diff --git a/PAMI/frequentPattern/basic/ECLATDiffset.py b/PAMI/frequentPattern/basic/ECLATDiffset.py index ef978d33..f535ae50 100644 --- a/PAMI/frequentPattern/basic/ECLATDiffset.py +++ b/PAMI/frequentPattern/basic/ECLATDiffset.py @@ -52,14 +52,13 @@ """ -# from abstract import * - from PAMI.frequentPattern.basic import abstract as _ab from deprecated import deprecated class ECLATDiffset(_ab._frequentPatterns): """ + :**Description**: ECLATDiffset uses diffset to extract the frequent patterns in a transactional database. :**Reference**: KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining @@ -323,7 +322,7 @@ def getRuntime(self): def getPatternsAsDataFrame(self): """ - Storing final frequent patterns in a dataframe + Storing final frequent patterns in a dataframe. :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame @@ -342,7 +341,7 @@ def getPatternsAsDataFrame(self): def save(self, outFile: str, seperator = "\t" ) -> None: """ - Complete set of frequent patterns will be loaded in to an output file + Complete set of frequent patterns will be loaded in to an output csv file. :param outFile: name of the output file :type outFile: csvfile @@ -362,7 +361,7 @@ def save(self, outFile: str, seperator = "\t" ) -> None: def getPatterns(self): """ - Function to send the set of frequent patterns after completion of the mining process + This function returns the frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict @@ -371,7 +370,7 @@ def getPatterns(self): def printResults(self): """ - This function is used to print the results + This function is used to print the results. """ print("Total number of Frequent Patterns:", len(self.getPatterns())) print("Total Memory in USS:", self.getMemoryUSS()) diff --git a/PAMI/frequentPattern/topk/FAE.py b/PAMI/frequentPattern/topk/FAE.py index a51b3d1c..2dba7edd 100644 --- a/PAMI/frequentPattern/topk/FAE.py +++ b/PAMI/frequentPattern/topk/FAE.py @@ -1,10 +1,13 @@ # Top - K is and algorithm to discover top frequent patterns in a transactional database. # # **Importing this algorithm into a python program** -# --------------------------------------------------------- # # import PAMI.frequentPattern.topK.FAE as alg # +# iFile = 'sampleDB.txt' +# +# K = 2 +# # obj = alg.FAE(iFile, K) # # obj.mine() @@ -31,9 +34,6 @@ # - - - __copyright__ = """ Copyright (C) 2021 Rage Uday Kiran @@ -57,49 +57,29 @@ class FAE(_ab._frequentPatterns): """ - :Description: Top - K is and algorithm to discover top frequent patterns in a transactional database. - - - :Reference: Zhi-Hong Deng, Guo-Dong Fang: Mining Top-Rank-K Frequent Patterns: DOI: 10.1109/ICMLC.2007.4370261 · Source: IEEE Xplore - https://ieeexplore.ieee.org/document/4370261 - - :param iFile: str : - Name of the Input file to mine complete set of frequent patterns - :param oFile: str : - Name of the output file to store complete set of frequent patterns - :param k: int : - User specified count of top frequent patterns - :param minimum: int : - Minimum number of frequent patterns to consider in analysis - - :param sep: str : - This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator. + About this algorithm + ==================== + :**Description**: Top - K is and algorithm to discover top frequent patterns in a transactional database. + :**Reference**: Zhi-Hong Deng, Guo-Dong Fang: Mining Top-Rank-K Frequent Patterns: DOI: 10.1109/ICMLC.2007.4370261 · Source: IEEE Xplore https://ieeexplore.ieee.org/document/4370261 - :Attributes: + :**Parameters**: - **iFile** (*str or URL or dataFrame*) -- *Name of the Input file to mine complete set of frequent patterns.* + - **oFile** (*str*) -- *Name of the output file to store complete set of frequent patterns.* + - **k** (*int*) -- *User specified count of top frequent patterns.* + **minimum** (*int*) -- *Minimum number of frequent patterns to consider in analysis.* + **sep** (*str*) -- *This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.* - startTime : float - To record the start time of the mining process + :**Attributes**: - **startTime** (*float*) -- *To record the start time of the mining process.* + - **endTime** (*float*) -- *To record the completion time of the mining process.* + - **finalPatterns** (*dict*) -- *Storing the complete set of patterns in a dictionary variable.* + - **memoryUSS** (*float*) -- *To store the total amount of USS memory consumed by the program.* + - **memoryRSS** (*float*) -- *To store the total amount of RSS memory consumed by the program.* - endTime : float - To record the completion time of the mining process + Execution methods + ================= - finalPatterns : dict - Storing the complete set of patterns in a dictionary variable - - memoryUSS : float - To store the total amount of USS memory consumed by the program - - memoryRSS : float - To store the total amount of RSS memory consumed by the program - - finalPatterns : dict - it represents to store the patterns - - - **Methods to execute code on terminal** - ------------------------------------------- + **Terminal command** .. code-block:: console @@ -109,45 +89,49 @@ class FAE(_ab._frequentPatterns): Example Usage: - (.venv) $ python3 FAE.py sampleDB.txt patterns.txt 10 - - .. note:: k will be considered as count of top frequent patterns to consider in analysis + (.venv) $ python3 FAE.py sampleDB.txt patterns.txt 10.0 + .. note:: k will be considered as count of top frequent patterns to consider in analysis. + **Calling from a python program** - **Importing this algorithm into a python program** - --------------------------------------------------------- .. code-block:: python - import PAMI.frequentPattern.topK.FAE as alg + import PAMI.frequentPattern.topK.FAE as alg + + iFile = 'sampleDB.txt' + + K = 2 - obj = alg.FAE(iFile, K) + obj = alg.FAE(iFile, K) - obj.mine() + obj.mine() - topKFrequentPatterns = obj.getPatterns() + topKFrequentPatterns = obj.getPatterns() - print("Total number of Frequent Patterns:", len(topKFrequentPatterns)) + print("Total number of Frequent Patterns:", len(topKFrequentPatterns)) - obj.save(oFile) + obj.save(oFile) - Df = obj.getPatternInDataFrame() + Df = obj.getPatternInDataFrame() - memUSS = obj.getMemoryUSS() + memUSS = obj.getMemoryUSS() - print("Total Memory in USS:", memUSS) + print("Total Memory in USS:", memUSS) - memRSS = obj.getMemoryRSS() + memRSS = obj.getMemoryRSS() - print("Total Memory in RSS", memRSS) + print("Total Memory in RSS", memRSS) - run = obj.getRuntime() + run = obj.getRuntime() - print("Total ExecutionTime in seconds:", run) + print("Total ExecutionTime in seconds:", run) - Credits: - -------- - The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran. + + Credits + ======= + + The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. """ @@ -166,8 +150,7 @@ class FAE(_ab._frequentPatterns): def _creatingItemSets(self): """ - Storing the complete transactions of the database/input file in a database variable - + Storing the complete transactions of the database/input file in a database variable """ self._Database = [] @@ -227,14 +210,15 @@ def _frequentOneItem(self): return plist def _save(self, prefix, suffix, tidSetI): - """Saves the patterns that satisfy the periodic frequent property. - - :param prefix: the prefix of a pattern - :type prefix: list - :param suffix: the suffix of a patterns - :type suffix: list - :param tidSetI: the timestamp of a patterns - :type tidSetI: list + """ + Saves the patterns that satisfy the periodic frequent property. + + :param prefix: the prefix of a pattern + :type prefix: list + :param suffix: the suffix of a patterns + :type suffix: list + :param tidSetI: the timestamp of a patterns + :type tidSetI: list """ if prefix is None: @@ -263,18 +247,16 @@ def _save(self, prefix, suffix, tidSetI): return def _Generation(self, prefix, itemSets, tidSets): - """Equivalence class is followed and checks for the patterns generated for periodic-frequent patterns. - - :param prefix: main equivalence prefix - :type prefix: periodic-frequent item or pattern - :param itemSets: patterns which are items combined with prefix and satisfying the periodicity - and frequent with their timestamps - :type itemSets: list - :param tidSets: timestamps of the items in the argument itemSets - :type tidSets: list - - - """ + """ + Equivalence class is followed and checks for the patterns generated for periodic-frequent patterns. + + :param prefix: main equivalence prefix + :type prefix: periodic-frequent item or pattern + :param itemSets: patterns which are items combined with prefix and satisfying the periodicity and frequent with their timestamps + :type itemSets: list + :param tidSets: timestamps of the items in the argument itemSets + :type tidSets: list + """ if len(itemSets) == 1: i = itemSets[0] tidI = tidSets[0] @@ -302,6 +284,7 @@ def _Generation(self, prefix, itemSets, tidSets): def _convert(self, value): """ to convert the type of user specified minSup value + :param value: user specified minSup value :type value: int or float or str :return: converted type @@ -321,13 +304,13 @@ def _convert(self, value): @deprecated("It is recommended to use 'mine()' instead of 'startMine()' for mining process. Starting from January 2025, 'startMine()' will be completely terminated.") def startMine(self): """ - Main function of the program + TopK Frequent pattern mining process will start from here """ self.mine() def mine(self): """ - Main function of the program + TopK Frequent pattern mining process will start from here """ self._startTime = _ab._time.time() if self._iFile is None: @@ -364,7 +347,6 @@ def getMemoryUSS(self): Total amount of USS memory consumed by the mining process will be retrieved from this function :return: returning USS memory consumed by the mining process - :rtype: float """ @@ -375,7 +357,6 @@ def getMemoryRSS(self): Total amount of RSS memory consumed by the mining process will be retrieved from this function :return: returning RSS memory consumed by the mining process - :rtype: float """ @@ -386,7 +367,6 @@ def getRuntime(self): Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process - :rtype: float """ @@ -417,7 +397,9 @@ def save(self, outFile): Complete set of frequent patterns will be loaded in to an output file :param outFile: name of the output file + :type outFile: csvfile + """ self._oFile = outFile writer = open(self._oFile, 'w+') @@ -430,7 +412,6 @@ def getPatterns(self): Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns - :rtype: dict """ return self._finalPatterns diff --git a/PAMI/subgraphMining/basic/gspan.py b/PAMI/subgraphMining/basic/gspan.py index e1c6dc70..2c26c5e3 100644 --- a/PAMI/subgraphMining/basic/gspan.py +++ b/PAMI/subgraphMining/basic/gspan.py @@ -649,4 +649,17 @@ def getFrequentSubgraphs(self): sb.append('\n'.join(subgraphDescription)) return '\n'.join(sb) + def getSubgraphGraphMapping(self): + """ + Return a list of mappings from subgraphs to the graph IDs they belong to in the format . + """ + mappings = [] + for i, subgraph in enumerate(self.frequentSubgraphs): + mapping = { + "FID": i, + "Clabel": str(subgraph.dfsCode), + "GIDs": list(subgraph.setOfGraphsIds) + } + mappings.append(mapping) + return mappings diff --git a/PAMI/subgraphMining/topK/graph.py b/PAMI/subgraphMining/topK/graph.py index 473fb76f..01bce433 100644 --- a/PAMI/subgraphMining/topK/graph.py +++ b/PAMI/subgraphMining/topK/graph.py @@ -44,7 +44,7 @@ def removeInfrequentLabel(self, label): for vertex in self.vMap.values(): edgesToRemove = [edge for edge in vertex.getEdgeList() - if edge.getV1() not in self.vMap or edge.getV2() not in self.vMap] + if edge.v1 not in self.vMap or edge.v2 not in self.vMap] for edge in edgesToRemove: vertex.getEdgeList().remove(edge) diff --git a/PAMI/subgraphMining/topK/tkg.py b/PAMI/subgraphMining/topK/tkg.py index b93e94cf..4eeab189 100644 --- a/PAMI/subgraphMining/topK/tkg.py +++ b/PAMI/subgraphMining/topK/tkg.py @@ -492,7 +492,7 @@ def removeInfrequentVertexPairs(self, graphDB): if TKG.ELIMINATE_INFREQUENT_VERTEX_PAIRS and count < self.minSup: v1.removeEdge(edge) - self.infrequentVertexPairsRemoved += 1 + self.infrequentVertexPairsRemovedCount += 1 elif TKG.ELIMINATE_INFREQUENT_EDGE_LABELS and \ mapEdgeLabelToSupport.get(edge.getEdgeLabel(), 0) < self.minSup: diff --git a/README.md b/README.md index ac116573..b69da82a 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,12 @@ PAttern MIning (PAMI) is a Python library containing several algorithms to disco *** # Recent Updates -- Version 2024.04.1.2: Introduced two new frequent subgraph mining algorithms, namely gspan and TKG. Optimized the frequent pattern mining algorithms. +- **Version 2024.05.01:** +In this latest version, the following updates have been made: + - Included two new algorithms, **Gspan and TKG**, for frequent subgraph mining. + - Updated three Synthetic Data Generator, **transactional database, temporal database, and geo-referenced transactional database**. + - Optimized the following frequent pattern mining algorithms: **Apriori, Aprioribitset, ECLAT, ECLATbitset, FPGrowth, and CHARM**. + - startMine() function has been deprecated to mine() function. Total number of algorithms: 83 @@ -142,10 +147,9 @@ from PAMI.frequentPattern.basic import FPGrowth as alg fileURL = "https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv" minSup=300 obj = alg.FPGrowth(iFile=fileURL, minSup=minSup, sep='\t') -obj.mine() +obj.startMine() obj.save('frequentPatternsAtMinSupCount300.txt') frequentPatternsDF= obj.getPatternsAsDataFrame() - print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns print('Runtime: ' + str(obj.getRuntime())) #measure the runtime print('Memory (RSS): ' + str(obj.getMemoryRSS())) @@ -225,7 +229,6 @@ We invite and encourage all community members to contribute, report bugs, fix bu *** # Tutorials - ### 0. Association Rule Mining | Basic | @@ -234,8 +237,6 @@ We invite and encourage all community members to contribute, report bugs, fix bu | Lift Open In Colab | | Leverage Open In Colab | - - ### 1. Pattern mining in binary transactional databases #### 1.1. Frequent pattern mining: [Sample](https://udaylab.github.io/PAMI/frequentPatternMining.html) @@ -546,6 +547,12 @@ We invite and encourage all community members to contribute, report bugs, fix bu | Transactional database Open In Colab | | | Temporal database Open In Colab | | Utility database (coming soon) | +| spatio-transactional database (coming soon) | +| spatio-temporal database (coming soon) | +| fuzzy transactional database (coming soon) | +| fuzzy temporal database (coming soon) | +| Sequence database generator (coming soon) | + #### 12.2. Converting a dataframe into a specific database type | Approaches | @@ -554,11 +561,11 @@ We invite and encourage all community members to contribute, report bugs, fix bu | Sparse dataframe to databases (coming soon) | #### 12.3. Gathering the statistical details of a database -| Approaches | -|--------------------------------------| -| Transactional database (coming soon) | -| Temporal database (coming soon) | -| Utility database (coming soon) | +| Approaches | +|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Transactional database Open In Colab | +| Temporal database Open In Colab | +| Utility database (coming soon) | #### 12.4. Generating Latex code for the experimental results | Approaches | @@ -569,7 +576,7 @@ We invite and encourage all community members to contribute, report bugs, fix bu # Real World Case Studies -1. Air pollution analytics Open In Colab +1. Air pollution analytics Open In Colab [Go to Top](#table-of-contents) diff --git a/notebooks/FuzzyPartialPeriodicPatternsInMultipleTimeSeries.ipynb b/notebooks/FuzzyPartialPeriodicPatternsInMultipleTimeSeries.ipynb index 125e05e1..705abb20 100644 --- a/notebooks/FuzzyPartialPeriodicPatternsInMultipleTimeSeries.ipynb +++ b/notebooks/FuzzyPartialPeriodicPatternsInMultipleTimeSeries.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/airPollutionAnalytics.ipynb b/notebooks/airPollutionAnalytics.ipynb index 65c3c93b..7c1bb963 100644 --- a/notebooks/airPollutionAnalytics.ipynb +++ b/notebooks/airPollutionAnalytics.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/correlatedPattern/basic/CoMine.ipynb b/notebooks/correlatedPattern/basic/CoMine.ipynb index ef7693b8..dda18f1c 100644 --- a/notebooks/correlatedPattern/basic/CoMine.ipynb +++ b/notebooks/correlatedPattern/basic/CoMine.ipynb @@ -23,7 +23,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/correlatedPattern/basic/CoMinePlus.ipynb b/notebooks/correlatedPattern/basic/CoMinePlus.ipynb index 4d9cd0a5..58227a76 100644 --- a/notebooks/correlatedPattern/basic/CoMinePlus.ipynb +++ b/notebooks/correlatedPattern/basic/CoMinePlus.ipynb @@ -23,7 +23,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/coveragePattern/basic/CMine.ipynb b/notebooks/coveragePattern/basic/CMine.ipynb index c2c6b92a..5e69bcd7 100644 --- a/notebooks/coveragePattern/basic/CMine.ipynb +++ b/notebooks/coveragePattern/basic/CMine.ipynb @@ -23,7 +23,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/coveragePattern/basic/CPPG.ipynb b/notebooks/coveragePattern/basic/CPPG.ipynb index aaa7a1f4..75294f5d 100644 --- a/notebooks/coveragePattern/basic/CPPG.ipynb +++ b/notebooks/coveragePattern/basic/CPPG.ipynb @@ -1,720 +1,720 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OLl_5kzY6tXz" - }, - "source": [ - "# Finding Coverage patterns in Transactional Databases using CPPG" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_uLUmXm57L7O" - }, - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find coverage patterns in a transactional database using the CPPG algorithm. In the final part, we describe an advanced approach, where we evaluate the CPPG algorithm on a dataset at different Minimum Relative Frequency values.\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GAbtl92E72nm" - }, - "source": [ - "# Prerequisites:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gA0Ogud176h-" - }, - "source": [ - "\n", - "\n", - "1. Installing the PAMI library\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nARoX2lM8IAC", - "outputId": "b54fe382-2763-436e-99bc-934863943b08" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", - 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"Building wheels for collected packages: JsonForm, JsonSir\n", - " Building wheel for JsonForm (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for JsonForm: filename=JsonForm-0.0.2-py3-none-any.whl size=3313 sha256=383f8eb9e12026ae4d1e461bcca3528f059fa9e7b355efa21d15dd3179f7bf3d\n", - " Stored in directory: /root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", - " Building wheel for JsonSir (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4753 sha256=3b427f295946228bcf24fd5e0d6e84371da336c5b571956e00a8c2bc690b3837\n", - " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", - "Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" - ] - } - ], - "source": [ - "!pip install -U pami #install the pami repository" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "clmAZNAr8YKs" - }, - "source": [ - "\n", - "\n", - "2. Downloading a sample dataset\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "T3qNOCC58dji", - "outputId": "c61dfb6b-2bc2-4941-b19a-ff41de13a1e7" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-23 11:59:10-- https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4019277 (3.8M) [text/csv]\n", - "Saving to: ‘Transactional_T10I4D100K.csv’\n", - "\n", - "Transactional_T10I4 100%[===================>] 3.83M 758KB/s in 5.4s \n", - "\n", - "2023-08-23 11:59:17 (722 KB/s) - ‘Transactional_T10I4D100K.csv’ saved [4019277/4019277]\n", - "\n" - ] - } - ], - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv #download a sample transactional database" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MQaVshhO8qiO" - }, - "source": [ - "\n", - "\n", - "3. Printing few lines of a dataset to know its format.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "AFNH4DG-8u8_", - "outputId": "86114f1c-bb7d-4e7f-8648-c22ca876f43d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ], - "source": [ - "!head -2 Transactional_T10I4D100K.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9143YYPN86ur" - }, - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4xHFzYNO9GtW" - }, - "source": [ - "## Part 1: Finding coverage patterns using CPPG" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WfoxUddK9Kh1" - }, - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum relative frequency (minRF) value." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JrxdjMqM9a8f", - "outputId": "774d2fde-55d0-49df-e58b-9496c692fc21" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99935\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883886940304302\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.101836193525791\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667097909135199\n", - "Variance in Transaction Sizes : 13.447741640067324\n" - ] - } - ], - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.TransactionalDatabase as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Transactional_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.TransactionalDatabase(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "z5oVNuP6-2BX" - }, - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "YyDgafh_-8UW", - "outputId": "6f56cbf8-89ef-4fd6-97e7-2afc2e815399" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ], - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HSRzh3pH_dRR" - }, - "source": [ - "### Step 3: Choosing an appropriate *minRF* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minRF_ value of 0.06 (in count). We can increase or decrease the _minRF_ based on the number of patterns being generated." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "zQwl9uQ3_u9E" - }, - "outputs": [], - "source": [ - "minRF = 0.06 #minRF is specified in count. However, the users can also specify different minRF value\n", - "minCS = 0.4 #minCS is specified in count. However, the users can also specify different minCS value\n", - "maxOR = 0.8 #minOR is specified in count. However, the users can also specify different minOR value" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OLl_5kzY6tXz" + }, + "source": [ + "# Finding Coverage patterns in Transactional Databases using CPPG" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_uLUmXm57L7O" + }, + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find coverage patterns in a transactional database using the CPPG algorithm. In the final part, we describe an advanced approach, where we evaluate the CPPG algorithm on a dataset at different Minimum Relative Frequency values.\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GAbtl92E72nm" + }, + "source": [ + "# Prerequisites:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gA0Ogud176h-" + }, + "source": [ + "\n", + "\n", + "1. Installing the PAMI library\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nARoX2lM8IAC", + "outputId": "b54fe382-2763-436e-99bc-934863943b08" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m10.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", + "Collecting resource (from pami)\n", + " Downloading Resource-0.2.1-py2.py3-none-any.whl (25 kB)\n", + "Collecting validators (from pami)\n", + " Downloading validators-0.21.2-py3-none-any.whl (25 kB)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.42.0)\n", + "Requirement 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Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from 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sha256=3b427f295946228bcf24fd5e0d6e84371da336c5b571956e00a8c2bc690b3837\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" + ] + } + ], + "source": [ + "!pip install -U pami #install the pami repository" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "clmAZNAr8YKs" + }, + "source": [ + "\n", + "\n", + "2. Downloading a sample dataset\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T3qNOCC58dji", + "outputId": "c61dfb6b-2bc2-4941-b19a-ff41de13a1e7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-23 11:59:10-- https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4019277 (3.8M) [text/csv]\n", + "Saving to: ‘Transactional_T10I4D100K.csv’\n", + "\n", + "Transactional_T10I4 100%[===================>] 3.83M 758KB/s in 5.4s \n", + "\n", + "2023-08-23 11:59:17 (722 KB/s) - ‘Transactional_T10I4D100K.csv’ saved [4019277/4019277]\n", + "\n" + ] + } + ], + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv #download a sample transactional database" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MQaVshhO8qiO" + }, + "source": [ + "\n", + "\n", + "3. Printing few lines of a dataset to know its format.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AFNH4DG-8u8_", + "outputId": "86114f1c-bb7d-4e7f-8648-c22ca876f43d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ], + "source": [ + "!head -2 Transactional_T10I4D100K.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9143YYPN86ur" + }, + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4xHFzYNO9GtW" + }, + "source": [ + "## Part 1: Finding coverage patterns using CPPG" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WfoxUddK9Kh1" + }, + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum relative frequency (minRF) value." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JrxdjMqM9a8f", + "outputId": "774d2fde-55d0-49df-e58b-9496c692fc21" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99935\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883886940304302\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.101836193525791\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667097909135199\n", + "Variance in Transaction Sizes : 13.447741640067324\n" + ] + } + ], + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TransactionalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Transactional_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TransactionalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z5oVNuP6-2BX" + }, + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 }, + "id": "YyDgafh_-8UW", + "outputId": "6f56cbf8-89ef-4fd6-97e7-2afc2e815399" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "h1MlLvka_2EE" - }, - "source": [ - "### Step 4: Mining coverage patterns using CPPG" + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6lrcJj-A_8om", - "outputId": "48b03646-50f7-4ad5-aa99-c0a158ddfe98" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "368 [['766'], ['529'], ['529'], ['529'], ['766'], ['766'], ['529'], ['529', '829'], ['829'], ['829'], ['529'], ['529'], ['529'], ['529'], ['829'], ['766'], ['829'], ['829'], ['529'], ['766'], ['766'], ['529'], ['766'], ['829'], ['766'], ['766'], ['829'], ['529'], ['529'], ['829'], ['766'], ['529'], ['829'], ['766'], ['829'], ['766'], ['829'], ['766'], ['529'], ['766'], ['829'], ['529'], ['766'], ['829'], ['766'], ['829'], ['766'], ['529'], ['829'], ['529'], ['529'], ['766'], ['829'], ['766'], ['766'], ['829'], ['529'], ['529'], ['766'], ['829'], ['829'], ['829'], ['529'], ['529'], ['766'], ['766'], 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" ], - "source": [ - "from PAMI.coveragePattern.basic import CPPG as alg #import the algorithm\n", - "\n", - "obj = alg.CPPG(iFile=inputFile, minRF=minRF, minCS=minCS, maxOR = maxOR, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('coveragePatternsAtMinRFCount0.06.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "IG5FVNIhFOpQ" - }, - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minRF_ value." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "S0B8zO3oFgiC", - "outputId": "41bba5b8-d802-4011-d866-9d952e5c1749" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "368:7821 \n", - "766:6259 \n", - "529:7053 \n", - "829:6802 \n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "!head coveragePatternsAtMinRFCount0.06.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7EiPK5UNFusb" - }, - "source": [ - "The storage format is: _coveragePattern:support_\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yNojbxSFF4cc" - }, - "source": [ - "## Part 2: Evaluating the CPPG algorithm on a dataset at different minRF values" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CP1gc7eQGImo" - }, - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "ZXCJ2hm0GuHj" - }, - "outputs": [], - "source": [ - "#Import the libraries\n", - "from PAMI.coveragePattern.basic import CPPG as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Transactional_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "minimumRelativeFrequencyCountList = [0.04, 0.05, 0.06, 0.07, 0.08]\n", - "#minimumRelativeFrequency must be specified between 0 to 1. E.g., minRFList = [0.004, 0.005, 0.006, 0.007, 0.008]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ztgFuehlHGj5" - }, - "source": [ - "### Step 2: Create a data frame to store the results of CPPG" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "znDZM2waHQVd" - }, - "outputs": [], - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minRF', 'minCS', 'maxOR', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of CPPG algorithm" - ] + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HSRzh3pH_dRR" + }, + "source": [ + "### Step 3: Choosing an appropriate *minRF* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minRF_ value of 0.06 (in count). We can increase or decrease the _minRF_ based on the number of patterns being generated." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "zQwl9uQ3_u9E" + }, + "outputs": [], + "source": [ + "minRF = 0.06 #minRF is specified in count. However, the users can also specify different minRF value\n", + "minCS = 0.4 #minCS is specified in count. However, the users can also specify different minCS value\n", + "maxOR = 0.8 #minOR is specified in count. However, the users can also specify different minOR value" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "h1MlLvka_2EE" + }, + "source": [ + "### Step 4: Mining coverage patterns using CPPG" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6lrcJj-A_8om", + "outputId": "48b03646-50f7-4ad5-aa99-c0a158ddfe98" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "368 [['766'], ['529'], ['529'], ['529'], ['766'], ['766'], ['529'], ['529', '829'], ['829'], ['829'], ['529'], ['529'], ['529'], ['529'], ['829'], ['766'], ['829'], ['829'], ['529'], ['766'], ['766'], ['529'], ['766'], ['829'], ['766'], ['766'], ['829'], ['529'], ['529'], ['829'], ['766'], ['529'], ['829'], ['766'], ['829'], ['766'], ['829'], ['766'], ['529'], ['766'], ['829'], ['529'], ['766'], ['829'], ['766'], ['829'], ['766'], ['529'], ['829'], ['529'], ['529'], ['766'], ['829'], 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PAMI.coveragePattern.basic import CPPG as alg #import the algorithm\n", + "\n", + "obj = alg.CPPG(iFile=inputFile, minRF=minRF, minCS=minCS, maxOR = maxOR, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('coveragePatternsAtMinRFCount0.06.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IG5FVNIhFOpQ" + }, + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minRF_ value." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S0B8zO3oFgiC", + "outputId": "41bba5b8-d802-4011-d866-9d952e5c1749" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "368:7821 \n", + "766:6259 \n", + "529:7053 \n", + "829:6802 \n" + ] + } + ], + "source": [ + "!head coveragePatternsAtMinRFCount0.06.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7EiPK5UNFusb" + }, + "source": [ + "The storage format is: _coveragePattern:support_\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yNojbxSFF4cc" + }, + "source": [ + "## Part 2: Evaluating the CPPG algorithm on a dataset at different minRF values" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CP1gc7eQGImo" + }, + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "ZXCJ2hm0GuHj" + }, + "outputs": [], + "source": [ + "#Import the libraries\n", + "from PAMI.coveragePattern.basic import CPPG as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Transactional_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "minimumRelativeFrequencyCountList = [0.04, 0.05, 0.06, 0.07, 0.08]\n", + "#minimumRelativeFrequency must be specified between 0 to 1. E.g., minRFList = [0.004, 0.005, 0.006, 0.007, 0.008]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ztgFuehlHGj5" + }, + "source": [ + "### Step 2: Create a data frame to store the results of CPPG" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "znDZM2waHQVd" + }, + "outputs": [], + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minRF', 'minCS', 'maxOR', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of CPPG algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aWPrPGaCHWhp" + }, + "source": [ + "### Step 3: Execute the algorithm at different minRF values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "78Vb5yT3HdHB", + "outputId": "9b70ad7f-3d01-4938-e458-ac72641443bf" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Coverage patterns were generated successfully using CPPG algorithm \n", + "368 [['766'], ['529'], ['529'], ['529'], ['766'], ['766'], ['529'], ['529', '829'], ['829'], ['829'], ['529'], ['529'], ['529'], ['529'], ['829'], ['766'], ['829'], ['829'], ['529'], ['766'], ['766'], ['529'], ['766'], ['829'], ['766'], ['766'], ['829'], ['529'], ['529'], ['829'], ['766'], ['529'], ['829'], ['766'], ['829'], ['766'], ['829'], ['766'], ['529'], ['766'], ['829'], ['529'], ['766'], ['829'], ['766'], ['829'], ['766'], ['529'], ['829'], ['529'], ['529'], ['766'], ['829'], ['766'], ['766'], ['829'], ['529'], ['529'], ['766'], ['829'], ['829'], ['829'], ['529'], ['529'], ['766'], ['766'], ['529'], ['529'], ['766'], ['766'], ['829'], ['529'], ['529'], ['829'], ['529'], ['766'], ['829'], ['829'], ['829'], ['529'], ['829'], ['766'], ['829'], ['766'], ['766'], ['529'], ['529'], ['829'], ['529'], ['529'], ['766'], ['829'], ['529'], ['829'], ['529', '829'], ['829'], ['529'], ['766'], ['766'], ['766'], 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['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529']]\n", + "529 []\n", + "Coverage patterns were generated successfully using CPPG algorithm \n", + "Coverage patterns were generated successfully using CPPG algorithm \n" + ] + } + ], + "source": [ + "for minRFCount in minimumRelativeFrequencyCountList:\n", + " obj = alg.CPPG(inputFile, minRF=minRFCount, minCS=minCS, maxOR=maxOR, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['CPPG', minRFCount, minCS, maxOR, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tzMnMDu7p2S3" + }, + "source": [ + "### Step 4: Print the result" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "t71hD-MzHmZM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c76bb5dc-a303-4751-f7ce-fea06e944300" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minRF minCS maxOR patterns runtime memory\n", + "0 CPPG 0.04 0.4 0.8 26 10.141634 361271296\n", + "1 CPPG 0.05 0.4 0.8 10 3.483257 281358336\n", + "2 CPPG 0.06 0.4 0.8 4 1.367797 265195520\n", + "3 CPPG 0.07 0.4 0.8 2 1.340577 263102464\n", + "4 CPPG 0.08 0.4 0.8 0 1.052483 271552512\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M4v1eWUKHqQT" + }, + "source": [ + "### Step 5: Visualizing the results" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "tGg9sBilHwX8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "outputId": "62b6c65f-9e7b-43bf-8e19-8e7803256d92" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "aWPrPGaCHWhp" - }, - "source": [ - "### Step 3: Execute the algorithm at different minRF values" + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "78Vb5yT3HdHB", - "outputId": "9b70ad7f-3d01-4938-e458-ac72641443bf" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Coverage patterns were generated successfully using CPPG algorithm \n", - "368 [['766'], ['529'], ['529'], ['529'], ['766'], ['766'], ['529'], ['529', '829'], ['829'], ['829'], ['529'], ['529'], ['529'], ['529'], ['829'], ['766'], ['829'], ['829'], ['529'], ['766'], ['766'], ['529'], ['766'], ['829'], ['766'], ['766'], ['829'], ['529'], ['529'], ['829'], ['766'], ['529'], ['829'], ['766'], ['829'], ['766'], ['829'], ['766'], ['529'], ['766'], ['829'], ['529'], ['766'], ['829'], ['766'], ['829'], ['766'], 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['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529'], ['529']]\n", - "529 []\n", - "Coverage patterns were generated successfully using CPPG algorithm \n", - "Coverage patterns were generated successfully using CPPG algorithm \n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "for minRFCount in minimumRelativeFrequencyCountList:\n", - " obj = alg.CPPG(inputFile, minRF=minRFCount, minCS=minCS, maxOR=maxOR, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['CPPG', minRFCount, minCS, maxOR, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "tzMnMDu7p2S3" - }, - "source": [ - "### Step 4: Print the result" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "t71hD-MzHmZM", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "c76bb5dc-a303-4751-f7ce-fea06e944300" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minRF minCS maxOR patterns runtime memory\n", - "0 CPPG 0.04 0.4 0.8 26 10.141634 361271296\n", - "1 CPPG 0.05 0.4 0.8 10 3.483257 281358336\n", - "2 CPPG 0.06 0.4 0.8 4 1.367797 265195520\n", - "3 CPPG 0.07 0.4 0.8 2 1.340577 263102464\n", - "4 CPPG 0.08 0.4 0.8 0 1.052483 271552512\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "print(result)" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "M4v1eWUKHqQT" - }, - "source": [ - "### Step 5: Visualizing the results" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "tGg9sBilHwX8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "62b6c65f-9e7b-43bf-8e19-8e7803256d92" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" - }, - "metadata": {} - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "result.plot(x='minRF', y='patterns', kind='line')\n", - "result.plot(x='minRF', y='runtime', kind='line')\n", - "result.plot(x='minRF', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "authorship_tag": "ABX9TyMogmCKifl2PhzwgbBEA727", - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "image/png": 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\n" + }, + "metadata": {} } + ], + "source": [ + "result.plot(x='minRF', y='patterns', kind='line')\n", + "result.plot(x='minRF', y='runtime', kind='line')\n", + "result.plot(x='minRF', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMogmCKifl2PhzwgbBEA727", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/extras/DF2DB/denseDF2DB.ipynb b/notebooks/extras/DF2DB/denseDF2DB.ipynb index b77457c2..5851a71b 100644 --- a/notebooks/extras/DF2DB/denseDF2DB.ipynb +++ b/notebooks/extras/DF2DB/denseDF2DB.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/extras/dbStats/MultipleTimeSeriesFuzzyDatabaseStats.ipynb b/notebooks/extras/dbStats/MultipleTimeSeriesFuzzyDatabaseStats.ipynb index 3a176289..a8d52954 100644 --- a/notebooks/extras/dbStats/MultipleTimeSeriesFuzzyDatabaseStats.ipynb +++ b/notebooks/extras/dbStats/MultipleTimeSeriesFuzzyDatabaseStats.ipynb @@ -23,7 +23,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/extras/dbStats/temporalDatabaseStats.ipynb b/notebooks/extras/dbStats/temporalDatabaseStats.ipynb index 184d5711..d6ffbc98 100644 --- a/notebooks/extras/dbStats/temporalDatabaseStats.ipynb +++ b/notebooks/extras/dbStats/temporalDatabaseStats.ipynb @@ -1,3211 +1,3211 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyNdqm5GWwwnMd3O55JUs3Bk", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Printing the statistics of a Temporal Database\n", + "> Illustration with 5-year nation-wide air pollution data of Japan" + ], + "metadata": { + "id": "HDeA6yStEG8o" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Introduction\n", + "\n", + "A temporal database is a collection of transactions ordered by their timestamp. We use the 5-year nation-wide air pollution (PM2.5) data of Japan for illustration purposes." + ], + "metadata": { + "id": "UmwzOJeOEO40" + } + }, + { + "cell_type": "markdown", + "source": [ + "__Format of a Temporal database:__ timestamp_separator_item1_separator_item2_separator_..._separator_itemN\n", + "\n", + "__Example:__\n", + "\n", + "* 1 a b c\n", + "* 2 d e\n", + "\n" + ], + "metadata": { + "id": "bo45f2hfEtMp" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Download the air pollution dataset [1]" + ], + "metadata": { + "id": "cfO_cDy_FCUl" + } + }, + { + "cell_type": "code", + "source": [ + "!wget https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv" + ], + "metadata": { "colab": { - "provenance": [], - "authorship_tag": "ABX9TyNdqm5GWwwnMd3O55JUs3Bk", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "base_uri": "https://localhost:8080/" }, - "language_info": { - "name": "python" + "id": "aWTdZkQsFFs6", + "outputId": "ac8ade57-7f08-442f-9843-b508be704d79" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-18 11:46:30-- https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.18, 2620:100:6035:18::a27d:5512\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.18|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: /s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv [following]\n", + "--2023-08-18 11:46:30-- https://www.dropbox.com/s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", + "Reusing existing connection to www.dropbox.com:443.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com/cd/0/inline/CCAbW15Mhm4ix1xO6840SqX_IA_8miqiM4ID02mGQUTASrvD6fVB6dDGMrvOYDsSpeoHnVzohatFb2lwZnJaJwBTJofk9AU52yjlh5BjuTsiY6Rw0BLZd712r83jlVrAcXe00zHIXK9r5FG6V-hvd5Uw/file# [following]\n", + "--2023-08-18 11:46:31-- https://uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com/cd/0/inline/CCAbW15Mhm4ix1xO6840SqX_IA_8miqiM4ID02mGQUTASrvD6fVB6dDGMrvOYDsSpeoHnVzohatFb2lwZnJaJwBTJofk9AU52yjlh5BjuTsiY6Rw0BLZd712r83jlVrAcXe00zHIXK9r5FG6V-hvd5Uw/file\n", + "Resolving uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com (uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com)... 162.125.80.15, 2620:100:6035:15::a27d:550f\n", + "Connecting to uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com (uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com)|162.125.80.15|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 262918270 (251M) [text/plain]\n", + "Saving to: ‘ETL_DATA_new.csv’\n", + "\n", + "ETL_DATA_new.csv 100%[===================>] 250.74M 30.3MB/s in 8.8s \n", + "\n", + "2023-08-18 11:46:40 (28.6 MB/s) - ‘ETL_DATA_new.csv’ saved [262918270/262918270]\n", + "\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Printing the statistics of a Temporal Database\n", - "> Illustration with 5-year nation-wide air pollution data of Japan" - ], - "metadata": { - "id": "HDeA6yStEG8o" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Introduction\n", - "\n", - "A temporal database is a collection of transactions ordered by their timestamp. We use the 5-year nation-wide air pollution (PM2.5) data of Japan for illustration purposes." - ], - "metadata": { - "id": "UmwzOJeOEO40" - } - }, - { - "cell_type": "markdown", - "source": [ - "__Format of a Temporal database:__ timestamp_separator_item1_separator_item2_separator_..._separator_itemN\n", - "\n", - "__Example:__\n", - "\n", - "* 1 a b c\n", - "* 2 d e\n", - "\n" - ], - "metadata": { - "id": "bo45f2hfEtMp" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Download the air pollution dataset [1]" - ], - "metadata": { - "id": "cfO_cDy_FCUl" - } - }, - { - "cell_type": "code", - "source": [ - "!wget https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "aWTdZkQsFFs6", - "outputId": "ac8ade57-7f08-442f-9843-b508be704d79" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-18 11:46:30-- https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", - "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.18, 2620:100:6035:18::a27d:5512\n", - "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.18|:443... connected.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: /s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv [following]\n", - "--2023-08-18 11:46:30-- https://www.dropbox.com/s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", - "Reusing existing connection to www.dropbox.com:443.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com/cd/0/inline/CCAbW15Mhm4ix1xO6840SqX_IA_8miqiM4ID02mGQUTASrvD6fVB6dDGMrvOYDsSpeoHnVzohatFb2lwZnJaJwBTJofk9AU52yjlh5BjuTsiY6Rw0BLZd712r83jlVrAcXe00zHIXK9r5FG6V-hvd5Uw/file# [following]\n", - "--2023-08-18 11:46:31-- https://uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com/cd/0/inline/CCAbW15Mhm4ix1xO6840SqX_IA_8miqiM4ID02mGQUTASrvD6fVB6dDGMrvOYDsSpeoHnVzohatFb2lwZnJaJwBTJofk9AU52yjlh5BjuTsiY6Rw0BLZd712r83jlVrAcXe00zHIXK9r5FG6V-hvd5Uw/file\n", - "Resolving uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com (uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com)... 162.125.80.15, 2620:100:6035:15::a27d:550f\n", - "Connecting to uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com (uc898f08b3c84666a5285c8d19ab.dl.dropboxusercontent.com)|162.125.80.15|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 262918270 (251M) [text/plain]\n", - "Saving to: ‘ETL_DATA_new.csv’\n", - "\n", - "ETL_DATA_new.csv 100%[===================>] 250.74M 30.3MB/s in 8.8s \n", - "\n", - "2023-08-18 11:46:40 (28.6 MB/s) - ‘ETL_DATA_new.csv’ saved [262918270/262918270]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Read the dataset and analyze it" - ], - "metadata": { - "id": "voX02Zn3FKJq" - } - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "dataset = pd.read_csv('ETL_DATA_new.csv', index_col=0)\n", - "\n", - "dataset\n", - "# you can notice that dataset is collected from 2018-01-01 01:00:00 hours to 2023-04-25 22:00:00 hours (5+ years)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 822 - }, - "id": "bw56d400FNVr", - "outputId": "dae6cb9a-8eec-48e0-c874-7a825fbbd352" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " TimeStamp Point(139.0794379 36.3727776) \\\n", - " \n", - "0 2018-01-01 01:00:00 NaN \n", - "1 2018-01-01 02:00:00 NaN \n", - "2 2018-01-01 03:00:00 NaN \n", - "3 2018-01-01 04:00:00 NaN \n", - "4 2018-01-01 05:00:00 NaN \n", - "... ... ... \n", - "46000 2023-04-25 18:00:00 NaN \n", - "46001 2023-04-25 19:00:00 NaN \n", - "46002 2023-04-25 20:00:00 NaN \n", - "46003 2023-04-25 21:00:00 NaN \n", - "46004 2023-04-25 22:00:00 NaN \n", - "\n", - " Point(139.1051411 36.3963822) Point(139.0960211 36.4047323) \\\n", - " \n", - "0 NaN 5.0 \n", - "1 NaN 11.0 \n", - "2 NaN 7.0 \n", - "3 NaN 5.0 \n", - "4 NaN 6.0 \n", - "... ... ... \n", - "46000 NaN NaN \n", - "46001 NaN NaN \n", - "46002 NaN NaN \n", - "46003 NaN NaN \n", - 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\n" - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "Point(139.0794379 36.3727776) 0.0\n", + "Point(139.1051411 36.3963822) 0.0\n", + "Point(139.0960211 36.4047323) 110.0\n", + "Point(139.0428727 36.3816035) 77.0\n", + "Point(138.9955116 36.33801589999999) 200.0\n", + " ... \n", + "Point(139.9074816 36.4445767) 0.0\n", + "Point(140.0934838 36.4673588) 137.0\n", + "Point(139.7422865 36.2305774) 0.0\n", + "Point(139.7151723 36.822353) 0.0\n", + "Point(140.1510903 36.6598314) 165.0\n", + "Length: 1764, dtype: float64" ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "maxValueInEachColumn.plot() #point the maximum values recorded by each sensor." + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 }, + "id": "DxoRJy7IGB6G", + "outputId": "65b8f554-2bac-4e2a-a00b-e1ed7bb273b8" + }, + "execution_count": 7, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "__Observation:__ we can see many sensors have recorded high PM2.5 values greater than 250. Such values are generally outliers/abnormalities and are not useful for the analysis." - ], - "metadata": { - "id": "P___73RpGE5I" - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Step 3.3.2: Replacing the values greater than 250 to zero." - ], - "metadata": { - "id": "hqJxCo2YGITg" - } - }, - { - "cell_type": "code", - "source": [ - "dataset.where(dataset <= 250, 0, inplace=True)\n", - "dataset.max().plot()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 447 - }, - "id": "2C0qq66LGKex", - "outputId": "4f4c256f-6a5d-4ff9-f1fd-9602ec33393a" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 8 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 7 }, { - "cell_type": "markdown", - "source": [ - "__Observation:__ We can notice that the maximum values of every sensor are no more than the 250 value.\n" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "54S7FESJGO7e" - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Step 3.3.3: Finding the minimum values recorded by each sensor" - ], - "metadata": { - "id": "Qca35mt0GO_C" - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ we can see many sensors have recorded high PM2.5 values greater than 250. Such values are generally outliers/abnormalities and are not useful for the analysis." + ], + "metadata": { + "id": "P___73RpGE5I" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.3.2: Replacing the values greater than 250 to zero." + ], + "metadata": { + "id": "hqJxCo2YGITg" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.where(dataset <= 250, 0, inplace=True)\n", + "dataset.max().plot()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 }, + "id": "2C0qq66LGKex", + "outputId": "4f4c256f-6a5d-4ff9-f1fd-9602ec33393a" + }, + "execution_count": 8, + "outputs": [ { - "cell_type": "code", - "source": [ - "minValueInEachColumn = dataset.min() #Reading the minimum PM2.5 value recorded by each sensor\n", - "minValueInEachColumn.plot() #ploting them\n", - "\n", - "#dataset.min().plot() #memory efficient approach" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 447 - }, - "id": "cFi4zU6TGY6q", - "outputId": "b95ba5fa-618c-424f-d546-57dbf9032be1" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 9 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 8 }, { - "cell_type": "markdown", - "source": [ - "__Observation:__ We can observe that many sensors have recorded negative PM2.5 values. Thus, we replace the negative PM2.5 values of each sensor with Zero" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "leL1D9LWGb50" - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Step 3.3.4: Replacing the values less than zero to 0" - ], - "metadata": { - "id": "gIJNLlp0Gb9C" - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ We can notice that the maximum values of every sensor are no more than the 250 value.\n" + ], + "metadata": { + "id": "54S7FESJGO7e" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.3.3: Finding the minimum values recorded by each sensor" + ], + "metadata": { + "id": "Qca35mt0GO_C" + } + }, + { + "cell_type": "code", + "source": [ + "minValueInEachColumn = dataset.min() #Reading the minimum PM2.5 value recorded by each sensor\n", + "minValueInEachColumn.plot() #ploting them\n", + "\n", + "#dataset.min().plot() #memory efficient approach" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 }, + "id": "cFi4zU6TGY6q", + "outputId": "b95ba5fa-618c-424f-d546-57dbf9032be1" + }, + "execution_count": 9, + "outputs": [ { - "cell_type": "code", - "source": [ - "dataset.where(dataset > 0, 0, inplace=True)\n", - "dataset.min().plot()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 447 - }, - "id": "aAf1XsUPGiGt", - "outputId": "0b228ae4-969a-4144-bcbf-30c2df830535" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 10 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 9 }, { - "cell_type": "markdown", - "source": [ - "__Observation:__ The abnormal values were replaced to 0." + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "Dm4aunbYGlZX" - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Step 3.4: Create a dataframe of the sensors having pm25 >= 15\n", - "(useful to prune those sensors that do not record any pm2.5 value)" - ], - "metadata": { - "id": "SQAnJ2lHGlco" - } + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ We can observe that many sensors have recorded negative PM2.5 values. Thus, we replace the negative PM2.5 values of each sensor with Zero" + ], + "metadata": { + "id": "leL1D9LWGb50" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.3.4: Replacing the values less than zero to 0" + ], + "metadata": { + "id": "gIJNLlp0Gb9C" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.where(dataset > 0, 0, inplace=True)\n", + "dataset.min().plot()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 }, + "id": "aAf1XsUPGiGt", + "outputId": "0b228ae4-969a-4144-bcbf-30c2df830535" + }, + "execution_count": 10, + "outputs": [ { - "cell_type": "code", - "source": [ - "thresholdValue = 15\n", - "pm25 = pd.DataFrame(columns=[\"long\", \"lat\", \"pm25\"])\n", - "for col in dataset[1:]:\n", - " res = [i for i in dataset[col].values if i >= thresholdValue]\n", - " if len(res) == 0 or col == \"Unnamed\":\n", - " dataset = dataset.drop([col], axis = 1)\n", - " else:\n", - " if \"Poi\" in col:\n", - " #print(\"Hey\")\n", - " col = col.strip(\"Point()\")\n", - " col = col.rstrip(\").1\")\n", - " long, lat = col.split()\n", - " pm25 = pm25._append({'long': float(long), 'lat': float(lat), 'pm25': len(res)}, ignore_index=True)\n", - "pm25.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - }, - "id": "ZIJYxkK8GtK3", - "outputId": "99a02c3c-5217-4f99-f10f-177e65756d64" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " long lat pm25\n", - "0 139.096021 36.404732 8204.0\n", - "1 139.042873 36.381603 8999.0\n", - "2 138.995512 36.338016 13929.0\n", - "3 139.342672 36.410566 12667.0\n", - "4 139.381732 36.290913 10391.0" - ], - "text/html": [ - "\n", - "
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" ], - "metadata": { - "id": "hOBFGw18GxIw" - } + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ The abnormal values were replaced to 0." + ], + "metadata": { + "id": "Dm4aunbYGlZX" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.4: Create a dataframe of the sensors having pm25 >= 15\n", + "(useful to prune those sensors that do not record any pm2.5 value)" + ], + "metadata": { + "id": "SQAnJ2lHGlco" + } + }, + { + "cell_type": "code", + "source": [ + "thresholdValue = 15\n", + "pm25 = pd.DataFrame(columns=[\"long\", \"lat\", \"pm25\"])\n", + "for col in dataset[1:]:\n", + " res = [i for i in dataset[col].values if i >= thresholdValue]\n", + " if len(res) == 0 or col == \"Unnamed\":\n", + " dataset = dataset.drop([col], axis = 1)\n", + " else:\n", + " if \"Poi\" in col:\n", + " #print(\"Hey\")\n", + " col = col.strip(\"Point()\")\n", + " col = col.rstrip(\").1\")\n", + " long, lat = col.split()\n", + " pm25 = pm25._append({'long': float(long), 'lat': float(lat), 'pm25': len(res)}, ignore_index=True)\n", + "pm25.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "ZIJYxkK8GtK3", + "outputId": "99a02c3c-5217-4f99-f10f-177e65756d64" + }, + "execution_count": 11, + "outputs": [ { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "import plotly.express as px\n", - "\n", - "fig = px.density_mapbox(pm25, lat = 'lat', lon = 'long', z = 'pm25',\n", - " radius = 8,\n", - " zoom = 6,\n", - " mapbox_style = 'open-street-map')\n", - "fig.show()" + "output_type": "execute_result", + "data": { + "text/plain": [ + " long lat pm25\n", + "0 139.096021 36.404732 8204.0\n", + "1 139.042873 36.381603 8999.0\n", + "2 138.995512 36.338016 13929.0\n", + "3 139.342672 36.410566 12667.0\n", + "4 139.381732 36.290913 10391.0" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 542 - }, - "id": "AaCHlyLKGtZv", - "outputId": "8fbf69e2-d516-4a21-bf52-a64e1058eb83" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
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\n" ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.5: Drawing the frequency heatmap of sensors\n", + "\n", + "* List item\n", + "\n", + "* List item\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "---\n", + "\n", + "\n", + "* List item\n", + "\n", + "\n", + "* List item\n", + "\n", + "\n", + "\n", + "The frequency heatmap provides cruical information regarding how frequently a particular sensor has recorded harmful levels of pollution" + ], + "metadata": { + "id": "hOBFGw18GxIw" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import plotly.express as px\n", + "\n", + "fig = px.density_mapbox(pm25, lat = 'lat', lon = 'long', z = 'pm25',\n", + " radius = 8,\n", + " zoom = 6,\n", + " mapbox_style = 'open-street-map')\n", + "fig.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 }, + "id": "AaCHlyLKGtZv", + "outputId": "8fbf69e2-d516-4a21-bf52-a64e1058eb83" + }, + "execution_count": 12, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "__Inference from the above figure:__ High PM2.5 levels were frequently observed at the south part of Japan, starting from Tokyo. " - ], - "metadata": { - "id": "H_2D-wfoG5ZF" - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Step 3.6: Printing the heat map of maximum PM2.5 value recorded by each sensor" - ], - "metadata": { - "id": "D2On4yk5G8kM" - } - }, - { - "cell_type": "code", - "source": [ - "maxPM25 = pd.DataFrame(columns=[\"long\", \"lat\", \"maxPM25\"])\n", - "for col in dataset[1:]:\n", - " res = [i for i in dataset[col].values if i >= 15]\n", - " if len(res) == 0 or col == \"Unnamed\":\n", - " dataset = dataset.drop([col], axis = 1)\n", - " else:\n", - " if \"Poi\" in col:\n", - " col = col.strip(\"Point()\")\n", - " col = col.rstrip(\").1\")\n", - " long, lat = col.split()\n", - " maxPM25 = maxPM25._append({'long': float(long), 'lat': float(lat), 'maxPM25': max(res)}, ignore_index=True)\n", - "maxPM25.head()\n", - "\n", - "import pandas as pd\n", - "import plotly.express as px\n", - "\n", - "fig = px.density_mapbox(maxPM25, lat = 'lat', lon = 'long', z = 'maxPM25',\n", - " radius = 8,\n", - " zoom = 6,\n", - " mapbox_style = 'open-street-map')\n", - "fig.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 542 - }, - "id": "9e8FHWw0G_51", - "outputId": "0c720fcf-7c68-41f9-c44c-30d1c286a8b9" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
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" + ], + "metadata": { + "id": "H_2D-wfoG5ZF" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.6: Printing the heat map of maximum PM2.5 value recorded by each sensor" + ], + "metadata": { + "id": "D2On4yk5G8kM" + } + }, + { + "cell_type": "code", + "source": [ + "maxPM25 = pd.DataFrame(columns=[\"long\", \"lat\", \"maxPM25\"])\n", + "for col in dataset[1:]:\n", + " res = [i for i in dataset[col].values if i >= 15]\n", + " if len(res) == 0 or col == \"Unnamed\":\n", + " dataset = dataset.drop([col], axis = 1)\n", + " else:\n", + " if \"Poi\" in col:\n", + " col = col.strip(\"Point()\")\n", + " col = col.rstrip(\").1\")\n", + " long, lat = col.split()\n", + " maxPM25 = maxPM25._append({'long': float(long), 'lat': float(lat), 'maxPM25': max(res)}, ignore_index=True)\n", + "maxPM25.head()\n", + "\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "\n", + "fig = px.density_mapbox(maxPM25, lat = 'lat', lon = 'long', z = 'maxPM25',\n", + " radius = 8,\n", + " zoom = 6,\n", + " mapbox_style = 'open-street-map')\n", + "fig.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 }, + "id": "9e8FHWw0G_51", + "outputId": "0c720fcf-7c68-41f9-c44c-30d1c286a8b9" + }, + "execution_count": 13, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Step 4: Installing the latest version of PAMI package" - ], - "metadata": { - "id": "1E1moYD_HDRs" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Rkif2EjkHGUz", - "outputId": "5a989e14-6885-4ed8-b96b-93c875971ed8" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: pami in /usr/local/lib/python3.10/dist-packages (2023.8.6.8)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", - 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Temporal Database" + ], + "metadata": { + "id": "OATEUGyZHTk-" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.dbStats import TemporalDatabase as tds\n", + "obj = tds.TemporalDatabase('PM24HeavyPollutionRecordingSensors.csv')\n", + "obj.run()\n", + "obj.printStats()\n", + "obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "eCNo_Qc9Qa_T", + "outputId": "7d0624d5-e9cc-478d-c441-2b7240045ba7" + }, + "execution_count": 16, + "outputs": [ { - "cell_type": "code", - "source": [ - "from PAMI.extras.DF2DB import DenseFormatDF as db\n", - "obj = db.DenseFormatDF(dataset, '>=', 35)\n", - "obj.createTemporal('PM24HeavyPollutionRecordingSensors.csv')" - ], - "metadata": { - "id": "Y8GWtI4aG4Xx" - }, - "execution_count": 15, - "outputs": [] + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size (total no of transactions) : 46005\n", + "Number of items : 1119\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 11.747201391153135\n", + "Maximum Transaction Size : 503\n", + "Standard Deviation Transaction Size : 33.61077193149669\n", + "Variance in Transaction Sizes : 1129.7085460433682\n", + "Sparsity : 0.989502054163402\n" + ] }, { - "cell_type": "markdown", - "source": [ - "### Step 6: Printing the statistics of the Temporal Database" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "OATEUGyZHTk-" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "from PAMI.extras.dbStats import temporalDatabaseStats as tds\n", - "obj = tds.temporalDatabaseStats('PM24HeavyPollutionRecordingSensors.csv')\n", - "obj.run()\n", - "obj.printStats()\n", - "obj.plotGraphs()" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "eCNo_Qc9Qa_T", - "outputId": "7d0624d5-e9cc-478d-c441-2b7240045ba7" - }, - "execution_count": 16, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size (total no of transactions) : 46005\n", - "Number of items : 1119\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 11.747201391153135\n", - "Maximum Transaction Size : 503\n", - "Standard Deviation Transaction Size : 33.61077193149669\n", - "Variance in Transaction Sizes : 1129.7085460433682\n", - "Sparsity : 0.989502054163402\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] + ] + } + ] } diff --git a/notebooks/extras/dbStats/transactionalDatabaseStats.ipynb b/notebooks/extras/dbStats/transactionalDatabaseStats.ipynb index 0b1f2082..6ee87df0 100644 --- a/notebooks/extras/dbStats/transactionalDatabaseStats.ipynb +++ b/notebooks/extras/dbStats/transactionalDatabaseStats.ipynb @@ -23,7 +23,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/extras/stats/Graphs.ipynb b/notebooks/extras/stats/Graphs.ipynb new file mode 100644 index 00000000..29cd7437 --- /dev/null +++ b/notebooks/extras/stats/Graphs.ipynb @@ -0,0 +1,35 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/extras/stats/SequenceDatabase.ipynb b/notebooks/extras/stats/SequenceDatabase.ipynb new file mode 100644 index 00000000..29cd7437 --- /dev/null +++ b/notebooks/extras/stats/SequenceDatabase.ipynb @@ -0,0 +1,35 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/extras/stats/SpatioTemporalDatabase.ipynb b/notebooks/extras/stats/SpatioTemporalDatabase.ipynb new file mode 100644 index 00000000..29cd7437 --- /dev/null +++ b/notebooks/extras/stats/SpatioTemporalDatabase.ipynb @@ -0,0 +1,35 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/extras/stats/SpatioTransactionalDatabase.ipynb b/notebooks/extras/stats/SpatioTransactionalDatabase.ipynb new file mode 100644 index 00000000..29cd7437 --- /dev/null +++ b/notebooks/extras/stats/SpatioTransactionalDatabase.ipynb @@ -0,0 +1,35 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/extras/stats/TemporalDatabase.ipynb b/notebooks/extras/stats/TemporalDatabase.ipynb new file mode 100644 index 00000000..ca325477 --- /dev/null +++ b/notebooks/extras/stats/TemporalDatabase.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Knowing the Statistics of a Temporal Database" + ], + "metadata": { + "id": "egIVPWRhqAnE" + }, + "id": "egIVPWRhqAnE" + }, + { + "cell_type": "markdown", + "source": [ + "In this notebook, we will learn the process to print the statistics of a transactional database. These statistics are crucial to specify the constraints, such as _minimum support_ and _maximum periodicity_ values." + ], + "metadata": { + "id": "f2nLzFBqqEkc" + }, + "id": "f2nLzFBqqEkc" + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Install the latest version of PAMI" + ], + "metadata": { + "id": "v8zmAJ6PqMsn" + }, + "id": "v8zmAJ6PqMsn" + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "collapsed": true, + "id": "initial_id", + "outputId": "db6f011e-f972-4041-b02f-7a7ca6a1bb41", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2024.5.24.1-py3-none-any.whl (999 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m999.9/999.9 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: 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Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4752 sha256=a568d3e81baf6c37835b0270a4962644486585a5cb647ead1a3504a71c442887\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, deprecated, sphinxcontrib-jquery, discord.py, sphinx-rtd-theme, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 deprecated-1.2.14 discord.py-2.3.2 pami-2024.5.24.1 python-easyconfig-0.1.7 resource-0.2.1 sphinx-rtd-theme-2.0.0 sphinxcontrib-jquery-4.1 validators-0.28.1\n" + ] + } + ], + "source": [ + "!pip install -U pami" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Download a sample temporal database" + ], + "metadata": { + "id": "81-jD-VNqTEY" + }, + "id": "81-jD-VNqTEY" + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv" + ], + "metadata": { + "id": "hXCQJoVfqYHu", + "outputId": "fce27d8f-0412-4c49-d77d-7e680d9d88fb", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "hXCQJoVfqYHu", + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-05-23 22:55:39-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.10MB/s in 6.4s \n", + "\n", + "2024-05-23 22:55:47 (703 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Check the format of the file by printing few lines" + ], + "metadata": { + "id": "jmAoj9GVqtp7" + }, + "id": "jmAoj9GVqtp7" + }, + { + "cell_type": "code", + "source": [ + "!head -6 Temporal_T10I4D100K.csv" + ], + "metadata": { + "id": "67yMe3z5qyJQ", + "outputId": "221408d7-d1b3-477a-cb78-141f6d8baae7", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "67yMe3z5qyJQ", + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n", + "3\t35\t249\t674\t712\t733\t759\t854\t950\r\n", + "4\t39\t422\t449\t704\t825\t857\t895\t937\t954\t964\r\n", + "5\t15\t229\t262\t283\t294\t352\t381\t708\t738\t766\t853\t883\t966\t978\r\n", + "6\t26\t104\t143\t320\t569\t620\t798\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Printing the statistics of this temporal database" + ], + "metadata": { + "id": "Az9vUh6Qq3y-" + }, + "id": "Az9vUh6Qq3y-" + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "print(\"---- Other Format -----\")\n", + "obj.printStats()\n", + "obj.plotGraphs()" + ], + "metadata": { + "id": "GNgjWXWHq9a6", + "outputId": "f4ca7204-ceee-486a-f5d0-04a95b069be3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 716 + } + }, + "id": "GNgjWXWHq9a6", + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n", + "---- Other Format -----\n", + "Database size : 99913\n", + "Number of items : 870\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Minimum Inter Arrival Period : 1\n", + "Average Inter Arrival Period : 1.0\n", + "Maximum Inter Arrival Period : 1\n", + "Minimum periodicity : 112\n", + "Average periodicity : 2124.6436781609195\n", + "Maximum periodicicty : 90296\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance : 13.447874088362232\n", + "Sparsity : 0.9883887027691103\n" + ] + }, + { + "output_type": "error", + "ename": "NameError", + "evalue": "name 'plt' is not defined", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"---- Other Format -----\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprintStats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotGraphs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PAMI/extras/dbStats/TemporalDatabase.py\u001b[0m in \u001b[0;36mplotGraphs\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0mitemFrequencies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetFrequenciesInRange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 430\u001b[0m \u001b[0mtransactionLength\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetTransanctionalLengthDistribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotLineGraphFromDictionary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitemFrequencies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Frequency'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'no of items'\u001b[0m\u001b[0;34m,\u001b[0m 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"nbformat_minor": 5 +} \ No newline at end of file diff --git a/notebooks/extras/stats/TransactionalDatabase.ipynb b/notebooks/extras/stats/TransactionalDatabase.ipynb new file mode 100644 index 00000000..9ff16ce2 --- /dev/null +++ b/notebooks/extras/stats/TransactionalDatabase.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Knowing the Statistics of a Transactional Database" + ], + "metadata": { + "id": "p-kw58iVmX4Q" + }, + "id": "p-kw58iVmX4Q" + }, + { + "cell_type": "markdown", + "source": [ + "In this notebook, we will learn the process to print the statistics of a transactional database. These statistics are crucial to specify the constraints, such as _minimum support_ and _minimum all-confidence_ values." + ], + "metadata": { + "id": "HgbP-3-JoR4L" + }, + "id": "HgbP-3-JoR4L" + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Install the latest version of PAMI" + ], + "metadata": { + "id": "avbt_SD9md9Q" + }, + "id": "avbt_SD9md9Q" + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "initial_id", + "outputId": "6ee1804b-dd5a-48f8-fdf8-d2906797f4b2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2024.5.24.1-py3-none-any.whl (999 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m999.9/999.9 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: 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Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4752 sha256=d4add26b3b17f0168825423e9a594a2b1c4320609bf91700c43c47bc1b56a2b5\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, deprecated, sphinxcontrib-jquery, discord.py, sphinx-rtd-theme, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 deprecated-1.2.14 discord.py-2.3.2 pami-2024.5.24.1 python-easyconfig-0.1.7 resource-0.2.1 sphinx-rtd-theme-2.0.0 sphinxcontrib-jquery-4.1 validators-0.28.1\n" + ] + } + ], + "source": [ + "!pip install -U pami" + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "_U60dX6UmkB5" + }, + "id": "_U60dX6UmkB5" + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Download a sample transactional database" + ], + "metadata": { + "id": "4caou5GcmxQo" + }, + "id": "4caou5GcmxQo" + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv #download a sample transactional database" + ], + "metadata": { + "id": "e19ZC-rym7kb", + "outputId": "ddb917d9-82ac-4773-e2d9-69fe3fc0b5f0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "e19ZC-rym7kb", + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-05-23 22:39:31-- https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4019277 (3.8M) [text/csv]\n", + "Saving to: ‘Transactional_T10I4D100K.csv’\n", + "\n", + "Transactional_T10I4 100%[===================>] 3.83M 1.31MB/s in 2.9s \n", + "\n", + "2024-05-23 22:39:34 (1.31 MB/s) - ‘Transactional_T10I4D100K.csv’ saved [4019277/4019277]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Check the format of the file by printing few lines" + ], + "metadata": { + "id": "H58ZA7Y-nDdn" + }, + "id": "H58ZA7Y-nDdn" + }, + { + "cell_type": "code", + "source": [ + "!head -6 Transactional_T10I4D100K.csv" + ], + "metadata": { + "id": "T_c3Msp_nHlQ", + "outputId": "dea70959-a840-47d6-bdd2-dfef113115a4", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "T_c3Msp_nHlQ", + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n", + "35\t249\t674\t712\t733\t759\t854\t950\r\n", + "39\t422\t449\t704\t825\t857\t895\t937\t954\t964\r\n", + "15\t229\t262\t283\t294\t352\t381\t708\t738\t766\t853\t883\t966\t978\r\n", + "26\t104\t143\t320\t569\t620\t798\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Printing the statistics of the file" + ], + "metadata": { + "id": "SZ2JniStnSci" + }, + "id": "SZ2JniStnSci" + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TransactionalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Transactional_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TransactionalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "obj.printStats()\n", + "obj.plotGraphs()" + ], + "metadata": { + "id": "rbG5FpYEnWl5", + "outputId": "d8c4d9f9-4d54-4b15-a8b5-62cee967850e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "id": "rbG5FpYEnWl5", + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99935\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883886940304302\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.101836193525791\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667097909135199\n", + "Variance in Transaction Sizes : 13.447741640067324\n", + "Database size (total no of transactions) : 99935\n", + "Number of items : 870\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.101836193525791\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667097909135199\n", + "Variance in Transaction Sizes : 13.447741640067324\n", + "Sparsity : 0.9883886940304302\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" + }, + "metadata": {} + } + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + }, + "colab": { + "provenance": [], + "include_colab_link": true + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/notebooks/extras/stats/UtilityDatabase.ipynb b/notebooks/extras/stats/UtilityDatabase.ipynb new file mode 100644 index 00000000..29cd7437 --- /dev/null +++ b/notebooks/extras/stats/UtilityDatabase.ipynb @@ -0,0 +1,35 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/faultTolerantFrequentPatterns/basic/FTFPGrowth.ipynb b/notebooks/faultTolerantFrequentPatterns/basic/FTFPGrowth.ipynb index 7a958dff..590bf7cb 100644 --- a/notebooks/faultTolerantFrequentPatterns/basic/FTFPGrowth.ipynb +++ b/notebooks/faultTolerantFrequentPatterns/basic/FTFPGrowth.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/frequentPattern/basic/ECLAT.ipynb b/notebooks/frequentPattern/basic/ECLAT.ipynb index cb53353a..1d1ff782 100644 --- a/notebooks/frequentPattern/basic/ECLAT.ipynb +++ b/notebooks/frequentPattern/basic/ECLAT.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/frequentPattern/basic/ECLATDiffset.ipynb b/notebooks/frequentPattern/basic/ECLATDiffset.ipynb index 3b26d65e..d15ba194 100644 --- a/notebooks/frequentPattern/basic/ECLATDiffset.ipynb +++ b/notebooks/frequentPattern/basic/ECLATDiffset.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/frequentPattern/basic/ECLATbitset.ipynb b/notebooks/frequentPattern/basic/ECLATbitset.ipynb index 9cfc6115..97420416 100644 --- a/notebooks/frequentPattern/basic/ECLATbitset.ipynb +++ b/notebooks/frequentPattern/basic/ECLATbitset.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/frequentPattern/basic/FPGrowth.ipynb b/notebooks/frequentPattern/basic/FPGrowth.ipynb index 0ff79396..4c60c8f7 100644 --- a/notebooks/frequentPattern/basic/FPGrowth.ipynb +++ b/notebooks/frequentPattern/basic/FPGrowth.ipynb @@ -23,7 +23,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/frequentPattern/closed/CHARM.ipynb b/notebooks/frequentPattern/closed/CHARM.ipynb index cc3eedc6..d8c8b8f0 100644 --- a/notebooks/frequentPattern/closed/CHARM.ipynb +++ b/notebooks/frequentPattern/closed/CHARM.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/frequentPattern/maximal/MaxFPGrowth.ipynb b/notebooks/frequentPattern/maximal/MaxFPGrowth.ipynb index d3bc632d..883c5766 100644 --- a/notebooks/frequentPattern/maximal/MaxFPGrowth.ipynb +++ b/notebooks/frequentPattern/maximal/MaxFPGrowth.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/frequentPattern/pyspark/parallelApriori.ipynb b/notebooks/frequentPattern/pyspark/parallelApriori.ipynb index 34287ecd..99875780 100644 --- a/notebooks/frequentPattern/pyspark/parallelApriori.ipynb +++ b/notebooks/frequentPattern/pyspark/parallelApriori.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/frequentPattern/pyspark/parallelECLAT.ipynb b/notebooks/frequentPattern/pyspark/parallelECLAT.ipynb index 97ce3934..95d018aa 100644 --- a/notebooks/frequentPattern/pyspark/parallelECLAT.ipynb +++ b/notebooks/frequentPattern/pyspark/parallelECLAT.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/frequentPattern/pyspark/parallelFPGrowth.ipynb b/notebooks/frequentPattern/pyspark/parallelFPGrowth.ipynb index e3269893..dc776098 100644 --- a/notebooks/frequentPattern/pyspark/parallelFPGrowth.ipynb +++ b/notebooks/frequentPattern/pyspark/parallelFPGrowth.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/frequentPattern/topk/FAE.ipynb b/notebooks/frequentPattern/topk/FAE.ipynb index 59481bde..151160be 100644 --- a/notebooks/frequentPattern/topk/FAE.ipynb +++ b/notebooks/frequentPattern/topk/FAE.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/fuzzyCorrelatedPattern/basic/FCPGrowth.ipynb b/notebooks/fuzzyCorrelatedPattern/basic/FCPGrowth.ipynb index 6126b722..7bf385a7 100644 --- a/notebooks/fuzzyCorrelatedPattern/basic/FCPGrowth.ipynb +++ b/notebooks/fuzzyCorrelatedPattern/basic/FCPGrowth.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/fuzzyFrequentPattern/basic/FFIMiner.ipynb b/notebooks/fuzzyFrequentPattern/basic/FFIMiner.ipynb index 28646457..52608e09 100644 --- a/notebooks/fuzzyFrequentPattern/basic/FFIMiner.ipynb +++ b/notebooks/fuzzyFrequentPattern/basic/FFIMiner.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/fuzzyGeoreferencedFrequentPattern/basic/FFSPMiner.ipynb b/notebooks/fuzzyGeoreferencedFrequentPattern/basic/FFSPMiner.ipynb index 3d9d2aa6..db41e72d 100644 --- a/notebooks/fuzzyGeoreferencedFrequentPattern/basic/FFSPMiner.ipynb +++ b/notebooks/fuzzyGeoreferencedFrequentPattern/basic/FFSPMiner.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/gdsstats.ipynb b/notebooks/gdsstats.ipynb index 32a5b51f..01a90494 100644 --- a/notebooks/gdsstats.ipynb +++ b/notebooks/gdsstats.ipynb @@ -141,7 +141,7 @@ } ], "source": [ - "from PAMI.extras.dbStats.temporalDatabaseStats import temporalDatabaseStats as alg\n", + "from PAMI.extras.dbStats.TemporalDatabase import TemporalDatabase as alg\n", "\n", "obj = alg(fileName)\n", "obj.run()\n", diff --git a/notebooks/georeferencedFrequentPattern/basic/SpatialECLAT.ipynb b/notebooks/georeferencedFrequentPattern/basic/SpatialECLAT.ipynb index e7af7c89..75e3a5af 100644 --- a/notebooks/georeferencedFrequentPattern/basic/SpatialECLAT.ipynb +++ b/notebooks/georeferencedFrequentPattern/basic/SpatialECLAT.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/georeferencedPeriodicFrequentPattern/basic/GPFPMiner.ipynb b/notebooks/georeferencedPeriodicFrequentPattern/basic/GPFPMiner.ipynb index ea6cacc4..8a2c73a5 100644 --- a/notebooks/georeferencedPeriodicFrequentPattern/basic/GPFPMiner.ipynb +++ b/notebooks/georeferencedPeriodicFrequentPattern/basic/GPFPMiner.ipynb @@ -1,718 +1,718 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XZ4vrXSQ1yEs" - }, - "source": [ - "# Finding Geo-Referenced (Spatial) Periodic Frequent patterns in Temporal Databases using GPFPMiner" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "roOSCMZX2Eb2" - }, - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Geo-Referenced (Spatial) Periodic Frequent patterns in a temporal database using the GPFPMiner algorithm. In the final part, we describe an advanced approach, where we evaluate the GPFPMiner algorithm on a dataset at different minimum support threshold values.\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TFlIIF_X2SzU" - }, - "source": [ - "# Prerequisites:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TqMwpaLw2XLu" - }, - "source": [ - "1. Installing the PAMI library" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "19e8dda1-db44-4730-f957-48dd053bf090" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", - 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] - } - ], - "source": [ - "!pip install -U pami #install the pami repository" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rYfvWHRN2oBs" - }, - "source": [ - "2. Downloading a sample dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "300820aa-5507-44cb-eff4-528f6e4662ba" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-08 13:33:44-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.02MB/s in 6.5s \n", - "\n", - "2023-09-08 13:33:52 (695 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ], - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "USUJbpXu3Gkw" - }, - "source": [ - "3. Printing few lines of a dataset to know its format." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "b7cca21e-554f-440e-f335-e2af36066273" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ], - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oQQdz3qn3Qwz" - }, - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "62Vkqg-C3WVZ" - }, - "source": [ - "## Part 1: Finding Geo-Referenced (Spatial) Periodic Frequent patterns using GPFPMiner" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gaxxPgXv3ecW" - }, - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "87f49162-ffa8-4513-efee-6fd8c12ad4e6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ], - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1oJIEK8A3wQS" - }, - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "d88bf225-3dcf-498b-9b1b-b9e910ed8e1a" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ], - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zpLiRCBp39k9" - }, - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "RP9ynbti4L48" - }, - "outputs": [], - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "neighborFile = '/content/t10_neighbours.txt'\n", - "maxmunPeriodCount = 5000 #user can specify different value." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XZ4vrXSQ1yEs" + }, + "source": [ + "# Finding Geo-Referenced (Spatial) Periodic Frequent patterns in Temporal Databases using GPFPMiner" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "roOSCMZX2Eb2" + }, + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Geo-Referenced (Spatial) Periodic Frequent patterns in a temporal database using the GPFPMiner algorithm. In the final part, we describe an advanced approach, where we evaluate the GPFPMiner algorithm on a dataset at different minimum support threshold values.\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TFlIIF_X2SzU" + }, + "source": [ + "# Prerequisites:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TqMwpaLw2XLu" + }, + "source": [ + "1. Installing the PAMI library" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "19e8dda1-db44-4730-f957-48dd053bf090" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m5.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", + "Collecting resource (from pami)\n", + " Downloading Resource-0.2.1-py2.py3-none-any.whl (25 kB)\n", + "Collecting validators (from pami)\n", + " Downloading validators-0.22.0-py3-none-any.whl (26 kB)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.42.1)\n", + "Requirement 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Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", + "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.30.2)\n", + "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.10.2)\n", + "Building wheels for collected packages: JsonForm, JsonSir\n", + " Building wheel for JsonForm (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonForm: filename=JsonForm-0.0.2-py3-none-any.whl size=3313 sha256=51d3215f8db1266733da55b2cb435ac44ee996d1477a5abc4d23a4870c1bba4f\n", + " Stored in directory: /root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=11caa622a1ea3ba1cfab01b6d4d71dd2bf2172c79bd515f0b334c768b94c9f94\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" + ] + } + ], + "source": [ + "!pip install -U pami #install the pami repository" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rYfvWHRN2oBs" + }, + "source": [ + "2. Downloading a sample dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "300820aa-5507-44cb-eff4-528f6e4662ba" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-08 13:33:44-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.02MB/s in 6.5s \n", + "\n", + "2023-09-08 13:33:52 (695 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ], + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "USUJbpXu3Gkw" + }, + "source": [ + "3. Printing few lines of a dataset to know its format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "b7cca21e-554f-440e-f335-e2af36066273" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ], + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oQQdz3qn3Qwz" + }, + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "62Vkqg-C3WVZ" + }, + "source": [ + "## Part 1: Finding Geo-Referenced (Spatial) Periodic Frequent patterns using GPFPMiner" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gaxxPgXv3ecW" + }, + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "87f49162-ffa8-4513-efee-6fd8c12ad4e6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ], + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1oJIEK8A3wQS" + }, + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 }, + "id": "y7pfaeJV34H_", + "outputId": "d88bf225-3dcf-498b-9b1b-b9e910ed8e1a" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "-Yr0r7zw4Q85" - }, - "source": [ - "### Step 4: Mining Geo-Referenced (Spatial) Periodic Frequent patterns using GPFPMiner" + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BZzrC2Pl4XGJ", - "outputId": "fafaeadd-3856-4667-c229-e1b5bf330e2e" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", - "Total No of patterns: Patterns Support Period\n", - "0 368\\t 7818 113\n", - "1 529\\t 7051 133\n", - "2 829\\t 6801 146\n", - "3 766\\t 6258 112\n", - "4 722\\t 5837 136\n", - ".. ... ... ...\n", - "784 426\\t 111 4525\n", - "785 191\\t 111 4449\n", - "786 856\\t 109 4260\n", - "787 102\\t 109 4201\n", - "788 330\\t 102 4598\n", - "\n", - "[789 rows x 3 columns]\n", - "Runtime: 1.8990938663482666\n", - "Memory (RSS): 361189376\n", - "Memory (USS): 313446400\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "from PAMI.geoReferencedPeriodicFrequentPattern.basic import GPFPMiner as alg #import the algorithm\n", - "\n", - "obj = alg.GPFPMiner(iFile=inputFile, minSup=minimumSupportCount, maxPer=maxmunPeriodCount, nFile=neighborFile, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('periodicFrequentPatternsMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(frequentPatternsDF)) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "3M8FtfKU4bhu" - }, - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b7IvA0IW4hBe", - "outputId": "69c18e51-9846-4302-fe0b-0ce3b5a4bfa3" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "368\t: 7818: 113 \n", - "529\t: 7051: 133 \n", - "829\t: 6801: 146 \n", - "766\t: 6258: 112 \n", - "722\t: 5837: 136 \n", - "354\t: 5829: 140 \n", - "684\t: 5403: 184 \n", - "217\t: 5365: 204 \n", - "494\t: 5096: 186 \n", - "419\t: 5047: 159 \n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "!head 'periodicFrequentPatternsMinSupCount100.txt'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j4CpTPXw4k9I" - }, - "source": [ - "The storage format is: _periodicFrequentPattern:support_\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kC71sBV74qY0" - }, - "source": [ - "## Part 2: Evaluating the GPFPMiner algorithm on a dataset at different minSup values" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EobpZCdu6G0Z" - }, - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "W96B78JT6KT2" - }, - "outputs": [], - "source": [ - "#Import the libraries\n", - "from PAMI.geoReferencedPeriodicFrequentPattern.basic import GPFPMiner as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "neighborFile = '/content/t10_neighbours.txt'\n", - "maximunPeriodCount = 5000\n", - "minimumSupportCountList = [100, 200, 300, 400, 500]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gS09HRlY6PPR" - }, - "source": [ - "### Step 2: Create a data frame to store the results of GPFPMiner" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0tbQu3re6VGI" - }, - "outputs": [], - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximunPeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of GPFPMiner algorithm" - ] + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zpLiRCBp39k9" + }, + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RP9ynbti4L48" + }, + "outputs": [], + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "neighborFile = '/content/t10_neighbours.txt'\n", + "maxmunPeriodCount = 5000 #user can specify different value." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Yr0r7zw4Q85" + }, + "source": [ + "### Step 4: Mining Geo-Referenced (Spatial) Periodic Frequent patterns using GPFPMiner" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BZzrC2Pl4XGJ", + "outputId": "fafaeadd-3856-4667-c229-e1b5bf330e2e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", + "Total No of patterns: Patterns Support Period\n", + "0 368\\t 7818 113\n", + "1 529\\t 7051 133\n", + "2 829\\t 6801 146\n", + "3 766\\t 6258 112\n", + "4 722\\t 5837 136\n", + ".. ... ... ...\n", + "784 426\\t 111 4525\n", + "785 191\\t 111 4449\n", + "786 856\\t 109 4260\n", + "787 102\\t 109 4201\n", + "788 330\\t 102 4598\n", + "\n", + "[789 rows x 3 columns]\n", + "Runtime: 1.8990938663482666\n", + "Memory (RSS): 361189376\n", + "Memory (USS): 313446400\n" + ] + } + ], + "source": [ + "from PAMI.geoReferencedPeriodicFrequentPattern.basic import GPFPMiner as alg #import the algorithm\n", + "\n", + "obj = alg.GPFPMiner(iFile=inputFile, minSup=minimumSupportCount, maxPer=maxmunPeriodCount, nFile=neighborFile, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('periodicFrequentPatternsMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(frequentPatternsDF)) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3M8FtfKU4bhu" + }, + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b7IvA0IW4hBe", + "outputId": "69c18e51-9846-4302-fe0b-0ce3b5a4bfa3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "368\t: 7818: 113 \n", + "529\t: 7051: 133 \n", + "829\t: 6801: 146 \n", + "766\t: 6258: 112 \n", + "722\t: 5837: 136 \n", + "354\t: 5829: 140 \n", + "684\t: 5403: 184 \n", + "217\t: 5365: 204 \n", + "494\t: 5096: 186 \n", + "419\t: 5047: 159 \n" + ] + } + ], + "source": [ + "!head 'periodicFrequentPatternsMinSupCount100.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j4CpTPXw4k9I" + }, + "source": [ + "The storage format is: _periodicFrequentPattern:support_\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kC71sBV74qY0" + }, + "source": [ + "## Part 2: Evaluating the GPFPMiner algorithm on a dataset at different minSup values" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EobpZCdu6G0Z" + }, + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "W96B78JT6KT2" + }, + "outputs": [], + "source": [ + "#Import the libraries\n", + "from PAMI.geoReferencedPeriodicFrequentPattern.basic import GPFPMiner as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "neighborFile = '/content/t10_neighbours.txt'\n", + "maximunPeriodCount = 5000\n", + "minimumSupportCountList = [100, 200, 300, 400, 500]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gS09HRlY6PPR" + }, + "source": [ + "### Step 2: Create a data frame to store the results of GPFPMiner" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0tbQu3re6VGI" + }, + "outputs": [], + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximunPeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of GPFPMiner algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kn2TtrbW6awD" + }, + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d9d75f4e-2417-4c54-8e46-f1e8b96e7a98" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n" + ] + } + ], + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.GPFPMiner(iFile=inputFile, minSup=minSupCount,maxPer=maximunPeriodCount, nFile=neighborFile, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['GPFPMiner', minSupCount, maximunPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NCQLc9pn7BYG" + }, + "source": [ + "### Step 4: Print the Result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7cbb59ad-c0f2-468e-9a21-190edb7532d9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximunPeriodCount patterns runtime memory\n", + "0 GPFPMiner 100 5000 789 3.037944 377323520\n", + "1 GPFPMiner 200 5000 741 2.996208 377651200\n", + "2 GPFPMiner 300 5000 692 1.996724 378114048\n", + "3 GPFPMiner 400 5000 630 1.900063 377974784\n", + "4 GPFPMiner 500 5000 569 1.944077 378048512\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S-prY3W27U4Z" + }, + "source": [ + "### Step 5: Visualizing the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "outputId": "50d6330a-f4df-47c9-8af6-16d985edaa20" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "kn2TtrbW6awD" - }, - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 14 }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d9d75f4e-2417-4c54-8e46-f1e8b96e7a98" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.GPFPMiner(iFile=inputFile, minSup=minSupCount,maxPer=maximunPeriodCount, nFile=neighborFile, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['GPFPMiner', minSupCount, maximunPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "NCQLc9pn7BYG" - }, - "source": [ - "### Step 4: Print the Result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "7cbb59ad-c0f2-468e-9a21-190edb7532d9" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximunPeriodCount patterns runtime memory\n", - "0 GPFPMiner 100 5000 789 3.037944 377323520\n", - "1 GPFPMiner 200 5000 741 2.996208 377651200\n", - "2 GPFPMiner 300 5000 692 1.996724 378114048\n", - "3 GPFPMiner 400 5000 630 1.900063 377974784\n", - "4 GPFPMiner 500 5000 569 1.944077 378048512\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "print(result)" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "S-prY3W27U4Z" - }, - "source": [ - "### Step 5: Visualizing the results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "50d6330a-f4df-47c9-8af6-16d985edaa20" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 14 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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6Hq8tikRiWp7U5REREVUoBhQ942pjht9G+uHrPk1gaaLAqes56P7DIczddQHFpWqpyyMiIqoQDCh6SBAEvN7KFbuntENHb0eUlImYt+ciuv9wCCevZUtdHhER0QtjQNFjjioTLB3SEgsGNoetuRIX0vPx2qJIfPnPWdwrLpO6PCIioufGgKLnBEFA9yYu2D2lHXo3rwm1CCw7lIyQ7w8i6hI3HyQiIv3EgGIgapgrMfeNZlgxvBWcH2w+uCwG0zfEI7eQmw8SEZF+YUAxMC83dMDOyUEY3NoNALD2aAo6zTmIPee4+SAREekPBhQDZGlihC96+eD30a1Rx9YMabmFGLnqGCZw80EiItITDCgGrHVdW2yfFIS3gupCJgCbT91Ex7kHufkgERHpPAYUA2diJMf0rl6ICAtEQydLZN0txsTfTyL0l2NIyymUujwiIqJHYkCpJprUssbm8DaY0tETRnIBu89loOOcA1h7NIWzKUREpHMYUKoRpUKGCR088M+Etmjmao28olJM3xCPgcticDWTmw8SEZHuYECphjwdLfHX2AB82M0LJkYyRF/ORMj3B/HTocvcfJCIiHQCA0o1JZcJGNW2LnZOaoeAerYoLFHji3/O4bXFUdx8kIiIJMeAUs252Zph9Sg/zH7NB5bGCpy6lo3uPxzC97u5+SAREUmHAYUgCAL6v+SGXVPaIdjLASVlIr7ffRE9FhzGKW4+SEREEmBAIQ0nKxMsG+qL+QOaw8ZcifNpeei9KBIzt57j5oNERFSlGFCoHEEQ0KPp/c0HezZzgVoElh68jC7zDuLI5UypyyMiomqCAYUeycZciXn9m2P5MF84qUxwJbMA/ZcewQcb45HHzQeJiKiSMaDQE3XwcsTOKUEY6Hd/88HVMSnoNPcg9p7n5oNERFR5GFDoqVQmRpjZ2wdrQv1Q29YMqTmFGLHyGCb9fgJZd4ulLo+IiAwQAwo9s4B6dtg+MQij/3/zwYiTN9FxzgH8feomb5dPREQVigGFtGKqlOP9rl7YMC4QDRwtkXm3GOPXnkDoL3FIz+Xmg0REVDEYUOi5NHO1xt/j22BSsMf/bz6YjuA5B/A7Nx8kIqIKwIBCz02pkGFSsCe2jG+LprWskFdYimkb4jHopxikZBZIXR4REekxBhR6YQ2cLLFhXCA+6Hp/88GoS/c3H1x+OJmbDxIR0XNhQKEKIZcJCA2qi+0Tg9C6rg3ulZRhxpaz6LskChfTufkgERFphwGFKlQdO3OsGdUaM3v7wMJYgRMp2eg2/zDm77nIzQeJiOiZaRVQ6tSpA0EQHnqEhYUBANq3b//QsTFjxpR7jZSUFHTr1g1mZmZwcHDA1KlTUVpaWnE9IsnJZAIG+rlh15QgvNLQAcVlaszZdQE9FhzG6evZUpdHRER6QKFN49jYWJSV/W/TuISEBHTs2BH9+vXTPBcaGorPP/9c87uZmZnm57KyMnTr1g1OTk6IiopCamoqhg4dCiMjI8ycOfNF+kE6yNnKFMuH+WLzqZv47O+zOJ+Wh14LIxHati4md/SEiZFc6hKJiEhHaTWDYm9vDycnJ81jy5YtqFevHtq1a6dpY2ZmVq6NSqXSHNu5cyfOnj2L3377Dc2aNUOXLl0wY8YMLFy4EMXFvCOpIRIEAT2b1cSuyUF4ten9zQd/PHgZXeYdQgw3HyQiosd47jUoxcXF+O233zBixAgIgqB5fvXq1bCzs0Pjxo0xffp0FBT873LT6Oho+Pj4wNHRUfNcSEgIcnNzcebMmce+V1FREXJzc8s9SL/YWhjjhwHNsWyoLxxVxki+fRdvLD2CDyO4+SARET3suQNKREQEsrOzMXz4cM1zAwcOxG+//YZ9+/Zh+vTp+PXXXzF48GDN8bS0tHLhBIDm97S0tMe+16xZs2BlZaV5uLq6Pm/ZJLGO3o7YNaUdBrx0fwx/O5KCkLkHsS8xQ+LKiIhIlwjic972MyQkBEqlEn///fdj2+zduxcdOnRAUlIS6tWrh9GjR+Pq1avYsWOHpk1BQQHMzc2xdetWdOnS5ZGvU1RUhKKiIs3vubm5cHV1RU5OTrmvkEi/RCXdxrQN8UjJuj/L9lrzmviouzdqmCslroyIiCpDbm4urKysnunz+7lmUK5evYrdu3dj1KhRT2zn5+cHAEhKSgIAODk5IT09vVybB787OTk99nWMjY2hUqnKPUj/BdS3w/ZJbTGqjTtkArDhxA0EzzmALae5+SARUXX3XAFlxYoVcHBwQLdu3Z7Y7uTJkwAAZ2dnAIC/vz/i4+ORkfG/6fxdu3ZBpVLB29v7eUohPWemVODD7t74a2wAPBwskHm3GOFrTuCtX+OQwc0HiYiqLa0DilqtxooVKzBs2DAoFP+7SvnSpUuYMWMG4uLicOXKFWzevBlDhw5FUFAQmjRpAgDo1KkTvL29MWTIEJw6dQo7duzAhx9+iLCwMBgbG1dcr0jvNHergS0T2mBCBw8oZAJ2nk1HhzkHsC72GmdTiIiqIa3XoOzcuRMhISFITEyEp6en5vlr165h8ODBSEhIwN27d+Hq6orevXvjww8/LPeVzNWrVzF27Fjs378f5ubmGDZsGGbPnl0u7DyNNt9hkf45l5qL9/46jdPXcwAAberbYdZrPnC1MXvKmUREpMu0+fx+7kWyUmJAMXylZWr8HJmM73ZeQFGpGqZGcrzbuQGG+teBXCY8/QWIiEjnVPoiWaLKppDLMDqoHrZPCsJL7vc3H/zs77PotyQKSRncfJCIyNAxoJBOc7czx++hrfFFr8awMFbgeEo2us47jAV7L6KkjJsPEhEZKgYU0nkymYDBrWtj5+QgvNzAHsVlany78wJ6LIhEwo0cqcsjIqJKwIBCesPF2hQ/D2+F799ohhpmRjiXmoueCyMxe9t5FJaUPf0FiIhIbzCgkF4RBAG9mtfErint0L2JM8rUIpYcuISu8w7haHKW1OUREVEFYUAhvWRnYYwFA1tg6ZCWcLA0xuXbd/H6j9H4eFMC8otKpS6PiIheEAMK6bVOjZywa0o7vOF7f/PBX6KvImTuQRy4cEviyoiI6EUwoJDeszI1wld9m2D1KD+42pjiRvY9DPv5KN5bfxq5hSVSl0dERM+BAYUMRmB9O+yYFIThAXUAAH8cu4aQuQexPzHjyScSEZHOYUAhg2KmVODTHo3wx+jWqG1rhtScQgxfEYupf55Czj3OphAR6QsGFDJIfnVtsX1iEEYEukMQgD/jriNk7kHsO8/ZFCIifcCAQgbLVCnHx696Y91b/nC3M0dabiHeXBmLt9edQk4BZ1OIiHQZAwoZvFZ1bLB1QluManN/NuWv49fRce4B7DmXLnVpRET0GAwoVC2YKuX4sLs31o/xR107c2TkFWHkqmOY8sdJZBcUS10eERH9BwMKVSsta9tg68S2GB1UFzIB2HDiBjrOPYhdZzmbQkSkSxhQqNoxMZLj/a5e+HNMAOram+NWXhFCfzmGSb+fwJ27nE0hItIFDChUbbWsXQNbJ7TFW+3uz6ZEnLyJjnMPYseZNKlLIyKq9hhQqFozMZJjehcv/DU2APUdLHA7vwhv/RqHCWtPIIuzKUREkmFAIQLQ3K0Gtoxvg7Ht60EmAJtP3USnuQewPSFV6tKIiKolBhSi/2diJMd7nRti47hAeDpa4HZ+Mcb8dhzha44jM79I6vKIiKoVBhSi/2jqao2/x7dB2Mv1IJcJ2HI6FZ3mHsTWeM6mEBFVFQYUokcwVsgxNaQhIsYFooGjJTLvFmPc6uMIW30ctzmbQkRU6RhQiJ7Ap5YVNo8PxIRX6kMuE/BP/P3ZlC2nb0IURanLIyIyWAwoRE9hrJBjSqcG2BQWiIZOlsi6W4zwNScwbvVx3MrjbAoRUWVgQCF6Ro1rWmFzeBtM7OABhUzAtoQ0dJp7AJtO3uBsChFRBWNAIdKCUiHD5I6e2BQeCC9nFe4UlGDi7yfx1q9xyMgrlLo8IiKDwYBC9BwauVhhc3ggJgd7QiETsPNsOjrNPYiIE5xNISKqCAwoRM/JSC7DxGAPbA5vg0YuKmQXlGDSHycR+kscMnI5m0JE9CIYUIhekLeLChFhgXi7oyeM5AJ2n0tHx7kHseH4dc6mEBE9JwYUogpgJJdhfAcP/D2+DRrXVCHnXgmmrDuFUauOIZ2zKUREWmNAIapADZ1U2DguEFNDGkApl2HP+Qx0nHMA6+M4m0JEpA0GFKIKZiSXIezl+vh7fBs0qWWF3MJSvPPnKYxYGYu0HM6mEBE9CwYUokrSwMkSG8YG4N3O92dT9iXeQse5B7Du2DXOphARPQUDClElUshlGNe+Pv6Z0AZNXa2RV1iKd9efxvAVsbiZfU/q8oiIdBYDClEV8HC0xF9j/DG9S0MoFTIcuHALIXMP4o/YFM6mEBE9AgMKURVRyGV4q109bJ3QFs3drJFXVIr3/orH0J+P4gZnU4iIymFAIapi9R0ssH5MAD7o6gVjhQyHLt5GyNyDWHuUsylERA8woBBJQC4TEBpUF1sntkXL2jWQX1SK6RviMWT5UVy/UyB1eUREkmNAIZJQPXsLrHvLHx92uz+bcjjp/mzK6pirnE0homqNAYVIYnKZgFFt62L7pCD41q6Bu8Vl+GBjAgb9FINrWZxNIaLqiQGFSEe425njj7f88XF3b5gYyRB1KRMh3x/Er9FXoFZzNoWIqhcGFCIdIpcJGNHGHdsnBuGlOjYoKC7DR5vOYOBPR5CSydkUIqo+GFCIdFAdO3P8Pro1Pn3VG6ZGchy5nIWQ7w9iVRRnU4ioemBAIdJRMpmA4YHu2D6pLfzcbXCvpAyfbD6DAcuO4GrmXanLIyKqVAwoRDqutq051oa2xuc9G8FMKUdMchY6f38IKyKTOZtCRAaLAYVID8hkAob618H2iUHwr2uLeyVl+Ozvs+i/9Aiu3OZsChEZHgYUIj3iZmuG1aP88EWvxjBXynH0ShY6zzuI5YeTUcbZFCIyIAwoRHpGJhMwuHVtbJ8UhMD6tigsUWPGlrN448doXL6VL3V5REQVggGFSE+52pjht5F+mNnbB+ZKOY5dvYMu8w7hp0OXOZtCRHqPAYVIjwmCgIF+btgxOQhtPexQVKrGF/+cQ78lUbjE2RQi0mMMKEQGoFYNM/wy4iXMfs0HFsYKHE/JRpd5h7D04CXOphCRXmJAITIQgiCg/0v3Z1OCPO1RXKrGzK3n0XdJFJIy8qQuj4hIKwwoRAamprUpVr3ZCl/3aQJLYwVOpGSj6/zDWLz/EkrL1FKXR0T0TBhQiAyQIAh4vZUrdk4JQvsG92dTvtp+Hn0WR+FiOmdTiEj3MaAQGTBnK1OsGN4K3/RtAksTBU5dz0G3+YexcF8SZ1OISKcxoBAZOEEQ0M/XFbsmt8MrDR1QXKbGNzsS8driKCSmcTaFiHQTAwpRNeFkZYLlw3zxXb+mUJkocPp6Dl794TAW7L2IEs6mEJGOYUAhqkYEQUCflrWwa0o7BHvdn035ducF9F4UiXOpuVKXR0SkwYBCVA05qkywbKgvvn+jGaxMjZBwIxc9FhzG/D2cTSEi3cCAQlRNCYKAXs1rYtfkIHT0dkRJmYg5uy6g18JInL3J2RQikhYDClE156AywdIhLTGvfzNYmxnhzM37synf776A4lLOphCRNBhQiAiCIKBns5rYOTkIIY0cUaoW8f3ui+i5MBJnbuZIXR4RVUMMKESk4WBpgiWDW+KHAc1Rw8wI51Jz0XNBJObs4mwKEVUtBhQiKkcQBLza1AW7prRDl8ZOKFWLmL/nInosOIyEG5xNIaKqwYBCRI9kZ2GMxYNbYuHAFrAxV+J8Wh56LozEdzsTUVRaJnV5RGTgGFCI6Im6NXHGrslB6NbEGWVqET/sTUKPHyJx+nq21KURkQHTKqDUqVMHgiA89AgLCwMAFBYWIiwsDLa2trCwsECfPn2Qnp5e7jVSUlLQrVs3mJmZwcHBAVOnTkVpaWnF9YiIKpythTEWDmyBRYNawNZcicT0PPReFIWvt5/nbAoRVQqtAkpsbCxSU1M1j127dgEA+vXrBwCYPHky/v77b/z55584cOAAbt68iddee01zfllZGbp164bi4mJERUVh1apVWLlyJT7++OMK7BIRVZauPs7YNaUdXm3qgjK1iEX7L6H7/MM4dS1b6tKIyMAIoiiKz3vypEmTsGXLFly8eBG5ubmwt7fHmjVr0LdvXwDA+fPn4eXlhejoaLRu3Rrbtm1D9+7dcfPmTTg6OgIAlixZgvfeew+3bt2CUql8pvfNzc2FlZUVcnJyoFKpnrd8InoB2xNS8WFEAm7nF0MmAKOD6mFSsAdMjORSl0ZEOkqbz+/nXoNSXFyM3377DSNGjIAgCIiLi0NJSQmCg4M1bRo2bAg3NzdER0cDAKKjo+Hj46MJJwAQEhKC3NxcnDlz5rHvVVRUhNzc3HIPIpJW58bO2DW5HXo2c4FaBJYcuITuPxzGiZQ7UpdGRAbguQNKREQEsrOzMXz4cABAWloalEolrK2ty7VzdHREWlqaps2/w8mD4w+OPc6sWbNgZWWlebi6uj5v2URUgWqYKzGvf3MsHdIS9pbGSMrIR5/FUZi19RwKS7g2hYie33MHlOXLl6NLly5wcXGpyHoeafr06cjJydE8rl27VunvSUTPrlMjJ+yaHITezWtCLQI/HryMrvMPIe4qZ1OI6Pk8V0C5evUqdu/ejVGjRmmec3JyQnFxMbKzs8u1TU9Ph5OTk6bNf6/qefD7gzaPYmxsDJVKVe5BRLrF2kyJuW80w09DfeFgaYzLt+6i75IofPnPWc6mEJHWniugrFixAg4ODujWrZvmuZYtW8LIyAh79uzRPJeYmIiUlBT4+/sDAPz9/REfH4+MjAxNm127dkGlUsHb2/t5+0BEOiTY2xG7JrdDnxa1IIrAskPJ6DrvEI5dyZK6NCLSI1pfxaNWq+Hu7o4BAwZg9uzZ5Y6NHTsWW7duxcqVK6FSqTB+/HgAQFRUFID7lxk3a9YMLi4u+Prrr5GWloYhQ4Zg1KhRmDlz5jPXwKt4iPTD3vPpmL4hHum5RRAEYESgO97p1ACmSl7pQ1QdVepVPLt370ZKSgpGjBjx0LG5c+eie/fu6NOnD4KCguDk5IQNGzZojsvlcmzZsgVyuRz+/v4YPHgwhg4dis8//1zbMohID7zS0BE7J7dDv5b3Z1OWH05Gl3kHcTSZsylE9GQvdB8UqXAGhUj/7EvMwPS/4pGWWwhBAIYH1MHUkAYwUyqkLo2IqkiV3AeFiEgbLzdwwM4pQXjD1xWiCKyIvIIu8w4h5nKm1KURkQ5iQCGiKqMyMcJXfZtg1YiX4GxlgquZBXhj6RF8uvkMCoq5JxcR/Q8DChFVuXae9tgxOQgDXrp/08WVUVfQ+ftDiL7E2RQiuo8BhYgkoTIxwqzXmuCXES+hprUpUrIKMGDZEXwUkYC7RZxNIaruGFCISFJBnvbYPqktBvm5AQB+PXIVXeYdQmJansSVEZGUGFCISHKWJkb4srcPVo/y08ym9FkchX3nM55+MhEZJAYUItIZgfXtsGV8G7Sua4P8olKMXBWL5YeToYd3QyCiF8SAQkQ6pYa5Er+M8EP/Vq5Qi8CMLWfxQUQCSsrUUpdGRFWIAYWIdI5SIcOs13zwYTcvCAKwJiYFw1ccRU5BidSlEVEVYUAhIp0kCAJGta2LZUN8Ya6UIzIpE70XRSL59l2pSyOiKsCAQkQ6LdjbEevHBqCmtSku376LXgsjEZV0W+qyiKiSMaAQkc7zclYhIiwQzd2skXOvBEN/Poo1MSlSl0VElYgBhYj0gr2lMdaGtkbPZi4oVYt4f2M8Zmw5izI1r/AhMkQMKESkN0yM5Pj+jWZ4u6MnAGD54WSE/nIMeYVcPEtkaBhQiEivCIKA8R08sGBgcxgrZNh7PgN9F0fjWlaB1KURUQViQCEivdS9iQvWveUPB0tjJKbnodfCSMRdzZK6LCKqIAwoRKS3mrpaY1N4IBq5qJB5txgDlsYg4sQNqcsiogrAgEJEes3ZyhR/jvFHJ29HFJepMemPk/h2RyLUXDxLpNcYUIhI75kpFVgyuCXGtq8HAFiwLwnha4/jXnGZxJUR0fNiQCEigyCTCXivc0N8268pjOQCtsan4fUfo5GWUyh1aUT0HBhQiMig9G1ZC2tCW8PGXIn4GznoufAwEm7kSF0WEWmJAYWIDE6rOjaIGBcIDwcLpOcWoe+SKGxPSJW6LCLSAgMKERkkN1sz/DUuAO087VFYosaY345j4b4kiCIXzxLpAwYUIjJYKhMjLB/mi+EBdQAA3+xIxNvrTqGolItniXQdAwoRGTSFXIZPezTCF70aQy4TsOHEDQxcFoPb+UVSl0ZET8CAQkTVwuDWtbHqzZdgaaJA3NU76LUwEolpeVKXRUSPwYBCRNVGGw87bBwXiNq2Zrh+5x76LI7CvvMZUpdFRI/AgEJE1Up9BwtEjAtE67o2yC8qxchVsVh+OJmLZ4l0DAMKEVU7NcyV+GWEH/q3coVaBGZsOYv3NyagpEwtdWlE9P8YUIioWlIqZJj1mg8+7OYFQQDWHk3BsJ+PIqegROrSiAgMKERUjQmCgFFt62LZEF+YK+WIupSJ3osikXz7rtSlEVV7DChEVO0Fezti/dgA1LQ2xeXbd9FrYSSikm5LXRZRtcaAQkQEwMtZhYiwQDR3s0bOvRIM/fko1sSkSF0WUbXFgEJE9P/sLY2xNrQ1ejR1QalaxPsb4zFjy1mUqXmFD1FVY0AhIvoXEyM55vVvhikdPQEAyw8nI/SXY8gr5OJZoqrEgEJE9B+CIGBCBw8sGNgcxgoZ9p7PQN/F0biWVSB1aUTVBgMKEdFjdG/ignVv+cPB0hiJ6XnotTAScVezpC6LqFpgQCEieoKmrtbYFB4Ib2cVMu8WY8DSGGw8cV3qsogMHgMKEdFTOFuZYv1Yf3TydkRxmRqT/ziFb3ckQs3Fs0SVhgGFiOgZmCkVWDK4Jca2rwcAWLAvCeFrj+NecZnElREZJgYUIqJnJJMJeK9zQ3zbrymM5AK2xqfh9R+jkZZTKHVpRAaHAYWISEt9W9bCmtDWsDFXIv5GDnouPIz46zlSl0VkUBhQiIieQ6s6NogYFwgPBwuk5xah349R2J6QKnVZRAaDAYWI6Dm52Zrhr3EBaOdpj8ISNcb8dhwL9yVBFLl4luhFMaAQEb0AlYkRlg/zxfCAOgCAb3Yk4u11p1BUysWzRC+CAYWI6AUp5DJ82qMRZvRqDLlMwIYTNzBwWQxu5xdJXRqR3mJAISKqIENa18bKN1vB0kSBuKt30GthJBLT8qQui0gvMaAQEVWgth722DguELVtzXD9zj30WRyFfeczpC6LSO8woBARVbD6DhaIGBeI1nVtkF9UipGrYrH8cDIXzxJpgQGFiKgS1DBX4pcRfnjD1xVqEZix5Sze35iAkjK11KUR6QUGFCKiSqJUyDC7jw8+6OoFQQDWHk3BsJ+PIrugWOrSiHQeAwoRUSUSBAGhQXWxbIgvzJVyRF3KRO9FUbh8K1/q0oh0GgMKEVEVCPZ2xPqxAahpbYrk23fRe1EUopJuS10Wkc5iQCEiqiJezipEhAWiuZs1cu6VYOjPR7EmJkXqsoh0EgMKEVEVsrc0xtrQ1ujR1AWlahHvb4zH53+fRZmaV/gQ/RsDChFRFTMxkmNe/2aY0tETAPBzZDJCfzmGvMISiSsj0h0MKEREEhAEARM6eGDBwOYwVsiw93wG+i6OxrWsAqlLI9IJDChERBLq3sQF697yh4OlMRLT89BrYSTirmZJXRaR5BhQiIgk1tTVGpvCA+HtrELm3WIMWBqDjSeuS10WkaQYUIiIdICzlSnWj/VHJ29HFJepMfmPU/hmx3mouXiWqikGFCIiHWGmVGDJ4JYY274eAGDhvksIW3Mc94rLJK6MqOoxoBAR6RCZTMB7nRvi235NYSQXsC0hDa//GI20nEKpSyOqUgwoREQ6qG/LWlgT2ho25krE38hBz4WHEX89R+qyiKoMAwoRkY5qVccGEeMC4eFggfTcIvT7MQrb4lOlLouoSjCgEBHpMDdbM/w1LgDtPO1RWKLG2NXHsXBfEkSRi2fJsGkdUG7cuIHBgwfD1tYWpqam8PHxwbFjxzTHhw8fDkEQyj06d+5c7jWysrIwaNAgqFQqWFtbY+TIkcjP586eRESPojIxwvJhvhgeUAcA8M2ORLy97hSKSrl4lgyXQpvGd+7cQWBgIF5++WVs27YN9vb2uHjxImrUqFGuXefOnbFixQrN78bGxuWODxo0CKmpqdi1axdKSkrw5ptvYvTo0VizZs0LdIWIyHAp5DJ82qMR6jlY4NPNZ7DhxA1czSrAj0Naws7C+OkvQKRnBFGLecJp06YhMjIShw4demyb4cOHIzs7GxEREY88fu7cOXh7eyM2Nha+vr4AgO3bt6Nr1664fv06XFxcnlpHbm4urKyskJOTA5VK9azlExEZhEMXb2Hc6uPIKyxFrRqmWD6sFRo4WUpdFtFTafP5rdVXPJs3b4avry/69esHBwcHNG/eHMuWLXuo3f79++Hg4IAGDRpg7NixyMzM1ByLjo6GtbW1JpwAQHBwMGQyGWJiYh75vkVFRcjNzS33ICKqrtp62GPjuEDUtjXD9Tv30GdxFPadz5C6LKIKpVVAuXz5MhYvXgwPDw/s2LEDY8eOxYQJE7Bq1SpNm86dO+OXX37Bnj178NVXX+HAgQPo0qULysruf1ealpYGBweHcq+rUChgY2ODtLS0R77vrFmzYGVlpXm4urpq208iIoNS38ECEeMC0bquDfKLSjFyVSyWH07m4lkyGFp9xaNUKuHr64uoqCjNcxMmTEBsbCyio6Mfec7ly5dRr1497N69Gx06dMDMmTOxatUqJCYmlmvn4OCAzz77DGPHjn3oNYqKilBUVKT5PTc3F66urvyKh4iqveJSNT6KSMAfx64BAAa85IbPezaCkZwXaZLuqbSveJydneHt7V3uOS8vL6SkpDz2nLp168LOzg5JSUkAACcnJ2RklJ+KLC0tRVZWFpycnB75GsbGxlCpVOUeREQEKBUyzO7jgw+6ekEQgLVHUzDs56PILiiWujSiF6JVQAkMDHxo5uPChQuoXbv2Y8+5fv06MjMz4ezsDADw9/dHdnY24uLiNG327t0LtVoNPz8/bcohIiIAgiAgNKgulg3xhblSjqhLmei9KAqXb/H2DaS/tAookydPxpEjRzBz5kwkJSVhzZo1WLp0KcLCwgAA+fn5mDp1Ko4cOYIrV65gz5496NmzJ+rXr4+QkBAA92dcOnfujNDQUBw9ehSRkZEIDw9H//79n+kKHiIierRgb0esHxuAmtamSL59F70XRSEq6bbUZRE9F63WoADAli1bMH36dFy8eBHu7u6YMmUKQkNDAQD37t1Dr169cOLECWRnZ8PFxQWdOnXCjBkz4OjoqHmNrKwshIeH4++//4ZMJkOfPn0wf/58WFhYPFMNvMyYiOjxbuUVYfSvx3AiJRsKmYDPezbGQD83qcsi0urzW+uAogsYUIiInqywpAzvrj+NzaduAgBGBLrjg25ekMsEiSuj6qzSFskSEZF+MDGSY17/ZpjS0RMA8HNkMkatikVeYYnElRE9GwYUIiIDJQgCJnTwwIKBzWGskGFf4i30XRyNa1kFUpdG9FQMKEREBq57Exese8sf9pbGSEzPQ6+FkYi7miV1WURPxIBCRFQNNHW1xubwQHg7q5B5txgDlsZg44nrUpdF9FgMKERE1YSzlSnWj/VHJ29HFJepMfmPU/hmx3mo1Xp3rQRVAwwoRETViJlSgSWDW2Js+3oAgIX7LiFszXHcKy6TuDKi8hhQiIiqGZlMwHudG+Lbfk1hJBewLSENr/8YjbScQqlLI9JgQCEiqqb6tqyF1aNao4aZEeJv5KDnwsOIv54jdVlEABhQiIiqtZfcbbAprA08HCyQnluEfj9GYVt8qtRlETGgEBFVd262ZvhrXADaedqjsESNsauPY8Hei9DDG42TAWFAISIiqEyMsHyYL4YH1AEAfLvzAqasO4WiUi6eJWkwoBAREQBAIZfh0x6NMKNXY8hlAjaeuIGBy2JwO79I6tKoGmJAISKicoa0ro2Vb7aCpYkCcVfvoNfCSCSm5UldFlUzDChERPSQth722DguELVtzXD9zj30WRyFfeczpC6LqhEGFCIieqT6DhaIGBcIP3cb5BeVYuSqWCw/nMzFs1QlGFCIiOixapgr8etIP7zh6wq1CMzYchbvb0xASZla6tLIwDGgEBHREykVMszu44MPunpBEIC1R1Mw7OejyC4olro0MmAMKERE9FSCICA0qC6WDfGFuVKOqEuZ6L0oCpdv5UtdGhkoBhQiInpmwd6OWD82ADWtTZF8+y56L4pCVNJtqcsiA8SAQkREWvFyVmFjWACau1kj514Jhv58FGtiUqQuiwwMAwoREWnNwdIEa0Nbo0dTF5SqRby/MR6f/30WZWpe4UMVgwGFiIiei4mRHPP6N8OUjp4AgJ8jkzFqVSzyCkskrowMAQMKERE9N0EQMKGDBxYMbA5jhQz7Em+h7+JoXMsqkLo00nMMKERE9MK6N3HBurf8YW9pjMT0PPRcGImjyVlSl0V6jAGFiIgqRFNXa2wOD0QjFxWy7hZj0E9H8EcsF8/S82FAISKiCuNsZYo/x/ijq48TSspEvPfX/cWzpbzzLGmJAYWIiCqUmVKBBQNaYFKwB4D7i2dHrDqGnHtcPEvPjgGFiIgqnEwmYFKwJxYNagETIxkOXriF3osieedZemYMKEREVGm6+jhj/ZgAOFuZ4PKtu+i1MBKHLt6SuizSAwwoRERUqRrXtMKm8EA0d7NGbmEphq+IxcrIZIgib+pGj8eAQkREle7BnWdfa1ETZWoRn/59Fu9vjEdxKRfP0qMxoBARUZUwMZLju35N8X7XhhAEYO3Raxi8PAZZd4ulLo10EAMKERFVGUEQMDqoHpYP84WFsQJHk7PQY8FhJKblSV0a6RgGFCIiqnKvNHTEhnEBcLMxw/U79/DaokjsOpsudVmkQxhQiIhIEp6OltgUFojWdW1wt7gMo389hkX7k7h4lgAwoBARkYRqmCvx60g/DG7tBlEEvt6eiMl/nERhSZnUpZHEGFCIiEhSRnIZvujlgxk9G0EuExBx8ibeWHoEGbmFUpdGEmJAISIinTDEvw5+GfESrEyNcOpaNnosiET89RypyyKJMKAQEZHOCKxvh01hgahnb4603EL0+zEKf5+6KXVZJAEGFCIi0il17MyxMSwQ7RvYo7BEjfFrT2DOzkSo1Vw8W50woBARkc5RmRhh+bBWCG3rDgCYvzcJ41YfR0FxqcSVUVVhQCEiIp0klwn4oJs3vunbBEq5DNvPpKHP4mhcv1MgdWlUBRhQiIhIp/XzdcXa0X6ws1DiXGouei2MxLErWVKXRZWMAYWIiHRey9o22BTeBl7OKtzOL8aAZUew7tg1qcuiSsSAQkREeqGmtSn+GuuPzo2cUFIm4t31p/HFlrMo4+JZg8SAQkREesNMqcCiQS0woYMHAOCnw8kYsTIWuYUlEldGFY0BhYiI9IpMJmBKR08sGNgcJkYyHLhwC70XRiL59l2pS6MKxIBCRER6qXsTF/z5VgCcVCa4dOsuei2MRGTSbanLogrCgEJERHrLp5YVNocHopmrNXLulWDoz0fxS/QV7ohsABhQiIhIrzmoTPD76Nbo3bwmytQiPt50Bh9GJKCkTC11afQCGFCIiEjvmRjJMef1pnivc0MIArA6JgVDlsfgzt1iqUuj58SAQkREBkEQBIxtXw/LhvjCXCnHkctZ6LkwEhfS86QujZ4DAwoRERmUYG9HbBgXCFcbU6RkFeC1RVHYcy5d6rJISwwoRERkcBo4WWJTWBv4udsgv6gUo345hh8PXOLiWT3CgEJERAbJxlyJX0f6YcBLbhBFYNa283h73SkUlpRJXRo9AwYUIiIyWEqFDDN7N8ZnPRpBLhOw4cQNDFh2BBl5hVKXRk/BgEJERAZNEAQMC6iDVW++BJWJAidSstFzQSQSbuRIXRo9AQMKERFVC2087BARFoi69uZIzSlE3yVR+Od0qtRl0WMwoBARUbVR194CG8cFIsjTHoUlaoStOY65uy5AzR2RdQ4DChERVStWpkb4eZgvRrZxBwDM23MRYWuOo6C4VOLK6N8YUIiIqNpRyGX4qLs3vu7TBEZyAdsS0tB3cTRuZN+TujT6fwwoRERUbb3eyhVrQlvD1lyJs6m56LkgEnFX70hdFoEBhYiIqrlWdWywKTwQDZ0scTu/CAOWHsH6uOtSl1XtMaAQEVG1V6uGGf4aG4BO3o4oLlPjnT9PYebWcyjj4lnJMKAQEREBMDdWYMnglhj/Sn0AwNKDlzFqVSzyCkskrqx60jqg3LhxA4MHD4atrS1MTU3h4+ODY8eOaY6LooiPP/4Yzs7OMDU1RXBwMC5evFjuNbKysjBo0CCoVCpYW1tj5MiRyM/Pf/HeEBERvQCZTMDbnRpgXv9mMFbIsC/xFnovisLVzLtSl1btaBVQ7ty5g8DAQBgZGWHbtm04e/YsvvvuO9SoUUPT5uuvv8b8+fOxZMkSxMTEwNzcHCEhISgs/N9thQcNGoQzZ85g165d2LJlCw4ePIjRo0dXXK+IiIheQM9mNbHuLX84qoyRlJGPngsjEXXpttRlVSuCqMXWjtOmTUNkZCQOHTr0yOOiKMLFxQVvv/023nnnHQBATk4OHB0dsXLlSvTv3x/nzp2Dt7c3YmNj4evrCwDYvn07unbtiuvXr8PFxeWpdeTm5sLKygo5OTlQqVTPWj4REZFW0nMLMfqXYzh1PQcKmYBPejTCkNa1pS5Lb2nz+a3VDMrmzZvh6+uLfv36wcHBAc2bN8eyZcs0x5OTk5GWlobg4GDNc1ZWVvDz80N0dDQAIDo6GtbW1ppwAgDBwcGQyWSIiYnRphwiIqJK5agywR9v+aNnMxeUqkV8FJGAjyISUFKmlro0g6dVQLl8+TIWL14MDw8P7NixA2PHjsWECROwatUqAEBaWhoAwNHRsdx5jo6OmmNpaWlwcHAod1yhUMDGxkbT5r+KioqQm5tb7kFERFQVTIzk+P6NZpga0gAA8OuRqxi6/Cju3C2WuDLDplVAUavVaNGiBWbOnInmzZtj9OjRCA0NxZIlSyqrPgDArFmzYGVlpXm4urpW6vsRERH9myAICHu5PpYOaQkzpRzRlzPRa1EkkjLypC7NYGkVUJydneHt7V3uOS8vL6SkpAAAnJycAADp6enl2qSnp2uOOTk5ISMjo9zx0tJSZGVladr81/Tp05GTk6N5XLt2TZuyiYiIKkSnRk7YMC4AtWqY4mpmAXovjMK+8xlPP5G0plVACQwMRGJiYrnnLly4gNq17y8Ycnd3h5OTE/bs2aM5npubi5iYGPj7+wMA/P39kZ2djbi4OE2bvXv3Qq1Ww8/P75Hva2xsDJVKVe5BREQkhYZOKmwKC8RLdWyQV1SKEatisfTgJWhxzQk9A60CyuTJk3HkyBHMnDkTSUlJWLNmDZYuXYqwsDAA96fAJk2ahC+++AKbN29GfHw8hg4dChcXF/Tq1QvA/RmXzp07IzQ0FEePHkVkZCTCw8PRv3//Z7qCh4iISGq2Fsb4bZQf+rdyhSgCM7eexzt/nkZRaZnUpRkMrS4zBoAtW7Zg+vTpuHjxItzd3TFlyhSEhoZqjouiiE8++QRLly5FdnY22rRpg0WLFsHT01PTJisrC+Hh4fj7778hk8nQp08fzJ8/HxYWFs9UAy8zJiIiXSCKIlZGXcGMLWehFoEWbtb4cYgv7C2NpS5NJ2nz+a11QNEFDChERKRLDl64hbA1x5FXWAoXKxMsHeqLxjWtpC5L51TafVCIiIjoYUGe9ogIC0RdO3PczClEvyXR2BafKnVZeo0BhYiIqALUs7fAxnGBaOthh3slZRi7+jjm7b7IxbPPiQGFiIiogliZGWHF8FZ4M7AOAGDu7gsIX3sC94q5eFZbDChEREQVSCGX4ZNXG2H2az4wkgv453Qq+v0YhdSce1KXplcYUIiIiCpB/5fc8NtIP9iYK5FwIxc9FkTieModqcvSGwwoRERElcSvri02hQWigaMlbuUVof/SI9hw/LrUZekFBhQiIqJK5Gpjhr/GBSDYyxHFpWpMWXcKs7edR5mai2efhAGFiIioklkYK7B0SEuMa18PALDkwCWM/uUY8gpLJK5MdzGgEBERVQGZTMC7nRtiXv9mUCpk2HM+A30WRyEls0Dq0nQSAwoREVEV6tmsJta95Q8HS2NcSM9Hz4WHEX0pU+qydA4DChERURVr5mqNzeFt0KSWFe4UlGDI8hisiUmRuiydwoBCREQkAScrE6x7yx+vNnVBqVrE+xvj8cmmBJSWqaUuTScwoBAREUnExEiO+f2b4Z1OngCAVdFXMWzFUWQXFEtcmfQYUIiIiCQkCALCX/HAksEtYaaUIzIpE70WRiIpI1/q0iTFgEJERKQDOjd2wvoxAahpbYormQXovTAS+xMzpC5LMgwoREREOsLbRYVN4YFoVacG8opKMWJlLH46dLla7ojMgEJERKRD7CyM8dsoP7zuWwtqEfjin3N4d/1pFJVWrx2RGVCIiIh0jLFCjq/6NMFH3b0hE4A/465j0LIY3M4vkrq0KsOAQkREpIMEQcDINu74eXgrWBorcOzqHfRcEImzN3OlLq1KMKAQERHpsPYNHLAxLBB1bM1wI/se+iyOwvaENKnLqnQMKERERDquvoMFIsIC0aa+He6VlGHMb3H4Yc9Fg148y4BCRESkB6zNlFj5ZisMD6gDAPhu1wVM+P0k7hUb5uJZBhQiIiI9oZDL8GmPRviyd2MoZAL+PnUTr/8YjbScQqlLq3AMKERERHpmkF9t/DrSDzXMjBB/Iwc9FhzGyWvZUpdVoRhQiIiI9JB/PVtsCmsDT0cLZOQV4fUfo7Hp5A2py6owDChERER6ys3WDH+NDUCHhg4oLlVj4u8n8dX281Cr9X/xLAMKERGRHrM0McLSob4Y064eAGDx/ksY/Wsc8otKJa7sxTCgEBER6Tm5TMC0Lg0x942mUCpk2H0uHX0WReFaVoHUpT03BhQiIiID0bt5LfwxujXsLY2RmJ6HngsjEXM5U+qyngsDChERkQFp7lYDm8MD0bimCll3izHopxisPZoidVlaY0AhIiIyMM5WpvjzrQB0a+KMUrWI6Rvi8enmMygtU0td2jNjQCEiIjJApko5FgxojikdPQEAK6Ou4M2VscgpKJG4smfDgEJERGSgBEHAhA4eWDyoBUyN5Dh08TZ6L4rEpVv5Upf2VAwoREREBq6LjzPWj/WHi5UJLt++i14LI3Hwwi2py3oiBhQiIqJqoJGLFTaFt0ELN2vkFZZi+Iqj+Plwss7uiMyAQkREVE3YWxpj7ejW6NuyFtQi8PmWs5i+IR7Fpbq3eJYBhYiIqBoxVsjxTd8m+KCrF2QC8HvsNQz+KQaZ+UVSl1YOAwoREVE1IwgCQoPqYvmwVrA0VuDolSz0WBCJc6m5UpemwYBCRERUTb3c0AEbxgWgtq0ZbmTfQ5/FUdh5Jk3qsgAwoBAREVVrHo6WiBgXiIB6tigoLsPoX+OwcF+S5ItnGVCIiIiquRrmSqwa8RKG+tcGAHyzIxFvrzslaUhhQCEiIiIYyWX4vGdjfNGrMRQyAU1qWUEQBMnqUUj2zkRERKRzBreujdZ1bVHP3lzSOhhQiIiIqJz6DhZSl8CveIiIiEj3MKAQERGRzmFAISIiIp3DgEJEREQ6hwGFiIiIdA4DChEREekcBhQiIiLSOQwoREREpHMYUIiIiEjnMKAQERGRzmFAISIiIp3DgEJEREQ6hwGFiIiIdI5e7mYsiiIAIDc3V+JKiIiI6Fk9+Nx+8Dn+JHoZUPLy8gAArq6uEldCRERE2srLy4OVldUT2wjis8QYHaNWq3Hz5k1YWlpCEIQKfe3c3Fy4urri2rVrUKlUFfrauoD903+G3kf2T/8Zeh8NvX9A5fVRFEXk5eXBxcUFMtmTV5no5QyKTCZDrVq1KvU9VCqVwf6HB7B/hsDQ+8j+6T9D76Oh9w+onD4+bebkAS6SJSIiIp3DgEJEREQ6hwHlP4yNjfHJJ5/A2NhY6lIqBfun/wy9j+yf/jP0Php6/wDd6KNeLpIlIiIiw8YZFCIiItI5DChERESkcxhQiIiISOcwoBAREZHOqRYB5eDBg3j11Vfh4uICQRAQERFR7rgoivj444/h7OwMU1NTBAcH4+LFi+XaZGVlYdCgQVCpVLC2tsbIkSORn59fhb14vKf1b/jw4RAEodyjc+fO5drocv9mzZqFVq1awdLSEg4ODujVqxcSExPLtSksLERYWBhsbW1hYWGBPn36ID09vVyblJQUdOvWDWZmZnBwcMDUqVNRWlpalV15rGfpY/v27R8axzFjxpRro6t9XLx4MZo0aaK56ZO/vz+2bdumOa7v4/e0/unz2D3K7NmzIQgCJk2apHlO38fwvx7VR30ex08//fSh2hs2bKg5rpPjJ1YDW7duFT/44ANxw4YNIgBx48aN5Y7Pnj1btLKyEiMiIsRTp06JPXr0EN3d3cV79+5p2nTu3Fls2rSpeOTIEfHQoUNi/fr1xQEDBlRxTx7taf0bNmyY2LlzZzE1NVXzyMrKKtdGl/sXEhIirlixQkxISBBPnjwpdu3aVXRzcxPz8/M1bcaMGSO6urqKe/bsEY8dOya2bt1aDAgI0BwvLS0VGzduLAYHB4snTpwQt27dKtrZ2YnTp0+XoksPeZY+tmvXTgwNDS03jjk5OZrjutzHzZs3i//884944cIFMTExUXz//fdFIyMjMSEhQRRF/R+/p/VPn8fuv44ePSrWqVNHbNKkiThx4kTN8/o+hv/2uD7q8zh+8sknYqNGjcrVfuvWLc1xXRy/ahFQ/u2/H+BqtVp0cnISv/nmG81z2dnZorGxsbh27VpRFEXx7NmzIgAxNjZW02bbtm2iIAjijRs3qqz2Z/G4gNKzZ8/HnqNP/RNFUczIyBABiAcOHBBF8f54GRkZiX/++aemzblz50QAYnR0tCiK90OcTCYT09LSNG0WL14sqlQqsaioqGo78Az+20dRvP+P47//sfwvfetjjRo1xJ9++skgx08U/9c/UTScscvLyxM9PDzEXbt2leuTIY3h4/ooivo9jp988onYtGnTRx7T1fGrFl/xPElycjLS0tIQHBysec7Kygp+fn6Ijo4GAERHR8Pa2hq+vr6aNsHBwZDJZIiJianymp/H/v374eDggAYNGmDs2LHIzMzUHNO3/uXk5AAAbGxsAABxcXEoKSkpN4YNGzaEm5tbuTH08fGBo6Ojpk1ISAhyc3Nx5syZKqz+2fy3jw+sXr0adnZ2aNy4MaZPn46CggLNMX3pY1lZGX7//XfcvXsX/v7+Bjd+/+3fA4YwdmFhYejWrVu5sQIM6+/g4/r4gD6P48WLF+Hi4oK6deti0KBBSElJAaC746eXmwVWpLS0NAAo94f+4PcHx9LS0uDg4FDuuEKhgI2NjaaNLuvcuTNee+01uLu749KlS3j//ffRpUsXREdHQy6X61X/1Go1Jk2ahMDAQDRu3BjA/fFRKpWwtrYu1/a/Y/ioMX5wTJc8qo8AMHDgQNSuXRsuLi44ffo03nvvPSQmJmLDhg0AdL+P8fHx8Pf3R2FhISwsLLBx40Z4e3vj5MmTBjF+j+sfoP9jBwC///47jh8/jtjY2IeOGcrfwSf1EdDvcfTz88PKlSvRoEEDpKam4rPPPkPbtm2RkJCgs+NX7QNKddC/f3/Nzz4+PmjSpAnq1auH/fv3o0OHDhJWpr2wsDAkJCTg8OHDUpdSaR7Xx9GjR2t+9vHxgbOzMzp06IBLly6hXr16VV2m1ho0aICTJ08iJycH69evx7Bhw3DgwAGpy6owj+uft7e33o/dtWvXMHHiROzatQsmJiZSl1MpnqWP+jyOXbp00fzcpEkT+Pn5oXbt2li3bh1MTU0lrOzxqv1XPE5OTgDw0Grl9PR0zTEnJydkZGSUO15aWoqsrCxNG31St25d2NnZISkpCYD+9C88PBxbtmzBvn37UKtWLc3zTk5OKC4uRnZ2drn2/x3DR43xg2O64nF9fBQ/Pz8AKDeOutxHpVKJ+vXro2XLlpg1axaaNm2KefPmGcz4Pa5/j6JvYxcXF4eMjAy0aNECCoUCCoUCBw4cwPz586FQKODo6Kj3Y/i0PpaVlT10jr6N479ZW1vD09MTSUlJOvt3sNoHFHd3dzg5OWHPnj2a53JzcxETE6P5/tjf3x/Z2dmIi4vTtNm7dy/UarXmP1B9cv36dWRmZsLZ2RmA7vdPFEWEh4dj48aN2Lt3L9zd3csdb9myJYyMjMqNYWJiIlJSUsqNYXx8fLkgtmvXLqhUKs00vJSe1sdHOXnyJACUG0dd7uN/qdVqFBUVGcT4PcqD/j2Kvo1dhw4dEB8fj5MnT2oevr6+GDRokOZnfR/Dp/VRLpc/dI6+jeO/5efn49KlS3B2dtbdv4OVsvRWx+Tl5YknTpwQT5w4IQIQ58yZI544cUK8evWqKIr3LzO2trYWN23aJJ4+fVrs2bPnIy8zbt68uRgTEyMePnxY9PDw0JnLcJ/Uv7y8PPGdd94Ro6OjxeTkZHH37t1iixYtRA8PD7GwsFDzGrrcv7Fjx4pWVlbi/v37y10iV1BQoGkzZswY0c3NTdy7d6947Ngx0d/fX/T399ccf3CJXKdOncSTJ0+K27dvF+3t7XXi8j9RfHofk5KSxM8//1w8duyYmJycLG7atEmsW7euGBQUpHkNXe7jtGnTxAMHDojJycni6dOnxWnTpomCIIg7d+4URVH/x+9J/dP3sXuc/17Rou9j+Cj/7qO+j+Pbb78t7t+/X0xOThYjIyPF4OBg0c7OTszIyBBFUTfHr1oElH379okAHnoMGzZMFMX7lxp/9NFHoqOjo2hsbCx26NBBTExMLPcamZmZ4oABA0QLCwtRpVKJb775ppiXlydBbx72pP4VFBSInTp1Eu3t7UUjIyOxdu3aYmhoaLlLxURRt/v3qL4BEFesWKFpc+/ePXHcuHFijRo1RDMzM7F3795iampqude5cuWK2KVLF9HU1FS0s7MT3377bbGkpKSKe/NoT+tjSkqKGBQUJNrY2IjGxsZi/fr1xalTp5a7B4Mo6m4fR4wYIdauXVtUKpWivb292KFDB004EUX9H78n9U/fx+5x/htQ9H0MH+XffdT3cXzjjTdEZ2dnUalUijVr1hTfeOMNMSkpSXNcF8dPEEVRrJy5GSIiIqLnU+3XoBAREZHuYUAhIiIincOAQkRERDqHAYWIiIh0DgMKERER6RwGFCIiItI5DChERESkcxhQiKhS7d+/H4IgPLTPBxHRkzCgEFGlCggIQGpqKqysrJ75nIKCAkyfPh316tWDiYkJ7O3t0a5dO2zatKkSKyUiXaKQugAiMmxKpVLr3U7HjBmDmJgY/PDDD/D29kZmZiaioqKQmZlZSVUSka7hDAoRaaV9+/YYP348Jk2ahBo1asDR0RHLli3D3bt38eabb8LS0hL169fHtm3bADz8Fc/KlSthbW2NHTt2wMvLCxYWFujcuTNSU1M177F582a8//776Nq1K+rUqYOWLVti/PjxGDFihKaNIAiIiIgoV5u1tTVWrlwJALhy5QoEQcDvv/+OgIAAmJiYoHHjxjhw4ECl/vkQUcVgQCEira1atQp2dnY4evQoxo8fj7Fjx6Jfv34ICAjA8ePH0alTJwwZMgQFBQWPPL+goADffvstfv31Vxw8eBApKSl45513NMednJywdetW5OXlvXCtU6dOxdtvv40TJ07A398fr776KmdiiPQAAwoRaa1p06b48MMP4eHhgenTp8PExAR2dnYIDQ2Fh4cHPv74Y2RmZuL06dOPPL+kpARLliyBr68vWrRogfDwcOzZs0dzfOnSpYiKioKtrS1atWqFyZMnIzIy8rlqDQ8PR58+feDl5YXFixfDysoKy5cvf67XIqKqw4BCRFpr0qSJ5me5XA5bW1v4+PhonnN0dAQAZGRkPPJ8MzMz1KtXT/O7s7NzubZBQUG4fPky9uzZg759++LMmTNo27YtZsyYoXWt/v7+mp8VCgV8fX1x7tw5rV+HiKoWAwoRac3IyKjc74IglHtOEAQAgFqtfubzRVF8qE3btm3x3nvvYefOnfj8888xY8YMFBcXP/ackpKS5+sQEekcBhQi0gve3t4oLS1FYWEhAMDe3r7cwtqLFy8+cs3LkSNHND+XlpYiLi4OXl5elV8wEb0QXmZMRDqnffv2GDBgAHx9fWFra4uzZ8/i/fffx8svvwyVSgUAeOWVV7BgwQL4+/ujrKwM77333kMzMwCwcOFCeHh4wMvLC3PnzsWdO3fKXQ1ERLqJMyhEpHNCQkKwatUqdOrUCV5eXhg/fjxCQkKwbt06TZvvvvsOrq6uaNu2LQYOHIh33nkHZmZmD73W7NmzMXv2bDRt2hSHDx/G5s2bYWdnV5XdIaLnIIj//RKXiMgAXLlyBe7u7jhx4gSaNWsmdTlEpCXOoBAREZHOYUAhIiIincOveIiIiEjncAaFiIiIdA4DChEREekcBhQiIiLSOQwoREREpHMYUIiIiEjnMKAQERGRzmFAISIiIp3DgEJEREQ6hwGFiIiIdM7/AR5+nSLXkmLmAAAAAElFTkSuQmCC\n" 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\n" 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\n" - }, - "metadata": {} - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "image/png": 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\n" + }, + "metadata": {} } + ], + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/georeferencedPeriodicFrequentPattern/basic/STEclat.ipynb b/notebooks/georeferencedPeriodicFrequentPattern/basic/STEclat.ipynb index 518d3244..e33ef0f4 100644 --- a/notebooks/georeferencedPeriodicFrequentPattern/basic/STEclat.ipynb +++ b/notebooks/georeferencedPeriodicFrequentPattern/basic/STEclat.ipynb @@ -1,718 +1,718 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XZ4vrXSQ1yEs" - }, - "source": [ - "# Finding Geo-Referenced (Spatial) Partial Periodic patterns in Temporal Databases using STEclat" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "roOSCMZX2Eb2" - }, - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Geo-Referenced (Spatial) Partial Periodic patterns in a temporal database using the STEclat algorithm. In the final part, we describe an advanced approach, where we evaluate the STEclat algorithm on a dataset at different minimum support threshold values.\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TFlIIF_X2SzU" - }, - "source": [ - "# Prerequisites:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TqMwpaLw2XLu" - }, - "source": [ - "1. Installing the PAMI library" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "4a1d11d6-3744-4d7c-eee6-685b62a685b1" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", - "Collecting resource (from pami)\n", - " Downloading Resource-0.2.1-py2.py3-none-any.whl (25 kB)\n", - "Collecting validators (from pami)\n", - " Downloading validators-0.22.0-py3-none-any.whl (26 kB)\n", - "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", - "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.42.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.4.5)\n", - 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" Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", - "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", - "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", - "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", - "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", - "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.30.2)\n", - "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.10.2)\n", - "Building wheels for collected packages: JsonForm, JsonSir\n", - " Building wheel for JsonForm (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for JsonForm: filename=JsonForm-0.0.2-py3-none-any.whl size=3313 sha256=bb7161b013faa1694f0ddcfee32113b3dfac7ddbb66a711db06bbf9948489ca7\n", - " Stored in directory: /root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", - " Building wheel for JsonSir (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=5fe4e741a5dbe0776d8c723eae41ca03b45b3d22247f44513bafbbdf24bec538\n", - " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", - "Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" - ] - } - ], - "source": [ - "!pip install -U pami #install the pami repository" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rYfvWHRN2oBs" - }, - "source": [ - "2. Downloading a sample dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "680bcb0e-c62c-4104-8144-b2b23cbdc1b6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-08 16:54:40-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.07MB/s in 4.1s \n", - "\n", - "2023-09-08 16:54:45 (1.07 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ], - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "USUJbpXu3Gkw" - }, - "source": [ - "3. Printing few lines of a dataset to know its format." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "a2aadc64-93c4-4634-b688-d2c2eefb89e1" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ], - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oQQdz3qn3Qwz" - }, - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "62Vkqg-C3WVZ" - }, - "source": [ - "## Part 1: Finding Geo-Referenced (Spatial) Partial Periodic patterns using STEclat" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gaxxPgXv3ecW" - }, - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "82883649-a129-49fe-b7db-040d5fce6446" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ], - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1oJIEK8A3wQS" - }, - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "50df6f7f-1529-40a6-b9a8-b49e994a46bb" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ], - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zpLiRCBp39k9" - }, - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "RP9ynbti4L48" - }, - "outputs": [], - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "neighborFile = '/content/t10_neighbours.txt'\n", - "maxmunPeriodCount = 4000 #user can specify different value." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XZ4vrXSQ1yEs" + }, + "source": [ + "# Finding Geo-Referenced (Spatial) Partial Periodic patterns in Temporal Databases using STEclat" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "roOSCMZX2Eb2" + }, + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Geo-Referenced (Spatial) Partial Periodic patterns in a temporal database using the STEclat algorithm. In the final part, we describe an advanced approach, where we evaluate the STEclat algorithm on a dataset at different minimum support threshold values.\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TFlIIF_X2SzU" + }, + "source": [ + "# Prerequisites:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TqMwpaLw2XLu" + }, + "source": [ + "1. Installing the PAMI library" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "4a1d11d6-3744-4d7c-eee6-685b62a685b1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m7.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", + "Collecting resource (from pami)\n", + " Downloading Resource-0.2.1-py2.py3-none-any.whl (25 kB)\n", + "Collecting validators (from pami)\n", + " Downloading validators-0.22.0-py3-none-any.whl (26 kB)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.42.1)\n", + "Requirement 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Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from 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sha256=5fe4e741a5dbe0776d8c723eae41ca03b45b3d22247f44513bafbbdf24bec538\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" + ] + } + ], + "source": [ + "!pip install -U pami #install the pami repository" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rYfvWHRN2oBs" + }, + "source": [ + "2. Downloading a sample dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "680bcb0e-c62c-4104-8144-b2b23cbdc1b6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-08 16:54:40-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.07MB/s in 4.1s \n", + "\n", + "2023-09-08 16:54:45 (1.07 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ], + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "USUJbpXu3Gkw" + }, + "source": [ + "3. Printing few lines of a dataset to know its format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "a2aadc64-93c4-4634-b688-d2c2eefb89e1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ], + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oQQdz3qn3Qwz" + }, + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "62Vkqg-C3WVZ" + }, + "source": [ + "## Part 1: Finding Geo-Referenced (Spatial) Partial Periodic patterns using STEclat" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gaxxPgXv3ecW" + }, + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "82883649-a129-49fe-b7db-040d5fce6446" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ], + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1oJIEK8A3wQS" + }, + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 }, + "id": "y7pfaeJV34H_", + "outputId": "50df6f7f-1529-40a6-b9a8-b49e994a46bb" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "-Yr0r7zw4Q85" - }, - "source": [ - "### Step 4: Mining Geo-Referenced (Spatial) Partial Periodic patterns using STEclat" + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BZzrC2Pl4XGJ", - "outputId": "5e1ce5c7-4ea6-4a6c-f156-109f22a56209" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", - "Total No of patterns: Patterns periodicSupport\n", - "0 368 7817\n", - "1 529 7050\n", - "2 829 6800\n", - "3 766 6257\n", - "4 722 5836\n", - ".. ... ...\n", - "790 62 108\n", - "791 426 107\n", - "792 856 107\n", - "793 102 107\n", - "794 199 107\n", - "\n", - "[795 rows x 2 columns]\n", - "Runtime: 3.1638286113739014\n", - "Memory (RSS): 346140672\n", - "Memory (USS): 299503616\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "from PAMI.georeferencedPartialPeriodicPattern.basic import STEclat as alg #import the algorithm\n", - "\n", - "obj = alg.STEclat(iFile=inputFile, minPS=minimumSupportCount, maxIAT=maxmunPeriodCount, nFile=neighborFile, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('partialPeriodicPatternsMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(frequentPatternsDF)) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "3M8FtfKU4bhu" - }, - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b7IvA0IW4hBe", - "outputId": "53e15230-e60d-4790-c0e7-53c15938e402" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "368: 7817 \n", - "529: 7050 \n", - "829: 6800 \n", - "766: 6257 \n", - "722: 5836 \n", - "354: 5828 \n", - "684: 5402 \n", - "217: 5364 \n", - "494: 5095 \n", - "419: 5046 \n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "!head 'partialPeriodicPatternsMinSupCount100.txt'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j4CpTPXw4k9I" - }, - "source": [ - "The storage format is: _periodicFrequentPattern:support_\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kC71sBV74qY0" - }, - "source": [ - "## Part 2: Evaluating the STEclat algorithm on a dataset at different minSup values" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EobpZCdu6G0Z" - }, - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "W96B78JT6KT2" - }, - "outputs": [], - "source": [ - "#Import the libraries\n", - "from PAMI.georeferencedPartialPeriodicPattern.basic import STEclat as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "neighborFile = '/content/t10_neighbours.txt'\n", - "maximunPeriodCount = 4000\n", - "minimumSupportCountList = [100, 200, 300, 400, 500]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gS09HRlY6PPR" - }, - "source": [ - "### Step 2: Create a data frame to store the results of STEclat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0tbQu3re6VGI" - }, - "outputs": [], - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximunPeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of STEclat algorithm" - ] + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zpLiRCBp39k9" + }, + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RP9ynbti4L48" + }, + "outputs": [], + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "neighborFile = '/content/t10_neighbours.txt'\n", + "maxmunPeriodCount = 4000 #user can specify different value." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Yr0r7zw4Q85" + }, + "source": [ + "### Step 4: Mining Geo-Referenced (Spatial) Partial Periodic patterns using STEclat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BZzrC2Pl4XGJ", + "outputId": "5e1ce5c7-4ea6-4a6c-f156-109f22a56209" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", + "Total No of patterns: Patterns periodicSupport\n", + "0 368 7817\n", + "1 529 7050\n", + "2 829 6800\n", + "3 766 6257\n", + "4 722 5836\n", + ".. ... ...\n", + "790 62 108\n", + "791 426 107\n", + "792 856 107\n", + "793 102 107\n", + "794 199 107\n", + "\n", + "[795 rows x 2 columns]\n", + "Runtime: 3.1638286113739014\n", + "Memory (RSS): 346140672\n", + "Memory (USS): 299503616\n" + ] + } + ], + "source": [ + "from PAMI.georeferencedPartialPeriodicPattern.basic import STEclat as alg #import the algorithm\n", + "\n", + "obj = alg.STEclat(iFile=inputFile, minPS=minimumSupportCount, maxIAT=maxmunPeriodCount, nFile=neighborFile, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('partialPeriodicPatternsMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(frequentPatternsDF)) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3M8FtfKU4bhu" + }, + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b7IvA0IW4hBe", + "outputId": "53e15230-e60d-4790-c0e7-53c15938e402" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "368: 7817 \n", + "529: 7050 \n", + "829: 6800 \n", + "766: 6257 \n", + "722: 5836 \n", + "354: 5828 \n", + "684: 5402 \n", + "217: 5364 \n", + "494: 5095 \n", + "419: 5046 \n" + ] + } + ], + "source": [ + "!head 'partialPeriodicPatternsMinSupCount100.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j4CpTPXw4k9I" + }, + "source": [ + "The storage format is: _periodicFrequentPattern:support_\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kC71sBV74qY0" + }, + "source": [ + "## Part 2: Evaluating the STEclat algorithm on a dataset at different minSup values" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EobpZCdu6G0Z" + }, + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "W96B78JT6KT2" + }, + "outputs": [], + "source": [ + "#Import the libraries\n", + "from PAMI.georeferencedPartialPeriodicPattern.basic import STEclat as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "neighborFile = '/content/t10_neighbours.txt'\n", + "maximunPeriodCount = 4000\n", + "minimumSupportCountList = [100, 200, 300, 400, 500]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gS09HRlY6PPR" + }, + "source": [ + "### Step 2: Create a data frame to store the results of STEclat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0tbQu3re6VGI" + }, + "outputs": [], + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximunPeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of STEclat algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kn2TtrbW6awD" + }, + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f12b58ae-3277-4f9b-a805-7943811f0cff" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", + "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n" + ] + } + ], + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.STEclat(iFile=inputFile, minPS=minSupCount, maxIAT=maximunPeriodCount, nFile=neighborFile, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['STEclat', minSupCount, maximunPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NCQLc9pn7BYG" + }, + "source": [ + "### Step 4: Print the Result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "79751643-0f7b-4fa5-cab7-516c01305f8b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximunPeriodCount patterns runtime memory\n", + "0 STEclat 100 4000 795 1.728645 348770304\n", + "1 STEclat 200 4000 741 1.696349 349192192\n", + "2 STEclat 300 4000 692 1.653258 349970432\n", + "3 STEclat 400 4000 629 1.547991 350089216\n", + "4 STEclat 500 4000 569 2.952726 350015488\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S-prY3W27U4Z" + }, + "source": [ + "### Step 5: Visualizing the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "outputId": "ca401ef6-0bed-424f-c350-1042e4d01426" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "kn2TtrbW6awD" - }, - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 14 }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "f12b58ae-3277-4f9b-a805-7943811f0cff" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n", - "Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.STEclat(iFile=inputFile, minPS=minSupCount, maxIAT=maximunPeriodCount, nFile=neighborFile, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['STEclat', minSupCount, maximunPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "NCQLc9pn7BYG" - }, - "source": [ - "### Step 4: Print the Result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "79751643-0f7b-4fa5-cab7-516c01305f8b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximunPeriodCount patterns runtime memory\n", - "0 STEclat 100 4000 795 1.728645 348770304\n", - "1 STEclat 200 4000 741 1.696349 349192192\n", - "2 STEclat 300 4000 692 1.653258 349970432\n", - "3 STEclat 400 4000 629 1.547991 350089216\n", - "4 STEclat 500 4000 569 2.952726 350015488\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "print(result)" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "S-prY3W27U4Z" - }, - "source": [ - "### Step 5: Visualizing the results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "ca401ef6-0bed-424f-c350-1042e4d01426" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 14 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" - }, - "metadata": {} - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "image/png": 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\n" + }, + "metadata": {} } + ], + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/highUtilityFrequentPatterns/basic/HUFIM.ipynb b/notebooks/highUtilityFrequentPatterns/basic/HUFIM.ipynb index 597c51c6..578d821d 100644 --- a/notebooks/highUtilityFrequentPatterns/basic/HUFIM.ipynb +++ b/notebooks/highUtilityFrequentPatterns/basic/HUFIM.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/highUtilityGeoreferencedFrequentPattern/basic/SHUFIM.ipynb b/notebooks/highUtilityGeoreferencedFrequentPattern/basic/SHUFIM.ipynb index 25acacb1..c44f5f1e 100644 --- a/notebooks/highUtilityGeoreferencedFrequentPattern/basic/SHUFIM.ipynb +++ b/notebooks/highUtilityGeoreferencedFrequentPattern/basic/SHUFIM.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/highUtilityPattern/basic/EFIM.ipynb b/notebooks/highUtilityPattern/basic/EFIM.ipynb index 583651c3..ef40d9b5 100644 --- a/notebooks/highUtilityPattern/basic/EFIM.ipynb +++ b/notebooks/highUtilityPattern/basic/EFIM.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/highUtilityPattern/basic/HMiner.ipynb b/notebooks/highUtilityPattern/basic/HMiner.ipynb index 9dbd7f0e..020f5a43 100644 --- a/notebooks/highUtilityPattern/basic/HMiner.ipynb +++ b/notebooks/highUtilityPattern/basic/HMiner.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/highUtilitySpatialPattern/basic/HDSHUIM.ipynb b/notebooks/highUtilitySpatialPattern/basic/HDSHUIM.ipynb index 22c7cc90..db8e92ba 100644 --- a/notebooks/highUtilitySpatialPattern/basic/HDSHUIM.ipynb +++ b/notebooks/highUtilitySpatialPattern/basic/HDSHUIM.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/highUtilitySpatialPattern/topK/TKSHUIM.ipynb b/notebooks/highUtilitySpatialPattern/topK/TKSHUIM.ipynb index 4078700e..cc9ee3c6 100644 --- a/notebooks/highUtilitySpatialPattern/topK/TKSHUIM.ipynb +++ b/notebooks/highUtilitySpatialPattern/topK/TKSHUIM.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/localPeriodicPattern/basic/LPPMBreadth.ipynb b/notebooks/localPeriodicPattern/basic/LPPMBreadth.ipynb index 013e8978..61e3cbcd 100644 --- a/notebooks/localPeriodicPattern/basic/LPPMBreadth.ipynb +++ b/notebooks/localPeriodicPattern/basic/LPPMBreadth.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/localPeriodicPattern/basic/LPPMDepth.ipynb b/notebooks/localPeriodicPattern/basic/LPPMDepth.ipynb index 1e334a14..31957220 100644 --- a/notebooks/localPeriodicPattern/basic/LPPMDepth.ipynb +++ b/notebooks/localPeriodicPattern/basic/LPPMDepth.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/localPeriodicPattern/basic/LPPgrowth.ipynb b/notebooks/localPeriodicPattern/basic/LPPgrowth.ipynb index 0ca756e2..8a613973 100644 --- a/notebooks/localPeriodicPattern/basic/LPPgrowth.ipynb +++ b/notebooks/localPeriodicPattern/basic/LPPgrowth.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -279,13 +279,13 @@ ], "source": [ "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", "\n", "#specify the file name\n", "inputFile = 'Temporal_T10I4D100K.csv'\n", "\n", "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", "\n", "#execute the class\n", "obj.run()\n", diff --git a/notebooks/multipleMinimumFrequentPatterns/basic/CFPGrowth.ipynb b/notebooks/multipleMinimumFrequentPatterns/basic/CFPGrowth.ipynb index 44b5b64c..4d1a2539 100644 --- a/notebooks/multipleMinimumFrequentPatterns/basic/CFPGrowth.ipynb +++ b/notebooks/multipleMinimumFrequentPatterns/basic/CFPGrowth.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/multipleMinimumFrequentPatterns/basic/CFPGrowthPlus.ipynb b/notebooks/multipleMinimumFrequentPatterns/basic/CFPGrowthPlus.ipynb index 50cc1929..3d170535 100644 --- a/notebooks/multipleMinimumFrequentPatterns/basic/CFPGrowthPlus.ipynb +++ b/notebooks/multipleMinimumFrequentPatterns/basic/CFPGrowthPlus.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/parallelPFPGrowth.ipynb b/notebooks/parallelPFPGrowth.ipynb index d4170d3b..68dd091d 100644 --- a/notebooks/parallelPFPGrowth.ipynb +++ b/notebooks/parallelPFPGrowth.ipynb @@ -1,772 +1,772 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true, + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding periodic-frequent patterns in big temporal databases using Parallel PFP-growth" + ], + "metadata": { + "id": "Aj6UkFAj3sHh" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find periodic-frequent patterns in a temporal database using the parallel PFP-growth algorithm. In the final part, we describe an advanced approach, where we evaluate the algorithm on a dataset at different *minimum support* threshold values using multiple cores (or worker machines)." + ], + "metadata": { + "id": "X-YPyS6G4AVR" + } + }, + { + "cell_type": "markdown", + "source": [ + "***" + ], + "metadata": { + "id": "XkW0ZZ276JtD" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Prerequisites:" + ], + "metadata": { + "id": "H8uDhbi55Use" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "z-avjjpTZzbf" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U 'pami[spark]' #install the pami repository" + ], + "metadata": { + "id": "2PdVic4l3-DQ", + "outputId": "6f856efb-c02b-4490-cf09-10e8914bf465", "colab": { - "provenance": [], - "toc_visible": true, - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" } + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pami[spark] in /usr/local/lib/python3.10/dist-packages (2023.7.28.5)\n", + "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (5.13.1)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (3.7.1)\n", + "Requirement already satisfied: resource in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (0.2.1)\n", + "Requirement already satisfied: validators in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (0.20.0)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (1.26.16)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (9.4.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (1.22.4)\n", + "Requirement already satisfied: pyspark in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (3.4.1)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (1.1.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (4.41.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (1.4.4)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (23.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (3.1.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pami[spark]) (2022.7.1)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->pami[spark]) (8.2.2)\n", + "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark->pami[spark]) (0.10.9.7)\n", + "Requirement already satisfied: JsonForm>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami[spark]) (0.0.2)\n", + "Requirement already satisfied: JsonSir>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami[spark]) (0.0.2)\n", + "Requirement already satisfied: python-easyconfig>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from resource->pami[spark]) (0.1.7)\n", + "Requirement already satisfied: decorator>=3.4.0 in /usr/local/lib/python3.10/dist-packages (from validators->pami[spark]) (4.4.2)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami[spark]) (4.3.3)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami[spark]) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami[spark]) (6.0.1)\n", + "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami[spark]) (23.1.0)\n", + "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami[spark]) (0.19.3)\n" + ] + } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding periodic-frequent patterns in big temporal databases using Parallel PFP-growth" - ], - "metadata": { - "id": "Aj6UkFAj3sHh" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find periodic-frequent patterns in a temporal database using the parallel PFP-growth algorithm. In the final part, we describe an advanced approach, where we evaluate the algorithm on a dataset at different *minimum support* threshold values using multiple cores (or worker machines)." - ], - "metadata": { - "id": "X-YPyS6G4AVR" - } - }, - { - "cell_type": "markdown", - "source": [ - "***" - ], - "metadata": { - "id": "XkW0ZZ276JtD" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Prerequisites:" - ], - "metadata": { - "id": "H8uDhbi55Use" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "z-avjjpTZzbf" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U 'pami[spark]' #install the pami repository" - ], - "metadata": { - "id": "2PdVic4l3-DQ", - "outputId": "6f856efb-c02b-4490-cf09-10e8914bf465", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: pami[spark] in /usr/local/lib/python3.10/dist-packages (2023.7.28.5)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (5.13.1)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (3.7.1)\n", - "Requirement already satisfied: resource in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (0.2.1)\n", - "Requirement already satisfied: validators in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (0.20.0)\n", - "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (1.26.16)\n", - "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (9.4.0)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (1.22.4)\n", - "Requirement already satisfied: pyspark in /usr/local/lib/python3.10/dist-packages (from pami[spark]) (3.4.1)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (1.1.0)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (0.11.0)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (4.41.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (1.4.4)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (23.1)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (3.1.0)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami[spark]) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pami[spark]) (2022.7.1)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->pami[spark]) (8.2.2)\n", - "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark->pami[spark]) (0.10.9.7)\n", - "Requirement already satisfied: JsonForm>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami[spark]) (0.0.2)\n", - "Requirement already satisfied: JsonSir>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami[spark]) (0.0.2)\n", - "Requirement already satisfied: python-easyconfig>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from resource->pami[spark]) (0.1.7)\n", - "Requirement already satisfied: decorator>=3.4.0 in /usr/local/lib/python3.10/dist-packages (from validators->pami[spark]) (4.4.2)\n", - "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami[spark]) (4.3.3)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami[spark]) (1.16.0)\n", - "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami[spark]) (6.0.1)\n", - "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami[spark]) (23.1.0)\n", - "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami[spark]) (0.19.3)\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "KZMafFx1Z7pn" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample transactional database" - ], - "metadata": { - "id": "SCtq3Erc5nEo", - "outputId": "d097fa1a-fd0a-411b-f5f2-99a2e10cc428", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "File ‘Temporal_T10I4D100K.csv’ already there; not retrieving.\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "nXgP5F4eaBTW" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "id": "7cHiIW59aS2T", - "outputId": "d8fab0a7-b1c6-4584-e61b-7e55f989eb3f", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "_format:_ every row contains a timestamp followed by a set of items seperated by a seperator.\n", - "\n", - "__Example:__\n", - "\n", - "timestamp1 item1 item2 item3 item4\n", - "\n", - "timestamp2 item1 item4 item6" - ], - "metadata": { - "id": "8HnTnR6MaebG" - } - }, - { - "cell_type": "markdown", - "source": [ - "***" - ], - "metadata": { - "id": "uJ8Z0ey_6FhV" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding periodic-frequent patterns using parallel PFP-growth" - ], - "metadata": { - "id": "pCnHao5L6PRR" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate *minimum support* (*minSup*) value." - ], - "metadata": { - "id": "XzsAMOgb6jjQ" - } - }, - { - "cell_type": "code", - "source": [ - "#specify the file name\n", - "inputFile = 'https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv'" - ], - "metadata": { - "id": "CkzdL-8lrCmk" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj. getVarianceTransactionLength()}')\n", - "print(f'Minimum period : {obj.getMinimumPeriod()}')\n", - "print(f'Average period : {obj.getAveragePeriod()}')\n", - "print(f'Maximum period : {obj.getMaximumPeriod()}')\n", - "\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "numberOfTransactionPerTimeStamp = obj.getNumberOfTransactionsPerTimestamp()\n", - "obj.save(itemFrequencies,'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "obj.save(numberOfTransactionPerTimeStamp, 'numberOfTransaction.csv')\n", - "\n", - "#Alternative apporach to derive the database statistics and plot the graphs\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "id": "3isxz2qx54te", - "outputId": "e5dc344b-2461-4e38-99c9-11e7f65992a9", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n", - "Minimum period : 1\n", - "Average period : 1.0\n", - "Maximum period : 1\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the items' frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "CnoHNwAC-kJQ" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, 100, 'Items\\' frequency graph', 'No of items', 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, 100, 'transaction distribution graph', 'transaction length', 'frequency')" - ], - "metadata": { - "id": "xwZmfh4H7XSR" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* and *maxPer* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 300 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated.\n", - "\n", - "\n", - " Similarly, let us choose a _maxPer_ value of 5000. That is, every periodic-frequent pattern must appear at least once in every 5000 transactions." - ], - "metadata": { - "id": "BtnhlIfLAEHR" - } - }, - { - "cell_type": "code", - "source": [ - "minSupValue=300 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maxPerValue=5000" - ], - "metadata": { - "id": "MHGkrW5e-zYR" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Choosing the number of worker machines\n", - "\n", - "Let us choose the number of worker machines equal to 2" - ], - "metadata": { - "id": "IJOBcmTm3zKO" - } - }, - { - "cell_type": "code", - "source": [ - "noWorkerMachines=2" - ], - "metadata": { - "id": "LjnMgn3S3-bE" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Mining frequent patterns using parallel FP-growth" - ], - "metadata": { - "id": "PlLmo9ZXDJkX" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.periodicFrequentPattern.pyspark import parallelPFPGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.parallelPFPGrowth(iFile=inputFile, minSup=minSupValue, maxPer=maxPerValue, numWorker=noWorkerMachines, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('periodicFrequentPatterns300_5000.txt') #save the patterns\n", - "\n", - "\n", - "patternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(patternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "pLV84IYcDHe3", - "outputId": "c0a25624-9ef6-4958-a0bc-73814a57b395", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Total No of patterns: 4529\n", - "Runtime: 233.28595542907715\n", - "Memory (RSS): 157249536\n", - "Memory (USS): 109367296\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 6: Investigating the generated patterns\n", - "\n", - "Open the patterns' file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "wE3A15V3FHjO" - } - }, - { - "cell_type": "code", - "source": [ - "!head periodicFrequentPatterns300_5000.txt" - ], - "metadata": { - "id": "nZ6dfQshDc9E", - "outputId": "97fb4eb0-afbf-4330-cf38-2c8a2fec906a", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "88:303 \n", - "916:306 \n", - "301:309 \n", - "301\t930:301 \n", - "301\t118:302 \n", - "301\t489:306 \n", - "301\t362:302 \n", - "437:313 \n", - "67:316 \n", - "67\t746:307 \n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "The storage format is: _periodicFrequentPattern:support:periodicity_\n", - "\n" - ], - "metadata": { - "id": "OSrlE5hwGEnH" - } - }, - { - "cell_type": "markdown", - "source": [ - "***" - ], - "metadata": { - "id": "HcaEFLGCHBjP" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the FP-growth algorithm on a dataset at different minSup values and worker machines" - ], - "metadata": { - "id": "FUO1nBPoHXJN" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "g90LiLg1Hzb-" - } - }, - { - "cell_type": "code", - "source": [ - "#Specify the input parameters\n", - "seperator='\\t'\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]\n", - "\n", - "numWorkers = [1,2, 3, 4]" - ], - "metadata": { - "id": "FfYBLxBPG5n_" - }, - "execution_count": 10, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of FP-growth" - ], - "metadata": { - "id": "2lvGcqTKJDee" - } - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "\n", - "result = pd.DataFrame(columns=['#workerMachines', 'minSup', 'patterns', 'runtime'])\n", - "#initialize a data frame to store the results of FPGrowth algorithm" - ], - "metadata": { - "id": "9Hl47i3ZICpp" - }, - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" - ], - "metadata": { - "id": "Hah3RHNNJf0f" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.periodicFrequentPattern.pyspark import parallelPFPGrowth as alg #import the algorithm\n", - "\n", - "for minSupValue in minimumSupportCountList:\n", - " for workers in numWorkers:\n", - " obj = alg.parallelPFPGrowth(iFile=inputFile, minSup=minSupValue, maxPer=maxPerValue, numWorker=workers, sep='\\t') #initialize\n", - " obj.mine() #start the mining process\n", - " result.loc[result.shape[0]] = [workers, minSupValue, len(obj.getPatterns()),obj.getRuntime()]" - ], - "metadata": { - "id": "9ivJHoSEJlky" - }, - "execution_count": 13, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the result" - ], - "metadata": { - "id": "qs3jpnTBJSd6" - } - }, - { - "cell_type": "code", - "source": [ - "print(result)" - ], - "metadata": { - "id": "MReBFwDFJ_3x", - "outputId": "ddd41dc7-c6e0-42ed-f332-6763838dca9e", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " #workerMachines minSup patterns runtime\n", - "0 1.0 100.0 25454.0 287.483394\n", - "1 2.0 100.0 25455.0 268.805671\n", - "2 3.0 100.0 25457.0 376.376910\n", - "3 4.0 100.0 25457.0 351.052995\n", - "4 5.0 100.0 25457.0 276.688836\n", - "5 6.0 100.0 25457.0 229.173701\n", - "6 7.0 100.0 25457.0 243.146675\n", - "7 8.0 100.0 25457.0 237.311443\n", - "8 9.0 100.0 25457.0 247.807653\n", - "9 10.0 100.0 25457.0 244.153801\n", - "10 1.0 150.0 25457.0 274.678433\n", - "11 2.0 150.0 25457.0 255.091034\n", - "12 3.0 150.0 25457.0 335.258194\n", - "13 4.0 150.0 25457.0 338.473147\n", - "14 5.0 150.0 25457.0 265.813371\n", - "15 6.0 150.0 25457.0 213.617335\n", - "16 7.0 150.0 25457.0 230.604153\n", - "17 8.0 150.0 25457.0 221.430903\n", - "18 9.0 150.0 25457.0 236.001616\n", - "19 10.0 150.0 25457.0 230.453704\n", - "20 1.0 200.0 25457.0 258.557297\n", - "21 2.0 200.0 25457.0 239.887953\n", - "22 3.0 200.0 25457.0 317.624678\n", - "23 4.0 200.0 25457.0 318.957440\n", - "24 5.0 200.0 25457.0 245.568537\n", - "25 6.0 200.0 25457.0 199.761284\n", - "26 7.0 200.0 25457.0 220.139179\n", - "27 8.0 200.0 25457.0 209.474587\n", - "28 9.0 200.0 25457.0 222.212832\n", - "29 10.0 200.0 25457.0 210.105144\n", - "30 1.0 250.0 25457.0 230.513641\n", - "31 2.0 250.0 25457.0 221.371173\n", - "32 3.0 250.0 25457.0 317.408155\n", - "33 4.0 250.0 25457.0 306.712078\n", - "34 5.0 250.0 25457.0 230.925884\n", - "35 6.0 250.0 25457.0 177.599180\n", - "36 7.0 250.0 25457.0 197.828111\n", - "37 8.0 250.0 25457.0 182.651038\n", - "38 9.0 250.0 25457.0 197.354614\n", - "39 10.0 250.0 25457.0 194.822553\n", - "40 1.0 300.0 25457.0 204.419644\n", - "41 2.0 300.0 25457.0 207.254872\n", - "42 3.0 300.0 25457.0 303.487077\n", - "43 4.0 300.0 25457.0 217.663208\n", - "44 5.0 300.0 25457.0 211.284912\n", - "45 6.0 300.0 25457.0 165.208382\n", - "46 7.0 300.0 25457.0 172.602902\n", - "47 8.0 300.0 25457.0 172.119099\n", - "48 9.0 300.0 25457.0 187.272733\n", - "49 10.0 300.0 25457.0 181.047893\n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "KZMafFx1Z7pn" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample transactional database" + ], + "metadata": { + "id": "SCtq3Erc5nEo", + "outputId": "d097fa1a-fd0a-411b-f5f2-99a2e10cc428", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "File ‘Temporal_T10I4D100K.csv’ already there; not retrieving.\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "nXgP5F4eaBTW" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "id": "7cHiIW59aS2T", + "outputId": "d8fab0a7-b1c6-4584-e61b-7e55f989eb3f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "_format:_ every row contains a timestamp followed by a set of items seperated by a seperator.\n", + "\n", + "__Example:__\n", + "\n", + "timestamp1 item1 item2 item3 item4\n", + "\n", + "timestamp2 item1 item4 item6" + ], + "metadata": { + "id": "8HnTnR6MaebG" + } + }, + { + "cell_type": "markdown", + "source": [ + "***" + ], + "metadata": { + "id": "uJ8Z0ey_6FhV" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding periodic-frequent patterns using parallel PFP-growth" + ], + "metadata": { + "id": "pCnHao5L6PRR" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate *minimum support* (*minSup*) value." + ], + "metadata": { + "id": "XzsAMOgb6jjQ" + } + }, + { + "cell_type": "code", + "source": [ + "#specify the file name\n", + "inputFile = 'https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv'" + ], + "metadata": { + "id": "CkzdL-8lrCmk" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj. getVarianceTransactionLength()}')\n", + "print(f'Minimum period : {obj.getMinimumPeriod()}')\n", + "print(f'Average period : {obj.getAveragePeriod()}')\n", + "print(f'Maximum period : {obj.getMaximumPeriod()}')\n", + "\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "numberOfTransactionPerTimeStamp = obj.getNumberOfTransactionsPerTimestamp()\n", + "obj.save(itemFrequencies,'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "obj.save(numberOfTransactionPerTimeStamp, 'numberOfTransaction.csv')\n", + "\n", + "#Alternative apporach to derive the database statistics and plot the graphs\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "id": "3isxz2qx54te", + "outputId": "e5dc344b-2461-4e38-99c9-11e7f65992a9", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n", + "Minimum period : 1\n", + "Average period : 1.0\n", + "Maximum period : 1\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the items' frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "CnoHNwAC-kJQ" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, 100, 'Items\\' frequency graph', 'No of items', 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, 100, 'transaction distribution graph', 'transaction length', 'frequency')" + ], + "metadata": { + "id": "xwZmfh4H7XSR" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* and *maxPer* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 300 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated.\n", + "\n", + "\n", + " Similarly, let us choose a _maxPer_ value of 5000. That is, every periodic-frequent pattern must appear at least once in every 5000 transactions." + ], + "metadata": { + "id": "BtnhlIfLAEHR" + } + }, + { + "cell_type": "code", + "source": [ + "minSupValue=300 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maxPerValue=5000" + ], + "metadata": { + "id": "MHGkrW5e-zYR" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Choosing the number of worker machines\n", + "\n", + "Let us choose the number of worker machines equal to 2" + ], + "metadata": { + "id": "IJOBcmTm3zKO" + } + }, + { + "cell_type": "code", + "source": [ + "noWorkerMachines=2" + ], + "metadata": { + "id": "LjnMgn3S3-bE" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Mining frequent patterns using parallel FP-growth" + ], + "metadata": { + "id": "PlLmo9ZXDJkX" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.periodicFrequentPattern.pyspark import parallelPFPGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.parallelPFPGrowth(iFile=inputFile, minSup=minSupValue, maxPer=maxPerValue, numWorker=noWorkerMachines, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('periodicFrequentPatterns300_5000.txt') #save the patterns\n", + "\n", + "\n", + "patternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(patternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "pLV84IYcDHe3", + "outputId": "c0a25624-9ef6-4958-a0bc-73814a57b395", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total No of patterns: 4529\n", + "Runtime: 233.28595542907715\n", + "Memory (RSS): 157249536\n", + "Memory (USS): 109367296\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 6: Investigating the generated patterns\n", + "\n", + "Open the patterns' file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "wE3A15V3FHjO" + } + }, + { + "cell_type": "code", + "source": [ + "!head periodicFrequentPatterns300_5000.txt" + ], + "metadata": { + "id": "nZ6dfQshDc9E", + "outputId": "97fb4eb0-afbf-4330-cf38-2c8a2fec906a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "88:303 \n", + "916:306 \n", + "301:309 \n", + "301\t930:301 \n", + "301\t118:302 \n", + "301\t489:306 \n", + "301\t362:302 \n", + "437:313 \n", + "67:316 \n", + "67\t746:307 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _periodicFrequentPattern:support:periodicity_\n", + "\n" + ], + "metadata": { + "id": "OSrlE5hwGEnH" + } + }, + { + "cell_type": "markdown", + "source": [ + "***" + ], + "metadata": { + "id": "HcaEFLGCHBjP" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the FP-growth algorithm on a dataset at different minSup values and worker machines" + ], + "metadata": { + "id": "FUO1nBPoHXJN" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "g90LiLg1Hzb-" + } + }, + { + "cell_type": "code", + "source": [ + "#Specify the input parameters\n", + "seperator='\\t'\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]\n", + "\n", + "numWorkers = [1,2, 3, 4]" + ], + "metadata": { + "id": "FfYBLxBPG5n_" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of FP-growth" + ], + "metadata": { + "id": "2lvGcqTKJDee" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "\n", + "result = pd.DataFrame(columns=['#workerMachines', 'minSup', 'patterns', 'runtime'])\n", + "#initialize a data frame to store the results of FPGrowth algorithm" + ], + "metadata": { + "id": "9Hl47i3ZICpp" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "Hah3RHNNJf0f" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.periodicFrequentPattern.pyspark import parallelPFPGrowth as alg #import the algorithm\n", + "\n", + "for minSupValue in minimumSupportCountList:\n", + " for workers in numWorkers:\n", + " obj = alg.parallelPFPGrowth(iFile=inputFile, minSup=minSupValue, maxPer=maxPerValue, numWorker=workers, sep='\\t') #initialize\n", + " obj.mine() #start the mining process\n", + " result.loc[result.shape[0]] = [workers, minSupValue, len(obj.getPatterns()),obj.getRuntime()]" + ], + "metadata": { + "id": "9ivJHoSEJlky" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the result" + ], + "metadata": { + "id": "qs3jpnTBJSd6" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "MReBFwDFJ_3x", + "outputId": "ddd41dc7-c6e0-42ed-f332-6763838dca9e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " #workerMachines minSup patterns runtime\n", + "0 1.0 100.0 25454.0 287.483394\n", + "1 2.0 100.0 25455.0 268.805671\n", + "2 3.0 100.0 25457.0 376.376910\n", + "3 4.0 100.0 25457.0 351.052995\n", + "4 5.0 100.0 25457.0 276.688836\n", + "5 6.0 100.0 25457.0 229.173701\n", + "6 7.0 100.0 25457.0 243.146675\n", + "7 8.0 100.0 25457.0 237.311443\n", + "8 9.0 100.0 25457.0 247.807653\n", + "9 10.0 100.0 25457.0 244.153801\n", + "10 1.0 150.0 25457.0 274.678433\n", + "11 2.0 150.0 25457.0 255.091034\n", + "12 3.0 150.0 25457.0 335.258194\n", + "13 4.0 150.0 25457.0 338.473147\n", + "14 5.0 150.0 25457.0 265.813371\n", + "15 6.0 150.0 25457.0 213.617335\n", + "16 7.0 150.0 25457.0 230.604153\n", + "17 8.0 150.0 25457.0 221.430903\n", + "18 9.0 150.0 25457.0 236.001616\n", + "19 10.0 150.0 25457.0 230.453704\n", + "20 1.0 200.0 25457.0 258.557297\n", + "21 2.0 200.0 25457.0 239.887953\n", + "22 3.0 200.0 25457.0 317.624678\n", + "23 4.0 200.0 25457.0 318.957440\n", + "24 5.0 200.0 25457.0 245.568537\n", + "25 6.0 200.0 25457.0 199.761284\n", + "26 7.0 200.0 25457.0 220.139179\n", + "27 8.0 200.0 25457.0 209.474587\n", + "28 9.0 200.0 25457.0 222.212832\n", + "29 10.0 200.0 25457.0 210.105144\n", + "30 1.0 250.0 25457.0 230.513641\n", + "31 2.0 250.0 25457.0 221.371173\n", + "32 3.0 250.0 25457.0 317.408155\n", + "33 4.0 250.0 25457.0 306.712078\n", + "34 5.0 250.0 25457.0 230.925884\n", + "35 6.0 250.0 25457.0 177.599180\n", + "36 7.0 250.0 25457.0 197.828111\n", + "37 8.0 250.0 25457.0 182.651038\n", + "38 9.0 250.0 25457.0 197.354614\n", + "39 10.0 250.0 25457.0 194.822553\n", + "40 1.0 300.0 25457.0 204.419644\n", + "41 2.0 300.0 25457.0 207.254872\n", + "42 3.0 300.0 25457.0 303.487077\n", + "43 4.0 300.0 25457.0 217.663208\n", + "44 5.0 300.0 25457.0 211.284912\n", + "45 6.0 300.0 25457.0 165.208382\n", + "46 7.0 300.0 25457.0 172.602902\n", + "47 8.0 300.0 25457.0 172.119099\n", + "48 9.0 300.0 25457.0 187.272733\n", + "49 10.0 300.0 25457.0 181.047893\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "6ELjr0iPKDSY" + } + }, + { + "cell_type": "code", + "source": [ + "import plotly.express as pe\n", + "\n", + "pe.line(result, x='minSup', y='patterns', color='#workerMachines').show()\n", + "pe.line(result, x='minSup', y='runtime', color='#workerMachines').show()" + ], + "metadata": { + "id": "mGET50RTasLT", + "outputId": "a1d1dd91-2b38-4b14-8417-1094688f30fa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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In the first part, we describe the basic approach to find partial periodic frequent patterns in a temporal database using the GPFgrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the GPFgrowth algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "604fec1f-960a-431d-f16a-258c60de34f5" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m8.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already 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(8.2.3)\n", + "Collecting JsonForm>=0.0.2 (from resource->pami)\n", + " Downloading JsonForm-0.0.2.tar.gz (2.4 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting JsonSir>=0.0.2 (from resource->pami)\n", + " Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in 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/root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=11313be4521d39f3aac246430f0fb0ebbead0c472772ae946c6c3275c3ba808b\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Partial Periodic Frequent Patterns in Temporal Databases using GPFgrowth" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find partial periodic frequent patterns in a temporal database using the GPFgrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the GPFgrowth algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "604fec1f-960a-431d-f16a-258c60de34f5" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "aceb0ca6-a05c-4a25-ff69-a8b91283775b" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 06:18:40-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.05MB/s in 4.2s \n", - "\n", - "2023-08-28 06:18:45 (1.05 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "5308258b-4ce4-4687-c2de-b921f6387af2" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Partial Periodic Frequent Patterns using GPFgrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "af3ac2f7-3566-4a95-869b-2de41add00cc" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "0a8bd9e1-3df8-4fb4-cf0d-2de0f3bd9e14" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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t27dHRkYGVq5cCbVafce1pojozhiSiIhMyJIlS7Bu3Tp06dIFkZGRdX7/U089hZ9//hnLly+HTCZDt27dsHLlSvTp06f+iyVq4jgniYiIiMgAzkkiIiIiMoAhiYiIiMgAzkm6D1qtFqmpqbCzs7uvxdiIiIhIekIIFBYWwtPTE2Zmde8XYki6D6mpqbWelk5ERESNw7Vr1+Dl5VXn9zEk3Qc7OzsA1RdZpVJJXA0RERHdD7VaDW9vb933eF0xJN2HmiE2lUrFkERERNTIPOhUGU7cJiIiIjKAIYmIiIjIAIYkIiIiIgMYkoiIiIgMYEgiIiIiMoAhiYiIiMgAhiQiIiIiAxiSiIiIiAxgSCIiIiIygCGJiIiIyACGJCIiIiIDGJKIiIiIDGBIklh+SQVi0wulLoOIiIhuw5AkofjMQnR5bzdGLTkKIYTU5RAREdEtGJIk5OVoDZkMKCyvQl5JpdTlEBER0S0YkiRkaSGHh8oSAJCUUyxxNURERHQrhiSJ+ThbAwCSGZKIiIhMCkOSxHydbQAAyTklEldCREREt2JIkpgPQxIREZFJYkiSWM1wG+ckERERmRaGJIn9PSeJPUlERESmhCFJYjXDbbnFFVCXcRkAIiIiU8GQJDFbpTlcbBUAgBT2JhEREZkMhiQTUNObxHlJREREpoMhyQRwXhIREZHpYUgyAX+vlcSeJCIiIlPBkGQC/l4GgD1JREREpoIhyQT4sCeJiIjI5DAkmQDfmz1JGepylFZoJK6GiIiIAIYkk+BgrYDK0hwAkJLLITciIiJTwJBkInxduAwAERGRKWFIMhGcl0RERGRaGJJMhC/XSiIiIjIpDEkmopUTQxIREZEpYUgyEZyTREREZFoYkkxEzYKSqfmlqKjSSlwNERERMSSZCFdbJawVcmgFcD2PQ25ERERSY0gyETKZjPOSiIiITAhDkgmpedAt5yURERFJjyHJhPi4sCeJiIjIVDAkmRAfJy4oSUREZCoYkkwIF5QkIiIyHQxJJsTn5lpJ1/JKoNEKiashIiJq3iQPSTdu3MCLL74IZ2dnWFlZITg4GKdOndLtF0Lg7bffRosWLWBlZYVBgwYhLi5O7xi5ubkYO3YsVCoVHBwcMGnSJBQVFem1OX/+PHr37g1LS0t4e3vj448/Nsr51YWHyhIKuRkqNQKp+aVSl0NERNSsSRqS8vLy0KtXL1hYWGDHjh24dOkSPvvsMzg6OurafPzxx/jqq6+wdOlSHD9+HDY2NggPD0dZWZmuzdixYxETE4Pdu3dj69atOHjwIKZOnarbr1arMXjwYPj4+CA6OhqffPIJ3n33XSxfvtyo53svcjMZvJ2sAHDIjYiISHJCQm+88YYICwu7436tVis8PDzEJ598otuWn58vlEql+PHHH4UQQly6dEkAECdPntS12bFjh5DJZOLGjRtCCCG+/fZb4ejoKMrLy/U+u23btvdVZ0FBgQAgCgoK6nR+D+Ll1SeEzxtbxfdRSQ3+WURERE3Zw35/S9qTtGXLFnTv3h3PPvss3Nzc0LVrV3z33Xe6/YmJiUhPT8egQYN02+zt7dGjRw9ERUUBAKKiouDg4IDu3bvr2gwaNAhmZmY4fvy4rk2fPn2gUCh0bcLDwxEbG4u8vLxadZWXl0OtVuu9jMXHmXe4ERERmQJJQ9LVq1exZMkSBAYGYteuXZgxYwZmz56NNWvWAADS09MBAO7u7nrvc3d31+1LT0+Hm5ub3n5zc3M4OTnptTF0jFs/41aLFi2Cvb297uXt7V0PZ3t/fHiHGxERkUmQNCRptVp069YNH3zwAbp27YqpU6diypQpWLp0qZRlYeHChSgoKNC9rl27ZrTPZkgiIiIyDZKGpBYtWiAoKEhvW/v27ZGSkgIA8PDwAABkZGTotcnIyNDt8/DwQGZmpt7+qqoq5Obm6rUxdIxbP+NWSqUSKpVK72UsNY8mSc4thhBcBoCIiEgqkoakXr16ITY2Vm/blStX4OPjAwDw8/ODh4cH9uzZo9uvVqtx/PhxhIaGAgBCQ0ORn5+P6OhoXZu9e/dCq9WiR48eujYHDx5EZWWlrs3u3bvRtm1bvTvpTEFLRyvIzWQoq9Qis7Bc6nKIiIiaLUlD0rx583Ds2DF88MEHiI+Pxw8//IDly5cjIiICACCTyTB37lz8+9//xpYtW3DhwgWMGzcOnp6eGDZsGIDqnqcnnngCU6ZMwYkTJ3DkyBHMnDkTY8aMgaenJwDghRdegEKhwKRJkxATE4OffvoJixcvxquvvirVqd+RhdwMLR2qlwFIyubkbSIiIqmYS/nhjzzyCH777TcsXLgQ7733Hvz8/PDll19i7NixujYLFixAcXExpk6divz8fISFhWHnzp2wtLTUtVm/fj1mzpyJgQMHwszMDCNHjsRXX32l229vb48//vgDERERCAkJgYuLC95++229tZRMiY+zNVJyS5CcU4IerZ2lLoeIiKhZkglOfLkntVoNe3t7FBQUGGV+0lubL+L7Y8l4pZ8/FjzRrsE/j4iIqCl62O9vyR9LQrXp7nDL5R1uREREUmFIMkFcUJKIiEh6DEkmyLemJym7hMsAEBERSYQhyQR5O1WHpMLyKuQWV0hcDRERUfPEkGSCLC3kaGFfffce5yURERFJgyHJRP39eBLOSyIiIpICQ5KJqnk8SVI2e5KIiIikwJBkolrd7ElK4XAbERGRJBiSTJSuJ4nDbURERJJgSDJRf89JYk8SERGRFBiSTFTNgpK5xRVQl1VKXA0REVHzw5BkomyV5nCxVQAAUtibREREZHQMSSbMh/OSiIiIJMOQZMI4L4mIiEg6DEkmzMepZq0k9iQREREZG0OSCfN1udmTxLWSiIiIjI4hyYTVzEnio0mIiIiMjyHJhPnenJOUoS5HaYVG4mqIiIiaF4YkE+ZgrYDK0hwAH09CRERkbAxJJs7XhcsAEBERSYEhycRxXhIREZE0GJJMnI9T9bykJK6VREREZFQMSSauZkFJPpqEiIjIuBiSTBznJBEREUmDIcnE1fQkpeaXoryKywAQEREZC0OSiXO1VcJaIYdWANfzSqUuh4iIqNlgSDJxMpkMrZw4L4mIiMjYGJIaAV9nzksiIiIyNoakRqBmXlIye5KIiIiMhiGpEeCCkkRERMbHkNQI+LIniYiIyOgYkhoBn5trJV3LK0GVRitxNURERM0DQ1Ij4KGyhEJuhkqNQFpBmdTlEBERNQsMSY2A3EwGbycrABxyIyIiMhaGpEaCywAQEREZF0NSI9FKN3mbIYmIiMgYGJIaib97kjjcRkREZAwMSY1EzYKSfDQJERGRcTAkNRI1PUnJucXQaoXE1RARETV9DEmNREtHK8jNZCir1CKzsFzqcoiIiJo8hqRGwkJuhpYONcsAcPI2ERFRQ2NIakT4oFsiIiLjYUhqRGpCEtdKIiIiangMSY1IzeTtKxlFEldCRETU9DEkNSI9/JwBAPtiMzkviYiIqIExJDUiwV726NPGFRqtwLf7EqQuh4iIqEljSGpk5gwMBAD8cvo6ruVyAjcREVFDYUhqZEJ8HBEW4IIqrcCSA+xNIiIiaigMSY3Q7Ju9SZtOXUNqfqnE1RARETVNDEmN0KN+TujZ2gmVGoEl+9mbRERE1BAYkhqpOQPbAAB+OnkN6QVlEldDRETU9DAkNVI9WzvhUV8nVGi0WMq5SURERPWOIamRkslkurlJP55IQaaavUlERET1SdKQ9O6770Imk+m92rVrp9tfVlaGiIgIODs7w9bWFiNHjkRGRobeMVJSUjBkyBBYW1vDzc0Nr7/+OqqqqvTa7N+/H926dYNSqURAQAAiIyONcXoNrleAM7q1ckB5lRbLDl6VuhwiIqImRfKepA4dOiAtLU33Onz4sG7fvHnz8Pvvv2PTpk04cOAAUlNTMWLECN1+jUaDIUOGoKKiAkePHsWaNWsQGRmJt99+W9cmMTERQ4YMQf/+/XH27FnMnTsXkydPxq5du4x6ng3h1t6k9ceTkVVYLnFFRERETYdMCCGk+vB3330XmzdvxtmzZ2vtKygogKurK3744QeMGjUKAHD58mW0b98eUVFR6NmzJ3bs2IGhQ4ciNTUV7u7uAIClS5fijTfeQFZWFhQKBd544w1s27YNFy9e1B17zJgxyM/Px86dO++rTrVaDXt7exQUFEClUj38idcjIQSGfXsU567lY1qf1lj4VHupSyIiIjIJD/v9LXlPUlxcHDw9PdG6dWuMHTsWKSkpAIDo6GhUVlZi0KBBurbt2rVDq1atEBUVBQCIiopCcHCwLiABQHh4ONRqNWJiYnRtbj1GTZuaYzR2MpkMcwYGAADWRiUjp4i9SURERPVB0pDUo0cPREZGYufOnViyZAkSExPRu3dvFBYWIj09HQqFAg4ODnrvcXd3R3p6OgAgPT1dLyDV7K/Zd7c2arUapaWGF2IsLy+HWq3We5my/m3d0LGlCqWVGqw4nCh1OURERE2CpCHpySefxLPPPotOnTohPDwc27dvR35+PjZu3ChlWVi0aBHs7e11L29vb0nruReZTIbZA6rnJq09moS84gqJKyIiImr8JB9uu5WDgwPatGmD+Ph4eHh4oKKiAvn5+XptMjIy4OHhAQDw8PCodbdbzc/3aqNSqWBlZWWwjoULF6KgoED3unbtWn2cXoN6PMgd7VuoUFyhwaoj7E0iIiJ6WCYVkoqKipCQkIAWLVogJCQEFhYW2LNnj25/bGwsUlJSEBoaCgAIDQ3FhQsXkJmZqWuze/duqFQqBAUF6drceoyaNjXHMESpVEKlUum9TN2tc5MijyShoKRS4oqIiIgaN0lD0muvvYYDBw4gKSkJR48exfDhwyGXy/H888/D3t4ekyZNwquvvop9+/YhOjoaEydORGhoKHr27AkAGDx4MIKCgvDSSy/h3Llz2LVrF/71r38hIiICSqUSADB9+nRcvXoVCxYswOXLl/Htt99i48aNmDdvnpSn3iAGB3mgrbsdCsursPooe5OIiIgehqQh6fr163j++efRtm1bjB49Gs7Ozjh27BhcXV0BAF988QWGDh2KkSNHok+fPvDw8MCvv/6qe79cLsfWrVshl8sRGhqKF198EePGjcN7772na+Pn54dt27Zh9+7d6Ny5Mz777DOsWLEC4eHhRj/fhmZmJsOsm71Jqw4nQl3G3iQiIqIHJek6SY2FKa+TdDuNViD8y4OIzyzC/MfbYNbNxSaJiIiam0a/ThLVL7mZDLMGVPcmrTiciKLyqnu8g4iIiAxhSGqChnbyRGsXGxSUVmJtVJLU5RARETVKDElNkNxMhpk3e5O+O3gVxexNIiIiqjOGpCbqH5094etsjbySSqw7lix1OURERI0OQ1ITZS43Q0T/6t6k5QevorRCI3FFREREjQtDUhM2rGtLeDtZIae4AuuPszeJiIioLhiSmjALuRki+lX3Ji07eBVllexNIiIiul8MSU3ciG5eaOlghazCcvx4IkXqcoiIiBoNhqQmTmFuhlf6+wMAlh5IYG8SERHRfWJIagZGhXihhb0lMtTl2HTqmtTlEBERNQoMSc2A0lyOGf2qe5O+3Z+A8ir2JhEREd0LQ1IzMbq7N9zslEgrKMPP0delLoeIiMjkMSQ1E5YWckzve7M3aV8CKqq0EldERERk2hiSmpEXerSCi60SN/JL8dsZ9iYRERHdDUNSM1Ldm9QaAPD1vnhUatibREREdCcMSc3MCz1awdlGgWu5pdh85obU5RAREZkshqRmxlphjil9qnuTvtkXjyr2JhERERnEkNQMvdTTB47WFkjKKcHv51OlLoeIiMgkMSQ1QzZKc0zuXd2b9N+98dBohcQVERERmR6GpGZqXKgP7K0scDWrGFvZm0RERFQLQ1IzZWdpgUlhfgCqe5O07E0iIiLSw5DUjI1/zBd2luaIzyzCjovpUpdDRERkUhiSmjF7Kwu83KumNymOvUlERES3YEhq5l7u5QdbpTkupxfij0sZUpdDRERkMhiSmjl7awtMeMwXAPDVnjgIwd4kIiIigCGJAEwK84ONQo5LaWr8+Vem1OUQERGZBIYkgqONAuPYm0RERKSHIYkAAJPD/GBlIceFGwXYH5sldTlERESSY0giAICzrRIvhfoAAL5kbxIRERFDEv1tSu/WUJqb4dy1fByMy5a6HCIiIkkxJJGOq50SY3tU9yYt/vMKe5OIiKhZq3NIunr1akPUQSZiet/WUJib4XRKPo4m5EhdDhERkWTqHJICAgLQv39/rFu3DmVlZQ1RE0nITWWJFx5tBQBY/CfnJhERUfNV55B0+vRpdOrUCa+++io8PDwwbdo0nDhxoiFqI4lM69saCrkZTiTl4tjVXKnLISIikkSdQ1KXLl2wePFipKamYtWqVUhLS0NYWBg6duyIzz//HFlZvH28sWthb4XRj3gBqF43iYiIqDl64Inb5ubmGDFiBDZt2oSPPvoI8fHxeO211+Dt7Y1x48YhLS2tPuskI5vRLwAWchmirubgRCJ7k4iIqPl54JB06tQpvPLKK2jRogU+//xzvPbaa0hISMDu3buRmpqKZ555pj7rJCNr6WCFUSHeANibREREzVOdQ9Lnn3+O4OBgPPbYY0hNTcXatWuRnJyMf//73/Dz80Pv3r0RGRmJ06dPN0S9ZESv9POHuZkMh+OzEZ3M3iQiImpe6hySlixZghdeeAHJycnYvHkzhg4dCjMz/cO4ublh5cqV9VYkScPbyRojurUEAHy1J17iaoiIiIxLJniP9z2p1WrY29ujoKAAKpVK6nKMKjmnGAM+OwCNVmBzRC908XaQuiQiIqL78rDf33XuSVq9ejU2bdpUa/umTZuwZs2aOhdAps3H2QbDulT3Jv2Xc5OIiKgZqXNIWrRoEVxcXGptd3NzwwcffFAvRZFpiejvDzMZsOdyJi5cL5C6HCIiIqOoc0hKSUmBn59fre0+Pj5ISUmpl6LItLR2tcU/OnsCAL7ay94kIiJqHuocktzc3HD+/Pla28+dOwdnZ+d6KYpMz8wBAZDJgN2XMhCTyt4kIiJq+uockp5//nnMnj0b+/btg0ajgUajwd69ezFnzhyMGTOmIWokExDgZoehnap7k77eyzvdiIio6TOv6xvef/99JCUlYeDAgTA3r367VqvFuHHjOCepiZs1IAC/n0vFjovpuJyuRjuP5nWnHxERNS8PvATAlStXcO7cOVhZWSE4OBg+Pj71XZvJaM5LANzulfXR2H4hHUM6tcA3L3STuhwiIqI7etjv7zr3JNVo06YN2rRp86Bvp0Zq1oBAbL+Qju0X0hCXUYhAdzupSyIiImoQdQ5JGo0GkZGR2LNnDzIzM6HVavX27927t96KI9PTvoUK4R3csSsmA1/vi8fiMV2lLomIiKhB1DkkzZkzB5GRkRgyZAg6duwImUzWEHWRCZs1IBC7YjLw+7lUzB4YCH9XW6lLIiIiqnd1DkkbNmzAxo0b8dRTTzVEPdQIdGxpj0Ht3fDnX5n4Zm88Pn+ui9QlERER1bs6LwGgUCgQEBDQELVQIzJrQCAAYPPZG0jKLpa4GiIiovpX55A0f/58LF68GHwubvPW2dsB/dq6QiuAb/Zx3SQiImp66jzcdvjwYezbtw87duxAhw4dYGFhobf/119/rbfiyLTNHhiI/bFZ+PXMDcweGAhvJ2upSyIiIqo3de5JcnBwwPDhw9G3b1+4uLjA3t5e7/WgPvzwQ8hkMsydO1e3raysDBEREXB2doatrS1GjhyJjIwMvfelpKRgyJAhsLa2hpubG15//XVUVVXptdm/fz+6desGpVKJgIAAREZGPnCd9LdurRzRO9AFGq1gbxIRETU5de5JWr16db0XcfLkSSxbtgydOnXS2z5v3jxs27YNmzZtgr29PWbOnIkRI0bgyJEjAKqXIxgyZAg8PDxw9OhRpKWlYdy4cbCwsNCt/p2YmIghQ4Zg+vTpWL9+Pfbs2YPJkyejRYsWCA8Pr/dzaW7mDAzEobhs/Bx9HTMHBMDLkb1JRETUNNS5JwkAqqqq8Oeff2LZsmUoLCwEAKSmpqKoqKjOxyoqKsLYsWPx3XffwdHRUbe9oKAAK1euxOeff44BAwYgJCQEq1evxtGjR3Hs2DEAwB9//IFLly5h3bp16NKlC5588km8//77+Oabb1BRUQEAWLp0Kfz8/PDZZ5+hffv2mDlzJkaNGoUvvvjiQU6dbtPd1wmP+TujSiuwZH+C1OUQERHVmzqHpOTkZAQHB+OZZ55BREQEsrKyAAAfffQRXnvttToXEBERgSFDhmDQoEF626Ojo1FZWam3vV27dmjVqhWioqIAAFFRUQgODoa7u7uuTXh4ONRqNWJiYnRtbj92eHi47hiGlJeXQ61W673ozuYMrL7TbeOpa0jNL5W4GiIiovpR55A0Z84cdO/eHXl5ebCystJtHz58OPbs2VOnY23YsAGnT5/GokWLau1LT0+HQqGAg4OD3nZ3d3ekp6fr2twakGr21+y7Wxu1Wo3SUsNf6IsWLdKbZ+Xt7V2n82puerR2Rg8/J1RqBJYeYG8SERE1DXUOSYcOHcK//vUvKBQKve2+vr64cePGfR/n2rVrmDNnDtavXw9LS8u6ltGgFi5ciIKCAt3r2rVrUpdk8mp6kzacuIb0gjKJqyEiInp4dQ5JWq0WGo2m1vbr16/Dzu7+H3YaHR2NzMxMdOvWDebm5jA3N8eBAwfw1VdfwdzcHO7u7qioqEB+fr7e+zIyMuDh4QEA8PDwqHW3W83P92qjUqn0esJupVQqoVKp9F50d6H+zuju44gKjRbLDrI3iYiIGr86h6TBgwfjyy+/1P0sk8lQVFSEd955p06PKhk4cCAuXLiAs2fP6l7du3fH2LFjdX+2sLDQG8KLjY1FSkoKQkNDAQChoaG4cOECMjMzdW12794NlUqFoKAgXZvbhwF3796tOwbVD5lMhjmDqnuTfjiegsxC9iYREVHjJhN1XDr7+vXrCA8PhxACcXFx6N69O+Li4uDi4oKDBw/Czc3tgYvp168funTpogthM2bMwPbt2xEZGQmVSoVZs2YBAI4ePQqgegmALl26wNPTEx9//DHS09Px0ksvYfLkyXpLAHTs2BERERF4+eWXsXfvXsyePRvbtm277yUA1Go17O3tUVBQwF6luxBCYMSSoziTko/JYX7419AgqUsiIqJm7GG/v+u8TpKXlxfOnTuHDRs24Pz58ygqKsKkSZMwduzYOw5fPagvvvgCZmZmGDlyJMrLyxEeHo5vv/1Wt18ul2Pr1q2YMWMGQkNDYWNjg/Hjx+O9997TtfHz88O2bdswb948LF68GF5eXlixYgXXSGoAMpkMswcGYuLqk1h3PBlPBnsgxMdJ6rKIiIgeSJ17kpoj9iTdPyEExq06gUNx2bCykOO7cd0RFugidVlERNQMPez3d51D0tq1a++6f9y4cXUuwtQxJNVNaYUG09ZF4+CVLCjkZvjvC10R3sFD6rKIiKiZMXpIunVVbACorKxESUkJFAoFrK2tkZubW+ciTB1DUt2VV2kwd8NZ7LiYDrmZDJ8+2wnDu3pJXRYRETUjD/v9Xee72/Ly8vReRUVFiI2NRVhYGH788cc6F0BNk9Jcjv8+3xUju3lBoxWY99M5fH8sWeqyiIiI7tsDPbvtdoGBgfjwww8xZ86c+jgcNRHmcjN8MqoTJjzmCwB4a/NFfLs/XtqiiIiI7lO9hCQAMDc3R2pqan0djpoIMzMZ3nk6CDP7BwAAPt4Zi493XgbvFyAiIlNX5yUAtmzZovezEAJpaWn4+uuv0atXr3orjJoOmUyG18LbwtbSHB/uuIxv9yegqLwK7z7dAWZmMqnLIyIiMqjOIWnYsGF6P8tkMri6umLAgAH47LPP6qsuaoKm9/WHrdIcb/3vItZGJaOorAofj+oEc3m9dWgSERHVmzqHJK1W2xB1UDPxYk8f2Fma49WN5/DrmRsorqjCV893hdJcLnVpREREevhPeDK6Z7q0xNIXQ6AwN8OumAxMXnMKJRVVUpdFRESkp87rJL366qv33fbzzz+vc0GmiOskNYwj8dmYsvYUSio06O7jiJUTHoG9lYXUZRERURNh9Ge3nTlzBmfOnEFlZSXatm0LALhy5Qrkcjm6deumayeTcUIu3V2vABd8P6kHJq4+gVPJeXh++TF8P+lRONsqpS6NiIio7sNtTz/9NPr06YPr16/j9OnTOH36NK5du4b+/ftj6NCh2LdvH/bt24e9e/c2RL3UxIT4OGLD1FC42CpwKU2N0cuikFZQKnVZREREdR9ua9myJf744w906NBBb/vFixcxePDgJrlWEofbGt7VrCK8uOI4UgvK0NLBCj9M6QEfZxupyyIiokbM6I8lUavVyMrKqrU9KysLhYWFdS6ACABau9pi04zH4OtsjRv5pXh2aRRi0/n7RERE0qlzSBo+fDgmTpyIX3/9FdevX8f169fxyy+/YNKkSRgxYkRD1EjNREsHK2ycHop2HnbILCzHc8ujcO5avtRlERFRM1Xn4baSkhK89tprWLVqFSorKwFUP5Jk0qRJ+OSTT2Bj0/SGSDjcZlz5JRWYsPokzl7Lh63SHCvGd0fP1s5Sl0VERI3Mw35/1zkk1SguLkZCQgIAwN/fv0mGoxoMScZXVF6FKWtOIepqDpTmZlj6Ygj6t3OTuiwiImpEjD4nqUZaWhrS0tIQGBgIGxsbPrCU6pWt0hyrJz6Cge3cUF6lxZS1p7D1fNO7KYCIiExXnUNSTk4OBg4ciDZt2uCpp55CWloaAGDSpEmYP39+vRdIzZelhRxLXwrB0509UaUVmP3jGWw8eU3qsoiIqJmoc0iaN28eLCwskJKSAmtra9325557Djt37qzX4ogs5Gb48rkueP5Rb2gFsOCX81h5OFHqsoiIqBmo84rbf/zxB3bt2gUvLy+97YGBgUhOTq63wohqyM1k+GB4MOwsLbD84FW8v/USisqqMHtgAFd2JyKiBlPnnqTi4mK9HqQaubm5UCr5OAlqGDKZDAufbIf5j7cBAHzx5xX8Z9tfnAtHREQNps4hqXfv3li7dq3uZ5lMBq1Wi48//hj9+/ev1+KIbiWTyTBrYCDeHhoEAFhxOBELf70AjZZBiYiI6l+dh9s+/vhjDBw4EKdOnUJFRQUWLFiAmJgY5Obm4siRIw1RI5Gel8P8YGtpjjd/OY8NJ6+hqLwKXzzXBRbyB75Zk4iIqJY6f6t07NgRV65cQVhYGJ555hkUFxdjxIgROHPmDPz9/RuiRqJaRnf3xn+f7wYLuQxbz6dh2vfRKKvUSF0WERE1IXVaTLKyshJPPPEEli5disDAwIasy6RwMUnTtS82E9O/j0Z5lRY9WzthxfhHYKuscwcpERE1QUZdTNLCwgLnz5+v84cQNZT+bd2w9uVHYas0x7GruRi74jjySyqkLouIiJqAOg+3vfjii1i5cmVD1EL0QHq0dsYPU3rA0doC567l47llx5CpLpO6LCIiauTqPC5RVVWFVatW4c8//0RISEitZ7Z9/vnn9VYc0f3q5OWAn6aF4sUVxxGbUYjRy6KwbnIPeDnWXq6CiIjoftzXnKTz58+jY8eOMDMzu+tt/jKZDHv37q3XAk0B5yQ1Hik5JXhhxTFczytFC3tLrJvcA/6utlKXRUREEnjY7+/7CklyuRxpaWlwc3ND69atcfLkSTg7Oz9QwY0RQ1Ljkl5QhhdXHkd8ZhGcbRRYO+lRdPC0l7osIiIyMqNM3HZwcEBiYvXzspKSkqDVauv8QUTG4mFviZ+m9kQHTxVyiiswZvkxRCfnSl0WERE1Mvc1J2nkyJHo27cvWrRoAZlMhu7du0Mulxtse/Xq1XotkOhBONsq8ePUnpgUeRInk/Lw4ooT+G5cd4QFukhdGhERNRL3vU7Szp07ER8fj9mzZ+O9996DnZ2dwXZz5syp1wJNAYfbGq/SCg2mfn8Kh+KyoZCb4esXumJwBw+pyyIiIiMwypykW02cOBFfffXVHUNSU8SQ1LiVV2kw58ez2BmTDrmZDJ8+2wnDu3pJXRYRETUwoy4mCQCrV69uVgGJGj+luRxfv9AVI7t5QaMVeHXjOXx/LFnqsoiIyMTxiaDULJjLzfDJqE4YH+oDIYC3Nl/Ekv0JUpdFREQmjCGJmg0zMxne/UcHzOwfAAD4aOdlfLzzMuo44kxERM0EQxI1KzKZDK+Ft8WbT7YDAHy7PwHvbImBVsugRERE+hiSqFma3tcf/x7WETIZsDYqGa/9fA5VGq7/RUREf2NIombrxZ4++GJ0F8jNZPj19A1E/HAa5VUaqcsiIiITwZBEzdqwri2xZGw3KORm2BWTgclrTqGkokrqsoiIyAQwJFGzN7iDB1ZPfATWCjkOxWVj3MoTKCitlLosIiKSGEMSEYBeAS74flIPqCzNcSo5Dy98dww5ReVSl0VERBJiSCK6KcTHERumhsLFVoGYVDVGL4tCWkGp1GUREZFEGJKIbhHkqcJP00LRwt4SCVnFeHZpFJJziqUui4iIJMCQRHQbf1dbbJoeCl9na1zPK8WzS6NwJaNQ6rKIiMjIGJKIDPBytMbG6aFo52GHzMJyjF4WhXPX8qUui4iIjIghiegO3OwssWFqT3T2dkB+SSXGrjiO41dzpC6LiIiMhCGJ6C4crBVYP7kHQls7o6i8CuNWncC+y5lSl0VEREbAkER0D7ZKc6ye+AgGtnNDeZUWU9aewrbzaVKXRUREDYwhieg+WFrIsfSlEDzd2RNVWoFZP57GxpPXpC6LiIgaEEMS0X2ykJvhy+e64PlHvaEVwIJfzmPl4USpyyIiogYiaUhasmQJOnXqBJVKBZVKhdDQUOzYsUO3v6ysDBEREXB2doatrS1GjhyJjIwMvWOkpKRgyJAhsLa2hpubG15//XVUVek/e2v//v3o1q0blEolAgICEBkZaYzToyZIbibDB8ODMaW3HwDg/a2XsPjPOAghJK6MiIjqm6QhycvLCx9++CGio6Nx6tQpDBgwAM888wxiYmIAAPPmzcPvv/+OTZs24cCBA0hNTcWIESN079doNBgyZAgqKipw9OhRrFmzBpGRkXj77bd1bRITEzFkyBD0798fZ8+exdy5czF58mTs2rXL6OdLTYNMJsM/n2qPVx9vAwD44s8r+GD7XwxKRERNjEyY2N/sTk5O+OSTTzBq1Ci4urrihx9+wKhRowAAly9fRvv27REVFYWePXtix44dGDp0KFJTU+Hu7g4AWLp0Kd544w1kZWVBoVDgjTfewLZt23Dx4kXdZ4wZMwb5+fnYuXPnfdWkVqthb2+PgoICqFSq+j9parRWHU7Ee1svAQCef9Qb/x4WDLmZTOKqiIgIePjvb5OZk6TRaLBhwwYUFxcjNDQU0dHRqKysxKBBg3Rt2rVrh1atWiEqKgoAEBUVheDgYF1AAoDw8HCo1Wpdb1RUVJTeMWra1ByD6GG8HOaHj0d2gpkM+PHENczZcAaVGq3UZRERUT0wl7qACxcuIDQ0FGVlZbC1tcVvv/2GoKAgnD17FgqFAg4ODnrt3d3dkZ6eDgBIT0/XC0g1+2v23a2NWq1GaWkprKysatVUXl6O8vK/nwCvVqsf+jyp6Rr9iDdslOaY+9MZbD2fhpIKDb4d2w2WFnKpSyMioocgeU9S27ZtcfbsWRw/fhwzZszA+PHjcenSJUlrWrRoEezt7XUvb29vSesh0zekUwssH9cdSnMz7L2ciQmrT6CovOrebyQiIpMleUhSKBQICAhASEgIFi1ahM6dO2Px4sXw8PBARUUF8vPz9dpnZGTAw8MDAODh4VHrbrean+/VRqVSGexFAoCFCxeioKBA97p2jevh0L31b+uGtS8/ClulOY5dzcXYFceRX1IhdVlERPSAJA9Jt9NqtSgvL0dISAgsLCywZ88e3b7Y2FikpKQgNDQUABAaGooLFy4gM/Pvx0Ts3r0bKpUKQUFBuja3HqOmTc0xDFEqlbplCWpeRPejR2tn/DClBxysLXDuWj6eW3YMmYVlUpdFREQPQNKQtHDhQhw8eBBJSUm4cOECFi5ciP3792Ps2LGwt7fHpEmT8Oqrr2Lfvn2Ijo7GxIkTERoaip49ewIABg8ejKCgILz00ks4d+4cdu3ahX/961+IiIiAUqkEAEyfPh1Xr17FggULcPnyZXz77bfYuHEj5s2bJ+WpUxPWycsBG6eFws1OidiMQoxeGoXreSVSl0VERHUkaUjKzMzEuHHj0LZtWwwcOBAnT57Erl278PjjjwMAvvjiCwwdOhQjR45Enz594OHhgV9//VX3frlcjq1bt0IulyM0NBQvvvgixo0bh/fee0/Xxs/PD9u2bcPu3bvRuXNnfPbZZ1ixYgXCw8ONfr7UfLRxt8Om6aHwcrRCUk4Jnl0ahYSsIqnLIiKiOjC5dZJMEddJogeVXlCGsSuOISGrGM42Cqyd9Cg6eNpLXRYRUbPQZNZJImqKPOwtsXFaKDp4qpBTXIExy48hOjlP6rKIiOg+MCQRNTBnWyV+nNoT3X0cUVhWhZdWHsfhuGypyyIiontgSCIyApWlBdZOehS9A11QUqHBy5En8UdMutRlERHRXTAkERmJtcIcK8Z3xxMdPFCh0WLG+tPYfOaG1GUREdEdMCQRGZHSXI6vX+iKEd1aQqMVmLfxLNYdS5a6LCIiMoAhicjIzOVm+HRUZ4wP9YEQwL82X8SS/QlSl0VERLdhSCKSgJmZDO/+owMi+vsDAD7aeRmf7LoMrshBRGQ6GJKIJCKTyfB6eDu8+WQ7AMA3+xLw7pYYaLUMSkREpoAhiUhi0/v64/1hHSGTAWuikvHaz+dQpdFKXRYRUbPHkERkAl7q6YMvRneB3EyGX0/fwMwfzqC8SiN1WUREzRpDEpGJGNa1JZaM7QaF3Aw7Y9Ixec0plFRUSV0WEVGzxZBEZEIGd/DAqgmPwMpCjkNx2Ri38gTUZZVSl0VE1CwxJBGZmLBAF6yb3AMqS3OcSs7D88uPIaeoXOqyiIiaHYYkIhMU4uOIH6f2hLONAjGpaoxeFoX0gjKpyyIialYYkohMVAdPe2ycHooW9pZIyCrGqKVHkZxTLHVZRETNBkMSkQnzd7XFpumh8HW2xvW8Ujy7NApXMgqlLouIqFlgSCIycV6O1tg4PRRt3e2QWViO0cuicP56vtRlERE1eQxJRI2Am50lfprWE529HZBfUokXvjuO41dzpC6LiKhJkwk+LOqe1Go17O3tUVBQAJVKJXU51IwVlVdh8pqTOHY1FzIZ0MFThd6Brugd6IIQH0cozeVSl0hEZDIe9vubIek+MCSRKSmr1GD+pnPYdj5Nb7uVhRw9WjvpQlOgmy1kMplEVRIRSY8hyQgYksgUZRaW4Uh8Ng5dycah+GxkFeqvpeSuUuoCU68AF7jYKiWqlIhIGgxJRsCQRKZOCIHYjEJdYDp+NQflVfoPye3gqUJYoAv6BLoixMcRlhYcmiOipo0hyQgYkqixKavU4FRSHg7FZ+HQlWxcSlPr7be0MMOjfs7oE+iC3oGuaOPOoTkianoYkoyAIYkau6zCchxNyMbBK9k4FJeFzNuG5tzslAgLdEHvQBeEBbjC1Y5Dc0TU+DEkGQFDEjUlQgjEZRbh4JUsHIrLxvHEHJRV6g/NtW+hQp9AF4QFuuARXycOzRFRo8SQZAQMSdSUlVVqcDo5DwfjsnE4PgsXb+gPzSnNzfConxN63xyaa+dhx6E5ImoUGJKMgCGJmpOconIcjs/G4bhsHIrLRrpa/8G6rnZKhAXUDM25wE1lKVGlRER3x5BkBAxJ1FwJIRCfWYRDcdVzmY5dzUVppUavTTsPO10v06N+HJojItPBkGQEDElE1cqrNDidnI9DcdXzmS6mFuDWv0EU5mZ41Ld6aC4s0AXtPVQwM+PQHBFJgyHJCBiSiAzLLa6oXtDyZmhKK9AfmnOxVSAswAVhNxe1dOfQHBEZEUOSETAkEd2bEAIJWcU4FJeFw3HZiLqag5IK/aG5Nu62ulXAe/g5w0rBoTkiajgMSUbAkERUdxVVWpxOybs5ATwL52/cNjQnN0N3X0ddaApqwaE5IqpfDElGwJBE9PDyiitwNCFHNzR3I79Ub7+zjQK9bt411zvQFR72HJojoofDkGQEDElE9UsIgcTsYt1dc1EJOSi+bWgu0M1W96y5Hq2dYK0wl6haImqsGJKMgCGJqGFVarQ4k/L3XXPnr+dDe8vfTBZyGUJ8qofm+gS6ooMnh+aI6N4YkoyAIYnIuPJLaobmsnHwSlatoTlHawv0CqjuZQoLdIGng5VElRKRKWNIMgKGJCLpCCGQlFOCw3FZOBiXjaiEHBSVV+m18Xe1qe5lalN915yNkkNzRMSQZBQMSUSmo1Kjxblr+Th4cz7TuWu1h+a6tXLUTQDv2NIecg7NETVLDElGwJBEZLoKSisRlVD9nLmDcVm4lqs/NOdgbYFe/i66VcC9HK0lqpSIjI0hyQgYkogaj+Scv++aOxqfg8LbhuZau9joepl6+jvDlkNzRE0WQ5IRMCQRNU5VGi3OXc+/GZqycfZaPjS3jM2Zm/09NBcW6IJOXg4cmiNqQhiSjIAhiahpUJdVIurmgpaH47KRlFOit9/eygK9ApwRFlC9Cri3E4fmiBozhiQjYEgiappSckpwKL46MB2Jz4a6TH9ozs/FBmE3VwEP9XeGnaWFRJUS0YNgSDIChiSipq9Ko8X5GwW6Z82dTtEfmpObydCtlUN1L1MbF3RqaQ9zuZmEFRPRvTAkGQFDElHzU3hzaO5wfPV8psTsYr39dpbm1XfNtXFB7wBXtHLm0ByRqWFIMgKGJCK6lltyMzBl4Uh8DgpKK/X2+zhbV08AD3DFYwHOUHFojkhyDElGwJBERLfSaAUu3CjAoSvVz5o7nZKHqtuG5rp4OyAswAV92rigs5cDh+aIJMCQZAQMSUR0N0XlVTh28665Q/HZuJp129Cc0hyh/s7o3cYVfQJd4ONsI1GlRM0LQ5IRMCQRUV1czyupngAeX33XXH6J/tCct5NV9bPmAl0Q6u8CeysOzRE1BIYkI2BIIqIHpdEKxKQWVD825UoWTqfkoVLz91+7ZjKgs7cDegdWr83UxdsBFhyaI6oXDElGwJBERPWluLwKxxNzcPBK9STwhNuG5mxrhuZuPjrF19kaMhlXASd6EAxJRsCQREQNJTW/FIdvPpz3SHw28m4bmvNytNIFpsf8neFgrZCoUqLGhyHJCBiSiMgYtFqBmFQ1DsVn4dCVbJxKzq01NBfs5YA+N0NT11YcmiO6G4YkI2BIIiIplFRU4XhiLg7dHJqLyyzS22+jkN8cmquez+TnYsOhOaJbPOz3t6T/BFm0aBEeeeQR2NnZwc3NDcOGDUNsbKxem7KyMkRERMDZ2Rm2trYYOXIkMjIy9NqkpKRgyJAhsLa2hpubG15//XVUVek/g2n//v3o1q0blEolAgICEBkZ2dCnR0T0UKwV5ujf1g1vPx2E3a/2xbGFA/HJqE74R2dPONsoUFyhwZ9/ZeKdLTEY8NkBhH20D2/+ch5bz6cir7hC6vKJGj1Je5KeeOIJjBkzBo888giqqqrwz3/+ExcvXsSlS5dgY1O9jsiMGTOwbds2REZGwt7eHjNnzoSZmRmOHDkCANBoNOjSpQs8PDzwySefIC0tDePGjcOUKVPwwQcfAAASExPRsWNHTJ8+HZMnT8aePXswd+5cbNu2DeHh4feskz1JRGRqtFqBS2lqHIrLxuH4LJxMzEOFRqvbL5MBnVraI+zm0Fy3Vo5QmHNojpqXJjXclpWVBTc3Nxw4cAB9+vRBQUEBXF1d8cMPP2DUqFEAgMuXL6N9+/aIiopCz549sWPHDgwdOhSpqalwd3cHACxduhRvvPEGsrKyoFAo8MYbb2Dbtm24ePGi7rPGjBmD/Px87Ny58551MSQRkakrrdDgeGLOzQf0ZiM2o1Bvv7VCjp6t/75rzt+VQ3PU9D3s97d5A9T0wAoKCgAATk5OAIDo6GhUVlZi0KBBujbt2rVDq1atdCEpKioKwcHBuoAEAOHh4ZgxYwZiYmLQtWtXREVF6R2jps3cuXMN1lFeXo7y8nLdz2q1ur5OkYioQVgp5OjX1g392roBADLUZTcDUxYOx2cju6gCey9nYu/lTABAC3tLXWDqFeACJxveNUd0O5MJSVqtFnPnzkWvXr3QsWNHAEB6ejoUCgUcHBz02rq7uyM9PV3X5taAVLO/Zt/d2qjVapSWlsLKykpv36JFi/D//t//q7dzIyIyNneVJUaGeGFkiBe0WoHL6YXVj02Jy8aJpFykFZRh46nr2HjqOmQyoKOnffUDegNdEOLjCKW5XOpTIJKcyYSkiIgIXLx4EYcPH5a6FCxcuBCvvvqq7me1Wg1vb28JKyIienBmZjIEeaoQ5KnCtL7+KKvU4ERiri40XU4vxIUbBbhwowDf7k+AlYUcPVs7Iezmo1MC3Gw5NEfNkkmEpJkzZ2Lr1q04ePAgvLy8dNs9PDxQUVGB/Px8vd6kjIwMeHh46NqcOHFC73g1d7/d2ub2O+IyMjKgUqlq9SIBgFKphFKprJdzIyIyNZYWcvRp44o+bVwBAJnqMhyOz765qGU2sovKsS82C/tiswAAHirLmxPAXRAW4AJnW/79SM2DpCFJCIFZs2bht99+w/79++Hn56e3PyQkBBYWFtizZw9GjhwJAIiNjUVKSgpCQ0MBAKGhofjPf/6DzMxMuLlVj8Xv3r0bKpUKQUFBujbbt2/XO/bu3bt1xyAias7cVJYY0c0LI7p5QYjqobmaVcBPJOYiXV2Gn6Ov4+fo6wCADp4q3QN6Q3w5NEdNl6R3t73yyiv44Ycf8L///Q9t27bVbbe3t9f18MyYMQPbt29HZGQkVCoVZs2aBQA4evQogL+XAPD09MTHH3+M9PR0vPTSS5g8eXKtJQAiIiLw8ssvY+/evZg9ezaXACAiuoeySg1OJeXhUFwWDsZl4680/RtZLC3M0MPv77vm2rhzaI5MR6NeAuBO/yOtXr0aEyZMAFC9mOT8+fPx448/ory8HOHh4fj22291Q2kAkJycjBkzZmD//v2wsbHB+PHj8eGHH8Lc/O+Osv3792PevHm4dOkSvLy88NZbb+k+414YkoiIqmUVluNIfHUv0+G4bGQWluvtd7NT6lYA7xXgAlc7Ds2RdBp1SGosGJKIiGoTQuBKRpFuAvjxxByUVWr12gS1UOl6mbr7OsLSgkNzZDwMSUbAkEREdG9llRpEJ+fh0M31mWJS9YfmlOZmeNTPCX0CXdG7jQvauttxaI4aFEOSETAkERHVXXZR9dBcTWjKUOsPzbnaKdE7wAW921QPzbnZWUpUKTVVDElGwJBERPRwhBCIzyzCwZuB6fjVXJRWavTatPOwQ582rggLcMGjfk4cmqOHxpBkBAxJRET1q7zq76G5w3HZuJhagFu/jRTmZujh54SwgOr5TO1bcGiO6o4hyQgYkoiIGlZOUTmOJOTg8M1J4GkFZXr7XWyVCAtw1t0556bi0BzdG0OSETAkEREZjxACCVnFurvmjl3NQUmF/tBcW3e76rvm2rjiUV8nWCk4NEe1MSQZAUMSEZF0Kqq0OJ2SpwtNF27cNjQnN8Mjfo7oHVg9nymohQpmZhyaI4Yko2BIIiIyHXnFFTiSkI1DV6ongafeNjTnbKNA2M3nzPUOdIWHPYfmmiuGJCNgSCIiMk1CCFzNLsbhm3fNRSXkoPi2obk27rYIC6hem6mHnxOsFSbxbHcyAoYkI2BIIiJqHCqqtDh7LV/3rLnz1/NrDc2F+DiidxsX9Al05dBcE8eQZAQMSUREjVN+SQWOJuRUh6Yr2biRX6q338lGgV4BLjcfneKCFvZWElVKDYEhyQgYkoiIGj8hBJJySnSB6djVHBSVV+m1CXCz1QWmHn7OsFFyaK4xY0gyAoYkIqKmp1JTMzRXPZ/p3LV8aG/5RrSQy6qH5m6uzdTB0x5yDs01KgxJRsCQRETU9BWUVCLqajYOxmXj4JUsXM/TH5pztLbAYwEu6BPogrBAV7R04NCcqWNIMgKGJCKi5kUIgeScEhyKz8ahK9V3zRXeNjTX2tUGfW72MvVo7QxbDs2ZHIYkI2BIIiJq3qo0Wpy7no+DV7JxOD4bZ6/lQ3PL2Jy5mQzdfBx1vUzBLTk0ZwoYkoyAIYmIiG5VUFqJqIQcHI6vXgU8OadEb7+9lQXCAlwQdnMSuJejtUSVNm8MSUbAkERERHeTklOCQ/FZOHQlG0cSslFYpj805+dic/OuOVf0bO0EO0sLiSptXhiSjIAhiYiI7lf10FyBbhXwMwaG5rq2ctDdNdfJy4FDcw2EIckIGJKIiOhBqcsqcSwhB4fiquczJWYX6+1XWZrfXNCyOjR5O3Forr4wJBkBQxIREdWXa7klNwNTFg7HZUN929Ccr7M1ege6IizQBaH+zlBxaO6BMSQZAUMSERE1BI1W4Pz1/JtDc9k4nZKHqluG5uRmMnTxdtDNZ+rsZQ9zuZmEFTcuDElGwJBERETGUFRedXNorvquuau3Dc3ZWZrjMX9n9A50RZ9AV7Ry5tDc3TAkGQFDEhERSeF6Xomul+lIQjbySyr19rdystb1MoX6O8PeikNzt2JIMgKGJCIikppGK3DxRoGulyk6WX9ozkwGdPF2QFigK/oEuqCztwMsmvnQHEOSETAkERGRqSkqr8Lxqzm6B/QmZN02NKc0R6i/s66nycfZGjJZ81pqgCHJCBiSiIjI1KXml+JwXDYOxmXhSHw28m4bmvN2skJYQHUv02P+LrC3bvpDcwxJRsCQREREjYlWKxCTqsbBuCwcistCdHIeKjX6Q3OdvBx0z5rr2qppDs0xJBkBQxIRETVmxeVVOJGYi4Nx1WszxWUW6e23VZqjZ+uaoTkX+LnYNImhOYYkI2BIIiKipiStoLR6Qcubq4DnFlfo7W/pYKWby9QrwBkO1gqJKn04DElGwJBERERNlVYrcClNrZsAfiopDxUarW6/7ObQXO+A6l6mrq0coTBvHENzDElGwJBERETNRUlF9dBcTWi6kqE/NGetkCP05tBcWKAr/F1Nd2iOIckIGJKIiKi5Si8ow+H46sB0JD4b2UX6Q3Oe9pa6Z831CnCBk43pDM0xJBkBQxIREVH10Nxf6WrdfKYTSbmoqNIfmgtuaY+wgOr5TCE+0g7NMSQZAUMSERFRbaUVGpxIysXhm6uAX04v1NtvrZCjh59T9bPm2rjA39XWqENzDElGwJBERER0b5nqmqG56ld2Ubnefg+VZfVdc21c0cvfGc62ygathyHJCBiSiIiI6kYIgcvphbpnzZ1IzEX5LUNzANCxpQq9A13RO9AFIT6OUJrL67UGhiQjYEgiIiJ6OGWVGpxMytX1Mv2Vptbbb6s0x8n/GwQrRf0FpYf9/javt0qIiIiI7sDSQn6z18gVAJBZWIYjtwzNeTla1WtAqg8MSURERGR0bnaWGN7VC8O7ekEIUeuBvKagcSyZSURERE2WTCYzqfWVajAkERERERnAkERERERkAEMSERERkQEMSUREREQGMCQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZwJBEREREZABDEhEREZEBDElEREREBjAkERERERlgLnUBjYEQAgCgVqslroSIiIjuV833ds33eF0xJN2HwsJCAIC3t7fElRAREVFdFRYWwt7evs7vk4kHjVfNiFarRWpqKuzs7CCTyR7qWGq1Gt7e3rh27RpUKlU9Vdj08bo9GF63B8Pr9uB47R4Mr9uDudd1E0KgsLAQnp6eMDOr+wwj9iTdBzMzM3h5edXrMVUqFf9HeAC8bg+G1+3B8Lo9OF67B8Pr9mDudt0epAepBiduExERERnAkERERERkAEOSkSmVSrzzzjtQKpVSl9Ko8Lo9GF63B8Pr9uB47R4Mr9uDaejrxonbRERERAawJ4mIiIjIAIYkIiIiIgMYkoiIiIgMYEgiIiIiMoAhyci++eYb+Pr6wtLSEj169MCJEyekLklSBw8exNNPPw1PT0/IZDJs3rxZb78QAm+//TZatGgBKysrDBo0CHFxcXptcnNzMXbsWKhUKjg4OGDSpEkoKioy4lkY16JFi/DII4/Azs4Obm5uGDZsGGJjY/XalJWVISIiAs7OzrC1tcXIkSORkZGh1yYlJQVDhgyBtbU13Nzc8Prrr6OqqsqYp2JUS5YsQadOnXSLzoWGhmLHjh26/bxm9+fDDz+ETCbD3Llzddt47Qx79913IZPJ9F7t2rXT7ed1u7MbN27gxRdfhLOzM6ysrBAcHIxTp07p9hvtu0GQ0WzYsEEoFAqxatUqERMTI6ZMmSIcHBxERkaG1KVJZvv27eL//u//xK+//ioAiN9++01v/4cffijs7e3F5s2bxblz58Q//vEP4efnJ0pLS3VtnnjiCdG5c2dx7NgxcejQIREQECCef/55I5+J8YSHh4vVq1eLixcvirNnz4qnnnpKtGrVShQVFenaTJ8+XXh7e4s9e/aIU6dOiZ49e4rHHntMt7+qqkp07NhRDBo0SJw5c0Zs375duLi4iIULF0pxSkaxZcsWsW3bNnHlyhURGxsr/vnPfwoLCwtx8eJFIQSv2f04ceKE8PX1FZ06dRJz5szRbee1M+ydd94RHTp0EGlpabpXVlaWbj+vm2G5ubnCx8dHTJgwQRw/flxcvXpV7Nq1S8THx+vaGOu7gSHJiB599FERERGh+1mj0QhPT0+xaNEiCasyHbeHJK1WKzw8PMQnn3yi25afny+USqX48ccfhRBCXLp0SQAQJ0+e1LXZsWOHkMlk4saNG0arXUqZmZkCgDhw4IAQovoaWVhYiE2bNuna/PXXXwKAiIqKEkJUh1MzMzORnp6ua7NkyRKhUqlEeXm5cU9AQo6OjmLFihW8ZvehsLBQBAYGit27d4u+ffvqQhKv3Z298847onPnzgb38brd2RtvvCHCwsLuuN+Y3w0cbjOSiooKREdHY9CgQbptZmZmGDRoEKKioiSszHQlJiYiPT1d75rZ29ujR48eumsWFRUFBwcHdO/eXddm0KBBMDMzw/Hjx41esxQKCgoAAE5OTgCA6OhoVFZW6l23du3aoVWrVnrXLTg4GO7u7ro24eHhUKvViImJMWL10tBoNNiwYQOKi4sRGhrKa3YfIiIiMGTIEL1rBPD37V7i4uLg6emJ1q1bY+zYsUhJSQHA63Y3W7ZsQffu3fHss8/Czc0NXbt2xXfffafbb8zvBoYkI8nOzoZGo9H7ZQcAd3d3pKenS1SVaau5Lne7Zunp6XBzc9Pbb25uDicnp2ZxXbVaLebOnYtevXqhY8eOAKqviUKhgIODg17b26+boetas6+punDhAmxtbaFUKjF9+nT89ttvCAoK4jW7hw0bNuD06dNYtGhRrX28dnfWo0cPREZGYufOnViyZAkSExPRu3dvFBYW8rrdxdWrV7FkyRIEBgZi165dmDFjBmbPno01a9YAMO53g/nDnAgRSSsiIgIXL17E4cOHpS6lUWjbti3Onj2LgoIC/Pzzzxg/fjwOHDggdVkm7dq1a5gzZw52794NS0tLqctpVJ588kndnzt16oQePXrAx8cHGzduhJWVlYSVmTatVovu3bvjgw8+AAB07doVFy9exNKlSzF+/Hij1sKeJCNxcXGBXC6vdedCRkYGPDw8JKrKtNVcl7tdMw8PD2RmZurtr6qqQm5ubpO/rjNnzsTWrVuxb98+eHl56bZ7eHigoqIC+fn5eu1vv26GrmvNvqZKoVAgICAAISEhWLRoETp37ozFixfzmt1FdHQ0MjMz0a1bN5ibm8Pc3BwHDhzAV199BXNzc7i7u/Pa3ScHBwe0adMG8fHx/J27ixYtWiAoKEhvW/v27XVDlcb8bmBIMhKFQoGQkBDs2bNHt02r1WLPnj0IDQ2VsDLT5efnBw8PD71rplarcfz4cd01Cw0NRX5+PqKjo3Vt9u7dC61Wix49ehi9ZmMQQmDmzJn47bffsHfvXvj5+entDwkJgYWFhd51i42NRUpKit51u3Dhgt5fIrt374ZKpar1l1NTptVqUV5ezmt2FwMHDsSFCxdw9uxZ3at79+4YO3as7s+8dvenqKgICQkJaNGiBX/n7qJXr161ljW5cuUKfHx8ABj5u6Hu887pQW3YsEEolUoRGRkpLl26JKZOnSocHBz07lxobgoLC8WZM2fEmTNnBADx+eefizNnzojk5GQhRPVtng4ODuJ///ufOH/+vHjmmWcM3ubZtWtXcfz4cXH48GERGBjYpJcAmDFjhrC3txf79+/Xu7W4pKRE12b69OmiVatWYu/eveLUqVMiNDRUhIaG6vbX3Fo8ePBgcfbsWbFz507h6urapG8tfvPNN8WBAwdEYmKiOH/+vHjzzTeFTCYTf/zxhxCC16wubr27TQheuzuZP3++2L9/v0hMTBRHjhwRgwYNEi4uLiIzM1MIwet2JydOnBDm5ubiP//5j4iLixPr168X1tbWYt26dbo2xvpuYEgysv/+97+iVatWQqFQiEcffVQcO3ZM6pIktW/fPgGg1mv8+PFCiOpbPd966y3h7u4ulEqlGDhwoIiNjdU7Rk5Ojnj++eeFra2tUKlUYuLEiaKwsFCCszEOQ9cLgFi9erWuTWlpqXjllVeEo6OjsLa2FsOHDxdpaWl6x0lKShJPPvmksLKyEi4uLmL+/PmisrLSyGdjPC+//LLw8fERCoVCuLq6ioEDB+oCkhC8ZnVxe0jitTPsueeeEy1atBAKhUK0bNlSPPfcc3pr/fC63dnvv/8uOnbsKJRKpWjXrp1Yvny53n5jfTfIhBCijj1hRERERE0e5yQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZwJBEREREZABDEhEREZEBDElE1Gykp6fj8ccfh42NTa2nr9eYMGEChg0bZtS6iMg0MSQRkdFMmDABMpkMH374od72zZs3QyaTNfjnf/HFF0hLS8PZs2dx5coVg20WL16MyMhI3c/9+vXD3LlzG7w2IjI9DElEZFSWlpb46KOPkJeXZ/TPTkhIQEhICAIDA+Hm5mawjb29/R17mYioeWFIIiKjGjRoEDw8PLBo0aK7tvvll1/QoUMHKJVK+Pr64rPPPrvnsZcsWQJ/f38oFAq0bdsW33//vW6fr68vfvnlF6xduxYymQwTJkwweIxbh9smTJiAAwcOYPHixZDJZJDJZEhKSgIAXLx4EU8++SRsbW3h7u6Ol156CdnZ2brj9OvXD7NmzcLcuXPh6OgId3d3fPfddyguLsbEiRNhZ2eHgIAA7NixQ/eevLw8jB07Fq6urrCyskJgYCBWr159z/MmoobBkERERiWXy/HBBx/gv//9L65fv26wTXR0NEaPHo0xY8bgwoULePfdd/HWW2/pDYPd7rfffsOcOXMwf/58XLx4EdOmTcPEiROxb98+AMDJkyfxxBNPYPTo0UhLS8PixYvvWevixYsRGhqKKVOmIC0tDWlpafD29kZ+fj4GDBiArl274tSpU9i5cycyMjIwevRovfevWbMGLi4uOHHiBGbNmoUZM2bg2WefxWOPPYbTp09j8ODBeOmll1BSUgIAeOutt3Dp0iXs2LEDf/31F5YsWQIXF5f7vLJEVN/4gFsiMpoJEyYgPz8fmzdvRmhoKIKCgrBy5Ups3rwZw4cPR81fR2PHjkVWVhb++OMP3XsXLFiAbdu2ISYmxuCxe/XqhQ4dOmD58uW6baNHj0ZxcTG2bdsGABg2bBgcHBzuGrZurRGo7hHq0qULvvzyS12bf//73zh06BB27dql23b9+nV4e3sjNjYWbdq0Qb9+/aDRaHDo0CEAgEajgb29PUaMGIG1a9cCqJ5I3qJFC0RFRaFnz574xz/+ARcXF6xater+LyoRNRj2JBGRJD766COsWbMGf/31V619f/31F3r16qW3rVevXoiLi4NGozF4vDu9x9DxH9a5c+ewb98+2Nra6l7t2rUDUD3vqUanTp10f5bL5XB2dkZwcLBum7u7OwAgMzMTADBjxgxs2LABXbp0wYIFC3D06NF6r52I7h9DEhFJok+fPggPD8fChQulLqXOioqK8PTTT+Ps2bN6r7i4OPTp00fXzsLCQu99MplMb1vNHX1arRYA8OSTTyI5ORnz5s1DamoqBg4ciNdee80IZ0REhjAkEZFkPvzwQ/z++++IiorS296+fXscOXJEb9uRI0fQpk0byOVyg8e603uCgoIeqkaFQlGr96pbt26IiYmBr68vAgIC9F42NjYP9Xmurq4YP3481q1bhy+//FJv+JCIjIshiYgkExwcjLFjx+Krr77S2z5//nzs2bMH77//Pq5cuYI1a9bg66+/vmuvyuuvv47IyEgsWbIEcXFx+Pzzz/Hrr78+dE+Mr68vjh8/jqSkJGRnZ0Or1SIiIgK5ubl4/vnncfLkSSQkJGDXrl2YOHHiHYcD78fbb7+N//3vf4iPj0dMTAy2bt2K9u3bP1T9RPTgGJKISFLvvfeebripRrdu3bBx40Zs2LABHTt2xNtvv4333nvvjrftA9WTshcvXoxPP/0UHTp0wLJly7B69Wr069fvoep77bXXIJfLERQUBFdXV6SkpMDT0xNHjhyBRqPB4MGDERwcjLlz58LBwQFmZg/+16pCocDChQvRqVMn9OnTB3K5HBs2bHio+onowfHuNiIiIiID2JNEREREZABDEhEREZEBDElEREREBjAkERERERnAkERERERkAEMSERERkQEMSUREREQGMCQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZ8P8BtZITKLvbaEYAAAAASUVORK5CYII=\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "maxPerCount = 500 #maxPerCount is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "minPRcount = 0.5 #minPRcount is specified in count. However, the users can also specify different minPRcount value.\n", - "minSup=100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Partial Periodic Frequent Patterns using GPFgrowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.partialPeriodicFrequentPattern.basic import GPFgrowth as alg #import the algorithm\n", - "\n", - "obj = alg.GPFgrowth(iFile=inputFile, minSup=minSup,maxPer=maxPerCount,minPR=minPRcount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('partialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "9ecc8c8f-afb5-4ae0-9579-ed2234a8da56" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Total No of patterns: 20743\n", - "Runtime: 86.12961840629578\n", - "Memory (RSS): 729460736\n", - "Memory (USS): 682782720\n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "aceb0ca6-a05c-4a25-ff69-a8b91283775b" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 06:18:40-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.05MB/s in 4.2s \n", + "\n", + "2023-08-28 06:18:45 (1.05 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "5308258b-4ce4-4687-c2de-b921f6387af2" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Partial Periodic Frequent Patterns using GPFgrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "af3ac2f7-3566-4a95-869b-2de41add00cc" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "0a8bd9e1-3df8-4fb4-cf0d-2de0f3bd9e14" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "3M8FtfKU4bhu" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "!head partialPeriodicFrequentPatternsAtMinSupCount100.txt" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "bcd8a294-c1e8-44e8-89e0-a8503b8dd712" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "662:113:0.5175438596491229\n", - "222:126:0.5118110236220472\n", - "23:128:0.5271317829457365\n", - "23\t395:119:0.5083333333333333\n", - "985:132:0.5037593984962406\n", - "49:137:0.5579710144927537\n", - "702:137:0.5\n", - "702\t956:135:0.5073529411764706\n", - "16:149:0.5333333333333333\n", - "891:151:0.5\n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "maxPerCount = 500 #maxPerCount is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "minPRcount = 0.5 #minPRcount is specified in count. However, the users can also specify different minPRcount value.\n", + "minSup=100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Partial Periodic Frequent Patterns using GPFgrowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.partialPeriodicFrequentPattern.basic import GPFgrowth as alg #import the algorithm\n", + "\n", + "obj = alg.GPFgrowth(iFile=inputFile, minSup=minSup,maxPer=maxPerCount,minPR=minPRcount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('partialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9ecc8c8f-afb5-4ae0-9579-ed2234a8da56" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total No of patterns: 20743\n", + "Runtime: 86.12961840629578\n", + "Memory (RSS): 729460736\n", + "Memory (USS): 682782720\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head partialPeriodicFrequentPatternsAtMinSupCount100.txt" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bcd8a294-c1e8-44e8-89e0-a8503b8dd712" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "662:113:0.5175438596491229\n", + "222:126:0.5118110236220472\n", + "23:128:0.5271317829457365\n", + "23\t395:119:0.5083333333333333\n", + "985:132:0.5037593984962406\n", + "49:137:0.5579710144927537\n", + "702:137:0.5\n", + "702\t956:135:0.5073529411764706\n", + "16:149:0.5333333333333333\n", + "891:151:0.5\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _partialperiodicfrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the GPFgrowth algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.partialPeriodicFrequentPattern.basic import GPFgrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maxPerCount = 500\n", + "minPRcount = 0.5\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of GPFgrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maxperCount','minPRcount','patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of GPFgrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.GPFgrowth(inputFile, minSup=minSupCount, maxPer=maxPerCount,minPR=minPRcount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['GPFgrowth', minSupCount, maxPerCount, minPRcount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e44be19e-60c9-4623-97ab-f8264abcffb4" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maxperCount minPRcount patterns runtime memory\n", + "0 GPFgrowth 100 500 0.5 20743 90.717150 775041024\n", + "1 GPFgrowth 150 500 0.5 19017 91.783152 771985408\n", + "2 GPFgrowth 200 500 0.5 13235 91.547446 784392192\n", + "3 GPFgrowth 250 500 0.5 7674 88.452363 790614016\n", + "4 GPFgrowth 300 500 0.5 4529 96.684099 790396928\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "b863cc66-3295-431a-9c29-bb94ba184c0e" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _partialperiodicfrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the GPFgrowth algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.partialPeriodicFrequentPattern.basic import GPFgrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maxPerCount = 500\n", - "minPRcount = 0.5\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of GPFgrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maxperCount','minPRcount','patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of GPFgrowth algorithm" - ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" - ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.GPFgrowth(inputFile, minSup=minSupCount, maxPer=maxPerCount,minPR=minPRcount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['GPFgrowth', minSupCount, maxPerCount, minPRcount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "cZNXmKqp6ea1" - }, - "execution_count": 11, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } - }, - { - "cell_type": "code", - "source": [ - "print(result)" - ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "e44be19e-60c9-4623-97ab-f8264abcffb4" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maxperCount minPRcount patterns runtime memory\n", - "0 GPFgrowth 100 500 0.5 20743 90.717150 775041024\n", - "1 GPFgrowth 150 500 0.5 19017 91.783152 771985408\n", - "2 GPFgrowth 200 500 0.5 13235 91.547446 784392192\n", - "3 GPFgrowth 250 500 0.5 7674 88.452363 790614016\n", - "4 GPFgrowth 300 500 0.5 4529 96.684099 790396928\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "b863cc66-3295-431a-9c29-bb94ba184c0e" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] + ] + } + ] } diff --git a/notebooks/partialPeriodicFrequentPattern/basic/PPF_DFS.ipynb b/notebooks/partialPeriodicFrequentPattern/basic/PPF_DFS.ipynb index 900a38b9..24b15b2a 100644 --- a/notebooks/partialPeriodicFrequentPattern/basic/PPF_DFS.ipynb +++ b/notebooks/partialPeriodicFrequentPattern/basic/PPF_DFS.ipynb @@ -1,669 +1,669 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Partial Periodic Frequent Patterns in Temporal Databases using PPF_DFS" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find partial periodic frequent patterns in a temporal database using the PPF_DFS algorithm. In the final part, we describe an advanced approach, where we evaluate the PPF_DFS algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "7ca0d322-3e92-4a41-ffc1-be43e782b4df" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pami in /usr/local/lib/python3.10/dist-packages (2023.8.6.8)\n", + "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", + "Requirement already satisfied: resource in /usr/local/lib/python3.10/dist-packages (from pami) (0.2.1)\n", + "Requirement already satisfied: validators in /usr/local/lib/python3.10/dist-packages (from pami) (0.21.2)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.4.4)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (23.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pami) (2023.3)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->pami) (8.2.3)\n", + "Requirement already satisfied: JsonForm>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.0.2)\n", + "Requirement already satisfied: JsonSir>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.0.2)\n", + "Requirement already satisfied: python-easyconfig>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.1.7)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", + "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.30.2)\n", + "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.9.2)\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Partial Periodic Frequent Patterns in Temporal Databases using PPF_DFS" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find partial periodic frequent patterns in a temporal database using the PPF_DFS algorithm. In the final part, we describe an advanced approach, where we evaluate the PPF_DFS algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "7ca0d322-3e92-4a41-ffc1-be43e782b4df" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: pami in /usr/local/lib/python3.10/dist-packages (2023.8.6.8)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", - "Requirement already satisfied: resource in /usr/local/lib/python3.10/dist-packages (from pami) (0.2.1)\n", - "Requirement already satisfied: validators in /usr/local/lib/python3.10/dist-packages (from pami) (0.21.2)\n", - "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", - "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.42.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.4.4)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (23.1)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (3.1.1)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pami) (2023.3)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->pami) (8.2.3)\n", - "Requirement already satisfied: JsonForm>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.0.2)\n", - "Requirement already satisfied: JsonSir>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.0.2)\n", - "Requirement already satisfied: python-easyconfig>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.1.7)\n", - "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", - "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", - "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", - "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", - "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.30.2)\n", - "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.9.2)\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "0c1570a1-a4ea-433b-8414-53ec41523e80" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 07:02:46-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.08MB/s in 6.3s \n", - "\n", - "2023-08-28 07:02:55 (713 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "0a9ea599-fcfa-4d77-bc0f-0e79c671589b" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Partial Periodic Frequent Patterns using PPF_DFS" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "f9da22be-4d2e-484e-a1e5-6e4d7c636e66" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "e205786a-0c59-4270-ae98-c0d92d3b16e7" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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zM3z++ecoKSnB4sWLa3U81fnPf/6D0aNHo0+fPnjqqaeQnZ2NFStWICgoSK+gupuRI0diw4YNGDt2LEaMGIHExESsWrUKgYGB1T5Hu3bt0LdvX7zwwgsoKSnBRx99BBcXF7z22mtGOy4iAJyqgKiu3n77bdGiRQuhUCj0Lt8HcMfpA7766ivRvn17oVKpRMeOHcXq1avFggULqlxufqfnuPVS+5KSEvHqq6+KkJAQYWdnJ2xsbERISIhYuXKl3mNOnz4tIiIihK2trXB1dRXPPvusbtqDWy97F0KIU6dOibFjxwpHR0dhaWkp/P39xRtvvFGj4759GgAhhLh48aJ48MEHdc/Xs2dPsWnTJr02lZfmr1+/Xm97dZfm38m+fftEaGiosLCwEG3atBGrVq2q9n29PeM777wjevbsKRwdHYWVlZXo2LGjePfdd0VpaamujVqtFi+++KJwc3MTkiTpnrMy35IlS6rkudNUBTY2NuLixYtiyJAhwtraWnh4eIgFCxbopm2olJGRIcaPHy+sra2Fk5OTeO6558SpU6eqPOedsglRdaoCIYQ4ceKEiIyMFLa2tsLa2lrcd9994tChQ3ptKqcqOHbsmN72O02hUJ2ffvpJdOzYUahUKhEUFCQ2btwoxo8fLzp27FjlParu/dNqteI///mP8PX1FSqVSnTt2lVs2rRJTJ48udrpDpYsWSKWLl0qfHx8hEqlEv369ROxsbF6z1n5/t+uus8J0Z1IQtTjKEwiIqJbdOnSBW5ublXG29XF5cuX4efnhyVLlmDOnDlGe16iO+GYJyIiMrqysjKo1Wq9bXv37kVsbCwGDhwoTygiI+GYJyIiMrrr168jIiICjz/+OLy9vXH27FmsWrUKnp6eVSaoJGpsWDwREZHROTk5ITQ0FF9++SUyMjJgY2ODESNG4L333tOtyUjUWHHMExEREZEBOOaJiIiIyAAsnoiIiIgMwDFPRqLVapGcnAw7OzujLuNARERE9UcIgfz8fHh7e9d40l4WT0aSnJxslDWhiIiIqOFdvXoVLVu2rFFbFk9GYmdnB6D8zTfG2lBERERU//Ly8uDj46P7Hq8JFk9GUtlVZ29vz+KJiIiokTFkyA0HjBMREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBE1A8VlGrkjEBE1GSyeiJowrVZgWdQ5BC3Yjn//GgchhNyRiIgaPTO5AxBR/SgoUWPW2hhEnU4DAPx4JAnt3GzxdF8/mZMRETVuPPNE1ARdySzEuJUHEXU6DRZKBUZ29gIAvLvlDA5duCFzOiKixo3FE1ETc+D8DTyw4iDOpRXA3U6Ftc/1wvLHumJc1xbQaAWm/3gCV7OK5I5JRNRosXgiaiKEEPjqQCImfX0EuTfL0MXHEX+82BddWzlBkiT8Z1wwgls4ILuoDFP/F42bpRxETkRUGyyeiJqA4jIN5qw/ibc3nYZWAOO7tcRPU3vBw95S18bSXInPnwiFi40FzqTk4bVfTnIAORFRLbB4Imrk0vKK8cgXf+GXE9egkIA3Rgbig4c6w9JcWaWtt6MVVk7sBjOFhD9ik/HF/ksyJCYiatxYPBE1YieSsjFq+QHEXs2Bg5U5vns6DFP6+kGSpDs+JqyNCxaMCgQAvL/tLPady2iouERETQKLJ6JGav3xq3j087+Qnl+CDh622DijD/q2d63RYx/v5YtHuvtAK4AXfzyByzcK6zktEVHTweKJqJFRa7R48494vPrzSZRqtBgS6IEN0/rA18Wmxs8hSRLeGtMJXVs5Iq9Yjan/O47CEnU9piYiajpYPBE1ItmFpZi8+ihWH7wMAHh5cHusejwUtirD57tVmSmx6vFQuNmpcC6tAK+si+UAciKiGmDxRNRIJKTmY/SnB3HwQiasLZT4bGI3zLq/AxSKO49vuhcPe0usejwU5koJ2+JT8emeC0ZMTETUNLF4ImoEtp1KxdiVB5GUVYSWTlb45YXeGBbsZZTnDvV1wtujgwAAS6POYdeZNKM8LxFRU8XiicjE/Rx9Dc9/H42iUg3C27hg44y+CPCyN+prPNqzFR7v1QpCADN/isHFjAKjPj8RUVPC4onIhJ1Ly8frv8UBAB7v1QrfTekJZxuLenmt+SM7oUdrJ+SXqPHsd8eRV1xWL69DRNTYsXgiMlHFZRrM+PEEisu06NfeFW89EARzZf39lbUwU2DlxFB4OVjiUkYhZq+NgVbLAeRERLdj8URkot784zTOpRXA1VaFZQ93qdPA8Jpys1Ph8ydCYWGmwM4z6fho1/l6f00iosaGxRORCdp0MhlrjiZBkoCPHukCNztVg71255aOWDQ2GADwya7z2HYqtcFem4ioMWDxRGRikjKLMO+X8nFO0wa2rfGs4cY0PrQlnu7jBwB4ZV0MzqXlN3gGIiJTxeKJyISUqrV48ae/kV+iRqivE2ZFdJAty7+Hd0Tvti4oLNVg6nfHkVvEAeRERACLJyKT8sGOBN0iv5881hVm9ThA/F7MlAqsmNANLRytcDmzCPM3npItCxGRKWHxRGQi9iSk44v9lwAAix/sjBaOVjInApxtLLB8QlcAwNa4VOTe5NknIiIWT0QmIC2vGK+siwUATA73RWQnT5kT/aNbKyd08LBFqUaLqNOcfZyIiMUTkcw0WoGZP8Ugq7AUgV72mDc8QO5IVYzs7A0A+CM2WeYkRETyk7V42r9/P0aNGgVvb29IkoTffvtNb78QAvPnz4eXlxesrKwQERGB8+f1553JysrCxIkTYW9vD0dHR0yZMgUFBfpLS5w8eRL9+vWDpaUlfHx8sHjx4ipZ1q9fj44dO8LS0hLBwcHYsmWL0Y+XqDqf7rmAw5fKF/tdMaErLM2VckeqYmTn8nX0Dly4gazCUpnTEBHJS9biqbCwECEhIfj000+r3b948WJ88sknWLVqFY4cOQIbGxtERkaiuLhY12bixImIj49HVFQUNm3ahP3792Pq1Km6/Xl5eRgyZAh8fX0RHR2NJUuWYOHChfjiiy90bQ4dOoTHHnsMU6ZMwd9//40xY8ZgzJgxOHWKA2Spfh25lImPdp4DALwzJght3GxlTlS9Nm626ORtD41WcN4nIiJhIgCIX3/9VXdfq9UKT09PsWTJEt22nJwcoVKpxJo1a4QQQpw+fVoAEMeOHdO12bp1q5AkSVy/fl0IIcTKlSuFk5OTKCkp0bWZO3eu8Pf3191/+OGHxYgRI/TyhIWFieeee67G+XNzcwUAkZubW+PHUPOWVVAiwt7dKXznbhKz1v4td5x7+mzvBeE7d5N49PPDckchIjKa2nx/m+yYp8TERKSmpiIiIkK3zcHBAWFhYTh8+DAA4PDhw3B0dET37t11bSIiIqBQKHDkyBFdm/79+8PC4p/FVCMjI5GQkIDs7Gxdm1tfp7JN5etUp6SkBHl5eXo3opoSQmDO+lik5hWjjZsN3h4dJHekexoRXN5191diJtLziu/Rmoio6TLZ4ik1tbxrwMPDQ2+7h4eHbl9qairc3d319puZmcHZ2VmvTXXPcetr3KlN5f7qLFq0CA4ODrqbj4+PoYdIzdjXBy9j19l0WJgpsPyxrrBRmckd6Z58nK3RtZUjhAC2xKXIHYeISDYmWzyZunnz5iE3N1d3u3r1qtyRqJGIu5aL97aeAQC8PiIAnbwdZE5Uc6Mqr7o7yeKJiJovky2ePD3L57lJS9OfVyYtLU23z9PTE+np6Xr71Wo1srKy9NpU9xy3vsad2lTur45KpYK9vb3ejehe8ovLMGPNCZRpBCI7eeCJXr5yRzLIiM5ekCQg+ko2rufclDsOEZEsTLZ48vPzg6enJ3bt2qXblpeXhyNHjiA8PBwAEB4ejpycHERHR+va7N69G1qtFmFhYbo2+/fvR1nZPzMjR0VFwd/fH05OTro2t75OZZvK1yEyBiEE/u/XU7iSWYQWjlZYPD4EkiTJHcsgHvaW6NnaGQCw+STnfCKi5knW4qmgoAAxMTGIiYkBUD5IPCYmBklJSZAkCTNnzsQ777yDjRs3Ii4uDpMmTYK3tzfGjBkDAAgICMDQoUPx7LPP4ujRozh48CBmzJiBRx99FN7e5d0LEyZMgIWFBaZMmYL4+HisXbsWH3/8MWbPnq3L8fLLL2Pbtm1YunQpzp49i4ULF+L48eOYMWNGQ78l1IStP34NG2OToVRI+OSxLnCwNpc7Uq2MCin/u7WJXXdE1FzV27V/NbBnzx4BoMpt8uTJQojy6QreeOMN4eHhIVQqlRg8eLBISEjQe47MzEzx2GOPCVtbW2Fvby+eeuopkZ+fr9cmNjZW9O3bV6hUKtGiRQvx3nvvVcmybt060aFDB2FhYSE6deokNm/ebNCxcKoCuptzqXnC//UtwnfuJrFi93m549TJjfxi0WbeZuE7d5NIzCiQOw4RUZ3U5vtbEkIIGWu3JiMvLw8ODg7Izc3l+CfSU1ymwegVB5GQlo9+7V3x7VM9oVA0ru662z3x1RH8ef4G5gzpgBmD2ssdh4io1mrz/W2yY56ImoqVey4gIS0frrYqLHu4S6MvnIB/rrpj1x0RNUcsnojqWWWB8fqIALjZqWROYxyRnTxhrpRwNjUf59Py5Y5DRNSgWDwR1aPEG4W4dKMQ5koJgwPc7/2ARsLB2hwDOrgB4JxPRNT8sHgiqke7z5bPQ9bTzxl2lo3z6ro7GVnZdRebDA6dJKLmhMUTUT3afbZ88tX7/JvOWadKEYEeUJkpcOlGIeKTubYjETUfLJ6I6kl+cRmOJmYBAAYHeNyjdeNjqzLDoI7lRSEHjhNRc8LiiaieHDh/A2UaAT9XG/i52sgdp178M2Emu+6IqPlg8URUTyrHO1WenWmK7vN3h42FEteybyLmao7ccYiIGgSLJ6J6oNUK7EkoL54GN+HiycpCiYjA8i7JP2LZdUdEzQOLJ6J6EHc9FzcKSmGrMkP3ioV0m6rKCTM3xyVDq2XXHRE1fSyeiOrBroouu/4dXGFh1rT/mvXr4Ao7SzOk5ZXg2OUsueMQEdW7pv2vOpFMmvIUBbdTmSkxtJMnAOCPk8kypyEiqn8snoiMLC2vGKeu50GSgIHNoHgC/rnqbmtcKtQarcxpiIjqF4snIiPbU9FlF9LSscmsZXcvvdu6wNnGApmFpTh8KVPuOERE9YrFE5GRNYcpCm5nplRgWFBF110su+6IqGlj8URkRCVqDQ5cuAGgeRVPwD9r3W07lYpSNbvuiKjpYvFEZERHLmWhqFQDD3sVOnnbyx2nQfX0c4a7nQp5xWr8eT5D7jhERPWGxROREd3aZSdJksxpGpZSIWF4sBcArnVHRE0biyciIxFCYFczmqKgOpVX3e2IT0VxmUbmNERE9YPFE5GRXMwowNWsm7AwU6BPO1e548iiWytHtHC0QmGpRnfVIRFRU8PiichIdp0pLxbC27jARmUmcxp5SJKEkZ3ZdUdETRuLJyIjaY5TFFSnsutu19k0FJaoZU5DRGR8LJ6IjCC3qAzHr2QDYPHUydserV2sUVymxc4zaXLHISIyOhZPREaw73wGNFqB9u628HG2ljuOrCRJ0p19+iOWXXdE1PSweCIygsrB0YMCmvdZp0qVxdO+c+nIvVkmcxoiIuNi8URURxqtwN6EiuKpmU5RcLsOHnbo4GGLMo3AjvhUueMQERkViyeiOoq5mo3sojLYW5oh1NdJ7jgmY1TFci1/8Ko7ImpiWDwR1VHlFAUD/d1hpuRfqUojK7ruDl64gazCUpnTEBEZD/+lJ6ojTlFQPT9XGwS1sIdGK7D1FM8+EVHTweKJqA6u59zE2dR8KCRgQAc3ueOYHF3XXWyyzEmIiIyHxRNRHVSederWyglONhYypzE9IypmGz+SmIX0vGKZ0xARGQeLJ6I64BQFd9fSyRrdWjlCCGBzHLvuiKhpYPFEVEs3SzU4eOEGAGBwRw+Z05iukRVddxvZdUdETQSLJ6JaOnzpBkrUWrRwtEIHD1u545iskZ29oFRI+DspBxfSC+SOQ0RUZyyeiGqpcoqCQR3dIUmSzGlMl7u9JQZWDKZff/yqzGmIiOqOxRNRLQghOEWBAR7u4QMA+OXENZRptDKnISKqGxZPRLVwNjUfKbnFsDRXILyti9xxTN6gju5wtVXhRkGprugkImqsWDwR1UJlAdCnrSsszZUypzF95koFxndrAYBdd0TU+LF4IqqF3ZyiwGAPdS/vutuTkME5n4ioUWPxRGSgrMJSnEjKBsDxToZo526L7r5O0GgFfj5xTe44RES1xuKJyED7zqVDCCDAyx5eDlZyx2lUKgeOrz9+DUIImdMQEdUOiyciA1VOUTCYZ50MNiLYCzYWSiTeKMTRxCy54xAR1QqLJyIDlGm02HcuAwBwH4sng9mozHQzjq/lwHEiaqRYPBEZIPpKNvKL1XC2sUAXH0e54zRKlV13W+JSkFdcJnMaIiLDsXgiMkDlVXYD/d2gVHBW8dro1soR7dxtUVymxR9c746IGiEWT0QG4KzidSdJEh6pmLZg3XFedUdEjQ+LJ6IaSsoswoX0ApgpJPRr7yZ3nEZtbLcWMFNIiL2ag4TUfLnjEBEZhMUTUQ3tPpsGAOje2gkOVuYyp2ncXG1ViAjwAACsPcaB40TUuLB4IqqhXWcrpyjwkDlJ0/BIxcDxX/++hhK1RuY0REQ1x+KJqAYKS9Q4cql8XiJOUWAc/dq7wsNeheyiMuw8zcWCiajxYPFEVAMHLtxAqUYLXxdrtHWzkTtOk2CmVODB0JYAOOcTETUuJl08aTQavPHGG/Dz84OVlRXatm2Lt99+W29ZByEE5s+fDy8vL1hZWSEiIgLnz5/Xe56srCxMnDgR9vb2cHR0xJQpU1BQUKDX5uTJk+jXrx8sLS3h4+ODxYsXN8gxUuOw+8w/V9lJEqcoMJaHK666+/N8Bq7n3JQ5DRFRzZh08fT+++/js88+w4oVK3DmzBm8//77WLx4MZYvX65rs3jxYnzyySdYtWoVjhw5AhsbG0RGRqK4+J9V2ydOnIj4+HhERUVh06ZN2L9/P6ZOnarbn5eXhyFDhsDX1xfR0dFYsmQJFi5ciC+++KJBj5dMk1YrsCeBUxTUB18XG/Rq4wwhgJ85bQERNRbChI0YMUI8/fTTetvGjRsnJk6cKIQQQqvVCk9PT7FkyRLd/pycHKFSqcSaNWuEEEKcPn1aABDHjh3Ttdm6dauQJElcv35dCCHEypUrhZOTkygpKdG1mTt3rvD3969x1tzcXAFA5ObmGn6gZNJOXs0RvnM3icA3toriMrXccZqcDSeuCt+5m0Sf93YJjUYrdxwiamZq8/1t0meeevfujV27duHcuXMAgNjYWBw4cADDhg0DACQmJiI1NRURERG6xzg4OCAsLAyHDx8GABw+fBiOjo7o3r27rk1ERAQUCgWOHDmia9O/f39YWFjo2kRGRiIhIQHZ2dnVZispKUFeXp7ejZqmXRVTFPRt7wqVmVLmNE3PsCAv2Fma4Vr2TRy+lCl3HCKiezLp4ulf//oXHn30UXTs2BHm5ubo2rUrZs6ciYkTJwIAUlNTAQAeHvqXjnt4eOj2paamwt1dv6vFzMwMzs7Oem2qe45bX+N2ixYtgoODg+7m4+NTx6MlU7XtVPlngFMU1A9LcyVGd6lYLJhzPhFRI2DSxdO6devwww8/4Mcff8SJEyfw7bff4oMPPsC3334rdzTMmzcPubm5utvVq/xHvylKSM3H2dR8mCslDOnE4qm+PNK9FQBgW3wqcou4WDARmTaTLp5effVV3dmn4OBgPPHEE5g1axYWLVoEAPD09AQApKWl6T0uLS1Nt8/T0xPp6fpzyKjVamRlZem1qe45bn2N26lUKtjb2+vdqOn5LeY6AGCgvzscrS3u0ZpqK6iFPQK87FGq1urecyIiU2XSxVNRUREUCv2ISqUSWq0WAODn5wdPT0/s2rVLtz8vLw9HjhxBeHg4ACA8PBw5OTmIjo7Wtdm9eze0Wi3CwsJ0bfbv34+ysn/+xxsVFQV/f384OTnV2/GRadNqBTbGJAMAxnZtIXOapk2SJDzcvWLOJ3bdEZGJM+niadSoUXj33XexefNmXL58Gb/++iuWLVuGsWPHAij/B3fmzJl45513sHHjRsTFxWHSpEnw9vbGmDFjAAABAQEYOnQonn32WRw9ehQHDx7EjBkz8Oijj8Lbu3ycxYQJE2BhYYEpU6YgPj4ea9euxccff4zZs2fLdehkAo5fycb1nJuwU5lxioIGMKZLC1goFTidkodT13PljkNEdEdmcge4m+XLl+ONN97AtGnTkJ6eDm9vbzz33HOYP3++rs1rr72GwsJCTJ06FTk5Oejbty+2bdsGS0tLXZsffvgBM2bMwODBg6FQKDB+/Hh88sknuv0ODg7YsWMHpk+fjtDQULi6umL+/Pl6c0FR8/Pr3+XdR0ODPGFpzqvs6puTjQWGdPLAppMpWHvsKoJaOMgdiYioWpIQt0zXTbWWl5cHBwcH5ObmcvxTE1Cq1qLHuzuRe7MMPzwThj7tXOWO1Cz8eT4DT3x1FHaWZjj2fxEsWomo3tXm+9uku+2I5LI3IR25N8vgbqdCrzYucsdpNvq0dUULRyvkF6uxPb76aUKIiOTG4omoGr9XDBR/IMQbSgXXsmsoCoWEhzhwnIhMHIsnotvkF5dh55nyqSrG8Cq7BvdQdx9IEnDoYiaSMovkjkNEVAWLJ6LbbDuVihK1Fu3cbdHJm+PXGloLRyv0rRhjtj6aZ5+IyPSweCK6TeUkjWO6eEOS2GUnh4e7ly939HP0NWi0vKaFiEwLiyeiW6TlFePQxfLFaUd3YZedXIZ08oCjtTlScoux/3yG3HGIiPSweCK6xR+xyRACCPV1go+ztdxxmi2VmRJjKorXdRw4TkQmhsUT0S10XXYcKC67R3qUd93tPJOGzIISmdMQEf2DxRNRhQvp+Th1PQ9mCgkjgr3kjtPsBXjZo3NLB5RphG62dyIiU8DiiajCb3+Xz+00oIMbnG0sZE5DwD8Dx9cdvwouhkBEpoLFExEAIQR+jy0/uzGaXXYm44Eu3rA0V+BcWgFirubIHYeICACLJyIAwImkbFzNugkbCyXuD/CQOw5VsLc0x/Cg8i7Udcc5cJyITAOLJyL802UX2ckTVhZcjNaUPFwxcPyP2BQUl2lkTkNExOKJCGUaLTadLC+eeJWd6Qnzc4aHvQoFJWocvpQpdxwiIhZPRPvPZSC7qAyutir0busidxy6jSRJuD+wvCt1R3yazGmIiFg8EeG3mPKzTqNCvGCm5F8JU3R/oCeA8jmftFyuhYhkxm8KatYKStSIOp0KALoZrcn09GrjDFuVGTLySxB7LUfuOETUzLF4omZtR3wqisu08HO1QeeWDnLHoTtQmSkx0N8NALDjNLvuiEheLJ6oWavsshvdxRuSJMmchu6mctxTFIsnIpIZiydqtjLyS3DgfAYAdtk1Bvd1dIe5UsKF9AJcyiiQOw4RNWMsnqjZ+iM2GVoBdPFxRGtXG7nj0D3YW5qjV5vyqyF59omI5MTiiZqt32PKl2MZ08Vb5iRUU+y6IyJTwOKJmqXEG4WIvZYLpULCyBAWT41FRMXSOdFJ2cjIL5E5DRE1VyyeqFn67e/ys05927nC1VYlcxqqKW9HKwS3cIAQwO6zPPtERPJg8UTNjhBC12U3lsuxNDrsuiMiubF4omYn5moOLmcWwcpcqfsipsZjSKfy39mf52+gqFQtcxoiao5YPFGz83vF3E5DOnnARmUmcxoylL+HHXycrVCi1mL/uRtyxyGiZojFEzUrao0Wm06WF0+c26lxkiQJ9weUr3XHrjsikgOLJ2pWDly4gRsFpXC2sUDf9q5yx6Faquy623U2DWqNVuY0RNTcsHiiZqWyy25kZy+YK/nxb6y6+zrB0docOUVlOH4lW+44RNTM8NuDmo2iUjW2x6cCAMbwKrtGzUypwKCO7gCAHfHsuiOihsXiiZqNqNNpKCrVwNfFGl19HOWOQ3U0JLBi3NOZVAghZE5DRM0JiydqNionxhwd4g1JkmROQ3XVv4MrVGYKXM26iYS0fLnjEFEzwuKJmoXMghLsP19+Wftodtk1CdYWZuhXMeifXXdE1JBYPFGzsDkuBRqtQHALB7R1s5U7DhkJZxsnIjmweKJm4deKLjsOFG9aBnX0gCQBcddzkZxzU+44RNRMsHiiJu9KZiH+TsqBQgJGhXjJHYeMyM1OhdBWTgCAnWd49omIGgaLJ2ryKud26tPOFe52ljKnIWNj1x0RNTQWT9Tk/RFbXjyN5nIsTVJl8XT4YiZyb5bJnIaImgODi6dLly7VRw6ienEpowDn0wtgppB0X7LUtLRxs0U7d1uotQJ7E9LljkNEzYDBxVO7du1w33334fvvv0dxcXF9ZCIymsqunPC2LnCwMpc5DdUXdt0RUUMyuHg6ceIEOnfujNmzZ8PT0xPPPfccjh49Wh/ZiOpsR8WX6RCedWrSKounvQkZKFFrZE5DRE2dwcVTly5d8PHHHyM5ORlff/01UlJS0LdvXwQFBWHZsmXIyMioj5xEBsvIL8GJpPJFYyNYPDVpXVo6ws1OhYISNf66lCV3HCJq4mo9YNzMzAzjxo3D+vXr8f777+PChQuYM2cOfHx8MGnSJKSkpBgzJ5HBdp1JgxBA55YO8HKwkjsO1SOFQkJEQGXXXarMaYioqat18XT8+HFMmzYNXl5eWLZsGebMmYOLFy8iKioKycnJGD16tDFzEhmMXXbNy5Bbxj1ptVwomIjqj5mhD1i2bBlWr16NhIQEDB8+HN999x2GDx8OhaK8DvPz88M333yD1q1bGzsrUY0VlKhx4EL5WnZDOnnKnIYaQnhbF9hYKJGWV4K467kI8XGUOxIRNVEGn3n67LPPMGHCBFy5cgW//fYbRo4cqSucKrm7u+Orr74yWkgiQ+0/l4FStRatXazR3p1r2TUHluZKDPB3A8Cr7oiofhl85un8+fP3bGNhYYHJkyfXKhCRMVR+ed4f6AFJkmROQw1lSKAntsSlYsfpVMyJ9Jc7DhE1UQafeVq9ejXWr19fZfv69evx7bffGiUUUV2UabTYVbHOGbvsmpf7/N2hVEg4l1aAK5mFcschoibK4OJp0aJFcHV1rbLd3d0d//nPf4wSiqgujiZmIa9YDRcbC3SrWDSWmgcHa3OE+TkDYNcdEdUfg4unpKQk+Pn5Vdnu6+uLpKQko4S61fXr1/H444/DxcUFVlZWCA4OxvHjx3X7hRCYP38+vLy8YGVlhYiIiCpdi1lZWZg4cSLs7e3h6OiIKVOmoKCgQK/NyZMn0a9fP1haWsLHxweLFy82+rFQw9gRX36pekSAB5QKdtk1N5VX3e2IZ/FERPXD4OLJ3d0dJ0+erLI9NjYWLi4uRglVKTs7G3369IG5uTm2bt2K06dPY+nSpXBy+udswuLFi/HJJ59g1apVOHLkCGxsbBAZGam3dMzEiRMRHx+PqKgobNq0Cfv378fUqVN1+/Py8jBkyBD4+voiOjoaS5YswcKFC/HFF18Y9Xio/gkh9MY7UfNTOSHq8StZyCwokTkNETVJwkCvvfaa8PX1Fbt37xZqtVqo1Wqxa9cu4evrK1555RVDn+6u5s6dK/r27XvH/VqtVnh6eoolS5botuXk5AiVSiXWrFkjhBDi9OnTAoA4duyYrs3WrVuFJEni+vXrQgghVq5cKZycnERJSYnea/v7+9c4a25urgAgcnNza/wYMr64aznCd+4m0fH1reJmqVruOCSTYR/tF75zN4m1x5LkjkJEJq42398Gn3l6++23ERYWhsGDB8PKygpWVlYYMmQIBg0aZPQxTxs3bkT37t3x0EMPwd3dHV27dsV///tf3f7ExESkpqYiIiJCt83BwQFhYWE4fPgwAODw4cNwdHRE9+7ddW0iIiKgUChw5MgRXZv+/fvDwsJC1yYyMhIJCQnIzs426jFR/arsshvQwQ2W5kqZ05BchnTiQsFEVH8MLp4sLCywdu1anD17Fj/88AM2bNiAixcv4uuvv9YrPozh0qVL+Oyzz9C+fXts374dL7zwAl566SXdVX2pqeVflB4e+t0zHh4eun2pqalwd3fX229mZgZnZ2e9NtU9x62vcbuSkhLk5eXp3Uh+ulnFO7HLrjmr7LL983wGbpZyoWAiMi6D53mq1KFDB3To0MGYWarQarXo3r277oxW165dcerUKaxatUr2eaQWLVqEN998U9YMpC8pswhnU/OhVEgY1NH93g+gJivQyx4tHK1wPecm/jyfwSkriMioDD7zpNFo8NVXX2HChAmIiIjAoEGD9G7G5OXlhcDAQL1tAQEBuqv6PD3L/0FMS9M/NZ+Wlqbb5+npifT0dL39arUaWVlZem2qe45bX+N28+bNQ25uru529erV2hwiGdGOigVhe7Z2hqO1cc+CUuMiSZLu7BO77ojI2Awunl5++WW8/PLL0Gg0CAoKQkhIiN7NmPr06YOEhAS9befOnYOvry+A8nX0PD09sWvXLt3+vLw8HDlyBOHh4QCA8PBw5OTkIDo6Wtdm9+7d0Gq1CAsL07XZv38/ysrKdG2ioqLg7++vd2XfrVQqFezt7fVuJC922dGtKqcs2HU2HRouFExExmToqHQXFxexefNmQx9WK0ePHhVmZmbi3XffFefPnxc//PCDsLa2Ft9//72uzXvvvSccHR3F77//Lk6ePClGjx4t/Pz8xM2bN3Vthg4dKrp27SqOHDkiDhw4INq3by8ee+wx3f6cnBzh4eEhnnjiCXHq1Cnx008/CWtra/H555/XOCuvtpNXZkGJ8PvXJuE7d5O4mlUodxwyAaVqjQhesE34zt0kjlzKlDsOEZmoBrnazsLCAu3atTN+FVeNHj164Ndff8WaNWsQFBSEt99+Gx999BEmTpyoa/Paa6/hxRdfxNSpU9GjRw8UFBRg27ZtsLS01LX54Ycf0LFjRwwePBjDhw9H37599eZwcnBwwI4dO5CYmIjQ0FC88sormD9/vt5cUGTadp1Jg1aUj3Vp6WQtdxwyAeZKBQYHVHbdVX/hBxFRbUhCCIPOZy9duhSXLl3CihUruODqLfLy8uDg4IDc3Fx24cng2e+OI+p0GmZGtMfMiPq9kIEajy1xKZj2wwn4ulhj75yB/DeLiKqozfe3wVfbHThwAHv27MHWrVvRqVMnmJub6+3fsGGDoU9JVCc3SzX483wGAGBIIK+qon/07+AGCzMFrmQW4Xx6ATp42MkdiYiaAIOLJ0dHR4wdO7Y+shDVyv7zGSgu06KlkxUCvPjlSP+wVZmhT1sX7EnIQNTpNBZPRGQUBhdPq1evro8cRLV261p27Jah290f6Ik9CRnYEZ+K6fc1zHhNImraDB4wDpTPk7Rz5058/vnnyM/PBwAkJyejoKDAqOGI7kWt0WLXmYopCthlR9WICHSHJAGx13KRnHNT7jhE1AQYXDxduXIFwcHBGD16NKZPn46MjPKxJu+//z7mzJlj9IBEd3P8Sjayi8rgaG2OHq2rn5OLmjd3O0v08HUGAGw6mSxzGiJqCmo1SWb37t2RnZ0NKysr3faxY8fqTVZJ1BB2xJefdRrc0QNmylqdSKVm4IEu3gCA32NYPBFR3Rn8bfPnn3/i9ddfr7IIcOvWrXH9+nWjBSO6FyEEos6Uz99TuRQHUXWGB3vBTCEhPjkPFzM4vICI6sbg4kmr1UKjqbpK+bVr12BnxytZqOGcTc3H1aybUJkp0L+Dq9xxyIQ521igb/vyz8hGnn0iojoyuHgaMmQIPvroI919SZJQUFCABQsWYPjw4cbMRnRXlV12/dq7wdrC4AtHqZkZXdF1tzE2GQbODUxEpMfg4mnp0qU4ePAgAgMDUVxcjAkTJui67N5///36yEhUrR0VS25wIWCqifsDPaEyUyDxRiFOXc+TOw4RNWIG/3e9ZcuWiI2NxU8//YSTJ0+ioKAAU6ZMwcSJE/UGkBPVp+s5NxGfnAeFBAzu6C53HGoEbFVmiAj0wOaTKdgYex3BLR3kjkREjVSt+jrMzMzw+OOPGzsLUY1FxZefderu6wwXW5XMaaixeCDEG5tPpuCP2BTMGxYAhYKTqhKR4Qwunr777ru77p80aVKtwxDV1I6KWcXZZUeGGOjvBjtLM6TmFePo5Sz0auMidyQiaoQMLp5efvllvftlZWUoKiqChYUFrK2tWTxRvcspKsWRxCwAnKKADKMyU2JYkCfWHb+GjbHJLJ6IqFYMHjCenZ2tdysoKEBCQgL69u2LNWvW1EdGIj27z6ZDoxXw97CDr4uN3HGokXkgpAUAYEtcCkrVWpnTEFFjZJQpmdu3b4/33nuvylkpovoQxS47qoPwti5wtVUhp6gMBy5kyB2HiBoho61nYWZmhuRkTj5H9au4TIN958q/8LgQMNWGUiFhZGcvAJwwk4hqx+AxTxs3btS7L4RASkoKVqxYgT59+hgtGFF1Dl64gaJSDbwcLBHUwl7uONRIPdDFG98cuowdp9Nws1QDKwul3JGIqBExuHgaM2aM3n1JkuDm5oZBgwZh6dKlxspFVK3KWcXvD/SAJPEyc6qdrj6O8HG2wtWsm9h5Jg2jQrzljkREjYjBxZNWywGWJA+NVmDX2YrxTuyyozqQJAkPhHjj0z0XsTE2mcUTERnEaGOeiOrb30nZuFFQCjtLM4S1cZY7DjVylVfd7U1IR25RmcxpiKgxMfjM0+zZs2vcdtmyZYY+PdEdVU6MObijO8yVrPupbvw97eDvYYeEtHxsi0/BIz1ayR2JiBoJg4unv//+G3///TfKysrg7+8PADh37hyUSiW6deuma8fxKGRMQgjsqFiS5X522ZGRPNDFG0u2J2BjbDKLJyKqMYOLp1GjRsHOzg7ffvstnJycAJRPnPnUU0+hX79+eOWVV4wekuhCegEuZxbBQqnAAH83ueNQE/FASHnxdOhiJtLziuFubyl3JCJqBAzu+1i6dCkWLVqkK5wAwMnJCe+88w6vtqN6U9ll16edC2xVtVrPmqgKH2drdGvlCCGATSdT5I5DRI2EwcVTXl4eMjKqzsqbkZGB/Px8o4Qiul1ll92QTuyyI+N6oOJKu42xnDCTiGrG4OJp7NixeOqpp7BhwwZcu3YN165dwy+//IIpU6Zg3Lhx9ZGRmrnU3GLEXsuFJAGDA9zljkNNzIjO3lBIQMzVHFzJLJQ7DhE1AgYXT6tWrcKwYcMwYcIE+Pr6wtfXFxMmTMDQoUOxcuXK+shIzVzUmfIuu64+jnC345gUMi43OxX6tHMFAPzBs09EVAMGF0/W1tZYuXIlMjMzdVfeZWVlYeXKlbCx4Qr3ZHzssqP6VjlJ5u8xyRBCyJyGiExdrSfLSUlJQUpKCtq3bw8bGxv+g0P1IqeoFIcvZgIoX5KFqD5EdvKEhVKB8+kFOJvKsZtEdHcGF0+ZmZkYPHgwOnTogOHDhyMlpfwKlSlTpnCaAjK6HafToNYKdPS0Q1s3W7njUBPlYGWO+zqWT4HBgeNEdC8GF0+zZs2Cubk5kpKSYG1trdv+yCOPYNu2bUYNR7S54vLxEcFeMiehpq5yuZaN7LojonsweMKcHTt2YPv27WjZsqXe9vbt2+PKlStGC0aUU1SKgxduAACGd2bxRPVrcIA7bCyUuJ5zEyeSshHqy/UTiah6Bp95Kiws1DvjVCkrKwsqlcoooYgAYEc8u+yo4ViaKxFZcVHCxhh23RHRnRlcPPXr1w/fffed7r4kSdBqtVi8eDHuu+8+o4aj5m1zHLvsqGGN6lJ+1d3muBSoNVqZ0xCRqTK4227x4sUYPHgwjh8/jtLSUrz22muIj49HVlYWDh48WB8ZqRlilx3JoW87VzjbWOBGQSkOXcxE/w5cR5GIqjL4zFNQUBDOnTuHvn37YvTo0SgsLMS4cePw999/o23btvWRkZohdtmRHMyVCgwPrui641V3RHQHBp15Kisrw9ChQ7Fq1Sr83//9X31lIsKmii67kTzrRA3sgZAW+P6vJGw/lYp3xgTB0lwpdyQiMjEGnXkyNzfHyZMn6ysLEQAgu7AUhyq77DjeiRpYd18neDtYIr9Ejb0J6XLHISITZHC33eOPP46vvvqqPrIQAQB2nE6FWisQ4GWPNuyyowamUEi65VrYdUdE1TF4wLharcbXX3+NnTt3IjQ0tMp6dsuWLTNaOGqeNseVr2U3Iphr2ZE8RoV44/P9l7DrTDryi8tgZ2kudyQiMiE1Kp5OnjyJoKAgKBQKnDp1Ct26dQMAnDt3Tq+dJEnGT0jNSnbhLVfZscuOZNLJ2x5t3WxwMaMQO+LTMD605b0fRETNRo2Kp65duyIlJQXu7u64cuUKjh07BhcXl/rORs3QjtOp0LDLjmQmSRIeCGmBD3eew8bYZBZPRKSnRmOeHB0dkZiYCAC4fPkytFpOHkf1Y9NJXmVHpuGBigkzD1y4gcyCEpnTEJEpqdGZp/Hjx2PAgAHw8vKCJEno3r07lMrqL9+9dOmSUQNS85FdWD4xIcAuO5Kfn6sNOrd0wMlrudgSl4InwlvLHYmITESNiqcvvvgC48aNw4ULF/DSSy/h2WefhZ2dXX1no2Zme3x5l12glz38XG3u/QCievZAiDdOXsvFxthkFk9EpFPjq+2GDh0KAIiOjsbLL7/M4omMTreWHbvsyESM7OyNd7ecwbHL2biecxMtHK3kjkREJsDgeZ5Wr17NwomMLuuWLjsuBEymwtPBEmF+zgCAPzjnExFVMLh4IqoPOyq67Dp526M1u+zIhDwQ0gIAsDGGxRMRlWPxRCahssuOA8XJ1AwL8oS5UsLplDwkpObLHYeITACLJ5Idu+zIlDnZWGBwRw8AwA9HrsichohMQaMqnt577z1IkoSZM2fqthUXF2P69OlwcXGBra0txo8fj7S0NL3HJSUlYcSIEbC2toa7uzteffVVqNVqvTZ79+5Ft27doFKp0K5dO3zzzTcNcEQE/HOVHbvsyFQ9Ee4LANhw4joKStT3aE1ETV2jKZ6OHTuGzz//HJ07d9bbPmvWLPzxxx9Yv3499u3bh+TkZIwbN063X6PRYMSIESgtLcWhQ4fw7bff4ptvvsH8+fN1bRITEzFixAjcd999iImJwcyZM/HMM89g+/btDXZ8zdkWXmVHJq53Wxe0cbNBQYkav/59Xe44RCSzRlE8FRQUYOLEifjvf/8LJycn3fbc3Fx89dVXWLZsGQYNGoTQ0FCsXr0ahw4dwl9//QUA2LFjB06fPo3vv/8eXbp0wbBhw/D222/j008/RWlpKQBg1apV8PPzw9KlSxEQEIAZM2bgwQcfxIcffijL8TYn7LKjxkCSJDzRq/zs0/eHr0AIIXMiIpJToyiepk+fjhEjRiAiIkJve3R0NMrKyvS2d+zYEa1atcLhw4cBAIcPH0ZwcDA8PDx0bSIjI5GXl4f4+Hhdm9ufOzIyUvcc1SkpKUFeXp7ejQxX2WUX1MIevi7ssiPTNa5bS1iZK5GQlo+jiVlyxyEiGZl88fTTTz/hxIkTWLRoUZV9qampsLCwgKOjo952Dw8PpKam6trcWjhV7q/cd7c2eXl5uHnzZrW5Fi1aBAcHB93Nx8enVsfX3G0+yavsqHFwsDLHmK7l69397y8OHCdqzky6eLp69Spefvll/PDDD7C0tJQ7jp558+YhNzdXd7t69arckRqdzIISHL7ELjtqPB6v6LrbdioV6fnFMqchIrmYdPEUHR2N9PR0dOvWDWZmZjAzM8O+ffvwySefwMzMDB4eHigtLUVOTo7e49LS0uDp6QkA8PT0rHL1XeX9e7Wxt7eHlVX1yzGoVCrY29vr3cgw2+PT2GVHjUonbweE+jpBrRVYe5T/YSJqrky6eBo8eDDi4uIQExOju3Xv3h0TJ07U/Wxubo5du3bpHpOQkICkpCSEh4cDAMLDwxEXF4f09HRdm6ioKNjb2yMwMFDX5tbnqGxT+RxUPzbHlc/YPCLYW+YkRDVXOXD8x6NJUGu0MqchIjnUeGFgOdjZ2SEoKEhvm42NDVxcXHTbp0yZgtmzZ8PZ2Rn29vZ48cUXER4ejl69egEAhgwZgsDAQDzxxBNYvHgxUlNT8frrr2P69OlQqVQAgOeffx4rVqzAa6+9hqeffhq7d+/GunXrsHnz5oY94GYks6AEh3mVHTVCw4I98fYmC6TkFmPnmXQMDfKUOxIRNTCTPvNUEx9++CFGjhyJ8ePHo3///vD09MSGDRt0+5VKJTZt2gSlUonw8HA8/vjjmDRpEt566y1dGz8/P2zevBlRUVEICQnB0qVL8eWXXyIyMlKOQ2oWtsWnQiuA4BYOaOViLXccohpTmSnxSI/yC0S+58BxomZJEpywxCjy8vLg4OCA3Nxcjn+qgYlf/oWDFzIxd2hHvDCwrdxxiAxyLbsI/RbvgRDArlcGoK2brdyRiKiWavP93ejPPFHjc4NddtTItXSyxuCO7gB49omoOWLxRA1uO7vsqAl4Irw1AODn6GsoKuV6d0TNCYsnanCVE2NyLTtqzPq1c4WvizXyi9X4PSZZ7jhE1IBYPFGDulFQgr84MSY1AQqFhMfDyqct+B/XuyNqVlg8UYPadqq8y65zSwf4OLPLjhq3h7q3hMpMgdMpeTiRlCN3HCJqICyeqEFtiavosuNZJ2oCHK0t8EBI+SSvHDhO1HyweKIGc2uXHRcCpqbiifDyrrvNJ1Nwo6BE5jRE1BBYPFGDqeyyC2GXHTUhnVs6IqSlA0o1Wqw7zvXuiJoDFk/UYCqvsuNZJ2pqKqct+OGvJGi0HDhO1NSxeKIGkZFfgiOJ7LKjpmlkZy84Wpvjes5N7Dmbfu8HEFGjxuKJGkTlWnbssqOmyNJciYe7l6939z8OHCdq8lg8UYPYwokxqYmbGNYKkgTsO5eBK5mFcschonrE4onq3a1ddsOCWDxR0+TrYoMBHdwAcNoCoqaOxRPVO12XnY8ju+yoSXuiV/m0BeuOX0NxmUbmNERUX1g8Ub3bfLJ83a8RwZ4yJyGqXwP93dHC0Qq5N8vwRyzXuyNqqlg8Ub26UVCCo4lZANhlR02fUiFhYq9WANh1R9SUsXiierX7TDq0AghuwavsqHl4pLsPLJQKxF7LRezVHLnjEFE9YPFE9WrH6VQAwP2BHjInIWoYLrYq3VWlnLaAqGli8UT1pqhUjT/P3wAADOnE4omaj8crBo7/EZuM7MJSmdMQkbGxeKJ6s//cDZSotfBxtoK/h53ccYgaTLdWjgj0skeJWov10VzvjqipYfFE9SbqdBoAYEigJyRJkjkNUcORJAmTwsvPPn3/VxK0XO+OqElh8UT1Qq3RYtfZ8uKJ452oOXqgizfsLM2QlFWE/ecz5I5DREbE4onqxfEr2cgpKoOTtTm6+zrJHYeowVlbmOHB0JYAgP8d5sBxoqaExRPVix3x5WedBnX0gJmSHzNqnioHju9OSMfVrCKZ0xCRsfBbjYxOCKGbooBX2VFz1tbNFn3buUII4MejSXLHISIjYfFERnc2NR/Xsm9CZaZAv/aucschklXl2ae1x66iRM317oiaAhZPZHSVXXb92rvB2sJM5jRE8ooIcIeXgyWyCkux7vg1ueMQkRGweCKjizrDLjuiSmZKBZ4f0BYA8PHO8ygsUcuciIjqisUTGdX1nJs4dT0PCgkY3NFd7jhEJuGxnq3QytkaNwpK8PWBRLnjEFEdsXgio9pZMTFmd19nuNiqZE5DZBoszBSYE+kPAPh8/yVkFpTInIiI6oLFExkVFwImqt7IYC8Et3BAQYkay3dfkDsOEdUBiycymtyiMvx1KQsAiyei2ykUEv41rCMA4IcjV5CUyXmfiBorFk9kNHsS0qHRCnTwsEVrVxu54xCZnD7tXNGvvSvKNAJLoxLkjkNEtcTiiYxGNzFmoKfMSYhM19yh5Weffo9JxqnruTKnIaLaYPFERlFcpsG+hPLFT9llR3RnQS0cMLqLNwDg/W1nZU5DRLXB4omM4vDFTBSWauBpb4ngFg5yxyEyaXOG+MNcKeHP8zdw4PwNueMQkYFYPJFR7KiYoiAi0B0KhSRzGiLT5uNsrVu25b1tZ6DVCpkTEZEhWDxRnWm1AjvPlBdPHO9EVDMz7msHW5UZTl3Pw6a4FLnjEJEBWDxRncVcy0FGfgnsVGbo1cZF7jhEjYKLrQrP9W8DAPhgewJK1VqZExFRTbF4ojqrXAh4YEd3WJjxI0VUU1P6+cHNToWkrCKsOZokdxwiqiF+01Gd/TNFAa+yIzKEtYUZZka0BwB8sus88ovLZE5ERDXB4onq5EJ6AS5lFMJcKWGgv5vccYganYe7+6CNqw0yC0vx3z+5aDBRY8DiieokquIqu/C2rrCzNJc5DVHjY65U4NWKRYO//PMS0vOLZU5ERPfC4onqJIoLARPV2dAgT4T4OKKoVIPlu7hoMJGpY/FEtZaeX4y/r+YAAO4PYPFEVFuSJGFexaLBa44mIfFGocyJiOhuWDxRre06kw4hgJCWDvB0sJQ7DlGj1quNCwZ1dIdaK/DBDi4aTGTKWDxRre2Ir7jKrhMnxiQyhteG+kOSgM0nUxBbcVaXiEwPiyeqlYISNQ5eyATA8U5ExtLR0x7jurYEACzaegZCcNkWIlPE4olqZf+5DJRqtGjtYo327rZyxyFqMmYP6QALMwX+upSFfecy5I5DRNVg8US1Utlld3+gBySJCwETGUsLRytMDq9YNHjrWS4aTGSCWDyRwco0Wuw+mw6A452I6sP0+9rBztIMZ1Pz8XvsdbnjENFtTLp4WrRoEXr06AE7Ozu4u7tjzJgxSEjQvwqluLgY06dPh4uLC2xtbTF+/HikpaXptUlKSsKIESNgbW0Nd3d3vPrqq1Cr1Xpt9u7di27dukGlUqFdu3b45ptv6vvwGq2jiVnIK1bDxcYC3Vo5yR2HqMlxtLbAtIHtAAAfbD+HErVG5kREdCuTLp727duH6dOn46+//kJUVBTKysowZMgQFBb+MwfKrFmz8Mcff2D9+vXYt28fkpOTMW7cON1+jUaDESNGoLS0FIcOHcK3336Lb775BvPnz9e1SUxMxIgRI3DfffchJiYGM2fOxDPPPIPt27c36PE2FpWzig8OcIdSwS47ovrwVJ/W8LS3xPWcm/jf4StyxyGiW0iiEV3OkZGRAXd3d+zbtw/9+/dHbm4u3Nzc8OOPP+LBBx8EAJw9exYBAQE4fPgwevXqha1bt2LkyJFITk6Gh0f5VWGrVq3C3LlzkZGRAQsLC8ydOxebN2/GqVOndK/16KOPIicnB9u2batRtry8PDg4OCA3Nxf29vbGP3gTIYRAn/d2Izm3GF9O6o4IXmlHVG/WHkvC3F/i4Ghtjv2v3Qd7LoFEZHS1+f426TNPt8vNzQUAODs7AwCio6NRVlaGiIgIXZuOHTuiVatWOHz4MADg8OHDCA4O1hVOABAZGYm8vDzEx8fr2tz6HJVtKp+jOiUlJcjLy9O7NQfxyXlIzi2GlbkSfdu7yh2HqEkb360l2rvbIqeoDJ/vuyh3HCKq0GiKJ61Wi5kzZ6JPnz4ICgoCAKSmpsLCwgKOjo56bT08PJCamqprc2vhVLm/ct/d2uTl5eHmzZvV5lm0aBEcHBx0Nx8fnzofY2Owo6LLrn8HV1iaK2VOQ9S0mSkVeG1o+bItXx1IRGouFw0mMgWNpniaPn06Tp06hZ9++knuKACAefPmITc3V3e7evWq3JEaxD9TFPAqO6KGEBHgju6+Tigu0+LVn2M5dQGRCWgUxdOMGTOwadMm7NmzBy1bttRt9/T0RGlpKXJycvTap6WlwdPTU9fm9qvvKu/fq429vT2srKyqzaRSqWBvb693a+quZhXhbGo+FBIwuKO73HGImgVJkrBoXDAszRX48/wNfL7/ktyRiJo9ky6ehBCYMWMGfv31V+zevRt+fn56+0NDQ2Fubo5du3bptiUkJCApKQnh4eEAgPDwcMTFxSE9PV3XJioqCvb29ggMDNS1ufU5KttUPgeVq+yy69HaGU42FjKnIWo+2nvY4c0HOgEAPtiRgOgr2TInImreTLp4mj59Or7//nv8+OOPsLOzQ2pqKlJTU3XjkBwcHDBlyhTMnj0be/bsQXR0NJ566imEh4ejV69eAIAhQ4YgMDAQTzzxBGJjY7F9+3a8/vrrmD59OlQqFQDg+eefx6VLl/Daa6/h7NmzWLlyJdatW4dZs2bJduymKOo0FwImksvD3X0wKsQbGq3AS2v+Rm5RmdyRiJotky6ePvvsM+Tm5mLgwIHw8vLS3dauXatr8+GHH2LkyJEYP348+vfvD09PT2zYsEG3X6lUYtOmTVAqlQgPD8fjjz+OSZMm4a233tK18fPzw+bNmxEVFYWQkBAsXboUX375JSIjIxv0eE1ZdmEpjiZmAQCGcHoCogYnSRL+MzYIrZytcT3nJv614SQXDiaSSaOa58mUNfV5nn6OvoY562PR0dMO22b2lzsOUbN18loOxn92CGUagXfGBOHxXr5yRyJq1Jr8PE8kH3bZEZmGzi0dMbdi+oK3Np3GmZTmMccckSlh8UT3VFymwf5zNwCwy47IFDzdxw/3+buhVK3FjB9PoKhUfe8HEZHRsHiiezpw/gZulmng7WCJTt5Nr0uSqLFRKCR88FAIPOxVuJhRiIUb4+WORNSssHiie9pxunJiTA9IEhcCJjIFLrYqfPRIV0gSsO74Nfwec13uSETNBosnuiuNVmDXmfI5sjjeici0hLd1wYuD2gMA/r0hDpdvFMqciKh5YPFEd7U9PhWZhaVwsDJHTz9nueMQ0W1eGtQOPVs7o7BUgxlrTqBErZE7ElGTx+KJ7kirFfhk13kAwOTerWGu5MeFyNSYKRX4+LEucLQ2x6nreVi8LUHuSERNHr8N6Y6izqThbGo+bFVmeLpPa7njENEdeDlY4YMHQwAAXx1IxK4zafd4BBHVBYsnqpYQt5518oWjNdeyIzJlEYEeeKriPzlz1sciJfemvIGImjAWT1StPQnpiE/Og7WFElP6tpE7DhHVwL+GdURQC3tkF5Xh5Z9ioNFyAQmi+sDiiaoQQuDjXRcAAE/08oWzDc86ETUGKjMllj/WDTYWShxNzNKdPSYi42LxRFXsP38DsVdzYGmuwDP9eNaJqDHxc7XBu2ODAQDLd5/H4YuZMicianpYPJGeW8c6TejpCzc7lcyJiMhQY7q2wEOhLaEVwMy1fyOrsFTuSERNCosn0nP4Yiair2TDwkyB5wbwrBNRY/Xm6E5o42aDtLwSzFkfCyE4/onIWFg8kZ6PK846PdbDBx72ljKnIaLasrYww6cTusHCTIHdZ9Px1YFEuSMRNRksnkjnyKVMHEnMgrlSwnMD2sodh4jqKMDLHm+MDAQAvL/tLI5fzpI5EVHTwOKJdJbvLr/C7qHuPvB2tJI5DREZw+NhrTAsyBNlGoHJXx/FX5c4gJyorlg8EQAg+ko2Dly4ATOFhBd41omoyZAkCUsfDkGfdi4oLNVg8tdHsTchXe5YRI0aiycCUH5JMwCM69YCPs7WMqchImOytjDDV5N7YFBHd5SotXj2u+PYdipV7lhEjRaLJ0Ls1RzsTciAUiFh+n3t5I5DRPXA0lyJVY+HYkSwF8o0AtN/PIHf/r4udyyiRonFE+nOOo3u4g1fFxuZ0xBRfbEwU+DjR7tgfLeW0GgFZq2LwZqjSXLHImp0WDw1c6eu52LnmXRIEnjWiagZMFMqsOTBzni8VysIAczbEMdpDIgMxOKpmVtRcYXdqM7eaOtmK3MaImoICoWEt0cHYWr/8olw3950Git2cx08oppi8dSMnU3Nw7b4VEgSMGMQzzoRNSeSJGHesI6YGdEeAPDBjnNYvO0sZyInqgEWT81Y5bxOw4O80MHDTuY0RNTQJEnCzIgO+PfwjgCAlXsv4s0/TkOrZQFFdDcsnpqpC+n52BKXAoBnnYiau6n92+LtMUEAgG8OXca8DXHQsIAiuiMWT83Uit0XIAQwJNADAV72cschIpk90csXHzwUAoUErD1+FbPWxqBMo5U7FpFJYvHUDCXeKMTG2GQAwEuD28uchohMxYOhLbH8sW4wU0jYGJuMaT+cQIlaI3csIpPD4qkZ+nTPBWgFMKijO4JaOMgdh4hMyIjOXvj8iVBYmCkQdToNz3x7HDdLWUAR3YrFUzOTlFmEXytmFX6RY52IqBqDAzyw+skesDJX4s/zNzD566PILy6TOxaRyWDx1Mys3HsBGq1A/w5u6NrKSe44RGSi+rRzxf+m9ISdygxHL2fh8S+PILuwVO5YRCaBxVMzci27CL+cuAYAeIlnnYjoHrq3dsaPz/aCo7U5Yq/lYvgnf+LQxRtyxyKSHYunZmTVvoso0wj0buuC7q2d5Y5DRI1AcEsHrHsuHH6uNkjJLcbEL49g0ZYzHEhOzRqLp2YiJfcm1h2rOOvEK+yIyAAdPOyw6cW+eKynD4QAPt9/CWM/PYTzaflyRyOSBYunZuLzfZdQqtGiZ2tn9GrjInccImpkbFRmWDSuM754IhRO1uY4nZKHkcsP4NtDl7mkCzU7LJ6agfS8Yqw5mgSAZ52IqG6GdPLE9pn9MaCDG0rUWizYGI8nVx9Den6x3NGIGgyLp2bgi/2XUKLWolsrR/Rpx7NORFQ37vaW+OapHnjzgU5QmSmw71wGhn70J6JOp8kdjahBsHhq4uKTc/H9kSsAgBcHt4ckSTInIqKmQJIkTO7dGn+82BcBXvbIKizFs98dx7wNcSgqVcsdj6hesXhqwn6PuY7xnx1CcZkWob5OGNjBTe5IRNTEdPCww2/Te2Nq/zaQJGDN0SSM+OQAYq/myB2NqN6weGqC1Bot3t18Gi//FIPiMi36d3DD15N78KwTEdULlZkS/x4egB+mhMHLwRKJNwox/rNDWLH7PDRaDianpofFUxOTXViKyauP4r9/JgIApg1si9VP9oCDtbnMyYioqevdzhXbXu6PEZ29oNYKfLDjHB75/DCuZhXJHY3IqFg8NSHxybkYteIADl7IhLWFEisndsNrQztCqeAZJyJqGA7W5ljxWFcsezgEtiozHL+SjWEf/4kNJ65xSgNqMiTBT7NR5OXlwcHBAbm5ubC3t2/w1/895jrm/nISxWVa+LpY44snusPf067BcxARVbqaVYRZa2Nw/Eo2ACC4hQNGdPbC8CAvtHKxljkdUbnafH+zeDISuYontUaL97ed1XXT9e/ghuWPdmU3HRGZBLVGi1X7LuLjXedRpvnn6yaohT2GBXlheLAX/FxtZExIzR2LJxnJUTxlF5ZixpoTOHghE0D5+KZXhvizm46ITM6NghJsj0/F1rhUHL6UqTeQPMDLHsODPDG8sxfautnKmJKaIxZPMmro4ik+ORfP/S8a17JvwtpCiQ8eCsHwYK96f10iorrKLChB1Ok0bI5LwaGL+oWUv4cdhgV7YkSwF9p7cOgB1T8WTzJqyOKJ45uIqKnILixF1Ok0bDmVgoMXbuh17bVzt8XwYC8MD/aEv4cdp1uhesHiSUYNUTypNVq8t/UsvjzA8U1E1PTkFpUh6kwatsal4M/zN1Cq0er2tXG1QZ92rgj0tkeglz38Pe1gaa6UMS01FSyeZFTfxVNWYSle5PgmImom8orLsOtMGrbEpWLfuQyUqrV6+xUS0NbNFp287SsKKgcEetvD2cZCpsTUWLF4klF9Fk/xybmY+l00rudwfBMRNT/5xWXYdy4DcddycTolD/HJecgqLK22rae9JQK97cuLKq/ywsrHyRoK/keT7oDFk4zqq3jafDIFr6yP4fgmIqIKQgik55fgdHIeTqfk4XRyHuKTc3E5s/qZzG1VZgjwskNrFxu426vgbmcJdzuV7mc3OxW7AJux2nx/m9Vzpkbn008/xZIlS5CamoqQkBAsX74cPXv2lC2PnaUZStVaDOjghk84vomICJIkwcPeEh72lrivo7tue0GJGmdT/imoTqfk4WxqPgpK1Dh2ORvHLmff8TntLc3gbl9RVNmpdD+72VUUW/YquNqoYGdpxrNYxDNPt1q7di0mTZqEVatWISwsDB999BHWr1+PhIQEuLu73/Wx9dlt99elTPRo7czxTUREBlJrtLh0oxCnk/NwPecm0vOKkZ5fUnErRlpeSZXxVHejVEhwtDKHo7U5nKwt4GhtASdrczjb/PNz5Z9ONhYVbcxhruRqaKaK3XZ1FBYWhh49emDFihUAAK1WCx8fH7z44ov417/+ddfHyr08CxERGU4IgbybaqTnF+sKqvS8kn8KrLxiZFT8XFCirvXr2KnMYGtpBjOlBHOFAmZKCUqFAuZKCWYKCWbKyp//+dNMKcFcqdDtN1NIUCokKCQJSgWgqPxZkqBQlP9Zub3858q2/+w3q3g9paL8ucv/LM9ipqjMUv76ylt+rnyM3LNFWJkr4WKrMupzstuuDkpLSxEdHY158+bptikUCkRERODw4cNV2peUlKCkpER3Py8vr0FyEhGR8UiSBAdrczhYm99zUs7iMg1yisqQXVSK7KJS3c85RWXIKqy6LbuoFLk3yyAEkF+iRn4dii8q90CINz55rKvcMVg8Vbpx4wY0Gg08PDz0tnt4eODs2bNV2i9atAhvvvlmQ8UjIiKZWZor4emghKeDZY0fo9EK5N4sL6SKSjQo02qh1gioNVqUaSv+1AhotAJqbfnPt+5Ta8Q/j9EKaLUCGlHx560/CwGNFvr7RXkbbcWfGi2g0Wqh1gqoK16zTKst/1Mjqu7TaCtyVWTRyt9RZaY0jeErLJ5qad68eZg9e7bufl5eHnx8fGRMREREpkapkOBsY8H5p5oYFk8VXF1doVQqkZaWprc9LS0Nnp6eVdqrVCqoVMbtdyUiIiLTx+H/FSwsLBAaGopdu3bptmm1WuzatQvh4eEyJiMiIiJTwjNPt5g9ezYmT56M7t27o2fPnvjoo49QWFiIp556Su5oREREZCJYPN3ikUceQUZGBubPn4/U1FR06dIF27ZtqzKInIiIiJovzvNkJJzniYiIqPGpzfc3xzwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDLsxhJ5UTteXl5MichIiKimqr83jZkwRUWT0aSn58PAPDx8ZE5CRERERkqPz8fDg4ONWrLte2MRKvVIjk5GXZ2dpAkSW9fXl4efHx8cPXqVa57Vwt8/+qO72Hd8P2rO76HdcP3r+7u9B4KIZCfnw9vb28oFDUbzcQzT0aiUCjQsmXLu7axt7fnh74O+P7VHd/DuuH7V3d8D+uG71/dVfce1vSMUyUOGCciIiIyAIsnIiIiIgOweGoAKpUKCxYsgEqlkjtKo8T3r+74HtYN37+643tYN3z/6s6Y7yEHjBMREREZgGeeiIiIiAzA4omIiIjIACyeiIiIiAzA4omIiIjIACye6tmnn36K1q1bw9LSEmFhYTh69KjckRqNhQsXQpIkvVvHjh3ljmWy9u/fj1GjRsHb2xuSJOG3337T2y+EwPz58+Hl5QUrKytERETg/Pnz8oQ1Ufd6D5988skqn8mhQ4fKE9YELVq0CD169ICdnR3c3d0xZswYJCQk6LUpLi7G9OnT4eLiAltbW4wfPx5paWkyJTYtNXn/Bg4cWOUz+Pzzz8uU2PR89tln6Ny5s24izPDwcGzdulW331ifPxZP9Wjt2rWYPXs2FixYgBMnTiAkJASRkZFIT0+XO1qj0alTJ6SkpOhuBw4ckDuSySosLERISAg+/fTTavcvXrwYn3zyCVatWoUjR47AxsYGkZGRKC4ubuCkpute7yEADB06VO8zuWbNmgZMaNr27duH6dOn46+//kJUVBTKysowZMgQFBYW6trMmjULf/zxB9avX499+/YhOTkZ48aNkzG16ajJ+wcAzz77rN5ncPHixTIlNj0tW7bEe++9h+joaBw/fhyDBg3C6NGjER8fD8CInz9B9aZnz55i+vTpuvsajUZ4e3uLRYsWyZiq8ViwYIEICQmRO0ajBED8+uuvuvtarVZ4enqKJUuW6Lbl5OQIlUol1qxZI0NC03f7eyiEEJMnTxajR4+WJU9jlJ6eLgCIffv2CSHKP3Pm5uZi/fr1ujZnzpwRAMThw4flimmybn//hBBiwIAB4uWXX5YvVCPk5OQkvvzyS6N+/njmqZ6UlpYiOjoaERERum0KhQIRERE4fPiwjMkal/Pnz8Pb2xtt2rTBxIkTkZSUJHekRikxMRGpqal6n0cHBweEhYXx82igvXv3wt3dHf7+/njhhReQmZkpdySTlZubCwBwdnYGAERHR6OsrEzvc9ixY0e0atWKn8Nq3P7+Vfrhhx/g6uqKoKAgzJs3D0VFRXLEM3kajQY//fQTCgsLER4ebtTPHxcGric3btyARqOBh4eH3nYPDw+cPXtWplSNS1hYGL755hv4+/sjJSUFb775Jvr164dTp07Bzs5O7niNSmpqKgBU+3ms3Ef3NnToUIwbNw5+fn64ePEi/v3vf2PYsGE4fPgwlEql3PFMilarxcyZM9GnTx8EBQUBKP8cWlhYwNHRUa8tP4dVVff+AcCECRPg6+sLb29vnDx5EnPnzkVCQgI2bNggY1rTEhcXh/DwcBQXF8PW1ha//vorAgMDERMTY7TPH4snMlnDhg3T/dy5c2eEhYXB19cX69atw5QpU2RMRs3Vo48+qvs5ODgYnTt3Rtu2bbF3714MHjxYxmSmZ/r06Th16hTHKdbSnd6/qVOn6n4ODg6Gl5cXBg8ejIsXL6Jt27YNHdMk+fv7IyYmBrm5ufj5558xefJk7Nu3z6ivwW67euLq6gqlUlllFH9aWho8PT1lStW4OTo6okOHDrhw4YLcURqdys8cP4/G1aZNG7i6uvIzeZsZM2Zg06ZN2LNnD1q2bKnb7unpidLSUuTk5Oi15+dQ353ev+qEhYUBAD+Dt7CwsEC7du0QGhqKRYsWISQkBB9//LFRP38snuqJhYUFQkNDsWvXLt02rVaLXbt2ITw8XMZkjVdBQQEuXrwILy8vuaM0On5+fvD09NT7PObl5eHIkSP8PNbBtWvXkJmZyc9kBSEEZsyYgV9//RW7d++Gn5+f3v7Q0FCYm5vrfQ4TEhKQlJTEzyHu/f5VJyYmBgD4GbwLrVaLkpISo37+2G1Xj2bPno3Jkyeje/fu6NmzJz766CMUFhbiqaeekjtaozBnzhyMGjUKvr6+SE5OxoIFC6BUKvHYY4/JHc0kFRQU6P3vMzExETExMXB2dkarVq0wc+ZMvPPOO2jfvj38/PzwxhtvwNvbG2PGjJEvtIm523vo7OyMN998E+PHj4enpycuXryI1157De3atUNkZKSMqU3H9OnT8eOPP+L333+HnZ2dbhyJg4MDrKys4ODggClTpmD27NlwdnaGvb09XnzxRYSHh6NXr14yp5ffvd6/ixcv4scff8Tw4cPh4uKCkydPYtasWejfvz86d+4sc3rTMG/ePAwbNgytWrVCfn4+fvzxR+zduxfbt2837ufPuBcE0u2WL18uWrVqJSwsLETPnj3FX3/9JXekRuORRx4RXl5ewsLCQrRo0UI88sgj4sKFC3LHMll79uwRAKrcJk+eLIQon67gjTfeEB4eHkKlUonBgweLhIQEeUObmLu9h0VFRWLIkCHCzc1NmJubC19fX/Hss8+K1NRUuWObjOreOwBi9erVujY3b94U06ZNE05OTsLa2lqMHTtWpKSkyBfahNzr/UtKShL9+/cXzs7OQqVSiXbt2olXX31V5ObmyhvchDz99NPC19dXWFhYCDc3NzF48GCxY8cO3X5jff4kIYSoa6VHRERE1FxwzBMRERGRAVg8ERERERmAxRMRERGRAVg8ERERERmAxRMRERGRAVg8ERERERmAxRMRERGRAVg8ERHdZu/evZAkqcoaWMY2cOBAzJw5s15fo6Ya6piJmgIWT0RkEFP6wjeG6o6nd+/eSElJgYODgzyh6llT+x0SNTQWT0RkdEIIqNVquWPUmoWFBTw9PSFJktxRiMgEsXgiohp78sknsW/fPnz88ceQJAmSJOHy5cu6Lp+tW7ciNDQUKpUKBw4cwMWLFzF69Gh4eHjA1tYWPXr0wM6dO/Wes3Xr1vjPf/6Dp59+GnZ2dmjVqhW++OIL3f7S0lLMmDEDXl5esLS0hK+vLxYtWqTbv2zZMgQHB8PGxgY+Pj6YNm0aCgoK9F7j4MGDGDhwIKytreHk5ITIyEhkZ2ff83hu7cL65Zdf0KlTJ6hUKrRu3RpLly416DhqoqSkBHPmzEGLFi1gY2ODsLAw7N27V7f/m2++gaOjI7Zv346AgADY2tpi6NChSElJ0bVRq9V46aWX4OjoCBcXF8ydOxeTJ0/WLQB9p2OuFB0dje7du8Pa2hq9e/dGQkKCQcdA1CwYbTU+ImrycnJyRHh4uHj22WdFSkqKSElJEWq1WregbufOncWOHTvEhQsXRGZmpoiJiRGrVq0ScXFx4ty5c+L1118XlpaW4sqVK7rn9PX1Fc7OzuLTTz8V58+fF4sWLRIKhUKcPXtWCCHEkiVLhI+Pj9i/f7+4fPmy+PPPP8WPP/6oe/yHH34odu/eLRITE8WuXbuEv7+/eOGFF3T7//77b6FSqcQLL7wgYmJixKlTp8Ty5ctFRkbGPY8nOztbCCHE8ePHhUKhEG+99ZZISEgQq1evFlZWVnoL3t7rOKozYMAA8fLLL+vuP/PMM6J3795i//794sKFC2LJkiVCpVKJc+fOCSGEWL16tTA3NxcRERHi2LFjIjo6WgQEBIgJEybonuOdd94Rzs7OYsOGDeLMmTPi+eefF/b29mL06NE1+h2GhYWJvXv3ivj4eNGvXz/Ru3dvgz8nRE0diyciMsjtX/hCCN0X72+//XbPx3fq1EksX75cd9/X11c8/vjjuvtarVa4u7uLzz77TAghxIsvvigGDRoktFptjfKtX79euLi46O4/9thjok+fPrU6nsriacKECeL+++/Xa/Pqq6+KwMDAGh/HvV77ypUrQqlUiuvXr+u1GTx4sJg3b54Qorx4AiAuXLig2//pp58KDw8P3X0PDw+xZMkS3X21Wi1atWqlK57udcw7d+7Ubdu8ebMAIG7evHnHYyBqjthtR0RG0717d737BQUFmDNnDgICAuDo6AhbW1ucOXMGSUlJeu06d+6s+1mSJHh6eiI9PR1AeTdTTEwM/P398dJLL2HHjh16j925cycGDx6MFi1awM7ODk888QQyMzNRVFQEAIiJicHgwYPrdFxnzpxBnz599Lb16dMH58+fh0ajqdFx3EtcXBw0Gg06dOgAW1tb3W3fvn24ePGirp21tTXatm2ru+/l5aV7jdzcXKSlpaFnz566/UqlEqGhoTU+1luPwcvLCwBqfAxEzYWZ3AGIqOmwsbHRuz9nzhxERUXhgw8+QLt27WBlZYUHH3wQpaWleu3Mzc317kuSBK1WCwDo1q0bEhMTsXXrVuzcuRMPP/wwIiIi8PPPP+Py5csYOXIkXnjhBbz77rtwdnbGgQMHMGXKFJSWlsLa2hpWVlb1e9A1PI57KSgogFKpRHR0NJRKpd4+W1vbu76GEKKWiau69fkrB8zX9BiImgueeSIig1hYWOidbbmbgwcP4sknn8TYsWMRHBwMT09PvcHJNWVvb49HHnkE//3vf7F27Vr88ssvyMrKQnR0NLRaLZYuXYpevXqhQ4cOSE5O1nts586dsWvXrjodT0BAAA4ePFjl2Dp06FCl0Kmtrl27QqPRID09He3atdO7eXp61ug5HBwc4OHhgWPHjum2aTQanDhxQq+dIb9DIqqKZ56IyCCtW7fGkSNHcPnyZdja2sLZ2fmObdu3b48NGzZg1KhRkCQJb7zxhsFnMZYtWwYvLy907doVCoUC69evh6enJxwdHdGuXTuUlZVh+fLlGDVqFA4ePIhVq1bpPX7evHkIDg7GtGnT8Pzzz8PCwgJ79uzBQw89BFdX1xodzyuvvIIePXrg7bffxiOPPILDhw9jxYoVWLlypUHHcjcdOnTAxIkTMWnSJCxduhRdu3ZFRkYGdu3ahc6dO2PEiBE1ep4XX3wRixYtQrt27dCxY0csX74c2dnZetMuGPI7JKKqeOaJiAwyZ84cKJVKBAYGws3Nrcr4pVstW7YMTk5O6N27N0aNGoXIyEh069bNoNezs7PD4sWL0b17d/To0QOXL1/Gli1boFAoEBISgmXLluH9999HUFAQfvjhB71pDIDyomTHjh2IjY1Fz549ER4ejt9//x1mZmY1Pp5u3bph3bp1+OmnnxAUFIT58+fjrbfewpNPPmnQsdzL6tWrMWnSJLzyyivw9/fHmDFjcOzYMbRq1arGzzF37lw89thjmDRpEsLDw2Fra4vIyEhYWlrq2hjyOySiqiRhzM5yIiIyKVqtFgEBAXj44Yfx9ttvyx2HqElgtx0RURNy5coV7NixAwMGDEBJSQlWrFiBxMRETJgwQe5oRE0Gu+2IiJoQhUKBb775Bj169ECfPn0QFxeHnTt3IiAgQO5oRE0Gu+2IiIiIDMAzT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQG+H9n6W6W5uqvUwAAAABJRU5ErkJggg==\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "maxPerCount = 500 #maxPerCount is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "minPRcount = 0.5 #minPRcount is specified in count. However, the users can also specify different minPRcount value.\n", - "minSup=100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Partial Periodic Frequent Patterns using PPF_DFS" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.partialPeriodicFrequentPattern.basic import PPF_DFS as alg #import the algorithm\n", - "\n", - "obj = alg.PPF_DFS(iFile=inputFile, minSup=minSup,maxPer=maxPerCount,minPR=minPRcount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('partialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "3c6aff70-bcb4-4132-f1ff-a2ea7dfac62d" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Total No of patterns: 20743\n", - "Runtime: 78.65053415298462\n", - "Memory (RSS): 259076096\n", - "Memory (USS): 212385792\n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "0c1570a1-a4ea-433b-8414-53ec41523e80" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 07:02:46-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.08MB/s in 6.3s \n", + "\n", + "2023-08-28 07:02:55 (713 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "0a9ea599-fcfa-4d77-bc0f-0e79c671589b" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Partial Periodic Frequent Patterns using PPF_DFS" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "f9da22be-4d2e-484e-a1e5-6e4d7c636e66" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "e205786a-0c59-4270-ae98-c0d92d3b16e7" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "3M8FtfKU4bhu" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "!head partialPeriodicFrequentPatternsAtMinSupCount100.txt" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "28614a6e-3fbd-453e-953c-c2bebc486ef8" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "352\t527\t319\t48\t676\t720\t529\t368\t595:147:0.5540540540540541\n", - "527\t319\t48\t676\t720\t529\t368\t352:149:0.5666666666666667\n", - "527\t319\t48\t676\t720\t529\t368\t595:148:0.5570469798657718\n", - "319\t48\t676\t720\t529\t368\t527:150:0.5695364238410596\n", - "352\t319\t48\t676\t720\t529\t368\t595:150:0.5562913907284768\n", - "319\t48\t676\t720\t529\t368\t352:152:0.5686274509803921\n", - "319\t48\t676\t720\t529\t368\t595:151:0.5592105263157895\n", - "48\t676\t720\t529\t368\t319:153:0.5714285714285714\n", - "352\t527\t48\t676\t720\t529\t368\t595:149:0.5533333333333333\n", - "527\t48\t676\t720\t529\t368\t352:151:0.5657894736842105\n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "maxPerCount = 500 #maxPerCount is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "minPRcount = 0.5 #minPRcount is specified in count. However, the users can also specify different minPRcount value.\n", + "minSup=100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Partial Periodic Frequent Patterns using PPF_DFS" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.partialPeriodicFrequentPattern.basic import PPF_DFS as alg #import the algorithm\n", + "\n", + "obj = alg.PPF_DFS(iFile=inputFile, minSup=minSup,maxPer=maxPerCount,minPR=minPRcount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('partialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3c6aff70-bcb4-4132-f1ff-a2ea7dfac62d" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total No of patterns: 20743\n", + "Runtime: 78.65053415298462\n", + "Memory (RSS): 259076096\n", + "Memory (USS): 212385792\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head partialPeriodicFrequentPatternsAtMinSupCount100.txt" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "28614a6e-3fbd-453e-953c-c2bebc486ef8" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "352\t527\t319\t48\t676\t720\t529\t368\t595:147:0.5540540540540541\n", + "527\t319\t48\t676\t720\t529\t368\t352:149:0.5666666666666667\n", + "527\t319\t48\t676\t720\t529\t368\t595:148:0.5570469798657718\n", + "319\t48\t676\t720\t529\t368\t527:150:0.5695364238410596\n", + "352\t319\t48\t676\t720\t529\t368\t595:150:0.5562913907284768\n", + "319\t48\t676\t720\t529\t368\t352:152:0.5686274509803921\n", + "319\t48\t676\t720\t529\t368\t595:151:0.5592105263157895\n", + "48\t676\t720\t529\t368\t319:153:0.5714285714285714\n", + "352\t527\t48\t676\t720\t529\t368\t595:149:0.5533333333333333\n", + "527\t48\t676\t720\t529\t368\t352:151:0.5657894736842105\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _partialperiodicfrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the PPF_DFS algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.partialPeriodicFrequentPattern.basic import PPF_DFS as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maxPerCount = 500\n", + "minPRcount = 0.5\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of PPF_DFS" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maxperCount','minPRcount','patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of PPF_DFS algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.PPF_DFS(inputFile, minSup=minSupCount, maxPer=maxPerCount,minPR=minPRcount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['PPF_DFS', minSupCount, maxPerCount, minPRcount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a772ae1b-d41f-4961-933a-04b868a8a1b1" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maxperCount minPRcount patterns runtime memory\n", + "0 PPF_DFS 100 500 0.5 20743 135.342738 275902464\n", + "1 PPF_DFS 150 500 0.5 19017 212.406846 288145408\n", + "2 PPF_DFS 200 500 0.5 13235 288.571058 300556288\n", + "3 PPF_DFS 250 500 0.5 7674 370.978486 311742464\n", + "4 PPF_DFS 300 500 0.5 4529 471.526392 323899392\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "2f925604-6497-418a-e1d8-7e5e53df8e18" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _partialperiodicfrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the PPF_DFS algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.partialPeriodicFrequentPattern.basic import PPF_DFS as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maxPerCount = 500\n", - "minPRcount = 0.5\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of PPF_DFS" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maxperCount','minPRcount','patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of PPF_DFS algorithm" - ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" - ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.PPF_DFS(inputFile, minSup=minSupCount, maxPer=maxPerCount,minPR=minPRcount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['PPF_DFS', minSupCount, maxPerCount, minPRcount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "cZNXmKqp6ea1" - }, - "execution_count": 11, - "outputs": [] + "image/png": 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qZDLesWNH2NnZYdeuXcZlaWlpyMjIQFhYGAAgLCwMJ06cMPlONDY2FiqVCiEhIcaaO7dRUVOxDYVCgY4dO5rUGAwG7Nq1y1hD1U8ul6FjoDvefjIYe9/shR2Tu2NKeHOE1Lt9E9GDF3Lw/uZT6DZvN57+4k98uecczmXlSd02ERHVUVU6u+rVV1/FmjVr8OuvvyIoKMi43NXVFQ4Ot//nPmHCBGzbtg0xMTFQqVSYNGkSAODAgQMAbp9C3q5dO/j5+WH+/PnQaDQYPnw4xo4di3//+98Abp9C3qpVK0ycOBFjxozB7t278dprr2Hr1q2IjIwEcPsU8pEjR+Lrr79Gly5d8J///Afr1q3D6dOn7zpW516s9WKAUricU2j8hOfwpVu4829VY28n9P3vJzxtGrjyjAYiInoo5r5/Vynk3OvNacWKFRg1ahSA2xcDfOONN7B27VqUlJQgMjISX331lcnXSJcuXcKECROwd+9eODk5YeTIkZg3bx5sbf/37dnevXsxZcoUnDp1Cg0aNMCsWbOMz1Hhiy++wIIFC6DRaNCuXTssXrwYoaGh5k6HIaeG3Mi7fRPRHSc1OHD+JvTl//srVs/VHhEhvohsqUaXRh6w5U1EiYioimok5FgbhpyapyvWY8/p26em7027gcLScuOYu6Md+gTfDjzdm3nB3o4HAhIR0f0x5JiBIefRKtaX48+zN7EzRYPfUzNxq/B/p3A6KmzQK8gbkS3V6N3CByp73kSUiIgqx5BjBoYc6ZSVG5B4MQe/pWRiZ4oG17XFxjE7GxnCmnihb0s1/hHiC28XnvZPRET/w5BjBoYcyyCEwImrWuxM0WDHSQ3O3/jfvdBkMqBjgLvx1PQAT0cJOyUiIkvAkGMGhhzLdC4r//bFB1M0OHZFazIWXE+FyJa3j+NpoXbhmVpERHUQQ44ZGHIs37XcIvyWosHOlEwkXjS9iWiAhyMiW/qibys12vu7Q86biBIR1QkMOWZgyKldcgpK8XtqJn5L0SDu7E2UlhmMY94uSvwjxBd9W6rRtbEnFLY8NZ2IyFox5JiBIaf2Kigpw74zN7AzRYPdqVnIKykzjrnY26LPf28i2jPIG44K3kSUiMiaMOSYgSHHOpSWGXDg/E3sTMlE7KlM3MwvMY4p/3vvrciWaoQH+8DNkTcRJSKq7RhyzMCQY33KDQJHMm5h50kNdp7S4HJOkXHMRi5DaCMP9G2lRkSIGmpXewk7JSKiB8WQYwaGHOsmhEDq9TzjPbVOa0xvFtrW3814plYTb2eJuiQioqpiyDEDQ07dcim74L+BJxNJl26ZjDXzcTZei6dVfRVPTScismAMOWZgyKm7snTF+O3U7astx5/PRtkdp6bXd3PAP0J88WInf4T48e8FEZGlYcgxA0MOAYC2UI/daZnYeTIT+87cQJH+9k1E7WxkWD6yM3o095a4QyIiuhNDjhkYcuivikrL8cfZG/g+/hL+PHcT9nZyrI4KRaeGHlK3RkRE/2Xu+zevmEZ0BweFDSJaqvHdqM7o2dwbxXoDRq84hJNXtfdfmYiILApDDlElFLZyLH25I7o09EBeSRlGfJeIc1n5UrdFRERVwJBDdA8OChssG9UJreqrkFNQipeXJeByTqHUbRERkZkYcoj+hsreDt+PCUVTH2dodMUYvjwBWXnFUrdFRERmYMghug8PJwVWR4XC38MBF7MLMXxZInILS6Vui4iI7oMhh8gMald7/BDVFT4uSqRl5mHkikPIv+OmoEREZHkYcojMFODpiNVjQ+HmaIdjl3MxbuVhFP/3mjpERGR5GHKIqqC5rwtWju4CZ6Ut4i9kI3rNEejLDVK3RURElWDIIaqitv5uWDayE5S2cvyemoU31h1DuaHOXlOTiMhiMeQQPYCujT2x9OWOsJXLsOnYNcz+9STq8MXDiYgsEkMO0QPq3cIHi15qB5kM+CEhA/N2nGbQISKyIAw5RA9hQFs/zH22NQDg630X8NXe8xJ3REREFRhyiB7S4C4BeLd/MABgwc40rDxwUdqGiIgIAEMOUbUY270xXnuiKQDgvU0p+L+kKxJ3REREDDlE1WTKP5pjVLeGAIBpPx/DjpMaaRsiIqrjGHKIqolMJsPsp0LwfMcGMAjgtbVH8cfZG1K3RURUZ1U55MTFxWHAgAHw8/ODTCbDxo0bTcbz8/MRHR2NBg0awMHBASEhIVi6dKlJTXFxMSZOnAhPT084Oztj0KBByMzMNKnJyMhA//794ejoCB8fH0ybNg1lZaaX0d+7dy86dOgApVKJpk2bIiYmpqrTIapWcrkM855rjX6t1CgtN2D890lIupQjdVtERHVSlUNOQUEB2rZtiy+//LLS8alTp2LHjh1YvXo1UlNTMXnyZERHR2PTpk3GmilTpmDz5s1Yv3499u3bh2vXruG5554zjpeXl6N///4oLS3FgQMHsHLlSsTExGD27NnGmvT0dPTv3x+9e/dGcnIyJk+ejLFjx2Lnzp1VnRJRtbK1keM/g9uhR3NvFOnLMWrFIaRc00rdFhFR3SMeAgCxYcMGk2UtW7YUH3zwgcmyDh06iHfeeUcIIURubq6ws7MT69evN46npqYKACI+Pl4IIcS2bduEXC4XGo3GWLNkyRKhUqlESUmJEEKIt956S7Rs2dLkeV566SURGRlpdv9arVYAEFqt1ux1iMxVWFImnl+yXwRO3yI6fPCbOJeVJ3VLRERWwdz372o/Jqdbt27YtGkTrl69CiEE9uzZgzNnziAiIgIAkJSUBL1ej/DwcOM6LVq0QEBAAOLj4wEA8fHxaN26NXx9fY01kZGR0Ol0SElJMdbcuY2KmoptVKakpAQ6nc7kQVRTHBQ2WD6qM1r6qZBdUIqXlyXgyq1CqdsiIqozqj3kfP755wgJCUGDBg2gUCjQt29ffPnll+jRowcAQKPRQKFQwM3NzWQ9X19faDQaY82dAadivGLs72p0Oh2Kiooq7W3u3LlwdXU1Pvz9/R96vkR/R2Vvh+/HdEETbydc1xbj5WUJyMorlrotIqI6oUZCzsGDB7Fp0yYkJSVh4cKFmDhxIn7//ffqfqoqmzlzJrRarfFx+fJlqVuiOsDTWYnVY0NR380BF7MLMWJ5IrSFeqnbIiKyetUacoqKivD222/j008/xYABA9CmTRtER0fjpZdewieffAIAUKvVKC0tRW5ursm6mZmZUKvVxpq/nm1V8fP9alQqFRwcHCrtT6lUQqVSmTyIHoV6rg74YWwovF2UOK3Jw6iYRBSUlN1/RSIiemDVGnL0ej30ej3kctPN2tjYwGAwAAA6duwIOzs77Nq1yzielpaGjIwMhIWFAQDCwsJw4sQJZGVlGWtiY2OhUqkQEhJirLlzGxU1FdsgsjQNvZywOioUbo52OJqRi3HfH0axvlzqtoiIrFaVQ05+fj6Sk5ORnJwM4Pap3MnJycjIyIBKpULPnj0xbdo07N27F+np6YiJicH333+PZ599FgDg6uqKqKgoTJ06FXv27EFSUhJGjx6NsLAwdO3aFQAQERGBkJAQDB8+HMeOHcPOnTvx7rvvYuLEiVAqlQCAV155BRcuXMBbb72F06dP46uvvsK6deswZcqUavrVEFW/ILULVo7uAieFDQ6cz0b0mqPQlxukbouIyDpV9bStPXv2CAB3PUaOHCmEEOL69eti1KhRws/PT9jb24ugoCCxcOFCYTAYjNsoKioSr776qnB3dxeOjo7i2WefFdevXzd5nosXL4p+/foJBwcH4eXlJd544w2h1+vv6qVdu3ZCoVCIxo0bixUrVlRpLjyFnKRy4NxN0eydbSJw+hbx+tojorzccP+ViIhICGH++7dMCCEkzFiS0ul0cHV1hVar5fE59MjtSs3EP1clocwgMCw0AB8NbAWZTCZ1W0REFs/c92/eu4pIIn2CffHpS+0gkwE/JGRg/s40qVsiIrIqDDlEEnq6rR/+NbA1AGDJ3vP4au85iTsiIrIeDDlEEhsaGoC3n2wBAJi/Iw2r4i9K2xARkZVgyCGyAON7NMGkJ5oCAGb9moINR69I3BERUe3HkENkIab+ozlGdWsIAHhz/XHsTNFI2xARUS3HkENkIWQyGWY/FYJBHRqg3CAwac1R/Hn2ptRtERHVWgw5RBZELpfh40Gt0belGqXlBoxfdRhJl25J3RYRUa3EkENkYWxt5PhsSDt0b+aFwtJyjF6RiFPXdFK3RURU6zDkEFkgpa0Nvh7eEZ0C3aErLsOI7xJw4Ua+1G0REdUqDDlEFspRYYvlozojpJ4KN/NL8fKyBFzNLZK6LSKiWoMhh8iCuTrY4fuoLmjs7YRr2mK8vCwBN/JKpG6LiKhWYMghsnBezkr8MDYU9d0ckH6zAMOXJ0BbqJe6LSIii8eQQ1QL1HN1wOqxofByVuK0Jg+jYhJRUFImdVtERBaNIYeolmjk5YTVY7vA1cEORzNyMX7VYRTry6Vui4jIYjHkENUiLdQqxIzuDEeFDfafy8Zra4+irNwgdVtERBaJIYeolmkf4I5lIztBYSvHb6cy8dbPx2EwCKnbIiKyOAw5RLVQtyZe+GpoB9jKZfjl6FW8tykFQjDoEBHdiSGHqJYKD/HFwhfbQiYDVh28hAU706RuiYjIojDkENViz7Srj48GtgIAfLX3PJbsPS9xR0REloMhh6iWGxYaiBn9WgAAPt5xGqsPXpK4IyIiy8CQQ2QFXunZBBN7NwEAzPr1JH5NvipxR0RE0mPIIbISb0YEYWRYIIQApq47hthTmVK3REQkKYYcIishk8nw3oCWeK5DfZQbBCauOYL9525K3RYRkWQYcoisiFwuw/xBbRDZ0helZQaM+/4wjmTckrotIiJJMOQQWRlbGzkWD2mP7s28UFhajlHfJSL1uk7qtoiIHjmGHCIrpLS1wdfDO6JDgBt0xWUYvjwR6TcLpG6LiOiRYsghslKOClusGN0FwfVUuJlfgpeXJeBabpHUbRERPTIMOURWzNXBDquiuqCxlxOu5hbh5WUJuJlfInVbRESPBEMOkZXzclZi9dhQ1HdzwIWbBRixPBHaIr3UbRER1TiGHKI6wM/NAavHhsLLWYlT13UYE3MIhaVlUrdFRFSjGHKI6ohGXk5YFdUFKntbJF26hX+uSkJJWbnUbRER1Zgqh5y4uDgMGDAAfn5+kMlk2Lhx4101qampePrpp+Hq6gonJyd07twZGRkZxvHi4mJMnDgRnp6ecHZ2xqBBg5CZaXp11oyMDPTv3x+Ojo7w8fHBtGnTUFZm+j/PvXv3okOHDlAqlWjatCliYmKqOh2iOiW4ngoxY7rAUWGDP87exGtrj6Ks3CB1W0RENaLKIaegoABt27bFl19+Wen4+fPn8fjjj6NFixbYu3cvjh8/jlmzZsHe3t5YM2XKFGzevBnr16/Hvn37cO3aNTz33HPG8fLycvTv3x+lpaU4cOAAVq5ciZiYGMyePdtYk56ejv79+6N3795ITk7G5MmTMXbsWOzcubOqUyKqUzoEuOPbEZ2gsJFjZ0om3vq/4zAYhNRtERFVO5kQ4oH/dZPJZNiwYQMGDhxoXDZ48GDY2dlh1apVla6j1Wrh7e2NNWvW4PnnnwcAnD59GsHBwYiPj0fXrl2xfft2PPXUU7h27Rp8fX0BAEuXLsX06dNx48YNKBQKTJ8+HVu3bsXJkydNnjs3Nxc7duyo9LlLSkpQUvK/M0t0Oh38/f2h1WqhUqke9NdAVCv9lqLBhB+OoNwgMDIsEHOebgmZTCZ1W0RE96XT6eDq6nrf9+9qPSbHYDBg69ataN68OSIjI+Hj44PQ0FCTr7SSkpKg1+sRHh5uXNaiRQsEBAQgPj4eABAfH4/WrVsbAw4AREZGQqfTISUlxVhz5zYqaiq2UZm5c+fC1dXV+PD396+OaRPVShEt1Vj4QlvIZMDK+EtY+NsZqVsiIqpW1RpysrKykJ+fj3nz5qFv37747bff8Oyzz+K5557Dvn37AAAajQYKhQJubm4m6/r6+kKj0Rhr7gw4FeMVY39Xo9PpUFRU+QXPZs6cCa1Wa3xcvnz5oedMVJsNbF8fHz7TCgDwxZ5z+HrfeYk7IiKqPrbVuTGD4fYBjM888wymTJkCAGjXrh0OHDiApUuXomfPntX5dFWmVCqhVCol7YHI0rzcNRB5xWX4eMdpzN1+Gs72thgWGih1W0RED61aP8nx8vKCra0tQkJCTJYHBwcbz65Sq9UoLS1Fbm6uSU1mZibUarWx5q9nW1X8fL8alUoFBweHapsTUV0woVcTvNqrCQDg3Y0n8WvyVYk7IiJ6eNUachQKBTp37oy0tDST5WfOnEFg4O3/GXbs2BF2dnbYtWuXcTwtLQ0ZGRkICwsDAISFheHEiRPIysoy1sTGxkKlUhkDVFhYmMk2KmoqtkFEVTMtMgjDuwZCCGDqumP4/VTm/VciIrJgVf66Kj8/H+fOnTP+nJ6ejuTkZHh4eCAgIADTpk3DSy+9hB49eqB3797YsWMHNm/ejL179wIAXF1dERUVhalTp8LDwwMqlQqTJk1CWFgYunbtCgCIiIhASEgIhg8fjvnz50Oj0eDdd9/FxIkTjV83vfLKK/jiiy/w1ltvYcyYMdi9ezfWrVuHrVu3VsOvhajukclkeP/plsgvKcOGo1fx6pojiBndGd2aeEndGhHRgxFVtGfPHgHgrsfIkSONNcuXLxdNmzYV9vb2om3btmLjxo0m2ygqKhKvvvqqcHd3F46OjuLZZ58V169fN6m5ePGi6Nevn3BwcBBeXl7ijTfeEHq9/q5e2rVrJxQKhWjcuLFYsWJFleai1WoFAKHVaqu0HpE105eVi7ErD4nA6VtEyKzt4silHKlbIiIyYe7790NdJ6e2M/c8e6K6plhfjqiVh7D/XDZcHezw0z+7ooWarxEisgySXCeHiKyDvZ0NvhneCR0C3KAt0uPlZYm4eLNA6raIiKqEIYeIKuWktMWKUV3QQu2Cm/klGLYsAddyK78GFRGRJWLIIaJ7cnW0w6qoUDTycsLV3CK8vDwBN/NL7r8iEZEFYMghor/l7aLE6rGh8HO1x4UbBRixPBHaIr3UbRER3RdDDhHdV303B6weGwovZwVOXdchKuYQCkvLpG6LiOhvMeQQkVkaezvj+zGhUNnb4vClW/jnqiSUlJVL3RYR0T0x5BCR2UL8VFgxugscFTb44+xNvL42GWXlBqnbIiKqFEMOEVVJx0B3fDO8ExQ2cuxI0WD6/52AwVBnL7dFRBaMIYeIquzxZl74fGh72Mhl+L8jV/DBllOow9cVJSILxZBDRA8ksqUaC55vAwCIOXARi2LPSNwREZEphhwiemDPdWiAD59pCQBYvPscvo27IHFHRET/w5BDRA9leFhDTIsMAgD8a1sq1iZmSNwREdFtDDlE9NAm9m6KV3o2AQC8veEENh27JnFHREQMOURUTab3DcLLXQMgBDD1p2TsPp0pdUtEVMcx5BBRtZDJZPjg6VZ4pp0fygwCE1YfQfz5bKnbIqI6jCGHiKqNXC7DJy+0RXiwL0rKDBi78hCOXc6Vui0iqqMYcoioWtnZyPHF0Pbo1sQTBaXlGLkiEWmaPKnbIqI6iCGHiKqdvZ0Nvh3RCe383ZBbqMfLyxNwKbtA6raIqI5hyCGiGuGktEXM6M5ooXbBjbwSDFuWgOvaIqnbIqI6hCGHiGqMm6MC30d1QUNPR1y5VYSXlyUgO79E6raIqI5gyCGiGuXjYo/VY0NRz9Ue528UYMR3idAV66Vui4jqAIYcIqpxDdwdsXpsKDydFEi5pkNUzCEUlZZL3RYRWTmGHCJ6JJp4O+P7qC5wsbfFoYu38M/VSSgtM0jdFhFZMYYcInpkWvq5ImZ0ZzjY2SDuzA1M/ukoysoZdIioZjDkENEj1THQA9+M6AiFjRzbTmgw85cTMBiE1G0RkRViyCGiR657M28sHtIeNnIZ1iddwYdbT0EIBh0iql4MOUQkib6t1Jg/qA0AYMX+i1j0+1mJOyIia8OQQ0SSGdSxAd5/uiUAYPGus1j2xwWJOyIia8KQQ0SSGtmtId6MaA4A+GhrKn5MzJC4IyKyFgw5RCS5ib2b4p89GgMAZm44gS3Hr0ncERFZgyqHnLi4OAwYMAB+fn6QyWTYuHHjPWtfeeUVyGQy/Oc//zFZnpOTg2HDhkGlUsHNzQ1RUVHIz883qTl+/Di6d+8Oe3t7+Pv7Y/78+Xdtf/369WjRogXs7e3RunVrbNu2rarTISILIJPJMKNfCwwNDYAQwOQfk7HndJbUbRFRLVflkFNQUIC2bdviyy+//Nu6DRs24ODBg/Dz87trbNiwYUhJSUFsbCy2bNmCuLg4jB8/3jiu0+kQERGBwMBAJCUlYcGCBZgzZw6++eYbY82BAwcwZMgQREVF4ejRoxg4cCAGDhyIkydPVnVKRGQBZDIZPnymFZ5p54cyg8Arq5Nw8EK21G0RUW0mHgIAsWHDhruWX7lyRdSvX1+cPHlSBAYGikWLFhnHTp06JQCIQ4cOGZdt375dyGQycfXqVSGEEF999ZVwd3cXJSUlxprp06eLoKAg488vvvii6N+/v8nzhoaGin/+859m96/VagUAodVqzV6HiGpWaVm5iIpJFIHTt4iWs3eIY5dvSd0SEVkYc9+/q/2YHIPBgOHDh2PatGlo2bLlXePx8fFwc3NDp06djMvCw8Mhl8uRkJBgrOnRowcUCoWxJjIyEmlpabh165axJjw83GTbkZGRiI+Pv2dvJSUl0Ol0Jg8isix2NnJ8MbQDwhp7Ir+kDCO+S8SZzDyp2yKiWqjaQ87HH38MW1tbvPbaa5WOazQa+Pj4mCyztbWFh4cHNBqNscbX19ekpuLn+9VUjFdm7ty5cHV1NT78/f2rNjkieiTs7Wzw7chOaOvvhtxCPV5eloCM7EKp2yKiWqZaQ05SUhI+++wzxMTEQCaTVeemq8XMmTOh1WqNj8uXL0vdEhHdg7PSFitHd0aQrwuy8kowbPlBaLTFUrdFRLVItYacP/74A1lZWQgICICtrS1sbW1x6dIlvPHGG2jYsCEAQK1WIyvL9KyJsrIy5OTkQK1WG2syMzNNaip+vl9NxXhllEolVCqVyYOILJebowKrorog0NMRl3OK8PLyBOQUlErdFhHVEtUacoYPH47jx48jOTnZ+PDz88O0adOwc+dOAEBYWBhyc3ORlJRkXG/37t0wGAwIDQ011sTFxUGv1xtrYmNjERQUBHd3d2PNrl27TJ4/NjYWYWFh1TklIpKYj8oeq6NCUc/VHuey8jHyu0ToivX3X5GI6rwqh5z8/HxjgAGA9PR0JCcnIyMjA56enmjVqpXJw87ODmq1GkFBQQCA4OBg9O3bF+PGjUNiYiL279+P6OhoDB482Hi6+dChQ6FQKBAVFYWUlBT89NNP+OyzzzB16lRjH6+//jp27NiBhQsX4vTp05gzZw4OHz6M6Ojoavi1EJEl8fdwxKqoUHg6KXDiqhZjYw6jqLRc6raIyNJV9bStPXv2CAB3PUaOHFlp/V9PIRdCiOzsbDFkyBDh7OwsVCqVGD16tMjLyzOpOXbsmHj88ceFUqkU9evXF/Pmzbtr2+vWrRPNmzcXCoVCtGzZUmzdurVKc+Ep5ES1y4kruaLVeztE4PQtYsTyBFGiL5e6JSKSgLnv3zIhhJAwY0lKp9PB1dUVWq2Wx+cQ1RKHL+bg5eUJKNYb0L91PSwe0h42css70YGIao6579+8dxUR1SqdGnrg6+GdYGcjw9YT1zHzl+Oow/9XI6K/wZBDRLVOz+beWDy4PeQyYN3hK/hoayqDDhHdhSGHiGqlfq3rYf7zbQEAy/9Mx2e7zkrcERFZGoYcIqq1nu/YAHMGhAAA/vP7WSz/M13ijojIkjDkEFGtNuqxRnjjH80BAB9uOYV1h3glcyK6jSGHiGq96CeaYnyPxgCAGb8cx9bj1yXuiIgsAUMOEdV6MpkMM/u1wJAu/jAIYPJPR7EnLev+KxKRVWPIISKrIJPJ8NHA1niqTT3oywUmrE5CwoVsqdsiIgkx5BCR1bCRy7DopXZ4ooUPivUGjIk5hKRLt6Rui4gkwpBDRFbFzkaOr4Z1QLcmnigoLceo7xJx7HKu1G0RkQQYcojI6tjb2WDZyE7o0sgDeSVlGL48ASevaqVui4geMYYcIrJKjgpbrBjVGZ0C3aErLsPLyxOQel0ndVtE9Agx5BCR1XJS2mLF6M5o5++G3EI9hi1LwJnMPKnbIqJHhCGHiKyai70dVo7pgtb1XZFTUIqh3ybgXFa+1G0R0SPAkENEVs/VwQ6roroguJ4KN/NLMPTbg0i/WSB1W0RUwxhyiKhOcHNU4IexoQjydUFW3u2gk5FdKHVbRFSDGHKIqM7wcFLgh3GhaOrjjOvaYgz59iCu3GLQIbJWDDlEVKd4OSuxZmwoGns54WpuEYZ+m4Dr2iKp2yKiGsCQQ0R1jo/KHmvGdUWgpyMycgox9NsEZOqKpW6LiKoZQw4R1Ulq19tBp4G7A9JvFmDotwdxI69E6raIqBox5BBRnVXfzQFrx3WFn6s9zt8owLBlB5Gdz6BDZC0YcoioTvP3cMSacV3hq1LiTGY+hi1LwK2CUqnbIqJqwJBDRHVeQy8nrBnXFd4uSpzW5GH4dwnQFuqlbouIHhJDDhERgCbezlgzNhSeTgqcvKrDiO8SoCtm0CGqzRhyiIj+q5mvC34YFwp3Rzscu6LFqO8SkV9SJnVbRPSAGHKIiO7QQq3C6rGhcHWww5GMXIxZcQiFpQw6RLURQw4R0V+09HPFqqgucFHaIvFiDqJiDqOotFzqtoioihhyiIgq0aaBG1ZGdYGz0hbxF7IxftVhFOsZdIhqE4YcIqJ76BDgjhWjO8NRYYM/zt7EhNVJKClj0CGqLRhyiIj+RueGHvhuVGfY28mxJ+0GJv5wFKVlBqnbIiIzVDnkxMXFYcCAAfDz84NMJsPGjRuNY3q9HtOnT0fr1q3h5OQEPz8/jBgxAteuXTPZRk5ODoYNGwaVSgU3NzdERUUhPz/fpOb48ePo3r077O3t4e/vj/nz59/Vy/r169GiRQvY29ujdevW2LZtW1WnQ0R0X10be2L5yM5Q2srxe2omXlt7FPpyBh0iS1flkFNQUIC2bdviyy+/vGussLAQR44cwaxZs3DkyBH88ssvSEtLw9NPP21SN2zYMKSkpCA2NhZbtmxBXFwcxo8fbxzX6XSIiIhAYGAgkpKSsGDBAsyZMwfffPONsebAgQMYMmQIoqKicPToUQwcOBADBw7EyZMnqzolIqL7eqypF74e3hEKGzl2pGgwdd0xlDHoEFk0mRBCPPDKMhk2bNiAgQMH3rPm0KFD6NKlCy5duoSAgACkpqYiJCQEhw4dQqdOnQAAO3bswJNPPokrV67Az88PS5YswTvvvAONRgOFQgEAmDFjBjZu3IjTp08DAF566SUUFBRgy5Ytxufq2rUr2rVrh6VLl5rVv06ng6urK7RaLVQq1QP+FoioLtmVmolXVidBXy7wbPv6+OSFtrCRy6Rui6hOMff9u8aPydFqtZDJZHBzcwMAxMfHw83NzRhwACA8PBxyuRwJCQnGmh49ehgDDgBERkYiLS0Nt27dMtaEh4ebPFdkZCTi4+Pv2UtJSQl0Op3Jg4ioKvoE++KLoR1gK5dhw9GrmPF/x2EwPPD/FYmoBtVoyCkuLsb06dMxZMgQY9LSaDTw8fExqbO1tYWHhwc0Go2xxtfX16Sm4uf71VSMV2bu3LlwdXU1Pvz9/R9ugkRUJ0W2VOOzwe1hI5dhfdIVvLPxJIMOkQWqsZCj1+vx4osvQgiBJUuW1NTTVMnMmTOh1WqNj8uXL0vdEhHVUv3b1MOnL7aFXAasTczAnM0peIhv/4moBtjWxEYrAs6lS5ewe/duk+/L1Go1srKyTOrLysqQk5MDtVptrMnMzDSpqfj5fjUV45VRKpVQKpUPPjEiojs8064+ysoF3vz5GL6PvwRbuRyzngqGTMZjdIgsQbV/klMRcM6ePYvff/8dnp6eJuNhYWHIzc1FUlKScdnu3bthMBgQGhpqrImLi4Ne/787AMfGxiIoKAju7u7Gml27dplsOzY2FmFhYdU9JSKiexrUsQHmPdcaAPDd/nTM236an+gQWYgqh5z8/HwkJycjOTkZAJCeno7k5GRkZGRAr9fj+eefx+HDh/HDDz+gvLwcGo0GGo0GpaWlAIDg4GD07dsX48aNQ2JiIvbv34/o6GgMHjwYfn5+AIChQ4dCoVAgKioKKSkp+Omnn/DZZ59h6tSpxj5ef/117NixAwsXLsTp06cxZ84cHD58GNHR0dXwayEiMt9LnQPw0cBWAICv4y7g09gzEndERAAAUUV79uwRAO56jBw5UqSnp1c6BkDs2bPHuI3s7GwxZMgQ4ezsLFQqlRg9erTIy8szeZ5jx46Jxx9/XCiVSlG/fn0xb968u3pZt26daN68uVAoFKJly5Zi69atVZqLVqsVAIRWq63qr4GI6C4r/rwgAqdvEYHTt4j/xJ6Ruh0iq2Xu+/dDXSentuN1coioui374wI+2poKAJgWGYSJvZtK3BGR9bGY6+QQEdUlY7s3xlt9gwAAC3am4du4CxJ3RFR3MeQQEVWzV3s1xdR/NAcA/GtbKlbsT5e4I6K6iSGHiKgGvNanGSY9cfurqvc3n8Kqg5ck7oio7mHIISKqIVP/0Ryv9GwCAJi18SR+TMyQuCOiuoUhh4iohshkMkzvG4SoxxsBAGZuOIGfk65I3BVR3cGQQ0RUg2QyGd7tH4yRYYEQApj28zH8mnxV6raI6gSGHCKiGiaTyTDn6ZYYGhoAIYApPyVj6/HrUrdFZPUYcoiIHgGZTIaPnmmFFzs1gEEAr/14FDtOaqRui8iqMeQQET0icrkMc59rg+fa10e5QWDS2iP4/VTm/VckogfCkENE9AjZyGVY8EJbDGjrB325wKs/HMHetCyp2yKySgw5RESPmI1chkUvtkW/VmqUlhswflUS/jx7U+q2iKwOQw4RkQRsbeRYPKQ9/hHii9IyA8Z+fwjx57OlbovIqjDkEBFJxM5Gji+GtkfvIG8U6w2IWnkIhy7mSN0WkdVgyCEikpDS1gZLXu6I7s28UFhajlHfJSLp0i2p2yKyCgw5REQSs7ezwbcjOqFbE08U/DfoHLucK3VbRLUeQw4RkQWwt7PBspGd0KWRB/JKyjB8eQJOXtVK3RZRrcaQQ0RkIRwVtvhuVGd0DHSHrrgMLy9PQOp1ndRtEdVaDDlERBbEWWmLmNGd0dbfDbmFegxbloAzmXlSt0VUKzHkEBFZGBd7O3w/pgta1Vchp6AUQ79NwPkb+VK3RVTrMOQQEVkgVwc7rI4KRXA9FW7ml2Dotwdx8WaB1G0R1SoMOUREFsrNUYEfxoYiyNcFmboSDPn2IC7nFErdFlGtwZBDRGTBPJwUWD02FE28nXBdW4zB3xzElVsMOkTmYMghIrJw3i5KrB3XFY28nHA1twhDv03AdW2R1G0RWTyGHCKiWsBHZY8140IR4OGIjJxCDP02AVm6YqnbIrJoDDlERLVEPVcHrBkXivpuDki/WYAh3x7EjbwSqdsislgMOUREtUgDd0f8OL4r6rna4/yNAgxbdhDZ+Qw6RJVhyCEiqmX8PRyxdlxX+KqUOJOZj5eXJyK3sFTqtogsDkMOEVEt1NDLCWvGdYWXsxKp13V4eXkCtEV6qdsisigMOUREtVQTb2esHRcKTycFTl7VYcR3idAVM+gQVWDIISKqxZr5umD12FC4Odrh2OVcjF5xCPklZVK3RWQRGHKIiGq54HoqrI4KhcreFkmXbmFMzCEUljLoEFU55MTFxWHAgAHw8/ODTCbDxo0bTcaFEJg9ezbq1asHBwcHhIeH4+zZsyY1OTk5GDZsGFQqFdzc3BAVFYX8fNObzx0/fhzdu3eHvb09/P39MX/+/Lt6Wb9+PVq0aAF7e3u0bt0a27Ztq+p0iIisQqv6rlgVFQoXpS0S03MwduVhFJWWS90WkaSqHHIKCgrQtm1bfPnll5WOz58/H4sXL8bSpUuRkJAAJycnREZGorj4fxetGjZsGFJSUhAbG4stW7YgLi4O48ePN47rdDpEREQgMDAQSUlJWLBgAebMmYNvvvnGWHPgwAEMGTIEUVFROHr0KAYOHIiBAwfi5MmTVZ0SEZFVaOvvhpVRXeCksMGB89kYv+owivUMOlSHiYcAQGzYsMH4s8FgEGq1WixYsMC4LDc3VyiVSrF27VohhBCnTp0SAMShQ4eMNdu3bxcymUxcvXpVCCHEV199Jdzd3UVJSYmxZvr06SIoKMj484svvij69+9v0k9oaKj45z//aXb/Wq1WABBardbsdYiILF1ierYInrVdBE7fIkZ9lyCK9WVSt0RUrcx9/67WY3LS09Oh0WgQHh5uXObq6orQ0FDEx8cDAOLj4+Hm5oZOnToZa8LDwyGXy5GQkGCs6dGjBxQKhbEmMjISaWlpuHXrlrHmzuepqKl4nsqUlJRAp9OZPIiIrE3nhh5YPrIz7O3k2JN2AxN/OIrSMoPUbRE9ctUacjQaDQDA19fXZLmvr69xTKPRwMfHx2Tc1tYWHh4eJjWVbePO57hXTcV4ZebOnQtXV1fjw9/fv6pTJCKqFcKaeGLZiM5Q2Mrxe2omXv/xKPTlDDpUt9Sps6tmzpwJrVZrfFy+fFnqloiIaszjzbzwzfCOUNjIsf2kBlPXHUMZgw7VIdUactRqNQAgMzPTZHlmZqZxTK1WIysry2S8rKwMOTk5JjWVbePO57hXTcV4ZZRKJVQqlcmDiMia9QrywZKXO8DORobNx67hrZ+Po9wgpG6L6JGo1pDTqFEjqNVq7Nq1y7hMp9MhISEBYWFhAICwsDDk5uYiKSnJWLN7924YDAaEhoYaa+Li4qDX/+/KnbGxsQgKCoK7u7ux5s7nqaipeB4iIrqtT7AvPh/SATZyGX45ehUz/u84DAw6VAdUOeTk5+cjOTkZycnJAG4fbJycnIyMjAzIZDJMnjwZH330ETZt2oQTJ05gxIgR8PPzw8CBAwEAwcHB6Nu3L8aNG4fExETs378f0dHRGDx4MPz8/AAAQ4cOhUKhQFRUFFJSUvDTTz/hs88+w9SpU419vP7669ixYwcWLlyI06dPY86cOTh8+DCio6Mf/rdCRGRl+rZSY/Hg9pDLgPVJV/DOxpMQgkGHrFxVT9vas2ePAHDXY+TIkUKI26eRz5o1S/j6+gqlUin69Okj0tLSTLaRnZ0thgwZIpydnYVKpRKjR48WeXl5JjXHjh0Tjz/+uFAqlaJ+/fpi3rx5d/Wybt060bx5c6FQKETLli3F1q1bqzQXnkJORHXNxqNXRMMZW0Tg9C1i1sYTwmAwSN0SUZWZ+/4tE6LuRnmdTgdXV1dotVoen0NEdcbPSVcw7edjEAIY81gjzHoqGDKZTOq2iMxm7vt3nTq7ioiIgOc7NsDcZ1sDAL7bn455O07zqyuySgw5RER10OAuAfhwYCsAwNf7LuDT2DMSd0RU/RhyiIjqqOFdA/HegBAAwOe7z2HxrrP3WYOodmHIISKqw0Y/1gjvPBkMAPg09gy+2ntO4o6Iqg9DDhFRHTeuR2O81TcIADB/Rxq+jbsgcUdE1YMhh4iI8GqvppgS3hwA8K9tqVixP13ijogeHkMOEREBAF7r0xTRvZsCAN7ffAqrDl6SuCOih8OQQ0REAACZTIY3Iprjnz0bAwBmbTyJnw5lSNwV0YNjyCEiIiOZTIYZfVtgzGONAAAzfjmBn5OuSNwV0YNhyCEiIhMymQyzngrGiLBACAFM+/kYfk2+KnVbRFXGkENERHeRyWSYM6AlhnQJgBDAlJ+SsfX4danbIqoShhwiIqqUXC7Dvwa2wgsdG8AggNd+PIodJzVSt0VkNoYcIiK6J7lchnmD2uDZ9vVRbhCYtPYIdqVmSt0WkVkYcoiI6G/ZyGVY8HwbDGjrB325wITVR7A3LUvqtojuiyGHiIjuy9ZGjk9fbIt+rdQoLTdg/Kok/Hn2ptRtEf0thhwiIjKLnY0cnw1uj/BgX5SWGTD2+0OIP58tdVtE98SQQ0REZlPYyvHlsPboHeSNYr0BUSsP4dDFHKnbIqoUQw4REVWJ0tYGS17uiO7NvFBYWo5R3yXiSMYtqdsiugtDDhERVZm9nQ2+Gd4JYY09UVBajpHLE3H8Sq7UbRGZYMghIqIH4qCwwfJRndCloQfySsrw8rIEnLyqlbotIiOGHCIiemCOClt8N7ozOga6Q1dchpeXJyD1uk7qtogAMOQQEdFDclbaYsXozmjr74bcQj1eXpaAs5l5UrdFxJBDREQPT2Vvh+/HdEGr+ipkF5RiyLcJOH8jX+q2qI5jyCEiomrh6mCH1VGhCK6nws38Egz99iAu3iyQui2qwxhyiIio2rg5KrA6qgua+zojU3c76FzOKZS6LaqjGHKIiKhaeTor8cPYrmji7YRr2mIM/uYgruYWSd0W1UEMOUREVO28XZRYO64rGnk54WpuEYZ8cxDXtQw69Ggx5BARUY3wUdljzbhQBHg4IiOnEEO/TUCWrljqtqgOYcghIqIaU8/VAWvGhaK+mwPSbxZgyLcHcSOvROq2qI5gyCEiohrVwN0Ra8d1RT1Xe5y/UYCXlyUgp6BU6raoDqj2kFNeXo5Zs2ahUaNGcHBwQJMmTfDhhx9CCGGsEUJg9uzZqFevHhwcHBAeHo6zZ8+abCcnJwfDhg2DSqWCm5sboqKikJ9ves2F48ePo3v37rC3t4e/vz/mz59f3dMhIqJqEOB5O+j4uCiRlpmHYcsSkFvIoEM1q9pDzscff4wlS5bgiy++QGpqKj7++GPMnz8fn3/+ubFm/vz5WLx4MZYuXYqEhAQ4OTkhMjISxcX/+6522LBhSElJQWxsLLZs2YK4uDiMHz/eOK7T6RAREYHAwEAkJSVhwYIFmDNnDr755pvqnhIREVWDhl5OWDOuK7yclUi9rsPw5YnQFumlbousmEzc+RFLNXjqqafg6+uL5cuXG5cNGjQIDg4OWL16NYQQ8PPzwxtvvIE333wTAKDVauHr64uYmBgMHjwYqampCAkJwaFDh9CpUycAwI4dO/Dkk0/iypUr8PPzw5IlS/DOO+9Ao9FAoVAAAGbMmIGNGzfi9OnTZvWq0+ng6uoKrVYLlUpVnb8GIiK6hzOZeRjyzUFkF5Sirb8bVkd1gYu9ndRtUS1i7vt3tX+S061bN+zatQtnzpwBABw7dgx//vkn+vXrBwBIT0+HRqNBeHi4cR1XV1eEhoYiPj4eABAfHw83NzdjwAGA8PBwyOVyJCQkGGt69OhhDDgAEBkZibS0NNy6davS3kpKSqDT6UweRET0aDX3dcHqsaFwc7TDscu5GLXiEPJLyqRui6xQtYecGTNmYPDgwWjRogXs7OzQvn17TJ48GcOGDQMAaDQaAICvr6/Jer6+vsYxjUYDHx8fk3FbW1t4eHiY1FS2jTuf46/mzp0LV1dX48Pf3/8hZ0tERA8iuJ4Kq6NCobK3RdKlWxgTcwiFpQw6VL2qPeSsW7cOP/zwA9asWYMjR45g5cqV+OSTT7By5crqfqoqmzlzJrRarfFx+fJlqVsiIqqzWtV3xaqoULgobZGYnoOxKw+jWF8udVtkRao95EybNs34aU7r1q0xfPhwTJkyBXPnzgUAqNVqAEBmZqbJepmZmcYxtVqNrKwsk/GysjLk5OSY1FS2jTuf46+USiVUKpXJg4iIpNPW3w0xY7rASWGDA+ezMe57Bh2qPtUecgoLCyGXm27WxsYGBoMBANCoUSOo1Wrs2rXLOK7T6ZCQkICwsDAAQFhYGHJzc5GUlGSs2b17NwwGA0JDQ401cXFx0Ov/d2R+bGwsgoKC4O7uXt3TIiKiGtIx0B0rRneBg50N/jh7ExNWJ6GkjEGHHl61h5wBAwbgX//6F7Zu3YqLFy9iw4YN+PTTT/Hss88CAGQyGSZPnoyPPvoImzZtwokTJzBixAj4+flh4MCBAIDg4GD07dsX48aNQ2JiIvbv34/o6GgMHjwYfn5+AIChQ4dCoVAgKioKKSkp+Omnn/DZZ59h6tSp1T0lIiKqYV0aeeC7UZ1hbyfHnrQbiF5zFPpyg9RtUW0nqplOpxOvv/66CAgIEPb29qJx48binXfeESUlJcYag8EgZs2aJXx9fYVSqRR9+vQRaWlpJtvJzs4WQ4YMEc7OzkKlUonRo0eLvLw8k5pjx46Jxx9/XCiVSlG/fn0xb968KvWq1WoFAKHVah98wkREVG3+OHNDNHtnmwicvkW8suqwKC0rl7olskDmvn9X+3VyahNeJ4eIyPLsTcvC+O+TUFpuQDMfZ8weEILuzbylbossiGTXySEiInoYvYJ88PXwjnB3tMPZrHwMX56IsSsP4+LNAqlbo1qGIYeIiCxO7xY+2Ptmb4x5rBFs5TL8npqJfyzah7nbU5FXzFtBkHn4dRW/riIismjnsvLwwZZUxJ25AQDwclbirb5BeL5DA8jlMom7IymY+/7NkMOQQ0Rk8YQQ2JOWhQ+3pCL9v19bta7vijlPh6BjoIfE3dGjxpBjBoYcIqLapbTMgJUHLmLxrrPI++/9rp5p54cZ/VqgnquDxN3Ro8KQYwaGHCKi2ulGXgkW/paGnw5fhhCAg50NJvRqgvE9GsPezkbq9qiGMeSYgSGHiKh2O3lVi/c3p+DQxVsAgPpuDnj7yWA82VoNmYzH61grhhwzMOQQEdV+QghsOX4dc7el4pq2GMDtKyi/NyAELf1cJe6OagJDjhkYcoiIrEdRaTm+jjuPpfvOo1hvgEwGDO4cgDcjmsPTWSl1e1SNGHLMwJBDRGR9ruYWYd7209h87BoAwMXeFq/3aYYRYQ2hsOXl4awBQ44ZGHKIiKxXYnoO3t+cgpRrOgBAY28nzHoqBL2DfCTujB4WQ44ZGHKIiKxbuUHg56TLWLAzDTfzSwEAvYO88e5TIWji7Sxxd/SgGHLMwJBDRFQ36Ir1+GL3OazYnw59uYCtXIZR3RpiUp9mcHWwk7o9qiKGHDMw5BAR1S0XbuTjX1tTset0FgDA00mBNyOD8GInf9jwFhG1BkOOGRhyiIjqpr1pWfhwyymcv3H7FhEh9VR4b0AIQht7StwZmYMhxwwMOUREdZe+3IBV8Zew6PczyCu+fYuI/m3qYWa/Fmjg7ihxd/R3GHLMwJBDRETZ+SX4NPYM1iZmwCAApa0c/+zZBK/0bAxHha3U7VElGHLMwJBDREQVTl3T4f3NKUhIzwEA1HO1x4x+LfB0Wz/eIsLCMOSYgSGHiIjuJITAjpMafLQ1FVdziwAAnQLd8d6AlmjdgLeIsBQMOWZgyCEiosoU68ux7I8L+HLPeRTpyyGTAS90bIA3I4Pg42IvdXt1HkOOGRhyiIjo72i0xfh4x2lsOHoVAOCstMWkJ5pi1GMNobS1kbi7uoshxwwMOUREZI6kS7fwweYUHLuiBQA09HTEu/1D0CfYh8frSIAhxwwMOUREZC6DQeCXo1fx8Y7TuJFXAgDo3swLs58KQTNfF4m7q1sYcszAkENERFWVX1KGL/ecw/I/0lFaboCNXIbhXQMxJbw5XB15i4hHgSHHDAw5RET0oC5lF+BfW1Px26lMAIC7ox2mRgRhSGd/2NrIJe7OujHkmIEhh4iIHtafZ2/igy0pOJOZDwBooXbB7AEh6NbES+LOrBdDjhkYcoiIqDqUlRuwJjEDC387A22RHgDQt6Ua7/QPhr8HbxFR3RhyzMCQQ0RE1elWQSn+8/sZrE7IQLlBQGErx7jujfBqr6ZwUvIWEdWFIccMDDlERFQT0jR5+GBLCvafywYA+LgoMaNfCwxsVx9yOU85f1gMOWZgyCEiopoihEDsqUx8tDUVGTmFAID2AW54b0BLtPN3k7a5Wo4hxwwMOUREVNNKysrx3Z8X8cXusygoLQcAPNehPqb3bQFfFW8R8SDMff+ukXPcrl69ipdffhmenp5wcHBA69atcfjwYeO4EAKzZ89GvXr14ODggPDwcJw9e9ZkGzk5ORg2bBhUKhXc3NwQFRWF/Px8k5rjx4+je/fusLe3h7+/P+bPn18T0yEiInpgSlsbTOjVBHve7IXnOzYAAPxy5Cp6f7IXX+45h2J9ucQdWq9qDzm3bt3CY489Bjs7O2zfvh2nTp3CwoUL4e7ubqyZP38+Fi9ejKVLlyIhIQFOTk6IjIxEcXGxsWbYsGFISUlBbGwstmzZgri4OIwfP944rtPpEBERgcDAQCQlJWHBggWYM2cOvvnmm+qeEhER0UPzUdnjkxfa4teJj6F9gBsKS8uxYGca/rFoH3ac1KAOf7FSY6r966oZM2Zg//79+OOPPyodF0LAz88Pb7zxBt58800AgFarha+vL2JiYjB48GCkpqYiJCQEhw4dQqdOnQAAO3bswJNPPokrV67Az88PS5YswTvvvAONRgOFQmF87o0bN+L06dOVPndJSQlKSkqMP+t0Ovj7+/PrKiIieqQMBoFNx65h7vZUZOpuvy91a+KJ2QNC0ELN96P7kezrqk2bNqFTp0544YUX4OPjg/bt2+Pbb781jqenp0Oj0SA8PNy4zNXVFaGhoYiPjwcAxMfHw83NzRhwACA8PBxyuRwJCQnGmh49ehgDDgBERkYiLS0Nt27dqrS3uXPnwtXV1fjw9/ev1rkTERGZQy6XYWD7+tj9Ri9MeqIpFLZyHDifjSc/+wOzfz2JWwWlUrdoFao95Fy4cAFLlixBs2bNsHPnTkyYMAGvvfYaVq5cCQDQaDQAAF9fX5P1fH19jWMajQY+Pj4m47a2tvDw8DCpqWwbdz7HX82cORNardb4uHz58kPOloiI6ME5KW3xRkQQdk3tiSdbq2EQwPfxl9Drk72I2Z8OfblB6hZrtWq/MpHBYECnTp3w73//GwDQvn17nDx5EkuXLsXIkSOr++mqRKlUQqlUStoDERHRX/l7OOKrYR0Rfz4b729OwWlNHuZsPoUfEjIwe0AIujfzlrrFWqnaP8mpV68eQkJCTJYFBwcjIyMDAKBWqwEAmZmZJjWZmZnGMbVajaysLJPxsrIy5OTkmNRUto07n4OIiKg2CWviia2vdce/nm0Fd0c7nM3Kx/DliRi78jAu3iyQur1ap9pDzmOPPYa0tDSTZWfOnEFgYCAAoFGjRlCr1di1a5dxXKfTISEhAWFhYQCAsLAw5ObmIikpyVize/duGAwGhIaGGmvi4uKg1+uNNbGxsQgKCjI5k4uIiKg2sZHLMCw0EHvf7I0xjzWCrVyG31Mz8Y9F+zB3eyryS8qkbrHWqPaQM2XKFBw8eBD//ve/ce7cOaxZswbffPMNJk6cCACQyWSYPHkyPvroI2zatAknTpzAiBEj4Ofnh4EDBwK4/clP3759MW7cOCQmJmL//v2Ijo7G4MGD4efnBwAYOnQoFAoFoqKikJKSgp9++gmfffYZpk6dWt1TIiIieuRcHe0we0AIdkzujh7NvaEvF/h63wX0/mQv1h++DIOBp5zfT41c8XjLli2YOXMmzp49i0aNGmHq1KkYN26ccVwIgffeew/ffPMNcnNz8fjjj+Orr75C8+bNjTU5OTmIjo7G5s2bIZfLMWjQICxevBjOzs7GmuPHj2PixIk4dOgQvLy8MGnSJEyfPt3sPnnFYyIiqg2EENiTloUPt6Qi/b9fW7Vp4Ir3BoSgY6CHxN09erytgxkYcoiIqDYpLTNg5YGLWLzrLPL++7XVM+38MKNfC9RzdZC4u0eHIccMDDlERFQb3cgrwcLf0vDT4csQAnCwu33riPE9GsPezkbq9mocQ44ZGHKIiKg2O3lVi/c3p+DQxdsXwa3v5oC3nwzGk63VkMlkEndXcxhyzMCQQ0REtZ0QAluOX8fcbam4pr19D8jQRh6YPSAELf1cJe6uZjDkmIEhh4iIrEVRaTm+jjuPpfvOo1hvgFwGvNQ5AG9GNIens3VdCJchxwwMOUREZG2u5hZh3vbT2HzsGgDAxd4Wr/dphhFhDaGwrfYrx0iCIccMDDlERGStEtNz8P7mFKRc0wEAGns7YfZTIegV5HOfNS0fQ44ZGHKIiMialRsEfk66jAU703Az//adzZ9o4YN3+wejsbfzfda2XAw5ZmDIISKiukBXrMcXu89hxf506MsFbOUyjH6sISb1aQaVvZ3U7VUZQ44ZGHKIiKguuXAjH//amopdp2/fBNvTSYE3I4PwYid/2MhrzynnDDlmYMghIqK6aG9aFj7ccgrnb9y+RURIPRXeGxCC0MaeEndmHoYcMzDkEBFRXaUvN2BV/CUs+v0M8opv3yKif5t6ePvJYNR3s+xbRDDkmIEhh4iI6rrs/BJ8GnsGaxMzYBCA0laOV3o2wSs9m8BBYZm3iGDIMQNDDhER0W2nrunw/uYUJKTnAADqudpj5pPBGNCmnsXdIoIhxwwMOURERP8jhMCOkxp8tDUVV3OLAACdAt3x3oCWaN3Acm4RwZBjBoYcIiKiuxXry7Hsjwv4cs95FOnLIZMBL3RsgDcjg+DjYi91eww55mDIISIiujeNthgf7ziNDUevAgCclbZ4rU9TjOrWSNJbRDDkmIEhh4iI6P6SLt3CB5tTcOyKFgDQyMsJ7/YPxhMtfCQ5XochxwwMOUREROYxGAR+OXoVH+84jRt5JQCAHs29MfupYDT1cXmkvTDkmIEhh4iIqGryS8rw5Z5zWP5HOkrLDbCRyzAiLBCT+zSHq+OjuUUEQ44ZGHKIiIgezKXsAvxrayp+O5UJAHB3tMPUiCAM6ewPW5uaPV6HIccMDDlEREQP58+zN/HBlhScycwHALRQu2D2gBB0a+JVY8/JkGMGhhwiIqKHV1ZuwJrEDCz87Qy0RXoAQL9Warz9ZDD8PRyr/fnMff+W7vwvIiIisgq2NnKMCGuIvW/2wsiwQNjIZdh+UoM+n+7DjpPXJeuLIYeIiIiqhbuTAu8/0wrbXuuOx5p6wk4uQ4cAd8n6sZXsmYmIiMgqBaldsDoqFBk5hfBRSXeFZH6SQ0RERNVOJpMh0NNJ0h4YcoiIiMgqMeQQERGRVWLIISIiIqvEkENERERWqcZDzrx58yCTyTB58mTjsuLiYkycOBGenp5wdnbGoEGDkJmZabJeRkYG+vfvD0dHR/j4+GDatGkoKyszqdm7dy86dOgApVKJpk2bIiYmpqanQ0RERLVEjYacQ4cO4euvv0abNm1Mlk+ZMgWbN2/G+vXrsW/fPly7dg3PPfeccby8vBz9+/dHaWkpDhw4gJUrVyImJgazZ8821qSnp6N///7o3bs3kpOTMXnyZIwdOxY7d+6sySkRERFRbSFqSF5enmjWrJmIjY0VPXv2FK+//roQQojc3FxhZ2cn1q9fb6xNTU0VAER8fLwQQoht27YJuVwuNBqNsWbJkiVCpVKJkpISIYQQb731lmjZsqXJc7700ksiMjLS7B61Wq0AILRa7YNOk4iIiB4xc9+/a+yTnIkTJ6J///4IDw83WZ6UlAS9Xm+yvEWLFggICEB8fDwAID4+Hq1bt4avr6+xJjIyEjqdDikpKcaav247MjLSuI3KlJSUQKfTmTyIiIjIOtXIFY9//PFHHDlyBIcOHbprTKPRQKFQwM3NzWS5r68vNBqNsebOgFMxXjH2dzU6nQ5FRUVwcHC467nnzp2L999//4HnRURERLVHtX+Sc/nyZbz++uv44YcfYG8v3aWcKzNz5kxotVrj4/Lly1K3RERERDWk2kNOUlISsrKy0KFDB9ja2sLW1hb79u3D4sWLYWtrC19fX5SWliI3N9dkvczMTKjVagCAWq2+62yrip/vV6NSqSr9FAcAlEolVCqVyYOIiIisU7WHnD59+uDEiRNITk42Pjp16oRhw4YZ/2xnZ4ddu3YZ10lLS0NGRgbCwsIAAGFhYThx4gSysrKMNbGxsVCpVAgJCTHW3LmNipqKbRAREVHdVu3H5Li4uKBVq1Ymy5ycnODp6WlcHhUVhalTp8LDwwMqlQqTJk1CWFgYunbtCgCIiIhASEgIhg8fjvnz50Oj0eDdd9/FxIkToVQqAQCvvPIKvvjiC7z11lsYM2YMdu/ejXXr1mHr1q3VPSUiIiKqhWrkwOP7WbRoEeRyOQYNGoSSkhJERkbiq6++Mo7b2Nhgy5YtmDBhAsLCwuDk5ISRI0figw8+MNY0atQIW7duxZQpU/DZZ5+hQYMGWLZsGSIjI83uQwgBADzLioiIqBapeN+ueB+/F5m4X4UVu3LlCvz9/aVug4iIiB7A5cuX0aBBg3uO1+mQYzAYcO3aNbi4uEAmk1XbdnU6Hfz9/XH58mWrPbjZ2ufI+dV+1j5Hzq/2s/Y51uT8hBDIy8uDn58f5PJ7H14syddVlkIul/9tAnxYdeEMLmufI+dX+1n7HDm/2s/a51hT83N1db1vDe9CTkRERFaJIYeIiIisEkNODVAqlXjvvfeMp7tbI2ufI+dX+1n7HDm/2s/a52gJ86vTBx4TERGR9eInOURERGSVGHKIiIjIKjHkEBERkVViyCEiIiKrxJBTBXFxcRgwYAD8/Pwgk8mwceNGk3EhBGbPno169erBwcEB4eHhOHv2rElNTk4Ohg0bBpVKBTc3N0RFRSE/P/8RzuLe/m5+er0e06dPR+vWreHk5AQ/Pz+MGDEC165dM9lGw4YNIZPJTB7z5s17xDOp3P3236hRo+7qvW/fviY1lrz/gPvP8a/zq3gsWLDAWGPJ+3Du3Lno3LkzXFxc4OPjg4EDByItLc2kpri4GBMnToSnpyecnZ0xaNAgZGZmmtRkZGSgf//+cHR0hI+PD6ZNm4aysrJHOZVK3W9+OTk5mDRpEoKCguDg4ICAgAC89tpr0Gq1JtupbB//+OOPj3o6dzFn//Xq1euu3l955RWTGkvdf8D953jx4sV7vg7Xr19vrLPUfbhkyRK0adPGeIG/sLAwbN++3Thuaa8/hpwqKCgoQNu2bfHll19WOj5//nwsXrwYS5cuRUJCApycnBAZGYni4mJjzbBhw5CSkoLY2Fhs2bIFcXFxGD9+/KOawt/6u/kVFhbiyJEjmDVrFo4cOYJffvkFaWlpePrpp++q/eCDD3D9+nXjY9KkSY+i/fu63/4DgL59+5r0vnbtWpNxS95/wP3neOfcrl+/ju+++w4ymQyDBg0yqbPUfbhv3z5MnDgRBw8eRGxsLPR6PSIiIlBQUGCsmTJlCjZv3oz169dj3759uHbtGp577jnjeHl5Ofr374/S0lIcOHAAK1euRExMDGbPni3FlEzcb37Xrl3DtWvX8Mknn+DkyZOIiYnBjh07EBUVdde2VqxYYbIPBw4c+Ihnczdz9h8AjBs3zqT3+fPnG8csef8B95+jv7//Xa/D999/H87OzujXr5/JtixxHzZo0ADz5s1DUlISDh8+jCeeeALPPPMMUlJSAFjg60/QAwEgNmzYYPzZYDAItVotFixYYFyWm5srlEqlWLt2rRBCiFOnTgkA4tChQ8aa7du3C5lMJq5evfrIejfHX+dXmcTERAFAXLp0ybgsMDBQLFq0qGabqwaVzW/kyJHimWeeuec6tWn/CWHePnzmmWfEE088YbKstuxDIYTIysoSAMS+ffuEELdfc3Z2dmL9+vXGmtTUVAFAxMfHCyGE2LZtm5DL5UKj0RhrlixZIlQqlSgpKXm0E7iPv86vMuvWrRMKhULo9XrjMnP2vSWobH49e/YUr7/++j3XqU37Twjz9mG7du3EmDFjTJbVln0ohBDu7u5i2bJlFvn64yc51SQ9PR0ajQbh4eHGZa6urggNDUV8fDwAID4+Hm5ubujUqZOxJjw8HHK5HAkJCY+854el1Wohk8ng5uZmsnzevHnw9PRE+/btsWDBAov5GNkce/fuhY+PD4KCgjBhwgRkZ2cbx6xt/2VmZmLr1q2VfgpQW/Zhxdc0Hh4eAICkpCTo9XqT12GLFi0QEBBg8jps3bo1fH19jTWRkZHQ6XTG/41air/O7141KpUKtramtyKcOHEivLy80KVLF3z33XcQFnhJtHvN74cffoCXlxdatWqFmTNnorCw0DhWm/YfcP99mJSUhOTk5Epfh5a+D8vLy/Hjjz+ioKAAYWFhFvn6q9M36KxOGo0GAEx2XMXPFWMajQY+Pj4m47a2tvDw8DDW1BbFxcWYPn06hgwZYnLjtddeew0dOnSAh4cHDhw4gJkzZ+L69ev49NNPJezWPH379sVzzz2HRo0a4fz583j77bfRr18/xMfHw8bGxqr2HwCsXLkSLi4uJh8lA7VnHxoMBkyePBmPPfYYWrVqBeD2a0yhUNwVvP/6OqzsdVoxZikqm99f3bx5Ex9++OFdX5l+8MEHeOKJJ+Do6IjffvsNr776KvLz8/Haa689itbNcq/5DR06FIGBgfDz88Px48cxffp0pKWl4ZdffgFQe/YfYN4+XL58OYKDg9GtWzeT5Za8D0+cOIGwsDAUFxfD2dkZGzZsQEhICJKTky3u9ceQQ1Wm1+vx4osvQgiBJUuWmIxNnTrV+Oc2bdpAoVDgn//8J+bOnWvxly4fPHiw8c+tW7dGmzZt0KRJE+zduxd9+vSRsLOa8d1332HYsGGwt7c3WV5b9uHEiRNx8uRJ/Pnnn1K3UiPuNz+dTof+/fsjJCQEc+bMMRmbNWuW8c/t27dHQUEBFixYYBFvkBXuNb87A1vr1q1Rr1499OnTB+fPn0eTJk0edZsP5X77sKioCGvWrDHZXxUseR8GBQUhOTkZWq0WP//8M0aOHIl9+/ZJ3Val+HVVNVGr1QBw11HkmZmZxjG1Wo2srCyT8bKyMuTk5BhrLF1FwLl06RJiY2NNPsWpTGhoKMrKynDx4sVH02A1aty4Mby8vHDu3DkA1rH/Kvzxxx9IS0vD2LFj71trifswOjoaW7ZswZ49e9CgQQPjcrVajdLSUuTm5prU//V1WNnrtGLMEtxrfhXy8vLQt29fuLi4YMOGDbCzs/vb7YWGhuLKlSsoKSmpqZar5H7zu1NoaCgAmLwOLX3/AebN8eeff0ZhYSFGjBhx3+1Z0j5UKBRo2rQpOnbsiLlz56Jt27b47LPPLPL1x5BTTRo1agS1Wo1du3YZl+l0OiQkJCAsLAwAEBYWhtzcXCQlJRlrdu/eDYPBYHwhW7KKgHP27Fn8/vvv8PT0vO86ycnJkMvld33NUxtcuXIF2dnZqFevHoDav//utHz5cnTs2BFt27a9b60l7UMhBKKjo7Fhwwbs3r0bjRo1Mhnv2LEj7OzsTF6HaWlpyMjIMHkdnjhxwiSwVgT2kJCQRzORe7jf/IDb/65ERERAoVBg06ZNd30SV5nk5GS4u7tL/kmcOfP7q+TkZAAweR1a6v4DqjbH5cuX4+mnn4a3t/d9t2sp+7AyBoMBJSUllvn6q/ZDma1YXl6eOHr0qDh69KgAID799FNx9OhR49lF8+bNE25ubuLXX38Vx48fF88884xo1KiRKCoqMm6jb9++on379iIhIUH8+eefolmzZmLIkCFSTcnE382vtLRUPP3006JBgwYiOTlZXL9+3fioOCL+wIEDYtGiRSI5OVmcP39erF69Wnh7e4sRI0ZIPLPb/m5+eXl54s033xTx8fEiPT1d/P7776JDhw6iWbNmori42LgNS95/Qtz/76gQQmi1WuHo6CiWLFly1/qWvg8nTJggXF1dxd69e03+DhYWFhprXnnlFREQECB2794tDh8+LMLCwkRYWJhxvKysTLRq1UpERESI5ORksWPHDuHt7S1mzpwpxZRM3G9+Wq1WhIaGitatW4tz586Z1JSVlQkhhNi0aZP49ttvxYkTJ8TZs2fFV199JRwdHcXs2bOlnJoQ4v7zO3funPjggw/E4cOHRXp6uvj1119F48aNRY8ePYzbsOT9J4R5f0eFEOLs2bNCJpOJ7du337UNS96HM2bMEPv27RPp6eni+PHjYsaMGUImk4nffvtNCGF5rz+GnCrYs2ePAHDXY+TIkUKI26eRz5o1S/j6+gqlUin69Okj0tLSTLaRnZ0thgwZIpydnYVKpRKjR48WeXl5Eszmbn83v/T09ErHAIg9e/YIIYRISkoSoaGhwtXVVdjb24vg4GDx73//2yQkSOnv5ldYWCgiIiKEt7e3sLOzE4GBgWLcuHEmpzkKYdn7T4j7/x0VQoivv/5aODg4iNzc3LvWt/R9eK+/gytWrDDWFBUViVdffVW4u7sLR0dH8eyzz4rr16+bbOfixYuiX79+wsHBQXh5eYk33njD5BRsqdxvfvfavwBEenq6EOL2ZQ3atWsnnJ2dhZOTk2jbtq1YunSpKC8vl25i/3W/+WVkZIgePXoIDw8PoVQqRdOmTcW0adOEVqs12Y6l7j8hzPs7KoQQM2fOFP7+/pXuF0veh2PGjBGBgYFCoVAIb29v0adPH2PAEcLyXn8yISzsnDQiIiKiasBjcoiIiMgqMeQQERGRVWLIISIiIqvEkENERERWiSGHiIiIrBJDDhEREVklhhwiIiKySgw5REREZJUYcoioVti7dy9kMtldN/8jIroXhhwiqhW6deuG69evw9XV1ex1CgsLMXPmTDRp0gT29vbw9vZGz5498euvv9Zgp0RkKWylboCIyBwKhQJqtbpK67zyyitISEjA559/jpCQEGRnZ+PAgQPIzs6uoS6JyJLwkxwikkSvXr0wadIkTJ48Ge7u7vD19cW3336LgoICjB49Gi4uLmjatCm2b98O4O6vq2JiYuDm5oadO3ciODgYzs7O6Nu3L65fv258jk2bNuHtt9/Gk08+iYYNG6Jjx46YNGkSxowZY6yRyWTYuHGjSW9ubm6IiYkBAFy8eBEymQw//vgjunXrBnt7e7Rq1Qr79u2r0d8PET08hhwikszKlSvh5eWFxMRETJo0CRMmTMALL7yAbt264ciRI4iIiMDw4cNRWFhY6fqFhYX45JNPsGrVKsTFxSEjIwNvvvmmcVytVmPbtm3Iy8t76F6nTZuGN954A0ePHkVYWBgGDBjAT4SILBxDDhFJpm3btnj33XfRrFkzzJw5E/b29vDy8sK4cePQrFkzzJ49G9nZ2Th+/Hil6+v1eixduhSdOnVChw4dEB0djV27dhnHv/nmGxw4cACenp7o3LkzpkyZgv379z9Qr9HR0Rg0aBCCg4OxZMkSuLq6Yvny5Q+0LSJ6NBhyiEgybdq0Mf7ZxsYGnp6eaN26tXGZr68vACArK6vS9R0dHdGkSRPjz/Xq1TOp7dGjBy5cuIBdu3bh+eefR0pKCrp3744PP/ywyr2GhYUZ/2xra4tOnTohNTW1ytshokeHIYeIJGNnZ2fys0wmM1kmk8kAAAaDwez1hRB31XTv3h3Tp0/Hb7/9hg8++AAffvghSktL77mOXq9/sAkRkUVhyCGiOiUkJARlZWUoLi4GAHh7e5scrHz27NlKjwE6ePCg8c9lZWVISkpCcHBwzTdMRA+Mp5ATkdXq1asXhgwZgk6dOsHT0xOnTp3C22+/jd69e0OlUgEAnnjiCXzxxRcICwtDeXk5pk+fftcnRADw5ZdfolmzZggODsaiRYtw69Ytk7O0iMjy8JMcIrJakZGRWLlyJSIiIhAcHIxJkyYhMjIS69atM9YsXLgQ/v7+6N69O4YOHYo333wTjo6Od21r3rx5mDdvHtq2bYs///wTmzZtgpeX16OcDhFVkUz89ctoIiIyunjxIho1aoSjR4+iXbt2UrdDRFXAT3KIiIjIKjHkEBERkVXi11VERERklfhJDhEREVklhhwiIiKySgw5REREZJUYcoiIiMgqMeQQERGRVWLIISIiIqvEkENERERWiSGHiIiIrNL/A5rkGinhIeirAAAAAElFTkSuQmCC\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } - }, - { - "cell_type": "code", - "source": [ - "print(result)" - ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a772ae1b-d41f-4961-933a-04b868a8a1b1" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maxperCount minPRcount patterns runtime memory\n", - "0 PPF_DFS 100 500 0.5 20743 135.342738 275902464\n", - "1 PPF_DFS 150 500 0.5 19017 212.406846 288145408\n", - "2 PPF_DFS 200 500 0.5 13235 288.571058 300556288\n", - "3 PPF_DFS 250 500 0.5 7674 370.978486 311742464\n", - "4 PPF_DFS 300 500 0.5 4529 471.526392 323899392\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "2f925604-6497-418a-e1d8-7e5e53df8e18" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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YnULamVzcXS08dWsLhrWvr9MyF1GmMjJ+/Hh69uxJ3bp1ycrKIikpiRUrVrBs2TIAhg4dSu3atUlMTATg1Vdf5ZlnniEpKYn69etz7Nj5Zujr64uvr/POsS8iIlWXYRjM+vUgL369nQKrjTrVvZk+OI7oiECzo9mtMpWREydOMHToUNLT0wkICCAqKoply5Zxyy23AJCWloaLy/9PXTJjxgwKCgq44447Smzn2Wef5bnnnrv69CIiInYkK6+Q8Qs28/WmdABuaVmT1+6IJsCnapyWKa+rvmakMuiaERERsXfbjmaSkJTC/lM5uLlYGNezOff/rUGVPi1TadeMiIiIVGWGYTBn3SGe+3Ir+UU2wgO8eGdwHG3qVTc7msNQGRERESmnnPwiJn6xhYUbzt/m5KZmNZg6IIbq1TxMTuZYVEZERETKYdfxLIbPSmbvyRxcXSw83q0Z/9OpIS4uVfe0THmpjIiIiJTR/OTDTPxiM3mFNmr6e/LOoDhuaHD5W53IpamMiIiIlNK5AivPLNrCvOTDAHRsEsIbA2MI8fU0OZljUxkREREphT0nskmYncLO41m4WGBM16Yk3NRYp2UqgMqIiIjIFSxKPcL4BZvJLbAS4uvJ24NiaN8oxOxYTkNlRERE5BLyCq08/9U2Pv0tDYD4hsG8NSiGUD8vk5M5F5URERGRi9h/KocRs1PYnp6JxQKjujThkZub4KrTMhVOZUREROQvvt50lHGfbyY7v4jgah68eVcMHZvUMDuW01IZERER+T/5RVZe/mY7/1lzEIAb6gfxzuBYavrrtMy1pDIiIiICpJ3OJSEphc1HMgAY0bkRj97SFDdXlyusKVdLZURERKq8pVuO8cT8jWTlFRHo484bA2K4qXmo2bGqDJURERGpsgqKbExasoOPftkPQFzdQKYNjiM80NvkZFWLyoiIiFRJh3/PZWTSBlIPnQXgwY4NeLJHc9x1WqbSqYyIiEiV88P24zw6dyMZ5wrx93Lj9QEx3NKyptmxqiyVERERqTIKrTZeW7aT93/aB0B0nQCmDY4jIsjH5GRVm8qIiIhUCekZ5xiVtIH1B38H4J729Zlwaws83HRaxmwqIyIi4vRW7DzBmM9S+T23ED9PNybfEUXPyFpmx5L/ozIiIiJOq8hq443vdzF9+V4AWtf2Z/rgOOoFVzM5mfyZyoiIiDil45l5PPzpBtbuPwPA3e3q8dRtLfBydzU5mfyVyoiIiDidX/ac4pE5GziVXUA1D1cm9Y+iV3S42bHkElRGRETEaVhtBm//sJu3f9yNYUDzMD/eHRJHwxq+ZkeTy1AZERERp3AyK5/Rn23glz2nAbjr+gie691Kp2UcgMqIiIg4vF/3nWbUpxs4mZWPt7srr/Rrze2xdcyOJaWkMiIiIg7LZjOYsXIvr3+7E5sBTUJ9eXdIHE1q+pkdTcpAZURERBzSmZwCxnyWyspdJwHoH1eHF/u2wsdDb22ORntMREQczvoDZxiZtIFjmXl4urnwYt/WDLguwuxYUk4qIyIi4jBsNoMPft7H5GU7sdoMGtaoxrtD4mge5m92NLkKKiMiIuIQzuYW8Njcjfyw4wQAfWLCefn2SHw99Vbm6LQHRUTE7qWk/c6opA0cOXsODzcXnu3VksE31MVisZgdTSqAyoiIiNgtwzD4cNV+Ji3ZQZHNoH6wD9MGx9G6doDZ0aQCqYyIiIhdyjhXyJPzN7Js63EAbousxaT+kfh5uZucTCqayoiIiNidTYfPkpCUwqEz5/BwdWHi31twd7t6Oi3jpFRGRETEbhiGwX9/PchLX2+nwGojIsib6YPjiKoTaHY0uYZURkRExC5k5RUy7vPNfLM5HYBuLWsy5c5oArx1WsbZqYyIiIjpth7NIGF2CgdO5+LmYmH8rS24r0N9nZapIlRGRETENIZh8Olvh3juq60UFNmoHejNO4Njiatb3exoUolURkRExBQ5+UVMWLiZRalHAbi5eSivD4gm0MfD5GRS2VRGRESk0u08lsWI2cnsPZmDq4uFJ7o3458dG+LiotMyVZHKiIiIVKq56w/xzKIt5BXaCPP34p3BsVxfP8jsWGIilREREakUuQVFPP3FVj5POQxAp6Y1eGNANMG+niYnE7OpjIiIyDW350QWw2elsPtENi4WePSWpozo3FinZQRQGRERkWts4YbDTFiwhXOFVmr4efL2XbHENwo2O5bYEZURERG5JvIKrTz35VbmrDsEQIfGwbw5MJYafjotIyWpjIiISIXbdzKbEbNT2HEsC4sFHrm5CaO6NMFVp2XkIlRGRESkQn218SjjPt9EToGVEF8P3hwYy9+ahJgdS+yYyoiIiFSIvEIrL32zjVm/pgHQtkEQbw+Kpaa/l8nJxN6pjIiIyFU7eDqHhKQUthzJBCDhpkaM6doUN1cXk5OJI1AZERGRq7J0SzpPzNtEVn4R1X3ceWNgDJ2bhZodSxyIyoiIiJRLbkERiYt38N9fDwLQpl513hkUS3igt8nJxNGojIiISJltSPudR+duZP+pHAD+2akhT3RvhrtOy0g5qIyIiEipFVptTPtxD9OW78FqMwjz9+K1O6P1aRm5KiojIiJSKntPZvPoZ6lsPJwBQO/ocF7s05oAH3eTk4mjUxkREZHLMgyD//56kFcWbyev0Ia/lxsv3R5J7+hws6OJk1AZERGRSzqemccT8zfx066TAPytcQhT7oyiVoAuUpWKozIiIiIX9c2mdJ76YjNncwvxdHNhfM/mDI2vrzvtSoVTGRERkRIyzhXy3JdbWbjhCACta/vz5sAYGof6mZxMnJXKiIiIFFu95xSPz9vI0Yw8XCyQcFNjRnVpgoebPrIr106ZfrtmzJhBVFQU/v7++Pv7Ex8fz5IlSy65/NatW+nfvz/169fHYrHw5ptvXm1eERG5BvIKrbz49TYG/3stRzPyqBfsw7yH2vNYt2YqInLNlek3rE6dOkyaNInk5GTWr19Ply5d6NOnD1u3br3o8rm5uTRs2JBJkyYRFhZWIYFFRKRibTmSQe9pq/hw1X4ABrety+KHO9KmXnWTk0lVYTEMw7iaDQQFBTFlyhTuv//+yy5Xv359Ro8ezejRo8v8MzIzMwkICCAjIwN/f/9yJhURkT+z2gze/2kvb3y3i0KrQYivJ5PviKRL85pmRxMnUdr373JfM2K1Wpk3bx45OTnEx8eXdzMXlZ+fT35+fvH3mZmZFbp9EZGqLu10Lo/OTWX9wd8B6N6qJq/cHkmwr6fJyaQqKnMZ2bx5M/Hx8eTl5eHr68vChQtp2bJlhYZKTEzk+eefr9BtiojI+QnM5q4/xAtfbSOnwIqvpxvP9mrJHW3qYLHoI7tijjJfldSsWTNSU1NZu3Ytw4cPZ9iwYWzbtq1CQ40fP56MjIzir0OHDlXo9kVEqqJT2fk8+J9kxn6+mZwCKzfUD2LJIx2587oIFRExVZmPjHh4eNC4cWMA2rRpw7p163jrrbd4//33KyyUp6cnnp46VCgiUlG+23accZ9v4nROAe6uFh7v1owHOjbEVROYiR246nlGbDZbies7RETEfmTnF/HiV9v4bP35I8zNw/yYOiCGluH6MIDYjzKVkfHjx9OzZ0/q1q1LVlYWSUlJrFixgmXLlgEwdOhQateuTWJiIgAFBQXFp3AKCgo4cuQIqamp+Pr6Fh9dERGRa2P9gTOMmZvKoTPnsFjgnx0b8mi3pni6uZodTaSEMpWREydOMHToUNLT0wkICCAqKoply5Zxyy23AJCWloaLy/9fhnL06FFiY2OLv3/ttdd47bXXuPHGG1mxYkXFjEBEREooKLLx5ve7eG/lXmwG1A705vUB0bRrGGx2NJGLuup5RiqD5hkRESmdXcezGD0nlW3p56dE6B9Xh2d7t8Tfy93kZFIVXfN5RkRExH7YbAYf/bKfyct2UlBko7qPO6/cHknPyFpmRxO5IpUREREHd+TsOZ6Yt5HVe08DcFOzGrzaP4pQfy+Tk4mUjsqIiIiDMgyDRalHeXrRFrLyivB2d2Xi31sw+Ia6mjdEHIrKiIiIAzqbW8BTX2zhm03pAMREBPLGwBgahFQzOZlI2amMiIg4mJW7TvLEvI2cyMrH1cXCIzc3YUTnRri5lnlSbRG7oDIiIuIgzhVYSVyynf+sOQhAwxrVeHNgDFF1As0NJnKVVEZERBzAxkNnGfNZKvtO5QBwT/v6jO3RHG8PTWAmjk9lRETEjhVZbUxfvpe3f9yN1WZQ09+TKXdE06lpDbOjiVQYlRERETu172Q2Y+ZuZOOhswDcFlWLl/u2JtDHw9xgIhVMZURExM4YhsGstWm8/M028gpt+Hm58VLf1vSODtdHdsUpqYyIiNiRE5l5PPn5JlbsPAlAh8bBTLkjmvBAb5OTiVw7KiMiInZiyeZ0xi/czNncQjzcXBjXozn3tK+Pi4uOhohzUxkRETFZZl4hzy3ayoINRwBoFe7PmwNjaFLTz+RkIpVDZURExERr9p7m8XkbOXL2HC4WGNG5MQ/f3AQPN01gJlWHyoiIiAnyCq28/u1O/r1qP4YBdYN8eGNgNG3qBZkdTaTSqYyIiFSybUczGfNZKjuPZwEw6IYIJt7Wkmqe+idZqib95ouIVBKrzeBfP+1j6nc7KbQahPh6MKlfFF1b1jQ7moipVEZERCrBoTO5PDo3lXUHfgfglpY1mdQvkmBfT5OTiZhPZURE5BoyDIN5yYd5/sut5BRYqebhyrO9WnHndXU0gZnI/1EZERG5Rk5l5zN+wWa+23YcgOvrV2fqgBgignxMTiZiX1RGRESuge+3HWfcgk2cyi7A3dXCo7c045+dGuKqCcxELqAyIiJSgXLyi3jpm218+tshAJrW9OWNgTG0Cg8wOZmI/VIZERGpIMkHzzDms42kncnFYoH7OzTg8e7N8HJ3NTuaiF1TGRERuUoFRTbe+mEXM1bsxWZAeIAXrw2Ipn2jELOjiTgElRERkauw+3gWoz9LZevRTAD6xdbm2d6tCPB2NzmZiONQGRERKQebzWDm6gNMWrqDgiIbgT7uvHJ7JLdG1jI7mojDURkRESmj9IxzPD5vI7/sOQ3AjU1rMOWOKEL9vUxOJuKYVEZERMpgUeoRnv5iC5l5RXi5u/DUbS35R9u6msBM5CqojIiIlMLZ3AKeXrSVrzYeBSC6TgBvDIyhYQ1fk5OJOD6VERGRK/h590ken7eR45n5uLpYGNWlMQk3Ncbd1cXsaCJOQWVEROQSzhVYeXXpDmauPgBAw5BqvDEwhuiIQFNziTgblRERkYvYdPgsYz5LZe/JHACGxtdjfM8WeHtoAjORiqYyIiLyJ0VWG++u2MvbP+ymyGYQ6ufJ5Dui6Nws1OxoIk5LZURE5P/sP5XDmM9SST10FoDbImvxUt/WVK/mYW4wESenMiIiVZ5hGCT9lsZLX2/nXKEVPy83XuzTmj4x4frIrkglUBkRkSrtRFYeY+dvYvnOkwDENwzmtQHR1A70NjmZSNWhMiIiVdbSLemMX7CZ33ML8XBz4cnuzbivQwNcXHQ0RKQyqYyISJWTmVfI819u4/OUwwC0qOXPmwNjaBbmZ3IykapJZUREqpRf953msbkbOXL2HBYLPHRjI8Z0bYqHmyYwEzGLyoiIVAn5RVamfruLf/28D8OAiCBvpg6I4fr6QWZHE6nyVEZExOltT89kzGep7DiWBcDA6yJ4uldLfD31T6CIPdArUUScltVm8O+f9/H6t7sosNoIrubBpP5R3NKyptnRRORPVEZExCkdOpPLY/M28tv+MwB0bVGTSf0jCfH1NDmZiPyVyoiIOBXDMJiffJjnv9pGdn4RPh6uPNurJQOui9AEZiJ2SmVERJzG6ex8JizczLKtxwFoU686UwdEUy+4msnJRORyVEZExCn8uOM4T87fzKnsfNxcLIy5pSkP3dgIV01gJmL3VEZExKHl5Bfx8uLtJK1NA6BJqC9vDIyhde0Ak5OJSGmpjIiIw0pJ+51HP0vlwOlcAO7/WwOe6N4ML3dXk5OJSFmojIiIwym02nj7h91MX74HmwG1Arx47c5oOjQOMTuaiJSDyoiIOJQ9J7IY89lGNh/JAKBvTDjP92lNgLe7yclEpLxURkTEIdhsBv9Zc4DEJTvIL7IR4O3Oy7e35u9R4WZHE5GrpDIiInbvWEYeT8zfyM+7TwHQsUkIU+6IJizAy+RkIlIRVEZExK59tfEoTy3cTGZeEV7uLky4tQV3t6unCcxEnIjKiIjYpYzcQp5etIUvNx4FILpOAFMHxtCohq/JyUSkoqmMiIjdWbX7FI/P28ixzDxcXSyMvKkxI7s0xt3VxexoInINqIyIiN3IK7Ty6tIdfPzLAQAahFRj6oBoYutWNzeYiFxTKiMiYhc2H85gzNxU9pzIBuAf7eoy4dYW+HjonykRZ6dXuYiYqshq472Ve3nz+90U2Qxq+Hky+Y4obmoWanY0EakkKiMiYprDv+fy8KcbSEk7C0DP1mG8fHskQdU8zA0mIpWqTFeDzZgxg6ioKPz9/fH39yc+Pp4lS5Zcdp158+bRvHlzvLy8iIyMZPHixVcVWEScw4qdJ/j7O6tISTuLn6cbUwdE8+6QOBURkSqoTGWkTp06TJo0ieTkZNavX0+XLl3o06cPW7duvejyq1evZtCgQdx///1s2LCBvn370rdvX7Zs2VIh4UXE8VhtBlO/28W9M9dxNreQqDoBLH6kI/3i6mjuEJEqymIYhnE1GwgKCmLKlCncf//9Fzw3cOBAcnJy+Prrr4sfa9euHTExMbz33nul/hmZmZkEBASQkZGBv7//1cQVEROdySngkTkbimdSHdK2Ls/0aomnm+6yK+KMSvv+Xe5rRqxWK/PmzSMnJ4f4+PiLLrNmzRoeffTREo91796dL7744rLbzs/PJz8/v/j7zMzM8sYUETuRkvY7CbNTSM/Iw8vdhVduj6RfXB2zY4mIHShzGdm8eTPx8fHk5eXh6+vLwoULadmy5UWXPXbsGDVr1izxWM2aNTl27Nhlf0ZiYiLPP/98WaOJiB0yDIP/rDnIS99so9Bq0DCkGjP+0YZmYX5mRxMRO1Hm6QybNWtGamoqa9euZfjw4QwbNoxt27ZVaKjx48eTkZFR/HXo0KEK3b6IVI6c/CIenpPKs19updBq0LN1GItGdlAREZESynxkxMPDg8aNGwPQpk0b1q1bx1tvvcX7779/wbJhYWEcP368xGPHjx8nLCzssj/D09MTT0/PskYTETuy50QWD81KYc+JbFxdLIzv2Zz7/9ZAF6mKyAWu+kYPNputxPUdfxYfH88PP/xQ4rHvvvvukteYiIhz+GrjUXpP+4U9J7IJ9fNkzj/b8UDHhioiInJRZToyMn78eHr27EndunXJysoiKSmJFStWsGzZMgCGDh1K7dq1SUxMBOCRRx7hxhtv5PXXX+e2225jzpw5rF+/nn/9618VPxIRMV1BkY1XFm9n5uoDAMQ3DObtQbHU8NORThG5tDKVkRMnTjB06FDS09MJCAggKiqKZcuWccsttwCQlpaGi8v/H2xp3749SUlJTJw4kQkTJtCkSRO++OILWrduXbGjEBHTpWecI2F2SvFsqsM7N+KxW5ripjvtisgVXPU8I5VB84yI2LdVu0/x8JwNnMkpwM/LjakDYrilZc0rrygiTu2azzMiImKzGUxfvoep3+/CMKBVuD8zhrShbrCP2dFExIGojIhIuZzNLWDMZ6ks33kSgIHXRfB8n1Z4uWs2VREpG5URESmzTYfPMnxWCkfOnsPTzYUX+7ZmwHURZscSEQelMiIipWYYBkm/pfH8l9sosNqoG+TDjH/E0So8wOxoIuLAVEZEpFTOFVh56ovNLEg5AkDXFjV5fUA0Ad7uJicTEUenMiIiV7T/VA7DZyWz41gWLhZ4skdz/qeTJjETkYqhMiIil7V0SzqPz9tEdn4RIb6evDMolvhGwWbHEhEnojIiIhdVaLUxeekOPvh5PwA31A9i2uBYQv29TE4mIs5GZURELnA8M4+RSSmsO/A7AP/s1JAnujfDXbOpisg1oDIiIiWs2XuaUZ9u4FR2Pr6ebrx2ZxQ9WtcyO5aIODGVEREBzn9s972V+5iybAc2A5qH+fHukDga1vA1O5qIODmVEREh41whj8/byHfbjgPQL642L/eNxNtDs6mKyLWnMiJSxW09msGI2SkcPJ2Lh6sLz/VuxaAbIvSxXRGpNCojIlXY3HWHeHrRFvKLbNSp7s2MIW2IrKPZVEWkcqmMiFRBeYVWnlm0hbnrDwNwU7MavDEwhkAfD5OTiUhVpDIiUsWknc7loVnJbEvPxGKBx25pyojOjXFx0WkZETGHyohIFfLdtuM8OjeVrLwigqp58PZdsfytSYjZsUSkilMZEakCiqw2Xv9uFzNW7AUgrm4g04fEUSvA2+RkIiIqIyJO72RWPg9/uoE1+04DcE/7+ky4tQUebppNVUTsg8qIiBNbd+AMCbNTOJGVTzUPVyb1j6JXdLjZsURESlAZEXFChmHw4ar9JC7ZgdVm0CTUlxn/aEPjUM2mKiL2R2VExMlk5RXy5PxNLNlyDIDe0eEk9oukmqde7iJin/Svk4gT2XEsk+GzUth/Kgd3VwtP/70ld7erp9lURcSuqYyIOIkFKYeZsHAzeYU2wgO8mD4kjti61c2OJSJyRSojIg4uv8jKC19tY/baNAA6NgnhrbtiCaqm2VRFxDGojIg4sENncklISmHT4QwsFni4SxMevrkJrppNVUQciMqIiINavvMEYz5L5WxuIYE+7rw5MIbOzULNjiUiUmYqIyIOxmozeOv7XbyzfA+GAdF1Apg+JI461X3MjiYiUi4qIyIO5HR2PqM/S+Xn3acAuLtdPSb+vQWebq4mJxMRKT+VEREHkZL2OwmzU0jPyMPb3ZXEfpH0ja1tdiwRkaumMiJi5wzD4JPVB3h58XYKrQYNQ6ox4x9taBbmZ3Y0EZEKoTIiYsdy8osYt2AzX208CsCtkWG82j8KPy93k5OJiFQclRERO7XnRBYPzUphz4ls3FwsjL+1Bfd1qK/ZVEXE6aiMiNihLzceZdznm8gtsFLT35Ppg+O4rn6Q2bFERK4JlRERO1JQZOOVxduZufoAAO0bBfP2oFhCfD3NDSYicg2pjIjYiaNnz5GQlMKGtLMAJNzUiEdvaabZVEXE6amMiNiBn3ef5JE5qZzJKcDfy42pA2Lo2rKm2bFERCqFyoiIiWw2g2nL9/DG97swDGgV7s+MIW2oG6zZVEWk6lAZETHJ7zkFjJmbyoqdJwEYdEMEz/ZqhZe7ZlMVkapFZUTEBJsOn2X4rBSOnD2Hp5sLL/VtzZ3XRZgdS0TEFCojIpXIMAySfkvj+S+3UWC1US/YhxlD2tAy3N/saCIiplEZEakk5wqsPLVwMws2HAGgW8uaTLkzmgBvzaYqIlWbyohIJdh3Mpvhs1LYeTwLVxcLT3Zvxj87NdRsqiIiqIyIXHNLNqfzxPxNZOcXEeLrybTBsbRrGGx2LBERu6EyInKNFFptvLpkB/9etR+AG+oHMW1wLKH+XiYnExGxLyojItfA8cw8RialsO7A7wD8T6eGPNG9GW6uLiYnExGxPyojIhVszd7TjPo0hVPZBfh5ujHlzmh6tA4zO5aIiN1SGRGpIDabwXs/7eW1ZTuxGdA8zI8Z/2hDg5BqZkcTEbFrKiMiFSDjXCGPzd3I99uPA9A/rg4v9W2Nt4dmUxURuRKVEZGrtPVoBsNnpZB2JhcPNxee792Ku66P0Md2RURKSWVE5Cp8ti6NpxdtpaDIRp3q3swY0obIOgFmxxIRcSgqIyLlkFdo5ZlFW5i7/jAAXZqHMnVANIE+HiYnExFxPCojImV08HQOw2elsC09ExcLPNatGcNvbISLi07LiIiUh8qISBl8u/UYj83bSFZeEcHVPHh7UCwdGoeYHUtExKGpjIiUQpHVxmvf7uK9lXsBaFOvOtMHxxEWoNlURUSulsqIyBWczMpn1Kcp/LrvDAD3dWjA+Fub467ZVEVEKoTKiMhlrDtwhoTZKZzIyqeahyuv3hHF36PCzY4lIuJUVEZELsIwDD5ctZ/EJTuw2gyahPoy4x9taBzqa3Y0ERGnozIi8hdZeYU8OX8TS7YcA6BPTDiv3B5JNU+9XEREroUynfROTEzk+uuvx8/Pj9DQUPr27cvOnTsvu05hYSEvvPACjRo1wsvLi+joaJYuXXpVoUWulR3HMuk97ReWbDmGu6uFF/u25s2BMSoiIiLXUJnKyMqVK0lISODXX3/lu+++o7CwkG7dupGTk3PJdSZOnMj777/PO++8w7Zt23jooYe4/fbb2bBhw1WHF6lIC1IO03f6L+w/lUPtQG/mPdSeu9vV07TuIiLXmMUwDKO8K588eZLQ0FBWrlxJp06dLrpMeHg4Tz31FAkJCcWP9e/fH29vb2bNmlWqn5OZmUlAQAAZGRn4+/uXN67IReUVWnnh620krU0DoFPTGrw5MIagappNVUTkapT2/fuqjj1nZGQAEBQUdMll8vPz8fIqOReDt7c3q1atuuw6+fn5xd9nZmZeTUyRSzp0JpcRs1PYfCQDiwUeubkJo7o0wVWzqYqIVJpyT5Rgs9kYPXo0HTp0oHXr1pdcrnv37kydOpXdu3djs9n47rvvWLBgAenp6ZdcJzExkYCAgOKviIiI8sYUuaTlO07w93dWsflIBoE+7sy89wZGd22qIiIiUsnKfZpm+PDhLFmyhFWrVlGnTp1LLnfy5EkefPBBvvrqKywWC40aNaJr16589NFHnDt37qLrXOzISEREhE7TSIWw2gze+n4Xb/+4B4DoiEDeHRJH7UBvk5OJiDiXa3qaZuTIkXz99df89NNPly0iADVq1OCLL74gLy+P06dPEx4ezrhx42jYsOEl1/H09MTT07M80UQu63R2PqM/S+Xn3acAGBpfj6dua4Gnm6vJyUREqq4ylRHDMBg1ahQLFy5kxYoVNGjQoNTrenl5Ubt2bQoLC/n8888ZMGBAmcOKXI3kg78zMimF9Iw8vN1dmdQ/kj4xtc2OJSJS5ZWpjCQkJJCUlMSiRYvw8/Pj2LHzk0IFBATg7X3+EPfQoUOpXbs2iYmJAKxdu5YjR44QExPDkSNHeO6557DZbDz55JMVPBSRizMMg5mrD/DyN9spshk0rFGN9/7RhqY1/cyOJiIilLGMzJgxA4DOnTuXePzjjz/mnnvuASAtLQ0Xl/+/LjYvL4+JEyeyb98+fH19ufXWW/nvf/9LYGDgVQUXKY2c/CLGfr6Jrzedv2D6tqhavNo/Cl9NYiYiYjeuap6RyqJ5RqQ8dh/P4qFZyew9mYObi4UJt7bg3g71NYmZiEglqZR5RkTs1ZcbjzLu803kFlip6e/Ju0PiaFPv0vPhiIiIeVRGxKkUFNl4+ZttfLLmIADtGwXz9qBYQnz16SwREXulMiJO4+jZcyQkpbAh7SwAI29qzJhbNImZiIi9UxkRp/Dz7pM8MieVMzkFBHi788bAaLo0r2l2LBERKQWVEXFoNpvBOz/u4c0fdmEY0Lq2PzOGtCEiyMfsaCIiUkoqI+Kwfs8pYMzcVFbsPAnAoBvq8myvlni5azZVERFHojIiDikl7XdGzk7haEYenm4uvHx7JHe0ufytCURExD6pjIhDMQyDj345QOLi87OpNgipxrtD4mhRS/PPiIg4KpURcRiZeYU8OW8TS7eevw3BbVG1mNQvEj8vd5OTiYjI1VAZEYew5UgGCUkpHDydi7urhYm3tWRofD3Npioi4gRURsSuGYbBp78d4rmvtlJQZKN2oDfvDokjOiLQ7GgiIlJBVEbEbuUWFDFx4RYWbDgCwM3NQ3l9QDSBPh4mJxMRkYqkMiJ2affxLEbMTmH3iWxcXSw80b0Z/+zYEBfNpioi4nRURsTufLHhCOMXbOZcoZVQP0/eGRRL24bBZscSEZFrRGVE7EZeoZUXvt5G0to0ADo0Duatu3STOxERZ6cyInbh4OkcRsxOYevRTCwWGNWlCY/c3EQ3uRMRqQJURsR0S7cc44n5G8nKKyKomgdvDoyhU9MaZscSEZFKojIipim02pi0ZAcfrtoPwHX1qvPO4FhqBXibnExERCqTyoiY4ujZc4xMSiEl7SwA/+zUkCe6N8Pd1cXcYCIiUulURqTSrdh5gjGfpfJ7biF+Xm68fmc03VqFmR1LRERMojIilcZqM3jz+11MW74Hw4DI2gFMHxxH3WAfs6OJiIiJVEakUpzIyuORT1NZs+80AHe3q8dTt7XAy93V5GQiImI2lRG55n7dd5pRn27gZFY+Ph6uJPaLpE9MbbNjiYiInVAZkWvGZjOYsXIvr3+7E5sBTWv68u6QNjQO9TU7moiI2BGVEbkmfs8p4NG5qSzfeRKAfnG1ealva3w89CsnIiIl6Z1BKtyGtN8ZmbSBI2fP4enmwgt9WjHguggsFs2mKiIiF1IZkQpjGAYzVx/glcXbKbQa1A/24d0hbWgZ7m92NBERsWMqI1IhMvMKGff5JhZvPgbArZFhvNo/Cj8vd5OTiYiIvVMZkau27WgmI2Ync+B0Lu6uFp66tQXD2tfXaRkRESkVlREpN8MwmLv+EM8s2kp+kY3agd5MGxxLbN3qZkcTEREHojIi5ZJbUMTEL7awIOUIADc1q8HUATFUr+ZhcjIREXE0KiNSZntOZDNidjK7jmfjYoHHuzfjoU6NcHHRaRkRESk7lREpk0WpRxi/YDO5BVZq+HnyzqBY2jUMNjuWiIg4MJURKZW8QisvfbONWb+mAdC+UTBv3RVLDT9Pk5OJiIijUxmRK0o7ncuIpGS2HMnEYoFRNzXmka5NcdVpGRERqQAqI3JZy7Ye4/F5G8nKK6K6jztvDIyhc7NQs2OJiIgTURmRiyq02pi8dAcf/LwfgLi6gUwbHEd4oLfJyURExNmojMgF0jPOMTJpA8kHfwfggb81YGzP5ri7upicTEREnJHKiJTw066TjP4slTM5Bfh5uTHljmh6tA4zO5aIiDgxlREBwGozeOv7XbyzfA+GAa3C/Xl3SBz1gquZHU1ERJycyohwMiuf0Z9t4Jc9pwEY3LYuz/y9JV7uriYnExGRqkBlpIpbu+80oz7dwImsfHw8XHnl9kj6xtY2O5aIiFQhKiNVlM1m8P5P+3jt251YbQZNQn2Z8Y84Gof6mR1NRESqGJWRKuhsbgGPzd3IDztOAHB7bG1evr01Ph76dRARkcqnd58qJvXQWRJmp3Dk7Dk83Fx4vncr7ro+AotFs6mKiIg5VEaqCMMw+GT1AV5evJ1Cq0G9YB/eHRJHq/AAs6OJiEgVpzJSBWTlFTLu8818szkdgB6twph8ZxT+Xu4mJxMREVEZcXrb0zMZMTuF/adycHOxMOHWFtzbob5Oy4iIiN1QGXFic9cd4ulFW8gvshEe4MW0IXHE1a1udiwREZESVEac0LkCK08v2sL85MMAdG5WgzcGxFC9mofJyURERC6kMuJk9p7MZsSsFHYez8LFAo91a8bwGxvh4qLTMiIiYp9URpzIVxuPMu7zTeQUWAnx9eTtQTG0bxRidiwREZHLUhlxAvlFVl76ejv//fUgAO0aBvH2oFhC/bxMTiYiInJlKiMO7tCZXBKSUth0OAOAhJsaMaZrU9xcXUxOJiIiUjoqIw7su23HeWxuKpl5RQT6uPPGwBhuahZqdiwREZEyURlxQIVWG68t28n7P+0DILZuINMGx1E70NvkZCIiImWnMuJgjmXkMerTFNYd+B2A+zo0YFzP5ni46bSMiIg4JpURB/Lz7pM8MieVMzkF+Hm6MfmOKHpG1jI7loiIyFVRGXEAVpvB2z/s5u0fd2MY0LKWP+8OiaN+SDWzo4mIiFy1Mh3bT0xM5Prrr8fPz4/Q0FD69u3Lzp07r7jem2++SbNmzfD29iYiIoIxY8aQl5dX7tBVyansfIZ99Btv/XC+iAy6IYIFI9qriIiIiNMo05GRlStXkpCQwPXXX09RURETJkygW7dubNu2jWrVLv7mmJSUxLhx4/joo49o3749u3bt4p577sFisTB16tQKGYSzWnfgDCOTUjiemY+3uysv396afnF1zI4lIiJSocpURpYuXVri+5kzZxIaGkpycjKdOnW66DqrV6+mQ4cODB48GID69eszaNAg1q5dW87Izs8wDP710z4mL9uJ1WbQONSXd4fE0bSmn9nRREREKtxVfQQjI+P8RFtBQUGXXKZ9+/YkJyfz22+/AbBv3z4WL17Mrbfeesl18vPzyczMLPFVVWTkFvLgf9aTuGQHVptBn5hwFiV0UBERERGnVe4LWG02G6NHj6ZDhw60bt36kssNHjyYU6dO8be//Q3DMCgqKuKhhx5iwoQJl1wnMTGR559/vrzRHNbGQ2dJSErh8O/n8HB14dneLRl8Q10sFt3kTkREnFe5j4wkJCSwZcsW5syZc9nlVqxYwSuvvMK7775LSkoKCxYs4JtvvuHFF1+85Drjx48nIyOj+OvQoUPljekQDMPgP2sOcOd7azj8+znqBvmwYER7hrStpyIiIiJOz2IYhlHWlUaOHMmiRYv46aefaNCgwWWX7dixI+3atWPKlCnFj82aNYt//vOfZGdn4+Jy5T6UmZlJQEAAGRkZ+Pv7lzWuXcvOL2L8gs18tfEoAN1b1WTyHdEEeLubnExEROTqlPb9u0ynaQzDYNSoUSxcuJAVK1ZcsYgA5ObmXlA4XF1di7dXle04lsmIWSnsO5WDm4uFcT2bc//fGuhoiIiIVCllKiMJCQkkJSWxaNEi/Pz8OHbsGAABAQF4e5+/L8rQoUOpXbs2iYmJAPTq1YupU6cSGxtL27Zt2bNnD08//TS9evUqLiVV0bz1h3h60RbyCm3UCvBi2uBY2tS79IXAIiIizqpMZWTGjBkAdO7cucTjH3/8Mffccw8AaWlpJY6ETJw4EYvFwsSJEzly5Ag1atSgV69evPzyy1eX3EGdK7DyzKItzEs+DECnpjV4c2AMQdU8TE4mIiJijnJdM1LZnOWakX0nsxkxO4Udx7JwscCYrk1JuKkxLi46LSMiIs7nmlwzIuX39aajjJ2/iZwCKyG+Hrx9VyztG4eYHUtERMR0KiPXWH6RlVe+2c4naw4CcEODIKYNiiXU38vkZCIiIvZBZeQaOnQml5FJKWw8fH6m2hGdG/HoLU1xc72qiW9FREScisrINfLD9uM8OncjGecKCfB2542B0XRpXtPsWCIiInZHZaSCFVltTPl2J++v3AdAdEQg0wfHUqe6j8nJRERE7JPKSAU6npnHqKQN/HbgDAD3tK/PhFtb4OGm0zIiIiKXojJSQVbtPsUjczZwOqcAX083Xu0fxW1RtcyOJSIiYvdURq6S1WYw7cc9vPnDLgwDmof58e6QOBrW8DU7moiIiENQGbkKp7PzGf1ZKj/vPgXAXddH8FzvVni5V91p7kVERMpKZaSc1h84w8ikDRzLzMPL3YWX+kZyR5s6ZscSERFxOCojZWQYBv/+eT+Tlu7AajNoWKMaM4a0oVmYn9nRREREHJLKSBlk5Bby+PyNfLftOAC9osNJ7BeJr6f+GkVERMpL76KltPlwBiOSkjl05hweri483asl/2hbF4tFN7kTERG5GiojV2AYBrPWpvHiV9sosNqICPLm3cFtiKwTYHY0ERERp6AychnZ+UVMWLCZLzceBeCWljV57Y5oAnzcTU4mIiLiPFRGLmHnsSyGz05m38kcXF0sjOvRnAc6NtBpGRERkQqmMnIR85MPM/GLzeQV2gjz92La4Fiuqx9kdiwRERGnpDLyJ3mFVp5dtJXP1h8CoGOTEN4cGEOwr6fJyURERJyXysj/2X8qh+GzktlxLAuLBUbf3JSRXRrj6qLTMiIiIteSygiweHM6T87fRHZ+EcHVPHjrrlj+1iTE7FgiIiJVQpUuIwVFNl5ZvJ2Zqw8AcH396rwzKI6wAC9zg4mIiFQhVbaMnCuwctcHv7Lx0FkAHrqxEY93a4qbq4u5wURERKqYKltGvD1caRHmx4FTObx+ZzRdW9Y0O5KIiEiVZDEMwzA7xJVkZmYSEBBARkYG/v7+FbbdvEIrp7LzqVPdp8K2KSIiIueV9v27Sp+T8HJ3VRERERExWZUuIyIiImI+lRERERExlcqIiIiImEplREREREylMiIiIiKmUhkRERERU6mMiIiIiKlURkRERMRUKiMiIiJiKpURERERMZXKiIiIiJhKZURERERMpTIiIiIipnIzO0BpGIYBnL8VsYiIiDiGP963/3gfvxSHKCNZWVkAREREmJxEREREyiorK4uAgIBLPm8xrlRX7IDNZuPo0aP4+flhsVgqbLuZmZlERERw6NAh/P39K2y79sTZx6jxOT5nH6PG5/icfYzXcnyGYZCVlUV4eDguLpe+MsQhjoy4uLhQp06da7Z9f39/p/wF+zNnH6PG5/icfYwan+Nz9jFeq/Fd7ojIH3QBq4iIiJhKZURERERMVaXLiKenJ88++yyenp5mR7lmnH2MGp/jc/YxanyOz9nHaA/jc4gLWEVERMR5VekjIyIiImI+lRERERExlcqIiIiImMopy8hPP/1Er169CA8Px2Kx8MUXX5R43jAMnnnmGWrVqoW3tzddu3Zl9+7dJZY5c+YMQ4YMwd/fn8DAQO6//36ys7MrcRSXdrnxFRYWMnbsWCIjI6lWrRrh4eEMHTqUo0ePlthG/fr1sVgsJb4mTZpUySO5uCvtv3vuueeC7D169CixjD3vP7jyGP86vj++pkyZUryMPe/DxMRErr/+evz8/AgNDaVv377s3LmzxDJ5eXkkJCQQHByMr68v/fv35/jx4yWWSUtL47bbbsPHx4fQ0FCeeOIJioqKKnMoF3Wl8Z05c4ZRo0bRrFkzvL29qVu3Lg8//DAZGRkltnOxfTxnzpzKHs4FSrP/OnfufEH2hx56qMQy9rr/4MpjPHDgwCVfh/PmzStezl734YwZM4iKiiqeOyQ+Pp4lS5YUP29vrz+nLCM5OTlER0czffr0iz4/efJk3n77bd577z3Wrl1LtWrV6N69O3l5ecXLDBkyhK1bt/Ldd9/x9ddf89NPP/HPf/6zsoZwWZcbX25uLikpKTz99NOkpKSwYMECdu7cSe/evS9Y9oUXXiA9Pb34a9SoUZUR/4qutP8AevToUSL7p59+WuJ5e95/cOUx/nls6enpfPTRR1gsFvr3719iOXvdhytXriQhIYFff/2V7777jsLCQrp160ZOTk7xMmPGjOGrr75i3rx5rFy5kqNHj9KvX7/i561WK7fddhsFBQWsXr2aTz75hJkzZ/LMM8+YMaQSrjS+o0ePcvToUV577TW2bNnCzJkzWbp0Kffff/8F2/r4449L7MO+fftW8mguVJr9B/Dggw+WyD558uTi5+x5/8GVxxgREXHB6/D555/H19eXnj17ltiWPe7DOnXqMGnSJJKTk1m/fj1dunShT58+bN26FbDD15/h5ABj4cKFxd/bbDYjLCzMmDJlSvFjZ8+eNTw9PY1PP/3UMAzD2LZtmwEY69atK15myZIlhsViMY4cOVJp2Uvjr+O7mN9++80AjIMHDxY/Vq9ePeONN964tuEqwMXGN2zYMKNPnz6XXMeR9p9hlG4f9unTx+jSpUuJxxxlHxqGYZw4ccIAjJUrVxqGcf415+7ubsybN694me3btxuAsWbNGsMwDGPx4sWGi4uLcezYseJlZsyYYfj7+xv5+fmVO4Ar+Ov4Lmbu3LmGh4eHUVhYWPxYafa9PbjY+G688UbjkUceueQ6jrT/DKN0+zAmJsa47777SjzmKPvQMAyjevXqxr///W+7fP055ZGRy9m/fz/Hjh2ja9euxY8FBATQtm1b1qxZA8CaNWsIDAzkuuuuK16ma9euuLi4sHbt2krPfLUyMjKwWCwEBgaWeHzSpEkEBwcTGxvLlClT7ObwaWmsWLGC0NBQmjVrxvDhwzl9+nTxc862/44fP84333xz0f9VO8o+/OP0RFBQEADJyckUFhaWeB02b96cunXrlngdRkZGUrNmzeJlunfvTmZmZvH/7uzFX8d3qWX8/f1xcyt5F46EhARCQkK44YYb+Oijj654d1MzXGp8s2fPJiQkhNatWzN+/Hhyc3OLn3Ok/QdX3ofJycmkpqZe9HVo7/vQarUyZ84ccnJyiI+Pt8vXn0Pcm6YiHTt2DKDEX/Af3//x3LFjxwgNDS3xvJubG0FBQcXLOIq8vDzGjh3LoEGDStxz4OGHHyYuLo6goCBWr17N+PHjSU9PZ+rUqSamLZ0ePXrQr18/GjRowN69e5kwYQI9e/ZkzZo1uLq6OtX+A/jkk0/w8/MrcQgVHGcf2mw2Ro8eTYcOHWjdujVw/jXm4eFxQUH+6+vwYq/TP56zFxcb31+dOnWKF1988YJThS+88AJdunTBx8eHb7/9lhEjRpCdnc3DDz9cGdFL5VLjGzx4MPXq1SM8PJxNmzYxduxYdu7cyYIFCwDH2X9Qun344Ycf0qJFC9q3b1/icXveh5s3byY+Pp68vDx8fX1ZuHAhLVu2JDU11e5ef1WujFQlhYWFDBgwAMMwmDFjRonnHn300eI/R0VF4eHhwf/8z/+QmJho97MM3nXXXcV/joyMJCoqikaNGrFixQpuvvlmE5NdGx999BFDhgzBy8urxOOOsg8TEhLYsmULq1atMjvKNXGl8WVmZnLbbbfRsmVLnnvuuRLPPf3008V/jo2NJScnhylTptjFG9kfLjW+PxeryMhIatWqxc0338zevXtp1KhRZce8Klfah+fOnSMpKanE/vqDPe/DZs2akZqaSkZGBvPnz2fYsGGsXLnS7FgXVeVO04SFhQFccNXw8ePHi58LCwvjxIkTJZ4vKirizJkzxcvYuz+KyMGDB/nuu++ueCfGtm3bUlRUxIEDByonYAVq2LAhISEh7NmzB3CO/feHn3/+mZ07d/LAAw9ccVl73IcjR47k66+/Zvny5SXuvB0WFkZBQQFnz54tsfxfX4cXe53+8Zw9uNT4/pCVlUWPHj3w8/Nj4cKFuLu7X3Z7bdu25fDhw+Tn51+ryGVypfH9Wdu2bQFKvA7tff9B6cY4f/58cnNzGTp06BW3Z0/70MPDg8aNG9OmTRsSExOJjo7mrbfessvXX5UrIw0aNCAsLIwffvih+LHMzEzWrl1LfHw8APHx8Zw9e5bk5OTiZX788UdsNlvxC86e/VFEdu/ezffff09wcPAV10lNTcXFxeWC0xuO4PDhw5w+fZpatWoBjr///uzDDz+kTZs2REdHX3FZe9qHhmEwcuRIFi5cyI8//kiDBg1KPN+mTRvc3d1LvA537txJWlpaidfh5s2bSxTLP4p1y5YtK2cgl3Cl8cH5f1e6deuGh4cHX3755QVHti4mNTWV6tWrm35kqzTj+6vU1FSAEq9De91/ULYxfvjhh/Tu3ZsaNWpccbv2sg8vxmazkZ+fb5+vvwq/JNYOZGVlGRs2bDA2bNhgAMbUqVONDRs2FH+aZNKkSUZgYKCxaNEiY9OmTUafPn2MBg0aGOfOnSveRo8ePYzY2Fhj7dq1xqpVq4wmTZoYgwYNMmtIJVxufAUFBUbv3r2NOnXqGKmpqUZ6enrx1x9XQK9evdp44403jNTUVGPv3r3GrFmzjBo1ahhDhw41eWTnXW58WVlZxuOPP26sWbPG2L9/v/H9998bcXFxRpMmTYy8vLzibdjz/jOMK/+OGoZhZGRkGD4+PsaMGTMuWN/e9+Hw4cONgIAAY8WKFSV+B3Nzc4uXeeihh4y6desaP/74o7F+/XojPj7eiI+PL36+qKjIaN26tdGtWzcjNTXVWLp0qVGjRg1j/PjxZgyphCuNLyMjw2jbtq0RGRlp7Nmzp8QyRUVFhmEYxpdffml88MEHxubNm43du3cb7777ruHj42M888wzZg7NMIwrj2/Pnj3GCy+8YKxfv97Yv3+/sWjRIqNhw4ZGp06dirdhz/vPMEr3O2oYhrF7927DYrEYS5YsuWAb9rwPx40bZ6xcudLYv3+/sWnTJmPcuHGGxWIxvv32W8Mw7O/155RlZPny5QZwwdewYcMMwzj/8d6nn37aqFmzpuHp6WncfPPNxs6dO0ts4/Tp08agQYMMX19fw9/f37j33nuNrKwsE0ZzocuNb//+/Rd9DjCWL19uGIZhJCcnG23btjUCAgIMLy8vo0WLFsYrr7xS4s3cTJcbX25urtGtWzejRo0ahru7u1GvXj3jwQcfLPHxM8Ow7/1nGFf+HTUMw3j//fcNb29v4+zZsxesb+/78FK/gx9//HHxMufOnTNGjBhhVK9e3fDx8TFuv/12Iz09vcR2Dhw4YPTs2dPw9vY2QkJCjMcee6zER2PNcqXxXWr/Asb+/fsNwzj/cfOYmBjD19fXqFatmhEdHW289957htVqNW9g/+dK40tLSzM6depkBAUFGZ6enkbjxo2NJ554wsjIyCixHXvdf4ZRut9RwzCM8ePHGxERERfdL/a8D++77z6jXr16hoeHh1GjRg3j5ptvLi4ihmF/rz/dtVdERERMVeWuGRERERH7ojIiIiIiplIZEREREVOpjIiIiIipVEZERETEVCojIiIiYiqVERERETGVyoiIiIiYSmVERCrUihUrsFgsF9yES0TkUlRGRKRCtW/fnvT0dAICAkq9Tm5uLuPHj6dRo0Z4eXlRo0YNbrzxRhYtWnQNk4qIvXAzO4CIOBcPD48y32L8oYceYu3atbzzzju0bNmS06dPs3r1ak6fPn2NUoqIPdGRERG5rM6dOzNq1ChGjx5N9erVqVmzJh988AE5OTnce++9+Pn50bhxY5YsWQJceJpm5syZBAYGsmzZMlq0aIGvry89evQgPT29+Gd8+eWXTJgwgVtvvZX69evTpk0bRo0axX333Ve8jMVi4YsvviiRLTAwkJkzZwJw4MABLBYLc+bMoX379nh5edG6dWtWrlx5Tf9+ROTqqYyIyBV98sknhISE8NtvvzFq1CiGDx/OnXfeSfv27UlJSaFbt27cfffd5ObmXnT93NxcXnvtNf773//y008/kZaWxuOPP178fFhYGIsXLyYrK+uqsz7xxBM89thjbNiwgfj4eHr16qUjLCJ2TmVERK4oOjqaiRMn0qRJE8aPH4+XlxchISE8+OCDNGnShGeeeYbTp0+zadOmi65fWFjIe++9x3XXXUdcXBwjR47khx9+KH7+X//6F6tXryY4OJjrr7+eMWPG8Msvv5Qr68iRI+nfvz8tWrRgxowZBAQE8OGHH5ZrWyJSOVRGROSKoqKiiv/s6upKcHAwkZGRxY/VrFkTgBMnTlx0fR8fHxo1alT8fa1atUos26lTJ/bt28cPP/zAHXfcwdatW+nYsSMvvvhimbPGx8cX/9nNzY3rrruO7du3l3k7IlJ5VEZE5Irc3d1LfG+xWEo8ZrFYALDZbKVe3zCMC5bp2LEjY8eO5dtvv+WFF17gxRdfpKCg4JLrFBYWlm9AImJXVEZExC61bNmSoqIi8vLyAKhRo0aJi15379590WtUfv311+I/FxUVkZycTIsWLa59YBEpN320V0RM17lzZwYNGsR1111HcHAw27ZtY8KECdx00034+/sD0KVLF6ZNm0Z8fDxWq5WxY8decMQFYPr06TRp0oQWLVrwxhtv8Pvvv5f4VI6I2B8dGRER03Xv3p1PPvmEbt260aJFC0aNGkX37t2ZO3du8TKvv/46ERERdOzYkcGDB/P444/j4+NzwbYmTZrEpEmTiI6OZtWqVXz55ZeEhIRU5nBEpIwsxl9PwoqIOKADBw7QoEEDNmzYQExMjNlxRKQMdGRERERETKUyIiIiIqbSaRoRERExlY6MiIiIiKlURkRERMRUKiMiIiJiKpURERERMZXKiIiIiJhKZURERERMpTIiIiIiplIZEREREVOpjIiIiIip/hdsdES+eKwrWAAAAABJRU5ErkJggg==\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/partialPeriodicPattern/basic/GThreePGrowth.ipynb b/notebooks/partialPeriodicPattern/basic/GThreePGrowth.ipynb index 88d1089b..86afb19d 100644 --- a/notebooks/partialPeriodicPattern/basic/GThreePGrowth.ipynb +++ b/notebooks/partialPeriodicPattern/basic/GThreePGrowth.ipynb @@ -1,717 +1,717 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Partial Periodic Frequent patterns in Temporal Databases using GThreePGrowth" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Partial Periodic Frequent patterns in a temporal database using the GThreePGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the GThreePGrowth algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "27f054ad-f3b4-4ce0-821c-bba0ded13d1f" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m8.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", + "Collecting resource (from pami)\n", + " Downloading Resource-0.2.1-py2.py3-none-any.whl (25 kB)\n", + "Collecting validators (from pami)\n", + " Downloading validators-0.22.0-py3-none-any.whl (26 kB)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.4.4)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (23.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pami) (2023.3)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->pami) (8.2.3)\n", + "Collecting JsonForm>=0.0.2 (from resource->pami)\n", + " Downloading JsonForm-0.0.2.tar.gz (2.4 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting JsonSir>=0.0.2 (from resource->pami)\n", + " Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in 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/root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=2e15281ac1387c8071f2e5c606fd9b8f3c64c4b33fffa5b838e5a26b962cdc6f\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Partial Periodic Frequent patterns in Temporal Databases using GThreePGrowth" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Partial Periodic Frequent patterns in a temporal database using the GThreePGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the GThreePGrowth algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "27f054ad-f3b4-4ce0-821c-bba0ded13d1f" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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" Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", - " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", - "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", - "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", - "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", - "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", - 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Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "64bf4f93-d753-44f1-bf9c-5699c5d1e370" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-07 06:53:49-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.11MB/s in 6.4s \n", - "\n", - "2023-09-07 06:53:58 (698 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "3e93e4c4-a7c0-47b5-e96f-8f7c682d5d84" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Partial Periodic Frequent patterns using GThreePGrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "dc9b9a17-bac4-4c34-f0c4-66e4e50ea262" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "05736c75-83c5-4186-c070-1e846856fc56" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "PeriodCount = 500 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1.\n", - "relativePSCount=0.2 #relativePSCount is specified in count. However, the users can also specify different relativePSCount value." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Partial Periodic Frequent patterns using GThreePGrowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.partialPeriodicPattern.basic import GThreePGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.GThreePGrowth(iFile=inputFile, minPS=minimumSupportCount, period=PeriodCount, relativePS=relativePSCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('partialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "e50da460-9b4b-4605-824c-b73ddf4d3179" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "760\n", - "Partial Periodic Patterns were generated successfully using Generalized 3PGrowth algorithm \n", - "Total No of patterns: 8445\n", - "Runtime: 28.928328275680542\n", - "Memory (RSS): 618270720\n", - "Memory (USS): 570109952\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'partialPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "61a6b4e1-fd6d-427a-e462-7ec0870d6c79" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "29 :[101, 1, 170] \n", - "847 :[103, 1, 179] \n", - "247 :[103, 1, 181] \n", - "609 :[103, 1, 184] \n", - "47 :[106, 1, 175] \n", - "47 27 :[102, 0.5862068965517241] \n", - "47 322 :[103, 0.5919540229885057] \n", - "931 :[108, 1, 170] \n", - "324 :[110, 1, 173] \n", - "324 750 :[100, 0.5813953488372093] \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "64bf4f93-d753-44f1-bf9c-5699c5d1e370" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-07 06:53:49-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.11MB/s in 6.4s \n", + "\n", + "2023-09-07 06:53:58 (698 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "3e93e4c4-a7c0-47b5-e96f-8f7c682d5d84" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Partial Periodic Frequent patterns using GThreePGrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "dc9b9a17-bac4-4c34-f0c4-66e4e50ea262" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "05736c75-83c5-4186-c070-1e846856fc56" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _partialPeriodicfrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the GThreePGrowth algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.partialPeriodicPattern.basic import GThreePGrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "PeriodCount = 500\n", - "relativePSCount=0.2\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of GThreePGrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'relativePS', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of GThreePGrowth algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 13, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.GThreePGrowth(iFile=inputFile, minPS=minSupCount, period=PeriodCount, relativePS=relativePSCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['GThreePGrowth', minSupCount, PeriodCount, relativePSCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "52b160c4-7e28-4169-a292-2d5dd17010b3" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "760\n", - "Partial Periodic Patterns were generated successfully using Generalized 3PGrowth algorithm \n", - "733\n", - "Partial Periodic Patterns were generated successfully using Generalized 3PGrowth algorithm \n", - "707\n", - "Partial Periodic Patterns were generated successfully using Generalized 3PGrowth algorithm \n", - "682\n", - "Partial Periodic Patterns were generated successfully using Generalized 3PGrowth algorithm \n", - "654\n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "PeriodCount = 500 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1.\n", + "relativePSCount=0.2 #relativePSCount is specified in count. However, the users can also specify different relativePSCount value." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Partial Periodic Frequent patterns using GThreePGrowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.partialPeriodicPattern.basic import GThreePGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.GThreePGrowth(iFile=inputFile, minPS=minimumSupportCount, period=PeriodCount, relativePS=relativePSCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('partialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e50da460-9b4b-4605-824c-b73ddf4d3179" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "760\n", + "Partial Periodic Patterns were generated successfully using Generalized 3PGrowth algorithm \n", + "Total No of patterns: 8445\n", + "Runtime: 28.928328275680542\n", + "Memory (RSS): 618270720\n", + "Memory (USS): 570109952\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'partialPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "61a6b4e1-fd6d-427a-e462-7ec0870d6c79" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "29 :[101, 1, 170] \n", + "847 :[103, 1, 179] \n", + "247 :[103, 1, 181] \n", + "609 :[103, 1, 184] \n", + "47 :[106, 1, 175] \n", + "47 27 :[102, 0.5862068965517241] \n", + "47 322 :[103, 0.5919540229885057] \n", + "931 :[108, 1, 170] \n", + "324 :[110, 1, 173] \n", + "324 750 :[100, 0.5813953488372093] \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _partialPeriodicfrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the GThreePGrowth algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.partialPeriodicPattern.basic import GThreePGrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "PeriodCount = 500\n", + "relativePSCount=0.2\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of GThreePGrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'relativePS', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of GThreePGrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.GThreePGrowth(iFile=inputFile, minPS=minSupCount, period=PeriodCount, relativePS=relativePSCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['GThreePGrowth', minSupCount, PeriodCount, relativePSCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "52b160c4-7e28-4169-a292-2d5dd17010b3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "760\n", + "Partial Periodic Patterns were generated successfully using Generalized 3PGrowth algorithm \n", + "733\n", + "Partial Periodic Patterns were generated successfully using Generalized 3PGrowth algorithm \n", + "707\n", + "Partial Periodic Patterns were generated successfully using Generalized 3PGrowth algorithm \n", + "682\n", + "Partial Periodic Patterns were generated successfully using Generalized 3PGrowth algorithm \n", + "654\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ddcc4502-b696-40b7-b619-446017c112fc" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup PeriodCount relativePS patterns runtime \\\n", + "0 GThreePGrowth 100 500 0.2 8445 23.717345 \n", + "1 GThreePGrowth 150 500 0.2 6350 23.018478 \n", + "2 GThreePGrowth 200 500 0.2 4661 24.229812 \n", + "3 GThreePGrowth 250 500 0.2 2783 21.167337 \n", + "4 GThreePGrowth 300 500 0.2 1992 20.532729 \n", + "\n", + " memory \n", + "0 622518272 \n", + "1 621395968 \n", + "2 618188800 \n", + "3 614481920 \n", + "4 610136064 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "797ce125-457a-4764-b301-671762bc02ed" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 20 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "ddcc4502-b696-40b7-b619-446017c112fc" - }, - "execution_count": 19, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup PeriodCount relativePS patterns runtime \\\n", - "0 GThreePGrowth 100 500 0.2 8445 23.717345 \n", - "1 GThreePGrowth 150 500 0.2 6350 23.018478 \n", - "2 GThreePGrowth 200 500 0.2 4661 24.229812 \n", - "3 GThreePGrowth 250 500 0.2 2783 21.167337 \n", - "4 GThreePGrowth 300 500 0.2 1992 20.532729 \n", - "\n", - " memory \n", - "0 622518272 \n", - "1 621395968 \n", - "2 618188800 \n", - "3 614481920 \n", - "4 610136064 \n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "797ce125-457a-4764-b301-671762bc02ed" - }, - "execution_count": 20, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 20 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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k5OSU6rNz507jlltuMcxms1GrVi1j6tSpZSnVyM7ONgAjOzu7TPs5muLiEmPykkSj7sSlRt2JS405Gw7ZuyQREZFyc6Xv31oLyQkYhsHU5fuZtf4QAJN6NmPEbQ3tXJWIiMj1p7WQKhCTycRzPZvx1J2NAJiyfD9vrz5ABc2eIiIil6UA4yRMJhPjuzdlQmRTAP616mdeX5mkECMiIpWSAoyTib6jES9ENQfg/bUH+fuyfQoxIiJS6SjAOKFhXRvw6n03AfDRhmRe/GY3JSUKMSIiUnkowDipQRH1+Ge/lphM8NnmFJ5btItihRgREakkFGCc2EMd6vCvB1vjYoIF244xfkECRcUl9i5LRESk3CnAOLk+bWvzzoBw3FxMfJOQypgvdlBQpBAjIiIVmwJMBRDVqiYzHmmHh6sLy3en8+Tn8eQVFtu7LBERkXKjAFNB/CUsiA8Gt8Ps5sKP+zIZ/sk2zhcoxIiISMWkAFOB3N40kDmPdcDL3ZX/HDjJ4zFbOJdfZO+yRERErjsFmAqmc6MafDr0ZnzMbmw+dJrBH2/Bmld4+R1FRESciAJMBdS+XjU+G9YRi6cb8UfOMGh2HFm5BfYuS0RE5LpRgKmg2oT6M294J6p6u7PzWDYDPozj1Nl8e5clIiJyXSjAVGAtavkx/4kIaviY2Zdmpf8Hm8nMybN3WSIiItdMAaaCaxrsy5cjOhFs8eRA5ln6z9pMWvZ5e5clIiJyTRRgKoGGAT4sGBFBLX8vDp08x4OzYjl6OtfeZYmIiFw1BZhKok51bxaMjKBudW+Onj7PQ7NiST55zt5liYiIXBUFmEqklr8XC0ZE0DCgCqnZeTw0K5ZfMnPsXZaIiEiZKcBUMkEWT+Y/EUGzYF8yc/J5aNZm9qVZ7V2WiIhImSjAVEIBvma+GN6JFrUsnDpXwIAPN5N4LNveZYmIiFwxBZhKqmoVDz4f1ok2of5k5Rby8OzNxB85Y++yRERErogCTCXm5+XOZ8M6cnO9auTkFTH4ozjiDp2yd1kiIiKXpQBTyfmY3YgZ0oEujapzrqCYR+dsYcOBk/YuS0RE5E8pwAjeHm589GgH7mgaQF5hCUPmbmXN/gx7lyUiIvKHFGAEAE93V2YOakf3sCAKikoY8Wk8K3an27ssERGRS1KAERuzmyvvDQynV6uaFBYbRM/bzrc7U+1dloiIyEUUYKQUd1cX3urflr7htSguMRg7fwdfxR+zd1kiIiKlKMDIRVxdTLxxf2sG3BxKiQHPLtzJvLgUe5clIiJiowAjl+TiYuK1Pi15rHM9AP66OJE5G5PtW5SIiMh/KcDIHzKZTPztnjBG3NoAgJe/28vMdQftXJWIiIgCjFyGyWTiuZ7NeOquxgBMXb6ft348gGEYdq5MREQqMwUYuSyTycT4vzRhQmRTAKb/+DOvr0xSiBEREbtRgJErFn1HI16Iag7A+2sP8urSfQoxIiJiFwowUibDujbg1ftuAuDjjcm8+M1uSkoUYkRE5MZSgJEyGxRRj2n9WmEywWebU5j49S6KFWJEROQGUoCRq/Jgh1CmP9gGFxMsjD/G+AUJFBWX2LssERGpJBRg5Kr1bluLdx8Ox83FxDcJqYz5YgcFRQoxIiJS/hRg5Jrc3bImMx9ph4erC8t3pzPqs3jyCovtXZaIiFRwCjByzbqFBfHho+0xu7mwen8mwz/ZxvkChRgRESk/CjByXdzWJIA5j3fA28OV/xw4yeMxWziXX2TvskREpIJSgJHrpnPDGnwy5GZ8zG5sPnSawR9vwZpXaO+yRESkAlKAkeuqfb1qfDasIxZPN+KPnOGR2XFk5RbYuywREalgyhRg6tWrh8lkuugRHR0NQF5eHtHR0VSvXh0fHx/69etHRkZGqWOkpKQQFRWFt7c3gYGBTJgwgaKi0h81rF27lvDwcMxmM40aNSImJubaRik3VJtQf754ohNVvd3ZdSybAR/Gcepsvr3LEhGRCqRMAWbr1q2kpaXZHqtWrQLggQceAGDcuHF89913LFy4kHXr1pGamkrfvn1t+xcXFxMVFUVBQQGbNm1i7ty5xMTEMHnyZFuf5ORkoqKiuOOOO0hISGDs2LEMGzaMlStXXo/xyg1yU4gfX46IoIaPmX1pVvp/sJlMa569yxIRkQrCZFzDYjZjx45l6dKlHDhwAKvVSkBAAPPmzeP+++8HYP/+/TRv3pzY2Fg6derE8uXL6dWrF6mpqQQFBQEwc+ZMJk6cyIkTJ/Dw8GDixIksW7aM3bt3256nf//+ZGVlsWLFij+sJT8/n/z8//0v32q1EhoaSnZ2NhaL5WqHKNfo0ImzPPxhHOnWPOrXqMK84R2p6edl77JERMRBWa1W/Pz8Lvv+fdX3wBQUFPDZZ58xZMgQTCYT8fHxFBYW0q1bN1ufZs2aUadOHWJjYwGIjY2lZcuWtvACEBkZidVqZc+ePbY+vz3GhT4XjvFHpkyZgp+fn+0RGhp6tUOT66hBgA8LRkRQy9+L5JPneHBWLEdP59q7LBERcXJXHWCWLFlCVlYWjz32GADp6el4eHjg7+9fql9QUBDp6em2Pr8NLxfaL7T9WR+r1cr58+f/sJ5JkyaRnZ1texw9evRqhybXWZ3q3iwYGUHd6t4cPX2eh2bFknzynL3LEhERJ3bVAeajjz6iZ8+ehISEXM96rprZbMZisZR6iOOo5e/FghERNAyoQmp2Hg/NiuVARo69yxIRESd1VQHmyJEj/PjjjwwbNsy2LTg4mIKCArKyskr1zcjIIDg42Nbn97OSLvx+uT4WiwUvL9074cyCLJ58OSKCZsG+ZObk0/+DzexNtdq7LBERcUJXFWDmzJlDYGAgUVFRtm3t2rXD3d2d1atX27YlJSWRkpJCREQEABERESQmJpKZmWnrs2rVKiwWC2FhYbY+vz3GhT4XjiHOrYaPmS+Gd6JFLQunzhUw4MPN7DqWZe+yRETEyZQ5wJSUlDBnzhweffRR3NzcbNv9/PwYOnQo48eP56effiI+Pp7HH3+ciIgIOnXqBED37t0JCwtj0KBB7Ny5k5UrV/LCCy8QHR2N2WwGYOTIkRw6dIj/+7//Y//+/bz//vssWLCAcePGXachi71VreLB58M60baOP9nnCxn4YRzxR87YuywREXEiZQ4wP/74IykpKQwZMuSitunTp9OrVy/69evHrbfeSnBwMIsWLbK1u7q6snTpUlxdXYmIiOCRRx5h8ODBvPLKK7Y+9evXZ9myZaxatYrWrVvz5ptvMnv2bCIjI69yiOKI/Lzc+XRoR26uV42c/CIGfRTH5kOn7F2WiIg4iWv6HhhHdqXzyMW+cguKeOKTeDb8chJPdxc+HNyero0D7F2WiIjYSbl/D4zI9eDt4cbsR9tzR9MA8gpLGDp3G2v2Z1x+RxERqdQUYMTuPN1dmTmoHd3DgigoKmHEp/Gs2J1u77JERMSBKcCIQzC7ufLewHB6tapJYbFB9LztfLsz1d5liYiIg1KAEYfh7urCW/3b0je8FsUlBmPn7+Cr+GP2LktERByQAow4FFcXE2/c35oBN4dSYsCzC3fyedwRe5clIiIORgFGHI6Li4nX+rTksc71AHh+8W4+3pBs36JERMShKMCIQzKZTPztnjBG3NYAgFeW7mXG2oN2rkpERByFAow4LJPJxHM9mvHUXY0B+OeK/fz7x5+poF9dJCIiZaAAIw7NZDIx/i9NmBDZFIB//3iAaSuTFGJERCo5BRhxCtF3NOKFqOYAzFh7kFeX7lOIERGpxBRgxGkM69qAV3u3AODjjcm8sGQ3JSUKMSIilZECjDiVQZ3qMq1fK0wm+DwuhYlf76JYIUZEpNJRgBGn82CHUKY/2AZXFxML448xfkECRcUl9i5LRERuIAUYcUq929binQFtcXMx8U1CKqPn7aCgSCFGRKSyUIARp3V3y5rMfKQdHq4urNiTzsjP4skrLLZ3WSIicgMowIhT6xYWxIePtsfs5sKa/ZkM/2Qb5wsUYkREKjoFGHF6tzUJIObxm/H2cOU/B07y2JwtnMsvsndZIiJSjhRgpEKIaFidT4bcjI/Zjbjk0wz6KA5rXqG9yxIRkXKiACMVRvt61fh8WEcsnm5sT8nikdlxZOUW2LssEREpBwowUqG0DvXniyc6Ua2KB7uOZTPgwzhOnc23d1kiInKdKcBIhXNTiB/zn+hEDR8z+9Ks9P9gM5nWPHuXJSIi15ECjFRITYJ8WTCiE8EWTw5knuWhDzaTmnXe3mWJiMh1ogAjFVaDAB8WjIiglr8XySfP8eCsWI6ezrV3WSIich0owEiFVqe6NwtGRlCvujfHzpznwVmxJJ88Z++yRETkGinASIVXy9+LL0dE0DCgCmnZeTw4K5YDGTn2LktERK6BAoxUCkEWT74cEUGzYF9O5OTz0Aeb2XTwpL3LEhGRq6QAI5VGDR8zXwzvRKvafpw+V8Ajs+OYue4ghmHYuzQRESkjBRipVKpW8eDLJyLoF16bEgOmLt/PqM+2k6Nv7RURcSoKMFLpeHm48sYDrfhHnxa4u5pYsSed+97dyM+6L0ZExGkowEilZDKZGNixLgtGRFDTz5NDJ8/R+72NfLcz1d6liYjIFVCAkUqtbZ2qLB1zC50bVie3oJgxX+zgle/2UlhcYu/SRETkTyjASKVX3cfMJ0NuZtTtDQH4eGMyD3+o5QdERByZAowI4ObqwsQezfhgUDt8zW5sPXyGqHc2sCX5tL1LExGRS1CAEfmN7jcF8+2YW2ga9Ov3xQz4cDMfbUjWVGsREQejACPyO/VrVGFxdGfuaxNCcYnBq0v3MuaLHZzLL7J3aSIi8l8KMCKX4O3hxr8fasNL94Th5mJi6a40er+3kYMnztq7NBERQQFG5A+ZTCYe61Kf+U90ItDXzIHMs9z37kZW7E6zd2kiIpWeAozIZbSvV42lT93CzfWrcTa/iJGfbWfK9/so0lRrERG7UYARuQKBvp58Pqwjw7vWB2DW+kM88lEcJ3Ly7VyZiEjlpAAjcoXcXV14PiqM9weGU8XDlc2HTnPPOxuIP3LG3qWJiFQ6CjAiZXR3y5p8M7oLDQOqkG7No/8HsXwSe1hTrUVEbqAyB5jjx4/zyCOPUL16dby8vGjZsiXbtm2ztRuGweTJk6lZsyZeXl5069aNAwcOlDrG6dOnGThwIBaLBX9/f4YOHcrZs6Vnd+zatYuuXbvi6elJaGgo06ZNu8ohilx/jQJ9+Wb0LdzdMpjCYoPJ3+xh/IKdnC8otndpIiKVQpkCzJkzZ+jSpQvu7u4sX76cvXv38uabb1K1alVbn2nTpvH2228zc+ZM4uLiqFKlCpGRkeTl/e9r2QcOHMiePXtYtWoVS5cuZf369TzxxBO2dqvVSvfu3albty7x8fG8/vrrvPTSS3zwwQfXYcgi14eP2Y33Hg7nhajmuLqYWLzjOH3e38jhk+fsXZqISIVnMspw3fu5555j48aN/Oc//7lku2EYhISE8Mwzz/Dss88CkJ2dTVBQEDExMfTv3599+/YRFhbG1q1bad++PQArVqzg7rvv5tixY4SEhDBjxgyef/550tPT8fDwsD33kiVL2L9//yWfOz8/n/z8/91QabVaCQ0NJTs7G4vFcqVDFLkqmw+dYvS87Zw8W4CvpxvTH2xDt7Age5clIuJ0rFYrfn5+l33/LtMVmG+//Zb27dvzwAMPEBgYSNu2bfnwww9t7cnJyaSnp9OtWzfbNj8/Pzp27EhsbCwAsbGx+Pv728ILQLdu3XBxcSEuLs7W59Zbb7WFF4DIyEiSkpI4c+bSN0xOmTIFPz8/2yM0NLQsQxO5Jp0aVGfpmK60q1uVnLwihn2yjTdWJlFcovtiRETKQ5kCzKFDh5gxYwaNGzdm5cqVjBo1iqeeeoq5c+cCkJ6eDkBQUOn/eQYFBdna0tPTCQwMLNXu5uZGtWrVSvW51DF++xy/N2nSJLKzs22Po0ePlmVoItcs2M+TL4Z34rHO9QB496dfeGzOFk6fK7BvYSIiFZBbWTqXlJTQvn17XnvtNQDatm3L7t27mTlzJo8++mi5FHilzGYzZrPZrjWIeLi58NK9N9G2jj/PfZ3Ifw6c5J53NvD+wHBah/rbuzwRkQqjTFdgatasSVhYWKltzZs3JyUlBYDg4GAAMjIySvXJyMiwtQUHB5OZmVmqvaioiNOnT5fqc6lj/PY5RBzZfW1qsSS6C/VrVOF41nkemBnLF1tSNNVaROQ6KVOA6dKlC0lJSaW2/fzzz9StWxeA+vXrExwczOrVq23tVquVuLg4IiIiAIiIiCArK4v4+HhbnzVr1lBSUkLHjh1tfdavX09hYaGtz6pVq2jatGmpGU8ijqxpsC/fjO7CX8KCKCguYdKiRCZ+vYu8Qk21FhG5VmUKMOPGjWPz5s289tpr/PLLL8ybN48PPviA6Oho4NfF78aOHcvf//53vv32WxITExk8eDAhISH07t0b+PWKTY8ePRg+fDhbtmxh48aNjB49mv79+xMSEgLAww8/jIeHB0OHDmXPnj18+eWXvPXWW4wfP/76jl6knFk83Zn1SDv+r0dTXEywYNsx+s3YxNHTufYuTUTEuRll9N133xktWrQwzGaz0axZM+ODDz4o1V5SUmK8+OKLRlBQkGE2m4277rrLSEpKKtXn1KlTxoABAwwfHx/DYrEYjz/+uJGTk1Oqz86dO41bbrnFMJvNRq1atYypU6eWqc7s7GwDMLKzs8s6RJFyseHACaPtKz8YdScuNVq9tNJYsz/D3iWJiDicK33/LtP3wDiTK51HLnIjpWadZ9Tn29l5NAuTCZ6+qzFP3dkYFxeTvUsTEXEI5fI9MCJybUL8vVgwohMDO9bBMODfPx5g6NytZOVqqrWISFkowIjcYGY3V/7RpyVvPNAas5sLPyWd4J53N7D7eLa9SxMRcRoKMCJ2cn+72ix6sjOh1bw4evo8/WZsYuE2fQGjiMiVUIARsaObQvxYOrordzYLJL+ohAlf7eKvixPJL9JUaxGRP6MAI2Jnft7uzB7cnvF/aYLJBPPiUnhwZizHs87buzQREYelACPiAFxcTDx1V2PmPNYBPy93dh7L5p53NrDhwEl7lyYi4pAUYEQcyO1NA1k65hZa1LJw+lwBgz+O472ffqFEq1qLiJSiACPiYEKrefPVyM481D6UEgNeX5nEE5/Gk32+8PI7i4hUEgowIg7I092Vf97fiql9W+Lh5sKP+zK4790N7E+32rs0ERGHoAAj4sD631yHr0ZGUMvfi8Oncun93kaW7Dhu77JEROxOAUbEwbWq7c93Y26ha+Ma5BWWMPbLBP72zW4KikrsXZqIiN0owIg4gWpVPIh5/GbG3NkIgLmxR+j/QSzp2Xl2rkxExD4UYESchKuLiWe6N2X24Pb4erqxPSWLXu/8h9iDp+xdmojIDacAI+JkuoUF8d3oW2gW7MvJswU88lEcH64/RAVdWF5E5JIUYEScUL0aVVj8ZBf6tq1FcYnBP77fR/S87ZzNL7J3aSIiN4QCjIiT8vJw5c0HW/Nq7xa4u5r4PjGd+97dwC+ZOfYuTUSk3CnAiDgxk8nEoE51+XJEBMEWTw6eOMe9725k6a5Ue5cmIlKuFGBEKoDwOlVZ+tQtRDSoTm5BMaPn7eDvS/dSWKyp1iJSMSnAiFQQNXzMfDr0Zkbe1hCA2RuSGTg7jswcTbUWkYpHAUakAnFzdeG5ns2Y+Ug4PmY3tiSfptfbG9h2+LS9SxMRua4UYEQqoB4tavLN6C40DvQhMyef/h9sZs7GZE21FpEKQwFGpIJqGODDkugu9GpVk6ISg5e/28vT8xM4p6nWIlIBKMCIVGBVzG68M6Atk3uF4eZi4tudqfR5fyOHTpy1d2kiItdEAUakgjOZTAy5pT5fPNGJAF8zP2ec5d53N7Jid7q9SxMRuWoKMCKVRId61Vg25hZurleNs/lFjPwsnqnL91OkqdYi4oQUYEQqkUCLJ58P78jQW+oDMHPdQQZ/vIWTZ/PtXJmISNkowIhUMu6uLrzYK4x3H26Lt4crmw6e4p53NrAj5Yy9SxMRuWIKMCKVVK9WIXwT3YUGAVVIy87jwVmxfLb5iKZai4hTUIARqcQaB/nyTXQXetwUTGGxwQtLdvPMwp2cLyi2d2kiIn9KAUakkvP1dGfGI+FM6tkMFxMs2n6cPu9v5Mipc/YuTUTkDynAiAgmk4kRtzXks2EdqeHjwf70HHq9s4HV+zLsXZqIyCUpwIiITeeGNVg6pivhdfzJySti6Nxt/OuHJIpLdF+MiDgWBRgRKSXYz5P5T0TwaERdAN5e8wuPx2zlzLkCO1cmIvI/CjAichEPNxdevq8F0x9qjae7C+t/PkGvdzaQeCzb3qWJiAAKMCLyJ/q0rc3iJ7tQt7o3x7PO02/mJr7cmmLvskREFGBE5M81r2nh29G30K15IAVFJUz8OpGJX+0ir1BTrUXEfhRgROSy/Lzc+WBQeyZENsXFBF9uO8oDM2M5ejrX3qWJSCWlACMiV8TFxUT0HY2YO+Rmqnq7k3g8m3ve3cC6n0/YuzQRqYQUYESkTLo2DmDpU11pVduPrNxCHpuzhbdXH6BEU61F5AZSgBGRMqvl78WCEREMuLkOhgH/WvUzwz7ZRnZuob1LE5FKQgFGRK6Kp7srU/q2ZNr9rfBwc2HN/kzueXcDe1Ot9i5NRCqBMgWYl156CZPJVOrRrFkzW3teXh7R0dFUr14dHx8f+vXrR0ZG6a8iT0lJISoqCm9vbwIDA5kwYQJFRUWl+qxdu5bw8HDMZjONGjUiJibm6kcoIuXqwfahLBrVmdpVvUg5nUuf9zfydfwxe5clIhVcma/A3HTTTaSlpdkeGzZssLWNGzeO7777joULF7Ju3TpSU1Pp27evrb24uJioqCgKCgrYtGkTc+fOJSYmhsmTJ9v6JCcnExUVxR133EFCQgJjx45l2LBhrFy58hqHKiLlpUUtP5aOuYXbmgSQX1TCMwt38sKSRPKLNNVaRMqHyTCMK77z7qWXXmLJkiUkJCRc1JadnU1AQADz5s3j/vvvB2D//v00b96c2NhYOnXqxPLly+nVqxepqakEBQUBMHPmTCZOnMiJEyfw8PBg4sSJLFu2jN27d9uO3b9/f7KyslixYsUf1pafn09+fr7td6vVSmhoKNnZ2Vgslisdoohcg5ISg7dWH+DtNQcwDGgd6s+MgeGE+HvZuzQRcRJWqxU/P7/Lvn+X+QrMgQMHCAkJoUGDBgwcOJCUlF+/lTM+Pp7CwkK6detm69usWTPq1KlDbGwsALGxsbRs2dIWXgAiIyOxWq3s2bPH1ue3x7jQ58Ix/siUKVPw8/OzPUJDQ8s6NBG5Ri4uJsb9pQkfP9YBPy93dh7Notc7G9j4y0l7lyYiFUyZAkzHjh2JiYlhxYoVzJgxg+TkZLp27UpOTg7p6el4eHjg7+9fap+goCDS09MBSE9PLxVeLrRfaPuzPlarlfPnz/9hbZMmTSI7O9v2OHr0aFmGJiLX0R1NA1k65hZuCrFw+lwBgz6KY8bag5Thgq+IyJ9yK0vnnj172n5u1aoVHTt2pG7duixYsAAvL/teIjabzZjNZrvWICL/E1rNm69HdeaFJbv5Kv4Y/1yxn4SjZ3j9gdZYPN3tXZ6IOLlrmkbt7+9PkyZN+OWXXwgODqagoICsrKxSfTIyMggODgYgODj4ollJF36/XB+LxWL3kCQiZePp7srr97fitT4t8XB1YeWeDO57dyNJ6Tn2Lk1EnNw1BZizZ89y8OBBatasSbt27XB3d2f16tW29qSkJFJSUoiIiAAgIiKCxMREMjMzbX1WrVqFxWIhLCzM1ue3x7jQ58IxRMS5mEwmHu5Yh4UjIwjx8yT55Dl6v7eRbxKO27s0EXFiZZqF9Oyzz3LPPfdQt25dUlNT+dvf/kZCQgJ79+4lICCAUaNG8f333xMTE4PFYmHMmDEAbNq0Cfh1GnWbNm0ICQlh2rRppKenM2jQIIYNG8Zrr70G/DqNukWLFkRHRzNkyBDWrFnDU089xbJly4iMjLzigV3pXcwicuOcPlfAU1/sYMN/b+p9rHM9/np3czzc9J2aIvKrcpmFdOzYMQYMGEDTpk158MEHqV69Ops3byYgIACA6dOn06tXL/r168ett95KcHAwixYtsu3v6urK0qVLcXV1JSIigkceeYTBgwfzyiuv2PrUr1+fZcuWsWrVKlq3bs2bb77J7NmzyxReRMQxVaviwdwhNxN9R0MAYjYd5uEPN5NhzbNzZSLibMp0BcaZ6AqMiGNbtTeD8V8mkJNfRA0fM+893JaODarbuywRsbNy+x4YEZHr4S9hQXw75haaBfty8mw+D8+OY/Z/DmmqtYhcEQUYEbGb+jWqsOjJzvRuE0JxicHfl+1j9LwdnM0vuvzOIlKpKcCIiF15e7gx/aE2vHLfTbi5mFiWmEbv9zbyS+ZZe5cmIg5MAUZE7M5kMjE4oh5fjuhEkMXML5lnue/dDSxPTLN3aSLioBRgRMRhtKtbjaVjutKxfjXOFRQz6vPtvPb9PoqKS+xdmog4GAUYEXEoAb5mPh/WkRG3NgDgg/WHGDg7jhM5+ZfZU0QqEwUYEXE4bq4uTLq7OTMGhlPFw5W45NP0euc/xB85be/SRMRBKMCIiMPq2bIm34y+hUaBPmRY83lo1mbmbjqsqdYiogAjIo6tUaAP30R3IapVTYpKDP727R7GfZlAboGmWotUZgowIuLwqpjdeHdAW16Iao6ri4klCan0mxFLWvZ5e5cmInaiACMiTsFkMjGsawPmDetIDR8P9qVZ6fPeJvamWu1dmojYgQKMiDiVjg2qs/jJLjQK9CHdmscDMzex7ucT9i5LRG4wBRgRcTqh1bz5emRnOjX49ftihsRs5YstKfYuS0RuIAUYEXFKft7ufDKkI33b1qK4xGDSokSmrdhPSYlmKIlUBgowIuK0PNxcePPB1jx9V2MA3l97kKfm7yCvsNjOlYlIeVOAERGnZjKZGPeXJrzxQGvcXEws3ZXGI7PjOHOuwN6liUg5UoARkQrh/na1+WTIzfh6urHtyBn6ztjE4ZPn7F2WiJQTBRgRqTA6N6rB16M6U8vfi+ST5+g7YxPxR87YuywRKQcKMCJSoTQJ8mVxdGda1vLj9LkCBny4me8T0+xdlohcZwowIlLhBPp68uWITnRrHkhBUQlPfr6dD9Yf1BpKIhWIAoyIVEjeHm7MGtSeRyPqAvDa9/t58ZvdFBWX2LkyEbkeFGBEpMJydTHx0r038WKvMEwm+GxzCsM/2ca5fC0EKeLsFGBEpEIzmUwMvaU+MwaGY3Zz4aekEzw4K5YMa569SxORa6AAIyKVQo8WNZn/RCeqV/FgT6qVPu9tZH+6FoIUcVYKMCJSabStU5XFT3ahQUAVUrPzuH9GLP85oIUgRZyRAoyIVCp1qnuzaFRnOtavxtn8Ih6fs5UFW4/auywRKSMFGBGpdPy9Pfhk6M30bhNCUYnB/329izdWJmmatYgTUYARkUrJ7ObK9IfaMObORgC8+9MvjP0ygfwiLQQp4gwUYESk0jKZTDzTvSnT+rXCzcXENwmpDPpoC1m5WghSxNEpwIhIpfdgh1DmPN4BX7MbW5JP03fGJlJO5dq7LBH5EwowIiJA18YBLBwVQYifJ4dOnKPP+xvZkaKFIEUclQKMiMh/NQu2sDi6CzeFWDh1roD+H2xmxW4tBCniiBRgRER+I8jiyYIREdzZLJD8ohJGfb6d2f85pBlKIg5GAUZE5HeqmN34YFA7HulUB8OAvy/bx0vf7qG4RCFGxFEowIiIXIKbqwuv3teC5+9uDsDc2COM+HQbuQVaCFLEESjAiIj8AZPJxPBbG/D+fxeC/HFfJg/N2kymFoIUsTsFGBGRy7i7ZU3mDe9EtSoeJB7Pps/7m/g5I8feZYlUagowIiJXoF3dqix+sjP1a1TheNZ5+s3YxKZfTtq7LJFKSwFGROQK1a1ehUWjOtOhXlVy8ooY/PEWvoo/Zu+yRColBRgRkTKoWsWDT4d25J7Wvy4E+ezCnUxf9bOmWYvcYAowIiJl5OnuylsPteHJ2xsC8NbqAzyzYCcFRSV2rkyk8rimADN16lRMJhNjx461bcvLyyM6Oprq1avj4+NDv379yMjIKLVfSkoKUVFReHt7ExgYyIQJEygqKj01ce3atYSHh2M2m2nUqBExMTHXUqqIyHXl4mLi/3o0Y0rflri6mFi04ziDP44jO7fQ3qWJVApXHWC2bt3KrFmzaNWqVant48aN47vvvmPhwoWsW7eO1NRU+vbta2svLi4mKiqKgoICNm3axNy5c4mJiWHy5Mm2PsnJyURFRXHHHXeQkJDA2LFjGTZsGCtXrrzackVEysWAm+vw8WMd8DG7sfnQafrN3MTR01oIUqS8mYyr+OD27NmzhIeH8/777/P3v/+dNm3a8O9//5vs7GwCAgKYN28e999/PwD79++nefPmxMbG0qlTJ5YvX06vXr1ITU0lKCgIgJkzZzJx4kROnDiBh4cHEydOZNmyZezevdv2nP379ycrK4sVK1Zcsqb8/Hzy8/Ntv1utVkJDQ8nOzsZisZR1iCIiZbI31cqQmK2kW/Oo4ePBR492oHWov73LEnE6VqsVPz+/y75/X9UVmOjoaKKioujWrVup7fHx8RQWFpba3qxZM+rUqUNsbCwAsbGxtGzZ0hZeACIjI7FarezZs8fW5/fHjoyMtB3jUqZMmYKfn5/tERoaejVDExG5KmEhFpZEd6F5TQsnzxbw0Aex/LAn3d5liVRYZQ4w8+fPZ/v27UyZMuWitvT0dDw8PPD39y+1PSgoiPT0dFuf34aXC+0X2v6sj9Vq5fz585esa9KkSWRnZ9seR48eLevQRESuSbCfJwtHRnBbkwDyCksY8Vk8czYm27sskQqpTAHm6NGjPP3003z++ed4enqWV01XxWw2Y7FYSj1ERG40H7MbHz3angE3/7oQ5Mvf7eXl77QQpMj1VqYAEx8fT2ZmJuHh4bi5ueHm5sa6det4++23cXNzIygoiIKCArKyskrtl5GRQXBwMADBwcEXzUq68Pvl+lgsFry8vMo0QBGRG83N1YXX+rTguZ7NAJiz8TCjPovnfEGxnSsTqTjKFGDuuusuEhMTSUhIsD3at2/PwIEDbT+7u7uzevVq2z5JSUmkpKQQEREBQEREBImJiWRmZtr6rFq1CovFQlhYmK3Pb49xoc+FY4iIODqTycTI2xryzoC2eLi58MPeDPp/EMuJnPzL7ywil3VVs5B+6/bbb7fNQgIYNWoU33//PTExMVgsFsaMGQPApk2bgF+nUbdp04aQkBCmTZtGeno6gwYNYtiwYbz22mvAr9OoW7RoQXR0NEOGDGHNmjU89dRTLFu2jMjIyCuq60rvYhYRKW/bDp9m+CfbOJNbSO2qXsQ83oFGgb72LkvEIZXrLKQ/M336dHr16kW/fv249dZbCQ4OZtGiRbZ2V1dXli5diqurKxERETzyyCMMHjyYV155xdanfv36LFu2jFWrVtG6dWvefPNNZs+efcXhRUTEkbSvV41FT3ahXnVvjp05T9/3N7HpoBaCFLkW13wFxlHpCoyIOJrT5woY/sk24o+cwd3VxD/7taJveG17lyXiUOx2BUZERC6tWhUPPh/WkaiWNSksNhi/YCdv/XhAC0GKXAUFGBGRG8jT3ZV3BrRlxG0NAJj+489M+GqXFoIUKSMFGBGRG8zFxcSkns35e+8WuJjgq/hjPB6zhezzWghS5EopwIiI2Mkjnery0aMd8PZwZeMvp3hg5iaOndFCkCJXQgFGRMSO7mgWyIIREQRZzPyccZY+728i8Vi2vcsScXgKMCIidtailh+Ln+xCs2BfTuTk8+CsWH7cm3H5HUUqMQUYEREHEOLvxcKREXRtXIPzhcU88ek2Pok9bO+yRByWAoyIiIPw9XTn48c60L9DKCUGTP5mD39fupcSLQQpchEFGBERB+Lu6sKUvi2ZENkUgNkbknny8+1aCFLkdxRgREQcjMlkIvqORrzVvw0eri6s2JPOgA83c/KsFoIUuUABRkTEQd3XphafDeuIn5c7CUez6PP+Rg6eOGvvskQcggKMiIgDu7l+NRY92Zk61bw5evrXhSDjDp2yd1kidqcAIyLi4BoG+LDoyc60CfUn+3whgz7awjcJx+1dlohdKcCIiDiBGj5m5j/RiR43BVNQXMLT8xN4d40WgpTKSwFGRMRJeLq78v7AcIZ3rQ/AGz/8zMSvd1FYrIUgpfJRgBERcSIuLiaejwrjlftuwsUEC7YdY0jMVqx5WghSKhcFGBERJzQ4oh4fDm6Pl7sr/zlwkgdnxpKadd7eZYncMAowIiJO6q7mQSwYEUGAr5n96Tn0fm8ju49rIUipHBRgREScWMvafiyJ7kKTIB8y/7sQ5E/7M+1dlki5U4AREXFytfy9+GpUZ7o0qk5uQTFD527ls81H7F2WSLlSgBERqQAsnu7Meexm7m9XmxIDXliymynf79NCkFJhKcCIiFQQHm4uvH5/K8b/pQkAs9YfYswXO8gr1EKQUvEowIiIVCAmk4mn7mrM9Ida4+5qYlliGg9/uJlTWghSKhgFGBGRCqhP29p8MqQjFk83tqdk0XfGJg5pIUipQBRgREQqqIiG1Vn0ZGdqV/XiyKlc+s7YxNbDp+1dlsh1oQAjIlKBNQr0ZfGTXWgd6k9WbiEDP4zju52p9i5L5JopwIiIVHABvmbmD+9E97AgCopLGPPFDmasPaiFIMWpKcCIiFQCXh6uzHikHUO6/LoQ5D9X7Oevi3dTpIUgxUkpwIiIVBKuLiYm3xPG3+4Jw2SCL7akMHTuNs7mF9m7NJEyU4AREalkHu9Sn1mPtMPT3YV1P5/ggZmxpGVrIUhxLgowIiKVUPebgvnyiQhq+JjZl2alz3ub2JtqtXdZIldMAUZEpJJqHerP4ic70yjQh3RrHg/M3MTaJC0EKc5BAUZEpBILrebN1yM706lBNc4VFDN07jbmxaXYuyyRy1KAERGp5Py83flkSEf6tq1FcYnBXxcn8s8V+7UQpDg0BRgREcHDzYU3H2zN2G6NAZix9iBPzddCkOK4FGBERAT4dSHIsd2a8MYDrXFzMbF0VxqPzI7jzLkCe5cmchEFGBERKeX+drX5ZMjN+Hq6se3IGfrO2MThk+fsXZZIKQowIiJykc6NavD1qM7U8vci+eQ5+s7YRPyRM/YuS8RGAUZERC6pSZAvi6M707KWH6fPFTDgw818n5hm77JEAAUYERH5E4G+nnw5ohPdmgdSUFTCk59vZ9Y6LQQp9qcAIyIif8rbw41Zg9rzaERdAKYs388LS7QQpNhXmQLMjBkzaNWqFRaLBYvFQkREBMuXL7e15+XlER0dTfXq1fHx8aFfv35kZGSUOkZKSgpRUVF4e3sTGBjIhAkTKCoqvZDY2rVrCQ8Px2w206hRI2JiYq5+hCIics1cXUy8dO9NvNjr14UgP49LYfgn2zinhSDFTsoUYGrXrs3UqVOJj49n27Zt3Hnnndx3333s2bMHgHHjxvHdd9+xcOFC1q1bR2pqKn379rXtX1xcTFRUFAUFBWzatIm5c+cSExPD5MmTbX2Sk5OJiorijjvuICEhgbFjxzJs2DBWrlx5nYYsIiJXw2QyMfSW+swY+OtCkD8lneDBWbFkWPPsXZpUQibjGj/IrFatGq+//jr3338/AQEBzJs3j/vvvx+A/fv307x5c2JjY+nUqRPLly+nV69epKamEhQUBMDMmTOZOHEiJ06cwMPDg4kTJ7Js2TJ2795te47+/fuTlZXFihUr/rCO/Px88vPzbb9brVZCQ0PJzs7GYrFcyxBFROR3dqScYdjcbZw6V0BNP0/mPN6BZsH6t1aundVqxc/P77Lv31d9D0xxcTHz58/n3LlzREREEB8fT2FhId26dbP1adasGXXq1CE2NhaA2NhYWrZsaQsvAJGRkVitVttVnNjY2FLHuNDnwjH+yJQpU/Dz87M9QkNDr3ZoIiJyGW3rVGXxk11oEFCFtOw87p8Ry38OnLB3WVKJlDnAJCYm4uPjg9lsZuTIkSxevJiwsDDS09Px8PDA39+/VP+goCDS09MBSE9PLxVeLrRfaPuzPlarlfPnz/9hXZMmTSI7O9v2OHr0aFmHJiIiZVCnujeLRnWmY/1qnM0v4vE5W1mwVf/2yo1R5gDTtGlTEhISiIuLY9SoUTz66KPs3bu3PGorE7PZbLu5+MJDRETKl7+3B58MvZnebUIoKjH4v6938cbKJE2zlnJX5gDj4eFBo0aNaNeuHVOmTKF169a89dZbBAcHU1BQQFZWVqn+GRkZBAcHAxAcHHzRrKQLv1+uj8ViwcvLq6zliohIOTO7uTL9oTaMubMRAO/+9Atjv0wgv0gLQUr5uebvgSkpKSE/P5927drh7u7O6tWrbW1JSUmkpKQQEREBQEREBImJiWRmZtr6rFq1CovFQlhYmK3Pb49xoc+FY4iIiOMxmUw8070p0/q1ws3FxDcJqQyavYWsXC0EKeWjTAFm0qRJrF+/nsOHD5OYmMikSZNYu3YtAwcOxM/Pj6FDhzJ+/Hh++ukn4uPjefzxx4mIiKBTp04AdO/enbCwMAYNGsTOnTtZuXIlL7zwAtHR0ZjNZgBGjhzJoUOH+L//+z/279/P+++/z4IFCxg3btz1H72IiFxXD3YIZc7jHfA1u7Hl8Gn6zthEyqlce5clFVCZAkxmZiaDBw+madOm3HXXXWzdupWVK1fyl7/8BYDp06fTq1cv+vXrx6233kpwcDCLFi2y7e/q6srSpUtxdXUlIiKCRx55hMGDB/PKK6/Y+tSvX59ly5axatUqWrduzZtvvsns2bOJjIy8TkMWEZHy1LVxAAtHRRDi58mhE+fo8/5GtqdoIUi5vq75e2Ac1ZXOIxcRkfKRYc1jSMxW9qRaMbu58Fb/NvRoUdPeZYmDK/fvgREREfkzQRZPFoyI4M5mgeQXlTDq8+3M/s8hzVCS60IBRkREyk0VsxsfDGrHI53qYBjw92X7eOnbPRSXKMTItVGAERGRcuXm6sKr97Xg+bubAzA39ghPfLJNM5TkmugeGBERuWG+T0xj3JcJ5BeV4OHqwl3NA+kbXpvbmgTg4ab/U8uVv38rwIiIyA0Vf+QMzy9OZH96jm1bVW937m0dQp/w2rSu7YfJZLJjhWJPCjAKMCIiDm1vqpXFO46xJCGVEzn5tu0NAqrQt20teretRe2q3nasUOxBAUYBRkTEKRQVl7Dx4CkWbT/Gyj3p5BWW2No61q9Gv/Da9GwZjK+nux2rlBtFAUYBRkTE6ZzNL2J5YhqLdxwn9tApLrxDmd1c6H5TMH3Da9G1UQ3cXHW/TEWlAKMAIyLi1I5nnWfJjuMs3nGcXzLP2rbX8DFzX5sQ+rStxU0hFt0vU8EowCjAiIhUCIZhkHg8m0Xbj/PtzlROn/vf9OumQb70Ca9F7za1CPbztGOVcr0owCjAiIhUOIXFJaz/+QSLth9n1b4MCop+vV/GZIIuDWvQN7wWkTcFU8XsZudK5WopwCjAiIhUaNnnC/k+MY3F24+z5fBp23ZvD1d63BRMn/BadG5YA1cXfcTkTBRgFGBERCqNo6dzWbzjOIu2H+PwqVzb9iCLmd5tatE3vDZNg33tWKFcKQUYBRgRkUrHMAx2HM1i0fZjfLczjezzhba2sJoW+obX4t42IQT66n4ZR6UAowAjIlKp5RcV89P+EyzafoyfkjIpLP717c7VxUTXxjXoG16b7mFBeLq72rlS+S0FGAUYERH5rzPnCli6K5VFO46zIyXLtt3H7MbdLYPp07Y2HetXw0X3y9idAowCjIiIXMKhE2dZsuM4i3Yc59iZ87bttfy96N02hD5ta9Mo0MeOFVZuCjAKMCIi8idKSgy2HTnDou3HWLYrjZz8Iltb69p+9A2vzT2tQ6hWxcOOVVY+CjAKMCIicoXyCov5cV8Gi7cfZ+3PJygu+fWt0c3FxO1NA+kbXos7mwXqfpkbQAFGAUZERK7CybP5fJuQyuIdx0k8nm3bbvF0o1frEPq2rUW7ulW1hEE5UYBRgBERkWt0ICOHRTuOs2THcdKy82zb61b3/u/3y9SibvUqdqyw4lGAUYAREZHrpLjEIO7QKb7efpwVu9M4V1Bsa2tXtyp9w2vRq2UIft7udqyyYlCAUYAREZFykFtQxA97Mli04zgbDpzgv7fL4OHqwl3NA+nTtha3Nw3Ew83FvoU6KQUYBRgRESlnGdY8vkk4zqLtx9mfnmPbXtXbnXtah9A3vData/vpfpkyUIBRgBERkRtob6qVxTuOsSQhlRM5+bbtDWpUoW94LXq3rUXtqt52rNA5KMAowIiIiB0UFZew8eApFm0/xso96eQVltjaOtavRr/w2vRsGYyvp+6XuRQFGAUYERGxs7P5RSxPTGPxjuPEHjrFhXdcs5sL3W8Kpm/bWnRtXAM3V90vc4ECjAKMiIg4kNSs8yz57/0yv2SetW2v4WPm3tYh9A2vxU0hlkp/v4wCjAKMiIg4IMMw2H3cytfbj/HtzlROnyuwtTUJ8qFveG16t6lFsJ+nHau0HwUYBRgREXFwhcUlrP/5BIu2H2fVvgwKin69X8Zkgi4Na9CnbS16tAimitnNzpXeOAowCjAiIuJEss8X8n1iGou3H2fL4dO27V7urvRoEUzf8Fp0blgDV5eK/RGTAowCjIiIOKmjp3NZvOM4i7Yf4/CpXNv2IIuZ3m1q0Se8Fs2CK+Z7mwKMAoyIiDg5wzDYcTSLRduP8d3ONLLPF9rawmpa6Btei3vbhBDoW3Hul1GAUYAREZEKJL+omJ/2n2DxjmOs2Z9JYfGvb9+uLia6Nv71fpnuYcF4ebjaudJrowCjACMiIhXUmXMFLE1MY9H2Y+xIybJt9zG70bNFMH3Da9OxfjVcnPB+GQUYBRgREakEDp04y5Idx1m04zjHzpy3ba/l70XvtiH0aVubRoE+dqywbBRgFGBERKQSKSkx2HbkDIu2H2NZYho5eUW2tta1/egbXpt7WodQrYqHHau8PAUYBRgREamk8gqL+XFfBou3H2ftzycoLvn1rd7NxcTtTQPoG16bO5sF4unuePfLKMAowIiIiHDybD7f7Uxl0fbjJB7Ptm23eLoR1SqEfuG1aFe3qsMsYaAAowAjIiJSyoGMHBbtOM6SHcdJy86zba9TzZs+bWvRN7wWdatXsWOFCjAKMCIiIn+gpMRg86FTfL39OCt2p3GuoNjW1q5uVfq0rUWvVjXx977x98tc6ft3mdbvnjJlCh06dMDX15fAwEB69+5NUlJSqT55eXlER0dTvXp1fHx86NevHxkZGaX6pKSkEBUVhbe3N4GBgUyYMIGioqJSfdauXUt4eDhms5lGjRoRExNTllJFRETkD7i4mOjcqAZvPtiarS90498PteHWJgG4mCD+yBleWLKbm/+xmpGfxvPDnnTbGk2OpExXYHr06EH//v3p0KEDRUVF/PWvf2X37t3s3buXKlV+veQ0atQoli1bRkxMDH5+fowePRoXFxc2btwIQHFxMW3atCE4OJjXX3+dtLQ0Bg8ezPDhw3nttdcASE5OpkWLFowcOZJhw4axevVqxo4dy7Jly4iMjLyiWnUFRkREpGwyrXl8k5DK19uPsT89x7a9qrc797QOoW94bVrX9ivX+2VuyEdIJ06cIDAwkHXr1nHrrbeSnZ1NQEAA8+bN4/777wdg//79NG/enNjYWDp16sTy5cvp1asXqampBAUFATBz5kwmTpzIiRMn8PDwYOLEiSxbtozdu3fbnqt///5kZWWxYsWK6/oHICIiIhfbm2pl8Y5jLElI5UROvm17gxpV6Btei95ta1G7qvd1f95y+Qjp97Kzf72buVq1agDEx8dTWFhIt27dbH2aNWtGnTp1iI2NBSA2NpaWLVvawgtAZGQkVquVPXv22Pr89hgX+lw4xqXk5+djtVpLPUREROTqhIVYeD4qjNjn7mTukJvp3SYET3cXDp08xxs//Mwt//yJORuT7Vaf29XuWFJSwtixY+nSpQstWrQAID09HQ8PD/z9/Uv1DQoKIj093dbnt+HlQvuFtj/rY7VaOX/+PF5eXhfVM2XKFF5++eWrHY6IiIhcgpurC7c1CeC2JgGczS9ixe50Fm0/RuyhU7SvW81+dV3tjtHR0ezevZsNGzZcz3qu2qRJkxg/frztd6vVSmhoqB0rEhERqVh8zG7c364297erTXp2HkEWs91quaoAM3r0aJYuXcr69eupXbu2bXtwcDAFBQVkZWWVugqTkZFBcHCwrc+WLVtKHe/CLKXf9vn9zKWMjAwsFsslr74AmM1mzGb7/UGKiIhUJsF+nnZ9/jLdA2MYBqNHj2bx4sWsWbOG+vXrl2pv164d7u7urF692rYtKSmJlJQUIiIiAIiIiCAxMZHMzExbn1WrVmGxWAgLC7P1+e0xLvS5cAwRERGp3Mo0C+nJJ59k3rx5fPPNNzRt2tS23c/Pz3ZlZNSoUXz//ffExMRgsVgYM2YMAJs2bQL+N406JCSEadOmkZ6ezqBBgxg2bNhF06ijo6MZMmQIa9as4amnntI0ahERkQruit+/jTIALvmYM2eOrc/58+eNJ5980qhatarh7e1t9OnTx0hLSyt1nMOHDxs9e/Y0vLy8jBo1ahjPPPOMUVhYWKrPTz/9ZLRp08bw8PAwGjRoUOo5rkR2drYBGNnZ2WXaT0REROznSt+/tZSAiIiIOIwb8j0wIiIiIvagACMiIiJORwFGREREnI4CjIiIiDgdBRgRERFxOgowIiIi4nQUYERERMTpKMCIiIiI01GAEREREadzVatRO4MLXzBstVrtXImIiIhcqQvv25dbKKDCBpicnBwAQkND7VyJiIiIlFVOTg5+fn5/2F5h10IqKSkhNTUVX19fTCbTdTuu1WolNDSUo0ePVtg1lir6GDU+51fRx1jRxwcVf4wa39UzDIOcnBxCQkJwcfnjO10q7BUYFxcXateuXW7Ht1gsFfIv5W9V9DFqfM6voo+xoo8PKv4YNb6r82dXXi7QTbwiIiLidBRgRERExOkowJSR2Wzmb3/7G2az2d6llJuKPkaNz/lV9DFW9PFBxR+jxlf+KuxNvCIiIlJx6QqMiIiIOB0FGBEREXE6CjAiIiLidBRgRERExOkowPzX+vXrueeeewgJCcFkMrFkyZJS7YZhMHnyZGrWrImXlxfdunXjwIEDpfqcPn2agQMHYrFY8Pf3Z+jQoZw9e/YGjuKP/dn4CgsLmThxIi1btqRKlSqEhIQwePBgUlNTSx2jXr16mEymUo+pU6fe4JFc2uXO32OPPXZR7T169CjVx5HPH1x+jL8f34XH66+/buvjyOdwypQpdOjQAV9fXwIDA+nduzdJSUml+uTl5REdHU316tXx8fGhX79+ZGRklOqTkpJCVFQU3t7eBAYGMmHCBIqKim7kUC7pcuM7ffo0Y8aMoWnTpnh5eVGnTh2eeuopsrOzSx3nUud4/vz5N3o4F7mS83f77bdfVPvIkSNL9XHU8weXH+Phw4f/8HW4cOFCWz9HPYczZsygVatWti+ni4iIYPny5bZ2R3v9KcD817lz52jdujXvvffeJdunTZvG22+/zcyZM4mLi6NKlSpERkaSl5dn6zNw4ED27NnDqlWrWLp0KevXr+eJJ564UUP4U382vtzcXLZv386LL77I9u3bWbRoEUlJSdx7770X9X3llVdIS0uzPcaMGXMjyr+sy50/gB49epSq/YsvvijV7sjnDy4/xt+OLS0tjY8//hiTyUS/fv1K9XPUc7hu3Tqio6PZvHkzq1atorCwkO7du3Pu3Dlbn3HjxvHdd9+xcOFC1q1bR2pqKn379rW1FxcXExUVRUFBAZs2bWLu3LnExMQwefJkewyplMuNLzU1ldTUVN544w12795NTEwMK1asYOjQoRcda86cOaXOYe/evW/waC52JecPYPjw4aVqnzZtmq3Nkc8fXH6MoaGhF70OX375ZXx8fOjZs2epYzniOaxduzZTp04lPj6ebdu2ceedd3LfffexZ88ewAFff4ZcBDAWL15s+72kpMQIDg42Xn/9ddu2rKwsw2w2G1988YVhGIaxd+9eAzC2bt1q67N8+XLDZDIZx48fv2G1X4nfj+9StmzZYgDGkSNHbNvq1q1rTJ8+vXyLuw4uNb5HH33UuO+++/5wH2c6f4ZxZefwvvvuM+68885S25zlHBqGYWRmZhqAsW7dOsMwfn3Nubu7GwsXLrT12bdvnwEYsbGxhmEYxvfff2+4uLgY6enptj4zZswwLBaLkZ+ff2MHcBm/H9+lLFiwwPDw8DAKCwtt267k3DuCS43vtttuM55++uk/3MeZzp9hXNk5bNOmjTFkyJBS25zlHBqGYVStWtWYPXu2Q77+dAXmCiQnJ5Oenk63bt1s2/z8/OjYsSOxsbEAxMbG4u/vT/v27W19unXrhouLC3FxcTe85muVnZ2NyWTC39+/1PapU6dSvXp12rZty+uvv+4wl3avxNq1awkMDKRp06aMGjWKU6dO2doq2vnLyMhg2bJll/zfu7OcwwsfnVSrVg2A+Ph4CgsLS70OmzVrRp06dUq9Dlu2bElQUJCtT2RkJFar1fa/SEfx+/H9UR+LxYKbW+ll66Kjo6lRowY333wzH3/8MYYDfp3XH43v888/p0aNGrRo0YJJkyaRm5tra3Om8weXP4fx8fEkJCRc8nXo6OewuLiY+fPnc+7cOSIiIhzy9VdhF3O8ntLT0wFKnZQLv19oS09PJzAwsFS7m5sb1apVs/VxFnl5eUycOJEBAwaUWqTrqaeeIjw8nGrVqrFp0yYmTZpEWloa//rXv+xY7ZXp0aMHffv2pX79+hw8eJC//vWv9OzZk9jYWFxdXSvU+QOYO3cuvr6+pS7vgvOcw5KSEsaOHUuXLl1o0aIF8OtrzMPD46JQ/fvX4aVepxfaHMWlxvd7J0+e5NVXX73oY8xXXnmFO++8E29vb3744QeefPJJzp49y1NPPXUjSr8ifzS+hx9+mLp16xISEsKuXbuYOHEiSUlJLFq0CHCe8wdXdg4/+ugjmjdvTufOnUttd+RzmJiYSEREBHl5efj4+LB48WLCwsJISEhwuNefAoyUUlhYyIMPPohhGMyYMaNU2/jx420/t2rVCg8PD0aMGMGUKVMc/uuy+/fvb/u5ZcuWtGrVioYNG7J27VruuusuO1ZWPj7++GMGDhyIp6dnqe3Ocg6jo6PZvXs3GzZssHcp5eJy47NarURFRREWFsZLL71Uqu3FF1+0/dy2bVvOnTvH66+/7hBvfhf80fh+G8ZatmxJzZo1ueuuuzh48CANGza80WVek8udw/PnzzNv3rxS5+sCRz6HTZs2JSEhgezsbL766iseffRR1q1bZ++yLkkfIV2B4OBggIvuts7IyLC1BQcHk5mZWaq9qKiI06dP2/o4ugvh5ciRI6xateqyS6R37NiRoqIiDh8+fGMKvI4aNGhAjRo1+OWXX4CKcf4u+M9//kNSUhLDhg27bF9HPIejR49m6dKl/PTTT9SuXdu2PTg4mIKCArKyskr1//3r8FKv0wttjuCPxndBTk4OPXr0wNfXl8WLF+Pu7v6nx+vYsSPHjh0jPz+/vEouk8uN77c6duwIUOp16OjnD65sjF999RW5ubkMHjz4ssdzpHPo4eFBo0aNaNeuHVOmTKF169a89dZbDvn6U4C5AvXr1yc4OJjVq1fbtlmtVuLi4oiIiAAgIiKCrKws4uPjbX3WrFlDSUmJ7UXqyC6ElwMHDvDjjz9SvXr1y+6TkJCAi4vLRR+9OINjx45x6tQpatasCTj/+futjz76iHbt2tG6devL9nWkc2gYBqNHj2bx4sWsWbOG+vXrl2pv164d7u7upV6HSUlJpKSklHodJiYmlgqjF8J4WFjYjRnIH7jc+ODXf1e6d++Oh4cH33777UVX0C4lISGBqlWr2v0K2pWM7/cSEhIASr0OHfX8QdnG+NFHH3HvvfcSEBBw2eM6yjm8lJKSEvLz8x3z9Xfdbwt2Ujk5OcaOHTuMHTt2GIDxr3/9y9ixY4dtFs7UqVMNf39/45tvvjF27dpl3HfffUb9+vWN8+fP247Ro0cPo23btkZcXJyxYcMGo3HjxsaAAQPsNaRS/mx8BQUFxr333mvUrl3bSEhIMNLS0myPC3eOb9q0yZg+fbqRkJBgHDx40Pjss8+MgIAAY/DgwXYe2a/+bHw5OTnGs88+a8TGxhrJycnGjz/+aISHhxuNGzc28vLybMdw5PNnGJf/O2oYhpGdnW14e3sbM2bMuGh/Rz+Ho0aNMvz8/Iy1a9eW+juYm5tr6zNy5EijTp06xpo1a4xt27YZERERRkREhK29qKjIaNGihdG9e3cjISHBWLFihREQEGBMmjTJHkMq5XLjy87ONjp27Gi0bNnS+OWXX0r1KSoqMgzDML799lvjww8/NBITE40DBw4Y77//vuHt7W1MnjzZnkMzDOPy4/vll1+MV155xdi2bZuRnJxsfPPNN0aDBg2MW2+91XYMRz5/hnFlf0cNwzAOHDhgmEwmY/ny5Rcdw5HP4XPPPWesW7fOSE5ONnbt2mU899xzhslkMn744QfDMBzv9acA818//fSTAVz0ePTRRw3D+HUq9YsvvmgEBQUZZrPZuOuuu4ykpKRSxzh16pQxYMAAw8fHx7BYLMbjjz9u5OTk2GE0F/uz8SUnJ1+yDTB++uknwzAMIz4+3ujYsaPh5+dneHp6Gs2bNzdee+21UgHAnv5sfLm5uUb37t2NgIAAw93d3ahbt64xfPjwUlP9DMOxz59hXP7vqGEYxqxZswwvLy8jKyvrov0d/Rz+0d/BOXPm2PqcP3/eePLJJ42qVasa3t7eRp8+fYy0tLRSxzl8+LDRs2dPw8vLy6hRo4bxzDPPlJqGbC+XG98fnV/ASE5ONgzj16n9bdq0MXx8fIwqVaoYrVu3NmbOnGkUFxfbb2D/dbnxpaSkGLfeeqtRrVo1w2w2G40aNTImTJhgZGdnlzqOo54/w7iyv6OGYRiTJk0yQkNDL3leHPkcDhkyxKhbt67h4eFhBAQEGHfddZctvBiG473+TIbhYHO3RERERC5D98CIiIiI01GAEREREaejACMiIiJORwFGREREnI4CjIiIiDgdBRgRERFxOgowIiIi4nQUYERERMTpKMCIiN2tXbsWk8l00UJxIiJ/RAFGROyuc+fOpKWl4efnd8X75ObmMmnSJBo2bIinpycBAQHcdtttfPPNN+VYqYg4Cjd7FyAi4uHhQXBwcJn2GTlyJHFxcbzzzjuEhYVx6tQpNm3axKlTp8qpShFxJLoCIyLX3e23386YMWMYO3YsVatWJSgoiA8//JBz587x+OOP4+vrS6NGjVi+fDlw8UdIMTEx+Pv7s3LlSpo3b46Pjw89evQgLS3N9hzffvstf/3rX7n77rupV68e7dq1Y8yYMQwZMsTWx2QysWTJklK1+fv7ExMTA8Dhw4cxmUzMnz+fzp074+npSYsWLVi3bl25/vmIyLVTgBGRcjF37lxq1KjBli1bGDNmDKNGjeKBBx6gc+fObN++ne7duzNo0CByc3MvuX9ubi5vvPEGn376KevXryclJYVnn33W1h4cHMz3339PTk7ONdc6YcIEnnnmGXbs2EFERAT33HOPruSIODgFGBEpF61bt+aFF16gcePGTJo0CU9PT2rUqMHw4cNp3LgxkydP5tSpU+zateuS+xcWFjJz5kzat29PeHg4o0ePZvXq1bb2Dz74gE2bNlG9enU6dOjAuHHj2Lhx41XVOnr0aPr160fz5s2ZMWMGfn5+fPTRR1d1LBG5MRRgRKRctGrVyvazq6sr1atXp2XLlrZtQUFBAGRmZl5yf29vbxo2bGj7vWbNmqX63nrrrRw6dIjVq1dz//33s2fPHrp27cqrr75a5lojIiJsP7u5udG+fXv27dtX5uOIyI2jACMi5cLd3b3U7yaTqdQ2k8kEQElJyRXvbxjGRX26du3KxIkT+eGHH3jllVd49dVXKSgo+MN9CgsLr25AIuJQFGBEpMIICwujqKiIvLw8AAICAkrd+HvgwIFL3nOzefNm289FRUXEx8fTvHnz8i9YRK6aplGLiFO6/fbbGTBgAO3bt6d69ers3buXv/71r9xxxx1YLBYA7rzzTt59910iIiIoLi5m4sSJF13ZAXjvvfdo3LgxzZs3Z/r06Zw5c6bUbCYRcTy6AiMiTikyMpK5c+fSvXt3mjdvzpgxY4iMjGTBggW2Pm+++SahoaF07dqVhx9+mGeffRZvb++LjjV16lSmTp1K69at2bBhA99++y01atS4kcMRkTIyGb//gFhEpJI4fPgw9evXZ8eOHbRp08be5YhIGegKjIiIiDgdBRgRERFxOvoISURERJyOrsCIiIiI01GAEREREaejACMiIiJORwFGREREnI4CjIiIiDgdBRgRERFxOgowIiIi4nQUYERERMTp/D+izBYG+hcqXQAAAABJRU5ErkJggg==\n" 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\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/partialPeriodicPattern/basic/PPPGrowth.ipynb b/notebooks/partialPeriodicPattern/basic/PPPGrowth.ipynb index db917df4..278a6830 100644 --- a/notebooks/partialPeriodicPattern/basic/PPPGrowth.ipynb +++ b/notebooks/partialPeriodicPattern/basic/PPPGrowth.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Partial Periodic Frequent patterns in Temporal Databases using PPPGrowth" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Partial Periodic Frequent patterns in a temporal database using the PPPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the PPPGrowth algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "8c0d17b3-1e6c-413b-9a80-9c278a13e872" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m7.2 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already 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(8.2.3)\n", + "Collecting JsonForm>=0.0.2 (from resource->pami)\n", + " Downloading JsonForm-0.0.2.tar.gz (2.4 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting JsonSir>=0.0.2 (from resource->pami)\n", + " Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in 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/root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=583caab11062938b6e0b1c75a97284d0f100f8900927ba21a0fe06ba033ebe13\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Partial Periodic Frequent patterns in Temporal Databases using PPPGrowth" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Partial Periodic Frequent patterns in a temporal database using the PPPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the PPPGrowth algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "8c0d17b3-1e6c-413b-9a80-9c278a13e872" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "2b3f73bf-e2df-473a-f9ba-8a9d905dc080" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-07 07:10:40-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.26MB/s in 3.5s \n", - "\n", - "2023-09-07 07:10:45 (1.26 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "fba11a8c-7d1a-4dff-cc19-e6acd413228e" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Partial Periodic Frequent patterns using PPPGrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "44cc54a6-21f9-4542-f32d-3b0ef79f0a3e" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "43cace24-8cc7-41e0-9589-66185c2c0362" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Partial Periodic Frequent patterns using PPPGrowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.partialPeriodicPattern.basic import PPPGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.PPPGrowth(iFile=inputFile, minPS=minimumSupportCount, period=PeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('partialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "c812508f-4812-45d8-a817-7b2877967614" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", - "Total No of patterns: 27134\n", - "Runtime: 24.31682300567627\n", - "Memory (RSS): 607637504\n", - "Memory (USS): 561012736\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'partialPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8055eefd-65ec-48fb-a674-efcef3cd2ba7" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "729:100 \n", - "330:101 \n", - "199:107 \n", - "62:108 \n", - "102:108 \n", - "856:108 \n", - "856\t490:102 \n", - "856\t490\t906:102 \n", - "856\t906:102 \n", - "426:110 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "2b3f73bf-e2df-473a-f9ba-8a9d905dc080" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-07 07:10:40-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.26MB/s in 3.5s \n", + "\n", + "2023-09-07 07:10:45 (1.26 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "fba11a8c-7d1a-4dff-cc19-e6acd413228e" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Partial Periodic Frequent patterns using PPPGrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "44cc54a6-21f9-4542-f32d-3b0ef79f0a3e" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "43cace24-8cc7-41e0-9589-66185c2c0362" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _partialPeriodicfrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the PPPGrowth algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.partialPeriodicPattern.basic import PPPGrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "PeriodCount = 5000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of PPPGrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of PPPGrowth algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.PPPGrowth(iFile=inputFile, minPS=minSupCount, period=PeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['PPPGrowth', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "50120557-8cea-4704-eb23-cdd1298aa909" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", - "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", - "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", - "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", - "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Partial Periodic Frequent patterns using PPPGrowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.partialPeriodicPattern.basic import PPPGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.PPPGrowth(iFile=inputFile, minPS=minimumSupportCount, period=PeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('partialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c812508f-4812-45d8-a817-7b2877967614" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", + "Total No of patterns: 27134\n", + "Runtime: 24.31682300567627\n", + "Memory (RSS): 607637504\n", + "Memory (USS): 561012736\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'partialPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8055eefd-65ec-48fb-a674-efcef3cd2ba7" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "729:100 \n", + "330:101 \n", + "199:107 \n", + "62:108 \n", + "102:108 \n", + "856:108 \n", + "856\t490:102 \n", + "856\t490\t906:102 \n", + "856\t906:102 \n", + "426:110 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _partialPeriodicfrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the PPPGrowth algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.partialPeriodicPattern.basic import PPPGrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "PeriodCount = 5000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of PPPGrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of PPPGrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.PPPGrowth(iFile=inputFile, minPS=minSupCount, period=PeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['PPPGrowth', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "50120557-8cea-4704-eb23-cdd1298aa909" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", + "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", + "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", + "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", + "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4c3503c5-052f-401e-cb99-da9bab9128a6" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup PeriodCount patterns runtime memory\n", + "0 PPPGrowth 100 5000 27134 24.994847 613310464\n", + "1 PPPGrowth 150 5000 18967 23.964055 611307520\n", + "2 PPPGrowth 200 5000 13131 26.192323 609583104\n", + "3 PPPGrowth 250 5000 7604 24.071209 606945280\n", + "4 PPPGrowth 300 5000 4482 19.968367 603377664\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "a779056a-8b09-4063-a47c-b2974671f790" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 14 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4c3503c5-052f-401e-cb99-da9bab9128a6" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup PeriodCount patterns runtime memory\n", - "0 PPPGrowth 100 5000 27134 24.994847 613310464\n", - "1 PPPGrowth 150 5000 18967 23.964055 611307520\n", - "2 PPPGrowth 200 5000 13131 26.192323 609583104\n", - "3 PPPGrowth 250 5000 7604 24.071209 606945280\n", - "4 PPPGrowth 300 5000 4482 19.968367 603377664\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "a779056a-8b09-4063-a47c-b2974671f790" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 14 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/partialPeriodicPattern/basic/PPP_ECLAT.ipynb b/notebooks/partialPeriodicPattern/basic/PPP_ECLAT.ipynb index 3fe9cff3..18db8686 100644 --- a/notebooks/partialPeriodicPattern/basic/PPP_ECLAT.ipynb +++ b/notebooks/partialPeriodicPattern/basic/PPP_ECLAT.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Partial Periodic Frequent patterns in Temporal Databases using PPP_ECLAT" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Partial Periodic Frequent patterns in a temporal database using the PPP_ECLAT algorithm. In the final part, we describe an advanced approach, where we evaluate the PPP_ECLAT algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "c238a93c-60cd-485e-cc17-17e3395adb2e" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m6.5 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already 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PPP_ECLAT" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Partial Periodic Frequent patterns in a temporal database using the PPP_ECLAT algorithm. In the final part, we describe an advanced approach, where we evaluate the PPP_ECLAT algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "c238a93c-60cd-485e-cc17-17e3395adb2e" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "ea167cea-1a5e-42cb-d61f-01769b64b209" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-07 07:23:35-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.33MB/s in 3.3s \n", - "\n", - "2023-09-07 07:23:39 (1.33 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "8011ff82-6e34-4370-8ca1-4ef882a54634" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Partial Periodic Frequent patterns using PPP_ECLAT" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "91dfa2c2-b28e-4e29-bf71-6384b2264a39" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "452c8d0d-ea58-4a88-d34e-f6c418a440f6" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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t27dHRkYGVq5cCbVafce1pojozhiSiIhMyJIlS7Bu3Tp06dIFkZGRdX7/U089hZ9//hnLly+HTCZDt27dsHLlSvTp06f+iyVq4jgniYiIiMgAzkkiIiIiMoAhiYiIiMgAzkm6D1qtFqmpqbCzs7uvxdiIiIhIekIIFBYWwtPTE2Zmde8XYki6D6mpqbWelk5ERESNw7Vr1+Dl5VXn9zEk3Qc7OzsA1RdZpVJJXA0RERHdD7VaDW9vb933eF0xJN2HmiE2lUrFkERERNTIPOhUGU7cJiIiIjKAIYmIiIjIAIYkIiIiIgMYkoiIiIgMYEgiIiIiMoAhiYiIiMgAhiQiIiIiAxiSiIiIiAxgSCIiIiIygCGJiIiIyACGJCIiIiIDGJKIiIiIDGBIklh+SQVi0wulLoOIiIhuw5AkofjMQnR5bzdGLTkKIYTU5RAREdEtGJIk5OVoDZkMKCyvQl5JpdTlEBER0S0YkiRkaSGHh8oSAJCUUyxxNURERHQrhiSJ+ThbAwCSGZKIiIhMCkOSxHydbQAAyTklEldCREREt2JIkpgPQxIREZFJYkiSWM1wG+ckERERmRaGJIn9PSeJPUlERESmhCFJYjXDbbnFFVCXcRkAIiIiU8GQJDFbpTlcbBUAgBT2JhEREZkMhiQTUNObxHlJREREpoMhyQRwXhIREZHpYUgyAX+vlcSeJCIiIlPBkGQC/l4GgD1JREREpoIhyQT4sCeJiIjI5DAkmQDfmz1JGepylFZoJK6GiIiIAIYkk+BgrYDK0hwAkJLLITciIiJTwJBkInxduAwAERGRKWFIMhGcl0RERGRaGJJMhC/XSiIiIjIpDEkmopUTQxIREZEpYUgyEZyTREREZFoYkkxEzYKSqfmlqKjSSlwNERERMSSZCFdbJawVcmgFcD2PQ25ERERSY0gyETKZjPOSiIiITAhDkgmpedAt5yURERFJjyHJhPi4sCeJiIjIVDAkmRAfJy4oSUREZCoYkkwIF5QkIiIyHQxJJsTn5lpJ1/JKoNEKiashIiJq3iQPSTdu3MCLL74IZ2dnWFlZITg4GKdOndLtF0Lg7bffRosWLWBlZYVBgwYhLi5O7xi5ubkYO3YsVCoVHBwcMGnSJBQVFem1OX/+PHr37g1LS0t4e3vj448/Nsr51YWHyhIKuRkqNQKp+aVSl0NERNSsSRqS8vLy0KtXL1hYWGDHjh24dOkSPvvsMzg6OurafPzxx/jqq6+wdOlSHD9+HDY2NggPD0dZWZmuzdixYxETE4Pdu3dj69atOHjwIKZOnarbr1arMXjwYPj4+CA6OhqffPIJ3n33XSxfvtyo53svcjMZvJ2sAHDIjYiISHJCQm+88YYICwu7436tVis8PDzEJ598otuWn58vlEql+PHHH4UQQly6dEkAECdPntS12bFjh5DJZOLGjRtCCCG+/fZb4ejoKMrLy/U+u23btvdVZ0FBgQAgCgoK6nR+D+Ll1SeEzxtbxfdRSQ3+WURERE3Zw35/S9qTtGXLFnTv3h3PPvss3Nzc0LVrV3z33Xe6/YmJiUhPT8egQYN02+zt7dGjRw9ERUUBAKKiouDg4IDu3bvr2gwaNAhmZmY4fvy4rk2fPn2gUCh0bcLDwxEbG4u8vLxadZWXl0OtVuu9jMXHmXe4ERERmQJJQ9LVq1exZMkSBAYGYteuXZgxYwZmz56NNWvWAADS09MBAO7u7nrvc3d31+1LT0+Hm5ub3n5zc3M4OTnptTF0jFs/41aLFi2Cvb297uXt7V0PZ3t/fHiHGxERkUmQNCRptVp069YNH3zwAbp27YqpU6diypQpWLp0qZRlYeHChSgoKNC9rl27ZrTPZkgiIiIyDZKGpBYtWiAoKEhvW/v27ZGSkgIA8PDwAABkZGTotcnIyNDt8/DwQGZmpt7+qqoq5Obm6rUxdIxbP+NWSqUSKpVK72UsNY8mSc4thhBcBoCIiEgqkoakXr16ITY2Vm/blStX4OPjAwDw8/ODh4cH9uzZo9uvVqtx/PhxhIaGAgBCQ0ORn5+P6OhoXZu9e/dCq9WiR48eujYHDx5EZWWlrs3u3bvRtm1bvTvpTEFLRyvIzWQoq9Qis7Bc6nKIiIiaLUlD0rx583Ds2DF88MEHiI+Pxw8//IDly5cjIiICACCTyTB37lz8+9//xpYtW3DhwgWMGzcOnp6eGDZsGIDqnqcnnngCU6ZMwYkTJ3DkyBHMnDkTY8aMgaenJwDghRdegEKhwKRJkxATE4OffvoJixcvxquvvirVqd+RhdwMLR2qlwFIyubkbSIiIqmYS/nhjzzyCH777TcsXLgQ7733Hvz8/PDll19i7NixujYLFixAcXExpk6divz8fISFhWHnzp2wtLTUtVm/fj1mzpyJgQMHwszMDCNHjsRXX32l229vb48//vgDERERCAkJgYuLC95++229tZRMiY+zNVJyS5CcU4IerZ2lLoeIiKhZkglOfLkntVoNe3t7FBQUGGV+0lubL+L7Y8l4pZ8/FjzRrsE/j4iIqCl62O9vyR9LQrXp7nDL5R1uREREUmFIMkFcUJKIiEh6DEkmyLemJym7hMsAEBERSYQhyQR5O1WHpMLyKuQWV0hcDRERUfPEkGSCLC3kaGFfffce5yURERFJgyHJRP39eBLOSyIiIpICQ5KJqnk8SVI2e5KIiIikwJBkolrd7ElK4XAbERGRJBiSTJSuJ4nDbURERJJgSDJRf89JYk8SERGRFBiSTFTNgpK5xRVQl1VKXA0REVHzw5BkomyV5nCxVQAAUtibREREZHQMSSbMh/OSiIiIJMOQZMI4L4mIiEg6DEkmzMepZq0k9iQREREZG0OSCfN1udmTxLWSiIiIjI4hyYTVzEnio0mIiIiMjyHJhPnenJOUoS5HaYVG4mqIiIiaF4YkE+ZgrYDK0hwAH09CRERkbAxJJs7XhcsAEBERSYEhycRxXhIREZE0GJJMnI9T9bykJK6VREREZFQMSSauZkFJPpqEiIjIuBiSTBznJBEREUmDIcnE1fQkpeaXoryKywAQEREZC0OSiXO1VcJaIYdWANfzSqUuh4iIqNlgSDJxMpkMrZw4L4mIiMjYGJIaAV9nzksiIiIyNoakRqBmXlIye5KIiIiMhiGpEeCCkkRERMbHkNQI+LIniYiIyOgYkhoBn5trJV3LK0GVRitxNURERM0DQ1Ij4KGyhEJuhkqNQFpBmdTlEBERNQsMSY2A3EwGbycrABxyIyIiMhaGpEaCywAQEREZF0NSI9FKN3mbIYmIiMgYGJIaib97kjjcRkREZAwMSY1EzYKSfDQJERGRcTAkNRI1PUnJucXQaoXE1RARETV9DEmNREtHK8jNZCir1CKzsFzqcoiIiJo8hqRGwkJuhpYONcsAcPI2ERFRQ2NIakT4oFsiIiLjYUhqRGpCEtdKIiIiangMSY1IzeTtKxlFEldCRETU9DEkNSI9/JwBAPtiMzkviYiIqIExJDUiwV726NPGFRqtwLf7EqQuh4iIqEljSGpk5gwMBAD8cvo6ruVyAjcREVFDYUhqZEJ8HBEW4IIqrcCSA+xNIiIiaigMSY3Q7Ju9SZtOXUNqfqnE1RARETVNDEmN0KN+TujZ2gmVGoEl+9mbRERE1BAYkhqpOQPbAAB+OnkN6QVlEldDRETU9DAkNVI9WzvhUV8nVGi0WMq5SURERPWOIamRkslkurlJP55IQaaavUlERET1SdKQ9O6770Imk+m92rVrp9tfVlaGiIgIODs7w9bWFiNHjkRGRobeMVJSUjBkyBBYW1vDzc0Nr7/+OqqqqvTa7N+/H926dYNSqURAQAAiIyONcXoNrleAM7q1ckB5lRbLDl6VuhwiIqImRfKepA4dOiAtLU33Onz4sG7fvHnz8Pvvv2PTpk04cOAAUlNTMWLECN1+jUaDIUOGoKKiAkePHsWaNWsQGRmJt99+W9cmMTERQ4YMQf/+/XH27FnMnTsXkydPxq5du4x6ng3h1t6k9ceTkVVYLnFFRERETYdMCCGk+vB3330XmzdvxtmzZ2vtKygogKurK3744QeMGjUKAHD58mW0b98eUVFR6NmzJ3bs2IGhQ4ciNTUV7u7uAIClS5fijTfeQFZWFhQKBd544w1s27YNFy9e1B17zJgxyM/Px86dO++rTrVaDXt7exQUFEClUj38idcjIQSGfXsU567lY1qf1lj4VHupSyIiIjIJD/v9LXlPUlxcHDw9PdG6dWuMHTsWKSkpAIDo6GhUVlZi0KBBurbt2rVDq1atEBUVBQCIiopCcHCwLiABQHh4ONRqNWJiYnRtbj1GTZuaYzR2MpkMcwYGAADWRiUjp4i9SURERPVB0pDUo0cPREZGYufOnViyZAkSExPRu3dvFBYWIj09HQqFAg4ODnrvcXd3R3p6OgAgPT1dLyDV7K/Zd7c2arUapaWGF2IsLy+HWq3We5my/m3d0LGlCqWVGqw4nCh1OURERE2CpCHpySefxLPPPotOnTohPDwc27dvR35+PjZu3ChlWVi0aBHs7e11L29vb0nruReZTIbZA6rnJq09moS84gqJKyIiImr8JB9uu5WDgwPatGmD+Ph4eHh4oKKiAvn5+XptMjIy4OHhAQDw8PCodbdbzc/3aqNSqWBlZWWwjoULF6KgoED3unbtWn2cXoN6PMgd7VuoUFyhwaoj7E0iIiJ6WCYVkoqKipCQkIAWLVogJCQEFhYW2LNnj25/bGwsUlJSEBoaCgAIDQ3FhQsXkJmZqWuze/duqFQqBAUF6drceoyaNjXHMESpVEKlUum9TN2tc5MijyShoKRS4oqIiIgaN0lD0muvvYYDBw4gKSkJR48exfDhwyGXy/H888/D3t4ekyZNwquvvop9+/YhOjoaEydORGhoKHr27AkAGDx4MIKCgvDSSy/h3Llz2LVrF/71r38hIiICSqUSADB9+nRcvXoVCxYswOXLl/Htt99i48aNmDdvnpSn3iAGB3mgrbsdCsursPooe5OIiIgehqQh6fr163j++efRtm1bjB49Gs7Ozjh27BhcXV0BAF988QWGDh2KkSNHok+fPvDw8MCvv/6qe79cLsfWrVshl8sRGhqKF198EePGjcN7772na+Pn54dt27Zh9+7d6Ny5Mz777DOsWLEC4eHhRj/fhmZmJsOsm71Jqw4nQl3G3iQiIqIHJek6SY2FKa+TdDuNViD8y4OIzyzC/MfbYNbNxSaJiIiam0a/ThLVL7mZDLMGVPcmrTiciKLyqnu8g4iIiAxhSGqChnbyRGsXGxSUVmJtVJLU5RARETVKDElNkNxMhpk3e5O+O3gVxexNIiIiqjOGpCbqH5094etsjbySSqw7lix1OURERI0OQ1ITZS43Q0T/6t6k5QevorRCI3FFREREjQtDUhM2rGtLeDtZIae4AuuPszeJiIioLhiSmjALuRki+lX3Ji07eBVllexNIiIiul8MSU3ciG5eaOlghazCcvx4IkXqcoiIiBoNhqQmTmFuhlf6+wMAlh5IYG8SERHRfWJIagZGhXihhb0lMtTl2HTqmtTlEBERNQoMSc2A0lyOGf2qe5O+3Z+A8ir2JhEREd0LQ1IzMbq7N9zslEgrKMPP0delLoeIiMjkMSQ1E5YWckzve7M3aV8CKqq0EldERERk2hiSmpEXerSCi60SN/JL8dsZ9iYRERHdDUNSM1Ldm9QaAPD1vnhUatibREREdCcMSc3MCz1awdlGgWu5pdh85obU5RAREZkshqRmxlphjil9qnuTvtkXjyr2JhERERnEkNQMvdTTB47WFkjKKcHv51OlLoeIiMgkMSQ1QzZKc0zuXd2b9N+98dBohcQVERERmR6GpGZqXKgP7K0scDWrGFvZm0RERFQLQ1IzZWdpgUlhfgCqe5O07E0iIiLSw5DUjI1/zBd2luaIzyzCjovpUpdDRERkUhiSmjF7Kwu83KumNymOvUlERES3YEhq5l7u5QdbpTkupxfij0sZUpdDRERkMhiSmjl7awtMeMwXAPDVnjgIwd4kIiIigCGJAEwK84ONQo5LaWr8+Vem1OUQERGZBIYkgqONAuPYm0RERKSHIYkAAJPD/GBlIceFGwXYH5sldTlERESSY0giAICzrRIvhfoAAL5kbxIRERFDEv1tSu/WUJqb4dy1fByMy5a6HCIiIkkxJJGOq50SY3tU9yYt/vMKe5OIiKhZq3NIunr1akPUQSZiet/WUJib4XRKPo4m5EhdDhERkWTqHJICAgLQv39/rFu3DmVlZQ1RE0nITWWJFx5tBQBY/CfnJhERUfNV55B0+vRpdOrUCa+++io8PDwwbdo0nDhxoiFqI4lM69saCrkZTiTl4tjVXKnLISIikkSdQ1KXLl2wePFipKamYtWqVUhLS0NYWBg6duyIzz//HFlZvH28sWthb4XRj3gBqF43iYiIqDl64Inb5ubmGDFiBDZt2oSPPvoI8fHxeO211+Dt7Y1x48YhLS2tPuskI5vRLwAWchmirubgRCJ7k4iIqPl54JB06tQpvPLKK2jRogU+//xzvPbaa0hISMDu3buRmpqKZ555pj7rJCNr6WCFUSHeANibREREzVOdQ9Lnn3+O4OBgPPbYY0hNTcXatWuRnJyMf//73/Dz80Pv3r0RGRmJ06dPN0S9ZESv9POHuZkMh+OzEZ3M3iQiImpe6hySlixZghdeeAHJycnYvHkzhg4dCjMz/cO4ublh5cqV9VYkScPbyRojurUEAHy1J17iaoiIiIxLJniP9z2p1WrY29ujoKAAKpVK6nKMKjmnGAM+OwCNVmBzRC908XaQuiQiIqL78rDf33XuSVq9ejU2bdpUa/umTZuwZs2aOhdAps3H2QbDulT3Jv2Xc5OIiKgZqXNIWrRoEVxcXGptd3NzwwcffFAvRZFpiejvDzMZsOdyJi5cL5C6HCIiIqOoc0hKSUmBn59fre0+Pj5ISUmpl6LItLR2tcU/OnsCAL7ay94kIiJqHuocktzc3HD+/Pla28+dOwdnZ+d6KYpMz8wBAZDJgN2XMhCTyt4kIiJq+uockp5//nnMnj0b+/btg0ajgUajwd69ezFnzhyMGTOmIWokExDgZoehnap7k77eyzvdiIio6TOv6xvef/99JCUlYeDAgTA3r367VqvFuHHjOCepiZs1IAC/n0vFjovpuJyuRjuP5nWnHxERNS8PvATAlStXcO7cOVhZWSE4OBg+Pj71XZvJaM5LANzulfXR2H4hHUM6tcA3L3STuhwiIqI7etjv7zr3JNVo06YN2rRp86Bvp0Zq1oBAbL+Qju0X0hCXUYhAdzupSyIiImoQdQ5JGo0GkZGR2LNnDzIzM6HVavX27927t96KI9PTvoUK4R3csSsmA1/vi8fiMV2lLomIiKhB1DkkzZkzB5GRkRgyZAg6duwImUzWEHWRCZs1IBC7YjLw+7lUzB4YCH9XW6lLIiIiqnd1DkkbNmzAxo0b8dRTTzVEPdQIdGxpj0Ht3fDnX5n4Zm88Pn+ui9QlERER1bs6LwGgUCgQEBDQELVQIzJrQCAAYPPZG0jKLpa4GiIiovpX55A0f/58LF68GHwubvPW2dsB/dq6QiuAb/Zx3SQiImp66jzcdvjwYezbtw87duxAhw4dYGFhobf/119/rbfiyLTNHhiI/bFZ+PXMDcweGAhvJ2upSyIiIqo3de5JcnBwwPDhw9G3b1+4uLjA3t5e7/WgPvzwQ8hkMsydO1e3raysDBEREXB2doatrS1GjhyJjIwMvfelpKRgyJAhsLa2hpubG15//XVUVVXptdm/fz+6desGpVKJgIAAREZGPnCd9LdurRzRO9AFGq1gbxIRETU5de5JWr16db0XcfLkSSxbtgydOnXS2z5v3jxs27YNmzZtgr29PWbOnIkRI0bgyJEjAKqXIxgyZAg8PDxw9OhRpKWlYdy4cbCwsNCt/p2YmIghQ4Zg+vTpWL9+Pfbs2YPJkyejRYsWCA8Pr/dzaW7mDAzEobhs/Bx9HTMHBMDLkb1JRETUNNS5JwkAqqqq8Oeff2LZsmUoLCwEAKSmpqKoqKjOxyoqKsLYsWPx3XffwdHRUbe9oKAAK1euxOeff44BAwYgJCQEq1evxtGjR3Hs2DEAwB9//IFLly5h3bp16NKlC5588km8//77+Oabb1BRUQEAWLp0Kfz8/PDZZ5+hffv2mDlzJkaNGoUvvvjiQU6dbtPd1wmP+TujSiuwZH+C1OUQERHVmzqHpOTkZAQHB+OZZ55BREQEsrKyAAAfffQRXnvttToXEBERgSFDhmDQoEF626Ojo1FZWam3vV27dmjVqhWioqIAAFFRUQgODoa7u7uuTXh4ONRqNWJiYnRtbj92eHi47hiGlJeXQ61W673ozuYMrL7TbeOpa0jNL5W4GiIiovpR55A0Z84cdO/eHXl5ebCystJtHz58OPbs2VOnY23YsAGnT5/GokWLau1LT0+HQqGAg4OD3nZ3d3ekp6fr2twakGr21+y7Wxu1Wo3SUsNf6IsWLdKbZ+Xt7V2n82puerR2Rg8/J1RqBJYeYG8SERE1DXUOSYcOHcK//vUvKBQKve2+vr64cePGfR/n2rVrmDNnDtavXw9LS8u6ltGgFi5ciIKCAt3r2rVrUpdk8mp6kzacuIb0gjKJqyEiInp4dQ5JWq0WGo2m1vbr16/Dzu7+H3YaHR2NzMxMdOvWDebm5jA3N8eBAwfw1VdfwdzcHO7u7qioqEB+fr7e+zIyMuDh4QEA8PDwqHW3W83P92qjUqn0esJupVQqoVKp9F50d6H+zuju44gKjRbLDrI3iYiIGr86h6TBgwfjyy+/1P0sk8lQVFSEd955p06PKhk4cCAuXLiAs2fP6l7du3fH2LFjdX+2sLDQG8KLjY1FSkoKQkNDAQChoaG4cOECMjMzdW12794NlUqFoKAgXZvbhwF3796tOwbVD5lMhjmDqnuTfjiegsxC9iYREVHjJhN1XDr7+vXrCA8PhxACcXFx6N69O+Li4uDi4oKDBw/Czc3tgYvp168funTpogthM2bMwPbt2xEZGQmVSoVZs2YBAI4ePQqgegmALl26wNPTEx9//DHS09Px0ksvYfLkyXpLAHTs2BERERF4+eWXsXfvXsyePRvbtm277yUA1Go17O3tUVBQwF6luxBCYMSSoziTko/JYX7419AgqUsiIqJm7GG/v+u8TpKXlxfOnTuHDRs24Pz58ygqKsKkSZMwduzYOw5fPagvvvgCZmZmGDlyJMrLyxEeHo5vv/1Wt18ul2Pr1q2YMWMGQkNDYWNjg/Hjx+O9997TtfHz88O2bdswb948LF68GF5eXlixYgXXSGoAMpkMswcGYuLqk1h3PBlPBnsgxMdJ6rKIiIgeSJ17kpoj9iTdPyEExq06gUNx2bCykOO7cd0RFugidVlERNQMPez3d51D0tq1a++6f9y4cXUuwtQxJNVNaYUG09ZF4+CVLCjkZvjvC10R3sFD6rKIiKiZMXpIunVVbACorKxESUkJFAoFrK2tkZubW+ciTB1DUt2VV2kwd8NZ7LiYDrmZDJ8+2wnDu3pJXRYRETUjD/v9Xee72/Ly8vReRUVFiI2NRVhYGH788cc6F0BNk9Jcjv8+3xUju3lBoxWY99M5fH8sWeqyiIiI7tsDPbvtdoGBgfjwww8xZ86c+jgcNRHmcjN8MqoTJjzmCwB4a/NFfLs/XtqiiIiI7lO9hCQAMDc3R2pqan0djpoIMzMZ3nk6CDP7BwAAPt4Zi493XgbvFyAiIlNX5yUAtmzZovezEAJpaWn4+uuv0atXr3orjJoOmUyG18LbwtbSHB/uuIxv9yegqLwK7z7dAWZmMqnLIyIiMqjOIWnYsGF6P8tkMri6umLAgAH47LPP6qsuaoKm9/WHrdIcb/3vItZGJaOorAofj+oEc3m9dWgSERHVmzqHJK1W2xB1UDPxYk8f2Fma49WN5/DrmRsorqjCV893hdJcLnVpREREevhPeDK6Z7q0xNIXQ6AwN8OumAxMXnMKJRVVUpdFRESkp87rJL366qv33fbzzz+vc0GmiOskNYwj8dmYsvYUSio06O7jiJUTHoG9lYXUZRERURNh9Ge3nTlzBmfOnEFlZSXatm0LALhy5Qrkcjm6deumayeTcUIu3V2vABd8P6kHJq4+gVPJeXh++TF8P+lRONsqpS6NiIio7sNtTz/9NPr06YPr16/j9OnTOH36NK5du4b+/ftj6NCh2LdvH/bt24e9e/c2RL3UxIT4OGLD1FC42CpwKU2N0cuikFZQKnVZREREdR9ua9myJf744w906NBBb/vFixcxePDgJrlWEofbGt7VrCK8uOI4UgvK0NLBCj9M6QEfZxupyyIiokbM6I8lUavVyMrKqrU9KysLhYWFdS6ACABau9pi04zH4OtsjRv5pXh2aRRi0/n7RERE0qlzSBo+fDgmTpyIX3/9FdevX8f169fxyy+/YNKkSRgxYkRD1EjNREsHK2ycHop2HnbILCzHc8ujcO5avtRlERFRM1Xn4baSkhK89tprWLVqFSorKwFUP5Jk0qRJ+OSTT2Bj0/SGSDjcZlz5JRWYsPokzl7Lh63SHCvGd0fP1s5Sl0VERI3Mw35/1zkk1SguLkZCQgIAwN/fv0mGoxoMScZXVF6FKWtOIepqDpTmZlj6Ygj6t3OTuiwiImpEjD4nqUZaWhrS0tIQGBgIGxsbPrCU6pWt0hyrJz6Cge3cUF6lxZS1p7D1fNO7KYCIiExXnUNSTk4OBg4ciDZt2uCpp55CWloaAGDSpEmYP39+vRdIzZelhRxLXwrB0509UaUVmP3jGWw8eU3qsoiIqJmoc0iaN28eLCwskJKSAmtra9325557Djt37qzX4ogs5Gb48rkueP5Rb2gFsOCX81h5OFHqsoiIqBmo84rbf/zxB3bt2gUvLy+97YGBgUhOTq63wohqyM1k+GB4MOwsLbD84FW8v/USisqqMHtgAFd2JyKiBlPnnqTi4mK9HqQaubm5UCr5OAlqGDKZDAufbIf5j7cBAHzx5xX8Z9tfnAtHREQNps4hqXfv3li7dq3uZ5lMBq1Wi48//hj9+/ev1+KIbiWTyTBrYCDeHhoEAFhxOBELf70AjZZBiYiI6l+dh9s+/vhjDBw4EKdOnUJFRQUWLFiAmJgY5Obm4siRIw1RI5Gel8P8YGtpjjd/OY8NJ6+hqLwKXzzXBRbyB75Zk4iIqJY6f6t07NgRV65cQVhYGJ555hkUFxdjxIgROHPmDPz9/RuiRqJaRnf3xn+f7wYLuQxbz6dh2vfRKKvUSF0WERE1IXVaTLKyshJPPPEEli5disDAwIasy6RwMUnTtS82E9O/j0Z5lRY9WzthxfhHYKuscwcpERE1QUZdTNLCwgLnz5+v84cQNZT+bd2w9uVHYas0x7GruRi74jjySyqkLouIiJqAOg+3vfjii1i5cmVD1EL0QHq0dsYPU3rA0doC567l47llx5CpLpO6LCIiauTqPC5RVVWFVatW4c8//0RISEitZ7Z9/vnn9VYc0f3q5OWAn6aF4sUVxxGbUYjRy6KwbnIPeDnWXq6CiIjoftzXnKTz58+jY8eOMDMzu+tt/jKZDHv37q3XAk0B5yQ1Hik5JXhhxTFczytFC3tLrJvcA/6utlKXRUREEnjY7+/7CklyuRxpaWlwc3ND69atcfLkSTg7Oz9QwY0RQ1Ljkl5QhhdXHkd8ZhGcbRRYO+lRdPC0l7osIiIyMqNM3HZwcEBiYvXzspKSkqDVauv8QUTG4mFviZ+m9kQHTxVyiiswZvkxRCfnSl0WERE1Mvc1J2nkyJHo27cvWrRoAZlMhu7du0Mulxtse/Xq1XotkOhBONsq8ePUnpgUeRInk/Lw4ooT+G5cd4QFukhdGhERNRL3vU7Szp07ER8fj9mzZ+O9996DnZ2dwXZz5syp1wJNAYfbGq/SCg2mfn8Kh+KyoZCb4esXumJwBw+pyyIiIiMwypykW02cOBFfffXVHUNSU8SQ1LiVV2kw58ez2BmTDrmZDJ8+2wnDu3pJXRYRETUwoy4mCQCrV69uVgGJGj+luRxfv9AVI7t5QaMVeHXjOXx/LFnqsoiIyMTxiaDULJjLzfDJqE4YH+oDIYC3Nl/Ekv0JUpdFREQmjCGJmg0zMxne/UcHzOwfAAD4aOdlfLzzMuo44kxERM0EQxI1KzKZDK+Ft8WbT7YDAHy7PwHvbImBVsugRERE+hiSqFma3tcf/x7WETIZsDYqGa/9fA5VGq7/RUREf2NIombrxZ4++GJ0F8jNZPj19A1E/HAa5VUaqcsiIiITwZBEzdqwri2xZGw3KORm2BWTgclrTqGkokrqsoiIyAQwJFGzN7iDB1ZPfATWCjkOxWVj3MoTKCitlLosIiKSGEMSEYBeAS74flIPqCzNcSo5Dy98dww5ReVSl0VERBJiSCK6KcTHERumhsLFVoGYVDVGL4tCWkGp1GUREZFEGJKIbhHkqcJP00LRwt4SCVnFeHZpFJJziqUui4iIJMCQRHQbf1dbbJoeCl9na1zPK8WzS6NwJaNQ6rKIiMjIGJKIDPBytMbG6aFo52GHzMJyjF4WhXPX8qUui4iIjIghiegO3OwssWFqT3T2dkB+SSXGrjiO41dzpC6LiIiMhCGJ6C4crBVYP7kHQls7o6i8CuNWncC+y5lSl0VEREbAkER0D7ZKc6ye+AgGtnNDeZUWU9aewrbzaVKXRUREDYwhieg+WFrIsfSlEDzd2RNVWoFZP57GxpPXpC6LiIgaEEMS0X2ykJvhy+e64PlHvaEVwIJfzmPl4USpyyIiogYiaUhasmQJOnXqBJVKBZVKhdDQUOzYsUO3v6ysDBEREXB2doatrS1GjhyJjIwMvWOkpKRgyJAhsLa2hpubG15//XVUVek/e2v//v3o1q0blEolAgICEBkZaYzToyZIbibDB8ODMaW3HwDg/a2XsPjPOAghJK6MiIjqm6QhycvLCx9++CGio6Nx6tQpDBgwAM888wxiYmIAAPPmzcPvv/+OTZs24cCBA0hNTcWIESN079doNBgyZAgqKipw9OhRrFmzBpGRkXj77bd1bRITEzFkyBD0798fZ8+exdy5czF58mTs2rXL6OdLTYNMJsM/n2qPVx9vAwD44s8r+GD7XwxKRERNjEyY2N/sTk5O+OSTTzBq1Ci4urrihx9+wKhRowAAly9fRvv27REVFYWePXtix44dGDp0KFJTU+Hu7g4AWLp0Kd544w1kZWVBoVDgjTfewLZt23Dx4kXdZ4wZMwb5+fnYuXPnfdWkVqthb2+PgoICqFSq+j9parRWHU7Ee1svAQCef9Qb/x4WDLmZTOKqiIgIePjvb5OZk6TRaLBhwwYUFxcjNDQU0dHRqKysxKBBg3Rt2rVrh1atWiEqKgoAEBUVheDgYF1AAoDw8HCo1Wpdb1RUVJTeMWra1ByD6GG8HOaHj0d2gpkM+PHENczZcAaVGq3UZRERUT0wl7qACxcuIDQ0FGVlZbC1tcVvv/2GoKAgnD17FgqFAg4ODnrt3d3dkZ6eDgBIT0/XC0g1+2v23a2NWq1GaWkprKysatVUXl6O8vK/nwCvVqsf+jyp6Rr9iDdslOaY+9MZbD2fhpIKDb4d2w2WFnKpSyMioocgeU9S27ZtcfbsWRw/fhwzZszA+PHjcenSJUlrWrRoEezt7XUvb29vSesh0zekUwssH9cdSnMz7L2ciQmrT6CovOrebyQiIpMleUhSKBQICAhASEgIFi1ahM6dO2Px4sXw8PBARUUF8vPz9dpnZGTAw8MDAODh4VHrbrean+/VRqVSGexFAoCFCxeioKBA97p2jevh0L31b+uGtS8/ClulOY5dzcXYFceRX1IhdVlERPSAJA9Jt9NqtSgvL0dISAgsLCywZ88e3b7Y2FikpKQgNDQUABAaGooLFy4gM/Pvx0Ts3r0bKpUKQUFBuja3HqOmTc0xDFEqlbplCWpeRPejR2tn/DClBxysLXDuWj6eW3YMmYVlUpdFREQPQNKQtHDhQhw8eBBJSUm4cOECFi5ciP3792Ps2LGwt7fHpEmT8Oqrr2Lfvn2Ijo7GxIkTERoaip49ewIABg8ejKCgILz00ks4d+4cdu3ahX/961+IiIiAUqkEAEyfPh1Xr17FggULcPnyZXz77bfYuHEj5s2bJ+WpUxPWycsBG6eFws1OidiMQoxeGoXreSVSl0VERHUkaUjKzMzEuHHj0LZtWwwcOBAnT57Erl278PjjjwMAvvjiCwwdOhQjR45Enz594OHhgV9//VX3frlcjq1bt0IulyM0NBQvvvgixo0bh/fee0/Xxs/PD9u2bcPu3bvRuXNnfPbZZ1ixYgXCw8ONfr7UfLRxt8Om6aHwcrRCUk4Jnl0ahYSsIqnLIiKiOjC5dZJMEddJogeVXlCGsSuOISGrGM42Cqyd9Cg6eNpLXRYRUbPQZNZJImqKPOwtsXFaKDp4qpBTXIExy48hOjlP6rKIiOg+MCQRNTBnWyV+nNoT3X0cUVhWhZdWHsfhuGypyyIiontgSCIyApWlBdZOehS9A11QUqHBy5En8UdMutRlERHRXTAkERmJtcIcK8Z3xxMdPFCh0WLG+tPYfOaG1GUREdEdMCQRGZHSXI6vX+iKEd1aQqMVmLfxLNYdS5a6LCIiMoAhicjIzOVm+HRUZ4wP9YEQwL82X8SS/QlSl0VERLdhSCKSgJmZDO/+owMi+vsDAD7aeRmf7LoMrshBRGQ6GJKIJCKTyfB6eDu8+WQ7AMA3+xLw7pYYaLUMSkREpoAhiUhi0/v64/1hHSGTAWuikvHaz+dQpdFKXRYRUbPHkERkAl7q6YMvRneB3EyGX0/fwMwfzqC8SiN1WUREzRpDEpGJGNa1JZaM7QaF3Aw7Y9Ixec0plFRUSV0WEVGzxZBEZEIGd/DAqgmPwMpCjkNx2Ri38gTUZZVSl0VE1CwxJBGZmLBAF6yb3AMqS3OcSs7D88uPIaeoXOqyiIiaHYYkIhMU4uOIH6f2hLONAjGpaoxeFoX0gjKpyyIialYYkohMVAdPe2ycHooW9pZIyCrGqKVHkZxTLHVZRETNBkMSkQnzd7XFpumh8HW2xvW8Ujy7NApXMgqlLouIqFlgSCIycV6O1tg4PRRt3e2QWViO0cuicP56vtRlERE1eQxJRI2Am50lfprWE529HZBfUokXvjuO41dzpC6LiKhJkwk+LOqe1Go17O3tUVBQAJVKJXU51IwVlVdh8pqTOHY1FzIZ0MFThd6Brugd6IIQH0cozeVSl0hEZDIe9vubIek+MCSRKSmr1GD+pnPYdj5Nb7uVhRw9WjvpQlOgmy1kMplEVRIRSY8hyQgYksgUZRaW4Uh8Ng5dycah+GxkFeqvpeSuUuoCU68AF7jYKiWqlIhIGgxJRsCQRKZOCIHYjEJdYDp+NQflVfoPye3gqUJYoAv6BLoixMcRlhYcmiOipo0hyQgYkqixKavU4FRSHg7FZ+HQlWxcSlPr7be0MMOjfs7oE+iC3oGuaOPOoTkianoYkoyAIYkau6zCchxNyMbBK9k4FJeFzNuG5tzslAgLdEHvQBeEBbjC1Y5Dc0TU+DEkGQFDEjUlQgjEZRbh4JUsHIrLxvHEHJRV6g/NtW+hQp9AF4QFuuARXycOzRFRo8SQZAQMSdSUlVVqcDo5DwfjsnE4PgsXb+gPzSnNzfConxN63xyaa+dhx6E5ImoUGJKMgCGJmpOconIcjs/G4bhsHIrLRrpa/8G6rnZKhAXUDM25wE1lKVGlRER3x5BkBAxJ1FwJIRCfWYRDcdVzmY5dzUVppUavTTsPO10v06N+HJojItPBkGQEDElE1cqrNDidnI9DcdXzmS6mFuDWv0EU5mZ41Ld6aC4s0AXtPVQwM+PQHBFJgyHJCBiSiAzLLa6oXtDyZmhKK9AfmnOxVSAswAVhNxe1dOfQHBEZEUOSETAkEd2bEAIJWcU4FJeFw3HZiLqag5IK/aG5Nu62ulXAe/g5w0rBoTkiajgMSUbAkERUdxVVWpxOybs5ATwL52/cNjQnN0N3X0ddaApqwaE5IqpfDElGwJBE9PDyiitwNCFHNzR3I79Ub7+zjQK9bt411zvQFR72HJojoofDkGQEDElE9UsIgcTsYt1dc1EJOSi+bWgu0M1W96y5Hq2dYK0wl6haImqsGJKMgCGJqGFVarQ4k/L3XXPnr+dDe8vfTBZyGUJ8qofm+gS6ooMnh+aI6N4YkoyAIYnIuPJLaobmsnHwSlatoTlHawv0CqjuZQoLdIGng5VElRKRKWNIMgKGJCLpCCGQlFOCw3FZOBiXjaiEHBSVV+m18Xe1qe5lalN915yNkkNzRMSQZBQMSUSmo1Kjxblr+Th4cz7TuWu1h+a6tXLUTQDv2NIecg7NETVLDElGwJBEZLoKSisRlVD9nLmDcVm4lqs/NOdgbYFe/i66VcC9HK0lqpSIjI0hyQgYkogaj+Scv++aOxqfg8LbhuZau9joepl6+jvDlkNzRE0WQ5IRMCQRNU5VGi3OXc+/GZqycfZaPjS3jM2Zm/09NBcW6IJOXg4cmiNqQhiSjIAhiahpUJdVIurmgpaH47KRlFOit9/eygK9ApwRFlC9Cri3E4fmiBozhiQjYEgiappSckpwKL46MB2Jz4a6TH9ozs/FBmE3VwEP9XeGnaWFRJUS0YNgSDIChiSipq9Ko8X5GwW6Z82dTtEfmpObydCtlUN1L1MbF3RqaQ9zuZmEFRPRvTAkGQFDElHzU3hzaO5wfPV8psTsYr39dpbm1XfNtXFB7wBXtHLm0ByRqWFIMgKGJCK6lltyMzBl4Uh8DgpKK/X2+zhbV08AD3DFYwHOUHFojkhyDElGwJBERLfSaAUu3CjAoSvVz5o7nZKHqtuG5rp4OyAswAV92rigs5cDh+aIJMCQZAQMSUR0N0XlVTh28665Q/HZuJp129Cc0hyh/s7o3cYVfQJd4ONsI1GlRM0LQ5IRMCQRUV1czyupngAeX33XXH6J/tCct5NV9bPmAl0Q6u8CeysOzRE1BIYkI2BIIqIHpdEKxKQWVD825UoWTqfkoVLz91+7ZjKgs7cDegdWr83UxdsBFhyaI6oXDElGwJBERPWluLwKxxNzcPBK9STwhNuG5mxrhuZuPjrF19kaMhlXASd6EAxJRsCQREQNJTW/FIdvPpz3SHw28m4bmvNytNIFpsf8neFgrZCoUqLGhyHJCBiSiMgYtFqBmFQ1DsVn4dCVbJxKzq01NBfs5YA+N0NT11YcmiO6G4YkI2BIIiIplFRU4XhiLg7dHJqLyyzS22+jkN8cmquez+TnYsOhOaJbPOz3t6T/BFm0aBEeeeQR2NnZwc3NDcOGDUNsbKxem7KyMkRERMDZ2Rm2trYYOXIkMjIy9NqkpKRgyJAhsLa2hpubG15//XVUVek/g2n//v3o1q0blEolAgICEBkZ2dCnR0T0UKwV5ujf1g1vPx2E3a/2xbGFA/HJqE74R2dPONsoUFyhwZ9/ZeKdLTEY8NkBhH20D2/+ch5bz6cir7hC6vKJGj1Je5KeeOIJjBkzBo888giqqqrwz3/+ExcvXsSlS5dgY1O9jsiMGTOwbds2REZGwt7eHjNnzoSZmRmOHDkCANBoNOjSpQs8PDzwySefIC0tDePGjcOUKVPwwQcfAAASExPRsWNHTJ8+HZMnT8aePXswd+5cbNu2DeHh4feskz1JRGRqtFqBS2lqHIrLxuH4LJxMzEOFRqvbL5MBnVraI+zm0Fy3Vo5QmHNojpqXJjXclpWVBTc3Nxw4cAB9+vRBQUEBXF1d8cMPP2DUqFEAgMuXL6N9+/aIiopCz549sWPHDgwdOhSpqalwd3cHACxduhRvvPEGsrKyoFAo8MYbb2Dbtm24ePGi7rPGjBmD/Px87Ny58551MSQRkakrrdDgeGLOzQf0ZiM2o1Bvv7VCjp6t/75rzt+VQ3PU9D3s97d5A9T0wAoKCgAATk5OAIDo6GhUVlZi0KBBujbt2rVDq1atdCEpKioKwcHBuoAEAOHh4ZgxYwZiYmLQtWtXREVF6R2jps3cuXMN1lFeXo7y8nLdz2q1ur5OkYioQVgp5OjX1g392roBADLUZTcDUxYOx2cju6gCey9nYu/lTABAC3tLXWDqFeACJxveNUd0O5MJSVqtFnPnzkWvXr3QsWNHAEB6ejoUCgUcHBz02rq7uyM9PV3X5taAVLO/Zt/d2qjVapSWlsLKykpv36JFi/D//t//q7dzIyIyNneVJUaGeGFkiBe0WoHL6YXVj02Jy8aJpFykFZRh46nr2HjqOmQyoKOnffUDegNdEOLjCKW5XOpTIJKcyYSkiIgIXLx4EYcPH5a6FCxcuBCvvvqq7me1Wg1vb28JKyIienBmZjIEeaoQ5KnCtL7+KKvU4ERiri40XU4vxIUbBbhwowDf7k+AlYUcPVs7Iezmo1MC3Gw5NEfNkkmEpJkzZ2Lr1q04ePAgvLy8dNs9PDxQUVGB/Px8vd6kjIwMeHh46NqcOHFC73g1d7/d2ub2O+IyMjKgUqlq9SIBgFKphFKprJdzIyIyNZYWcvRp44o+bVwBAJnqMhyOz765qGU2sovKsS82C/tiswAAHirLmxPAXRAW4AJnW/79SM2DpCFJCIFZs2bht99+w/79++Hn56e3PyQkBBYWFtizZw9GjhwJAIiNjUVKSgpCQ0MBAKGhofjPf/6DzMxMuLlVj8Xv3r0bKpUKQUFBujbbt2/XO/bu3bt1xyAias7cVJYY0c0LI7p5QYjqobmaVcBPJOYiXV2Gn6Ov4+fo6wCADp4q3QN6Q3w5NEdNl6R3t73yyiv44Ycf8L///Q9t27bVbbe3t9f18MyYMQPbt29HZGQkVCoVZs2aBQA4evQogL+XAPD09MTHH3+M9PR0vPTSS5g8eXKtJQAiIiLw8ssvY+/evZg9ezaXACAiuoeySg1OJeXhUFwWDsZl4680/RtZLC3M0MPv77vm2rhzaI5MR6NeAuBO/yOtXr0aEyZMAFC9mOT8+fPx448/ory8HOHh4fj22291Q2kAkJycjBkzZmD//v2wsbHB+PHj8eGHH8Lc/O+Osv3792PevHm4dOkSvLy88NZbb+k+414YkoiIqmUVluNIfHUv0+G4bGQWluvtd7NT6lYA7xXgAlc7Ds2RdBp1SGosGJKIiGoTQuBKRpFuAvjxxByUVWr12gS1UOl6mbr7OsLSgkNzZDwMSUbAkEREdG9llRpEJ+fh0M31mWJS9YfmlOZmeNTPCX0CXdG7jQvauttxaI4aFEOSETAkERHVXXZR9dBcTWjKUOsPzbnaKdE7wAW921QPzbnZWUpUKTVVDElGwJBERPRwhBCIzyzCwZuB6fjVXJRWavTatPOwQ582rggLcMGjfk4cmqOHxpBkBAxJRET1q7zq76G5w3HZuJhagFu/jRTmZujh54SwgOr5TO1bcGiO6o4hyQgYkoiIGlZOUTmOJOTg8M1J4GkFZXr7XWyVCAtw1t0556bi0BzdG0OSETAkEREZjxACCVnFurvmjl3NQUmF/tBcW3e76rvm2rjiUV8nWCk4NEe1MSQZAUMSEZF0Kqq0OJ2SpwtNF27cNjQnN8Mjfo7oHVg9nymohQpmZhyaI4Yko2BIIiIyHXnFFTiSkI1DV6ongafeNjTnbKNA2M3nzPUOdIWHPYfmmiuGJCNgSCIiMk1CCFzNLsbhm3fNRSXkoPi2obk27rYIC6hem6mHnxOsFSbxbHcyAoYkI2BIIiJqHCqqtDh7LV/3rLnz1/NrDc2F+DiidxsX9Al05dBcE8eQZAQMSUREjVN+SQWOJuRUh6Yr2biRX6q338lGgV4BLjcfneKCFvZWElVKDYEhyQgYkoiIGj8hBJJySnSB6djVHBSVV+m1CXCz1QWmHn7OsFFyaK4xY0gyAoYkIqKmp1JTMzRXPZ/p3LV8aG/5RrSQy6qH5m6uzdTB0x5yDs01KgxJRsCQRETU9BWUVCLqajYOxmXj4JUsXM/TH5pztLbAYwEu6BPogrBAV7R04NCcqWNIMgKGJCKi5kUIgeScEhyKz8ahK9V3zRXeNjTX2tUGfW72MvVo7QxbDs2ZHIYkI2BIIiJq3qo0Wpy7no+DV7JxOD4bZ6/lQ3PL2Jy5mQzdfBx1vUzBLTk0ZwoYkoyAIYmIiG5VUFqJqIQcHI6vXgU8OadEb7+9lQXCAlwQdnMSuJejtUSVNm8MSUbAkERERHeTklOCQ/FZOHQlG0cSslFYpj805+dic/OuOVf0bO0EO0sLiSptXhiSjIAhiYiI7lf10FyBbhXwMwaG5rq2ctDdNdfJy4FDcw2EIckIGJKIiOhBqcsqcSwhB4fiquczJWYX6+1XWZrfXNCyOjR5O3Forr4wJBkBQxIREdWXa7klNwNTFg7HZUN929Ccr7M1ege6IizQBaH+zlBxaO6BMSQZAUMSERE1BI1W4Pz1/JtDc9k4nZKHqluG5uRmMnTxdtDNZ+rsZQ9zuZmEFTcuDElGwJBERETGUFRedXNorvquuau3Dc3ZWZrjMX9n9A50RZ9AV7Ry5tDc3TAkGQFDEhERSeF6Xomul+lIQjbySyr19rdystb1MoX6O8PeikNzt2JIMgKGJCIikppGK3DxRoGulyk6WX9ozkwGdPF2QFigK/oEuqCztwMsmvnQHEOSETAkERGRqSkqr8Lxqzm6B/QmZN02NKc0R6i/s66nycfZGjJZ81pqgCHJCBiSiIjI1KXml+JwXDYOxmXhSHw28m4bmvN2skJYQHUv02P+LrC3bvpDcwxJRsCQREREjYlWKxCTqsbBuCwcistCdHIeKjX6Q3OdvBx0z5rr2qppDs0xJBkBQxIRETVmxeVVOJGYi4Nx1WszxWUW6e23VZqjZ+uaoTkX+LnYNImhOYYkI2BIIiKipiStoLR6Qcubq4DnFlfo7W/pYKWby9QrwBkO1gqJKn04DElGwJBERERNlVYrcClNrZsAfiopDxUarW6/7ObQXO+A6l6mrq0coTBvHENzDElGwJBERETNRUlF9dBcTWi6kqE/NGetkCP05tBcWKAr/F1Nd2iOIckIGJKIiKi5Si8ow+H46sB0JD4b2UX6Q3Oe9pa6Z831CnCBk43pDM0xJBkBQxIREVH10Nxf6WrdfKYTSbmoqNIfmgtuaY+wgOr5TCE+0g7NMSQZAUMSERFRbaUVGpxIysXhm6uAX04v1NtvrZCjh59T9bPm2rjA39XWqENzDElGwJBERER0b5nqmqG56ld2Ubnefg+VZfVdc21c0cvfGc62ygathyHJCBiSiIiI6kYIgcvphbpnzZ1IzEX5LUNzANCxpQq9A13RO9AFIT6OUJrL67UGhiQjYEgiIiJ6OGWVGpxMytX1Mv2Vptbbb6s0x8n/GwQrRf0FpYf9/javt0qIiIiI7sDSQn6z18gVAJBZWIYjtwzNeTla1WtAqg8MSURERGR0bnaWGN7VC8O7ekEIUeuBvKagcSyZSURERE2WTCYzqfWVajAkERERERnAkERERERkAEMSERERkQEMSUREREQGMCQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZwJBEREREZABDEhEREZEBDElEREREBjAkERERERlgLnUBjYEQAgCgVqslroSIiIjuV833ds33eF0xJN2HwsJCAIC3t7fElRAREVFdFRYWwt7evs7vk4kHjVfNiFarRWpqKuzs7CCTyR7qWGq1Gt7e3rh27RpUKlU9Vdj08bo9GF63B8Pr9uB47R4Mr9uDudd1E0KgsLAQnp6eMDOr+wwj9iTdBzMzM3h5edXrMVUqFf9HeAC8bg+G1+3B8Lo9OF67B8Pr9mDudt0epAepBiduExERERnAkERERERkAEOSkSmVSrzzzjtQKpVSl9Ko8Lo9GF63B8Pr9uB47R4Mr9uDaejrxonbRERERAawJ4mIiIjIAIYkIiIiIgMYkoiIiIgMYEgiIiIiMoAhyci++eYb+Pr6wtLSEj169MCJEyekLklSBw8exNNPPw1PT0/IZDJs3rxZb78QAm+//TZatGgBKysrDBo0CHFxcXptcnNzMXbsWKhUKjg4OGDSpEkoKioy4lkY16JFi/DII4/Azs4Obm5uGDZsGGJjY/XalJWVISIiAs7OzrC1tcXIkSORkZGh1yYlJQVDhgyBtbU13Nzc8Prrr6OqqsqYp2JUS5YsQadOnXSLzoWGhmLHjh26/bxm9+fDDz+ETCbD3Llzddt47Qx79913IZPJ9F7t2rXT7ed1u7MbN27gxRdfhLOzM6ysrBAcHIxTp07p9hvtu0GQ0WzYsEEoFAqxatUqERMTI6ZMmSIcHBxERkaG1KVJZvv27eL//u//xK+//ioAiN9++01v/4cffijs7e3F5s2bxblz58Q//vEP4efnJ0pLS3VtnnjiCdG5c2dx7NgxcejQIREQECCef/55I5+J8YSHh4vVq1eLixcvirNnz4qnnnpKtGrVShQVFenaTJ8+XXh7e4s9e/aIU6dOiZ49e4rHHntMt7+qqkp07NhRDBo0SJw5c0Zs375duLi4iIULF0pxSkaxZcsWsW3bNnHlyhURGxsr/vnPfwoLCwtx8eJFIQSv2f04ceKE8PX1FZ06dRJz5szRbee1M+ydd94RHTp0EGlpabpXVlaWbj+vm2G5ubnCx8dHTJgwQRw/flxcvXpV7Nq1S8THx+vaGOu7gSHJiB599FERERGh+1mj0QhPT0+xaNEiCasyHbeHJK1WKzw8PMQnn3yi25afny+USqX48ccfhRBCXLp0SQAQJ0+e1LXZsWOHkMlk4saNG0arXUqZmZkCgDhw4IAQovoaWVhYiE2bNuna/PXXXwKAiIqKEkJUh1MzMzORnp6ua7NkyRKhUqlEeXm5cU9AQo6OjmLFihW8ZvehsLBQBAYGit27d4u+ffvqQhKv3Z298847onPnzgb38brd2RtvvCHCwsLuuN+Y3w0cbjOSiooKREdHY9CgQbptZmZmGDRoEKKioiSszHQlJiYiPT1d75rZ29ujR48eumsWFRUFBwcHdO/eXddm0KBBMDMzw/Hjx41esxQKCgoAAE5OTgCA6OhoVFZW6l23du3aoVWrVnrXLTg4GO7u7ro24eHhUKvViImJMWL10tBoNNiwYQOKi4sRGhrKa3YfIiIiMGTIEL1rBPD37V7i4uLg6emJ1q1bY+zYsUhJSQHA63Y3W7ZsQffu3fHss8/Czc0NXbt2xXfffafbb8zvBoYkI8nOzoZGo9H7ZQcAd3d3pKenS1SVaau5Lne7Zunp6XBzc9Pbb25uDicnp2ZxXbVaLebOnYtevXqhY8eOAKqviUKhgIODg17b26+boetas6+punDhAmxtbaFUKjF9+nT89ttvCAoK4jW7hw0bNuD06dNYtGhRrX28dnfWo0cPREZGYufOnViyZAkSExPRu3dvFBYW8rrdxdWrV7FkyRIEBgZi165dmDFjBmbPno01a9YAMO53g/nDnAgRSSsiIgIXL17E4cOHpS6lUWjbti3Onj2LgoIC/Pzzzxg/fjwOHDggdVkm7dq1a5gzZw52794NS0tLqctpVJ588kndnzt16oQePXrAx8cHGzduhJWVlYSVmTatVovu3bvjgw8+AAB07doVFy9exNKlSzF+/Hij1sKeJCNxcXGBXC6vdedCRkYGPDw8JKrKtNVcl7tdMw8PD2RmZurtr6qqQm5ubpO/rjNnzsTWrVuxb98+eHl56bZ7eHigoqIC+fn5eu1vv26GrmvNvqZKoVAgICAAISEhWLRoETp37ozFixfzmt1FdHQ0MjMz0a1bN5ibm8Pc3BwHDhzAV199BXNzc7i7u/Pa3ScHBwe0adMG8fHx/J27ixYtWiAoKEhvW/v27XVDlcb8bmBIMhKFQoGQkBDs2bNHt02r1WLPnj0IDQ2VsDLT5efnBw8PD71rplarcfz4cd01Cw0NRX5+PqKjo3Vt9u7dC61Wix49ehi9ZmMQQmDmzJn47bffsHfvXvj5+entDwkJgYWFhd51i42NRUpKit51u3Dhgt5fIrt374ZKpar1l1NTptVqUV5ezmt2FwMHDsSFCxdw9uxZ3at79+4YO3as7s+8dvenqKgICQkJaNGiBX/n7qJXr161ljW5cuUKfHx8ABj5u6Hu887pQW3YsEEolUoRGRkpLl26JKZOnSocHBz07lxobgoLC8WZM2fEmTNnBADx+eefizNnzojk5GQhRPVtng4ODuJ///ufOH/+vHjmmWcM3ubZtWtXcfz4cXH48GERGBjYpJcAmDFjhrC3txf79+/Xu7W4pKRE12b69OmiVatWYu/eveLUqVMiNDRUhIaG6vbX3Fo8ePBgcfbsWbFz507h6urapG8tfvPNN8WBAwdEYmKiOH/+vHjzzTeFTCYTf/zxhxCC16wubr27TQheuzuZP3++2L9/v0hMTBRHjhwRgwYNEi4uLiIzM1MIwet2JydOnBDm5ubiP//5j4iLixPr168X1tbWYt26dbo2xvpuYEgysv/+97+iVatWQqFQiEcffVQcO3ZM6pIktW/fPgGg1mv8+PFCiOpbPd966y3h7u4ulEqlGDhwoIiNjdU7Rk5Ojnj++eeFra2tUKlUYuLEiaKwsFCCszEOQ9cLgFi9erWuTWlpqXjllVeEo6OjsLa2FsOHDxdpaWl6x0lKShJPPvmksLKyEi4uLmL+/PmisrLSyGdjPC+//LLw8fERCoVCuLq6ioEDB+oCkhC8ZnVxe0jitTPsueeeEy1atBAKhUK0bNlSPPfcc3pr/fC63dnvv/8uOnbsKJRKpWjXrp1Yvny53n5jfTfIhBCijj1hRERERE0e5yQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZwJBEREREZABDEhEREZEBDElE1Gykp6fj8ccfh42NTa2nr9eYMGEChg0bZtS6iMg0MSQRkdFMmDABMpkMH374od72zZs3QyaTNfjnf/HFF0hLS8PZs2dx5coVg20WL16MyMhI3c/9+vXD3LlzG7w2IjI9DElEZFSWlpb46KOPkJeXZ/TPTkhIQEhICAIDA+Hm5mawjb29/R17mYioeWFIIiKjGjRoEDw8PLBo0aK7tvvll1/QoUMHKJVK+Pr64rPPPrvnsZcsWQJ/f38oFAq0bdsW33//vW6fr68vfvnlF6xduxYymQwTJkwweIxbh9smTJiAAwcOYPHixZDJZJDJZEhKSgIAXLx4EU8++SRsbW3h7u6Ol156CdnZ2brj9OvXD7NmzcLcuXPh6OgId3d3fPfddyguLsbEiRNhZ2eHgIAA7NixQ/eevLw8jB07Fq6urrCyskJgYCBWr159z/MmoobBkERERiWXy/HBBx/gv//9L65fv26wTXR0NEaPHo0xY8bgwoULePfdd/HWW2/pDYPd7rfffsOcOXMwf/58XLx4EdOmTcPEiROxb98+AMDJkyfxxBNPYPTo0UhLS8PixYvvWevixYsRGhqKKVOmIC0tDWlpafD29kZ+fj4GDBiArl274tSpU9i5cycyMjIwevRovfevWbMGLi4uOHHiBGbNmoUZM2bg2WefxWOPPYbTp09j8ODBeOmll1BSUgIAeOutt3Dp0iXs2LEDf/31F5YsWQIXF5f7vLJEVN/4gFsiMpoJEyYgPz8fmzdvRmhoKIKCgrBy5Ups3rwZw4cPR81fR2PHjkVWVhb++OMP3XsXLFiAbdu2ISYmxuCxe/XqhQ4dOmD58uW6baNHj0ZxcTG2bdsGABg2bBgcHBzuGrZurRGo7hHq0qULvvzyS12bf//73zh06BB27dql23b9+nV4e3sjNjYWbdq0Qb9+/aDRaHDo0CEAgEajgb29PUaMGIG1a9cCqJ5I3qJFC0RFRaFnz574xz/+ARcXF6xater+LyoRNRj2JBGRJD766COsWbMGf/31V619f/31F3r16qW3rVevXoiLi4NGozF4vDu9x9DxH9a5c+ewb98+2Nra6l7t2rUDUD3vqUanTp10f5bL5XB2dkZwcLBum7u7OwAgMzMTADBjxgxs2LABXbp0wYIFC3D06NF6r52I7h9DEhFJok+fPggPD8fChQulLqXOioqK8PTTT+Ps2bN6r7i4OPTp00fXzsLCQu99MplMb1vNHX1arRYA8OSTTyI5ORnz5s1DamoqBg4ciNdee80IZ0REhjAkEZFkPvzwQ/z++++IiorS296+fXscOXJEb9uRI0fQpk0byOVyg8e603uCgoIeqkaFQlGr96pbt26IiYmBr68vAgIC9F42NjYP9Xmurq4YP3481q1bhy+//FJv+JCIjIshiYgkExwcjLFjx+Krr77S2z5//nzs2bMH77//Pq5cuYI1a9bg66+/vmuvyuuvv47IyEgsWbIEcXFx+Pzzz/Hrr78+dE+Mr68vjh8/jqSkJGRnZ0Or1SIiIgK5ubl4/vnncfLkSSQkJGDXrl2YOHHiHYcD78fbb7+N//3vf4iPj0dMTAy2bt2K9u3bP1T9RPTgGJKISFLvvfeebripRrdu3bBx40Zs2LABHTt2xNtvv4333nvvjrftA9WTshcvXoxPP/0UHTp0wLJly7B69Wr069fvoep77bXXIJfLERQUBFdXV6SkpMDT0xNHjhyBRqPB4MGDERwcjLlz58LBwQFmZg/+16pCocDChQvRqVMn9OnTB3K5HBs2bHio+onowfHuNiIiIiID2JNEREREZABDEhEREZEBDElEREREBjAkERERERnAkERERERkAEMSERERkQEMSUREREQGMCQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZ8P8BtZITKLvbaEYAAAAASUVORK5CYII=\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Partial Periodic Frequent patterns using PPP_ECLAT" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.partialPeriodicPattern.basic import PPP_ECLAT as alg #import the algorithm\n", - "\n", - "obj = alg.PPP_ECLAT(iFile=inputFile, minPS=minimumSupportCount, period=PeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('partialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "df0f46b4-1d32-4275-dbb3-fc9945ddcb30" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n", - "Total No of patterns: 27134\n", - "Runtime: 56.02991461753845\n", - "Memory (RSS): 258572288\n", - "Memory (USS): 211623936\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'partialPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "ca663937-e3aa-4d02-ca3a-ef20e303a2f2" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "352\t527\t319\t48\t676\t720\t529\t368\t595:146 \n", - "527\t319\t48\t676\t720\t529\t368\t352:148 \n", - "527\t319\t48\t676\t720\t529\t368\t595:147 \n", - "319\t48\t676\t720\t529\t368\t527:149 \n", - "352\t319\t48\t676\t720\t529\t368\t595:149 \n", - "319\t48\t676\t720\t529\t368\t352:151 \n", - "319\t48\t676\t720\t529\t368\t595:150 \n", - "48\t676\t720\t529\t368\t319:152 \n", - "352\t527\t48\t676\t720\t529\t368\t595:148 \n", - "527\t48\t676\t720\t529\t368\t352:150 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "ea167cea-1a5e-42cb-d61f-01769b64b209" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-07 07:23:35-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.33MB/s in 3.3s \n", + "\n", + "2023-09-07 07:23:39 (1.33 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "8011ff82-6e34-4370-8ca1-4ef882a54634" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Partial Periodic Frequent patterns using PPP_ECLAT" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "91dfa2c2-b28e-4e29-bf71-6384b2264a39" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "452c8d0d-ea58-4a88-d34e-f6c418a440f6" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _partialPeriodicfrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the PPP_ECLAT algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.partialPeriodicPattern.basic import PPP_ECLAT as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "PeriodCount = 5000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of PPP_ECLAT" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of PPP_ECLAT algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.PPP_ECLAT(iFile=inputFile, minPS=minSupCount, period=PeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['PPP_ECLAT', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "44a6f6b7-e473-45e1-87c0-fcf5aa3e7a1a" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n", - "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n", - "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n", - "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n", - "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Partial Periodic Frequent patterns using PPP_ECLAT" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.partialPeriodicPattern.basic import PPP_ECLAT as alg #import the algorithm\n", + "\n", + "obj = alg.PPP_ECLAT(iFile=inputFile, minPS=minimumSupportCount, period=PeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('partialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "df0f46b4-1d32-4275-dbb3-fc9945ddcb30" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n", + "Total No of patterns: 27134\n", + "Runtime: 56.02991461753845\n", + "Memory (RSS): 258572288\n", + "Memory (USS): 211623936\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'partialPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ca663937-e3aa-4d02-ca3a-ef20e303a2f2" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "352\t527\t319\t48\t676\t720\t529\t368\t595:146 \n", + "527\t319\t48\t676\t720\t529\t368\t352:148 \n", + "527\t319\t48\t676\t720\t529\t368\t595:147 \n", + "319\t48\t676\t720\t529\t368\t527:149 \n", + "352\t319\t48\t676\t720\t529\t368\t595:149 \n", + "319\t48\t676\t720\t529\t368\t352:151 \n", + "319\t48\t676\t720\t529\t368\t595:150 \n", + "48\t676\t720\t529\t368\t319:152 \n", + "352\t527\t48\t676\t720\t529\t368\t595:148 \n", + "527\t48\t676\t720\t529\t368\t352:150 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _partialPeriodicfrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the PPP_ECLAT algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.partialPeriodicPattern.basic import PPP_ECLAT as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "PeriodCount = 5000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of PPP_ECLAT" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of PPP_ECLAT algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.PPP_ECLAT(iFile=inputFile, minPS=minSupCount, period=PeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['PPP_ECLAT', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "44a6f6b7-e473-45e1-87c0-fcf5aa3e7a1a" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n", + "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n", + "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n", + "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n", + "Partial Periodic Patterns were generated successfully using 3PEclat algorithm\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c43f62ad-2695-446a-f06e-bcaef0e8f903" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup PeriodCount patterns runtime memory\n", + "0 PPP_ECLAT 100 5000 27134 58.729703 264630272\n", + "1 PPP_ECLAT 150 5000 18967 50.933669 265129984\n", + "2 PPP_ECLAT 200 5000 13131 45.622379 264982528\n", + "3 PPP_ECLAT 250 5000 7604 44.114617 264933376\n", + "4 PPP_ECLAT 300 5000 4482 41.221311 264564736\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "e6cd1734-7fa1-45e3-8392-d1f98b709dec" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "c43f62ad-2695-446a-f06e-bcaef0e8f903" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup PeriodCount patterns runtime memory\n", - "0 PPP_ECLAT 100 5000 27134 58.729703 264630272\n", - "1 PPP_ECLAT 150 5000 18967 50.933669 265129984\n", - "2 PPP_ECLAT 200 5000 13131 45.622379 264982528\n", - "3 PPP_ECLAT 250 5000 7604 44.114617 264933376\n", - "4 PPP_ECLAT 300 5000 4482 41.221311 264564736\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "e6cd1734-7fa1-45e3-8392-d1f98b709dec" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" 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1ahU0Go3+kZXFKe0tVa1Wh6+TsgG0vHhlV3mkvxu+XfyIQdv3P46ksO2biKgLtCvUCIKAqKgo7N27F7GxsfDz82v1PWPHjkVaWhp0Op3+uZSUFKhUKlhbWzf7nrKyMqSnp0OlUrWnPAM2NjZwdHQ0eJBlOvZTPgrKauDmYI2IAe7d/v3vb/v+x5FUtn0TEXWBdoUatVqN7du3Y8eOHVAoFMjLy0NeXh4qKyv12yxcuNCg5XrRokUoKirC0qVLkZKSggMHDmD16tVQq9X6bd58803ExcUhIyMD8fHxePrppyGTyTB//nz9Nnl5eUhKStKP3ly5cgVJSUkoKirq8MGTZWicm+bpEG9YycSZb7Kx7Xvd3KGwkUv1bd9XszWi1ENEZI7a9S/8hg0boNFoEBERAZVKpX98+eWX+m0yMzORm5ur/9rX1xeHDh1CQkICgoODsWTJEixduhQrV67Ub3P79m3Mnz8fAwYMwLx58+Dq6oqzZ8/C3f3n/1Vv3LgRISEhePnllwEA48ePR0hICPbt29fhgyfzd7e0Gsd+qr+PpbsvPTVndpgP9r42Fr1df17t+8uETLHLIiIyCxLBQuaQLikpgZOTEzQaDS9FWZBNJ27g/8Vcx1BfZ3yjHit2OXqaylq8sSsJR67XB65nhvviTzPY9k1EdL/2/P7m2k9ktgRBwO7E+hvEO7p4ZVdx6mGFT58djremDoBUAnx5IQtzNsZztW8ioofAUENm6/JtDVLulMFGLsWTQ73ELqeJ+9u+r2aX4Il/ncIxtn0TEXUIQw2ZrcZRmqmDPeHUw0rkalqmb/v2dYamshYvbE3A+sNs+yYiai+GGjJLVbVa7EvKAdC5i1d2FW/nHtj1ymj8z+j6tu9/Hk3F81vZ9k1E1B4MNWSWvv/xDkqq6uDt3ANj/N3ELqdNbOQyvD9zCD6YNxS2VlLEpbDtm4ioPRhqyCw1LoswO9QbMmnXLF7ZVWaF+uCrRWz7JiJqL4YaMjvZxZU4lVYAAJgTJv7cNB0xyMsR+6IeweRAJWrqdFix5wp+G/0Dqmq1YpdGRGS0GGrI7HyVeBuCAIzyc0EvV9NdFfv+tu9dF26z7ZuI6AEYasisCIKA6Iv1yyIYwwzCD4tt30REbcdQQ2bl/M0i3CqsgL21DI8P8RS7nE7zSH837GfbNxHRAzHUkFlpXLxyerAKdtZykavpXF4ttH3fK2fbNxERwFBDZqS8ug4xV+oXU51nBpeemtNS2/eV22z7JiJiqCGzceBKLipqtOjrZo+w3j3FLqdLzQr9ebXv7OJKzN4Yj53n2fZNRJaNoYbMhn5umjAfSCSmNTdNRwSqGtu+PVBTp8PKr9j2TUSWjaGGzMLNgnIkZNyDVALMDjX+ZRE6S33bdxjbvomIwFBDZiK6YfHKcf3d4elkK3I13avFtu+f2PZNRJaFoYZMnlYnYE9iNgDTWLyyqzS2fQ9rbPveloAPDqdAy7ZvIrIQDDVk8k6lFSCvpArOdlaYMshD7HJE5eXcA1++MhrPju4NQQA+PJqKF9j2TUQWgqGGTF7jDcIzhnrBRi4TuRrx2chl+PPMILZ9E5HFYaghk1ZcUYPvr90BYB7LInSmJm3fG9j2TUTmjaGGTNq+H3JQo9VhoKcCg70cxS7H6Bi0fWvZ9k1E5o2hhkza7gs/L15pCXPTdERzbd+zN7Dtm4jMD0MNmayf8kpwJVsDuVSCmcO8xC7HqDW2ff/nxfq272s5bPsmIvPDUEMmq3GUZlKgEq4ONiJXYxrG9jNs+35+K9u+ich8MNSQSarV6vD1pfq5acx18cqu8su2b6C+7ZurfROROWCoIZMU+1M+Cstr4K6wwYQAd7HLMTmNbd/rn6lv+z7R0PZ9+Xax2KUREXUYQw2ZpMa5aWaFeEMu449xRz0dUt/23aeh7XvOhjP47/lMCAIvRxGR6eFvAzI5+aVVOJZ8F4BlL4vQWQJVjvgm6hFMGVTf9r3qqyv4bfRltn0TkclhqCGT8/WlbGh1Aob5OqOfUiF2OWbBqYcVPvmfMPx2Wn3b9+7E+rbvzEK2fROR6WCoIZMiCMIv5qbhKE1nkkoleC2ivu3bVd/2fZJt30RkMhhqyKT8cFuD1Pwy2MileHIo56bpCmP7uWH/kvq275Kquvq27++T2fZNREaPoYZMSuMNwo8FecLR1krkasyXyum+tu/YNPxmy3m2fRORUWOoIZNRVavFvh9yAHDxyu5wf9v3ydQCtn0TkVFjqCGTcehaHkqr6uDt3APhfV3FLsdisO2biEwFQw2ZjMYbhGeH+UAq5eKV3SlQ5Yh9i9n2TUTGjaGGTEJ2cSVOpxcAAOaGsetJDI62Tdu+Z/2bbd9EZDwYasgk7Em8DUEARvd1ga+LndjlWKz7275/zK1v+4796Y7YpRERMdSQ8dPpBEQn1l964uKVxqGx7TukV33b9wtbL7Dtm4hEx1BDRu98RhEyiyrgYCPHY0EqscuhBiqnHvjyf8OxMJxt30RkHBhqyOg13iD8RLAKPaxlIldDv2Qtl+K9GUH4xzPDDNq+f8gqFrs0IrJADDVk1Mqq6xBzJRcAl0UwZjNDvPG1+ue277kbz2DHObZ9E1H3Yqgho3bgcg4qa7Xo626P0F49xS6HHmCgp2Hb99t7r+Attn0TUTdiqCGj1njpaU6YDyQSzk1j7BrbvldMGwipBIhm2zcRdSOGGjJaN+6W4cKte5BKgNmhvPRkKqRSCRZF+GP7fW3fR6+z7ZuIuhZDDRmtxjbuCQHu8HC0Fbkaaq8x97V9v7jtAtax7ZuIuhBDDRklrU7Anov1oYaLV5qu+9u+/9XQ9l3Etm8i6gIMNWSUTqTexZ2SajjbWWFSoFLscughNNf2/STbvomoCzDUkFGKbrhBeOYwb9jIOTeNOWDbNxF1NYYaMjrFFTU4/GP9TaVzuHilWWls+478Rdv3m7vZ9k1EnaNdoWbNmjUYMWIEFAoFlEolZs6cieTk5FbfV1xcDLVaDZVKBRsbGwQEBCAmJkb/+h//+EdIJBKDx8CBAw32UVVVBbVaDVdXVzg4OGD27Nm4c4fdFObom6Qc1Gh1CFQ5IsjbSexyqJM52lrhk2d/bvvec5Ft30TUOdoVauLi4qBWq3H27FkcPnwYtbW1iIyMRHl5eYvvqampwZQpU5CRkYHo6GgkJydj06ZN8Pb2Nthu8ODByM3N1T9OnTpl8Pry5cvx7bffYvfu3YiLi0NOTg5mzZrVnvLJROxOzAIAzOMMwmZLImHbNxF1Pnl7Nj548KDB11u3boVSqURiYiLGjx/f7Hs2b96MoqIixMfHw8rKCgDQp0+fpoXI5fD09Gx2HxqNBp999hl27NiBiRMnAgC2bNmCwMBAnD17FqNHj27PYZARu55bgqvZJbCSSTBjmHfrbyCT1tj2/doXF3EpsxgvbruAxRP7YdnkAMiknGyRiNrnoe6p0Wg0AAAXF5cWt9m3bx/Cw8OhVqvh4eGBoKAgrF69Glqt4TX01NRUeHl5oW/fvliwYAEyMzP1ryUmJqK2thaTJ0/WPzdw4ED06tULZ86cafb7VldXo6SkxOBBxq9xBuHJgR5wsbcWuRrqDo1t38+x7ZuIHlKHQ41Op8OyZcswduxYBAUFtbjdjRs3EB0dDa1Wi5iYGLz77rtYt24d3n//ff02o0aNwtatW3Hw4EFs2LABN2/exLhx41BaWgoAyMvLg7W1NZydnQ327eHhgby8vGa/75o1a+Dk5KR/+PpyrhNjV1Onw9dJ2QC4eKWlsZZL8aeGtu8eVjK2fRNRh3Q41KjValy9ehU7d+584HY6nQ5KpRKffvopwsLC8Mwzz+Cdd97Bxo0b9ds89thjmDt3LoKDgzF16lTExMSguLgYu3bt6mh5WLVqFTQajf6RlZXV4X1R94j96Q6KymugVNhgfH93scshEcwM8cZe9RiDtu8vzt1i2zcRtUmHQk1UVBT279+PY8eOwcfnwf+jVqlUCAgIgEz281wjgYGByMvLQ01N88PLzs7OCAgIQFpaGgDA09MTNTU1KC4uNtjuzp07Ld6HY2NjA0dHR4MHGbfGS09Ph3pDLuNsA5bq/rbvd/ZexZu7L6Oyhm3fRPRg7frNIQgCoqKisHfvXsTGxsLPz6/V94wdOxZpaWnQ6XT651JSUqBSqWBt3fw9E2VlZUhPT4dKpQIAhIWFwcrKCkePHtVvk5ycjMzMTISHh7fnEMhI5ZdW4XjKXQDA3DBeKrR0jW3fKx/7Rdv3hnjcKmy505KIqF2hRq1WY/v27dixYwcUCgXy8vKQl5eHyspK/TYLFy7EqlWr9F8vWrQIRUVFWLp0KVJSUnDgwAGsXr0aarVav82bb76JuLg4ZGRkID4+Hk8//TRkMhnmz58PAHBycsKLL76I119/HceOHUNiYiKef/55hIeHs/PJTOy9mA2tTkBoL2f0UzqIXQ4ZAYlEglcn/Nz2fT23BE/86xTbvomoRe0KNRs2bIBGo0FERARUKpX+8eWXX+q3yczMRG5urv5rX19fHDp0CAkJCQgODsaSJUuwdOlSrFy5Ur/N7du3MX/+fAwYMADz5s2Dq6srzp49C3f3n++rWL9+PZ544gnMnj0b48ePh6enJ7766quHOXYyEoIgYHciF6+k5v1yte/ShtW+/36Iq30TUVMSwULuwCspKYGTkxM0Gg3vrzEylzLv4el/x8PWSoqEdyZDYWsldklkhGrqdPh/B37EtjO3AADj+rvhn78KYes/kZlrz+9v3o1JotvVcIPwY0EqBhpqUXNt3098eBJJbPsmogYMNSSqyhot9v+QAwCYy8UrqQ0aV/v2c7NHjqYK8zaewfazbPsmIoYaEtmha3kora6DT88eGN3XVexyyEQM8FTgm6ixmDq4vu37d19fxeu7fsDl28W814bIgrVr7Seizta4eOWcMB9IudYPtYOjrRU2/k8YPjlxA389+BP2XsrG3kvZUNjKMbqvK8b6u2JMPzf0VzpAIuHPFpElYKgh0dy+V4H49EIAwOxQXnqi9mts+w7xdcb/nbqJszcKUVpVh8M/3sHhH+tbv90cbDDG3xVj/F0xtp8bfF3sRK6aiLoKQw2JZk9iNgQBGOPvyl809FBG9XXFqL6uqNPqcC2nBPHphYhPL0BCRhEKyqqx74cc7Gu4d8unZw99wAnv6wqlo63I1RNRZ2FLN4lCpxMw4e/HkFVUifXPDMXTIRypoc5XXadFUmYxTqcX4kx6AS5lFqPuvntu+ikdMNbfFeH+9SHHyY4deETGpD2/vzlSQ6I4e7MQWUWVUNjIMW2wSuxyyEzZyGX6URxMCUB5dR0SMopwJr0Qp9MLcC2nBGn5ZUjLL8O2M7cgkQBBXk71l6v6uWFEn56ws+Y/k0Smgp9WEkV0w9w0TwxVoYe1rJWtiTqHvY0cEQOUiBigBAAUV9Tg7I0ixKcXID69EGn5ZbiSrcGVbA0+OXEDVjIJhvk6Y4y/G8b4u2JYL2fYyPnzSmSsePmJul1pVS1G/L8jqKrVYc+iMQjr3VPskogAAPklVfr7cU6nFSK7uNLgdVsrKUb0cdGHnCBvJ8jYtUfUpXj5iYzagcu5qKrVwd/dHqG9nMUuh0hP6WiLmSHemBniDQDILKzQj+LEpxeioKwaJ1MLcDK1AAD07eONNx6zfZxIXAw11O1+uXglfwGQMevlaoderr3wq5G9IAgCUvPLcDqtPuS01j4+xt8NvVzZ1UfUnXj5ibpV+t0yTFoXB5lUgjMrJ7KdlkyWVifgarbGoH28qlZnsE1j+3jj5Sr+vBO1Hy8/kdGKbhilmRDgzn/gyaTJpBIM9XXGUF9nLIrw17ePN4acS5nFuH2vErsu3NYv2tpP6aAPOaP7usDZjiuME3UmjtRQt6nT6jBmbSzyS6uxYUEoHhvCVm4yX+XVdbhw6x7iGy5XXc3R4Jf/2kokwGAvR4z1d0O4vytG9HGBvQ3/n0l0P47UkFE6mVqA/NJq9LSzwqRAD7HLIepS9jZyTAhwx4QAdwCApqIWZ27UTwIYn16I1PwyXM0uwdXsEnxy4gbkUglCejkjvOFSVQjbx4najaGGuk3j4pUzQ7xhLecC8WRZnOysMC3IE9OCPAHUt4+fuVGI02k/t48nZNxDQsY9fHg0le3jRB3AUEPd4l55DY78mA8AmBvmK3I1ROJTOtpixjBvzBhW3z6eVVSh76xqrX18jL8bAjzYPk50P4Ya6hbfJGWjRqvDYC9HDPLiPU1E9/N1scOvRhq2j8f/on28pEn7uLX+UtVYfzf4uvRgyCGLx1BD3UI/N00YF64kao1EIkGAhwIBHgr8ZqwftDoB13Lq28dPpzWuPl6Db3/IwbcNq497O/9i9XF/V3iwu5AsELufqMtdy9Fg+oenYC2T4tzbk9DTnm2sRA+juk6LH7I0OJ1WgDPphbiUdQ+12qarjzdOBDi6ryvbx8lksfuJjMruhjk6Jg9SMtAQdQIbuQwj/Vww0s8Fy6cAFTV1SMi4V7+kQ1p9+3jj6uOfN6w+PtjLUX/TMdvHyVzxp5q6VE2dDt8kZQPgDcJEXcXOumn7+Nmbhfp7cn7ZPv5pQ/v4MF9njOnH9nEyL7z8RF3quyu5WPTFRXg42uD0iomQy9jKTdTd8kurcCa9EPFphTidXoDb95pffTy8obNqCNvHyYjw8hMZjcYbhGeF+jDQEIlEqWjaPt64+vjptPvbx5OhsJVjlN/Pq4+zfZxMBUdqqMvkl1Rh9Jqj0AlA7BsT0NfdQeySiOg+giAgLb9M31nV2D7+S24O1hjdtz7gjPF3RS8XO4Yc6jYcqSGj8NWlbOgEIKx3TwYaIiMlkUjQ30OB/h4KPDemj0H7eHx6IRJu1reP77+ci/2XcwH83D4+pl/95Sq2j5Ox4EgNdQlBEDDpgzjcuFuOtbOG4Fcje4ldEhF1QE2dDklZxfrOqubax/3d7THG3w1j+7F9nDpfe35/M9RQl0i8dQ+zN8TD1kqKhHcmQ2FrJXZJRNQJKmrqcCHjHk6n18+RcyW76erjg1SO+kkAR7J9nB4SLz+R6KIbFq98fIiKgYbIjNhZyzE+wB3j72sfP9NwT05qfhmu5ZTgWs597eP+rhjTz43t49SlOFJDna6yRosR/+8Iyqrr8N+XRyPc31Xskoiom/yyfTz+RgGyigzbx23kDauPN9yPE+TlyM5IeiCO1JCoDl7LRVl1HXxdemCUn4vY5RBRN3pQ+3h8eiHullbjVFoBTqU1tI/byDGqYfXxqUGe8HbuIe4BkEnjSA11ul9vOov49EIsnxyApZP7i10OERmJ1trHFbZyfLd0HHx62olYJRkbjtSQaOr/V1YIiQSYHeYtdjlEZESaax//MacE8ekF+PJCFm7cLce/jqbhL3OCxS6VTBQvZFKnim6YQXiMvyv/t0VEDySTSjDExwmvTPDH3+YMBQBEX7yNjIJykSsjU8VQQ51GpxP0oYaLVxJRe4T17omJA5XQ6gT882iq2OWQiWKooU5z9kYhsosrobCVY1qQp9jlEJGJeX1KAADg66RspN4pFbkaMkUMNdRpGhevfHKoF2ytOA8FEbVPkLcTpg32hCAA/zjC0RpqP4Ya6hQlVbX47mr9ujBzw3xEroaITNXyKQGQSIADV3JxLUcjdjlkYhhqqFMcuJyLqlod+ikdMMzXWexyiMhEDfBU4MlgLwDA+sMpIldDpoahhjrF7gv1yyLMDfOBRCIRuRoiMmXLJveHVAIcuZ6PpKxiscshE8JQQw8tLb8UFzOLIZNK8HQo56YhoofT190Bs0PrL2Ov+z5Z5GrIlDDU0ENrvEE4IsAdSoWtyNUQkTlYMqk/5FIJTqYW4PzNIrHLIRPBUEMPpU6rw1cXswEAc4dzbhoi6hy+LnZ4ZkT9vyl//z4ZFrKiDz0khhp6KCdS7+JuaTVc7K0xcaBS7HKIyIxETewHa7kU528W4XRaodjlkAlgqKGHsvtC/aWnmcO8YS3njxMRdR6VUw8sGNULAEdrqG34W4g6rKi8Bkeu3wEAzB3OuWmIqPMtivBHDysZkrKKEftTvtjlkJFjqKEO+/pSNmq1AoK8HRGoevBy8EREHaFU2OK5MX0AAB8cToFOx9Eaalm7Qs2aNWswYsQIKBQKKJVKzJw5E8nJrbfbFRcXQ61WQ6VSwcbGBgEBAYiJiWl227Vr10IikWDZsmUGz6enp+Ppp5+Gu7s7HB0dMW/ePNy5c6c95VMn283FK4moG7wyvi8cbOS4llOCQ9fyxC6HjFi7Qk1cXBzUajXOnj2Lw4cPo7a2FpGRkSgvb3mZ+JqaGkyZMgUZGRmIjo5GcnIyNm3aBG/vpvOZJCQk4JNPPkFwcLDB8+Xl5YiMjIREIkFsbCxOnz6NmpoaPPnkk9DpdO05BOokV7M1uJ5bAmuZFDOGeYldDhGZsZ721njhET8A9aM1Wo7WUAvk7dn44MGDBl9v3boVSqUSiYmJGD9+fLPv2bx5M4qKihAfHw8rKysAQJ8+fZpsV1ZWhgULFmDTpk14//33DV47ffo0MjIycOnSJTg61l/m2LZtG3r27InY2FhMnjy5PYdBnSC6YZRmymAPONtZi1wNEZm7Fx/xw7b4DKTml2H/5RzMGMaJPqmph7qnRqOpX2zMxcWlxW327duH8PBwqNVqeHh4ICgoCKtXr4ZWqzXYTq1WY/r06c0GlOrqakgkEtjY2Oifs7W1hVQqxalTpx7mEKgDquu0+DqpYW4aLl5JRN3AqYcV/nd8XwD1a0LVaTlKT011ONTodDosW7YMY8eORVBQUIvb3bhxA9HR0dBqtYiJicG7776LdevWGYzG7Ny5ExcvXsSaNWua3cfo0aNhb2+PFStWoKKiAuXl5XjzzTeh1WqRm5vb7Huqq6tRUlJi8KDOcfR6PoorauHpaItx/d3FLoeILMRvxvSBq701Mgor9JN+Ev1Sh0ONWq3G1atXsXPnzgdup9PpoFQq8emnnyIsLAzPPPMM3nnnHWzcuBEAkJWVhaVLl+KLL76ArW3zU+y7u7tj9+7d+Pbbb+Hg4AAnJycUFxcjNDQUUmnzh7BmzRo4OTnpH76+vJm1szQuXjkr1BsyKRevJKLuYW8jx6IIfwDAP4+moqaOozVkqEOhJioqCvv378exY8fg4/Pgyw8qlQoBAQGQyWT65wIDA5GXl4eamhokJiYiPz8foaGhkMvlkMvliIuLw4cffgi5XK6/TBUZGYn09HTk5+ejoKAA//nPf5CdnY2+ffs2+31XrVoFjUajf2RlZXXkUOk+eZoqxKXcBQDM4aUnIupm/zO6N5QKG2QXV+LLC/x3nQy1K9QIgoCoqCjs3bsXsbGx8PPza/U9Y8eORVpamkGXUkpKClQqFaytrTFp0iRcuXIFSUlJ+sfw4cOxYMECJCUlGYQhAHBzc4OzszNiY2ORn5+Pp556qtnva2NjA0dHR4MHPbyvLt2GTgCG9+6Jvu4OYpdDRBbG1kqGqIn9AAAfxaaiqlbbyjvIkrQr1KjVamzfvh07duyAQqFAXl4e8vLyUFlZqd9m4cKFWLVqlf7rRYsWoaioCEuXLkVKSgoOHDiA1atXQ61WAwAUCgWCgoIMHvb29nB1dTW4V2fLli04e/Ys0tPTsX37dsydOxfLly/HgAEDHvbvgNpIEARENyyLMI+LVxKRSJ4Z4Qtv5x64U1KNL85lil0OGZF2hZoNGzZAo9EgIiICKpVK//jyyy/122RmZhrcvOvr64tDhw4hISEBwcHBWLJkCZYuXYqVK1e2q9Dk5GTMnDkTgYGBeO+99/DOO+/g73//e7v2QQ/nYuY93CgoRw8rGR4PVoldDhFZKBu5DEsm1Y/WbDiehvLqOpErImMhESxkhbCSkhI4OTlBo9HwUlQHrdxzGTsTsjA71Afr5g0VuxwismC1Wh0mfxCHW4UV+O20AXgtop/YJVEXac/vb679RG1SUVOH/ZfrR+C4eCURic1KJsWyyf0BAJ/E3UBJVa3IFZExYKihNjl4NQ9l1XXo5WKHUX4tT7ZIRNRdnhrqjX5KB2gqa7H51E2xyyEjwFBDbbKroXVyTpgPJBLOTUNE4pNJJXh9SgAA4LOTN3GvvEbkikhsDDXUqszCCpy9UQSJBJjNuWmIyIhMG+yJQJUjSqvr8OnJG2KXQyJjqKFWRV+sb+N+pJ8bvJ17iFwNEdHPpFIJ3mgYrdl6OgMFZdUiV0RiYqihB9LpBOxpWJGbMwgTkTGaFKjEUF9nVNZqseF4utjlkIgYauiBztwoRHZxJRS2ckwd7Cl2OURETUgkP4/W/OfsLeRpqkSuiMTCUEMP1Lh45VNDvWBrJWtlayIicYzr74aRfVxQU6fDx8fSxC6HRMJQQy0qqarFd1fzAABzuSwCERkxiUSCNyLrR2t2JmQiq6hC5IpIDAw11KJvf8hBdZ0O/ZUOGOrjJHY5REQPNKqvKx7p54ZarYB/xaaKXQ6JgKGGWrT7F4tXcm4aIjIFrzeM1uy5mI2bBeUiV0PdjaGGmpWWX4qkrGLIpBLMDPEWuxwiojYJ7dUTkwYqodUJ+OeRFLHLoW7GUEPNahyleXSAEu4KG5GrISJqu+UNnVDf/JCDlDulIldD3Ymhhpqo0+rw1aVsAFy8kohMT5C3Ex4L8oQgAOsPc7TGkjDUUBNxKXdxt7QarvbWmDhQKXY5RETttnxKACQS4LurebiarRG7HOomDDXUROPilTNDvGEl448IEZmeAA8FZgz1AsDRGkvC31hkoLCsGkev5wOo73oiIjJVSycHQCaV4OhP+biYeU/scqgbMNSQga+TclCnExDs44QBngqxyyEi6jA/N3vMDq3v3uRojWVgqCE9QRD0yyLM5eKVRGQGFk/sDyuZBCdTC3D2RqHY5VAXY6ghvWs5JfgprxTWcimeGsq5aYjI9Pm62OGZEfWX0j/4PgWCIIhcEXUlhhrSaxyliRzkASc7K5GrISLqHFGP9oe1XIrzGUU4lVYgdjnUhRhqCABQVavF10k5ALh4JRGZF08nWzw7ujcA4O8crTFrDDUEADhy/Q40lbVQOdnikX5uYpdDRNSpFkX4o4eVDD9kFes7PMn8MNQQgJ+XRZgd6gOZlItXEpF5cXOwwW/G9gEArDucAp2OozXmiKGGkKepwsnUuwCAOex6IiIz9cr4vlDYyHE9twQHr+WJXQ51AYYawp6Lt6ETgJF9XNDHzV7scoiIuoSznTVeeMQPAPDB4RRoOVpjdhhqLJwgCIhOrL/0NIeLVxKRmXtxnB+celghLb8M+37IFrsc6mQMNRYu8dY93Cwoh521DNOHqMQuh4ioSznaWuGVCX0BAP88koparU7kiqgzMdRYuMYbhB8fooK9jVzkaoiIut5vxvSBm4M1Mgor8NXF22KXQ52IocaCVdTUYf/lhrlpeIMwEVkIO2s5Xp3gDwD48Ggaquu0IldEnYWhxoLFXMlDeY0WfVztMNLPRexyiIi6zf+M7g0PRxtkF1diV0KW2OVQJ2GosWCNyyLMCfOBRMK5aYjIcthayRA1sT8A4F+xaaiq5WiNOWCosVCZhRU4d7MIEgkwK5SXnojI8jwz3Bfezj2QX1qN7WdviV0OdQKGGgsVnVg/SvNIPzd4OfcQuRoiou5nLZdi6aT60Zp/H09HeXWdyBXRw2KosUA6nYA9F+vnZ+DilURkyWaFeqOPqx2KymuwNT5D7HLoITHUWKD49EJkF1fC0VaOyEEeYpdDRCQauUyKZZMDAACfxKVDU1krckX0MBhqLNCuhhuEZwzzhq2VTORqiIjE9eRQL/RXOqCkqg6fnbopdjn0EBhqLIymshaHGhZym8tlEYiIIJNK8PqU+tGazadu4l55jcgVUUcx1FiYb3/IQXWdDgM8FBji7SR2OURERmHqYE8MUjmirLoOn5y4IXY51EEMNRZmd8PilXOHc24aIqJGUqkEb0TWj9Zsi8/A3dJqkSuijmCosSCpd0rxQ1Yx5FIJZoZ4i10OEZFRmThQiWG+zqis1WLD8XSxy6EOYKixII2jNI8OVMLNwUbkaoiIjItEIsGbkQMAANvP3UKuplLkiqi9GGosRK1Wp1+NlotXEhE1b2w/V4z0c0FNnQ4fxaaJXQ61E0ONhTiefBcFZTVwc7DGowOVYpdDRGSUJBIJ3mjohNp1IQtZRRUiV0TtwVBjIRoXr3w6xBtWMp52IqKWjOrrinH93VCrFfDh0VSxy6F24G83C1BQVo3Yn/IBcFkEIqK2eKPh3po9F2/jxt0ykauhtmKosQBfX8pGnU7AUB8nBHgoxC6HiMjoDfN1xuRAJXQC8I8jHK0xFQw1Zk4QBEQ3dD3N4SgNEVGbLW+4t+bbyzlIzisVuRpqC4YaM3c1uwQ/5ZXCWi7FU8FeYpdDRGQyBns54fEhnhAEYP3hFLHLoTZoV6hZs2YNRowYAYVCAaVSiZkzZyI5ObnV9xUXF0OtVkOlUsHGxgYBAQGIiYlpdtu1a9dCIpFg2bJlBs/n5eXh2WefhaenJ+zt7REaGoo9e/a0p3yL1Lh45bTBnnCysxK5GiIi07J8cgAkEuDgtTxczdaIXQ61ol2hJi4uDmq1GmfPnsXhw4dRW1uLyMhIlJeXt/iempoaTJkyBRkZGYiOjkZycjI2bdoEb++mM9omJCTgk08+QXBwcJPXFi5ciOTkZOzbtw9XrlzBrFmzMG/ePFy6dKk9h2BRqmq1+CYpGwAXryQi6oj+HgrMHFb/++oDjtYYPXl7Nj548KDB11u3boVSqURiYiLGjx/f7Hs2b96MoqIixMfHw8qqfqSgT58+TbYrKyvDggULsGnTJrz//vtNXo+Pj8eGDRswcuRIAMDvfvc7rF+/HomJiQgJCWnPYViMwz/eQUlVHbycbDHG303scoiITNLSSf2x74ccxP6Uj8Rb9xDWu6fYJVELHuqeGo2mfijOxcWlxW327duH8PBwqNVqeHh4ICgoCKtXr4ZWqzXYTq1WY/r06Zg8eXKz+xkzZgy+/PJLFBUVQafTYefOnaiqqkJERESz21dXV6OkpMTgYWkal0WYHeYDmZSLVxIRdUQfN3vMCa0f7f7gcOu3XJB4OhxqdDodli1bhrFjxyIoKKjF7W7cuIHo6GhotVrExMTg3Xffxbp16wxGY3bu3ImLFy9izZo1Le5n165dqK2thaurK2xsbPDKK69g79696NevX7Pbr1mzBk5OTvqHr69ldf7kaipxMvUuAGAOl0UgInooiyf1g5VMgtNphTiTXih2OdSCDocatVqNq1evYufOnQ/cTqfTQalU4tNPP0VYWBieeeYZvPPOO9i4cSMAICsrC0uXLsUXX3wBW1vbFvfz7rvvori4GEeOHMGFCxfw+uuvY968ebhy5Uqz269atQoajUb/yMrK6uihmqSvLmZDEICRfi7o7WovdjlERCbNp6cd5o/sBaB+tEYQBJEroua0656aRlFRUdi/fz9OnDgBH58HjwKoVCpYWVlBJpPpnwsMDEReXh5qamqQmJiI/Px8hIaG6l/XarU4ceIEPvroI1RXVyMjIwMfffQRrl69isGDBwMAhg4dipMnT+Ljjz/WB6RfsrGxgY2NZa5ELQiCflkELl5JRNQ51I/2w5cJWUjIuIcTqQWYEOAudkl0n3aN1AiCgKioKOzduxexsbHw8/Nr9T1jx45FWloadDqd/rmUlBSoVCpYW1tj0qRJuHLlCpKSkvSP4cOHY8GCBUhKSoJMJkNFRf2CYlKpYbkymcxgv1QvIeMeMgorYG8tw+NDVGKXQ0RkFjwcbfHs6N4AgA++52iNMWpXqFGr1di+fTt27NgBhUKBvLw85OXlobKyUr/NwoULsWrVKv3XixYtQlFREZYuXYqUlBQcOHAAq1evhlqtBgAoFAoEBQUZPOzt7eHq6qq/V2fgwIHo168fXnnlFZw/fx7p6elYt24dDh8+jJkzZ3bCX4N5aRylmR6sgr1NhwbjiIioGa9G+MPOWoYfbmtw5Hq+2OXQfdoVajZs2ACNRoOIiAioVCr948svv9Rvk5mZidzcXP3Xvr6+OHToEBISEhAcHIwlS5Zg6dKlWLlyZZu/r5WVFWJiYuDu7o4nn3wSwcHB+Pzzz7Ft2zY8/vjj7TkEs1deXYcDV+r//rl4JRFR53JzsMFvxvQBAKz7Phk6HUdrjIlEsJDxs5KSEjg5OUGj0cDR0VHscrrM7gtZeCv6Mvzc7BH7xgRIJGzlJiLqTMUVNRj3l2Mora7DR78OwRNcgqZLtef3N9d+MjONc9PMCfNhoCEi6gLOdtZ4aVxfAPVrQmk5WmM0GGrMyK3Ccpy/WQSpBJgV2nQZCiIi6hwvPNIHznZWSL9brl+OhsTHUGNGohtGacb1d4fKqYfI1RARmS+FrRVeGe8PAPjHkVTUatmJawwYasyEVifoQw0XryQi6nrPjekNNwdrZBZVYE/Dv78kLoYaM3E6rQC5mio49bDC5EAPscshIjJ7dtZyLIqoX6rnw6OpqK7TtvIO6moMNWai8QbhGcO8YGsla2VrIiLqDAtG9YKnoy1yNFXYed6yluMxRgw1ZkBTUYtD1/IAAHPDODcNEVF3sbWSIWpi/WjNR8fSUFnD0RoxMdSYgX2Xc1BTp8NATwWCvM13Dh4iImM0b7gvfHr2wN3Samw/e0vsciwaQ40ZiG5YFoFz0xARdT9ruRRLJvUHAGyIS0dZdZ3IFVkuhhoTl5xXih9uayCXSvB0COemISISw6wQb/i52aOovAZbT98UuxyLxVBj4hoXr5wUqISrg43I1RARWSa5TIplk+tHaz49cQOaylqRK7JMDDUmrFarw9cNM1nyBmEiInE9GeyFAA8HlFTV4bOTN8QuxyIx1JiwYz/lo6CsBm4ONogY4C52OUREFk0qleD1KQEAgM9O3URReY3IFVkehhoT1jg3zaxQb8hlPJVERGKbOtgTg70cUV6jxScn0sUux+LwN6GJKiirxrGf8gEAc8O4LAIRkTGQSCR4I7J+tGZbfAbyS6tErsiyMNSYqK8vZaNOJ2CorzP6eyjELoeIiBo8OkCJkF7OqKrV4d/HOFrTnRhqTJAgCNjV0PU0j4tXEhEZFYlEgjcjBwAAdpzLRE5xpcgVWQ6GGhN0+bYGKXfKYCOX4smhXmKXQ0RE9xnj74pRfi6o0erw0bE0scuxGAw1Jmh3Yv0ozbQgTzjaWolcDRER3a/+3pr60ZpdCVnILKwQuSLLwFBjYqpqtdiXlAOAc9MQERmzkX4uGB/gjjqdgA9jU8UuxyIw1JiY73+8g5KqOng798AYf1exyyEiogd4o2Hemq8u3kb63TKRqzF/DDUmpnFZhNmh3pBKuXglEZExG+rrjMmBHtAJwD+OcLSmqzHUmJCc4kqcSisAAMzhpSciIpPQOMvwtz/k4Ke8EpGrMW8MNSZkT+JtCAIwuq8LernaiV0OERG1wSAvR0wPVgEA1h9OEbka88ZQYyIEQUD0xfplEXiDMBGRaVk+uT+kEuDQtTu4clsjdjlmi6HGRJy/WYRbhRVwsJHjsSGeYpdDRETt0E+pwMxh3gCAdYeTRa7GfDHUmIjGxSunD1HBzloucjVERNReSyf3h0wqwfHku0i8VSR2OWaJocYElFfXIeZKLgBgLpdFICIySb1d7fULEK/7nvfWdAWGGhNw4EouKmq06Otmj7DePcUuh4iIOmjxpP6wlkkRn16I+PQCscsxOww1JiD6Qv2lpznDfSCRcG4aIiJT5e3cA/NH1jd7fPB9CgRBELki88JQY+RuFpTjfEYRpBJgdigvPRERmTr1o/1gI5fiwq17iEu5K3Y5ZoWhxshFNyxeOT7AHR6OtiJXQ0RED0vpaIuF4b0BAB8c5mhNZ2KoMWJanYA9idkAODcNEZE5eXWCP+ysZbh8W4PDP94RuxyzwVBjxE6lFSCvpArOdlaYPEgpdjlERNRJXB1s8MJYPwD1ozU6HUdrOgNDjRFrXLxyxlAv2MhlIldDRESd6eVxfaGwleOnvFIcaJi2gx4OQ42RKq6owfcNQ5Jzh/PSExGRuXGys8LL4/oCANYfSUGdVidyRaaPocZI7fshBzV1OgSqHBHk7SR2OURE1AWeH9sHPe2scONuOb5JyhG7HJPHUGOkdl9oXLySbdxEROZKYWuFVyb4AwD+cTQFtRyteSgMNUbop7wSXMnWwEomwcwQb7HLISKiLrQwvDfcHGyQVVSp/w8tdQxDjRFq/KGeNNADLvbWIldDRERdyc5aDvWj9aM1/4pNRVWtVuSKTBdDjZGp1erw9aWGuWm4eCURkUWYP7IXVE62yNVUYef5TLHLMVkMNUYm9qd8FJbXwF1hgwkB7mKXQ0RE3cDWSoaoif0AAB8dS0dlDUdrOoKhxsg0XnqaFeoNuYynh4jIUswN84WvSw8UlFXjP2czxC7HJPG3phHJL63CseR8AFwWgYjI0ljLpVgysT8AYMPxdJRV14lckelhqDEiX1/KhlYnIKSXM/opHcQuh4iIutnTId7o62aPexW12HLqptjlmByGGiMhCMIv5qbhKA0RkSWSy6RYNiUAAPDpyRvQVNSKXJFpYagxEj/c1iA1vwy2VlI8MVQldjlERCSSJ4aoMMBDgdKqOvzfqRtil2NSGGqMROPildMGe8LR1krkaoiISCxSqQTLG0ZrNp+6icKyapErMh3tCjVr1qzBiBEjoFAooFQqMXPmTCQnJ7f6vuLiYqjVaqhUKtjY2CAgIAAxMTHNbrt27VpIJBIsW7ZM/1xGRgYkEkmzj927d7fnEIxSVa0W+36oX/NjHhevJCKyeFMHeyDI2xHlNVp8coKjNW3VrlATFxcHtVqNs2fP4vDhw6itrUVkZCTKy8tbfE9NTQ2mTJmCjIwMREdHIzk5GZs2bYK3d9Pp/xMSEvDJJ58gODjY4HlfX1/k5uYaPP70pz/BwcEBjz32WHsOwSgdupaH0qo6+PTsgdF9XcUuh4iIRCaRSPBG5AAAwOdnMpBfUiVyRaZB3p6NDx48aPD11q1boVQqkZiYiPHjxzf7ns2bN6OoqAjx8fGwsqq/rNKnT58m25WVlWHBggXYtGkT3n//fYPXZDIZPD09DZ7bu3cv5s2bBwcH0+8SarxBeHaoD6RSicjVEBGRMYgIcEdoL2dczCzGv4+n449PDRa7JKP3UPfUaDQaAICLi0uL2+zbtw/h4eFQq9Xw8PBAUFAQVq9eDa3WcLZEtVqN6dOnY/Lkya1+38TERCQlJeHFF198mPKNQnZxJU6nFwAA5nBFbiIiaiCRSPBmw2jNjnOZyC6uFLki49eukZpf0ul0WLZsGcaOHYugoKAWt7tx4wZiY2OxYMECxMTEIC0tDa+99hpqa2vxhz/8AQCwc+dOXLx4EQkJCW363p999hkCAwMxZsyYFreprq5GdfXPN1eVlJS08ci6157E2xAEILyvK3xd7MQuh4iIjMiYfm4I7+uKMzcK8VFsGtbMGiJ2SUatwyM1arUaV69exc6dOx+4nU6ng1KpxKeffoqwsDA888wzeOedd7Bx40YAQFZWFpYuXYovvvgCtra2rX7fyspK7Nixo9VRmjVr1sDJyUn/8PU1vhtwdToB0YkNc9Nw8UoiImrGG5H1nVC7L2ThVmHL97BSB0NNVFQU9u/fj2PHjsHH58G/jFUqFQICAiCTyfTPBQYGIi8vDzU1NUhMTER+fj5CQ0Mhl8shl8sRFxeHDz/8EHK5vMllqujoaFRUVGDhwoUP/L6rVq2CRqPRP7KysjpyqF3qfEYRMosq4GAjx2NBnJuGiIiaGt7HBRMC3FGnE/DPo6lil2PU2hVqBEFAVFQU9u7di9jYWPj5+bX6nrFjxyItLQ06nU7/XEpKClQqFaytrTFp0iRcuXIFSUlJ+sfw4cOxYMECJCUlGYQhoP7S01NPPQV39wevYG1jYwNHR0eDh7FpvEH4yaEq9LCWtbI1ERFZqsbRmq8vZSMtv0zkaoxXu0KNWq3G9u3bsWPHDigUCuTl5SEvLw+VlT/fvLRw4UKsWrVK//WiRYtQVFSEpUuXIiUlBQcOHMDq1auhVqsBAAqFAkFBQQYPe3t7uLq6NrlXJy0tDSdOnMBLL730MMdsFMqq6xBzJRcAMIfLIhAR0QME+zhjyiAP6ATgH0dSxC7HaLUr1GzYsAEajQYRERFQqVT6x5dffqnfJjMzE7m5ufqvfX19cejQISQkJCA4OBhLlizB0qVLsXLlynYXu3nzZvj4+CAyMrLd7zU2By7noLJWi77u9gjt5Sx2OUREZOReb5hleP/lXFzPNc7mF7FJBEEQxC6iO5SUlMDJyQkajcYoLkXN2RCPC7fuYcW0gVgU4S92OUREZAKidlzE/su5mDLIA5sWDhe7nG7Rnt/fXPtJBDfuluHCrXuQSoBZoU1nViYiImrOsskBkEqAwz/eweXbxWKXY3QYakTQ2MY9IcAdHo6tt7ETEREBQD+lA2aG1P9neN33vLfmfgw13UyrE/DVxWwAwFwuXklERO20dFJ/yKUSxKXcxYWMIrHLMSoMNd3sZOpd5JVUoaedFSYFKsUuh4iITExvV3v9f4o5WmOIoaabNc5NM2OYN2zknJuGiIjab/HEfrCWSXHmRiHi0wrELsdoMNR0o+KKGhz+8Q4ALotAREQd5+XcA78e1QsA8Pfvk2EhjcytYqjpRt8k5aBGq8MglSMGezmJXQ4REZmw1yL8YWslxcXMYhxPuSt2OUaBoaYb7U6sX3+KozRERPSwlI62WBjeBwCwjqM1ABhqus313BJczS6BlUyCGcM4Nw0RET28V8b3hb21DFezS3Do2h2xyxEdQ003abxBeMogD7jYW4tcDRERmQNXBxu88Ej94tLrD6dAp7Ps0RqGmm5QU6fD10kNc9Nw8UoiIupELz3SFwpbOZLvlGL/ldzW32DGGGq6QexPd1BUXgOlwgbj+ruJXQ4REZkRJzsr/O+4vgCAfxxOQZ1WJ3JF4mGo6QaNl55mhfpALuNfORERda7nH/FDTzsr3Cgox95L2WKXIxr+hu1i+aVV+lY7dj0REVFXcLCRY1GEPwDgw9hU1NRZ5mgNQ00X23sxG1qdgNBezvB3dxC7HCIiMlPPju4Dd4UNsooq9VOIWBqGmi4kCAJ2N6zIzcUriYioK/WwlkHdMFrzr6NpqKrVilxR92Oo6UJJWcVIyy+DrZUUTwSrxC6HiIjM3PxRveDlZIu8kir893ym2OV0O4aaLtQ4SvN4kAoKWyuRqyEiInNnI5chamJ/AMDHx9JRUVMnckXdi6Gmi1TWaPFtUg4AYA5vECYiom4yd7gPernYoaCsGp+fuSV2Od2KoaaLHLqWh9LqOvj07IHRfq5il0NERBbCSibF0kn1ozWfxKWjtKpW5Iq6D0NNF2m883xOmA+kUonI1RARkSWZGeKNvu72uFdRiy2nM8Qup9sw1HSB2/cqEJ9eCACYHcpLT0RE1L1kUgmWTw4AAGw6cQPFFTUiV9Q9GGq6wJ7EbAgCMLafK3xd7MQuh4iILND0ISoM9FSgtLoOm07eELucbsFQ08l0OgHRF+svPXHxSiIiEotUKsHyKfWjNVtOZ6CwrFrkiroeQ00nO3ezCFlFlVDYyDF1sKfY5RARkQWLHOSBId5OqKjRYmNcutjldDmGmk62+0L9KM0TQ73Qw1omcjVERGTJJBIJ3oisH635/Mwt3CmpErmirsVQ04lKq2oRczUXABevJCIi4zAhwB3De/dEdZ0O/z6WJnY5XYqhphMduJyLqlod/N3tEeLrLHY5REREkEgkeL1htGbH+UzcvlchckVdh6GmE/1y8UqJhHPTEBGRcRjj74Yx/q6o1Qr4KNZ8R2sYajpJ+t0yJN66B5lUglkh3mKXQ0REZKDx3prdibeRUVAucjVdg6Gmk0Q3jNJEBLhD6WgrcjVERESGwnq7IGKAO7Q6AR8eTRW7nC7BUNMJ6rQ67NFfeuINwkREZJzemDIAALA3KRtp+aUiV9P5GGo6wcnUAuSXVsPF3hoTB3qIXQ4REVGzhvg4YepgDwgCsP6I+Y3WMNR0gsbFK2cM84K1nH+lRERkvJZPCYBEUt+x+2NOidjldCr+Bn5I98prcOTHfABcFoGIiIzfQE9HPBHsBQD44HCKyNV0Loaah5R2twwKWzmCvB0xyMtR7HKIiIhatWxyf0glwJHrd5CUVSx2OZ2GoeYhjejjgrNvT8KGBWFil0JERNQm/u4OeDqkvrHFnEZrGGo6gZVMCl8XO7HLICIiarOlk/pDLpXgRMpdnL9ZJHY5nYKhhoiIyAL1crXDvBH194Ku+z4ZgiCIXNHDY6ghIiKyUIsn9oO1XIpzN4sQn14odjkPjaGGiIjIQqmceuDXI3sBAP5uBqM1DDVEREQW7LVH/WFrJcWlzGIcS84Xu5yHwlBDRERkwZQKWzw3pg8AYN33KSY9WsNQQ0REZOFeGe8Pe2sZruWU4NC1PLHL6TCGGiIiIgvnYm+NFx/xA1A/b41WZ5qjNQw1REREhBfH9YWjrRwpd8qw/3KO2OV0CEMNERERwamHFf53fF8AwD+OpKJOqxO5ovZjqCEiIiIAwG/G+sHF3ho3C8rx1aVssctpN4YaIiIiAgA42MixaII/AOCfR1JRU2daozXtCjVr1qzBiBEjoFAooFQqMXPmTCQnJ7f6vuLiYqjVaqhUKtjY2CAgIAAxMTHNbrt27VpIJBIsW7asyWtnzpzBxIkTYW9vD0dHR4wfPx6VlZXtOQQiIiJ6gP8Z3RvuChtkF1di14Ussctpl3aFmri4OKjVapw9exaHDx9GbW0tIiMjUV5e3uJ7ampqMGXKFGRkZCA6OhrJycnYtGkTvL29m2ybkJCATz75BMHBwU1eO3PmDKZNm4bIyEicP38eCQkJiIqKglTKwSYiIqLO0sNahqhH+wEA/hWbiqparcgVtZ28PRsfPHjQ4OutW7dCqVQiMTER48ePb/Y9mzdvRlFREeLj42FlZQUA6NOnT5PtysrKsGDBAmzatAnvv/9+k9eXL1+OJUuWYOXKlfrnBgwY0J7yiYiIqA1+NdIXn8SlI0dThS/OZerbvY3dQw1zaDQaAICLi0uL2+zbtw/h4eFQq9Xw8PBAUFAQVq9eDa3WMPmp1WpMnz4dkydPbrKP/Px8nDt3DkqlEmPGjIGHhwcmTJiAU6dOtfh9q6urUVJSYvAgIiKi1tnIZVgyqT8AYMPxNFTU1IlcUdt0ONTodDosW7YMY8eORVBQUIvb3bhxA9HR0dBqtYiJicG7776LdevWGYzG7Ny5ExcvXsSaNWta3AcA/PGPf8TLL7+MgwcPIjQ0FJMmTUJqamqz71mzZg2cnJz0D19f344eKhERkcWZHeaDXi52KCirwbb4W2KX0yYdDjVqtRpXr17Fzp07H7idTqeDUqnEp59+irCwMDzzzDN45513sHHjRgBAVlYWli5dii+++AK2trYt7gMAXnnlFTz//PMICQnB+vXrMWDAAGzevLnZ96xatQoajUb/yMoyrZudiIiIxGQlk2LZ5PrRmk9OpKO0qlbkilrXoVATFRWF/fv349ixY/Dx8XngtiqVCgEBAZDJZPrnAgMDkZeXh5qaGiQmJiI/Px+hoaGQy+WQy+WIi4vDhx9+CLlcDq1WC5VKBQAYNGiQwb4DAwORmZnZ7Pe1sbGBo6OjwYOIiIjabsYwb/i726O4ohabT2WIXU6r2hVqBEFAVFQU9u7di9jYWPj5tX7j0NixY5GWlqYfbQGAlJQUqFQqWFtbY9KkSbhy5QqSkpL0j+HDh2PBggVISkqCTCZDnz594OXl1aR9PCUlBb17927PIRAREVEbyaQSLJ8SAAD4v5M3UFxRI3JFD9auUKNWq7F9+3bs2LEDCoUCeXl5yMvLM5grZuHChVi1apX+60WLFqGoqAhLly5FSkoKDhw4gNWrV0OtVgMAFAoFgoKCDB729vZwdXXV36sjkUjw1ltv4cMPP0R0dDTS0tLw7rvv4qeffsKLL77YGX8PRERE1IzHg1QY6KlAaXUdPj1xQ+xyHqhdLd0bNmwAAERERBg8v2XLFvzmN78BAGRmZhrMHePr64tDhw5h+fLlCA4Ohre3N5YuXYoVK1a0q9Bly5ahqqoKy5cvR1FREYYOHYrDhw/D39+/XfshIiKitpNKJXgjcgBe/vwCtpzOwAuP+MHNwUbsspolEQTBNNcXb6eSkhI4OTlBo9Hw/hoiIqJ2EAQBMz8+jR9ua/DSI3743RODWn9TJ2nP729Ox0tEREQPJJFI8Hpk/YS3/zl7C3maKpErah5DDREREbVqfH83jOjTE9V1Onx8LE3scprFUENEREStkkgkeH1K/WjNzoRM3L5XIXJFTTHUEBERUZuE+7tibD9X1GoF/Ouo8Y3WMNQQERFRmzWO1kRfvI2MgnKRqzHEUENERERtFta7JyYOVEKrE/DPo82vvygWhhoiIiJql9cbZhn+OikbqXdKRa7mZww1RERE1C5B3k6YNtgTggCsP5Iidjl6DDVERETUbsunBEAiAWKu5OFajkbscgAw1BAREVEHDPBU4MlgLwDA+sPGMVrDUENEREQdsmxyf0glwJHr+biUeU/schhqiIiIqGP6ujtgdqgPAOADIxitYaghIiKiDlsyqT/kUglOphbg3I1CUWthqCEiIqIO83WxwzMjfAEA675PgSAIotXCUENEREQPJWpiP1jLpbCWS1FeoxWtDrlo35mIiIjMgsqpB44sn4Bernai1sGRGiIiInpoYgcagKGGiIiIzARDDREREZkFhhoiIiIyCww1REREZBYYaoiIiMgsMNQQERGRWWCoISIiIrPAUENERERmgaGGiIiIzAJDDREREZkFhhoiIiIyCww1REREZBYYaoiIiMgsyMUuoLsIggAAKCkpEbkSIiIiaqvG39uNv8cfxGJCTWlpKQDA19dX5EqIiIiovUpLS+Hk5PTAbSRCW6KPGdDpdMjJyYFCoYBEIunUfZeUlMDX1xdZWVlwdHTs1H0bA3M/PsD8j5HHZ/rM/Rh5fKavq45REASUlpbCy8sLUumD75qxmJEaqVQKHx+fLv0ejo6OZvvDCpj/8QHmf4w8PtNn7sfI4zN9XXGMrY3QNOKNwkRERGQWGGqIiIjILDDUdAIbGxv84Q9/gI2NjdildAlzPz7A/I+Rx2f6zP0YeXymzxiO0WJuFCYiIiLzxpEaIiIiMgsMNURERGQWGGqIiIjILDDUtODEiRN48skn4eXlBYlEgq+//trgdUEQ8Pvf/x4qlQo9evTA5MmTkZqaarBNUVERFixYAEdHRzg7O+PFF19EWVlZNx7Fgz3oGGtra7FixQoMGTIE9vb28PLywsKFC5GTk2Owjz59+kAikRg81q5d281H0rzWzuFvfvObJrVPmzbNYBtjPoetHd/9x9b4+Nvf/qbfxpjP35o1azBixAgoFAoolUrMnDkTycnJBttUVVVBrVbD1dUVDg4OmD17Nu7cuWOwTWZmJqZPnw47OzsolUq89dZbqKur685DaVFrx1hUVITFixdjwIAB6NGjB3r16oUlS5ZAo9EY7Ke587xz587uPpwm2nIOIyIimtT+6quvGmxjrOewtePLyMho8XO4e/du/XbGev4AYMOGDQgODtbPPRMeHo7vvvtO/7qxfQYZalpQXl6OoUOH4uOPP2729b/+9a/48MMPsXHjRpw7dw729vaYOnUqqqqq9NssWLAA165dw+HDh7F//36cOHEC//u//9tdh9CqBx1jRUUFLl68iHfffRcXL17EV199heTkZDz11FNNtn3vvfeQm5urfyxevLg7ym9Va+cQAKZNm2ZQ+3//+1+D1435HLZ2fL88rtzcXGzevBkSiQSzZ8822M5Yz19cXBzUajXOnj2Lw4cPo7a2FpGRkSgvL9dvs3z5cnz77bfYvXs34uLikJOTg1mzZulf12q1mD59OmpqahAfH49t27Zh69at+P3vfy/GITXR2jHm5OQgJycHf//733H16lVs3boVBw8exIsvvthkX1u2bDE4jzNnzuzmo2mqLecQAF5++WWD2v/617/qXzPmc9ja8fn6+jb5HP7pT3+Cg4MDHnvsMYN9GeP5AwAfHx+sXbsWiYmJuHDhAiZOnIgZM2bg2rVrAIzwMyhQqwAIe/fu1X+t0+kET09P4W9/+5v+ueLiYsHGxkb473//KwiCIPz4448CACEhIUG/zXfffSdIJBIhOzu722pvq/uPsTnnz58XAAi3bt3SP9e7d29h/fr1XVtcJ2ju+J577jlhxowZLb7HlM5hW87fjBkzhIkTJxo8ZyrnTxAEIT8/XwAgxMXFCYJQ/5mzsrISdu/erd/m+vXrAgDhzJkzgiAIQkxMjCCVSoW8vDz9Nhs2bBAcHR2F6urq7j2ANrj/GJuza9cuwdraWqitrdU/15bzbwyaO74JEyYIS5cubfE9pnQO23L+hg0bJrzwwgsGz5nK+WvUs2dP4f/+7/+M8jPIkZoOuHnzJvLy8jB58mT9c05OThg1ahTOnDkDADhz5gycnZ0xfPhw/TaTJ0+GVCrFuXPnur3mzqDRaCCRSODs7Gzw/Nq1a+Hq6oqQkBD87W9/M4ph4bY6fvw4lEolBgwYgEWLFqGwsFD/mjmdwzt37uDAgQPN/g/fVM5f4yUXFxcXAEBiYiJqa2sNPocDBw5Er169DD6HQ4YMgYeHh36bqVOnoqSkRP8/TWNy/zG2tI2joyPkcsNVbtRqNdzc3DBy5Ehs3ry5TSsad7eWju+LL76Am5sbgoKCsGrVKlRUVOhfM6Vz2Nr5S0xMRFJSUrOfQ1M4f1qtFjt37kR5eTnCw8ON8jNoMWs/daa8vDwAMDhJjV83vpaXlwelUmnwulwuh4uLi34bU1JVVYUVK1Zg/vz5Bmt6LFmyBKGhoXBxcUF8fDxWrVqF3NxcfPDBByJW2zbTpk3DrFmz4Ofnh/T0dLz99tt47LHHcObMGchkMrM6h9u2bYNCoTAYFgZM5/zpdDosW7YMY8eORVBQEID6z5i1tXWTkH3/57C5z2nja8akuWO8X0FBAf785z83uQT63nvvYeLEibCzs8P333+P1157DWVlZViyZEl3lN4mLR3fr3/9a/Tu3RteXl64fPkyVqxYgeTkZHz11VcATOcctuX8ffbZZwgMDMSYMWMMnjf283flyhWEh4ejqqoKDg4O2Lt3LwYNGoSkpCSj+wwy1FCramtrMW/ePAiCgA0bNhi89vrrr+v/HBwcDGtra7zyyitYs2aN0c+c+atf/Ur/5yFDhiA4OBj+/v44fvw4Jk2aJGJlnW/z5s1YsGABbG1tDZ43lfOnVqtx9epVnDp1SuxSukxrx1hSUoLp06dj0KBB+OMf/2jw2rvvvqv/c0hICMrLy/G3v/3NaH4pAi0f3y8D2pAhQ6BSqTBp0iSkp6fD39+/u8vssNbOX2VlJXbs2GFwrhoZ+/kbMGAAkpKSoNFoEB0djeeeew5xcXFil9UsXn7qAE9PTwBocof3nTt39K95enoiPz/f4PW6ujoUFRXptzEFjYHm1q1bOHz4cKsrr44aNQp1dXXIyMjongI7Ud++feHm5oa0tDQA5nMOT548ieTkZLz00kutbmuM5y8qKgr79+/HsWPH4OPjo3/e09MTNTU1KC4uNtj+/s9hc5/TxteMRUvH2Ki0tBTTpk2DQqHA3r17YWVl9cD9jRo1Crdv30Z1dXVXldwurR3fL40aNQoADD6Hxn4O23J80dHRqKiowMKFC1vdn7GdP2tra/Tr1w9hYWFYs2YNhg4din/+859G+RlkqOkAPz8/eHp64ujRo/rnSkpKcO7cOYSHhwMAwsPDUVxcjMTERP02sbGx0Ol0+g+tsWsMNKmpqThy5AhcXV1bfU9SUhKkUmmTyzam4Pbt2ygsLIRKpQJgHucQqB/yDgsLw9ChQ1vd1pjOnyAIiIqKwt69exEbGws/Pz+D18PCwmBlZWXwOUxOTkZmZqbB5/DKlSsG4bQxnA8aNKh7DuQBWjtGoP7flsjISFhbW2Pfvn1NRtuak5SUhJ49e4o+2taW47tfUlISABh8Do31HLbn+D777DM89dRTcHd3b3W/xnL+WqLT6VBdXW2cn8FOv/XYTJSWlgqXLl0SLl26JAAQPvjgA+HSpUv6zp+1a9cKzs7OwjfffCNcvnxZmDFjhuDn5ydUVlbq9zFt2jQhJCREOHfunHDq1Cmhf//+wvz588U6pCYedIw1NTXCU089Jfj4+AhJSUlCbm6u/tF4x3p8fLywfv16ISkpSUhPTxe2b98uuLu7CwsXLhT5yOo96PhKS0uFN998Uzhz5oxw8+ZN4ciRI0JoaKjQv39/oaqqSr8PYz6Hrf2MCoIgaDQawc7OTtiwYUOT9xv7+Vu0aJHg5OQkHD9+3ODnr6KiQr/Nq6++KvTq1UuIjY0VLly4IISHhwvh4eH61+vq6oSgoCAhMjJSSEpKEg4ePCi4u7sLq1atEuOQmmjtGDUajTBq1ChhyJAhQlpamsE2dXV1giAIwr59+4RNmzYJV65cEVJTU4V///vfgp2dnfD73/9ezEMTBKH140tLSxPee+894cKFC8LNmzeFb775Rujbt68wfvx4/T6M+Ry25WdUEAQhNTVVkEgkwnfffddkH8Z8/gRBEFauXCnExcUJN2/eFC5fviysXLlSkEgkwvfffy8IgvF9BhlqWnDs2DEBQJPHc889JwhCfVv3u+++K3h4eAg2NjbCpEmThOTkZIN9FBYWCvPnzxccHBwER0dH4fnnnxdKS0tFOJrmPegYb9682exrAIRjx44JgiAIiYmJwqhRowQnJyfB1tZWCAwMFFavXm0QCsT0oOOrqKgQIiMjBXd3d8HKykro3bu38PLLLxu0HQqCcZ/D1n5GBUEQPvnkE6FHjx5CcXFxk/cb+/lr6edvy5Yt+m0qKyuF1157TejZs6dgZ2cnPP3000Jubq7BfjIyMoTHHntM6NGjh+Dm5ia88cYbBu3QYmrtGFs6xwCEmzdvCoJQP83AsGHDBAcHB8He3l4YOnSosHHjRkGr1Yp3YA1aO77MzExh/PjxgouLi2BjYyP069dPeOuttwSNRmOwH2M9h235GRUEQVi1apXg6+vb7Dkx5vMnCILwwgsvCL179xasra0Fd3d3YdKkSfpAIwjG9xnkKt1ERERkFnhPDREREZkFhhoiIiIyCww1REREZBYYaoiIiMgsMNQQERGRWWCoISIiIrPAUENERERmgaGGiIiIzAJDDREZnePHj0MikTRZKI+I6EEYaojI6IwZMwa5ublwcnJq83sqKiqwatUq+Pv7w9bWFu7u7pgwYQK++eabLqyUiIyJXOwCiIjuZ21tDU9Pz3a959VXX8W5c+fwr3/9C4MGDUJhYSHi4+NRWFjYRVUSkbHhSA0RdbmIiAgsXrwYy5YtQ8+ePeHh4YFNmzahvLwczz//PBQKBfr164fvvvsOQNPLT1u3boWzszMOHTqEwMBAODg4YNq0acjNzdV/j3379uHtt9/G448/jj59+iAsLAyLFy/GCy+8oN9GIpHg66+/NqjN2dkZW7duBQBkZGRAIpFg586dGDNmDGxtbREUFIS4uLgu/fshos7BUENE3WLbtm1wc3PD+fPnsXjxYixatAhz587FmDFjcPHiRURGRuLZZ59FRUVFs++vqKjA3//+d/znP//BiRMnkJmZiTfffFP/uqenJ2JiYlBaWvrQtb711lt44403cOnSJYSHh+PJJ5/kiA+RCWCoIaJuMXToUPzud79D//79sWrVKtja2sLNzQ0vv/wy+vfvj9///vcoLCzE5cuXm31/bW0tNm7ciOHDhyM0NBRRUVE4evSo/vVPP/0U8fHxcHV1xYgRI7B8+XKcPn26Q7VGRUVh9uzZCAwMxIYNG+Dk5ITPPvusQ/siou7DUENE3SI4OFj/Z5lMBldXVwwZMkT/nIeHBwAgPz+/2ffb2dnB399f/7VKpTLYdvz48bhx4waOHj2KOXPm4Nq1axg3bhz+/Oc/t7vW8PBw/Z/lcjmGDx+O69evt3s/RNS9GGqIqFtYWVkZfC2RSAyek0gkAACdTtfm9wuC0GSbcePGYcWKFfj+++/x3nvv4c9//jNqampafE9tbW3HDoiIjA5DDRGZrUGDBqGurg5VVVUAAHd3d4Obi1NTU5u9h+fs2bP6P9fV1SExMRGBgYFdXzARPRS2dBORWYiIiMD8+fMxfPhwuLq64scff8Tbb7+NRx99FI6OjgCAiRMn4qOPPkJ4eDi0Wi1WrFjRZAQIAD7++GP0798fgYGBWL9+Pe7du2fQRUVExokjNURkFqZOnYpt27YhMjISgYGBWLx4MaZOnYpdu3bpt1m3bh18fX0xbtw4/PrXv8abb74JOzu7Jvtau3Yt1q5di6FDh+LUqVPYt28f3NzcuvNwiKgDJML9F5iJiCxURkYG/Pz8cOnSJQwbNkzscoionThSQ0RERGaBoYaIiIjMAi8/ERERkVngSA0RERGZBYYaIiIiMgsMNURERGQWGGqIiIjILDDUEBERkVlgqCEiIiKzwFBDREREZoGhhoiIiMwCQw0RERGZhf8PVutfyzEsepUAAAAASUVORK5CYII=\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/partialPeriodicPattern/closed/PPPClose.ipynb b/notebooks/partialPeriodicPattern/closed/PPPClose.ipynb index 3659c595..991393da 100644 --- a/notebooks/partialPeriodicPattern/closed/PPPClose.ipynb +++ b/notebooks/partialPeriodicPattern/closed/PPPClose.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Closed Partial Periodic Frequent patterns in Temporal Databases using PPPClose" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Closed Partial Periodic Frequent patterns in a temporal database using the PPPClose algorithm. In the final part, we describe an advanced approach, where we evaluate the PPPClose algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "d5a982f2-af84-466e-bb84-09f7ab9e5255" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/835.0 kB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K \u001B[91m━━\u001B[0m\u001B[91m╸\u001B[0m\u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m61.4/835.0 kB\u001B[0m 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python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Closed Partial Periodic Frequent patterns in Temporal Databases using PPPClose" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Closed Partial Periodic Frequent patterns in a temporal database using the PPPClose algorithm. In the final part, we describe an advanced approach, where we evaluate the PPPClose algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "d5a982f2-af84-466e-bb84-09f7ab9e5255" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/835.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/835.0 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m829.4/835.0 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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" Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", - "Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "5cd7bb72-90d7-4135-aea3-f0d2e4877068" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-07 07:39:38-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 762KB/s in 5.9s \n", - "\n", - "2023-09-07 07:39:46 (762 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "8fdd27c3-0373-4b43-d3ea-b9768917c07b" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Closed Partial Periodic Frequent patterns using PPPClose" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "f115d671-553f-49ee-fd69-a5f93682bb86" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "ad49cbdc-90ae-4f5c-ed69-ce781db917a8" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Closed Partial Periodic Frequent patterns using PPPClose" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.partialPeriodicPattern.closed import PPPClose as alg #import the algorithm\n", - "\n", - "obj = alg.PPPClose(iFile=inputFile, periodicSupport=minimumSupportCount, period=PeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('ClosedPartialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a4ddcc50-9002-489b-fef6-8c97b76ba9c6" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n", - "Total No of patterns: 26354\n", - "Runtime: 48.7431743144989\n", - "Memory (RSS): 262389760\n", - "Memory (USS): 215556096\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'ClosedPartialPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "56a4d7ac-9103-4a7c-c33b-864018a6af6b" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "729:100 \n", - "662:111 \n", - "330:101 \n", - "191\t339\t426\t914:101 \n", - "191\t426\t90\t914:100 \n", - "191\t426\t90:101 \n", - "191\t276\t426\t825:100 \n", - "191\t276\t426\t914:100 \n", - "191\t276\t426:101 \n", - "191\t426\t825\t914:102 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "5cd7bb72-90d7-4135-aea3-f0d2e4877068" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-07 07:39:38-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 762KB/s in 5.9s \n", + "\n", + "2023-09-07 07:39:46 (762 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "8fdd27c3-0373-4b43-d3ea-b9768917c07b" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Closed Partial Periodic Frequent patterns using PPPClose" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "f115d671-553f-49ee-fd69-a5f93682bb86" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "ad49cbdc-90ae-4f5c-ed69-ce781db917a8" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _closedPartialPeriodicfrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the PPPClose algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.partialPeriodicPattern.closed import PPPClose as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "PeriodCount = 5000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 13, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of PPPClose" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of PPPClose algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 20, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.PPPClose(iFile=inputFile, periodicSupport=minSupCount, period=PeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['PPPClose', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5ec63dc8-f069-4113-f9e1-605b2ceba9d0" - }, - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n", - "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n", - "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n", - "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n", - "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Closed Partial Periodic Frequent patterns using PPPClose" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.partialPeriodicPattern.closed import PPPClose as alg #import the algorithm\n", + "\n", + "obj = alg.PPPClose(iFile=inputFile, periodicSupport=minimumSupportCount, period=PeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('ClosedPartialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a4ddcc50-9002-489b-fef6-8c97b76ba9c6" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n", + "Total No of patterns: 26354\n", + "Runtime: 48.7431743144989\n", + "Memory (RSS): 262389760\n", + "Memory (USS): 215556096\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'ClosedPartialPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "56a4d7ac-9103-4a7c-c33b-864018a6af6b" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "729:100 \n", + "662:111 \n", + "330:101 \n", + "191\t339\t426\t914:101 \n", + "191\t426\t90\t914:100 \n", + "191\t426\t90:101 \n", + "191\t276\t426\t825:100 \n", + "191\t276\t426\t914:100 \n", + "191\t276\t426:101 \n", + "191\t426\t825\t914:102 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _closedPartialPeriodicfrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the PPPClose algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.partialPeriodicPattern.closed import PPPClose as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "PeriodCount = 5000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of PPPClose" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of PPPClose algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.PPPClose(iFile=inputFile, periodicSupport=minSupCount, period=PeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['PPPClose', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5ec63dc8-f069-4113-f9e1-605b2ceba9d0" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n", + "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n", + "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n", + "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n", + "Closed periodic frequent patterns were generated successfully using PPPClose algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ebe0fbfc-15ed-4ad6-d005-804795b95803" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup PeriodCount patterns runtime memory\n", + "0 PPPClose 100 5000 26354 41.247799 374771712\n", + "1 PPPClose 150 5000 18456 37.799484 374128640\n", + "2 PPPClose 200 5000 12975 33.970583 373219328\n", + "3 PPPClose 250 5000 7553 33.471501 371965952\n", + "4 PPPClose 300 5000 4442 31.019934 371499008\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "4a0a56d0-8a3b-48fa-b9be-9a8c0a6e62b8" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 23 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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KpfDz8xPHjh2ryHSM8mKAj6PT6cS/d5wX3rO3i4bvbBe7ziTLHYmIiKhalff1m8s6GOl1ch5FCIF3fj6DH2NuQGmmwKoxndG1qbPcsYiIiKpFtVwnh+QhSRI+GeSLvq3dUazVYdK6GJxMuit3LCIiIqPCklNDmSkk/HdoOwQ1dUZ+sRZjVh9HfGqO3LGIiIiMBktODaYyN8OKVzuivZcDNAUlGLkyCkkZ+Y9/IBERUS3AklPD2ajMsXpMZzR3s0N6ThFeXRmF9OxCuWMRERHJjiXHBDhYK7FuvB+8nKyRlJmPkSujkZVfLHcsIiIiWbHkmAhXtSXWj/eHq50K8Wk5GBt+HHlFpXLHIiIikg1LjgnxqmONdeP9YW9lgZNJWZi8PhZFpVq5YxEREcmCJcfENHe3w+qxnWGtNMPhy3cw44c4aLlyORER1UIsOSaog5cjvhnZCUozBXadTcW7v5xBLb7mIxER1VIsOSaqa1NnLB7WDgoJ+DHmBubvusiiQ0REtQpLjgnr07ouFgy6tzDqN4euYdmBqzInIiIiqj4sOSZuSGdP/DOkJQDg09/jsf7YdZkTERERVQ+WnFpgQlAjTH2uCQDg/d/O4re4WzInIiIiqnosObXEzH80w8gu3hACeHPTKey/mC53JCIioirFklNLSJKED19ohQHtPFCqE5i8PhbRCZlyxyIiIqoyLDm1iEIh4bOX2+K5Fq4oKtVhfPhxnL2lkTsWERFRlWDJqWUszBRYNqID/Bo6IaeoFKNXRePa7Vy5YxEREVU6lpxayNLCDN+N7oRWHmpk5BVj5MpoJGcVyB2LiIioUrHk1FJqSwusGeeHRs42uJVVgJEro5CRWyR3LCIiokrDklOLOduqsG6CPzzsLXH1dh7GrD6OnMISuWMRERFVCpacWq6egxXWTfCHk40SZ25pMGFNDApLuHI5ERHVfCw5hMYutlg7zg+2KnNEJWQibMMJlGh1csciIiJ6Kiw5BABoXc8eK0d3gspcgT8vpOPtn05Dp+OCnkREVHOx5JCef6M6WP5qB5grJGw5eQsfbjvHlcuJiKjGYskhA8+1cMPnQ9pCkoA1kdfx5Z+X5Y5ERET0RFhy6D4D2tXDRy+0AgAs3nsZK48kyJyIiIio4lhy6IFGBjTAW72bAQA+3n4em2NuyJyIiIioYlhy6KFCezTBhK4NAQCzfz6N38+lypyIiIio/Fhy6KEkScJ7IS3xcsf60Alg6oaTOHrljtyxiIiIyoUlhx5JkiTMH+SLPq3cUazVYeLaGMTdyJI7FhER0WOx5NBjmZspsGhYOzzTpA7yi7UYszoal9Jy5I5FRET0SCw5VC4qczN8M7IT2nk6ICu/BCNXRuFGZr7csYiIiB6KJYfKzUZljvCxndHMzRZp2UV4dWUU0nMK5Y5FRET0QCw5VCEO1kqsG+8PTycrXM/Ix6iV0dDkc+VyIiIyPiw5VGFuakusH+8PFzsVLqbmYGx4NPKLS+WORUREZIAlh56Idx0brBvvB7WlOU4kZeG1dbEoKtXKHYuIiEiPJYeeWAt3NVaP9YOVhRkOX76DmT+egpYrlxMRkZFgyaGn0tHbEd+M6ggLMwk7zqTgvS1nuHI5EREZBZYcempBTV2waGh7KCTgh+M3sGD3RbkjERERseRQ5ejnWxfzB/kCAL4+eA3LD1yVOREREdV2LDlUaV7p7IX3+rUEAPxn90V8H3Vd5kRERFSbseRQpZrYrRFCezQGAPzz17PYdipZ5kRERFRbseRQpXurd3OM8PeCEMAbP8bhQHy63JGIiKgWYsmhSidJEj4a0Br923qgVCcweX0sYhIz5Y5FRES1DEsOVQkzhYQvhrRFj+YuKCzRYWz4cZxL1sgdi4iIahGWHKoyFmYKLBvREZ0bOCKnsBSjV0Uj4U6e3LGIiKiWYMmhKmWlNMN3ozvDp64ad3KL8ep3UUjRFMgdi4iIagGWHKpy9lYWWDveD42cbXArqwAjV0YjM69Y7lhERGTiWHKoWjjbqrB2vB/q2lviSnouxqyORk5hidyxiIjIhLHkULWp72iNdeP94WSjxOmbGkxcG4PCEq5cTkREVYMlh6pVE1dbrBnrB1uVOY5dy0TYhpMo1erkjkVERCaIJYeqnW99e3w3uhOU5gr8eSENb/90GjodVy4nIqLKxZJDsujSqA6WDe8AM4WEX07ewkfbz0MIFh0iIqo8LDkkm14+bvjs5TYAgPCIRCzae1nmREREZEpYckhWL7avjw9faAUA+O+fl7H6aILMiYiIyFSw5JDsRgc2wMx/NAMAfLjtPH6OvSlzIiIiMgUsOWQUpj7XBOOeaQgAePvn0/jjXKrMiYiIqKZjySGjIEkS/hnSEi91rA+tTiBs40lEXL0jdywiIqrBWHLIaCgUEhYM8kVvHzcUl+owcU0MTt3IkjsWERHVUCw5ZFTMzRRYPKw9AhvXQV6xFmNWR+NyWo7csYiIqAZiySGjY2lhhm9GdUJbTwfczS/ByJXRuJGZL3csIiKqYVhyyCjZqswRPqYzmrraIjW7ECNXRuF2TpHcsYiIqAZhySGj5WijxLrx/qjvaIXEjHyMWhUNTQFXLiciovJhySGj5m5vifXj/eFsq8KFlGyMCz+O/OJSuWMREVENwJJDRq+Bsw3WjfeD2tIcsdfvYsr6Eygu5crlRET0aCw5VCO0rKvG6rGdYWVhhoOXbuONTXHQcuVyIiJ6hAqVnPnz56Nz586ws7ODq6srBg4ciPj4eIMxhYWFCA0NRZ06dWBra4vBgwcjLS3NYExSUhJCQkJgbW0NV1dXzJo1C6WlhqcgDhw4gA4dOkClUqFJkyYIDw+/L8/SpUvRoEEDWFpawt/fH9HR0RWZDtUwHb2dsGJkR1iYSdhxOgX//PUsVy4nIqKHqlDJOXjwIEJDQ3Hs2DHs2bMHJSUl6N27N/Ly8vRj3njjDWzbtg2bN2/GwYMHkZycjEGDBum3a7VahISEoLi4GBEREVizZg3Cw8Mxd+5c/ZiEhASEhISgR48eiIuLw4wZMzBhwgT8/vvv+jE//vgjZs6ciQ8++AAnTpxA27ZtERwcjPT09Kf5e5CRe7aZC/77SnsoJGBjdBIW/h7/+AcREVHtJJ5Cenq6ACAOHjwohBAiKytLWFhYiM2bN+vHXLhwQQAQkZGRQgghdu7cKRQKhUhNTdWPWb58uVCr1aKoqEgIIcTbb78tWrVqZfC7XnnlFREcHKz/2c/PT4SGhup/1mq1wsPDQ8yfP/+heQsLC4VGo9Hfbty4IQAIjUbzFH8FksPGqOvCe/Z24T17u1h+4IrccYiIqBppNJpyvX4/1WdyNBoNAMDJyQkAEBsbi5KSEvTq1Us/pkWLFvDy8kJkZCQAIDIyEr6+vnBzc9OPCQ4ORnZ2Ns6dO6cf89d9lI0p20dxcTFiY2MNxigUCvTq1Us/5kHmz58Pe3t7/c3T0/Nppk8yGurnhTl9WwAAFuy6iI3RSTInIiIiY/PEJUen02HGjBl45pln0Lp1awBAamoqlEolHBwcDMa6ubkhNTVVP+avBadse9m2R43Jzs5GQUEB7ty5A61W+8AxZft4kDlz5kCj0ehvN27cqPjEyWi89mxjTOneGADw7pYz2H46WeZERERkTMyf9IGhoaE4e/Ysjhw5Upl5qpRKpYJKpZI7BlWit4ObQ1NQgg1RSXjjxzjYWVrg2WYucsciIiIj8ETv5ISFhWH79u3Yv38/6tevr7/f3d0dxcXFyMrKMhiflpYGd3d3/Zi/f9uq7OfHjVGr1bCysoKzszPMzMweOKZsH1Q7SJKEjwe0xvNt6qJEKzB5XSxir2fKHYuIiIxAhUqOEAJhYWHYsmUL9u3bh4YNGxps79ixIywsLLB37179ffHx8UhKSkJAQAAAICAgAGfOnDH4FtSePXugVqvh4+OjH/PXfZSNKduHUqlEx44dDcbodDrs3btXP4ZqDzOFhC+GtMOzzVxQUKLFmNXHcT45W+5YREQkt4p8mnnKlCnC3t5eHDhwQKSkpOhv+fn5+jGTJ08WXl5eYt++fSImJkYEBASIgIAA/fbS0lLRunVr0bt3bxEXFyd2794tXFxcxJw5c/Rjrl27JqytrcWsWbPEhQsXxNKlS4WZmZnYvXu3fswPP/wgVCqVCA8PF+fPnxeTJk0SDg4OBt/aepzyfjqbaob8olIxeNlR4T17u+j48R6RcDtX7khERFQFyvv6XaGSA+CBt9WrV+vHFBQUiNdff104OjoKa2tr8eKLL4qUlBSD/SQmJoq+ffsKKysr4ezsLN58801RUlJiMGb//v2iXbt2QqlUikaNGhn8jjJfffWV8PLyEkqlUvj5+Yljx45VZDosOSYoK79Y9PnvIeE9e7t4ZsFekZJVIHckIiKqZOV9/ZaEqL2XjM3Ozoa9vT00Gg3UarXccaiS3M4pwssrIpCYkY+mrrbY9FoAHG2UcsciIqJKUt7Xb65dRSbHxU6FdeP94a62xOX0XIxZHY3cIq5cTkRU27DkkEnydLLG+gl+cLS2wKmbGkxaG4PCEq3csYiIqBqx5JDJauJqhzXj/GCjNEPE1QxM23gSpVqd3LGIiKiasOSQSWtT3wHfju4EpbkCf5xPw+yfz0Cnq7UfQyMiqlVYcsjkBTZ2xtLhHWCmkPDziZv4eMd51OLP2xMR1RosOVQr/MPHDZ++1AYAsPpoIr7ad0XmREREVNVYcqjWGNShPj7of++q2l/suYTwowkyJyIioqrEkkO1ythnGmJGr6YAgHnbzmPLyZsyJyIioqrCkkO1zvSeTTEmsAEA4K3Np/Hn+bRHP4CIiGoklhyqdSRJwtznfTCoQz1odQKvbziByKsZcsciIqJKxpJDtZJCIWHh4Dbo1dINxaU6TFwbg9M3s+SORURElYglh2otczMFlgxvj4BGdZBbVIrRq6JxJT1H7lhERFRJWHKoVrO0MMO3ozuhbX173M0vwciV0bh5N1/uWEREVAlYcqjWs1WZY/VYPzRxtUWKphAjV0bjdk6R3LGIiOgpseQQAXCyUWLdeD/Uc7BCwp08jF4VDU1BidyxiIjoKbDkEP1PXXsrrJ/gD2dbFc6nZGPkyijcyOSpKyKimoolh+gvGjrbYO04P9hbWeD0TQ1CFh/G7+dS5Y5FRERPgCWH6G98PNTYPrUr2no6ILuwFK+ti8WH286hqFQrdzQiIqoAlhyiB/B0ssbm1wIwMaghgHuLer60PBJJGTx9RURUU7DkED2E0lyB90J88N2oTnCwtsCZW/dOX+08kyJ3NCIiKgeWHKLH6OXjhh3TgtDR2xE5RaV4/fsTeP/Xsygs4ekrIiJjxpJDVA71HKzww6QumPxsYwDAumPXMWhZBBLu5MmcjIiIHoYlh6icLMwUeKdvC6we2xlONkqcT8nG84sP47e4W3JHIyKiB2DJIaqgHs1dsXNaEPwaOCGvWIvpP8Rhzi+nefqKiMjIsOQQPQF3e0tsmOiPqc81gSQBG6NvYODSo7iSnit3NCIi+h+WHKInZG6mwJu9m2PtOD842ypxMTUHLyw5gl9O3JQ7GhERgSWH6KkFNXXBzmlBCGhUB/nFWszcdAqzNp9CfnGp3NGIiGo1lhyiSuCqtsT6Cf6Y0aspJAnYHHsTA5YcxaW0HLmjERHVWiw5RJXETCFhRq9m+H6CP1zsVLicnosXlhzBppgbEELIHY+IqNZhySGqZIGNnbFrehCCmjqjsESHt386jZmbTiGviKeviIiqE0sOURVwtlVhzVg/zApuDoUEbDl5C/2XHMGFlGy5oxER1RosOURVRKGQENqjCX6YFAB3tSWu3c7DgKVHsSEqiaeviIiqAUsOURXza+iEndOD0L25C4pLdXh3yxlM+yEOOYUlckcjIjJpLDlE1cDJRolVozvjnb4tYKaQsO1UMvp/dQRnb2nkjkZEZLJYcoiqiUIhYfKzjbHptS7wsLdEYkY+Bi2LwNrIRJ6+IiKqAiw5RNWso/e901e9WrqiWKvD3N/OIXTDCWTz9BURUaViySGSgYO1Et+O6oR/hrSEuULCzjOpCFl8GKduZMkdjYjIZLDkEMlEkiRMCGqEn6YEor6jFW5kFuClFRFYdSSBp6+IiCoBSw6RzNp5OmDHtCAEt3JDiVbgo+3nMWldLLLyi+WORkRUo7HkEBkBeysLrHi1Iz58oRWUZgrsOZ+GkMVHcCLprtzRiIhqLJYcIiMhSRJGBzbAz1MC4V3HGreyCjBkRSS+OXQVOh1PXxERVRRLDpGR8a1vj21TuyKkTV2U6gQ+2XkRE9bG4G4eT18REVUESw6REVJbWmDJsPb418DWUJorsO9iOvotPozjiZlyRyMiqjFYcoiMlCRJeLWLN7a8HoiGzjZI0RRi6DfHsOzAFZ6+IiIqB5YcIiPXyuPe6asB7Tyg1Qks3B2PMeHHcSe3SO5oRERGjSWHqAawVZnjv6+0w38G+0JlrsChS7fRb9FhHLuWIXc0IiKjxZJDVENIkoRXOntha1hXNHaxQXpOEYZ/ewyL916GlqeviIjuw5JDVMM0d7fDtqldMbhDfegE8MWeSxi1KgrpOYVyRyMiMiosOUQ1kLXSHJ8PaYvPXm4LKwszHL2SgX6LjuDolTtyRyMiMhosOUQ12Esd62Nr2DNo5maLO7lFeHVlFL7Yc4mnr4iIwJJDVOM1dbPDb6FdMbSzJ4QAFu+9jOHfHkNaNk9fEVHtxpJDZAKslGZYMLgNFg1tBxulGaISMtFv0WEcvHRb7mhERLJhySEyIQPa1cO2qV3Rsq4aGXnFGL0qGgt3X0SpVid3NCKiaseSQ2RiGrnYYsvrgRjh7wUAWHbgKoZ9ewwpmgKZkxERVS+WHCITZGlhhn+/6Islw9vDVmWO44l30W/RYey/mC53NCKiasOSQ2TCnm/jge1Tu6J1PTXu5pdgbPhxzN95ASU8fUVEtQBLDpGJa+Bsg5+nBGJMYAMAwNeHrmHI15G4eTdf3mBERFWMJYeoFlCZm2HeC62w4tUOsLM0x8mkLIQsPoI/zqXKHY2IqMqw5BDVIn1a18XOaUFoW98emoISTFoXiw+3nUNxKU9fEZHpYckhqmU8nayxeXIgxndtCABYfTQRL6+IwI1Mnr4iItPCkkNUCynNFXj/eR98O6oT7K0scOqmBv0WH8auMylyRyMiqjQsOUS12D983LBjWld08HJATmEppnx/AnN/O4vCEq3c0YiInhpLDlEtV9/RGj++FoDXnm0EAFgbeR2Dl0cg8U6ezMmIiJ4OSw4RwcJMgTl9W2L1mM5wtLbAueRsPP/VEWw7lSx3NCKiJ8aSQ0R6PVq4Yuf0IHRu4IjcolJM3XgS7245w9NXRFQjVbjkHDp0CP3794eHhwckScKvv/5qsH3MmDGQJMng1qdPH4MxmZmZGDFiBNRqNRwcHDB+/Hjk5uYajDl9+jSCgoJgaWkJT09PLFy48L4smzdvRosWLWBpaQlfX1/s3LmzotMhor+pa2+FjRO7ILRHY0gSsCEqCQOXHsXV27mPfzARkRGpcMnJy8tD27ZtsXTp0oeO6dOnD1JSUvS3jRs3GmwfMWIEzp07hz179mD79u04dOgQJk2apN+enZ2N3r17w9vbG7Gxsfj0008xb948fPPNN/oxERERGDZsGMaPH4+TJ09i4MCBGDhwIM6ePVvRKRHR35ibKTAruAXWjPVDHRslLqbmoP9XR7Dl5E25oxERlZskhBBP/GBJwpYtWzBw4ED9fWPGjEFWVtZ97/CUuXDhAnx8fHD8+HF06tQJALB7927069cPN2/ehIeHB5YvX4733nsPqampUCqVAIB33nkHv/76Ky5evAgAeOWVV5CXl4ft27fr992lSxe0a9cOK1asKFf+7Oxs2NvbQ6PRQK1WP8FfgMj0pWUXYvoPJ3HsWiYAYEin+vjwhdawUprJnIyIaqvyvn5XyWdyDhw4AFdXVzRv3hxTpkxBRkaGfltkZCQcHBz0BQcAevXqBYVCgaioKP2Ybt266QsOAAQHByM+Ph53797Vj+nVq5fB7w0ODkZkZORDcxUVFSE7O9vgRkSP5qa2xPcTumB6z6aQJGBTzE0MWHoEl9Ny5I5GRPRIlV5y+vTpg7Vr12Lv3r34z3/+g4MHD6Jv377Qau99cDE1NRWurq4GjzE3N4eTkxNSU1P1Y9zc3AzGlP38uDFl2x9k/vz5sLe31988PT2fbrJEtYSZQsIb/2iG78f7w8VOhUtpuei/5Ag2x9yQOxoR0UNVeskZOnQoXnjhBfj6+mLgwIHYvn07jh8/jgMHDlT2r6qwOXPmQKPR6G83bvB/oIkqIrCJM3ZOC0LXJs4oLNFh1k+nMXNTHPKKSuWORkR0nyr/CnmjRo3g7OyMK1euAADc3d2Rnp5uMKa0tBSZmZlwd3fXj0lLSzMYU/bz48aUbX8QlUoFtVptcCOiinGxU2HNOD+8+Y9mUEjALydu4YUlR3Axlad/ici4VHnJuXnzJjIyMlC3bl0AQEBAALKyshAbG6sfs2/fPuh0Ovj7++vHHDp0CCUlJfoxe/bsQfPmzeHo6Kgfs3fvXoPftWfPHgQEBFT1lIhqPTOFhKk9m2LDxC5wU6tw9XYeBiw5io3RSXiK7zIQEVWqCpec3NxcxMXFIS4uDgCQkJCAuLg4JCUlITc3F7NmzcKxY8eQmJiIvXv3YsCAAWjSpAmCg4MBAC1btkSfPn0wceJEREdH4+jRowgLC8PQoUPh4eEBABg+fDiUSiXGjx+Pc+fO4ccff8SiRYswc+ZMfY7p06dj9+7d+Pzzz3Hx4kXMmzcPMTExCAsLq4Q/CxGVR5dGdbBzWhCebeaColId5vxyBtN/iEMuT18RkTEQFbR//34B4L7b6NGjRX5+vujdu7dwcXERFhYWwtvbW0ycOFGkpqYa7CMjI0MMGzZM2NraCrVaLcaOHStycnIMxpw6dUp07dpVqFQqUa9ePbFgwYL7smzatEk0a9ZMKJVK0apVK7Fjx44KzUWj0QgAQqPRVPTPQER/odXqxLL9V0SjOTuE9+ztovun+8XZW1lyxyIiE1Xe1++nuk5OTcfr5BBVrpjETEzdeBIpmkIozRV4/3kfvOrvBUmS5I5GRCZE1uvkEFHt1KmBE3ZOC0LPFq4oLtXh/V/PImzDSWQXljz+wURElYwlh4gqlaONEt+N7oR/hrSEuULCjjMpeH7xEZy+mSV3NCKqZVhyiKjSSZKECUGNsHlyAOo5WCEpMx+Dl0dg9dEEfvuKiKoNSw4RVZn2Xo7YOS0IvX3cUKIV+HDbeUxeHwtNPk9fEVHVY8khoiplb22Br0d2xAf9fWBhJuH3c2not/gwTibdlTsaEZk4lhwiqnKSJGHsMw3x85RAeDlZ41ZWAV5eEYnvDl/j6SsiqjIsOURUbdrUd8D2aV3Rz9cdpTqBf+24gAlrYnA3r1juaERkglhyiKhaqS0tsHR4B3w8sDWU5grsvZiOkMWHEXs9U+5oRGRiWHKIqNpJkoSRXbyx5fVANHS2QbKmEEO+PoblB65Cp+PpKyKqHCw5RCSbVh722Da1K15o6wGtTuA/uy9ibPhxZOQWyR2NiEwASw4RycpWZY5FQ9th/iBfqMwVOHjpNvotPoyoaxlyRyOiGo4lh4hkJ0kShvl54dfQZ9DIxQZp2UUY9u0xfLX3MrQ8fUVET4glh4iMRsu6amwL64pB7etBJ4DP91zC6FXRuJ3D01dEVHEsOURkVGxU5vjilXb49KU2sLRQ4MiVO+i3+DAirtyROxoR1TAsOURklF7u5IltYV3R1NUWt3OKMGJlFL7cc4mnr4io3FhyiMhoNXWzw9awrhjSqT6EABbtvYwR3x1Denah3NGIqAZgySEio2alNMPCl9riy1fawlpphmPXMtFv8WEcvnxb7mhEZORYcoioRnixfX1sDeuKFu52uJNbjFGrovHZ7/Eo1erkjkZERoolh4hqjCautvg19BkM9/eCEMCS/Vcw/NsopGgK5I5GREaIJYeIahRLCzN88qIvFg9rD1uVOaITM9Fv0WHsv5gudzQiMjIsOURUI73Q1gPbpnZFKw817uaXYGz4cczfeQElPH1FRP/DkkNENVZDZxv8PCUQowK8AQBfH7qGV76OxK0snr4iIpYcIqrhLC3M8NGA1lg2ogPsVOY4kZSFfosOY8/5NLmjEZHMWHKIyCT0862LHdOC0Ka+PTQFJZi4NgYfbTuP4lKeviKqrVhyiMhkeNWxxk+TAzHumYYAgFVHEzB4eQSuZ+TJnIyI5MCSQ0QmRWmuwNz+Pvh2VCfYW1ngzC0NQhYfwdZTyXJHI6JqxpJDRCbpHz5u2DU9CJ0bOCK3qBTTNp7EOz+fRkGxVu5oRFRNWHKIyGR5OFhh48QumPpcE0gS8MPxGxiw9AgupeXIHY2IqgFLDhGZNHMzBd7s3Rzrx/vDxU6FS2m5eGHJEWyMToIQXNGcyJSx5BBRrfBME2fsnBaEoKbOKCzRYc4vZzB140nkFJbIHY2IqghLDhHVGi52KqwZ64fZfVrATCFh++kUhCw+gtM3s+SORkRVgCWHiGoVhULClO6Nsem1ANRzsEJSZj4GL4/Ad4ev8fQVkYlhySGiWqmjtyN2TgtCn1buKNEK/GvHBYxfE4PMvGK5oxFRJWHJIaJay97aAstf7YCPB7SC0lyBfRfT0W/RYURdy5A7GhFVApYcIqrVJEnCyIAG2PJ6IBo52yA1uxDDvj2GRX9ehlbH01dENRlLDhERgFYe9tg2tSsGd6gPnQC+/PMSRnx3DGnZhXJHI6InxJJDRPQ/NipzfD6kLb4Y0hbWSjMcu5aJvosOY398utzRiOgJsOQQEf3NoA71sW1qV7Ssq0ZmXjHGrj6OT3Ze4IrmRDUMSw4R0QM0drHFltcDMTrAGwDwzaFrePnrSNzIzJc5GRGVF0sOEdFDWFqY4cMBrbHi1Y5QW5rj1I0s9Ft8GDvPpMgdjYjKgSWHiOgx+rR2x87pQejg5YCcwlK8/v0JvLflDApLuKI5kTFjySEiKof6jtb48bUATOneGADwfVQSBi49iivpXNGcyFix5BARlZOFmQKz+7TA2nF+cLZV4mJqDvp/dRSbjt/gkhBERoglh4iogro1c8HO6UF4pkkdFJRo8fbPpzHjxzjkFpXKHY2I/oIlh4joCbjaWWLtOH/MCm4OM4WE3+KS8fziwzh7SyN3NCL6H5YcIqInZKaQENqjCX6c1AUe9pZIzMjHoGURWH00gaeviIwASw4R0VPq1MAJO6cH4R8+bijW6vDhtvOYtC4WWflc0ZxITiw5RESVwMFaiW9GdsS8/j5Qmimw53wa+i06jOOJmXJHI6q1WHKIiCqJJEkY80xD/PJ6IBrUsUayphBDvzmGJfu4ojmRHFhyiIgqWet69tg+LQgD23lAqxP47I9LGLUqCuk5XNGcqDqx5BARVQFblTm+fKUdFr7UBlYWZjh6JQP9Fh3GoUu35Y5GVGuw5BARVRFJkjCkkye2TX0GLdztcCe3GKNWReM/uy+iRMsVzYmqGksOEVEVa+Jqh19Dn8EIfy8AwPIDV/HK15G4eZcrmhNVJZYcIqJqYGlhhn+/6IulwzvAztIcJ5Ky0G/RYew+yxXNiaoKSw4RUTUKaVMXO6cFoZ2nA7ILSzF5/QnM/e0sVzQnqgIsOURE1czTyRqbJwfgtW6NAABrI6/jxWURuHo7V+ZkRKaFJYeISAYWZgrM6dcSq8d2hpONEhdSstH/qyP4Ofam3NGITAZLDhGRjHo0d8Wu6UEIaFQH+cVavLn5FGZuikMeVzQnemosOUREMnNTW2L9BH/M/EczKCTglxO30H/JEZxPzpY7GlGNxpJDRGQEzBQSpvVsio0Tu8BdbYlrt/MwcNlRrItM5IrmRE+IJYeIyIj4N6qDndOD0LOFK4pLdXj/t3OYsv4ENPklckcjqnFYcoiIjIyTjRLfje6Ef4a0hIWZhN3nUtFv8WHEXr8rdzSiGoUlh4jICEmShAlBjfDzlEB417HGrawCDPk6EssOXIGOK5oTlQtLDhGREWtT3wHbp3ZF/7b3VjRfuDseo1dH43ZOkdzRiIweSw4RkZGzs7TA4qHtsGCQLywtFDh8+Q76LT6Mo1fuyB2NyKix5BAR1QCSJGGonxe2hnVFMzdb3M4pwqsro/DZ7/Eo5YrmRA9U4ZJz6NAh9O/fHx4eHpAkCb/++qvBdiEE5s6di7p168LKygq9evXC5cuXDcZkZmZixIgRUKvVcHBwwPjx45Gba3g589OnTyMoKAiWlpbw9PTEwoUL78uyefNmtGjRApaWlvD19cXOnTsrOh0iohqlmZsdfgvtimF+nhACWLL/CoZ+cwzJWQVyRyMyOhUuOXl5eWjbti2WLl36wO0LFy7E4sWLsWLFCkRFRcHGxgbBwcEoLCzUjxkxYgTOnTuHPXv2YPv27Th06BAmTZqk356dnY3evXvD29sbsbGx+PTTTzFv3jx88803+jEREREYNmwYxo8fj5MnT2LgwIEYOHAgzp49W9EpERHVKFZKM8wf1AZfDWsPW5U5Yq7fRd9Fh7HnfJrc0YiMi3gKAMSWLVv0P+t0OuHu7i4+/fRT/X1ZWVlCpVKJjRs3CiGEOH/+vAAgjh8/rh+za9cuIUmSuHXrlhBCiGXLlglHR0dRVFSkHzN79mzRvHlz/c9DhgwRISEhBnn8/f3Fa6+9Vu78Go1GABAajabcjyEiMiaJd3JF/68OC+/Z24X37O3ig9/OisKSUrljEVWp8r5+V+pnchISEpCamopevXrp77O3t4e/vz8iIyMBAJGRkXBwcECnTp30Y3r16gWFQoGoqCj9mG7dukGpVOrHBAcHIz4+Hnfv3tWP+evvKRtT9nsepKioCNnZ2QY3IqKazLuODX6aHIjxXRsCAMIjEjF4eQQS7uTJnIxIfpVaclJTUwEAbm5uBve7ubnpt6WmpsLV1dVgu7m5OZycnAzGPGgff/0dDxtTtv1B5s+fD3t7e/3N09OzolMkIjI6SnMF3n/eBytHd4KjtQXO3srG84sP47e4W3JHI5JVrfp21Zw5c6DRaPS3GzduyB2JiKjS9Gzphp3Tg+DX0Al5xVpM/yEOb/90CvnFXNGcaqdKLTnu7u4AgLQ0ww+/paWl6be5u7sjPT3dYHtpaSkyMzMNxjxoH3/9HQ8bU7b9QVQqFdRqtcGNiMiU1LW3wsaJXTC9Z1NIErAp5iZeWHIUF1N5ep5qn0otOQ0bNoS7uzv27t2rvy87OxtRUVEICAgAAAQEBCArKwuxsbH6Mfv27YNOp4O/v79+zKFDh1BS8v8L0u3ZswfNmzeHo6Ojfsxff0/ZmLLfQ0RUW5kpJLzxj2bYMKELXO1UuJKeiwFLjuL7qOtc0ZxqlQqXnNzcXMTFxSEuLg7AvQ8bx8XFISkpCZIkYcaMGfjXv/6FrVu34syZMxg1ahQ8PDwwcOBAAEDLli3Rp08fTJw4EdHR0Th69CjCwsIwdOhQeHh4AACGDx8OpVKJ8ePH49y5c/jxxx+xaNEizJw5U59j+vTp2L17Nz7//HNcvHgR8+bNQ0xMDMLCwp7+r0JEZAICGtfBrulB6N7cBUWlOry35SzCNpyEpoArmlMtUdGvbe3fv18AuO82evRoIcS9r5G///77ws3NTahUKtGzZ08RHx9vsI+MjAwxbNgwYWtrK9RqtRg7dqzIyckxGHPq1CnRtWtXoVKpRL169cSCBQvuy7Jp0ybRrFkzoVQqRatWrcSOHTsqNBd+hZyIagOtVie+PnhFNJ6zQ3jP3i6eWbBXnEy6K3csoidW3tdvSYja+95ldnY27O3todFo+PkcIjJ5J5PuYurGk7h5twDmCglv92mOCV0bQaGQ5I5GVCHlff2uVd+uIiKqzdp7OWLHtCCE+NZFqU7gk50XMW7NcWTkckVzMk0sOUREtYi9lQWWDG+Pf7/YGipzBQ7E30a/xYcReTVD7mhElY4lh4iolpEkCSP8vfFb2DNo7GKDtOwiDP/uGL7YcwlaXa39BAOZIJYcIqJaqoW7GtumdsWQTvUhBLB472UM+/YYUjWFj38wUQ3AkkNEVItZK82x8KW2WDS0HWyUZohOyETfRYew7yJXNKeajyWHiIgwoF09bJ8WhNb11LibX4Jx4TH41/bzKC7VyR2N6Imx5BAREQCgobMNfp4SiDGBDQAA3x1JwEsrInA9gyuaU83EkkNERHoqczPMe6EVvh3VCfZWFjh9U4OQxUew7VSy3NGIKowlh4iI7vMPHzfsmh6ETt6OyC0qxdSNJzHnl9MoKNbKHY2o3FhyiIjogTwcrPDDpC4I69EEkgRsjL6BAUuP4FJajtzRiMqFJYeIiB7K3EyBt4KbY904fzjbqnApLRcvLDmCH6KTuKI5GT2WHCIieqyuTZ2xa3oQgpo6o7BEh3d+OYNpP8Qhp5ArmpPxYskhIqJycbFTYc1YP8zu0wJmCgnbTiXj+a+O4PTNLLmjET0QSw4REZWbQiFhSvfG2PRaAOo5WOF6Rj4GL4/AyiMJPH1FRoclh4iIKqyjtyN2TgtCn1buKNEKfLz9PCasicHdvGK5oxHpseQQEdETsbe2wPJXO+DjAa2gNFdg78V09F10GNEJmXJHIwLAkkNERE9BkiSMDGiALa8HopGzDVKzCzH0m0gs3nuZK5qT7FhyiIjoqbXysMe2qV0xqEM96ATwxZ5LePW7KKRlc0Vzkg9LDhERVQoblTm+GNIOn7/cFtZKM0Rey0C/RYdxID5d7mhUS7HkEBFRpRrcsT62Te2KlnXVyMgrxpjVxzF/5wWUaLmiOVUvlhwiIqp0jV1sseX1QIwK8AYAfH3oGl5eEYkbmfkyJ6PahCWHiIiqhKWFGT4a0BorXu0AtaU54m5kod/iw9h5JkXuaFRLsOQQEVGV6tO6LnZOD0IHLwfkFJbi9e9P4L0tZ1BYwhXNqWqx5BARUZWr72iNH18LwJTujQEA30clYeDSo7iSnitzMjJlLDlERFQtLMwUmN2nBdaO84OzrRIXU3PQ/6sj2Bxzg0tCUJVgySEiomrVrZkLdk4PwjNN6qCgRItZP53GzE2nkFtUKnc0MjEsOUREVO1c7Syxdpw/ZgU3h5lCwpaTt9D/qyM4e0sjdzQyISw5REQkCzOFhNAeTfDDpC7wsLdEwp08DFoWgfCjXNGcKgdLDhERyapzAyfsnB6EXi3dUKzVYd6285i0LhZZ+VzRnJ4OSw4REcnOwVqJb0d1xAf9faA0U2DP+TT0XXQYayIS+VkdemKSqMXvCWZnZ8Pe3h4ajQZqtVruOEREBODsLQ3CNpxAYsa9qyPbqswxuEM9jApsgMYutjKnI2NQ3tdvlhyWHCIio5NfXIrNMTexJjIR127n6e8PauqMUQEN8FwLV5gpJBkTkpxYcsqBJYeIyLgJIXDkyh2sibiOvRfTUPaKVd/RCiO7eGNIJ0842ijlDUnVjiWnHFhyiIhqjhuZ+Vh/7Dp+OH4DmoISAIDKXIGB7ephVKA3WnnYy5yQqgtLTjmw5BAR1TwFxVpsO5WM8IhEnE/J1t/fydsRowMboE9rd1iY8Xs1powlpxxYcoiIai4hBGKv38WayOvYdSYFpbp7L2eudioM9/fCcH8vuNpZypySqgJLTjmw5BARmYa07EJsiErChugk3M4pAgBYmEno27ouRgd6o4OXIySJH1Q2FSw55cCSQ0RkWopLddh1NgVrI68j9vpd/f2t66kxKqABXmjrAUsLMxkTUmVgySkHlhwiItN19pYGayIS8dupZBSX6gAAjtYWeKWzF17t4oX6jtYyJ6QnxZJTDiw5RESmLzOvGD8ev4H1x67jVlYBAEAhAT1bumF0QAM806QOT2XVMCw55cCSQ0RUe2h1AnsvpGFNZCKOXsnQ39/YxQajAxtgUIf6sFWZy5iQyoslpxxYcoiIaqcr6TlYG3kdP8feRF6xFsC95SNe6lgfIwO8uXyEkWPJKQeWHCKi2i2nsAQ/x97E2sjruHbHcPmI0QEN0IPLRxgllpxyYMkhIiIA0OkEjl69gzURidh7MV2/fISn0/8vH+FgzeUjjAVLTjmw5BAR0d89avmI0YEN4OPB1wu5seSUA0sOERE9TEGxFltP3UJ4xHVc+MvyEZ0bOGJUAJePkBNLTjmw5BAR0eMIIRBz/S7WRCRi99lUg+UjRvh7Y5i/J5ePqGYsOeXAkkNERBWRll2I76OSsCEqCXdy/3/5iH6+dTEqoAE6eDnwmjvVgCWnHFhyiIjoSZQtH7EmIhEnkrL097eup8bogAboz+UjqhRLTjmw5BAR0dM6c1ODNZGJ2MrlI6oNS045sOQQEVFledjyEb1aumF0YAMENubyEZWFJaccWHKIiKiylWp12HsxHWv/tnxEE1dbjA7wxotcPuKpseSUA0sOERFVpctp/1s+4sRN5P9v+Qg7lTkGc/mIp8KSUw4sOUREVB2y/7J8RMLflo8YE9gA3Ztz+YiKYMkpB5YcIiKqTjqdwJEr95aP2BfP5SOeFEtOObDkEBGRXJIy8rE+6jp+/MvyEZYW95aPGBXA5SMehSWnHFhyiIhIbgXFWvwWdwvhEYm4mJqjv9+vgRNGBXojuBWXj/g7lpxyYMkhIiJjIYTA8cS7WBN5b/kI7f+Wj3BT31s+Yqgfl48ow5JTDiw5RERkjFI1hdgQdR0bopNwJ7cYwL3lI0J862JUYAO096zdy0ew5JQDSw4RERmzolItdp9NRXhEIk7+ZfkI33r2GB3YAM+3qVsrl49gySkHlhwiIqopTt/MwpqI69h22nD5iKF+Xni1izfqOVjJnLD6sOSUA0sOERHVNBm5Rfgx5gbWR15HsqYQwL3lI/7h44bRAQ0QUAuWj2DJKQeWHCIiqqlKtTr8eeHe8hERV/9/+YimrrYYFdgAg9rXg42JLh/BklMOLDlERGQKLqXlYG1kIn45ceu+5SNGBXijkYktH8GSUw4sOUREZEqyC0vwU8xNrDtmuHxEt2YuGB3gbTLLR7DklANLDhERmSKdTuDwlTtY+7flI7ycrPXLR9hbW8gb8imU9/W70i+hOG/ePEiSZHBr0aKFfnthYSFCQ0NRp04d2NraYvDgwUhLSzPYR1JSEkJCQmBtbQ1XV1fMmjULpaWlBmMOHDiADh06QKVSoUmTJggPD6/sqRAREdVICoWEZ5u5YOWYzjjwVndMDGoItaU5kjLz8e+dF+A//0/M+eU0LqRkyx21SlXJdaJbtWqFlJQU/e3IkSP6bW+88Qa2bduGzZs34+DBg0hOTsagQYP027VaLUJCQlBcXIyIiAisWbMG4eHhmDt3rn5MQkICQkJC0KNHD8TFxWHGjBmYMGECfv/996qYDhERUY3lXccG74X4IOrdXpg/yBct3O1QWKLDxugb6LvoMIasiMSO0yko0erkjlrpKv101bx58/Drr78iLi7uvm0ajQYuLi7YsGEDXnrpJQDAxYsX0bJlS0RGRqJLly7YtWsXnn/+eSQnJ8PNzQ0AsGLFCsyePRu3b9+GUqnE7NmzsWPHDpw9e1a/76FDhyIrKwu7d+9+aLaioiIUFRXpf87OzoanpydPVxERUa2hXz4iIhG7z92/fMQwPy+42KlkTvlosp2uAoDLly/Dw8MDjRo1wogRI5CUlAQAiI2NRUlJCXr16qUf26JFC3h5eSEyMhIAEBkZCV9fX33BAYDg4GBkZ2fj3Llz+jF/3UfZmLJ9PMz8+fNhb2+vv3l6elbKfImIiGoKSZLg19AJS0d0wNHZz2Hqc03gbKtEWnYRvthzCYEL9mLGDydxIukuavrHdiu95Pj7+yM8PBy7d+/G8uXLkZCQgKCgIOTk5CA1NRVKpRIODg4Gj3Fzc0NqaioAIDU11aDglG0v2/aoMdnZ2SgoKHhotjlz5kCj0ehvN27ceNrpEhER1Vju9pZ4s3dzHH3nOfz3lXZo5+mAEq3Ar3HJGLQsAgOWHsVPsTdRWKKVO+oTqfSrBPXt21f/7zZt2sDf3x/e3t7YtGkTrKzkveS0SqWCSmXcb8ERERFVN5W5GQa2r4eB7evh1I0srI28jm2nknH6pgZvbT6FT3ZewNDOnhhRw5aPqJLTVX/l4OCAZs2a4cqVK3B3d0dxcTGysrIMxqSlpcHd3R0A4O7uft+3rcp+ftwYtVote5EiIiKqydp6OuDzIW0ROec5zApuDg97S2TmFWPZgasI+s8+vLYuBhFX79SIU1lVXnJyc3Nx9epV1K1bFx07doSFhQX27t2r3x4fH4+kpCQEBAQAAAICAnDmzBmkp6frx+zZswdqtRo+Pj76MX/dR9mYsn0QERHR06ljq0JojyY49HYPrHi1AwIa1YFOAL+fS8Pwb6PQ+8tDWHfsOvKKSh+/M5lU+rer3nrrLfTv3x/e3t5ITk7GBx98gLi4OJw/fx4uLi6YMmUKdu7cifDwcKjVakydOhUAEBERAeDeV8jbtWsHDw8PLFy4EKmpqRg5ciQmTJiATz75BMC9r5C3bt0aoaGhGDduHPbt24dp06Zhx44dCA4OLndWXgyQiIio/C6l5WBNxL3lIwpK/n/5iJc61ceogAZo6GxTLTlku+Lx0KFDcejQIWRkZMDFxQVdu3bFv//9bzRu3BjAvYsBvvnmm9i4cSOKiooQHByMZcuW6U9FAcD169cxZcoUHDhwADY2Nhg9ejQWLFgAc/P//wjRgQMH8MYbb+D8+fOoX78+3n//fYwZM6ZCWVlyiIiIKk5TUIKfY29ibWQiEjPy9fc/28wFowO90b2ZKxRVuHwEl3UoB5YcIiKiJ6fTCRy6fBtrI69j/9+WjxgV4I2XO1bN8hEsOeXAkkNERFQ5Eu/kYf2x69gUcwPZhfc+p2NpocCGiV3QwcuxUn+XrBcDJCIiotqlgbMN/vm8D4692xOfvHhv+QgbpTlaecj3JkKlXyeHiIiIai9rpTmG+3thmJ8nkjWFUJmbyZaF7+QQERFRpZMkSfYLB7LkEBERkUliySEiIiKTxJJDREREJoklh4iIiEwSSw4RERGZJJYcIiIiMkksOURERGSSWHKIiIjIJLHkEBERkUliySEiIiKTxJJDREREJoklh4iIiEwSSw4RERGZJHO5A8hJCAEAyM7OljkJERERlVfZ63bZ6/jD1OqSk5OTAwDw9PSUOQkRERFVVE5ODuzt7R+6XRKPq0EmTKfTITk5GXZ2dpAkqdL2m52dDU9PT9y4cQNqtbrS9mtMTH2OnF/NZ+pz5PxqPlOfY1XOTwiBnJwceHh4QKF4+CdvavU7OQqFAvXr16+y/avVapP8D/evTH2OnF/NZ+pz5PxqPlOfY1XN71Hv4JThB4+JiIjIJLHkEBERkUliyakCKpUKH3zwAVQqldxRqoypz5Hzq/lMfY6cX81n6nM0hvnV6g8eExERkeniOzlERERkklhyiIiIyCSx5BAREZFJYskhIiIik8SSUwGHDh1C//794eHhAUmS8OuvvxpsF0Jg7ty5qFu3LqysrNCrVy9cvnzZYExmZiZGjBgBtVoNBwcHjB8/Hrm5udU4i4d71PxKSkowe/Zs+Pr6wsbGBh4eHhg1ahSSk5MN9tGgQQNIkmRwW7BgQTXP5MEed/zGjBlzX/Y+ffoYjDHm4wc8fo5/n1/Z7dNPP9WPMdZjOH/+fHTu3Bl2dnZwdXXFwIEDER8fbzCmsLAQoaGhqFOnDmxtbTF48GCkpaUZjElKSkJISAisra3h6uqKWbNmobS0tDqn8lCPm2NmZiamTp2K5s2bw8rKCl5eXpg2bRo0Go3Bfh50jH/44Yfqns59ynMMu3fvfl/2yZMnG4ypyccwMTHxoc/DzZs368cZ6zFcvnw52rRpo7/AX0BAAHbt2qXfbmzPQZacCsjLy0Pbtm2xdOnSB25fuHAhFi9ejBUrViAqKgo2NjYIDg5GYWGhfsyIESNw7tw57NmzB9u3b8ehQ4cwadKk6prCIz1qfvn5+Thx4gTef/99nDhxAr/88gvi4+Pxwgsv3Df2o48+QkpKiv42derU6oj/WI87fgDQp08fg+wbN2402G7Mxw94/Bz/OreUlBSsWrUKkiRh8ODBBuOM8RgePHgQoaGhOHbsGPbs2YOSkhL07t0beXl5+jFvvPEGtm3bhs2bN+PgwYNITk7GoEGD9Nu1Wi1CQkJQXFyMiIgIrFmzBuHh4Zg7d64cU7rP4+aYnJyM5ORkfPbZZzh79izCw8Oxe/dujB8//r59rV692uAYDhw4sJpnc7/yHEMAmDhxokH2hQsX6rfV9GPo6el53/Pwww8/hK2tLfr27WuwL2M8hvXr18eCBQsQGxuLmJgYPPfccxgwYADOnTsHwAifg4KeCACxZcsW/c86nU64u7uLTz/9VH9fVlaWUKlUYuPGjUIIIc6fPy8AiOPHj+vH7Nq1S0iSJG7dulVt2cvj7/N7kOjoaAFAXL9+XX+ft7e3+PLLL6s2XCV40PxGjx4tBgwY8NDH1KTjJ0T5juGAAQPEc889Z3BfTTmG6enpAoA4ePCgEOLe883CwkJs3rxZP+bChQsCgIiMjBRCCLFz506hUChEamqqfszy5cuFWq0WRUVF1TuBcvj7HB9k06ZNQqlUipKSEv195Tn2xuBB83v22WfF9OnTH/oYUzyG7dq1E+PGjTO4r6YcQyGEcHR0FN99951RPgf5Tk4lSUhIQGpqKnr16qW/z97eHv7+/oiMjAQAREZGwsHBAZ06ddKP6dWrFxQKBaKioqo989PSaDSQJAkODg4G9y9YsAB16tRB+/bt8emnnxrN28jlceDAAbi6uqJ58+aYMmUKMjIy9NtM7filpaVhx44dD3wXoCYcw7JTNE5OTgCA2NhYlJSUGDwHW7RoAS8vL4PnoK+vL9zc3PRjgoODkZ2drf9/osbk73N82Bi1Wg1zc8OlCENDQ+Hs7Aw/Pz+sWrUKwggvifaw+X3//fdwdnZG69atMWfOHOTn5+u3mdoxjI2NRVxc3AOfh8Z+DLVaLX744Qfk5eUhICDAKJ+DtXqBzsqUmpoKAAYHruznsm2pqalwdXU12G5ubg4nJyf9mJqisLAQs2fPxrBhwwwWXps2bRo6dOgAJycnREREYM6cOUhJScEXX3whY9ry6dOnDwYNGoSGDRvi6tWrePfdd9G3b19ERkbCzMzMpI4fAKxZswZ2dnYGbyUDNeMY6nQ6zJgxA8888wxat24N4N7zS6lU3le6//4cfNBztGybMXnQHP/uzp07+Pjjj+87ZfrRRx/hueeeg7W1Nf744w+8/vrryM3NxbRp06ojerk8bH7Dhw+Ht7c3PDw8cPr0acyePRvx8fH45ZdfAJjeMVy5ciVatmyJwMBAg/uN+RieOXMGAQEBKCwshK2tLbZs2QIfHx/ExcUZ3XOQJYcqrKSkBEOGDIEQAsuXLzfYNnPmTP2/27RpA6VSiddeew3z5883+kuXDx06VP9vX19ftGnTBo0bN8aBAwfQs2dPGZNVjVWrVmHEiBGwtLQ0uL8mHMPQ0FCcPXsWR44ckTtKlXncHLOzsxESEgIfHx/MmzfPYNv777+v/3f79u2Rl5eHTz/91CheIMs8bH5/LWy+vr6oW7cuevbsiatXr6Jx48bVHfOpPO4YFhQUYMOGDQbHq4wxH8PmzZsjLi4OGo0GP/30E0aPHo2DBw/KHeuBeLqqkri7uwPAfZ8iT0tL029zd3dHenq6wfbS0lJkZmbqxxi7soJz/fp17Nmzx+BdnAfx9/dHaWkpEhMTqydgJWrUqBGcnZ1x5coVAKZx/MocPnwY8fHxmDBhwmPHGtsxDAsLw/bt27F//37Ur19ff7+7uzuKi4uRlZVlMP7vz8EHPUfLthmLh82xTE5ODvr06QM7Ozts2bIFFhYWj9yfv78/bt68iaKioqqKXCGPm99f+fv7A4DB89AUjiEA/PTTT8jPz8eoUaMeuz9jOoZKpRJNmjRBx44dMX/+fLRt2xaLFi0yyucgS04ladiwIdzd3bF37179fdnZ2YiKikJAQAAAICAgAFlZWYiNjdWP2bdvH3Q6nf6JbMzKCs7ly5fx559/ok6dOo99TFxcHBQKxX2neWqCmzdvIiMjA3Xr1gVQ84/fX61cuRIdO3ZE27ZtHzvWWI6hEAJhYWHYsmUL9u3bh4YNGxps79ixIywsLAyeg/Hx8UhKSjJ4Dp45c8agrJaVdR8fn+qZyCM8bo7Avf9d6d27N5RKJbZu3XrfO3EPEhcXB0dHR9nfiSvP/P4uLi4OAAyehzX9GJZZuXIlXnjhBbi4uDx2v8ZyDB9Ep9OhqKjIOJ+Dlf5RZhOWk5MjTp48KU6ePCkAiC+++EKcPHlS/+2iBQsWCAcHB/Hbb7+J06dPiwEDBoiGDRuKgoIC/T769Okj2rdvL6KiosSRI0dE06ZNxbBhw+SakoFHza+4uFi88MILon79+iIuLk6kpKTob2WfiI+IiBBffvmliIuLE1evXhXr168XLi4uYtSoUTLP7J5HzS8nJ0e89dZbIjIyUiQkJIg///xTdOjQQTRt2lQUFhbq92HMx0+Ix/83KoQQGo1GWFtbi+XLl9/3eGM+hlOmTBH29vbiwIEDBv/95efn68dMnjxZeHl5iX379omYmBgREBAgAgIC9NtLS0tF69atRe/evUVcXJzYvXu3cHFxEXPmzJFjSvd53Bw1Go3w9/cXvr6+4sqVKwZjSktLhRBCbN26VXz77bfizJkz4vLly2LZsmXC2tpazJ07V86pCSEeP78rV66Ijz76SMTExIiEhATx22+/iUaNGolu3brp91HTj2GZy5cvC0mSxK5du+7bhzEfw3feeUccPHhQJCQkiNOnT4t33nlHSJIk/vjjDyGE8T0HWXIqYP/+/QLAfbfRo0cLIe59jfz9998Xbm5uQqVSiZ49e4r4+HiDfWRkZIhhw4YJW1tboVarxdixY0VOTo4Ms7nfo+aXkJDwwG0AxP79+4UQQsTGxgp/f39hb28vLC0tRcuWLcUnn3xiUBLk9Kj55efni969ewsXFxdhYWEhvL29xcSJEw2+5iiEcR8/IR7/36gQQnz99dfCyspKZGVl3fd4Yz6GD/vvb/Xq1foxBQUF4vXXXxeOjo7C2tpavPjiiyIlJcVgP4mJiaJv377CyspKODs7izfffNPg69dyetwcH3Z8AYiEhAQhxL3LGrRr107Y2toKGxsb0bZtW7FixQqh1Wrlm9j/PG5+SUlJolu3bsLJyUmoVCrRpEkTMWvWLKHRaAz2U5OPYZk5c+YIT0/PBx4XYz6G48aNE97e3kKpVAoXFxfRs2dPfcERwvieg5IQRvadNCIiIqJKwM/kEBERkUliySEiIiKTxJJDREREJoklh4iIiEwSSw4RERGZJJYcIiIiMkksOURERGSSWHKIiIjIJLHkEFGNcODAAUiSdN/if0RED8OSQ0Q1QmBgIFJSUmBvb1/ux+Tn52POnDlo3LgxLC0t4eLigmeffRa//fZbFSYlImNhLncAIqLyUCqVcHd3r9BjJk+ejKioKHz11Vfw8fFBRkYGIiIikJGRUUUpiciY8J0cIpJF9+7dMXXqVMyYMQOOjo5wc3PDt99+i7y8PIwdOxZ2dnZo0qQJdu3aBeD+01Xh4eFwcHDA77//jpYtW8LW1hZ9+vRBSkqK/nds3boV7777Lvr164cGDRqgY8eOmDp1KsaNG6cfI0kSfv31V4NsDg4OCA8PBwAkJiZCkiT88MMPCAwMhKWlJVq3bo2DBw9W6d+HiJ4eSw4RyWbNmjVwdnZGdHQ0pk6diilTpuDll19GYGAgTpw4gd69e2PkyJHIz89/4OPz8/Px2WefYd26dTh06BCSkpLw1ltv6be7u7tj586dyMnJeeqss2bNwptvvomTJ08iICAA/fv35ztCREaOJYeIZNO2bVv885//RNOmTTFnzhxYWlrC2dkZEydORNOmTTF37lxkZGTg9OnTD3x8SUkJVqxYgU6dOqFDhw4ICwvD3r179du/+eYbREREoE6dOujcuTPeeOMNHD169ImyhoWFYfDgwWjZsiWWL18Oe3t7rFy58on2RUTVgyWHiGTTpk0b/b/NzMxQp04d+Pr66u9zc3MDAKSnpz/w8dbW1mjcuLH+57p16xqM7datG65du4a9e/fipZdewrlz5xAUFISPP/64wlkDAgL0/zY3N0enTp1w4cKFCu+HiKoPSw4RycbCwsLgZ0mSDO6TJAkAoNPpyv14IcR9Y4KCgjB79mz88ccf+Oijj/Dxxx+juLj4oY8pKSl5sgkRkVFhySGiWsXHxwelpaUoLCwEALi4uBh8WPny5csP/AzQsWPH9P8uLS1FbGwsWrZsWfWBieiJ8SvkRGSyunfvjmHDhqFTp06oU6cOzp8/j3fffRc9evSAWq0GADz33HNYsmQJAgICoNVqMXv27PveIQKApUuXomnTpmjZsiW+/PJL3L171+BbWkRkfPhODhGZrODgYKxZswa9e/dGy5YtMXXqVAQHB2PTpk36MZ9//jk8PT0RFBSE4cOH46233oK1tfV9+1qwYAEWLFiAtm3b4siRI9i6dSucnZ2rczpEVEGS+PvJaCIi0ktMTETDhg1x8uRJtGvXTu44RFQBfCeHiIiITBJLDhEREZkknq4iIiIik8R3coiIiMgkseQQERGRSWLJISIiIpPEkkNEREQmiSWHiIiITBJLDhEREZkklhwiIiIySSw5REREZJL+DzQYkxl+vaLQAAAAAElFTkSuQmCC\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "ebe0fbfc-15ed-4ad6-d005-804795b95803" - }, - "execution_count": 22, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup PeriodCount patterns runtime memory\n", - "0 PPPClose 100 5000 26354 41.247799 374771712\n", - "1 PPPClose 150 5000 18456 37.799484 374128640\n", - "2 PPPClose 200 5000 12975 33.970583 373219328\n", - "3 PPPClose 250 5000 7553 33.471501 371965952\n", - "4 PPPClose 300 5000 4442 31.019934 371499008\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "4a0a56d0-8a3b-48fa-b9be-9a8c0a6e62b8" - }, - "execution_count": 23, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 23 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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+2agiZkAAABYgfLiRa9s11Mu3XC4PQ/pw3QElfrKJAAIAsDvCh5u5tl1DvXxre3kY0v+t26/H5xNAAAD2RfhwQ9e1a6iXSmZA5q3dr78vIIAAAOyH8OGmBl9+iV68uTiAfLBmv/6+YDMBBABgF4QPN3Z9+0v0ws3tSgLIPj35KQEEAFD7uNTWzd3QvpEk6cEPf9Z7q/fJMKRJg9vIMLgMFwBQO5j5gG5o30jP/6WdDEN6d1XxDIhpMgMCAKgdhA9Ikm7q2EhTTwsg/1i4hQACAKgVhA+U+UvHRnrupjgZhvT2yl/1FAEEAFALCB8o56+dovTvkgAyd+WvevqzrQQQAECNInzgLDd3itK/b4yTJM1ZsZcAAgCoUYQPVOjmzlH6901tJRUHkGc+J4AAAGoG4QPndEvnaE25sTiAzP5pr/75xTYCCACg2ggfOK9bu0RrckkAmfljip4lgAAAqonwgQsa0iVa/7qhOIDM+DFF//qSAAIAuHiED1TK0K7RevaGNpKk6T+kaPKiXwggAICLQvhApQ3r2liTri8OIG8t36MpiwkgAICqI3ygSm7v1liTBl8mSXpz2R79e/F2AggAoEoIH6iy2+Ob6JmSAPLGst16bgkBBABQeYQPXJTh8U309HXFAWTa0t2aSgABAFQS4QMXbUT3Jnrq2taSpP8u3a0XvtpBAAEAXBDhA9VyxxUx+kdJAHnt+1168WsCCADg/AgfqLY7r4jRxD8XB5D/fLdLLxFAAADnQfhAjUj4U4yeuKaVJOnV73bppW92WlwRAMBRET5QY0b1aPpHAPl2p17+ZofFFQEAHBHhAzVqVI+m+vvVxQHk5W926hVmQAAAZyB8oMaN7tlUj1/dUpL00jc79Oq3BBAAwB8IH6gVd/VspsRBxQHkxa936D8EEABACcIHas3dVzbTowOLA8gLX+/Qa98RQAAAhA/UsjG9mulvA1tIkp7/aode/36XxRUBAKxG+ECtu7dXrB4ZUBxApi7Zrv8uJYAAgDsjfMAuxl71RwB5bvF2TVu62+KKAABWIXzAbsZeFauH+l0qSfr34l/0xjICCAC4I8IH7Gpcn+Z6sCSATFn0i95aTgABAHdD+IDdje/TXA/0LQ4g//ryF01fvsfiigAA9kT4gCXu79tcE/o2lyQ9++U2zfiBAAIA7oLwActM6HupxvcpDiD//IIAAgDuokrhY9q0aYqLi5PNZpPNZlN8fLwWLVpU9vihQ4d0++23KzIyUgEBAerQoYP+97//1XjRcB0P9G2u8b1jJRUHkJk/plhcEQCgtlUpfDRq1EhTpkxRUlKS1q1bp969e2vw4MHasmWLJGn48OHavn27Fi5cqE2bNunGG2/UzTffrA0bNtRK8XB+hmHogX6XalxJAJn0+VbNIoAAgEszTNM0q7ODkJAQTZ06VSNHjlRgYKCmTZum22+/vezx0NBQ/fvf/9aoUaMqtb+MjAwFBwcrPT1dNputOqXBiZimqRe+2qHXSlZA/ce1rXXnFTEWVwUAqKyqfH5fdM9HYWGh5s2bp6ysLMXHx0uSunfvrv/7v//T8ePHVVRUpHnz5iknJ0e9evU6535yc3OVkZFR7gb3YxiGHup/qe7t1UyS9PRnWzV3xV5riwIA1Aqvqj5h06ZNio+PV05OjgIDAzV//ny1bt1akvThhx/qlltuUWhoqLy8vOTv76/58+crNjb2nPubPHmynn766YsfAVyGYRh6ZEALmZKmLd2tfyzcIsOQhsc3sbo0AEANqvLMR4sWLZScnKzVq1drzJgxGjFihLZu3SpJevLJJ3XixAl98803WrdunR588EHdfPPN2rRp0zn3l5iYqPT09LLb/v37L340cHqGYehvA1roniuLZ0AmfrpF76zca21RAIAaVe2ej759+6pZs2b629/+ptjYWG3evFmXXXZZucdjY2P1xhtvVGp/9HxAKu4BmbL4F725rPjy20nXt9Ht3RpbXBUA4Fzs0vNRqqioSLm5ucrOzi7eoUf5XXp6eqqoqKi6LwM3YxiGHhvYUnf1bCpJenLBZr276leLqwIA1IQq9XwkJiZq0KBBio6OVmZmpt5//30tXbpUS5YsUcuWLRUbG6u7775bzz//vEJDQ7VgwQJ9/fXX+vzzz2urfrgwwzCUOKilTNPU9B9S9MSCzfIwDA3tGm11aQCAaqhS+EhLS9Pw4cOVmpqq4OBgxcXFacmSJerXr58k6csvv9Rjjz2ma6+9VidPnlRsbKzmzp2rq6++ulaKh+szDEOPX91KpinN+DFFj8/fJC8PQzd3jrK6NADARap2z0dNo+cDFTFNs2wFVA9Deuv2TurbOsLqsgAAJeza8wHYg2EYeuKaVrq5UyMVmdJ9H6xX0q+/W10WAOAiED7gNAzD0LM3tNVVLcKVk1+kkXPXalfaSavLAgBUEeEDTsXb00OvD+ugdlF1dSI7XyNmrdHhjByrywIAVAHhA07H38dLs0Z0UkxYgH47cUojZq1RRk6+1WUBACqJ8AGnFBroq7cTuigs0Fe/HMrU3W8nKbeg0OqyAACVQPiA04oK8decOzsr0NdLK/cc00Mf/qyiIoe6eAsAUAHCB5xam0uC9cZtHeXtaejzjan65xfb5GBXjwMAzkD4gNP7U/MwPf/XdpKkWT+laPoPeyyuCABwPoQPuITBl1+iv1/dSpL0ry9/0fwNByyuCABwLoQPuIzRPZtq1J9iJEmPfLRRy3ccsbgiAEBFCB9wKY9f3UrXtWuogiJTY95N0ubf0q0uCQBwBsIHXIqHh6Gpf41T92ahysor1B2z12jfsWyrywIAnIbwAZfj6+WpN2/vqFYNbDp6Mk/DZ63W0ZO5VpcFAChB+IBLCvLz1tw7O6tRvTraeyxbI+esVVZugdVlAQBE+IALq2/z09yELqrn762fD6Rr7PvrlV9YZHVZAOD2CB9wac3CAzXrjs7y8/bQ0u1H9Nj/NrEIGQBYjPABl9c+up5eH9pBnh6G/rf+gJ7/arvVJQGAWyN8wC30aRWhf93QRpL0+ve7NXfFXmsLAgA3RviA27ilc7Qe7HepJOmpz7Zo0aZUiysCAPdE+IBbGdc7VsO6Rss0pfv/L1mr9xyzuiQAcDuED7gVwzD0zOA26t86QnkFRRr19jptP5RpdVkA4FYIH3A7nh6GXh3SXp0a11NmToFGzFqjgydOWV0WALgNwgfckp+3p2aM6KTY+oE6lJGjEbPW6ER2ntVlAYBbIHzAbdX199HchC6KtPlpZ9pJjX57nXLyC60uCwBcHuEDbu2SunU0J6Gzgvy8tHbv77p/3gYVFrEIGQDUJsIH3F7LSJumD+8kH08PLdlyWP9YuJlVUAGgFhE+AEndmobq5Vsvl2FI767ap9e/32V1SQDgsggfQImr2zbQU9deJkl6/qsd+nDtfosrAgDXRPgATjOiexON6dVMkpQ4f5O+++WwxRUBgOshfABn+NuAFrqxwyUqLDJ173vrtWHf71aXBAAuhfABnMEwDP37pjhdeWm4cvKLlDBnrfYcOWl1WQDgMggfQAW8PT3032EdFNcoWL9n52v4rDVKy8yxuiwAcAmED+AcAny9NOuOzmoS6q8Dv5/SHbPWKjMn3+qyAMDpET6A8wgL9NXchC4KC/TR1tQM3fNukvIKiqwuCwCcGuEDuIDGoQGafUcXBfh46qddx/TwRz+riFVQAeCiET6ASmjbKFjTbusoLw9DC38+qMmLtlldEgA4LcIHUEk9Lw3Xc3+JkyRN/yFFM37YY3FFAOCcCB9AFdzYoZEeG9RSkvTPL7bp0+TfLK4IAJwP4QOoort7NtWdVzSRJD380c/6addRawsCACdD+ACqyDAMPXlNa10T10D5habufidJWw6mW10WADgNwgdwETw8DL14czt1axqik7kFumP2Wu0/nm11WQDgFAgfwEXy9fLUW8M7qWVkkI5k5mr4rDU6npVndVkA4PAIH0A12Py8NTehiy6pW0cpR7OUMGetsvMKrC4LABwa4QOopgibn+YmdFZdf28l7z+h+97foIJCVkEFgHMhfAA1ILZ+kGaO6CRfLw9990uaHp+/SabJKqgAUBHCB1BDOjYO0WtDO8jDkD5cd0Avfb3D6pIAwCERPoAa1K91hJ69oa0k6dXvdundVb9aXBEAOB7CB1DDhnSJ1v19mkuSJn66WYs3H7K4IgBwLIQPoBZM6NtcQ7pEqciUxs/boLV7j1tdEgA4DMIHUAsMw9CkwW3Ut1WE8gqKNHLOWu08nGl1WQDgEAgfQC3x8vTQf4a0V4fousrIKdCIWWuUmn7K6rIAwHKED6AW1fHx1MwRndUsPEAH03N0x6y1Sj+Vb3VZAGApwgdQy+oF+GhuQhfVD/LV9sOZGv32OuXkF1pdFgBYhvAB2EGjev6am9BFQb5eWpNyXA/8X7IKi1iEDIB7InwAdtKqgU1vDu8oH08PLdp8SE9/toVVUAG4pSqFj2nTpikuLk42m002m03x8fFatGhRuW1Wrlyp3r17KyAgQDabTT179tSpUzTZAZLUvVmYXrylnQxDenvlr/rv0t1WlwQAdlel8NGoUSNNmTJFSUlJWrdunXr37q3Bgwdry5YtkoqDx8CBA9W/f3+tWbNGa9eu1X333ScPDyZYgFJ/jmuoJ69pLUmaumS7Pk46YHFFAGBfhlnNed+QkBBNnTpVI0eOVLdu3dSvXz9NmjSp0s/Pzc1Vbm5u2Z8zMjIUFRWl9PR02Wy26pQGOLTJi7bpzWV75OlhaMaITrqqRX2rSwKAi5aRkaHg4OBKfX5f9JREYWGh5s2bp6ysLMXHxystLU2rV69W/fr11b17d0VEROjKK6/Ujz/+eN79TJ48WcHBwWW3qKioiy0JcCqPDmipG9pfosIiU/e+u14/7z9hdUkAYBdVDh+bNm1SYGCgfH19dc8992j+/Plq3bq19uzZI0l66qmnNHr0aC1evFgdOnRQnz59tHPnznPuLzExUenp6WW3/fv3X/xoACfi4WHo3zfFqUfzMJ3KL1TCnLVKOZpldVkAUOuqHD5atGih5ORkrV69WmPGjNGIESO0detWFRUVSZLuvvtu3XnnnWrfvr1eeukltWjRQrNmzTrn/nx9fcsaWEtvgLvw8fLQtNs6qs0lNh3LytPwWat1JDP3wk8EACdW5fDh4+Oj2NhYdezYUZMnT1a7du30yiuvqEGDBpKk1q1bl9u+VatW2rdvX81UC7igQF8vzb6ji6JD/LX/+CndOWeNTuYWWF0WANSaal+GUlRUpNzcXDVp0kQNGzbU9u3byz2+Y8cONW7cuLovA7i08CBfzU3ootAAH23+LUNj3k1SXkGR1WUBQK2oUvhITEzU8uXLtXfvXm3atEmJiYlaunSphg0bJsMw9Mgjj+jVV1/Vxx9/rF27dunJJ5/UL7/8opEjR9ZW/YDLiAkL0Kw7OquOt6d+2HlUj/5vo4pYBRWAC/KqysZpaWkaPny4UlNTFRwcrLi4OC1ZskT9+vWTJE2YMEE5OTl64IEHdPz4cbVr105ff/21mjVrVivFA66mXVRdTbutg0bNXaf5G35TfZuvEge1srosAKhR1V7no6ZV5TphwFV9nHRAD3/0syRp4p9bK+FPMRZXBADnZ5d1PgDUnr90bKRHBrSQJE36Yqs++/mgxRUBQM0hfAAO6t5ezTQivrFMU3row5+1YvdRq0sCgBpB+AAclGEYmnjtZbq6baTyCot099tJ2noww+qyAKDaCB+AA/P0MPTizZerS0yIMnMLdMfsNdp/PNvqsgCgWggfgIPz8/bU9OGd1CIiSGmZuRoxe41+z8qzuiwAuGiED8AJBNfx1pyEzmoQ7Kc9R7I0cu5ancortLosALgohA/ASTQIrqO3E7oouI631u87oXEfrFdBIaugAnA+hA/AiTSPCNKMEZ3k6+Whb7al6clPN8vBluoBgAsifABOpnOTEL06pL08DOmDNfv18jc7rS4JAKqE8AE4oQGXReqZwW0kSa98u1Pvr+abowE4D8IH4KRu69ZY43vHSpKeWLBJX289bHFFAFA5hA/AiT3Q71Ld0ilKRaZ03/vrlfTrcatLAoALInwATswwDD17Qxv1bllfuQVFGjl3nXalZVpdFgCcF+EDcHJenh56bWh7XR5VVyey8zVi1lodzsixuiwAOCfCB+AC/H28NOuOzmoaFqDfTpzSiFlrlH4q3+qyAKBChA/ARYQE+GhuQheFB/nql0OZuvuddcotYBVUAI6H8AG4kKgQf825s7MCfb20as9xPfjhzyoqYhEyAI6F8AG4mMsaBuvN2zvK29PQFxtT9cznW1kFFYBDIXwALuiK2DA9/9d2kqQ5K/bqzeV7LK4IAP5A+ABc1ODLL9ET17SSJE1Z9Is+WX/A4ooAoBjhA3Bho3o01egeMZKkv328Uct2HLG4IgAgfAAuL3FQKw2+vKEKikyNeTdJGw+csLokAG6O8AG4OA8PQ1P/0k5/ig1Tdl6hEuas1a/HsqwuC4AbI3wAbsDHy0PTbuug1g1sOnoyT8NnrdHRk7lWlwXATRE+ADcR5OetOQmd1aheHf16LFsJc9YqK7fA6rIAuCHCB+BG6gf56e2ELgoJ8NHGA+ka89565RcWWV0WADdD+ADcTNPwQM0c0Ul1vD21fMcRPfq/jSxCBsCuCB+AG2ofXU+vD2svTw9Dn6z/Tc8t2W51SQDcCOEDcFO9W0Zo8o1tJUnTlu7WnJ9SLK4IgLsgfABu7OZOUXq4/6WSpKc/36ovNqZaXBEAd0D4ANzc2KtidXu3xjJN6YH/S9aqPcesLgmAiyN8AG7OMAw9dd1lGnBZhPIKizT67XX65VCG1WUBcGGEDwDy9DD0yq3t1blJPWXmFGjErDX67cQpq8sC4KIIHwAkSX7enpoxvLOa1w/U4YxcjZi1Riey86wuC4ALInwAKBPs7625CV0UafPTrrSTGjl3nXLyC60uC4CLIXwAKKdh3Tqam9BFNj8vJf36u8Z/sEGFRSxCBqDmED4AnKVFZJCmD+8kHy8PfbX1sJ78dDOroAKoMYQPABXq2jRUr9xyuQxDen/1Pv3nu11WlwTARRA+AJzToLYN9PR1l0mSXvx6h+at2WdxRQBcAeEDwHkNj2+isVc1kyT9fcFmfbvtsMUVAXB2hA8AF/Rw/xb6S8dGKiwyNfb99Vq/73erSwLgxAgfAC7IMAxNvrGterUIV05+kUbOWavdR05aXRYAJ0X4AFAp3p4e+u+wDmrXKFi/Z+dr+Mw1SsvIsbosAE6I8AGg0vx9vDTrjs5qEuqv306c0ojZa5WRk291WQCcDOEDQJWEBvrq7YSuCgv00bbUDN3zTpJyC1gFFUDlET4AVFl0qL/m3NlFAT6eWrH7mB7+aKOKWAUVQCURPgBclDaXBOuN2zvKy8PQZz8f1LNfbrO6JABOgvAB4KL1aB6u5//aTpI088cUTV++x+KKADgDwgeAarm+/SVKHNRSkvTsl9u0YMNvFlcEwNERPgBU2109myrhihhJ0iMf/6wfdx61uCIAjozwAaDaDMPQE9e00p/jGii/0NTd76zT5t/SrS4LgIMifACoER4ehl64uZ3im4YqK69Qd8xeq33Hsq0uC4ADInwAqDG+Xp56c3hHtYwM0tGTuRoxe42Oncy1uiwADobwAaBG2fy8NTehiy6pW0cpR7OUMGetsvMKrC4LgAOpUviYNm2a4uLiZLPZZLPZFB8fr0WLFp21nWmaGjRokAzD0IIFC2qqVgBOIsLmp7dHdlFdf2/9fCBdY99br/zCIqvLAuAgqhQ+GjVqpClTpigpKUnr1q1T7969NXjwYG3ZsqXcdi+//LIMw6jRQgE4l2bhgZo5orP8vD30/fYjSvxkk0yTVVABVDF8XHvttbr66qvVvHlzXXrppXr22WcVGBioVatWlW2TnJysF154QbNmzarxYgE4l46N6+m1IR3kYUgfJx3QC1/tsLokAA7gons+CgsLNW/ePGVlZSk+Pl6SlJ2draFDh+r1119XZGRkpfaTm5urjIyMcjcArqNv6wj964a2kqTXvt+ld1butbYgAJbzquoTNm3apPj4eOXk5CgwMFDz589X69atJUkPPPCAunfvrsGDB1d6f5MnT9bTTz9d1TIAOJFbu0TrcEauXvpmh578dIv+890uNQkLUExoQPF/S26NQ/3l5+1pdbkAaplhVvEkbF5envbt26f09HR9/PHHmjFjhpYtW6Zdu3bpoYce0oYNGxQYGFi8c8PQ/Pnzdf31159zf7m5ucrN/eNSvIyMDEVFRSk9PV02m+3iRgXA4ZimqX9+sU0zf0w55zaGITWw+ZULJE1KAkp0iL98vLhAD3BUGRkZCg4OrtTnd5XDx5n69u2rZs2aqU6dOnr11Vfl4fHHXw6FhYXy8PBQjx49tHTp0hovHoDzST+Vr71Hs5RSctt7LEt7j2Zpz9EsZeac+5JcD0NqVM+/ZMbEvziYlASUS+rWkZcnwQSwUlU+v6t82uVMRUVFys3N1dNPP61Ro0aVe6xt27Z66aWXdO2111b3ZQC4iOA63moXVVftouqWu980TR3PytPeY1lKOZqtlKMntfdodllAyc4r1L7j2dp3PFvLz9int6ehqBD/stM4TcIC1LTkvw1sfvLw4Oo7wJFUKXwkJiZq0KBBio6OVmZmpt5//30tXbpUS5YsUWRkZIVNptHR0YqJiamxggG4JsMwFBroq9BAX3VsHFLuMdM0dSQzV3uOFs+SpJTMlhQHk2zlFRRpz5Es7TmSddZ+fb081Pj0mZLQP8JJeJAvywIAFqhS+EhLS9Pw4cOVmpqq4OBgxcXFacmSJerXr19t1QcAMgxD9W1+qm/zU7emoeUeKyoylZqRU3bqZu9pAWXfsWzlFhRpx+GT2nH45Fn7DfDxVOPQkt6SMH/FhAUqJsxfTUIDFBLgQzABakm1ez5qGj0fAGpKQWGRfjtxqniGpGSWpDSgHPg9W0Xn+dsvyM+r7NRNk9DTGmDDAhRcx9t+gwCchF0bTmsa4QOAPeQVFGn/79lKOZJV0mdS2vyard9OnDrvc0MCfNQk9LSZktMCSoBvtVvpAKdk14ZTAHBGPl4eahYeqGbhgWc9lpNfqF+P/dHsmnLkjz6TtMxcHc/K0/GsPK3fd+Ks59YP8i3rLYkJ/yOUsIYJ8AdmPgCgCk7mFpScwiltei25MudYto5n5Z3zeYYhNQyuoyYlPSWla5jEhAcoqh5rmMD5cdoFACxQE2uYNA0LUJNQf9YwgdMhfACAAzlzDZMzA0p2XuE5n3vmGianN76yhgkcCT0fAOBAanMNk+Ll5/3/6DMpCSesYQJHxswHADio09cwKZspKQko+49nK7/w3H99l61hEn76F/ixhglqD6ddAMDFVWcNE5ufV9mpmyahAWoa/scX+LGGCS4W4QMA3Fh11zApuxIn7I/G1yahrGGC86PnAwDcWE2sYZL06+9nPbd0DZOuMSG6t1es6viwbgkuDjMfAABJFa9hUjpzcuYaJs3CA/TKre3V5pJgi6qFo+G0CwCgRpWuYbL9UKZe+Hq7DmfkytvT0N8GtNTIP8VwyS8IHwCA2vN7Vp4e/d9GfbX1sCSpR/MwPf/Xdoqw+VlcGaxUlc9vls0DAFRJvQAfvXl7R/3rhrby8/bQDzuPauDLy/V1SRgBLoTwAQCoMsMwNLRrtD4f10OXNbTp9+x8jX57nf4+f5NOnWfFVkAifAAAqiG2fqA+ube77urZVJL03up9uva1H7XlYLrFlcGRET4AANXi6+Wpx69upXdHdlWEzVe70k7q+td/0vTle1R0vtXO4LYIHwCAGvGn5mFafH9P9W8dofxCU89+uU0jZq9RWkaO1aXBwRA+AAA1pqJm1AE0o+IMhA8AQI2iGRUXQvgAANQKmlFxLoQPAECtOb0ZtX5QcTPqDa+v0IwfaEZ1Z4QPAECt+1PzMC2e0FP9Wkcor7BI//yCZlR3RvgAANhFSICP3qIZFSJ8AADsiGZUSIQPAIAFaEZ1b4QPAIAlaEZ1X4QPAIClaEZ1P4QPAIDlSptRn72hDc2oboDwAQBwCIZhaFjXxvp83J/UusEfzahPLKAZ1dUQPgAADiW2fpDmj/2jGfXdVTSjuhrCBwDA4ZQ2o74zsgvNqC6I8AEAcFg9mofTjOqCCB8AAIdWUTPqwFd+oBnViRE+AAAO78xm1ONZeTSjOjHCBwDAaZQ2o47uESPpj2bUrQczLK4MVUH4AAA4FV8vT/39mtblmlGvf/0nmlGdCOEDAOCUSptR+7aiGdXZED4AAE4rJMBH04ef3Yz6Dc2oDo3wAQBwahU1o46iGdWhET4AAC6BZlTnQfgAALgMmlGdA+EDAOByaEZ1bIQPAIBLKm1G/ef1NKM6GsIHAMBlGYah27oVN6O2ohnVYRA+AAAuL7Z+kBbQjOowCB8AALdAM6rjIHwAANwKzajWI3wAANzOuZpRv91GM6o9ED4AAG6pombUkXPX6ckFm2lGrWWEDwCAWyttRh31p+Jm1HdW/Uozai0jfAAA3J6vl6ee+HNrvZ3QReE0o9Y6wgcAACV6XhquJWc0o94xZy3NqDWM8AEAwGnObEZdvuMIzag1jPABAMAZzteMmpNPM2p1VSl8TJs2TXFxcbLZbLLZbIqPj9eiRYskScePH9e4cePUokUL1alTR9HR0Ro/frzS09NrpXAAAGpbhc2o/6EZtbqqFD4aNWqkKVOmKCkpSevWrVPv3r01ePBgbdmyRQcPHtTBgwf1/PPPa/PmzZozZ44WL16skSNH1lbtAADUujObUXfSjFpthmma1fo/FxISoqlTp1YYMj766CPddtttysrKkpeXV6X2l5GRoeDgYKWnp8tms1WnNAAAatSxk7l69H8b9c22NEnFDarP/zVO9YP8LK7MelX5/L7ono/CwkLNmzdPWVlZio+Pr3Cb0gLOFzxyc3OVkZFR7gYAgCMKDfTV9OGdNOn6NvL1KmlGfZlm1KqqcvjYtGmTAgMD5evrq3vuuUfz589X69atz9ru6NGjmjRpku66667z7m/y5MkKDg4uu0VFRVW1JAAA7MYwDN3erbG+GE8z6sWq8mmXvLw87du3T+np6fr44481Y8YMLVu2rFwAycjIUL9+/RQSEqKFCxfK29v7nPvLzc1Vbm5uuedGRUVx2gUA4PByCwr13OLtmvljiiSpef1AvTqkvVo1cL/Pr6qcdql2z0ffvn3VrFkzvfnmm5KkzMxMDRgwQP7+/vr888/l51e182D0fAAAnM3yHUf00Ec/60hmrnw8PfTooJa6s3sTeXgYVpdmN3bp+ShVVFRUNnORkZGh/v37y8fHRwsXLqxy8AAAwBn1vDRci+/vob6t6iuvsEiTPt9avDJqJiujVqRK4SMxMVHLly/X3r17tWnTJiUmJmrp0qUaNmxYWfDIysrSzJkzlZGRoUOHDunQoUMqLOQcGADAtVXUjDqIZtQKVe761xJpaWkaPny4UlNTFRwcrLi4OC1ZskT9+vXT0qVLtXr1aklSbGxsueelpKSoSZMmNVY0AACOqLQZtVtMiMbPS9a21AyNnLtOw+Mb6/GrW8nP29PqEh1CtXs+aho9HwAAV+Buzah27fkAAABn8/Xy1JNnrIw6+LWfNPPHFLdfGZXwAQBALaIZ9WyEDwAAahnNqOURPgAAsIPSZtTPxxWvjHqsZGXUiZ+638qohA8AAOyoeUSQFoztrpF/ipEkvb3yV137nx+1LdV9vtuM8AEAgJ25ezMq4QMAAIuUNqP2aelezaiEDwAALBQa6KsZIzpp0uDL3KYZlfABAIDFDMPQ7fFN9Nm4P6llZJDLN6MSPgAAcBCXRgTp0/uuKNeMet1rrteMSvgAAMCBlDajzk3oorBAX+04fFKDX/9Js1yoGZXwAQCAA7ry0nAtmVDSjFpQpGdcqBmV8AEAgIM6VzPqd784dzMq4QMAAAdWUTNqwhznbkYlfAAA4AQujQjSgrGu0YxK+AAAwEn4eZ+7GdU0nacZlfABAICTqbAZdbbzNKMSPgAAcEJnNqMuc6JmVMIHAABOylmbUQkfAAA4udJm1IQrnKMZlfABAIAL8PP21MRrW2vOnZ0dvhmV8AEAgAvp1aJ+hc2oRzJzrS6tDOEDAAAXU9qM+sxpzagDX17uMM2ohA8AAFyQYRga7qDNqIQPAABc2LmaUU9k51lWE+EDAAAXd2Yzamz9QAXX8basHi/LXhkAANhVrxb1tXhCD3l7eMgwDMvqIHwAAOBGwgJ9rS6B0y4AAMC+CB8AAMCuCB8AAMCuCB8AAMCuCB8AAMCuCB8AAMCuCB8AAMCuCB8AAMCuCB8AAMCuCB8AAMCuCB8AAMCuCB8AAMCuCB8AAMCuHO5bbU3TlCRlZGRYXAkAAKis0s/t0s/x83G48JGZmSlJioqKsrgSAABQVZmZmQoODj7vNoZZmYhiR0VFRTp48KCCgoJkGEaN7jsjI0NRUVHav3+/bDZbje7bEbj6+CTXHyPjc36uPkbG5/xqa4ymaSozM1MNGzaUh8f5uzocbubDw8NDjRo1qtXXsNlsLvtLJbn++CTXHyPjc36uPkbG5/xqY4wXmvEoRcMpAACwK8IHAACwK7cKH76+vvrHP/4hX19fq0upFa4+Psn1x8j4nJ+rj5HxOT9HGKPDNZwCAADX5lYzHwAAwHqEDwAAYFeEDwAAYFeEDwAAYFdOHz6WL1+ua6+9Vg0bNpRhGFqwYEG5x03T1MSJE9WgQQPVqVNHffv21c6dO8ttc/z4cQ0bNkw2m01169bVyJEjdfLkSTuO4vzON8b8/Hw9+uijatu2rQICAtSwYUMNHz5cBw8eLLePJk2ayDCMcrcpU6bYeSQVu9AxvOOOO86qfeDAgeW2ceRjeKHxnTm20tvUqVPLtnHk4zd58mR17txZQUFBql+/vq6//npt37693DY5OTkaO3asQkNDFRgYqJtuukmHDx8ut82+fft0zTXXyN/fX/Xr19cjjzyigoICew7lnC40xuPHj2vcuHFq0aKF6tSpo+joaI0fP17p6enl9lPRcZ43b569h3OWyhzDXr16nVX7PffcU24bRz2GFxrf3r17z/k+/Oijj8q2c9TjJ0nTpk1TXFxc2cJh8fHxWrRoUdnjjvYedPrwkZWVpXbt2un111+v8PHnnntOr776qt544w2tXr1aAQEBGjBggHJycsq2GTZsmLZs2aKvv/5an3/+uZYvX6677rrLXkO4oPONMTs7W+vXr9eTTz6p9evX65NPPtH27dt13XXXnbXtM888o9TU1LLbuHHj7FH+BV3oGErSwIEDy9X+wQcflHvckY/hhcZ3+rhSU1M1a9YsGYahm266qdx2jnr8li1bprFjx2rVqlX6+uuvlZ+fr/79+ysrK6tsmwceeECfffaZPvroIy1btkwHDx7UjTfeWPZ4YWGhrrnmGuXl5WnFihWaO3eu5syZo4kTJ1oxpLNcaIwHDx7UwYMH9fzzz2vz5s2aM2eOFi9erJEjR561r9mzZ5c7jtdff72dR3O2yhxDSRo9enS52p977rmyxxz5GF5ofFFRUWe9D59++mkFBgZq0KBB5fbliMdPkho1aqQpU6YoKSlJ69atU+/evTV48GBt2bJFkgO+B00XIsmcP39+2Z+LiorMyMhIc+rUqWX3nThxwvT19TU/+OAD0zRNc+vWraYkc+3atWXbLFq0yDQMw/ztt9/sVntlnTnGiqxZs8aUZP76669l9zVu3Nh86aWXare4GlDR+EaMGGEOHjz4nM9xpmNYmeM3ePBgs3fv3uXuc5bjZ5qmmZaWZkoyly1bZppm8XvO29vb/Oijj8q22bZtmynJXLlypWmapvnll1+aHh4e5qFDh8q2mTZtmmmz2czc3Fz7DqASzhxjRT788EPTx8fHzM/PL7uvMsffEVQ0viuvvNK8//77z/kcZzqGlTl+l19+uZmQkFDuPmc5fqXq1atnzpgxwyHfg04/83E+KSkpOnTokPr27Vt2X3BwsLp27aqVK1dKklauXKm6deuqU6dOZdv07dtXHh4eWr16td1rrgnp6ekyDEN169Ytd/+UKVMUGhqq9u3ba+rUqQ4xHVpZS5cuVf369dWiRQuNGTNGx44dK3vMlY7h4cOH9cUXX1T4L2ZnOX6lpxpCQkIkSUlJScrPzy/3PmzZsqWio6PLvQ/btm2riIiIsm0GDBigjIyMsn+5OZIzx3iubWw2m7y8yn+F1tixYxUWFqYuXbpo1qxZlfr6cXs71/jee+89hYWFqU2bNkpMTFR2dnbZY850DC90/JKSkpScnFzh+9AZjl9hYaHmzZunrKwsxcfHO+R70OG+WK4mHTp0SJLK/c8s/XPpY4cOHVL9+vXLPe7l5aWQkJCybZxJTk6OHn30UQ0ZMqTcFwaNHz9eHTp0UEhIiFasWKHExESlpqbqxRdftLDayhk4cKBuvPFGxcTEaPfu3Xr88cc1aNAgrVy5Up6eni51DOfOnaugoKBy06GS8xy/oqIiTZgwQVdccYXatGkjqfg95uPjc1YYPvN9WNH7tPQxR1LRGM909OhRTZo06axTf88884x69+4tf39/ffXVV7r33nt18uRJjR8/3h6lV8q5xjd06FA1btxYDRs21MaNG/Xoo49q+/bt+uSTTyQ5zzGszPGbOXOmWrVqpe7du5e739GP36ZNmxQfH6+cnBwFBgZq/vz5at26tZKTkx3uPejS4cPd5Ofn6+abb5Zpmpo2bVq5xx588MGyn+Pi4uTj46O7775bkydPdvhlhG+99dayn9u2bau4uDg1a9ZMS5cuVZ8+fSysrObNmjVLw4YNk5+fX7n7neX4jR07Vps3b9aPP/5odSm15kJjzMjI0DXXXKPWrVvrqaeeKvfYk08+WfZz+/btlZWVpalTpzrMh5d07vGdHqTatm2rBg0aqE+fPtq9e7eaNWtm7zIv2oWO36lTp/T++++XO1alHP34tWjRQsnJyUpPT9fHH3+sESNGaNmyZVaXVSGXPu0SGRkpSWd19B4+fLjsscjISKWlpZV7vKCgQMePHy/bxhmUBo9ff/1VX3/99QW/Jrlr164qKCjQ3r177VNgDWratKnCwsK0a9cuSa5zDH/44Qdt375do0aNuuC2jnj87rvvPn3++ef6/vvv1ahRo7L7IyMjlZeXpxMnTpTb/sz3YUXv09LHHMW5xlgqMzNTAwcOVFBQkObPny9vb+/z7q9r1646cOCAcnNza6vkKrnQ+E7XtWtXSSr3PnT0Y1iZ8X388cfKzs7W8OHDL7g/Rzt+Pj4+io2NVceOHTV58mS1a9dOr7zyikO+B106fMTExCgyMlLffvtt2X0ZGRlavXq14uPjJUnx8fE6ceKEkpKSyrb57rvvVFRUVPbmcnSlwWPnzp365ptvFBoaesHnJCcny8PD46zTFc7gwIEDOnbsmBo0aCDJNY6hVDzV27FjR7Vr1+6C2zrS8TNNU/fdd5/mz5+v7777TjExMeUe79ixo7y9vcu9D7dv3659+/aVex9u2rSpXIgsDdGtW7e2z0DO40JjlIr/bunfv798fHy0cOHCs2avKpKcnKx69epZPntVmfGdKTk5WZLKvQ8d9RhWZXwzZ87Uddddp/Dw8Avu11GO37kUFRUpNzfXMd+DNd7CameZmZnmhg0bzA0bNpiSzBdffNHcsGFD2ZUeU6ZMMevWrWt++umn5saNG83BgwebMTEx5qlTp8r2MXDgQLN9+/bm6tWrzR9//NFs3ry5OWTIEKuGdJbzjTEvL8+87rrrzEaNGpnJyclmampq2a20Q3nFihXmSy+9ZCYnJ5u7d+823333XTM8PNwcPny4xSMrdr7xZWZmmg8//LC5cuVKMyUlxfzmm2/MDh06mM2bNzdzcnLK9uHIx/BCv6OmaZrp6emmv7+/OW3atLOe7+jHb8yYMWZwcLC5dOnScr9/2dnZZdvcc889ZnR0tPndd9+Z69atM+Pj4834+PiyxwsKCsw2bdqY/fv3N5OTk83Fixeb4eHhZmJiohVDOsuFxpienm527drVbNu2rblr165y2xQUFJimaZoLFy40p0+fbm7atMncuXOn+d///tf09/c3J06caOXQTNO88Ph27dplPvPMM+a6devMlJQU89NPPzWbNm1q9uzZs2wfjnwMK/M7apqmuXPnTtMwDHPRokVn7cORj59pmuZjjz1mLlu2zExJSTE3btxoPvbYY6ZhGOZXX31lmqbjvQedPnx8//33pqSzbiNGjDBNs/hy2yeffNKMiIgwfX19zT59+pjbt28vt49jx46ZQ4YMMQMDA02bzWbeeeedZmZmpgWjqdj5xpiSklLhY5LM77//3jRN00xKSjK7du1qBgcHm35+fmarVq3Mf/3rX+U+vK10vvFlZ2eb/fv3N8PDw01vb2+zcePG5ujRo8tdDmaajn0ML/Q7apqm+eabb5p16tQxT5w4cdbzHf34nev3b/bs2WXbnDp1yrz33nvNevXqmf7+/uYNN9xgpqamltvP3r17zUGDBpl16tQxw8LCzIceeqjcZapWutAYz3WMJZkpKSmmaRZf/n355ZebgYGBZkBAgNmuXTvzjTfeMAsLC60bWIkLjW/fvn1mz549zZCQENPX19eMjY01H3nkETM9Pb3cfhz1GFbmd9Q0TTMxMdGMioqq8Jg48vEzTdNMSEgwGzdubPr4+Jjh4eFmnz59yoKHaTree9AwTQe8TggAALgsl+75AAAAjofwAQAA7IrwAQAA7IrwAQAA7IrwAQAA7IrwAQAA7IrwAQAA7IrwAQAA7IrwAeCiLV26VIZhnPWFVQBwPoQPABete/fuSk1NVXBwcKWfk52drcTERDVr1kx+fn4KDw/XlVdeqU8//bQWKwXgSLysLgCA8/Lx8any123fc889Wr16tf7zn/+odevWOnbsmFasWKFjx47VUpUAHA0zHwDK9OrVS+PGjdOECRNUr149RUREaPr06crKytKdd96poKAgxcbGatGiRZLOPu0yZ84c1a1bV0uWLFGrVq0UGBiogQMHKjU1tew1Fi5cqMcff1xXX321mjRpoo4dO2rcuHFKSEgo28YwDC1YsKBcbXXr1tWcOXMkSXv37pVhGJo3b566d+8uPz8/tWnTRsuWLavV/z8AagbhA0A5c+fOVVhYmNasWaNx48ZpzJgx+utf/6ru3btr/fr16t+/v26//XZlZ2dX+Pzs7Gw9//zzeuedd7R8+XLt27dPDz/8cNnjkZGR+vLLL5WZmVntWh955BE99NBD2rBhg+Lj43XttdcygwI4AcIHgHLatWunJ554Qs2bN1diYqL8/PwUFham0aNHq3nz5po4caKOHTumjRs3Vvj8/Px8vfHGG+rUqZM6dOig++67T99++23Z42+99ZZWrFih0NBQde7cWQ888IB++umni6r1vvvu00033aRWrVpp2rRpCg4O1syZMy9qXwDsh/ABoJy4uLiynz09PRUaGqq2bduW3RcRESFJSktLq/D5/v7+atasWdmfGzRoUG7bnj17as+ePfr222/1l7/8RVu2bFGPHj00adKkKtcaHx9f9rOXl5c6deqkbdu2VXk/AOyL8AGgHG9v73J/Ngyj3H2GYUiSioqKKv180zTP2qZHjx569NFH9dVXX+mZZ57RpEmTlJeXd87n5OfnX9yAADgcwgcAy7Vu3VoFBQXKycmRJIWHh5drUt25c2eFPSarVq0q+7mgoEBJSUlq1apV7RcMoFq41BaAXfXq1UtDhgxRp06dFBoaqq1bt+rxxx/XVVddJZvNJknq3bu3XnvtNcXHx6uwsFCPPvroWTMqkvT666+refPmatWqlV566SX9/vvv5a6aAeCYmPkAYFcDBgzQ3Llz1b9/f7Vq1Urjxo3TgAED9OGHH5Zt88ILLygqKko9evTQ0KFD9fDDD8vf3/+sfU2ZMkVTpkxRu3bt9OOPP2rhwoUKCwuz53AAXATDPPPEKgA4uL179yomJkYbNmzQ5ZdfbnU5AKqImQ8AAGBXhA8AAGBXnHYBAAB2xcwHAACwK8IHAACwK8IHAACwK8IHAACwK8IHAACwK8IHAACwK8IHAACwK8IHAACwq/8H1dyPR7QmhTkAAAAASUVORK5CYII=\n" 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\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/partialPeriodicPattern/maximal/Max3PGrowth.ipynb b/notebooks/partialPeriodicPattern/maximal/Max3PGrowth.ipynb index a6a1a898..665e9ce6 100644 --- a/notebooks/partialPeriodicPattern/maximal/Max3PGrowth.ipynb +++ b/notebooks/partialPeriodicPattern/maximal/Max3PGrowth.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Maximal Partial Periodic Frequent patterns in Temporal Databases using Max3PGrowth" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Maximal Partial Periodic Frequent patterns in a temporal database using the Max3PGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the Max3PGrowth algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "9299a283-7823-4e02-f9c1-d012787ddad5" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/835.0 kB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K \u001B[91m━━━━━━━━━━\u001B[0m\u001B[91m╸\u001B[0m\u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m225.3/835.0 kB\u001B[0m 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JsonForm, JsonSir\n", + " Building wheel for JsonForm (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonForm: filename=JsonForm-0.0.2-py3-none-any.whl size=3313 sha256=03116056d753c84302a95075b0b4ad12ab3ccba473cafab8cfc6e6c229e4e62e\n", + " Stored in directory: /root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=4d27488206a9da00a0b7473f9a2fdf2db3a103bc1e79fec2eca147db981f1d5a\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Maximal Partial Periodic Frequent patterns in Temporal Databases using Max3PGrowth" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Maximal Partial Periodic Frequent patterns in a temporal database using the Max3PGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the Max3PGrowth algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "9299a283-7823-4e02-f9c1-d012787ddad5" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/835.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.3/835.0 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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" Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", - "Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "5794bdf7-e651-4f30-df06-1ac348435fe3" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-07 08:02:58-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.13MB/s in 5.5s \n", - "\n", - "2023-09-07 08:03:05 (825 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "82407986-5639-4de4-eb61-ae9eb45c6e15" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Maximal Partial Periodic Frequent patterns using Max3PGrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "efccbab2-de2b-4aed-bbc8-5dc306d1f419" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "65bea099-38b4-49b5-a36f-886f080d77df" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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zM3z++ecoKSnB4sWLa3U81fnPf/6D0aNHo0+fPnjqqaeQnZ2NFStWICgoSK+gupuRI0diw4YNGDt2LEaMGIHExESsWrUKgYGB1T5Hu3bt0LdvX7zwwgsoKSnBRx99BBcXF7z22mtGOy4iAJyqgKiu3n77bdGiRQuhUCj0Lt8HcMfpA7766ivRvn17oVKpRMeOHcXq1avFggULqlxufqfnuPVS+5KSEvHqq6+KkJAQYWdnJ2xsbERISIhYuXKl3mNOnz4tIiIihK2trXB1dRXPPvusbtqDWy97F0KIU6dOibFjxwpHR0dhaWkp/P39xRtvvFGj4759GgAhhLh48aJ48MEHdc/Xs2dPsWnTJr02lZfmr1+/Xm97dZfm38m+fftEaGiosLCwEG3atBGrVq2q9n29PeM777wjevbsKRwdHYWVlZXo2LGjePfdd0VpaamujVqtFi+++KJwc3MTkiTpnrMy35IlS6rkudNUBTY2NuLixYtiyJAhwtraWnh4eIgFCxbopm2olJGRIcaPHy+sra2Fk5OTeO6558SpU6eqPOedsglRdaoCIYQ4ceKEiIyMFLa2tsLa2lrcd9994tChQ3ptKqcqOHbsmN72O02hUJ2ffvpJdOzYUahUKhEUFCQ2btwoxo8fLzp27FjlParu/dNqteI///mP8PX1FSqVSnTt2lVs2rRJTJ48udrpDpYsWSKWLl0qfHx8hEqlEv369ROxsbF6z1n5/t+uus8J0Z1IQtTjKEwiIqJbdOnSBW5ublXG29XF5cuX4efnhyVLlmDOnDlGe16iO+GYJyIiMrqysjKo1Wq9bXv37kVsbCwGDhwoTygiI+GYJyIiMrrr168jIiICjz/+OLy9vXH27FmsWrUKnp6eVSaoJGpsWDwREZHROTk5ITQ0FF9++SUyMjJgY2ODESNG4L333tOtyUjUWHHMExEREZEBOOaJiIiIyAAsnoiIiIgMwDFPRqLVapGcnAw7OzujLuNARERE9UcIgfz8fHh7e9d40l4WT0aSnJxslDWhiIiIqOFdvXoVLVu2rFFbFk9GYmdnB6D8zTfG2lBERERU//Ly8uDj46P7Hq8JFk9GUtlVZ29vz+KJiIiokTFkyA0HjBMREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBE1A8VlGrkjEBE1GSyeiJowrVZgWdQ5BC3Yjn//GgchhNyRiIgaPTO5AxBR/SgoUWPW2hhEnU4DAPx4JAnt3GzxdF8/mZMRETVuPPNE1ARdySzEuJUHEXU6DRZKBUZ29gIAvLvlDA5duCFzOiKixo3FE1ETc+D8DTyw4iDOpRXA3U6Ftc/1wvLHumJc1xbQaAWm/3gCV7OK5I5JRNRosXgiaiKEEPjqQCImfX0EuTfL0MXHEX+82BddWzlBkiT8Z1wwgls4ILuoDFP/F42bpRxETkRUGyyeiJqA4jIN5qw/ibc3nYZWAOO7tcRPU3vBw95S18bSXInPnwiFi40FzqTk4bVfTnIAORFRLbB4Imrk0vKK8cgXf+GXE9egkIA3Rgbig4c6w9JcWaWtt6MVVk7sBjOFhD9ik/HF/ksyJCYiatxYPBE1YieSsjFq+QHEXs2Bg5U5vns6DFP6+kGSpDs+JqyNCxaMCgQAvL/tLPady2iouERETQKLJ6JGav3xq3j087+Qnl+CDh622DijD/q2d63RYx/v5YtHuvtAK4AXfzyByzcK6zktEVHTweKJqJFRa7R48494vPrzSZRqtBgS6IEN0/rA18Wmxs8hSRLeGtMJXVs5Iq9Yjan/O47CEnU9piYiajpYPBE1ItmFpZi8+ihWH7wMAHh5cHusejwUtirD57tVmSmx6vFQuNmpcC6tAK+si+UAciKiGmDxRNRIJKTmY/SnB3HwQiasLZT4bGI3zLq/AxSKO49vuhcPe0usejwU5koJ2+JT8emeC0ZMTETUNLF4ImoEtp1KxdiVB5GUVYSWTlb45YXeGBbsZZTnDvV1wtujgwAAS6POYdeZNKM8LxFRU8XiicjE/Rx9Dc9/H42iUg3C27hg44y+CPCyN+prPNqzFR7v1QpCADN/isHFjAKjPj8RUVPC4onIhJ1Ly8frv8UBAB7v1QrfTekJZxuLenmt+SM7oUdrJ+SXqPHsd8eRV1xWL69DRNTYsXgiMlHFZRrM+PEEisu06NfeFW89EARzZf39lbUwU2DlxFB4OVjiUkYhZq+NgVbLAeRERLdj8URkot784zTOpRXA1VaFZQ93qdPA8Jpys1Ph8ydCYWGmwM4z6fho1/l6f00iosaGxRORCdp0MhlrjiZBkoCPHukCNztVg71255aOWDQ2GADwya7z2HYqtcFem4ioMWDxRGRikjKLMO+X8nFO0wa2rfGs4cY0PrQlnu7jBwB4ZV0MzqXlN3gGIiJTxeKJyISUqrV48ae/kV+iRqivE2ZFdJAty7+Hd0Tvti4oLNVg6nfHkVvEAeRERACLJyKT8sGOBN0iv5881hVm9ThA/F7MlAqsmNANLRytcDmzCPM3npItCxGRKWHxRGQi9iSk44v9lwAAix/sjBaOVjInApxtLLB8QlcAwNa4VOTe5NknIiIWT0QmIC2vGK+siwUATA73RWQnT5kT/aNbKyd08LBFqUaLqNOcfZyIiMUTkcw0WoGZP8Ugq7AUgV72mDc8QO5IVYzs7A0A+CM2WeYkRETyk7V42r9/P0aNGgVvb29IkoTffvtNb78QAvPnz4eXlxesrKwQERGB8+f1553JysrCxIkTYW9vD0dHR0yZMgUFBfpLS5w8eRL9+vWDpaUlfHx8sHjx4ipZ1q9fj44dO8LS0hLBwcHYsmWL0Y+XqDqf7rmAw5fKF/tdMaErLM2VckeqYmTn8nX0Dly4gazCUpnTEBHJS9biqbCwECEhIfj000+r3b948WJ88sknWLVqFY4cOQIbGxtERkaiuLhY12bixImIj49HVFQUNm3ahP3792Pq1Km6/Xl5eRgyZAh8fX0RHR2NJUuWYOHChfjiiy90bQ4dOoTHHnsMU6ZMwd9//40xY8ZgzJgxOHWKA2Spfh25lImPdp4DALwzJght3GxlTlS9Nm626ORtD41WcN4nIiJhIgCIX3/9VXdfq9UKT09PsWTJEt22nJwcoVKpxJo1a4QQQpw+fVoAEMeOHdO12bp1q5AkSVy/fl0IIcTKlSuFk5OTKCkp0bWZO3eu8Pf3191/+OGHxYgRI/TyhIWFieeee67G+XNzcwUAkZubW+PHUPOWVVAiwt7dKXznbhKz1v4td5x7+mzvBeE7d5N49PPDckchIjKa2nx/m+yYp8TERKSmpiIiIkK3zcHBAWFhYTh8+DAA4PDhw3B0dET37t11bSIiIqBQKHDkyBFdm/79+8PC4p/FVCMjI5GQkIDs7Gxdm1tfp7JN5etUp6SkBHl5eXo3opoSQmDO+lik5hWjjZsN3h4dJHekexoRXN5191diJtLziu/Rmoio6TLZ4ik1tbxrwMPDQ2+7h4eHbl9qairc3d319puZmcHZ2VmvTXXPcetr3KlN5f7qLFq0CA4ODrqbj4+PoYdIzdjXBy9j19l0WJgpsPyxrrBRmckd6Z58nK3RtZUjhAC2xKXIHYeISDYmWzyZunnz5iE3N1d3u3r1qtyRqJGIu5aL97aeAQC8PiIAnbwdZE5Uc6Mqr7o7yeKJiJovky2ePD3L57lJS9OfVyYtLU23z9PTE+np6Xr71Wo1srKy9NpU9xy3vsad2lTur45KpYK9vb3ejehe8ovLMGPNCZRpBCI7eeCJXr5yRzLIiM5ekCQg+ko2rufclDsOEZEsTLZ48vPzg6enJ3bt2qXblpeXhyNHjiA8PBwAEB4ejpycHERHR+va7N69G1qtFmFhYbo2+/fvR1nZPzMjR0VFwd/fH05OTro2t75OZZvK1yEyBiEE/u/XU7iSWYQWjlZYPD4EkiTJHcsgHvaW6NnaGQCw+STnfCKi5knW4qmgoAAxMTGIiYkBUD5IPCYmBklJSZAkCTNnzsQ777yDjRs3Ii4uDpMmTYK3tzfGjBkDAAgICMDQoUPx7LPP4ujRozh48CBmzJiBRx99FN7e5d0LEyZMgIWFBaZMmYL4+HisXbsWH3/8MWbPnq3L8fLLL2Pbtm1YunQpzp49i4ULF+L48eOYMWNGQ78l1IStP34NG2OToVRI+OSxLnCwNpc7Uq2MCin/u7WJXXdE1FzV27V/NbBnzx4BoMpt8uTJQojy6QreeOMN4eHhIVQqlRg8eLBISEjQe47MzEzx2GOPCVtbW2Fvby+eeuopkZ+fr9cmNjZW9O3bV6hUKtGiRQvx3nvvVcmybt060aFDB2FhYSE6deokNm/ebNCxcKoCuptzqXnC//UtwnfuJrFi93m549TJjfxi0WbeZuE7d5NIzCiQOw4RUZ3U5vtbEkIIGWu3JiMvLw8ODg7Izc3l+CfSU1ymwegVB5GQlo9+7V3x7VM9oVA0ru662z3x1RH8ef4G5gzpgBmD2ssdh4io1mrz/W2yY56ImoqVey4gIS0frrYqLHu4S6MvnIB/rrpj1x0RNUcsnojqWWWB8fqIALjZqWROYxyRnTxhrpRwNjUf59Py5Y5DRNSgWDwR1aPEG4W4dKMQ5koJgwPc7/2ARsLB2hwDOrgB4JxPRNT8sHgiqke7z5bPQ9bTzxl2lo3z6ro7GVnZdRebDA6dJKLmhMUTUT3afbZ88tX7/JvOWadKEYEeUJkpcOlGIeKTubYjETUfLJ6I6kl+cRmOJmYBAAYHeNyjdeNjqzLDoI7lRSEHjhNRc8LiiaieHDh/A2UaAT9XG/i52sgdp178M2Emu+6IqPlg8URUTyrHO1WenWmK7vN3h42FEteybyLmao7ccYiIGgSLJ6J6oNUK7EkoL54GN+HiycpCiYjA8i7JP2LZdUdEzQOLJ6J6EHc9FzcKSmGrMkP3ioV0m6rKCTM3xyVDq2XXHRE1fSyeiOrBroouu/4dXGFh1rT/mvXr4Ao7SzOk5ZXg2OUsueMQEdW7pv2vOpFMmvIUBbdTmSkxtJMnAOCPk8kypyEiqn8snoiMLC2vGKeu50GSgIHNoHgC/rnqbmtcKtQarcxpiIjqF4snIiPbU9FlF9LSscmsZXcvvdu6wNnGApmFpTh8KVPuOERE9YrFE5GRNYcpCm5nplRgWFBF110su+6IqGlj8URkRCVqDQ5cuAGgeRVPwD9r3W07lYpSNbvuiKjpYvFEZERHLmWhqFQDD3sVOnnbyx2nQfX0c4a7nQp5xWr8eT5D7jhERPWGxROREd3aZSdJksxpGpZSIWF4sBcArnVHRE0biyciIxFCYFczmqKgOpVX3e2IT0VxmUbmNERE9YPFE5GRXMwowNWsm7AwU6BPO1e548iiWytHtHC0QmGpRnfVIRFRU8PiichIdp0pLxbC27jARmUmcxp5SJKEkZ3ZdUdETRuLJyIjaY5TFFSnsutu19k0FJaoZU5DRGR8LJ6IjCC3qAzHr2QDYPHUydserV2sUVymxc4zaXLHISIyOhZPREaw73wGNFqB9u628HG2ljuOrCRJ0p19+iOWXXdE1PSweCIygsrB0YMCmvdZp0qVxdO+c+nIvVkmcxoiIuNi8URURxqtwN6EiuKpmU5RcLsOHnbo4GGLMo3AjvhUueMQERkViyeiOoq5mo3sojLYW5oh1NdJ7jgmY1TFci1/8Ko7ImpiWDwR1VHlFAUD/d1hpuRfqUojK7ruDl64gazCUpnTEBEZD/+lJ6ojTlFQPT9XGwS1sIdGK7D1FM8+EVHTweKJqA6u59zE2dR8KCRgQAc3ueOYHF3XXWyyzEmIiIyHxRNRHVSederWyglONhYypzE9IypmGz+SmIX0vGKZ0xARGQeLJ6I64BQFd9fSyRrdWjlCCGBzHLvuiKhpYPFEVEs3SzU4eOEGAGBwRw+Z05iukRVddxvZdUdETQSLJ6JaOnzpBkrUWrRwtEIHD1u545iskZ29oFRI+DspBxfSC+SOQ0RUZyyeiGqpcoqCQR3dIUmSzGlMl7u9JQZWDKZff/yqzGmIiOqOxRNRLQghOEWBAR7u4QMA+OXENZRptDKnISKqGxZPRLVwNjUfKbnFsDRXILyti9xxTN6gju5wtVXhRkGprugkImqsWDwR1UJlAdCnrSsszZUypzF95koFxndrAYBdd0TU+LF4IqqF3ZyiwGAPdS/vutuTkME5n4ioUWPxRGSgrMJSnEjKBsDxToZo526L7r5O0GgFfj5xTe44RES1xuKJyED7zqVDCCDAyx5eDlZyx2lUKgeOrz9+DUIImdMQEdUOiyciA1VOUTCYZ50MNiLYCzYWSiTeKMTRxCy54xAR1QqLJyIDlGm02HcuAwBwH4sng9mozHQzjq/lwHEiaqRYPBEZIPpKNvKL1XC2sUAXH0e54zRKlV13W+JSkFdcJnMaIiLDsXgiMkDlVXYD/d2gVHBW8dro1soR7dxtUVymxR9c746IGiEWT0QG4KzidSdJEh6pmLZg3XFedUdEjQ+LJ6IaSsoswoX0ApgpJPRr7yZ3nEZtbLcWMFNIiL2ag4TUfLnjEBEZhMUTUQ3tPpsGAOje2gkOVuYyp2ncXG1ViAjwAACsPcaB40TUuLB4IqqhXWcrpyjwkDlJ0/BIxcDxX/++hhK1RuY0REQ1x+KJqAYKS9Q4cql8XiJOUWAc/dq7wsNeheyiMuw8zcWCiajxYPFEVAMHLtxAqUYLXxdrtHWzkTtOk2CmVODB0JYAOOcTETUuJl08aTQavPHGG/Dz84OVlRXatm2Lt99+W29ZByEE5s+fDy8vL1hZWSEiIgLnz5/Xe56srCxMnDgR9vb2cHR0xJQpU1BQUKDX5uTJk+jXrx8sLS3h4+ODxYsXN8gxUuOw+8w/V9lJEqcoMJaHK666+/N8Bq7n3JQ5DRFRzZh08fT+++/js88+w4oVK3DmzBm8//77WLx4MZYvX65rs3jxYnzyySdYtWoVjhw5AhsbG0RGRqK4+J9V2ydOnIj4+HhERUVh06ZN2L9/P6ZOnarbn5eXhyFDhsDX1xfR0dFYsmQJFi5ciC+++KJBj5dMk1YrsCeBUxTUB18XG/Rq4wwhgJ85bQERNRbChI0YMUI8/fTTetvGjRsnJk6cKIQQQqvVCk9PT7FkyRLd/pycHKFSqcSaNWuEEEKcPn1aABDHjh3Ttdm6dauQJElcv35dCCHEypUrhZOTkygpKdG1mTt3rvD3969x1tzcXAFA5ObmGn6gZNJOXs0RvnM3icA3toriMrXccZqcDSeuCt+5m0Sf93YJjUYrdxwiamZq8/1t0meeevfujV27duHcuXMAgNjYWBw4cADDhg0DACQmJiI1NRURERG6xzg4OCAsLAyHDx8GABw+fBiOjo7o3r27rk1ERAQUCgWOHDmia9O/f39YWFjo2kRGRiIhIQHZ2dnVZispKUFeXp7ejZqmXRVTFPRt7wqVmVLmNE3PsCAv2Fma4Vr2TRy+lCl3HCKiezLp4ulf//oXHn30UXTs2BHm5ubo2rUrZs6ciYkTJwIAUlNTAQAeHvqXjnt4eOj2paamwt1dv6vFzMwMzs7Oem2qe45bX+N2ixYtgoODg+7m4+NTx6MlU7XtVPlngFMU1A9LcyVGd6lYLJhzPhFRI2DSxdO6devwww8/4Mcff8SJEyfw7bff4oMPPsC3334rdzTMmzcPubm5utvVq/xHvylKSM3H2dR8mCslDOnE4qm+PNK9FQBgW3wqcou4WDARmTaTLp5effVV3dmn4OBgPPHEE5g1axYWLVoEAPD09AQApKWl6T0uLS1Nt8/T0xPp6fpzyKjVamRlZem1qe45bn2N26lUKtjb2+vdqOn5LeY6AGCgvzscrS3u0ZpqK6iFPQK87FGq1urecyIiU2XSxVNRUREUCv2ISqUSWq0WAODn5wdPT0/s2rVLtz8vLw9HjhxBeHg4ACA8PBw5OTmIjo7Wtdm9eze0Wi3CwsJ0bfbv34+ysn/+xxsVFQV/f384OTnV2/GRadNqBTbGJAMAxnZtIXOapk2SJDzcvWLOJ3bdEZGJM+niadSoUXj33XexefNmXL58Gb/++iuWLVuGsWPHAij/B3fmzJl45513sHHjRsTFxWHSpEnw9vbGmDFjAAABAQEYOnQonn32WRw9ehQHDx7EjBkz8Oijj8Lbu3ycxYQJE2BhYYEpU6YgPj4ea9euxccff4zZs2fLdehkAo5fycb1nJuwU5lxioIGMKZLC1goFTidkodT13PljkNEdEdmcge4m+XLl+ONN97AtGnTkJ6eDm9vbzz33HOYP3++rs1rr72GwsJCTJ06FTk5Oejbty+2bdsGS0tLXZsffvgBM2bMwODBg6FQKDB+/Hh88sknuv0ODg7YsWMHpk+fjtDQULi6umL+/Pl6c0FR8/Pr3+XdR0ODPGFpzqvs6puTjQWGdPLAppMpWHvsKoJaOMgdiYioWpIQt0zXTbWWl5cHBwcH5ObmcvxTE1Cq1qLHuzuRe7MMPzwThj7tXOWO1Cz8eT4DT3x1FHaWZjj2fxEsWomo3tXm+9uku+2I5LI3IR25N8vgbqdCrzYucsdpNvq0dUULRyvkF6uxPb76aUKIiOTG4omoGr9XDBR/IMQbSgXXsmsoCoWEhzhwnIhMHIsnotvkF5dh55nyqSrG8Cq7BvdQdx9IEnDoYiaSMovkjkNEVAWLJ6LbbDuVihK1Fu3cbdHJm+PXGloLRyv0rRhjtj6aZ5+IyPSweCK6TeUkjWO6eEOS2GUnh4e7ly939HP0NWi0vKaFiEwLiyeiW6TlFePQxfLFaUd3YZedXIZ08oCjtTlScoux/3yG3HGIiPSweCK6xR+xyRACCPV1go+ztdxxmi2VmRJjKorXdRw4TkQmhsUT0S10XXYcKC67R3qUd93tPJOGzIISmdMQEf2DxRNRhQvp+Th1PQ9mCgkjgr3kjtPsBXjZo3NLB5RphG62dyIiU8DiiajCb3+Xz+00oIMbnG0sZE5DwD8Dx9cdvwouhkBEpoLFExEAIQR+jy0/uzGaXXYm44Eu3rA0V+BcWgFirubIHYeICACLJyIAwImkbFzNugkbCyXuD/CQOw5VsLc0x/Cg8i7Udcc5cJyITAOLJyL802UX2ckTVhZcjNaUPFwxcPyP2BQUl2lkTkNExOKJCGUaLTadLC+eeJWd6Qnzc4aHvQoFJWocvpQpdxwiIhZPRPvPZSC7qAyutir0busidxy6jSRJuD+wvCt1R3yazGmIiFg8EeG3mPKzTqNCvGCm5F8JU3R/oCeA8jmftFyuhYhkxm8KatYKStSIOp0KALoZrcn09GrjDFuVGTLySxB7LUfuOETUzLF4omZtR3wqisu08HO1QeeWDnLHoTtQmSkx0N8NALDjNLvuiEheLJ6oWavsshvdxRuSJMmchu6mctxTFIsnIpIZiydqtjLyS3DgfAYAdtk1Bvd1dIe5UsKF9AJcyiiQOw4RNWMsnqjZ+iM2GVoBdPFxRGtXG7nj0D3YW5qjV5vyqyF59omI5MTiiZqt32PKl2MZ08Vb5iRUU+y6IyJTwOKJmqXEG4WIvZYLpULCyBAWT41FRMXSOdFJ2cjIL5E5DRE1VyyeqFn67e/ys05927nC1VYlcxqqKW9HKwS3cIAQwO6zPPtERPJg8UTNjhBC12U3lsuxNDrsuiMiubF4omYn5moOLmcWwcpcqfsipsZjSKfy39mf52+gqFQtcxoiao5YPFGz83vF3E5DOnnARmUmcxoylL+HHXycrVCi1mL/uRtyxyGiZojFEzUrao0Wm06WF0+c26lxkiQJ9weUr3XHrjsikgOLJ2pWDly4gRsFpXC2sUDf9q5yx6Faquy623U2DWqNVuY0RNTcsHiiZqWyy25kZy+YK/nxb6y6+zrB0docOUVlOH4lW+44RNTM8NuDmo2iUjW2x6cCAMbwKrtGzUypwKCO7gCAHfHsuiOihsXiiZqNqNNpKCrVwNfFGl19HOWOQ3U0JLBi3NOZVAghZE5DRM0JiydqNionxhwd4g1JkmROQ3XVv4MrVGYKXM26iYS0fLnjEFEzwuKJmoXMghLsP19+Wftodtk1CdYWZuhXMeifXXdE1JBYPFGzsDkuBRqtQHALB7R1s5U7DhkJZxsnIjmweKJm4deKLjsOFG9aBnX0gCQBcddzkZxzU+44RNRMsHiiJu9KZiH+TsqBQgJGhXjJHYeMyM1OhdBWTgCAnWd49omIGgaLJ2ryKud26tPOFe52ljKnIWNj1x0RNTQWT9Tk/RFbXjyN5nIsTVJl8XT4YiZyb5bJnIaImgODi6dLly7VRw6ienEpowDn0wtgppB0X7LUtLRxs0U7d1uotQJ7E9LljkNEzYDBxVO7du1w33334fvvv0dxcXF9ZCIymsqunPC2LnCwMpc5DdUXdt0RUUMyuHg6ceIEOnfujNmzZ8PT0xPPPfccjh49Wh/ZiOpsR8WX6RCedWrSKounvQkZKFFrZE5DRE2dwcVTly5d8PHHHyM5ORlff/01UlJS0LdvXwQFBWHZsmXIyMioj5xEBsvIL8GJpPJFYyNYPDVpXVo6ws1OhYISNf66lCV3HCJq4mo9YNzMzAzjxo3D+vXr8f777+PChQuYM2cOfHx8MGnSJKSkpBgzJ5HBdp1JgxBA55YO8HKwkjsO1SOFQkJEQGXXXarMaYioqat18XT8+HFMmzYNXl5eWLZsGebMmYOLFy8iKioKycnJGD16tDFzEhmMXXbNy5Bbxj1ptVwomIjqj5mhD1i2bBlWr16NhIQEDB8+HN999x2GDx8OhaK8DvPz88M333yD1q1bGzsrUY0VlKhx4EL5WnZDOnnKnIYaQnhbF9hYKJGWV4K467kI8XGUOxIRNVEGn3n67LPPMGHCBFy5cgW//fYbRo4cqSucKrm7u+Orr74yWkgiQ+0/l4FStRatXazR3p1r2TUHluZKDPB3A8Cr7oiofhl85un8+fP3bGNhYYHJkyfXKhCRMVR+ed4f6AFJkmROQw1lSKAntsSlYsfpVMyJ9Jc7DhE1UQafeVq9ejXWr19fZfv69evx7bffGiUUUV2UabTYVbHOGbvsmpf7/N2hVEg4l1aAK5mFcschoibK4OJp0aJFcHV1rbLd3d0d//nPf4wSiqgujiZmIa9YDRcbC3SrWDSWmgcHa3OE+TkDYNcdEdUfg4unpKQk+Pn5Vdnu6+uLpKQko4S61fXr1/H444/DxcUFVlZWCA4OxvHjx3X7hRCYP38+vLy8YGVlhYiIiCpdi1lZWZg4cSLs7e3h6OiIKVOmoKCgQK/NyZMn0a9fP1haWsLHxweLFy82+rFQw9gRX36pekSAB5QKdtk1N5VX3e2IZ/FERPXD4OLJ3d0dJ0+erLI9NjYWLi4uRglVKTs7G3369IG5uTm2bt2K06dPY+nSpXBy+udswuLFi/HJJ59g1apVOHLkCGxsbBAZGam3dMzEiRMRHx+PqKgobNq0Cfv378fUqVN1+/Py8jBkyBD4+voiOjoaS5YswcKFC/HFF18Y9Xio/gkh9MY7UfNTOSHq8StZyCwokTkNETVJwkCvvfaa8PX1Fbt37xZqtVqo1Wqxa9cu4evrK1555RVDn+6u5s6dK/r27XvH/VqtVnh6eoolS5botuXk5AiVSiXWrFkjhBDi9OnTAoA4duyYrs3WrVuFJEni+vXrQgghVq5cKZycnERJSYnea/v7+9c4a25urgAgcnNza/wYMr64aznCd+4m0fH1reJmqVruOCSTYR/tF75zN4m1x5LkjkJEJq42398Gn3l6++23ERYWhsGDB8PKygpWVlYYMmQIBg0aZPQxTxs3bkT37t3x0EMPwd3dHV27dsV///tf3f7ExESkpqYiIiJCt83BwQFhYWE4fPgwAODw4cNwdHRE9+7ddW0iIiKgUChw5MgRXZv+/fvDwsJC1yYyMhIJCQnIzs426jFR/arsshvQwQ2W5kqZ05BchnTiQsFEVH8MLp4sLCywdu1anD17Fj/88AM2bNiAixcv4uuvv9YrPozh0qVL+Oyzz9C+fXts374dL7zwAl566SXdVX2pqeVflB4e+t0zHh4eun2pqalwd3fX229mZgZnZ2e9NtU9x62vcbuSkhLk5eXp3Uh+ulnFO7HLrjmr7LL983wGbpZyoWAiMi6D53mq1KFDB3To0MGYWarQarXo3r277oxW165dcerUKaxatUr2eaQWLVqEN998U9YMpC8pswhnU/OhVEgY1NH93g+gJivQyx4tHK1wPecm/jyfwSkriMioDD7zpNFo8NVXX2HChAmIiIjAoEGD9G7G5OXlhcDAQL1tAQEBuqv6PD3L/0FMS9M/NZ+Wlqbb5+npifT0dL39arUaWVlZem2qe45bX+N28+bNQ25uru529erV2hwiGdGOigVhe7Z2hqO1cc+CUuMiSZLu7BO77ojI2Awunl5++WW8/PLL0Gg0CAoKQkhIiN7NmPr06YOEhAS9befOnYOvry+A8nX0PD09sWvXLt3+vLw8HDlyBOHh4QCA8PBw5OTkIDo6Wtdm9+7d0Gq1CAsL07XZv38/ysrKdG2ioqLg7++vd2XfrVQqFezt7fVuJC922dGtKqcs2HU2HRouFExExmToqHQXFxexefNmQx9WK0ePHhVmZmbi3XffFefPnxc//PCDsLa2Ft9//72uzXvvvSccHR3F77//Lk6ePClGjx4t/Pz8xM2bN3Vthg4dKrp27SqOHDkiDhw4INq3by8ee+wx3f6cnBzh4eEhnnjiCXHq1Cnx008/CWtra/H555/XOCuvtpNXZkGJ8PvXJuE7d5O4mlUodxwyAaVqjQhesE34zt0kjlzKlDsOEZmoBrnazsLCAu3atTN+FVeNHj164Ndff8WaNWsQFBSEt99+Gx999BEmTpyoa/Paa6/hxRdfxNSpU9GjRw8UFBRg27ZtsLS01LX54Ycf0LFjRwwePBjDhw9H37599eZwcnBwwI4dO5CYmIjQ0FC88sormD9/vt5cUGTadp1Jg1aUj3Vp6WQtdxwyAeZKBQYHVHbdVX/hBxFRbUhCCIPOZy9duhSXLl3CihUruODqLfLy8uDg4IDc3Fx24cng2e+OI+p0GmZGtMfMiPq9kIEajy1xKZj2wwn4ulhj75yB/DeLiKqozfe3wVfbHThwAHv27MHWrVvRqVMnmJub6+3fsGGDoU9JVCc3SzX483wGAGBIIK+qon/07+AGCzMFrmQW4Xx6ATp42MkdiYiaAIOLJ0dHR4wdO7Y+shDVyv7zGSgu06KlkxUCvPjlSP+wVZmhT1sX7EnIQNTpNBZPRGQUBhdPq1evro8cRLV261p27Jah290f6Ik9CRnYEZ+K6fc1zHhNImraDB4wDpTPk7Rz5058/vnnyM/PBwAkJyejoKDAqOGI7kWt0WLXmYopCthlR9WICHSHJAGx13KRnHNT7jhE1AQYXDxduXIFwcHBGD16NKZPn46MjPKxJu+//z7mzJlj9IBEd3P8Sjayi8rgaG2OHq2rn5OLmjd3O0v08HUGAGw6mSxzGiJqCmo1SWb37t2RnZ0NKysr3faxY8fqTVZJ1BB2xJefdRrc0QNmylqdSKVm4IEu3gCA32NYPBFR3Rn8bfPnn3/i9ddfr7IIcOvWrXH9+nWjBSO6FyEEos6Uz99TuRQHUXWGB3vBTCEhPjkPFzM4vICI6sbg4kmr1UKjqbpK+bVr12BnxytZqOGcTc3H1aybUJkp0L+Dq9xxyIQ521igb/vyz8hGnn0iojoyuHgaMmQIPvroI919SZJQUFCABQsWYPjw4cbMRnRXlV12/dq7wdrC4AtHqZkZXdF1tzE2GQbODUxEpMfg4mnp0qU4ePAgAgMDUVxcjAkTJui67N5///36yEhUrR0VS25wIWCqifsDPaEyUyDxRiFOXc+TOw4RNWIG/3e9ZcuWiI2NxU8//YSTJ0+ioKAAU6ZMwcSJE/UGkBPVp+s5NxGfnAeFBAzu6C53HGoEbFVmiAj0wOaTKdgYex3BLR3kjkREjVSt+jrMzMzw+OOPGzsLUY1FxZefderu6wwXW5XMaaixeCDEG5tPpuCP2BTMGxYAhYKTqhKR4Qwunr777ru77p80aVKtwxDV1I6KWcXZZUeGGOjvBjtLM6TmFePo5Sz0auMidyQiaoQMLp5efvllvftlZWUoKiqChYUFrK2tWTxRvcspKsWRxCwAnKKADKMyU2JYkCfWHb+GjbHJLJ6IqFYMHjCenZ2tdysoKEBCQgL69u2LNWvW1EdGIj27z6ZDoxXw97CDr4uN3HGokXkgpAUAYEtcCkrVWpnTEFFjZJQpmdu3b4/33nuvylkpovoQxS47qoPwti5wtVUhp6gMBy5kyB2HiBoho61nYWZmhuRkTj5H9au4TIN958q/8LgQMNWGUiFhZGcvAJwwk4hqx+AxTxs3btS7L4RASkoKVqxYgT59+hgtGFF1Dl64gaJSDbwcLBHUwl7uONRIPdDFG98cuowdp9Nws1QDKwul3JGIqBExuHgaM2aM3n1JkuDm5oZBgwZh6dKlxspFVK3KWcXvD/SAJPEyc6qdrj6O8HG2wtWsm9h5Jg2jQrzljkREjYjBxZNWywGWJA+NVmDX2YrxTuyyozqQJAkPhHjj0z0XsTE2mcUTERnEaGOeiOrb30nZuFFQCjtLM4S1cZY7DjVylVfd7U1IR25RmcxpiKgxMfjM0+zZs2vcdtmyZYY+PdEdVU6MObijO8yVrPupbvw97eDvYYeEtHxsi0/BIz1ayR2JiBoJg4unv//+G3///TfKysrg7+8PADh37hyUSiW6deuma8fxKGRMQgjsqFiS5X522ZGRPNDFG0u2J2BjbDKLJyKqMYOLp1GjRsHOzg7ffvstnJycAJRPnPnUU0+hX79+eOWVV4wekuhCegEuZxbBQqnAAH83ueNQE/FASHnxdOhiJtLziuFubyl3JCJqBAzu+1i6dCkWLVqkK5wAwMnJCe+88w6vtqN6U9ll16edC2xVtVrPmqgKH2drdGvlCCGATSdT5I5DRI2EwcVTXl4eMjKqzsqbkZGB/Px8o4Qiul1ll92QTuyyI+N6oOJKu42xnDCTiGrG4OJp7NixeOqpp7BhwwZcu3YN165dwy+//IIpU6Zg3Lhx9ZGRmrnU3GLEXsuFJAGDA9zljkNNzIjO3lBIQMzVHFzJLJQ7DhE1AgYXT6tWrcKwYcMwYcIE+Pr6wtfXFxMmTMDQoUOxcuXK+shIzVzUmfIuu64+jnC345gUMi43OxX6tHMFAPzBs09EVAMGF0/W1tZYuXIlMjMzdVfeZWVlYeXKlbCx4Qr3ZHzssqP6VjlJ5u8xyRBCyJyGiExdrSfLSUlJQUpKCtq3bw8bGxv+g0P1IqeoFIcvZgIoX5KFqD5EdvKEhVKB8+kFOJvKsZtEdHcGF0+ZmZkYPHgwOnTogOHDhyMlpfwKlSlTpnCaAjK6HafToNYKdPS0Q1s3W7njUBPlYGWO+zqWT4HBgeNEdC8GF0+zZs2Cubk5kpKSYG1trdv+yCOPYNu2bUYNR7S54vLxEcFeMiehpq5yuZaN7LojonsweMKcHTt2YPv27WjZsqXe9vbt2+PKlStGC0aUU1SKgxduAACGd2bxRPVrcIA7bCyUuJ5zEyeSshHqy/UTiah6Bp95Kiws1DvjVCkrKwsqlcoooYgAYEc8u+yo4ViaKxFZcVHCxhh23RHRnRlcPPXr1w/fffed7r4kSdBqtVi8eDHuu+8+o4aj5m1zHLvsqGGN6lJ+1d3muBSoNVqZ0xCRqTK4227x4sUYPHgwjh8/jtLSUrz22muIj49HVlYWDh48WB8ZqRlilx3JoW87VzjbWOBGQSkOXcxE/w5cR5GIqjL4zFNQUBDOnTuHvn37YvTo0SgsLMS4cePw999/o23btvWRkZohdtmRHMyVCgwPrui641V3RHQHBp15Kisrw9ChQ7Fq1Sr83//9X31lIsKmii67kTzrRA3sgZAW+P6vJGw/lYp3xgTB0lwpdyQiMjEGnXkyNzfHyZMn6ysLEQAgu7AUhyq77DjeiRpYd18neDtYIr9Ejb0J6XLHISITZHC33eOPP46vvvqqPrIQAQB2nE6FWisQ4GWPNuyyowamUEi65VrYdUdE1TF4wLharcbXX3+NnTt3IjQ0tMp6dsuWLTNaOGqeNseVr2U3Iphr2ZE8RoV44/P9l7DrTDryi8tgZ2kudyQiMiE1Kp5OnjyJoKAgKBQKnDp1Ct26dQMAnDt3Tq+dJEnGT0jNSnbhLVfZscuOZNLJ2x5t3WxwMaMQO+LTMD605b0fRETNRo2Kp65duyIlJQXu7u64cuUKjh07BhcXl/rORs3QjtOp0LDLjmQmSRIeCGmBD3eew8bYZBZPRKSnRmOeHB0dkZiYCAC4fPkytFpOHkf1Y9NJXmVHpuGBigkzD1y4gcyCEpnTEJEpqdGZp/Hjx2PAgAHw8vKCJEno3r07lMrqL9+9dOmSUQNS85FdWD4xIcAuO5Kfn6sNOrd0wMlrudgSl4InwlvLHYmITESNiqcvvvgC48aNw4ULF/DSSy/h2WefhZ2dXX1no2Zme3x5l12glz38XG3u/QCievZAiDdOXsvFxthkFk9EpFPjq+2GDh0KAIiOjsbLL7/M4omMTreWHbvsyESM7OyNd7ecwbHL2biecxMtHK3kjkREJsDgeZ5Wr17NwomMLuuWLjsuBEymwtPBEmF+zgCAPzjnExFVMLh4IqoPOyq67Dp526M1u+zIhDwQ0gIAsDGGxRMRlWPxRCahssuOA8XJ1AwL8oS5UsLplDwkpObLHYeITACLJ5Idu+zIlDnZWGBwRw8AwA9HrsichohMQaMqnt577z1IkoSZM2fqthUXF2P69OlwcXGBra0txo8fj7S0NL3HJSUlYcSIEbC2toa7uzteffVVqNVqvTZ79+5Ft27doFKp0K5dO3zzzTcNcEQE/HOVHbvsyFQ9Ee4LANhw4joKStT3aE1ETV2jKZ6OHTuGzz//HJ07d9bbPmvWLPzxxx9Yv3499u3bh+TkZIwbN063X6PRYMSIESgtLcWhQ4fw7bff4ptvvsH8+fN1bRITEzFixAjcd999iImJwcyZM/HMM89g+/btDXZ8zdkWXmVHJq53Wxe0cbNBQYkav/59Xe44RCSzRlE8FRQUYOLEifjvf/8LJycn3fbc3Fx89dVXWLZsGQYNGoTQ0FCsXr0ahw4dwl9//QUA2LFjB06fPo3vv/8eXbp0wbBhw/D222/j008/RWlpKQBg1apV8PPzw9KlSxEQEIAZM2bgwQcfxIcffijL8TYn7LKjxkCSJDzRq/zs0/eHr0AIIXMiIpJToyiepk+fjhEjRiAiIkJve3R0NMrKyvS2d+zYEa1atcLhw4cBAIcPH0ZwcDA8PDx0bSIjI5GXl4f4+Hhdm9ufOzIyUvcc1SkpKUFeXp7ejQxX2WUX1MIevi7ssiPTNa5bS1iZK5GQlo+jiVlyxyEiGZl88fTTTz/hxIkTWLRoUZV9qampsLCwgKOjo952Dw8PpKam6trcWjhV7q/cd7c2eXl5uHnzZrW5Fi1aBAcHB93Nx8enVsfX3G0+yavsqHFwsDLHmK7l69397y8OHCdqzky6eLp69Spefvll/PDDD7C0tJQ7jp558+YhNzdXd7t69arckRqdzIISHL7ELjtqPB6v6LrbdioV6fnFMqchIrmYdPEUHR2N9PR0dOvWDWZmZjAzM8O+ffvwySefwMzMDB4eHigtLUVOTo7e49LS0uDp6QkA8PT0rHL1XeX9e7Wxt7eHlVX1yzGoVCrY29vr3cgw2+PT2GVHjUonbweE+jpBrRVYe5T/YSJqrky6eBo8eDDi4uIQExOju3Xv3h0TJ07U/Wxubo5du3bpHpOQkICkpCSEh4cDAMLDwxEXF4f09HRdm6ioKNjb2yMwMFDX5tbnqGxT+RxUPzbHlc/YPCLYW+YkRDVXOXD8x6NJUGu0MqchIjnUeGFgOdjZ2SEoKEhvm42NDVxcXHTbp0yZgtmzZ8PZ2Rn29vZ48cUXER4ejl69egEAhgwZgsDAQDzxxBNYvHgxUlNT8frrr2P69OlQqVQAgOeffx4rVqzAa6+9hqeffhq7d+/GunXrsHnz5oY94GYks6AEh3mVHTVCw4I98fYmC6TkFmPnmXQMDfKUOxIRNTCTPvNUEx9++CFGjhyJ8ePHo3///vD09MSGDRt0+5VKJTZt2gSlUonw8HA8/vjjmDRpEt566y1dGz8/P2zevBlRUVEICQnB0qVL8eWXXyIyMlKOQ2oWtsWnQiuA4BYOaOViLXccohpTmSnxSI/yC0S+58BxomZJEpywxCjy8vLg4OCA3Nxcjn+qgYlf/oWDFzIxd2hHvDCwrdxxiAxyLbsI/RbvgRDArlcGoK2brdyRiKiWavP93ejPPFHjc4NddtTItXSyxuCO7gB49omoOWLxRA1uO7vsqAl4Irw1AODn6GsoKuV6d0TNCYsnanCVE2NyLTtqzPq1c4WvizXyi9X4PSZZ7jhE1IBYPFGDulFQgr84MSY1AQqFhMfDyqct+B/XuyNqVlg8UYPadqq8y65zSwf4OLPLjhq3h7q3hMpMgdMpeTiRlCN3HCJqICyeqEFtiavosuNZJ2oCHK0t8EBI+SSvHDhO1HyweKIGc2uXHRcCpqbiifDyrrvNJ1Nwo6BE5jRE1BBYPFGDqeyyC2GXHTUhnVs6IqSlA0o1Wqw7zvXuiJoDFk/UYCqvsuNZJ2pqKqct+OGvJGi0HDhO1NSxeKIGkZFfgiOJ7LKjpmlkZy84Wpvjes5N7Dmbfu8HEFGjxuKJGkTlWnbssqOmyNJciYe7l6939z8OHCdq8lg8UYPYwokxqYmbGNYKkgTsO5eBK5mFcschonrE4onq3a1ddsOCWDxR0+TrYoMBHdwAcNoCoqaOxRPVO12XnY8ju+yoSXuiV/m0BeuOX0NxmUbmNERUX1g8Ub3bfLJ83a8RwZ4yJyGqXwP93dHC0Qq5N8vwRyzXuyNqqlg8Ub26UVCCo4lZANhlR02fUiFhYq9WANh1R9SUsXiierX7TDq0AghuwavsqHl4pLsPLJQKxF7LRezVHLnjEFE9YPFE9WrH6VQAwP2BHjInIWoYLrYq3VWlnLaAqGli8UT1pqhUjT/P3wAADOnE4omaj8crBo7/EZuM7MJSmdMQkbGxeKJ6s//cDZSotfBxtoK/h53ccYgaTLdWjgj0skeJWov10VzvjqipYfFE9SbqdBoAYEigJyRJkjkNUcORJAmTwsvPPn3/VxK0XO+OqElh8UT1Qq3RYtfZ8uKJ452oOXqgizfsLM2QlFWE/ecz5I5DREbE4onqxfEr2cgpKoOTtTm6+zrJHYeowVlbmOHB0JYAgP8d5sBxoqaExRPVix3x5WedBnX0gJmSHzNqnioHju9OSMfVrCKZ0xCRsfBbjYxOCKGbooBX2VFz1tbNFn3buUII4MejSXLHISIjYfFERnc2NR/Xsm9CZaZAv/aucschklXl2ae1x66iRM317oiaAhZPZHSVXXb92rvB2sJM5jRE8ooIcIeXgyWyCkux7vg1ueMQkRGweCKjizrDLjuiSmZKBZ4f0BYA8PHO8ygsUcuciIjqisUTGdX1nJs4dT0PCgkY3NFd7jhEJuGxnq3QytkaNwpK8PWBRLnjEFEdsXgio9pZMTFmd19nuNiqZE5DZBoszBSYE+kPAPh8/yVkFpTInIiI6oLFExkVFwImqt7IYC8Et3BAQYkay3dfkDsOEdUBiycymtyiMvx1KQsAiyei2ykUEv41rCMA4IcjV5CUyXmfiBorFk9kNHsS0qHRCnTwsEVrVxu54xCZnD7tXNGvvSvKNAJLoxLkjkNEtcTiiYxGNzFmoKfMSYhM19yh5Weffo9JxqnruTKnIaLaYPFERlFcpsG+hPLFT9llR3RnQS0cMLqLNwDg/W1nZU5DRLXB4omM4vDFTBSWauBpb4ngFg5yxyEyaXOG+MNcKeHP8zdw4PwNueMQkYFYPJFR7KiYoiAi0B0KhSRzGiLT5uNsrVu25b1tZ6DVCpkTEZEhWDxRnWm1AjvPlBdPHO9EVDMz7msHW5UZTl3Pw6a4FLnjEJEBWDxRncVcy0FGfgnsVGbo1cZF7jhEjYKLrQrP9W8DAPhgewJK1VqZExFRTbF4ojqrXAh4YEd3WJjxI0VUU1P6+cHNToWkrCKsOZokdxwiqiF+01Gd/TNFAa+yIzKEtYUZZka0BwB8sus88ovLZE5ERDXB4onq5EJ6AS5lFMJcKWGgv5vccYganYe7+6CNqw0yC0vx3z+5aDBRY8DiieokquIqu/C2rrCzNJc5DVHjY65U4NWKRYO//PMS0vOLZU5ERPfC4onqJIoLARPV2dAgT4T4OKKoVIPlu7hoMJGpY/FEtZaeX4y/r+YAAO4PYPFEVFuSJGFexaLBa44mIfFGocyJiOhuWDxRre06kw4hgJCWDvB0sJQ7DlGj1quNCwZ1dIdaK/DBDi4aTGTKWDxRre2Ir7jKrhMnxiQyhteG+kOSgM0nUxBbcVaXiEwPiyeqlYISNQ5eyATA8U5ExtLR0x7jurYEACzaegZCcNkWIlPE4olqZf+5DJRqtGjtYo327rZyxyFqMmYP6QALMwX+upSFfecy5I5DRNVg8US1Utlld3+gBySJCwETGUsLRytMDq9YNHjrWS4aTGSCWDyRwco0Wuw+mw6A452I6sP0+9rBztIMZ1Pz8XvsdbnjENFtTLp4WrRoEXr06AE7Ozu4u7tjzJgxSEjQvwqluLgY06dPh4uLC2xtbTF+/HikpaXptUlKSsKIESNgbW0Nd3d3vPrqq1Cr1Xpt9u7di27dukGlUqFdu3b45ptv6vvwGq2jiVnIK1bDxcYC3Vo5yR2HqMlxtLbAtIHtAAAfbD+HErVG5kREdCuTLp727duH6dOn46+//kJUVBTKysowZMgQFBb+MwfKrFmz8Mcff2D9+vXYt28fkpOTMW7cON1+jUaDESNGoLS0FIcOHcK3336Lb775BvPnz9e1SUxMxIgRI3DfffchJiYGM2fOxDPPPIPt27c36PE2FpWzig8OcIdSwS47ovrwVJ/W8LS3xPWcm/jf4StyxyGiW0iiEV3OkZGRAXd3d+zbtw/9+/dHbm4u3Nzc8OOPP+LBBx8EAJw9exYBAQE4fPgwevXqha1bt2LkyJFITk6Gh0f5VWGrVq3C3LlzkZGRAQsLC8ydOxebN2/GqVOndK/16KOPIicnB9u2batRtry8PDg4OCA3Nxf29vbGP3gTIYRAn/d2Izm3GF9O6o4IXmlHVG/WHkvC3F/i4Ghtjv2v3Qd7LoFEZHS1+f426TNPt8vNzQUAODs7AwCio6NRVlaGiIgIXZuOHTuiVatWOHz4MADg8OHDCA4O1hVOABAZGYm8vDzEx8fr2tz6HJVtKp+jOiUlJcjLy9O7NQfxyXlIzi2GlbkSfdu7yh2HqEkb360l2rvbIqeoDJ/vuyh3HCKq0GiKJ61Wi5kzZ6JPnz4ICgoCAKSmpsLCwgKOjo56bT08PJCamqprc2vhVLm/ct/d2uTl5eHmzZvV5lm0aBEcHBx0Nx8fnzofY2Owo6LLrn8HV1iaK2VOQ9S0mSkVeG1o+bItXx1IRGouFw0mMgWNpniaPn06Tp06hZ9++knuKACAefPmITc3V3e7evWq3JEaxD9TFPAqO6KGEBHgju6+Tigu0+LVn2M5dQGRCWgUxdOMGTOwadMm7NmzBy1bttRt9/T0RGlpKXJycvTap6WlwdPTU9fm9qvvKu/fq429vT2srKyqzaRSqWBvb693a+quZhXhbGo+FBIwuKO73HGImgVJkrBoXDAszRX48/wNfL7/ktyRiJo9ky6ehBCYMWMGfv31V+zevRt+fn56+0NDQ2Fubo5du3bptiUkJCApKQnh4eEAgPDwcMTFxSE9PV3XJioqCvb29ggMDNS1ufU5KttUPgeVq+yy69HaGU42FjKnIWo+2nvY4c0HOgEAPtiRgOgr2TInImreTLp4mj59Or7//nv8+OOPsLOzQ2pqKlJTU3XjkBwcHDBlyhTMnj0be/bsQXR0NJ566imEh4ejV69eAIAhQ4YgMDAQTzzxBGJjY7F9+3a8/vrrmD59OlQqFQDg+eefx6VLl/Daa6/h7NmzWLlyJdatW4dZs2bJduymKOo0FwImksvD3X0wKsQbGq3AS2v+Rm5RmdyRiJotky6ePvvsM+Tm5mLgwIHw8vLS3dauXatr8+GHH2LkyJEYP348+vfvD09PT2zYsEG3X6lUYtOmTVAqlQgPD8fjjz+OSZMm4a233tK18fPzw+bNmxEVFYWQkBAsXboUX375JSIjIxv0eE1ZdmEpjiZmAQCGcHoCogYnSRL+MzYIrZytcT3nJv614SQXDiaSSaOa58mUNfV5nn6OvoY562PR0dMO22b2lzsOUbN18loOxn92CGUagXfGBOHxXr5yRyJq1Jr8PE8kH3bZEZmGzi0dMbdi+oK3Np3GmZTmMccckSlh8UT3VFymwf5zNwCwy47IFDzdxw/3+buhVK3FjB9PoKhUfe8HEZHRsHiiezpw/gZulmng7WCJTt5Nr0uSqLFRKCR88FAIPOxVuJhRiIUb4+WORNSssHiie9pxunJiTA9IEhcCJjIFLrYqfPRIV0gSsO74Nfwec13uSETNBosnuiuNVmDXmfI5sjjeici0hLd1wYuD2gMA/r0hDpdvFMqciKh5YPFEd7U9PhWZhaVwsDJHTz9nueMQ0W1eGtQOPVs7o7BUgxlrTqBErZE7ElGTx+KJ7kirFfhk13kAwOTerWGu5MeFyNSYKRX4+LEucLQ2x6nreVi8LUHuSERNHr8N6Y6izqThbGo+bFVmeLpPa7njENEdeDlY4YMHQwAAXx1IxK4zafd4BBHVBYsnqpYQt5518oWjNdeyIzJlEYEeeKriPzlz1sciJfemvIGImjAWT1StPQnpiE/Og7WFElP6tpE7DhHVwL+GdURQC3tkF5Xh5Z9ioNFyAQmi+sDiiaoQQuDjXRcAAE/08oWzDc86ETUGKjMllj/WDTYWShxNzNKdPSYi42LxRFXsP38DsVdzYGmuwDP9eNaJqDHxc7XBu2ODAQDLd5/H4YuZMicianpYPJGeW8c6TejpCzc7lcyJiMhQY7q2wEOhLaEVwMy1fyOrsFTuSERNCosn0nP4Yiair2TDwkyB5wbwrBNRY/Xm6E5o42aDtLwSzFkfCyE4/onIWFg8kZ6PK846PdbDBx72ljKnIaLasrYww6cTusHCTIHdZ9Px1YFEuSMRNRksnkjnyKVMHEnMgrlSwnMD2sodh4jqKMDLHm+MDAQAvL/tLI5fzpI5EVHTwOKJdJbvLr/C7qHuPvB2tJI5DREZw+NhrTAsyBNlGoHJXx/FX5c4gJyorlg8EQAg+ko2Dly4ATOFhBd41omoyZAkCUsfDkGfdi4oLNVg8tdHsTchXe5YRI0aiycCUH5JMwCM69YCPs7WMqchImOytjDDV5N7YFBHd5SotXj2u+PYdipV7lhEjRaLJ0Ls1RzsTciAUiFh+n3t5I5DRPXA0lyJVY+HYkSwF8o0AtN/PIHf/r4udyyiRonFE+nOOo3u4g1fFxuZ0xBRfbEwU+DjR7tgfLeW0GgFZq2LwZqjSXLHImp0WDw1c6eu52LnmXRIEnjWiagZMFMqsOTBzni8VysIAczbEMdpDIgMxOKpmVtRcYXdqM7eaOtmK3MaImoICoWEt0cHYWr/8olw3950Git2cx08oppi8dSMnU3Nw7b4VEgSMGMQzzoRNSeSJGHesI6YGdEeAPDBjnNYvO0sZyInqgEWT81Y5bxOw4O80MHDTuY0RNTQJEnCzIgO+PfwjgCAlXsv4s0/TkOrZQFFdDcsnpqpC+n52BKXAoBnnYiau6n92+LtMUEAgG8OXca8DXHQsIAiuiMWT83Uit0XIAQwJNADAV72cschIpk90csXHzwUAoUErD1+FbPWxqBMo5U7FpFJYvHUDCXeKMTG2GQAwEuD28uchohMxYOhLbH8sW4wU0jYGJuMaT+cQIlaI3csIpPD4qkZ+nTPBWgFMKijO4JaOMgdh4hMyIjOXvj8iVBYmCkQdToNz3x7HDdLWUAR3YrFUzOTlFmEXytmFX6RY52IqBqDAzyw+skesDJX4s/zNzD566PILy6TOxaRyWDx1Mys3HsBGq1A/w5u6NrKSe44RGSi+rRzxf+m9ISdygxHL2fh8S+PILuwVO5YRCaBxVMzci27CL+cuAYAeIlnnYjoHrq3dsaPz/aCo7U5Yq/lYvgnf+LQxRtyxyKSHYunZmTVvoso0wj0buuC7q2d5Y5DRI1AcEsHrHsuHH6uNkjJLcbEL49g0ZYzHEhOzRqLp2YiJfcm1h2rOOvEK+yIyAAdPOyw6cW+eKynD4QAPt9/CWM/PYTzaflyRyOSBYunZuLzfZdQqtGiZ2tn9GrjInccImpkbFRmWDSuM754IhRO1uY4nZKHkcsP4NtDl7mkCzU7LJ6agfS8Yqw5mgSAZ52IqG6GdPLE9pn9MaCDG0rUWizYGI8nVx9Den6x3NGIGgyLp2bgi/2XUKLWolsrR/Rpx7NORFQ37vaW+OapHnjzgU5QmSmw71wGhn70J6JOp8kdjahBsHhq4uKTc/H9kSsAgBcHt4ckSTInIqKmQJIkTO7dGn+82BcBXvbIKizFs98dx7wNcSgqVcsdj6hesXhqwn6PuY7xnx1CcZkWob5OGNjBTe5IRNTEdPCww2/Te2Nq/zaQJGDN0SSM+OQAYq/myB2NqN6weGqC1Bot3t18Gi//FIPiMi36d3DD15N78KwTEdULlZkS/x4egB+mhMHLwRKJNwox/rNDWLH7PDRaDianpofFUxOTXViKyauP4r9/JgIApg1si9VP9oCDtbnMyYioqevdzhXbXu6PEZ29oNYKfLDjHB75/DCuZhXJHY3IqFg8NSHxybkYteIADl7IhLWFEisndsNrQztCqeAZJyJqGA7W5ljxWFcsezgEtiozHL+SjWEf/4kNJ65xSgNqMiTBT7NR5OXlwcHBAbm5ubC3t2/w1/895jrm/nISxWVa+LpY44snusPf067BcxARVbqaVYRZa2Nw/Eo2ACC4hQNGdPbC8CAvtHKxljkdUbnafH+zeDISuYontUaL97ed1XXT9e/ghuWPdmU3HRGZBLVGi1X7LuLjXedRpvnn6yaohT2GBXlheLAX/FxtZExIzR2LJxnJUTxlF5ZixpoTOHghE0D5+KZXhvizm46ITM6NghJsj0/F1rhUHL6UqTeQPMDLHsODPDG8sxfautnKmJKaIxZPMmro4ik+ORfP/S8a17JvwtpCiQ8eCsHwYK96f10iorrKLChB1Ok0bI5LwaGL+oWUv4cdhgV7YkSwF9p7cOgB1T8WTzJqyOKJ45uIqKnILixF1Ok0bDmVgoMXbuh17bVzt8XwYC8MD/aEv4cdp1uhesHiSUYNUTypNVq8t/UsvjzA8U1E1PTkFpUh6kwatsal4M/zN1Cq0er2tXG1QZ92rgj0tkeglz38Pe1gaa6UMS01FSyeZFTfxVNWYSle5PgmImom8orLsOtMGrbEpWLfuQyUqrV6+xUS0NbNFp287SsKKgcEetvD2cZCpsTUWLF4klF9Fk/xybmY+l00rudwfBMRNT/5xWXYdy4DcddycTolD/HJecgqLK22rae9JQK97cuLKq/ywsrHyRoK/keT7oDFk4zqq3jafDIFr6yP4fgmIqIKQgik55fgdHIeTqfk4XRyHuKTc3E5s/qZzG1VZgjwskNrFxu426vgbmcJdzuV7mc3OxW7AJux2nx/m9Vzpkbn008/xZIlS5CamoqQkBAsX74cPXv2lC2PnaUZStVaDOjghk84vomICJIkwcPeEh72lrivo7tue0GJGmdT/imoTqfk4WxqPgpK1Dh2ORvHLmff8TntLc3gbl9RVNmpdD+72VUUW/YquNqoYGdpxrNYxDNPt1q7di0mTZqEVatWISwsDB999BHWr1+PhIQEuLu73/Wx9dlt99elTPRo7czxTUREBlJrtLh0oxCnk/NwPecm0vOKkZ5fUnErRlpeSZXxVHejVEhwtDKHo7U5nKwt4GhtASdrczjb/PNz5Z9ONhYVbcxhruRqaKaK3XZ1FBYWhh49emDFihUAAK1WCx8fH7z44ov417/+ddfHyr08CxERGU4IgbybaqTnF+sKqvS8kn8KrLxiZFT8XFCirvXr2KnMYGtpBjOlBHOFAmZKCUqFAuZKCWYKCWbKyp//+dNMKcFcqdDtN1NIUCokKCQJSgWgqPxZkqBQlP9Zub3858q2/+w3q3g9paL8ucv/LM9ipqjMUv76ylt+rnyM3LNFWJkr4WKrMupzstuuDkpLSxEdHY158+bptikUCkRERODw4cNV2peUlKCkpER3Py8vr0FyEhGR8UiSBAdrczhYm99zUs7iMg1yisqQXVSK7KJS3c85RWXIKqy6LbuoFLk3yyAEkF+iRn4dii8q90CINz55rKvcMVg8Vbpx4wY0Gg08PDz0tnt4eODs2bNV2i9atAhvvvlmQ8UjIiKZWZor4emghKeDZY0fo9EK5N4sL6SKSjQo02qh1gioNVqUaSv+1AhotAJqbfnPt+5Ta8Q/j9EKaLUCGlHx560/CwGNFvr7RXkbbcWfGi2g0Wqh1gqoK16zTKst/1Mjqu7TaCtyVWTRyt9RZaY0jeErLJ5qad68eZg9e7bufl5eHnx8fGRMREREpkapkOBsY8H5p5oYFk8VXF1doVQqkZaWprc9LS0Nnp6eVdqrVCqoVMbtdyUiIiLTx+H/FSwsLBAaGopdu3bptmm1WuzatQvh4eEyJiMiIiJTwjNPt5g9ezYmT56M7t27o2fPnvjoo49QWFiIp556Su5oREREZCJYPN3ikUceQUZGBubPn4/U1FR06dIF27ZtqzKInIiIiJovzvNkJJzniYiIqPGpzfc3xzwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDLsxhJ5UTteXl5MichIiKimqr83jZkwRUWT0aSn58PAPDx8ZE5CRERERkqPz8fDg4ONWrLte2MRKvVIjk5GXZ2dpAkSW9fXl4efHx8cPXqVa57Vwt8/+qO72Hd8P2rO76HdcP3r+7u9B4KIZCfnw9vb28oFDUbzcQzT0aiUCjQsmXLu7axt7fnh74O+P7VHd/DuuH7V3d8D+uG71/dVfce1vSMUyUOGCciIiIyAIsnIiIiIgOweGoAKpUKCxYsgEqlkjtKo8T3r+74HtYN37+643tYN3z/6s6Y7yEHjBMREREZgGeeiIiIiAzA4omIiIjIACyeiIiIiAzA4omIiIjIACye6tmnn36K1q1bw9LSEmFhYTh69KjckRqNhQsXQpIkvVvHjh3ljmWy9u/fj1GjRsHb2xuSJOG3337T2y+EwPz58+Hl5QUrKytERETg/Pnz8oQ1Ufd6D5988skqn8mhQ4fKE9YELVq0CD169ICdnR3c3d0xZswYJCQk6LUpLi7G9OnT4eLiAltbW4wfPx5paWkyJTYtNXn/Bg4cWOUz+Pzzz8uU2PR89tln6Ny5s24izPDwcGzdulW331ifPxZP9Wjt2rWYPXs2FixYgBMnTiAkJASRkZFIT0+XO1qj0alTJ6SkpOhuBw4ckDuSySosLERISAg+/fTTavcvXrwYn3zyCVatWoUjR47AxsYGkZGRKC4ubuCkpute7yEADB06VO8zuWbNmgZMaNr27duH6dOn46+//kJUVBTKysowZMgQFBYW6trMmjULf/zxB9avX499+/YhOTkZ48aNkzG16ajJ+wcAzz77rN5ncPHixTIlNj0tW7bEe++9h+joaBw/fhyDBg3C6NGjER8fD8CInz9B9aZnz55i+vTpuvsajUZ4e3uLRYsWyZiq8ViwYIEICQmRO0ajBED8+uuvuvtarVZ4enqKJUuW6Lbl5OQIlUol1qxZI0NC03f7eyiEEJMnTxajR4+WJU9jlJ6eLgCIffv2CSHKP3Pm5uZi/fr1ujZnzpwRAMThw4flimmybn//hBBiwIAB4uWXX5YvVCPk5OQkvvzyS6N+/njmqZ6UlpYiOjoaERERum0KhQIRERE4fPiwjMkal/Pnz8Pb2xtt2rTBxIkTkZSUJHekRikxMRGpqal6n0cHBweEhYXx82igvXv3wt3dHf7+/njhhReQmZkpdySTlZubCwBwdnYGAERHR6OsrEzvc9ixY0e0atWKn8Nq3P7+Vfrhhx/g6uqKoKAgzJs3D0VFRXLEM3kajQY//fQTCgsLER4ebtTPHxcGric3btyARqOBh4eH3nYPDw+cPXtWplSNS1hYGL755hv4+/sjJSUFb775Jvr164dTp07Bzs5O7niNSmpqKgBU+3ms3Ef3NnToUIwbNw5+fn64ePEi/v3vf2PYsGE4fPgwlEql3PFMilarxcyZM9GnTx8EBQUBKP8cWlhYwNHRUa8tP4dVVff+AcCECRPg6+sLb29vnDx5EnPnzkVCQgI2bNggY1rTEhcXh/DwcBQXF8PW1ha//vorAgMDERMTY7TPH4snMlnDhg3T/dy5c2eEhYXB19cX69atw5QpU2RMRs3Vo48+qvs5ODgYnTt3Rtu2bbF3714MHjxYxmSmZ/r06Th16hTHKdbSnd6/qVOn6n4ODg6Gl5cXBg8ejIsXL6Jt27YNHdMk+fv7IyYmBrm5ufj5558xefJk7Nu3z6ivwW67euLq6gqlUlllFH9aWho8PT1lStW4OTo6okOHDrhw4YLcURqdys8cP4/G1aZNG7i6uvIzeZsZM2Zg06ZN2LNnD1q2bKnb7unpidLSUuTk5Oi15+dQ353ev+qEhYUBAD+Dt7CwsEC7du0QGhqKRYsWISQkBB9//LFRP38snuqJhYUFQkNDsWvXLt02rVaLXbt2ITw8XMZkjVdBQQEuXrwILy8vuaM0On5+fvD09NT7PObl5eHIkSP8PNbBtWvXkJmZyc9kBSEEZsyYgV9//RW7d++Gn5+f3v7Q0FCYm5vrfQ4TEhKQlJTEzyHu/f5VJyYmBgD4GbwLrVaLkpISo37+2G1Xj2bPno3Jkyeje/fu6NmzJz766CMUFhbiqaeekjtaozBnzhyMGjUKvr6+SE5OxoIFC6BUKvHYY4/JHc0kFRQU6P3vMzExETExMXB2dkarVq0wc+ZMvPPOO2jfvj38/PzwxhtvwNvbG2PGjJEvtIm523vo7OyMN998E+PHj4enpycuXryI1157De3atUNkZKSMqU3H9OnT8eOPP+L333+HnZ2dbhyJg4MDrKys4ODggClTpmD27NlwdnaGvb09XnzxRYSHh6NXr14yp5ffvd6/ixcv4scff8Tw4cPh4uKCkydPYtasWejfvz86d+4sc3rTMG/ePAwbNgytWrVCfn4+fvzxR+zduxfbt2837ufPuBcE0u2WL18uWrVqJSwsLETPnj3FX3/9JXekRuORRx4RXl5ewsLCQrRo0UI88sgj4sKFC3LHMll79uwRAKrcJk+eLIQon67gjTfeEB4eHkKlUonBgweLhIQEeUObmLu9h0VFRWLIkCHCzc1NmJubC19fX/Hss8+K1NRUuWObjOreOwBi9erVujY3b94U06ZNE05OTsLa2lqMHTtWpKSkyBfahNzr/UtKShL9+/cXzs7OQqVSiXbt2olXX31V5ObmyhvchDz99NPC19dXWFhYCDc3NzF48GCxY8cO3X5jff4kIYSoa6VHRERE1FxwzBMRERGRAVg8ERERERmAxRMRERGRAVg8ERERERmAxRMRERGRAVg8ERERERmAxRMRERGRAVg8ERHdZu/evZAkqcoaWMY2cOBAzJw5s15fo6Ya6piJmgIWT0RkEFP6wjeG6o6nd+/eSElJgYODgzyh6llT+x0SNTQWT0RkdEIIqNVquWPUmoWFBTw9PSFJktxRiMgEsXgiohp78sknsW/fPnz88ceQJAmSJOHy5cu6Lp+tW7ciNDQUKpUKBw4cwMWLFzF69Gh4eHjA1tYWPXr0wM6dO/Wes3Xr1vjPf/6Dp59+GnZ2dmjVqhW++OIL3f7S0lLMmDEDXl5esLS0hK+vLxYtWqTbv2zZMgQHB8PGxgY+Pj6YNm0aCgoK9F7j4MGDGDhwIKytreHk5ITIyEhkZ2ff83hu7cL65Zdf0KlTJ6hUKrRu3RpLly416DhqoqSkBHPmzEGLFi1gY2ODsLAw7N27V7f/m2++gaOjI7Zv346AgADY2tpi6NChSElJ0bVRq9V46aWX4OjoCBcXF8ydOxeTJ0/WLQB9p2OuFB0dje7du8Pa2hq9e/dGQkKCQcdA1CwYbTU+ImrycnJyRHh4uHj22WdFSkqKSElJEWq1WregbufOncWOHTvEhQsXRGZmpoiJiRGrVq0ScXFx4ty5c+L1118XlpaW4sqVK7rn9PX1Fc7OzuLTTz8V58+fF4sWLRIKhUKcPXtWCCHEkiVLhI+Pj9i/f7+4fPmy+PPPP8WPP/6oe/yHH34odu/eLRITE8WuXbuEv7+/eOGFF3T7//77b6FSqcQLL7wgYmJixKlTp8Ty5ctFRkbGPY8nOztbCCHE8ePHhUKhEG+99ZZISEgQq1evFlZWVnoL3t7rOKozYMAA8fLLL+vuP/PMM6J3795i//794sKFC2LJkiVCpVKJc+fOCSGEWL16tTA3NxcRERHi2LFjIjo6WgQEBIgJEybonuOdd94Rzs7OYsOGDeLMmTPi+eefF/b29mL06NE1+h2GhYWJvXv3ivj4eNGvXz/Ru3dvgz8nRE0diyciMsjtX/hCCN0X72+//XbPx3fq1EksX75cd9/X11c8/vjjuvtarVa4u7uLzz77TAghxIsvvigGDRoktFptjfKtX79euLi46O4/9thjok+fPrU6nsriacKECeL+++/Xa/Pqq6+KwMDAGh/HvV77ypUrQqlUiuvXr+u1GTx4sJg3b54Qorx4AiAuXLig2//pp58KDw8P3X0PDw+xZMkS3X21Wi1atWqlK57udcw7d+7Ubdu8ebMAIG7evHnHYyBqjthtR0RG0717d737BQUFmDNnDgICAuDo6AhbW1ucOXMGSUlJeu06d+6s+1mSJHh6eiI9PR1AeTdTTEwM/P398dJLL2HHjh16j925cycGDx6MFi1awM7ODk888QQyMzNRVFQEAIiJicHgwYPrdFxnzpxBnz599Lb16dMH58+fh0ajqdFx3EtcXBw0Gg06dOgAW1tb3W3fvn24ePGirp21tTXatm2ru+/l5aV7jdzcXKSlpaFnz566/UqlEqGhoTU+1luPwcvLCwBqfAxEzYWZ3AGIqOmwsbHRuz9nzhxERUXhgw8+QLt27WBlZYUHH3wQpaWleu3Mzc317kuSBK1WCwDo1q0bEhMTsXXrVuzcuRMPP/wwIiIi8PPPP+Py5csYOXIkXnjhBbz77rtwdnbGgQMHMGXKFJSWlsLa2hpWVlb1e9A1PI57KSgogFKpRHR0NJRKpd4+W1vbu76GEKKWiau69fkrB8zX9BiImgueeSIig1hYWOidbbmbgwcP4sknn8TYsWMRHBwMT09PvcHJNWVvb49HHnkE//3vf7F27Vr88ssvyMrKQnR0NLRaLZYuXYpevXqhQ4cOSE5O1nts586dsWvXrjodT0BAAA4ePFjl2Dp06FCl0Kmtrl27QqPRID09He3atdO7eXp61ug5HBwc4OHhgWPHjum2aTQanDhxQq+dIb9DIqqKZ56IyCCtW7fGkSNHcPnyZdja2sLZ2fmObdu3b48NGzZg1KhRkCQJb7zxhsFnMZYtWwYvLy907doVCoUC69evh6enJxwdHdGuXTuUlZVh+fLlGDVqFA4ePIhVq1bpPX7evHkIDg7GtGnT8Pzzz8PCwgJ79uzBQw89BFdX1xodzyuvvIIePXrg7bffxiOPPILDhw9jxYoVWLlypUHHcjcdOnTAxIkTMWnSJCxduhRdu3ZFRkYGdu3ahc6dO2PEiBE1ep4XX3wRixYtQrt27dCxY0csX74c2dnZetMuGPI7JKKqeOaJiAwyZ84cKJVKBAYGws3Nrcr4pVstW7YMTk5O6N27N0aNGoXIyEh069bNoNezs7PD4sWL0b17d/To0QOXL1/Gli1boFAoEBISgmXLluH9999HUFAQfvjhB71pDIDyomTHjh2IjY1Fz549ER4ejt9//x1mZmY1Pp5u3bph3bp1+OmnnxAUFIT58+fjrbfewpNPPmnQsdzL6tWrMWnSJLzyyivw9/fHmDFjcOzYMbRq1arGzzF37lw89thjmDRpEsLDw2Fra4vIyEhYWlrq2hjyOySiqiRhzM5yIiIyKVqtFgEBAXj44Yfx9ttvyx2HqElgtx0RURNy5coV7NixAwMGDEBJSQlWrFiBxMRETJgwQe5oRE0Gu+2IiJoQhUKBb775Bj169ECfPn0QFxeHnTt3IiAgQO5oRE0Gu+2IiIiIDMAzT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQG+H9n6W6W5uqvUwAAAABJRU5ErkJggg==\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Maximal Partial Periodic Frequent patterns using Max3PGrowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.partialPeriodicPattern.maximal import Max3PGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.Max3PGrowth(iFile=inputFile, periodicSupport=minimumSupportCount, period=PeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('maximalPartialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a2d96de7-c0ed-4b16-c6ee-64b5fbd679ab" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n", - "Total No of patterns: 3939\n", - "Runtime: 13.753859758377075\n", - "Memory (RSS): 606720000\n", - "Memory (USS): 558936064\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'maximalPartialPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "ecd9fe66-55f2-452a-e22b-e3257367a506" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "729:100 \n", - "330:101 \n", - "199:107 \n", - "62:108 \n", - "102:108 \n", - "906\t490\t856:102 \n", - "914\t90\t339\t426:100 \n", - "914\t90\t191\t426:100 \n", - "825\t276\t90\t426:100 \n", - "914\t276\t90\t426:100 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "5794bdf7-e651-4f30-df06-1ac348435fe3" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-07 08:02:58-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.13MB/s in 5.5s \n", + "\n", + "2023-09-07 08:03:05 (825 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "82407986-5639-4de4-eb61-ae9eb45c6e15" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Maximal Partial Periodic Frequent patterns using Max3PGrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "efccbab2-de2b-4aed-bbc8-5dc306d1f419" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "65bea099-38b4-49b5-a36f-886f080d77df" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _maximalPartialPeriodicfrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the Max3PGrowth algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.partialPeriodicPattern.maximal import Max3PGrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "PeriodCount = 5000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of Max3PGrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of Max3PGrowth algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.Max3PGrowth(iFile=inputFile, periodicSupport=minSupCount, period=PeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['Max3PGrowth', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "0027f524-0fd6-471d-f4e8-73da087d0d57" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n", - "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n", - "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n", - "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n", - "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Maximal Partial Periodic Frequent patterns using Max3PGrowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.partialPeriodicPattern.maximal import Max3PGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.Max3PGrowth(iFile=inputFile, periodicSupport=minimumSupportCount, period=PeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('maximalPartialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a2d96de7-c0ed-4b16-c6ee-64b5fbd679ab" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n", + "Total No of patterns: 3939\n", + "Runtime: 13.753859758377075\n", + "Memory (RSS): 606720000\n", + "Memory (USS): 558936064\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'maximalPartialPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ecd9fe66-55f2-452a-e22b-e3257367a506" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "729:100 \n", + "330:101 \n", + "199:107 \n", + "62:108 \n", + "102:108 \n", + "906\t490\t856:102 \n", + "914\t90\t339\t426:100 \n", + "914\t90\t191\t426:100 \n", + "825\t276\t90\t426:100 \n", + "914\t276\t90\t426:100 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _maximalPartialPeriodicfrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the Max3PGrowth algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.partialPeriodicPattern.maximal import Max3PGrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "PeriodCount = 5000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of Max3PGrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of Max3PGrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.Max3PGrowth(iFile=inputFile, periodicSupport=minSupCount, period=PeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['Max3PGrowth', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0027f524-0fd6-471d-f4e8-73da087d0d57" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n", + "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n", + "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n", + "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n", + "Maximal Partial Periodic Frequent patterns were generated successfully using MAX-3PGrowth algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "25e5ca57-1d64-4c3b-9150-13e259798c6f" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup PeriodCount patterns runtime memory\n", + "0 Max3PGrowth 100 5000 3939 14.151447 609136640\n", + "1 Max3PGrowth 150 5000 2378 12.423699 607219712\n", + "2 Max3PGrowth 200 5000 1930 12.888960 605229056\n", + "3 Max3PGrowth 250 5000 1579 12.538450 602316800\n", + "4 Max3PGrowth 300 5000 1284 11.184557 598999040\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "d8aaa3c6-2c24-496d-836e-9ad31d0dc0e2" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "25e5ca57-1d64-4c3b-9150-13e259798c6f" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup PeriodCount patterns runtime memory\n", - "0 Max3PGrowth 100 5000 3939 14.151447 609136640\n", - "1 Max3PGrowth 150 5000 2378 12.423699 607219712\n", - "2 Max3PGrowth 200 5000 1930 12.888960 605229056\n", - "3 Max3PGrowth 250 5000 1579 12.538450 602316800\n", - "4 Max3PGrowth 300 5000 1284 11.184557 598999040\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "d8aaa3c6-2c24-496d-836e-9ad31d0dc0e2" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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x/lCqtsshIiLSCwwwWmYpM8EbQerVqr/Ydwm5ReVaroiIiEj3McDogKd6d4S/mw1ulVfhoz+5WjUREVF9GGB0wJ2rVf98PB1nMthWTURE9DAMMDqibyc7PF7bVr1sO9uqiYiIHoYBRoeEjva+o606W9vlEBER6SwGGB3i1sECL9e2Vb/PtmoiIqIHYoDRMXMe7QonaxnSbpRgA9uqiYiI7osBRsdYykzwxmh1W/XnbKsmIiK6LwYYHTShd0f4dVS3VX8cydWqiYiI7sYAo4M02qpj03A2o1DLFREREekWBhgd1b+zHYJ7uqCGq1UTERHdgwFGh705ujukJkaIuZyHyLNsqyYiIqrDAKPDlHYWmD3UAwBXqyYiIroTA4yOmzvcE47WMlzJK8HGw6naLoeIiEgnMMDoOCuZCZYEeQMAPt97Cddvsa2aiIioUQFm7dq16NmzJ+RyOeRyOVQqFXbt2iXuHz58OCQSicZjzpw5GsdIS0tDcHAwLCws4OTkhCVLlqCqqkpjTFRUFPr06QOZTAZPT09s2LCh6TM0AE/3cYNvRzmK2FZNREQEoJEBxs3NDStXrkRcXByOHz+OkSNHYvz48Thz5ow4Zvbs2cjMzBQfq1atEvdVV1cjODgYFRUVOHz4MDZu3IgNGzZg6dKl4piUlBQEBwdjxIgRiI+Px8KFCzFr1izs2bOnBaarn4yMJFg67hEAQPixNJzLZFs1ERG1bxKhmf25dnZ2+OCDD/DSSy9h+PDh6NWrFz755JP7jt21axfGjRuHjIwMODs7AwDWrVuH0NBQ5ObmQiqVIjQ0FBEREUhMTBRfN2nSJOTn52P37t0PrKO8vBzl5bc/XiksLIRSqURBQQHkcnlzpqgzQn48gYiETAzxtMcPLw2ERCLRdklEREQtqrCwEDY2NvX+/m7yd2Cqq6sRHh6O4uJiqFQqcfuPP/4IBwcH+Pr6IiwsDCUlJeK+mJgY+Pn5ieEFAIKCglBYWChexYmJiUFAQIDGzwoKCkJMTMxD61mxYgVsbGzEh1KpbOrUdNabY9Rt1Ycu5eGvcznaLoeIiEhrGh1gEhISYGVlBZlMhjlz5mDr1q3w8VHfNfb555/HDz/8gP379yMsLAzff/89XnjhBfG1WVlZGuEFgPg8KyvroWMKCwtRWlr6wLrCwsJQUFAgPtLT0xs7NZ2ntLPArH/UtlVHnEVFVY2WKyIiItIOk8a+wNvbG/Hx8SgoKMAvv/yC6dOnIzo6Gj4+Pnj55ZfFcX5+fnBxccGoUaOQnJyMrl27tmjhd5PJZJDJZK36M3TBKyM8sfn4VaTmleD/YlIxa2gXbZdERETU5hp9BUYqlcLT0xN9+/bFihUr4O/vj08//fS+YwcOHAgAuHTpEgBAoVAgO1vzjrJ1zxUKxUPHyOVymJubN7Zcg2MlM8EbtW3Vn+69iDy2VRMRUTvU7PvA1NTUaHx59k7x8fEAABcXFwCASqVCQkICcnJuf38jMjIScrlc/BhKpVJh7969GseJjIzU+J5NezexrxsecZWjqKwKq/9iWzUREbU/jQowYWFhOHDgAFJTU5GQkICwsDBERUVhypQpSE5OxvLlyxEXF4fU1FRs27YN06ZNw7Bhw9CzZ08AQGBgIHx8fDB16lScOnUKe/bswdtvv42QkBDx4585c+bg8uXLeOONN3D+/HmsWbMGmzdvxqJFi1p+9nrK2EiCd8apA9+mo2lIyirSckVERERtq1EBJicnB9OmTYO3tzdGjRqF2NhY7NmzB4899hikUin++usvBAYGonv37njttdcwceJEbN++XXy9sbExduzYAWNjY6hUKrzwwguYNm0ali1bJo7x8PBAREQEIiMj4e/vj48++gjffPMNgoKCWm7WBmBQF3uM8VVwtWoiImqXmn0fGF3V0D5yfZaWV4KAj6NRUV2Db6f3w6gezvW/iIiISIe1+n1gSPvc7S0ws7at+r2Ic2yrJiKidoMBRs+FjOgKBysZUq4X4/9iUrVdDhERUZtggNFz1mamWBLUDYC6rfpGcYWWKyIiImp9DDAG4Om+Svi41LZVc7VqIiJqBxhgDMCdbdU/Hr3CtmoiIjJ4DDAGQtXVHqMfUbdVvxfBtmoiIjJsDDAGJGxsd0iNjfD3xevYn8TVqomIyHAxwBiQTvaWePEfnQEA7+04h8pqtlUTEZFhYoAxMPNGeMLBSorL14vxfcwVbZdDRETUKhhgDIy1mSleC1SvVv3JXxdwk23VRERkgBhgDNCz/ZTorrBGYVkVPuFq1UREZIAYYAyQsZEESx9Xt1X/cDQNF7PZVk1ERIaFAcZADe7qgEAfZ1TXCHgv4py2yyEiImpRDDAG7K2xPWBqLEH0hVzsP8+2aiIiMhwMMAass4MlZg5Rr1a9POIs26qJiMhgMMAYuJCRnrC3lOJybjF+OMK2aiIiMgwMMAZOrtFWfZFt1UREZBAYYNqB5/qr26oLSivx6d6L2i6HiIio2Rhg2gFjIwmW1q5W/f2RK7iUw7ZqIiLSbwww7cRgTwc8xrZqIiIyEAww7UhdW3VUUi5XqyYiIr3GANOOeDhYYsbgzgCA/0RwtWoiItJfDDDtzLyRXrCzlOJSzi1sOpqm7XKIiIiahAGmnbExN8Xix7oBAFb/dQH5JWyrJiIi/cMA0w5N6q+Et7M18kvYVk1ERPqJAaYdMjE2wjt1bdUxV3Ap55aWKyIiImocBph26h9eDgjo4YyqGgH/iTir7XKIiIgahQGmHftXsLqten9SLqLYVk1ERHqEAaYd83CwxHRVZwDAexHnUMW2aiIi0hMMMO3c/FFe6GBhqm6rPsa2aiIi0g8MMO2cjbkpFteuVv1x5AUUlFRquSIiIqL6McAQJvdXopuzFduqiYhIbzDAkEZb9f/FpCI5l23VRESk2xhgCAAw1MsRo7o7oapGwPtcrZqIiHQcAwyJ3gruARMjCfaez8GBC7naLoeIiOiBGGBI1NXRCtPEtuqzbKsmIiKdxQBDGhaM8oKthSkuZN/CT7Hp2i6HiIjovhhgSIONxe3Vqj/+MwkFpWyrJiIi3cMAQ/d4foA7vJyscLOkEp+zrZqIiHQQAwzd48626g2HU3GZbdVERKRjGGDovoZ1c8TIurbqnWyrJiIi3cIAQw/01lh1W/Vf53Lw90W2VRMRke5ggKEH8nSywlRVJwDAezu4WjUREekOBhh6qLq26qTsIoSzrZqIiHQEAww9lK2FFIsCatuqIy+wrZqIiHQCAwzV6/mB7vB0ssKN4gp8sY9t1UREpH0MMFQvU2MjvB3cA4C6rTrlerGWKyIiovaOAYYaZLi3E4Z7O6Kymm3VRESkfQww1GBvB/eAsZEEkWezcejSdW2XQ0RE7RgDDDWYp5M1pg5St1Uv33EW1TWClisiIqL2igGGGmXBKC/YmJvifFYRfmZbNRERaQkDDDVKB0spFgV4AQA++jMJhWVsqyYiorbXqACzdu1a9OzZE3K5HHK5HCqVCrt27RL3l5WVISQkBPb29rCyssLEiRORnZ2tcYy0tDQEBwfDwsICTk5OWLJkCaqqqjTGREVFoU+fPpDJZPD09MSGDRuaPkNqcVMGdUJXR0vkFVfgi32XtF0OERG1Q40KMG5ubli5ciXi4uJw/PhxjBw5EuPHj8eZM2cAAIsWLcL27duxZcsWREdHIyMjAxMmTBBfX11djeDgYFRUVODw4cPYuHEjNmzYgKVLl4pjUlJSEBwcjBEjRiA+Ph4LFy7ErFmzsGfPnhaaMjWXqbER3q5drXr9oRSksq2aiIjamEQQhGZ9E9POzg4ffPABnn76aTg6OmLTpk14+umnAQDnz59Hjx49EBMTg0GDBmHXrl0YN24cMjIy4OzsDABYt24dQkNDkZubC6lUitDQUERERCAxMVH8GZMmTUJ+fj52797d4LoKCwthY2ODgoICyOXy5kyRHmD6d8cQfSEXgT7O+O+0ftouh4iIDEBDf383+Tsw1dXVCA8PR3FxMVQqFeLi4lBZWYmAgABxTPfu3eHu7o6YmBgAQExMDPz8/MTwAgBBQUEoLCwUr+LExMRoHKNuTN0xHqS8vByFhYUaD2pddW3Vf57NxmG2VRMRURtqdIBJSEiAlZUVZDIZ5syZg61bt8LHxwdZWVmQSqWwtbXVGO/s7IysrCwAQFZWlkZ4qdtft+9hYwoLC1FaWvrAulasWAEbGxvxoVQqGzs1aiQvZ2u8MNAdALCMbdVERNSGGh1gvL29ER8fj6NHj2Lu3LmYPn06zp492xq1NUpYWBgKCgrER3o6W3zbwsKAbpCbmeB8VhE2H+d/cyIiahuNDjBSqRSenp7o27cvVqxYAX9/f3z66adQKBSoqKhAfn6+xvjs7GwoFAoAgEKhuKcrqe55fWPkcjnMzc0fWJdMJhO7o+oe1Po6WEqxsHa16g/3sK2aiIjaRrPvA1NTU4Py8nL07dsXpqam2Lt3r7gvKSkJaWlpUKlUAACVSoWEhATk5OSIYyIjIyGXy+Hj4yOOufMYdWPqjkG6Z6qqE7rUtlV/uZ9t1URE1PoaFWDCwsJw4MABpKamIiEhAWFhYYiKisKUKVNgY2ODl156CYsXL8b+/fsRFxeHF198ESqVCoMGDQIABAYGwsfHB1OnTsWpU6ewZ88evP322wgJCYFMJgMAzJkzB5cvX8Ybb7yB8+fPY82aNdi8eTMWLVrU8rOnFnHnatXrD6biSh7bqomIqHU1KsDk5ORg2rRp8Pb2xqhRoxAbG4s9e/bgscceAwCsXr0a48aNw8SJEzFs2DAoFAr89ttv4uuNjY2xY8cOGBsbQ6VS4YUXXsC0adOwbNkycYyHhwciIiIQGRkJf39/fPTRR/jmm28QFBTUQlOm1jDC2wlDvRxQUV2DFTvPa7scIiIycM2+D4yu4n1g2t6F7CKM/uQAagTgp9mDoOpqr+2SiIhIz7T6fWCI7tbN2RpTBnK1aiIian0MMNSiFj2mbqs+m1mIX+LYVk1ERK2DAYZalJ2lFAtq26o/2JOEIrZVExFRK2CAoRY3dVAndHGwxPVbFfhyf7K2yyEiIgPEAEMtTmpihH/VtlV/dzAFaXklWq6IiIgMDQMMtYqR3e9oq951TtvlEBGRgWGAoVYhkUjwdrAPjCTArsQsHLmcp+2SiIjIgDDAUKvxVljj+drVqtlWTURELYkBhlrVooBusDYzwZmMQvwad1Xb5RARkYFggKFWZW8lw4JRXgCAVXuScKu8SssVERGRIWCAoVY3TdUZne0tcP1WOdZwtWoiImoBDDDU6tRt1T4AgG8OpiD9BtuqiYioeRhgqE0E9HDCEE97VFTVYOUurlZNRETNwwBDbUIikeCdceq26oiETBxLuaHtkoiISI8xwFCb6a6QY/IAdVv1sh1nUMO2aiIiaiIGGGpTix/rBmuZCRKvFeKXE2yrJiKipmGAoTZlbyXDq7Vt1R+wrZqIiJqIAYba3PTB6rbq3KJyrI1iWzURETUeAwy1OamJEd4aq16t+uu/2VZNRESNxwBDWvGYjzMGd61tq97NtmoiImocBhjSCo226tOZiE1lWzURETUcAwxpTQ8XOZ7rX9tWvf0s26qJiKjBGGBIq14L7AYrmQkSrhXgt5PXtF0OERHpCQYY0ioHKxnmj/QEAKzafR7FbKsmIqIGYIAhrZsxpDPc7SyQU1SOddHJ2i6HiIj0AAMMaZ3MxFhsq/7vgcu4epNt1URE9HAMMKQTgh5xhqqLPcqravC/u5O0XQ4REek4BhjSCXVt1RIJsP1UBuKusK2aiIgejAGGdIaPqxyT+isBsK2aiIgejgGGdMrix7xhJTPBqasF2Mq2aiIiegAGGNIpjtYyzKtrq97DtmoiIro/BhjSOS/WtlVnF5bjK7ZVExHRfTDAkM5Rt1V3BwB8deAyruWXarkiIiLSNQwwpJOCHlFgoIeduq16F1erJiIiTQwwpJPubKvexrZqIiK6CwMM6SzfjjZ4tm9tW/WOc2yrJiIiEQMM6bTXgrrBUmqMU+n5+OMU26qJiEiNAYZ0mpO1GUJq26r/d1cSSirYVk1ERAwwpAdmDvGA0s4cWYVl+Cr6srbLISIiHcAAQzrPzNQYb41Rr1b91YFkZLCtmoio3WOAIb0w2leBAR52KKuswardbKsmImrvGGBIL0gkEiytbav+PT4DJ9JuarskIiLSIgYY0hu+HW3wTF83AFytmoiovWOAIb3yeqA3LKXGiE/Px7ZTGdouh4iItIQBhvSKk9wMr4xQt1Wv3HWebdVERO0UAwzpnZf+4YGOtuq26v8eYFs1EVF7xABDesfM1BhvjVW3Va+LTkZmAduqiYjaGwYY0ktj/RTo37lDbVt1krbLISKiNsYAQ3pJ3Vb9CCQSYOvJazjJtmoionaFAYb0lp+bDSb2qW2r3nEWgsC2aiKi9oIBhvTakiBvWEiNcTKNbdVERO0JAwzpNWe5GUJG1K1WfR6lFdVaroiIiNpCowLMihUr0L9/f1hbW8PJyQlPPvkkkpI0v0A5fPhwSCQSjcecOXM0xqSlpSE4OBgWFhZwcnLCkiVLUFWleT+PqKgo9OnTBzKZDJ6entiwYUPTZkgGr66tOqOgDF//zbZqIqL2oFEBJjo6GiEhIThy5AgiIyNRWVmJwMBAFBcXa4ybPXs2MjMzxceqVavEfdXV1QgODkZFRQUOHz6MjRs3YsOGDVi6dKk4JiUlBcHBwRgxYgTi4+OxcOFCzJo1C3v27GnmdMkQmZkaI2xsdwDA2qhkZBWUabkiIiJqbRKhGd98zM3NhZOTE6KjozFs2DAA6iswvXr1wieffHLf1+zatQvjxo1DRkYGnJ2dAQDr1q1DaGgocnNzIZVKERoaioiICCQmJoqvmzRpEvLz87F79+4G1VZYWAgbGxsUFBRALpc3dYqkJwRBwDPrYnD8yk1M6NMRHz/bS9slERFREzT093ezvgNTUFAAALCzs9PY/uOPP8LBwQG+vr4ICwtDSUmJuC8mJgZ+fn5ieAGAoKAgFBYW4syZM+KYgIAAjWMGBQUhJibmgbWUl5ejsLBQ40Hth0QiwdLHfQAAv524hvj0fO0WRERErarJAaampgYLFy7EkCFD4OvrK25//vnn8cMPP2D//v0ICwvD999/jxdeeEHcn5WVpRFeAIjPs7KyHjqmsLAQpaX3v+vqihUrYGNjIz6USmVTp0Z6qqeb7e226u1n2FZNRGTATJr6wpCQECQmJuLgwYMa219++WXxz35+fnBxccGoUaOQnJyMrl27Nr3SeoSFhWHx4sXi88LCQoaYduiN0d7YmZCJE2n52H46E0/4u2q7JCIiagVNugIzb9487NixA/v374ebm9tDxw4cOBAAcOnSJQCAQqFAdna2xpi65wqF4qFj5HI5zM3N7/tzZDIZ5HK5xoPaH2e5GV4Zrg7KK3eeQ1kl26qJiAxRowKMIAiYN28etm7din379sHDw6Pe18THxwMAXFxcAAAqlQoJCQnIyckRx0RGRkIul8PHx0ccs3fvXo3jREZGQqVSNaZcaqdmD+sCVxszdVs1V6smIjJIjQowISEh+OGHH7Bp0yZYW1sjKysLWVlZ4vdSkpOTsXz5csTFxSE1NRXbtm3DtGnTMGzYMPTs2RMAEBgYCB8fH0ydOhWnTp3Cnj178PbbbyMkJAQymQwAMGfOHFy+fBlvvPEGzp8/jzVr1mDz5s1YtGhRC0+fDJGZqTHerF2tek1UMrIL2VZNRGRoGtVGLZFI7rt9/fr1mDFjBtLT0/HCCy8gMTERxcXFUCqVeOqpp/D2229rfKRz5coVzJ07F1FRUbC0tMT06dOxcuVKmJjc/kpOVFQUFi1ahLNnz8LNzQ3vvPMOZsyY0eCJsY26fRMEARPXHsaJtHxM7OOGj57113ZJRETUAA39/d2s+8DoMgYYik/Px5NfHgIAbJs3BD3dbLVbEBER1atN7gNDpMt6KW0xoXdHAMCy7VytmojIkDDAkEF7Y3R3mJsa4/iVm4hIyNR2OURE1EIYYMigKWzMMLe2rXrFzvNsqyYiMhAMMGTwZg9Vt1Vfyy/FtwdTtF0OERG1AAYYMnjmUmOEjlGvVv3l/kvIYVs1EZHeY4ChduEJf1f0drdFSUU1Vu1J0nY5RETUTAww1C5IJBIsHae+0/MvcVeRcLVAyxUREVFzMMBQu9HbvQOeqm2r/p9tiSgordRyRURE1FQMMNSuvDHaG2amRjiRlo9//O8+rI68wCBDRKSHGGCoXXGxMcd30/vD29kaRWVV+HTvRQz933349K+LKCxjkCEi0hdcSoDapZoaAbsSs/Dp3gu4kH0LACA3M8GsoV3w4pDOsDYz1XKFRETtE9dCYoChBqipEbAzMROf/nURF3PUQcbG3BSzh3pg+mAGGSKitsYAwwBDjVBdIyAiIROf/nUBybnFAABbC1PMHtoF0wd3hpXMpJ4jEBFRS2CAYYChJqiuEbDjdAY+3XsRl2uDTAcLU8we1gXTVZ1hySBDRNSqGGAYYKgZqmsEbD+Vgc/2XsTl67eDzMvDumKaqhODDBFRK2GAYYChFlBVXYPtpzPw2d5LSKkNMnaWUvxzWBdMVXWChZRBhoioJTHAMMBQC6qqrsEf8Rn4fN9FpOaVAADsLaX456NdMHVQZ5hLjbVcIRGRYWCAYYChVlBVXYPfa4PMldog42AlxZxHu2LKwE4MMkREzcQAwwBDraiyugZbT17D5/suIv1GKQDAwUqGOY92wQuDOsHMlEGGiKgpGGAYYKgNVFbXYOuJa/hs30VcvakOMo7WMsx9tCueH+jOIENE1EgMMAww1IYqq2vwa9xVfL7vEq7lq4OMk7UMc4d3xeQBDDJERA3FAMMAQ1pQUVWDX09cxRd3BBlnuQyvDPfEc/2VDDJERPVggGGAIS2qqKrBlrh0fLnvEjIKygAACrkZQkZ0xbP9lZCZMMgQEd0PAwwDDOmA8qpqbDl+FV/uv4TM2iDjYmOGV0Z44tl+bgwyRER3YYBhgCEdUl5Vjc2x6fhyfzKyCtVBxtXGDCEjPfFMXyWkJkZarpCISDcwwDDAkA4qq6zGz7HpWBN1CdmF5QCAjrbmCBnhiaf7ujHIEFG7xwDDAEM6rKyyGuHH0rAmKhk5RbeDzPyRnpjY1w2mxgwyRNQ+McAwwJAeKKusxqajaVgbnYzc2iDj1kEdZCb0YZAhovaHAYYBhvRIWWU1fjyahrVRybh+Sx1k3O0sMG+kJ57q3ZFBhojaDQYYBhjSQ6UV1fjx6BWsi07G9VsVAIBO9haYN0IdZEwYZIjIwDHAMMCQHiutqMYPR9RBJq9YHWQ621tg/kgvjO/lyiBDRAaLAYYBhgxASUUVvo+5gq8OXMaN2iDj4WCJ+SM98YQ/gwwRGR4GGAYYMiDF5VX4/sgV/PeOINPFwRKvjvLC4/6uMDaSaLlCIqKWwQDDAEMGqLi8ChtjUvH1gcu4WVIJAOjiaIkFo7wwrieDDBHpPwYYBhgyYLfKq7DxcCq+/vsy8muDjKeTFV4d5YVgPxcGGSLSWwwwDDDUDhSVVdYGmRQUlKqDjNcdQcaIQYaI9AwDDAMMtSNFZZXYcEh9RaawrAoA0M3ZCgtGdcMYXwWDDBHpDQYYBhhqhwrLKrH+YCq+OXgZRbVBxtvZGgsCvDD6EQYZItJ9DDAMMNSOFZRWYv2hFHx7MEUMMt0V1lgY4IVAHwYZItJdDDAMMEQoKK3EtwdTsP5gCorK1UGmh4scC0Z5IegRZ0gkDDJEpFsYYBhgiEQFJZX49uBlfHcoFbdqg4yPixwLA7zwmA+DDBHpDgYYBhiie+SXVOCbv1Ow/lAKiiuqAQCPuMqxMKAbAno4McgQkdYxwDDAED3QzeIKfHPwMjYcShWDjG9HORaO6oZRDDJEpEUMMAwwRPW6UVyBr/++jI2HU1FSG2R6utlgYYAXRngzyBBR22OAYYAharAbxRX474HL+L+Y20HG380GCwO6Ybi3I4MMEbUZBhgGGKJGy7tVXhtkrqC0Uh1keiltsTDAC492Y5AhotbHAMMAQ9Rk18Ugk4qyyhoAQG93WywM6IZhXg4MMkTUahhgGGCImi23qBxfRSfjh6NXxCDTx90Wix7rhn94MsgQUctjgGGAIWoxOUVl+Cr6Mn44cgXlVeog069TBywM6IYhnvYMMkTUYhhgGGCIWlxOYRnWRifjx6NpqKgNMv07d8CigG5QdWWQIaLmY4BhgCFqNdmFZVgblYxNx24HmQEedlgY4IXBXR20XB0R6TMGGAYYolaXVVCGtVGX8NOxdFRUq4PMQA87LHqsGwZ1sddydUSkjxhgGGCI2kxmQSnWRiUj/I4go+pij4UBXhjIIENEjdDQ399GjTnoihUr0L9/f1hbW8PJyQlPPvkkkpKSNMaUlZUhJCQE9vb2sLKywsSJE5Gdna0xJi0tDcHBwbCwsICTkxOWLFmCqqoqjTFRUVHo06cPZDIZPD09sWHDhsaUSkRtyMXGHMvG+yJqyXC8MMgdpsYSxFzOw3P/PYLnvz6C2NQb2i6RiAxMowJMdHQ0QkJCcOTIEURGRqKyshKBgYEoLi4WxyxatAjbt2/Hli1bEB0djYyMDEyYMEHcX11djeDgYFRUVODw4cPYuHEjNmzYgKVLl4pjUlJSEBwcjBEjRiA+Ph4LFy7ErFmzsGfPnhaYMhG1Fldbc7z3pB+ilozAlIHqIHM4OQ/PrIvBC98cxXEGGSJqIc36CCk3NxdOTk6Ijo7GsGHDUFBQAEdHR2zatAlPP/00AOD8+fPo0aMHYmJiMGjQIOzatQvjxo1DRkYGnJ2dAQDr1q1DaGgocnNzIZVKERoaioiICCQmJoo/a9KkScjPz8fu3bvvW0t5eTnKy8vF54WFhVAqlfwIiUiLrt4swZf7k7HleDqqatT/1Az1csDCgG7o26mDlqsjIl3UKh8h3a2goAAAYGdnBwCIi4tDZWUlAgICxDHdu3eHu7s7YmJiAAAxMTHw8/MTwwsABAUFobCwEGfOnBHH3HmMujF1x7ifFStWwMbGRnwolcrmTI2IWoBbBwusmOCH/a8Px+QBSpgYSfD3xeuYuPYwpn13DCfSbmq7RCLSU00OMDU1NVi4cCGGDBkCX19fAEBWVhakUilsbW01xjo7OyMrK0scc2d4qdtft+9hYwoLC1FaWnrfesLCwlBQUCA+0tPTmzo1ImphSjsLrJjQE/tfH47n+ilhbCTBgQu5mLDmMKZ/dwzx6fnaLpGI9IxJU18YEhKCxMREHDx4sCXraTKZTAaZTKbtMojoIZR2Fvjfp3siZIQnvth/Eb+euIboC7mIvpCLEd6OWBjQDf5KW22XSUR6oElXYObNm4cdO3Zg//79cHNzE7crFApUVFQgPz9fY3x2djYUCoU45u6upLrn9Y2Ry+UwNzdvSslEpEPc7S2w6ml/7HvtUTzd1w3GRhLsT8rF+C8PYeaGWJy+mq/tEolIxzUqwAiCgHnz5mHr1q3Yt28fPDw8NPb37dsXpqam2Lt3r7gtKSkJaWlpUKlUAACVSoWEhATk5OSIYyIjIyGXy+Hj4yOOufMYdWPqjkFEhqGTvSU+fMYfexc/iol93GAkAfadz8ETXxzCrI2xSLhaoO0SiUhHNaoL6ZVXXsGmTZvwxx9/wNvbW9xuY2MjXhmZO3cudu7ciQ0bNkAul2P+/PkAgMOHDwNQt1H36tULrq6uWLVqFbKysjB16lTMmjUL77//PgB1G7Wvry9CQkIwc+ZM7Nu3D6+++ioiIiIQFBTUoFp5Izsi/ZNyvRif772I3+OvobZpCQE9nLEwwAu+HW20WxwRtYlWuRPvgxZqW79+PWbMmAFAfSO71157DT/99BPKy8sRFBSENWvWiB8PAcCVK1cwd+5cREVFwdLSEtOnT8fKlSthYnL7KzlRUVFYtGgRzp49Czc3N7zzzjviz2gIBhgi/ZWcewtf7LuEP+4IMoE+zlgQ4IVHXBlkiAwZlxJggCHSe5dybuHzfRex7VQG6v6lCnrEGQsDuqGHC9/XRIaIAYYBhshgXMopwmd7L2H76dtBZmR3J0we4I4R3o4wMW7WLa2ISIcwwDDAEBmci9lF+HTvRUQkZIpBxlkuwzN9lXiuvxJKOwvtFkhEzcYAwwBDZLCSc28h/Fgafj1xDTeKK8TtQ70c8Fx/JQJ9FJCa8KoMkT5igGGAITJ45VXV+OtsDsJj0/D3xevidjtLKSb26YhJA9zR1dFKixUSUWMxwDDAELUr6TdK8HNsOrbEpSO78PbCrgM622HSACXG+rnAzNRYixUSUUMwwDDAELVLVdU12J+Ui/BjadiflCO2YcvNTPBU7454rr87fFz5bwKRrmKAYYAhavcyC0rxy/GrCI9Nx7X82wvB+rvZYNIAdzzu7worWZOXhCOiVsAAwwBDRLVqagQcvHQd4bFp+PNMNqpqL8tYSo3xuL8rJg1wh7+bzQNv1klEbYcBhgGGiO7j+q1y/Bp3FT/HpuPy9WJxe3eFNSYPcMeTvTrCxsJUixUStW8MMAwwRPQQgiDgaMoN/BybjoiETFRU1QAAZCZGGOvngkn9lRjgYcerMkRtjAGGAYaIGqigpBJbT6q/K3M+q0jc3sXREpP6KzGxjxvsrWRarJCo/WCAYYAhokYSBAHx6fkIP5aO7aczUFJRDQAwNZYg0EeBSQOUGNLVAUZGvCpD1FoYYBhgiKgZbpVXYVt8Bn6OTcOpqwXidqWdOZ7rp8TTfZVQ2JhpsUIiw8QAwwBDRC3kbEYhwmPTsPXkNRSVVQEAjCTqBSUn9XfHcC4oSdRiGGAYYIiohZVWVGNnQibCY9MQm3pT3O4sl+HZfko8248LShI1FwMMAwwRtaJLOUUIP5aOX09cxc2SSgCARAL8w9MBkwe4I6CHMxeUJGoCBhgGGCJqA+VV1fjzTDZ+jk3HwUu3F5S0t5RiYl83PNdfyQUliRqBAYYBhojaWFpeCX4+noYtx68ip+iOBSU97DB5gBJjfLmgJFF9GGAYYIhIS6qqa7DvfA7CY9MRddeCkhP6uGHSACW6K/jvEtH9MMAwwBCRDsgsKMXm2KvYfPyuBSWVtpjcX4nH/V1hyQUliUQMMAwwRKRDqusWlDyWhsizmgtKPtHLFZP6u6MnF5QkYoBhgCEiXZVbVI5fT6gXlEy5Y0HJHi5yTB6gxPheHWFjzgUlqX1igGGAISIdJwgCjly+gfDYNOxKzNJYUDLYzwWTBrijf+cOvCpD7QoDDAMMEemR/JIKbD15DeHH0pGUfXtBya6OlpjU3x0T+nTkgpLULjDAMMAQkR4SBAEn0/MRfiwN209lorTyjgUlH1Fgcn93DO5qzwUlyWAxwDDAEJGeKyqrxLZTGQg/lo6Ea5oLSk7q746n+7rBWc4FJcmwMMAwwBCRAUm8VoCfY9Px+8lrKCpXLyhpbCTBCG8nTB6gxKPduKAkGQYGGAYYIjJApRXViEjIRPixNBy/cntBSYXcDM/2c8Oz/ZVw68AFJUl/McAwwBCRgbuYXYTw2HT8dteCkkO9HDG5vxKjuKAk6SEGGAYYImonyquqsedMNsKPpeFwcp643cFKiol91AtKduGCkqQnGGAYYIioHbqSV4yfY9OxJe4qcu9YUHKghx0mD3DHaF8FF5QkncYAwwBDRO1YZd2CksfSEH0hV1xQ0sbcFE/17ojJA9zhrbDWbpFE98EAwwBDRAQAyMgvxebj6dgcm46MgjJxe293W0zqr8S4nlxQknQHAwwDDBGRhuoaAX9fzEX4sXT8de72gpJWMhM87u+KyQOU8OvIBSVJuxhgGGCIiB4op6gMv8Zdw8+xaUjNKxG3+9QtKNm7I+RmXFCS2h4DDAMMEVG9amoEHEnJQ/ixdOxOzEJFtXpBSTNTIwT7uWLSACX6deKCktR2GGAYYIiIGuVmcQV+O3kN4cfScDHnlrjd08kKk/orMaGPG+wspVqskNoDBhgGGCKiJhEEASfS1AtK7jh9e0FJqbERAh9xxuQB7lB14YKS1DoYYBhgiIiarbCsEtviMxAem4bEa4Xidnc7CzzXX4ln+rrBiQtKUgtigGGAISJqUYnXCvDTsTT8EZ+BW3csKDmquxMmDVDi0W5OMOZVGWomBhgGGCKiVlFSUYWI05kIj01H3B0LSrrYmOGZfko828+NC0pSkzHAMMAQEbW6C9lFCD+Wjt9OXkX+HQtKDvNyxOQB6gUlTY25oCQ1HAMMAwwRUZspq6zGnjNZCD+WjpjLdy4oKcPTfdULSno4WGqxQtIXDDAMMEREWpF6vRg/H0/HluNXcf3W7QUlB3VRLygZ9AgXlKQHY4BhgCEi0qrK6hrsPZeD8Fj1gpJ1v21sLdQLSj7VuyOXLqB7MMAwwBAR6Yxr+aXYHJuOLcc1F5R062COsX4uGOvnAn83hhligGGAISLSQdU1Ag5czMUvx69i3/kc8SZ5ANDR1hxj/RQY4+eC3kpbhpl2igGGAYaISKeVVlQjKikHEQmZ2Hc+ByUVt8OMq40ZxtRememttOVdf9sRBhgGGCIivVFaUY3oCznYmZCFveeyUXxHmHGxMcNoXwWC/VzQx70Dw4yBY4BhgCEi0ktlldWIvpCLnQmZ2HsuR7zrLwAo5OowM9bPBf06McwYIgYYBhgiIr1XVlmNvy9ex86ETPx1NhtFd4QZJ2sZxtSFmc52XMbAQDDAMMAQERmU8qpq/H1BHWYi7wozjtYyjH5EHWYGeDDM6LOG/v5u9P2dDxw4gMcffxyurq6QSCT4/fffNfbPmDEDEolE4zF69GiNMTdu3MCUKVMgl8tha2uLl156Cbdu3dIYc/r0aQwdOhRmZmZQKpVYtWpVY0slIiIDIjMxRoCPMz5+rheOvxOA72b0w8Q+bpCbmSC3qBzfH7mCyV8fwcD39+Lt3xNwOPk6qqprtF02tRKTxr6guLgY/v7+mDlzJiZMmHDfMaNHj8b69evF5zKZTGP/lClTkJmZicjISFRWVuLFF1/Eyy+/jE2bNgFQp6/AwEAEBARg3bp1SEhIwMyZM2Fra4uXX365sSUTEZGBkZkYY2R3Z4zs7oyKKj8cSr6Onacz8efZbFy/VY4fjqThhyNpsLeUIqj2C8ADPexgwnWZDEazPkKSSCTYunUrnnzySXHbjBkzkJ+ff8+VmTrnzp2Dj48PYmNj0a9fPwDA7t27MXbsWFy9ehWurq5Yu3Yt/vWvfyErKwtSqRQA8Oabb+L333/H+fPnG1QbP0IiImp/KqpqcDhZ/THTn2ezxQUmAcDOUoqgR5wx1s8Fqi72DDM6qtU+QmqIqKgoODk5wdvbG3PnzkVe3u2FvWJiYmBrayuGFwAICAiAkZERjh49Ko4ZNmyYGF4AICgoCElJSbh58/bS7XcqLy9HYWGhxoOIiNoXqYkRhns7YdXT/oj9VwD+b+YATOqvRAcLU9worsBPx9Ix9dtj6P+fv/Dmr6dx4EIuKvkxk15q9EdI9Rk9ejQmTJgADw8PJCcn46233sKYMWMQExMDY2NjZGVlwcnJSbMIExPY2dkhKysLAJCVlQUPDw+NMc7OzuK+Dh063PNzV6xYgXfffbelp0NERHrK1NgIw7o5Ylg3Ryx/0hdHL99AREIm9pzJwo3iCoTHpiM8Nh22FqYI9FFfmRni6QBTXpnRCy0eYCZNmiT+2c/PDz179kTXrl0RFRWFUaNGtfSPE4WFhWHx4sXi88LCQiiVylb7eUREpD9MjY3wDy8H/MPLAcvHP4KjKbVhJjELecUV2Hz8KjYfvwobc1M85uOM4NowIzVhmNFVLR5g7talSxc4ODjg0qVLGDVqFBQKBXJycjTGVFVV4caNG1AoFAAAhUKB7OxsjTF1z+vG3E0mk93zZWEiIqK7mRgbYYinA4Z4OmD5eF8cTcnDzoRM7E5UfwH4l7ir+CXuKuRmJnjMR4HgngoM8XSAzMRY26XTHVo9wFy9ehV5eXlwcXEBAKhUKuTn5yMuLg59+/YFAOzbtw81NTUYOHCgOOZf//oXKisrYWpqCgCIjIyEt7f3fT8+IiIiagpjIwkGd3XA4K4OePcJX8Sm3sDOhEzsSsxCblE5fj1xFb+euAprMxM81kP9MdPQbgwzuqDRXUi3bt3CpUuXAAC9e/fGxx9/jBEjRsDOzg52dnZ49913MXHiRCgUCiQnJ+ONN95AUVEREhISxCskY8aMQXZ2NtatWye2Uffr109soy4oKIC3tzcCAwMRGhqKxMREzJw5E6tXr25wGzW7kIiIqKmqawQcvyPM5BSVi/usZSYI8HHGGF8FhnVzhJkpw0xLarU78UZFRWHEiBH3bJ8+fTrWrl2LJ598EidPnkR+fj5cXV0RGBiI5cuXi1/CBdQ3sps3bx62b98OIyMjTJw4EZ999hmsrKzEMadPn0ZISAhiY2Ph4OCA+fPnIzQ0tMF1MsAQEVFLqKkREJd2ExGnM7ErMRPZhbfDjJXMBKN6OGGsnwseZZhpEVxKgAGGiIhaWE2NgBNpN7EzIQu7EjORWVAm7rOUGmNkD2cE+ykw3NuJYaaJGGAYYIiIqBXV1Ag4mZ6v/pgpIRMZd4QZC6kxRnZXX5kZ4e0EcynDTEMxwDDAEBFRGxEEAfG1YWZnQhau5ZeK+8xN7wgz3R1hIW31/hm9xgDDAENERFogCAJOXy3AzoRMRCRk4urN22HGzNQII7zVYWZkdydYyhhm7sYAwwBDRERaJggCEq4VYGdCFnYmZCLtRom4T2aiDjNj/BQY1cMZVgwzABhgGGCIiEinCIKAMxmFiEjIxM6ETFzJ0wwzj3ZzRHBP9ZUZazNTLVaqXQwwDDBERKSjBEHA2cxC8TszKdeLxX1SEyMM83JEcE/1lRl5OwszDDAMMEREpAcEQcC5zCLsSlR/Z+Zy7h1hxtgIw7o5YIyvCwJ8nGFjbvhhhgGGAYaIiPSMIAhIyi7CztPqMJN8R5gxNZZgqJcjxvq54DEDDjMMMAwwRESk5y5kFyHitPo7MxdzbonbTY0lGOLpgLF+Lgj0cYathVSLVbYsBhgGGCIiMiAXs4vEbqak7CJxu4lRXZhRINBHgQ6W+h1mGGAYYIiIyEBdyrlV+wXgTJzP0gwzqq72CPZzQeAjCtjpYZhhgGGAISKidiA59xZ2JWQiIiEL5zILxe3GRhIM7mqPMb4uCHrEGfZWMi1W2XAMMAwwRETUzqRcLxavzJzJ0Awzg7rYYayfC4IeUcBBh8MMAwwDDBERtWOp14uxM1EdZhKv3Q4zRhJgoIc9xvZ0wehHFHC01q0wwwDDAENERAQASMsrEcPM6asF4nYjCTDAQ31lZrSvAk7WZlqsUo0BhgGGiIjoHuk3SmpvmpeFU+n54naJBOjf2Q7Bfi4Y46uAk1w7YYYBhgGGiIjooa7eLMGuhCxEJGQi/q4w069TB4z1c8EYXxcobNouzDDAMMAQERE12LX8Uuyq/QLwibR8jX39OnXAGD8XjPVTwMXGvFXrYIBhgCEiImqSjPxS7E5U3zTv+JWbGvv6uNtirJ8Lxvq5wNW25cMMAwwDDBERUbNlFZRhV+0XgI9fuYk7U8P/PO6DF4d4tOjPa+jvb5MW/alERERkUBQ2ZnhxiAdeHOKB7MIy7E5Uf2cmNvUG+rh30FpdvAJDREREjZZTVAZHKxkkEkmLHpdXYIiIiKjVaPueMUZa/elERERETcAAQ0RERHqHAYaIiIj0DgMMERER6R0GGCIiItI7DDBERESkdxhgiIiISO8wwBAREZHeYYAhIiIivcMAQ0RERHqHAYaIiIj0DgMMERER6R0GGCIiItI7BrsatSAIANTLchMREZF+qPu9Xfd7/EEMNsAUFRUBAJRKpZYrISIiosYqKiqCjY3NA/dLhPoijp6qqalBRkYGrK2tIZFIWuy4hYWFUCqVSE9Ph1wub7Hj6hJDnyPnp/8MfY6GPj/A8OfI+TWdIAgoKiqCq6srjIwe/E0Xg70CY2RkBDc3t1Y7vlwuN8i/lHcy9DlyfvrP0Odo6PMDDH+OnF/TPOzKSx1+iZeIiIj0DgMMERER6R0GmEaSyWT4n//5H8hkMm2X0moMfY6cn/4z9Dka+vwAw58j59f6DPZLvERERGS4eAWGiIiI9A4DDBEREekdBhgiIiLSOwwwREREpHcYYGodOHAAjz/+OFxdXSGRSPD7779r7BcEAUuXLoWLiwvMzc0REBCAixcvaoy5ceMGpkyZArlcDltbW7z00ku4detWG87iwR42v8rKSoSGhsLPzw+WlpZwdXXFtGnTkJGRoXGMzp07QyKRaDxWrlzZxjO5v/rO34wZM+6pffTo0RpjdPn8AfXP8e751T0++OADcYwun8MVK1agf//+sLa2hpOTE5588kkkJSVpjCkrK0NISAjs7e1hZWWFiRMnIjs7W2NMWloagoODYWFhAScnJyxZsgRVVVVtOZX7qm9+N27cwPz58+Ht7Q1zc3O4u7vj1VdfRUFBgcZx7neOw8PD23o692jI+Rs+fPg9tc+ZM0djjK6eP6D+Oaampj7wfbhlyxZxnK6ew7Vr16Jnz57izelUKhV27dol7te19x8DTK3i4mL4+/vjyy+/vO/+VatW4bPPPsO6detw9OhRWFpaIigoCGVlZeKYKVOm4MyZM4iMjMSOHTtw4MABvPzyy201hYd62PxKSkpw4sQJvPPOOzhx4gR+++03JCUl4Yknnrhn7LJly5CZmSk+5s+f3xbl16u+8wcAo0eP1qj9p59+0tivy+cPqH+Od84tMzMT3333HSQSCSZOnKgxTlfPYXR0NEJCQnDkyBFERkaisrISgYGBKC4uFscsWrQI27dvx5YtWxAdHY2MjAxMmDBB3F9dXY3g4GBUVFTg8OHD2LhxIzZs2IClS5dqY0oa6ptfRkYGMjIy8OGHHyIxMREbNmzA7t278dJLL91zrPXr12ucwyeffLKNZ3Ovhpw/AJg9e7ZG7atWrRL36fL5A+qfo1KpvOd9+O6778LKygpjxozROJYunkM3NzesXLkScXFxOH78OEaOHInx48fjzJkzAHTw/SfQPQAIW7duFZ/X1NQICoVC+OCDD8Rt+fn5gkwmE3766SdBEATh7NmzAgAhNjZWHLNr1y5BIpEI165da7PaG+Lu+d3PsWPHBADClStXxG2dOnUSVq9e3brFtYD7zW/69OnC+PHjH/gafTp/gtCwczh+/Hhh5MiRGtv05RwKgiDk5OQIAITo6GhBENTvOVNTU2HLli3imHPnzgkAhJiYGEEQBGHnzp2CkZGRkJWVJY5Zu3atIJfLhfLy8radQD3unt/9bN68WZBKpUJlZaW4rSHnXhfcb36PPvqosGDBgge+Rp/OnyA07Bz26tVLmDlzpsY2fTmHgiAIHTp0EL755hudfP/xCkwDpKSkICsrCwEBAeI2GxsbDBw4EDExMQCAmJgY2Nraol+/fuKYgIAAGBkZ4ejRo21ec3MVFBRAIpHA1tZWY/vKlSthb2+P3r1744MPPtCZS7sNERUVBScnJ3h7e2Pu3LnIy8sT9xna+cvOzkZERMR9/+9dX85h3UcndnZ2AIC4uDhUVlZqvA+7d+8Od3d3jfehn58fnJ2dxTFBQUEoLCwU/y9SV9w9vweNkcvlMDHRXLYuJCQEDg4OGDBgAL777jsIOng7rwfN78cff4SDgwN8fX0RFhaGkpIScZ8+nT+g/nMYFxeH+Pj4+74Pdf0cVldXIzw8HMXFxVCpVDr5/jPYxRxbUlZWFgBonJS653X7srKy4OTkpLHfxMQEdnZ24hh9UVZWhtDQUEyePFljka5XX30Vffr0gZ2dHQ4fPoywsDBkZmbi448/1mK1DTN69GhMmDABHh4eSE5OxltvvYUxY8YgJiYGxsbGBnX+AGDjxo2wtrbWuLwL6M85rKmpwcKFCzFkyBD4+voCUL/HpFLpPaH67vfh/d6ndft0xf3md7fr169j+fLl93yMuWzZMowcORIWFhb4888/8corr+DWrVt49dVX26L0BnnQ/J5//nl06tQJrq6uOH36NEJDQ5GUlITffvsNgP6cP6Bh5/Dbb79Fjx49MHjwYI3tunwOExISoFKpUFZWBisrK2zduhU+Pj6Ij4/XufcfAwxpqKysxLPPPgtBELB27VqNfYsXLxb/3LNnT0ilUvzzn//EihUrdP522ZMmTRL/7Ofnh549e6Jr166IiorCqFGjtFhZ6/juu+8wZcoUmJmZaWzXl3MYEhKCxMREHDx4UNultIr65ldYWIjg4GD4+Pjg3//+t8a+d955R/xz7969UVxcjA8++EAnfvnVedD87gxjfn5+cHFxwahRo5CcnIyuXbu2dZnNUt85LC0txaZNmzTOVx1dPofe3t6Ij49HQUEBfvnlF0yfPh3R0dHaLuu++BFSAygUCgC459vW2dnZ4j6FQoGcnByN/VVVVbhx44Y4RtfVhZcrV64gMjKy3iXSBw4ciKqqKqSmprZNgS2oS5cucHBwwKVLlwAYxvmr8/fffyMpKQmzZs2qd6wunsN58+Zhx44d2L9/P9zc3MTtCoUCFRUVyM/P1xh/9/vwfu/Tun264EHzq1NUVITRo0fD2toaW7duhamp6UOPN3DgQFy9ehXl5eWtVXKj1De/Ow0cOBAANN6Hun7+gIbN8ZdffkFJSQmmTZtW7/F06RxKpVJ4enqib9++WLFiBfz9/fHpp5/q5PuPAaYBPDw8oFAosHfvXnFbYWEhjh49CpVKBQBQqVTIz89HXFycOGbfvn2oqakR36S6rC68XLx4EX/99Rfs7e3rfU18fDyMjIzu+ehFH1y9ehV5eXlwcXEBoP/n707ffvst+vbtC39//3rH6tI5FAQB8+bNw9atW7Fv3z54eHho7O/bty9MTU013odJSUlIS0vTeB8mJCRohNG6MO7j49M2E3mA+uYHqP9dCQwMhFQqxbZt2+65gnY/8fHx6NChg9avoDVkfneLj48HAI33oa6eP6Bxc/z222/xxBNPwNHRsd7j6so5vJ+amhqUl5fr5vuvxb8WrKeKioqEkydPCidPnhQACB9//LFw8uRJsQtn5cqVgq2trfDHH38Ip0+fFsaPHy94eHgIpaWl4jFGjx4t9O7dWzh69Khw8OBBwcvLS5g8ebK2pqThYfOrqKgQnnjiCcHNzU2Ij48XMjMzxUfdN8cPHz4srF69WoiPjxeSk5OFH374QXB0dBSmTZum5ZmpPWx+RUVFwuuvvy7ExMQIKSkpwl9//SX06dNH8PLyEsrKysRj6PL5E4T6/44KgiAUFBQIFhYWwtq1a+95va6fw7lz5wo2NjZCVFSUxt/BkpISccycOXMEd3d3Yd++fcLx48cFlUolqFQqcX9VVZXg6+srBAYGCvHx8cLu3bsFR0dHISwsTBtT0lDf/AoKCoSBAwcKfn5+wqVLlzTGVFVVCYIgCNu2bRO+/vprISEhQbh48aKwZs0awcLCQli6dKk2pyYIQv3zu3TpkrBs2TLh+PHjQkpKivDHH38IXbp0EYYNGyYeQ5fPnyA07O+oIAjCxYsXBYlEIuzateueY+jyOXzzzTeF6OhoISUlRTh9+rTw5ptvChKJRPjzzz8FQdC99x8DTK39+/cLAO55TJ8+XRAEdSv1O++8Izg7OwsymUwYNWqUkJSUpHGMvLw8YfLkyYKVlZUgl8uFF198USgqKtLCbO71sPmlpKTcdx8AYf/+/YIgCEJcXJwwcOBAwcbGRjAzMxN69OghvP/++xoBQJseNr+SkhIhMDBQcHR0FExNTYVOnToJs2fP1mj1EwTdPn+CUP/fUUEQhK+++kowNzcX8vPz73m9rp/DB/0dXL9+vTimtLRUeOWVV4QOHToIFhYWwlNPPSVkZmZqHCc1NVUYM2aMYG5uLjg4OAivvfaaRhuyttQ3vwedXwBCSkqKIAjq1v5evXoJVlZWgqWlpeDv7y+sW7dOqK6u1t7EatU3v7S0NGHYsGGCnZ2dIJPJBE9PT2HJkiVCQUGBxnF09fwJQsP+jgqCIISFhQlKpfK+50WXz+HMmTOFTp06CVKpVHB0dBRGjRolhhdB0L33n0QQdKx3i4iIiKge/A4MERER6R0GGCIiItI7DDBERESkdxhgiIiISO8wwBAREZHeYYAhIiIivcMAQ0RERHqHAYaIiIj0DgMMEWldVFQUJBLJPQvFERE9CAMMEWnd4MGDkZmZCRsbmwa/pqSkBGFhYejatSvMzMzg6OiIRx99FH/88UcrVkpEusJE2wUQEUmlUigUika9Zs6cOTh69Cg+//xz+Pj4IC8vD4cPH0ZeXl4rVUlEuoRXYIioxQ0fPhzz58/HwoUL0aFDBzg7O+Prr79GcXExXnzxRVhbW8PT0xO7du0CcO9HSBs2bICtrS327NmDHj16wMrKCqNHj0ZmZqb4M7Zt24a33noLY8eORefOndG3b1/Mnz8fM2fOFMdIJBL8/vvvGrXZ2tpiw4YNAIDU1FRIJBKEh4dj8ODBMDMzg6+vL6Kjo1v1vw8RNR8DDBG1io0bN8LBwQHHjh3D/PnzMXfuXDzzzDMYPHgwTpw4gcDAQEydOhUlJSX3fX1JSQk+/PBDfP/99zhw4ADS0tLw+uuvi/sVCgV27tyJoqKiZte6ZMkSvPbaazh58iRUKhUef/xxXskh0nEMMETUKvz9/fH222/Dy8sLYWFhMDMzg4ODA2bPng0vLy8sXboUeXl5OH369H1fX1lZiXXr1qFfv37o06cP5s2bh71794r7//vf/+Lw4cOwt7dH//79sWjRIhw6dKhJtc6bNw8TJ05Ejx49sHbtWtjY2ODbb79t0rGIqG0wwBBRq+jZs6f4Z2NjY9jb28PPz0/c5uzsDADIycm57+stLCzQtWtX8bmLi4vG2GHDhuHy5cvYu3cvnn76aZw5cwZDhw7F8uXLG12rSqUS/2xiYoJ+/frh3LlzjT4OEbUdBhgiahWmpqYazyUSicY2iUQCAKipqWnw6wVBuGfM0KFDERoaij///BPLli3D8uXLUVFR8cDXVFZWNm1CRKRTGGCIyGD4+PigqqoKZWVlAABHR0eNL/5evHjxvt+5OXLkiPjnqqoqxMXFoUePHq1fMBE1GduoiUgvDR8+HJMnT0a/fv1gb2+Ps2fP4q233sKIESMgl8sBACNHjsQXX3wBlUqF6upqhIaG3nNlBwC+/PJLeHl5oUePHli9ejVu3ryp0c1ERLqHV2CISC8FBQVh48aNCAwMRI8ePTB//nwEBQVh8+bN4piPPvoISqUSQ4cOxfPPP4/XX38dFhYW9xxr5cqVWLlyJfz9/XHw4EFs27YNDg4ObTkdImokiXD3B8RERO1EamoqPDw8cPLkSfTq1Uvb5RBRI/AKDBEREekdBhgiIiLSO/wIiYiIiPQOr8AQERGR3mGAISIiIr3DAENERER6hwGGiIiI9A4DDBEREekdBhgiIiLSOwwwREREpHcYYIiIiEjv/D99OOK/Cyf/iAAAAABJRU5ErkJggg==\n" 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\n" 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8GeQJM0M9XMwqxItfH8P08JNIuVksdolEREQAALUuWrhz5w4CAgLQr18/REZGwtbWFsnJybCysqpzm9TUVAQFBWHWrFn4/vvvsX//fkyfPh2Ojo4IDAwEAHz44YdYv349wsPD0bFjR5w8eRJTpkyBhYUFFixY8HgzpIcy0JNiep82GOHrjC/2J+PbuGv462IOopNyMb6XK14e4AErEwOxyyQiomZMra+Eli5diiNHjuDQoUP1/gVLlixBREQEzp8/r1w2ZswY5OfnY+/evQCAZ599Fvb29ti0aZNyzMiRI2FkZITvvvuuXr+HXwk1nCu5xVj5x0Xsv5QLALAw0seCAR6Y0MsVBnp8sCIRETWcRvlKaPfu3fDz88OoUaNgZ2cHHx8fhIWFPXCb2NhYDBw4UGVZYGAgYmNjlT/37t0b+/fvx+XLlwEAZ86cweHDh/H000+rUx41EHc7U2ya3B3fTeuJDg5mKCirwvI9iRj0aQyiLmTzqzoiImpyagWWlJQUrF+/Hh4eHoiKisLs2bOxYMEChIeH17lNdnY27O3tVZbZ29ujsLAQZWVlAGrP3IwZMwYdOnSAvr4+fHx8sHDhQowbN67O/VZUVKCwsFDlRQ3rCQ8bRCzogw9HesPGVI60vFK89G08xobF4XxmgdjlERFRM6JWYFEoFPD19cWKFSvg4+ODmTNnYsaMGdiwYcNjFbFz5058//332LZtG06dOoXw8HB8/PHHDwxCK1euhIWFhfLl4uLyWDXQ/cmkEozu3grRr/XFvH7ukOtJEZdyG0O+OoxXfzyDnMJysUskIqJmQK3A4ujoCC8vL5Vlnp6eSE9Pr3MbBwcH5OTkqCzLycmBubk5jIxq77D62muvKc+yeHt7Y8KECQgODsbKlSvr3G9ISAgKCgqUr4yMDHWmQmoylevh1cD2OPBqXwzr6gRBAH6Kv46+q6Px+V/JKK2sFrtEIiLSYWoFloCAACQlJaksu3z5MlxdXevcxt/fH/v371dZtm/fPvj7+yt/Li0thVSqWopMJoNCoahzv3K5HObm5iovanwtLY3w+Rgf7JrTG91crVBWVYNP/7qM/h/H4Of461AoeH0LERE1PLUCS3BwMOLi4rBixQpcuXIF27ZtQ2hoKObOnascExISgokTJyp/njVrFlJSUrB48WJcunQJ69atw86dOxEcHKwcM2TIEHzwwQeIiIhAWloadu3ahU8++QTPPfdcA0yRGoNPKyv8NMsfX73oA2crI2QXluOVH89g2NojOJaSJ3Z5RESkY9S+0+2ePXsQEhKC5ORkuLm5YdGiRZgxY4Zy/eTJk5GWlobo6GjlsujoaAQHByMxMRHOzs5YtmwZJk+erFxfVFSEZcuWYdeuXcjNzYWTkxPGjh2Lt956CwYG9bv/B9uaxVNeVYOtR9Pw1YErKK6o/WpocEcHhDzTAa7WJiJXR0REmoy35qcmd6u4Ap/uu4ztx9OhEAB9mQSTe7fGvP4esDDSF7s8IiLSQAwsJJrLOUV4P+Ii/r58EwBgZayPhQPb4cWeraAv443niIjo/zGwkOiik3LxQcRFJOfWPpOora0J3gjyRL/2dpBI+ERoIiJiYBG7HPpHdY0CO05k4NN9l5FXUgkAeMLdBm8+64kODjxORETNHQMLaZTC8iqsPXgFWw6nobJGAakEGN3dBcH/awc7M0OxyyMiIpEwsJBGyrhdilV7LyHibBYAwMRAhjn93DHtCTcY6stEro6IiJoaAwtptJNpt7F8TyLOXK99JlFLSyMsHtweQ7s48foWIqJmhIGFNJ5CIeD3szfwYeQl3CiofSZRVxdLLHvWC91crUSujoiImgIDC2mN8qoafH0oBeuir6K0sgYA8GxnRywZ3AEuLYxFro6IiBoTAwtpndyicnzy52X8cDIDggAY6EkxNcANc/u1hZkhbzxHRKSLGFhIayXeKMT7EYk4erX2mUTWJgZYNKgdRvu5QI83niMi0ikMLKTVBEHAgUu5+OCPi0i5WQIAaGdvijeCvPBUO1uRqyMioobCwEI6oapGge/jruGz/cnIL60CAPRtb4s3nvGEh72ZyNUREdHjYmAhnVJQWoUvDyQjPDYNVTUCZFIJxvZwQfDAdrA2lYtdHhERPSIGFtJJqbdKsCryIqIu5AAAzOR6mNffHZMDWkOuxxvPERFpGwYW0mlxKXl4PyIR5zMLAQAuLYywdLAnnvF24I3niIi0CAML6TyFQsAvpzOxOuoScgorAAB+rlZ481kvdHWxFLc4IiKqFwYWajZKK6sR+ncKNsakoKyq9sZzw7s6YfHgDnCyNBK5OiIiehAGFmp2sgvKsToqCT+fug4AkOtJMfPJNpj1VFuYyPVEro6IiO6HgYWarXPXC7A8IhHHU28DAGzN5HhtUHs8380ZUimvbyEi0iQMLNSsCYKAqAs5WBl5EdfySgEAXZwt8PbQjvBtxQcrEhFpCgYWIgCV1QqEH03D5/uTUVxRDQAY6euMJYPbw87cUOTqiIiIgYXoX3KLyrF6bxJ+jK+9vsVUrof5/d0xJcANBnp8PhERkVgYWIjuIyEjH+/svoCEjHwAQBsbEywb4oV+7e3ELYyIqJliYCGqw937t6yKvIRbxbX3bxnQwQ7LnvVCaxsTkasjImpeGFiIHqKovApfHriCzYdTUa0QYCCTYuoTbpjX3x2mbIMmImoSDCxE9XQltxjL9yQi5vJNAICdmRwhz3TA8K4teZt/IqJGxsBCpAZBEHDgUi7e25OobIPu5mqFd4Z0hLezhcjVERHpLgYWokdQUV2DTYdT8dWBKyitrIFEAozp7oJXB7WHtalc7PKIiHQOAwvRY8guKMeqyIv4NeEGAMDMUA/BA9thgr8r9GVsgyYiaigMLEQN4GTabby9+wIu3CgEAHjYmeLtIR3xhIeNyJUREekGBhaiBlKjELDzZAZWRyXhdkklAGBwRwe8EeQJlxbGIldHRKTdGFiIGlhBaRU+/esyvo27hhqFALmeFC892Qaz+7rDyEAmdnlERFqJgYWokSRlF+Hd3y/g6NU8AICThSFeD/JEkLcj26CJiNTEwELUiGqfBp2N5XsuIjO/DADQ060F3hnaEZ6O/PtHRFRfDCxETaC8qgYbY1KwLvoKKqoVkEqAcT1dseh/7WBlYiB2eUREGo+BhagJXb9TipV/XELEuSwAgKWxPl4Z1B4v9mgFmZRfExER1YWBhUgEsVfz8O7vF3ApuwgA4OlojneGeKFnG2uRKyMi0kwMLEQiqa5RYNvxdKz58zIKyqoAAEO6OCHk6Q5wsjQSuToiIs3CwEIkstsllVjzZxK2HU+HIABG+jLM6dsWM55sA0N9tkETEQEMLGKXQ6R0PrMA7/5+ASfS7gAAXFoY4c0gLwzysmcbNBE1ewwsRBpEEATsPnMDK/+4hOzCcgBAHw8bvD3EC+52ZiJXR0QkHgYWIg1UWlmNdQevIvTvFFTWKKAnlWCif2u8PNADFkb6YpdHRNTkGFiINNi1vBK8H3ER+xJzAADWJgZYPLg9RnVzgZRt0ETUjDCwEGmBvy/fxLu/X8DVmyUAgM7OFnh7SEd0c7USuTIioqbBwEKkJapqFAg/mobP/0pGUUU1AGCEb0ssHdwBduaGIldHRNS4GFiItMzNogqsjrqEnSevAwBMDGSYP8ADUwJaQ67HNmgi0k0MLERa6kxGPt7efQEJGfkAADcbE7z1rBf6dbATtzAiokbAwEKkxRQKAbtOZ2LV3ku4WVQBAOjfwQ7LnvWCm42JyNURETUcBhYiHVBUXoWvDlzB5iOpqKoRoC+TYNoTbTCvvztM5Xpil0dE9NgYWIh0yNWbxXjv90TEXL4JALAzk2Pp0x0wvGtLtkETkVZjYCHSMYIg4MClXCzfk4i0vFIAgG8rS7wztCM6O1uKWxwR0SNiYCHSURXVNdh8OA1fHkhGaWUNJBLghW4ueG1we9iYysUuj4hILQwsRDoup7AcqyIvYdfpTACAmaEeFg5sh4n+rtCXSUWujoiofhhYiJqJ+Gu38fbuCzifWQgAcLczxTtDOuIJDxuRKyMiejgGFqJmpEYh4MeTGfgoKgm3SyoBAIEd7fFmkBdcWhiLXB0RUd3q+/mt9nnjzMxMjB8/HtbW1jAyMoK3tzdOnjz5wG2io6Ph6+sLuVwOd3d3bN26tUH2S0S1ZFIJxvRohYOv9MWUgNaQSSWIupCDAZ/EYM2fSSitrBa7RCKix6JWYLlz5w4CAgKgr6+PyMhIJCYmYs2aNbCyqvtBbampqQgKCkK/fv2QkJCAhQsXYvr06YiKinqs/RLRvSyM9fH2kI6IfLkPAtytUVmtwJcHrmDAmhj8fuYGdOSEKhE1Q2p9JbR06VIcOXIEhw4dqvcvWLJkCSIiInD+/HnlsjFjxiA/Px979+595P3+F78SIlIlCAKiLmTj/YiLuH6nDADQw60F3hnSEV5OfI8QkWZolK+Edu/eDT8/P4waNQp2dnbw8fFBWFjYA7eJjY3FwIEDVZYFBgYiNjb2sfZLRA8mkUgwuJMj/lr0FBb9rx0M9aU4nnobz355CMt+PY87/1zrQkSkDdQKLCkpKVi/fj08PDwQFRWF2bNnY8GCBQgPD69zm+zsbNjb26sss7e3R2FhIcrKyh55vxUVFSgsLFR5EdG9DPVlWDDAA/tf6Yugzo5QCMC3cdfQb000vo1NQ3WNQuwSiYgeSq3AolAo4OvrixUrVsDHxwczZ87EjBkzsGHDhscq4lH2u3LlSlhYWChfLi4uj1UDka5raWmEtS/6YvuMXujgYIb80ios++0Cnv3yMOJS8sQuj4jogdQKLI6OjvDy8lJZ5unpifT09Dq3cXBwQE5OjsqynJwcmJubw8jI6JH3GxISgoKCAuUrIyNDnakQNVv+ba2xZ/4TWD6sIyyM9HEpuwhjQuMwb9sp3MgvE7s8IqL7UiuwBAQEICkpSWXZ5cuX4erqWuc2/v7+2L9/v8qyffv2wd/f/7H2K5fLYW5urvIiovrRk0kxwb81ol/ti/G9WkEqAfaczUL/NdH4Yn8yyqtqxC6RiEiFWoElODgYcXFxWLFiBa5cuYJt27YhNDQUc+fOVY4JCQnBxIkTlT/PmjULKSkpWLx4MS5duoR169Zh586dCA4OVmu/RNTwrEwM8P5wb+yZ3wc93FqgvEqBT/ZdxsBPYrD3fDbboIlIY6h9p9s9e/YgJCQEycnJcHNzw6JFizBjxgzl+smTJyMtLQ3R0dHKZdHR0QgODkZiYiKcnZ2xbNkyTJ48Wa39PgzbmokejyAI2HM2Cyv+uIisgnIAwBPuNnh7iBc87M1Ero6IdBVvzU9Ej6S0shrro69i498pqKxWQCaVYJJ/a7w80AMWRvpil0dEOoaBhYgeS3peKd6PSMSfibUXzVubGOC1wPYY5ecCmVQicnVEpCsYWIioQRxKvol3f0/EldxiAIB3Swu8M9QL3VxbiFwZEekCBhYiajBVNQp8E3sNn+27jKKK2gcpjvBpiSVPd4C9uaHI1RGRNmNgIaIGd6u4Aqv3JmFnfAYEATAxkGFefw9MfaI15HoyscsjIi3EwEJEjeZMRj7e+f0CTqfnAwBaWxvjrSFe6N/B/sEbEhH9BwMLETUqhULArwmZWBl5CTeLKgAA/drbYtmzXmhjaypydUSkLRhYiKhJFFdU48sDydh8OBVVNQL0ZRJMDXDDvP7uMDNkGzQRPRgDCxE1qZSbxVi+JxEHk24CAGzN5Fg6uAOe82kJKdugiagODCxEJIoDl3Lw3u+JSMsrBQD4tLLEO0M6oouLpbiFEZFGYmAhItFUVNdgy5E0fLk/GSWVNZBIgBe6ueC1we1hYyoXuzwi0iAMLEQkupzCcnwYeQm/nM4EAJjJ9fDyQA9M6t0a+jK1nr1KRDqKgYWINEb8tTt4Z/cFnMssAAC425ni7SFe6ONhK3JlRCQ2BhYi0igKhYAf4zPw0d4k5JVUAgAGednjzSAvtLI2Frk6IhILAwsRaaSCsip8/lcywmPTUKMQYKAnxcw+bTCnX1sYG+iJXR4RNTEGFiLSaMk5RXj390QcvnILAOBoYYiQZzwxpLMjJBK2QRM1FwwsRKTxBEHAn4k5WL4nEdfvlAEAerRugbeHeqGjk4XI1RFRU2BgISKtUV5Vg7C/U7A2+grKqxSQSoAXe7bCK/9rDysTA7HLI6JGxMBCRFrnRn4ZVvxxEXvOZgEALIz08cqgdnixRyvosQ2aSCcxsBCR1opLycM7uy/gUnYRAKCDgxlWP98F3s78mohI19T385v/ZCEijdOrjTX2zH8Cy4d3gqWxPi5lF2HUxqPYez5b7NKISCQMLESkkfRkUkzo5YqDr/RFv/a2KK9SYPb38Qj9+yp05MQwEamBgYWINJqViQHCJvphor8rBAFY8cclvPHreVTVKMQujYiaEAMLEWk8PZkU7w7tiLee9YJEAmw7lo6pW0+gsLxK7NKIqIkwsBCRVpBIJJj6hBtCJ/jBSF+GQ8m38Pz6o7h+p1Ts0oioCTCwEJFW+Z+XPX6c5Q97czku5xRj+NqjSMjIF7ssImpkDCxEpHU6tbTAr3MD4OlojlvFFRgTGovIc1lil0VEjYiBhYi0kqOFEX6c5Y/+Hez+6SA6hY0x7CAi0lUMLESktUzlegid0A2Te7cGAKyMvITXd51jBxGRDmJgISKtpieT4p2hHfH2EC9IJcD24xnsICLSQQwsRKQTpgS4IWyiH4wNajuIRq47iozb7CAi0hUMLESkMwZ42mPnS7UdRMm5xXhu3RGcTr8jdllE1AAYWIhIp3RqaYHf5j4BL0dz3CquxJjQOPzBDiIircfAQkQ6x8HCED/O8seADnaoqFZgzvensD6aHURE2oyBhYh0kolcD6ET/ZQdRB/uvYSlP7ODiEhbMbAQkc6SSSV4Z2hHvDu0I6QS4IeTGZi85TgKythBRKRtGFiISOdN6t0aX0/yg4mBDEeu5GHkenYQEWkbBhYiahb6d7DHj7N6w8HcEFdyizF87RGcYgcRkdZgYCGiZsPLyRy/zg1ARydz5JVUYmxoHCLOsoOISBswsBBRs+JgYYidL/ljoGdtB9Hcbaew9uAVdhARaTgGFiJqdkzketg4wQ9TA9wAAKujkrDk57PsICLSYAwsRNQsyaQSvDXEC8uH1XYQ7Tx5HZM2s4OISFMxsBBRszbBvzU2Te4OEwMZjl5lBxGRpmJgIaJmr197O/w4qzccLf6/gyj+GjuIiDQJAwsREf6/g6hTy386iMLisOfsDbHLIqJ/MLAQEf3D3ry2g+h/XvaorFZg3rbT7CAi0hAMLERE/2JsoIcN47th2hP/30G0+KezqKxmBxGRmBhYiIj+QyaVYNmzXlg+vBOkEuDH+H86iErZQUQkFgYWIqI6TOjlquwgik3Jw4j1R5Cexw4iIjEwsBARPUC/9nb4aXZvOFkY4urNEgxfdwTx126LXRZRs8PAQkT0EJ6OtR1E3i0tcLukEmPDjmH3GXYQETUlBhYionqwMzfEDy/1wqB/OogWbD+Nrw4ks4OIqIkwsBAR1ZOxgR7Wj++GGX1qO4g+/vMyXv2RHURETYGBhYhIDTKpBG8EeeH94Z0gk0rw86nrmLj5GPJLK8UujUinMbAQET2C8b1csXlyd5jK9RCXchsj1h3FtbwSscsi0lkMLEREj+ipdrb4abY/WloaIeVWCYavPYKTaewgImoMDCxERI+hg4M5ds3tjc7OFrhTWoUXw47ht4RMscsi0jlqB5bMzEyMHz8e1tbWMDIygre3N06ePPnAbaKjo+Hr6wu5XA53d3ds3bq1zrGrVq2CRCLBwoUL1S2NiEgUdmaG+GGmPwI72qOyRoGXdyTgi/3sICJqSGoFljt37iAgIAD6+vqIjIxEYmIi1qxZAysrqzq3SU1NRVBQEPr164eEhAQsXLgQ06dPR1RU1D1jT5w4gY0bN6Jz587qz4SISERGBjKsH9cNM59sAwD4ZN9lvPLjGVRU14hcGZFu0FNn8IcffggXFxds2bJFuczNze2B22zYsAFubm5Ys2YNAMDT0xOHDx/Gp59+isDAQOW44uJijBs3DmFhYXj//ffVKYuISCNIpRK8/ownWlubYNlv5/HLqUxcv1OG0AndYGlsIHZ5RFpNrTMsu3fvhp+fH0aNGgU7Ozv4+PggLCzsgdvExsZi4MCBKssCAwMRGxursmzu3LkICgq6Z2xdKioqUFhYqPIiItIEL/ZshS2Tu8NMrofjqbfx3LqjSLvFDiKix6FWYElJScH69evh4eGBqKgozJ49GwsWLEB4eHid22RnZ8Pe3l5lmb29PQoLC1FWVgYA2LFjB06dOoWVK1fWu5aVK1fCwsJC+XJxcVFnKkREjerJdrb4aXZvtLQ0QuqtEjy37ghOsIOI6JGpFVgUCgV8fX2xYsUK+Pj4YObMmZgxYwY2bNjwyAVkZGTg5Zdfxvfffw9DQ8N6bxcSEoKCggLlKyMj45FrICJqDO0dzLBrbm90+aeDaFzYMfx6mh1ERI9CrcDi6OgILy8vlWWenp5IT0+vcxsHBwfk5OSoLMvJyYG5uTmMjIwQHx+P3Nxc+Pr6Qk9PD3p6eoiJicEXX3wBPT091NTc/4I1uVwOc3NzlRcRkaaxMzPEjpn+eLqTAyprFFj4QwI+++syO4iI1KRWYAkICEBSUpLKssuXL8PV1bXObfz9/bF//36VZfv27YO/vz8AYMCAATh37hwSEhKULz8/P4wbNw4JCQmQyWTqlEhEpHGMDGRY+6IvXnqqtoPos7+SsWgnO4iI1KFWl1BwcDB69+6NFStW4IUXXsDx48cRGhqK0NBQ5ZiQkBBkZmbim2++AQDMmjULX331FRYvXoypU6fiwIED2LlzJyIiIgAAZmZm6NSpk8rvMTExgbW19T3LiYi0lVQqQcjTtR1Eb/56HrtOZyLzThk2TugGKxN2EBE9jFpnWLp3745du3Zh+/bt6NSpE5YvX47PPvsM48aNU47JyspS+YrIzc0NERER2LdvH7p06YI1a9bg66+/VmlpJiJqLsb2aIXwKT1qO4jSbmPE+qNIZQcR0UNJBB35IrWwsBAWFhYoKCjg9SxEpPEu5xRhypYTyMwvg6WxPjaO74aebazFLouoydX385vPEiIiEkE7ezP8OjcAXV0skV9ahfGbjmHX6etil0WksRhYiIhEYmsmx46ZvfCMtwOqagQE/3AGn+5jBxHR/TCwEBGJyFBfhq/G+mJ237YAgM/3JyP4hwR2EBH9BwMLEZHIpFIJlgzugA9HekNPKsGvCTcw/utjuF1SKXZpRBqDgYWISEOM7t4K4VN7wMxQDyfS7uC5dUeQcrNY7LKINAIDCxGRBglwt8Evs3vD2coI1/JK8dy6o4hLyRO7LCLRMbAQEWkYj386iHxaWaKgrAoTNh3Dz/HsIKLmjYGFiEgD2ZjKsX1GLwR1dkRVjYBXfjyDT/5MYgcRNVsMLEREGspQX4Yvx/hgbr/aDqIvDlzByzsSUF7FDiJqfhhYiIg0mFQqwWuBHfDRyM7Qk0qw+0xtB1FecYXYpRE1KQYWIiIt8EJ3F2UH0clrd/DcuqO4yg4iakYYWIiItESAuw12zekNlxZGSL9dihHrjiL2KjuIqHlgYCEi0iLudmbYNScAvv90EE3cfAw/sYOImgEGFiIiLWNjKse2Gb3w7D8dRK/+eAYfRyVBoWAHEekuBhYiIi1kqC/DF//qIPrq4BUs2HGaHUSksxhYiIi0lLKD6PnaDqI9Z7PwYlgcO4hIJzGwEBFpuRf8XPDNtB4wN9TDqfR8PLfuKK7ksoOIdAsDCxGRDujd1ga/zAlAqxbG/3QQHcHRK7fELouowTCwEBHpCHc7U+ya0xvdXK1QWF6NiZuPY+fJDLHLImoQDCxERDrE2lSO76f3xJAuTqhWCFj801l8tPcSO4hI6zGwEBHpGEN9GT4f3RXz+7sDANZFX8V8dhCRlmNgISLSQVKpBK8Mao+PR3WBvkyCiLNZGBsWh1vsICItxcBCRKTDnu/mjG+m9oSFkT5Op+fjuXVHcCW3SOyyiNTGwEJEpOP821rjlzm94WptjIzbZXhu3VEcYQcRaRkGFiKiZqCtrSl2zQmAn6sVisqrMWnzcfxwIl3ssojqjYGFiKiZaGFigO+m98SwrrUdREt+PocP2UFEWoKBhYioGTHUl+Gz0V2xYIAHAGB99FXM384OItJ8DCxERM2MRCLBov+1w5q7HUTnsjAmNA43i9hBRJqLgYWIqJka2c0Z306r7SBKyKjtIErOYQcRaSYGFiKiZqxXG2vsmtMbra2Ncf1OGUasO4rDyewgIs3DwEJE1My1sTXFL3MC0L21FYoqqjF5y3HsOM4OItIsDCxERKTsIBr+TwfR0l/OYWXkRXYQkcZgYCEiIgCAXE+GT0d3xcKBtR1EG2NSMHfbKZRVsoOIxMfAQkREShKJBAsHtsOno7vAQCZF5PlsjAmLQ25RudilUTPHwEJERPd4zscZ303vCUtjfZzJyMdza4/iMjuISEQMLEREdF893Fpg15wAuNmYIDO/DCPXHcWh5Jtil0XNFAMLERHVyc3GBL/M7o0ebi3+6SA6gW3H2EFETY+BhYiIHsjKxADfTuuB53xaokYh4PVd57DiD3YQUdNiYCEiooeS68nwyQtdEDywHQAg9O8UzP4+nh1E1GQYWIiIqF4kEgleHuiBz8d0hYFMiqgLORgTGssOImoSDCxERKSWYV1b4vsZPWFlrI8z1wvw3NqjSMpmBxE1LgYWIiJSW/fW/+kgWn8UMZfZQUSNh4GFiIgeSet/dRAVV1Rj6tYT+C7umthlkY5iYCEiokd2t4NoxD8dRG/+eh4fRCSyg4gaHAMLERE9FrmeDGte6IJF/6vtIAo7lMoOImpwDCxERPTYJBIJFgxQ7SAaHRqL3EJ2EFHDYGAhIqIGM6xrS2z7p4Po7PUCDF97BJeyC8Uui3QAAwsRETUov386iNrYmOBGQTmeXx+L6KRcscsiLcfAQkREDa61jQl+mdMbvdrUdhBNCz+JX05dF7ss0mIMLERE1CgsjQ3wzdSeGOFb20H0yo9nsPNEhthlkZZiYCEiokZjoCfFx893wfherSAIwOKfz/JeLfRIGFiIiKhRSaUSLB/WCVMCWgMA3vz1PLYcSRW3KNI6DCxERNToJBIJ3nrWCy891QYA8O7viQj7O0XkqkibMLAQEVGTkEgkWDq4A+b3dwcAfPDHRaw9eEXkqkhbMLAQEVGTkUgkeGVQe+VdcVdHJeHTfZchCLyVPz2Y2oElMzMT48ePh7W1NYyMjODt7Y2TJ08+cJvo6Gj4+vpCLpfD3d0dW7duVVm/cuVKdO/eHWZmZrCzs8Pw4cORlJSkbmlERKQlFgzwwJLBHQAAn+9PxuqoJIYWeiC1AsudO3cQEBAAfX19REZGIjExEWvWrIGVlVWd26SmpiIoKAj9+vVDQkICFi5ciOnTpyMqKko5JiYmBnPnzkVcXBz27duHqqoqDBo0CCUlJY8+MyIi0miz+7bFm0GeAIB10Vex4o+LDC1UJ4mgxt+OpUuX4siRIzh06FC9f8GSJUsQERGB8+fPK5eNGTMG+fn52Lt37323uXnzJuzs7BATE4Mnn3yyXr+nsLAQFhYWKCgogLm5eb3rIyIicX0Tm4a3frsAAJjcuzXeHuIFiUQiclXUVOr7+a3WGZbdu3fDz88Po0aNgp2dHXx8fBAWFvbAbWJjYzFw4ECVZYGBgYiNja1zm4KCAgBAixYt6hxTUVGBwsJClRcREWmfif6tsXKENyQSYOvRNLzx63koFDzTQqrUCiwpKSlYv349PDw8EBUVhdmzZ2PBggUIDw+vc5vs7GzY29urLLO3t0dhYSHKysruGa9QKLBw4UIEBASgU6dOde535cqVsLCwUL5cXFzUmQoREWmQsT1a4aORnSGRANuOpWPJz2dRw9BC/6KnzmCFQgE/Pz+sWLECAODj44Pz589jw4YNmDRpUoMUNHfuXJw/fx6HDx9+4LiQkBAsWrRI+XNhYSFDCxGRFhvl5wIDPSmCf0jAj/HXUa0QsPr5ztCTsaGV1DzD4ujoCC8vL5Vlnp6eSE9Pr3MbBwcH5OTkqCzLycmBubk5jIyMVJbPmzcPe/bswcGDB+Hs7PzAWuRyOczNzVVeRESk3YZ1bYkvx/pCJpVg1+lMLPwhAVU1CrHLIg2gVmAJCAi4p9348uXLcHV1rXMbf39/7N+/X2XZvn374O/vr/xZEATMmzcPu3btwoEDB+Dm5qZOWUREpEOCOjti3Thf6Msk2HM2C/O2nUJlNUNLc6dWYAkODkZcXBxWrFiBK1euYNu2bQgNDcXcuXOVY0JCQjBx4kTlz7NmzUJKSgoWL16MS5cuYd26ddi5cyeCg4OVY+bOnYvvvvsO27Ztg5mZGbKzs5GdnX3fa1yIiEj3BXZ0wMYJ3WAgkyLqQg5mfxePiuoascsiEanV1gwAe/bsQUhICJKTk+Hm5oZFixZhxowZyvWTJ09GWloaoqOjlcuio6MRHByMxMREODs7Y9myZZg8efL/F1FH+9qWLVtUxj0I25qJiHTP35dvYsY3J1FRrcCT7WwROqEbDPVlYpdFDai+n99qBxZNxcBCRKSbjl65hWnhJ1FWVYPeba3x9SQ/GBuo1TNCGqxR7sNCRETU1Hq72yB8ag+YGMhw9GoeJm85geKKarHLoibGwEJERBqvh1sLfDOtJ8zkejieehuTNh9HYXmV2GVRE2JgISIirdDN1QrfTe8Jc0M9xF+7gwmbjqOglKGluWBgISIirdHFxRLbZvSClbE+zmTk48Wv43CnpFLssqgJMLAQEZFW6dTSAttn9oK1iQEu3CjE2LA43CquELssamQMLEREpHU6OJhjx8xesDWT41J2EcaGxiG3sFzssqgRMbAQEZFW8rA3ww8ze8HB3BDJucUYExqH7AKGFl3FwEJERFqrja0pfnipF1paGiHlVglGh8YiM593SddFDCxERKTVXK1N8MNLveDSwgjX8koxemMsMm6Xil0WNTAGFiIi0nrOVsbY+ZI/3GxMcP1OGV7YGIu0WyVil0UNiIGFiIh0gqOFEX6Y2QttbU2QVVCOFzbG4kpusdhlUQNhYCEiIp1hZ26IHTP90d7eDLlFFRgTGouk7CKxy6IGwMBCREQ6xdZMju0ze8HL0Ry3iisxNiwOiTcKxS6LHhMDCxER6ZwWJgbYNqMnOjtb4HZJbWg5d71A7LLoMTCwEBGRTrI0NsB303vCp5UlCsqq8OLXcTiVfkfssugRMbAQEZHOMjfUx7fTeqJ7aysUlVdj4qbjOJF2W+yy6BEwsBARkU4zleshfGoP+LexRnFFNSZtPo7Yq3lil0VqYmAhIiKdZ2ygh82Tu6OPhw1KK2swZetxHE6+JXZZpAYGFiIiahaMDGQIm+iHfu1tUV6lwNTwEziYlCt2WVRPDCxERNRsGOrLsGFCN/zPyx6V1Qq89E089iXmiF0W1QMDCxERNStyPRnWjfNFkLcjKmsUmP1dPCLPZYldFj0EAwsRETU7+jIpPh/TFcO6OqFaIWDe9tP4LSFT7LLoARhYiIioWdKTSfHJC10x0tcZNQoBwT8k4Of462KXRXVgYCEiomZLJpVg9fOdMbaHCxQC8OpPZ/DDiXSxy6L7YGAhIqJmTSqV4IPh3pjo7wpBAJb8fA7fxl0Tuyz6DwYWIiJq9qRSCd4d2hFTA9wAAMt+PY/Nh1NFror+jYGFiIgIgEQiwbJnPTHrqbYAgPf2JGJjzFWRq6K7GFiIiIj+IZFIsGRweyzo7w4AWBl5CV8dSBa5KgIYWIiIiFRIJBIsGtQer/yvHQDg4z8v45N9lyEIgsiVNW8MLERERPcxf4AHlj7dAQDwxf5kfBSVxNAiIgYWIiKiOsx6qi2WPesFAFgffRUfRFxkaBEJAwsREdEDTHvCDcuHdQQAfH04Fe/svgCFgqGlqTGwEBERPcQE/9ZYNcIbEgkQHnsNb/x6nqGliTGwEBER1cOYHq3w8fNdIJUA24+nY/HPZ1HD0NJkGFiIiIjqaWQ3Z3w6uitkUgl+ir+ORTsTUF2jELusZoGBhYiISA3DurbEl2N9oCeV4LeEG3h5RwKqGFoaHQMLERGRmp7xdsS6cb7Ql0kQcS4Lc78/hcpqhpbGxMBCRET0CAZ1dEDoBD8Y6EnxZ2IOZn0Xj/KqGrHL0lkMLERERI+oXwc7bJrkB7meFAcu5WLGNycZWhoJAwsREdFj6ONhiy1TusNIX4ZDybcwdesJlFZWi12WzmFgISIieky929rgm2k9YGIgw9GreZi8+QSKKxhaGhIDCxERUQPo3roFvp3eE2ZyPRxPu42Jm46hsLxK7LJ0BgMLERFRA/FtZYXvZ/SEhZE+TqXnY8LXx1BQytDSEBhYiIiIGlBnZ0tsm9ETVsb6OHO9AGPD4nC7pFLssrQeAwsREVED6+hkgR0z/WFjaoDErEK8GBaHW8UVYpel1RhYiIiIGkF7BzPsmOkPOzM5LmUXYUxoHHILy8UuS2sxsBARETUSdztT/PCSPxwtDHEltxijQ+OQVVAmdllaiYGFiIioEbnZmGDnS/5oaWmE1FslGL0xDtfvlIpdltZhYCEiImpkLi2M8cNLvdCqhTHSb5di9MY4pOcxtKiDgYWIiKgJOFvVhpY2NibIzC/D6NBYpN4qEbssrcHAQkRE1EQcLYywY2YvuNuZIqugHKM3xuJKbpHYZWkFBhYiIqImZGduiB0ze6GDgxlyiyowJjQOSdkMLQ/DwEJERNTEbEzl2D6jFzo6meNWcSXGhMbiwo0CscvSaAwsREREIrAyMcC26b3QxdkCd0qr8GLYMZy9ni92WRqLgYWIiEgkFsb6+HZ6T/i2skRBWRXGhR3DqfQ7YpelkdQOLJmZmRg/fjysra1hZGQEb29vnDx58oHbREdHw9fXF3K5HO7u7ti6des9Y9auXYvWrVvD0NAQPXv2xPHjx9UtjYiISOuYG+rjm2k90aN1CxRVVGPC18dwIu222GVpHLUCy507dxAQEAB9fX1ERkYiMTERa9asgZWVVZ3bpKamIigoCP369UNCQgIWLlyI6dOnIyoqSjnmhx9+wKJFi/D222/j1KlT6NKlCwIDA5Gbm/voMyMiItISpnI9bJ3aHb3bWqOksgYTNx3H0au3xC5Lo0gEQRDqO3jp0qU4cuQIDh06VO9fsGTJEkREROD8+fPKZWPGjEF+fj727t0LAOjZsye6d++Or776CgCgUCjg4uKC+fPnY+nSpfX6PYWFhbCwsEBBQQHMzc3rXR8REZGmKK+qwYxvTuJQ8i3I9aQIm+iHJ9vZil1Wo6rv57daZ1h2794NPz8/jBo1CnZ2dvDx8UFYWNgDt4mNjcXAgQNVlgUGBiI2NhYAUFlZifj4eJUxUqkUAwcOVI4hIiJqDgz1ZQib6If+HexQUa3A9G9O4uAlftsAqBlYUlJSsH79enh4eCAqKgqzZ8/GggULEB4eXuc22dnZsLe3V1lmb2+PwsJClJWV4datW6ipqbnvmOzs7Dr3W1FRgcLCQpUXERGRtjPUl2HD+G4I7GiPymoFZn57En9eqPvzsLlQK7AoFAr4+vpixYoV8PHxwcyZMzFjxgxs2LChseqr08qVK2FhYaF8ubi4NHkNREREjcFAT4qvXvRFUGdHVNUImPP9KfxxLkvsskSlVmBxdHSEl5eXyjJPT0+kp6fXuY2DgwNycnJUluXk5MDc3BxGRkawsbGBTCa77xgHB4c69xsSEoKCggLlKyMjQ52pEBERaTR9mRSfj+6K4V2dUK0QMH/7afyWkCl2WaJRK7AEBAQgKSlJZdnly5fh6upa5zb+/v7Yv3+/yrJ9+/bB398fAGBgYIBu3bqpjFEoFNi/f79yzP3I5XKYm5urvIiIiHSJnkyKNS90xfPdnFGjEBD8QwJ+ir8udlmiUCuwBAcHIy4uDitWrMCVK1ewbds2hIaGYu7cucoxISEhmDhxovLnWbNmISUlBYsXL8alS5ewbt067Ny5E8HBwcoxixYtQlhYGMLDw3Hx4kXMnj0bJSUlmDJlSgNMkYiISHvJpBJ8NLIzxvZoBYUAvPbTGew4Xvc3G7pKT53B3bt3x65duxASEoL33nsPbm5u+OyzzzBu3DjlmKysLJWviNzc3BAREYHg4GB8/vnncHZ2xtdff43AwEDlmNGjR+PmzZt46623kJ2dja5du2Lv3r33XIhLRETUHEmlEqx4rhMMZBKEx17D0l/OoapGgQn+rcUurcmodR8WTcb7sBARka4TBAEfRFzE14dTAQDLnvXCtCfcRK7q8TTKfViIiIhIPBKJBG8EeWJ237YAgOV7ErEh5qrIVTUNBhYiIiItIpFIsDiwPV4e4AEAWBV5CV/uTxa5qsbHwEJERKRlJBIJgv/XDq8OagcAWLPvMj75Mwk6cpXHfTGwEBERaal5/T3w+jMdAABfHLiCD/fqbmhhYCEiItJiM59si7eH1N7UdUPMVSzfc1EnQwsDCxERkZabEuCG5cM7AQA2H0nFW79dgEKhW6GFgYWIiEgHTOjlig9HekMiAb6Nu4bXd53TqdDCwEJERKQjRndvhTWjukAqAXacyMBrP51FjY6EFgYWIiIiHTLC1xmfjfGBTCrBz6euY9HOBFTXKMQu67ExsBAREemYoV2c8NVYH+hJJfgt4QZe3pGAKi0PLQwsREREOuhpb0esH98NBjIpIs5lYc73p1BRXSN2WY+MgYWIiEhH/c/LHhsndoOBnhT7EnMw69t4lFdpZ2hhYCEiItJh/drbYfOk7jDUl+Jg0k3M+OYkyiq1L7QwsBAREem4JzxssGVyDxgbyHAo+Rambj2B0spqsctSCwMLERFRM+Df1hrfTO0BU7keYlPyMGnzcRRXaE9oYWAhIiJqJvxat8C303rAzFAPJ9LuYMKmYygsrxK7rHphYCEiImpGfFpZYdv0XrAw0sfp9HyM//oY8ksrxS7roRhYiIiImhlvZwtsn9ELLUwMcPZ6AV4MO4bbJZodWhhYiIiImiEvJ3PsmNkLNqZyJGYVYmxoHG4WVYhdVp0YWIiIiJqpdvZm2DGzF+zM5EjKKcKY0FjkFpaLXdZ9MbAQERE1Y+52pvjhJX84Whji6s0SjA6NQ1ZBmdhl3YOBhYiIqJlzszHBzpf80dLSCKm3SvDCxlhk3C4VuywVDCxEREQElxbG2DnLH67Wxsi4XYYxoXFIz9Oc0MLAQkRERACAlpZG+GGmP9rYmCAzvwwvbIxFys1iscsCwMBCRERE/+JgYYgdL/WCh50psgvLMTo0Dldyi8Qui4GFiIiIVNmZGWLHzF7o4GCGm0UVGL0xDpeyC0WtiYGFiIiI7mFtKsf2Gb3QqaU58koqMTY0Dok3xAstDCxERER0X1YmBvh+ei90cbGElbEBbMwMRKtFT7TfTERERBrPwkgf303rgdLKGtiZGYpWBwMLERERPZCZoT7MDPVFrYFfCREREZHGY2AhIiIijcfAQkRERBqPgYWIiIg0HgMLERERaTwGFiIiItJ4DCxERESk8RhYiIiISOMxsBAREZHGY2AhIiIijcfAQkRERBqPgYWIiIg0HgMLERERaTydeVqzIAgAgMLCQpErISIiovq6+7l993O8LjoTWIqKigAALi4uIldCRERE6ioqKoKFhUWd6yXCwyKNllAoFLhx4wbMzMwgkUgabL+FhYVwcXFBRkYGzM3NG2y/mkTX58j5aT9dnyPnp/10fY6NOT9BEFBUVAQnJydIpXVfqaIzZ1ikUimcnZ0bbf/m5uY6+Zfw33R9jpyf9tP1OXJ+2k/X59hY83vQmZW7eNEtERERaTwGFiIiItJ4DCwPIZfL8fbbb0Mul4tdSqPR9TlyftpP1+fI+Wk/XZ+jJsxPZy66JSIiIt3FMyxERESk8RhYiIiISOMxsBAREZHGa7aB5e+//8aQIUPg5OQEiUSCX3/9VWW9IAh466234OjoCCMjIwwcOBDJyckqY27fvo1x48bB3NwclpaWmDZtGoqLi5twFnV70PyqqqqwZMkSeHt7w8TEBE5OTpg4cSJu3Lihso/WrVtDIpGovFatWtXEM7m/hx2/yZMn31P74MGDVcZo8vEDHj7H/87v7mv16tXKMZp6DFeuXInu3bvDzMwMdnZ2GD58OJKSklTGlJeXY+7cubC2toapqSlGjhyJnJwclTHp6ekICgqCsbEx7Ozs8Nprr6G6uropp1Knh83x9u3bmD9/Ptq3bw8jIyO0atUKCxYsQEFBgcp+7neMd+zY0dTTuUd9jmHfvn3vqX3WrFkqY7T5GKalpdX5Pvzxxx+V4zT1GK5fvx6dO3dW3lvF398fkZGRyvWa9h5stoGlpKQEXbp0wdq1a++7/qOPPsIXX3yBDRs24NixYzAxMUFgYCDKy8uVY8aNG4cLFy5g37592LNnD/7++2/MnDmzqabwQA+aX2lpKU6dOoVly5bh1KlT+OWXX5CUlIShQ4feM/a9995DVlaW8jV//vymKP+hHnb8AGDw4MEqtW/fvl1lvSYfP+Dhc/z33LKysrB582ZIJBKMHDlSZZwmHsOYmBjMnTsXcXFx2LdvH6qqqjBo0CCUlJQoxwQHB+P333/Hjz/+iJiYGNy4cQMjRoxQrq+pqUFQUBAqKytx9OhRhIeHY+vWrXjrrbfEmNI9HjbHGzdu4MaNG/j4449x/vx5bN26FXv37sW0adPu2deWLVtUjuHw4cObeDb3qs8xBIAZM2ao1P7RRx8p12n7MXRxcbnnffjuu+/C1NQUTz/9tMq+NPEYOjs7Y9WqVYiPj8fJkyfRv39/DBs2DBcuXACgge9BgQQAwq5du5Q/KxQKwcHBQVi9erVyWX5+viCXy4Xt27cLgiAIiYmJAgDhxIkTyjGRkZGCRCIRMjMzm6z2+vjv/O7n+PHjAgDh2rVrymWurq7Cp59+2rjFNYD7zW/SpEnCsGHD6txGm46fINTvGA4bNkzo37+/yjJtOYa5ubkCACEmJkYQhNr3m76+vvDjjz8qx1y8eFEAIMTGxgqCIAh//PGHIJVKhezsbOWY9evXC+bm5kJFRUXTTqAe/jvH+9m5c6dgYGAgVFVVKZfV59hrgvvN76mnnhJefvnlOrfRxWPYtWtXYerUqSrLtOUYCoIgWFlZCV9//bVGvgeb7RmWB0lNTUV2djYGDhyoXGZhYYGePXsiNjYWABAbGwtLS0v4+fkpxwwcOBBSqRTHjh1r8pofV0FBASQSCSwtLVWWr1q1CtbW1vDx8cHq1as15lRtfURHR8POzg7t27fH7NmzkZeXp1yna8cvJycHERER9/3XuTYcw7tfg7Ro0QIAEB8fj6qqKpX3YIcOHdCqVSuV96C3tzfs7e2VYwIDA1FYWKj8F6Im+e8c6xpjbm4OPT3Vp6bMnTsXNjY26NGjBzZv3vzQp9qKoa75ff/997CxsUGnTp0QEhKC0tJS5TpdO4bx8fFISEi47/tQ049hTU0NduzYgZKSEvj7+2vke1BnniXUkLKzswFA5SDc/fnuuuzsbNjZ2ams19PTQ4sWLZRjtEV5eTmWLFmCsWPHqjwjYsGCBfD19UWLFi1w9OhRhISEICsrC5988omI1dbP4MGDMWLECLi5ueHq1at4/fXX8fTTTyM2NhYymUynjh8AhIeHw8zMTOV0LaAdx1ChUGDhwoUICAhAp06dANS+vwwMDO4J0P99D97vPXp3nSa53xz/69atW1i+fPk9X0u+99576N+/P4yNjfHnn39izpw5KC4uxoIFC5qi9Hqpa34vvvgiXF1d4eTkhLNnz2LJkiVISkrCL7/8AkD3juGmTZvg6emJ3r17qyzX5GN47tw5+Pv7o7y8HKampti1axe8vLyQkJCgce9BBpZmrqqqCi+88AIEQcD69etV1i1atEj5586dO8PAwAAvvfQSVq5cqfF3cxwzZozyz97e3ujcuTPatm2L6OhoDBgwQMTKGsfmzZsxbtw4GBoaqizXhmM4d+5cnD9/HocPHxa7lEbzsDkWFhYiKCgIXl5eeOedd1TWLVu2TPlnHx8flJSUYPXq1RrxYXdXXfP7d/jy9vaGo6MjBgwYgKtXr6Jt27ZNXeZjedgxLCsrw7Zt21SO112afAzbt2+PhIQEFBQU4KeffsKkSZMQExMjdln3xa+E7sPBwQEA7rkaOicnR7nOwcEBubm5Kuurq6tx+/Zt5RhNdzesXLt2Dfv27XvoEzh79uyJ6upqpKWlNU2BDahNmzawsbHBlStXAOjG8bvr0KFDSEpKwvTp0x86VtOO4bx587Bnzx4cPHhQ5WnrDg4OqKysRH5+vsr4/74H7/cevbtOU9Q1x7uKioowePBgmJmZYdeuXdDX13/g/nr27Inr16+joqKisUpWy8Pm9289e/YEAJX3oS4cQwD46aefUFpaiokTJz50f5p0DA0MDODu7o5u3bph5cqV6NKlCz7//HONfA8ysNyHm5sbHBwcsH//fuWywsJCHDt2DP7+/gAAf39/5OfnIz4+XjnmwIEDUCgUyjelJrsbVpKTk/HXX3/B2tr6odskJCRAKpXe81WKNrh+/Try8vLg6OgIQPuP379t2rQJ3bp1Q5cuXR46VlOOoSAImDdvHnbt2oUDBw7Azc1NZX23bt2gr6+v8h5MSkpCenq6ynvw3LlzKsHzbvD28vJqmok8wMPmCNT+f2XQoEEwMDDA7t277zlDdj8JCQmwsrIS/QxZfeb3XwkJCQCg8j7U9mN416ZNmzB06FDY2to+dL+acgzvR6FQoKKiQjPfgw1+Ga+WKCoqEk6fPi2cPn1aACB88sknwunTp5VdMqtWrRIsLS2F3377TTh79qwwbNgwwc3NTSgrK1PuY/DgwYKPj49w7Ngx4fDhw4KHh4cwduxYsaak4kHzq6ysFIYOHSo4OzsLCQkJQlZWlvJ198ruo0ePCp9++qmQkJAgXL16Vfjuu+8EW1tbYeLEiSLPrNaD5ldUVCS8+uqrQmxsrJCamir89ddfgq+vr+Dh4SGUl5cr96HJx08QHv53VBAEoaCgQDA2NhbWr19/z/aafAxnz54tWFhYCNHR0Sp//0pLS5VjZs2aJbRq1Uo4cOCAcPLkScHf31/w9/dXrq+urhY6deokDBo0SEhISBD27t0r2NraCiEhIWJM6R4Pm2NBQYHQs2dPwdvbW7hy5YrKmOrqakEQBGH37t1CWFiYcO7cOSE5OVlYt26dYGxsLLz11ltiTk0QhIfP78qVK8J7770nnDx5UkhNTRV+++03oU2bNsKTTz6p3Ie2H8O7kpOTBYlEIkRGRt6zD00+hkuXLhViYmKE1NRU4ezZs8LSpUsFiUQi/Pnnn4IgaN57sNkGloMHDwoA7nlNmjRJEITa1uZly5YJ9vb2glwuFwYMGCAkJSWp7CMvL08YO3asYGpqKpibmwtTpkwRioqKRJjNvR40v9TU1PuuAyAcPHhQEARBiI+PF3r27ClYWFgIhoaGgqenp7BixQqVD3wxPWh+paWlwqBBgwRbW1tBX19fcHV1FWbMmKHSeicImn38BOHhf0cFQRA2btwoGBkZCfn5+fdsr8nHsK6/f1u2bFGOKSsrE+bMmSNYWVkJxsbGwnPPPSdkZWWp7CctLU14+umnBSMjI8HGxkZ45ZVXVFqCxfSwOdZ1fAEIqampgiDUttp37dpVMDU1FUxMTIQuXboIGzZsEGpqasSb2D8eNr/09HThySefFFq0aCHI5XLB3d1deO2114SCggKV/WjzMbwrJCREcHFxue9x0eRjOHXqVMHV1VUwMDAQbG1thQEDBijDiiBo3nuQT2smIiIijcdrWIiIiEjjMbAQERGRxmNgISIiIo3HwEJEREQaj4GFiIiINB4DCxEREWk8BhYiIiLSeAwsREREpPEYWIioyUVHR0MikdzzYDUiorowsBBRk+vduzeysrJgYWFR721KS0sREhKCtm3bwtDQELa2tnjqqafw22+/NWKlRKQp9MQugIiaHwMDA7UfPz9r1iwcO3YMX375Jby8vJCXl4ejR48iLy+vkaokIk3CMyxE9Nj69u2L+fPnY+HChbCysoK9vT3CwsJQUlKCKVOmwMzMDO7u7oiMjARw71dCW7duhaWlJaKiouDp6QlTU1MMHjwYWVlZyt+xe/duvP7663jmmWfQunVrdOvWDfPnz8fUqVOVYyQSCX799VeV2iwtLbF161YAQFpaGiQSCXbs2IHevXvD0NAQnTp1QkxMTKP+9yGix8fAQkQNIjw8HDY2Njh+/Djmz5+P2bNnY9SoUejduzdOnTqFQYMGYcKECSgtLb3v9qWlpfj444/x7bff4u+//0Z6ejpeffVV5XoHBwf88ccfKCoqeuxaX3vtNbzyyis4ffo0/P39MWTIEJ6pIdJwDCxE1CC6dOmCN998Ex4eHggJCYGhoSFsbGwwY8YMeHh44K233kJeXh7Onj173+2rqqqwYcMG+Pn5wdfXF/PmzcP+/fuV60NDQ3H06FFYW1uje/fuCA4OxpEjRx6p1nnz5mHkyJHw9PTE+vXrYWFhgU2bNj3SvoioaTCwEFGD6Ny5s/LPMpkM1tbW8Pb2Vi6zt7cHAOTm5t53e2NjY7Rt21b5s6Ojo8rYJ598EikpKdi/fz+ef/55XLhwAX369MHy5cvVrtXf31/5Zz09Pfj5+eHixYtq74eImg4DCxE1CH19fZWfJRKJyjKJRAIAUCgU9d5eEIR7xvTp0wdLlizBn3/+iffeew/Lly9HZWVlndtUVVU92oSISKMwsBCR1vLy8kJ1dTXKy8sBALa2tioX6iYnJ9/3mpm4uDjln6urqxEfHw9PT8/GL5iIHhnbmolIK/Tt2xdjx46Fn58frK2tkZiYiNdffx39+vWDubk5AKB///746quv4O/vj5qaGixZsuSeMzcAsHbtWnh4eMDT0xOffvop7ty5o9JtRESah2dYiEgrBAYGIjw8HIMGDYKnpyfmz5+PwMBA7Ny5UzlmzZo1cHFxQZ8+ffDiiy/i1VdfhbGx8T37WrVqFVatWoUuXbrg8OHD2L17N2xsbJpyOkSkJonw3y98iYh0VFpaGtzc3HD69Gl07dpV7HKISA08w0JEREQaj4GFiIiINB6/EiIiIiKNxzMsREREpPEYWIiIiEjjMbAQERGRxmNgISIiIo3HwEJEREQaj4GFiIiINB4DCxEREWk8BhYiIiLSeAwsREREpPH+D7G6vR3qwrQfAAAAAElFTkSuQmCC\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/partialPeriodicPattern/topk/k3PMiner.ipynb b/notebooks/partialPeriodicPattern/topk/k3PMiner.ipynb index e3ad9dc8..e796cdbf 100644 --- a/notebooks/partialPeriodicPattern/topk/k3PMiner.ipynb +++ b/notebooks/partialPeriodicPattern/topk/k3PMiner.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Top-K Partial Periodic Frequent patterns in Temporal Databases using k3PMiner" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Top-K Partial Periodic Frequent patterns in a temporal database using the k3PMiner algorithm. In the final part, we describe an advanced approach, where we evaluate the k3PMiner algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "ad79de69-fac5-45a9-c8d0-715f4e4617ec" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/835.0 kB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K \u001B[91m━━━━━━━\u001B[0m\u001B[91m╸\u001B[0m\u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m163.8/835.0 kB\u001B[0m 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JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Top-K Partial Periodic Frequent patterns in Temporal Databases using k3PMiner" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Top-K Partial Periodic Frequent patterns in a temporal database using the k3PMiner algorithm. In the final part, we describe an advanced approach, where we evaluate the k3PMiner algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "ad79de69-fac5-45a9-c8d0-715f4e4617ec" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/835.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m163.8/835.0 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m665.6/835.0 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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"Collecting JsonForm>=0.0.2 (from resource->pami)\n", - " Downloading JsonForm-0.0.2.tar.gz (2.4 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Collecting JsonSir>=0.0.2 (from resource->pami)\n", - " Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", - " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", - "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", - "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", - "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", - 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" Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", - "Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "d2a8446a-c488-4161-baf9-20cc6e4e3685" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-07 08:23:20-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.19MB/s in 3.7s \n", - "\n", - "2023-09-07 08:23:25 (1.19 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "0c7a1d21-0e3c-40a5-8d7f-82e2b25582d7" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Top-K Partial Periodic Frequent patterns using k3PMiner" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "2f820229-2cf0-4791-f9dd-542a2126b8db" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "9006abf4-d807-4210-fbe2-36ef7c744ec8" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 4 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Top-K Partial Periodic Frequent patterns using k3PMiner" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.partialPeriodicPattern.topk import k3PMiner as alg #import the algorithm\n", - "\n", - "obj = alg.k3PMiner(iFile=inputFile, k=minimumSupportCount, period=PeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('topKPartialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4d7b99ce-41a1-4eb3-fc84-d607baf4a0c5" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "TopK partial periodic patterns were generated successfully\n", - "Total No of patterns: 100\n", - "Runtime: 2.376168966293335\n", - "Memory (RSS): 253939712\n", - "Memory (USS): 205508608\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'topKPartialPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "1d514422-d861-4c38-d538-c9dbc23467a3" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "937:4675 \n", - "914:4036 \n", - "966:3918 \n", - "919:3705 \n", - "947:3688 \n", - "956:3622 \n", - "895:3380 \n", - "918:3007 \n", - "944:2790 \n", - "93:2774 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "d2a8446a-c488-4161-baf9-20cc6e4e3685" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-07 08:23:20-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.19MB/s in 3.7s \n", + "\n", + "2023-09-07 08:23:25 (1.19 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "0c7a1d21-0e3c-40a5-8d7f-82e2b25582d7" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Top-K Partial Periodic Frequent patterns using k3PMiner" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "2f820229-2cf0-4791-f9dd-542a2126b8db" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "9006abf4-d807-4210-fbe2-36ef7c744ec8" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 4 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _topKPartialPeriodicfrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the k3PMiner algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.partialPeriodicPattern.topk import k3PMiner as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "PeriodCount = 5000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of k3PMiner" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of k3PMiner algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 9, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.k3PMiner(iFile=inputFile, k=minSupCount, period=PeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['k3PMiner', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "0ceea19d-ce9d-49e5-99b6-9cf68d736812" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "TopK partial periodic patterns were generated successfully\n", - "TopK partial periodic patterns were generated successfully\n", - "TopK partial periodic patterns were generated successfully\n", - "TopK partial periodic patterns were generated successfully\n", - "TopK partial periodic patterns were generated successfully\n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Top-K Partial Periodic Frequent patterns using k3PMiner" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.partialPeriodicPattern.topk import k3PMiner as alg #import the algorithm\n", + "\n", + "obj = alg.k3PMiner(iFile=inputFile, k=minimumSupportCount, period=PeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('topKPartialPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4d7b99ce-41a1-4eb3-fc84-d607baf4a0c5" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "TopK partial periodic patterns were generated successfully\n", + "Total No of patterns: 100\n", + "Runtime: 2.376168966293335\n", + "Memory (RSS): 253939712\n", + "Memory (USS): 205508608\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'topKPartialPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1d514422-d861-4c38-d538-c9dbc23467a3" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "937:4675 \n", + "914:4036 \n", + "966:3918 \n", + "919:3705 \n", + "947:3688 \n", + "956:3622 \n", + "895:3380 \n", + "918:3007 \n", + "944:2790 \n", + "93:2774 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _topKPartialPeriodicfrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the k3PMiner algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.partialPeriodicPattern.topk import k3PMiner as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "PeriodCount = 5000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of k3PMiner" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of k3PMiner algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.k3PMiner(iFile=inputFile, k=minSupCount, period=PeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['k3PMiner', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0ceea19d-ce9d-49e5-99b6-9cf68d736812" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "TopK partial periodic patterns were generated successfully\n", + "TopK partial periodic patterns were generated successfully\n", + "TopK partial periodic patterns were generated successfully\n", + "TopK partial periodic patterns were generated successfully\n", + "TopK partial periodic patterns were generated successfully\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "be654d52-22a6-4e4d-b42a-cf364074a38f" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup PeriodCount patterns runtime memory\n", + "0 k3PMiner 100 5000 100 2.483504 266457088\n", + "1 k3PMiner 150 5000 150 3.455439 266907648\n", + "2 k3PMiner 200 5000 200 6.762334 267304960\n", + "3 k3PMiner 250 5000 250 8.694199 267083776\n", + "4 k3PMiner 300 5000 300 14.277166 267206656\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "6c624b0d-f435-4f8e-88b6-46b1d3792afc" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 12 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "be654d52-22a6-4e4d-b42a-cf364074a38f" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup PeriodCount patterns runtime memory\n", - "0 k3PMiner 100 5000 100 2.483504 266457088\n", - "1 k3PMiner 150 5000 150 3.455439 266907648\n", - "2 k3PMiner 200 5000 200 6.762334 267304960\n", - "3 k3PMiner 250 5000 250 8.694199 267083776\n", - "4 k3PMiner 300 5000 300 14.277166 267206656\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "6c624b0d-f435-4f8e-88b6-46b1d3792afc" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 12 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/partialPeriodicPatternInMultipleTimeSeries/basic/PPGrowth.ipynb b/notebooks/partialPeriodicPatternInMultipleTimeSeries/basic/PPGrowth.ipynb index 161dc3d2..f061f91c 100644 --- a/notebooks/partialPeriodicPatternInMultipleTimeSeries/basic/PPGrowth.ipynb +++ b/notebooks/partialPeriodicPatternInMultipleTimeSeries/basic/PPGrowth.ipynb @@ -1,728 +1,728 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Partial Periodic patterns in Multiple TimeSeries in a Temporal Databases using PPGrowth" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Partial Periodic patterns in Multiple TimeSeries in a temporal database using the PPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the PPGrowth algorithm on a dataset at different periodic support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "cd7d768e-aa53-42e5-d6a2-c5f6d5086949" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.11.15.4-py3-none-any.whl (883 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m883.9/883.9 kB\u001B[0m \u001B[31m7.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement 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/root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, sphinxcontrib-jquery, sphinx-rtd-theme, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.11.15.4 python-easyconfig-0.1.7 resource-0.2.1 sphinx-rtd-theme-1.3.0 sphinxcontrib-jquery-4.1 validators-0.22.0\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Partial Periodic patterns in Multiple TimeSeries in a Temporal Databases using PPGrowth" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Partial Periodic patterns in Multiple TimeSeries in a temporal database using the PPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the PPGrowth algorithm on a dataset at different periodic support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "cd7d768e-aa53-42e5-d6a2-c5f6d5086949" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.11.15.4-py3-none-any.whl (883 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.9/883.9 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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" Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", - "Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, sphinxcontrib-jquery, sphinx-rtd-theme, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.11.15.4 python-easyconfig-0.1.7 resource-0.2.1 sphinx-rtd-theme-1.3.0 sphinxcontrib-jquery-4.1 validators-0.22.0\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "43e8b140-6464-4d05-a7f6-181de5953ebf" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-11-17 02:43:33-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.37MB/s in 3.2s \n", - "\n", - "2023-11-17 02:43:37 (1.37 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "6659f2d6-4b65-45cf-9ffb-d222476b039a" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Partial Periodic patterns in Multiple TimeSeries using PPGrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate periodic support (periodicSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "c34b76e8-d9c6-4131-efbd-294c96b1d7f0" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "a8125803-a19e-4bab-be19-6dacb8ebf6d7" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *periodicSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _periodicSup_ value of 1000 (in count). We can increase or decrease the _periodicSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1.\n", - "periodicSupportCount = 1000" - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Partial Periodic patterns in Multiple TimeSeries using PPGrowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.partialPeriodicPatternInMultipleTimeSeries import PPGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.PPGrowth(iFile=inputFile, period=PeriodCount, periodicSupport=periodicSupportCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('partialPeriodicPatternsInMultipleTimeSeriesAtperSupCount1000.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "bf2dc6a3-60ee-4246-d3ff-4b90056f7cfd" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n", - "Total No of patterns: 385\n", - "Runtime: 14.044399976730347\n", - "Memory (RSS): 451764224\n", - "Memory (USS): 428314624\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'partialPeriodicPatternsInMultipleTimeSeriesAtperSupCount1000.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "6f043be5-347a-4b49-ab32-74640a7eb9c0" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "912:1008:504 \n", - "511:1014:748 \n", - "428:1019:833 \n", - "266:1020:788 \n", - "325:1020:732 \n", - "201:1029:600 \n", - "366:1030:1013 \n", - "823:1031:677 \n", - "928:1032:683 \n", - "258:1035:692 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "43e8b140-6464-4d05-a7f6-181de5953ebf" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-11-17 02:43:33-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.37MB/s in 3.2s \n", + "\n", + "2023-11-17 02:43:37 (1.37 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "6659f2d6-4b65-45cf-9ffb-d222476b039a" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Partial Periodic patterns in Multiple TimeSeries using PPGrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate periodic support (periodicSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "c34b76e8-d9c6-4131-efbd-294c96b1d7f0" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "a8125803-a19e-4bab-be19-6dacb8ebf6d7" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _partialPeriodicPatternInMultipleTimeseries:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the PPGrowth algorithm on a dataset at different periodicSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.partialPeriodicPatternInMultipleTimeSeries import PPGrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "PeriodCount = 5000\n", - "periodicSupportCountList = [1000, 1500, 2000, 2500, 3000]\n", - "#periodicSupport can also specified between 0 to 1. E.g., periodicSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 13, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of PPGrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of PPGrowth algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 14, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different periodicSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in periodicSupportCountList:\n", - " obj = alg.PPGrowth(iFile=inputFile, periodicSupport=minSupCount, period=PeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['PPGrowth', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d4b4a9da-0dbc-472d-8a7c-ba21c9c5d74f" - }, - "execution_count": 15, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n", - "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n", - "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n", - "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n", - "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *periodicSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _periodicSup_ value of 1000 (in count). We can increase or decrease the _periodicSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "PeriodCount = 5000 #PeriodCount is specified in count. However, the users can also specify PeriodCount between 0 and 1.\n", + "periodicSupportCount = 1000" + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Partial Periodic patterns in Multiple TimeSeries using PPGrowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.partialPeriodicPatternInMultipleTimeSeries import PPGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.PPGrowth(iFile=inputFile, period=PeriodCount, periodicSupport=periodicSupportCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('partialPeriodicPatternsInMultipleTimeSeriesAtperSupCount1000.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bf2dc6a3-60ee-4246-d3ff-4b90056f7cfd" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n", + "Total No of patterns: 385\n", + "Runtime: 14.044399976730347\n", + "Memory (RSS): 451764224\n", + "Memory (USS): 428314624\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'partialPeriodicPatternsInMultipleTimeSeriesAtperSupCount1000.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6f043be5-347a-4b49-ab32-74640a7eb9c0" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "912:1008:504 \n", + "511:1014:748 \n", + "428:1019:833 \n", + "266:1020:788 \n", + "325:1020:732 \n", + "201:1029:600 \n", + "366:1030:1013 \n", + "823:1031:677 \n", + "928:1032:683 \n", + "258:1035:692 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _partialPeriodicPatternInMultipleTimeseries:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the PPGrowth algorithm on a dataset at different periodicSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.partialPeriodicPatternInMultipleTimeSeries import PPGrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "PeriodCount = 5000\n", + "periodicSupportCountList = [1000, 1500, 2000, 2500, 3000]\n", + "#periodicSupport can also specified between 0 to 1. E.g., periodicSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of PPGrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'PeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of PPGrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different periodicSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in periodicSupportCountList:\n", + " obj = alg.PPGrowth(iFile=inputFile, periodicSupport=minSupCount, period=PeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['PPGrowth', minSupCount, PeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d4b4a9da-0dbc-472d-8a7c-ba21c9c5d74f" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n", + "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n", + "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n", + "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n", + "Periodic Frequent patterns were generated successfully using PPGrowth algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "98791e8a-08c7-42cf-cfac-f2c31899428b" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup PeriodCount patterns runtime memory\n", + "0 PPGrowth 1000 5000 385 12.224450 452337664\n", + "1 PPGrowth 1500 5000 237 9.264858 374980608\n", + "2 PPGrowth 2000 5000 155 6.491828 322408448\n", + "3 PPGrowth 2500 5000 107 5.214154 308989952\n", + "4 PPGrowth 3000 5000 60 5.486989 301764608\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "6c55d324-debb-4ba3-993b-e84f6078d8bb" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 17 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "98791e8a-08c7-42cf-cfac-f2c31899428b" - }, - "execution_count": 16, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup PeriodCount patterns runtime memory\n", - "0 PPGrowth 1000 5000 385 12.224450 452337664\n", - "1 PPGrowth 1500 5000 237 9.264858 374980608\n", - "2 PPGrowth 2000 5000 155 6.491828 322408448\n", - "3 PPGrowth 2500 5000 107 5.214154 308989952\n", - "4 PPGrowth 3000 5000 60 5.486989 301764608\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "6c55d324-debb-4ba3-993b-e84f6078d8bb" - }, - "execution_count": 17, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 17 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/partialPeriodicPatternMiningPollutionDemo.ipynb b/notebooks/partialPeriodicPatternMiningPollutionDemo.ipynb index ad389b13..bbb2cd13 100644 --- a/notebooks/partialPeriodicPatternMiningPollutionDemo.ipynb +++ b/notebooks/partialPeriodicPatternMiningPollutionDemo.ipynb @@ -1,3404 +1,3404 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Walk through: Discovering Periodic-Frequent Patterns from Big Multiple Time Series Data\n", + "\n", + "\n", + "> Illustration with 5-year nation-wide air pollution data of Japan\n", + "\n" + ], + "metadata": { + "id": "9kxtMcdavLld" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Introduction\n", + "\n", + "Multiple time series data is ubiqutous. Beneficial patterns that can empower the users with competitive knowledge are hidden in this series. This article describe the process to discover frequently occurring patterns (or frequent patterns) in a multiple time series. We use the 5-year nation-wide air pollution (PM2.5) data of Japan for illustration purposes." + ], + "metadata": { + "id": "V_0ZTN7ExFVq" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Download the air pollution dataset [1]\n", + "\n" + ], + "metadata": { + "id": "zABbPAWlzzf-" + } + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "P6B2vwnWu6oa", "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "base_uri": "https://localhost:8080/" }, - "language_info": { - "name": "python" + "outputId": "9c74c1fb-7a6f-44ab-9d7a-084cdb0b7c46" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-07 12:42:32-- https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.5.18, 2620:100:601d:18::a27d:512\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.5.18|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: /s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv [following]\n", + "--2023-08-07 12:42:32-- https://www.dropbox.com/s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", + "Reusing existing connection to www.dropbox.com:443.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com/cd/0/inline/CBVOtqCTVjBmQMQI0xLjY2l9y6BPBZtIQ4KS3OwAU9mViwmtZEJA9uGf7e3A4Pe5eXY5aG06uU25apewe6aaaqcPwpAWLgwjnvn4y2-GcrrfKIdrxukXQQcXaRKqwmYFFp2z5zJCqjOKIgqLyYAWYEZH/file# [following]\n", + "--2023-08-07 12:42:33-- https://uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com/cd/0/inline/CBVOtqCTVjBmQMQI0xLjY2l9y6BPBZtIQ4KS3OwAU9mViwmtZEJA9uGf7e3A4Pe5eXY5aG06uU25apewe6aaaqcPwpAWLgwjnvn4y2-GcrrfKIdrxukXQQcXaRKqwmYFFp2z5zJCqjOKIgqLyYAWYEZH/file\n", + "Resolving uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com (uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com)... 162.125.5.15, 2620:100:601d:15::a27d:50f\n", + "Connecting to uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com (uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com)|162.125.5.15|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 262918270 (251M) [text/plain]\n", + "Saving to: ‘ETL_DATA_new.csv’\n", + "\n", + "ETL_DATA_new.csv 100%[===================>] 250.74M 79.1MB/s in 3.3s \n", + "\n", + "2023-08-07 12:42:36 (76.0 MB/s) - ‘ETL_DATA_new.csv’ saved [262918270/262918270]\n", + "\n" + ] } + ], + "source": [ + "!wget https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Walk through: Discovering Periodic-Frequent Patterns from Big Multiple Time Series Data\n", - "\n", - "\n", - "> Illustration with 5-year nation-wide air pollution data of Japan\n", - "\n" - ], - "metadata": { - "id": "9kxtMcdavLld" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Introduction\n", - "\n", - "Multiple time series data is ubiqutous. Beneficial patterns that can empower the users with competitive knowledge are hidden in this series. This article describe the process to discover frequently occurring patterns (or frequent patterns) in a multiple time series. We use the 5-year nation-wide air pollution (PM2.5) data of Japan for illustration purposes." - ], - "metadata": { - "id": "V_0ZTN7ExFVq" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Download the air pollution dataset [1]\n", - "\n" - ], - "metadata": { - "id": "zABbPAWlzzf-" - } - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "P6B2vwnWu6oa", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "9c74c1fb-7a6f-44ab-9d7a-084cdb0b7c46" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-07 12:42:32-- https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", - "Resolving www.dropbox.com (www.dropbox.com)... 162.125.5.18, 2620:100:601d:18::a27d:512\n", - "Connecting to www.dropbox.com (www.dropbox.com)|162.125.5.18|:443... connected.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: /s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv [following]\n", - "--2023-08-07 12:42:32-- https://www.dropbox.com/s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", - "Reusing existing connection to www.dropbox.com:443.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com/cd/0/inline/CBVOtqCTVjBmQMQI0xLjY2l9y6BPBZtIQ4KS3OwAU9mViwmtZEJA9uGf7e3A4Pe5eXY5aG06uU25apewe6aaaqcPwpAWLgwjnvn4y2-GcrrfKIdrxukXQQcXaRKqwmYFFp2z5zJCqjOKIgqLyYAWYEZH/file# [following]\n", - "--2023-08-07 12:42:33-- https://uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com/cd/0/inline/CBVOtqCTVjBmQMQI0xLjY2l9y6BPBZtIQ4KS3OwAU9mViwmtZEJA9uGf7e3A4Pe5eXY5aG06uU25apewe6aaaqcPwpAWLgwjnvn4y2-GcrrfKIdrxukXQQcXaRKqwmYFFp2z5zJCqjOKIgqLyYAWYEZH/file\n", - "Resolving uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com (uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com)... 162.125.5.15, 2620:100:601d:15::a27d:50f\n", - "Connecting to uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com (uc62c1b13b180bb22e72be0e66c4.dl.dropboxusercontent.com)|162.125.5.15|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 262918270 (251M) [text/plain]\n", - "Saving to: ‘ETL_DATA_new.csv’\n", - "\n", - "ETL_DATA_new.csv 100%[===================>] 250.74M 79.1MB/s in 3.3s \n", - "\n", - "2023-08-07 12:42:36 (76.0 MB/s) - ‘ETL_DATA_new.csv’ saved [262918270/262918270]\n", - "\n" - ] - } - ], - "source": [ - "!wget https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv" - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Read the dataset and analyze it" - ], - "metadata": { - "id": "DYJOJKOq3Qr2" - } - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "dataset = pd.read_csv('ETL_DATA_new.csv', index_col=0)\n", - "\n", - "dataset\n", - "# you can notice that dataset is collected from 2018-01-01 01:00:00 hours to 2023-04-25 22:00:00 hours (5+ years)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 693 - }, - "id": "vsonMLxu1CMG", - "outputId": "87f977b4-53cf-429f-a44e-3fdb4dbd479c" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " TimeStamp Point(139.0794379 36.3727776) \\\n", - " \n", - "0 2018-01-01 01:00:00 NaN \n", - "1 2018-01-01 02:00:00 NaN \n", - "2 2018-01-01 03:00:00 NaN \n", - "3 2018-01-01 04:00:00 NaN \n", - "4 2018-01-01 05:00:00 NaN \n", - "... ... ... \n", - "46000 2023-04-25 18:00:00 NaN \n", - "46001 2023-04-25 19:00:00 NaN \n", - "46002 2023-04-25 20:00:00 NaN \n", - "46003 2023-04-25 21:00:00 NaN \n", - "46004 2023-04-25 22:00:00 NaN \n", - "\n", - " Point(139.1051411 36.3963822) Point(139.0960211 36.4047323) \\\n", - " \n", - "0 NaN 5.0 \n", - "1 NaN 11.0 \n", - "2 NaN 7.0 \n", - "3 NaN 5.0 \n", - "4 NaN 6.0 \n", - "... ... ... \n", - "46000 NaN NaN \n", - "46001 NaN NaN \n", - "46002 NaN NaN \n", - "46003 NaN NaN \n", - "46004 NaN NaN \n", - "\n", - " Point(139.0428727 36.3816035) Point(138.9955116 36.33801589999999) \\\n", - " \n", - "0 13.0 18.0 \n", - "1 12.0 22.0 \n", - "2 12.0 19.0 \n", - "3 11.0 16.0 \n", - "4 11.0 10.0 \n", - "... ... ... \n", - "46000 22.0 3.0 \n", - "46001 21.0 2.0 \n", - "46002 20.0 10.0 \n", - "46003 19.0 2.0 \n", - "46004 19.0 1.0 \n", - "\n", - " Point(139.342672 36.4105658) Point(139.3526243 36.3695416) \\\n", - " \n", - "0 20.0 NaN \n", - "1 15.0 NaN \n", - "2 16.0 NaN \n", - "3 11.0 NaN \n", - "4 8.0 NaN \n", - "... ... ... \n", - "46000 15.0 NaN \n", - "46001 19.0 NaN \n", - "46002 19.0 NaN \n", - "46003 15.0 NaN \n", - "46004 17.0 NaN \n", - "\n", - " Point(139.1945766 36.31351160000001) Point(139.2076974 36.3034767) \\\n", - " \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "46000 NaN NaN \n", - "46001 NaN NaN \n", - "46002 NaN NaN \n", - "46003 NaN NaN \n", - "46004 NaN NaN \n", - "\n", - " ... 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\n" ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.3: Fillup the missing values (NaN) with zero" + ], + "metadata": { + "id": "Ym9fqZJO6FAH" + } + }, + { + "cell_type": "code", + "source": [ + "dataset = dataset.fillna(0)\n", + "dataset.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331 }, + "id": "Qka2TNy86OCx", + "outputId": "d9b46e43-ab5d-42e3-d477-b940cca5325e" + }, + "execution_count": 6, + "outputs": [ { - "cell_type": "code", - "source": [ - "maxValueInEachColumn.plot() #point the maximum values recorded by each sensor." + "output_type": "execute_result", + "data": { + "text/plain": [ + " Point(139.0794379 36.3727776) Point(139.1051411 36.3963822) \\\n", + " \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Point(139.0960211 36.4047323) Point(139.0428727 36.3816035) \\\n", + " \n", + "0 5.0 13.0 \n", + "1 11.0 12.0 \n", + "2 7.0 12.0 \n", + "3 5.0 11.0 \n", + "4 6.0 11.0 \n", + "\n", + " Point(138.9955116 36.33801589999999) Point(139.342672 36.4105658) \\\n", + " \n", + "0 18.0 20.0 \n", + "1 22.0 15.0 \n", + "2 19.0 16.0 \n", + "3 16.0 11.0 \n", + "4 10.0 8.0 \n", + "\n", + " Point(139.3526243 36.3695416) Point(139.1945766 36.31351160000001) \\\n", + " \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Point(139.2076974 36.3034767) Point(139.3817322 36.2909131) ... \\\n", + " ... \n", + "0 0.0 23.0 ... \n", + "1 0.0 32.0 ... \n", + "2 0.0 32.0 ... \n", + "3 0.0 28.0 ... \n", + "4 0.0 27.0 ... \n", + "\n", + " Point(139.9239959 36.8062129) Point(139.9418164 36.7656467) \\\n", + " \n", + "0 1.0 0.0 \n", + "1 0.0 0.0 \n", + "2 2.0 0.0 \n", + "3 3.0 0.0 \n", + "4 5.0 0.0 \n", + "\n", + " Point(140.0549894 36.9688923) Point(139.8775674 36.3847082) \\\n", + " \n", + "0 0.0 0.0 \n", + "1 6.0 0.0 \n", + "2 0.0 0.0 \n", + "3 2.0 0.0 \n", + "4 4.0 0.0 \n", + "\n", + " Point(139.9101767 36.4393022) Point(139.9074816 36.4445767) \\\n", + " \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Point(140.0934838 36.4673588) Point(139.7422865 36.2305774) \\\n", + " \n", + "0 6.0 0.0 \n", + "1 9.0 0.0 \n", + "2 10.0 0.0 \n", + "3 11.0 0.0 \n", + "4 8.0 0.0 \n", + "\n", + " Point(139.7151723 36.822353) Point(140.1510903 36.6598314) \n", + " \n", + "0 0.0 4.0 \n", + "1 0.0 5.0 \n", + "2 0.0 6.0 \n", + "3 0.0 11.0 \n", + "4 0.0 6.0 \n", + "\n", + "[5 rows x 1764 columns]" ], - "metadata": { - "id": "Wmvv1z6mZwA2", - "outputId": "1fdfd6b4-0f26-4115-f33f-a0cb35a69122", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - } - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 8 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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xqCUiIiIii8egloiIyEIV1ymG3WWoLGJQS0REREQWj0EtEREREVk8BrVERESlDDuKUVnEoJaIiIiILB6DWiIiIgvFN4oR3cagloiIiIgsHoNaIiIiIrJ4DGqJiIhKGXYUo7KIQS0RERERWTwGtURERKUMO4pRWcSgloiIyEJJsS/KJSp7GNQSERERkcVjUEtERFTKsKMYlUUMaomIiIjI4jGoJSIiKmXYUYzKIga1REREFoqxK9FtDGqJiIiIyOIxqCUiIipl2FGMyiIGtURERERk8RjUEhERlTLsKEZlEYNaIiIiC8XQleg2BrVEREREZPEY1BIREZUy7ChGZRGDWiIiIiKyeAxqiYiIShl2FKOyiEEtERGRpWLwSqRgUEtEREREFo9BLRERUSnDjmJUFjGoJSIiIiKLx6CWiIiolGFHMSqLGNQSERFZKIauRLcxqCUiIiIii8egloiIqJRhRzEqixjUEhEREZHFY1BLRERUyrCjGJVFDGqJiIgsFGNXottKVVA7YcIEqFQqDBo0SJl269Yt9O/fH87OzrC3t0fHjh0RHx9v9r3Y2Fi0adMGtra2cHNzw8cff4ycnByzNFu2bEHdunWh1+sREBCA+fPnF/r96dOnw8/PD9bW1ggODsa+ffseRTGJiIiI6A6lJqjdv38/vv/+e9SuXdts+gcffIA//vgDy5cvx9atW3H58mW88soryvzc3Fy0adMGWVlZ2LVrFxYsWID58+dj5MiRSprz58+jTZs2aNq0KaKjozFo0CC89dZbWL9+vZJm6dKlCAsLw6hRo3Dw4EEEBQUhNDQUCQkJj77wREREBbCjGJVFpSKovXnzJrp27YoffvgB5cqVU6YnJydj7ty5+Oabb9CsWTPUq1cP8+bNw65du7Bnzx4AwIYNG3D8+HEsXLgQderUQevWrfHZZ59h+vTpyMrKAgDMmjUL/v7+mDRpEqpXr44BAwagU6dOmDx5svJb33zzDfr06YOePXsiMDAQs2bNgq2tLX788cfHuzKIiIiIyqBSEdT2798fbdq0QYsWLcymR0ZGIjs722x6tWrV4Ovri927dwMAdu/ejVq1asHd3V1JExoaipSUFBw7dkxJc+eyQ0NDlWVkZWUhMjLSLI1arUaLFi2UNEXJzMxESkqK2YeIiOi/YkcxKou0JZ2B/2rJkiU4ePAg9u/fX2heXFwcdDodHB0dzaa7u7sjLi5OSVMwoDXNN827W5qUlBRkZGTgxo0byM3NLTLNyZMni837+PHjMWbMmHsrKBER0R0YvBLdZtE1tRcvXsT777+PRYsWwdrauqSzc9+GDRuG5ORk5XPx4sWSzhIRERGRRbLooDYyMhIJCQmoW7cutFottFottm7dim+//RZarRbu7u7IyspCUlKS2ffi4+Ph4eEBAPDw8Cg0GoLp739LYzAYYGNjAxcXF2g0miLTmJZRFL1eD4PBYPYhIiL6r9hRjMoiiw5qmzdvjiNHjiA6Olr51K9fH127dlX+b2VlhYiICOU7MTExiI2NRUhICAAgJCQER44cMRulYOPGjTAYDAgMDFTSFFyGKY1pGTqdDvXq1TNLk5eXh4iICCUNERERET06Ft2m1sHBATVr1jSbZmdnB2dnZ2V67969ERYWBicnJxgMBgwcOBAhISFo2LAhAKBly5YIDAzEG2+8gYkTJyIuLg4jRoxA//79odfrAQDvvPMOpk2bhsGDB6NXr17YvHkzli1bhjVr1ii/GxYWhh49eqB+/fp4+umnMWXKFKSlpaFnz56PaW0QERERlV0WHdTei8mTJ0OtVqNjx47IzMxEaGgoZsyYoczXaDRYvXo1+vXrh5CQENjZ2aFHjx4YO3asksbf3x9r1qzBBx98gKlTp8Lb2xtz5sxBaGiokqZz585ITEzEyJEjERcXhzp16iA8PLxQ5zEiIqKHpbhuYuxARmWRSrjnPzFSUlJgNBqRnJzM9rVERPSv5u08jzF/HC803ctojcvJtwAAFya0edzZKnN4/X4yWHSbWiIiIiIigEEtERFRqcPRD6gsYlBLRERERBaPQS0REZGFKq5XDLvLUFnEoJaIiIiILB6DWiIiIiKyeAxqiYiIShl2FKOyiEEtEREREVk8BrVEREQWim8UI7qNQS0RERERWTwGtURERERk8RjUEhERlTLsKEZlEYNaIiIiIrJ4DGqJiIgsVHEdwthRjMoiBrVEREREZPEY1BIRERGRxWNQS0REVMqwoxiVRQxqiYiIiMjiMaglIiIqZdhRjMoiBrVEREREZPEY1BIRERGRxWNQS0REVMqwoxiVRQxqiYiIiMjiMaglIiKyUMX1B2NHMSqLGNQSERERkcVjUEtEREREFo9BLRERUSnDjmJUFjGoJSIiIiKLx6CWiIjIQgmK7hDGjmJUFjGoJSIiIiKLx6CWiIiIiCweg1oiIqJShh3FqCxiUEtEREREFo9BLRERkYXiG8WIbmNQS0REREQWj0EtEREREVk8BrVERESlDDuKUVnEoJaIiIiILB6DWiIiIgtVXHcwdhSjsohBLRERERFZPIsPamfOnInatWvDYDDAYDAgJCQE69atU+bfunUL/fv3h7OzM+zt7dGxY0fEx8ebLSM2NhZt2rSBra0t3Nzc8PHHHyMnJ8cszZYtW1C3bl3o9XoEBARg/vz5hfIyffp0+Pn5wdraGsHBwdi3b98jKTMRERERmbP4oNbb2xsTJkxAZGQkDhw4gGbNmqF9+/Y4duwYAOCDDz7AH3/8geXLl2Pr1q24fPkyXnnlFeX7ubm5aNOmDbKysrBr1y4sWLAA8+fPx8iRI5U058+fR5s2bdC0aVNER0dj0KBBeOutt7B+/XolzdKlSxEWFoZRo0bh4MGDCAoKQmhoKBISEh7fyiAiIgI7ilHZpJJS2PDGyckJX331FTp16gRXV1csXrwYnTp1AgCcPHkS1atXx+7du9GwYUOsW7cObdu2xeXLl+Hu7g4AmDVrFoYMGYLExETodDoMGTIEa9aswdGjR5Xf6NKlC5KSkhAeHg4ACA4ORoMGDTBt2jQAQF5eHnx8fDBw4EAMHTr0nvKdkpICo9GI5ORkGAyGh7lKiIioFJq19SwmrDtZaHp5Rxv8nZQBALgwoc3jzlaZw+v3k8Hia2oLys3NxZIlS5CWloaQkBBERkYiOzsbLVq0UNJUq1YNvr6+2L17NwBg9+7dqFWrlhLQAkBoaChSUlKU2t7du3ebLcOUxrSMrKwsREZGmqVRq9Vo0aKFkoaIqCwSEew6exVXb2aWdFZKJb5RjOg2bUln4GE4cuQIQkJCcOvWLdjb22PFihUIDAxEdHQ0dDodHB0dzdK7u7sjLi4OABAXF2cW0Jrmm+bdLU1KSgoyMjJw48YN5ObmFpnm5MnCd9AmmZmZyMy8faJPSUm5v4ITET3h1h+LxzsLI2Gr0+D42FYlnR0iKsVKRU1t1apVER0djb1796Jfv37o0aMHjh8/XtLZ+lfjx4+H0WhUPj4+PiWdJSKih2rzyfyOuelZuSWcEyIq7UpFUKvT6RAQEIB69eph/PjxCAoKwtSpU+Hh4YGsrCwkJSWZpY+Pj4eHhwcAwMPDo9BoCKa//y2NwWCAjY0NXFxcoNFoikxjWkZRhg0bhuTkZOVz8eLFByo/ERFRQewoRmVRqQhq75SXl4fMzEzUq1cPVlZWiIiIUObFxMQgNjYWISEhAICQkBAcOXLEbJSCjRs3wmAwIDAwUElTcBmmNKZl6HQ61KtXzyxNXl4eIiIilDRF0ev1ylBkpg8RERER3T+Lb1M7bNgwtG7dGr6+vkhNTcXixYuxZcsWrF+/HkajEb1790ZYWBicnJxgMBgwcOBAhISEoGHDhgCAli1bIjAwEG+88QYmTpyIuLg4jBgxAv3794derwcAvPPOO5g2bRoGDx6MXr16YfPmzVi2bBnWrFmj5CMsLAw9evRA/fr18fTTT2PKlClIS0tDz549S2S9EBFR6SfFvFOMHcWoLLL4oDYhIQHdu3fHlStXYDQaUbt2baxfvx4vvPACAGDy5MlQq9Xo2LEjMjMzERoaihkzZijf12g0WL16Nfr164eQkBDY2dmhR48eGDt2rJLG398fa9aswQcffICpU6fC29sbc+bMQWhoqJKmc+fOSExMxMiRIxEXF4c6deogPDy8UOcxIiIiInr4SuU4tZaK49wRUWkz+JdDWHbgEgCOl/oozNhyBhPDYwpN9zJa43LyLQBc748Dr99PhlLZppaIiIiIyhYGtURERKUMRz+gsohBLRERkYXiG8WIbmNQS0REREQWj0EtEREREVk8BrVEREREZPEY1BIREZUy7ChGZRGDWiIiolKGHcWoLGJQS0REREQWj0EtEREREVk8BrVEREREZPEY1BIREZUy7ChGZRGDWiIiIgtVXIcwdhSjsohBLRERERFZPAa1RERERGTxGNQSERERkcVjUEtERFTKsKMYlUUMaomIiCxUcf3B2FGMyiIGtURERERk8RjUEhEREZHFY1BLRERERBaPQS0REVEpw45iVBYxqCUiIrJQxXUHY0cxKosY1BIRERGRxWNQS0REREQWj0EtEREREVk8BrVERESlDDuKUVnEoJaIiMhC8Y1iRLcxqCUiIiIii8egloiIiIgsHoNaIiIiIrJ4DGqJiIhKGXYUo7KIQS0REZGFkmLeKcaOYlQWMaglIiIiIovHoJaIiIiILB6DWiIiIiKyeAxqiYiIShl2FKOyiEEtERGRheIbxYhuY1BLRERERBaPQS0RERERWTwGtURERERk8Sw+qB0/fjwaNGgABwcHuLm5oUOHDoiJiTFLc+vWLfTv3x/Ozs6wt7dHx44dER8fb5YmNjYWbdq0ga2tLdzc3PDxxx8jJyfHLM2WLVtQt25d6PV6BAQEYP78+YXyM336dPj5+cHa2hrBwcHYt2/fQy8zERHR3bCjGJVFFh/Ubt26Ff3798eePXuwceNGZGdno2XLlkhLS1PSfPDBB/jjjz+wfPlybN26FZcvX8Yrr7yizM/NzUWbNm2QlZWFXbt2YcGCBZg/fz5GjhyppDl//jzatGmDpk2bIjo6GoMGDcJbb72F9evXK2mWLl2KsLAwjBo1CgcPHkRQUBBCQ0ORkJDweFYGERGVKcV1B2NHMSqLVFLK9vzExES4ublh69ataNy4MZKTk+Hq6orFixejU6dOAICTJ0+ievXq2L17Nxo2bIh169ahbdu2uHz5Mtzd3QEAs2bNwpAhQ5CYmAidTochQ4ZgzZo1OHr0qPJbXbp0QVJSEsLDwwEAwcHBaNCgAaZNmwYAyMvLg4+PDwYOHIihQ4f+a95TUlJgNBqRnJwMg8HwsFcNEdFjN/iXQ1h24BIA4MKENiWcm9Lnm42n8G3E6ULTvYzWuJx8CwDX++PA6/eTweJrau+UnJwMAHBycgIAREZGIjs7Gy1atFDSVKtWDb6+vti9ezcAYPfu3ahVq5YS0AJAaGgoUlJScOzYMSVNwWWY0piWkZWVhcjISLM0arUaLVq0UNLcKTMzEykpKWYfIiIiIrp/pSqozcvLw6BBg/DMM8+gZs2aAIC4uDjodDo4OjqapXV3d0dcXJySpmBAa5pvmne3NCkpKcjIyMDVq1eRm5tbZBrTMu40fvx4GI1G5ePj4/NgBSciIiIq40pVUNu/f38cPXoUS5YsKems3JNhw4YhOTlZ+Vy8eLGks0RERKUAO4pRWaQt6Qw8LAMGDMDq1auxbds2eHt7K9M9PDyQlZWFpKQks9ra+Ph4eHh4KGnuHKXANDpCwTR3jpgQHx8Pg8EAGxsbaDQaaDSaItOYlnEnvV4PvV7/YAUmIiIqpltMKesuQ3RPLL6mVkQwYMAArFixAps3b4a/v7/Z/Hr16sHKygoRERHKtJiYGMTGxiIkJAQAEBISgiNHjpiNUrBx40YYDAYEBgYqaQouw5TGtAydTod69eqZpcnLy0NERISShoiIiIgeDYuvqe3fvz8WL16M33//HQ4ODkr7VaPRCBsbGxiNRvTu3RthYWFwcnKCwWDAwIEDERISgoYNGwIAWrZsicDAQLzxxhuYOHEi4uLiMGLECPTv31+pSX3nnXcwbdo0DB48GL169cLmzZuxbNkyrFmzRslLWFgYevTogfr16+Ppp5/GlClTkJaWhp49ez7+FUNERERUhlh8UDtz5kwAwPPPP282fd68eXjzzTcBAJMnT4ZarUbHjh2RmZmJ0NBQzJgxQ0mr0WiwevVq9OvXDyEhIbCzs0OPHj0wduxYJY2/vz/WrFmDDz74AFOnToW3tzfmzJmD0NBQJU3nzp2RmJiIkSNHIi4uDnXq1EF4eHihzmNERERE9HCVunFqLRnHuSOi0obj1D5a32yIwbebzxSaznFqHy9ev58MFt+mloiIqKxirRTRbQxqiYiIiMjiMaglIiIiIovHoJaIiIiILB6DWiIiIiKyeAxqiYiILBTHLyK6jUEtEREREVk8BrVEREREZPEY1BIRERGRxWNQS0REREQWj0EtERGRhRK+U4xIwaCWiIiIiCweg1oiIiIisngMaomIiIjI4jGoJSKiR0YFVUlngYjKCAa1RET0yLAj06PFN4oR3cagloiIiIgsHoNaIiIiIrJ4DGqJiIiIyOIxqCUiIiIii8egloiIyEKxnxjRbQxqiYjokeGQXkT0uDCoJSKiR4ZDehHR48KgloiIiIgsHoNaIiIiIrJ4DGqJiIgsFN8oRnQbg1oiIiIisngMaomIiIjI4jGoJSKiR4ZDehHR48KgloiIHhkO6UVEjwuDWiIiIgvFmwai2xjUEhEREZHFY1BLRERERBaPQS0RERERWTwGtURERERk8RjUEhHRI8MhvR4x9hMjUjCoJSKiR4a984nocWFQS0REREQWj0EtEREREVk8BrVEREREZPEsPqjdtm0bXnrpJXh5eUGlUmHlypVm80UEI0eOhKenJ2xsbNCiRQucPn3aLM3169fRtWtXGAwGODo6onfv3rh586ZZmsOHD+O5556DtbU1fHx8MHHixEJ5Wb58OapVqwZra2vUqlULa9eufejlJSKyJOwo9mixxTLRbRYf1KalpSEoKAjTp08vcv7EiRPx7bffYtasWdi7dy/s7OwQGhqKW7duKWm6du2KY8eOYePGjVi9ejW2bduGvn37KvNTUlLQsmVLVKhQAZGRkfjqq68wevRozJ49W0mza9cuvPbaa+jduzeioqLQoUMHdOjQAUePHn10hSciIiIiAIBKRErNjZ5KpcKKFSvQoUMHAPm1tF5eXvjwww/x0UcfAQCSk5Ph7u6O+fPno0uXLjhx4gQCAwOxf/9+1K9fHwAQHh6OF198EZcuXYKXlxdmzpyJ4cOHIy4uDjqdDgAwdOhQrFy5EidPngQAdO7cGWlpaVi9erWSn4YNG6JOnTqYNWvWPeU/JSUFRqMRycnJMBgMD2u1EBGVmCG/HMbSAxcBABcmtCnh3JQ+49aewOxt5wpN9zJa43JyfuUN1/ujx+v3k8Hia2rv5vz584iLi0OLFi2UaUajEcHBwdi9ezcAYPfu3XB0dFQCWgBo0aIF1Go19u7dq6Rp3LixEtACQGhoKGJiYnDjxg0lTcHfMaUx/U5RMjMzkZKSYvahB7f68GV0/3EfrqdllXRWiOgfHNKLiB6XUh3UxsXFAQDc3d3Npru7uyvz4uLi4ObmZjZfq9XCycnJLE1Ryyj4G8WlMc0vyvjx42E0GpWPj4/P/RaRChiwOArbTiXiq/UnSzorRERE9JiV6qD2STds2DAkJycrn4sXL5Z0lkqFG2nZJZ0FIiIiesxKdVDr4eEBAIiPjzebHh8fr8zz8PBAQkKC2fycnBxcv37dLE1Ryyj4G8WlMc0vil6vh8FgMPsQERHdq1LULYboPyvVQa2/vz88PDwQERGhTEtJScHevXsREhICAAgJCUFSUhIiIyOVNJs3b0ZeXh6Cg4OVNNu2bUN29u0awI0bN6Jq1aooV66ckqbg75jSmH6HiKgs4pBeRPS4WHxQe/PmTURHRyM6OhpAfuew6OhoxMbGQqVSYdCgQfj888+xatUqHDlyBN27d4eXl5cyQkL16tXRqlUr9OnTB/v27cPOnTsxYMAAdOnSBV5eXgCA119/HTqdDr1798axY8ewdOlSTJ06FWFhYUo+3n//fYSHh2PSpEk4efIkRo8ejQMHDmDAgAGPe5UQERERlTnaks7Af3XgwAE0bdpU+dsUaPbo0QPz58/H4MGDkZaWhr59+yIpKQnPPvsswsPDYW1trXxn0aJFGDBgAJo3bw61Wo2OHTvi22+/VeYbjUZs2LAB/fv3R7169eDi4oKRI0eajWXbqFEjLF68GCNGjMAnn3yCypUrY+XKlahZs+ZjWAtEREREZZvFB7XPP//8XdsUqVQqjB07FmPHji02jZOTExYvXnzX36lduza2b99+1zSvvvoqXn311btnmKgUuHg9HeUdbaBW89Ey3R2H9CKix8Ximx8Q0eO1cM9feG7inxi+km/LIypp7CdGdBuDWiK6L19viAEA/LwvtoRzQkREdBuDWiIiIiKyeAxqiYgszM4zVzH2j+PIzMkt6az8Kw7pRUSPi8V3FCMiKmu6ztkLAHB10KPf85VKODdERE8G1tQSEVmo2OvpJZ0FKmHsJ0Z0G4NaIiJ6ZDikFxE9LgxqiYiIiMjiMaglIiIiIovHoJaIiIiILB6DWiIiemQ4pNejxTeKEd3GoJaIiIiILB6DWiIiIiKyeAxqiYjokeGQXkT0uDCoJSIiIiKLx6CWiIgeGXYUe7RYE050G4NaIiIiIrJ4DGqJyCLl5QkSUm+VdDaIiOgJwaCWStSt7FwcuZQM4WCLdJ8GLY3G019E4M+TCSWdFSIiegIwqKUS1XvBfrw0bQcW74st6ayQhVl16DIAYPqfZ0o4J0RE9CRgUEslaueZawCA/+3+q4RzQkT7L1zHpA0xyMrJe2jLZEemR+thPuTaeDweR/9OfngLJHrMGNTSE4GtD+hBqdi5/qF5ddZufLf5DP63J/8m869raUi9lV3CuSKTK8kZGPhzFCL/uv7Ql30yLgV9fjqAtt/tMJsuIog4EY+/kzIe+m/eq8OXktBl9m4cvpRUYnkgy8CgloiIzJxLvImziTfR5KstaDgu4j8ti0N6PTwfLT+EPw5dRseZux/6ss8nphU5fdOJBPRecADPTNj80H/zXnWatRt7zl3Hq7MefrmpdGFQS0REhew8cxUAkJaVW8I5IZMLV9Mf2bKLe1i2++y1R/ab98rUHCbzITaLodJJW9IZIALY7o6IqDhL98fif3v+4hB2RP+CQS09Edimlqj0ExGo2Aj6vg359cgj/w1uFSoN2PyAnjh/XUvDuiNXOHathTsVn4o528891J709HhYypGXmJqJXWevPpHnirjkW+j70wHs+qcZBxE9eqyppSdCwUtSk6+2AABmv1EPLWt4lEh+6L9rOXkbACAnT/BOk0olnBsqKQWbFok83NEqnvlyM7Jy8jDvzQZoWs3t4S34IfhkxRFsPpmADcfjcWFCm5LODlGZwJpaemJFxt4o6SxYjOOXU3A28eZj/917qYWdsO7kY8gJPWprDl/BcxM348ilJ2ccU9P+t/VUYgnnpLBLNx5dp65H7Ums+Sa6Fwxq6YnAk+iDS07PxovfbkfzSVsf+3ps8MUmpGXmAABupGXh5j//L6tmbT2L36P/LulsPBL9Fx/ExesZeGdh5H19rySH9MrNE2w+GY+rNzMBAL9H/41uc/aiw/Sd+HHH+Xtaxri1JxC2NPq+jy1LO6UVzO7xKykllo97kZ6Vg4V7/kJ8CjvOkTkGtfREsLDz/xMlrsCJPe8xr8jkjGzsPHMV6Vk5eOqzjag5an2R6bJzS3+72pNxKZiw7iTeXxL92H6zJPpc3cp+8CG+HvdxvuzARfSafwAvTt0OAHh/STR2nLmK6ItJGLv6+L9+X0Qwe9s5/Bb1N07F39+TkAcpq4hg2G+HMXXT6fv6zsPW5tsdOB2f+tCX+7B8tvoERqw8ynFrqRAGtfTkYqR730oqeCw4fmZuEZF1w3ERyCjl451eT8t67L9ZErWBljR4wcbj8QCAhNTMB/p+wX35Xo+to38nY2L4SaQ/wFOL41dS8PO+i5i86dR9f/dh238hv/nXkzjcomm7xl633CYe9GgwqKUnwrli3mZDxTuXeBPfbIhBSoHXmBYVUN5p7ZErGPrr4Yc6KoG6wJmkqIv/tbQsHCzlbaQLPmYvbc1pCrYP/S9FS896vM1TNOr/FoHnPMCjj7bf7cCMLWdxOfn+H43fyr597JS2fYjocWBQS0+McyXQ0cmStfl2B77dfAafrjyqTMvJ/fcL4buLDmLJ/otYeuDiQ8mHAFAXqL7jW38eLBh6EI+q1vTOeGr4iqNFJ7xPtUZvwMI9fz2UZd0LK83jD2r/i4LbM/sejuWH+tv3kGbVocuPPB9E/wWDWnosMnNyMW7tibu+cvHQpaTHl6FSIOOfto0n4263fcvJu/eAMvERdbK4lZ2LQxeTHsmyLcXjagZyL5V5u85exa6z/22s1LgHqHUszoiVDydAvhcxcffeLjQzJxcLdl3A+au3nxrl3mdg+V9rVwsGliXdDt0UYBd8AvHez1FIfMCmHPciPSsHP+44b9EjR1DJYlBLj8VPu/7C7G3n8NoPe0o6K6VCZk7R7VPvpfnBo1DwAjz2j+NoP31nieSjpGw6Hm+2bz/uWrbiZGTl4vUf9uL1H/b+p0f/2fdxs/QkOXsfzZp+2HYOo1YdQ9OvtyjT7rfcpraeD8O9BrXF7WkFp99LU6M7l5Ocka2MbFLQo2xCMjE8BmNXH0e7aWXr/EEPD4NaeizOXWWb2f9CRMw6Wm0/VXTNW/Z9BLUPK+was+qYWUecNUeuPKQlW463fjpg9nfOY6pl+7fmB2kFApD/0lGvLLwVbu/564WmFWzOcy9NES5c+2/nuYI/kfUf96GC+f1h+7l/TX9nJfOEdSfRaMLm/5SH+2Uab/hhd7q8kpyBZQcuFlsZQKUHg1p6TP79glCS41k+6UasPIrqI8NxMi5//MjcYh5zFve49EpyBl7/YQ82HIt76Hm7nHwLwx7Du+ktyeNqi/lvT7sL1tz/l1r8h/0o/HJSxkNd3r34t6YBqiLuEAo258m9h1rb/3oOK3gz9F9r+wsuK/zovx/3RZ1TkjOykXfH9CfjBuf+1s1L3+3E4F8OY8afZx9RfuhJwaC2DBIRXLv56NpFPSwFT1sJqbfM2rrdTZ4IjlxKfuC78pzcPCSlF11TkJyeXegCfzMz5z+N3XkvFu2NBQBM23zmrumKe1w68vdj2HX2Gvr+r/iB83+JvIQpRQwllJaZ86+1fHH30D7324jTWHP4wWpxc/ME5xJvIu+fwOxJ7xn+KC/8xZU9OT0byRnZSLmVjSvJ+UFjZoHe9PfTge/GHTVl/yXAOhFXeCD/B60BzMnNQ2qB0T7uR1E1nwXXZVHhaMGa2kfdpCQ5IxuXbtwO9rP/4z50I/32erKx0tyenpalnK/y8kSpFc0r5qbnznPbnftRTm4ekjOycT0tS1lGccsqKaaXb/wZk1DCOaFHjUFtGTT01yOo9/mmu3baetIEj4tA06+33FMnhQ3H4/HStB0IW3bogX6r29y9qDN2Iy7eMQZiQsotBI3dgA4F2oveys5FzVHrUe3TcBz9+9G8PvReg3kA2F/EI1QARb55587Y6KPlhzBl02mz16Bm5eSh7mcb0XB8xH++UO09fx39Fx98oO82mhCBZpO2ovU/g+gP/uWwMu/o3yn/uSNUQQdjbyjrS0SUC+L9eJg1tSuj/sYzEzbj6N/JSLmVbfaI2lS5mJSehZAJEQgaswG1R29AyPjNWH34MrJybwck9xPUhh+LM9ufHzTASrmVjcMP8bW63X/ch9pjNiAh9QGGy8oqXIaCtddFjf6V85Bquk0yc3KLrKW+kZaFoDEb8OHy2+esrzbE/OffM9Fb5V/qY+JSUf+LTWjz7XaICD5cfgh1P9uIg7E3ii3fvwW1r/2wB0FjNqDuZxsxaGk0ElJvocEXmzDq98IdAq/ezCzRG1I+Cyz9GNQ+AtOnT4efnx+sra0RHByMffv2lXSWzJiGcvo24t7fWvM45Ingjbl7C01Pz8pRArCTRdT6FOd+awWPX07B30kZ2HMuPzC8c/iaiJP5d/nHLt/OQ8FeugXfbrPv/PV7euR3L5bsi73ntEN/y28GsHhvLGZuuf2o7d8uyAXnX03LVHq7X0nOQGZOfk1M2kPqIGK6qCWk3Cp0Y7XhWFyRbzKKT8kPLGP+mbc88pIyLyM7vzPUw3gD0rHLyXhlxi6EjI8AAHy9IQb1P9+EdffZTri4NrWXkzLQ96cD2HPu3m8oBy2Nxt9JGWj73Q7UHr0Bqw/f3i//upaGRXv/wqn4m0i/ozb905VHzcY9/bfa4zuDjWUHbq/jWw/41ONh9JQ/k3BTeXKy6+w1iOQf28UFR7l5glvZuYX2+aLKkFEgYPu35gc3inl6cz+6zdmLRhM249jl/EDfVIYdZwrflK05fAWTN55C5F9F36ia3EsNsnc5WwDA91vPIjdPcDYxDckZ2VgRlf9K51lbzhbbpKngPgQU3o9ML2kA8s+ZP+36C9fSsrBgt/nQbbvPXkP9zzdh+EMY/eLf4uLT8al4+38HlPWssKQ3h9ADYVD7kC1duhRhYWEYNWoUDh48iKCgIISGhiIh4cl77CEQZOfmYfa2s7hQAh25TI9ITU7F38T204VP7gWHE7p5q+jA6m53/3O2n8MvkZfwzYYYRF9MQnZunlkTguT0bIQtjcaL327HMwUei2Zk5eLj5YfwwdJonIxLwdwC74q/djMTzSZtQYtvtt1O/88FUkTwf9/vxjsLIxF77d+HpklMzbx7j+IizsN3a+OYnJGNT1YcwZfhJ5XfLyqmnfbn7aYMBQPWKZtOo+H4CKw7csXsAnbzn57Q/7WixVTT02rqdrz2wx7sOnMVyenZWHvkCvr+LxIvTN72L0so2htz9yEvT5CZk4udZ67eV81aWmYOes7bhw+WRgO4vb6m/9MGb9SqY/eVl7VH4lBn7AbsOnPVrHZq+Ioj2HA8Hl1mP/goIAVfw7vzzDUMX3EU//d94deFZmTnmtWqFfX4PTkjG35D18Bv6Boc+Kv4l2M86KP3uwXSBY/ZnNw8nI5PLXQcd5uzFy2+2YqWk7eZNQm6eSvHbJ+ev+uC8v/Xf9iDZ7/cXKizUVE1xvN23v7enYeZiJg1PxiwOArfb719o7jpeDzGrz2BSzfS/0mbh22nE4stL3A7AFx+4BL2nruGBl9swqpDlxFZzLqfGnEaHWcW/SpY0/6dkvHvzTF+3heLDcfi8Ns/QSwAdJ1zuwJhw/F4s6cfBd15M/BvHdhmF+iUdrjAMI3fbMyveV68995v0u9HXp4gL08gInhh8jasPxaP7nPNK5QY0pZ+KnnSG6dZmODgYDRo0ADTpk0DAOTl5cHHxwcDBw7E0KFD7/rdlJQUGI1GJCcnw2AwPNR8Zefm4Yft55CWmaNcqO9Uxd0eqbdyEFrDA462VvA0WqN9nfK4cC0Nx/5Owb7z11HR1Q56rRq7zl5Dk6quaFzZFR5Ga0TFJuGPQ5fhXc4GWo0a1Twc4GKvh0YNbD99Fb9HX0Z0gbFLB7eqim8jTpvVAtjrtUrgBABOdjpU93TAzjMP3kxiSKtq+DL8ZKHpLvY6bAprgu4/7nuoj0dXD3w2vzbun7ari98KRnBFZ+XNRjm5eWYvKghbFo2V0fk1b02ruqKubzm8WNsTlVztkZsnuHkrB28vPKDUHge42eOdJpXw0fLim1Y4WGuR+k/w/8eAZ1HL24jQyduUWs6Czo9/EckZ2Ui9lYPnJv5pNs/aSo0FPZ9G5wIBWAO/cmY1Mw+iqrsD/tf7aTw9Lr82NKSiM3bfUXN5btyLSM/OxZhVx6BSmdcabh/ctFBeC3o92BeL98birWf90bGeN96ctw/xKZkIe6EKoi8m4fmqrngh0B2roi+jvp8T9py7hszsXHx7R3vlTvW88UuBGuE53eujqocDdFo13A3WCD8ah2UHLiJPBFti7h7MVHSxwws13LFs/0WlrWMDv3Ko4GyHT9sGYtWhyyhna4U9565h++mr+OtaOt5oWAHt63ih0394v31dX0ccjE0CAMzoWhen4lNR0dUeLQPdodeq8X/f736g7dmiujsqudlhUPMqsNKoMO3PMzhyKRm5Inj3+QCcTbyJL8NP4uatnGKbYlRxt8cP3etj/bE4jFubf4z6OdsiyMcR3uVsMLBZZVT7NLzYPEztUscswK/hZcCbjfzw8T/B2aRXg8we5xeljo8jVvZ/BgDw1oL92HQiv/LBTqdBWlYuansbC50fVrzbCAv3xOLXg7f3jYHNAnAl+ZbZ/lLQu89XwqUbGcrTn6ruDkUej/ejqrsDAr0MSm3r41LV3QFta3vi9WBfjFt70mw9FGdm17rot+h206MLE9pgwa4LWHvkCtrXKY+XnyoPG50GfkPXmKUREdzMzEGt0RuU6asGPGM25Fenet5oUd0NI3/PH4WlVQ0PhBfTIdZKo8JrT/uiUz1v1PZ2fIDSF+9RXr/p3jGofYiysrJga2uLX375BR06dFCm9+jRA0lJSfj999/N0mdmZiIz8/bjuZSUFPj4+Dz0g+JUfCpaPmDtF1m2FtXdsPVU4hMzbirRk6iiqx3SMnOUZi5U8rRq1SMbRaRWeSP+GPjsQ10mg9onA5sfPERXr15Fbm4u3N3dzaa7u7sjLq7wneP48eNhNBqVj4+PzyPJF9/OUnZtOpHAgJboX5xLTCu1AW2PkAolnYUH8iiHxWsZ6P7vicgiaUs6A2XZsGHDEBYWpvxtqql92Co426F/00qw0qjx17V0HPjrOuJTMtEy0B0atQo1vAxwN1hjzeErqFehHC5cS8ezAS74eV8sDsbeQAM/J6hUQFUPBzjZ6mCwscL/1c/P54krKRi16hi6BvuiWTU3ZOXkwU6vRcY/HTUSUjLhbK/Dr5GXMDXiNDwdrdG9oR+SMrKQkpEDB2stdp65iviUTKhVgI+TLSq62iH2ejp0GjXiUzJxJTkDzaq5o5ytFVL+Gc5HrVLBXq+Fi4MeGVm5eLayC/adv47Iv25Aq1YpQ+OcjEuBr5MtckXQtKobDl9KRvTFJLSp5QlPozUW7L6Ap3zLYUiragj0NECrUSEhNRMbjsWhfZ3yiE+5hf0XruPlp8rDTqe93W4WwPnENGw/k4hgf2f4ONngTMJNONnpoNdqICL4Pfoytp9OxLOVXXHySgpir6dDBHi3aSXY6rQ4k3AT2bl5aF7dDdZWGjjb6bBg11+IjL2Bl5/yQnpWLrbGJCL2ejrq+5VD48qusNNr8dPuCzh8KRl1fcuh93P+OJNwE6NXHUN6Vi6CfBxhp9PAxV6PjOxcVHG3R1xyJrafTkS7IC/otGp4OdogpJIzfth2Dn8nZcDDYI3K7va4mZmLlIxsHP07GTqtGk/5OuJqahaSM7KhUavQNdgXG47H49KNdKRl5qJZNTfEpdzC9bQsHL+cgv9r4INgfyeM/qfJwPNV3dC3cUVsjUnE52uOo5KrPTKyc3E28SZaVM/f914IdMcfhy7DYG2F1Fs5iIy9gUBPA6w0angY9biVnYdnA1zgZtDjTMJNfL7mBF4IdEe34ApIuZWNADd72Om0WLT3L9Qqb4S3ky02n4jHqkOXcTYxDU/7OeHDllVw6UYGZm87h64NffHHocuw02vx28G/EehpQEZ2LkQEdSuUQ3auwM/ZFieupGDvuet48xk/vFrPByfiUpCckY0zCTex++w16LVqvB7sixpeRoxadRSBnka0CHSDXqtGdU8D0rNyceTvZKhVKngYrPFb1CWcTbiJTScS0LqmR35nppw8aFT5neCu3sxERVc75OUBsdfTkXorGx+2rIrcPMHG4/FwcdChmkf+eqnibo/E1ExU9XCAvV6L41dSEBWbBO9yNmhXxwvZuYK1h68g+lISgv55fL76n06Tzau5ITMnD1U9HDB3x3l0D6mANxv5wdFWh94L9iMqNgkTO9VGvQrl8Mehy2hV0wO/HfwbFV3s0LKGB3acuYrT8ak4+ncy/oxJRM3yBjTwc4JapcKO01dxPT0LbWp54urNTLSt7YU/Dl2Gq4MeJ+NS0Ka2F15+qjystWqsOXIFaw5fga+TLX49eAk30rNR0cUOarUKZxJuwtHWCg7WWtT0MuJWdi6qeDhg15lrqOvriPiUTMSl3ML7LSrDzUGPhJRMzNhyBvsv3EDzam547WlfrD8Wh4s30uHmYI3QGh74Oyl/n1116DJaVHeDg7UVbKw0+DMmAX9dS0cNLwMqu9sjNw+IvZ6Gv66lY3S7Gth0Ih47z1yFl9EGNbyMsLfWorqnA07FpSJXgC0xCTibcBPZeYI2tTxR2d0eG47lHyOta3qiZQ13nL+ahtWHr8C7nA0uXs9AeUdrGG11iL6YhEMXk+DlaIOOdcujW8MK0GvVUKlU+CXyEj5afggvBXnBxV6HhNRM1CpvxPnENDjaWeHFmp7YceYqwo/GoVGAM16q7YWtpxJx/HIKXB30GNGmOn6PvozK7vao7e2IUS/VwNWbmYi6mAR7vRauDnqsPnQZ289cRZ/nKmLTiXgkpWf/0x79Giq52uHNRn4I9DLC0dYK5Wx1WHPkClIysnHkUjIiTsbjvWaV0eGp8vh+21msir6Maa/XReqtHExcfxI5uYI8EXiXs8Ghi8no9aw/LlxNQ8OKTsjJE1xLy0INLwP0Wg3Grj6GQE8D4lMyYaVR4eL1DDQKcEZOriD6YhK0GhVsdRrcSMvOP5fn5TdJaFjRGTY6NZIzsnHwryTcSM+CjZUGTau54XpaFtIycxBxMgF5IhDJb7qlVgFP+zuh93P+D/06S08GNj94iO63+cGd+PiCiIjI8vD6/WRg84OHSKfToV69eoiIiFCm5eXlISIiAiEhISWYMyIiIqLSjc0PHrKwsDD06NED9evXx9NPP40pU6YgLS0NPXv2LOmsEREREZVaDGofss6dOyMxMREjR45EXFwc6tSpg/Dw8EKdx4iIiIjo4WGb2icI2+QQERFZHl6/nwxsU0tEREREFo9BLRERERFZPAa1RERERGTxGNQSERERkcVjUEtEREREFo9BLRERERFZPAa1RERERGTxGNQSERERkcVjUEtEREREFo+vyX2CmF7ulpKSUsI5ISIiontlum7zJa0li0HtEyQ1NRUA4OPjU8I5ISIiovuVmpoKo9FY0tkos1TC24onRl5eHi5fvgwHBweoVKqHuuyUlBT4+Pjg4sWLZfK91Cw/y8/yl83yl+WyAyz/4yq/iCA1NRVeXl5Qq9mys6SwpvYJolar4e3t/Uh/w2AwlMkTmwnLz/Kz/GWz/GW57ADL/zjKzxraksfbCSIiIiKyeAxqiYiIiMjiMagtI/R6PUaNGgW9Xl/SWSkRLD/Lz/KXzfKX5bIDLH9ZL39Zw45iRERERGTxWFNLRERERBaPQS0RERERWTwGtURERERk8RjUEhEREZHFe6xB7fz58+Ho6PjQlvfpp5+ib9++D215jxrLz/LfT/mHDh2KgQMHFju/tJd/1qxZeOmll4qdX9rL37BhQ/z666/Fzi/t5S9N+/+DnPu6dOmCSZMmFTnPksr+oMLDw1GnTh3k5eWVdFZKxL8d/6XdA5df7kOPHj0EgAAQKysrqVSpkowZM0ays7Pv6fvp6ekSHx9/Pz8pTZo0kffff7/Q9CtXroiDg4NcuHBBmbZ161Zp27ateHp6CgBZsWJFoe+NGjVKqlatKra2tuLo6CjNmzeXPXv2mKWJjIyUFi1aiNFoFCcnJ+nTp4+kpqbetfx5eXny6aefioeHh1hbW0vz5s3l1KlTyjL//PNP5bt3fvbt26ekW7p0qQQFBYmNjY34+vpKxYoViy2/ra2tqNVqCQoKMiu/0WgUAGJtbS0ODg7SsGFDWbt2rfLd8+fPF5uXZcuWyeeffy4hISGi1+tFo9GIvb29uLu7S82aNe+6/Y8fPy6+vr6iUqkEgGi1WmnRooWcOHFCRESeffbZQr+nVqvF1dVV3n33XRGRIvPUqlUr6du3r1SsWFGsra3Fzs5ObG1tBYD4+/vLggULZN68ecWWCYDo9Xr59ddfxd/fXzQajTJdpVKJvb29sh+4u7sXuwwvL6+7/oanp6dYW1uLk5NTsWnKlSsnarVaNBqNaDQaKVeunDg6OopOpxN3d3cJCAgQJycnsba2loCAAFGpVPLmm29KTEyMtGvXTgwGg2g0GtFqtQJAvv/+exERiY6OlmbNmom1tbXyW1qtttA+btr/9Xq9ks7Ozk569eolqampIiIybdo0cXV1Ncu3h4eH2f5vKoOVlZXUqVNH5syZIz169BC9Xi8qlUpUKpVYWVmJlZWVODo6Sp8+fSQhIUHi4+MlNzdXAgICzJZvMBikf//+kpycLCIiV69elfLlywsAadSoUbHrs3z58vLNN9+Y7f/29vbK/IL7/+zZs8XHx0fUarWy7U3/v3HjhoiYn+MKfuzt7aVixYpm01QqlZQrV05GjBghmZmZIiLSp08fZdvc+dFoNFKrVq277kNubm7i4eEhtra24u/vL1ZWVvLee+/JSy+9JD4+PsqxdefnxRdflC1btkj16tWV/btcuXLSoEEDASCTJ0822/6m/cTOzk7ZFlFRUSIi0rZtW9HpdGb5bt++fbHrBoAEBQVJzZo1RaPRiFqtFpVKpfw/MDBQRER8fHzuWnbTcRocHCz29vbi7OwsAKRevXr/+j1vb2+pV6+eWFlZKdN0Op3Z9h81alSR5x+9Xi8ODg7y3HPPyfXr16VXr15K+U3lKLhcrVYrNjY2otfrxWg0ynPPPfev+fu3z+jRo+XXX3+VFi1aiIuLizg4OIidnZ1YWVlJUlKSnDlzRjp06CC2trbKPuDo6Cjt27eXkydPytatW8Xd3V3Zn03Hp5eXlwwcOFCSkpLk/fffL/K3nZycZPDgwZKdnS2RkZFSpUoVZTlWVlZiNBpFp9OJl5eXfP755zJjxgypVauWODg4iIODgzRo0EDatm0r5cuXF7VaXez+D0BsbGxEo9GIXq8XvV4v9vb2YmNjI46OjuLr6yvVq1cXnU6nXNPq168vffr0KXZ5bdq0kfPnzxe7bzo5OSnXvXHjxkn9+vWV64etra3odDqpXbu2rFu3TkREvv/+e2nSpInZ/m/6VK1a9a7X8S+//FLq1asndnZ2yjnaVJaFCxcq+Th+/Li89NJLYjAYxNbWVjmvtG/f3uwa/8cff0hAQIAsX75c6tWrJ0ajUWxtbSUoKEh++ukns7S//vqrvPDCC8q1x3QsF9SkSZNCeX777beV+dHR0dKlSxfx9vYWa2trqVatmkyZMqXQchYuXCi1a9cWGxsb8fDwkJ49e8rVq1fN0ty4cUPeffdd8fDwEJ1OJ5UrV5Y1a9Yo83NycmTEiBHi5+cn1tbWUrFiRRk7dqzk5eUVKn9ubm6hPNzNfdfUtmrVCleuXMHp06fx4YcfYvTo0fjqq6/u6bs2NjZwc3O7358s0pw5c9CoUSNUqFBBmZaWloagoCBMnz692O9VqVIF06ZNw5EjR7Bjxw74+fmhZcuWSExMBABcvnwZLVq0QEBAAPbu3Yvw8HAcO3YMb775JoDiyz9x4kR8++23mDVrFvbu3Qs7OzuEhobi1q1bAIBGjRrhypUrZp+33noL/v7+qF+/PgBg3bp16Nq1K9555x0cPXoUM2bMwKVLl3Do0KFC5Zg2bRpEBC1atChU/nfffRcAMGnSJBw4cADNmjVD+/btcezYMQCAj48P5s6dixo1asDDwwNjxozBmDFjYG9vj9atWyMrKwuNGjVCdnY2rKysEBUVhaVLlyI2NhZ+fn5Flv/s2bN49tlnUa1aNcyePRtbtmzBxIkToVar0bJlS+Tm5kKj0aBPnz4YNWoU3N3dMX36dERGRmLTpk0IDQ1VyjFv3jxlHYWEhKBSpUqoV68e5s2bh+HDhwPIfx2hXq/H559/jv79+8Pe3h4LFy7E+++/j169egEAatSogZCQEFy5cgV//fUXtm3bhuDgYEydOhWffPIJWrduDQDIzc2Fu7s7WrZsifXr1+PQoUM4dOgQPvzwQ3z22WcAAGdnZ2RlZcFoNOLQoUPYt28fJkyYAJVKheDgYGg0GkyZMgVHjx7FwoUL4eTkBFdXVwDAjz/+iGbNmqFy5coYMGAAunfvjokTJ0Kv1yM7OxsZGRn45JNPMGHCBHTt2hXbtm3DiRMnMGrUKIgINm/ejLZt2yInJwfjx49H37594e/vDwCwsrICAERGRsLe3h6dOnVCz549lXX57rvvmu3jVapUwYQJEwAAtra2ymsj//zzT/Tt2xdLly7FoEGDkJaWhnr16qFevXrQaDRwd3dX9v/GjRujZs2a+PDDD5GTkwMXFxe89dZbOHz4MPr164fp06dDq9VCq9WiWrVq8PHxwbFjx9CvXz+4ubmhSZMmuHz5MoD82rg//vgDw4cPx6ZNm/DOO+8AAHr37o3atWsDgNkxs2HDBsyYMQOBgYHQ6/UYP348RowYgdmzZyMtLQ01a9aEvb09AMDa2tps/1+5ciWaN2+O6dOn45133kG1atWU9XT16lXl/9bW1vj444/x4Ycf4n//+x8iIiKwcuVKpKamws7ODv/3f/+Hn376CWPGjEFSUhImT56MTz75BACQlJSEOnXqAABGjRqFli1bwsvLC9WrV0flypXx9ttvAwDKly+v7GeHDh1CZGSksi/9+uuvOHz4MAYOHIjs7Gzs3bsXTZs2xbJlyxAVFYVVq1bBy8sLBoMBR48ehUajwauvvopJkyYhPj4eH3/8MQCgXbt2uHbtGry8vJSymba/q6srGjVqhJycHFy4cEGZv3TpUqxduxbt2rVDlSpVoNFooFKpkJmZqWz/kJAQdOnSRdn/w8LCUK5cOXz66af4+OOP8f3338PZ2RkuLi5wdnZWlq9WqzF27FhcuXIFJ06cQGBgIJo0aQKDwYArV64gICAA2dnZaNasGaKjo7FhwwZotVocP35c2f4xMTE4dOgQ/Pz8YDQacfHiRTg5OaF8+fKoXbs2unXrpqzjnJwc/PTTT8r2T0hIQPny5bF06VL88MMPsLW1Re3atWFlZYX169djwIABGDJkCBYvXgwfHx+0b99euV7l5OSgVatWOHPmDOzs7BAUFISsrCy8+eabcHV1hYuLC2JjYzF9+nQsWLAAffr0gY2NDV599VU4ODggICAA//d//4crV65g9OjR+Oabb+Dt7Q0/Pz9YWVlh/vz52LZtG1544QWsXbsWkZGRcHZ2RnZ2NsaMGYOWLVtCpVKhTp06aNasmVLmrKwstGzZEteuXUOtWrWUmu1u3bqhdu3acHR0RHh4OHr37q2cMz7//HNotVrUqlULdnZ2mD17NlatWoUBAwagcePGOHPmDL788ku0adMGKpUK6enp6NatG1atWoWnn34a3t7emDBhAiIjI3HgwAFkZ2dj9erV+Oyzz/DJJ5/ggw8+gEajwfDhw1G/fn3lo1Kp0KlTJ7z55puoWLEicnJyEBgYiMzMTHz++eewsrLCpUuX8Oqrryr745tvvonIyEhcuXIFe/fuhV6vx8CBA/Hcc8+hbt26SE1NxSuvvIKpU6fiypUr2LdvH3r37o3JkydDo9GgcuXKyrK2bt2K/v37o3PnzrCzs0NgYCBcXFzQs2dPvPzyy4iKikJ6ejpatWqF5557DgBw8uRJZd/bsWPHXa/jDRo0wPDhw/HWW2/h7bffRv/+/ZGbm4uGDRuie/fuWL16tdl1csuWLVi7di2SkpIQHByMO7Vu3Rqpqak4ffo0hg8fjt27d+Pw4cPo2bMnevbsifXr1ytp09LS8Oyzz+LLL78stJyC+vTpY5b3iRMnKvMiIyPh5uaGhQsX4tixYxg+fDiGDRuGadOmKWl27tyJ7t27o3fv3jh27BiWL1+Offv2oU+fPkqarKwsvPDCC7hw4QJ++eUXxMTE4IcffkD58uWVNF9++SVmzpyJadOm4cSJE/jyyy8xceJEfPfdd4XKv27duruWqZD7iYB79OhR6G7ihRdekIYNG4qIyPXr1+WNN94QR0dHsbGxkVatWpnVVs6bN0+MRqPy96hRo5S7jgoVKojBYJDOnTtLSkqK8nu4487i/PnzIiJSo0YNmTZtWrF5BYquqb1TcnKyAJBNmzaJSP6dmpubm9ndweHDhwWAvPzyy8WW38PDQ8aMGWNWfpVKpdQi3Vn+rKwssbW1FQ8PD6X8VlZW4u3tfU/lNxgM0qpVK2Ud/lv5y5UrJ3PmzBERkUuXLkn58uXl6NGjUqFCBZk8ebLUqVNHevXqpaQfNmyY+Pn5mW2vZs2aiVqtVvJXsPydO3eWV199tdD2X7VqlQCQM2fOSNWqVUWn04mNjY1s2rSpyO0PQBYvXlxs+Z966in56KOPpFKlSspyw8LC5JlnnjFbz/inhunOO9o7Va1aVQDI77//brYfmAwYMEDUarWMGTNGmjVrJgAkKyvLrPy2trbSq1cvZf93cHBQankAyPz588XKykr69OmjrM9169Ypd/PNmzcXa2vr+9r/q1evrtTwFAXIr+Fu2rRpoX28devWAkD69Omj1L6PGzdOVCqVBAUFiZ2dnbKv5Obmio2NjQQGBirHv52dnbJeX3jhBalTp44AkCFDhsgbb7yh1KJbWVlJ165dRaVSSWRkpFJTA0C2b98uAMTPz0/Z/tbW1mJjYyPffPONNGnSRFq2bFlk+bOyskSj0UibNm1EROTll1+Wbt26iYjI4MGDpVu3bgJAbG1tlfVRcP83ycnJUWosV65cqaxzGxsbpWazoOK2v6+vr/j7+8vWrVvFxsZGeVLy1FNPKfs/ABk2bJgyz8/Pz2z/L1eunACQjh073tP29/X1FQDy9ddfi4ODgxw4cEC0Wq2cPHlS2f5OTk5mx7hJ586dZcSIEfL666+b1UhFRUXJ008/Lf379xeR2+erO7d/wadnxZ3/TU8CmjZtqqwzZ2dnsba2VrZTrVq1xMfHR2xsbJSnayqVSpKSku5a/oSEBAEg3bt3lxUrVohKpTJ7Ymcqv7e3t4wZM0bZ/i+99JJyrgwODpYRI0aYHRtJSUlKLeP+/ftFROTEiRPKb4eGhsr+/fsFgMTGxiplN10fOnTooJz7HBwcpGPHjsrygfzafpOPPvpIqSkz1Yq++uqrd932hw8fFpH8a99XX30lKpVKli9fLgBk69atZmVfsWKF/P7776JSqWTx4sWi0+nk9OnTAkB69eol9evXNyv7qlWrlBropk2byvHjx0Wr1Srnxbp16xY6Hkxq1Kgh1tbWZsdX3bp1Zfjw4cr3VSqVNG3aVJlvyvfKlSvlxRdflJ49eyrrceDAgcp2+uuvv5Tz/PLly0Wr1UpcXJxYWVnJTz/9JKtWrRKVSmV2TIqIrFixQgBIz549C+XX09NTpk2bpuxHW7dulVdeeUW6du2qpDGtf9MTnOJkZWWJq6urjB07tsj5Tz31lIwYMUIpY+fOnZVzVU5OjjRq1Eh5ynVnbCEi0rNnTyV9Ucu9k+kpbHE1tUU99b2bd99912y7ffXVV1KxYkWzNN9++62UL19e+XvmzJlSsWLFQtukoDZt2pjFGyJSaBuIFF/+u/nPbWptbGyQlZUFIP+u6sCBA1i1ahV2794NEcGLL76I7OzsYr9/9uxZrFy5EqtXr8bq1auxdetWpSZp6tSpCAkJMbu78PHxwfXr13H8+HGlhvNBZWVlYfbs2TAajQgKCgIAZGZmQqfTQa2+vWpsbGwAAAkJCUWWPzU1FXFxcdi8ebNZ+R0dHTFmzJgiy79q1Sqkp6cjNTVVKX9ISAiuXr1qVv5KlSoBAPbt26eU/7vvvkNKSgpGjBjxr2XMzc3FkiVLkJaWhpCQEOTl5eGNN97Axx9/jBo1agAALl68iOjoaPTu3Vv5XmZmplILaKLRaJCXl4fIyEiz8mdlZWHNmjU4cuQIfvvtN4gIKlWqhMuXL6N79+7w8/ODj4+Pss4zMjLQo0cPfPnllzh8+DCGDRuGOXPmYPXq1QCAvn37wsXFBYcPH0alSpXw1ltvKdvflLezZ8/C09MTPj4+sLGxwb59+wqt59zcXAwbNsyslrqgW7duIS4uDtbW1oiKijLbD0x5nT9/PkQEL7/8Ms6dOwcnJyez9ZKZmYn09HT07t0bb775Jvbv3w8nJyd07twZWq0WALB582bY2tqa7a8///wztFot8vLy8Oeff0KlUqF169bYsmWLsv179eqlLMPX1xevv/46nJyckJCQgJMnTwKAsn8UJT09HY6Ojmb7+Lx583DixAk4OjrC3d0d165dg9FoRM+ePaFWq3H48GGkpaVBrVbjqaeeUu6u4+PjleU2atQIS5cuxfXr12Ftba085di2bRsOHDiAPn36wM3NDWq1GkuXLkXDhg2VGuGMjAx4eXlh7969AIC//voLI0eOxJQpU1C1alXk5eXh008/xU8//WTWnvLXX39V9v9FixYhNzcXb7/9NqKiorBr1y40adIEmzdvxvLly82e1Ny5/xeUlJSknLtq1qxptl+EhYVBo9HAxcUFo0ePRkJCQpHb37T/Ozo6ok2bNnB2dsYff/yh/Hb37t3h4OCAypUro0qVKsr3Ll26hIkTJ+LIkSOYMmUKnJyc0LZtW+zcudPs+Nfr9bCysjI7/509exaxsbGoXbs25s+fjy5duiAiIgIVK1bE6tWr4efnByD/6YKnp6dZmefNm4dz587hnXfewapVq2AwGODg4AAAyM7ORmRkpNnTHwDw8vLC9evXzaYtWrQILi4u2LNnD86fP4/09HSz8//UqVMBADt27ICNjY2yzjIzM2EwGDBlyhQ4OzsjISEB2dnZeP755+Hp6QkRweuvv47c3FyMHTtWaYda8Pw3a9YsAPnHx9y5c9GiRQuzJ3YmGRkZcHR0VLa/j48PTp8+DXd3d+zduxdr1qxBYGAgVCoVPv30U8yfPx85OTkwGo3KsVqtWjWUK1cOAHDjxg1UrVoVzs7OmDt3LvR6PW7duoW5c+fC3t4eMTExWLVqFebOnYvU1FTs2bMHM2fOhE6ng6urKzQajZI309O32NhY6HQ6eHp6Yvv27YWufdbW1lCpVFCr1ahUqZJy7QsODoZarca2bdsAAE5OTmZlT01NxaJFi9CoUSOkpaXBYDAo5xJTjX9QUJBS62xjY4OcnBxotVocPHgQM2bMQMWKFbF27VoAwIkTJ/DWW28V2g9MT7kyMzNRsWJFiAj+/PNPnDp1Cg0bNsSiRYuU6UOGDFG+FxoaCrVajRkzZiApKQkGgwFz585F9erVzdoe+/r6wt3dHdu3b0e9evWgVqsxcOBA2NjY4IUXXsD//vc/tGjRotC1au7cuShXrpxy3ikoMzMT1tbWSE5OVtadjY0NduzYUSht9erVUbFiRXTt2hWxsbGF5q9atQrXrl0zezoGACKCiIgIxMTEoHHjxkhOTka5cuWwZs0aVKlSBaGhoTAYDDhx4gScnZ0LLdfk6aefxvbt24td7v0yHbc1a9bEsGHDkJ6eftf0ycnJZvtWSEgILl68iLVr10JEEB8fj19++QUvvviikmbVqlUICQlB//794e7ujpo1a2LcuHHIzc1V0jRq1AgRERE4deoUgPzjYceOHcrT0+LKf0/uJwIueDeRl5cnGzduFL1eLx999JGcOnVKAMjOnTuV9FevXhUbGxtZtmyZiBRdU2tra2tW8/fxxx9LcHCw8ndRdxdRUVHK3XJxcJea2j/++EPs7OxEpVKJl5eXWZvWo0ePilarlYkTJ0pmZqZcv35dOnbsqNytFlX+1157TbmbLlj+du3aiUajKbL8rVu3loCAALPyf//996LVaqVatWqSm5srMTExSs3Wrl27RETk1KlTSq1ObGzsXWtqTW1ijUaj0p5l3Lhx8sILLyhtVypUqCCNGjWS6tWrm31//fr1olKpxNbWVnJycuTSpUvi5uam1KQWLH+/fv2U8g8cOFCGDh2qtEFTqVTy7bffikj+/qPT6USr1Yqnp6fY2NiIWq2WJk2aSNWqVSUzM1OeeeYZCQwMlIMHD8qECRNEpVJJkyZNRERk+vTpZm3btm3bJvv371fawV6+fFlERHbt2iVAflvMLVu2SNu2bcVgMMjFixfNtr9pOQAK7Qci+e2bASg1KS4uLtK6dWuz7a/RaMTJyUnZ/9u1ayddu3YVX19fGTRokFJj1q9fP5k3b55YWVkp29S0bLVaLZ999plUqFBBatasKVZWVqLT6UStVis1csuWLZN69eqZtQO92z5uml9wHz916pS4ublJ+/btzdo9m8ptasMIQHx9feWXX36RAwcOiNFoFJVKJV26dJH27dvLjRs35IUXXlDSWllZKW251q1bV6iW6dtvv1WOIZVKpbSbNP1tSvfiiy+Ki4uLVKpUSURut0PX6XQyf/58pWzPPPOM8rtqtVrGjh0rV69eFR8fH6XGyrTMO/f/O7f/nW1qf/75Z+nVq5fMnTtXJk+ebLafFLX9dTqd6HQ6qV+/vnL8T58+3Wz929jYyJdffqkc/88//7wEBARI3759lXbHAGTv3r1m5z/T/gdAcnNzZfDgwUotOABZuHCh8r23335bWa99+/ZVaoObNm2q1NSatv/MmTOVdrdarVZWrlwpAGT9+vVm5xpTfmvUqCGOjo7K+f/777+XdevWyezZs8XKykrs7e2V/WHnzp0SFhambFeVSiXe3t4ikl9DbGtrK+7u7hIWFiZ6vV7UarUYDAYpV66cfPnll9KlSxfRarVK/kxPPbZs2aJsP39/fwHy+yJoNBpZunRpsft/we2/du1aWbZsmYSFhZltn9mzZ8ugQYNEq9WKWq2WKlWqmC3L9Hu1a9cWkfwnd6aaZQDKfNO53/QkBMhv9+rg4CCvvvqq2bWv4DlAp9PJ559/XujaV7FiRaUm3cHBQd5//33l3GY6xnx9fc2eUg0ePFhZbsOGDSUmJkZ8fX3lk08+kcTEROndu7eyTNNTih9//FFpF6zRaKRdu3aF2m6/9dZbUqdOHaXW7vDhw0rbUYPBIM2bN1f2J41Go/xGw4YNpXHjxqJSqQpto5o1ayr7s0qlkqpVq8qFCxcKXdOeeuop5YnUli1bRKPRKPkLCQkpVJv6999/i0ajkcDAwCJrJl977TUJDAyU559/Xho1aiQbNmwQGxsb0el0SpoJEyYIkP9EKTw8XEJCQsTX19csVjFta9M5QUQkKSlJ7OzsRKvVil6vl7lz58rSpUtFp9PJli1bBMh/gjRgwABxdXWVESNGiEqlktDQ0CJran///XdRq9Vy/fr1Qsstyt1qar///nsJDw+Xw4cPy8KFC6V8+fLy8ssvF7kcEZGdO3eKVquV9evXm01ftmyZ2NvbK081XnrpJbNaWVOfjV69esmBAwdkyZIl4uTkZPZUMTc3V4YMGSIqlUq0Wq2oVCoZN25cseW/n3a19x3UajQasbOzU4KT7t27y82bN+X3338XrVYrOTk5Zt+pU6eO8gioqKDW1InA5JtvvhF/f3/l76KCWtOBnZCQUHzB7nLBv3nzppw+fVp2794tvXr1Ej8/P7MObIsWLRJ3d3fl4Pzoo4/E3d1d6tatW2T5N23apBzQBcv/6quviqOjY6HyX7x4UdRqtbz66qtm5c/Ly5OmTZsqnRPKlSsnFSpUEACyZ88eycnJkfr168vHH3+slP9uQe2MGTPkwIEDMnToUHFxcZFly5aJu7u7/P3330o6X19fsba2lq+//rrQMjp37qyc6GxtbaVu3bpKsFyw/KbHWiqVSnJyciQpKUlOnToljRo1UgLYjIwMmTdvnnIiXb9+vXTv3l25KKvVagkPDy+0/StUqKA8tktKSpLDhw9Lw4YNlZOtp6enciKPi4sz2z9MZcrKypJKlSopj2tu3rwpkyZNEr1eL88995x4enrKa6+9Vmg/aNy4sRKUbdiwQdzc3ESlUpltfysrK/niiy/k999/F41GI/7+/lK/fn1p1aqVZGVlKfk8cOCAzJs3TwwGg5w4cUKCgoKUdfbZZ5+JSH5jfyD/senhw4dl9uzZyomjYcOG0rp1a9mxY4dZp7iiTm5HjhwRANK2bVuzfTwoKEhmzpwpo0aNEj8/P+ndu7c4OTkp5S4Y1Jo6oInkN3VQqVTSsGFDpdOMaR9t27atfPLJJ2Yd1DQajVStWtWsw4gpkDYFKzExMcrNxC+//CIAxGg0iouLi7L9TUGtra2tzJgxQ0RELl68qFzQtmzZIrNnzxYnJyepX7++DBkyxGz/t7a2Ntv/jx07pmz/Dz/8UBwcHJTOiwWbSRVketxs6tB45/a3t7eXpk2biqenp3L8JyUlCQD5/PPPleDlwoULyvFvOmZN57+6deuKlZWViNw+/23evFlsbW2VzlXp6emSmJgoMTExMmXKFGXd1apVS0RE6Uzz66+/Kjd5X3/9tZJu0qRJUr9+fZk5c6ZMnDhR6tatKz179hQnJyfx9va+56C2qPPf6tWrlfNfRkaGhIaGSmBgoKxYsUK5QcvLy1Nu6kzbqV27dgLkN0nRarVy+PBhcXFxEaPRKAcPHpStW7cqzTVMNyWmYxuAfPLJJ+Ls7Kx00jNZtGiRAPk32EVtf9P5ukuXLmJlZSUuLi4SHx+vdMQrLqhVq9VKZYhKpZIXX3xRNm/eLCEhIQJAUlNTJT09XYxGowwdOlQqV64sfn5+AkA+++wz5dpnKoO3t7ds2LBB9Hq9VK9eXSZNmqTs+4sWLRK1Wq3cLPzwww9mHRU7deokLi4uYm9vr9ysi4gkJiYKABk1apQEBweL0WhUzkWmff/06dPy3nvvmVU8jB8/XoD8JhQFb5qbN2+u5MPUhOjkyZOSmZkpp0+flgMHDkjTpk1Fo9HItGnT5NChQzJ+/HixtbWVCRMmSEhIiNJpraD09HSxtrYWlUolo0aNkt27d0vHjh2lRo0a8sknn5hd0xo1aiSDBw+WK1euKMfDwoULZevWrdKkSRNp3ry5WQejcePGibOzszRu3LjIoDYhIUHZLhqNRqpUqSLvvvuu0jRG5Pa5xxQw37hxQwwGg1kTC9N1/JdfflGm5ebmyunTpyUqKkq+/vprsbOzE71eLwsWLJC///5b2XZ+fn5K5+2XXnpJ/Pz8igxqN2zYIACU7WZartFolD///LNQ+rsFtXeKiIgQIL9px52OHDkiLi4uyrXJ5NixY+Lp6SkTJ06UQ4cOSXh4uNSqVcusKUHlypXFx8fHLBaaNGmSeHh4KH///PPP4u3tLT///LMcPnxYfvrpJ3FycjKrvChY/vT09H8tj8l9B7UtWrSQ06dPy19//WU26sGDBrV3BmSTJ0+WChUqKH8XFdSaasViYmKKL9hdgto7BQQEFHmXEBcXJ6mpqXLz5k2lRrGo8p89e7bIoLZx48bi4uJSqPxjx45V7tKKK/+lS5ckMzNT6S2dkJAgN27cUE6upn9NF3iNRiMRERHFlr958+YSEhKiBBamj+nk5ePjU6j8pvz+/fffkp6eLh06dFAunAXLn5mZqdQ4Fiy/qWbJyspKFi9eLPPmzVMu8hcvXpRhw4YJAAkPDxc3NzeZPXt2oe1vavN569YtZZpp++t0Olm4cKHMmDFDHBwclLu5Xr16FSp/p06dpEuXLiKSf0DZ2NjI6tWrzbZ/wf3gwoULolKpxGg0KheETp06CZBfa/rXX38pF+mEhATljtK0Pk0nctPfTZo0Mdv/TRd04HYN8/HjxwWAWfsk00lcrVYrIwOYyg+gUHujY8eOKTXqBctvuiAWrOUoWBvTs2dPs3nbt29XvlupUiWxsrKSWrVqKbWkGzduNDv+a9euLWq1Wq5du6b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\n" ] - }, - { - "cell_type": "markdown", - "source": [ - "__Observation:__ we can see many sensors have recorded high PM2.5 values greater than 250. Such values are generally outliers/abnormalities and are not useful for the analysis." - ], - "metadata": { - "id": "tBrwAabsaJmE" - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Step 3.4.2: Replacing the values greater than 250 to zero." - ], - "metadata": { - "id": "6ZAm2FtlaxQT" - } - }, + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.4: Removing abnormal values in the data" + ], + "metadata": { + "id": "MJeJRYFfZRfW" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.4.1: Finding the maximum values recorded by each sensor" + ], + "metadata": { + "id": "oFNpo4dJa3u7" + } + }, + { + "cell_type": "code", + "source": [ + "maxValueInEachColumn = dataset.max() #Gathering the maximum PM2.5 value recorded by each sensor\n", + "maxValueInEachColumn #Printing the maxValue of each sensor" + ], + "metadata": { + "id": "FT2EIM1ZVzRf", + "outputId": "c9e91d72-9dbf-4cbe-dff6-339335e43228", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 7, + "outputs": [ { - "cell_type": "code", - "source": [ - "dataset.where(dataset <= 250, 0, inplace=True)\n", - "dataset.max().plot()" - ], - "metadata": { - "id": "7Vx27DZ_bG8L", - "outputId": "4e252887-0fb1-4973-a905-55b870106690", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - } - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 9 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "Point(139.0794379 36.3727776) 0.0\n", + "Point(139.1051411 36.3963822) 0.0\n", + "Point(139.0960211 36.4047323) 110.0\n", + "Point(139.0428727 36.3816035) 77.0\n", + "Point(138.9955116 36.33801589999999) 200.0\n", + " ... \n", + "Point(139.9074816 36.4445767) 0.0\n", + "Point(140.0934838 36.4673588) 137.0\n", + "Point(139.7422865 36.2305774) 0.0\n", + "Point(139.7151723 36.822353) 0.0\n", + "Point(140.1510903 36.6598314) 165.0\n", + "Length: 1764, dtype: float64" ] - }, - { - "cell_type": "markdown", - "source": [ - "__Observation:__ We can notice that the maximum values of every sensor are no more than the 250 value.\n", - "\n", - "We will now check for the minimum values recorded by the sensors." - ], - "metadata": { - "id": "opYvY-uQb1fR" - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Step 3.4.3: Finding the minimum values recorded by each sensor" - ], - "metadata": { - "id": "JGSmOe8EcI8l" - } - }, + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "maxValueInEachColumn.plot() #point the maximum values recorded by each sensor." + ], + "metadata": { + "id": "Wmvv1z6mZwA2", + "outputId": "1fdfd6b4-0f26-4115-f33f-a0cb35a69122", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + } + }, + "execution_count": 8, + "outputs": [ { - "cell_type": "code", - "source": [ - "minValueInEachColumn = dataset.min() #Reading the minimum PM2.5 value recorded by each sensor\n", - "minValueInEachColumn.plot() #ploting them\n", - "\n", - "#dataset.min().plot() #memory efficient approach" - ], - "metadata": { - "id": "zwbbXYqCZEo2", - "outputId": "e3efa79b-1eb3-4719-9119-defe85615fb1", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - } - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 10 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 8 }, { - "cell_type": "markdown", - "source": [ - "__Observation:__ We can observe that many sensors have recorded negative PM2.5 values. Thus, we replace the negative PM2.5 values of each sensor with Zero" - ], - "metadata": { - "id": "buCv95fkc2sO" - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Step 3.4.5: Replacing the values less than zero to 0" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "OYlUYzY1dTjW" - } - }, + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ we can see many sensors have recorded high PM2.5 values greater than 250. Such values are generally outliers/abnormalities and are not useful for the analysis." + ], + "metadata": { + "id": "tBrwAabsaJmE" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.4.2: Replacing the values greater than 250 to zero." + ], + "metadata": { + "id": "6ZAm2FtlaxQT" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.where(dataset <= 250, 0, inplace=True)\n", + "dataset.max().plot()" + ], + "metadata": { + "id": "7Vx27DZ_bG8L", + "outputId": "4e252887-0fb1-4973-a905-55b870106690", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + } + }, + "execution_count": 9, + "outputs": [ { - "cell_type": "code", - "source": [ - "dataset.where(dataset > 0, 0, inplace=True)\n", - "dataset.min().plot()" - ], - "metadata": { - "id": "Q0DaB2g_daf8", - "outputId": "e02a5869-b619-4907-c08f-955b11fb6ab7", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - } - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 11 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 9 }, { - "cell_type": "markdown", - "source": [ - "__Observation:__ The abnormal values were replaced to 0." + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "U1O0xBeJd8Sl" - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Step 3.5: Create a dataframe of the sensors having pm25 >= 15\n", - "(useful to prune those sensors that do not record any pm2.5 value)" - ], - "metadata": { - "id": "AyJCyA6n97Oq" - } - }, + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ We can notice that the maximum values of every sensor are no more than the 250 value.\n", + "\n", + "We will now check for the minimum values recorded by the sensors." + ], + "metadata": { + "id": "opYvY-uQb1fR" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.4.3: Finding the minimum values recorded by each sensor" + ], + "metadata": { + "id": "JGSmOe8EcI8l" + } + }, + { + "cell_type": "code", + "source": [ + "minValueInEachColumn = dataset.min() #Reading the minimum PM2.5 value recorded by each sensor\n", + "minValueInEachColumn.plot() #ploting them\n", + "\n", + "#dataset.min().plot() #memory efficient approach" + ], + "metadata": { + "id": "zwbbXYqCZEo2", + "outputId": "e3efa79b-1eb3-4719-9119-defe85615fb1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + } + }, + "execution_count": 10, + "outputs": [ { - "cell_type": "code", - "source": [ - "thresholdValue = 15\n", - "pm25 = pd.DataFrame(columns=[\"long\", \"lat\", \"pm25\"])\n", - "for col in dataset[1:]:\n", - " res = [i for i in dataset[col].values if i >= thresholdValue]\n", - " if len(res) == 0 or col == \"Unnamed\":\n", - " dataset = dataset.drop([col], axis = 1)\n", - " else:\n", - " if \"Poi\" in col:\n", - " #print(\"Hey\")\n", - " col = col.strip(\"Point()\")\n", - " col = col.rstrip(\").1\")\n", - " long, lat = col.split()\n", - " pm25 = pm25._append({'long': float(long), 'lat': float(lat), 'pm25': len(res)}, ignore_index=True)\n", - "pm25.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - }, - "id": "51ureCEF_jv6", - "outputId": "b61bd0fd-2620-48ae-92c1-431ca93e4274" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " long lat pm25\n", - "0 139.096021 36.404732 8204.0\n", - "1 139.042873 36.381603 8999.0\n", - "2 138.995512 36.338016 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\n" - ] - }, - "metadata": {}, - "execution_count": 12 - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 10 }, { - "cell_type": "markdown", - "source": [ - "#### Step 3.6: Drawing the frequency heatmap of sensors\n", - "\n", - "The frequency heatmap provides cruical information regarding how frequently a particular sensor has recorded harmful levels of pollution" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "3_LVZ8q0AXua" - } - }, + "image/png": 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rfmA6vl11pWxDHq6CzvVCCgW1WrUKvR4Kx8CmkZi69qDFZz8Na6oc282r+WLbqObwdjLgamYO9Bq10m84oZInYoLdMHP9Qazakx8kjmtXHU2nbCxURieDFhtebgwfZyMC3U1Fru/iPN8sEu9uOKz8XXDbAcB7XePw8ebjRX634M2s2ZSOMfjrYgb+PPPvWA6DTo1PetdHRAkB6Kyn6+D8o5nwdTGiUWVvmPQaGHUa1A3zUNIEe5hw8tK/Nca31oj+PqoFXExaZGTmQq9VIydPYNRp0KqGH34b0RyuJh2qjSl8HPzXzeOtXn+sJsa1q46o0fnLcjRoGdDaOM5+UMoGDx6MDz74AIsWLcL+/fvRv39/XL9+HUlJSRWdNQBApweC0SU+RPnbPEgGyA8Ky4u38+3VBo98pBoejPTEyEeqKhcRZ6MOJr0GQe4mdIgNQLS/C2KD3W4rUHYx6uBsLBwkVfK27KKREOGJvo0jlL89nfSoEeiKBUkP4IfBjTCrc200qOSJuV3jlDRvPF4TQH4tTainA26Xq0kHJ4MW056KVQJaIL+mKeSf5Ux+spbFwIbKxdS+F9WcptfmX4A9nQwwFDGABwBMBd5X/xMoOOi1mNs1Do/XDlTW8Rf9EuDmcHdBZq0gV+X/XeuHINjDhKp+zgjx+HddBbgaMbx1NYvvPVTZG7HBbqji64SwEtbrz8OaYv+EVkog8mX/BhjdphriQt3xcLTvXeW5JDM71UZssBv+r1c9APn7SFEeCPOA6p91+vpjNVE7xA0fPFMXc7rUwef9EjDjqVjM6xYH7T+B0rON8ve7euEecDXp8HC0L+qGumNM22hlmd7OBiRW/7dMgx+ugjcfr4XaIW6Y2zUOy55NgF6rhpuDHm4OenSOD0GNQBfEh3vgs77/BpT1IzyVQXizO9cplPfHaweiZSmsO3P5zcw3FEt6x6NJlDd+eaUpfnmlKTrEBqBb/VCY9BroNGr0fDAcUb7OWPZsAvxdTViQ9IDFcgru7/6uJrg7/LsNXE3/7qcOBq2yXxcUHeCCEa2rwfmf2vuCbr1Z9XE2QqVSwcWoKzQQLsrPGVUKBEvuDjq83y2u0GAkV5NOaUGZ0+Xf9e1czKAlT0c9qvo5Y3y76hj8cBW0jw0AALzYvDISIjzRukZ+C2D3hFC4mnRQF9OcXjfUHa1r+KGKrxOq+jmjS3wIHA1aTH6yFmKC3VAj0AWJ1X0RF+JeaBmLe/17Xh3dphrUapVyc+juqC9yUGDB86K590Pnf647UzvGwNvZAINWA3dHPRwNWott5edqhEmvwcc96ynvOeo1ePPxmrfVLW5M22jUC/fApA41oFKpYNBqMLtzHdQOccOwxKr/+X2ybqypLWVPPfUUzp8/jzFjxiA1NRWxsbFYvXo1fH1L/6J6J1xNOly5kY2mVX0Q5G5SajNyCnS4uvXCU1aWPZtgcRIzm/5UDB6rHaQ0ST7bKAImvQZLeheu1QHy8zujU+1SydOinvXQ8K0fAeR3jfj0nwv/H68+DPmneRUAmkb5KN/55JbapqfrheDpeiEoC/+rG4z/1f23v/XK3acx8JPkQulurd0q9HkxFz3VP6Om0zKyLYJPlUqFaU/F3l2mC3ipRRW82KLyXX3Xy8mA5UXUvict2IYfU87jnadro11MgPL+r8ObKf+PDXZD74ciCn23NFTzd7HI1+0cPxHeTvj6OcuyPBBmmWbEI9Uw4hHLwB4AejYMR8+GxdcqAyi0bLPXH6tZ5PsatQp7xicWer9zfAjGtI1WApbmVX2U/sWleZp4MNILD0Z6KX/fejyPeTTa4u+mUT748Jm66P3PwNdbA1Vn47+XtFtbnf4r38UdG3dDBRUSq/shcXx+0Gk+p93I/rcSoZq/C46/2QYA0HzqRlw9n2OxjGn/i0G7mACLVqWZnWpjZoF19F6B4BEovowatapQWiC/Vaaolq0Ib0ccPX8dbWr5o2FlLyWft6t6gGuh915/rGax+2FRGlXxLvJ3nQxai+5aBZnT33qctKnljza1/G/7t8l6MagtAwMHDrzvuhusG9wIKalX0TDSCyqV6p+aDyNe/Wbvf3+5lNUL9yjy/aJqUspLcQGJh2PRtW8VrbgL8H9dmAt+fusgtc3Dm+FGVi7cHEq/zC2rl/5N3TtP18bBs9cQF+pe6su+G0Wt+hbVfAq/aSUK1sC983RtVB+7pgJz86+CNaq31q4WPIdY/r/oY7zgO6U5aEhVTKPXzQJBrUX6IvIW6ulwW92kCiruHHqn59YlveOxYudpdHrg3m/S/+M+m6hUsfuBnfBxNuKhyt7KybNeuAeCPRwqfGTsuAI1MeU9uZwKRZ9trWGSu7u9eJV04XbQa+HpVDYDF83NzaXJ2ai7bwJa4N/+nQVNucO+p/er0py3+l6pLYLaktM+UScIXk4GPFY7sOgEquID5Htx63HY859+28NbF938XdTN6N3c5BdXBPUdHn7+riY827gSXO+yqxGQX1Z3Bx1GtYn+78REpYRBrZ2r6GmKnyqFmoDSMqlDDRh1arzzdOl0aShLxV3wtP9x9SqvLia3MpRBUHu/uXXdvtSiSpnUele0ir7p0xRTG1uUqf+LwW8jmhW7HQoGgaVaU3vL36PbVMPGl5ugW/3QItO//WQMXIxaPFT5364Yd5Of0qqpLQ39GlfCH68+XOLsO0Sl7f65/aYKkVfBV6iSTtzlfTLsWj8UnR4IvuMmv/uJ5g7a+gTlt+3NfZJt2a27cgX2prFpBe/bbifwK+l4Lvjt0uxTe2sQqVarEFbCfOE1g1yxc0xLbDx4Dj8fulDkMm5HcTetFXUzWx6/Gxvshp0n0/BEMTPMkH1hUGvn8kpvmsS7UtSF5KvnGmDbsUvFToNVpvmxkoC2uHC0NC/MpclQjjNrVJSK7BNeniq6mHdSU/tfLPrdlmZN7V0sSq1WWQSBd1O24u5pi+oaYyuWD3gQF65lFpoKkOwTg1o7V141tdEBrgAKz7NZ1IWkTog76oSUb19JWznl30mTZXlW0ttD9wN7eTpRRXc/KDjDx62zfRSM3W5na1imr9igFrAMZO+q+0Ex37H1XdOrjMYCkPVhUGvnyusC9fQDwbiZlYuESp7/nbic2GLlxf1aU6u3khrwe2GL+9P9yCLwu8eVXpqBbGkst+DhezeH8v3W/YCovDGotXPlVVOr1ajRp1HZzBdqj4ob4He/1hbaw0X11uZiWy1xRW/Kgvv4PXcZKKOy3G227rU7RLGzH9jqzkh0C9uvPqESVfRAMSpd/zX7QUHc8qXrfr2hsDUFa0Hvtaa2rDbZ3d7EFfza3ZTtfpr9gKgiMKi1c6/+8+jNF5pFVnBOync0vq260/koqfQwbCh/91pTW1bdD+42W/c6CK6437WlG66KnoaS7m/sfmDnaoe4I2VSK7uYcske3ElNLdHduNuYwlGvwfWsop+odbdKCtZuJyYsqwrMu62pVVt0rSi932VFLdkLXgHpvgloK3LGA2s76dcI/PfZ6gWfd387FTLO/zwdqmmUd6nnCwBaVfez+LssniZGZcPPxQgAaF3D7z9S3rn/6x2Pqn7O+KR3fKkt8967H/z7/egAF+X/lbyLn1O2LFkOFLubKb1sv/uBPfTPp7vHmlqqcNtHt8Dl61kI9ayYC4k1CnAzKf93Merw7cCG0GvVt3XC/+WVZvg77YbFRbw0vfpoNK5l5uCXw/mTyG8d0bxMfue+YwMPX1g7uBFOXMywuGm61d2Wq06IO1YPanSXOSvavTZMFCxLo8pe+LJ/A1y6nlVhs7QUPH7vbkqvYt63xp2R6C4wqKUK5+Vk4DyD96ikpxXdytVBd0/PdP8vOo0KtUPclKDW3V4mRbeBrn4uRl2JAS1Q8fPUFnSvNbUm3b+tVCqVCnGh5dtadCv1Pfeptc95aonM2C5IZKXMNwINI73+I2X50qhUqOpXNrXARAXd6wAo80DZ+wXnqf1vHChGJWFNLZGV+mbgg/h+zxl0qhdS0VmxoFap8EhNP0zsUAO1g90qOjtUyu6n+OjWYK3g37czs0FggW4894OC8dpddT+wg9kPiErCoJbsVlk9IrO8BLqZ0Puh+++BFmpV/jPsu9UPreislC/r24WsUiWf/K42Rt29NzTeTwH6re7u4Qu23/3AlmqdqfQxqCWiUqWy105Nt7SK8uJbNhz0WuwZ1xK6Unj08v22jQruQqU5T+39Vk6issKglohK1b0O3qH72/3QpdHZWHYDHStSwf6ipftEsbvOEpFVsdc6FSIqI5w+iOje3c10ZfbwmFwOFKOSMKglolJlQ9fPO2Mn5b6ft681P1DlVnfV/aCYKzoHipG9YFBLdqvg4DBrvwDeT3gBJbo7Besg76b7gT08Jpf9g6kkDGqJqFTZUlMnUUW5m8PIHrofEJWEQS0RlSp7rajVl8JofLJvBbuL3k2NZHHHHoNashc8CxNRqbLX5sE6Ie6oW8GPWSX7xtkPyN5xSi8iumdB7ibUC/eAp6O+orNSYdRqFd7vFoe4ST9UdFbslrXfT9UKckWEt+NdP+nM2Vj0Jd3FZJtToBHdikEtEexm4HqZmva/2IrOQoWzh1pqzqhUdnQaNda91Piua1ZrBLjirSdq4tL1bAgEJy9loGmUD3xdjKWbUaL7FINaslt2EH8QkZW5l9lD1GoVnnogpBRzQ2Rd2KeWiIhuG28Gieh+xaCWiIiIiKweg1oiIrIJtvREMSK6cwxqiQCOFKNSwd2IiKjiMKglIiIiIqvHoJaI7hmbeomIqKIxqCWie8a5S+2HNW/rqn7OFZ0FIipDDGqJiMgmFHz4RVEPwlg+4EE8XjuwPLNEROWIQS0RABWH+NwTdj+wH9a8rY06DcK8HCs6G0RURhjUkt2yh0ealhdrbpIuTdyl7FfNQNeKzgKR3eNjcomIiO5Rs6o+mNkpFtX8XSo6K0R2i0EtERHZjbKqTFepVGgfy/66RBWJ3Q+IiMgmFOz+UVzwyp4y1k3Y14lKwKCWiIiIiKweg1qyW3xOPJU2zqJBVLY4wJdKwqCWiIj+05SOMXA16TC3a1xFZ4WIqEgcKEZERP/pybggPFEnkDVldN9Y+XzDis4C3WdYU0tERLflfg9o2aXI9hUcKFaDcwPTLRjUEhEREZHVY1BLdut2pv8huiPckYjK1P3eWkAVi0EtEREREVk9BrVEREREZPUY1BIRkW0o0DTNOYNtE58oRiVhUEtEd6RHgzAAQPOqPhWbESIiogIY1BKBgw/uxPPNKuOLfgmY3aVORWflvsPd6P7XvFr+zZiHo76Cc0J3g+dqKgkfvkBEd0SjVqFumEdFZ4PorlQPcMXGl5vA29lQ0VkholLGoJaIiOxKmJdjRWeBiMoAux8QEZFN4BPFbB8HilFJGNQSERERkdVjUEsEPgiKSgf3I6KyxYFiVBKbDmqPHz+OXr16ITw8HCaTCZUqVcLYsWORlZVlkW737t146KGHYDQaERwcjMmTJxda1ueff46qVavCaDSiZs2aWLVqlcXnIoIxY8bA398fJpMJLVq0wKFDh8q0fERERESUz6aD2gMHDiAvLw/vv/8+9u3bh+nTp2Pu3LkYOXKkkiY9PR0tW7ZEaGgoduzYgbfffhvjxo3DvHnzlDSbN2/G008/jV69eiE5ORkdOnRAhw4dsHfvXiXN5MmT8c4772Du3LnYunUrHB0dkZiYiJs3b5ZrmYmIiIjskU3PftCqVSu0atVK+TsiIgIpKSl47733MGXKFADAkiVLkJWVhY8++gh6vR7Vq1fHzp07MW3aNPTt2xcAMHPmTLRq1QpDhw4FAEycOBHr1q3DrFmzMHfuXIgIZsyYgdGjR6N9+/YAgI8//hi+vr5Yvnw5OnXqVM4lJyKyPwVbptlIbZs4UIxKYtM1tUW5cuUKPDz+nWNzy5YtaNSoEfT6fyfiTkxMREpKCi5fvqykadGihcVyEhMTsWXLFgDAsWPHkJqaapHG1dUV8fHxSpqiZGZmIj093eJFRERERHfOroLaw4cP491338Wzzz6rvJeamgpfX1+LdOa/U1NTS0xT8POC3ysqTVHeeOMNuLq6Kq/g4OC7LBndK449oNLAQSxEZYvHGJXEKoPa4cOHQ6VSlfg6cOCAxXf+/vtvtGrVCh07dkSfPn0qKOeWRowYgStXriivkydPVnSWiIiIiKySVfapHTJkCHr06FFimoiICOX/p0+fRtOmTdGgQQOLAWAA4Ofnh7Nnz1q8Z/7bz8+vxDQFPze/5+/vb5EmNja22DwaDAYYDHxUIxEREdG9ssqg1tvbG97e3reV9u+//0bTpk0RFxeHBQsWQK22rJxOSEjAqFGjkJ2dDZ1OBwBYt24doqKi4O7urqRZv349Bg0apHxv3bp1SEhIAACEh4fDz88P69evV4LY9PR0bN26Ff3797/H0hIR0e1QgSPFbB0HilFJrLL7we36+++/0aRJE4SEhGDKlCk4f/48UlNTLfq5du7cGXq9Hr169cK+ffuwdOlSzJw5E4MHD1bSvPjii1i9ejWmTp2KAwcOYNy4cdi+fTsGDhwIIL+Pz6BBgzBp0iSsWLECe/bswTPPPIOAgAB06NChvItNREREZHessqb2dq1btw6HDx/G4cOHERQUZPGZ+W7P1dUVa9euxYABAxAXFwcvLy+MGTNGmc4LABo0aIBPPvkEo0ePxsiRI1G5cmUsX74cNWrUUNIMGzYM169fR9++fZGWloaGDRti9erVMBqN5VNYuicqVusQEd33OFCMSmLTQW2PHj3+s+8tANSqVQs///xziWk6duyIjh07Fvu5SqXChAkTMGHChDvNJhHZCF5uiYgqjk13PyAiIiIi+8CgloiIbILlE8VYb26LOFCMSsKgloiIiIisHoNaIiIiIrJ6DGqJAI7woVLBgdlEZYuzH1BJGNQSERERkdVjUEtERDahYB0eK/RsEweKUUkY1BIRERGR1WNQS0RERERWj0EtEdhUSaWDc6MSlS0OFKOSMKglIiIiIqvHoJaIiGwO6/OI7A+DWiIiIrIKnP2ASsKgloiIiIisHoNaIrCpkkoHx7AQlS0OFKOSMKglIiIiIqvHoJaIiGwOa/SI7A+DWiIiIrIKHChGJWFQS0RERERWj0EtEdhUSURkDXiuppIwqCUiIiIiq8egloiIbA7r84jsD4NaIiIisgocKEYlYVBLRERERFaPQS0R2FRJpYNjWIjKFgeKUUkY1BIRERGR1WNQS0RENqFgJR4r9IjsD4NaIrprzzaKAACMbhNdwTkhAjiGyPZxoBiVRFvRGSAi6zXikWp4rmkkXE26is7KfYc1hURE5Ys1tURgAHIvGNAWjRVKRKWPA8WoJAxqiYhKiYrzaBARVRgGtUREZBMsBorxBoPI7jCoJSIim8AuH7aPA8WoJAxqiYjKALv+ERGVLwa1RERlgBVKRETli0EtEdj/jkoHa2eJyhZnP6CSMKglIiKbYBHvMPYhsjsMaomIyCawy4ft40AxKgmDWiKiMsBWUiKi8sWgloioDLBCiYiofDGoJQJr1ah0cDciKlscKEYlYVBLREQ2weKJYox9iOwOg1oiIrIJ7PJh+zhQjErCoJaIqAywppCIqHwxqCUiKgOsUCIiKl8MaomISgkHsRCVLR5jVBIGtUREZBMsBopVXDaIqIIwqCUiIpvALh+2jwPFqCQMaomIygBbSYmIyheDWiKiMsAKJSKi8sWglgisVaPSwd2IqGxxoBiVhEEtERHZBMsnijH4IbI3DGqJiMgmsMuH7eNAMSoJg1oiojLAikIiovLFoJaIqAywQomIqHwxqCUCoOIQHyoFrJ0lKlvsK00lsZugNjMzE7GxsVCpVNi5c6fFZ7t378ZDDz0Eo9GI4OBgTJ48udD3P//8c1StWhVGoxE1a9bEqlWrLD4XEYwZMwb+/v4wmUxo0aIFDh06VJZFIiKiAvhEMSL7ZjdB7bBhwxAQEFDo/fT0dLRs2RKhoaHYsWMH3n77bYwbNw7z5s1T0mzevBlPP/00evXqheTkZHTo0AEdOnTA3r17lTSTJ0/GO++8g7lz52Lr1q1wdHREYmIibt68WS7lIyKyd+zyYfs4UIxKYhdB7ffff4+1a9diypQphT5bsmQJsrKy8NFHH6F69ero1KkTXnjhBUybNk1JM3PmTLRq1QpDhw5FtWrVMHHiRNSpUwezZs0CkH+QzZgxA6NHj0b79u1Rq1YtfPzxxzh9+jSWL19eXsUkovsIW0mJiMqXzQe1Z8+eRZ8+ffB///d/cHBwKPT5li1b0KhRI+j1euW9xMREpKSk4PLly0qaFi1aWHwvMTERW7ZsAQAcO3YMqampFmlcXV0RHx+vpClKZmYm0tPTLV5EREREdOdsOqgVEfTo0QP9+vVD3bp1i0yTmpoKX19fi/fMf6emppaYpuDnBb9XVJqivPHGG3B1dVVewcHBd1A6Kk2sVaPSxlZSotLHgWJUEqsMaocPHw6VSlXi68CBA3j33Xdx9epVjBgxoqKzXKQRI0bgypUryuvkyZMVnSUiuge84FYsyyeKVVw+iKhiaCs6A3djyJAh6NGjR4lpIiIisGHDBmzZsgUGg8His7p166JLly5YtGgR/Pz8cPbsWYvPzX/7+fkp/xaVpuDn5vf8/f0t0sTGxhabR4PBUChvRER0d1g7bvs4UIxKYpVBrbe3N7y9vf8z3TvvvINJkyYpf58+fRqJiYlYunQp4uPjAQAJCQkYNWoUsrOzodPpAADr1q1DVFQU3N3dlTTr16/HoEGDlGWtW7cOCQkJAIDw8HD4+flh/fr1ShCbnp6OrVu3on///qVRZCKyMqwpJCIqX1YZ1N6ukJAQi7+dnJwAAJUqVUJQUBAAoHPnzhg/fjx69eqFV155BXv37sXMmTMxffp05XsvvvgiGjdujKlTp6JNmzb47LPPsH37dmXaL5VKhUGDBmHSpEmoXLkywsPD8eqrryIgIAAdOnQon8LSHWPMQUREZDtsOqi9Ha6urli7di0GDBiAuLg4eHl5YcyYMejbt6+SpkGDBvjkk08wevRojBw5EpUrV8by5ctRo0YNJc2wYcNw/fp19O3bF2lpaWjYsCFWr14No9FYEcWi28BGLCpLbCUlIipfdhXUhoWFFdkfp1atWvj5559L/G7Hjh3RsWPHYj9XqVSYMGECJkyYcM/5pPLHWlsi62f5RDEe1baIgzGpJFY5+wEREdGtWDtOZN8Y1BIRlQFWKBGVPs5+QCVhUEt2izEHERGR7WBQS3aL9/tUllihRERUvhjUEoGDD4hsAZ8oZvt4rqaSMKglIiKbwNpxIvvGoJaIqAywQomo9HGgGJWEQS3ZLcYcREREtoNBLdkt3u9TWWKFEhFR+WJQSwTW2hLZAnb5sH0cKEYlYVBLRERERFaPQS0RURlghVL5Y5cP28eBYlQSBrVktxhzEBER2Q4GtWS3eL9PZYkVSkRE5YtBLRHYVExkC/hEMdvHgWJUEga1RERERGT1GNQSEZUBViiVP3b5sH0cKEYlYVBLdosxBxERke1gUEt2i/f7VJZYoUREVL4Y1BKBgw+IbIHFQDG2xdgknqupJAxqiYiIiMjqMaglIioDrFAqf+zyYfs4UIxKwqCW7BZjDiIiItvBoJbsFu/3iYiIbAeDWiKiMsBW0vLHJ4rZPg4Uo5IwqCUiIiIiq8egloioDLBCqfyxdtz2caAYlYRBLRERERFZPQa1ZLdYkUZERGQ7GNSS3WIjFpUltpKWP8snihGRvWFQS0RERFaBsx9QSRjUEhGVAV57yx9rx20fB4pRSRjUEhEREZHVY1BLdosVaURERLaDQS3ZLTZiUVliK2n5s3yiGG9biewNg1oiIiKyCrxZoZIwqCUiKgO89pY/1o7bPg4Uo5IwqCUiIiIiq8egluwWK9KIiIhsB4NasltsxKKyxFbS8scnihHZNwa1REREZBU4UIxKwqCWiKgM8Npb/lg7bvs4UIxKwqCWiIiIiKweg1qyW6xIIyIish0MaslusRGLyhJbScuf5RPFKi4fRFQxGNQSERGRVeBAMSoJg1oiojLAa2/5Y+247eNAMSoJg1oiIiIisnoMaslusSKNiIjIdjCoJbvFRiwqS2wlLX+WA8V420pkbxjUEhERkVXgzQqVhEEtEVEZ4LW3/LF23PZxoBiVhEEtEREREVk9BrVkt1iRRkREZDsY1JLdYiMWkW1hlw8i+8agloiIiKwCB4pRSewiqP3uu+8QHx8Pk8kEd3d3dOjQweLzEydOoE2bNnBwcICPjw+GDh2KnJwcizQbN25EnTp1YDAYEBkZiYULFxb6ndmzZyMsLAxGoxHx8fHYtm1bGZaKiIgK4hgiIvtm80Htl19+iW7duiEpKQm7du3Cr7/+is6dOyuf5+bmok2bNsjKysLmzZuxaNEiLFy4EGPGjFHSHDt2DG3atEHTpk2xc+dODBo0CL1798aaNWuUNEuXLsXgwYMxduxY/PHHH4iJiUFiYiLOnTtXruUlIiKyVZz9gEpi00FtTk4OXnzxRbz99tvo168fqlSpgujoaPzvf/9T0qxduxZ//vknFi9ejNjYWLRu3RoTJ07E7NmzkZWVBQCYO3cuwsPDMXXqVFSrVg0DBw7Ek08+ienTpyvLmTZtGvr06YOkpCRER0dj7ty5cHBwwEcffVTu5abbw0YsIiIi22HTQe0ff/yBv//+G2q1GrVr14a/vz9at26NvXv3Kmm2bNmCmjVrwtfXV3kvMTER6enp2Ldvn5KmRYsWFstOTEzEli1bAABZWVnYsWOHRRq1Wo0WLVooaYqSmZmJ9PR0ixcREd0ddrcksm82HdQePXoUADBu3DiMHj0aK1euhLu7O5o0aYJLly4BAFJTUy0CWgDK36mpqSWmSU9Px40bN3DhwgXk5uYWmca8jKK88cYbcHV1VV7BwcH3VmC6I2zEIiIish1WGdQOHz4cKpWqxNeBAweQl5cHABg1ahSeeOIJxMXFYcGCBVCpVPj8888ruBTAiBEjcOXKFeV18uTJis4SEZHVYndL28fZD6gk2orOwN0YMmQIevToUWKaiIgInDlzBgAQHR2tvG8wGBAREYETJ04AAPz8/ArNUnD27FnlM/O/5vcKpnFxcYHJZIJGo4FGoykyjXkZRTEYDDAYDCWWg4iIiPJxoBiVxCqDWm9vb3h7e/9nuri4OBgMBqSkpKBhw4YAgOzsbBw/fhyhoaEAgISEBLz22ms4d+4cfHx8AADr1q2Di4uLEgwnJCRg1apVFstet24dEhISAAB6vR5xcXFYv369Ml1YXl4e1q9fj4EDB5ZKman08X6fiIjIdlhl94Pb5eLign79+mHs2LFYu3YtUlJS0L9/fwBAx44dAQAtW7ZEdHQ0unXrhl27dmHNmjUYPXo0BgwYoNSi9uvXD0ePHsWwYcNw4MABzJkzB8uWLcNLL72k/NbgwYPxwQcfYNGiRdi/fz/69++P69evIykpqfwLTkRERGRnrLKm9k68/fbb0Gq16NatG27cuIH4+Hhs2LAB7u7uAACNRoOVK1eif//+SEhIgKOjI7p3744JEyYoywgPD8d3332Hl156CTNnzkRQUBA+/PBDJCYmKmmeeuopnD9/HmPGjEFqaipiY2OxevXqQoPH6P7BRiwi28LulkT2zeaDWp1OhylTpmDKlCnFpgkNDS3UveBWTZo0QXJycolpBg4cyO4GREREZYQDxagkNt39gIiI7AfHENk+DhSjkjCoJbvF+30iIiLbwaCWiIiIiKweg1qyW2zEIrIt7G5JZN8Y1BIREZFV4EAxKgmDWiIisgkcQ2T7OFCMSsKgluwW7/eJiIhsB4NaIiIiIrJ6DGrJbrERi8i2sLslkX1jUEtERERWgQPFqCQMaomIyCZwDJHt40AxKgmDWrJbvN8nIiKyHQxqiYiIiMjqMaglu8VGLCLbwu6WRPaNQS0RERFZBQ4Uo5IwqCUiIpvAMUS2jwPFqCQMaslu8X6fiIjIdjCoJSIiIiKrx6CW7BYbsagsqdgWUO7Y3ZLIvjGoJSIqA8LbJqJSx4FiVBIGtUREZBM4hsj2caAYlYRBLdkt3u8TERHZDga1RERERGT1GNQSEZUBDhQrf+xuSWTfGNSS3WLPLCpLHChGRFS+GNQSEZFN4BgiIvvGoJbsFlsqiYiIbAeDWiIiIiKyegxqiYjKAAeKlT8OFCOybwxqyW6x+x2VJQ4UIyIqXwxqiYjIJnCgGJF9Y1BLRERERFaPQS3ZLXa/IyIish0MaomIygAHipU/DhQjsm8MaslusfsdlSUOFCMiKl8MaomIyCZwoBiRfWNQS0RERERWj0Et2S12vyMiIrIdDGqJiMoAB4qVPw4UI7JvDGrJbrH7HZUlDhQjIipfDGqJiMgmcKAYkX1jUEtEREREVo9BLdktdr8jIiKyHQxqiYjKAAeKlT8OFCOybwxqyW6x+x2VJQ4UIyIqXwxqiYjIJnCgmO1TsTqeSsCgloiIiKyC8M6FSsCgluwW7/eJiIhsB4NaIqIywIFi5Y8t00T2jUEt2S02YhEREdkOBrVERGWAsx8QlT4OFKOSMKglIiKbwDFEto8DxagkDGrJbvF+n4iIyHYwqCUiKgMcKFb+2DJNZN8Y1BIRERGR1WNQS3aLPbOoLHGgGBFR+bL5oPbgwYNo3749vLy84OLigoYNG+LHH3+0SHPixAm0adMGDg4O8PHxwdChQ5GTk2ORZuPGjahTpw4MBgMiIyOxcOHCQr81e/ZshIWFwWg0Ij4+Htu2bSvLohERUQEcQ0Rk32w+qG3bti1ycnKwYcMG7NixAzExMWjbti1SU1MBALm5uWjTpg2ysrKwefNmLFq0CAsXLsSYMWOUZRw7dgxt2rRB06ZNsXPnTgwaNAi9e/fGmjVrlDRLly7F4MGDMXbsWPzxxx+IiYlBYmIizp07V+5lptvD7ndERES2w6aD2gsXLuDQoUMYPnw4atWqhcqVK+PNN99ERkYG9u7dCwBYu3Yt/vzzTyxevBixsbFo3bo1Jk6ciNmzZyMrKwsAMHfuXISHh2Pq1KmoVq0aBg4ciCeffBLTp09XfmvatGno06cPkpKSEB0djblz58LBwQEfffRRhZSdiCoWB4qVPw4UI7JvNh3Uenp6IioqCh9//DGuX7+OnJwcvP/++/Dx8UFcXBwAYMuWLahZsyZ8fX2V7yUmJiI9PR379u1T0rRo0cJi2YmJidiyZQsAICsrCzt27LBIo1ar0aJFCyVNUTIzM5Genm7xIiIiIqI7p63oDJQllUqFH374AR06dICzszPUajV8fHywevVquLu7AwBSU1MtAloAyt/mLgrFpUlPT8eNGzdw+fJl5ObmFpnmwIEDxebvjTfewPjx4++5nHR32P2OyhIHihERlS+rrKkdPnw4VCpVia8DBw5ARDBgwAD4+Pjg559/xrZt29ChQwc8+uijOHPmTEUXAyNGjMCVK1eU18mTJys6S0REVosDxYjsm1XW1A4ZMgQ9evQoMU1ERAQ2bNiAlStX4vLly3BxcQEAzJkzB+vWrcOiRYswfPhw+Pn5FZql4OzZswAAPz8/5V/zewXTuLi4wGQyQaPRQKPRFJnGvIyiGAwGGAyG2yozlT52vyMiIrIdVhnUent7w9vb+z/TZWRkAMjv31qQWq1GXl4eACAhIQGvvfYazp07Bx8fHwDAunXr4OLigujoaCXNqlWrLJaxbt06JCQkAAD0ej3i4uKwfv16dOjQAQCQl5eH9evXY+DAgXdfUCKyWhwoVv44UIzIvlll94PblZCQAHd3d3Tv3h27du3CwYMHMXToUGWKLgBo2bIloqOj0a1bN+zatQtr1qzB6NGjMWDAAKUWtV+/fjh69CiGDRuGAwcOYM6cOVi2bBleeukl5bcGDx6MDz74AIsWLcL+/fvRv39/XL9+HUlJSRVSdiIiIiJ7YpU1tbfLy8sLq1evxqhRo9CsWTNkZ2ejevXq+OabbxATEwMA0Gg0WLlyJfr374+EhAQ4Ojqie/fumDBhgrKc8PBwfPfdd3jppZcwc+ZMBAUF4cMPP0RiYqKS5qmnnsL58+cxZswYpKamIjY2FqtXry40eIzuH+x+R2WJA8WIiMqXTQe1AFC3bl2LhyQUJTQ0tFD3gls1adIEycnJJaYZOHAguxsQEVUQDhQjsm823f2AqCTsfkdERGQ7GNQSEZUBDhQrfxwoRmTfGNQSERERkdVjUEt2i93vqCxxoBgRUfliUEtERDaBA8WI7BuDWrJb7H5HRERkOxjUEhGVAQ4UK38cKEZk3xjUEhEREZHVY1BLdovd76gscaAYEVH5YlBLREQ2gQPFiOwbg1qyW+x+R0REZDsY1BIRlQEOFCt/HChGZN8Y1BIRERGR1WNQS3aL3e+oLHGgGBFR+WJQS0RENoEDxYjsG4NaslvsfkdERGQ7GNQSEZUBDhQrfxwoRmTfGNQSERERkdVjUEtEREREVo9BLRFRGeDsB+WPA8WI7BuDWiIiIiKyegxqiYjKAAeKlT8OFCOybwxqiYiIiMjqMaglIiIiIqvHoJaIqAxwoFj540AxIvvGoJaIiIiIrB6DWiKiMsCBYuWPA8WI7BuDWiIiIiKyegxqiYiIiMjqMaglu8UxJVSWOFCs/HGgGJF9Y1BLRERERFaPQS3ZLY4pobLEgWLljwPFiOwbg1oiIiIisnoMaomIiIjI6jGoJbvFMSVUljhQrPxxoBiRfWNQS0RERERWj0Et2S2OKaGyxIFi5Y8DxYjsG4NaIiIiIrJ6DGqJiIiIyOoxqCW7xTElVJY4UIyIqHwxqCUiIiIiq8egluwWx5RQWeJAMSKi8sWgloiIiIisHoNaIiIiIrJ6DGrJbnEYD5UlDhQjIipfDGqJiIiIyOoxqCW7xWE8VJY4UIyIqHwxqCUiIiIiq8egloiIiIisHoNaIiIiIrJ6DGqJiIiIyOoxqCUiIiIiq8egloiIiIisHoNaIiIiIrJ6DGqJiIiIyOoxqCUiIiIiq2fVQe1rr72GBg0awMHBAW5ubkWmOXHiBNq0aQMHBwf4+Phg6NChyMnJsUizceNG1KlTBwaDAZGRkVi4cGGh5cyePRthYWEwGo2Ij4/Htm3bLD6/efMmBgwYAE9PTzg5OeGJJ57A2bNnS6uoRERERFQCqw5qs7Ky0LFjR/Tv37/Iz3Nzc9GmTRtkZWVh8+bNWLRoERYuXIgxY8YoaY4dO4Y2bdqgadOm2LlzJwYNGoTevXtjzZo1SpqlS5di8ODBGDt2LP744w/ExMQgMTER586dU9K89NJL+Pbbb/H5559j06ZNOH36NB5//PGyKzwRERERKaw6qB0/fjxeeukl1KxZs8jP165diz///BOLFy9GbGwsWrdujYkTJ2L27NnIysoCAMydOxfh4eGYOnUqqlWrhoEDB+LJJ5/E9OnTleVMmzYNffr0QVJSEqKjozF37lw4ODjgo48+AgBcuXIF8+fPx7Rp09CsWTPExcVhwYIF2Lx5M3777beyXxFEREREds6qg9r/smXLFtSsWRO+vr7Ke4mJiUhPT8e+ffuUNC1atLD4XmJiIrZs2QIgvzZ4x44dFmnUajVatGihpNmxYweys7Mt0lStWhUhISFKmqJkZmYiPT3d4kVEREREd86mg9rU1FSLgBaA8ndqamqJadLT03Hjxg1cuHABubm5RaYpuAy9Xl+oX2/BNEV544034OrqqryCg4Pvqpx0d6r4Old0FsiGVfXj/lXeKvOYtnmtavgD4PFFRdNWdAZuNXz4cLz11lslptm/fz+qVq1aTjkqOyNGjMDgwYOVv9PT0xnYlqMGkV6Y9r8YVPbhyZFKz3cvNMSfp9PRJMq7orNid+qEuGNu1zoI8XCs6KxQGZnYoTriIzzQvKpPRWeF7kP3XVA7ZMgQ9OjRo8Q0ERERt7UsPz+/QrMUmGck8PPzU/69dZaCs2fPwsXFBSaTCRqNBhqNpsg0BZeRlZWFtLQ0i9ragmmKYjAYYDAYbqssVDYerxNU0VkgG1M9wBXVA1wrOht2y1yTR7bJQa/F/+qy8oeKdt91P/D29kbVqlVLfOn1+ttaVkJCAvbs2WMxS8G6devg4uKC6OhoJc369estvrdu3TokJCQAAPR6PeLi4izS5OXlYf369UqauLg46HQ6izQpKSk4ceKEkoaIiIiIys59V1N7J06cOIFLly7hxIkTyM3Nxc6dOwEAkZGRcHJyQsuWLREdHY1u3bph8uTJSE1NxejRozFgwAClhrRfv36YNWsWhg0bhp49e2LDhg1YtmwZvvvuO+V3Bg8ejO7du6Nu3bqoV68eZsyYgevXryMpKQkA4Orqil69emHw4MHw8PCAi4sLnn/+eSQkJKB+/frlvl6IiIiI7I5Yse7duwuAQq8ff/xRSXP8+HFp3bq1mEwm8fLykiFDhkh2drbFcn788UeJjY0VvV4vERERsmDBgkK/9e6770pISIjo9XqpV6+e/Pbbbxaf37hxQ5577jlxd3cXBwcHeeyxx+TMmTN3VJ4rV64IALly5codfY+IiIgqDq/f9weViEgFxtRUQHp6OlxdXXHlyhW4uLhUdHaIiIjoNvD6fX+47/rUEhERERHdKQa1RERERGT1GNQSERERkdVjUEtEREREVo9BLRERERFZPQa1RERERGT1GNQSERERkdVjUEtEREREVo9BLRERERFZPW1FZ4D+ZX64W3p6egXnhIiIiG6X+brNh7RWLAa195GrV68CAIKDgys4J0RERHSnrl69CldX14rOht1SCW8r7ht5eXk4ffo0nJ2doVKpSnXZ6enpCA4OxsmTJ+3yudQsP8vP8ttn+e257ADLX17lFxFcvXoVAQEBUKvZs7OisKb2PqJWqxEUFFSmv+Hi4mKXJzYzlp/lZ/nts/z2XHaA5S+P8rOGtuLxdoKIiIiIrB6DWiIiIiKyegxq7YTBYMDYsWNhMBgqOisVguVn+Vl++yy/PZcdYPntvfz2hgPFiIiIiMjqsaaWiIiIiKweg1oiIiIisnoMaomIiIjI6jGoJSIiIiKrV65B7cKFC+Hm5lZqy3v11VfRt2/fUlteWWP5Wf47Kf/w4cPx/PPPF/u5rZd/7ty5ePTRR4v93NbLX79+fXz55ZfFfm7r5bel/f9uzn2dOnXC1KlTi/zMmsp+t1avXo3Y2Fjk5eVVdFYqxH8d/7burssvd6B79+4CQACITqeTSpUqyfjx4yU7O/u2vp+RkSFnz569k5+Uxo0by4svvljo/TNnzoizs7McP35ceW/Tpk3Stm1b8ff3FwDy9ddfF/re2LFjJSoqShwcHMTNzU2aN28uv/32m0WaHTt2SIsWLcTV1VU8PDykT58+cvXq1RLLn5eXJ6+++qr4+fmJ0WiU5s2by8GDB5Vl/vjjj8p3b31t27ZNSbd06VKJiYkRk8kkISEhEhERUWz5HRwcRK1WS0xMjEX5XV1dBYAYjUZxdnaW+vXry6pVq5TvHjt2rNi8LFu2TCZNmiQJCQliMBhEo9GIk5OT+Pr6So0aNUrc/n/++aeEhISISqUSAKLVaqVFixayf/9+ERFp2LBhod9Tq9Xi7e0tzz33nIhIkXlq1aqV9O3bVyIiIsRoNIqjo6M4ODgIAAkPD5dFixbJggULii0TADEYDPLll19KeHi4aDQa5X2VSiVOTk7KfuDr61vsMgICAkr8DX9/fzEajeLh4VFsGnd3d1Gr1aLRaESj0Yi7u7u4ubmJXq8XX19fiYyMFA8PDzEajRIZGSkqlUp69OghKSkp0q5dO3FxcRGNRiNarVYAyPvvvy8iIjt37pRmzZqJ0WhUfkur1Rbax837v8FgUNI5OjpKz5495erVqyIiMmvWLPH29rbIt5+fn8X+by6DTqeT2NhY+fDDD6V79+5iMBhEpVKJSqUSnU4nOp1O3NzcpE+fPnLu3Dk5e/as5ObmSmRkpMXyXVxcZMCAAXLlyhUREblw4YIEBgYKAGnQoEGx6zMwMFCmTZtmsf87OTkpnxfc/+fNmyfBwcGiVquVbW/+/+XLl0XE8hxX8OXk5CQREREW76lUKnF3d5fRo0dLZmamiIj06dNH2Ta3vjQajdSsWbPEfcjHx0f8/PzEwcFBwsPDRafTyQsvvCCPPvqoBAcHK8fWra9HHnlENm7cKNWqVVP2b3d3d3nggQcEgEyfPt1i+5v3E0dHR2VbJCcni4hI27ZtRa/XW+S7ffv2xa4bABITEyM1atQQjUYjarVaVCqV8v/o6GgREQkODi6x7ObjND4+XpycnMTT01MASFxc3H9+LygoSOLi4kSn0ynv6fV6i+0/duzYIs8/BoNBnJ2d5aGHHpJLly5Jz549lfKby1FwuVqtVkwmkxgMBnF1dZWHHnroP/P3X69x48bJl19+KS1atBAvLy9xdnYWR0dH0el0kpaWJocPH5YOHTqIg4ODsg+4ublJ+/bt5cCBA7Jp0ybx9fVV9mfz8RkQECDPP/+8pKWlyYsvvljkb3t4eMiwYcMkOztbduzYIVWqVFGWo9PpxNXVVfR6vQQEBMikSZNkzpw5UrNmTXF2dhZnZ2d54IEHpG3bthIYGChqtbrY/R+AmEwm0Wg0YjAYxGAwiJOTk5hMJnFzc5OQkBCpVq2a6PV65ZpWt25d6dOnT7HLa9OmjRw7dqzYfdPDw0O57r3++utSt25d5frh4OAger1eatWqJd9//72IiLz//vvSuHFji/3f/IqKiirxOv7WW29JXFycODo6Kudoc1kWL16s5OPPP/+URx99VFxcXMTBwUE5r7Rv397iGv/tt99KZGSkfP755xIXFyeurq7i4OAgMTEx8vHHH1uk/fLLL+Xhhx9Wrj3mY7mgxo0bF8rzs88+q3y+c+dO6dSpkwQFBYnRaJSqVavKjBkzCi1n8eLFUqtWLTGZTOLn5ydJSUly4cIFizSXL1+W5557Tvz8/ESv10vlypXlu+++Uz7PycmR0aNHS1hYmBiNRomIiJAJEyZIXl5eofLn5uYWykNJ7rimtlWrVjhz5gwOHTqEIUOGYNy4cXj77bdv67smkwk+Pj53+pNF+vDDD9GgQQOEhoYq712/fh0xMTGYPXt2sd+rUqUKZs2ahT179uCXX35BWFgYWrZsifPnzwMATp8+jRYtWiAyMhJbt27F6tWrsW/fPvTo0QNA8eWfPHky3nnnHcydOxdbt26Fo6MjEhMTcfPmTQBAgwYNcObMGYtX7969ER4ejrp16wIAvv/+e3Tp0gX9+vXD3r17MWfOHJw6dQq7du0qVI5Zs2ZBRNCiRYtC5X/uuecAAFOnTsX27dvRrFkztG/fHvv27QMABAcHY/78+ahevTr8/Pwwfvx4jB8/Hk5OTmjdujWysrLQoEEDZGdnQ6fTITk5GUuXLsWJEycQFhZWZPmPHDmChg0bomrVqpg3bx42btyIyZMnQ61Wo2XLlsjNzYVGo0GfPn0wduxY+Pr6Yvbs2dixYwd++OEHJCYmKuVYsGCBso4SEhJQqVIlxMXFYcGCBRg1ahSA/McRGgwGTJo0CQMGDICTkxMWL16MF198ET179gQAVK9eHQkJCThz5gz++usv/PTTT4iPj8fMmTMxcuRItG7dGgCQm5sLX19ftGzZEmvWrMGuXbuwa9cuDBkyBBMnTgQAeHp6IisrC66urti1axe2bduGN998EyqVCvHx8dBoNJgxYwb27t2LxYsXw8PDA97e3gCAjz76CM2aNUPlypUxcOBAPPPMM5g8eTIMBgOys7Nx48YNjBw5Em+++Sa6dOmCn376Cfv378fYsWMhItiwYQPatm2LnJwcvPHGG+jbty/Cw8MBADqdDgCwY8cOODk54cknn0RSUpKyLp977jmLfbxKlSp48803AQAODg7KYyN//PFH9O3bF0uXLsWgQYNw/fp1xMXFIS4uDhqNBr6+vsr+36hRI9SoUQNDhgxBTk4OvLy80Lt3b+zevRv9+/fH7NmzodVqodVqUbVqVQQHB2Pfvn3o378/fHx80LhxY5w+fRpAfm3ct99+i1GjRuGHH35Av379AAC9evVCrVq1AMDimFm7di3mzJmD6OhoGAwGvPHGGxg9ejTmzZuH69evo0aNGnBycgIAGI1Gi/1/+fLlaN68OWbPno1+/fqhatWqynq6cOGC8n+j0YihQ4diyJAh+L//+z+sX78ey5cvx9WrV+Ho6Ij//e9/+PjjjzF+/HikpaVh+vTpGDlyJAAgLS0NsbGxAICxY8eiZcuWCAgIQLVq1VC5cmU8++yzAIDAwEBlP9u1axd27Nih7Etffvkldu/ejeeffx7Z2dnYunUrmjZtimXLliE5ORkrVqxAQEAAXFxcsHfvXmg0GnTs2BFTp07F2bNnMXToUABAu3btcPHiRQQEBChlM29/b29vNGjQADk5OTh+/Ljy+dKlS7Fq1Sq0a9cOVapUgUajgUqlQmZmprL9ExIS0KlTJ2X/Hzx4MNzd3fHqq69i6NCheP/99+Hp6QkvLy94enoqy1er1ZgwYQLOnDmD/fv3Izo6Go0bN4aLiwvOnDmDyMhIZGdno1mzZti5cyfWrl0LrVaLP//8U9n+KSkp2LVrF8LCwuDq6oqTJ0/Cw8MDgYGBqFWrFrp27aqs45ycHHz88cfK9j937hwCAwOxdOlSfPDBB3BwcECtWrWg0+mwZs0aDBw4EK+88go++eQTBAcHo3379sr1KicnB61atcLhw4fh6OiImJgYZGVloUePHvD29oaXlxdOnDiB2bNnY9GiRejTpw9MJhM6duwIZ2dnREZG4n//+x/OnDmDcePGYdq0aQgKCkJYWBh0Oh0WLlyIn376CQ8//DBWrVqFHTt2wNPTE9nZ2Rg/fjxatmwJlUqF2NhYNGvWTClzVlYWWrZsiYsXL6JmzZpKzXbXrl1Rq1YtuLm5YfXq1ejVq5dyzpg0aRK0Wi1q1qwJR0dHzJs3DytWrMDAgQPRqFEjHD58GG+99RbatGkDlUqFjIwMdO3aFStWrEC9evUQFBSEN998Ezt27MD27duRnZ2NlStXYuLEiRg5ciReeuklaDQajBo1CnXr1lVeKpUKTz75JHr06IGIiAjk5OQgOjoamZmZmDRpEnQ6HU6dOoWOHTsq+2OPHj2wY8cOnDlzBlu3boXBYMDzzz+Phx56CHXq1MHVq1fx+OOPY+bMmThz5gy2bduGXr16Yfr06dBoNKhcubKyrE2bNmHAgAF46qmn4OjoiOjoaHh5eSEpKQmPPfYYkpOTkZGRgVatWuGhhx4CABw4cEDZ93755ZcSr+MPPPAARo0ahd69e+PZZ5/FgAEDkJubi/r16+OZZ57BypUrLa6TGzduxKpVq5CWlob4+HjcqnXr1rh69SoOHTqEUaNGYcuWLdi9ezeSkpKQlJSENWvWKGmvX7+Ohg0b4q233iq0nIL69OljkffJkycrn+3YsQM+Pj5YvHgx9u3bh1GjRmHEiBGYNWuWkubXX3/FM888g169emHfvn34/PPPsW3bNvTp00dJk5WVhYcffhjHjx/HF198gZSUFHzwwQcIDAxU0rz11lt47733MGvWLOzfvx9vvfUWJk+ejHfffbdQ+b///vsSy1TInUTA3bt3L3Q38fDDD0v9+vVFROTSpUvSrVs3cXNzE5PJJK1atbKorVywYIG4uroqf48dO1a56wgNDRUXFxd56qmnJD09Xfk93HJncezYMRERqV69usyaNavYvAJF19Te6sqVKwJAfvjhBxHJv1Pz8fGxuDvYvXu3AJDHHnus2PL7+fnJ+PHjLcqvUqmUWqRby5+VlSUODg7i5+enlF+n00lQUNBtld/FxUVatWqlrMP/Kr+7u7t8+OGHIiJy6tQpCQwMlL1790poaKhMnz5dYmNjpWfPnkr6ESNGSFhYmMX2atasmajVaiV/Bcv/1FNPSceOHQtt/xUrVggAOXz4sERFRYlerxeTySQ//PBDkdsfgHzyySfFlr927dry8ssvS6VKlZTlDh48WB588EGL9Yx/aphuvaO9VVRUlACQb775xmI/MBs4cKCo1WoZP368NGvWTABIVlaWRfkdHBykZ8+eyv7v7Oys1PIAkIULF4pOp5M+ffoo6/P7779X7uabN28uRqPxjvb/atWqKTU8RQHya7ibNm1aaB9v3bq1AJA+ffoote+vv/66qFQqiYmJEUdHR2Vfyc3NFZPJJNHR0crx7+joqKzXhx9+WGJjYwWAvPLKK9KtWzelFl2n00mXLl1EpVLJjh07lJoaAPLzzz8LAAkLC1O2v9FoFJPJJNOmTZPGjRtLy5Ytiyx/VlaWaDQaadOmjYiIPPbYY9K1a1cRERk2bJh07dpVAIiDg4OyPgru/2Y5OTlKjeXy5cuVdW4ymZSazYKK2/4hISESHh4umzZtEpPJpLSU1K5dW9n/AciIESOUz8LCwiz2f3d3dwEgTzzxxG1t/5CQEAEgU6ZMEWdnZ9m+fbtotVo5cOCAsv09PDwsjnGzp556SkaPHi2dO3e2qJFKTk6WevXqyYABA0Tk3/PVrdu/YOtZced/c0tA06ZNlXXm6ekpRqNR2U41a9aU4OBgMZlMSuuaSqWStLS0Est/7tw5ASDPPPOMfP3116JSqSxa7MzlDwoKkvHjxyvb/9FHH1XOlfHx8TJ69GiLYyMtLU2pZfz9999FRGT//v3KbycmJsrvv/8uAOTEiRNK2c3Xhw4dOijnPmdnZ3niiSeU5QP5tf1mL7/8slJTZq4V7dixY4nbfvfu3SKSf+17++23RaVSyeeffy4AZNOmTRZl//rrr+Wbb74RlUoln3zyiej1ejl06JAAkJ49e0rdunUtyr5ixQqlBrpp06by559/ilarVc6LderUKXQ8mFWvXl2MRqPF8VWnTh0ZNWqU8n2VSiVNmzZVPjfne/ny5fLII49IUlKSsh6ff/55ZTv99ddfynn+888/F61WK6mpqaLT6eTjjz+WFStWiEqlsjgmRUS+/vprASBJSUmF8uvv7y+zZs1S9qNNmzbJ448/Ll26dFHSmNe/uQWnOFlZWeLt7S0TJkwo8vPatWvL6NGjlTI+9dRTyrkqJydHGjRooLRy3RpbiIgkJSUp6Yta7q3MrbDF1dQW1epbkueee85iu7399tsSERFhkeadd96RwMBA5e/33ntPIiIiCm2Tgtq0aWMRb4hIoW0gUnz5S3LPfWpNJhOysrIA5N9Vbd++HStWrMCWLVsgInjkkUeQnZ1d7PePHDmC5cuXY+XKlVi5ciU2bdqk1CTNnDkTCQkJFncXwcHBuHTpEv7880+lhvNuZWVlYd68eXB1dUVMTAwAIDMzE3q9Hmr1v6vGZDIBAM6dO1dk+a9evYrU1FRs2LDBovxubm4YP358keVfsWIFMjIycPXqVaX8CQkJuHDhgkX5K1WqBADYtm2bUv53330X6enpGD169H+WMTc3F5999hmuX7+OhIQE5OXloVu3bhg6dCiqV68OADh58iR27tyJXr16Kd/LzMxUagHNNBoN8vLysGPHDovyZ2Vl4bvvvsOePXvw1VdfQURQqVIlnD59Gs888wzCwsIQHBysrPMbN26ge/fueOutt7B7926MGDECH374IVauXAkA6Nu3L7y8vLB7925UqlQJvXv3Vra/OW9HjhyBv78/goODYTKZsG3btkLrOTc3FyNGjLCopS7o5s2bSE1NhdFoRHJyssV+YM7rwoULISJ47LHHcPToUXh4eFisl8zMTGRkZKBXr17o0aMHfv/9d3h4eOCpp56CVqsFAGzYsAEODg4W++unn34KrVaLvLw8/Pjjj1CpVGjdujU2btyobP+ePXsqywgJCUHnzp3h4eGBc+fO4cCBAwCg7B9FycjIgJubm8U+vmDBAuzfvx9ubm7w9fXFxYsX4erqiqSkJKjVauzevRvXr1+HWq1G7dq1lbvrs2fPKstt0KABli5dikuXLsFoNCqtHD/99BO2b9+OPn36wMfHB2q1GkuXLkX9+vWVGuEbN24gICAAW7duBQD89ddfGDNmDGbMmIGoqCjk5eXh1Vdfxccff2zRn/LLL79U9v8lS5YgNzcXzz77LJKTk7F582Y0btwYGzZswOeff27RUnPr/l9QWlqacu6qUaOGxX4xePBgaDQaeHl5Ydy4cTh37lyR29+8/7u5uaFNmzbw9PTEt99+q/z2M888A2dnZ1SuXBlVqlRRvnfq1ClMnjwZe/bswYwZM+Dh4YG2bdvi119/tTj+DQYDdDqdxfnvyJEjOHHiBGrVqoWFCxeiU6dOWL9+PSIiIrBy5UqEhYUByG9d8Pf3tyjzggULcPToUfTr1w8rVqyAi4sLnJ2dAQDZ2dnYsWOHResPAAQEBODSpUsW7y1ZsgReXl747bffcOzYMWRkZFic/2fOnAkA+OWXX2AymZR1lpmZCRcXF8yYMQOenp44d+4csrOz0aRJE/j7+0NE0LlzZ+Tm5mLChAlKP9SC57+5c+cCyD8+5s+fjxYtWli02JnduHEDbm5uyvYPDg7GoUOH4Ovri61bt+K7775DdHQ0VCoVXn31VSxcuBA5OTlwdXVVjtWqVavC3d0dAHD58mVERUXB09MT8+fPh8FgwM2bNzF//nw4OTkhJSUFK1aswPz583H16lX89ttveO+996DX6+Ht7Q2NRqPkzdz6duLECej1evj7++Pnn38udO0zGo1QqVRQq9WoVKmScu2Lj4+HWq3GTz/9BADw8PCwKPvVq1exZMkSNGjQANevX4eLi4tyLjHX+MfExCi1ziaTCTk5OdBqtfjjjz8wZ84cREREYNWqVQCA/fv3o3fv3oX2A3MrV2ZmJiIiIiAi+PHHH3Hw4EHUr18fS5YsUd5/5ZVXlO8lJiZCrVZjzpw5SEtLg4uLC+bPn49q1apZ9D0OCQmBr68vfv75Z8TFxUGtVuP555+HyWTCww8/jP/7v/9DixYtCl2r5s+fD3d3d+W8U1BmZiaMRiOuXLmirDuTyYRffvmlUNpq1aohIiICXbp0wYkTJwp9vmLFCly8eNGidQwARATr169HSkoKGjVqhCtXrsDd3R3fffcdqlSpgsTERLi4uGD//v3w9PQstFyzevXq4eeffy52uXfKfNzWqFEDI0aMQEZGRonpr1y5YrFvJSQk4OTJk1i1ahVEBGfPnsUXX3yBRx55REmzYsUKJCQkYMCAAfD19UWNGjXw+uuvIzc3V0nToEEDrF+/HgcPHgSQfzz88ssvSutpceW/LXcSARe8m8jLy5N169aJwWCQl19+WQ4ePCgA5Ndff1XSX7hwQUwmkyxbtkxEiq6pdXBwsKj5Gzp0qMTHxyt/F3V3kZycrNwtFwcl1NR+++234ujoKCqVSgICAiz6tO7du1e0Wq1MnjxZMjMz5dKlS/LEE08od6tFlf/pp59W7qYLlr9du3ai0WiKLH/r1q0lMjLSovzvv/++aLVaqVq1quTm5kpKSopSs7V582YRETl48KBSq3PixIkSa2rNfWJdXV2V/iyvv/66PPzww0rfldDQUGnQoIFUq1bN4vtr1qwRlUolDg4OkpOTI6dOnRIfHx+lJrVg+fv376+U//nnn5fhw4crfdBUKpW88847IpK//+j1etFqteLv7y8mk0nUarU0btxYoqKiJDMzUx588EGJjo6WP/74Q958801RqVTSuHFjERGZPXu2Rd+2n376SX7//XelH+zp06dFRGTz5s0C5PfF3Lhxo7Rt21ZcXFzk5MmTFtvfvBwAhfYDkfz+zQCUmhQvLy9p3bq1xfbXaDTi4eGh7P/t2rWTLl26SEhIiAwaNEipMevfv78sWLBAdDqdsk3Ny1ar1TJx4kQJDQ2VGjVqiE6nE71eL2q1WqmRW7ZsmcTFxVn0Ay1pHzd/XnAfP3jwoPj4+Ej79u0t+j2by23uwwhAQkJC5IsvvpDt27eLq6urqFQq6dSpk7Rv314uX74sDz/8sJJWp9Mpfbm+//77QrVM77zzjnIMqVQqpd+k+W9zukceeUS8vLykUqVKIvJvP3S9Xi8LFy5Uyvbggw8qv6tWq2XChAly4cIFCQ4OVmqszMu8df+/dfvf2qf2008/lZ49e8r8+fNl+vTpFvtJUdtfr9eLXq+XunXrKsf/7NmzLda/yWSSt956Szn+mzRpIpGRkdK3b1+l3zEA2bp1q8X5z7z/AZDc3FwZNmyYUgsOQBYvXqx879lnn1XWa9++fZXa4KZNmyo1tebt/9577yn9brVarSxfvlwAyJo1ayzONeb8Vq9eXdzc3JTz//vvvy/ff/+9zJs3T3Q6nTg5OSn7w6+//iqDBw9WtqtKpZKgoCARya8hdnBwEF9fXxk8eLAYDAZRq9Xi4uIi7u7u8tZbb0mnTp1Eq9Uq+TO3emzcuFHZfuHh4QLkj0XQaDSydOnSYvf/gtt/1apVsmzZMhk8eLDF9pk3b54MGjRItFqtqNVqqVKlisWyzL9Xq1YtEclvuTPXLANQPjef+80tIUB+v1dnZ2fp2LGjxbWv4DlAr9fLpEmTCl37IiIilJp0Z2dnefHFF5Vzm/kYCwkJsWilGjZsmLLc+vXrS0pKioSEhMjIkSPl/Pnz0qtXL2WZ5laKjz76SOkXrNFopF27doX6bvfu3VtiY2OVWrvdu3crfUddXFykefPmyv6k0WiU36hfv740atRIVCpVoW1Uo0YNZX9WqVQSFRUlx48fL3RNq127ttIitXHjRtFoNEr+EhISCtWm/v3336LRaCQ6OrrImsmnn35aoqOjpUmTJtKgQQNZu3atmEwm0ev1Spo333xTgPwWpdWrV0tCQoKEhIRYxCrmbW0+J4iIpKWliaOjo2i1WjEYDDJ//nxZunSp6PV62bhxowD5LUgDBw4Ub29vGT16tKhUKklMTCyypvabb74RtVotly5dKrTcopRUU/v+++/L6tWrZffu3bJ48WIJDAyUxx57rMjliIj8+uuvotVqZc2aNRbvL1u2TJycnJRWjUcffdSiVtY8ZqNnz56yfft2+eyzz8TDw8OiVTE3N1deeeUVUalUotVqRaVSyeuvv15s+e+kX+0dB7UajUYcHR2V4OSZZ56Ra9euyTfffCNarVZycnIsvhMbG6s0ARUV1JoHEZhNmzZNwsPDlb+LCmrNB/a5c+eKL1gJF/xr167JoUOHZMuWLdKzZ08JCwuzGMC2ZMkS8fX1VQ7Ol19+WXx9faVOnTpFlv+HH35QDuiC5e/YsaO4ubkVKv/JkydFrVZLx44dLcqfl5cnTZs2VQYnuLu7S2hoqACQ3377TXJycqRu3boydOhQpfwlBbVz5syR7du3y/Dhw8XLy0uWLVsmvr6+8vfffyvpQkJCxGg0ypQpUwot46mnnlJOdA4ODlKnTh0lWC5YfnOzlkqlkpycHElLS5ODBw9KgwYNlAD2xo0bsmDBAuVEumbNGnnmmWeUi7JarZbVq1cX2v6hoaFKs11aWprs3r1b6tevr5xs/f39lRN5amqqxf5hLlNWVpZUqlRJaa65du2aTJ06VQwGgzz00EPi7+8vTz/9dKH9oFGjRkpQtnbtWvHx8RGVSmWx/XU6nbz22mvyzTffiEajkfDwcKlbt660atVKsrKylHxu375dFixYIC4uLrJ//36JiYlR1tnEiRNFJL+zP5DfbLp7926ZN2+ecuKoX7++tG7dWn755ReLQXFFndz27NkjAKRt27YW+3hMTIy89957MnbsWAkLC5NevXqJh4eHUu6CQa15AJpIflcHlUol9evXVwbNmPfRtm3bysiRIy0GqGk0GomKirIYMGIOpM3BSkpKinIz8cUXXwgAcXV1FS8vL2X7m4NaBwcHmTNnjoiInDx5Urmgbdy4UebNmyceHh5St25deeWVVyz2f6PRaLH/79u3T9n+Q4YMEWdnZ2XwYsFuUgWZm5vNAxpv3f5OTk7StGlT8ff3V47/tLQ0ASCTJk1Sgpfjx48rx7/5mDWf/+rUqSM6nU5E/j3/bdiwQRwcHJTBVRkZGXL+/HlJSUmRGTNmKOuuZs2aIiLKYJovv/xSucmbMmWKkm7q1KlSt25dee+992Ty5MlSp04dSUpKEg8PDwkKCrrtoLao89/KlSuV89+NGzckMTFRoqOj5euvv1Zu0PLy8pSbOvN2ateunQD5XVK0Wq3s3r1bvLy8xNXVVf744w/ZtGmT0l3DfFNiPrYByMiRI8XT01MZpGe2ZMkSAfJvsIva/ubzdadOnUSn04mXl5ecPXtWGYhXXFCrVquVyhCVSiWPPPKIbNiwQRISEgSAXL16VTIyMsTV1VWGDx8ulStXlrCwMAEgEydOVK595jIEBQXJ2rVrxWAwSLVq1WTq1KnKvr9kyRJRq9XKzcIHH3xgMVDxySefFC8vL3FyclJu1kVEzp8/LwBk7NixEh8fL66ursq5yLzvHzp0SF544QWLioc33nhDgPwuFAVvmps3b67kw9yF6MCBA5KZmSmHDh2S7du3S9OmTUWj0cisWbNk165d8sYbb4iDg4O8+eabkpCQoAxaKygjI0OMRqOoVCoZO3asbNmyRZ544gmpXr26jBw50uKa1qBBAxk2bJicOXNGOR4WL14smzZtksaNG0vz5s0tBhi9/vrr4unpKY0aNSoyqD137pyyXTQajVSpUkWee+45pWuMyL/nHnPAfPnyZXFxcbHoYmG+jn/xxRfKe7m5uXLo0CFJTk6WKVOmiKOjoxgMBlm0aJH8/fffyrYLCwtTBm8/+uijEhYWVmRQu3btWgGgbDfzcl1dXeXHH38slL6koPZW69evFyC/a8et9uzZI15eXsq1yWzfvn3i7+8vkydPll27dsnq1aulZs2aFl0JKleuLMHBwRax0NSpU8XPz0/5+9NPP5WgoCD59NNPZffu3fLxxx+Lh4eHReVFwfJnZGT8Z3nM7jiobdGihRw6dEj++usvi1kP7jaovTUgmz59uoSGhip/FxXUmmvFUlJSii9YCUHtrSIjI4u8S0hNTZWrV6/KtWvXlBrFosp/5MiRIoPaRo0aiZeXV6HyT5gwQblLK678p06dkszMTGW09Llz5+Ty5cvKydX8r/kCr9FoZP369cWWv3nz5pKQkKAEFuaX+eQVHBxcqPzm/P7999+SkZEhHTp0UC6cBcufmZmp1DgWLL+5Zkmn08knn3wiCxYsUC7yJ0+elBEjRggAWb16tfj4+Mi8efMKbX9zn8+bN28q75m3v16vl8WLF8ucOXPE2dlZuZvr2bNnofI/+eST0qlTJxHJP6BMJpOsXLnSYvsX3A+OHz8uKpVKXF1dlQvCk08+KUB+relff/2lXKTPnTun3FGa16f5RG7+u3Hjxhb7v/mCDvxbw/znn38KAIv+SeaTuFqtVmYGMJcfQKH+Rvv27VNq1AuW33xBLFjLUbA2JikpyeKzn3/+WflupUqVRKfTSc2aNZVa0nXr1lkc/7Vq1RK1Wi0XL15Ubjbr1asnbm5uAkAef/xxi5qfguvGXEtUcHYJ88j5ghdxEZEJEyYoLRXm43/ixIkWM0oUXLZGo5H58+dL8+bNpW/fviKS3y/M1dVVfv/9d+Xi9eqrrxba/83MZUhMTFS2/7Zt2yQyMlK6desmL7zwQpHb3XxMmPs1FhXU1qxZUzkmbt68KdOnTxdfX19xdHSU999/X6pVqyZardYiPwW3v/ncOGbMGNFqtTJ9+nRlG966P9663m99mbe/eb8x57dSpUrK7BdFnf+uXbumfL99+/ZSq1YtZTS0eRaNzZs3W/R1vzUfKpVK2rZtK8HBwRbHv3nmg48++khE8o9tc39yc2tIQeZju6jzn3n7Hz16VADI//3f/0ndunXF3d1dXn/9dWnSpIlyc1WQeX+rVauWvPbaa4UCafNN2ZIlS+Tjjz9WzgmxsbESHh4ujo6O8sQTTyjL7dmzp/j6+irnfvMsHy+++KKEhoYqZahRo4ZSsXD+/HkREfntt98EgHTp0kXUarUMHz680P4K5LemmdddUcGPSH4liqenpzg5OSnnnpiYGHn55Zdl8ODBotFo5Nq1a6JSqUSv1yvbee3atcoyMjIyRKfTSUxMjLJ+RUR69eoliYmJyg1YwQBRRJSKGfNxLZJ/HXFwcJAnnnjC4rpYtWpVefvtt2X06NHi5eUlsbGxymcnT54UALJlyxalTJGRkTJo0KBi+5AOGDBAgoKCZP/+/XLq1CnJy8uTYcOGWVQw3RrUiuTPxFBwfZuv48X1Hd24caNotVpluZmZmaLVauW5556zOA4Kno81Go1FkPnZZ5+Jo6NjoWX36tVLWrZsWej9Owlqzdtz9erVFu+bryEjR44s9J2uXbtabDMRUcZHmK9jjRo1kubNm1ukWbVqlQBQjpugoKBCY6ImTpwoUVFRFu8VV/6S3HGfWkdHR0RGRiIkJETpowPk9z3JyclR+soBwMWLF5GSkoLo6Og7/RmFXq+36IsB5PcjdHFxwZ9//nnXyy0oLy9PGd1bkK+vL5ycnLB06VIYjUYEBAQUWf7w8HB4eXlZlD89PR2//fYb0tPTC5V/wYIFeOaZZyz6WN0qMDAQer0eFy5cgJ+fH7y9veHi4oI9e/YgOTkZTk5OmDZtGvr164eoqCjs3LmzyBGUBcsYEhKC3bt3Y+fOncpLr9ejSpUqWLduXbHfDQgIgMlkwtGjR2EymdC+fXuL8uv1etSqVQt5eXkW23/v3r3IzMy0GD1t/k5KSgpSU1MB5I/Cv3DhQrH94gwGAwwGg/KeefsD+f0AP/vsM7Rt2xZqtRrXrl3DsmXLLJaRm5uLPXv2wN/fH59++imSkpLw6aefok2bNsq6yczMtNgPPvroI6jVavTs2bNQfy1vb2+EhIRg4cKFaNeuHby9vREcHIy8vDxUrVoV27Ztw65du5RRm/Hx8ViwYIHy/X379in9aAEov3nw4EGoVCqL/SInJ0f5v7mfd8Hyi4jFcps2bYru3bsXWo8A0L9/f+zcuRPLly8HADzxxBOIiopCUFAQbty4gby8PNSoUQMajQYpKSnKujl9+jTy8vLg5OQEvV4PIH//LHj8Ozs7Iy8vDwcOHIC3tzcOHTqE33//Xemz5evrC61WC6PRCABKv9PAwEAMHz4cADBkyBAAgI+PD3bu3IkPP/xQWf7QoUMhIliwYAGSkpIsjv+8vDz4+flZ7NdA/iwGO3fuxGOPPaZs28mTJ2PixIlYvXq1RR9nc9/aW506dQppaWlQq9UW+2CXLl1Qt25dLFiwACNGjFD6gy9atEj5/cGDByMvLw8ajabI8wvwb79eNzc3GAwGHD58GOfOncNbb72Fvn374ubNm8osGmaVKlVS1mPTpk0BAA8++CBycnLQsGFD7N69GwDw8ssvK+vz5ZdfxooVK5RZBb766iv873//Q1hYGLy8vADkz3wQExOD9evXW/zemTNnlH51RZ3/zOXNzc3F7t278cMPP8DT0xMXL17E5cuXAfy7jzs7OyvbqHnz5spvvPvuuwgJCVGOCTNzP/mwsDDl2O7Xrx8cHR1x4sQJi3EABY/tWxU8tsPCwhAQEIA9e/bgyJEjUKvVyMzMxMWLF6FSqXDlyhVl3EBKSopSBn9/fzg7O0On01mcE8zn94MHD2L+/Plo164d1Go1Dhw4gGPHjsHZ2Vk5js1lqFOnTqE85uTk4Pr160oZ/P39cf78eQQFBSnbqG7dutDr9VixYgXy8vKUWXluNW7cuELnrVupVCpkZWXByckJn376KYKDg6FWq6FWq5GYmIjc3Fy8++67yhgT8zmh4Hk6Ozsb2dnZEBGLfdw8/sK8LbRarbJfbdy4ETNmzACQf4yYqdVqqFQqi3PazZs3ceTIEdSuXRtpaWm4dOmSxTY3nyvNc9lu2rQJhw8ftkhjJiIYOHAgvv76a2zYsAFVq1ZFYGAgcnJy8OWXX6J9+/bFrqtr164p4zjMyzJfx4tazxs3bkSbNm1Qt25d5fjV6/V44IEHcPnyZezZs0c5Dpo2bYrAwEA0bdoUO3fuVMafAPnX0Nq1axdafnExy50wH7cF+90XvIa89tprhb6TkZFhMd4I+HcbmLfbgw8+iMOHD1vML3zw4EH4+/sr14/ilnPrnMTFlb9EdxIBFzdCz6x9+/YSHR0tP//8s+zcuVNatWolkZGRyp3M3dTU9unTRx544AE5duyYnD9/XqmNe/zxx2XIkCEW37169aokJycrfW6nTZsmycnJ8tdff4lI/p3JiBEjZMuWLXL8+HHZvn27JCUlicFgkL179yrLeffdd2XHjh2SkpIis2bNEpPJJDNnzrQof1RUlHz11VfKd958803RarUSHBwsCxculCZNmojJZJJKlSpZlN/cR2///v2Fyn/+/Hl58sknJSAgQJKTk+WFF15Q+gYVV/6CyzCXPykpSQDI6NGjZdmyZdK/f39RqVQWd9gionQbKHiHLZI/4jQ5OVkZ2f/FF18oy6hXr16R5f/qq69EpVKJs7OzvPbaa0o/W6PRKO7u7rJ161Z57LHHlP53wcHByhyFbdu2lejoaPnqq6/kqaeeEl9fXwkPD5dhw4aJRqMRHx8fGTp0qPzwww+yfv16GTNmjHh7e4ter5f27duLh4eHHDt2TK5evSpjxoxRaoFGjRoln3zyiTz66KNiNBpl4sSJolKp5IUXXpDu3bvLjBkz5NFHHxWdTicdOnRQ9oPc3Fyldu6rr76S48ePy/r168Xb21scHBzkyJEjSs3n999/L1euXJH4+HhxcXGRyMhIWb58ufzwww9StWpVAfJnP/juu++kZ8+e4uDgIB4eHko/M41GIxs3bpRvvvlGvL29pUqVKuLt7S3h4eHy9ttvi1qtFr1eL25ubtKmTRv55ZdfZPny5Upf2yFDhkhycrKsXbtWvLy8JDExUTp16iQAZNCgQfLee+9Jx44dxWAwyLZt25T9v3HjxuLp6SkuLi6i1+slJCREnn76afnss8+Uri/jx4+Xhx9+WJlztlWrVtKoUSOJjIxU+okePnxYaeYOCwsTb29vGThwoDKHpcFgUEa9d+7cWVxdXaVOnTpKDby3t7fExsZK7dq1pVq1ahIeHi7+/v4SFRUly5YtU2rkjx07pnTPmDNnjrRs2VJ69uwpH374oTg7O8uoUaMK7f8Gg8Fi/3/mmWdErVbL66+/Lt9//73Mnj1bmcd10aJF8uuvv8oTTzwhL774okydOlW6desm/v7+olarpXnz5uLt7S0mk0lCQ0PFwcFBvv32Wzlz5ozyatGihfj4+MiHH34oQH5favNctmfPnpU333xTjEajNGrUSMLCwpTmb7VaLWPGjJENGzaITqcTR0dHqVSpkvzxxx/i4+Mjnp6eMm7cONmwYYMcPXpU1q9fr3R9Mbdg5ObmSkxMjNSpU0c+/fRTpSazfv36EhgYKNOnTy90/uvTp4+4u7sr/R+Tk5Pls88+E71eL+PHj5fOnTsrfSRjYmKkdevWEh8fLxMmTJDQ0FCZO3eufPPNNxIeHi6enp6i1+vF09NTpk+fLlOnTpWoqChlho8PP/xQWrVqJU5OTnLkyBFZvHix0opj7rZkbg41z9G7Y8cOcXZ2FpVKJfv375fp06eLwWCQy5cvS3BwsEVz5pIlS0Sj0cjw4cOV2s1BgwbJggULlO3fvn176dKli3z11VfSo0cPZU5VvV4vzz77rBiNRnn66aeVOaKnTp0qlSpVUvr8NWnSRL766itl7uXPPvtM9u7dK127dhWtVqt0FRsyZIg0btxYXFxcxGg0isFgkEmTJomrq6uMHDlSdDqdPPPMMxIeHi7vv/++srxWrVoJAHnttdekUqVK0qpVK/Hw8JAOHTrIzJkz5ddff5XOnTsrXYCeffZZZd9btWqVvPLKK/LRRx8p+17VqlUlMDBQjh8/LkeOHJE2bdrI+PHj5YcffpCuXbsqNd7NmjUTnU4nX3/9tbRu3VocHBxkypQp4u/vr7RWtWjRQuLj45W5UCdOnCjHjh2T3bt3K61JAwcOlLFjx8r48ePFYDAo/X79/f2lT58+EhISIlOnThWj0Si+vr6iUqmke/fusmnTJvn111+lffv24uTkJI0bNxadTifJycnywQcfiKOjo1y/fl2GDBkiQP4sIgcPHpQdO3ZIYmKihIaGKs3TXbt2lRo1akhycrLExcVJ586dJTk5Wfbt2yf9+/cXV1dXmTNnjnz44Yfy22+/yddffy1NmjSR8PBwuXz5spw5c0aSk5OVmVeWLl0qCxYskMaNG4uXl5fSCmXuwmKeg10kv9vD2rVrZfHixWI0GqVZs2ai1WplypQpcubMGbl48aKy/8ybN08OHTok7777rmg0GmnVqpUSW3Tr1k2pETa3Dq9du1aOHDkif/75p0yZMkW0Wq188MEHym9fvHhRkpOT5bvvvhMA8tlnn0lycrKcOXNGREQOHz4sEyZMkO3bt8uxY8fkm2++kYiICGnUqJGyjD179oi3t7d07drV4rxWsJvnggULRKvVypw5c+TIkSPyyy+/SN26dZW4QETkxIkT4uzsLAMHDpSUlBRZuXKl+Pj4yKRJk5Q03bt3l8DAQFm5cqUcO3ZMvvrqK/Hy8pJhw4ZZxCKNGzcudmaJ4pRqUGue0sU8DUxiYuJtTelV0K1BbUpKitSvX19pUjJPabNq1SoJDAy06EBc3MTI3bt3FxGRGzduyGOPPSYBAQGi1+vF399f2rVrV2iAULdu3cTDw0OZmNk8fVHB8gOQBQsWKN/Jy8uTl19+WelbqFarpWHDhoXKr9PppEGDBkWW//z58xIaGqoM0GrevLksW7asxPKPGTNGWUZx5ff39y8U0IrkT9ul0Wgsph0zl7Oo5RQcKHdr+UXymw8KNivqdDpp27atHDhwQE6cOCFVqlRRBgqZHyKg0WjksccekxMnTsj3338vAQEBSnOM+eEOderUsWg+NQcCKpVK2rVrp0xjVFz5AwMD5Y8//ih2knSDwWCxH5j7Fjo5OYmHh4cYDAYJCwuTKlWqSGJiotLE4+3tLbm5uSVOyI1/mkE3bNigTENW1Euj0UjTpk0tHv5QvXp18fDwkMjISGWKpFsHuJlf5j66Rb2Cg4Nl27ZthfZ/c59XR0dHSUpKUh6+MGPGDGWATlEvc/O3s7OzODg4SK1ataRr166FHtiAf5rUqlWrJh9//LFFd5aC20Kj0UhERIS88sor8vrrryvrwBycBQYGKvt/UFCQVK9eXXnIQ2xsrMyZM6fE7WDe/803AiW9IiIiLPoCm288QkNDpUqVKhYPILn1tW/fPov9F4BERkYq+6d5+rhbXz169JDc3NxijzvzQ1aA/ADf3Fzt4eFhcf4zT5N068vR0VGmT59eaPs7OTmJi4uLMlDM3GRZr169EteRua+5VqtVBrz913ot+DIajVKtWjVlikCDwWBx7jWfQ8w3kaGhocr2b9eunaSlpSk3eubyFzWx/K3b/8knn7R46Ih50I3BYJCEhAT5+eef5caNG9KzZ0+LAam3vszdphwdHcXd3V2aNWsma9askejoaIv9RqVSSeXKlWXbtm3Kvh8TEyO+vr4W+fivV8GBqsWlMXe5KulVo0aNQg9G0Gg0Eh8fr/Tv7NKli7KuCz5AxcfHR3r06CEXL14UAMpNjLe3tzRs2FBatmwpPj4+Spcb8wAyR0dHOXHihNy4cUOee+65Ih9qYN4W5oqEW1+dO3cWEZGEhARp0KCB1K5dWxwdHcXb21vatWunBJZpaWkWA/AKvsw3HEW9GjRooIwzKeoBHQCkXr16Ft0Cnn76aeU6bjZq1CiJjIwstouPecDz/PnzJTIyUoxGo8TExMjy5cstYovGjRtL9+7d5dSpU6LT6eT5559X0ru7u0tCQoJ89tlnFr9d3MOHxo4dKyL5gWajRo2U61lkZKQMHTpU6dJWUtkLxmMi+VN4RUdHK1PxdenSRU6dOmWRZvPmzRIfHy8Gg0EiIiLktddes+iamJ6eLi+++KIypiciIkJGjRpl0a3HXP6CfcZvxx0FtfeTvLw8eeCBB5Q5Te0Ny2/75V+1apVUq1atyCf22UP59+7dKz4+Psq8pQXZQ/mHDRsmffr0KfIzeyi/ve//c+bMkYcffrjQ+/ZQdpH8Sh4PDw85evRoRWelQpR0/NuDuy3/Pc9TW1FUKhXmzZtn0efQnrD8tl/+69evY8GCBRZ9V83sofxnzpzBxx9/DFdX10Kf2UP5fXx8lCfa3coeym/v+79Op7N4wpKZPZQdAI4fP445c+YoT0KzNyUd//bgbsuvEinQK5uIiIiIyApZbU0tEREREZEZg1oiIiIisnoMaomIiIjI6jGoJSIiIiKrx6CWiIiIiKweg1oiIiIisnoMaomIiIjI6jGoJSIiIiKrx6CWiIiIiKze/wOjXo/Wza46qAAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ We can observe that many sensors have recorded negative PM2.5 values. Thus, we replace the negative PM2.5 values of each sensor with Zero" + ], + "metadata": { + "id": "buCv95fkc2sO" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.4.5: Replacing the values less than zero to 0" + ], + "metadata": { + "id": "OYlUYzY1dTjW" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.where(dataset > 0, 0, inplace=True)\n", + "dataset.min().plot()" + ], + "metadata": { + "id": "Q0DaB2g_daf8", + "outputId": "e02a5869-b619-4907-c08f-955b11fb6ab7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + } + }, + "execution_count": 11, + "outputs": [ { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "import plotly.express as px\n", - "\n", - "fig = px.density_mapbox(pm25, lat = 'lat', lon = 'long', z = 'pm25',\n", - " radius = 8,\n", - " zoom = 6,\n", - " mapbox_style = 'open-street-map')\n", - "fig.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 542 - }, - "id": "t105pivfBTRh", - "outputId": "3f4893b7-6a7d-4ba1-8154-e5725bfcf126" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
\n", - "
\n", - "\n", - "" - ] - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 11 }, { - "cell_type": "markdown", - "source": [ - "__Inference from the above figure:__ High PM2.5 levels were frequently observed at the south part of Japan, starting from Tokyo. " + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "Z-ppzs3AB7xM" - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Step 3.7: Printing the heat map of maximum PM2.5 value recorded by each sensor" - ], - "metadata": { - "id": "1QDznnGOClCq" - } + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ The abnormal values were replaced to 0." + ], + "metadata": { + "id": "U1O0xBeJd8Sl" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.5: Create a dataframe of the sensors having pm25 >= 15\n", + "(useful to prune those sensors that do not record any pm2.5 value)" + ], + "metadata": { + "id": "AyJCyA6n97Oq" + } + }, + { + "cell_type": "code", + "source": [ + "thresholdValue = 15\n", + "pm25 = pd.DataFrame(columns=[\"long\", \"lat\", \"pm25\"])\n", + "for col in dataset[1:]:\n", + " res = [i for i in dataset[col].values if i >= thresholdValue]\n", + " if len(res) == 0 or col == \"Unnamed\":\n", + " dataset = dataset.drop([col], axis = 1)\n", + " else:\n", + " if \"Poi\" in col:\n", + " #print(\"Hey\")\n", + " col = col.strip(\"Point()\")\n", + " col = col.rstrip(\").1\")\n", + " long, lat = col.split()\n", + " pm25 = pm25._append({'long': float(long), 'lat': float(lat), 'pm25': len(res)}, ignore_index=True)\n", + "pm25.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "51ureCEF_jv6", + "outputId": "b61bd0fd-2620-48ae-92c1-431ca93e4274" + }, + "execution_count": 12, + "outputs": [ { - "cell_type": "code", - "source": [ - "maxPM25 = pd.DataFrame(columns=[\"long\", \"lat\", \"maxPM25\"])\n", - "for col in dataset[1:]:\n", - " res = [i for i in dataset[col].values if i >= 15]\n", - " if len(res) == 0 or col == \"Unnamed\":\n", - " dataset = dataset.drop([col], axis = 1)\n", - " else:\n", - " if \"Poi\" in col:\n", - " col = col.strip(\"Point()\")\n", - " col = col.rstrip(\").1\")\n", - " long, lat = col.split()\n", - " maxPM25 = maxPM25._append({'long': float(long), 'lat': float(lat), 'maxPM25': max(res)}, ignore_index=True)\n", - "maxPM25.head()\n", - "\n", - "import pandas as pd\n", - "import plotly.express as px\n", - "\n", - "fig = px.density_mapbox(maxPM25, lat = 'lat', lon = 'long', z = 'maxPM25',\n", - " radius = 8,\n", - " zoom = 6,\n", - " mapbox_style = 'open-street-map')\n", - "fig.show()" + "output_type": "execute_result", + "data": { + "text/plain": [ + " long lat pm25\n", + "0 139.096021 36.404732 8204.0\n", + "1 139.042873 36.381603 8999.0\n", + "2 138.995512 36.338016 13929.0\n", + "3 139.342672 36.410566 12667.0\n", + "4 139.381732 36.290913 10391.0" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 542 - }, - "id": "90j1c_T9DVtL", - "outputId": "91a93f7a-1f80-4ac6-a108-6d90a1055d91" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
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\n" ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.6: Drawing the frequency heatmap of sensors\n", + "\n", + "The frequency heatmap provides cruical information regarding how frequently a particular sensor has recorded harmful levels of pollution" + ], + "metadata": { + "id": "3_LVZ8q0AXua" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import plotly.express as px\n", + "\n", + "fig = px.density_mapbox(pm25, lat = 'lat', lon = 'long', z = 'pm25',\n", + " radius = 8,\n", + " zoom = 6,\n", + " mapbox_style = 'open-street-map')\n", + "fig.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 }, + "id": "t105pivfBTRh", + "outputId": "3f4893b7-6a7d-4ba1-8154-e5725bfcf126" + }, + "execution_count": 13, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Step 4: Installing the latest version of PAMI package" - ], - "metadata": { - "id": "eJ_MqM0dpnmM" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Im02B5LdO6cJ", - "outputId": "7199ddd1-c85b-401b-9d78-b5b10b6cc8a2" - }, - "execution_count": 15, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: pami in /usr/local/lib/python3.10/dist-packages (2023.8.6.2)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.13.1)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", - "Requirement already satisfied: resource in /usr/local/lib/python3.10/dist-packages (from pami) (0.2.1)\n", - "Requirement already satisfied: validators in /usr/local/lib/python3.10/dist-packages (from pami) (0.20.0)\n", - "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (1.26.16)\n", - "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.22.4)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.41.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.4.4)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (23.1)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (3.1.0)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pami) (2022.7.1)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->pami) (8.2.2)\n", - "Requirement already satisfied: JsonForm>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.0.2)\n", - "Requirement already satisfied: JsonSir>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.0.2)\n", - "Requirement already satisfied: python-easyconfig>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.1.7)\n", - "Requirement already satisfied: decorator>=3.4.0 in /usr/local/lib/python3.10/dist-packages (from validators->pami) (4.4.2)\n", - "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.3.3)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", - "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", - "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", - "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.19.3)\n" - ] - } + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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\n", + "\n", + "" ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Inference from the above figure:__ High PM2.5 levels were frequently observed at the south part of Japan, starting from Tokyo. " + ], + "metadata": { + "id": "Z-ppzs3AB7xM" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.7: Printing the heat map of maximum PM2.5 value recorded by each sensor" + ], + "metadata": { + "id": "1QDznnGOClCq" + } + }, + { + "cell_type": "code", + "source": [ + "maxPM25 = pd.DataFrame(columns=[\"long\", \"lat\", \"maxPM25\"])\n", + "for col in dataset[1:]:\n", + " res = [i for i in dataset[col].values if i >= 15]\n", + " if len(res) == 0 or col == \"Unnamed\":\n", + " dataset = dataset.drop([col], axis = 1)\n", + " else:\n", + " if \"Poi\" in col:\n", + " col = col.strip(\"Point()\")\n", + " col = col.rstrip(\").1\")\n", + " long, lat = col.split()\n", + " maxPM25 = maxPM25._append({'long': float(long), 'lat': float(lat), 'maxPM25': max(res)}, ignore_index=True)\n", + "maxPM25.head()\n", + "\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "\n", + "fig = px.density_mapbox(maxPM25, lat = 'lat', lon = 'long', z = 'maxPM25',\n", + " radius = 8,\n", + " zoom = 6,\n", + " mapbox_style = 'open-street-map')\n", + "fig.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 }, + "id": "90j1c_T9DVtL", + "outputId": "91a93f7a-1f80-4ac6-a108-6d90a1055d91" + }, + "execution_count": 14, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Step 5: Converting the data frame into a temporal database" - ], - "metadata": { - "id": "FsT19X0fp1ux" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.extras.DF2DB import DenseFormatDF as db\n", - "obj = db.DenseFormatDF(dataset, '>=', 35)\n", - "obj.createTemporal('temporalDatabasePM25HeavyPollution.csv')" - ], - "metadata": { - "id": "smUkVkF4O3By" - }, - "execution_count": 16, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 6: Printing new lines of the created temporal database" - ], - "metadata": { - "id": "5q43oauYTlc_" - } - }, - { - "cell_type": "code", - "source": [ - "!head -5 temporalDatabasePM25HeavyPollution.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "D0En4pJ8TtHF", - "outputId": "8e80c57e-ae03-4f40-ceec-011872a4ed4a" - }, - "execution_count": 17, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "0\tPoint(139.750635 35.7977241)\tPoint(139.8841693 35.8422674)\tPoint(139.3819898 36.2229999)\tPoint(139.9206727 35.684037)\tPoint(139.9785953 35.6880639)\tPoint(139.9033705 35.7876179)\tPoint(139.9123927 35.7995549)\tPoint(139.880097 35.953403)\tPoint(139.9035901 35.8570293)\tPoint(139.9012134 35.6552406)\tPoint(139.8356927 35.6967785)\tPoint(139.7209595 35.6108138)\tPoint(139.7054233 35.7609043)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8773135 35.6821907)\tPoint(137.1418898 34.9626945)\tPoint(136.8905668 35.0122987)\tPoint(136.6548337 35.0051925)\tPoint(139.4949175 36.2914457)\n", - 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python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", + "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.19.3)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Converting the data frame into a temporal database" + ], + "metadata": { + "id": "FsT19X0fp1ux" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.DF2DB import DenseFormatDF as db\n", + "obj = db.DenseFormatDF(dataset, '>=', 35)\n", + "obj.createTemporal('temporalDatabasePM25HeavyPollution.csv')" + ], + "metadata": { + "id": "smUkVkF4O3By" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 6: Printing new lines of the created temporal database" + ], + "metadata": { + "id": "5q43oauYTlc_" + } + }, + { + "cell_type": "code", + "source": [ + "!head -5 temporalDatabasePM25HeavyPollution.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "D0En4pJ8TtHF", + "outputId": "8e80c57e-ae03-4f40-ceec-011872a4ed4a" + }, + "execution_count": 17, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Step 8: Mining Periodic-Frequent Patterns using PFP-growth algorithm" - ], - "metadata": { - "id": "3Xw24J5sEP3n" - } + "output_type": "stream", + "name": "stdout", + "text": [ + "0\tPoint(139.750635 35.7977241)\tPoint(139.8841693 35.8422674)\tPoint(139.3819898 36.2229999)\tPoint(139.9206727 35.684037)\tPoint(139.9785953 35.6880639)\tPoint(139.9033705 35.7876179)\tPoint(139.9123927 35.7995549)\tPoint(139.880097 35.953403)\tPoint(139.9035901 35.8570293)\tPoint(139.9012134 35.6552406)\tPoint(139.8356927 35.6967785)\tPoint(139.7209595 35.6108138)\tPoint(139.7054233 35.7609043)\tPoint(139.8257782 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35.7609043)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8776074 35.744251)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8859668 35.7099429)\tPoint(139.8773135 35.6821907)\tPoint(139.871021 35.6536708)\n", + "4\tPoint(139.9033705 35.7876179)\tPoint(139.880097 35.953403)\tPoint(139.9012134 35.6552406)\tPoint(139.8356927 35.6967785)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8692678 35.7864558)\tPoint(139.871021 35.6536708)\tPoint(139.6849913 35.5003919)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 7: Printing the statistics of the database" + ], + "metadata": { + "id": "rkppthohO2yJ" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.dbStats import TemporalDatabase as tempDS\n", + "obj = tempDS.TemporalDatabase('temporalDatabasePM25HeavyPollution.csv')\n", + "obj.run()\n", + "obj.printStats()\n", + "#obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "xm6W5dCBPPbo", + "outputId": "9fde0a5e-b889-4eef-d9a8-699bf5a4d522" + }, + "execution_count": 18, + "outputs": [ { - "cell_type": "code", - "source": [ - "from PAMI.partialPeriodicPattern.basic import PPPGrowth as alg\n", - "obj = alg.PPPGrowth('temporalDatabasePM25HeavyPollution.csv',periodicSupport=500, period=1000, sep='\\t')\n", - "obj.mine()\n", - "obj.printResults()\n", - "obj.save('soramame_periodicFrequentPatterns.txt')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oGprbukCQFXY", - "outputId": "eeaf4213-d2f1-472a-b2a7-007acfa6afee" - }, - "execution_count": 20, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", - "Total number of Partial Periodic Patterns: 1084\n", - "Total Memory in USS: 8719978496\n", - "Total Memory in RSS 8766881792\n", - "Total ExecutionTime in ms: 22.24203848838806\n" - ] - } - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 31462\n", + "Number of items : 1119\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 16.545896637213147\n", + "Maximum Transaction Size : 503\n", + "Minimum Inter Arrival Period : 1\n", + "Average Inter Arrival Period : 1.0\n", + "Maximum Inter Arrival Period : 1\n", + "Minimum periodicity : 590\n", + "Average periodicity : 5948.814119749776\n", + "Maximum periodicicty : 31415\n", + "Standard Deviation Transaction Size : 39.73276427461552\n", + "Variance : 1578.7427362530073\n", + "Sparsity : 0.9852136759274235\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 8: Mining Periodic-Frequent Patterns using PFP-growth algorithm" + ], + "metadata": { + "id": "3Xw24J5sEP3n" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.partialPeriodicPattern.basic import PPPGrowth as alg\n", + "obj = alg.PPPGrowth('temporalDatabasePM25HeavyPollution.csv',periodicSupport=500, period=1000, sep='\\t')\n", + "obj.mine()\n", + "obj.printResults()\n", + "obj.save('soramame_periodicFrequentPatterns.txt')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "oGprbukCQFXY", + "outputId": "eeaf4213-d2f1-472a-b2a7-007acfa6afee" + }, + "execution_count": 20, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Step 9: Printing some of the generated periodic-frequent patterns" - ], - "metadata": { - "id": "xiV9dSvpZd7O" - } - }, + "output_type": "stream", + "name": "stdout", + "text": [ + "Partial Periodic Patterns were generated successfully using 3PGrowth algorithm \n", + "Total number of Partial Periodic Patterns: 1084\n", + "Total Memory in USS: 8719978496\n", + "Total Memory in RSS 8766881792\n", + "Total ExecutionTime in ms: 22.24203848838806\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 9: Printing some of the generated periodic-frequent patterns" + ], + "metadata": { + "id": "xiV9dSvpZd7O" + } + }, + { + "cell_type": "code", + "source": [ + "!head soramame_periodicFrequentPatterns.txt" + ], + "metadata": { + "id": "rihfOjuEZl1M", + "outputId": "7399d68a-ae95-4e84-de6c-fd83b3187eb9", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 21, + "outputs": [ { - "cell_type": "code", - "source": [ - "!head soramame_periodicFrequentPatterns.txt" - ], - "metadata": { - "id": "rihfOjuEZl1M", - "outputId": "7399d68a-ae95-4e84-de6c-fd83b3187eb9", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Point(139.7098858 35.5963547):500 \n", - "Point(140.1026808 39.703907).1:501 \n", - "Point(139.5597075 36.0854272):502 \n", - "Point(135.516357 34.8128173):503 \n", - "Point(136.9765267 35.2033163):506 \n", - "Point(139.664222 35.7653547):507 \n", - "Point(132.221021 35.0112753):508 \n", - "Point(139.9035901 35.8570293):509 \n", - "Point(136.2315365 36.031118):510 \n", - "Point(140.1708865 35.6678777):510 \n" - ] - } - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Point(139.7098858 35.5963547):500 \n", + "Point(140.1026808 39.703907).1:501 \n", + "Point(139.5597075 36.0854272):502 \n", + "Point(135.516357 34.8128173):503 \n", + "Point(136.9765267 35.2033163):506 \n", + "Point(139.664222 35.7653547):507 \n", + "Point(132.221021 35.0112753):508 \n", + "Point(139.9035901 35.8570293):509 \n", + "Point(136.2315365 36.031118):510 \n", + "Point(140.1708865 35.6678777):510 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 7: Visualization of Generated Patterns" + ], + "metadata": { + "id": "A9X6ymGdQI08" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.graph import visualizePatterns as fig\n", + "\n", + "obj = fig.visualizePatterns('soramame_periodicFrequentPatterns.txt',10)\n", + "obj.visualize(width=1000,height=900)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "XCX7QBEuQITs", + "outputId": "569e75bf-e61b-4c8e-8e7d-119a9945e436" + }, + "execution_count": 22, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Step 7: Visualization of Generated Patterns" - ], - "metadata": { - "id": "A9X6ymGdQI08" - } + "output_type": "stream", + "name": "stdout", + "text": [ + "Number \t Pattern\n", + "2\tPoint(139.6623882 36.0656055)\tPoint(140.0900293 35.5199404)\tPoint(139.6681689 36.0701176)\n", + "3\tPoint(139.6623882 36.0656055)\tPoint(140.5781487 39.3100292)\tPoint(139.6681689 36.0701176)\n", + "4\tPoint(139.6623882 36.0656055)\tPoint(139.9717795 35.8439578)\tPoint(139.6681689 36.0701176)\n", + "5\tPoint(139.6681689 36.0701176)\tPoint(130.6999328 33.0093764)\tPoint(130.6384926 32.5010333)\n", + "6\tPoint(130.8163704 32.7914033)\tPoint(130.6384926 32.5010333)\tPoint(130.6999328 33.0093764)\n", + "7\tPoint(130.6999328 33.0093764)\tPoint(140.5781487 39.3100292)\tPoint(130.6384926 32.5010333)\n", + "8\tPoint(130.7998865 33.868205)\tPoint(130.6939898 33.89368080000001)\tPoint(130.8073796 33.9015062)\n", + "9\tPoint(139.0432674 36.64710669999999)\tPoint(139.6623882 36.0656055)\tPoint(139.6681689 36.0701176)\n", + "10\tPoint(130.6939898 33.89368080000001)\tPoint(130.8293822 33.8952617)\tPoint(130.8073796 33.9015062)\n", + "11\tPoint(133.7758944 34.6007931)\tPoint(133.8362679 34.5899827)\tPoint(133.7570366 34.5800143)\tPoint(133.8171478 34.6075268)\n" + ] }, { - "cell_type": "code", - "source": [ - "from PAMI.extras.graph import visualizePatterns as fig\n", - "\n", - "obj = fig.visualizePatterns('soramame_periodicFrequentPatterns.txt',10)\n", - "obj.visualize(width=1000,height=900)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "XCX7QBEuQITs", - "outputId": "569e75bf-e61b-4c8e-8e7d-119a9945e436" - }, - "execution_count": 22, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Number \t Pattern\n", - "2\tPoint(139.6623882 36.0656055)\tPoint(140.0900293 35.5199404)\tPoint(139.6681689 36.0701176)\n", - "3\tPoint(139.6623882 36.0656055)\tPoint(140.5781487 39.3100292)\tPoint(139.6681689 36.0701176)\n", - "4\tPoint(139.6623882 36.0656055)\tPoint(139.9717795 35.8439578)\tPoint(139.6681689 36.0701176)\n", - "5\tPoint(139.6681689 36.0701176)\tPoint(130.6999328 33.0093764)\tPoint(130.6384926 32.5010333)\n", - "6\tPoint(130.8163704 32.7914033)\tPoint(130.6384926 32.5010333)\tPoint(130.6999328 33.0093764)\n", - "7\tPoint(130.6999328 33.0093764)\tPoint(140.5781487 39.3100292)\tPoint(130.6384926 32.5010333)\n", - "8\tPoint(130.7998865 33.868205)\tPoint(130.6939898 33.89368080000001)\tPoint(130.8073796 33.9015062)\n", - "9\tPoint(139.0432674 36.64710669999999)\tPoint(139.6623882 36.0656055)\tPoint(139.6681689 36.0701176)\n", - "10\tPoint(130.6939898 33.89368080000001)\tPoint(130.8293822 33.8952617)\tPoint(130.8073796 33.9015062)\n", - "11\tPoint(133.7758944 34.6007931)\tPoint(133.8362679 34.5899827)\tPoint(133.7570366 34.5800143)\tPoint(133.8171478 34.6075268)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
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\n", + "\n", + "" ] - }, - { - "cell_type": "markdown", - "source": [ - "References:\n", - "\n", - "\n", - "1. RAGE, Uday kiran (2023), “5+ Years of Multiple Time Series Data of Hourly PM2.5 Recordings Gathered from Various Sensors Located throughout Japan (1-1-2018 to 25-4-2023)”, Mendeley Data, V1, doi: 10.17632/phgrnvykmr.1\n", - "2. PAMI: PAttern Mining toolkit. https://github.com/UdayLab/PAMI\n", - "\n" - ], - "metadata": { - "id": "NqQ1qbZY2120" - } + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + }, + { + "cell_type": "markdown", + "source": [ + "References:\n", + "\n", + "\n", + "1. RAGE, Uday kiran (2023), “5+ Years of Multiple Time Series Data of Hourly PM2.5 Recordings Gathered from Various Sensors Located throughout Japan (1-1-2018 to 25-4-2023)”, Mendeley Data, V1, doi: 10.17632/phgrnvykmr.1\n", + "2. PAMI: PAttern Mining toolkit. https://github.com/UdayLab/PAMI\n", + "\n" + ], + "metadata": { + "id": "NqQ1qbZY2120" + } + } + ] +} diff --git a/notebooks/periodicCorrelatedPattern/basic/EPCPGrowth.ipynb b/notebooks/periodicCorrelatedPattern/basic/EPCPGrowth.ipynb index 7febbce5..27f5b21f 100644 --- a/notebooks/periodicCorrelatedPattern/basic/EPCPGrowth.ipynb +++ b/notebooks/periodicCorrelatedPattern/basic/EPCPGrowth.ipynb @@ -1,714 +1,714 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Correlated Periodic Frequent patterns in Temporal Databases using EPCPGrowth" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Correlated Periodic Frequent patterns in a temporal database using the EPCPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the EPCPGrowth algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "159ccfcc-8efa-4fed-cb24-87c187a20c77" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m15.5 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already 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(8.2.3)\n", + "Collecting JsonForm>=0.0.2 (from resource->pami)\n", + " Downloading JsonForm-0.0.2.tar.gz (2.4 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting JsonSir>=0.0.2 (from resource->pami)\n", + " Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in 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/root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=ef5920d11691f28280c289db69c41cac40f13da0d02f092320425f6e59a1f570\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Correlated Periodic Frequent patterns in Temporal Databases using EPCPGrowth" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Correlated Periodic Frequent patterns in a temporal database using the EPCPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the EPCPGrowth algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "159ccfcc-8efa-4fed-cb24-87c187a20c77" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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" Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", - " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", - "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", - "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", - "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", - "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", - 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"Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "b1285039-6864-4428-d4ad-582e70317e3a" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 07:35:28-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.06MB/s in 6.4s \n", - "\n", - "2023-08-28 07:35:36 (699 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "2abb2944-e83e-4b7f-9995-3d7e0068139a" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Correlated Periodic Frequent patterns using EPCPGrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "dfc401cf-2a22-4843-a212-3b9c00d23813" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "f1e4fe7c-e3d3-459e-a3cd-3f63b9472881" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 8000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1.\n", - "minAllConfCount = 0.5 #minAllConfCount is specified in count. However, the users can also specify different minAllConfCount va;ue.\n", - "maxPerAllmaxPerConfCount = 4.0 #maxPerAllmaxPerConfCount is specified in count. However, the users can also specify maxPerAllmaxPerConfCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Correlated Periodic Frequent patterns using EPCPGowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.periodicCorrelatedPattern.basic import EPCPGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.EPCPGrowth(iFile=inputFile, minSup=minimumSupportCount, minAllConf=minAllConfCount, maxPerAllConf=maximumPeriodCount, maxPer=maxPerAllmaxPerConfCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('correlatedPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4f314a11-6f89-4b80-a635-4d0e110e424e" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n", - "Total No of patterns: 843\n", - "Runtime: 19.488892793655396\n", - "Memory (RSS): 604864512\n", - "Memory (USS): 557834240\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'correlatedPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "74451681-83da-4c58-beca-9d44aa9a65a2" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "330:102:4598:1:1 \n", - "729:102:6798:1:1 \n", - "102:109:4201:1:1 \n", - "199:109:5444:1:1 \n", - "856:109:4260:1:1 \n", - "62:110:7051:1:1 \n", - "191:111:4449:1:1 \n", - "191\t426:107:5431:0.963963963963964:1.2207237581478985 \n", - "426:111:4525:1:1 \n", - "146:113:3550:1:1 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "b1285039-6864-4428-d4ad-582e70317e3a" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 07:35:28-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.06MB/s in 6.4s \n", + "\n", + "2023-08-28 07:35:36 (699 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "2abb2944-e83e-4b7f-9995-3d7e0068139a" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Correlated Periodic Frequent patterns using EPCPGrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "dfc401cf-2a22-4843-a212-3b9c00d23813" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "f1e4fe7c-e3d3-459e-a3cd-3f63b9472881" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _correlatedPeriodicFrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the EPCPGrowth algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.periodicCorrelatedPattern.basic import EPCPGrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 8000\n", - "minAllConfCount = 0.5\n", - "maxPerAllmaxPerConfCount = 4.0\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of EPCPGrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'minAllConfCount', 'maximumPeriodCount', 'maxPerAllmaxPerConfCount','patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of EPCPGrowth algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.EPCPGrowth(inputFile, minSup=minSupCount, minAllConf=minAllConfCount, maxPerAllConf=maximumPeriodCount, maxPer=maxPerAllmaxPerConfCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['EPCPGrowth', minSupCount, minAllConfCount, maximumPeriodCount, maxPerAllmaxPerConfCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a02d3350-7e6c-4d9d-f164-6920a830cc1a" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n", - "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n", - "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n", - "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n", - "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 8000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1.\n", + "minAllConfCount = 0.5 #minAllConfCount is specified in count. However, the users can also specify different minAllConfCount va;ue.\n", + "maxPerAllmaxPerConfCount = 4.0 #maxPerAllmaxPerConfCount is specified in count. However, the users can also specify maxPerAllmaxPerConfCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Correlated Periodic Frequent patterns using EPCPGowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.periodicCorrelatedPattern.basic import EPCPGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.EPCPGrowth(iFile=inputFile, minSup=minimumSupportCount, minAllConf=minAllConfCount, maxPerAllConf=maximumPeriodCount, maxPer=maxPerAllmaxPerConfCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('correlatedPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4f314a11-6f89-4b80-a635-4d0e110e424e" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n", + "Total No of patterns: 843\n", + "Runtime: 19.488892793655396\n", + "Memory (RSS): 604864512\n", + "Memory (USS): 557834240\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'correlatedPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "74451681-83da-4c58-beca-9d44aa9a65a2" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "330:102:4598:1:1 \n", + "729:102:6798:1:1 \n", + "102:109:4201:1:1 \n", + "199:109:5444:1:1 \n", + "856:109:4260:1:1 \n", + "62:110:7051:1:1 \n", + "191:111:4449:1:1 \n", + "191\t426:107:5431:0.963963963963964:1.2207237581478985 \n", + "426:111:4525:1:1 \n", + "146:113:3550:1:1 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _correlatedPeriodicFrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the EPCPGrowth algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.periodicCorrelatedPattern.basic import EPCPGrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 8000\n", + "minAllConfCount = 0.5\n", + "maxPerAllmaxPerConfCount = 4.0\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of EPCPGrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'minAllConfCount', 'maximumPeriodCount', 'maxPerAllmaxPerConfCount','patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of EPCPGrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.EPCPGrowth(inputFile, minSup=minSupCount, minAllConf=minAllConfCount, maxPerAllConf=maximumPeriodCount, maxPer=maxPerAllmaxPerConfCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['EPCPGrowth', minSupCount, minAllConfCount, maximumPeriodCount, maxPerAllmaxPerConfCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a02d3350-7e6c-4d9d-f164-6920a830cc1a" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n", + "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n", + "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n", + "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n", + "Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1299755d-4913-44ac-8980-649a8b63c173" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup minAllConfCount maximumPeriodCount \\\n", + "0 EPCPGrowth 100 0.5 8000 \n", + "1 EPCPGrowth 150 0.5 8000 \n", + "2 EPCPGrowth 200 0.5 8000 \n", + "3 EPCPGrowth 250 0.5 8000 \n", + "4 EPCPGrowth 300 0.5 8000 \n", + "\n", + " maxPerAllmaxPerConfCount patterns runtime memory \n", + "0 4.0 843 21.011028 606879744 \n", + "1 4.0 808 17.925914 605224960 \n", + "2 4.0 782 18.608613 603942912 \n", + "3 4.0 753 16.907374 601681920 \n", + "4 4.0 723 16.624251 598376448 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "e03fb5ed-7f20-40aa-b160-25f1e606b56e" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "1299755d-4913-44ac-8980-649a8b63c173" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup minAllConfCount maximumPeriodCount \\\n", - "0 EPCPGrowth 100 0.5 8000 \n", - "1 EPCPGrowth 150 0.5 8000 \n", - "2 EPCPGrowth 200 0.5 8000 \n", - "3 EPCPGrowth 250 0.5 8000 \n", - "4 EPCPGrowth 300 0.5 8000 \n", - "\n", - " maxPerAllmaxPerConfCount patterns runtime memory \n", - "0 4.0 843 21.011028 606879744 \n", - "1 4.0 808 17.925914 605224960 \n", - "2 4.0 782 18.608613 603942912 \n", - "3 4.0 753 16.907374 601681920 \n", - "4 4.0 723 16.624251 598376448 \n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "e03fb5ed-7f20-40aa-b160-25f1e606b56e" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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VVftjeP6TrVy9lmp2NBERkYdKBSUP6FDzEb7oWx8fDxd2nY2n48wtnLx8zexYIiIiD40KSh7RoGxRvh0UToifB2euJtNx5hZ2nI4zO5aIiMhDoYKSh5QP8GLJoMeoEeJLfHI6z38axfI9F8yOJSIikuNUUPIY/8JuLOrfkBahgaRl2BiycDczfz2hBw2KiEi+ooKSB3m4OjHzhTq8+FhpACauPsy/vttPRqYeNCgiIvmDCkoe5WS18Eb7RxnbLhSLBRZEnaXf5zu4lpphdjQREZEHpoKSx/VpVIZZL9TB3cXKr0cu89xHkVxKTDE7loiIyANRQckHWj4axKIBYRTzcuXAhUSenrGZwzGJZscSERG5byoo+UTNEF+WvvwYZf0LcTEhhc4zI9l07IrZsURERO6LCko+EuLnyZJB4TQo40dSaga952xj8Y5zZscSERHJNhWUfMbX05XP+9anQ81gMmwGf/9mL5N+OqLLkEVEJE9RQcmH3Jyd+OC5mgx5ojwAU9cd59XFe0jL0GXIIiKSN6ig5FMWi4VXW1RiYqdqOFktLNn9Gz0/iyIhOd3saCIiInelgpLPPVevJHN618PLzZmtJ+PoNGsL5+KSzY4lIiLyl1RQCoAmFf1Z/FIYQd7uHI+9xjMfbmHv+XizY4mIiNyRCkoBERrszXeDH6NKcW+uXEvluY+2svbgJbNjiYiI3JYKSgES5OPO4pca0qSiPzfSMxnwxQ7mbTltdiwREZFbqKAUMIXdXZjdqy7d6odgM+CNZQf4z4qD2Gy6DFlERByHCkoB5OJk5b/PVOPvrSoB8OmmU7y8YBcp6ZkmJxMREfmDCkoBZbFYePnx8kzpWhNXJyurD8TQ7ZOtXLmWanY0ERERFZSCrkPNR5jfrwE+Hi7sPhtPxw+3cPLyNbNjiYhIAaeCItQv48eSl8MJ8fPgbFwyHWduYdupOLNjiYhIAaaCIgCU8/di6cuPUTPEl/jkdF74NIrley6YHUtERAooFRSxK+blxsL+DWn5aCBpmTaGLNzNzF9P6EGDIiKS61RQJAsPVyc+7F6Hvo3KADBx9WH+sXQ/GZl60KCIiOQeFRS5hZPVwph2obzZPhSLBRZuO0vfeTu4lpphdjQRESkgVFDkjno/VoaPXqiDu4uV9Ucv02VWJDEJKWbHEhGRAiBbBSUzM5MxY8ZQpkwZPDw8KFeuHOPGjbOfo5Cens6oUaOoVq0ahQoVIjg4mJ49e3LhQtaTLePi4ujevTve3t74+vrSt29frl3Tpa2OqMWjQXw1IIxiXq4cvJjIMx9u5tDFRLNjiYhIPpetgjJx4kRmzpzJ9OnTOXToEBMnTuSdd95h2rRpACQnJ7Nr1y7GjBnDrl27WLJkCUeOHOGpp57Ksp7u3btz4MAB1qxZw4oVK9iwYQMDBgzIuVFJjqoR4svSlx+jfIAXFxNS6Dwrkg1HL5sdS0RE8jGLkY1LNNq1a0dgYCCzZ8+2v9epUyc8PDyYP3/+bZfZvn079evX58yZM5QsWZJDhw4RGhrK9u3bqVu3LgCrV6+mTZs2nD9/nuDg4FvWkZqaSmrq/93hNDExkZCQEBISEvD29r7nwcqDSUhOZ8AXO4g6FYez1cJ/n6lGl3ohZscSEZE8IjExER8fn3v6/Z2tPSjh4eH8/PPPHD16FIA9e/awadMmWrdufcdlEhISsFgs+Pr6AhAZGYmvr6+9nAA0b94cq9VKVFTUbdcxfvx4fHx87K+QEP1SNIOPpwuf963PM7UeIcNm8Pdv9/L+T0d0GbKIiOS4bBWU119/na5du1K5cmVcXFyoVasWw4cPp3v37redPyUlhVGjRtGtWzd7U4qJiSEgICDLfM7Ozvj5+RETE3Pb9YwePZqEhAT769y5c9mJLTnIzdmJSV1qMOSJ8gBMW3ecV76KJjVDDxoUEZGc45ydmRcvXsyCBQv48ssvefTRR4mOjmb48OEEBwfTq1evLPOmp6fTpUsXDMNg5syZDxTSzc0NNze3B1qH5ByLxcKrLSoRUsSTfyzdx3fRF4hJTOGjF+ri4+lidjwREckHsrUHZeTIkfa9KNWqVaNHjx688sorjB8/Pst8N8vJmTNnWLNmTZbjTEFBQcTGxmaZPyMjg7i4OIKCgh5gKJLbutQLYc6L9fByc2bryTg6ztzMubhks2OJiEg+kK2CkpycjNWadREnJydstv+7y+jNcnLs2DHWrl1L0aJFs8wfFhZGfHw8O3futL+3bt06bDYbDRo0uJ8xiIkaV/Dnm0FhFPdx58Tl6zzz4Wb2nIs3O5aIiORx2Soo7du35+233+aHH37g9OnTLF26lEmTJvHMM88Af5STZ599lh07drBgwQIyMzOJiYkhJiaGtLQ0AKpUqUKrVq3o378/27ZtY/PmzURERNC1a9fbXsEjjq9ykDdLX36M0OLeXLmWxnMfR/LTgdufTyQiInIvsnWZcVJSEmPGjGHp0qXExsYSHBxMt27dGDt2LK6urpw+fZoyZcrcdtlffvmFxx9/HPjjRm0REREsX74cq9VKp06dmDp1Kl5eXveUIzuXKUnuuZaaweAFu1h/9DIWC7zRLpTej93+vwcRESl4svP7O1sFxVGooDiujEwbY74/wMJtZwHo81gZ/tm2Ck5Wi8nJRETEbA/tPigid+PsZOW/z1RlVKvKAHy2+RQvL9jJjTRdhiwiIvdOBUVynMViYdDj5ZjWrRauTlZ+PHCJbp9s5cq11LsvLCIiggqKPETtawSzoH8DfD1diD4XzzMfbubEZT0UUkRE7k4FRR6qeqX9+HZQOCX9PDkXd4OOH25h26k4s2OJiIiDU0GRh66cvxdLXw6nVklfEm6k88KnUXwf/ZvZsURExIGpoEiuKOrlxsL+DWn1aBBpmTaGLYpmxi/H9aBBERG5LRUUyTXuLk582L02/Rr9cW+Ud388wj+W7iM903aXJUVEpKBRQZFcZbVa+Fe7UP791KNYLbBw2zn6ztvBtdQMs6OJiIgDUUERU/QKL83HPeri4eLEhqOX6TwrkosJN8yOJSIiDkIFRUzTPDSQr15qSDEvNw5dTOSZGVs4dDHR7FgiIuIAVFDEVNVL+LL05XDKB3gRk5hC51mRrD962exYIiJiMhUUMV2InyffDgynYVk/rqVm0Gfudr7aftbsWCIiYiIVFHEIPp4ufN6nAR1rPUKmzWDUt/t478cjugxZRKSAUkERh+HqbOX9LjUY+mQFAKb/cpzhX0WTmqEHDYqIFDQqKOJQLBYLI/5WkXeerY6z1cL30RfoMXsb8clpZkcTEZFcpIIiDqlL3RDmvlifwm7ObDsVR6eZWzgXl2x2LBERySUqKOKwGlUoxteDwgj2cefE5es88+Fmos/Fmx1LRERygQqKOLTKQd4sHfwYocW9uXItja4fR/LjgRizY4mIyEOmgiIOL9DbncUDw2hWyZ+UdBsD5+/ks02nzI4lIiIPkQqK5Alebs580rMuzzcoiWHAWysO8u/lB8i06TJkEZH8SAVF8gxnJytvP12V11tXBmDO5tMMmr+TG2m6DFlEJL9RQZE8xWKxMLBpOaY/XwtXZys/HbxE10+2cjkp1exoIiKSg1RQJE9qVz2YBf0a4Ovpwp5z8XScuZnjsdfMjiUiIjlEBUXyrHql/Vj68mOUKurJubgbdJq5haiTV82OJSIiOUAFRfK0MsUKsWRQOLVL+pJwI50es7fxffRvZscSEZEHpIIieV5RLze+7N+Q1lWDSMu0MWxRNP9cuo+klHSzo4mIyH1SQZF8wd3FiRnP1+alJmUBWBB1lr9N2sDag5dMTiYiIvdDBUXyDavVwug2VfiyfwNKFfUkJjGFfp/vYMjC3Vy5pqt8RETyEhUUyXfCyxVj9bAmvNSkLFYLLN9zgeaT1rNk13kMQzd2ExHJC1RQJF/ycHVidJsqfD+4EVWKexOfnM6IxXvoPWc753/XU5FFRBydCorka9VK+LAs4jFGtqyEq7OV9Ucv02LyBuZuPoVNt8kXEXFYKiiS77k4WRncrDyrhjWmXukiJKdl8ubyg3T+KJLjsUlmxxMRkdtQQZECo5y/F18NCGNch0cp5OrEzjO/02bKJqb+fIy0DJvZ8URE5H+ooEiBYrVa6BFWmjUjmvJE5QDSMm1MWnOUp6ZvYs+5eLPjiYjI/6eCIgVSsK8Hs3vVZUrXmvgVcuVwTBLPfLiZt384qKcji4g4ABUUKbAsFgsdaj7Cmlea8HTNYGwGfLLxFC0/2MDm41fMjiciUqCpoEiBV9TLjQ+61mJO73oE+7hzNi6Z7p9G8fdv9pCQrNvli4iYQQVF5P9rVjmAn0Y0pWdYKQAW7zhP88nrWb3/osnJREQKHhUUkf/h5ebMWx2q8vXAMMr6F+JyUioD5+9i0PydxCalmB1PRKTAUEERuY16pf1YObQxEc3K42y1sGp/DM3fX8/i7ed0u3wRkVyggiJyB+4uTrzWshLLIhpR7REfElMy+Pu3e3lhdhRnr+p2+SIiD5MKishdhAZ7s/TlcP7RpjJuzlY2H79Kiw/W8+nGk2TqdvkiIg+FCorIPXB2sjKgSTl+HN6EsLJFSUm38Z8fDtFx5hYOxySaHU9EJN9RQRHJhtLFCvFl/wZM6FiNwu7O7DkXT7upm5j00xFSM3SDNxGRnKKCIpJNFouFrvVLsnZEU1qEBpJhM5i67jhtp25i55k4s+OJiOQL2SoomZmZjBkzhjJlyuDh4UG5cuUYN25clqsaDMNg7NixFC9eHA8PD5o3b86xY8eyrCcuLo7u3bvj7e2Nr68vffv25dq1azkzIpFcEujtzkc96vBh99oU83LleOw1np0VyZvLDnA9NcPseCIieVq2CsrEiROZOXMm06dP59ChQ0ycOJF33nmHadOm2ed55513mDp1KrNmzSIqKopChQrRsmVLUlL+7x4S3bt358CBA6xZs4YVK1awYcMGBgwYkHOjEsklFouFNtWKs3ZEU56tUwLDgLlbTtNi8gZ+PRJrdjwRkTzLYmTjpg7t2rUjMDCQ2bNn29/r1KkTHh4ezJ8/H8MwCA4O5tVXX+W1114DICEhgcDAQObOnUvXrl05dOgQoaGhbN++nbp16wKwevVq2rRpw/nz5wkODr7lc1NTU0lNTbX/nJiYSEhICAkJCXh7e9/34EVy2sZjlxm9ZB/nf78BQMdajzCmXShFCrmanExExHyJiYn4+Pjc0+/vbO1BCQ8P5+eff+bo0aMA7Nmzh02bNtG6dWsATp06RUxMDM2bN7cv4+PjQ4MGDYiMjAQgMjISX19fezkBaN68OVarlaioqNt+7vjx4/Hx8bG/QkJCshNbJNc0ruDPT680oc9jZbBYYMnu32g+aT3L91zQDd5ERLIhWwXl9ddfp2vXrlSuXBkXFxdq1arF8OHD6d69OwAxMTEABAYGZlkuMDDQPi0mJoaAgIAs052dnfHz87PP82ejR48mISHB/jp37lx2YovkKk9XZ8a2D2XJoHAqBnpx9XoaQxbupv/nO7iYcMPseCIieUK2CsrixYtZsGABX375Jbt27WLevHm89957zJs372HlA8DNzQ1vb+8sLxFHV6tkEVYMacwrzSvi4mRh7aFYWkzawIKoM9h0gzcRkb+UrYIycuRI+16UatWq0aNHD1555RXGjx8PQFBQEACXLl3KstylS5fs04KCgoiNzXryYEZGBnFxcfZ5RPILV2crw5pX4IehjalV0pek1Az+uXQ/XT/ZysnLunJNROROslVQkpOTsVqzLuLk5ITNZgOgTJkyBAUF8fPPP9unJyYmEhUVRVhYGABhYWHEx8ezc+dO+zzr1q3DZrPRoEGD+x6IiCOrGFiYbwaGM7ZdKB4uTmw7FUerKRuZ+esJMjJtZscTEXE42Soo7du35+233+aHH37g9OnTLF26lEmTJvHMM88Af1xyOXz4cP7zn/+wbNky9u3bR8+ePQkODubpp58GoEqVKrRq1Yr+/fuzbds2Nm/eTEREBF27dr3tFTwi+YWT1UKfRmX46ZUmNK5QjLQMGxNXH6bDjM3s/y3B7HgiIg4lW5cZJyUlMWbMGJYuXUpsbCzBwcF069aNsWPH4ur6x2WUhmHwxhtv8PHHHxMfH0+jRo348MMPqVixon09cXFxREREsHz5cqxWK506dWLq1Kl4eXndU47sXKYk4ogMw+DbXb8xbsVBEm6k42S1MKBJWYY9WQF3Fyez44mIPBTZ+f2drYLiKFRQJL+4nJTKm8sP8MPeiwCUKVaICR2r0aBsUZOTiYjkvId2HxQRyVn+hd2Y8XxtPu5Rh0BvN05duc5zH2/ln0v3kZSSbnY8ERHTqKCIOIAWjwbx0ytN6Va/JAALos7yt0kbWHvw0l2WFBHJn1RQRByEj4cL4ztWY2H/hpQu6klMYgr9Pt/BkIW7uXIt9e4rEBHJR1RQRBxMWLmirB7ehJealsXJamH5ngs0n7SeJbvO63b5IlJgqKCIOCB3FydGt67Cdy8/RpXi3sQnpzNi8R56z9nO+d+TzY4nIvLQqaCIOLBqJXxYFvEYI1tWwtXZyvqjl2kxeQNzN5/S7fJFJF9TQRFxcC5OVgY3K8+qYY2pV7oIyWmZvLn8IJ0/iuR4bJLZ8UREHgoVFJE8opy/F18NCGPc01Up5OrEzjO/02bKJqb+fIy0DN0uX0TyFxUUkTzEarXQo2Ep1oxoyhOVA0jLtDFpzVGemr6JPefizY4nIpJjVFBE8qBgXw9m96rLlK418SvkyuGYJJ75cDNv/3CQG2mZZscTEXlgKigieZTFYqFDzUdYO6IpT9cMxmbAJxtP0fKDDWw+fsXseCIiD0QFRSSP8yvkygddazHnxXoE+7hzNi6Z7p9G8fdv9pCQrNvli0jepIIikk80qxTATyOa0jOsFACLd5yn+eT1rN5/0eRkIiLZp4Iiko94uTnzVoeqfDMwjHL+hbiclMrA+bsYNH8nsUkpZscTEblnKigi+VDd0n78MLQxEc3K42y1sGp/DM3fX8/i7ed0u3wRyRNUUETyKXcXJ15rWYllEY2oXsKHxJQM/v7tXl6YHcXZq7pdvog4NhUUkXwuNNibJYPC+WebKri7WNl8/CotPljPpxtPkqnb5YuIg1JBESkAnJ2s9G9Slh+HNyGsbFFS0m3854dDdJy5hcMxiWbHExG5hQqKSAFSqmghvuzfgAkdq1HY3Zk95+JpN3UTk346QmqGbvAmIo5DBUWkgLFYLHStX5K1I5rSIjSQDJvB1HXHaTt1EzvPxJkdT0QEUEERKbACvd35qEcdPuxem2JebhyPvcazsyJ5c9kBrqdmmB1PRAo4FRSRAsxisdCmWnHWjmhC5zolMAyYu+U0LSZv4NcjsWbHE5ECTAVFRPD1dOXdzjX4om99ShTx4Lf4G/Ses50RX0Xz+/U0s+OJSAGkgiIido0r+PPTK03o26gMVgss2f0bzSetZ/meC7rBm4jkKhUUEcnC09WZMe1C+XZQOBUDvbh6PY0hC3fT//MdXEy4YXY8ESkgVFBE5LZqlSzCiiGNeaV5RVycLKw9FEuLSRtYEHUGm27wJiIPmQqKiNyRq7OVYc0r8MPQxtQq6UtSagb/XLqfrp9s5eTla2bHE5F8TAVFRO6qYmBhvhkYzhvtQ/F0dWLbqThaTdnIzF9PkJFpMzueiORDKigick+crBZefKwMPw5vQuMKxUjLsDFx9WE6zNjM/t8SzI4nIvmMCoqIZEuInyef96nP+51r4OPhwoELiXSYsZmJqw+Tkq7b5YtIzlBBEZFss1gsdKpTgrUjmtK2enEybQYzfz1B6ykbiTp51ex4IpIPqKCIyH3zL+zGjOdr80nPugR6u3HqynWe+3gr/1y6j6SUdLPjiUgepoIiIg/sb6GBrBnRlG71SwKwIOosf5u0gbUHL5mcTETyKhUUEckR3u4ujO9YjUUDGlK6qCcxiSn0+3wHQxbu5sq1VLPjiUgeo4IiIjmqYdmirB7ehJealsXJamH5ngs0n7SeJbvO63b5InLPVFBEJMe5uzgxunUVvh/8GKHFvYlPTmfE4j30nrOd878nmx1PRPIAFRQReWiqPuLD9xGPMbJlJVydraw/epkWkzcwd/Mp3S5fRP6SCoqIPFQuTlYGNyvPqmGNqV/aj+S0TN5cfpDOH0VyPDbJ7Hgi4qBUUEQkV5Tz92LRgIb85+mqeLk5s/PM77SZsompPx8jLUO3yxeRrFRQRCTXWK0WXmhYip9eacKTlQNIy7Qxac1Rnpq+iT3n4s2OJyIORAVFRHJdsK8Hn/aqy9RutfAr5MrhmCSe+XAzb/9wkBtpul2+iKigiIhJLBYLT9UIZu2IpjxT6xFsBnyy8RQtP9jAluNXzI4nIiZTQRERU/kVcmXyczWZ82I9gn3cORuXzPOfRvGfFQdJzdDeFJGCSgVFRBxCs0oB/DSiKS80/ON2+Z9uOkXHD7dw4vI1k5OJiBlUUETEYXi5OfOfp6vxac+6FPF04cCFRNpN3cSibWd1F1qRAiZbBaV06dJYLJZbXoMHDwYgJiaGHj16EBQURKFChahduzbffvttlnXExcXRvXt3vL298fX1pW/fvly7pn8hicj/aR4ayOrhTWhUvhg30jN5fck+Bn+5i4RkPSFZpKDIVkHZvn07Fy9etL/WrFkDQOfOnQHo2bMnR44cYdmyZezbt4+OHTvSpUsXdu/ebV9H9+7dOXDgAGvWrGHFihVs2LCBAQMG5OCQRCQ/CPR25/M+9RndujLOVgsr98XQesoGtp2KMzuaiOQCi/EA+02HDx/OihUrOHbsGBaLBS8vL2bOnEmPHj3s8xQtWpSJEyfSr18/Dh06RGhoKNu3b6du3boArF69mjZt2nD+/HmCg4Nv+zmpqamkpv7f01ATExMJCQkhISEBb2/v+40vInnE3vPxDFsUzakr17FaIKJZeYY+WQFnJx2lFslLEhMT8fHxuaff3/f97U5LS2P+/Pn06dMHi8UCQHh4OF999RVxcXHYbDYWLVpESkoKjz/+OACRkZH4+vraywlA8+bNsVqtREVF3fGzxo8fj4+Pj/0VEhJyv7FFJA+qXsKXFUMa0blOCWwGTF13nC4fRXIuTg8eFMmv7rugfPfdd8THx9O7d2/7e4sXLyY9PZ2iRYvi5ubGSy+9xNKlSylfvjzwxzkqAQEBWdbj7OyMn58fMTExd/ys0aNHk5CQYH+dO3fufmOLSB5VyM2ZdzvXYFq3WhR2d2bX2XjaTNnI99G/mR1NRB6C+y4os2fPpnXr1lkOy4wZM4b4+HjWrl3Ljh07GDFiBF26dGHfvn0PFNLNzQ1vb+8sLxEpmNrXCGbl0MbULVWEpNQMhi2K5tXFe7iWmmF2NBHJQc73s9CZM2dYu3YtS5Yssb934sQJpk+fzv79+3n00UcBqFGjBhs3bmTGjBnMmjWLoKAgYmNjs6wrIyODuLg4goKCHmAYIlKQhPh5smhAQ6b/cpypPx/j213n2XEmjqlda1EjxNfseCKSA+5rD8qcOXMICAigbdu29veSk/84Fmy1Zl2lk5MTNtsfTyoNCwsjPj6enTt32qevW7cOm81GgwYN7ieKiBRQzk5WhjevyFcvhfGIrwdnribTaeYWZv56AptN90wRyeuyXVBsNhtz5syhV69eODv/3w6YypUrU758eV566SW2bdvGiRMneP/991mzZg1PP/00AFWqVKFVq1b079+fbdu2sXnzZiIiIujatesdr+AREfkr9Ur7sXJYY9pWL06GzWDi6sO8MDuKmIQUs6OJyAPIdkFZu3YtZ8+epU+fPlned3FxYeXKlfj7+9O+fXuqV6/O559/zrx582jTpo19vgULFlC5cmWefPJJ2rRpQ6NGjfj4448ffCQiUmD5eLgwvVst3nm2Op6uTmw5cZXWUzbw04E7n3wvIo7tge6DYpbsXEctIgXLycvXGLYomn2/JQDQo2Ep/tm2Cu4uTiYnE5FcuQ+KiIgjKuvvxbeDwnmpSVkAvth6hvbTNnE4JtHkZCKSHSooIpLvuDpbGd2mCl/0rY9/YTeOxV7jqembmbfltB46KJJHqKCISL7VuII/q4c15snKAaRl2Hhj2QH6ztvB1Wupd19YREylgiIi+VpRLzc+7VWXfz/1KK7OVtYdjqXVlI1sPHbZ7Ggi8hdUUEQk37NYLPQKL82yiMeoGOjF5aRUeszexn9XHiItw2Z2PBG5DRUUESkwKgd5syyiET0algLg4w0n6ThzMycvXzM5mYj8mQqKiBQo7i5OjHu6Kh/3qIOvpwv7f0uk7dRNLN5+TifQijgQFRQRKZBaPBrE6mFNCC9XlBvpmfz9271ELNxNwo10s6OJCCooIlKABfm4M79vA0a1qoyz1cIPey/SZspGtp+OMzuaSIGngiIiBZrVamHQ4+X4dlA4pYp68lv8DZ77KJLJa46SkakTaEXMooIiIgLUCPHlh6GN6VS7BDYDpvx8jK4fb+X878lmRxMpkFRQRET+Py83Z97vUoMpXWtS2M2ZHWd+p/WUjSzfc8HsaCIFjgqKiMifdKj5CCuHNaZ2SV+SUjIYsnA3r329h+upGWZHEykwVFBERG4jxM+TxS+FMfSJ8lgt8M3O87Sbtom95+PNjiZSIKigiIjcgbOTlREtKrGwf0OCfdw5deU6HT/cwqz1J7DZdM8UkYdJBUVE5C4alC3KqmFNaFMtiAybwYRVh+n52TZiE1PMjiaSb6mgiIjcAx9PF2Y8X5uJnarh4eLEpuNXaDVlI2sPXjI7mki+pIIiInKPLBYLz9UryfIhjXg02Ju462n0+3wHY7/fT0p6ptnxRPIVFRQRkWwqH+DFkpfD6d+4DACfR56hw/TNHIlJMjmZSP6hgiIich/cnJ34Z9tQ5vWpTzEvN45cSqL99E18HnlaDx0UyQEqKCIiD6BpRX9WD29Ms0r+pGXYGPv9Afp/voO462lmRxPJ01RQREQeUDEvNz7rXY832ofi6mRl7aFYWn2wgc3Hr5gdTSTPUkEREckBFouFFx8rw3eDH6N8gBexSam8MDuK8asOkZahhw6KZJcKiohIDgoN9mZ5RCO6NyiJYcBH60/y7KwtnLpy3exoInmKCoqISA7zcHXi7WeqMeuFOvh6urD3fAJtp27k6x3ndAKtyD1SQREReUhaVQ1i1bDGNCzrR3JaJiO/2cvQRdEk3Eg3O5qIw1NBERF5iIr7eLCgX0NGtqyEk9XC8j0XaDNlIztOx5kdTcShqaCIiDxkTlYLg5uV55uBYZT08+S3+Bt0+SiSKWuPkZGpE2hFbkcFRUQkl9QqWYQfhjaiY61HsBkwee1Run2yld/ib5gdTcThqKCIiOSiwu4uTHquJh88VxMvN2e2n/6dVh9s4Ie9F82OJuJQVFBEREzwdK1HWDm0MTVDfElKyWDwl7v4+zd7uJ6aYXY0EYeggiIiYpKSRT35emAYEc3KY7HA4h3naT9tE/t/SzA7mojpVFBEREzk4mTltZaVWNi/IcV93Dl55TrPfLiZTzacxGbTPVOk4FJBERFxAA3LFmXVsMa0ejSI9EyDt1ceotecbcQmppgdTcQUKigiIg7C19OVmS/UZnzHari7WNl47Aqtpmxk3eFLZkcTyXUqKCIiDsRisdCtfklWDGlEleLexF1Po8/cHby57AAp6ZlmxxPJNSooIiIOqHxAYZa+HE6fx8oAMHfLaZ6esZmjl5JMTiaSO1RQREQclLuLE2PbhzLnxXoU83LlcEwS7adt4outZ/TQQcn3VFBERBxcs0oBrBrWhKYV/UnNsDHmu/0M+GIncdfTzI4m8tCooIiI5AH+hd2Y07seY9qF4upkZc3BS7SesoEtx6+YHU3koVBBERHJI6xWC30blWHJy+GU9S/EpcRUus+OYuLqw6TroYOSz6igiIjkMVUf8WHFkEZ0q18Sw4CZv57g2ZlbOH3lutnRRHKMCoqISB7k6erM+I7VmNm9Nj4eLuw5n0DbqRv5dud5nUAr+YIKiohIHta6WnFWDWtMgzJ+XE/L5NWv9zD8q2gSU9LNjibyQFRQRETyuGBfD77s35DXWlTEyWrh++gLtJmykZ1nfjc7msh9y1ZBKV26NBaL5ZbX4MGD7fNERkbyxBNPUKhQIby9vWnSpAk3btywT4+Li6N79+54e3vj6+tL3759uXbtWs6NSESkAHKyWoh4ogKLXwqjRBEPzv9+gy4fRTLt52Nk6qGDkgdlq6Bs376dixcv2l9r1qwBoHPnzsAf5aRVq1a0aNGCbdu2sX37diIiIrBa/+9junfvzoEDB1izZg0rVqxgw4YNDBgwIAeHJCJScNUpVYSVwxrToWYwmTaD99ccpdvHW/kt/sbdFxZxIBbjAc6mGj58OCtWrODYsWNYLBYaNmzI3/72N8aNG3fb+Q8dOkRoaCjbt2+nbt26AKxevZo2bdpw/vx5goODb7tcamoqqamp9p8TExMJCQkhISEBb2/v+40vIpKvLdl1njHf7ed6Wibe7s5M6FSdNtWKmx1LCrDExER8fHzu6ff3fZ+DkpaWxvz58+nTpw8Wi4XY2FiioqIICAggPDycwMBAmjZtyqZNm+zLREZG4uvray8nAM2bN8dqtRIVFXXHzxo/fjw+Pj72V0hIyP3GFhEpMDrWLsHKYY2pEeJLYkoGLy/Yxevf7iU5LcPsaCJ3dd8F5bvvviM+Pp7evXsDcPLkSQDefPNN+vfvz+rVq6lduzZPPvkkx44dAyAmJoaAgIAs63F2dsbPz4+YmJg7ftbo0aNJSEiwv86dO3e/sUVECpRSRQvxzcAwXn68HBYLLNp+jnbTNrH/twSzo4n8pfsuKLNnz6Z169b2wzI22x93MXzppZd48cUXqVWrFpMnT6ZSpUp89tlnDxTSzc0Nb2/vLC8REbk3Lk5W/t6qMgv6NiDQ242Tl6/T8cMtfLrxJDadQCsO6r4KypkzZ1i7di39+vWzv1e8+B/HNUNDQ7PMW6VKFc6ePQtAUFAQsbGxWaZnZGQQFxdHUFDQ/UQREZF7FF6+GKuHNaFFaCBpmTb+88Mhes/dTmxSitnRRG5xXwVlzpw5BAQE0LZtW/t7pUuXJjg4mCNHjmSZ9+jRo5QqVQqAsLAw4uPj2blzp336unXrsNlsNGjQ4H6iiIhINhQp5MpHPerwn6er4uZsZcPRy7SZspFfDsfefWGRXJTtgmKz2ZgzZw69evXC2dnZ/r7FYmHkyJFMnTqVb775huPHjzNmzBgOHz5M3759gT/2prRq1Yr+/fuzbds2Nm/eTEREBF27dr3jFTwiIpKzLBYLLzQsxYohjagcVJgr19J4ce52/r38ACnpmWbHEwHu4zLjn376iZYtW3LkyBEqVqx4y/QJEyYwY8YM4uLiqFGjBu+88w6NGjWyT4+LiyMiIoLly5djtVrp1KkTU6dOxcvL654zZOcyJRERubOU9EwmrDrM3C2nAahS3Jtp3WpSPqCwucEkX8rO7+8Hug+KWVRQRERy1rrDlxj59V6uXk/D3cXKmHahPF+/JBaLxexoko/kyn1QREQk/3iiciCrhjWmcYVipKTb+OfS/Qycv5Pfr6eZHU0KKBUUEREBIMDbnXkv1udfbavg4mThxwOXaD1lI1tOXDE7mhRAKigiImJntVro17gsS19+jLLFChGTmEL3T6N498fDpGfazI4nBYgKioiI3KLqIz4sH9KI5+qGYBgw45cTdJ4VydmryWZHkwJCBUVERG6rkJszE5+tzozna+Pt7kz0uXjaTN3I0t3nzY4mBYAKioiI/KW21YuzangT6pUuwrXUDF75ag/DF+0mKSXd7GiSj6mgiIjIXT3i68HC/g0Z8beKOFktfBd9gTZTN7Lr7O9mR5N8SgVFRETuibOTlaFPVmDxSw15xNeDc3E36DwrkunrjpGphw5KDlNBERGRbKlTyo+VwxrTvkYwmTaD9346yvOfbOVC/A2zo0k+ooIiIiLZ5uPhwtSuNXmvcw08XZ2IOhVH6ykbWb3/otnRJJ9QQRERkftisVh4tk4JfhjamOolfEi4kc7A+bsYvWQfN9L00EF5MCooIiLyQMoUK8Q3A8MZ2LQcFgss3HaW9tM3cSQmyexokoepoIiIyANzdbbyeuvKzO/bgIDCbhyPvUaHGZv4avtZ8uAzacUBqKCIiEiOeax8MVb+z0MHR327j1e+iuZaaobZ0SSPUUEREZEcVczLjXkv1mdky0r2e6Y8NW0TBy8kmh1N8hAVFBERyXFWq4XBzcqzaEBDgrzdOXnlOk9/uJkFUWd0yEfuiQqKiIg8NPVK/3HPlCcqB5CWYeOfS/cTsVC3yZe7U0EREZGHyq+QK5/2rMs/2lTG2Wrhh70XaTdtE/vOJ5gdTRyYCoqIiDx0VquFAU3KsXhgGI/4enDmajKdZm5h7uZTOuQjt6WCIiIiuaZ2ySKsHNqYv4UGkpZp483lBxk0fxcJN3TIR7JSQRERkVzl4+nCxz3qMLZdKC5OFlYfiKHt1I1En4s3O5o4EBUUERHJdRaLhT6NyvDNwHBC/Dw4//sNnp25hU83ntQhHwFUUERExEQ1Qnz5YWhj2lQLIsNm8J8fDtH/8x3EJ6eZHU1MpoIiIiKm8nZ3YcbztRn3dFVcna2sPRRLmykb2XkmzuxoYiIVFBERMZ3FYqFHw1IsfTmcMsUKcSEhhS4fbWXW+hPYbDrkUxCpoIiIiMN4NNiH5UMa8VSNYDJtBhNWHabPvO1cvZZqdjTJZSooIiLiULzcnJnStSbjO1bDzdnKr0cu02bqRqJOXjU7muQiFRQREXE4FouFbvVL8n3EY5TzL8SlxFS6fbKV6euO6ZBPAaGCIiIiDqtykDfLIhrRsfYj2Ax476ej9JqzjctJOuST36mgiIiIQyvk5sykLjV599nqeLg4sfHYFdpM3ciW41fMjiYPkQqKiIjkCZ3rhrAs4jEqBnpxOSmV7rOjmLzmKJk65JMvqaCIiEieUSGwMN8PbsRzdUMwDJjy8zG6f7qVS4kpZkeTHKaCIiIieYqHqxMTn63OB8/VxNPVia0n42gzZSMbjl42O5rkIBUUERHJk56u9QgrhjSiSnFvrl5Po9ecbbz742EyMm1mR5McoIIiIiJ5Vll/L5a+HE73BiUxDJjxywm6fbKViwk3zI4mD0gFRURE8jR3FyfefqYa05+vhZebM9tP/06bKRv55XCs2dHkAaigiIhIvtCuejArhjSi6iPe/J6czotztzN+5SHSdcgnT1JBERGRfKN0sUJ8Oyic3uGlAfhow0m6fBTJ+d+TzQ0m2aaCIiIi+YqbsxNvPvUos16oTWF3Z3afjaft1E38dCDG7GiSDSooIiKSL7WqWpyVQxtTo4QPCTfSGfDFTt5afpC0DB3yyQtUUEREJN8K8fPk64Hh9GtUBoDPNp+i86wtnIvTIR9Hp4IiIiL5mquzlX+1C+XTnnXx8XBhz/kE2kzdyKp9F82OJn9BBUVERAqE5qGBrBzWmDqlipCUksGgBbsY+/1+UtIzzY4mt6GCIiIiBcYjvh4sGtCQgU3LAfB55Bk6zdzC6SvXTU4mf6aCIiIiBYqLk5XXW1dmzov18CvkyoELibSbtolley6YHU3+R7YKSunSpbFYLLe8Bg8enGU+wzBo3bo1FouF7777Lsu0s2fP0rZtWzw9PQkICGDkyJFkZGQ88EBERESyo1mlAFYObUz90n5cS81g6MLdjF6yT4d8HES2Csr27du5ePGi/bVmzRoAOnfunGW+Dz74AIvFcsvymZmZtG3blrS0NLZs2cK8efOYO3cuY8eOfYAhiIiI3J8gH3e+7N+AIU+Ux2KBhdvO8vSMzRyPvWZ2tALPYhiGcb8LDx8+nBUrVnDs2DF7IYmOjqZdu3bs2LGD4sWLs3TpUp5++mkAVq1aRbt27bhw4QKBgYEAzJo1i1GjRnH58mVcXV1v+zmpqamkpqbaf05MTCQkJISEhAS8vb3vN76IiIjdxmOXeeWraK5cS8PT1Yn/PF2VjrVLmB0rX0lMTMTHx+eefn/f9zkoaWlpzJ8/nz59+tjLSXJyMs8//zwzZswgKCjolmUiIyOpVq2avZwAtGzZksTERA4cOHDHzxo/fjw+Pj72V0hIyP3GFhERua3GFfxZObQxYWWLkpyWyYjFexj59R6S03Qaghnuu6B89913xMfH07t3b/t7r7zyCuHh4XTo0OG2y8TExGQpJ4D955iYO9+CePTo0SQkJNhf586du9/YIiIidxTg7c78fg14pXlFrBb4eud5OkzfzNFLSWZHK3Cc73fB2bNn07p1a4KDgwFYtmwZ69atY/fu3TkW7iY3Nzfc3NxyfL0iIiJ/5mS1MKx5BeqX8WPYot0ci73GU9M38VaHqnSuU+K251hKzruvPShnzpxh7dq19OvXz/7eunXrOHHiBL6+vjg7O+Ps/Ef36dSpE48//jgAQUFBXLp0Kcu6bv58u0NCIiIiZgkrV5SVwxrTuEIxUtJt/P2bvYxYvIfrqTrkkxvuq6DMmTOHgIAA2rZta3/v9ddfZ+/evURHR9tfAJMnT2bOnDkAhIWFsW/fPmJjY+3LrVmzBm9vb0JDQx9gGCIiIjmvmJcb816sz8iWlXCyWli6+zfaT9/EoYuJZkfL97J9iMdmszFnzhx69epl30sCf+wBud1ekJIlS1KmzB8PaWrRogWhoaH06NGDd955h5iYGP71r38xePBgHcIRERGHZLVaGNysPPXL+DHky92cvHydDjM282b7R+lWP0SHfB6SbO9BWbt2LWfPnqVPnz7Z/jAnJydWrFiBk5MTYWFhvPDCC/Ts2ZO33nor2+sSERHJTfVK+7FyWGOaVfInLcPGP5buY+iiaJJS0s2Oli890H1QzJKd66hFRERyks1m8Ommk7yz+ggZNoPSRT2Z/nxtqj7iY3Y0h5cr90EREREpiKxWCwOalOOrl8J4xNeD01eT6fjhFj6PPE0e/De/w1JBERERuQ91ShXhh6GN+FtoIGmZNsZ+f4CXF+wi4YYO+eQEFRQREZH75Ovpysc96jC2XSguThZW7Y+h3bSN7DkXb3a0PE8FRURE5AFYLBb6NCrDNwPDCfHz4FzcDZ6dtYXZm07pkM8DUEERERHJATVCfFkxpDGtqwaRnmkwbsVB+n++k/jkNLOj5UkqKCIiIjnEx8OFD7vXZlyHR3F1srL20CXaTt3EzjO/mx0tz1FBERERyUEWi4UeYaVZ8nI4pYt68lv8DZ77KJKP1p/AZtMhn3ulgiIiIvIQVH3Eh+VDGtG+RjAZNoPxqw7Td9524q7rkM+9UEERERF5SAq7uzC1a03Gd6yGm7OVX45cps2UjWw7FWd2NIengiIiIvIQWSwWutUvyXeDH6OsfyFiElPo9slWZvxyXId8/oIKioiISC6oUtyb5RGN6FjrETJtBu/+eIRec7Zx5Vqq2dEckgqKiIhILink5syk52ry7rPVcXexsvHYFVpP2ciWE1fMjuZwVFBERERyWee6ISyPaETFQC8uJ6XywqdRfLD2KJk65GOngiIiImKCCoGF+X5wI7rULYHNgA/WHqPH7ChiE1PMjuYQVFBERERM4uHqxDvP1mDyczXwdHViy4mrtJm6kY3HLpsdzXQqKCIiIiZ7plYJlg9pROWgwly5lkbPz7bx3o9HyMi0mR3NNCooIiIiDqCcvxffDX6M5xuUxDBg+i/Hef6TKGISCuYhHxUUERERB+Hu4sR/n6nGtG618HJzZtvpONpM3cgvR2LNjpbrVFBEREQcTPsawawY0oiqj3gTdz2NF+dsZ/yqQ6QXoEM+KigiIiIOqHSxQnw7KJze4aUB+Gj9SZ77KJLf4m+YGyyXqKCIiIg4KDdnJ9586lFmvVCbwu7O7DobT5spG1lz8JLZ0R46FRQREREH16pqcVYObUyNEj4k3Ein/+c7GLfiIGkZ+feQjwqKiIhIHhDi58nXA8Pp26gMALM3naLzR5Gci0s2OdnDoYIiIiKSR7g6WxnTLpRPetbFx8OFPefiaTN1I6v3XzQ7Wo5TQREREclj/hYayMphjald0peklAwGzt/FG9/vJzUj0+xoOUYFRUREJA96xNeDr14K46WmZQGYF3mGTjO3cPrKdZOT5QwVFBERkTzKxcnK6NZVmNO7HkU8Xdj/WyLtpm1ixd4LZkd7YCooIiIieVyzygGsHNaY+qX9uJaaQcSXu/nH0n2kpOfdQz4qKCIiIvlAcR8PvuzfgCFPlMdigS+jzvL0jM2cuHzN7Gj3RQVFREQkn3B2svJqi0p83qc+xbxcORyTRPtpm/hu929mR8s2FRQREZF8pnEFf1YObUxY2aIkp2Uy/KtoRn2zlxtpeeeQjwqKiIhIPhTg7c78fg0Y3rwCFgt8teMcHWZs4tilJLOj3RMVFBERkXzKyWphePOKLOjXAP/Cbhy9dI320zfx9Y5zZke7KxUUERGRfC68XDFWDWtM4wrFSEm3MfKbvYxYHM311Ayzo92RCoqIiEgBUMzLjXkv1mdky0pYLbBk1288NX0Th2MSzY52WyooIiIiBYTVamFws/IsGhBGkLc7Jy5fp8P0zSzcdhbDMMyOl4UKioiISAFTv4wfK4c1plklf1IzbIxeso+hi6JJSkk3O5qdCoqIiEgB5FfIldm96jG6dWWcrRaW77lA+2mb2P9bgtnRABUUERGRAstqtfBS03J89VIYj/h6cPpqMh0/3MIXkadNP+SjgiIiIlLA1SlVhB+GNqJ5lUDSMm2M+f4AIxbvMbWkqKCIiIgIvp6ufNKzDmPaheLiZKFWSV8sFotpeZxN+2QRERFxKBaLhb6NyvB4JX/KFitkahYVFBEREcminL+X2RF0iEdEREQcjwqKiIiIOJxsFZTSpUtjsVhueQ0ePJi4uDiGDBlCpUqV8PDwoGTJkgwdOpSEhKzXU589e5a2bdvi6elJQEAAI0eOJCPDcZ8FICIiIrkvW+egbN++nczMTPvP+/fv529/+xudO3fmwoULXLhwgffee4/Q0FDOnDnDwIEDuXDhAt988w0AmZmZtG3blqCgILZs2cLFixfp2bMnLi4u/Pe//83ZkYmIiEieZTEe4CLn4cOHs2LFCo4dO3bbS5G+/vprXnjhBa5fv46zszOrVq2iXbt2XLhwgcDAQABmzZrFqFGjuHz5Mq6urrf9nNTUVFJTU+0/JyYmEhISQkJCAt7e3vcbX0RERHJRYmIiPj4+9/T7+77PQUlLS2P+/Pn06dPnjtdJ3wzg7PzHjprIyEiqVatmLycALVu2JDExkQMHDtzxs8aPH4+Pj4/9FRIScr+xRUREJA+474Ly3XffER8fT+/evW87/cqVK4wbN44BAwbY34uJiclSTgD7zzExMXf8rNGjR5OQkGB/nTt37n5ji4iISB5w3/dBmT17Nq1btyY4OPiWaYmJibRt25bQ0FDefPPNB8kHgJubG25ubg+8HhEREckb7msPypkzZ1i7di39+vW7ZVpSUhKtWrWicOHCLF26FBcXF/u0oKAgLl26lGX+mz8HBQXdTxQRERHJh+6roMyZM4eAgADatm2b5f3ExERatGiBq6sry5Ytw93dPcv0sLAw9u3bR2xsrP29NWvW4O3tTWho6P1EERERkXwo2wXFZrMxZ84cevXqZT/5Ff6vnFy/fp3Zs2eTmJhITEwMMTEx9kuTW7RoQWhoKD169GDPnj38+OOP/Otf/2Lw4ME6hCMiIiJ22T4HZe3atZw9e5Y+ffpkeX/Xrl1ERUUBUL58+SzTTp06RenSpXFycmLFihUMGjSIsLAwChUqRK9evXjrrbceYAgiIiKS3zzQfVDMkp3rqEVERMQxZOf3d558mvHNTpWYmGhyEhEREblXN39v38u+kTxZUJKSkgB0wzYREZE8KCkpCR8fn7+cJ08e4rHZbFy4cIHChQvf8S629+vmbfTPnTuXLw8faXx5X34fo8aX9+X3Meb38cHDG6NhGCQlJREcHIzV+tfX6eTJPShWq5USJUo81M/w9vbOt//hgcaXH+T3MWp8eV9+H2N+Hx88nDHebc/JTfd9q3sRERGRh0UFRURERByOCsqfuLm58cYbb+TbG8dpfHlffh+jxpf35fcx5vfxgWOMMU+eJCsiIiL5m/agiIiIiMNRQRERERGHo4IiIiIiDkcFRURERBxOgSgoGzZsoH379gQHB2OxWPjuu++yTDcMg7Fjx1K8eHE8PDxo3rw5x44dyzJPXFwc3bt3x9vbG19fX/r27cu1a9dycRR39lfjS09PZ9SoUVSrVo1ChQoRHBxMz549uXDhQpZ1lC5dGovFkuU1YcKEXB7Jnd1tG/bu3fuW/K1atcoyT17dhsAtY7v5evfdd+3zOPI2HD9+PPXq1aNw4cIEBATw9NNPc+TIkSzzpKSkMHjwYIoWLYqXlxedOnXi0qVLWeY5e/Ysbdu2xdPTk4CAAEaOHElGRkZuDuW27ja+uLg4hgwZQqVKlfDw8KBkyZIMHTqUhISELOu53TZetGhRbg/ntu5lGz7++OO35B84cGCWefLqNjx9+vQdv4dff/21fT5H3YYzZ86kevXq9huvhYWFsWrVKvt0R/z+FYiCcv36dWrUqMGMGTNuO/2dd95h6tSpzJo1i6ioKAoVKkTLli1JSUmxz9O9e3cOHDjAmjVrWLFiBRs2bGDAgAG5NYS/9FfjS05OZteuXYwZM4Zdu3axZMkSjhw5wlNPPXXLvG+99RYXL160v4YMGZIb8e/J3bYhQKtWrbLkX7hwYZbpeXUbAlnGdfHiRT777DMsFgudOnXKMp+jbsP169czePBgtm7dypo1a0hPT6dFixZcv37dPs8rr7zC8uXL+frrr1m/fj0XLlygY8eO9umZmZm0bduWtLQ0tmzZwrx585g7dy5jx441Y0hZ3G18Fy5c4MKFC7z33nvs37+fuXPnsnr1avr27XvLuubMmZNlGz799NO5PJrbu5dtCNC/f/8s+d955x37tLy8DUNCQm75Hv773//Gy8uL1q1bZ1mXI27DEiVKMGHCBHbu3MmOHTt44okn6NChAwcOHAAc9PtnFDCAsXTpUvvPNpvNCAoKMt599137e/Hx8Yabm5uxcOFCwzAM4+DBgwZgbN++3T7PqlWrDIvFYvz222+5lv1e/Hl8t7Nt2zYDMM6cOWN/r1SpUsbkyZMfbrgccrsx9urVy+jQocMdl8lv27BDhw7GE088keW9vLQNY2NjDcBYv369YRh/fOdcXFyMr7/+2j7PoUOHDMCIjIw0DMMwVq5caVitViMmJsY+z8yZMw1vb28jNTU1dwdwF38e3+0sXrzYcHV1NdLT0+3v3cu2dxS3G2PTpk2NYcOG3XGZ/LYNa9asafTp0yfLe3lpGxYpUsT49NNPHfb7VyD2oPyVU6dOERMTQ/Pmze3v+fj40KBBAyIjIwGIjIzE19eXunXr2udp3rw5VquVqKioXM/8oBISErBYLPj6+mZ5f8KECRQtWpRatWrx7rvvOsRu1+z49ddfCQgIoFKlSgwaNIirV6/ap+WnbXjp0iV++OGH2/7rO69sw5uHNvz8/ADYuXMn6enpWb6HlStXpmTJklm+h9WqVSMwMNA+T8uWLUlMTLT/K9BR/Hl8d5rH29sbZ+esj0QbPHgwxYoVo379+nz22Wf39Fh6M9xpjAsWLKBYsWJUrVqV0aNHk5ycbJ+Wn7bhzp07iY6Ovu330NG3YWZmJosWLeL69euEhYU57PcvTz4sMCfFxMQAZPlLv/nzzWkxMTEEBARkme7s7Iyfn599nrwiJSWFUaNG0a1btywPgBo6dCi1a9fGz8+PLVu2MHr0aC5evMikSZNMTHvvWrVqRceOHSlTpgwnTpzgH//4B61btyYyMhInJ6d8tQ3nzZtH4cKFs+x+hbyzDW02G8OHD+exxx6jatWqwB/fMVdX11tK85+/h7f7nt6c5ihuN74/u3LlCuPGjbvlEONbb73FE088gaenJz/99BMvv/wy165dY+jQobkR/Z7daYzPP/88pUqVIjg4mL179zJq1CiOHDnCkiVLgPy1DWfPnk2VKlUIDw/P8r4jb8N9+/YRFhZGSkoKXl5eLF26lNDQUKKjox3y+1fgC0pBkp6eTpcuXTAMg5kzZ2aZNmLECPufq1evjqurKy+99BLjx4/PE7dz7tq1q/3P1apVo3r16pQrV45ff/2VJ5980sRkOe+zzz6je/fuuLu7Z3k/r2zDwYMHs3//fjZt2mR2lIfibuNLTEykbdu2hIaG8uabb2aZNmbMGPufa9WqxfXr13n33Xcd4pfb/7rTGP+3cFWrVo3ixYvz5JNPcuLECcqVK5fbMe/b3bbhjRs3+PLLL7Nsr5sceRtWqlSJ6OhoEhIS+Oabb+jVqxfr1683O9YdFfhDPEFBQQC3nK186dIl+7SgoCBiY2OzTM/IyCAuLs4+j6O7WU7OnDnDmjVr7vr47AYNGpCRkcHp06dzJ2AOK1u2LMWKFeP48eNA/tiGABs3buTIkSP069fvrvM64jaMiIhgxYoV/PLLL5QoUcL+flBQEGlpacTHx2eZ/8/fw9t9T29OcwR3Gt9NSUlJtGrVisKFC7N06VJcXFz+cn0NGjTg/PnzpKamPqzI2Xa3Mf6vBg0aAGT5Hub1bQjwzTffkJycTM+ePe+6Pkfahq6urpQvX546deowfvx4atSowZQpUxz2+1fgC0qZMmUICgri559/tr+XmJhIVFQUYWFhAISFhREfH8/OnTvt86xbtw6bzWb/Ajqym+Xk2LFjrF27lqJFi951mejoaKxW6y2HRfKK8+fPc/XqVYoXLw7k/W140+zZs6lTpw41atS467yOtA0NwyAiIoKlS5eybt06ypQpk2V6nTp1cHFxyfI9PHLkCGfPns3yPdy3b1+WonmzbIeGhubOQO7gbuODP/6/0qJFC1xdXVm2bNkte8BuJzo6miJFijjEHrB7GeOfRUdHA2T5HublbXjT7Nmzeeqpp/D397/reh1pG/6ZzWYjNTXVcb9/D+XUWweTlJRk7N6929i9e7cBGJMmTTJ2795tv4plwoQJhq+vr/H9998be/fuNTp06GCUKVPGuHHjhn0drVq1MmrVqmVERUUZmzZtMipUqGB069bNrCFl8VfjS0tLM5566imjRIkSRnR0tHHx4kX76+aZ11u2bDEmT55sREdHGydOnDDmz59v+Pv7Gz179jR5ZP/nr8aYlJRkvPbaa0ZkZKRx6tQpY+3atUbt2rWNChUqGCkpKfZ15NVteFNCQoLh6elpzJw585blHX0bDho0yPDx8TF+/fXXLP8NJicn2+cZOHCgUbJkSWPdunXGjh07jLCwMCMsLMw+PSMjw6hatarRokULIzo62li9erXh7+9vjB492owhZXG38SUkJBgNGjQwqlWrZhw/fjzLPBkZGYZhGMayZcuMTz75xNi3b59x7Ngx48MPPzQ8PT2NsWPHmjk0u7uN8fjx48Zbb71l7Nixwzh16pTx/fffG2XLljWaNGliX0de3oY3HTt2zLBYLMaqVatuWYcjb8PXX3/dWL9+vXHq1Clj7969xuuvv25YLBbjp59+MgzDMb9/BaKg/PLLLwZwy6tXr16GYfxxqfGYMWOMwMBAw83NzXjyySeNI0eOZFnH1atXjW7duhleXl6Gt7e38eKLLxpJSUkmjOZWfzW+U6dO3XYaYPzyyy+GYRjGzp07jQYNGhg+Pj6Gu7u7UaVKFeO///1vll/uZvurMSYnJxstWrQw/P39DRcXF6NUqVJG//79s1wOZxh5dxve9NFHHxkeHh5GfHz8Lcs7+ja803+Dc+bMsc9z48YN4+WXXzaKFClieHp6Gs8884xx8eLFLOs5ffq00bp1a8PDw8MoVqyY8eqrr2a5TNcsdxvfnbYvYJw6dcowjD8ue69Zs6bh5eVlFCpUyKhRo4Yxa9YsIzMz07yB/Y+7jfHs2bNGkyZNDD8/P8PNzc0oX768MXLkSCMhISHLevLqNrxp9OjRRkhIyG23iyNvwz59+hilSpUyXF1dDX9/f+PJJ5+0lxPDcMzvn8UwHOz6JxERESnwCvw5KCIiIuJ4VFBERETE4aigiIiIiMNRQRERERGHo4IiIiIiDkcFRURERByOCoqIiIg4HBUUERERcTgqKCLyUP36669YLJZbHkQmIvJXVFBE5KEKDw/n4sWL+Pj43PMyycnJjB49mnLlyuHu7o6/vz9Nmzbl+++/f4hJRcSROJsdQETyN1dX12w/jn3gwIFERUUxbdo0QkNDuXr1Klu2bOHq1asPKaWIOBrtQRGRbHn88ccZMmQIw4cPp0iRIgQGBvLJJ59w/fp1XnzxRQoXLkz58uVZtWoVcOshnrlz5+Lr68uPP/5IlSpV8PLyolWrVly8eNH+GcuWLeMf//gHbdq0oXTp0tSpU4chQ4bQp08f+zwWi4XvvvsuSzZfX1/mzp0LwOnTp7FYLCxatIjw8HDc3d2pWrUq69evf6h/PyKSM1RQRCTb5s2bR7Fixdi2bRtDhgxh0KBBdO7cmfDwcHbt2kWLFi3o0aMHycnJt10+OTmZ9957jy+++IINGzZw9uxZXnvtNfv0oKAgVq5cSVJS0gNnHTlyJK+++iq7d+8mLCyM9u3ba0+MSB6ggiIi2VajRg3+9a9/UaFCBUaPHo27uzvFihWjf//+VKhQgbFjx3L16lX27t172+XT09OZNWsWdevWpXbt2kRERPDzzz/bp3/88cds2bKFokWLUq9ePV555RU2b958X1kjIiLo1KkTVapUYebMmfj4+DB79uz7WpeI5B4VFBHJturVq9v/7OTkRNGiRalWrZr9vcDAQABiY2Nvu7ynpyflypWz/1y8ePEs8zZp0oSTJ0/y888/8+yzz3LgwAEaN27MuHHjsp01LCzM/mdnZ2fq1q3LoUOHsr0eEcldKigikm0uLi5ZfrZYLFnes1gsANhstnte3jCMW+Zp3Lgxo0aN4qeffuKtt95i3LhxpKWl3XGZ9PT0+xuQiDgcFRQRyRNCQ0PJyMggJSUFAH9//ywn1h47duy257xs3brV/ueMjAx27txJlSpVHn5gEXkgusxYRBzO448/Trdu3ahbty5Fixbl4MGD/OMf/6BZs2Z4e3sD8MQTTzB9+nTCwsLIzMxk1KhRt+yZAZgxYwYVKlSgSpUqTJ48md9//z3L1UAi4pi0B0VEHE7Lli2ZN28eLVq0oEqVKgwZMoSWLVuyePFi+zzvv/8+ISEhNG7cmOeff57XXnsNT0/PW9Y1YcIEJkyYQI0aNdi0aRPLli2jWLFiuTkcEbkPFuPPB3FFRPKB06dPU6ZMGXbv3k3NmjXNjiMi2aQ9KCIiIuJwVFBERETE4egQj4iIiDgc7UERERERh6OCIiIiIg5HBUVEREQcjgqKiIiIOBwVFBEREXE4KigiIiLicFRQRERExOGooIiIiIjD+X+MudSOHfc2QQAAAABJRU5ErkJggg==\n" 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] + ] + } + ] } diff --git a/notebooks/periodicFrequentPattern/basic/PFECLAT.ipynb b/notebooks/periodicFrequentPattern/basic/PFECLAT.ipynb index 7867c427..daedf699 100644 --- a/notebooks/periodicFrequentPattern/basic/PFECLAT.ipynb +++ b/notebooks/periodicFrequentPattern/basic/PFECLAT.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Periodic Frequent patterns in Temporal Databases using PFECLAT" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Periodic Frequent patterns in a temporal database using the PFECLAT algorithm. In the final part, we describe an advanced approach, where we evaluate the PFECLAT algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "530d85aa-dc1b-4d83-a3aa-8f39072ace63" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m6.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already 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- "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Periodic Frequent patterns in a temporal database using the PFECLAT algorithm. In the final part, we describe an advanced approach, where we evaluate the PFECLAT algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "530d85aa-dc1b-4d83-a3aa-8f39072ace63" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "f255e02d-f9b2-4a68-b3cd-eafee2332669" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 07:56:02-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.25MB/s in 5.0s \n", - "\n", - "2023-08-28 07:56:09 (904 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "075c364f-23c9-41c6-ae62-c2effc761177" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Periodic Frequent patterns using PFECLAT" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "2c83e7e6-3955-4047-ff3f-fae90b47135a" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "faaaef32-6f7f-46ef-a15d-2629eb1c16a0" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Periodic Frequent patterns using PFECLAT" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.periodicFrequentPattern.basic import PFECLAT as alg #import the algorithm\n", - "\n", - "obj = alg.PFECLAT(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('periodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "7c58b016-9cb6-47b9-aa34-db2830300b7a" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n", - "Total No of patterns: 25454\n", - "Runtime: 57.06001043319702\n", - "Memory (RSS): 525512704\n", - "Memory (USS): 478879744\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'periodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "06d71aa9-6394-415d-f16e-50fa2c034de8" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "25:1390:472 \n", - "52:1978:385 \n", - "164:744:914 \n", - "240:1398:461 \n", - "274:2625:311 \n", - "328:663:1191 \n", - "368:7818:113 \n", - "448:1367:404 \n", - "538:3981:283 \n", - "561:2781:283 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "f255e02d-f9b2-4a68-b3cd-eafee2332669" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 07:56:02-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.25MB/s in 5.0s \n", + "\n", + "2023-08-28 07:56:09 (904 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "075c364f-23c9-41c6-ae62-c2effc761177" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Periodic Frequent patterns using PFECLAT" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "2c83e7e6-3955-4047-ff3f-fae90b47135a" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "faaaef32-6f7f-46ef-a15d-2629eb1c16a0" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _periodicFrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the PFECLAT algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.periodicFrequentPattern.basic import PFECLAT as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 5000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of PFECLAT" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of PFECLAT algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.PFECLAT(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['PFECLAT', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "474b3eed-d733-4542-c28f-294bcd0bd220" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n", - "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n", - "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n", - "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n", - "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Periodic Frequent patterns using PFECLAT" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.periodicFrequentPattern.basic import PFECLAT as alg #import the algorithm\n", + "\n", + "obj = alg.PFECLAT(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('periodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7c58b016-9cb6-47b9-aa34-db2830300b7a" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n", + "Total No of patterns: 25454\n", + "Runtime: 57.06001043319702\n", + "Memory (RSS): 525512704\n", + "Memory (USS): 478879744\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'periodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "06d71aa9-6394-415d-f16e-50fa2c034de8" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "25:1390:472 \n", + "52:1978:385 \n", + "164:744:914 \n", + "240:1398:461 \n", + "274:2625:311 \n", + "328:663:1191 \n", + "368:7818:113 \n", + "448:1367:404 \n", + "538:3981:283 \n", + "561:2781:283 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _periodicFrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the PFECLAT algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.periodicFrequentPattern.basic import PFECLAT as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 5000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of PFECLAT" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of PFECLAT algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.PFECLAT(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['PFECLAT', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "474b3eed-d733-4542-c28f-294bcd0bd220" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n", + "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n", + "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n", + "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n", + "Periodic-Frequent patterns were generated successfully using PFECLAT algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d40855ec-f650-46c9-a551-d9a7e5e1a582" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximumPeriodCount patterns runtime memory\n", + "0 PFECLAT 100 5000 25454 55.942703 538968064\n", + "1 PFECLAT 150 5000 18976 36.741215 540770304\n", + "2 PFECLAT 200 5000 13231 25.113022 541171712\n", + "3 PFECLAT 250 5000 7673 19.293699 544583680\n", + "4 PFECLAT 300 5000 4529 14.802631 546689024\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "405b3a61-181f-4215-8289-f8132c915f64" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d40855ec-f650-46c9-a551-d9a7e5e1a582" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximumPeriodCount patterns runtime memory\n", - "0 PFECLAT 100 5000 25454 55.942703 538968064\n", - "1 PFECLAT 150 5000 18976 36.741215 540770304\n", - "2 PFECLAT 200 5000 13231 25.113022 541171712\n", - "3 PFECLAT 250 5000 7673 19.293699 544583680\n", - "4 PFECLAT 300 5000 4529 14.802631 546689024\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "405b3a61-181f-4215-8289-f8132c915f64" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] + ] + } + ] } diff --git a/notebooks/periodicFrequentPattern/basic/PFPGrowth.ipynb b/notebooks/periodicFrequentPattern/basic/PFPGrowth.ipynb index bcfd5226..c902e776 100644 --- a/notebooks/periodicFrequentPattern/basic/PFPGrowth.ipynb +++ b/notebooks/periodicFrequentPattern/basic/PFPGrowth.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Periodic Frequent patterns in Temporal Databases using PFPGrowth" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Periodic Frequent patterns in a temporal database using the PFPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the PFPGrowth algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "5839f85f-379c-4cfa-e21b-c89aa1c0ee67" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m10.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already 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(8.2.3)\n", + "Collecting JsonForm>=0.0.2 (from resource->pami)\n", + " Downloading JsonForm-0.0.2.tar.gz (2.4 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting JsonSir>=0.0.2 (from resource->pami)\n", + " Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in 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/root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=b9aee4c96b3f77242e129e8368b2b15ad8e7717ef0881ae90de267fae2f4401c\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Periodic Frequent patterns in Temporal Databases using PFPGrowth" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Periodic Frequent patterns in a temporal database using the PFPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the PFPGrowth algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "5839f85f-379c-4cfa-e21b-c89aa1c0ee67" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "ee5a26bb-4cc7-4949-943d-cd4ae23e550f" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 08:10:49-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 862KB/s in 5.2s \n", - "\n", - "2023-08-28 08:10:56 (862 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "69e1657d-e988-484e-9944-b506b53883df" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Periodic Frequent patterns using PFPGrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "4906ae9c-fb50-414d-d715-801a0269e123" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "d58f64be-f85a-47a2-b136-4500bb9506d5" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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zM3z++ecoKSnB4sWLa3U81fnPf/6D0aNHo0+fPnjqqaeQnZ2NFStWICgoSK+gupuRI0diw4YNGDt2LEaMGIHExESsWrUKgYGB1T5Hu3bt0LdvX7zwwgsoKSnBRx99BBcXF7z22mtGOy4iAJyqgKiu3n77bdGiRQuhUCj0Lt8HcMfpA7766ivRvn17oVKpRMeOHcXq1avFggULqlxufqfnuPVS+5KSEvHqq6+KkJAQYWdnJ2xsbERISIhYuXKl3mNOnz4tIiIihK2trXB1dRXPPvusbtqDWy97F0KIU6dOibFjxwpHR0dhaWkp/P39xRtvvFGj4759GgAhhLh48aJ48MEHdc/Xs2dPsWnTJr02lZfmr1+/Xm97dZfm38m+fftEaGiosLCwEG3atBGrVq2q9n29PeM777wjevbsKRwdHYWVlZXo2LGjePfdd0VpaamujVqtFi+++KJwc3MTkiTpnrMy35IlS6rkudNUBTY2NuLixYtiyJAhwtraWnh4eIgFCxbopm2olJGRIcaPHy+sra2Fk5OTeO6558SpU6eqPOedsglRdaoCIYQ4ceKEiIyMFLa2tsLa2lrcd9994tChQ3ptKqcqOHbsmN72O02hUJ2ffvpJdOzYUahUKhEUFCQ2btwoxo8fLzp27FjlParu/dNqteI///mP8PX1FSqVSnTt2lVs2rRJTJ48udrpDpYsWSKWLl0qfHx8hEqlEv369ROxsbF6z1n5/t+uus8J0Z1IQtTjKEwiIqJbdOnSBW5ublXG29XF5cuX4efnhyVLlmDOnDlGe16iO+GYJyIiMrqysjKo1Wq9bXv37kVsbCwGDhwoTygiI+GYJyIiMrrr168jIiICjz/+OLy9vXH27FmsWrUKnp6eVSaoJGpsWDwREZHROTk5ITQ0FF9++SUyMjJgY2ODESNG4L333tOtyUjUWHHMExEREZEBOOaJiIiIyAAsnoiIiIgMwDFPRqLVapGcnAw7OzujLuNARERE9UcIgfz8fHh7e9d40l4WT0aSnJxslDWhiIiIqOFdvXoVLVu2rFFbFk9GYmdnB6D8zTfG2lBERERU//Ly8uDj46P7Hq8JFk9GUtlVZ29vz+KJiIiokTFkyA0HjBMREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBE1A8VlGrkjEBE1GSyeiJowrVZgWdQ5BC3Yjn//GgchhNyRiIgaPTO5AxBR/SgoUWPW2hhEnU4DAPx4JAnt3GzxdF8/mZMRETVuPPNE1ARdySzEuJUHEXU6DRZKBUZ29gIAvLvlDA5duCFzOiKixo3FE1ETc+D8DTyw4iDOpRXA3U6Ftc/1wvLHumJc1xbQaAWm/3gCV7OK5I5JRNRosXgiaiKEEPjqQCImfX0EuTfL0MXHEX+82BddWzlBkiT8Z1wwgls4ILuoDFP/F42bpRxETkRUGyyeiJqA4jIN5qw/ibc3nYZWAOO7tcRPU3vBw95S18bSXInPnwiFi40FzqTk4bVfTnIAORFRLbB4Imrk0vKK8cgXf+GXE9egkIA3Rgbig4c6w9JcWaWtt6MVVk7sBjOFhD9ik/HF/ksyJCYiatxYPBE1YieSsjFq+QHEXs2Bg5U5vns6DFP6+kGSpDs+JqyNCxaMCgQAvL/tLPady2iouERETQKLJ6JGav3xq3j087+Qnl+CDh622DijD/q2d63RYx/v5YtHuvtAK4AXfzyByzcK6zktEVHTweKJqJFRa7R48494vPrzSZRqtBgS6IEN0/rA18Wmxs8hSRLeGtMJXVs5Iq9Yjan/O47CEnU9piYiajpYPBE1ItmFpZi8+ihWH7wMAHh5cHusejwUtirD57tVmSmx6vFQuNmpcC6tAK+si+UAciKiGmDxRNRIJKTmY/SnB3HwQiasLZT4bGI3zLq/AxSKO49vuhcPe0usejwU5koJ2+JT8emeC0ZMTETUNLF4ImoEtp1KxdiVB5GUVYSWTlb45YXeGBbsZZTnDvV1wtujgwAAS6POYdeZNKM8LxFRU8XiicjE/Rx9Dc9/H42iUg3C27hg44y+CPCyN+prPNqzFR7v1QpCADN/isHFjAKjPj8RUVPC4onIhJ1Ly8frv8UBAB7v1QrfTekJZxuLenmt+SM7oUdrJ+SXqPHsd8eRV1xWL69DRNTYsXgiMlHFZRrM+PEEisu06NfeFW89EARzZf39lbUwU2DlxFB4OVjiUkYhZq+NgVbLAeRERLdj8URkot784zTOpRXA1VaFZQ93qdPA8Jpys1Ph8ydCYWGmwM4z6fho1/l6f00iosaGxRORCdp0MhlrjiZBkoCPHukCNztVg71255aOWDQ2GADwya7z2HYqtcFem4ioMWDxRGRikjKLMO+X8nFO0wa2rfGs4cY0PrQlnu7jBwB4ZV0MzqXlN3gGIiJTxeKJyISUqrV48ae/kV+iRqivE2ZFdJAty7+Hd0Tvti4oLNVg6nfHkVvEAeRERACLJyKT8sGOBN0iv5881hVm9ThA/F7MlAqsmNANLRytcDmzCPM3npItCxGRKWHxRGQi9iSk44v9lwAAix/sjBaOVjInApxtLLB8QlcAwNa4VOTe5NknIiIWT0QmIC2vGK+siwUATA73RWQnT5kT/aNbKyd08LBFqUaLqNOcfZyIiMUTkcw0WoGZP8Ugq7AUgV72mDc8QO5IVYzs7A0A+CM2WeYkRETyk7V42r9/P0aNGgVvb29IkoTffvtNb78QAvPnz4eXlxesrKwQERGB8+f1553JysrCxIkTYW9vD0dHR0yZMgUFBfpLS5w8eRL9+vWDpaUlfHx8sHjx4ipZ1q9fj44dO8LS0hLBwcHYsmWL0Y+XqDqf7rmAw5fKF/tdMaErLM2VckeqYmTn8nX0Dly4gazCUpnTEBHJS9biqbCwECEhIfj000+r3b948WJ88sknWLVqFY4cOQIbGxtERkaiuLhY12bixImIj49HVFQUNm3ahP3792Pq1Km6/Xl5eRgyZAh8fX0RHR2NJUuWYOHChfjiiy90bQ4dOoTHHnsMU6ZMwd9//40xY8ZgzJgxOHWKA2Spfh25lImPdp4DALwzJght3GxlTlS9Nm626ORtD41WcN4nIiJhIgCIX3/9VXdfq9UKT09PsWTJEt22nJwcoVKpxJo1a4QQQpw+fVoAEMeOHdO12bp1q5AkSVy/fl0IIcTKlSuFk5OTKCkp0bWZO3eu8Pf3191/+OGHxYgRI/TyhIWFieeee67G+XNzcwUAkZubW+PHUPOWVVAiwt7dKXznbhKz1v4td5x7+mzvBeE7d5N49PPDckchIjKa2nx/m+yYp8TERKSmpiIiIkK3zcHBAWFhYTh8+DAA4PDhw3B0dET37t11bSIiIqBQKHDkyBFdm/79+8PC4p/FVCMjI5GQkIDs7Gxdm1tfp7JN5etUp6SkBHl5eXo3opoSQmDO+lik5hWjjZsN3h4dJHekexoRXN5191diJtLziu/Rmoio6TLZ4ik1tbxrwMPDQ2+7h4eHbl9qairc3d319puZmcHZ2VmvTXXPcetr3KlN5f7qLFq0CA4ODrqbj4+PoYdIzdjXBy9j19l0WJgpsPyxrrBRmckd6Z58nK3RtZUjhAC2xKXIHYeISDYmWzyZunnz5iE3N1d3u3r1qtyRqJGIu5aL97aeAQC8PiIAnbwdZE5Uc6Mqr7o7yeKJiJovky2ePD3L57lJS9OfVyYtLU23z9PTE+np6Xr71Wo1srKy9NpU9xy3vsad2lTur45KpYK9vb3ejehe8ovLMGPNCZRpBCI7eeCJXr5yRzLIiM5ekCQg+ko2rufclDsOEZEsTLZ48vPzg6enJ3bt2qXblpeXhyNHjiA8PBwAEB4ejpycHERHR+va7N69G1qtFmFhYbo2+/fvR1nZPzMjR0VFwd/fH05OTro2t75OZZvK1yEyBiEE/u/XU7iSWYQWjlZYPD4EkiTJHcsgHvaW6NnaGQCw+STnfCKi5knW4qmgoAAxMTGIiYkBUD5IPCYmBklJSZAkCTNnzsQ777yDjRs3Ii4uDpMmTYK3tzfGjBkDAAgICMDQoUPx7LPP4ujRozh48CBmzJiBRx99FN7e5d0LEyZMgIWFBaZMmYL4+HisXbsWH3/8MWbPnq3L8fLLL2Pbtm1YunQpzp49i4ULF+L48eOYMWNGQ78l1IStP34NG2OToVRI+OSxLnCwNpc7Uq2MCin/u7WJXXdE1FzV27V/NbBnzx4BoMpt8uTJQojy6QreeOMN4eHhIVQqlRg8eLBISEjQe47MzEzx2GOPCVtbW2Fvby+eeuopkZ+fr9cmNjZW9O3bV6hUKtGiRQvx3nvvVcmybt060aFDB2FhYSE6deokNm/ebNCxcKoCuptzqXnC//UtwnfuJrFi93m549TJjfxi0WbeZuE7d5NIzCiQOw4RUZ3U5vtbEkIIGWu3JiMvLw8ODg7Izc3l+CfSU1ymwegVB5GQlo9+7V3x7VM9oVA0ru662z3x1RH8ef4G5gzpgBmD2ssdh4io1mrz/W2yY56ImoqVey4gIS0frrYqLHu4S6MvnIB/rrpj1x0RNUcsnojqWWWB8fqIALjZqWROYxyRnTxhrpRwNjUf59Py5Y5DRNSgWDwR1aPEG4W4dKMQ5koJgwPc7/2ARsLB2hwDOrgB4JxPRNT8sHgiqke7z5bPQ9bTzxl2lo3z6ro7GVnZdRebDA6dJKLmhMUTUT3afbZ88tX7/JvOWadKEYEeUJkpcOlGIeKTubYjETUfLJ6I6kl+cRmOJmYBAAYHeNyjdeNjqzLDoI7lRSEHjhNRc8LiiaieHDh/A2UaAT9XG/i52sgdp178M2Emu+6IqPlg8URUTyrHO1WenWmK7vN3h42FEteybyLmao7ccYiIGgSLJ6J6oNUK7EkoL54GN+HiycpCiYjA8i7JP2LZdUdEzQOLJ6J6EHc9FzcKSmGrMkP3ioV0m6rKCTM3xyVDq2XXHRE1fSyeiOrBroouu/4dXGFh1rT/mvXr4Ao7SzOk5ZXg2OUsueMQEdW7pv2vOpFMmvIUBbdTmSkxtJMnAOCPk8kypyEiqn8snoiMLC2vGKeu50GSgIHNoHgC/rnqbmtcKtQarcxpiIjqF4snIiPbU9FlF9LSscmsZXcvvdu6wNnGApmFpTh8KVPuOERE9YrFE5GRNYcpCm5nplRgWFBF110su+6IqGlj8URkRCVqDQ5cuAGgeRVPwD9r3W07lYpSNbvuiKjpYvFEZERHLmWhqFQDD3sVOnnbyx2nQfX0c4a7nQp5xWr8eT5D7jhERPWGxROREd3aZSdJksxpGpZSIWF4sBcArnVHRE0biyciIxFCYFczmqKgOpVX3e2IT0VxmUbmNERE9YPFE5GRXMwowNWsm7AwU6BPO1e548iiWytHtHC0QmGpRnfVIRFRU8PiichIdp0pLxbC27jARmUmcxp5SJKEkZ3ZdUdETRuLJyIjaY5TFFSnsutu19k0FJaoZU5DRGR8LJ6IjCC3qAzHr2QDYPHUydserV2sUVymxc4zaXLHISIyOhZPREaw73wGNFqB9u628HG2ljuOrCRJ0p19+iOWXXdE1PSweCIygsrB0YMCmvdZp0qVxdO+c+nIvVkmcxoiIuNi8URURxqtwN6EiuKpmU5RcLsOHnbo4GGLMo3AjvhUueMQERkViyeiOoq5mo3sojLYW5oh1NdJ7jgmY1TFci1/8Ko7ImpiWDwR1VHlFAUD/d1hpuRfqUojK7ruDl64gazCUpnTEBEZD/+lJ6ojTlFQPT9XGwS1sIdGK7D1FM8+EVHTweKJqA6u59zE2dR8KCRgQAc3ueOYHF3XXWyyzEmIiIyHxRNRHVSederWyglONhYypzE9IypmGz+SmIX0vGKZ0xARGQeLJ6I64BQFd9fSyRrdWjlCCGBzHLvuiKhpYPFEVEs3SzU4eOEGAGBwRw+Z05iukRVddxvZdUdETQSLJ6JaOnzpBkrUWrRwtEIHD1u545iskZ29oFRI+DspBxfSC+SOQ0RUZyyeiGqpcoqCQR3dIUmSzGlMl7u9JQZWDKZff/yqzGmIiOqOxRNRLQghOEWBAR7u4QMA+OXENZRptDKnISKqGxZPRLVwNjUfKbnFsDRXILyti9xxTN6gju5wtVXhRkGprugkImqsWDwR1UJlAdCnrSsszZUypzF95koFxndrAYBdd0TU+LF4IqqF3ZyiwGAPdS/vutuTkME5n4ioUWPxRGSgrMJSnEjKBsDxToZo526L7r5O0GgFfj5xTe44RES1xuKJyED7zqVDCCDAyx5eDlZyx2lUKgeOrz9+DUIImdMQEdUOiyciA1VOUTCYZ50MNiLYCzYWSiTeKMTRxCy54xAR1QqLJyIDlGm02HcuAwBwH4sng9mozHQzjq/lwHEiaqRYPBEZIPpKNvKL1XC2sUAXH0e54zRKlV13W+JSkFdcJnMaIiLDsXgiMkDlVXYD/d2gVHBW8dro1soR7dxtUVymxR9c746IGiEWT0QG4KzidSdJEh6pmLZg3XFedUdEjQ+LJ6IaSsoswoX0ApgpJPRr7yZ3nEZtbLcWMFNIiL2ag4TUfLnjEBEZhMUTUQ3tPpsGAOje2gkOVuYyp2ncXG1ViAjwAACsPcaB40TUuLB4IqqhXWcrpyjwkDlJ0/BIxcDxX/++hhK1RuY0REQ1x+KJqAYKS9Q4cql8XiJOUWAc/dq7wsNeheyiMuw8zcWCiajxYPFEVAMHLtxAqUYLXxdrtHWzkTtOk2CmVODB0JYAOOcTETUuJl08aTQavPHGG/Dz84OVlRXatm2Lt99+W29ZByEE5s+fDy8vL1hZWSEiIgLnz5/Xe56srCxMnDgR9vb2cHR0xJQpU1BQUKDX5uTJk+jXrx8sLS3h4+ODxYsXN8gxUuOw+8w/V9lJEqcoMJaHK666+/N8Bq7n3JQ5DRFRzZh08fT+++/js88+w4oVK3DmzBm8//77WLx4MZYvX65rs3jxYnzyySdYtWoVjhw5AhsbG0RGRqK4+J9V2ydOnIj4+HhERUVh06ZN2L9/P6ZOnarbn5eXhyFDhsDX1xfR0dFYsmQJFi5ciC+++KJBj5dMk1YrsCeBUxTUB18XG/Rq4wwhgJ85bQERNRbChI0YMUI8/fTTetvGjRsnJk6cKIQQQqvVCk9PT7FkyRLd/pycHKFSqcSaNWuEEEKcPn1aABDHjh3Ttdm6dauQJElcv35dCCHEypUrhZOTkygpKdG1mTt3rvD3969x1tzcXAFA5ObmGn6gZNJOXs0RvnM3icA3toriMrXccZqcDSeuCt+5m0Sf93YJjUYrdxwiamZq8/1t0meeevfujV27duHcuXMAgNjYWBw4cADDhg0DACQmJiI1NRURERG6xzg4OCAsLAyHDx8GABw+fBiOjo7o3r27rk1ERAQUCgWOHDmia9O/f39YWFjo2kRGRiIhIQHZ2dnVZispKUFeXp7ejZqmXRVTFPRt7wqVmVLmNE3PsCAv2Fma4Vr2TRy+lCl3HCKiezLp4ulf//oXHn30UXTs2BHm5ubo2rUrZs6ciYkTJwIAUlNTAQAeHvqXjnt4eOj2paamwt1dv6vFzMwMzs7Oem2qe45bX+N2ixYtgoODg+7m4+NTx6MlU7XtVPlngFMU1A9LcyVGd6lYLJhzPhFRI2DSxdO6devwww8/4Mcff8SJEyfw7bff4oMPPsC3334rdzTMmzcPubm5utvVq/xHvylKSM3H2dR8mCslDOnE4qm+PNK9FQBgW3wqcou4WDARmTaTLp5effVV3dmn4OBgPPHEE5g1axYWLVoEAPD09AQApKWl6T0uLS1Nt8/T0xPp6fpzyKjVamRlZem1qe45bn2N26lUKtjb2+vdqOn5LeY6AGCgvzscrS3u0ZpqK6iFPQK87FGq1urecyIiU2XSxVNRUREUCv2ISqUSWq0WAODn5wdPT0/s2rVLtz8vLw9HjhxBeHg4ACA8PBw5OTmIjo7Wtdm9eze0Wi3CwsJ0bfbv34+ysn/+xxsVFQV/f384OTnV2/GRadNqBTbGJAMAxnZtIXOapk2SJDzcvWLOJ3bdEZGJM+niadSoUXj33XexefNmXL58Gb/++iuWLVuGsWPHAij/B3fmzJl45513sHHjRsTFxWHSpEnw9vbGmDFjAAABAQEYOnQonn32WRw9ehQHDx7EjBkz8Oijj8Lbu3ycxYQJE2BhYYEpU6YgPj4ea9euxccff4zZs2fLdehkAo5fycb1nJuwU5lxioIGMKZLC1goFTidkodT13PljkNEdEdmcge4m+XLl+ONN97AtGnTkJ6eDm9vbzz33HOYP3++rs1rr72GwsJCTJ06FTk5Oejbty+2bdsGS0tLXZsffvgBM2bMwODBg6FQKDB+/Hh88sknuv0ODg7YsWMHpk+fjtDQULi6umL+/Pl6c0FR8/Pr3+XdR0ODPGFpzqvs6puTjQWGdPLAppMpWHvsKoJaOMgdiYioWpIQt0zXTbWWl5cHBwcH5ObmcvxTE1Cq1qLHuzuRe7MMPzwThj7tXOWO1Cz8eT4DT3x1FHaWZjj2fxEsWomo3tXm+9uku+2I5LI3IR25N8vgbqdCrzYucsdpNvq0dUULRyvkF6uxPb76aUKIiOTG4omoGr9XDBR/IMQbSgXXsmsoCoWEhzhwnIhMHIsnotvkF5dh55nyqSrG8Cq7BvdQdx9IEnDoYiaSMovkjkNEVAWLJ6LbbDuVihK1Fu3cbdHJm+PXGloLRyv0rRhjtj6aZ5+IyPSweCK6TeUkjWO6eEOS2GUnh4e7ly939HP0NWi0vKaFiEwLiyeiW6TlFePQxfLFaUd3YZedXIZ08oCjtTlScoux/3yG3HGIiPSweCK6xR+xyRACCPV1go+ztdxxmi2VmRJjKorXdRw4TkQmhsUT0S10XXYcKC67R3qUd93tPJOGzIISmdMQEf2DxRNRhQvp+Th1PQ9mCgkjgr3kjtPsBXjZo3NLB5RphG62dyIiU8DiiajCb3+Xz+00oIMbnG0sZE5DwD8Dx9cdvwouhkBEpoLFExEAIQR+jy0/uzGaXXYm44Eu3rA0V+BcWgFirubIHYeICACLJyIAwImkbFzNugkbCyXuD/CQOw5VsLc0x/Cg8i7Udcc5cJyITAOLJyL802UX2ckTVhZcjNaUPFwxcPyP2BQUl2lkTkNExOKJCGUaLTadLC+eeJWd6Qnzc4aHvQoFJWocvpQpdxwiIhZPRPvPZSC7qAyutir0busidxy6jSRJuD+wvCt1R3yazGmIiFg8EeG3mPKzTqNCvGCm5F8JU3R/oCeA8jmftFyuhYhkxm8KatYKStSIOp0KALoZrcn09GrjDFuVGTLySxB7LUfuOETUzLF4omZtR3wqisu08HO1QeeWDnLHoTtQmSkx0N8NALDjNLvuiEheLJ6oWavsshvdxRuSJMmchu6mctxTFIsnIpIZiydqtjLyS3DgfAYAdtk1Bvd1dIe5UsKF9AJcyiiQOw4RNWMsnqjZ+iM2GVoBdPFxRGtXG7nj0D3YW5qjV5vyqyF59omI5MTiiZqt32PKl2MZ08Vb5iRUU+y6IyJTwOKJmqXEG4WIvZYLpULCyBAWT41FRMXSOdFJ2cjIL5E5DRE1VyyeqFn67e/ys05927nC1VYlcxqqKW9HKwS3cIAQwO6zPPtERPJg8UTNjhBC12U3lsuxNDrsuiMiubF4omYn5moOLmcWwcpcqfsipsZjSKfy39mf52+gqFQtcxoiao5YPFGz83vF3E5DOnnARmUmcxoylL+HHXycrVCi1mL/uRtyxyGiZojFEzUrao0Wm06WF0+c26lxkiQJ9weUr3XHrjsikgOLJ2pWDly4gRsFpXC2sUDf9q5yx6Faquy623U2DWqNVuY0RNTcsHiiZqWyy25kZy+YK/nxb6y6+zrB0docOUVlOH4lW+44RNTM8NuDmo2iUjW2x6cCAMbwKrtGzUypwKCO7gCAHfHsuiOihsXiiZqNqNNpKCrVwNfFGl19HOWOQ3U0JLBi3NOZVAghZE5DRM0JiydqNionxhwd4g1JkmROQ3XVv4MrVGYKXM26iYS0fLnjEFEzwuKJmoXMghLsP19+Wftodtk1CdYWZuhXMeifXXdE1JBYPFGzsDkuBRqtQHALB7R1s5U7DhkJZxsnIjmweKJm4deKLjsOFG9aBnX0gCQBcddzkZxzU+44RNRMsHiiJu9KZiH+TsqBQgJGhXjJHYeMyM1OhdBWTgCAnWd49omIGgaLJ2ryKud26tPOFe52ljKnIWNj1x0RNTQWT9Tk/RFbXjyN5nIsTVJl8XT4YiZyb5bJnIaImgODi6dLly7VRw6ienEpowDn0wtgppB0X7LUtLRxs0U7d1uotQJ7E9LljkNEzYDBxVO7du1w33334fvvv0dxcXF9ZCIymsqunPC2LnCwMpc5DdUXdt0RUUMyuHg6ceIEOnfujNmzZ8PT0xPPPfccjh49Wh/ZiOpsR8WX6RCedWrSKounvQkZKFFrZE5DRE2dwcVTly5d8PHHHyM5ORlff/01UlJS0LdvXwQFBWHZsmXIyMioj5xEBsvIL8GJpPJFYyNYPDVpXVo6ws1OhYISNf66lCV3HCJq4mo9YNzMzAzjxo3D+vXr8f777+PChQuYM2cOfHx8MGnSJKSkpBgzJ5HBdp1JgxBA55YO8HKwkjsO1SOFQkJEQGXXXarMaYioqat18XT8+HFMmzYNXl5eWLZsGebMmYOLFy8iKioKycnJGD16tDFzEhmMXXbNy5Bbxj1ptVwomIjqj5mhD1i2bBlWr16NhIQEDB8+HN999x2GDx8OhaK8DvPz88M333yD1q1bGzsrUY0VlKhx4EL5WnZDOnnKnIYaQnhbF9hYKJGWV4K467kI8XGUOxIRNVEGn3n67LPPMGHCBFy5cgW//fYbRo4cqSucKrm7u+Orr74yWkgiQ+0/l4FStRatXazR3p1r2TUHluZKDPB3A8Cr7oiofhl85un8+fP3bGNhYYHJkyfXKhCRMVR+ed4f6AFJkmROQw1lSKAntsSlYsfpVMyJ9Jc7DhE1UQafeVq9ejXWr19fZfv69evx7bffGiUUUV2UabTYVbHOGbvsmpf7/N2hVEg4l1aAK5mFcschoibK4OJp0aJFcHV1rbLd3d0d//nPf4wSiqgujiZmIa9YDRcbC3SrWDSWmgcHa3OE+TkDYNcdEdUfg4unpKQk+Pn5Vdnu6+uLpKQko4S61fXr1/H444/DxcUFVlZWCA4OxvHjx3X7hRCYP38+vLy8YGVlhYiIiCpdi1lZWZg4cSLs7e3h6OiIKVOmoKCgQK/NyZMn0a9fP1haWsLHxweLFy82+rFQw9gRX36pekSAB5QKdtk1N5VX3e2IZ/FERPXD4OLJ3d0dJ0+erLI9NjYWLi4uRglVKTs7G3369IG5uTm2bt2K06dPY+nSpXBy+udswuLFi/HJJ59g1apVOHLkCGxsbBAZGam3dMzEiRMRHx+PqKgobNq0Cfv378fUqVN1+/Py8jBkyBD4+voiOjoaS5YswcKFC/HFF18Y9Xio/gkh9MY7UfNTOSHq8StZyCwokTkNETVJwkCvvfaa8PX1Fbt37xZqtVqo1Wqxa9cu4evrK1555RVDn+6u5s6dK/r27XvH/VqtVnh6eoolS5botuXk5AiVSiXWrFkjhBDi9OnTAoA4duyYrs3WrVuFJEni+vXrQgghVq5cKZycnERJSYnea/v7+9c4a25urgAgcnNza/wYMr64aznCd+4m0fH1reJmqVruOCSTYR/tF75zN4m1x5LkjkJEJq42398Gn3l6++23ERYWhsGDB8PKygpWVlYYMmQIBg0aZPQxTxs3bkT37t3x0EMPwd3dHV27dsV///tf3f7ExESkpqYiIiJCt83BwQFhYWE4fPgwAODw4cNwdHRE9+7ddW0iIiKgUChw5MgRXZv+/fvDwsJC1yYyMhIJCQnIzs426jFR/arsshvQwQ2W5kqZ05BchnTiQsFEVH8MLp4sLCywdu1anD17Fj/88AM2bNiAixcv4uuvv9YrPozh0qVL+Oyzz9C+fXts374dL7zwAl566SXdVX2pqeVflB4e+t0zHh4eun2pqalwd3fX229mZgZnZ2e9NtU9x62vcbuSkhLk5eXp3Uh+ulnFO7HLrjmr7LL983wGbpZyoWAiMi6D53mq1KFDB3To0MGYWarQarXo3r277oxW165dcerUKaxatUr2eaQWLVqEN998U9YMpC8pswhnU/OhVEgY1NH93g+gJivQyx4tHK1wPecm/jyfwSkriMioDD7zpNFo8NVXX2HChAmIiIjAoEGD9G7G5OXlhcDAQL1tAQEBuqv6PD3L/0FMS9M/NZ+Wlqbb5+npifT0dL39arUaWVlZem2qe45bX+N28+bNQ25uru529erV2hwiGdGOigVhe7Z2hqO1cc+CUuMiSZLu7BO77ojI2Awunl5++WW8/PLL0Gg0CAoKQkhIiN7NmPr06YOEhAS9befOnYOvry+A8nX0PD09sWvXLt3+vLw8HDlyBOHh4QCA8PBw5OTkIDo6Wtdm9+7d0Gq1CAsL07XZv38/ysrKdG2ioqLg7++vd2XfrVQqFezt7fVuJC922dGtKqcs2HU2HRouFExExmToqHQXFxexefNmQx9WK0ePHhVmZmbi3XffFefPnxc//PCDsLa2Ft9//72uzXvvvSccHR3F77//Lk6ePClGjx4t/Pz8xM2bN3Vthg4dKrp27SqOHDkiDhw4INq3by8ee+wx3f6cnBzh4eEhnnjiCXHq1Cnx008/CWtra/H555/XOCuvtpNXZkGJ8PvXJuE7d5O4mlUodxwyAaVqjQhesE34zt0kjlzKlDsOEZmoBrnazsLCAu3atTN+FVeNHj164Ndff8WaNWsQFBSEt99+Gx999BEmTpyoa/Paa6/hxRdfxNSpU9GjRw8UFBRg27ZtsLS01LX54Ycf0LFjRwwePBjDhw9H37599eZwcnBwwI4dO5CYmIjQ0FC88sormD9/vt5cUGTadp1Jg1aUj3Vp6WQtdxwyAeZKBQYHVHbdVX/hBxFRbUhCCIPOZy9duhSXLl3CihUruODqLfLy8uDg4IDc3Fx24cng2e+OI+p0GmZGtMfMiPq9kIEajy1xKZj2wwn4ulhj75yB/DeLiKqozfe3wVfbHThwAHv27MHWrVvRqVMnmJub6+3fsGGDoU9JVCc3SzX483wGAGBIIK+qon/07+AGCzMFrmQW4Xx6ATp42MkdiYiaAIOLJ0dHR4wdO7Y+shDVyv7zGSgu06KlkxUCvPjlSP+wVZmhT1sX7EnIQNTpNBZPRGQUBhdPq1evro8cRLV261p27Jah290f6Ik9CRnYEZ+K6fc1zHhNImraDB4wDpTPk7Rz5058/vnnyM/PBwAkJyejoKDAqOGI7kWt0WLXmYopCthlR9WICHSHJAGx13KRnHNT7jhE1AQYXDxduXIFwcHBGD16NKZPn46MjPKxJu+//z7mzJlj9IBEd3P8Sjayi8rgaG2OHq2rn5OLmjd3O0v08HUGAGw6mSxzGiJqCmo1SWb37t2RnZ0NKysr3faxY8fqTVZJ1BB2xJefdRrc0QNmylqdSKVm4IEu3gCA32NYPBFR3Rn8bfPnn3/i9ddfr7IIcOvWrXH9+nWjBSO6FyEEos6Uz99TuRQHUXWGB3vBTCEhPjkPFzM4vICI6sbg4kmr1UKjqbpK+bVr12BnxytZqOGcTc3H1aybUJkp0L+Dq9xxyIQ521igb/vyz8hGnn0iojoyuHgaMmQIPvroI919SZJQUFCABQsWYPjw4cbMRnRXlV12/dq7wdrC4AtHqZkZXdF1tzE2GQbODUxEpMfg4mnp0qU4ePAgAgMDUVxcjAkTJui67N5///36yEhUrR0VS25wIWCqifsDPaEyUyDxRiFOXc+TOw4RNWIG/3e9ZcuWiI2NxU8//YSTJ0+ioKAAU6ZMwcSJE/UGkBPVp+s5NxGfnAeFBAzu6C53HGoEbFVmiAj0wOaTKdgYex3BLR3kjkREjVSt+jrMzMzw+OOPGzsLUY1FxZefderu6wwXW5XMaaixeCDEG5tPpuCP2BTMGxYAhYKTqhKR4Qwunr777ru77p80aVKtwxDV1I6KWcXZZUeGGOjvBjtLM6TmFePo5Sz0auMidyQiaoQMLp5efvllvftlZWUoKiqChYUFrK2tWTxRvcspKsWRxCwAnKKADKMyU2JYkCfWHb+GjbHJLJ6IqFYMHjCenZ2tdysoKEBCQgL69u2LNWvW1EdGIj27z6ZDoxXw97CDr4uN3HGokXkgpAUAYEtcCkrVWpnTEFFjZJQpmdu3b4/33nuvylkpovoQxS47qoPwti5wtVUhp6gMBy5kyB2HiBoho61nYWZmhuRkTj5H9au4TIN958q/8LgQMNWGUiFhZGcvAJwwk4hqx+AxTxs3btS7L4RASkoKVqxYgT59+hgtGFF1Dl64gaJSDbwcLBHUwl7uONRIPdDFG98cuowdp9Nws1QDKwul3JGIqBExuHgaM2aM3n1JkuDm5oZBgwZh6dKlxspFVK3KWcXvD/SAJPEyc6qdrj6O8HG2wtWsm9h5Jg2jQrzljkREjYjBxZNWywGWJA+NVmDX2YrxTuyyozqQJAkPhHjj0z0XsTE2mcUTERnEaGOeiOrb30nZuFFQCjtLM4S1cZY7DjVylVfd7U1IR25RmcxpiKgxMfjM0+zZs2vcdtmyZYY+PdEdVU6MObijO8yVrPupbvw97eDvYYeEtHxsi0/BIz1ayR2JiBoJg4unv//+G3///TfKysrg7+8PADh37hyUSiW6deuma8fxKGRMQgjsqFiS5X522ZGRPNDFG0u2J2BjbDKLJyKqMYOLp1GjRsHOzg7ffvstnJycAJRPnPnUU0+hX79+eOWVV4wekuhCegEuZxbBQqnAAH83ueNQE/FASHnxdOhiJtLziuFubyl3JCJqBAzu+1i6dCkWLVqkK5wAwMnJCe+88w6vtqN6U9ll16edC2xVtVrPmqgKH2drdGvlCCGATSdT5I5DRI2EwcVTXl4eMjKqzsqbkZGB/Px8o4Qiul1ll92QTuyyI+N6oOJKu42xnDCTiGrG4OJp7NixeOqpp7BhwwZcu3YN165dwy+//IIpU6Zg3Lhx9ZGRmrnU3GLEXsuFJAGDA9zljkNNzIjO3lBIQMzVHFzJLJQ7DhE1AgYXT6tWrcKwYcMwYcIE+Pr6wtfXFxMmTMDQoUOxcuXK+shIzVzUmfIuu64+jnC345gUMi43OxX6tHMFAPzBs09EVAMGF0/W1tZYuXIlMjMzdVfeZWVlYeXKlbCx4Qr3ZHzssqP6VjlJ5u8xyRBCyJyGiExdrSfLSUlJQUpKCtq3bw8bGxv+g0P1IqeoFIcvZgIoX5KFqD5EdvKEhVKB8+kFOJvKsZtEdHcGF0+ZmZkYPHgwOnTogOHDhyMlpfwKlSlTpnCaAjK6HafToNYKdPS0Q1s3W7njUBPlYGWO+zqWT4HBgeNEdC8GF0+zZs2Cubk5kpKSYG1trdv+yCOPYNu2bUYNR7S54vLxEcFeMiehpq5yuZaN7LojonsweMKcHTt2YPv27WjZsqXe9vbt2+PKlStGC0aUU1SKgxduAACGd2bxRPVrcIA7bCyUuJ5zEyeSshHqy/UTiah6Bp95Kiws1DvjVCkrKwsqlcoooYgAYEc8u+yo4ViaKxFZcVHCxhh23RHRnRlcPPXr1w/fffed7r4kSdBqtVi8eDHuu+8+o4aj5m1zHLvsqGGN6lJ+1d3muBSoNVqZ0xCRqTK4227x4sUYPHgwjh8/jtLSUrz22muIj49HVlYWDh48WB8ZqRlilx3JoW87VzjbWOBGQSkOXcxE/w5cR5GIqjL4zFNQUBDOnTuHvn37YvTo0SgsLMS4cePw999/o23btvWRkZohdtmRHMyVCgwPrui641V3RHQHBp15Kisrw9ChQ7Fq1Sr83//9X31lIsKmii67kTzrRA3sgZAW+P6vJGw/lYp3xgTB0lwpdyQiMjEGnXkyNzfHyZMn6ysLEQAgu7AUhyq77DjeiRpYd18neDtYIr9Ejb0J6XLHISITZHC33eOPP46vvvqqPrIQAQB2nE6FWisQ4GWPNuyyowamUEi65VrYdUdE1TF4wLharcbXX3+NnTt3IjQ0tMp6dsuWLTNaOGqeNseVr2U3Iphr2ZE8RoV44/P9l7DrTDryi8tgZ2kudyQiMiE1Kp5OnjyJoKAgKBQKnDp1Ct26dQMAnDt3Tq+dJEnGT0jNSnbhLVfZscuOZNLJ2x5t3WxwMaMQO+LTMD605b0fRETNRo2Kp65duyIlJQXu7u64cuUKjh07BhcXl/rORs3QjtOp0LDLjmQmSRIeCGmBD3eew8bYZBZPRKSnRmOeHB0dkZiYCAC4fPkytFpOHkf1Y9NJXmVHpuGBigkzD1y4gcyCEpnTEJEpqdGZp/Hjx2PAgAHw8vKCJEno3r07lMrqL9+9dOmSUQNS85FdWD4xIcAuO5Kfn6sNOrd0wMlrudgSl4InwlvLHYmITESNiqcvvvgC48aNw4ULF/DSSy/h2WefhZ2dXX1no2Zme3x5l12glz38XG3u/QCievZAiDdOXsvFxthkFk9EpFPjq+2GDh0KAIiOjsbLL7/M4omMTreWHbvsyESM7OyNd7ecwbHL2biecxMtHK3kjkREJsDgeZ5Wr17NwomMLuuWLjsuBEymwtPBEmF+zgCAPzjnExFVMLh4IqoPOyq67Dp526M1u+zIhDwQ0gIAsDGGxRMRlWPxRCahssuOA8XJ1AwL8oS5UsLplDwkpObLHYeITACLJ5Idu+zIlDnZWGBwRw8AwA9HrsichohMQaMqnt577z1IkoSZM2fqthUXF2P69OlwcXGBra0txo8fj7S0NL3HJSUlYcSIEbC2toa7uzteffVVqNVqvTZ79+5Ft27doFKp0K5dO3zzzTcNcEQE/HOVHbvsyFQ9Ee4LANhw4joKStT3aE1ETV2jKZ6OHTuGzz//HJ07d9bbPmvWLPzxxx9Yv3499u3bh+TkZIwbN063X6PRYMSIESgtLcWhQ4fw7bff4ptvvsH8+fN1bRITEzFixAjcd999iImJwcyZM/HMM89g+/btDXZ8zdkWXmVHJq53Wxe0cbNBQYkav/59Xe44RCSzRlE8FRQUYOLEifjvf/8LJycn3fbc3Fx89dVXWLZsGQYNGoTQ0FCsXr0ahw4dwl9//QUA2LFjB06fPo3vv/8eXbp0wbBhw/D222/j008/RWlpKQBg1apV8PPzw9KlSxEQEIAZM2bgwQcfxIcffijL8TYn7LKjxkCSJDzRq/zs0/eHr0AIIXMiIpJToyiepk+fjhEjRiAiIkJve3R0NMrKyvS2d+zYEa1atcLhw4cBAIcPH0ZwcDA8PDx0bSIjI5GXl4f4+Hhdm9ufOzIyUvcc1SkpKUFeXp7ejQxX2WUX1MIevi7ssiPTNa5bS1iZK5GQlo+jiVlyxyEiGZl88fTTTz/hxIkTWLRoUZV9qampsLCwgKOjo952Dw8PpKam6trcWjhV7q/cd7c2eXl5uHnzZrW5Fi1aBAcHB93Nx8enVsfX3G0+yavsqHFwsDLHmK7l69397y8OHCdqzky6eLp69Spefvll/PDDD7C0tJQ7jp558+YhNzdXd7t69arckRqdzIISHL7ELjtqPB6v6LrbdioV6fnFMqchIrmYdPEUHR2N9PR0dOvWDWZmZjAzM8O+ffvwySefwMzMDB4eHigtLUVOTo7e49LS0uDp6QkA8PT0rHL1XeX9e7Wxt7eHlVX1yzGoVCrY29vr3cgw2+PT2GVHjUonbweE+jpBrRVYe5T/YSJqrky6eBo8eDDi4uIQExOju3Xv3h0TJ07U/Wxubo5du3bpHpOQkICkpCSEh4cDAMLDwxEXF4f09HRdm6ioKNjb2yMwMFDX5tbnqGxT+RxUPzbHlc/YPCLYW+YkRDVXOXD8x6NJUGu0MqchIjnUeGFgOdjZ2SEoKEhvm42NDVxcXHTbp0yZgtmzZ8PZ2Rn29vZ48cUXER4ejl69egEAhgwZgsDAQDzxxBNYvHgxUlNT8frrr2P69OlQqVQAgOeffx4rVqzAa6+9hqeffhq7d+/GunXrsHnz5oY94GYks6AEh3mVHTVCw4I98fYmC6TkFmPnmXQMDfKUOxIRNTCTPvNUEx9++CFGjhyJ8ePHo3///vD09MSGDRt0+5VKJTZt2gSlUonw8HA8/vjjmDRpEt566y1dGz8/P2zevBlRUVEICQnB0qVL8eWXXyIyMlKOQ2oWtsWnQiuA4BYOaOViLXccohpTmSnxSI/yC0S+58BxomZJEpywxCjy8vLg4OCA3Nxcjn+qgYlf/oWDFzIxd2hHvDCwrdxxiAxyLbsI/RbvgRDArlcGoK2brdyRiKiWavP93ejPPFHjc4NddtTItXSyxuCO7gB49omoOWLxRA1uO7vsqAl4Irw1AODn6GsoKuV6d0TNCYsnanCVE2NyLTtqzPq1c4WvizXyi9X4PSZZ7jhE1IBYPFGDulFQgr84MSY1AQqFhMfDyqct+B/XuyNqVlg8UYPadqq8y65zSwf4OLPLjhq3h7q3hMpMgdMpeTiRlCN3HCJqICyeqEFtiavosuNZJ2oCHK0t8EBI+SSvHDhO1HyweKIGc2uXHRcCpqbiifDyrrvNJ1Nwo6BE5jRE1BBYPFGDqeyyC2GXHTUhnVs6IqSlA0o1Wqw7zvXuiJoDFk/UYCqvsuNZJ2pqKqct+OGvJGi0HDhO1NSxeKIGkZFfgiOJ7LKjpmlkZy84Wpvjes5N7Dmbfu8HEFGjxuKJGkTlWnbssqOmyNJciYe7l6939z8OHCdq8lg8UYPYwokxqYmbGNYKkgTsO5eBK5mFcschonrE4onq3a1ddsOCWDxR0+TrYoMBHdwAcNoCoqaOxRPVO12XnY8ju+yoSXuiV/m0BeuOX0NxmUbmNERUX1g8Ub3bfLJ83a8RwZ4yJyGqXwP93dHC0Qq5N8vwRyzXuyNqqlg8Ub26UVCCo4lZANhlR02fUiFhYq9WANh1R9SUsXiierX7TDq0AghuwavsqHl4pLsPLJQKxF7LRezVHLnjEFE9YPFE9WrH6VQAwP2BHjInIWoYLrYq3VWlnLaAqGli8UT1pqhUjT/P3wAADOnE4omaj8crBo7/EZuM7MJSmdMQkbGxeKJ6s//cDZSotfBxtoK/h53ccYgaTLdWjgj0skeJWov10VzvjqipYfFE9SbqdBoAYEigJyRJkjkNUcORJAmTwsvPPn3/VxK0XO+OqElh8UT1Qq3RYtfZ8uKJ452oOXqgizfsLM2QlFWE/ecz5I5DREbE4onqxfEr2cgpKoOTtTm6+zrJHYeowVlbmOHB0JYAgP8d5sBxoqaExRPVix3x5WedBnX0gJmSHzNqnioHju9OSMfVrCKZ0xCRsfBbjYxOCKGbooBX2VFz1tbNFn3buUII4MejSXLHISIjYfFERnc2NR/Xsm9CZaZAv/aucschklXl2ae1x66iRM317oiaAhZPZHSVXXb92rvB2sJM5jRE8ooIcIeXgyWyCkux7vg1ueMQkRGweCKjizrDLjuiSmZKBZ4f0BYA8PHO8ygsUcuciIjqisUTGdX1nJs4dT0PCgkY3NFd7jhEJuGxnq3QytkaNwpK8PWBRLnjEFEdsXgio9pZMTFmd19nuNiqZE5DZBoszBSYE+kPAPh8/yVkFpTInIiI6oLFExkVFwImqt7IYC8Et3BAQYkay3dfkDsOEdUBiycymtyiMvx1KQsAiyei2ykUEv41rCMA4IcjV5CUyXmfiBorFk9kNHsS0qHRCnTwsEVrVxu54xCZnD7tXNGvvSvKNAJLoxLkjkNEtcTiiYxGNzFmoKfMSYhM19yh5Weffo9JxqnruTKnIaLaYPFERlFcpsG+hPLFT9llR3RnQS0cMLqLNwDg/W1nZU5DRLXB4omM4vDFTBSWauBpb4ngFg5yxyEyaXOG+MNcKeHP8zdw4PwNueMQkYFYPJFR7KiYoiAi0B0KhSRzGiLT5uNsrVu25b1tZ6DVCpkTEZEhWDxRnWm1AjvPlBdPHO9EVDMz7msHW5UZTl3Pw6a4FLnjEJEBWDxRncVcy0FGfgnsVGbo1cZF7jhEjYKLrQrP9W8DAPhgewJK1VqZExFRTbF4ojqrXAh4YEd3WJjxI0VUU1P6+cHNToWkrCKsOZokdxwiqiF+01Gd/TNFAa+yIzKEtYUZZka0BwB8sus88ovLZE5ERDXB4onq5EJ6AS5lFMJcKWGgv5vccYganYe7+6CNqw0yC0vx3z+5aDBRY8DiieokquIqu/C2rrCzNJc5DVHjY65U4NWKRYO//PMS0vOLZU5ERPfC4onqJIoLARPV2dAgT4T4OKKoVIPlu7hoMJGpY/FEtZaeX4y/r+YAAO4PYPFEVFuSJGFexaLBa44mIfFGocyJiOhuWDxRre06kw4hgJCWDvB0sJQ7DlGj1quNCwZ1dIdaK/DBDi4aTGTKWDxRre2Ir7jKrhMnxiQyhteG+kOSgM0nUxBbcVaXiEwPiyeqlYISNQ5eyATA8U5ExtLR0x7jurYEACzaegZCcNkWIlPE4olqZf+5DJRqtGjtYo327rZyxyFqMmYP6QALMwX+upSFfecy5I5DRNVg8US1Utlld3+gBySJCwETGUsLRytMDq9YNHjrWS4aTGSCWDyRwco0Wuw+mw6A452I6sP0+9rBztIMZ1Pz8XvsdbnjENFtTLp4WrRoEXr06AE7Ozu4u7tjzJgxSEjQvwqluLgY06dPh4uLC2xtbTF+/HikpaXptUlKSsKIESNgbW0Nd3d3vPrqq1Cr1Xpt9u7di27dukGlUqFdu3b45ptv6vvwGq2jiVnIK1bDxcYC3Vo5yR2HqMlxtLbAtIHtAAAfbD+HErVG5kREdCuTLp727duH6dOn46+//kJUVBTKysowZMgQFBb+MwfKrFmz8Mcff2D9+vXYt28fkpOTMW7cON1+jUaDESNGoLS0FIcOHcK3336Lb775BvPnz9e1SUxMxIgRI3DfffchJiYGM2fOxDPPPIPt27c36PE2FpWzig8OcIdSwS47ovrwVJ/W8LS3xPWcm/jf4StyxyGiW0iiEV3OkZGRAXd3d+zbtw/9+/dHbm4u3Nzc8OOPP+LBBx8EAJw9exYBAQE4fPgwevXqha1bt2LkyJFITk6Gh0f5VWGrVq3C3LlzkZGRAQsLC8ydOxebN2/GqVOndK/16KOPIicnB9u2batRtry8PDg4OCA3Nxf29vbGP3gTIYRAn/d2Izm3GF9O6o4IXmlHVG/WHkvC3F/i4Ghtjv2v3Qd7LoFEZHS1+f426TNPt8vNzQUAODs7AwCio6NRVlaGiIgIXZuOHTuiVatWOHz4MADg8OHDCA4O1hVOABAZGYm8vDzEx8fr2tz6HJVtKp+jOiUlJcjLy9O7NQfxyXlIzi2GlbkSfdu7yh2HqEkb360l2rvbIqeoDJ/vuyh3HCKq0GiKJ61Wi5kzZ6JPnz4ICgoCAKSmpsLCwgKOjo56bT08PJCamqprc2vhVLm/ct/d2uTl5eHmzZvV5lm0aBEcHBx0Nx8fnzofY2Owo6LLrn8HV1iaK2VOQ9S0mSkVeG1o+bItXx1IRGouFw0mMgWNpniaPn06Tp06hZ9++knuKACAefPmITc3V3e7evWq3JEaxD9TFPAqO6KGEBHgju6+Tigu0+LVn2M5dQGRCWgUxdOMGTOwadMm7NmzBy1bttRt9/T0RGlpKXJycvTap6WlwdPTU9fm9qvvKu/fq429vT2srKyqzaRSqWBvb693a+quZhXhbGo+FBIwuKO73HGImgVJkrBoXDAszRX48/wNfL7/ktyRiJo9ky6ehBCYMWMGfv31V+zevRt+fn56+0NDQ2Fubo5du3bptiUkJCApKQnh4eEAgPDwcMTFxSE9PV3XJioqCvb29ggMDNS1ufU5KttUPgeVq+yy69HaGU42FjKnIWo+2nvY4c0HOgEAPtiRgOgr2TInImreTLp4mj59Or7//nv8+OOPsLOzQ2pqKlJTU3XjkBwcHDBlyhTMnj0be/bsQXR0NJ566imEh4ejV69eAIAhQ4YgMDAQTzzxBGJjY7F9+3a8/vrrmD59OlQqFQDg+eefx6VLl/Daa6/h7NmzWLlyJdatW4dZs2bJduymKOo0FwImksvD3X0wKsQbGq3AS2v+Rm5RmdyRiJotky6ePvvsM+Tm5mLgwIHw8vLS3dauXatr8+GHH2LkyJEYP348+vfvD09PT2zYsEG3X6lUYtOmTVAqlQgPD8fjjz+OSZMm4a233tK18fPzw+bNmxEVFYWQkBAsXboUX375JSIjIxv0eE1ZdmEpjiZmAQCGcHoCogYnSRL+MzYIrZytcT3nJv614SQXDiaSSaOa58mUNfV5nn6OvoY562PR0dMO22b2lzsOUbN18loOxn92CGUagXfGBOHxXr5yRyJq1Jr8PE8kH3bZEZmGzi0dMbdi+oK3Np3GmZTmMccckSlh8UT3VFymwf5zNwCwy47IFDzdxw/3+buhVK3FjB9PoKhUfe8HEZHRsHiiezpw/gZulmng7WCJTt5Nr0uSqLFRKCR88FAIPOxVuJhRiIUb4+WORNSssHiie9pxunJiTA9IEhcCJjIFLrYqfPRIV0gSsO74Nfwec13uSETNBosnuiuNVmDXmfI5sjjeici0hLd1wYuD2gMA/r0hDpdvFMqciKh5YPFEd7U9PhWZhaVwsDJHTz9nueMQ0W1eGtQOPVs7o7BUgxlrTqBErZE7ElGTx+KJ7kirFfhk13kAwOTerWGu5MeFyNSYKRX4+LEucLQ2x6nreVi8LUHuSERNHr8N6Y6izqThbGo+bFVmeLpPa7njENEdeDlY4YMHQwAAXx1IxK4zafd4BBHVBYsnqpYQt5518oWjNdeyIzJlEYEeeKriPzlz1sciJfemvIGImjAWT1StPQnpiE/Og7WFElP6tpE7DhHVwL+GdURQC3tkF5Xh5Z9ioNFyAQmi+sDiiaoQQuDjXRcAAE/08oWzDc86ETUGKjMllj/WDTYWShxNzNKdPSYi42LxRFXsP38DsVdzYGmuwDP9eNaJqDHxc7XBu2ODAQDLd5/H4YuZMicianpYPJGeW8c6TejpCzc7lcyJiMhQY7q2wEOhLaEVwMy1fyOrsFTuSERNCosn0nP4Yiair2TDwkyB5wbwrBNRY/Xm6E5o42aDtLwSzFkfCyE4/onIWFg8kZ6PK846PdbDBx72ljKnIaLasrYww6cTusHCTIHdZ9Px1YFEuSMRNRksnkjnyKVMHEnMgrlSwnMD2sodh4jqKMDLHm+MDAQAvL/tLI5fzpI5EVHTwOKJdJbvLr/C7qHuPvB2tJI5DREZw+NhrTAsyBNlGoHJXx/FX5c4gJyorlg8EQAg+ko2Dly4ATOFhBd41omoyZAkCUsfDkGfdi4oLNVg8tdHsTchXe5YRI0aiycCUH5JMwCM69YCPs7WMqchImOytjDDV5N7YFBHd5SotXj2u+PYdipV7lhEjRaLJ0Ls1RzsTciAUiFh+n3t5I5DRPXA0lyJVY+HYkSwF8o0AtN/PIHf/r4udyyiRonFE+nOOo3u4g1fFxuZ0xBRfbEwU+DjR7tgfLeW0GgFZq2LwZqjSXLHImp0WDw1c6eu52LnmXRIEnjWiagZMFMqsOTBzni8VysIAczbEMdpDIgMxOKpmVtRcYXdqM7eaOtmK3MaImoICoWEt0cHYWr/8olw3950Git2cx08oppi8dSMnU3Nw7b4VEgSMGMQzzoRNSeSJGHesI6YGdEeAPDBjnNYvO0sZyInqgEWT81Y5bxOw4O80MHDTuY0RNTQJEnCzIgO+PfwjgCAlXsv4s0/TkOrZQFFdDcsnpqpC+n52BKXAoBnnYiau6n92+LtMUEAgG8OXca8DXHQsIAiuiMWT83Uit0XIAQwJNADAV72cschIpk90csXHzwUAoUErD1+FbPWxqBMo5U7FpFJYvHUDCXeKMTG2GQAwEuD28uchohMxYOhLbH8sW4wU0jYGJuMaT+cQIlaI3csIpPD4qkZ+nTPBWgFMKijO4JaOMgdh4hMyIjOXvj8iVBYmCkQdToNz3x7HDdLWUAR3YrFUzOTlFmEXytmFX6RY52IqBqDAzyw+skesDJX4s/zNzD566PILy6TOxaRyWDx1Mys3HsBGq1A/w5u6NrKSe44RGSi+rRzxf+m9ISdygxHL2fh8S+PILuwVO5YRCaBxVMzci27CL+cuAYAeIlnnYjoHrq3dsaPz/aCo7U5Yq/lYvgnf+LQxRtyxyKSHYunZmTVvoso0wj0buuC7q2d5Y5DRI1AcEsHrHsuHH6uNkjJLcbEL49g0ZYzHEhOzRqLp2YiJfcm1h2rOOvEK+yIyAAdPOyw6cW+eKynD4QAPt9/CWM/PYTzaflyRyOSBYunZuLzfZdQqtGiZ2tn9GrjInccImpkbFRmWDSuM754IhRO1uY4nZKHkcsP4NtDl7mkCzU7LJ6agfS8Yqw5mgSAZ52IqG6GdPLE9pn9MaCDG0rUWizYGI8nVx9Den6x3NGIGgyLp2bgi/2XUKLWolsrR/Rpx7NORFQ37vaW+OapHnjzgU5QmSmw71wGhn70J6JOp8kdjahBsHhq4uKTc/H9kSsAgBcHt4ckSTInIqKmQJIkTO7dGn+82BcBXvbIKizFs98dx7wNcSgqVcsdj6hesXhqwn6PuY7xnx1CcZkWob5OGNjBTe5IRNTEdPCww2/Te2Nq/zaQJGDN0SSM+OQAYq/myB2NqN6weGqC1Bot3t18Gi//FIPiMi36d3DD15N78KwTEdULlZkS/x4egB+mhMHLwRKJNwox/rNDWLH7PDRaDianpofFUxOTXViKyauP4r9/JgIApg1si9VP9oCDtbnMyYioqevdzhXbXu6PEZ29oNYKfLDjHB75/DCuZhXJHY3IqFg8NSHxybkYteIADl7IhLWFEisndsNrQztCqeAZJyJqGA7W5ljxWFcsezgEtiozHL+SjWEf/4kNJ65xSgNqMiTBT7NR5OXlwcHBAbm5ubC3t2/w1/895jrm/nISxWVa+LpY44snusPf067BcxARVbqaVYRZa2Nw/Eo2ACC4hQNGdPbC8CAvtHKxljkdUbnafH+zeDISuYontUaL97ed1XXT9e/ghuWPdmU3HRGZBLVGi1X7LuLjXedRpvnn6yaohT2GBXlheLAX/FxtZExIzR2LJxnJUTxlF5ZixpoTOHghE0D5+KZXhvizm46ITM6NghJsj0/F1rhUHL6UqTeQPMDLHsODPDG8sxfautnKmJKaIxZPMmro4ik+ORfP/S8a17JvwtpCiQ8eCsHwYK96f10iorrKLChB1Ok0bI5LwaGL+oWUv4cdhgV7YkSwF9p7cOgB1T8WTzJqyOKJ45uIqKnILixF1Ok0bDmVgoMXbuh17bVzt8XwYC8MD/aEv4cdp1uhesHiSUYNUTypNVq8t/UsvjzA8U1E1PTkFpUh6kwatsal4M/zN1Cq0er2tXG1QZ92rgj0tkeglz38Pe1gaa6UMS01FSyeZFTfxVNWYSle5PgmImom8orLsOtMGrbEpWLfuQyUqrV6+xUS0NbNFp287SsKKgcEetvD2cZCpsTUWLF4klF9Fk/xybmY+l00rudwfBMRNT/5xWXYdy4DcddycTolD/HJecgqLK22rae9JQK97cuLKq/ywsrHyRoK/keT7oDFk4zqq3jafDIFr6yP4fgmIqIKQgik55fgdHIeTqfk4XRyHuKTc3E5s/qZzG1VZgjwskNrFxu426vgbmcJdzuV7mc3OxW7AJux2nx/m9Vzpkbn008/xZIlS5CamoqQkBAsX74cPXv2lC2PnaUZStVaDOjghk84vomICJIkwcPeEh72lrivo7tue0GJGmdT/imoTqfk4WxqPgpK1Dh2ORvHLmff8TntLc3gbl9RVNmpdD+72VUUW/YquNqoYGdpxrNYxDNPt1q7di0mTZqEVatWISwsDB999BHWr1+PhIQEuLu73/Wx9dlt99elTPRo7czxTUREBlJrtLh0oxCnk/NwPecm0vOKkZ5fUnErRlpeSZXxVHejVEhwtDKHo7U5nKwt4GhtASdrczjb/PNz5Z9ONhYVbcxhruRqaKaK3XZ1FBYWhh49emDFihUAAK1WCx8fH7z44ov417/+ddfHyr08CxERGU4IgbybaqTnF+sKqvS8kn8KrLxiZFT8XFCirvXr2KnMYGtpBjOlBHOFAmZKCUqFAuZKCWYKCWbKyp//+dNMKcFcqdDtN1NIUCokKCQJSgWgqPxZkqBQlP9Zub3858q2/+w3q3g9paL8ucv/LM9ipqjMUv76ylt+rnyM3LNFWJkr4WKrMupzstuuDkpLSxEdHY158+bptikUCkRERODw4cNV2peUlKCkpER3Py8vr0FyEhGR8UiSBAdrczhYm99zUs7iMg1yisqQXVSK7KJS3c85RWXIKqy6LbuoFLk3yyAEkF+iRn4dii8q90CINz55rKvcMVg8Vbpx4wY0Gg08PDz0tnt4eODs2bNV2i9atAhvvvlmQ8UjIiKZWZor4emghKeDZY0fo9EK5N4sL6SKSjQo02qh1gioNVqUaSv+1AhotAJqbfnPt+5Ta8Q/j9EKaLUCGlHx560/CwGNFvr7RXkbbcWfGi2g0Wqh1gqoK16zTKst/1Mjqu7TaCtyVWTRyt9RZaY0jeErLJ5qad68eZg9e7bufl5eHnx8fGRMREREpkapkOBsY8H5p5oYFk8VXF1doVQqkZaWprc9LS0Nnp6eVdqrVCqoVMbtdyUiIiLTx+H/FSwsLBAaGopdu3bptmm1WuzatQvh4eEyJiMiIiJTwjNPt5g9ezYmT56M7t27o2fPnvjoo49QWFiIp556Su5oREREZCJYPN3ikUceQUZGBubPn4/U1FR06dIF27ZtqzKInIiIiJovzvNkJJzniYiIqPGpzfc3xzwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDLsxhJ5UTteXl5MichIiKimqr83jZkwRUWT0aSn58PAPDx8ZE5CRERERkqPz8fDg4ONWrLte2MRKvVIjk5GXZ2dpAkSW9fXl4efHx8cPXqVa57Vwt8/+qO72Hd8P2rO76HdcP3r+7u9B4KIZCfnw9vb28oFDUbzcQzT0aiUCjQsmXLu7axt7fnh74O+P7VHd/DuuH7V3d8D+uG71/dVfce1vSMUyUOGCciIiIyAIsnIiIiIgOweGoAKpUKCxYsgEqlkjtKo8T3r+74HtYN37+643tYN3z/6s6Y7yEHjBMREREZgGeeiIiIiAzA4omIiIjIACyeiIiIiAzA4omIiIjIACye6tmnn36K1q1bw9LSEmFhYTh69KjckRqNhQsXQpIkvVvHjh3ljmWy9u/fj1GjRsHb2xuSJOG3337T2y+EwPz58+Hl5QUrKytERETg/Pnz8oQ1Ufd6D5988skqn8mhQ4fKE9YELVq0CD169ICdnR3c3d0xZswYJCQk6LUpLi7G9OnT4eLiAltbW4wfPx5paWkyJTYtNXn/Bg4cWOUz+Pzzz8uU2PR89tln6Ny5s24izPDwcGzdulW331ifPxZP9Wjt2rWYPXs2FixYgBMnTiAkJASRkZFIT0+XO1qj0alTJ6SkpOhuBw4ckDuSySosLERISAg+/fTTavcvXrwYn3zyCVatWoUjR47AxsYGkZGRKC4ubuCkpute7yEADB06VO8zuWbNmgZMaNr27duH6dOn46+//kJUVBTKysowZMgQFBYW6trMmjULf/zxB9avX499+/YhOTkZ48aNkzG16ajJ+wcAzz77rN5ncPHixTIlNj0tW7bEe++9h+joaBw/fhyDBg3C6NGjER8fD8CInz9B9aZnz55i+vTpuvsajUZ4e3uLRYsWyZiq8ViwYIEICQmRO0ajBED8+uuvuvtarVZ4enqKJUuW6Lbl5OQIlUol1qxZI0NC03f7eyiEEJMnTxajR4+WJU9jlJ6eLgCIffv2CSHKP3Pm5uZi/fr1ujZnzpwRAMThw4flimmybn//hBBiwIAB4uWXX5YvVCPk5OQkvvzyS6N+/njmqZ6UlpYiOjoaERERum0KhQIRERE4fPiwjMkal/Pnz8Pb2xtt2rTBxIkTkZSUJHekRikxMRGpqal6n0cHBweEhYXx82igvXv3wt3dHf7+/njhhReQmZkpdySTlZubCwBwdnYGAERHR6OsrEzvc9ixY0e0atWKn8Nq3P7+Vfrhhx/g6uqKoKAgzJs3D0VFRXLEM3kajQY//fQTCgsLER4ebtTPHxcGric3btyARqOBh4eH3nYPDw+cPXtWplSNS1hYGL755hv4+/sjJSUFb775Jvr164dTp07Bzs5O7niNSmpqKgBU+3ms3Ef3NnToUIwbNw5+fn64ePEi/v3vf2PYsGE4fPgwlEql3PFMilarxcyZM9GnTx8EBQUBKP8cWlhYwNHRUa8tP4dVVff+AcCECRPg6+sLb29vnDx5EnPnzkVCQgI2bNggY1rTEhcXh/DwcBQXF8PW1ha//vorAgMDERMTY7TPH4snMlnDhg3T/dy5c2eEhYXB19cX69atw5QpU2RMRs3Vo48+qvs5ODgYnTt3Rtu2bbF3714MHjxYxmSmZ/r06Th16hTHKdbSnd6/qVOn6n4ODg6Gl5cXBg8ejIsXL6Jt27YNHdMk+fv7IyYmBrm5ufj5558xefJk7Nu3z6ivwW67euLq6gqlUlllFH9aWho8PT1lStW4OTo6okOHDrhw4YLcURqdys8cP4/G1aZNG7i6uvIzeZsZM2Zg06ZN2LNnD1q2bKnb7unpidLSUuTk5Oi15+dQ353ev+qEhYUBAD+Dt7CwsEC7du0QGhqKRYsWISQkBB9//LFRP38snuqJhYUFQkNDsWvXLt02rVaLXbt2ITw8XMZkjVdBQQEuXrwILy8vuaM0On5+fvD09NT7PObl5eHIkSP8PNbBtWvXkJmZyc9kBSEEZsyYgV9//RW7d++Gn5+f3v7Q0FCYm5vrfQ4TEhKQlJTEzyHu/f5VJyYmBgD4GbwLrVaLkpISo37+2G1Xj2bPno3Jkyeje/fu6NmzJz766CMUFhbiqaeekjtaozBnzhyMGjUKvr6+SE5OxoIFC6BUKvHYY4/JHc0kFRQU6P3vMzExETExMXB2dkarVq0wc+ZMvPPOO2jfvj38/PzwxhtvwNvbG2PGjJEvtIm523vo7OyMN998E+PHj4enpycuXryI1157De3atUNkZKSMqU3H9OnT8eOPP+L333+HnZ2dbhyJg4MDrKys4ODggClTpmD27NlwdnaGvb09XnzxRYSHh6NXr14yp5ffvd6/ixcv4scff8Tw4cPh4uKCkydPYtasWejfvz86d+4sc3rTMG/ePAwbNgytWrVCfn4+fvzxR+zduxfbt2837ufPuBcE0u2WL18uWrVqJSwsLETPnj3FX3/9JXekRuORRx4RXl5ewsLCQrRo0UI88sgj4sKFC3LHMll79uwRAKrcJk+eLIQon67gjTfeEB4eHkKlUonBgweLhIQEeUObmLu9h0VFRWLIkCHCzc1NmJubC19fX/Hss8+K1NRUuWObjOreOwBi9erVujY3b94U06ZNE05OTsLa2lqMHTtWpKSkyBfahNzr/UtKShL9+/cXzs7OQqVSiXbt2olXX31V5ObmyhvchDz99NPC19dXWFhYCDc3NzF48GCxY8cO3X5jff4kIYSoa6VHRERE1FxwzBMRERGRAVg8ERERERmAxRMRERGRAVg8ERERERmAxRMRERGRAVg8ERERERmAxRMRERGRAVg8ERHdZu/evZAkqcoaWMY2cOBAzJw5s15fo6Ya6piJmgIWT0RkEFP6wjeG6o6nd+/eSElJgYODgzyh6llT+x0SNTQWT0RkdEIIqNVquWPUmoWFBTw9PSFJktxRiMgEsXgiohp78sknsW/fPnz88ceQJAmSJOHy5cu6Lp+tW7ciNDQUKpUKBw4cwMWLFzF69Gh4eHjA1tYWPXr0wM6dO/Wes3Xr1vjPf/6Dp59+GnZ2dmjVqhW++OIL3f7S0lLMmDEDXl5esLS0hK+vLxYtWqTbv2zZMgQHB8PGxgY+Pj6YNm0aCgoK9F7j4MGDGDhwIKytreHk5ITIyEhkZ2ff83hu7cL65Zdf0KlTJ6hUKrRu3RpLly416DhqoqSkBHPmzEGLFi1gY2ODsLAw7N27V7f/m2++gaOjI7Zv346AgADY2tpi6NChSElJ0bVRq9V46aWX4OjoCBcXF8ydOxeTJ0/WLQB9p2OuFB0dje7du8Pa2hq9e/dGQkKCQcdA1CwYbTU+ImrycnJyRHh4uHj22WdFSkqKSElJEWq1WregbufOncWOHTvEhQsXRGZmpoiJiRGrVq0ScXFx4ty5c+L1118XlpaW4sqVK7rn9PX1Fc7OzuLTTz8V58+fF4sWLRIKhUKcPXtWCCHEkiVLhI+Pj9i/f7+4fPmy+PPPP8WPP/6oe/yHH34odu/eLRITE8WuXbuEv7+/eOGFF3T7//77b6FSqcQLL7wgYmJixKlTp8Ty5ctFRkbGPY8nOztbCCHE8ePHhUKhEG+99ZZISEgQq1evFlZWVnoL3t7rOKozYMAA8fLLL+vuP/PMM6J3795i//794sKFC2LJkiVCpVKJc+fOCSGEWL16tTA3NxcRERHi2LFjIjo6WgQEBIgJEybonuOdd94Rzs7OYsOGDeLMmTPi+eefF/b29mL06NE1+h2GhYWJvXv3ivj4eNGvXz/Ru3dvgz8nRE0diyciMsjtX/hCCN0X72+//XbPx3fq1EksX75cd9/X11c8/vjjuvtarVa4u7uLzz77TAghxIsvvigGDRoktFptjfKtX79euLi46O4/9thjok+fPrU6nsriacKECeL+++/Xa/Pqq6+KwMDAGh/HvV77ypUrQqlUiuvXr+u1GTx4sJg3b54Qorx4AiAuXLig2//pp58KDw8P3X0PDw+xZMkS3X21Wi1atWqlK57udcw7d+7Ubdu8ebMAIG7evHnHYyBqjthtR0RG0717d737BQUFmDNnDgICAuDo6AhbW1ucOXMGSUlJeu06d+6s+1mSJHh6eiI9PR1AeTdTTEwM/P398dJLL2HHjh16j925cycGDx6MFi1awM7ODk888QQyMzNRVFQEAIiJicHgwYPrdFxnzpxBnz599Lb16dMH58+fh0ajqdFx3EtcXBw0Gg06dOgAW1tb3W3fvn24ePGirp21tTXatm2ru+/l5aV7jdzcXKSlpaFnz566/UqlEqGhoTU+1luPwcvLCwBqfAxEzYWZ3AGIqOmwsbHRuz9nzhxERUXhgw8+QLt27WBlZYUHH3wQpaWleu3Mzc317kuSBK1WCwDo1q0bEhMTsXXrVuzcuRMPP/wwIiIi8PPPP+Py5csYOXIkXnjhBbz77rtwdnbGgQMHMGXKFJSWlsLa2hpWVlb1e9A1PI57KSgogFKpRHR0NJRKpd4+W1vbu76GEKKWiau69fkrB8zX9BiImgueeSIig1hYWOidbbmbgwcP4sknn8TYsWMRHBwMT09PvcHJNWVvb49HHnkE//3vf7F27Vr88ssvyMrKQnR0NLRaLZYuXYpevXqhQ4cOSE5O1nts586dsWvXrjodT0BAAA4ePFjl2Dp06FCl0Kmtrl27QqPRID09He3atdO7eXp61ug5HBwc4OHhgWPHjum2aTQanDhxQq+dIb9DIqqKZ56IyCCtW7fGkSNHcPnyZdja2sLZ2fmObdu3b48NGzZg1KhRkCQJb7zxhsFnMZYtWwYvLy907doVCoUC69evh6enJxwdHdGuXTuUlZVh+fLlGDVqFA4ePIhVq1bpPX7evHkIDg7GtGnT8Pzzz8PCwgJ79uzBQw89BFdX1xodzyuvvIIePXrg7bffxiOPPILDhw9jxYoVWLlypUHHcjcdOnTAxIkTMWnSJCxduhRdu3ZFRkYGdu3ahc6dO2PEiBE1ep4XX3wRixYtQrt27dCxY0csX74c2dnZetMuGPI7JKKqeOaJiAwyZ84cKJVKBAYGws3Nrcr4pVstW7YMTk5O6N27N0aNGoXIyEh069bNoNezs7PD4sWL0b17d/To0QOXL1/Gli1boFAoEBISgmXLluH9999HUFAQfvjhB71pDIDyomTHjh2IjY1Fz549ER4ejt9//x1mZmY1Pp5u3bph3bp1+OmnnxAUFIT58+fjrbfewpNPPmnQsdzL6tWrMWnSJLzyyivw9/fHmDFjcOzYMbRq1arGzzF37lw89thjmDRpEsLDw2Fra4vIyEhYWlrq2hjyOySiqiRhzM5yIiIyKVqtFgEBAXj44Yfx9ttvyx2HqElgtx0RURNy5coV7NixAwMGDEBJSQlWrFiBxMRETJgwQe5oRE0Gu+2IiJoQhUKBb775Bj169ECfPn0QFxeHnTt3IiAgQO5oRE0Gu+2IiIiIDMAzT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQG+H9n6W6W5uqvUwAAAABJRU5ErkJggg==\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Periodic Frequent patterns using PFPGrowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.periodicFrequentPattern.basic import PFPGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.PFPGrowth(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('periodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "bda0daea-5361-4c66-d8d7-d9ca5551dcd4" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", - "Total No of patterns: 25454\n", - "Runtime: 21.575378894805908\n", - "Memory (RSS): 610709504\n", - "Memory (USS): 563843072\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'periodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "ac970cc4-f00e-47f5-c786-5451b65293bf" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "330\t:102:4598 \n", - "102\t:109:4201 \n", - "856\t:109:4260 \n", - "856\t490\t:103:4431 \n", - "856\t490\t906\t:103:4431 \n", - "856\t906\t:103:4431 \n", - "191\t:111:4449 \n", - "191\t339\t:106:4449 \n", - "191\t339\t90\t:102:4449 \n", - "191\t339\t90\t914\t:102:4449 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "ee5a26bb-4cc7-4949-943d-cd4ae23e550f" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 08:10:49-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 862KB/s in 5.2s \n", + "\n", + "2023-08-28 08:10:56 (862 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "69e1657d-e988-484e-9944-b506b53883df" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Periodic Frequent patterns using PFPGrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "4906ae9c-fb50-414d-d715-801a0269e123" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "d58f64be-f85a-47a2-b136-4500bb9506d5" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _periodicFrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the PFPGrowth algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.periodicFrequentPattern.basic import PFPGrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 5000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of PFPGrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of PFPGrowth algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.PFPGrowth(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['PFPGrowth', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "272bcaef-c7dc-4563-9144-60666f7df85a" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", - "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", - "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", - "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", - "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Periodic Frequent patterns using PFPGrowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.periodicFrequentPattern.basic import PFPGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.PFPGrowth(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('periodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bda0daea-5361-4c66-d8d7-d9ca5551dcd4" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", + "Total No of patterns: 25454\n", + "Runtime: 21.575378894805908\n", + "Memory (RSS): 610709504\n", + "Memory (USS): 563843072\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'periodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ac970cc4-f00e-47f5-c786-5451b65293bf" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "330\t:102:4598 \n", + "102\t:109:4201 \n", + "856\t:109:4260 \n", + "856\t490\t:103:4431 \n", + "856\t490\t906\t:103:4431 \n", + "856\t906\t:103:4431 \n", + "191\t:111:4449 \n", + "191\t339\t:106:4449 \n", + "191\t339\t90\t:102:4449 \n", + "191\t339\t90\t914\t:102:4449 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _periodicFrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the PFPGrowth algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.periodicFrequentPattern.basic import PFPGrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 5000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of PFPGrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of PFPGrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.PFPGrowth(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['PFPGrowth', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "272bcaef-c7dc-4563-9144-60666f7df85a" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", + "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", + "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", + "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", + "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "89a32439-1b80-43ae-f9b3-9bc05e411e2b" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximumPeriodCount patterns runtime memory\n", + "0 PFPGrowth 100 5000 25454 24.550280 614481920\n", + "1 PFPGrowth 150 5000 18976 21.977171 612569088\n", + "2 PFPGrowth 200 5000 13231 21.550775 610091008\n", + "3 PFPGrowth 250 5000 7673 19.546447 606396416\n", + "4 PFPGrowth 300 5000 4529 19.533360 602624000\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "a0978be3-192d-4d38-bb6f-aa307861e8b1" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "89a32439-1b80-43ae-f9b3-9bc05e411e2b" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximumPeriodCount patterns runtime memory\n", - "0 PFPGrowth 100 5000 25454 24.550280 614481920\n", - "1 PFPGrowth 150 5000 18976 21.977171 612569088\n", - "2 PFPGrowth 200 5000 13231 21.550775 610091008\n", - "3 PFPGrowth 250 5000 7673 19.546447 606396416\n", - "4 PFPGrowth 300 5000 4529 19.533360 602624000\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "a0978be3-192d-4d38-bb6f-aa307861e8b1" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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iIqoKQ04946yywHczeqFbc3uoC0vx3JdxOMB5V0RERJUw5NRD9lZKfBPqhyfaOqKwtByh645ix0nOuyIiIroXQ049ZaU0xeqJ3THUxxWl5QIvbDqB9YevyF0WERGRwWDIqceUpgr875kumNDLA0IAr287g0+jLnDKLhERERhy6j0ThYS3hnfACwPbAAA+jPwDb+88B42GQYeIiBo2hhwjIEkS5v6jLd4Y6g0AWHsoGS9vOYnSco3MlREREcmHIceITOnjiWXP+MBEIeHHE39i5vp4FJWWy10WERGRLBhyjMzIrm5YPfHOvKuo81mYuOYI1IWcd0VERA0PQ44RerL93XlXFqY4knITY1cdRlZekdxlERER1SmGHCPV07MxvpvuDwcbc5xLz8XTK2M574qIiBoUhhwj5u2qwg+z/OHe2BJXsgswegXnXRERUcPBkGPkPJpY4/uZd+ZdZeVx3hURETUcDDkNgLPKAptn+HPeFRERNSgMOQ2EnZVZpXlX2znvioiIjBhDTgNSMe9q2N15Vy9y3hURERkxhpwGpmLe1UT/v+ZdfcJ5V0REZIQYchoghULCm8M64MW7864+ivwDb+5I5LwrIiIyKgw5DZQkSXjpH22x+O68q4iYFMzjvCsiIjIiDDkN3OQ+nvjfM11gqpCw9cSfmLE+HoUlnHdFRET1H0MOYUTXZlg9sTsszBTYez4LE9fGcd4VERHVeww5BAAY0N5JO+/qaMotzrsiIqJ6jyGHtHq0aIzNMzjvioiIjANDDunwalp53tX5jFy5yyIiIqo2hhyqxKOJNX6Y2RvtXe7Ou1oZi/grN+Uui4iIqFoYcqhKTioLfDfdH74ejZBbVIbxX8Zhf1KW3GURERHpjSGH7svOygzfhPihfztHFJVqELruGH5K+FPusoiIiPTCkEMPZKk0weqJ3TG8iyvKNAJzvkvA+tgUucsiIiJ6KIYceigzEwWWjemCSRXzrn46i49/47wrIiIybAw5pBeFQsLiYR0wJ/DOvKtlv3HeFRERGTaGHNKbJEmYE9gWbw7rAODOvKu5mxM474qIiAwSQw5V26TeLfDx2DvzrrYlpGH618c474qIiAwOQw49kuFd/pp3tS/pOuddERGRwWHIoUc2oL0Tvgnxg+ruvKtnvojlvCsiIjIYDDn0WLq3aIzvZvjD0dYc5zPy8NSKWKRmc94VERHJjyGHHptXUxV+mNkbzRtbIfVmAUavjMG5dM67IiIieTHkUI1o3sQK38/0R3sXW1zPK8YzX8TiWArnXRERkXwYcqjGVMy76n533tVza+Kw7zznXRERkTwYcqhG2VmZYX2IHwbcnXc17WvOuyIiInkw5FCNs1SaYNXE7hhxz7yrrznvioiI6hhDDtUKMxMFPhrTBZN7t4AQwKKfzuJ/v/3BeVdERFRnGHKo1igUEt4Y6o2XAtsCAP732wUs3n6W866IiKhOMORQrZIkCS8GtsFbwztAkoB1sVfwEuddERFRHWDIoTox0b8F/vfMnXlXPyWkYRrnXRERUS1jyKE6M7xLM6yedGfe1f6k65iwJg7qAs67IiKi2sGQQ3VqQDsnbAi9M+/q2JVbeGZVLLJyOe+KiIhqHkMO1Tlfj8bYPNMfThXzrlZy3hUREdU8hhySRXsXFb6f2RseTTjvioiIagdDDsmmeRMrbJnpD6+mKlzPK8YYzrsiIqIaxJBDsnKytcCm6b3Qo0Uj5HHeFRER1SCGHJKdnaUZvp7qhyfbO2nnXW07wXlXRET0eBhyyCBYKk3wxQRfjOzaTDvvKuJQstxlERFRPcaQQwbDzESBD5/2weTeLQAAi3ckYlkk510REdGjYcghg1Ix72ruP+7Mu/o46gLe4LwrIiJ6BAw5ZHAkScILA9vg7bvzrr6OvYI53yWgpIzzroiISH8MOWSwJvi3wMdju8JUIWH7yTRMX895V0REpD+GHDJow3xc8eWk7rA0M8H+pOt4jvOuiIhITww5ZPD6t3PCN6F+sLM0Q/yVWxjzBeddERHRwzHkUL3g69EIm2fcmXeVlJmH0StjcCX7ttxlERGRAWPIoXqjnYstfph1Z97V1ZuFGL0iFolpnHdFRERVY8ihesW98V/zrm7kF+OZVbE4ynlXRERUBYYcqncqzbv6Mg57z2fKXRYRERmYaoWcJUuWoEePHrC1tYWTkxNGjBiBpKQknTVFRUUICwtDkyZNYGNjg9GjRyMzU/cFKDU1FcHBwbCysoKTkxPmz5+PsrIynTX79+9Ht27dYG5ujtatWyMiIqJSPcuXL0eLFi1gYWEBPz8/HDlypDqHQ/VYxbyrge2dUFymwbSv4znvioiIdFQr5Bw4cABhYWE4fPgwIiMjUVpaikGDBuH27b8uAH3ppZewY8cObNmyBQcOHEBaWhpGjRql3V5eXo7g4GCUlJQgJiYG69atQ0REBBYtWqRdk5ycjODgYAwYMAAJCQmYM2cOQkND8csvv2jXfPfdd5g7dy7eeOMNHD9+HD4+PggKCkJWFidYNxSWShOsvDvvqvzuvKuvOO+KiIgqiMeQlZUlAIgDBw4IIYTIyckRZmZmYsuWLdo1586dEwBEbGysEEKIXbt2CYVCITIyMrRrVqxYIVQqlSguLhZCCPHKK6+IDh066PyuZ555RgQFBWl/7tmzpwgLC9P+XF5eLlxdXcWSJUvuW29RUZFQq9Xa29WrVwUAoVarH+NvgeRWXq4Ri7efER4LdgqPBTvFh78mCY1GI3dZRERUS9RqtV6v3491TY5arQYANG7cGAAQHx+P0tJSBAYGate0b98ezZs3R2xsLAAgNjYWnTp1grOzs3ZNUFAQcnNzcfbsWe2ae/dRsaZiHyUlJYiPj9dZo1AoEBgYqF1TlSVLlsDOzk57c3d3f5zDJwOhUEhY9C9vzLs77+qTqAtY9BPnXRERNXSPHHI0Gg3mzJmDPn36oGPHjgCAjIwMKJVK2Nvb66x1dnZGRkaGds29Aadie8W2B63Jzc1FYWEhbty4gfLy8irXVOyjKgsXLoRardberl69Wv0DJ4MkSRJmD2yDt0d0hCQB6w9fwYucd0VE1KCZPuoDw8LCcObMGURHR9dkPbXK3Nwc5ubmcpdBtWhCLw/YW5ph7uYE7DiZhtzCUqx4rhuslI/8T52IiOqpR3onJzw8HDt37sS+ffvg5uamvd/FxQUlJSXIycnRWZ+ZmQkXFxftmr9/26ri54etUalUsLS0hIODA0xMTKpcU7EPariG+rjiy0k9YGlmggN/XMdzX8Yhp6BE7rKIiKiOVSvkCCEQHh6OrVu3Yu/evfD09NTZ7uvrCzMzM0RFRWnvS0pKQmpqKvz9/QEA/v7+OH36tM63oCIjI6FSqeDt7a1dc+8+KtZU7EOpVMLX11dnjUajQVRUlHYNNWxPtHXUzrs6npqDZ744jEzOuyIialiqczXzrFmzhJ2dndi/f79IT0/X3goKCrRrZs6cKZo3by727t0rjh07Jvz9/YW/v792e1lZmejYsaMYNGiQSEhIEHv27BGOjo5i4cKF2jWXL18WVlZWYv78+eLcuXNi+fLlwsTEROzZs0e7ZtOmTcLc3FxERESIxMREMX36dGFvb6/zra2H0ffqbKq/zqfnih7/Fyk8FuwUfd+NEsnX8+UuiYiIHpO+r9/VCjkAqrx99dVX2jWFhYXi+eefF40aNRJWVlZi5MiRIj09XWc/KSkpYsiQIcLS0lI4ODiIefPmidLSUp01+/btE126dBFKpVK0bNlS53dU+PTTT0Xz5s2FUqkUPXv2FIcPH67O4TDkNBCp2bfFE+/tFR4LdgrftyPF2T95vomI6jN9X78lIUSD/Z5tbm4u7OzsoFaroVKp5C6HatH1vGJMWnsEiem5sLUwxZpJPdDTs7HcZRER0SPQ9/Wbs6uoQXC0NcemGb3Qs0Vj5BWVYcKaOESd47wrIiJjxpBDDYbKwgxfh/REoNedeVfT18fjx+PX5C6LiIhqCUMONSgWZiZY8ZwvRt2ddzV380msjea8KyIiY8SQQw2OmYkCHzztg6l97rRAeGtnIj76NQkN+PI0IiKjxJBDDZJCIeH1f3nh5UF3513tvYjXfzqDcs67IiIyGgw51GBJkoTwJ9vg/+7Ou/rmcCpe3HSC866IiIwEQw41eM/18sCn47rCzETCzlPpCP36GApKyuQui4iIHhNDDhGAf3X+a97VwT+uYzznXRER1XsMOUR3PdHWERum3Zl3dSI1B6NXxCDlxm25yyIiokfEkEN0j27NG2HLTH+4qCxw6fptDF9+CNEXbshdFhERPQKGHKK/aetsi+3hfdC1uT3UhaWYuDYOa6OT+RVzIqJ6hiGHqApOKgtsmt4LT/m6QSPu9NJ55ftTKC4rl7s0IiLSE0MO0X2Ym5rg/ac64/V/eUMhAVvir2HcqsPIyiuSuzQiItIDQw7RA0iShJC+noiY0hMqC1McT83B8M8O4fQ1tdylERHRQzDkEOmhX1tH/BTeF60crZGuLsJTK2Ow/WSa3GUREdEDMOQQ6cnTwRpbw/rgyfZ3ppi/sPEE3ttzHhqOgiAiMkgMOUTVoLIww+qJ3THziVYAgM/3X8K0r48hr6hU5sqIiOjvGHKIqslEIeHVIe3x8dguMDdVIOp8FkZ+zsaBRESGhiGH6BEN79JM2zjwYlY+GwcSERkYhhyix9DZzZ6NA4mIDBRDDtFjclJZYOM03caBC35g40AiIrkx5BDVAAuzO40D/xPsBYUEbD52Dc+ujmPjQCIiGTHkENUQSZIQGtBS2zgw/sotNg4kIpIRQw5RDWPjQCIiw8CQQ1QLKhoHDmjnyMaBREQyYcghqiUqCzN8OakHGwcSEcmEIYeoFlU0DvzfM2wcSERU1xhyiOrAiK7NsHmGP5xV5mwcSERURxhyiOqIj7s9doT31TYOnPTVEXx1iI0DiYhqC0MOUR2qaBw4upsbyjUCb+5g40AiotrCkENUxyzMTPDB02wcSERU2xhyiGRQ0TjwKzYOJCKqNQw5RDJ6oq0jtoX1QUs2DiQiqnEMOUQya+log21/axz4/i9sHEhE9LgYcogMQEXjwBlPtAQALN93CdPXs3EgEdHjYMghMhAmCgkLh3hh2TM+UJoq8Nu5LIxi40AiokfGkENkYEZ2dcOWu40DL7BxIBHRI2PIITJAFY0Du7izcSAR0aNiyCEyUE4qC2yarts48NUfTrNxIBGRnhhyiAzY3xsHfnfsKp5dHYfrecVyl0ZEZPAYcogM3L2NA23vNg4c9lk0zvzJxoFERA/CkENUTzzR1hE//a1x4A42DiQiui+GHKJ6pKJxYP92jigq1WA2GwcSEd0XQw5RPaOyMMMaNg4kInoohhyieuh+jQOvZLNxIBFRBYYconrs740Dh312CIcusnEgERHAkENU7/m422N7eF/43G0cOHEtGwcSEQEMOURGwVllge+m98Kobs3YOJCI6C6GHCIjYWFmgg+f9mHjQCKiuxhyiIwIGwcSEf2FIYfICLFxIBERQw6R0aqqceAHvySxcSARNRgMOURGTNs4sN+dxoGf7buI6evj2TiQiBoEhhwiI2eikLDwn/c2Dsxk40AiahAYcogaiJFd3bCZjQOJqAFhyCFqQLpU0Tgwgo0DichIMeQQNTDaxoFd7zQOXLwjEQt/PI2SMo3cpRER1SiGHKIGyMLMBB+O8cG//3mnceCmo1fx7OrDbBxIREaFIYeogZIkCdP6tcTayT1ga2GKY1duYTgbBxKREWHIIWrg+rdzwra7jQPT2DiQiIwIQw4RoZWjDbY+3wdPtGXjQCIyHgw5RAQAsLM0w9rJlRsH5heXyVwZEdGjYcghIq2qGwceYuNAIqqXGHKIqJKKxoFOtub4IzMfw5ezcSAR1T8MOURUpS7u9tgx+07jwJwCNg4kovqHIYeI7ouNA4moPmPIIaIHYuNAIqqvGHKI6KHYOJCI6iOGHCLSm7ZxoMNfjQN3nmLjQCIyTNUOOQcPHsTQoUPh6uoKSZKwbds2ne2TJ0+GJEk6t8GDB+usuXnzJsaPHw+VSgV7e3uEhIQgPz9fZ82pU6cQEBAACwsLuLu747333qtUy5YtW9C+fXtYWFigU6dO2LVrV3UPh4iqqZWjDbaG/dU4MPxbNg4kIsNU7ZBz+/Zt+Pj4YPny5fddM3jwYKSnp2tvGzdu1Nk+fvx4nD17FpGRkdi5cycOHjyI6dOna7fn5uZi0KBB8PDwQHx8PN5//30sXrwYq1at0q6JiYnBuHHjEBISghMnTmDEiBEYMWIEzpw5U91DIqJqqmgcOP2exoEzvmHjQCIyLJJ4jO+DSpKErVu3YsSIEdr7Jk+ejJycnErv8FQ4d+4cvL29cfToUXTv3h0AsGfPHvzzn//EtWvX4OrqihUrVuDf//43MjIyoFQqAQCvvvoqtm3bhvPnzwMAnnnmGdy+fRs7d+7U7rtXr17o0qULVq5cqVf9ubm5sLOzg1qthkqleoS/ASL68fg1vHr3G1dtnW2wemJ3eDSxlrssIjJi+r5+18o1Ofv374eTkxPatWuHWbNmITs7W7stNjYW9vb22oADAIGBgVAoFIiLi9Ou6devnzbgAEBQUBCSkpJw69Yt7ZrAwECd3xsUFITY2Nj71lVcXIzc3FydGxE9nlHd2DiQiAxTjYecwYMH4+uvv0ZUVBTeffddHDhwAEOGDEF5eTkAICMjA05OTjqPMTU1RePGjZGRkaFd4+zsrLOm4ueHranYXpUlS5bAzs5Oe3N3d3+8gyUiAPc0DnSz0zYOXBeTwsaBRCSrGg85Y8eOxbBhw9CpUyeMGDECO3fuxNGjR7F///6a/lXVtnDhQqjVau3t6tWrcpdEZDScVRb4boY/Rt5tHPjG9rNsHEhEsqr1r5C3bNkSDg4OuHjxIgDAxcUFWVlZOmvKyspw8+ZNuLi4aNdkZmbqrKn4+WFrKrZXxdzcHCqVSudGRDXHwswEH43xwWv/bK9tHDj+y8O4kc/GgURU92o95Fy7dg3Z2dlo2rQpAMDf3x85OTmIj4/Xrtm7dy80Gg38/Py0aw4ePIjS0lLtmsjISLRr1w6NGjXSromKitL5XZGRkfD396/tQyKiB5AkCdP7tcKau40Dj6bcwrBP2TiQiOpetUNOfn4+EhISkJCQAABITk5GQkICUlNTkZ+fj/nz5+Pw4cNISUlBVFQUhg8fjtatWyMoKAgA4OXlhcGDB2PatGk4cuQIDh06hPDwcIwdOxaurq4AgGeffRZKpRIhISE4e/YsvvvuO3z88ceYO3euto4XX3wRe/bswYcffojz589j8eLFOHbsGMLDw2vgr4WIHtcANg4kIrmJatq3b58AUOk2adIkUVBQIAYNGiQcHR2FmZmZ8PDwENOmTRMZGRk6+8jOzhbjxo0TNjY2QqVSiSlTpoi8vDydNSdPnhR9+/YV5ubmolmzZmLp0qWVatm8ebNo27atUCqVokOHDuLnn3+u1rGo1WoBQKjV6ur+NRCRnnIKSsSENXHCY8FO4bFgp3h/z3lRXq6Ruywiqsf0ff1+rD459R375BDVjXKNwNLd57D692QAwD+8nbHsmS6wMTeVuTIiqo9k7ZNDRHQvE4WEfwd748OnfaA0VSAyMROjPj+E1OwCuUsjIiPGkENEdWa0rxu+m95L2zhw2PJoxLBxIBHVEoYcIqpTXZs30mkcOIGNA4moljDkEFGdY+NAIqoLDDlEJIt7GwdKbBxIRLWAIYeIZFPROHDtpB6wNf+rceDZNDYOJKLHx5BDRLIb0N4JW+9pHDh6RQx+PpUud1lEVM8x5BCRQWjtZIOtYX3Qr60jiko1CPv2OD78NQkaDS9IJqJHw5BDRAbDztIMX03ugWkBngCAT/dexIxv4pFfXCZzZURUHzHkEJFB0WkcaMLGgUT06BhyiMggjfZ1w6YZveB4b+PAS2wcSET6Y8ghIoPVrXkj7Ai/p3HgmiP4OpaNA4lIPww5RGTQXOx0Gwcu+uksXtvKxoFE9HAMOURk8CoaBy4ccqdx4MYjbBxIRA/HkENE9YIkSZjxhG7jwOGfHWLjQCK6L4YcIqpXKhoHejpY48+cQjYOJKL7YsghonqntZMNtj2v2zjwIzYOJKK/YcghonrJzsoMayd11zYO/GTvRcxk40AiugdDDhHVW6YmCp3Ggb8mZmL05zFsHEhEABhyiMgI3Ns4MCkzj40DiQgAQw4RGYmKxoGd72kcGHEomY0DiRowhhwiMhoudhbYPMMfI7q4olwjsHhHIuZtPonCknK5SyMiGTDkEJFRsTAzwbJnuuA/wV4wUUj48cSfGL0iBldv8jodooaGIYeIjI4kSQgNaIn1IT3RxFqJxPRcDP0sGr9fuC53aURUhxhyiMho9W7lgB2z/7pOZ9LaI1ix/xKv0yFqIBhyiMioudpbYvMMf4zp7gaNAN7dcx7PbzjOfjpEDQBDDhEZPQszE7w7ujPeGdkRZiYSdp/JwMjlh3D5er7cpRFRLWLIIaIGQZIkjPfzwKbp/nBWmeNCVj6Gf3YIkYmZcpdGRLWEIYeIGhRfj0bYMbsverZojLziMkz7+hjnXhEZKYYcImpwnGwtsGGaHyb3bgHgztyrkHVHoS4olbcwIqpRDDlE1CCZmSiweFgHfDTGB+amCuxLuo5hy6NxPiNX7tKIqIYw5BBRgzaqmxt+mNUbbo0scSW7ACOXx2D7yTS5yyKiGsCQQ0QNXsdmdtgR3hcBbRxQWFqOFzaewDs/J6KsXCN3aUT0GBhyiIgANLJWImJKT8zq3woAsPr3ZExYcwTZ+cUyV0ZEj4ohh4joLhOFhAWD22PF+G6wVpog9nI2hn4ajZNXc+QujYgeAUMOEdHfDOnUFNvC+qClgzXS1EV4+otYbD56Ve6yiKiaGHKIiKrQxtkW28L7INDLGSVlGrzywym8tvU0isvK5S6NiPTEkENEdB8qCzOsmuCLef9oC0kCvo1LxdhVh5GZWyR3aUSkB4YcIqIHUCgkzB7YBmsn9YDKwhQnUnMQ/Ek0jiTflLs0InoIhhwiIj0MaO+E7eF90d7FFjfyi/Hs6sOIOJQMITgOgshQMeQQEemphYM1fny+N4b6uKJMI7B4RyLmbT6JolJep0NkiBhyiIiqwUppik/GdsF/gr1gopDw44k/MXpFDK7eLJC7NCL6G4YcIqJqkiQJoQEtsT6kJ5pYK3E2LRdDP4vG7xeuy10aEd2DIYeI6BH1buWAHbP7orObHXIKSjFp7RGs2H+J1+kQGQiGHCKix+Bqb4nNM/wxprsbNAJ4d895PL/hOPKLy+QujajBY8ghInpMFmYmeHd0Z7wzsiPMTCTsPpOBkcsP4fL1fLlLI2rQGHKIiGqAJEkY7+eBTdP94awyx4WsfAz/7BAiEzPlLo2owWLIISKqQb4ejbBjdl/0bNEYecVlmPb1MXwU+Qc0Gl6nQ1TXGHKIiGqYk60FNkzzw+TeLQAAn0RdQMi6o1AXlMpbGFEDw5BDRFQLzEwUWDysAz4a4wNzUwX2JV3HsOXROJ+RK3dpRA0GQw4RUS0a1c0NP8zqDbdGlriSXYCRy2Ow/WSa3GURNQgMOUREtaxjMzvsCO+LgDYOKCwtxwsbT+CdnxNRVq6RuzQio8aQQ0RUBxpZKxExpSdm9W8FAFj9ezImrDmC7PximSsjMl4MOUREdcREIWHB4PZYMb4brJUmiL2cjaGfRuPk1Ry5SyMySgw5RER1bEinptgW1gctHayRpi7C01/EYvPRq3KXRWR0GHKIiGTQxtkW28L7INDLGSVlGrzywyn8e+tplJTxOh2imsKQQ0QkE5WFGVZN8MW8f7SFJAEb4lLxzKpYZOYWyV0akVFgyCEikpFCIWH2wDZYO6kHVBamOJGag+BPonE05abcpRHVeww5REQGYEB7J2wP74v2Lra4kV+McasOY11MCoTgOAiiR8WQQ0RkIFo4WOPH53tjqI8ryjQCb2w/i3mbT6KotFzu0ojqJYYcIiIDYqU0xSdju+A/wV4wUUj48cSfGL0iBldvFshdGlG9w5BDRGRgJElCaEBLrA/piSbWSpxNy8Wwz6Lx+4XrcpdGVK8w5BARGajerRywY3ZfdHazw62CUkxaewQr9l/idTpEemLIISIyYK72ltg8wx9jurtBI4B395xH2LfHkV9cJndpRAaPIYeIyMBZmJng3dGd8c7IjjAzkbDrdAZGLj+Ey9fz5S6NyKAx5BAR1QOSJGG8nwc2TfeHs8ocF7LyMfyzQ4hMzJS7NCKDxZBDRFSP+Ho0wo7ZfdGjRSPkFZdh2tfH8FHkH9BoeJ0O0d8x5BAR1TNOthbYENoLk3u3AAB8EnUBIeuOQl1QKm9hRAaGIYeIqB5SmiqweFgHfDTGB+amCuxLuo5hy6NxPiNX7tKIDEa1Q87BgwcxdOhQuLq6QpIkbNu2TWe7EAKLFi1C06ZNYWlpicDAQFy4cEFnzc2bNzF+/HioVCrY29sjJCQE+fm6F9CdOnUKAQEBsLCwgLu7O957771KtWzZsgXt27eHhYUFOnXqhF27dlX3cIiI6rVR3dzww6zecGtkiSvZBRi5PAbbT6bJXRaRQah2yLl9+zZ8fHywfPnyKre/9957+OSTT7By5UrExcXB2toaQUFBKCr6a6ru+PHjcfbsWURGRmLnzp04ePAgpk+frt2em5uLQYMGwcPDA/Hx8Xj//fexePFirFq1SrsmJiYG48aNQ0hICE6cOIERI0ZgxIgROHPmTHUPiYioXuvYzA47wvsioI0DCkvL8cLGE3jn50SUlWvkLo1IXuIxABBbt27V/qzRaISLi4t4//33tffl5OQIc3NzsXHjRiGEEImJiQKAOHr0qHbN7t27hSRJ4s8//xRCCPH555+LRo0aieLiYu2aBQsWiHbt2ml/HjNmjAgODtapx8/PT8yYMUPv+tVqtQAg1Gq13o8hIjJUZeUasXT3OeGxYKfwWLBTjP0iVtzIK5K7LKIap+/rd41ek5OcnIyMjAwEBgZq77Ozs4Ofnx9iY2MBALGxsbC3t0f37t21awIDA6FQKBAXF6dd069fPyiVSu2aoKAgJCUl4datW9o19/6eijUVv6cqxcXFyM3N1bkRERkLE4WEBYPbY8X4brBWmiD2cjaGfhqNU9dy5C6NSBY1GnIyMjIAAM7Ozjr3Ozs7a7dlZGTAyclJZ7upqSkaN26ss6aqfdz7O+63pmJ7VZYsWQI7Ozvtzd3dvbqHSERk8IZ0aoptYX3Q0sEaaeoiPLUyFpuPXpW7LKI616C+XbVw4UKo1Wrt7epVPumJyDi1cbbFtvA+CPRyRkmZBq/8cAr/3noaJWW8TocajhoNOS4uLgCAzEzdDpyZmZnabS4uLsjKytLZXlZWhps3b+qsqWof9/6O+62p2F4Vc3NzqFQqnRsRkbFSWZhh1QRfzPtHW0gSsCEuFWNXxSIzt+jhDyYyAjUacjw9PeHi4oKoqCjtfbm5uYiLi4O/vz8AwN/fHzk5OYiPj9eu2bt3LzQaDfz8/LRrDh48iNLSvxpbRUZGol27dmjUqJF2zb2/p2JNxe8hIiJAoZAwe2AbrJ3UAyoLUxxPzUHwJ9E4mnJT7tKIal21Q05+fj4SEhKQkJAA4M7FxgkJCUhNTYUkSZgzZw7+7//+D9u3b8fp06cxceJEuLq6YsSIEQAALy8vDB48GNOmTcORI0dw6NAhhIeHY+zYsXB1dQUAPPvss1AqlQgJCcHZs2fx3Xff4eOPP8bcuXO1dbz44ovYs2cPPvzwQ5w/fx6LFy/GsWPHEB4e/vh/K0RERmZAeydsD++L9i62uJFfjHGrDmNdTAqE4DgIMmLV/drWvn37BIBKt0mTJgkh7nyN/PXXXxfOzs7C3NxcDBw4UCQlJensIzs7W4wbN07Y2NgIlUolpkyZIvLy8nTWnDx5UvTt21eYm5uLZs2aiaVLl1aqZfPmzaJt27ZCqVSKDh06iJ9//rlax8KvkBNRQ3O7uFSEf3tc+zXzl747IQpLyuQui6ha9H39loRouDE+NzcXdnZ2UKvVvD6HiBoMIQTWRCdjye7zKNcIdHBVYeVzvnBvbCV3aUR60ff1u0F9u4qIiABJkhAa0BLrQ3qiibUSZ9NyMeyzaERfuCF3aUQ1iiGHiKiB6t3KATtm90VnNzvcKijFxLVxWHngEq/TIaPBkENE1IC52lti8wx/jOnuBo0Alu4+j7BvjyO/uEzu0ogeG0MOEVEDZ2FmgndHd8Y7IzvCzETCrtMZGLn8EC5fz5e7NKLHwpBDRESQJAnj/Tywabo/nFXmuJCVj+GfHUJkYubDH0xkoBhyiIhIy9ejEXbM7oseLRohr7gM074+ho8i/4BGw+t0qP5hyCEiIh1OthbYENoLk3u3AAB8EnUBIeuOQl1Y+uAHEhkYhhwiIqpEaarA4mEd8NEYH5ibKrAv6TqGfRaN8xm5cpdGpDeGHCIiuq9R3dzww6zeaGZviSvZBRi5PAY7TqbJXRaRXhhyiIjogTo2s8PO2X0R0MYBhaXlmL3xBN75ORFl5Rq5SyN6IIYcIiJ6qEbWSkRM6YlZ/VsBAFb/nowJa44gO79Y5sqI7o8hh4iI9GKikLBgcHusGN8N1koTxF7OxtBPo3HqWo7cpRFViSGHiIiqZUinptgW1gctHayRpi7CUytjsfnYVbnLIqqEIYeIiKqtjbMttoX3QaCXM0rKNHjl+1P499bTKCnjdTpkOBhyiIjokagszLBqgi/m/aMtJAnYEJeKsatikZlbJHdpRAAYcoiI6DEoFBJmD2yDtZN6QGVhiuOpOfjXp9E4mnJT7tKIGHKIiOjxDWjvhO3hfdHexRbX84oxbtVhrItJgRAcB0HyYcghIqIa0cLBGj8+3xtDfVxRphF4Y/tZzNtyEkWl5XKXRg0UQw4REdUYK6UpPhnbBf8J9oKJQsKPx//E6BUxuHqzQO7SqAFiyCEioholSRJCA1pifUhPNLZW4mxaLoZ9Fo3oCzfkLo0aGIYcIiKqFb1bOWDH7L7o7GaHWwWlmLg2DisPXOJ1OlRnGHKIiKjWNLO3xOYZ/hjT3Q0aASzdfR5h3x5HfnGZ3KVRA8CQQ0REtcrCzATvju6Md0Z2hJmJhF2nMzBy+SFcvp4vd2lk5BhyiIio1kmShPF+Htg03R/OKnNcyMrH8M8O4bfETLlLIyPGkENERHXG16MRdszuix4tGiGvuAyhXx/DR5F/QKPhdTpU8xhyiIioTjnZWmBDaC9M7t0CAPBJ1AWErDsKdWGpvIWR0WHIISKiOqc0VWDxsA74aIwPzE0V2Jd0HcM+i0ZSRp7cpZERYcghIiLZjOrmhh9m9UYze0tcyS7AiOWHsONkmtxlkZFgyCEiIll1bGaHnbP7IqCNAwpLyzF74wm883Miyso1cpdG9RxDDhERya6RtRIRU3piVv9WAIDVvydjwpojSEzLlbkyqs8k0YBbT+bm5sLOzg5qtRoqlUrucoiICMDu0+mYt+UkCkruDPbs29oBIQGe6N/WEZIkyVwdGQJ9X78ZchhyiIgMzsWsfPzvtz+w+0wGyu9+vbyNkw1C+npiRNdmsDAzkblCkhNDjh4YcoiIDNu1WwWIOJSCTUevakdBONgoMaFXCzzXqzma2JjLXCHJgSFHDww5RET1Q25RKb47chVfHUpGmroIAGBuqsCobm4I6euJ1k42MldIdYkhRw8MOURE9UtpuQa7z2Tgy98v49Q1tfb+ge2dEBLgCf+WTXjdTgPAkKMHhhwiovpJCIGjKbew+vfL+O1cJipeyTq4qhAa4Il/dXaFmQm/QGysGHL0wJBDRFT/Jd+4jbXRydgSfxVFpXd667ioLDC5TwuM69kcdpZmMldINY0hRw8MOURExuPW7RJ8eyQVETEpuJ5XDACwUppgTHd3hPT1hHtjK5krpJrCkKMHhhwiIuNTXFaO7QlpWBOdjPN3Z2EpJCCogwtCA1rC16ORzBXS42LI0QNDDhGR8RJCIPriDaz+PRkH/7iuvb9rc3tMC2iJoA4uMFHwIuX6iCFHDww5REQNQ1JGHtZEX8a2E2kouTsTy72xJab28cTT3d1hY24qc4VUHQw5emDIISJqWLLyivBN7BWsP3wFtwpKAQC2FqZ41q85JvdugaZ2ljJXSPpgyNEDQw4RUcNUWFKOH45fw9roZFy+cRsAYKqQ8K/OTREa0BIdm9nJXCE9CEOOHhhyiIgaNo1GYO/5LHwZfRmHL9/U3t+rZWNMC2iJAe2coOB1OwaHIUcPDDlERFTh9DU1voy+jJ2n0rVDQVs6WiOkrydGd3PjUFADwpCjB4YcIiL6u7ScQqyLScG3R1KRV3RnKGhjayWe6+WBCb084GjLoaByY8jRA0MOERHdT35xGTYfvYq1h5Jx7VYhAEBpqsDILs0QGuCJNs62MlfYcDHk6IEhh4iIHqasXINfzmZi9e+XkXA1R3t//3aOCO3bEn1acyhoXWPI0QNDDhERVUf8lZtYfTAZvyRmaIeCtnexRWhASwzzcYXSlENB6wJDjh4YcoiI6FFcyb6Nrw6lYPOxqygoKQcAONmaY1LvFhjv1xz2VkqZKzRuDDl6YMghIqLHoS4ovTsUNBmZuXeGglqameDp7m6Y2scTLRysZa7QODHk6IEhh4iIakJJmQY/n07D6oPJSEzPBQBIEvAPL2dM69cS3T0a8bqdGsSQoweGHCIiqklCCMReysbq3y9jX9JfQ0F93OwQGtASQzq6wNSE1+08LoYcPTDkEBFRbbmYlYc10cn44fifKCm7MxS0mb0lpvRpgWd6uMPWwkzmCusvhhw9MOQQEVFtu5FfjG8OX8H62CvIvl0CALA1N8XYnu6Y3McTzew5FLS6GHL0wJBDRER1pai0HNtO/Ikvo5NxMSsfAGCikPDPTk0xLcATnd3s5S2wHmHI0QNDDhER1TWNRuDAH9fxZfRlHLqYrb2/Z4vGCA3wxEAvZ5hwKOgDMeTogSGHiIjkdDZNjTXRydhxMg2l5Xdejls0sUJIX0885esOSyWHglaFIUcPDDlERGQIMtRFWBebgg2HryD37lBQeyszPOfngYn+HnBSWchcoWFhyNEDQw4RERmS28Vl+D7+GtZEJyP1ZgEAQGmiwLAurggN8ER7F75WAQw5emHIISIiQ1SuEYhMzMSXv1/GsSu3tPcHtHFAaEBL9Gvj0KCbCzLk6IEhh4iIDN2J1Fv4MjoZu0+nQ3P3Fbutsw1C+7bE8K6uMDdteNftMOTogSGHiIjqi6s3C/DVoRR8dzQVt+8OBXWwMcdEfw8818sDja0bzlBQhhw9MOQQEVF9k1tUik1HUvHVoRSkq4sAABZmCozu5oapfT3RytFG5gprH0OOHhhyiIioviot12DX6XR8+XsyTv+p1t4f6OWE0ICW8PNsbLTX7TDk6IEhh4iI6jshBI4k38Tq35MRdT4TFa/qHZupMC2gJf7ZqSnMjGwoKEOOHhhyiIjImFy+nn93KOg1FJXeGQra1M4Ck3u3wNiezWFnaRxDQRly9MCQQ0RExujm7RJsOHwF62Kv4EZ+MQDAWmmCZ3o0x5Q+LeDe2ErmCh8PQ44eGHKIiMiYFZeV46eENKz5PRlJmXkAAIUEDOnYFKEBnujavJHMFT4ahhw9MOQQEVFDIITA7xduYPXvl/H7hRva+309GmFagCf+4e1Sr4aC6vv6XeNXIi1evBiSJOnc2rdvr91eVFSEsLAwNGnSBDY2Nhg9ejQyMzN19pGamorg4GBYWVnByckJ8+fPR1lZmc6a/fv3o1u3bjA3N0fr1q0RERFR04dCRERkFCRJQr+2jlgf4oc9cwLwtK8blCYKxF+5hZnfHMeAD/Yj4lAybheXPXxn9UitXG7doUMHpKena2/R0dHabS+99BJ27NiBLVu24MCBA0hLS8OoUaO028vLyxEcHIySkhLExMRg3bp1iIiIwKJFi7RrkpOTERwcjAEDBiAhIQFz5sxBaGgofvnll9o4HCIiIqPR3kWF95/2QfSrAxA+oDXsrcyQerMAi3ckwn9JFN7dcx4Zd/vv1Hc1/nHV4sWLsW3bNiQkJFTaplar4ejoiG+//RZPPfUUAOD8+fPw8vJCbGwsevXqhd27d+Nf//oX0tLS4OzsDABYuXIlFixYgOvXr0OpVGLBggX4+eefcebMGe2+x44di5ycHOzZs+e+tRUXF6O4uFj7c25uLtzd3flxFRERNViFJeX4/vg1rI1ORvKN2wAAU4WEYT6uCAnwRAdXO5krrEy2j6sA4MKFC3B1dUXLli0xfvx4pKamAgDi4+NRWlqKwMBA7dr27dujefPmiI2NBQDExsaiU6dO2oADAEFBQcjNzcXZs2e1a+7dR8Wain3cz5IlS2BnZ6e9ubu718jxEhER1VeWShNM6OWBqLlPYPXE7ujp2RhlGoEfT/yJ4E+i8ezqw9h3PgsaTf27hLfGQ46fnx8iIiKwZ88erFixAsnJyQgICEBeXh4yMjKgVCphb2+v8xhnZ2dkZGQAADIyMnQCTsX2im0PWpObm4vCwsL71rZw4UKo1Wrt7erVq497uEREREZBoZDwD29nbJ7hj+3hfTDMxxUmCgkxl7IxJeIoBv3vIDYeSUVRabncperNtKZ3OGTIEO2fO3fuDD8/P3h4eGDz5s2wtLSs6V9XLebm5jA3N5e1BiIiIkPX2c0en4zrigVD2mNdTAo2xqXiYlY+Fv54Gh/8koTnenlggr8HHGwM+zW11vs829vbo23btrh48SJcXFxQUlKCnJwcnTWZmZlwcXEBALi4uFT6tlXFzw9bo1KpZA9SRERExqKZvSVe+6cXYhY+if8Ee6GZvSWyb5fg46gL6L10Lxb+eAoXs/LkLvO+aj3k5Ofn49KlS2jatCl8fX1hZmaGqKgo7fakpCSkpqbC398fAODv74/Tp08jKytLuyYyMhIqlQre3t7aNffuo2JNxT6IiIio5thamCE0oCUOzO+Pz57tCh93e5SUabDxyFUEfnQQU746gkMXb8DQWu/V+LerXn75ZQwdOhQeHh5IS0vDG2+8gYSEBCQmJsLR0RGzZs3Crl27EBERAZVKhdmzZwMAYmJiANz5CnmXLl3g6uqK9957DxkZGZgwYQJCQ0Px3//+F8Cdr5B37NgRYWFhmDp1Kvbu3YsXXngBP//8M4KCgvSulc0AiYiIqk8Igfgrt7D698v4NfGvoaDeTVUIDfDEvzq7Qmlae++jyNbxeOzYsTh48CCys7Ph6OiIvn374p133kGrVq0A3GkGOG/ePGzcuBHFxcUICgrC559/rv0oCgCuXLmCWbNmYf/+/bC2tsakSZOwdOlSmJr+dQnR/v378dJLLyExMRFubm54/fXXMXny5GrVypBDRET0eK5k38ba6GRsPnYNhXcvSnZWmWNS7xYY39MDdlY1PxSUYx30wJBDRERUM3IKSvDtkVREHEpBVt6dnnSWZibYMM0P3Wp4Rpa+r981/u0qIiIianjsrZR4vn9rhPZtiR0n07D698u4kV+CDq7yvYnAkENEREQ1RmmqwGhfN4zq1gxp6iKYm5rIVkutf7uKiIiIGh5JktDMXt62Lgw5REREZJQYcoiIiMgoMeQQERGRUWLIISIiIqPEkENERERGiSGHiIiIjBJDDhERERklhhwiIiIySgw5REREZJQYcoiIiMgoMeQQERGRUWLIISIiIqPEkENERERGyVTuAuQkhAAA5ObmylwJERER6avidbvidfx+GnTIycvLAwC4u7vLXAkRERFVV15eHuzs7O67XRIPi0FGTKPRIC0tDba2tpAkqcb2m5ubC3d3d1y9ehUqlarG9mtIjP0YeXz1n7EfI4+v/jP2Y6zN4xNCIC8vD66urlAo7n/lTYN+J0ehUMDNza3W9q9SqYzyH+69jP0YeXz1n7EfI4+v/jP2Y6yt43vQOzgVeOExERERGSWGHCIiIjJKDDm1wNzcHG+88QbMzc3lLqXWGPsx8vjqP2M/Rh5f/Wfsx2gIx9egLzwmIiIi48V3coiIiMgoMeQQERGRUWLIISIiIqPEkENERERGiSGnGg4ePIihQ4fC1dUVkiRh27ZtOtuFEFi0aBGaNm0KS0tLBAYG4sKFCzprbt68ifHjx0OlUsHe3h4hISHIz8+vw6O4vwcdX2lpKRYsWIBOnTrB2toarq6umDhxItLS0nT20aJFC0iSpHNbunRpHR9J1R52/iZPnlyp9sGDB+usMeTzBzz8GP9+fBW3999/X7vGUM/hkiVL0KNHD9ja2sLJyQkjRoxAUlKSzpqioiKEhYWhSZMmsLGxwejRo5GZmamzJjU1FcHBwbCysoKTkxPmz5+PsrKyujyU+3rYMd68eROzZ89Gu3btYGlpiebNm+OFF16AWq3W2U9V53jTpk11fTiV6HMO+/fvX6n2mTNn6qypz+cwJSXlvs/DLVu2aNcZ6jlcsWIFOnfurG3w5+/vj927d2u3G9pzkCGnGm7fvg0fHx8sX768yu3vvfcePvnkE6xcuRJxcXGwtrZGUFAQioqKtGvGjx+Ps2fPIjIyEjt37sTBgwcxffr0ujqEB3rQ8RUUFOD48eN4/fXXcfz4cfz4449ISkrCsGHDKq196623kJ6err3Nnj27Lsp/qIedPwAYPHiwTu0bN27U2W7I5w94+DHee2zp6elYu3YtJEnC6NGjddYZ4jk8cOAAwsLCcPjwYURGRqK0tBSDBg3C7du3tWteeukl7NixA1u2bMGBAweQlpaGUaNGabeXl5cjODgYJSUliImJwbp16xAREYFFixbJcUiVPOwY09LSkJaWhg8++ABnzpxBREQE9uzZg5CQkEr7+uqrr3TO4YgRI+r4aCrT5xwCwLRp03Rqf++997Tb6vs5dHd3r/Q8fPPNN2FjY4MhQ4bo7MsQz6GbmxuWLl2K+Ph4HDt2DE8++SSGDx+Os2fPAjDA56CgRwJAbN26VfuzRqMRLi4u4v3339fel5OTI8zNzcXGjRuFEEIkJiYKAOLo0aPaNbt37xaSJIk///yzzmrXx9+PrypHjhwRAMSVK1e093l4eIhly5bVbnE1oKrjmzRpkhg+fPh9H1Ofzp8Q+p3D4cOHiyeffFLnvvpyDrOysgQAceDAASHEneebmZmZ2LJli3bNuXPnBAARGxsrhBBi165dQqFQiIyMDO2aFStWCJVKJYqLi+v2APTw92OsyubNm4VSqRSlpaXa+/Q594agquN74oknxIsvvnjfxxjjOezSpYuYOnWqzn315RwKIUSjRo3El19+aZDPQb6TU0OSk5ORkZGBwMBA7X12dnbw8/NDbGwsACA2Nhb29vbo3r27dk1gYCAUCgXi4uLqvObHpVarIUkS7O3tde5funQpmjRpgq5du+L99983mLeR9bF//344OTmhXbt2mDVrFrKzs7XbjO38ZWZm4ueff67yXYD6cA4rPqJp3LgxACA+Ph6lpaU6z8H27dujefPmOs/BTp06wdnZWbsmKCgIubm52v8TNSR/P8b7rVGpVDA11R1FGBYWBgcHB/Ts2RNr166FMMCWaPc7vg0bNsDBwQEdO3bEwoULUVBQoN1mbOcwPj4eCQkJVT4PDf0clpeXY9OmTbh9+zb8/f0N8jnYoAd01qSMjAwA0DlxFT9XbMvIyICTk5POdlNTUzRu3Fi7pr4oKirCggULMG7cOJ3Bay+88AK6deuGxo0bIyYmBgsXLkR6ejo++ugjGavVz+DBgzFq1Ch4enri0qVLeO211zBkyBDExsbCxMTEqM4fAKxbtw62trY6byUD9eMcajQazJkzB3369EHHjh0B3Hl+KZXKSqH778/Bqp6jFdsMSVXH+Hc3btzA22+/Xekj07feegtPPvkkrKys8Ouvv+L5559Hfn4+XnjhhbooXS/3O75nn30WHh4ecHV1xalTp7BgwQIkJSXhxx9/BGB853DNmjXw8vJC7969de435HN4+vRp+Pv7o6ioCDY2Nti6dSu8vb2RkJBgcM9BhhyqttLSUowZMwZCCKxYsUJn29y5c7V/7ty5M5RKJWbMmIElS5YYfOvysWPHav/cqVMndO7cGa1atcL+/fsxcOBAGSurHWvXrsX48eNhYWGhc399OIdhYWE4c+YMoqOj5S6l1jzsGHNzcxEcHAxvb28sXrxYZ9vrr7+u/XPXrl1x+/ZtvP/++wbxAlnhfsd3b2Dr1KkTmjZtioEDB+LSpUto1apVXZf5WB52DgsLC/Htt9/qnK8KhnwO27Vrh4SEBKjVanz//feYNGkSDhw4IHdZVeLHVTXExcUFACpdRZ6Zmand5uLigqysLJ3tZWVluHnzpnaNoasIOFeuXEFkZKTOuzhV8fPzQ1lZGVJSUuqmwBrUsmVLODg44OLFiwCM4/xV+P3335GUlITQ0NCHrjW0cxgeHo6dO3di3759cHNz097v4uKCkpIS5OTk6Kz/+3OwqudoxTZDcb9jrJCXl4fBgwfD1tYWW7duhZmZ2QP35+fnh2vXrqG4uLi2Sq6Whx3fvfz8/ABA53loDOcQAL7//nsUFBRg4sSJD92fIZ1DpVKJ1q1bw9fXF0uWLIGPjw8+/vhjg3wOMuTUEE9PT7i4uCAqKkp7X25uLuLi4uDv7w8A8Pf3R05ODuLj47Vr9u7dC41Go30iG7KKgHPhwgX89ttvaNKkyUMfk5CQAIVCUeljnvrg2rVryM7ORtOmTQHU//N3rzVr1sDX1xc+Pj4PXWso51AIgfDwcGzduhV79+6Fp6enznZfX1+YmZnpPAeTkpKQmpqq8xw8ffq0TlitCOve3t51cyAP8LBjBO78d2XQoEFQKpXYvn17pXfiqpKQkIBGjRrJ/k6cPsf3dwkJCQCg8zys7+ewwpo1azBs2DA4Ojo+dL+Gcg6rotFoUFxcbJjPwRq/lNmI5eXliRMnTogTJ04IAOKjjz4SJ06c0H67aOnSpcLe3l789NNP4tSpU2L48OHC09NTFBYWavcxePBg0bVrVxEXFyeio6NFmzZtxLhx4+Q6JB0POr6SkhIxbNgw4ebmJhISEkR6err2VnFFfExMjFi2bJlISEgQly5dEt98841wdHQUEydOlPnI7njQ8eXl5YmXX35ZxMbGiuTkZPHbb7+Jbt26iTZt2oiioiLtPgz5/Anx8H+jQgihVquFlZWVWLFiRaXHG/I5nDVrlrCzsxP79+/X+fdXUFCgXTNz5kzRvHlzsXfvXnHs2DHh7+8v/P39tdvLyspEx44dxaBBg0RCQoLYs2ePcHR0FAsXLpTjkCp52DGq1Wrh5+cnOnXqJC5evKizpqysTAghxPbt28Xq1avF6dOnxYULF8Tnn38urKysxKJFi+Q8NCHEw4/v4sWL4q233hLHjh0TycnJ4qeffhItW7YU/fr10+6jvp/DChcuXBCSJIndu3dX2ochn8NXX31VHDhwQCQnJ4tTp06JV199VUiSJH799VchhOE9BxlyqmHfvn0CQKXbpEmThBB3vkb++uuvC2dnZ2Fubi4GDhwokpKSdPaRnZ0txo0bJ2xsbIRKpRJTpkwReXl5MhxNZQ86vuTk5Cq3ARD79u0TQggRHx8v/Pz8hJ2dnbCwsBBeXl7iv//9r05IkNODjq+goEAMGjRIODo6CjMzM+Hh4SGmTZum8zVHIQz7/Anx8H+jQgjxxRdfCEtLS5GTk1Pp8YZ8Du/37++rr77SriksLBTPP/+8aNSokbCyshIjR44U6enpOvtJSUkRQ4YMEZaWlsLBwUHMmzdP5+vXcnrYMd7v/AIQycnJQog7bQ26dOkibGxshLW1tfDx8RErV64U5eXl8h3YXQ87vtTUVNGvXz/RuHFjYW5uLlq3bi3mz58v1Gq1zn7q8zmssHDhQuHu7l7leTHkczh16lTh4eEhlEqlcHR0FAMHDtQGHCEM7zkoCWFg30kjIiIiqgG8JoeIiIiMEkMOERERGSWGHCIiIjJKDDlERERklBhyiIiIyCgx5BAREZFRYsghIiIio8SQQ0REREaJIYeI6oX9+/dDkqRKw/+IiO6HIYeI6oXevXsjPT0ddnZ2ej+moKAACxcuRKtWrWBhYQFHR0c88cQT+Omnn2qxUiIyFKZyF0BEpA+lUgkXF5dqPWbmzJmIi4vDp59+Cm9vb2RnZyMmJgbZ2dm1VCURGRK+k0NEsujfvz9mz56NOXPmoFGjRnB2dsbq1atx+/ZtTJkyBba2tmjdujV2794NoPLHVREREbC3t8cvv/wCLy8v2NjYYPDgwUhPT9f+ju3bt+O1117DP//5T7Ro0QK+vr6YPXs2pk6dql0jSRK2bdumU5u9vT0iIiIAACkpKZAkCZs2bULv3r1hYWGBjh074sCBA7X690NEj48hh4hks27dOjg4OODIkSOYPXs2Zs2ahaeffhq9e/fG8ePHMWjQIEyYMAEFBQVVPr6goAAffPAB1q9fj4MHDyI1NRUvv/yydruLiwt27dqFvLy8x651/vz5mDdvHk6cOAF/f38MHTqU7wgRGTiGHCKSjY+PD/7zn/+gTZs2WLhwISwsLODg4IBp06ahTZs2WLRoEbKzs3Hq1KkqH19aWoqVK1eie/fu6NatG8LDwxEVFaXdvmrVKsTExKBJkybo0aMHXnrpJRw6dOiRag0PD8fo0aPh5eWFFStWwM7ODmvWrHmkfRFR3WDIISLZdO7cWftnExMTNGnSBJ06ddLe5+zsDADIysqq8vFWVlZo1aqV9uemTZvqrO3Xrx8uX76MqKgoPPXUUzh79iwCAgLw9ttvV7tWf39/7Z9NTU3RvXt3nDt3rtr7IaK6w5BDRLIxMzPT+VmSJJ37JEkCAGg0Gr0fL4SotCYgIAALFizAr7/+irfeegtvv/02SkpK7vuY0tLSRzsgIjIoDDlE1KB4e3ujrKwMRUVFAABHR0edi5UvXLhQ5TVAhw8f1v65rKwM8fHx8PLyqv2CieiR8SvkRGS0+vfvj3HjxqF79+5o0qQJEhMT8dprr2HAgAFQqVQAgCeffBKfffYZ/P39UV5ejgULFlR6hwgAli9fjjZt2sDLywvLli3DrVu3dL6lRUSGh+/kEJHRCgoKwrp16zBo0CB4eXlh9uzZCAoKwubNm7VrPvzwQ7i7uyMgIADPPvssXn75ZVhZWVXa19KlS7F06VL4+PggOjoa27dvh4ODQ10eDhFVkyT+/mE0ERFppaSkwNPTEydOnECXLl3kLoeIqoHv5BAREZFRYsghIiIio8SPq4iIiMgo8Z0cIiIiMkoMOURERGSUGHKIiIjIKDHkEBERkVFiyCEiIiKjxJBDRERERokhh4iIiIwSQw4REREZpf8H9kku6m+eG8sAAAAASUVORK5CYII=\n" 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] + ] + } + ] } diff --git a/notebooks/periodicFrequentPattern/basic/PFPGrowthPlus.ipynb b/notebooks/periodicFrequentPattern/basic/PFPGrowthPlus.ipynb index 5bf9ebb1..7c610ec7 100644 --- a/notebooks/periodicFrequentPattern/basic/PFPGrowthPlus.ipynb +++ b/notebooks/periodicFrequentPattern/basic/PFPGrowthPlus.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Periodic Frequent patterns in Temporal Databases using PFPGrowthPlus" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Periodic Frequent patterns in a temporal database using the PFPGrowthPlus algorithm. In the final part, we describe an advanced approach, where we evaluate the PFPGrowthPlus algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "db8dc0bb-f311-4668-84d9-85627c9a6b05" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m10.3 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", + "Collecting resource (from pami)\n", + " Downloading Resource-0.2.1-py2.py3-none-any.whl (25 kB)\n", + "Collecting validators (from pami)\n", + " Downloading validators-0.21.2-py3-none-any.whl (25 kB)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", + "Requirement already 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(8.2.3)\n", + "Collecting JsonForm>=0.0.2 (from resource->pami)\n", + " Downloading JsonForm-0.0.2.tar.gz (2.4 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting JsonSir>=0.0.2 (from resource->pami)\n", + " Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in 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- ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Periodic Frequent patterns in a temporal database using the PFPGrowthPlus algorithm. In the final part, we describe an advanced approach, where we evaluate the PFPGrowthPlus algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "db8dc0bb-f311-4668-84d9-85627c9a6b05" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "70eb00c3-ca1c-4fd0-80d4-d01bfb9b5043" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 08:25:10-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 2.51MB/s in 1.7s \n", - "\n", - "2023-08-28 08:25:12 (2.51 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "b3306282-e95a-4541-f0df-821e439cb105" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Periodic Frequent patterns using PFPGrowthPlus" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "b5603be9-a4f3-43b6-874d-c0da41a910db" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "001e35af-3e3d-43a6-952b-3a30fc278a8a" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Periodic Frequent patterns using PFPGrowthPlus" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.periodicFrequentPattern.basic import PFPGrowthPlus as alg #import the algorithm\n", - "\n", - "obj = alg.PFPGrowthPlus(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('periodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "ab6cd69f-67d0-42e1-99f2-9232025ad969" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n", - "Total No of patterns: 25454\n", - "Runtime: 26.73489499092102\n", - "Memory (RSS): 609894400\n", - "Memory (USS): 562933760\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'periodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8aa26461-bb48-4a55-d328-f4377aadc102" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "330\t:102:4598 \n", - "102\t:109:4201 \n", - "856\t:109:4260 \n", - "856\t490\t:103:4431 \n", - "856\t490\t906\t:103:4431 \n", - "856\t906\t:103:4431 \n", - "191\t:111:4449 \n", - "191\t339\t:106:4449 \n", - "191\t339\t90\t:102:4449 \n", - "191\t339\t90\t914\t:102:4449 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "70eb00c3-ca1c-4fd0-80d4-d01bfb9b5043" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 08:25:10-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 2.51MB/s in 1.7s \n", + "\n", + "2023-08-28 08:25:12 (2.51 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "b3306282-e95a-4541-f0df-821e439cb105" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Periodic Frequent patterns using PFPGrowthPlus" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "b5603be9-a4f3-43b6-874d-c0da41a910db" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "001e35af-3e3d-43a6-952b-3a30fc278a8a" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _periodicFrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the PFPGrowthPlus algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.periodicFrequentPattern.basic import PFPGrowthPlus as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 5000\n", - "minimumSupportCountList = [100, 200, 300, 400, 500]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of PFPGrowthPlus" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of PFPGrowthPlus algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.PFPGrowthPlus(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['PFPGrowthPlus', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "990324ff-ba76-4fa6-d6a6-9cd1ed09ddf9" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n", - "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n", - "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n", - "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n", - "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Periodic Frequent patterns using PFPGrowthPlus" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.periodicFrequentPattern.basic import PFPGrowthPlus as alg #import the algorithm\n", + "\n", + "obj = alg.PFPGrowthPlus(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('periodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ab6cd69f-67d0-42e1-99f2-9232025ad969" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n", + "Total No of patterns: 25454\n", + "Runtime: 26.73489499092102\n", + "Memory (RSS): 609894400\n", + "Memory (USS): 562933760\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'periodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8aa26461-bb48-4a55-d328-f4377aadc102" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "330\t:102:4598 \n", + "102\t:109:4201 \n", + "856\t:109:4260 \n", + "856\t490\t:103:4431 \n", + "856\t490\t906\t:103:4431 \n", + "856\t906\t:103:4431 \n", + "191\t:111:4449 \n", + "191\t339\t:106:4449 \n", + "191\t339\t90\t:102:4449 \n", + "191\t339\t90\t914\t:102:4449 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _periodicFrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the PFPGrowthPlus algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.periodicFrequentPattern.basic import PFPGrowthPlus as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 5000\n", + "minimumSupportCountList = [100, 200, 300, 400, 500]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of PFPGrowthPlus" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of PFPGrowthPlus algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.PFPGrowthPlus(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['PFPGrowthPlus', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "990324ff-ba76-4fa6-d6a6-9cd1ed09ddf9" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n", + "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n", + "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n", + "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n", + "periodic-frequent patterns were generated successfully using PFPGrowth++ algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "2b219af8-48a5-45f6-9ec5-d6d036af8a4b" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximumPeriodCount patterns runtime memory\n", + "0 PFPGrowthPlus 100 5000 25454 22.967165 614412288\n", + "1 PFPGrowthPlus 200 5000 13231 19.419037 609107968\n", + "2 PFPGrowthPlus 300 5000 4529 18.698286 602087424\n", + "3 PFPGrowthPlus 400 5000 1999 15.910377 591564800\n", + "4 PFPGrowthPlus 500 5000 1072 15.570678 579190784\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "bae99aac-5960-4605-c062-8d65fb6fac17" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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VVkRERLeCIccGebur8dbI2mGr5ckncOBMkbwFERER2SGGHBv1YHgAHurNYSsiIqJbxZBjwxY9GgYfdxWO5pfgo1+52oqIiKgxGHJsmFcbFf56edhqRfIJZJwukrcgIiIiO8KQY+OGhQXgkYhAmAUwa00GKqo5bEVERNQQDDl24M1HesHHXY1jBSX4O4etiIiIGoQhxw60baPC3x6rHbb6dNsJ7M+9JHNFREREto8hx07E9vLHyD4ctiIiImoohhw7svCRXvD1UOPE+VIsTTgqdzlEREQ2jSHHjni6qfD2Y+EAgH/9dhL7OGxFRER0XQw5duaBUB0ev6Mdh62IiIhugiHHDr3xcC/4eahx8nwpPticLXc5RERENokhxw5p3Zyx+PHaYavPtucg7feLMldERERkexhy7NTQEB1G9W0PIYDZaw5w2IqIiOgqDDl2bMHDodBp1Dh5oRTv/8JhKyIioisx5NgxraszljzeGwDw+Y4c7DnFYSsiIqI6DDl2bkhPPzzRr27YKgPlVRy2IiIiAhhyHMLrD4XCX+OCU4VleI/DVkRERAAYchyC1tUZi0fVrrZauTMHu3M4bEVERMSQ4yCG9PDDU/2Daoet/puBsqoauUsiIiKSFUOOA/nLQyEI0Lrg98IyvLuJw1ZERNS6MeQ4EI2LM5aMql1tFb/zFHadLJS5IiIiIvkw5DiY+7r7YvSAIADAq/89wGErIiJqtRhyHNBrD4agnacrci+W4Z2fj8hdDhERkSwYchyQh4szllxebbUq5XfsPHFB5oqIiIhaHkOOgxrYzRdjIoMB1A5blVZy2IqIiFoXhhwHVjdsdeZSOZZw2IqIiFoZhhwH5q52wrt/ql1ttXrX79h5nMNWRETUejDkOLh7uvrgmbtqh61m//cASjhsRURErQRDTiswb3gI2rd1xdmicizeeFjucoiIiFoEQ04r0OaKYauvUnOx/RiHrYiIyPEx5LQSd3fxwXNRHQAAc74/gOKKapkrIiIial4MOa3InGE9EeRVO2z19kautiIiIsfGkNOKtFE74d1REQCAr3fnYtvR8zJXRERE1HwYclqZqC7eGH93RwDA3O8PwMhhKyIiclAMOa3Qq8N6oIO3G84ZKvD2Bq62IiIix8SQ0wq5qZzw7qja1Vbf7DmNpOwCmSsiIiJqegw5rVRk5z+Greb9cJDDVkRE5HAYclqxV4f1QEdvN+QZKvDX9Vlyl0NERNSkGHJaMTeVE957IgKSBHy39wy2HuGwFREROQ6GnFbuzo5emHB3JwDA3B8OwFDOYSsiInIMDDmE2bE90MmnDfKNlXiLw1ZEROQgGHIIriol3vtTb0gS8N+0M9hyJF/ukoiIiG4bQw4BAPp39MKkey4PW31/EIYyDlsREZF9Y8ghi1mxPdDZpw0Kiivx5vpMucshIiK6LY0KOYsXL8add94JDw8P+Pn5YeTIkcjOzrZqU1FRgenTp8Pb2xvu7u4YNWoU8vOthz9yc3MxYsQIuLm5wc/PD7Nnz0ZNTY1Vm6SkJPTt2xdqtRpdu3ZFfHz8NfUsW7YMHTt2hIuLCyIjI7F79+7GdIeu4uKsxHtPREAhAT/sO4tfszhsRURE9qtRISc5ORnTp0/Hrl27kJCQgOrqasTExKC0tNTS5uWXX8b//vc/rFmzBsnJyTh37hwef/xxy36TyYQRI0agqqoKO3fuxKpVqxAfH48FCxZY2uTk5GDEiBEYMmQI0tPTMWPGDEyePBm//PKLpc23336LmTNn4o033sC+ffsQERGB2NhYFBRwGfTt6NehLSYP7AwAmLf2IIrKqmSuiIiI6BaJ21BQUCAAiOTkZCGEEEVFRcLZ2VmsWbPG0ubw4cMCgEhJSRFCCLFx40ahUCiEXq+3tFm+fLnQaDSisrJSCCHEq6++Knr16mX1WU899ZSIjY21/DxgwAAxffp0y88mk0kEBgaKxYsXN7h+g8EgAAiDwdCIXju+8qoacf/7W0WHOevFjG/2y10OERGRlYZ+f9/WnByDwQAA8PLyAgCkpaWhuroa0dHRljY9e/ZEcHAwUlJSAAApKSkIDw+HTqeztImNjYXRaERmZqalzZXHqGtTd4yqqiqkpaVZtVEoFIiOjra0qU9lZSWMRqPVRtdycVbi/cvDVmv3n8XmTL3cJRERETXaLYccs9mMGTNm4J577kFYWBgAQK/XQ6VSwdPT06qtTqeDXq+3tLky4NTtr9t3ozZGoxHl5eW4cOECTCZTvW3qjlGfxYsXQ6vVWragoKDGd7yVuCO4LaYMqh22em3tIVwq5bAVERHZl1sOOdOnT8ehQ4fwzTffNGU9zWrevHkwGAyW7fTp03KXZNNeju6Orn7uuFBSiYX/42orIiKyL7cUcuLi4rB+/Xps3boV7du3t7zu7++PqqoqFBUVWbXPz8+Hv7+/pc3Vq63qfr5ZG41GA1dXV/j4+ECpVNbbpu4Y9VGr1dBoNFYbXd+Vw1Y/pp/DpkMctiIiIvvRqJAjhEBcXBzWrl2LLVu2oFOnTlb7+/XrB2dnZyQmJlpey87ORm5uLqKiogAAUVFROHjwoNUqqISEBGg0GoSGhlraXHmMujZ1x1CpVOjXr59VG7PZjMTEREsbahp9gjzx/H1dAACvrzuIixy2IiIie9GY2czTpk0TWq1WJCUliby8PMtWVlZmafPCCy+I4OBgsWXLFrF3714RFRUloqKiLPtrampEWFiYiImJEenp6WLTpk3C19dXzJs3z9Lm5MmTws3NTcyePVscPnxYLFu2TCiVSrFp0yZLm2+++Uao1WoRHx8vsrKyxNSpU4Wnp6fVqq2b4eqqhqmorhEPfJgkOsxZL+L+s0/ucoiIqJVr6Pd3o0IOgHq3lStXWtqUl5eLP//5z6Jt27bCzc1NPPbYYyIvL8/qOKdOnRLDhw8Xrq6uwsfHR7zyyiuiurraqs3WrVtFnz59hEqlEp07d7b6jDqffPKJCA4OFiqVSgwYMEDs2rWrMd1hyGmEjNOXROd5G0SHOevFxgPn5C6HiIhasYZ+f0tCCCHXVSS5GY1GaLVaGAwGzs9pgPd+OYJlW0/Au40Km18eBG93tdwlERFRK9TQ728+u4oa7P+GdkMPnQcKS6uw4CeutiIiItvGkEMNpnaqXW2lVEjYcCAPGw7kyV0SERHRdTHkUKOEt9fiz4NrV1vN//EQLpRUylwRERFR/RhyqNFevL8bevp74GJpFRb8eEjucoiIiOrFkEONpnJS4P0nIuCkkLDxoB7rD5yTuyQiIqJrMOTQLQlrp8Wfh3QFAMxfdwjnizlsRUREtoUhh25Z3JCuCAnQ4FJZNeavO4RWfDcCIiKyQQw5dMtqh616w0khYVOmHv/jaisiIrIhDDl0W3oFahF3f+2w1YIfD6GguELmioiIiGox5NBtmz6kK0IDNCgqq8brazlsRUREtoEhh26bs1KBD56MgLNSwuasfPyUwdVWREQkP4YcahIhARq8eH83AMCCHzNRYOSwFRERyYshh5rMtMFdENZOA0N5NV7jsBUREcmMIYeajLOy9iaBzkoJvx7Ox7r0s3KXRERErRhDDjWpnv4avDS0dtjqjR8zkc9hKyIikglDDjW5F+7rgvB2WhgravDaDwc5bEVERLJgyKEm53R5tZVKqUDikQL8sI/DVkRE1PIYcqhZdNd54KXo2mGrhf/LhN7AYSsiImpZDDnUbJ4f1BkR7bUorqjBvB8OcNiKiIhaFEMONRuny6utVEoFtmafx3/TzshdEhERtSIMOdSsuuk88PID3QEAi/6XhTxDucwVERFRa8GQQ81uysBOiAjyRHFlDeZ+z9VWRETUMhhyqNk5KRX44IneUDkpkHz0PNbs5bAVERE1P4YcahFd/TzwyuVhq7fWZ+FcEYetiIioeTHkUIuZPLAz7gi+PGzFmwQSEVEzY8ihFqNUSHj/iQionRTYdvQ8vt1zWu6SiIjIgTHkUIvq4uuOWTE9AAB/3XAYZzlsRUREzYQhh1rcxHs7oV+HtiiprMHc73mTQCIiah4MOdTilAoJ7/2pN9ROCvx27AK+3s1hKyIianoMOSSLzr7umB1bO2z1tw1ZOH2xTOaKiIjI0TDkkGwm3NMJ/Tu0RWmVCXP5bCsiImpiDDkkG6VCwntPRMDFWYEdxwvxVWqu3CUREZEDYcghWXXyaYNXY3sCAN7eeJjDVkRE1GQYckh24+/uiAEdvVBWZcKr/z0As5nDVkREdPsYckh2CoWEd//UG67OSqScLMRXqb/LXRIRETkAhhyyCR192mDOsNrVVot/PoLcQg5bERHR7WHIIZvxXFRHRHaqHbaa/d8MDlsREdFtYcghm6FQSHjvTxFwUymRmnMRq3dx2IqIiG4dQw7ZlGBvN8wdXrvaasnPR/B7YanMFRERkb1iyCGb80xkB9zV2Qvl1SbM5morIiK6RQw5ZHOuHLbanXMRq1JOyV0SERHZIYYcsklBXm6Y92AIAOCdTUdw6gKHrYiIqHEYcshmjR0QjLu7eKOi2szVVkRE1GgMOWSzFAoJ74zqjTYqJfacuoSVO0/JXRIREdkRhhyyaUFebnhtRO2w1Xu/HMHJ8yUyV0RERPaCIYds3pgBwbi3q8/lYasDMHHYioiIGoAhh2yeJElYMioc7monpP1+CSt35MhdEhER2QGGHLIL7du64S+WYatsnOCwFRER3QRDDtmNp+8MwsBuPqisMWP2mgwOWxER0Q0x5JDdkKTa1VYeaifsyy3C59tPyl0SERHZMIYcsiuBnq54/aHaYav3Nx/F8QIOWxERUf0YcsjuPNk/CPd190VVjRmzOGxFRETXwZBDdqdutZWHixPSTxfhX79x2IqIiK7FkEN2KUDrivkPhQIAPkw4imP5xTJXREREtoYhh+zWE/3aY0iPP4atakxmuUsiIiIbwpBDdkuSJCx+vDc8XJyQccaATzlsRUREV2DIIbvmr3XBGw/3AgD8PeEYjnLYioiILmPIIbs3qm873N/TD1UmDlsREdEfGh1ytm3bhocffhiBgYGQJAnr1q2z2j9+/HhIkmS1DRs2zKrNxYsXMXbsWGg0Gnh6emLSpEkoKbG+38mBAwcwcOBAuLi4ICgoCO++++41taxZswY9e/aEi4sLwsPDsXHjxsZ2hxxA7bBVODQuTjhwxoD/t43DVkREdAshp7S0FBEREVi2bNl12wwbNgx5eXmW7euvv7baP3bsWGRmZiIhIQHr16/Htm3bMHXqVMt+o9GImJgYdOjQAWlpaXjvvfewcOFCfPrpp5Y2O3fuxOjRozFp0iTs378fI0eOxMiRI3Ho0KHGdokcgE7jgoWPXB62+vUojuiNMldERERyk4QQt3wnNUmSsHbtWowcOdLy2vjx41FUVHTNFZ46hw8fRmhoKPbs2YP+/fsDADZt2oQHH3wQZ86cQWBgIJYvX46//OUv0Ov1UKlUAIC5c+di3bp1OHLkCADgqaeeQmlpKdavX2859l133YU+ffpgxYoVDarfaDRCq9XCYDBAo9Hcwm+AbIkQAlP+vRe/Hi5AWDsN1v75HjgrOSJLRORoGvr93SzfAElJSfDz80OPHj0wbdo0FBYWWvalpKTA09PTEnAAIDo6GgqFAqmpqZY2gwYNsgQcAIiNjUV2djYuXbpkaRMdHW31ubGxsUhJSbluXZWVlTAajVYbOQ5JkvD2Y+HQujrj0FkjViSdkLskIiKSUZOHnGHDhuHf//43EhMT8c477yA5ORnDhw+HyWQCAOj1evj5+Vm9x8nJCV5eXtDr9ZY2Op3Oqk3dzzdrU7e/PosXL4ZWq7VsQUFBt9dZsjl+Ghe8eXnY6uMtx3A4j0GWiKi1avKQ8/TTT+ORRx5BeHg4Ro4cifXr12PPnj1ISkpq6o9qtHnz5sFgMFi206dPy10SNYNH+wTigVAdqk0Cs9ZkoJqrrYiIWqVmn7DQuXNn+Pj44Pjx4wAAf39/FBQUWLWpqanBxYsX4e/vb2mTn59v1abu55u1qdtfH7VaDY1GY7WR45EkCX97LAyebs7IPGfEP7dy2IqIqDVq9pBz5swZFBYWIiAgAAAQFRWFoqIipKWlWdps2bIFZrMZkZGRljbbtm1DdXW1pU1CQgJ69OiBtm3bWtokJiZafVZCQgKioqKau0tkB/w8/hi2+mTLMWSeM8hcERERtbRGh5ySkhKkp6cjPT0dAJCTk4P09HTk5uaipKQEs2fPxq5du3Dq1CkkJibi0UcfRdeuXREbGwsACAkJwbBhwzBlyhTs3r0bO3bsQFxcHJ5++mkEBgYCAMaMGQOVSoVJkyYhMzMT3377LT766CPMnDnTUsdLL72ETZs24YMPPsCRI0ewcOFC7N27F3FxcU3wayFH8EhEIGJ76VBjFpi15gCqajhsRUTUqohG2rp1qwBwzTZu3DhRVlYmYmJihK+vr3B2dhYdOnQQU6ZMEXq93uoYhYWFYvTo0cLd3V1oNBoxYcIEUVxcbNUmIyND3HvvvUKtVot27dqJJUuWXFPLd999J7p37y5UKpXo1auX2LBhQ6P6YjAYBABhMBga+2sgO1FgrBB93vxFdJizXny4OVvucoiIqAk09Pv7tu6TY+94n5zW4X8Z5/Di1/vhpJCwbvo9CGunlbskIiK6DbLeJ4fIljzUOwDDw/wvD1tlcNiKiKiVYMghhydJEt4aGQavNioc0RfjH1uOyV0SERG1AIYcahV83NV469EwAMCypBM4dJarrYiIHB1DDrUaI3oHYER4AEyXh60qa0xyl0RERM2IIYdalUWP9oL35WGrTxKPy10OERE1I4YcalW83dX468jaYavlySdw4EyRvAUREVGzYcihVmd4eAAe6s1hKyIiR8eQQ63SokfD4OOuwtH8Enz0K1dbERE5IoYcapW82qjw15HhAIAVySeQcbpI3oKIiKjJMeRQqzUszB+PRATCLIBZazJQUc1hKyIiR8KQQ63am4/0go+7GscKSvB3DlsRETkUhhxq1dq2UeHtx2pXW3267QT2516SuSIiImoqDDnU6sX08sfIPhy2IiJyNAw5RAAWPtILvh5qnDhfiqUJR+Uuh4iImgBDDhEATzcV3n6sdrXVv347iX0ctiIisnsMOUSXPRCqw+N3tOOwFRGRg2DIIbrCGw/3gp+HGifPl+LdTdlyl0NERLeBIYfoClo3Zyx+vHbY6osdOfhie47MFRER0a1iyCG6ytAQHWZEdwMALFqfhTV7T8tcERER3QqGHKJ6vDS0Gybe0wkAMOf7A/j5YJ7MFRERUWMx5BDVQ5IkzH8oBE/2bw+zAP7vm/1IPnpe7rKIiKgRGHKIrkOSJCx+vDdGhAeg2iTw/Oq92HPqotxlERFRAzHkEN2AUiFh6VN9cF93X1RUmzFx5R4cOmuQuywiImoAhhyim1A5KbDimX4Y0NELxZU1eO6L3TheUCJ3WUREdBMMOUQN4KpS4rPx/RHWToOLpVV45rNUnL5YJndZRER0Aww5RA2kcXHGqgkD0MW3DfTGCjz7eSoKiivkLouIiK6DIYeoEbzd1fhq8l1o39YVpwrL8Oxnu1FUViV3WUREVA+GHKJG8te64KvJkfD1UCM7vxjjVu5BSWWN3GUREdFVGHKIbkEH7zb4clIkPN2ckXG6CFP/vZcP9CQisjEMOUS3qIe/B1ZNGIA2KiV2nihE3H/2o9pklrssIiK6jCGH6DZEBHnis3F3QuWkwK+H8zF7TQbMZiF3WUREBIYcotsW1cUby8f2hZNCwrr0c5j/4yEIwaBDRCQ3hhyiJjA0RIcPn+oDSQK+Ss3Fu79ky10SEVGrx5BD1EQeiQjE30aGAwCWJ53AP5OOy1wREVHrxpBD1ITGRAZj3vCeAIB3N2VjdcopeQsiImrFGHKImtjz93VB3JCuAID5P2Zi7f4zMldERNQ6MeQQNYNXYrpjXFQHAMCsNQewOVMvc0VERK0PQw5RM5AkCW883AuP920Hk1kg7j/7seP4BbnLIiJqVRhyiJqJQiHh3VG9EROqQ5XJjCn/3ot9uZfkLouIqNVgyCFqRk5KBT4Zcwfu7eqDsioTxn+xG4fzjHKXRUTUKjDkEDUztZMSnz7XD32DPWGsqMGzn+9GzoVSucsiInJ4DDlELcBN5YSVEwYgJECDCyWVeOazVJwrKpe7LCIih8aQQ9RCtK7O+PfEAejs0wZni8rxzGepuFBSKXdZREQOiyGHqAX5eqixenIkArUuOHmhFM99vhuG8mq5yyIickgMOUQtrJ2nK76cHAkfdxWy8oyYGL8HZVU1cpdFRORwGHKIZNDZ1x3/nhgJjYsT0n6/hOdXp6GyxiR3WUREDoUhh0gmoYEarJwwAG4qJX47dgEvfZ2OGpNZ7rKIiBwGQw6RjPp1aItPn+0PlVKBTZl6zPn+IMxmIXdZREQOgSGHSGb3dvPBJ2PugFIh4ft9Z7BofRaEYNAhIrpdDDlENiC2lz/e+1NvAED8zlNYmnBU5oqIiOwfQw6RjXi8b3sserQXAODjLcfxr20nZa6IiMi+MeQQ2ZDnojpidmwPAMDfNh7G17tzZa6IiMh+MeQQ2Zg/D+6C5+/rDAB4be1B/C/jnMwVERHZJ4YcIhsjSRLmDuuJMZHBEAJ4+dt0bDmSL3dZRER2hyGHyAZJkoS3Hg3Do30CUWMWmPblPuw6WSh3WUREdoUhh8hGKRUS3n8iAtEhfqisMWNS/B5knC6SuywiIrvBkENkw5yVCvxjTF9EdfZGaZUJ41buxtH8YrnLIiKyCww5RDbOxVmJf43rj4ggTxSVVeOZz1KRW1gmd1lERDav0SFn27ZtePjhhxEYGAhJkrBu3Tqr/UIILFiwAAEBAXB1dUV0dDSOHTtm1ebixYsYO3YsNBoNPD09MWnSJJSUlFi1OXDgAAYOHAgXFxcEBQXh3XffvaaWNWvWoGfPnnBxcUF4eDg2btzY2O4Q2QV3tRNWTbgTPXQeKCiuxNjPd0FvqJC7LCIim9bokFNaWoqIiAgsW7as3v3vvvsuPv74Y6xYsQKpqalo06YNYmNjUVHxx3+Qx44di8zMTCQkJGD9+vXYtm0bpk6datlvNBoRExODDh06IC0tDe+99x4WLlyITz/91NJm586dGD16NCZNmoT9+/dj5MiRGDlyJA4dOtTYLhHZBU83FVZPGoAO3m44fbEcz3yeioulVXKXRURku8RtACDWrl1r+dlsNgt/f3/x3nvvWV4rKioSarVafP3110IIIbKysgQAsWfPHkubn3/+WUiSJM6ePSuEEOKf//ynaNu2raisrLS0mTNnjujRo4fl5yeffFKMGDHCqp7IyEjx/PPPN7h+g8EgAAiDwdDg9xDJLbewVET+7VfRYc568dDHvwlDeZXcJRERtaiGfn836ZycnJwc6PV6REdHW17TarWIjIxESkoKACAlJQWenp7o37+/pU10dDQUCgVSU1MtbQYNGgSVSmVpExsbi+zsbFy6dMnS5srPqWtT9zn1qayshNFotNqI7E2Qlxu+nBwJrzYqHDxrwOT4vSivMsldFhGRzWnSkKPX6wEAOp3O6nWdTmfZp9fr4efnZ7XfyckJXl5eVm3qO8aVn3G9NnX767N48WJotVrLFhQU1NguEtmErn7u+PfEAfBQO2H3qYuY9lUaqmrMcpdFRGRTWtXqqnnz5sFgMFi206dPy10S0S0La6fFFxPuhIuzAknZ5/Hyt+kwmYXcZRER2YwmDTn+/v4AgPx861vQ5+fnW/b5+/ujoKDAan9NTQ0uXrxo1aa+Y1z5GddrU7e/Pmq1GhqNxmojsmd3dvTC/3u2P5yVEjYczMNrPxyEEAw6RERAE4ecTp06wd/fH4mJiZbXjEYjUlNTERUVBQCIiopCUVER0tLSLG22bNkCs9mMyMhIS5tt27ahurra0iYhIQE9evRA27ZtLW2u/Jy6NnWfQ9Ra3NfdFx89fQcUEvDt3tP424bDDDpERLiFkFNSUoL09HSkp6cDqJ1snJ6ejtzcXEiShBkzZuCvf/0rfvrpJxw8eBDPPfccAgMDMXLkSABASEgIhg0bhilTpmD37t3YsWMH4uLi8PTTTyMwMBAAMGbMGKhUKkyaNAmZmZn49ttv8dFHH2HmzJmWOl566SVs2rQJH3zwAY4cOYKFCxdi7969iIuLu/3fCpGdeTA8AEtG9QYAfLY9B59sOS5zRURENqCxy7a2bt0qAFyzjRs3TghRu4x8/vz5QqfTCbVaLYYOHSqys7OtjlFYWChGjx4t3N3dhUajERMmTBDFxcVWbTIyMsS9994r1Gq1aNeunViyZMk1tXz33Xeie/fuQqVSiV69eokNGzY0qi9cQk6O5vPfTooOc9aLDnPWi89/Oyl3OUREzaKh39+SEK33urbRaIRWq4XBYOD8HHIYH/16DEt/PQoAePdPvfFkf64iJCLH0tDv71a1uoqoNfi/oV0x6d5OAIC53x/AzwfzZK6IiEgeDDlEDkaSJLw+IgRP9Q+CWQD/981+JB89L3dZREQtjiGHyAFJkoS3Hw/HiPAAVJsEnl+9F3tOXZS7LCKiFsWQQ+SglAoJS5/qg8E9fFFRbcbElXtw6KxB7rKIiFoMQw6RA1M5KbB8bD8M6OiF4soaPPfFbhwvKJG7LCKiFsGQQ+TgXFVKfDa+P8LaaXCxtArPfJaK0xfL5C6LiKjZMeQQtQIaF2f8e2Ikuvq5Q2+swDOfp6LAWCF3WUREzYohh6iV8GqjwpeTItG+rSt+LyzDs5/vRlFZldxlERE1G4YcolbEX+uCryZHws9Djez8YoxbuQcllTVyl0VE1CwYcohamQ7ebfDl5Eh4ujkj43QRpqzai4pqk9xlERE1OYYcolaou84DqyYMQBuVEiknCxH3n32oNpnlLouIqEkx5BC1UhFBnvhs3J1QOynw6+ECzFqTAbO51T7KjogcEEMOUSsW1cUby5/pCyeFhB/Tz2H+j4fQip/ZS0QOhiGHqJW7v6cOHz7VB5IEfJWai3c2ZctdEhFRk2DIISI8EhGIv40MBwCsSD6BfyYdl7kiIqLbx5BDRACAMZHBeO3BngCAdzdlY3XKKXkLIiK6TQw5RGQxdVAXvHh/VwDA/B8zsXb/GZkrIiK6dQw5RGRl5gPdMf7ujgCAWWsOYHOmXt6CiIhuEUMOEVmRJAkLHgrFqL7tYTILxP1nP3YcvyB3WUREjcaQQ0TXUCgkvDMqHLG9dKgymTHl33uR9vslucsiImoUhhwiqpeTUoGPR9+Bgd18UFZlwoSVu3E4zyh3WUREDcaQQ0TXpXZS4v892w/9OrSFsaIGz36+GzkXSuUui4ioQRhyiOiG3FRO+GL8nQgN0OBCSSWe+SwVZ4vK5S6LiOimGHKI6Ka0rs7496QB6OzTBmeLyvHsZ6m4UFIpd1lERDfEkENEDeLjrsaXkyPRztMVJy+U4rnPd8NQXi13WURE18WQQ0QNFujpii8nR8LHXY2sPCMmxu9BWVWN3GUREdWLIYeIGqWTTxusnjQAGhcnpP1+Cc+vTkNljUnusoiIrsGQQ0SNFhKgQfzEAXBTKfHbsQv4v6/3o8ZklrssIiIrDDlEdEv6BrfFv57rD5VSgV8y8/Hq9wdgNgu5yyIismDIIaJbdk9XH/xjzB1QKiT8sO8sFq3PghAMOkRkGxhyiOi2xPTyx/tP9AYAxO88hQ8TjspcERFRLYYcIrptj93RHm892gsA8MmW4/h02wmZKyIiYsghoibybFRHzI7tAQB4e+MRfL07V+aKiKi1Y8ghoiYzfUhXvHBfFwDAa2sP4qeMczJXREStGUMOETWpOcN6YGxkMIQAZn6bji1H8uUuiYhaKYYcImpSkiThrUfD8GifQNSYBaZ9uQ+7ThbKXRYRtUIMOUTU5BQKCe8/EYHoED9U1pgxKX4PMk4XyV0WEbUyDDlE1CyclQr8Y0xfRHX2RmmVCeNW7sbR/GK5yyKiVoQhh4iajYuzEv8a1x99gjxRVFaNZz5LRW5hmdxlEVErwZBDRM3KXe2E+Al3oofOAwXFlRjz2S7oDRVyl0VErQBDDhE1O083FVZPGoAO3m44c6kcz3yeioulVXKXRUQOjiGHiFqEn8YFX06KRIDWBccLSjDui90wVlTLXRYROTCGHCJqMUFeblg9KRJebVQ4eNaAyfF7UV5lkrssInJQDDlE1KK6+rnj3xMHwEPthN2nLmLaV2moqjHLXRYROSCGHCJqcWHttPhiwp1wcVYgKfs8Xv42HSazkLssInIwDDlEJIs7O3rh/z3bH85KCRsO5uG1Hw5CCAYdImo6DDlEJJv7uvvi46fvgEICvt17Gn/dcJhBh4iaDEMOEclqeHgA3hnVGwDw+fYcfJx4XOaKiMhRMOQQkeye6B+EBQ+FAgCW/noUX2zPkbkiInIEDDlEZBMm3tsJL0d3BwAsWp+F7/aelrkiIrJ3DDlEZDP+b2hXTL63EwBg7vcHsPFgnswVEZE9Y8ghIpshSRL+MiIET/UPglkAL32zH8lHz8tdFhHZKYYcIrIpkiTh7cfDMaJ3AKpNAs+v3os9py7KXRYR2SGGHCKyOUqFhKVP9sHgHr6oqDZj4so9OHTWIHdZRGRnGHKIyCapnBRYPrYfBnTyQnFlDZ77YjeOF5TIXRYR2RGGHCKyWa4qJT4f1x/h7bS4WFqFZz5LxemLZXKXRUR2giGHiGyah4szVk0cgG5+7tAbK/DM56koMFbIXRYR2QGGHCKyeV5tVFg9KRJBXq74vbAMz36+G0VlVXKXRUQ2rslDzsKFCyFJktXWs2dPy/6KigpMnz4d3t7ecHd3x6hRo5Cfn291jNzcXIwYMQJubm7w8/PD7NmzUVNTY9UmKSkJffv2hVqtRteuXREfH9/UXSEiG+KvdcFXk+6Cn4ca2fnFGLdyD0oqa27+RiJqtZrlSk6vXr2Ql5dn2bZv327Z9/LLL+N///sf1qxZg+TkZJw7dw6PP/64Zb/JZMKIESNQVVWFnTt3YtWqVYiPj8eCBQssbXJycjBixAgMGTIE6enpmDFjBiZPnoxffvmlObpDRDYi2NsNX06ORFs3Z2ScLsKUVXtRUW2SuywislGSaOJH/i5cuBDr1q1Denr6NfsMBgN8fX3xn//8B3/6058AAEeOHEFISAhSUlJw11134eeff8ZDDz2Ec+fOQafTAQBWrFiBOXPm4Pz581CpVJgzZw42bNiAQ4cOWY799NNPo6ioCJs2bWpwrUajEVqtFgaDARqN5vY6TkQt5sCZIoz5VypKKmsQHeKH5c/0g7OSo+9ErUVDv7+b5b8Kx44dQ2BgIDp37oyxY8ciNzcXAJCWlobq6mpER0db2vbs2RPBwcFISUkBAKSkpCA8PNwScAAgNjYWRqMRmZmZljZXHqOuTd0xrqeyshJGo9FqIyL707u9Jz4b1x9qJwV+PVyAWWsyYDY36f+vEZEDaPKQExkZifj4eGzatAnLly9HTk4OBg4ciOLiYuj1eqhUKnh6elq9R6fTQa/XAwD0er1VwKnbX7fvRm2MRiPKy8uvW9vixYuh1WotW1BQ0O12l4hkcldnb6x4ph+cFBJ+TD+H+T8eQhNfmCYiO9fkIWf48OF44okn0Lt3b8TGxmLjxo0oKirCd99919Qf1Wjz5s2DwWCwbKdP8ynHRPZsSE8/LH2qDyQJ+Co1F+9sypa7JCKyIc0+iO3p6Ynu3bvj+PHj8Pf3R1VVFYqKiqza5Ofnw9/fHwDg7+9/zWqrup9v1kaj0cDV1fW6tajVamg0GquNiOzbwxGBePuxcADAiuQTWLb1uMwVEZGtaPaQU1JSghMnTiAgIAD9+vWDs7MzEhMTLfuzs7ORm5uLqKgoAEBUVBQOHjyIgoICS5uEhARoNBqEhoZa2lx5jLo2dccgotZl9IBg/OXBEADAe79kY3XKKXkLIiKb0OQhZ9asWUhOTsapU6ewc+dOPPbYY1AqlRg9ejS0Wi0mTZqEmTNnYuvWrUhLS8OECRMQFRWFu+66CwAQExOD0NBQPPvss8jIyMAvv/yC119/HdOnT4darQYAvPDCCzh58iReffVVHDlyBP/85z/x3Xff4eWXX27q7hCRnZgyqDNevL8rAGD+j5lYu/+MzBURkdycmvqAZ86cwejRo1FYWAhfX1/ce++92LVrF3x9fQEAS5cuhUKhwKhRo1BZWYnY2Fj885//tLxfqVRi/fr1mDZtGqKiotCmTRuMGzcOixYtsrTp1KkTNmzYgJdffhkfffQR2rdvj88++wyxsbFN3R0isiMzH+iO4ooaxO88hVlrDuBwXjFie/njjiBPKBSS3OURUQtr8vvk2BPeJ4fI8ZjNAq9+fwD/TfvjSo6PuxrRIX54IFSHe7r6wMVZKWOFRHS7Gvr9zZDDkEPkcMxmgZ8P6bEpU4+kIwUovuLxD67OStzX3RcPhOpwf08/tG2jkrFSIroVDDkNwJBD5PiqasxIzSlEQlY+ErLykWf44wnmSoWE/h3a4oFQHWJC/RHs7SZjpUTUUAw5DcCQQ9S6CCFw6KwRCVl6bM7KxxF9sdX+nv4eeCBUhwdCdQhvp4UkcR4PkS1iyGkAhhyi1u30xTJszspHQpYee05dgumKR0P4a1wsgeeuzt5QOfHZWES2giGnARhyiKhOUVkVthwpQEJWPpKPnkdZ1R9PN/dQO+G+HrXzeIb09IPGxVnGSomIIacBGHKIqD4V1SbsPHHh8jyeAlwoqbTsc1JIiOrijQdCdYgO0SHQ8/p3WSei5sGQ0wAMOUR0M2azQPqZIsvE5eMFJVb7w9pp8ECIP2J66dDT34PzeIhaAENOAzDkEFFjnTxfYgk8abmXcOV/Qdu3dbXM4xnQ0QtOSs7jIWoODDkNwJBDRLfjfHElthypDTy/HbuAyhqzZZ/W1Rn39/RDTKgOg7r7oo26yW8wT9RqMeQ0AEMOETWVsqoa/Hasdh5P4uF8XCqrtuxTOSlwTxdvPBDqj+hQP/h5uMhYKZH9Y8hpAIYcImoOJrNA2u+XsDlTj4TD+fi9sMxq/x3BnpdvQKhDF193zuMhaiSGnAZgyCGi5iaEwLGC2nk8mzP1yDhjsNrfyaeNZR5P3+C2UPJBokQ3xZDTAAw5RNTS8o0VlonLKScKUWX6Yx6PdxsVhob44YFQfwzsxgeJEl0PQ04DMOQQkZyKK6qRfPQ8ErLyseVIAYor/niQqIuzAgO7+SImVIehITp48UGiRBYMOQ3AkENEtqLaZMbunIuWqzxni8ot+xQS0L+Dl2VYq6NPGxkrJZIfQ04DMOQQkS0SQiArz4jNmbWBJyvPaLW/m587Ynrp8ECoP3q300LBeTzUyjDkNABDDhHZgzOXyvBrVj42Z+UjNeei1YNE/TzUiL58hefuLt5QO3EeDzk+hpwGYMghIntjKKvG1uzaB4kmZReg9IoHibZRKTG4h1/tg0R7+EHrxgeJkmNiyGkAhhwismeVNSaknCjE5qx8/JqVj4Ji6weJDujkhZhQHaJDdWjf1k3GSomaFkNOAzDkEJGjMJsFDpw1ICFLj4SsfBzNt36QaGiAxjJxuVeghjcgJLvGkNMADDlE5KhOXSi1rNTa+/tFXDGNB+08r3iQaCcvOPNBomRnGHIagCGHiFqDwpJKbDlSO49n27HzqKj+4waEGhcnDOlZO4/nvu6+8HDhPB6yfQw5DcCQQ0StTXmVCduPX0BClh6JhwtQWFpl2adSKhDVxdtylUen4YNEyTYx5DQAQw4RtWYms8D+3Eu1z9XKykfOhVKr/RHttbUPEu3lj25+fJAo2Q6GnAZgyCEiqiWEwInzJdh8eR7P/twiq/0dvN3wQEjtFZ7+Hb34IFGSFUNOAzDkEBHVr8BYgV8PFyAhS48dJwpRVfPHPJ62bs64v6cOMb10GNjNB24qJxkrpdaIIacBGHKIiG6utLIG2y4/SDTxSAEM5dWWfWonBQZ288EDlx8k6uOulrFSai0YchqAIYeIqHFqTGbsPvXHg0TPXPrjQaKSBPQLbmuZuNzZ113GSsmRMeQ0AEMOEdGtE0LgiL748sRlPQ6dtX6QaBffNngg1B8xvXTo096TDxKlJsOQ0wAMOURETedcUTl+PVx7hSflRCFqrrgDoa+HGtEhfpcfJOoDF2c+SJRuHUNOAzDkEBE1D2NFNZKyz2Nzph7J2edRXFlj2eemUmJQN1/E9NLh/p5+8HRTyVgp2SOGnAZgyCEian5VNWbsOllomcejN1ZY9ikVEu7s2LZ2WCtUhyAvPkiUbo4hpwEYcoiIWpYQAgfPGiyB54i+2Gp/T38PxITq8ECoP8La8UGiVD+GnAZgyCEiklduYRkSDucjIUuP3TnWDxL1cVejg7cbArQuCPR0RYDWBQFaVwR61v7p3UbFycytFENOAzDkEBHZjkulVVYPEi2rMt2wvUqpgL/WxToEeboi8IowpHV15tUgB8SQ0wAMOUREtqmi2oTDeUacK6pAnqH8jz8NFcgrKsf5kko05NvL1VmJAE8XBGqvCkFX/Omu5h2b7U1Dv795ZomIyOa4OCtxR3Bb3BFc//6qGjPyjRXIM1wVgi7/mWeowMXSKpRXm3DyfClOni+t/0AAPFycakPQ5WGwq0NQgNaFS97tFEMOERHZHZWTAkFebjdcjVVRbaoNQUV/XAE6dzkU5RVV4JyhHMUVNSiuqEF2RTGy84uveyyvNqpr5gTV/RmgdYG/1gXOSkVzdJVuA0MOERE5JBdnJTr5tEEnnzbXbVNSWXNtCCqqvRJ07nIYKq824WJpFS6WViHznLHe40gS4OuuvmZOUMDlK0SBWlf4eqj59PYWxpBDREStlrvaCd10Huim86h3vxAChvLqa+YE5RkqcO7yn3pDBapMZhQUV6KguBIZp+v/LCeFBJ3G5aoJ0nV/rw1D3m1UnCjdhBhyiIiIrkOSJHi6qeDppkJoYP0TXM1mgcLSqmvmBNWFoLyicuQXV6LGLHC2qBxni8qB3y/VeyyVk+LysJiL9Twhy3whV2hcnRiEGoghh4iI6DYoFBJ8PdTw9VCjd/v629SYzDhfUvlHCCr6Yzis7grR+eJKVNWY8XthGX4vLLvu57mplNe9d1Ddn224YgwAQw4REVGzc1IqLk9SdgXQtt42dSvGzl01J+jKK0SXyqpRVmXCifOlOHGDFWMaF6d67x1UNz/Iv5WsGGPIISIisgENWTFWXmW6djjsymGyogoUV9bAWFEDo774msdmXMm7jcpq2Xygp6vV0nmdhxpOdr5ijCGHiIjITriqlOjs647Ovu7XbVNcUX3NnKCrl85XVJtRWFqFwtIqHDpb/4oxhQT4ebjc8GaKPu5qm360BkMOERGRA/FwcYaHizO632DFWFFZ9TVzgq4MQ3pDBapNAnpjBfTGCuxHUb3HclbWrhirb5J03byhtm7yPVqDIYeIiKgVkSQJbduo0LaNCr0CtfW2MZsFLpRWXjMn6Mol9PnG2iB05lI5zlwqv+7nbXnlvhteeWpODDlERERkRaGQ4OfhAj8PF0QEedbbpubyvYGu91iNc0UVKCytvDzZWh4MOURERNRoTkoFAj1dEejpin4d6m9TWWOC2km+VVz2PW2aiIiIbJacAQdgyCEiIiIHxZBDREREDokhh4iIiBwSQw4RERE5JIYcIiIickgMOUREROSQGHKIiIjIITHkEBERkUOy+5CzbNkydOzYES4uLoiMjMTu3bvlLomIiIhsgF2HnG+//RYzZ87EG2+8gX379iEiIgKxsbEoKCiQuzQiIiKSmV2HnA8//BBTpkzBhAkTEBoaihUrVsDNzQ1ffPGF3KURERGRzOw25FRVVSEtLQ3R0dGW1xQKBaKjo5GSklLveyorK2E0Gq02IiIickx2G3IuXLgAk8kEnU5n9bpOp4Ner6/3PYsXL4ZWq7VsQUFBLVEqERERycBJ7gJa0rx58zBz5kzLzwaDAcHBwbyiQ0REZEfqvreFEDdsZ7chx8fHB0qlEvn5+Vav5+fnw9/fv973qNVqqNVqy891vyRe0SEiIrI/xcXF0Gq1191vtyFHpVKhX79+SExMxMiRIwEAZrMZiYmJiIuLa9AxAgMDcfr0aXh4eECSpCarzWg0IigoCKdPn4ZGo2my49oSR+8j+2f/HL2P7J/9c/Q+Nmf/hBAoLi5GYGDgDdvZbcgBgJkzZ2LcuHHo378/BgwYgL///e8oLS3FhAkTGvR+hUKB9u3bN1t9Go3GIf/BvZKj95H9s3+O3kf2z/45eh+bq383uoJTx65DzlNPPYXz589jwYIF0Ov16NOnDzZt2nTNZGQiIiJqfew65ABAXFxcg4eniIiIqPWw2yXktkytVuONN96wmuTsaBy9j+yf/XP0PrJ/9s/R+2gL/ZPEzdZfEREREdkhXskhIiIih8SQQ0RERA6JIYeIiIgcEkMOEREROSSGnEbYtm0bHn74YQQGBkKSJKxbt85qvxACCxYsQEBAAFxdXREdHY1jx45Ztbl48SLGjh0LjUYDT09PTJo0CSUlJS3Yi+u7Wf/Gjx8PSZKstmHDhlm1seX+LV68GHfeeSc8PDzg5+eHkSNHIjs726pNRUUFpk+fDm9vb7i7u2PUqFHXPDokNzcXI0aMgJubG/z8/DB79mzU1NS0ZFfq1ZD+DR48+Jpz+MILL1i1sdX+AcDy5cvRu3dvy83FoqKi8PPPP1v22/P5A27eP3s/f1dbsmQJJEnCjBkzLK/Z+zm8Un39s/dzuHDhwmvq79mzp2W/zZ0/QQ22ceNG8Ze//EX88MMPAoBYu3at1f4lS5YIrVYr1q1bJzIyMsQjjzwiOnXqJMrLyy1thg0bJiIiIsSuXbvEb7/9Jrp27SpGjx7dwj2p3836N27cODFs2DCRl5dn2S5evGjVxpb7FxsbK1auXCkOHTok0tPTxYMPPiiCg4NFSUmJpc0LL7wggoKCRGJioti7d6+46667xN13323ZX1NTI8LCwkR0dLTYv3+/2Lhxo/Dx8RHz5s2To0tWGtK/++67T0yZMsXqHBoMBst+W+6fEEL89NNPYsOGDeLo0aMiOztbvPbaa8LZ2VkcOnRICGHf50+Im/fP3s/flXbv3i06duwoevfuLV566SXL6/Z+Dutcr3/2fg7feOMN0atXL6v6z58/b9lva+ePIecWXR0CzGaz8Pf3F++9957ltaKiIqFWq8XXX38thBAiKytLABB79uyxtPn555+FJEni7NmzLVZ7Q1wv5Dz66KPXfY899U8IIQoKCgQAkZycLISoPV/Ozs5izZo1ljaHDx8WAERKSooQojYIKhQKodfrLW2WL18uNBqNqKysbNkO3MTV/ROi9j+wV/4H92r21L86bdu2FZ999pnDnb86df0TwnHOX3FxsejWrZtISEiw6pOjnMPr9U8I+z+Hb7zxhoiIiKh3ny2ePw5XNZGcnBzo9XpER0dbXtNqtYiMjERKSgoAICUlBZ6enujfv7+lTXR0NBQKBVJTU1u85luRlJQEPz8/9OjRA9OmTUNhYaFln731z2AwAAC8vLwAAGlpaaiurrY6hz179kRwcLDVOQwPD7d6dEhsbCyMRiMyMzNbsPqbu7p/db766iv4+PggLCwM8+bNQ1lZmWWfPfXPZDLhm2++QWlpKaKiohzu/F3dvzqOcP6mT5+OESNGWJ0rwHH+Hbxe/+rY+zk8duwYAgMD0blzZ4wdOxa5ubkAbPP82f1jHWyFXq8HgGuem6XT6Sz79Ho9/Pz8rPY7OTnBy8vL0saWDRs2DI8//jg6deqEEydO4LXXXsPw4cORkpICpVJpV/0zm82YMWMG7rnnHoSFhQGoPT8qlQqenp5Wba8+h/Wd47p9tqK+/gHAmDFj0KFDBwQGBuLAgQOYM2cOsrOz8cMPPwCwj/4dPHgQUVFRqKiogLu7O9auXYvQ0FCkp6c7xPm7Xv8Axzh/33zzDfbt24c9e/Zcs88R/h28Uf8A+z+HkZGRiI+PR48ePZCXl4c333wTAwcOxKFDh2zy/DHkUIM9/fTTlr+Hh4ejd+/e6NKlC5KSkjB06FAZK2u86dOn49ChQ9i+fbvcpTSL6/Vv6tSplr+Hh4cjICAAQ4cOxYkTJ9ClS5eWLvOW9OjRA+np6TAYDPjvf/+LcePGITk5We6ymsz1+hcaGmr35+/06dN46aWXkJCQABcXF7nLaXIN6Z+9n8Phw4db/t67d29ERkaiQ4cO+O677+Dq6ipjZfXjcFUT8ff3B4BrZpHn5+db9vn7+6OgoMBqf01NDS5evGhpY086d+4MHx8fHD9+HID99C8uLg7r16/H1q1b0b59e8vr/v7+qKqqQlFRkVX7q89hfee4bp8tuF7/6hMZGQkAVufQ1vunUqnQtWtX9OvXD4sXL0ZERAQ++ugjhzl/1+tffezt/KWlpaGgoAB9+/aFk5MTnJyckJycjI8//hhOTk7Q6XR2fQ5v1j+TyXTNe+ztHF7N09MT3bt3x/Hjx23y30GGnCbSqVMn+Pv7IzEx0fKa0WhEamqqZTw9KioKRUVFSEtLs7TZsmULzGaz5R90e3LmzBkUFhYiICAAgO33TwiBuLg4rF27Flu2bEGnTp2s9vfr1w/Ozs5W5zA7Oxu5ublW5/DgwYNWYS4hIQEajcYypCCXm/WvPunp6QBgdQ5ttX/XYzabUVlZaffn73rq+lcfezt/Q4cOxcGDB5Genm7Z+vfvj7Fjx1r+bs/n8Gb9UyqV17zH3s7h1UpKSnDixAkEBATY5r+DTT6V2YEVFxeL/fv3i/379wsA4sMPPxT79+8Xv//+uxCidgm5p6en+PHHH8WBAwfEo48+Wu8S8jvuuEOkpqaK7du3i27dutnMEusb9a+4uFjMmjVLpKSkiJycHPHrr7+Kvn37im7duomKigrLMWy5f9OmTRNarVYkJSVZLX8sKyuztHnhhRdEcHCw2LJli9i7d6+IiooSUVFRlv11yx9jYmJEenq62LRpk/D19bWJ5Z0369/x48fFokWLxN69e0VOTo748ccfRefOncWgQYMsx7Dl/gkhxNy5c0VycrLIyckRBw4cEHPnzhWSJInNmzcLIez7/Alx4/45wvmrz9Wrjez9HF7tyv45wjl85ZVXRFJSksjJyRE7duwQ0dHRwsfHRxQUFAghbO/8MeQ0wtatWwWAa7Zx48YJIWqXkc+fP1/odDqhVqvF0KFDRXZ2ttUxCgsLxejRo4W7u7vQaDRiwoQJori4WIbeXOtG/SsrKxMxMTHC19dXODs7iw4dOogpU6ZYLQMUwrb7V1/fAIiVK1da2pSXl4s///nPom3btsLNzU089thjIi8vz+o4p06dEsOHDxeurq7Cx8dHvPLKK6K6urqFe3Otm/UvNzdXDBo0SHh5eQm1Wi26du0qZs+ebXWPDiFst39CCDFx4kTRoUMHoVKphK+vrxg6dKgl4Ahh3+dPiBv3zxHOX32uDjn2fg6vdmX/HOEcPvXUUyIgIECoVCrRrl078dRTT4njx49b9tva+ZOEEKLprw8RERERyYtzcoiIiMghMeQQERGRQ2LIISIiIofEkENEREQOiSGHiIiIHBJDDhERETkkhhwiIiJySAw5RERE5JAYcojILiQlJUGSpGse/kdEdD0MOURkF+6++27k5eVBq9U2+D1lZWWYN28eunTpAhcXF/j6+uK+++7Djz/+2IyVEpGtcJK7ACKihlCpVPD392/Ue1544QWkpqbik08+QWhoKAoLC7Fz504UFhY2U5VEZEt4JYeIZDF48GC8+OKLmDFjBtq2bQudTod//etfKC0txYQJE+Dh4YGuXbvi559/BnDtcFV8fDw8PT3xyy+/ICQkBO7u7hg2bBjy8vIsn/HTTz/htddew4MPPoiOHTuiX79+ePHFFzFx4kRLG0mSsG7dOqvaPD09ER8fDwA4deoUJEnCN998g7vvvhsuLi4ICwtDcnJys/5+iOj2MeQQkWxWrVoFHx8f7N69Gy+++CKmTZuGJ554AnfffTf27duHmJgYPPvssygrK6v3/WVlZXj//fexevVqbNu2Dbm5uZg1a5Zlv7+/PzZu3Iji4uLbrnX27Nl45ZVXsH//fkRFReHhhx/mFSEiG8eQQ0SyiYiIwOuvv45u3bph3rx5cHFxgY+PD6ZMmYJu3bphwYIFKCwsxIEDB+p9f3V1NVasWIH+/fujb9++iIuLQ2JiomX/p59+ip07d8Lb2xt33nknXn75ZezYseOWao2Li8OoUaMQEhKC5cuXQ6vV4vPPP7+lYxFRy2DIISLZ9O7d2/J3pVIJb29vhIeHW17T6XQAgIKCgnrf7+bmhi5dulh+DggIsGo7aNAgnDx5EomJifjTn/6EzMxMDBw4EG+99Vaja42KirL83cnJCf3798fhw4cbfRwiajkMOUQkG2dnZ6ufJUmyek2SJACA2Wxu8PuFENe0GThwIObMmYPNmzdj0aJFeOutt1BVVXXd91RXV99ah4jIpjDkEFGrEhoaipqaGlRUVAAAfH19rSYrHzt2rN45QLt27bL8vaamBmlpaQgJCWn+gonolnEJORE5rMGDB2P06NHo378/vL29kZWVhddeew1DhgyBRqMBANx///34xz/+gaioKJhMJsyZM+eaK0QAsGzZMnTr1g0hISFYunQpLl26ZLVKi4hsD6/kEJHDio2NxapVqxATE4OQkBC8+OKLiI2NxXfffWdp88EHHyAoKAgDBw7EmDFjMGvWLLi5uV1zrCVLlmDJkiWIiIjA9u3b8dNPP8HHx6clu0NEjSSJqwejiYjI4tSpU+jUqRP279+PPn36yF0OETUCr+QQERGRQ2LIISIiIofE4SoiIiJySLySQ0RERA6JIYeIiIgcEkMOEREROSSGHCIiInJIDDlERETkkBhyiIiIyCEx5BAREZFDYsghIiIih/T/ASU8zO1/RlDUAAAAAElFTkSuQmCC\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "2b219af8-48a5-45f6-9ec5-d6d036af8a4b" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximumPeriodCount patterns runtime memory\n", - "0 PFPGrowthPlus 100 5000 25454 22.967165 614412288\n", - "1 PFPGrowthPlus 200 5000 13231 19.419037 609107968\n", - "2 PFPGrowthPlus 300 5000 4529 18.698286 602087424\n", - "3 PFPGrowthPlus 400 5000 1999 15.910377 591564800\n", - "4 PFPGrowthPlus 500 5000 1072 15.570678 579190784\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "bae99aac-5960-4605-c062-8d65fb6fac17" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] + ] + } + ] } diff --git a/notebooks/periodicFrequentPattern/basic/PFPMC.ipynb b/notebooks/periodicFrequentPattern/basic/PFPMC.ipynb index 4a85d3f2..7a321d52 100644 --- a/notebooks/periodicFrequentPattern/basic/PFPMC.ipynb +++ b/notebooks/periodicFrequentPattern/basic/PFPMC.ipynb @@ -1,713 +1,713 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XZ4vrXSQ1yEs" - }, - "source": [ - "# Finding Periodic Frequent patterns in Temporal Databases using PFPMC" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "roOSCMZX2Eb2" - }, - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Periodic Frequent patterns in a temporal database using the PFPMC algorithm. In the final part, we describe an advanced approach, where we evaluate the PFPMC algorithm on a dataset at different minimum support threshold values.\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TFlIIF_X2SzU" - }, - "source": [ - "# Prerequisites:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TqMwpaLw2XLu" - }, - "source": [ - "1. Installing the PAMI library" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "9833be73-ed95-4d02-eb55-3ca689c35176" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pami in /usr/local/lib/python3.10/dist-packages (2023.11.15.4)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", - "Requirement already satisfied: resource in /usr/local/lib/python3.10/dist-packages (from pami) (0.2.1)\n", - "Requirement already satisfied: validators in /usr/local/lib/python3.10/dist-packages (from pami) (0.22.0)\n", - 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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.5.0->sphinx<8,>=1.6->sphinx-rtd-theme->pami) (2023.7.22)\n", - "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", - "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", - "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.30.2)\n", - "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.12.0)\n" - ] - } - ], - "source": [ - "!pip install -U pami #install the pami repository" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rYfvWHRN2oBs" - }, - "source": [ - "2. Downloading a sample dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "aabbad14-e263-49c6-9198-01e0ba7ca5dd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2023-11-16 13:36:02-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1007KB/s in 6.9s \n", - "\n", - "2023-11-16 13:36:10 (654 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ], - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "USUJbpXu3Gkw" - }, - "source": [ - "3. Printing few lines of a dataset to know its format." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "eee0f4a4-36aa-43eb-879b-33828f3aa07d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ], - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oQQdz3qn3Qwz" - }, - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "62Vkqg-C3WVZ" - }, - "source": [ - "## Part 1: Finding Periodic Frequent patterns using PFPMC" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gaxxPgXv3ecW" - }, - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "background_save": true, - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "399e80d3-ce10-4ffd-b1fa-2e47f44ed968" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ], - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1oJIEK8A3wQS" - }, - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "background_save": true - }, - "id": "y7pfaeJV34H_", - "outputId": "19cf95f8-d20a-4cf0-b5e8-cc8b3ec265e8" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zpLiRCBp39k9" - }, - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 3000 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "background_save": true - }, - "id": "RP9ynbti4L48" - }, - "outputs": [], - "source": [ - "minimumSupportCount = 3000 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 4000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-Yr0r7zw4Q85" - }, - "source": [ - "### Step 4: Mining Periodic Frequent patterns using PFPMC" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "background_save": true - }, - "id": "BZzrC2Pl4XGJ", - "outputId": "291d6075-4d52-411d-e970-13f51674e365" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n", - "Total No of patterns: 60\n", - "Runtime: 98.44582390785217\n", - "Memory (RSS): 498446336\n", - "Memory (USS): 481484800\n" - ] - } - ], - "source": [ - "from PAMI.periodicFrequentPattern.basic import PFPMC as alg #import the algorithm\n", - "\n", - "obj = alg.PFPMC(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('periodicFrequentPatternsAtMinSupCount3000.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3M8FtfKU4bhu" - }, - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "background_save": true - }, - "id": "b7IvA0IW4hBe", - "outputId": "bc26822e-c6b9-4288-b353-2907201a1ca3" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "368:7818:113 \n", - "538:3981:283 \n", - "775:3766:228 \n", - "825:3082:271 \n", - "39:4252:236 \n", - "120:4968:174 \n", - "205:3602:231 \n", - "401:3665:277 \n", - "895:3380:255 \n", - "937:4675:201 \n" - ] - } - ], - "source": [ - "!head 'periodicFrequentPatternsAtMinSupCount3000.txt'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j4CpTPXw4k9I" - }, - "source": [ - "The storage format is: _periodicfrequentPattern:support_\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kC71sBV74qY0" - }, - "source": [ - "## Part 2: Evaluating the PFPMC algorithm on a dataset at different minSup values" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EobpZCdu6G0Z" - }, - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "background_save": true - }, - "id": "W96B78JT6KT2" - }, - "outputs": [], - "source": [ - "#Import the libraries\n", - "from PAMI.periodicFrequentPattern.basic import PFPMC as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 4000\n", - "minimumSupportCountList = [3000, 3500, 4000, 4500, 5000]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gS09HRlY6PPR" - }, - "source": [ - "### Step 2: Create a data frame to store the results of PFPMC" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XZ4vrXSQ1yEs" + }, + "source": [ + "# Finding Periodic Frequent patterns in Temporal Databases using PFPMC" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "roOSCMZX2Eb2" + }, + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Periodic Frequent patterns in a temporal database using the PFPMC algorithm. In the final part, we describe an advanced approach, where we evaluate the PFPMC algorithm on a dataset at different minimum support threshold values.\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TFlIIF_X2SzU" + }, + "source": [ + "# Prerequisites:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TqMwpaLw2XLu" + }, + "source": [ + "1. Installing the PAMI library" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "9833be73-ed95-4d02-eb55-3ca689c35176" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pami in /usr/local/lib/python3.10/dist-packages (2023.11.15.4)\n", + "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", + "Requirement already satisfied: resource in /usr/local/lib/python3.10/dist-packages (from pami) (0.2.1)\n", + "Requirement already 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jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", + "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.30.2)\n", + "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.12.0)\n" + ] + } + ], + "source": [ + "!pip install -U pami #install the pami repository" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rYfvWHRN2oBs" + }, + "source": [ + "2. Downloading a sample dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "aabbad14-e263-49c6-9198-01e0ba7ca5dd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-11-16 13:36:02-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1007KB/s in 6.9s \n", + "\n", + "2023-11-16 13:36:10 (654 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ], + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "USUJbpXu3Gkw" + }, + "source": [ + "3. Printing few lines of a dataset to know its format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "eee0f4a4-36aa-43eb-879b-33828f3aa07d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ], + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oQQdz3qn3Qwz" + }, + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "62Vkqg-C3WVZ" + }, + "source": [ + "## Part 1: Finding Periodic Frequent patterns using PFPMC" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gaxxPgXv3ecW" + }, + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true, + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "399e80d3-ce10-4ffd-b1fa-2e47f44ed968" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ], + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1oJIEK8A3wQS" + }, + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true }, + "id": "y7pfaeJV34H_", + "outputId": "19cf95f8-d20a-4cf0-b5e8-cc8b3ec265e8" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "background_save": true - }, - "id": "0tbQu3re6VGI" - }, - "outputs": [], - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of PFPMC algorithm" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": { - "id": "kn2TtrbW6awD" - }, - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "background_save": true - }, - "id": "cZNXmKqp6ea1", - "outputId": "9e09c07b-6f12-41c1-9dd9-f06bcac45955" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n", - "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n", - "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n", - "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n", - "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n" - ] - } - ], - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.PFPMC(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['PFPMC', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + "data": { + "image/png": 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555+jpKQECxcurNfxVOedd97B/fffj759++LRRx9FTk4Oli1bhtDQUKNC6nZGjhyJtWvXYsyYMRgxYgQSEhKwfPlydOzYsdp9tG3bFv369cNTTz2FkpISfPjhh3B1dcWLL75osuOiFk6u0/aImos333xTtGrVSigUCqPT8AHcchmAr7/+WrRr106oVCoREhIiVqxYIebPn1/ltPFb7ePGU+ZLSkrECy+8ILp06SIcHByEnZ2d6NKli/j000+NnnP69GkRGRkp7O3thZubm3j88ccNyxfcePq6EELExsaKMWPGCCcnJ2FtbS2Cg4PFa6+9Vqvjvvl0fiGEuHjxonjggQcM++vVq5fYsGGDUZvKU+xXr15ttL26U+xvZffu3aJ79+7CyspKtGnTRixfvrza9/XmjG+99Zbo1auXcHJyEjY2NiIkJES8/fbborS01NCmvLxcPPPMM8Ld3V1IkmTYZ2W+RYsWVclzqyUH7OzsxMWLF8XQoUOFra2t8PT0FPPnzzcsv1ApMzNTjBs3Ttja2gpnZ2fxxBNPiNjY2Cr7vFU2IaouOSCEEEePHhVRUVHC3t5e2Nrairvvvlvs37/fqE3lkgOHDx822n6rpRCq89NPP4mQkBChUqlEaGio+O2338S4ceNESEhIlfeouvdPr9eLd955R/j7+wuVSiW6du0qNmzYIKZMmVLtsgWLFi0SixcvFr6+vkKlUon+/fuLEydOGO2z8v2/WXWfE6KbSUI04OxKIiKiG9x1111wd3evMp/uTiQmJiIwMBCLFi3CnDlzTLZfoptxThMREZlcWVkZysvLjbbt2rULJ06cwKBBg+QJRXSHOKeJiIhMLjk5GZGRkXjkkUfg4+ODs2fPYvny5fDy8qqysCRRU8GiiYiITM7Z2Rndu3fHV199hczMTNjZ2WHEiBF49913DddMJGpqOKeJiIiIqBY4p4mIiIioFlg0EREREdUC5zSZiF6vR0pKChwcHEx6uQUiIiJqOEII5OXlwcfHp8bFdlk0mUhKSopJrtlEREREje/KlSto3br1bduwaDIRBwcHABVvuimu3UREREQNT6vVwtfX1/A9fjssmkykckjO0dGRRRMREVETU5upNZwITkRERFQLLJqIiIiIaoFFExEREVEtsGgiIiIiqgUWTURERES1wKKJiIiIqBZYNBERERHVAosmIiIiolpg0URERERUCyyaiIiIiGqBRRMRERFRLbBoImqhUjVF2H8xC6maIrmjEBE1CbxgL1ELtOpwEuauPQW9ABQSED02DON7+skdi4jIrLGniaiFSdUU4eXrBRMA6AXwytpY9jgREdWARRNRCyKEwCc7LkAI4+06IZCYVShPKCKiJoLDc0QtREFJOV5ccxIbT6ZW+7ifi00jJyIialrY00TUAlzMzMfoT/Zh48lUWCgk3NfFB0rJuM2qw1fkCUdE1ESwp4momdscm4Y5q08gv6QcHg4qfDqpG3oEuGDuvSFIzCpEbHIu3v7jLD7ecQFBHva4/65WckcmIjJLLJqImimdXuD9rfH4bNdFAECvABcsm9QVHg7WAABvtQ281TaICHJFVn4pPt9zCS/8chJ+Lrbo6ucsZ3QiIrPE4TmiZii7oBRTvjlkKJj+3TcQ3z8ebiiYbvbisBAMCfFAabke0/93BCm5PJOOiOhmLJqImpkTV3Ixaule7L2QBRtLJT6e0BXzRnWEpfLW/9yVCgkfTeiKYE8HZOaV4LFv/0ZhaXkjpiYiMn8smoiakZ8OJeHB5QeQnFuEQDc7rJ/RF/d18anVc+1VFvhqSg+42lnhdKoWs1edgF4van4iEVELwaKJqBkoLtPh5TUn8fLaUyjV6XFPR0/8OrMvgr0c6rQfXxdbfP6v7rBSKrA5Lg1LYs41UGIioqaHRRNRE3c1pxAPfX4APx2+AkkCXogKxuePdIejtWW99tcjwAXvjA0DACzbeQHrjyWbMi4RUZPFs+eImqBUTRESsgqQmVeCBb/FIaewDE62lvj44a4Y0N79jvf/QPfWuJCRj+W7L+LFNSfh52qLbjyjjohaOBZNRE3MjRfbrRTWSo1PJ3WDr4utyV7nxahgXMjIx7Yz6Zj+3yP4dWZftHLiquFE1HJxeI6oCUnVFFUpmCQASyd0NWnBBAAKhYQPH74LIV4OyMqvOKOuoIRn1BFRy8WiiagJScgqwM0ntAkAqZriBnm9yjPq3OytcCZVi+dXHecZdUTUYrFoImpCysurFixKSUKAm2l7mW7U2tkWn/+rB6yUCmw9nY73t8Y32GsREZkzFk1ETYS2uAzzfos12qaUJLwzNhTe6oada9Td3xnvjqs4o+7TXRex7tjVBn09IiJzxIngRE2AEAIvrj6JxGuFaOVkg6+n9EBOYRkC3GwbvGCqNLZbxRl1n+66iJd+OQU/Fzt09+cZdUTUcrCniagJ+GZfIjbHpcFSKeGTSd0Q4u2IiCDXRiuYKs0ZGoyhHT1RqtPjif/9jaOXs7H/YhZSNbxWHRE1f5IQgrM6TUCr1UKtVkOj0cDR0VHuONSMHLmcjfGf/4VyvcDr93XClD4BsuYpKCnHA8sP4Eyq1rBNIQHRY8MwvqefjMmIiOquLt/f7GkiMmPX8ksw84djKNcLjOzsjckR/nJHgp3KAu+MCTXaphfAK2tj2eNERM0aiyYiM6XTC8xadRypmmK0cbfDu+M6Q5IkuWMBAIrKdFW26YRAYlahDGmIiBoHiyYiM7VsxwX8eT4L1pYKfDapO+xV5nPeRqCbHRQ31W8NvfQBEZHcWDQRmaE/z2fiw+3nAABvjw5DsJeDzImMeattED02DDfWTY2x9AERkZxkLZr27NmDUaNGwcfHB5IkYf369UaPCyEwb948eHt7w8bGBpGRkTh//rxRm+zsbEyaNAmOjo5wcnLCtGnTkJ+fb9Tm5MmT6N+/P6ytreHr64uFCxdWybJ69WqEhITA2toaYWFh+OOPP0x+vES1kaopwnM/HYcQwIRevhjXvbXckao1vqcfVj3RG0DFRPDBIZ4yJyIialiyFk0FBQXo0qULPvnkk2ofX7hwIT7++GMsX74cBw8ehJ2dHaKiolBc/M8lIyZNmoS4uDjExMRgw4YN2LNnD6ZPn254XKvVYujQofD398eRI0ewaNEiLFiwAF988YWhzf79+zFhwgRMmzYNx44dw+jRozF69GjExhovJEjU0Mp0esz84RiyC0rR0dsR80d1kjvSbfUKdEWX1mroBbDxZIrccYiIGpYwEwDEunXrDPf1er3w8vISixYtMmzLzc0VKpVK/Pjjj0IIIU6fPi0AiMOHDxvabNq0SUiSJJKTk4UQQnz66afC2dlZlJSUGNq89NJLIjg42HD/oYceEiNGjDDKEx4eLp544ola59doNAKA0Gg0tX4O0c3e2hAn/F/aIELnbxaJWflyx6mVr/+8JPxf2iDuX7ZX7ihERHVWl+9vs53TlJCQgLS0NERGRhq2qdVqhIeH48CBAwCAAwcOwMnJCT169DC0iYyMhEKhwMGDBw1tBgwYACsrK0ObqKgoxMfHIycnx9DmxtepbFP5OtUpKSmBVqs1uhHdic2xafjyzwQAwKIHusDf1U7mRLUzsos3FBJw/EouErMK5I5DRNRgzLZoSktLAwB4ehrPk/D09DQ8lpaWBg8PD6PHLSws4OLiYtSmun3c+Bq3alP5eHWio6OhVqsNN19f37oeIpHB5WsFeGH1CQDA4/0DMSzUS+ZEtefhYI2+bd0AAL8e5xAdETVfZls0mbu5c+dCo9EYbleuXJE7EjVRxWU6PPXdUeSVlKOHvzNeHBYid6Q6G31XKwDAr8eTIXiRASJqpsy2aPLyqvhNOz093Wh7enq64TEvLy9kZGQYPV5eXo7s7GyjNtXt48bXuFWbysero1Kp4OjoaHQjqo/Xf4/D6VQtXO2ssGxiN1gqzfaf5S0N7eQJlYUCl7IKEJvMoWoiap7M9n/nwMBAeHl5Yfv27YZtWq0WBw8eREREBAAgIiICubm5OHLkiKHNjh07oNfrER4ebmizZ88elJWVGdrExMQgODgYzs7OhjY3vk5lm8rXIWooa45cxY+HrkCSgI8e7govtbXckerFwdoSkR0rhrjXH0+WOQ0RUcOQtWjKz8/H8ePHcfz4cQAVk7+PHz+OpKQkSJKEWbNm4a233sJvv/2GU6dOYfLkyfDx8cHo0aMBAB06dMCwYcPw+OOP49ChQ9i3bx9mzpyJhx9+GD4+PgCAiRMnwsrKCtOmTUNcXBxWrVqFjz76CLNnzzbkeO6557B582YsXrwYZ8+exYIFC/D3339j5syZjf2WUAtyNk2L/1t/CgAwa0h79GvnJnOiO1M5RPf7iRTo9ByiI6JmqOFP5ru1nTt3CgBVblOmTBFCVCw78NprrwlPT0+hUqnEkCFDRHx8vNE+rl27JiZMmCDs7e2Fo6OjePTRR0VeXp5RmxMnToh+/foJlUolWrVqJd59990qWX7++WfRvn17YWVlJTp16iQ2btxYp2PhkgNUF+fT80Tvd7YJ/5c2iEe++kvodHq5I92xkjKd6PL6FuH/0gbx57lMueMQEdVKXb6/JSE4a9MUtFot1Go1NBoN5zfRbf10KAkvrz1luP/aiA6Y1r+NjIlM55V1p/DDwSQ80L013n+wi9xxiIhqVJfvb7Od00TUHKVqijD3hoIJAN754yxSNUUyJTKtyiG6zbFpKC7TyZyGiMi0WDQRNaKErALc3LWrEwKJWYWy5DG1Hv7O8FFbI7+kHDvOZtT8BCKiJoRFE1Ej8lHbVNmmlCQEuNnKkMb0FAoJ913vbVp/jGfREVHzwqKJqBFdyso3uq+UJLwzNhTe1RRTTdXorhVnru6Kz4SmsKyG1kRETYeF3AGIWpKY0xVDVmO7tsKDPXwR4GbbrAomAAjxckSIlwPOpuXhj9hUTOjlJ3ckIiKTYE8TUSPR6wW2nalYeX5011aICHJtdgVTpfs5REdEzRCLJqJGcjJZg8y8EtirLBDexkXuOA3qvrsqhugOJmQjJbd5nBlIRMSiiaiRxJxOAwAMbO8OlYVS5jQNq5WTDXoFVBSGv59IkTkNEZFpsGgiaiTbrs9nuuf6Ndqau/uvTwhff5xFExE1DyyaiBpB0rVCxKfnQamQMCjYXe44jWJEmDcslRLOpGpxLj1P7jhERHeMRRNRI4i5PgG8V4ALnGytZE7TOJxsrTCwvQcATggnouaBRRNRI9h2uqJoimwhQ3OVKtds+vV4CvR6XuaSiJo2Fk1EDSy3sBSHErMBAPd0aFlFU2QHT9hZKZGcW4QjSTlyxyEiuiMsmoga2K74TOj0AsGeDvBzbR6XS6kta0slokK9AAC/HucQHRE1bSyaiBpY5XymyI4eMieRx+jrC11uPJmKMp1e5jRERPXHoomoAZWU67A7PhMAcE9HL5nTyKNPkCvc7FXIKSzDnnOZcschIqo3Fk1EDejgpWzkl5TD3UGFzq3UcseRhYVSgVFdvAFwzSYiatpYNBE1oMprzUV28IBCIcmcRj6VQ3Qxp9OQX1Iucxoiovph0UTUQIQQhqUGWsoq4LfSubUagW52KC7TY2tcmtxxiIjqhUUTUQOJS9EiRVMMG0sl+gS5yR1HVpIk4b4u/6zZRETUFLFoImogMdd7mQa0d4O1ZfO+QG9tjO5aMUS390IWsvJLZE5DRFR3LJqIGsg/85la9tBcpUA3O3RprYZOL7DhBHubiKjpYdFE1ABScosQl6KFQgIGh7TM9Zmqc//1CeE8i46ImiIWTUQNoLKXqbu/M1ztVTKnMR8ju3hDIQHHr+QiMatA7jhERHXCoomoAVTOZ+LQnDEPB2v0bVsxKZ4TwomoqWHRRGRi2uIy/HXpGgAuNVCdyiG6X08kQwghcxoiotpj0URkYnvOZaJMJ9DG3Q5t3O3ljmN2ojp5QmWhwKXMAsQma+WOQ0RUayyaiEzMsKAlh+aq5WBticjrPXDrjyfLnIaIqPZYNBGZUJlOjx1nMwBwaO52Ki+r8vuJFOj0HKIjoqaBRRORCR1OzIa2uByudlbo6ucsdxyzNbC9O5xsLZGRV4IDF6/JHYeIqFZYNBGZ0LbTFb1Mg0M8oGzBF+itiZWFAveGeQMAvvjzIlI1RTInIiKqGYsmIhMRQiDmTMXFaCM5NFcjB5UFAGDPuSz0fXcHVh1OkjkREdHtsWgiMpFz6fm4kl0ElYUC/du17Av01iRVU4Qv/7xkuK8XwCtrY9njRERmjUUTkYnEnK7oZerX1g22VhYypzFvCVkFuHn+t04IJGYVyhOIiKgWWDQRmUjMmYr5TByaq1mgmx1unvKlkIAAN1t5AhER1QKLJiITyNAW48SVXADAEF6gt0beahtEjw2DUvqnchrQ3h3eahsZUxER3R6LJiIT2Ha9l+kuXyd4OFrLnKZpGN/TD3tfvhvPR7YDAMQma1Gm08uciojo1lg0EZnAtjPXVwHn0FydeKtt8PTdbeHuoEJWfolhNXUiInPEoonoDhWUlGPvhSwALJrqw1KpwEM9WgMAfjjEZQeIyHyxaCK6Q3+ez0JpuR5+LrZo58EL9NbHwz39IEkV72XSNZ5BR0TmiUUT0R2qHJqL7OAJSeIq4PXh62KL/u3cAQA/cpFLIjJTLJqI7oBOL3iBXhOZ2MsXALD67ysoLeeEcCIyPyyaiO7A0aQcZBeUQm1jiZ4BvEDvnRjSwfP6hPBSQ+8dEZE5YdFEdAdirp/tNTjEAxZK/nO6E0YTwg9yiI6IzA//lye6A5WnyEd24NCcKVROCN97IQuXrxXIHYeIyAiLJqJ6upiZj0tZBbBUShjQnhfoNQWjCeGHrsichojIGIsmonqqHJqLCHKDg7WlzGmaj4m9/AAAvxzhhHAiMi8smojqqXJo7p4OvNacKQ3p4AGP6xPCY7hCOBGZERZNRPVwLb8ER5JyAACRXGrApComhFcsP/AjVwgnIjPCoomoHrafzYAQQGgrR3irbeSO0+yM7+lrmBCemMUJ4URkHlg0EdUDz5prWL4uthhwfUL4T4c5IZyIzINZF006nQ6vvfYaAgMDYWNjg6CgILz55psQQhjaCCEwb948eHt7w8bGBpGRkTh//rzRfrKzszFp0iQ4OjrCyckJ06ZNQ35+vlGbkydPon///rC2toavry8WLlzYKMdITU9xmQ5/nucFehvaxHBOCCci82LWRdN7772Hzz77DMuWLcOZM2fw3nvvYeHChVi6dKmhzcKFC/Hxxx9j+fLlOHjwIOzs7BAVFYXi4mJDm0mTJiEuLg4xMTHYsGED9uzZg+nTpxse12q1GDp0KPz9/XHkyBEsWrQICxYswBdffNGox0tNw+8nUlBUpoOXozU6ejvKHafZGhzyz4TwrafT5I5DRARJ3NhtY2ZGjhwJT09PfP3114Zt48aNg42NDb777jsIIeDj44P//Oc/mDNnDgBAo9HA09MTK1euxMMPP4wzZ86gY8eOOHz4MHr06AEA2Lx5M+69915cvXoVPj4++Oyzz/B///d/SEtLg5WVFQDg5Zdfxvr163H27NlaZdVqtVCr1dBoNHB05Bdpc7XqcBJeWnMKACABeHdcGMb39JM3VDO2eGs8lu64gL5tXfH9Y73ljkNEzVBdvr/NuqepT58+2L59O86dOwcAOHHiBPbu3Yvhw4cDABISEpCWlobIyEjDc9RqNcLDw3HgwAEAwIEDB+Dk5GQomAAgMjISCoUCBw8eNLQZMGCAoWACgKioKMTHxyMnJ6fabCUlJdBqtUY3at5SNUWYu/aU4b4A8MraWKRqiuQL1cxVTgjfd+EaJ4QTkezMumh6+eWX8fDDDyMkJASWlpbo2rUrZs2ahUmTJgEA0tIquuw9PY3nlXh6ehoeS0tLg4eH8To6FhYWcHFxMWpT3T5ufI2bRUdHQ61WG26+vr53eLRk7hKyCqC/qV9WJwQSswrlCdQCtHa2xcD211cIP8zlB4hIXmZdNP3888/4/vvv8cMPP+Do0aP49ttv8f777+Pbb7+VOxrmzp0LjUZjuF25wjN8mrtAN7sq25SShAA3WxnStByGFcL/vsoJ4UQkKwu5A9zOCy+8YOhtAoCwsDBcvnwZ0dHRmDJlCry8vAAA6enp8Pb2NjwvPT0dd911FwDAy8sLGRkZRvstLy9Hdna24fleXl5ITzdeebjyfmWbm6lUKqhUqjs/SGoynGysoLJQoOT6F7dSkvDO2FCu09TABod4wNNRhXRtCbaeTsPIzj5yRyKiFsqse5oKCwuhUBhHVCqV0OsrvrQCAwPh5eWF7du3Gx7XarU4ePAgIiIiAAARERHIzc3FkSNHDG127NgBvV6P8PBwQ5s9e/agrKzM0CYmJgbBwcFwdnZusOOjpmXr6TSUlOvh7WSNHx4Lx96X7+Yk8EZgoVRg/PUVwn84yCE6IpKPWRdNo0aNwttvv42NGzciMTER69atw5IlSzBmzBgAgCRJmDVrFt566y389ttvOHXqFCZPngwfHx+MHj0aANChQwcMGzYMjz/+OA4dOoR9+/Zh5syZePjhh+HjU/Eb68SJE2FlZYVp06YhLi4Oq1atwkcffYTZs2fLdehkhtYcTQYAPNjdF33aurGHqRE9dH1C+P6L15DACeFEJBOzHp5bunQpXnvtNTz99NPIyMiAj48PnnjiCcybN8/Q5sUXX0RBQQGmT5+O3Nxc9OvXD5s3b4a1tbWhzffff4+ZM2diyJAhUCgUGDduHD7++GPD42q1Glu3bsWMGTPQvXt3uLm5Yd68eUZrOVHLlq4txt7zmQCAcd1ayZym5WntbItB7d2xMz4TPx1Kwtx7O8gdiYhaILNep6kp4TpNzdvnuy8ietNZ9Axwxuon+8gdp0XaGpeG6f87Ahc7KxyYOxgqC6XckYioGWg26zQRmQMhBNYcvQoAGNuttcxpWq7KCeHZBaXYGpde8xOIiEyMRRNRDWKTtTiXng8rCwVGdPau+QnUIDghnIjkxqKJqAaVvUxDO3rC0dpS5jQt2/heflBIwIFL13ApM7/mJxARmRCLJqLbKC3X47cTKQCAcd05NCe3Vk42GBRcscL/T4e5oCwRNS4WTUS3sftcJrILSuHuoEL/tm5yxyEAEypXCD9yFSXlOpnTEFFLwqKJ6DbWHKkYmht9lw8slPznYg7uDnaHl6M1sgtKsYUTwomoEfFbgOgWcgtLsf1sxZcyh+bMh4VSgYd6VkwI/5ETwomoEbFoIrqF30+koEwn0NHbESFeXHvLnIzv6csJ4UTU6Fg0Ed3CL9cvm8JeJvPDCeFEJAcWTUTVuJCRjxNXcqFUSLivi4/ccagaEzkhnIgaGYsmomqsvb4206D27nB3UMmchqoz6IYJ4Uu3n0eqpkjuSETUzLFoIrqJXi+w7ljF0Bwvm2K+LJQKdGpVMdds2c6L6PvuDqw6zInhRNRwWDQR3eTApWtI1RTD0doCQzp4yB2HbiFVU4SdZzMM9/UCeGVtLHuciKjBsGgiuknlZVNGdvGBtaVS5jR0KwlZBdAL4206IZCYVShPICJq9lg0Ed2goKQcm2PTAADjODRn1gLd7KCQjLcpJQkBbrbyBCKiZo9FE9ENNsWmobBUh0A3O3Tzc5I7Dt2Gt9oG0WPDjAqnl4aHwFttI18oImrWWDQR3aDyrLmxXVtBkqQaWpPcxvf0w76XB6O9pz0AwNqS/6URUcPh/zBE1yXnFuHApWsAgDHdWsmchmrLW22DB7tXXFZl06k0mdMQUXPGoonouvXHkiEE0LuNC1o7c15MUzIs1AsAcDDhGrILSmVOQ0TNFYsmIgBCCKw5UjE0xwngTY+viy06+ThCL4Btp9PljkNEzRSLJiIAx6/k4lJWAWwslRge5i13HKqHYZ0qeps2xabKnISImisWTUT4Z22mYaFesFdZyJyG6qNyiG7fhWvQFpfJnIaImiMWTdTilZTr8PuJit4JDs01Xe08HRDkbodSnd5opXAiIlNh0UQt3o4zGdAUlcHL0RoRQa5yx6E7UNnbVLlAKRGRKbFoohavcmhuTLdWUN68xDQ1KcM6VcxH2xWfiaJSncxpiKi5YdFELVpWfgl2xWcCAMZxbaYmL7SVI1o52aCoTIc95zPljkNEzQyLJmrRfjuegnK9QJfWarT1cJA7Dt0hSZI4REdEDYZFE7Voa49dv2wKJ4A3G5VF07Yz6Sgt18uchoiaExZN1GLFp+UhNlkLS6WEUV185I5DJtLdzxnuDirkFZcbLotDRGQKLJqoxaqcAH53sAdc7KxkTkOmolBIGNrREwCH6IjItFg0UYtUrtNj3bFkAMC47hyaa24qh+hiTqdBpxcypyGi5oJFE7VIey9kITOvBM62lrg72EPuOGRivdu4Qm1jiaz8UvydmC13HCJqJlg0UYu09mhFL9N9XXxgZcF/Bs2NpVKByA7Xh+jiOERHRKbBbwtqcbTFZdhy/YuUQ3PNV+UQ3ZbYNAjBIToiunMsmqjF2XQqFSXlerT1sEdYK7XccaiB9G/nBlsrJVI0xTh5VSN3HCJqBlg0UYuz5sj1CeDdWkOSeNmU5sraUom7Qyrmq3GIjohMgUUTtSh/J2bj0PWJwaO7cm2m5m5Yp39WB+cQHRHdKRZN1GKsOpyEB5cfMNzfc47XJmvu7g7xgJWFAglZBTiXni93HCJq4lg0UYuQqinC3LWncGNfwytrY5GqKZItEzU8e5UFBrRzA8CFLonoztW5aLp06VJD5CBqUAlZBbh5jUOdEEjMKpQnEDWaqOtDdJtiU2VOQkRNXZ2LprZt2+Luu+/Gd999h+Li4obIRGRygW52uHnKt1KSEOBmK0seajyRHTyhVEg4m5aHxKwCueMQURNW56Lp6NGj6Ny5M2bPng0vLy888cQTOHToUENkIzIZb7UNPBxVhvtKScI7Y0PhrbaRMRU1Bmc7K/Ru4wIAhvW5iIjqo85F01133YWPPvoIKSkp+Oabb5Camop+/fohNDQUS5YsQWYmJ9eS+bmUmY90bQmUEvDV5B7Y+/LdGN/TT+5Y1EiGhXoDADZxXhMR3YF6TwS3sLDA2LFjsXr1arz33nu4cOEC5syZA19fX0yePBmpqZw/QOaj8suybzt3RHb0ZA9TCxPV0ROSBBy/ksvJ/0RUb/Uumv7++288/fTT8Pb2xpIlSzBnzhxcvHgRMTExSElJwf3332/KnER3pHJYpnLdHmpZPByt0c3PGQCwNS5d5jRE1FTVuWhasmQJwsLC0KdPH6SkpOC///0vLl++jLfeeguBgYHo378/Vq5ciaNHjzZEXqI6u5pTiJNXNVBIwNBOnnLHIZkMD/1noUsiovqoc9H02WefYeLEibh8+TLWr1+PkSNHQqEw3o2Hhwe+/vprk4UkuhOVX5I9A1zgZq+qoTU1V5VLDxxMuIZr+SUypyGipsiirk84f/58jW2srKwwZcqUegUiMrXKoqnyqvfUMvm62KKTjyPiUrTYdiadJwIQUZ3VuadpxYoVWL16dZXtq1evxrfffmuSUESmkqEtxpGkHAAsmohDdER0Z+pcNEVHR8PNza3Kdg8PD7zzzjsmCUVkKltOp0MI4C5fJ54xR4bCee+FLGiLy2ROQ0RNTZ2LpqSkJAQGBlbZ7u/vj6SkJJOEulFycjIeeeQRuLq6wsbGBmFhYfj7778NjwshMG/ePHh7e8PGxgaRkZFVhhCzs7MxadIkODo6wsnJCdOmTUN+vvHFO0+ePIn+/fvD2toavr6+WLhwocmPhRrf5uuXzmAvEwFAWw8HBLnboUwnsPNshtxxiKiJqXPR5OHhgZMnT1bZfuLECbi6upokVKWcnBz07dsXlpaW2LRpE06fPo3FixfD2dnZ0GbhwoX4+OOPsXz5chw8eBB2dnaIiooyusTLpEmTEBcXh5iYGGzYsAF79uzB9OnTDY9rtVoMHToU/v7+OHLkCBYtWoQFCxbgiy++MOnxUOPKKSjFX5eyAfwzLEM0/PpClxyiI6I6E3X04osvCn9/f7Fjxw5RXl4uysvLxfbt24W/v7/4z3/+U9fd3dZLL70k+vXrd8vH9Xq98PLyEosWLTJsy83NFSqVSvz4449CCCFOnz4tAIjDhw8b2mzatElIkiSSk5OFEEJ8+umnwtnZWZSUlBi9dnBwcK2zajQaAUBoNJpaP4ca1qrDScL/pQ1i2Id75I5CZuTU1Vzh/9IGEfLqJlFYUi53HCKSWV2+v+vc0/Tmm28iPDwcQ4YMgY2NDWxsbDB06FAMHjzY5HOafvvtN/To0QMPPvggPDw80LVrV3z55ZeGxxMSEpCWlobIyEjDNrVajfDwcBw4cAAAcODAATg5OaFHjx6GNpGRkVAoFDh48KChzYABA2BlZWVoExUVhfj4eOTk5Jj0mKjxGM6a44KWdINOPo5o5WSDojIddp/jZZ+IqPbqXDRZWVlh1apVOHv2LL7//nusXbsWFy9exDfffGNUdJjCpUuX8Nlnn6Fdu3bYsmULnnrqKTz77LOGs/TS0iq+FD09jRcs9PT0NDyWlpYGDw8Po8ctLCzg4uJi1Ka6fdz4GjcrKSmBVqs1upH5yCsuw97zWQCA4WEsmugfkiQZ5rjxAr5EVBd1XqepUvv27dG+fXtTZqlCr9ejR48ehh6srl27IjY2FsuXL5d9Hajo6Gi8/vrrsmagW9txNgOlOj3auNuhnYe93HHIzAwP9cLXexOw7Uw6Ssv1sLKo9xWliKgFqXPRpNPpsHLlSmzfvh0ZGRnQ6/VGj+/YscNk4by9vdGxY0ejbR06dMCaNWsAAF5eFb8tpqenw9vb29AmPT0dd911l6FNRobxWTLl5eXIzs42PN/Lywvp6cbXo6q8X9nmZnPnzsXs2bMN97VaLXx9fet6iNRAbhyakyRJ5jRkbrr5OcPdQYXMvBLsv5iFQcEeNT+JiFq8Ov969dxzz+G5556DTqdDaGgounTpYnQzpb59+yI+Pt5o27lz5+Dv7w8ACAwMhJeXF7Zv3254XKvV4uDBg4iIiAAAREREIDc3F0eOHDG02bFjB/R6PcLDww1t9uzZg7Kyf9ZtiYmJQXBwsNGZejdSqVRwdHQ0upF5KCrVYVd8xVyVyjOliG6kUEgY2rFiCJ5DdERUa3WdZe7q6io2btxYrxnqdXXo0CFhYWEh3n77bXH+/Hnx/fffC1tbW/Hdd98Z2rz77rvCyclJ/Prrr+LkyZPi/vvvF4GBgaKoqMjQZtiwYaJr167i4MGDYu/evaJdu3ZiwoQJhsdzc3OFp6en+Ne//iViY2PFTz/9JGxtbcXnn39e66w8e858bDqVKvxf2iD6vrtd6PV6ueOQmfrzXKbwf2mD6PbGVlGu4+eEqKWqy/d3nYfnrKys0LZtW9NXb9Xo2bMn1q1bh7lz5+KNN95AYGAgPvzwQ0yaNMnQ5sUXX0RBQQGmT5+O3Nxc9OvXD5s3b4a1tbWhzffff4+ZM2diyJAhUCgUGDduHD7++GPD42q1Glu3bsWMGTPQvXt3uLm5Yd68eUZrOVHTYVjQkkNzdBvhbVygtrHEtYJSHE7MRu82pl1njoiaH0kIIeryhMWLF+PSpUtYtmwZv5BuoNVqoVarodFoOFQno5JyHXq8uQ15JeX45ckI9AhwkTsSmbH//HwCa45exdQ+AVhwXye54xCRDOry/V3nnqa9e/di586d2LRpEzp16gRLS0ujx9euXVvXXRKZzP6L15BXUg4PBxW6+VU/H42o0vBQL6w5ehVb4tIwf1RH/iJIRLdV56LJyckJY8aMaYgsRHds86mKSb1RnbygUPALkG6vXzs32Fopkaopxnd/XUZkR09e2JmIbqnORdOKFSsaIgfRHSvX6bH19PWlBnitOaoFa0slgjzsceqqBq/9Gof5v8UhemwYxvf0kzsaEZmheq3oVl5ejm3btuHzzz9HXl4eACAlJQX5+fkmDUdUF4cSs5FTWAZnW0uEB3IuE9UsVVOE2Ksaw329AF5ZG4tUTZGMqYjIXNW5p+ny5csYNmwYkpKSUFJSgnvuuQcODg547733UFJSguXLlzdETqIaVS5oeU9HT1goucIz1SwhqwA3nwmjEwKJWYUcpiOiKuq1uGWPHj2Qk5MDG5t//lMZM2aM0SKTRI1Jrxf/rALOoTmqpUA3O9w89U0pSQhws5UnEBGZtToXTX/++SdeffXVKhfnDQgIQHJyssmCEdXFsSu5yMgrgYPKAn3buskdh5oIb7UNoseGGRVOb9zfib1MRFStOhdNer0eOp2uyvarV6/CwcHBJKGI6qpyQcvBHTygslDKnIaakvE9/bDnxbvhZFOxfIq7g0rmRERkrupcNA0dOhQffvih4b4kScjPz8f8+fNx7733mjIbUa0IIbDphgv0EtVVa2dbPNijNQBg3TH2mBNR9epcNC1evBj79u1Dx44dUVxcjIkTJxqG5t57772GyEh0W3EpWlzNKYK1pQIDg93ljkNN1OiurQAA289kQFNYVkNrImqJ6nz2XOvWrXHixAn89NNPOHnyJPLz8zFt2jRMmjTJaGI4UWOpnAA+qL0HbK3q/JEmAgB09HZEsKcD4tPz8EdsKib04lpNRGSsXt8wFhYWeOSRR0ydhaheNl2fzzQ8jENzVH+SJGFMt1Z4d9NZrDuazKKJiKqoc9H03//+97aPT548ud5hiOrqfHoeLmYWwFIp4e4QD7njUBN3/10+eG/zWRxKzMaV7EL4unDpASL6R52Lpueee87ofllZGQoLC2FlZQVbW1sWTdSoKofm+rV1g6O1ZQ2tiW7PW22DiDau2H/xGn49noyZg9vJHYmIzEidJ4Ln5OQY3fLz8xEfH49+/frhxx9/bIiMRLdUedbc8FBvmZNQczHm+oTwtceSIcTN64UTUUtmkmtNtGvXDu+++26VXiiihpR0rRCnU7VQKiREdvSUOw41E8NCvaCyUOBSZgFO3nBdOiIik12gy8LCAikpKabaHVGNNsdVTAAPD3SBi51VDa2JasfB2hJDr6/3xTWbiOhGdZ7T9NtvvxndF0IgNTUVy5YtQ9++fU0WjKgm/wzN8aw5Mq2xXVvh9xMp+P1ECv5vRAdY8gLQRIR6FE2jR482ui9JEtzd3TF48GAsXrzYVLmIbitVU4RjSbkAYOgVIDKVfu3c4GpnhWsFpfjzfCYGh3D4l4jqUTTp9fqGyEFUJ1uu9zJ193eGp6O1zGmoubFUKjCqiw9W7k/EumMpLJqICIAJ5zQRNabNcRyao4Y1tlvFWXRb49KQV8zLqhBRPXqaZs+eXeu2S5YsqevuiWp0Lb8EhxKyAQBRHJqjBhLWSo027na4lFmATbFpeKiHr9yRiEhmdS6ajh07hmPHjqGsrAzBwcEAgHPnzkGpVKJbt26GdpIkmS4l0Q22nk6HXgChrRy5YjM1GEmSMLZrK7y/9RzWH0tm0UREdS+aRo0aBQcHB3z77bdwdnYGULHg5aOPPor+/fvjP//5j8lDEt1oMxe0pEZy/10VRdOBS9eQqimCt5oXJSdqyeo8p2nx4sWIjo42FEwA4OzsjLfeeotnz1GD0xSVYf/FLAAcmqOG5+tii16BLhACWH+M69ARtXR1Lpq0Wi0yMzOrbM/MzEReXp5JQhHdyvYz6SjTCbTzsEdbD3u541ALUHlZlXXHrvKyKkQtXJ2LpjFjxuDRRx/F2rVrcfXqVVy9ehVr1qzBtGnTMHbs2IbISASgYm2mHw4mAeBZc9R47g3zhpWFAufS83E6VSt3HCKSUZ3nNC1fvhxz5szBxIkTUVZWcRquhYUFpk2bhkWLFpk8IBEArDqchLlrT0F//Rd9nmhAjUVtY4nIDh7441Qa1h1NRicftdyRiEgmkqhnf3NBQQEuXrwIAAgKCoKdnZ1JgzU1Wq0WarUaGo0Gjo6OcsdpVlI1Rej77g5DwQQASgnY+/JgTsylRrE1Lg3T/3cE7g4q/DV3CJQKFu1EzUVdvr/rvbhlamoqUlNT0a5dO9jZ2XGsnxpMQlaBUcEEADoBJGYVyhOIWpxBwR5wtrVEZl4J9l3IkjsOEcmkzkXTtWvXMGTIELRv3x733nsvUlMrrjQ/bdo0LjdADSLQzQ43/2KvlCQEuHGNJmocVhYKjOzsAwBYdyxZ5jREJJc6F03PP/88LC0tkZSUBFvbf760xo8fj82bN5s0HBEAeKtt8GCP1ob7Sgl4Z2woh+aoUY2+fhbd5tg0FJSUy5yGiORQ54ngW7duxZYtW9C6dWuj7e3atcPly5dNFozoRhnaEgAV1wN7ISqYBRM1um5+TghwtUXitUJsPZ2GMV1b1/wkImpW6tzTVFBQYNTDVCk7OxsqlcokoYhulFNQij/PV8wjeXpQEAsmkoUkSYbeprVHOURH1BLVuWjq378//vvf/xruS5IEvV6PhQsX4u677zZpOCIA2BSbhnK9QAdvR7T1cJA7DrVgo++qKJr2XchChrZY5jRE1NjqPDy3cOFCDBkyBH///TdKS0vx4osvIi4uDtnZ2di3b19DZKQW7rcTFb/V39fFR+Yk1NIFuNmhm58Tjibl4rcTKXisfxu5IxFRI6pzT1NoaCjOnTuHfv364f7770dBQQHGjh2LY8eOISgoqCEyUguWri3GwYRsAMDIzrxAL8lvTLeKuUwcoiNqeerU01RWVoZhw4Zh+fLl+L//+7+GykRksOFkKoSomITr68IlBkh+I8O88cbvcTidqkV8Wh6CvThkTNRS1KmnydLSEidPnmyoLERV/Hai4sryHJojc+FsZ4VBwR4AuGYTUUtT5+G5Rx55BF9//XVDZCEyknStECeu5EIhAfdyaI7MyNjrZ9H9ejwZ+puXqyeiZqvOE8HLy8vxzTffYNu2bejevXuVa84tWbLEZOGoZfv9ZEUvU0SQKzwcrGVOQ/SPu0M84GBtgVRNMf5KuIY+QW5yRyKiRlCrounkyZMIDQ2FQqFAbGwsunXrBgA4d+6cUTteeZ5M6bfjHJoj82RtqcTIzt748dAVrDuazKKJqIWoVdHUtWtXpKamwsPDA5cvX8bhw4fh6ura0NmoBYtPy0N8eh4slRKGdeLQHJmfMV1b48dDV7ApNg1v3B8KGyul3JGIqIHVak6Tk5MTEhISAACJiYnQ6/UNGoro9+sTwAe2d4fa1lLmNERV9fB3RisnG+SXlGPbmXS54xBRI6hVT9O4ceMwcOBAeHt7Q5Ik9OjRA0pl9b9VXbp0yaQBqeURQhjOmhvFoTkyUwqFhDFdW2HZzgtYdyyZn1WiFqBWRdMXX3yBsWPH4sKFC3j22Wfx+OOPw8GBa5NQwzh5VYOk7ELYWCpxT0dPueMQ3dLo60XT7nOZyMovgZs9r79J1JzV+uy5YcOGAQCOHDmC5557jkUTNZjKXqbIjp6wtarzCZ5Ejaathz06t1bj5FUNNpxIwdS+gXJHIqIGVOd1mlasWMGCiRqMTi+w4fpSA6O4NhM1AWOur9m06vAV7L+YhVRNkcyJiKih1LloImpIhxOzka4tgaO1BQYGu8sdh6hGo7r4QJKAM2l5mPjlQfR9dwdWHU6SOxYRNQAWTWRWKofmhoV6QWXBU7jJ/JXp9BA3LAquF8Ara2PZ40TUDLFoIrNRptNj06lUADxrjpqOhKyCKtt0QiAxq1CGNETUkJpU0fTuu+9CkiTMmjXLsK24uBgzZsyAq6sr7O3tMW7cOKSnG6+ZkpSUhBEjRsDW1hYeHh544YUXUF5ebtRm165d6NatG1QqFdq2bYuVK1c2whHRjfZeyEJOYRnc7K0Q0YaLp1LTEOhmB8VNF0NQShIC3GzlCUREDabJFE2HDx/G559/js6dOxttf/755/H7779j9erV2L17N1JSUjB27FjD4zqdDiNGjEBpaSn279+Pb7/9FitXrsS8efMMbRISEjBixAjcfffdOH78OGbNmoXHHnsMW7ZsabTjI+D365dNGRHmDQtlk/loUgvnrbZB9Ngw3Fg3vT0mFN5qG9kyEVHDaBLfTPn5+Zg0aRK+/PJLODs7G7ZrNBp8/fXXWLJkCQYPHozu3btjxYoV2L9/P/766y8AwNatW3H69Gl89913uOuuuzB8+HC8+eab+OSTT1BaWgoAWL58OQIDA7F48WJ06NABM2fOxAMPPIAPPvhAluNtiYrLdNgSlwaAQ3PU9Izv6YfNs/rDUllROgW62dXwDCJqippE0TRjxgyMGDECkZGRRtuPHDmCsrIyo+0hISHw8/PDgQMHAAAHDhxAWFgYPD3/WSQxKioKWq0WcXFxhjY37zsqKsqwj+qUlJRAq9Ua3aj+dp7NQEGpDq2cbNDNz7nmJxCZmWAvRzzQ3RcAsHJ/orxhiKhBmH3R9NNPP+Ho0aOIjo6u8lhaWhqsrKzg5ORktN3T0xNpaWmGNjcWTJWPVz52uzZarRZFRdWfARMdHQ21Wm24+fr61uv4qELlWXMju3hDcfMEEaImYmqfAADAlrg0JOfy7Dmi5sasi6YrV67gueeew/fffw9ra2u54xiZO3cuNBqN4XblyhW5IzVZecVl2H42AwAwqjOH5qjpCvZyQN+2rtAL4H8HLssdh4hMzKyLpiNHjiAjIwPdunWDhYUFLCwssHv3bnz88cewsLCAp6cnSktLkZuba/S89PR0eHl5AQC8vLyqnE1Xeb+mNo6OjrCxqX4yp0qlgqOjo9GN6ifmdDpKy/Vo426HTj58H6lpm9qn4lIqPx5KQlGpTuY0RGRKZl00DRkyBKdOncLx48cNtx49emDSpEmGv1taWmL79u2G58THxyMpKQkREREAgIiICJw6dQoZGRmGNjExMXB0dETHjh0NbW7cR2Wbyn1Qw6ocmruviw8kiUNz1LQNDvGAr4sNNEVlWH88We44RGRCZn01VAcHB4SGhhpts7Ozg6urq2H7tGnTMHv2bLi4uMDR0RHPPPMMIiIi0Lt3bwDA0KFD0bFjR/zrX//CwoULkZaWhldffRUzZsyASlVxRfInn3wSy5Ytw4svvoh///vf2LFjB37++Wds3LixcQ+4BcouKMXe81kAeNYcNQ9KhYQpEQF4a+MZrNyXiId7+vKXAaJmwqx7mmrjgw8+wMiRIzFu3DgMGDAAXl5eWLt2reFxpVKJDRs2QKlUIiIiAo888ggmT56MN954w9AmMDAQGzduRExMDLp06YLFixfjq6++QlRUlByH1KJsik1FuV6gk48jgtzt5Y5DZBIP9vCFrZUS8el5OHDpmtxxiMhEJCFuvGoS1ZdWq4VarYZGo+H8pjoY//kBHEzIxtzhIXhiYJDccYhM5rX1sfjfX5dxT0dPfDm5h9xxiOgW6vL93eR7mqjpStMU41BiNgBgJIfmqJmZ0scfALDtTDquZPM6dETNAYsmks2GkykQAujh74xWTrzkBDUvbT0c0L+dG4QA/nsgUe44RGQCLJpINr9XnjV3F3uZqHl6tG8AAOCnw1dQUFJ++8ZEZPZYNJEsErMKcOKqBgoJGB7qLXccogYxqL0HAlxtkVdcjnXHuPwAUVPHoolkseFkRS9T37ZucHdQyZyGqGEoFBKmXL+0ysr9ieB5N0RNG4smkkXlgpZcm4mauwe6t4adlRIXMvKx90KW3HGI6A6waKJGdzZNi3Pp+bBSKhDVyUvuOEQNysHaEg/2qLig98p9ifKGIaI7wqKJGl3lBPCBwe5Q21jKnIao4U2OqFh+YEd8BhKzCmROQ0T1xaKJGpUQAr+fSAVQca05opagjbs97g52v778wGW54xBRPbFookZ1/EoukrILYWulxJAOHnLHIWo0U/sGAgBW/30F+Vx+gKhJYtFEjaqylymygydsrcz6etFEJtW/rRvauNshr6Qca45clTsOEdUDiyZqNDq9MCw1wKE5amkUCglTry8/8O3+ROj1XH6AqKlh0USN5mDCNWTklcDR2gID2rvLHYeo0Y3t1hoOKgtcyirAnvOZcschojpi0USNZtXhKwCAge3dYWXBjx61PPYqCzzU8/ryA/sT5Q1DRHXGby5qFN//dRm/Hq8YmttwKhWrDifJnIhIHpMj/CFJwK74TFzMzJc7DhHVAYsmanCpmiK8+mus4b4QwCtrY5GqKZIxFZE8/F3tMCSk4szR/7K3iahJYdFEDS4hqwA3X3JLJwQSswrlCUQks6l9KpYf+OXIVWiLy2ROQ0S1xaKJGpyvs02VbUpJQoCbrQxpiOTXt60r2nnYo6BUh1/+5vIDRE0FiyZqcFdzio3uKyUJ74wNhbe6ajFF1BJIkoSpfQMAAN8e4PIDRE0FVxekBrc5tmJByxFhXnikdwAC3GxZMFGLN6ZrK7y36SwuXyvEzvgMDOngKXckIqoBe5qoQen1Alvi0gEA47q3RkSQKwsmIgC2VhZ4uJcfAC4/QNRUsGiiBnX8ai7StMWwV1mgb1s3ueMQmZV/9faHQgL+PJ+F8+l5cschohqwaKIGtTk2DQAwOMQDKgulzGmIzIuviy0irw/LfXsgUd4wRFQjFk3UYIQQhqJpWKiXzGmIzNOjfSuWH1hzJBmaIi4/QGTOWDRRgzmdqkVSdiFUFgoMCua15oiq07uNC0K8HFBUpsPCzWe56CuRGWPRRA1my/VepoHt3WFrxRM1iaojSRI6+TgCAL4/mIS+7+7gZYaIzBSLJmowm64XTcPDODRHdCupmiKsO5ZsuK/nZYaIzBaLJmoQFzLycT4jH5ZKCYNDuP4M0a0kZBXg5rUteZkhIvPEookaxJa4il6mPkFuUNtYypyGyHwFutlBIRlvkwBeZojIDLFoogbBs+aIasdbbYPosWFQSv9UTpZKCcqbKykikh2LJjK5K9mFOJWsgUIC7unIoTmimozv6Ye9L9+NHx4LRwdvR5TqBD6IOSd3LCK6CYsmMrnKobmeAS5ws1fJnIaoafBW26BPWze8eX8nAMCqw1cQn8ZVwonMCYsmMrnKobnhHJojqrMeAS4YHuoFvQDe/uOM3HGI6AYsmsikMrTFOJKUAwCIYtFEVC8vDw+BpVLCnnOZ2BWfIXccIrqORROZ1JbT6RACuMvXCd5qG7njEDVJ/q52mBIRAAB4548zKNfp5Q1ERABYNJGJbeFZc0Qm8czgdnCytcS59Hz8/PdVueMQEVg0kQnlFJTiwKVrAIBhnVg0Ed0Jta0lnhvSDgCwJCYeecW8mC+R3Fg0kclsO5MOnV4gxMsBAW52cschavImhfsj0M0OWfmlWL77otxxiFo8Fk1kMv+cNectcxKi5sHKQoGXh4cAAL76MwHJubweHZGcWDSRSeSXlOPP81kAOJ+JyJSGdvREeKALSsr1WLT5rNxxiFo0Fk1kEjvOZqBUp0cbNzu097SXOw5RsyFJEl4d0REAsP54Ck5cyZU3EFELxqKJTKLyrLmoUC9IEq+ZRWRKYa3VGNu1FQDg7Y1nIISQORFRy8Siie5YcZkOO68vwMdVwIkaxpyoYFhbKnAoMdtwqSIialwsmuiO7TmXicJSHVo52SCslVruOETNko+TDR7v3wYA8O6msygt54KXRI2NRRPdscqz5qI6cWiOqCE9MTAIbvYqJF4rxP/+uix3HKIWh0UT3ZHScj22nUkHwLPmiBqavcoCc4a2BwB8vP08cgtLZU5E1LKwaKI7cuDSNWiLy+Fmr0J3f2e54xA1ew/28EWIlwM0RWX4ePsFueMQtSgsmuiOVA7NDe3kCaWCQ3NEDU2pkPDKvR0AAP/7KxEJWQUyJyJqOVg0Ub3p9AIxpytXAefQHFFjGdDeHYOC3VGmE3h30xm54xC1GCyaqN7+TsxGVn4p1DaW6N3GVe44RC3KK/d2gEICtsSl4+D1C2UTUcNi0UT1tun60FxkB09YKvlRImpM7T0d8HAvPwDA23+cgV7PBS+JGppZf9NFR0ejZ8+ecHBwgIeHB0aPHo34+HijNsXFxZgxYwZcXV1hb2+PcePGIT093ahNUlISRowYAVtbW3h4eOCFF15AeXm5UZtdu3ahW7duUKlUaNu2LVauXNnQh9ekCSEMC+zxrDkieTwf2R72KgucvKrBryeS5Y5D1OyZddG0e/duzJgxA3/99RdiYmJQVlaGoUOHoqDgn4mPzz//PH7//XesXr0au3fvRkpKCsaOHWt4XKfTYcSIESgtLcX+/fvx7bffYuXKlZg3b56hTUJCAkaMGIG7774bx48fx6xZs/DYY49hy5YtjXq8TcmJqxqkaophZ6VE/3ZucschapHcHVR4alAQAGDR5ngUl+lkTkTUvEmiCV3EKDMzEx4eHti9ezcGDBgAjUYDd3d3/PDDD3jggQcAAGfPnkWHDh1w4MAB9O7dG5s2bcLIkSORkpICT09PAMDy5cvx0ksvITMzE1ZWVnjppZewceNGxMbGGl7r4YcfRm5uLjZv3lyrbFqtFmq1GhqNBo6OjqY/eDPz7qazWL77IkZ29sayid3kjkPUYhWX6TBk8W4k5xbhyYFtMKC9OwLd7OCttpE7GlGTUJfvb7PuabqZRqMBALi4uAAAjhw5grKyMkRGRhrahISEwM/PDwcOHAAAHDhwAGFhYYaCCQCioqKg1WoRFxdnaHPjPirbVO6jOiUlJdBqtUa3lkIIgc2xqQA4NEckN2tLJV4cFgwAWL77EiZ+eRB9392BVYeTZE5G1Pw0maJJr9dj1qxZ6Nu3L0JDQwEAaWlpsLKygpOTk1FbT09PpKWlGdrcWDBVPl752O3aaLVaFBUVVZsnOjoaarXacPP19b3jY2wq4tPzkHitEFYWCtwd7CF3HKIWr8dNC8vqBfDK2likaqr//4uI6qfJFE0zZsxAbGwsfvrpJ7mjAADmzp0LjUZjuF25ckXuSI1m06mKYnNAO3fYqSxkTkNEl7MLq2zTCYHErKrbiaj+msQ33syZM7Fhwwbs2bMHrVu3Nmz38vJCaWkpcnNzjXqb0tPT4eXlZWhz6NAho/1Vnl13Y5ubz7hLT0+Ho6MjbGyqnxegUqmgUqnu+Niaosqz5rigJZF5CHSzg0Kq6GGqpJCAADdb+UIRNUNm3dMkhMDMmTOxbt067NixA4GBgUaPd+/eHZaWlti+fbthW3x8PJKSkhAREQEAiIiIwKlTp5CRkWFoExMTA0dHR3Ts2NHQ5sZ9VLap3Af9IyGrAGfT8mChkBDZwbPmJxBRg/NW2yB6bBhuvJKRj5MNXOys5AtF1AyZddE0Y8YMfPfdd/jhhx/g4OCAtLQ0pKWlGeYZqdVqTJs2DbNnz8bOnTtx5MgRPProo4iIiEDv3r0BAEOHDkXHjh3xr3/9CydOnMCWLVvw6quvYsaMGYaeoieffBKXLl3Ciy++iLNnz+LTTz/Fzz//jOeff162YzdXldeaiwhyhdrWUuY0RFRpfE8/7Ht5MJZO6ApHawtczSnCWxt4iRUiUzLroumzzz6DRqPBoEGD4O3tbbitWrXK0OaDDz7AyJEjMW7cOAwYMABeXl5Yu3at4XGlUokNGzZAqVQiIiICjzzyCCZPnow33njD0CYwMBAbN25ETEwMunTpgsWLF+Orr75CVFRUox5vU8Cz5ojMl7faBqO6+OCjCV0BAP/76zJ+Pc5FL4lMpUmt02TOWsI6Tcm5Rej77g5IEnDolUi4O7TMOV1ETcH7W+KxbOcF2Fop8dvMvmjr4SB3JCKz1GzXaSJ5bbk+NNfT34UFE5GZe/6e9ugT5IrCUh2e+u4oCkvLa34SEd0Wiyaqtcr5TByaIzJ/SoWEjx7uCg8HFc5n5OPVdbHgwALRnWHRRLUSm6zBocRsAEAUiyaiJsHdQYWlE7pCqZCw9lgyfjrcctaTI2oILJqoRqsOJ2HU0r2G+3vPZ8qYhojqIryNK+YMrbjMyvzf4hCbrJE5EVHTxaKJbitVU4S5a0/hxk59Xp6BqGl5YkAbRHbwQGm5Hk9/fxSaojK5IxE1SSya6LYSsgqMVhkGeHkGoqZGoZCw+MG70NrZBknZhXhh9QnObyKqBxZNdFuBbnZVtikliZdnIGpi1LaW+HRSN1gpFdh6Oh1f702QOxJRk8OiiW7r5m58pSThnbGh8FZXf00+IjJfnVs74bWRHQAA7246i7+vn9xBRLXTJC7YS/L5aNt5AMDgEA883r8NAtxsWTARNWGP9PbH4cQc/HYiBTN/OIaNz/aDqz3XXSOqDfY00S3FpWiwKTYNkgS8PDwEEUGuLJiImjhJkhA9NgxB7nZI0xZj1qrj0N08cZGIqsWiiW7pg5iKXqZRnX3Q3pOXYCBqLuxUFvjske6wsVTiz/NZWLrjvNyRiJoEFk1UrZNXc7HtTDoUEvBcZDu54xCRibX3dMDbY0IBAB9tP48/uf4aUY1YNFG1lsScAwCM7toKQe72MqchooYwtltrTOjlByGA5346zvXXiGrAoomqOHI5B7viM6FUSHh2MHuZiJqz+aM6opOPI7ILSjHzh2NIyi7A/otZLKCIqsGz56iKD7dV9DI90K01AqpZp4mImg9rSyU+ndQNI5fuxZHLORi4cBcEAIUERI8Nw/iefnJHJDIb7GkiI4cSsvHn+SxYKCTMHNxW7jhE1Aj8Xe3wyvCK9Zsqz6PTC14yiehmLJrIyJKYeADAQz194evCVb+JWgr/alb55yWTiIyxaCKD/Rez8NelbFgpFZh5N3uZiFqSQDc7KCTjbRLASyYR3YBFEwEAhBBYsrViLtOEXr7wceIilkQtibfaBtFjw6C8oXASAFbuS+TFfYmu40RwAgD8eT4Lf1/OgcpCgafZy0TUIo3v6YcB7d2RmFWIfRcysWznRXy+5xJyCkvxzpgwWCj5eza1bCyaqKKX6fq6TI/09oeno7XMiYhILt5qG3irbRAR5Ao/Vzu8vOYkfv77KnILy/DxhK6wtlTKHZFINvy1gbAzPgPHr+TC2lKBJwcGyR2HiMzEQz188dkj3WFlocDW0+mYuuIQ8orL5I5FJBsWTS3cjb1MUyIC4O7Aq50T0T+iOnnh20d7wV5lgb8uZWPCl38hK79E7lhEsmDR1MJtPZ2O2GQt7KyUeIK9TERUjYggV/w0vTdc7awQm6zFg8sP4GoOlyKglodFUwum1wt8cL2XaWrfALjYWcmciIjMVWgrNVY/GYFWTjZIyCrAA58dwLn0PLljETUqFk0t2Oa4NJxNy4ODygKP928jdxwiMnNt3O2x5qk+aOdhjzRtMR5cfgBHk3LkjkXUaFg0tVC6G3qZ/t0vEE627GUiopp5qa2x+skIdPVzgqaoDJO+PIjd5zLljkXUKFg0tVAbTqbgfEY+HK0t8O9+gXLHIaImxMnWCt8/Fo4B7d1RVKbDY98exu8nUuSORdTgWDS1QOU6PT7adh4AMH1AG6htLGVORERNja2VBb6a3AMjO3ujTCfw7E/H8L+/Lssdi6hBcXHLFujX4ym4lFUAJ1tLTO3LXiYiqh8rCwU+ergrnG2t8L+/LuO19bFIulaAu4M9EOhuB281L8dEzQuLphamTKfHxzsqepmeGBAEexU/AkRUf0qFhDfu7wRnOyt8vP08vvwzAV/+mQCFBESPDcP4nn5yRyQyGQ7PtTDrjibj8rVCuNpZYUoff7njEFEzIEkSJvTyxQ3X+oVeAHPXnkKqpki2XESmxqKpBSkt1+Oj7RW9TE8NCoKtFXuZiMg0ErIKIG7aphfAF3suQa+/+RGipolFUwuy+sgVJOcWwd1BhUnh7GUiItMJdLODQqq6fcW+REz+5hCSc9njRE0fi6YWoqRch2U7LgAAZgwKgo0Vr1RORKbjrbZB9NgwKKWKykkpAfd18YG1pQJ7L2Rh2Ad7sPrvKxCCvU7UdHF8pgVI1RTh892XkKophpejNR7uxYmZRGR643v6YUB7dyRmFSLAzRbe6opLrvzn5+M4mpSLF345iS1xaXhnbBg8HKzljktUZ+xpauZWHU5C33d3YOX+RABA7zYusLZkLxMRNQxvtQ0iglwNyw0Eutlh9ZN98PLwEFgpFdh2JgNDP9iDDSe5GCY1PSyamrFUTRHmrj2FG+dg/nYihWezEFGjUiokPDkwCL8/0w+dfByRW1iGmT8cwzM/HkNOQanc8YhqjUVTMxZ7VYubT1rRCyAxq1CeQETUogV7OWDd033x7JB2UCok/H4iBUM/3IMdZ9PljkZUKyyamqnYZA3m/R5bZbtSkhDgZitDIiKiilXEZ9/THuue7oO2HvbIzCvBv1f+jRd/OYG84jK54xHdFoumZmjV4SSM/Ww/UnOL4WxnaTgNWClJeGdsKC9tQESy69zaCRue6YfH+wdCkoCf/76KYR/+if0XspCqKcL+i1mcSkBmRxI8/9MktFot1Go1NBoNHB0dZclQXKbDa+tjsfrIVQBAZAcPLH7wLhSWlRudzUJEZE4OJWRjzuoTSMqumDogARAAL8VCjaIu398smkxE7qLp8rUCPPndUZxJ1UIhAf8ZGoynBgZBUd1qc0REZqagpByvrjuFdceNz6pTSMCuFwbBz8VOpmTU3NXl+5vDc83A1rg0jFy6F2dStXC1s8J308Ix4+62LJiIqMmwU1ngwZ6+VbbrBTDsgz2Yveo4NsemoahUJ0M6ogpc3LIJK9fp8f7Wc1i++yIAoLu/Mz6Z2A1eai4aR0RNT+WlWG4+67ewTI+1x5Kx9lgyrC0VGNjeHcNCvTA4xBNqG0t5wlKLxOE5E2ns4bnMvBI88+NR/HUpGwAwrV8gXh4eAkslOw+JqOladTgJr6yNhU4IKCUJb40JRVsPe2yOTcOWuDRczflncriFQkJEkCuiOnlhaEdPeDj+8wtjqqYICVkFCHSz41xOui3OaZJBYxZNhxOzMeP7o8jIK4GdlRILH+iCEZ29G/Q1iYgaS6qmqNqTV4QQiEvRYmtcGjbHpeFcer7hMUkCuvk5I6qTJ8p1Au9vjYdecDI51YxFkwwao2gSQuDrvQmI3nQWOr1Ae097fPZIdwS52zfI6xERmbOErAJsiUvD5tg0HL+Se8t2CgnYPnsQAt05mZyqYtEkg4YsmlI1RTidosX//rqMXfGZAIDRd/ngnbFhsLXitDQiojRNMbaeTsOqw1cQl6Kttk0bNzuEeDugg5cjQrwdEeLlgNbONpCkqifNcHiv5WDRJIOGKppWHU7Cy2tPofKnpJSABfeH4pFwv2r/oRMRtWSpmiL0fXdHlcnkt+KgskCItwNCvBzRwdsRId4OiL2qwYLf4zi810LU5fub3RRmLFVThJfXnMKN//YFKhatZMFERFSVt9oG0WPDjCaTvzM2FINDPHEmVYuzaVqcTc3D6VQtLmbmI6+kHIcTc3A4Mafa/ekF8PKaU8gtLEM7T3u421vD3UEFV3urGk+8YW9V88Oi6SaffPIJFi1ahLS0NHTp0gVLly5Fr169ZMmSkFWAm39ZqrzgLv8BEhFVb3xPPwxo715lMrm7gzsGtHc3tCst1+NSVn5FMZWahzNpeThxJReaIuNr4AkA0ZvOGm2TJMDF1gruDqqKm73qn787qBCbrMHXexPuuLfKFIUXizfT4fDcDVatWoXJkydj+fLlCA8Px4cffojVq1cjPj4eHh4et31uQwzPVdfNrJQk7H35bn7wiYgaQHX/70oAerdxQV5JOTLzSpCVXwpdbcf/btDKyRpuDtZwsrGEs60lnGyt4GRrWXHfzgpqG0s4V26ztcIfp1Lxf+tO3VHhtepwEuauvbN9mEvh1lDFH+c01VN4eDh69uyJZcuWAQD0ej18fX3xzDPP4OWXX77tcxtyTtPN3cwcWyciajg1/b+r1wtkF5YiM6/kn1t+xZ8ZeSW4kJGHM6l5DZIt0M0WNpYWsFRKsFAqoFRIFX9XKGCplKBUVGy3VEgo1enxx6k0o+dLACaE+8HR2hJKRcUv4gqFBIVU8dyKPwGFVPH341dy8fuJFIjrz32wR2v0besGy+uvbaGQrme49f0tcWlYEnPOULi9GBWMkV186nTcG06kYOGWhllGgkVTPZSWlsLW1ha//PILRo8ebdg+ZcoU5Obm4tdffzVqX1JSgpKSEsN9rVYLX1/fBjt7jhfcJSJqPHfy/251vVUKCfh0UjcoFQrkFJZCU1iGnMJS5BaVIbewFLmFZcgpLIOmsBQ5hWUoKuPlYm7HlKMunAheD1lZWdDpdPD09DTa7unpibNnz1ZpHx0djddff71RsnmrbVgsERE1ojv5f/dWk9GHhdZ+EeLErAIMXryrSuH14cN3QW1jhXKdHmU6AZ1eoFxf8fdynR7l+n/+zC4oxWe7LhrNja3sabKxVEKnF9ALYfSnTl+xJqBOCKRriw1XnbhRR29H2KssUK7XQ6cXRjmM7wsUl5Ujv6RqAWipkGp9fVS9XqDspuFQnRCyzO9l0VRPc+fOxezZsw33K3uaiIiIbjUZvbYC3OyqLbzu69KqTvvxd7Wt9xSPW82r/Xpqj1ofz632seel2vcS3WofAW62tXq+KbFous7NzQ1KpRLp6elG29PT0+Hl5VWlvUqlgkqlaqx4RETUxNzpKMGdFl53uo9b9Zg1xX2YCuc03SA8PBy9evXC0qVLAVRMBPfz88PMmTNlmwhOREQkJ1PMqzWXfVSHc5rqafbs2ZgyZQp69OiBXr164cMPP0RBQQEeffRRuaMRERHJwhTzas1lH3eKRdMNxo8fj8zMTMybNw9paWm46667sHnz5iqTw4mIiKjl4fCciXB4joiIqOmpy/f37S+cQ0REREQAWDQRERER1QqLJiIiIqJaYNFEREREVAssmoiIiIhqgUUTERERUS2waCIiIiKqBRZNRERERLXAoomIiIioFngZFROpXFhdq9XKnISIiIhqq/J7uzYXSGHRZCJ5eXkAAF9fX5mTEBERUV3l5eVBrVbftg2vPWcier0eKSkpcHBwgCRJJt23VquFr68vrly5wuva3SG+l6bF99N0+F6aFt9P02nu76UQAnl5efDx8YFCcftZS+xpMhGFQoHWrVs36Gs4Ojo2yw+sHPhemhbfT9Phe2lafD9Npzm/lzX1MFXiRHAiIiKiWmDRRERERFQLLJqaAJVKhfnz50OlUskdpcnje2lafD9Nh++lafH9NB2+l//gRHAiIiKiWmBPExEREVEtsGgiIiIiqgUWTURERES1wKKJiIiIqBZYNJm5Tz75BAEBAbC2tkZ4eDgOHTokd6QmacGCBZAkyegWEhIid6wmYc+ePRg1ahR8fHwgSRLWr19v9LgQAvPmzYO3tzdsbGwQGRmJ8+fPyxO2Cajp/Zw6dWqVz+qwYcPkCWvmoqOj0bNnTzg4OMDDwwOjR49GfHy8UZvi4mLMmDEDrq6usLe3x7hx45Ceni5TYvNVm/dy0KBBVT6bTz75pEyJ5cGiyYytWrUKs2fPxvz583H06FF06dIFUVFRyMjIkDtak9SpUyekpqYabnv37pU7UpNQUFCALl264JNPPqn28YULF+Ljjz/G8uXLcfDgQdjZ2SEqKgrFxcWNnLRpqOn9BIBhw4YZfVZ//PHHRkzYdOzevRszZszAX3/9hZiYGJSVlWHo0KEoKCgwtHn++efx+++/Y/Xq1di9ezdSUlIwduxYGVObp9q8lwDw+OOPG302Fy5cKFNimQgyW7169RIzZsww3NfpdMLHx0dER0fLmKppmj9/vujSpYvcMZo8AGLdunWG+3q9Xnh5eYlFixYZtuXm5gqVSiV+/PFHGRI2LTe/n0IIMWXKFHH//ffLkqepy8jIEADE7t27hRAVn0VLS0uxevVqQ5szZ84IAOLAgQNyxWwSbn4vhRBi4MCB4rnnnpMvlBlgT5OZKi0txZEjRxAZGWnYplAoEBkZiQMHDsiYrOk6f/48fHx80KZNG0yaNAlJSUlyR2ryEhISkJaWZvQ5VavVCA8P5+f0DuzatQseHh4IDg7GU089hWvXrskdqUnQaDQAABcXFwDAkSNHUFZWZvT5DAkJgZ+fHz+fNbj5vaz0/fffw83NDaGhoZg7dy4KCwvliCcbXrDXTGVlZUGn08HT09Nou6enJ86ePStTqqYrPDwcK1euRHBwMFJTU/H666+jf//+iI2NhYODg9zxmqy0tDQAqPZzWvkY1c2wYcMwduxYBAYG4uLFi3jllVcwfPhwHDhwAEqlUu54Zkuv12PWrFno27cvQkNDAVR8Pq2srODk5GTUlp/P26vuvQSAiRMnwt/fHz4+Pjh58iReeuklxMfHY+3atTKmbVwsmqhFGD58uOHvnTt3Rnh4OPz9/fHzzz9j2rRpMiYjMvbwww8b/h4WFobOnTsjKCgIu3btwpAhQ2RMZt5mzJiB2NhYzlU0gVu9l9OnTzf8PSwsDN7e3hgyZAguXryIoKCgxo4pCw7PmSk3NzcolcoqZ3mkp6fDy8tLplTNh5OTE9q3b48LFy7IHaVJq/ws8nPacNq0aQM3Nzd+Vm9j5syZ2LBhA3bu3InWrVsbtnt5eaG0tBS5ublG7fn5vLVbvZfVCQ8PB4AW9dlk0WSmrKys0L17d2zfvt2wTa/XY/v27YiIiJAxWfOQn5+PixcvwtvbW+4oTVpgYCC8vLyMPqdarRYHDx7k59RErl69imvXrvGzWg0hBGbOnIl169Zhx44dCAwMNHq8e/fusLS0NPp8xsfHIykpiZ/Pm9T0Xlbn+PHjANCiPpscnjNjs2fPxpQpU9CjRw/06tULH374IQoKCvDoo4/KHa3JmTNnDkaNGgV/f3+kpKRg/vz5UCqVmDBhgtzRzF5+fr7Rb5IJCQk4fvw4XFxc4Ofnh1mzZuGtt95Cu3btEBgYiNdeew0+Pj4YPXq0fKHN2O3eTxcXF7z++usYN24cvLy8cPHiRbz44oto27YtoqKiZExtnmbMmIEffvgBv/76KxwcHAzzlNRqNWxsbKBWqzFt2jTMnj0bLi4ucHR0xDPPPIOIiAj07t1b5vTmpab38uLFi/jhhx9w7733wtXVFSdPnsTzzz+PAQMGoHPnzjKnb0Ryn75Ht7d06VLh5+cnrKysRK9evcRff/0ld6Qmafz48cLb21tYWVmJVq1aifHjx4sLFy7IHatJ2LlzpwBQ5TZlyhQhRMWyA6+99prw9PQUKpVKDBkyRMTHx8sb2ozd7v0sLCwUQ4cOFe7u7sLS0lL4+/uLxx9/XKSlpckd2yxV9z4CECtWrDC0KSoqEk8//bRwdnYWtra2YsyYMSI1NVW+0GaqpvcyKSlJDBgwQLi4uAiVSiXatm0rXnjhBaHRaOQN3sgkIYRozCKNiIiIqCninCYiIiKiWmDRRERERFQLLJqIiIiIaoFFExEREVEtsGgiIiIiqgUWTURERES1wKKJiIiIqBZYNBER3WTXrl2QJKnKNctMbdCgQZg1a1aDvkZtNdYxEzVlLJqIqE7M6YveFKo7nj59+iA1NRVqtVqeUA2suf0MiRoLiyYiMjkhBMrLy+WOUW9WVlbw8vKCJElyRyEiM8KiiYhqberUqdi9ezc++ugjSJIESZKQmJhoGNrZtGkTunfvDpVKhb179+LixYu4//774enpCXt7e/Ts2RPbtm0z2mdAQADeeecd/Pvf/4aDgwP8/PzwxRdfGB4vLS3FzJkz4e3tDWtra/j7+yM6Otrw+JIlSxAWFgY7Ozv4+vri6aefRn5+vtFr7Nu3D4MGDYKtrS2cnZ0RFRWFnJycGo/nxqGqNWvWoFOnTlCpVAgICMDixYvrdBy1UVJSgjlz5qBVq1aws7NDeHg4du3aZXh85cqVcHJywpYtW9ChQwfY29tj2LBhSE1NNbQpLy/Hs88+CycnJ7i6uuKll17ClClTDBdQvtUxVzpy5Ah69OgBW1tb9OnTB/Hx8XU6BqJmTeZr3xFRE5KbmysiIiLE448/LlJTU0VqaqooLy83XIS2c+fOYuvWreLChQvi2rVr4vjx42L58uXi1KlT4ty5c+LVV18V1tbW4vLly4Z9+vv7CxcXF/HJJ5+I8+fPi+joaKFQKMTZs2eFEEIsWrRI+Pr6ij179ojExETx559/ih9++MHw/A8++EDs2LFDJCQkiO3bt4vg4GDx1FNPGR4/duyYUKlU4qmnnhLHjx8XsbGxYunSpSIzM7PG48nJyRFCCPH3338LhUIh3njjDREfHy9WrFghbGxsjC4MW9NxVGfgwIHiueeeM9x/7LHHRJ8+fcSePXvEhQsXxKJFi4RKpRLnzp0TQgixYsUKYWlpKSIjI8Xhw4fFkSNHRIcOHcTEiRMN+3jrrbeEi4uLWLt2rThz5ox48sknhaOjo7j//vtr9TMMDw8Xu3btEnFxcaJ///6iT58+df6cEDVXLJqIqE5u/qIXQhi+cNevX1/j8zt16iSWLl1quO/v7y8eeeQRw329Xi88PDzEZ599JoQQ4plnnhGDBw8Wer2+VvlWr14tXF1dDfcnTJgg+vbtW6/jqSyaJk6cKO655x6jNi+88ILo2LFjrY+jpte+fPmyUCqVIjk52ajNkCFDxNy5c4UQFUUTAHHhwgXD45988onw9PQ03Pf09BSLFi0y3C8vLxd+fn6GoqmmY962bZth28aNGwUAUVRUdMtjIGpJODxHRCbTo0cPo/v5+fmYM2cOOnToACcnJ9jb2+PMmTNISkoyate5c2fD3yVJgpeXFzIyMgBUDCcdP34cwcHBePbZZ7F161aj527btg1DhgxBq1at4ODggH/961+4du0aCgsLAQDHjx/HkCFD7ui4zpw5g759+xpt69u3L86fPw+dTler46jJqVOnoNPp0L59e9jb2xtuu3fvxsWLFw3tbG1tERQUZLjv7e1teA2NRoP09HT06tXL8LhSqUT37t1rfaw3HoO3tzcA1PoYiJo7C7kDEFHzYWdnZ3R/zpw5iImJwfvvv4+2bdvCxsYGDzzwAEpLS43aWVpaGt2XJAl6vR4A0K1bNyQkJGDTpk3Ytm0bHnroIURGRuKXX35BYmIiRo4ciaeeegpvv/02XFxcsHfvXkybNg2lpaWwtbWFjY1Nwx50LY+jJvn5+VAqlThy5AiUSqXRY/b29rd9DSFEPRNXdeP+KyfC1/YYiJo79jQRUZ1YWVkZ9a7czr59+zB16lSMGTMGYWFh8PLyMpp0XFuOjo4YP348vvzyS6xatQpr1qxBdnY2jhw5Ar1ej8WLF6N3795o3749UlJSjJ7buXNnbN++/Y6Op0OHDti3b1+VY2vfvn2VAqe+unbtCp1Oh4yMDLRt29bo5uXlVat9qNVqeHp64vDhw4ZtOp0OR48eNWpXl58hEf2DPU1EVCcBAQE4ePAgEhMTYW9vDxcXl1u2bdeuHdauXYtRo0ZBkiS89tprde61WLJkCby9vdG1a1coFAqsXr0aXl5ecHJyQtu2bVFWVoalS5di1KhR2LdvH5YvX270/Llz5yIsLAxPP/00nnzySVhZWWHnzp148MEH4ebmVqvj+c9//oOePXvizTffxPjx43HgwAEsW7YMn376aZ2O5Xbat2+PSZMmYfLkyVi8eDG6du2KzMxMbN++HZ07d8aIESNqtZ9nnnkG0dHRaNu2LUJCQrB06VLk5OQYLZ9Ql58hEf2DPU1EVCdz5syBUqlEx44d4e7uXmV+0o2WLFkCZ2dn9OnTB6NGjUJUVBS6detWp9dzcHDAwoUL0aNHD/Ts2ROJiYn4448/oFAo0KVLFyxZsgTvvfceQkND8f333xstRwBUFCNbt27FiRMn0KtXL0RERODXX3+FhYVFrY+nW7du+Pnnn/HTTz8hNDQU8+bNwxtvvIGpU6fW6VhqsmLFCkyePBn/+c9/EBwcjNGjR+Pw4cPw8/Or9T5eeuklTJgwAZMnT0ZERATs7e0RFRUFa2trQ5u6/AyJ6B+SMOVgOBERmRW9Xo8OHTrgoYcewptvvil3HKImjcNzRETNyOXLl7F161YMHDgQJSUlWLZsGRISEjBx4kS5oxE1eRyeIyJqRhQKBVauXImePXuib9++OHXqFLZt24YOHTrIHY2oyePwHBEREVEtsKeJiIiIqBZYNBERERHVAosmIiIiolpg0URERERUCyyaiIiIiGqBRRMRERFRLbBoIiIiIqoFFk1EREREtcCiiYiIiKgW/h+REIiqGxmemgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zpLiRCBp39k9" + }, + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 3000 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "RP9ynbti4L48" + }, + "outputs": [], + "source": [ + "minimumSupportCount = 3000 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 4000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Yr0r7zw4Q85" + }, + "source": [ + "### Step 4: Mining Periodic Frequent patterns using PFPMC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "BZzrC2Pl4XGJ", + "outputId": "291d6075-4d52-411d-e970-13f51674e365" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n", + "Total No of patterns: 60\n", + "Runtime: 98.44582390785217\n", + "Memory (RSS): 498446336\n", + "Memory (USS): 481484800\n" + ] + } + ], + "source": [ + "from PAMI.periodicFrequentPattern.basic import PFPMC as alg #import the algorithm\n", + "\n", + "obj = alg.PFPMC(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('periodicFrequentPatternsAtMinSupCount3000.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3M8FtfKU4bhu" + }, + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "b7IvA0IW4hBe", + "outputId": "bc26822e-c6b9-4288-b353-2907201a1ca3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "368:7818:113 \n", + "538:3981:283 \n", + "775:3766:228 \n", + "825:3082:271 \n", + "39:4252:236 \n", + "120:4968:174 \n", + "205:3602:231 \n", + "401:3665:277 \n", + "895:3380:255 \n", + "937:4675:201 \n" + ] + } + ], + "source": [ + "!head 'periodicFrequentPatternsAtMinSupCount3000.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j4CpTPXw4k9I" + }, + "source": [ + "The storage format is: _periodicfrequentPattern:support_\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kC71sBV74qY0" + }, + "source": [ + "## Part 2: Evaluating the PFPMC algorithm on a dataset at different minSup values" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EobpZCdu6G0Z" + }, + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "W96B78JT6KT2" + }, + "outputs": [], + "source": [ + "#Import the libraries\n", + "from PAMI.periodicFrequentPattern.basic import PFPMC as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 4000\n", + "minimumSupportCountList = [3000, 3500, 4000, 4500, 5000]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gS09HRlY6PPR" + }, + "source": [ + "### Step 2: Create a data frame to store the results of PFPMC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "0tbQu3re6VGI" + }, + "outputs": [], + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of PFPMC algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kn2TtrbW6awD" + }, + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "cZNXmKqp6ea1", + "outputId": "9e09c07b-6f12-41c1-9dd9-f06bcac45955" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n", + "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n", + "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n", + "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n", + "Periodic-Frequent patterns were generated successfully using PFPDiffset ECLAT algorithm \n" + ] + } + ], + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.PFPMC(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['PFPMC', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NCQLc9pn7BYG" + }, + "source": [ + "### Step 4: Print the Result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "kpkdWbyQ6j6M", + "outputId": "9bb38d40-8964-4f3f-ba71-3f56dcb58820" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " algorithm minSup maximumPeriodCount patterns runtime memory\n", + "0 PFPMC 3000 4000 60 96.989232 502587392\n", + "1 PFPMC 3500 4000 40 59.217767 422608896\n", + "2 PFPMC 4000 4000 26 41.785200 365383680\n", + "3 PFPMC 4500 4000 17 33.114026 328404992\n", + "4 PFPMC 5000 4000 10 29.964159 299024384\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S-prY3W27U4Z" + }, + "source": [ + "### Step 5: Visualizing the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true }, + "id": "zVEtHn5j7aYE", + "outputId": "4b39563e-7f95-4f06-9b95-a102624f6ad3" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "NCQLc9pn7BYG" - }, - "source": [ - "### Step 4: Print the Result" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "background_save": true - }, - "id": "kpkdWbyQ6j6M", - "outputId": "9bb38d40-8964-4f3f-ba71-3f56dcb58820" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " algorithm minSup maximumPeriodCount patterns runtime memory\n", - "0 PFPMC 3000 4000 60 96.989232 502587392\n", - "1 PFPMC 3500 4000 40 59.217767 422608896\n", - "2 PFPMC 4000 4000 26 41.785200 365383680\n", - "3 PFPMC 4500 4000 17 33.114026 328404992\n", - "4 PFPMC 5000 4000 10 29.964159 299024384\n" - ] - } - ], - "source": [ - "print(result)" + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "S-prY3W27U4Z" - }, - "source": [ - "### Step 5: Visualizing the results" + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + "data": { + "image/png": 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" ] + }, + "metadata": {}, + "output_type": "display_data" } - ], - "metadata": { - "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } + ], + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/periodicFrequentPattern/basic/PSGrowth.ipynb b/notebooks/periodicFrequentPattern/basic/PSGrowth.ipynb index ae3b83cf..43e0682b 100644 --- a/notebooks/periodicFrequentPattern/basic/PSGrowth.ipynb +++ b/notebooks/periodicFrequentPattern/basic/PSGrowth.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Periodic Frequent patterns in Temporal Databases using PSGrowth" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Periodic Frequent patterns in a temporal database using the PSGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the PSGrowth algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "0fed2a3c-c902-450b-f15a-5c86d9c4b133" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/835.0 kB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K \u001B[91m━━━━━━━━━━\u001B[0m\u001B[91m╸\u001B[0m\u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m225.3/835.0 kB\u001B[0m 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JsonForm, JsonSir\n", + " Building wheel for JsonForm (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonForm: filename=JsonForm-0.0.2-py3-none-any.whl size=3313 sha256=d6688b771f73eb448f75c61740cb11c83523e27d6859f6b7d2d9bb546f423112\n", + " Stored in directory: /root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=7a156d6c166b217dc789230c3f7d08a1a81f66180abdc2b0fa50340433911fc7\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Periodic Frequent patterns in Temporal Databases using PSGrowth" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Periodic Frequent patterns in a temporal database using the PSGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the PSGrowth algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "0fed2a3c-c902-450b-f15a-5c86d9c4b133" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/835.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.3/835.0 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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" Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", - "Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "44508052-6358-4856-cb90-4cd96f5af75d" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 08:38:13-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 754KB/s in 6.2s \n", - "\n", - "2023-08-28 08:38:22 (723 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "81f511fa-2081-4635-989f-63fc9928d210" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Periodic Frequent patterns using PSGrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "142e186f-6c99-4e97-e871-ecc0e1b1f062" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "5b19c606-13e7-4165-9975-cd74baebd23c" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Periodic Frequent patterns using PSGrowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.periodicFrequentPattern.basic import PSGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.PSGrowth(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('periodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5ec4ad64-593e-4656-e139-616e599912ef" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n", - "Total No of patterns: 25454\n", - "Runtime: 1693211973.927723\n", - "Memory (RSS): 704012288\n", - "Memory (USS): 657412096\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'periodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "9d8a959a-ec10-43a0-8770-e24b9412f4e4" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "330\t:102:4598 \n", - "102\t:109:4201 \n", - "856\t:109:4260 \n", - "856\t490\t:103:4431 \n", - "856\t906\t:103:4431 \n", - "856\t490\t906\t:103:4431 \n", - "191\t:111:4449 \n", - "191\t339\t:106:4865 \n", - "191\t339\t90\t:102:4865 \n", - "191\t339\t90\t914\t:0:0 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "44508052-6358-4856-cb90-4cd96f5af75d" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 08:38:13-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 754KB/s in 6.2s \n", + "\n", + "2023-08-28 08:38:22 (723 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "81f511fa-2081-4635-989f-63fc9928d210" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Periodic Frequent patterns using PSGrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "142e186f-6c99-4e97-e871-ecc0e1b1f062" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "5b19c606-13e7-4165-9975-cd74baebd23c" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _periodicFrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the PSGrowth algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.periodicFrequentPattern.basic import PSGrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 5000\n", - "minimumSupportCountList = [100, 200, 300, 400, 500]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of PSGrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of PSGrowth algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.PSGrowth(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['PSGrowth', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "aa3165d8-644d-48ec-c8eb-f310b65d3540" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n", - "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n", - "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n", - "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n", - "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Periodic Frequent patterns using PSGrowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.periodicFrequentPattern.basic import PSGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.PSGrowth(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('periodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5ec4ad64-593e-4656-e139-616e599912ef" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n", + "Total No of patterns: 25454\n", + "Runtime: 1693211973.927723\n", + "Memory (RSS): 704012288\n", + "Memory (USS): 657412096\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'periodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9d8a959a-ec10-43a0-8770-e24b9412f4e4" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "330\t:102:4598 \n", + "102\t:109:4201 \n", + "856\t:109:4260 \n", + "856\t490\t:103:4431 \n", + "856\t906\t:103:4431 \n", + "856\t490\t906\t:103:4431 \n", + "191\t:111:4449 \n", + "191\t339\t:106:4865 \n", + "191\t339\t90\t:102:4865 \n", + "191\t339\t90\t914\t:0:0 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _periodicFrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the PSGrowth algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.periodicFrequentPattern.basic import PSGrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 5000\n", + "minimumSupportCountList = [100, 200, 300, 400, 500]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of PSGrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of PSGrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.PSGrowth(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['PSGrowth', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "aa3165d8-644d-48ec-c8eb-f310b65d3540" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n", + "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n", + "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n", + "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n", + "Periodic-Frequent patterns were generated successfully using PS-Growth algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4f584ef6-2d5f-46e9-d0b2-aa1e929b1873" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximumPeriodCount patterns runtime memory\n", + "0 PSGrowth 100 5000 25454 1.693212e+09 708530176\n", + "1 PSGrowth 200 5000 13231 1.693212e+09 704589824\n", + "2 PSGrowth 300 5000 4529 1.693212e+09 697524224\n", + "3 PSGrowth 400 5000 1999 1.693212e+09 693518336\n", + "4 PSGrowth 500 5000 1072 1.693212e+09 691421184\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "ec3aa886-bce7-4802-fece-2204eb220ece" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4f584ef6-2d5f-46e9-d0b2-aa1e929b1873" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximumPeriodCount patterns runtime memory\n", - "0 PSGrowth 100 5000 25454 1.693212e+09 708530176\n", - "1 PSGrowth 200 5000 13231 1.693212e+09 704589824\n", - "2 PSGrowth 300 5000 4529 1.693212e+09 697524224\n", - "3 PSGrowth 400 5000 1999 1.693212e+09 693518336\n", - "4 PSGrowth 500 5000 1072 1.693212e+09 691421184\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "ec3aa886-bce7-4802-fece-2204eb220ece" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] + ] + } + ] } diff --git a/notebooks/periodicFrequentPattern/closed/CPFPMiner.ipynb b/notebooks/periodicFrequentPattern/closed/CPFPMiner.ipynb index e8a852fa..affbeb6d 100644 --- a/notebooks/periodicFrequentPattern/closed/CPFPMiner.ipynb +++ b/notebooks/periodicFrequentPattern/closed/CPFPMiner.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Closed Periodic Frequent patterns in Temporal Databases using CPFPMiner" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Closed Periodic Frequent patterns in a temporal database using the CPFPMiner algorithm. In the final part, we describe an advanced approach, where we evaluate the CPFPMiner algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "c812d4ba-06c8-4a6e-c6c5-c8c1469e640b" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m7.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", + "Collecting resource (from pami)\n", + " Downloading Resource-0.2.1-py2.py3-none-any.whl (25 kB)\n", + "Collecting validators (from pami)\n", + " Downloading validators-0.21.2-py3-none-any.whl (25 kB)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.4.4)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (23.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pami) (2023.3)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->pami) (8.2.3)\n", + "Collecting JsonForm>=0.0.2 (from resource->pami)\n", + " Downloading JsonForm-0.0.2.tar.gz (2.4 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting JsonSir>=0.0.2 (from resource->pami)\n", + " Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", + "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.30.2)\n", + "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.9.2)\n", + "Building wheels for collected packages: JsonForm, JsonSir\n", + " Building wheel for JsonForm (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonForm: filename=JsonForm-0.0.2-py3-none-any.whl size=3313 sha256=6aff1c782a3f21a8e84433c58533d89047625d6340c467d0a307d7584a8fe093\n", + " Stored in directory: /root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=8284f0369b534f15f494911147afe1f57d0365822cd7618e8b91960b82601ae3\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Closed Periodic Frequent patterns in Temporal Databases using CPFPMiner" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Closed Periodic Frequent patterns in a temporal database using the CPFPMiner algorithm. In the final part, we describe an advanced approach, where we evaluate the CPFPMiner algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "c812d4ba-06c8-4a6e-c6c5-c8c1469e640b" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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" Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", - " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", - "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", - "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", - "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", - "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", - 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"Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "45049907-ea50-40c8-a9e3-741fbe73a1a5" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 09:02:44-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 853KB/s in 5.3s \n", - "\n", - "2023-08-28 09:02:51 (853 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "da8d734a-918b-47b1-98bf-3fdb8ff0a55a" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Closed Periodic Frequent patterns using CPFPMiner" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "9f821b8a-17bc-49bc-baf1-3a99a53949b4" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "0f573630-212b-4b16-feb0-fc87183900e0" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Closed Periodic Frequent patterns using CPFPMiner" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.periodicFrequentPattern.closed import CPFPMiner as alg #import the algorithm\n", - "\n", - "obj = alg.CPFPMiner(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('closedPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "fbb32cac-3a75-4eef-dbd4-41e1a6312de2" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n", - "Total No of patterns: 24703\n", - "Runtime: 40.61265206336975\n", - "Memory (RSS): 192118784\n", - "Memory (USS): 144490496\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'closedPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "449d0ea5-4b5c-4c7b-cb0d-c1d8f4eb3dc8" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "330:102:4598 \n", - "191\t339\t90\t914:102:4449 \n", - "191\t276\t339\t825\t914:100:4449 \n", - "191\t276\t339\t914:102:4449 \n", - "191\t339\t825\t914:103:4449 \n", - "191\t339\t914:106:4449 \n", - "191\t276\t825\t90\t914:101:4449 \n", - "191\t276\t825\t90:102:4449 \n", - "191\t276\t90\t914:103:4449 \n", - "191\t276\t90:104:4449 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "45049907-ea50-40c8-a9e3-741fbe73a1a5" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 09:02:44-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 853KB/s in 5.3s \n", + "\n", + "2023-08-28 09:02:51 (853 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "da8d734a-918b-47b1-98bf-3fdb8ff0a55a" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Closed Periodic Frequent patterns using CPFPMiner" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "9f821b8a-17bc-49bc-baf1-3a99a53949b4" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "0f573630-212b-4b16-feb0-fc87183900e0" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _closedPeriodicFrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the CPFPMiner algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.periodicFrequentPattern.closed import CPFPMiner as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 5000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of CPFPMiner" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of CPFPMiner algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.CPFPMiner(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['CPFPMiner', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "6d8644aa-c064-4869-9d76-634bab73c4ed" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n", - "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n", - "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n", - "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n", - "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Closed Periodic Frequent patterns using CPFPMiner" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.periodicFrequentPattern.closed import CPFPMiner as alg #import the algorithm\n", + "\n", + "obj = alg.CPFPMiner(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('closedPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fbb32cac-3a75-4eef-dbd4-41e1a6312de2" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n", + "Total No of patterns: 24703\n", + "Runtime: 40.61265206336975\n", + "Memory (RSS): 192118784\n", + "Memory (USS): 144490496\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'closedPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "449d0ea5-4b5c-4c7b-cb0d-c1d8f4eb3dc8" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "330:102:4598 \n", + "191\t339\t90\t914:102:4449 \n", + "191\t276\t339\t825\t914:100:4449 \n", + "191\t276\t339\t914:102:4449 \n", + "191\t339\t825\t914:103:4449 \n", + "191\t339\t914:106:4449 \n", + "191\t276\t825\t90\t914:101:4449 \n", + "191\t276\t825\t90:102:4449 \n", + "191\t276\t90\t914:103:4449 \n", + "191\t276\t90:104:4449 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _closedPeriodicFrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the CPFPMiner algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.periodicFrequentPattern.closed import CPFPMiner as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 5000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of CPFPMiner" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of CPFPMiner algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.CPFPMiner(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['CPFPMiner', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6d8644aa-c064-4869-9d76-634bab73c4ed" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n", + "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n", + "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n", + "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n", + "Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "74e4b421-6101-4d03-c84a-504a4328e3a5" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximumPeriodCount patterns runtime memory\n", + "0 CPFPMiner 100 5000 24703 39.146091 201682944\n", + "1 CPFPMiner 150 5000 18442 37.759825 204513280\n", + "2 CPFPMiner 200 5000 13075 34.709628 205938688\n", + "3 CPFPMiner 250 5000 7622 35.744624 207970304\n", + "4 CPFPMiner 300 5000 4486 31.335702 208179200\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "d103856d-2685-4f36-d940-5f6017c8310c" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "74e4b421-6101-4d03-c84a-504a4328e3a5" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximumPeriodCount patterns runtime memory\n", - "0 CPFPMiner 100 5000 24703 39.146091 201682944\n", - "1 CPFPMiner 150 5000 18442 37.759825 204513280\n", - "2 CPFPMiner 200 5000 13075 34.709628 205938688\n", - "3 CPFPMiner 250 5000 7622 35.744624 207970304\n", - "4 CPFPMiner 300 5000 4486 31.335702 208179200\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "d103856d-2685-4f36-d940-5f6017c8310c" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/periodicFrequentPattern/maximal/MaxPFGrowth.ipynb b/notebooks/periodicFrequentPattern/maximal/MaxPFGrowth.ipynb index 60d128e1..9ea1b935 100644 --- a/notebooks/periodicFrequentPattern/maximal/MaxPFGrowth.ipynb +++ b/notebooks/periodicFrequentPattern/maximal/MaxPFGrowth.ipynb @@ -1,703 +1,703 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Maximal Periodic Frequent patterns in Temporal Databases using MaxPFGrowth" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Maximal Periodic Frequent patterns in a temporal database using the MaxPFGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the MaxPFGrowth algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "2116b22f-44af-4e27-a6bc-364a51a12274" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/835.0 kB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K \u001B[91m━━━━━━━━━━\u001B[0m\u001B[91m╸\u001B[0m\u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m225.3/835.0 kB\u001B[0m 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JsonForm, JsonSir\n", + " Building wheel for JsonForm (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonForm: filename=JsonForm-0.0.2-py3-none-any.whl size=3313 sha256=3222038324761964038fcac7983ccebeb2aad78f18e0908710803addd526fdaa\n", + " Stored in directory: /root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=ffccebd4e9947ae1eb3b9541997d59dbf6b6e4b69d209d57033801526f573992\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Maximal Periodic Frequent patterns in Temporal Databases using MaxPFGrowth" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Maximal Periodic Frequent patterns in a temporal database using the MaxPFGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the MaxPFGrowth algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "2116b22f-44af-4e27-a6bc-364a51a12274" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/835.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.3/835.0 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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" Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", - "Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "7ad061a9-b215-41a7-d4f8-7164ff874e7d" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 09:17:09-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1006KB/s in 5.6s \n", - "\n", - "2023-08-28 09:17:16 (808 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "32d6f5a8-13d7-4c99-fa68-fc814884b024" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Maximal Periodic Frequent patterns using MaxPFGrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "5e153422-ba0e-4d11-af1d-02eb71c632e7" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "264355df-b35d-4c2a-ef32-222dd5fc61f2" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Maximal Periodic Frequent patterns using MaxPFGrowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.periodicFrequentPattern.maximal import MaxPFGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.MaxPFGrowth(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('maximalPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d17348da-bd62-4a78-b7e0-170722552dae" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n", - "Total No of patterns: 3443\n", - "Runtime: 13.052407026290894\n", - "Memory (RSS): 606658560\n", - "Memory (USS): 559308800\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'maximalPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "3cd989ab-db62-4b04-c41b-a3c017074adb" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "330:102:4598 \n", - "102:109:4201 \n", - "906\t490\t856:103:4431 \n", - "914\t90\t339\t191:102:4449 \n", - "914\t825\t276\t339\t191:100:4449 \n", - "914\t825\t276\t90\t191:101:4449 \n", - "914\t90\t339\t426:101:4525 \n", - "914\t825\t276\t90\t426:100:4525 \n", - "914\t825\t276\t339\t426:101:4525 \n", - "146:113:3550 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "7ad061a9-b215-41a7-d4f8-7164ff874e7d" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 09:17:09-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1006KB/s in 5.6s \n", + "\n", + "2023-08-28 09:17:16 (808 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "32d6f5a8-13d7-4c99-fa68-fc814884b024" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Maximal Periodic Frequent patterns using MaxPFGrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "5e153422-ba0e-4d11-af1d-02eb71c632e7" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "264355df-b35d-4c2a-ef32-222dd5fc61f2" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _maximalPeriodicFrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the MaxPFGrowth algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.periodicFrequentPattern.maximal import MaxPFGrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 5000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of MaxPFGrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of MaxPFGrowth algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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t27dHRkYGVq5cCbVafce1pojozhiSiIhMyJIlS7Bu3Tp06dIFkZGRdX7/U089hZ9//hnLly+HTCZDt27dsHLlSvTp06f+iyVq4jgniYiIiMgAzkkiIiIiMoAhiYiIiMgAzkm6D1qtFqmpqbCzs7uvxdiIiIhIekIIFBYWwtPTE2Zmde8XYki6D6mpqbWelk5ERESNw7Vr1+Dl5VXn9zEk3Qc7OzsA1RdZpVJJXA0RERHdD7VaDW9vb933eF0xJN2HmiE2lUrFkERERNTIPOhUGU7cJiIiIjKAIYmIiIjIAIYkIiIiIgMYkoiIiIgMYEgiIiIiMoAhiYiIiMgAhiQiIiIiAxiSiIiIiAxgSCIiIiIygCGJiIiIyACGJCIiIiIDGJKIiIiIDGBIklh+SQVi0wulLoOIiIhuw5AkofjMQnR5bzdGLTkKIYTU5RAREdEtGJIk5OVoDZkMKCyvQl5JpdTlEBER0S0YkiRkaSGHh8oSAJCUUyxxNURERHQrhiSJ+ThbAwCSGZKIiIhMCkOSxHydbQAAyTklEldCREREt2JIkpgPQxIREZFJYkiSWM1wG+ckERERmRaGJIn9PSeJPUlERESmhCFJYjXDbbnFFVCXcRkAIiIiU8GQJDFbpTlcbBUAgBT2JhEREZkMhiQTUNObxHlJREREpoMhyQRwXhIREZHpYUgyAX+vlcSeJCIiIlPBkGQC/l4GgD1JREREpoIhyQT4sCeJiIjI5DAkmQDfmz1JGepylFZoJK6GiIiIAIYkk+BgrYDK0hwAkJLLITciIiJTwJBkInxduAwAERGRKWFIMhGcl0RERGRaGJJMhC/XSiIiIjIpDEkmopUTQxIREZEpYUgyEZyTREREZFoYkkxEzYKSqfmlqKjSSlwNERERMSSZCFdbJawVcmgFcD2PQ25ERERSY0gyETKZjPOSiIiITAhDkgmpedAt5yURERFJjyHJhPi4sCeJiIjIVDAkmRAfJy4oSUREZCoYkkwIF5QkIiIyHQxJJsTn5lpJ1/JKoNEKiashIiJq3iQPSTdu3MCLL74IZ2dnWFlZITg4GKdOndLtF0Lg7bffRosWLWBlZYVBgwYhLi5O7xi5ubkYO3YsVCoVHBwcMGnSJBQVFem1OX/+PHr37g1LS0t4e3vj448/Nsr51YWHyhIKuRkqNQKp+aVSl0NERNSsSRqS8vLy0KtXL1hYWGDHjh24dOkSPvvsMzg6OurafPzxx/jqq6+wdOlSHD9+HDY2NggPD0dZWZmuzdixYxETE4Pdu3dj69atOHjwIKZOnarbr1arMXjwYPj4+CA6OhqffPIJ3n33XSxfvtyo53svcjMZvJ2sAHDIjYiISHJCQm+88YYICwu7436tVis8PDzEJ598otuWn58vlEql+PHHH4UQQly6dEkAECdPntS12bFjh5DJZOLGjRtCCCG+/fZb4ejoKMrLy/U+u23btvdVZ0FBgQAgCgoK6nR+D+Ll1SeEzxtbxfdRSQ3+WURERE3Zw35/S9qTtGXLFnTv3h3PPvss3Nzc0LVrV3z33Xe6/YmJiUhPT8egQYN02+zt7dGjRw9ERUUBAKKiouDg4IDu3bvr2gwaNAhmZmY4fvy4rk2fPn2gUCh0bcLDwxEbG4u8vLxadZWXl0OtVuu9jMXHmXe4ERERmQJJQ9LVq1exZMkSBAYGYteuXZgxYwZmz56NNWvWAADS09MBAO7u7nrvc3d31+1LT0+Hm5ub3n5zc3M4OTnptTF0jFs/41aLFi2Cvb297uXt7V0PZ3t/fHiHGxERkUmQNCRptVp069YNH3zwAbp27YqpU6diypQpWLp0qZRlYeHChSgoKNC9rl27ZrTPZkgiIiIyDZKGpBYtWiAoKEhvW/v27ZGSkgIA8PDwAABkZGTotcnIyNDt8/DwQGZmpt7+qqoq5Obm6rUxdIxbP+NWSqUSKpVK72UsNY8mSc4thhBcBoCIiEgqkoakXr16ITY2Vm/blStX4OPjAwDw8/ODh4cH9uzZo9uvVqtx/PhxhIaGAgBCQ0ORn5+P6OhoXZu9e/dCq9WiR48eujYHDx5EZWWlrs3u3bvRtm1bvTvpTEFLRyvIzWQoq9Qis7Bc6nKIiIiaLUlD0rx583Ds2DF88MEHiI+Pxw8//IDly5cjIiICACCTyTB37lz8+9//xpYtW3DhwgWMGzcOnp6eGDZsGIDqnqcnnngCU6ZMwYkTJ3DkyBHMnDkTY8aMgaenJwDghRdegEKhwKRJkxATE4OffvoJixcvxquvvirVqd+RhdwMLR2qlwFIyubkbSIiIqmYS/nhjzzyCH777TcsXLgQ7733Hvz8/PDll19i7NixujYLFixAcXExpk6divz8fISFhWHnzp2wtLTUtVm/fj1mzpyJgQMHwszMDCNHjsRXX32l229vb48//vgDERERCAkJgYuLC95++229tZRMiY+zNVJyS5CcU4IerZ2lLoeIiKhZkglOfLkntVoNe3t7FBQUGGV+0lubL+L7Y8l4pZ8/FjzRrsE/j4iIqCl62O9vyR9LQrXp7nDL5R1uREREUmFIMkFcUJKIiEh6DEkmyLemJym7hMsAEBERSYQhyQR5O1WHpMLyKuQWV0hcDRERUfPEkGSCLC3kaGFfffce5yURERFJgyHJRP39eBLOSyIiIpICQ5KJqnk8SVI2e5KIiIikwJBkolrd7ElK4XAbERGRJBiSTJSuJ4nDbURERJJgSDJRf89JYk8SERGRFBiSTFTNgpK5xRVQl1VKXA0REVHzw5BkomyV5nCxVQAAUtibREREZHQMSSbMh/OSiIiIJMOQZMI4L4mIiEg6DEkmzMepZq0k9iQREREZG0OSCfN1udmTxLWSiIiIjI4hyYTVzEnio0mIiIiMjyHJhPnenJOUoS5HaYVG4mqIiIiaF4YkE+ZgrYDK0hwAH09CRERkbAxJJs7XhcsAEBERSYEhycRxXhIREZE0GJJMnI9T9bykJK6VREREZFQMSSauZkFJPpqEiIjIuBiSTBznJBEREUmDIcnE1fQkpeaXoryKywAQEREZC0OSiXO1VcJaIYdWANfzSqUuh4iIqNlgSDJxMpkMrZw4L4mIiMjYGJIaAV9nzksiIiIyNoakRqBmXlIye5KIiIiMhiGpEeCCkkRERMbHkNQI+LIniYiIyOgYkhoBn5trJV3LK0GVRitxNURERM0DQ1Ij4KGyhEJuhkqNQFpBmdTlEBERNQsMSY2A3EwGbycrABxyIyIiMhaGpEaCywAQEREZF0NSI9FKN3mbIYmIiMgYGJIaib97kjjcRkREZAwMSY1EzYKSfDQJERGRcTAkNRI1PUnJucXQaoXE1RARETV9DEmNREtHK8jNZCir1CKzsFzqcoiIiJo8hqRGwkJuhpYONcsAcPI2ERFRQ2NIakT4oFsiIiLjYUhqRGpCEtdKIiIiangMSY1IzeTtKxlFEldCRETU9DEkNSI9/JwBAPtiMzkviYiIqIExJDUiwV726NPGFRqtwLf7EqQuh4iIqEljSGpk5gwMBAD8cvo6ruVyAjcREVFDYUhqZEJ8HBEW4IIqrcCSA+xNIiIiaigMSY3Q7Ju9SZtOXUNqfqnE1RARETVNDEmN0KN+TujZ2gmVGoEl+9mbRERE1BAYkhqpOQPbAAB+OnkN6QVlEldDRETU9DAkNVI9WzvhUV8nVGi0WMq5SURERPWOIamRkslkurlJP55IQaaavUlERET1SdKQ9O6770Imk+m92rVrp9tfVlaGiIgIODs7w9bWFiNHjkRGRobeMVJSUjBkyBBYW1vDzc0Nr7/+OqqqqvTa7N+/H926dYNSqURAQAAiIyONcXoNrleAM7q1ckB5lRbLDl6VuhwiIqImRfKepA4dOiAtLU33Onz4sG7fvHnz8Pvvv2PTpk04cOAAUlNTMWLECN1+jUaDIUOGoKKiAkePHsWaNWsQGRmJt99+W9cmMTERQ4YMQf/+/XH27FnMnTsXkydPxq5du4x6ng3h1t6k9ceTkVVYLnFFRERETYdMCCGk+vB3330XmzdvxtmzZ2vtKygogKurK3744QeMGjUKAHD58mW0b98eUVFR6NmzJ3bs2IGhQ4ciNTUV7u7uAIClS5fijTfeQFZWFhQKBd544w1s27YNFy9e1B17zJgxyM/Px86dO++rTrVaDXt7exQUFEClUj38idcjIQSGfXsU567lY1qf1lj4VHupSyIiIjIJD/v9LXlPUlxcHDw9PdG6dWuMHTsWKSkpAIDo6GhUVlZi0KBBurbt2rVDq1atEBUVBQCIiopCcHCwLiABQHh4ONRqNWJiYnRtbj1GTZuaYzR2MpkMcwYGAADWRiUjp4i9SURERPVB0pDUo0cPREZGYufOnViyZAkSExPRu3dvFBYWIj09HQqFAg4ODnrvcXd3R3p6OgAgPT1dLyDV7K/Zd7c2arUapaWGF2IsLy+HWq3We5my/m3d0LGlCqWVGqw4nCh1OURERE2CpCHpySefxLPPPotOnTohPDwc27dvR35+PjZu3ChlWVi0aBHs7e11L29vb0nruReZTIbZA6rnJq09moS84gqJKyIiImr8JB9uu5WDgwPatGmD+Ph4eHh4oKKiAvn5+XptMjIy4OHhAQDw8PCodbdbzc/3aqNSqWBlZWWwjoULF6KgoED3unbtWn2cXoN6PMgd7VuoUFyhwaoj7E0iIiJ6WCYVkoqKipCQkIAWLVogJCQEFhYW2LNnj25/bGwsUlJSEBoaCgAIDQ3FhQsXkJmZqWuze/duqFQqBAUF6drceoyaNjXHMESpVEKlUum9TN2tc5MijyShoKRS4oqIiIgaN0lD0muvvYYDBw4gKSkJR48exfDhwyGXy/H888/D3t4ekyZNwquvvop9+/YhOjoaEydORGhoKHr27AkAGDx4MIKCgvDSSy/h3Llz2LVrF/71r38hIiICSqUSADB9+nRcvXoVCxYswOXLl/Htt99i48aNmDdvnpSn3iAGB3mgrbsdCsursPooe5OIiIgehqQh6fr163j++efRtm1bjB49Gs7Ozjh27BhcXV0BAF988QWGDh2KkSNHok+fPvDw8MCvv/6qe79cLsfWrVshl8sRGhqKF198EePGjcN7772na+Pn54dt27Zh9+7d6Ny5Mz777DOsWLEC4eHhRj/fhmZmJsOsm71Jqw4nQl3G3iQiIqIHJek6SY2FKa+TdDuNViD8y4OIzyzC/MfbYNbNxSaJiIiam0a/ThLVL7mZDLMGVPcmrTiciKLyqnu8g4iIiAxhSGqChnbyRGsXGxSUVmJtVJLU5RARETVKDElNkNxMhpk3e5O+O3gVxexNIiIiqjOGpCbqH5094etsjbySSqw7lix1OURERI0OQ1ITZS43Q0T/6t6k5QevorRCI3FFREREjQtDUhM2rGtLeDtZIae4AuuPszeJiIioLhiSmjALuRki+lX3Ji07eBVllexNIiIiul8MSU3ciG5eaOlghazCcvx4IkXqcoiIiBoNhqQmTmFuhlf6+wMAlh5IYG8SERHRfWJIagZGhXihhb0lMtTl2HTqmtTlEBERNQoMSc2A0lyOGf2qe5O+3Z+A8ir2JhEREd0LQ1IzMbq7N9zslEgrKMPP0delLoeIiMjkMSQ1E5YWckzve7M3aV8CKqq0EldERERk2hiSmpEXerSCi60SN/JL8dsZ9iYRERHdDUNSM1Ldm9QaAPD1vnhUatibREREdCcMSc3MCz1awdlGgWu5pdh85obU5RAREZkshqRmxlphjil9qnuTvtkXjyr2JhERERnEkNQMvdTTB47WFkjKKcHv51OlLoeIiMgkMSQ1QzZKc0zuXd2b9N+98dBohcQVERERmR6GpGZqXKgP7K0scDWrGFvZm0RERFQLQ1IzZWdpgUlhfgCqe5O07E0iIiLSw5DUjI1/zBd2luaIzyzCjovpUpdDRERkUhiSmjF7Kwu83KumNymOvUlERES3YEhq5l7u5QdbpTkupxfij0sZUpdDRERkMhiSmjl7awtMeMwXAPDVnjgIwd4kIiIigCGJAEwK84ONQo5LaWr8+Vem1OUQERGZBIYkgqONAuPYm0RERKSHIYkAAJPD/GBlIceFGwXYH5sldTlERESSY0giAICzrRIvhfoAAL5kbxIRERFDEv1tSu/WUJqb4dy1fByMy5a6HCIiIkkxJJGOq50SY3tU9yYt/vMKe5OIiKhZq3NIunr1akPUQSZiet/WUJib4XRKPo4m5EhdDhERkWTqHJICAgLQv39/rFu3DmVlZQ1RE0nITWWJFx5tBQBY/CfnJhERUfNV55B0+vRpdOrUCa+++io8PDwwbdo0nDhxoiFqI4lM69saCrkZTiTl4tjVXKnLISIikkSdQ1KXLl2wePFipKamYtWqVUhLS0NYWBg6duyIzz//HFlZvH28sWthb4XRj3gBqF43iYiIqDl64Inb5ubmGDFiBDZt2oSPPvoI8fHxeO211+Dt7Y1x48YhLS2tPuskI5vRLwAWchmirubgRCJ7k4iIqPl54JB06tQpvPLKK2jRogU+//xzvPbaa0hISMDu3buRmpqKZ555pj7rJCNr6WCFUSHeANibREREzVOdQ9Lnn3+O4OBgPPbYY0hNTcXatWuRnJyMf//73/Dz80Pv3r0RGRmJ06dPN0S9ZESv9POHuZkMh+OzEZ3M3iQiImpe6hySlixZghdeeAHJycnYvHkzhg4dCjMz/cO4ublh5cqV9VYkScPbyRojurUEAHy1J17iaoiIiIxLJniP9z2p1WrY29ujoKAAKpVK6nKMKjmnGAM+OwCNVmBzRC908XaQuiQiIqL78rDf33XuSVq9ejU2bdpUa/umTZuwZs2aOhdAps3H2QbDulT3Jv2Xc5OIiKgZqXNIWrRoEVxcXGptd3NzwwcffFAvRZFpiejvDzMZsOdyJi5cL5C6HCIiIqOoc0hKSUmBn59fre0+Pj5ISUmpl6LItLR2tcU/OnsCAL7ay94kIiJqHuocktzc3HD+/Pla28+dOwdnZ+d6KYpMz8wBAZDJgN2XMhCTyt4kIiJq+uockp5//nnMnj0b+/btg0ajgUajwd69ezFnzhyMGTOmIWokExDgZoehnap7k77eyzvdiIio6TOv6xvef/99JCUlYeDAgTA3r367VqvFuHHjOCepiZs1IAC/n0vFjovpuJyuRjuP5nWnHxERNS8PvATAlStXcO7cOVhZWSE4OBg+Pj71XZvJaM5LANzulfXR2H4hHUM6tcA3L3STuhwiIqI7etjv7zr3JNVo06YN2rRp86Bvp0Zq1oBAbL+Qju0X0hCXUYhAdzupSyIiImoQdQ5JGo0GkZGR2LNnDzIzM6HVavX27927t96KI9PTvoUK4R3csSsmA1/vi8fiMV2lLomIiKhB1DkkzZkzB5GRkRgyZAg6duwImUzWEHWRCZs1IBC7YjLw+7lUzB4YCH9XW6lLIiIiqnd1DkkbNmzAxo0b8dRTTzVEPdQIdGxpj0Ht3fDnX5n4Zm88Pn+ui9QlERER1bs6LwGgUCgQEBDQELVQIzJrQCAAYPPZG0jKLpa4GiIiovpX55A0f/58LF68GHwubvPW2dsB/dq6QiuAb/Zx3SQiImp66jzcdvjwYezbtw87duxAhw4dYGFhobf/119/rbfiyLTNHhiI/bFZ+PXMDcweGAhvJ2upSyIiIqo3de5JcnBwwPDhw9G3b1+4uLjA3t5e7/WgPvzwQ8hkMsydO1e3raysDBEREXB2doatrS1GjhyJjIwMvfelpKRgyJAhsLa2hpubG15//XVUVVXptdm/fz+6desGpVKJgIAAREZGPnCd9LdurRzRO9AFGq1gbxIRETU5de5JWr16db0XcfLkSSxbtgydOnXS2z5v3jxs27YNmzZtgr29PWbOnIkRI0bgyJEjAKqXIxgyZAg8PDxw9OhRpKWlYdy4cbCwsNCt/p2YmIghQ4Zg+vTpWL9+Pfbs2YPJkyejRYsWCA8Pr/dzaW7mDAzEobhs/Bx9HTMHBMDLkb1JRETUNNS5JwkAqqqq8Oeff2LZsmUoLCwEAKSmpqKoqKjOxyoqKsLYsWPx3XffwdHRUbe9oKAAK1euxOeff44BAwYgJCQEq1evxtGjR3Hs2DEAwB9//IFLly5h3bp16NKlC5588km8//77+Oabb1BRUQEAWLp0Kfz8/PDZZ5+hffv2mDlzJkaNGoUvvvjiQU6dbtPd1wmP+TujSiuwZH+C1OUQERHVmzqHpOTkZAQHB+OZZ55BREQEsrKyAAAfffQRXnvttToXEBERgSFDhmDQoEF626Ojo1FZWam3vV27dmjVqhWioqIAAFFRUQgODoa7u7uuTXh4ONRqNWJiYnRtbj92eHi47hiGlJeXQ61W673ozuYMrL7TbeOpa0jNL5W4GiIiovpR55A0Z84cdO/eHXl5ebCystJtHz58OPbs2VOnY23YsAGnT5/GokWLau1LT0+HQqGAg4OD3nZ3d3ekp6fr2twakGr21+y7Wxu1Wo3SUsNf6IsWLdKbZ+Xt7V2n82puerR2Rg8/J1RqBJYeYG8SERE1DXUOSYcOHcK//vUvKBQKve2+vr64cePGfR/n2rVrmDNnDtavXw9LS8u6ltGgFi5ciIKCAt3r2rVrUpdk8mp6kzacuIb0gjKJqyEiInp4dQ5JWq0WGo2m1vbr16/Dzu7+H3YaHR2NzMxMdOvWDebm5jA3N8eBAwfw1VdfwdzcHO7u7qioqEB+fr7e+zIyMuDh4QEA8PDwqHW3W83P92qjUqn0esJupVQqoVKp9F50d6H+zuju44gKjRbLDrI3iYiIGr86h6TBgwfjyy+/1P0sk8lQVFSEd955p06PKhk4cCAuXLiAs2fP6l7du3fH2LFjdX+2sLDQG8KLjY1FSkoKQkNDAQChoaG4cOECMjMzdW12794NlUqFoKAgXZvbhwF3796tOwbVD5lMhjmDqnuTfjiegsxC9iYREVHjJhN1XDr7+vXrCA8PhxACcXFx6N69O+Li4uDi4oKDBw/Czc3tgYvp168funTpogthM2bMwPbt2xEZGQmVSoVZs2YBAI4ePQqgegmALl26wNPTEx9//DHS09Px0ksvYfLkyXpLAHTs2BERERF4+eWXsXfvXsyePRvbtm277yUA1Go17O3tUVBQwF6luxBCYMSSoziTko/JYX7419AgqUsiIqJm7GG/v+u8TpKXlxfOnTuHDRs24Pz58ygqKsKkSZMwduzYOw5fPagvvvgCZmZmGDlyJMrLyxEeHo5vv/1Wt18ul2Pr1q2YMWMGQkNDYWNjg/Hjx+O9997TtfHz88O2bdswb948LF68GF5eXlixYgXXSGoAMpkMswcGYuLqk1h3PBlPBnsgxMdJ6rKIiIgeSJ17kpoj9iTdPyEExq06gUNx2bCykOO7cd0RFugidVlERNQMPez3d51D0tq1a++6f9y4cXUuwtQxJNVNaYUG09ZF4+CVLCjkZvjvC10R3sFD6rKIiKiZMXpIunVVbACorKxESUkJFAoFrK2tkZubW+ciTB1DUt2VV2kwd8NZ7LiYDrmZDJ8+2wnDu3pJXRYRETUjD/v9Xee72/Ly8vReRUVFiI2NRVhYGH788cc6F0BNk9Jcjv8+3xUju3lBoxWY99M5fH8sWeqyiIiI7tsDPbvtdoGBgfjwww8xZ86c+jgcNRHmcjN8MqoTJjzmCwB4a/NFfLs/XtqiiIiI7lO9hCQAMDc3R2pqan0djpoIMzMZ3nk6CDP7BwAAPt4Zi493XgbvFyAiIlNX5yUAtmzZovezEAJpaWn4+uuv0atXr3orjJoOmUyG18LbwtbSHB/uuIxv9yegqLwK7z7dAWZmMqnLIyIiMqjOIWnYsGF6P8tkMri6umLAgAH47LPP6qsuaoKm9/WHrdIcb/3vItZGJaOorAofj+oEc3m9dWgSERHVmzqHJK1W2xB1UDPxYk8f2Fma49WN5/DrmRsorqjCV893hdJcLnVpREREevhPeDK6Z7q0xNIXQ6AwN8OumAxMXnMKJRVVUpdFRESkp87rJL366qv33fbzzz+vc0GmiOskNYwj8dmYsvYUSio06O7jiJUTHoG9lYXUZRERURNh9Ge3nTlzBmfOnEFlZSXatm0LALhy5Qrkcjm6deumayeTcUIu3V2vABd8P6kHJq4+gVPJeXh++TF8P+lRONsqpS6NiIio7sNtTz/9NPr06YPr16/j9OnTOH36NK5du4b+/ftj6NCh2LdvH/bt24e9e/c2RL3UxIT4OGLD1FC42CpwKU2N0cuikFZQKnVZREREdR9ua9myJf744w906NBBb/vFixcxePDgJrlWEofbGt7VrCK8uOI4UgvK0NLBCj9M6QEfZxupyyIiokbM6I8lUavVyMrKqrU9KysLhYWFdS6ACABau9pi04zH4OtsjRv5pXh2aRRi0/n7RERE0qlzSBo+fDgmTpyIX3/9FdevX8f169fxyy+/YNKkSRgxYkRD1EjNREsHK2ycHop2HnbILCzHc8ujcO5avtRlERFRM1Xn4baSkhK89tprWLVqFSorKwFUP5Jk0qRJ+OSTT2Bj0/SGSDjcZlz5JRWYsPokzl7Lh63SHCvGd0fP1s5Sl0VERI3Mw35/1zkk1SguLkZCQgIAwN/fv0mGoxoMScZXVF6FKWtOIepqDpTmZlj6Ygj6t3OTuiwiImpEjD4nqUZaWhrS0tIQGBgIGxsbPrCU6pWt0hyrJz6Cge3cUF6lxZS1p7D1fNO7KYCIiExXnUNSTk4OBg4ciDZt2uCpp55CWloaAGDSpEmYP39+vRdIzZelhRxLXwrB0509UaUVmP3jGWw8eU3qsoiIqJmoc0iaN28eLCwskJKSAmtra9325557Djt37qzX4ogs5Gb48rkueP5Rb2gFsOCX81h5OFHqsoiIqBmo84rbf/zxB3bt2gUvLy+97YGBgUhOTq63wohqyM1k+GB4MOwsLbD84FW8v/USisqqMHtgAFd2JyKiBlPnnqTi4mK9HqQaubm5UCr5OAlqGDKZDAufbIf5j7cBAHzx5xX8Z9tfnAtHREQNps4hqXfv3li7dq3uZ5lMBq1Wi48//hj9+/ev1+KIbiWTyTBrYCDeHhoEAFhxOBELf70AjZZBiYiI6l+dh9s+/vhjDBw4EKdOnUJFRQUWLFiAmJgY5Obm4siRIw1RI5Gel8P8YGtpjjd/OY8NJ6+hqLwKXzzXBRbyB75Zk4iIqJY6f6t07NgRV65cQVhYGJ555hkUFxdjxIgROHPmDPz9/RuiRqJaRnf3xn+f7wYLuQxbz6dh2vfRKKvUSF0WERE1IXVaTLKyshJPPPEEli5disDAwIasy6RwMUnTtS82E9O/j0Z5lRY9WzthxfhHYKuscwcpERE1QUZdTNLCwgLnz5+v84cQNZT+bd2w9uVHYas0x7GruRi74jjySyqkLouIiJqAOg+3vfjii1i5cmVD1EL0QHq0dsYPU3rA0doC567l47llx5CpLpO6LCIiauTqPC5RVVWFVatW4c8//0RISEitZ7Z9/vnn9VYc0f3q5OWAn6aF4sUVxxGbUYjRy6KwbnIPeDnWXq6CiIjoftzXnKTz58+jY8eOMDMzu+tt/jKZDHv37q3XAk0B5yQ1Hik5JXhhxTFczytFC3tLrJvcA/6utlKXRUREEnjY7+/7CklyuRxpaWlwc3ND69atcfLkSTg7Oz9QwY0RQ1Ljkl5QhhdXHkd8ZhGcbRRYO+lRdPC0l7osIiIyMqNM3HZwcEBiYvXzspKSkqDVauv8QUTG4mFviZ+m9kQHTxVyiiswZvkxRCfnSl0WERE1Mvc1J2nkyJHo27cvWrRoAZlMhu7du0Mulxtse/Xq1XotkOhBONsq8ePUnpgUeRInk/Lw4ooT+G5cd4QFukhdGhERNRL3vU7Szp07ER8fj9mzZ+O9996DnZ2dwXZz5syp1wJNAYfbGq/SCg2mfn8Kh+KyoZCb4esXumJwBw+pyyIiIiMwypykW02cOBFfffXVHUNSU8SQ1LiVV2kw58ez2BmTDrmZDJ8+2wnDu3pJXRYRETUwoy4mCQCrV69uVgGJGj+luRxfv9AVI7t5QaMVeHXjOXx/LFnqsoiIyMTxiaDULJjLzfDJqE4YH+oDIYC3Nl/Ekv0JUpdFREQmjCGJmg0zMxne/UcHzOwfAAD4aOdlfLzzMuo44kxERM0EQxI1KzKZDK+Ft8WbT7YDAHy7PwHvbImBVsugRERE+hiSqFma3tcf/x7WETIZsDYqGa/9fA5VGq7/RUREf2NIombrxZ4++GJ0F8jNZPj19A1E/HAa5VUaqcsiIiITwZBEzdqwri2xZGw3KORm2BWTgclrTqGkokrqsoiIyAQwJFGzN7iDB1ZPfATWCjkOxWVj3MoTKCitlLosIiKSGEMSEYBeAS74flIPqCzNcSo5Dy98dww5ReVSl0VERBJiSCK6KcTHERumhsLFVoGYVDVGL4tCWkGp1GUREZFEGJKIbhHkqcJP00LRwt4SCVnFeHZpFJJziqUui4iIJMCQRHQbf1dbbJoeCl9na1zPK8WzS6NwJaNQ6rKIiMjIGJKIDPBytMbG6aFo52GHzMJyjF4WhXPX8qUui4iIjIghiegO3OwssWFqT3T2dkB+SSXGrjiO41dzpC6LiIiMhCGJ6C4crBVYP7kHQls7o6i8CuNWncC+y5lSl0VEREbAkER0D7ZKc6ye+AgGtnNDeZUWU9aewrbzaVKXRUREDYwhieg+WFrIsfSlEDzd2RNVWoFZP57GxpPXpC6LiIgaEEMS0X2ykJvhy+e64PlHvaEVwIJfzmPl4USpyyIiogYiaUhasmQJOnXqBJVKBZVKhdDQUOzYsUO3v6ysDBEREXB2doatrS1GjhyJjIwMvWOkpKRgyJAhsLa2hpubG15//XVUVek/e2v//v3o1q0blEolAgICEBkZaYzToyZIbibDB8ODMaW3HwDg/a2XsPjPOAghJK6MiIjqm6QhycvLCx9++CGio6Nx6tQpDBgwAM888wxiYmIAAPPmzcPvv/+OTZs24cCBA0hNTcWIESN079doNBgyZAgqKipw9OhRrFmzBpGRkXj77bd1bRITEzFkyBD0798fZ8+exdy5czF58mTs2rXL6OdLTYNMJsM/n2qPVx9vAwD44s8r+GD7XwxKRERNjEyY2N/sTk5O+OSTTzBq1Ci4urrihx9+wKhRowAAly9fRvv27REVFYWePXtix44dGDp0KFJTU+Hu7g4AWLp0Kd544w1kZWVBoVDgjTfewLZt23Dx4kXdZ4wZMwb5+fnYuXPnfdWkVqthb2+PgoICqFSq+j9parRWHU7Ee1svAQCef9Qb/x4WDLmZTOKqiIgIePjvb5OZk6TRaLBhwwYUFxcjNDQU0dHRqKysxKBBg3Rt2rVrh1atWiEqKgoAEBUVheDgYF1AAoDw8HCo1Wpdb1RUVJTeMWra1ByD6GG8HOaHj0d2gpkM+PHENczZcAaVGq3UZRERUT0wl7qACxcuIDQ0FGVlZbC1tcVvv/2GoKAgnD17FgqFAg4ODnrt3d3dkZ6eDgBIT0/XC0g1+2v23a2NWq1GaWkprKysatVUXl6O8vK/nwCvVqsf+jyp6Rr9iDdslOaY+9MZbD2fhpIKDb4d2w2WFnKpSyMioocgeU9S27ZtcfbsWRw/fhwzZszA+PHjcenSJUlrWrRoEezt7XUvb29vSesh0zekUwssH9cdSnMz7L2ciQmrT6CovOrebyQiIpMleUhSKBQICAhASEgIFi1ahM6dO2Px4sXw8PBARUUF8vPz9dpnZGTAw8MDAODh4VHrbrean+/VRqVSGexFAoCFCxeioKBA97p2jevh0L31b+uGtS8/ClulOY5dzcXYFceRX1IhdVlERPSAJA9Jt9NqtSgvL0dISAgsLCywZ88e3b7Y2FikpKQgNDQUABAaGooLFy4gM/Pvx0Ts3r0bKpUKQUFBuja3HqOmTc0xDFEqlbplCWpeRPejR2tn/DClBxysLXDuWj6eW3YMmYVlUpdFREQPQNKQtHDhQhw8eBBJSUm4cOECFi5ciP3792Ps2LGwt7fHpEmT8Oqrr2Lfvn2Ijo7GxIkTERoaip49ewIABg8ejKCgILz00ks4d+4cdu3ahX/961+IiIiAUqkEAEyfPh1Xr17FggULcPnyZXz77bfYuHEj5s2bJ+WpUxPWycsBG6eFws1OidiMQoxeGoXreSVSl0VERHUkaUjKzMzEuHHj0LZtWwwcOBAnT57Erl278PjjjwMAvvjiCwwdOhQjR45Enz594OHhgV9//VX3frlcjq1bt0IulyM0NBQvvvgixo0bh/fee0/Xxs/PD9u2bcPu3bvRuXNnfPbZZ1ixYgXCw8ONfr7UfLRxt8Om6aHwcrRCUk4Jnl0ahYSsIqnLIiKiOjC5dZJMEddJogeVXlCGsSuOISGrGM42Cqyd9Cg6eNpLXRYRUbPQZNZJImqKPOwtsXFaKDp4qpBTXIExy48hOjlP6rKIiOg+MCQRNTBnWyV+nNoT3X0cUVhWhZdWHsfhuGypyyIiontgSCIyApWlBdZOehS9A11QUqHBy5En8UdMutRlERHRXTAkERmJtcIcK8Z3xxMdPFCh0WLG+tPYfOaG1GUREdEdMCQRGZHSXI6vX+iKEd1aQqMVmLfxLNYdS5a6LCIiMoAhicjIzOVm+HRUZ4wP9YEQwL82X8SS/QlSl0VERLdhSCKSgJmZDO/+owMi+vsDAD7aeRmf7LoMrshBRGQ6GJKIJCKTyfB6eDu8+WQ7AMA3+xLw7pYYaLUMSkREpoAhiUhi0/v64/1hHSGTAWuikvHaz+dQpdFKXRYRUbPHkERkAl7q6YMvRneB3EyGX0/fwMwfzqC8SiN1WUREzRpDEpGJGNa1JZaM7QaF3Aw7Y9Ixec0plFRUSV0WEVGzxZBEZEIGd/DAqgmPwMpCjkNx2Ri38gTUZZVSl0VE1CwxJBGZmLBAF6yb3AMqS3OcSs7D88uPIaeoXOqyiIiaHYYkIhMU4uOIH6f2hLONAjGpaoxeFoX0gjKpyyIialYYkohMVAdPe2ycHooW9pZIyCrGqKVHkZxTLHVZRETNBkMSkQnzd7XFpumh8HW2xvW8Ujy7NApXMgqlLouIqFlgSCIycV6O1tg4PRRt3e2QWViO0cuicP56vtRlERE1eQxJRI2Am50lfprWE529HZBfUokXvjuO41dzpC6LiKhJkwk+LOqe1Go17O3tUVBQAJVKJXU51IwVlVdh8pqTOHY1FzIZ0MFThd6Brugd6IIQH0cozeVSl0hEZDIe9vubIek+MCSRKSmr1GD+pnPYdj5Nb7uVhRw9WjvpQlOgmy1kMplEVRIRSY8hyQgYksgUZRaW4Uh8Ng5dycah+GxkFeqvpeSuUuoCU68AF7jYKiWqlIhIGgxJRsCQRKZOCIHYjEJdYDp+NQflVfoPye3gqUJYoAv6BLoixMcRlhYcmiOipo0hyQgYkqixKavU4FRSHg7FZ+HQlWxcSlPr7be0MMOjfs7oE+iC3oGuaOPOoTkianoYkoyAIYkau6zCchxNyMbBK9k4FJeFzNuG5tzslAgLdEHvQBeEBbjC1Y5Dc0TU+DEkGQFDEjUlQgjEZRbh4JUsHIrLxvHEHJRV6g/NtW+hQp9AF4QFuuARXycOzRFRo8SQZAQMSdSUlVVqcDo5DwfjsnE4PgsXb+gPzSnNzfConxN63xyaa+dhx6E5ImoUGJKMgCGJmpOconIcjs/G4bhsHIrLRrpa/8G6rnZKhAXUDM25wE1lKVGlRER3x5BkBAxJ1FwJIRCfWYRDcdVzmY5dzUVppUavTTsPO10v06N+HJojItPBkGQEDElE1cqrNDidnI9DcdXzmS6mFuDWv0EU5mZ41Ld6aC4s0AXtPVQwM+PQHBFJgyHJCBiSiAzLLa6oXtDyZmhKK9AfmnOxVSAswAVhNxe1dOfQHBEZEUOSETAkEd2bEAIJWcU4FJeFw3HZiLqag5IK/aG5Nu62ulXAe/g5w0rBoTkiajgMSUbAkERUdxVVWpxOybs5ATwL52/cNjQnN0N3X0ddaApqwaE5IqpfDElGwJBE9PDyiitwNCFHNzR3I79Ub7+zjQK9bt411zvQFR72HJojoofDkGQEDElE9UsIgcTsYt1dc1EJOSi+bWgu0M1W96y5Hq2dYK0wl6haImqsGJKMgCGJqGFVarQ4k/L3XXPnr+dDe8vfTBZyGUJ8qofm+gS6ooMnh+aI6N4YkoyAIYnIuPJLaobmsnHwSlatoTlHawv0CqjuZQoLdIGng5VElRKRKWNIMgKGJCLpCCGQlFOCw3FZOBiXjaiEHBSVV+m18Xe1qe5lalN915yNkkNzRMSQZBQMSUSmo1Kjxblr+Th4cz7TuWu1h+a6tXLUTQDv2NIecg7NETVLDElGwJBEZLoKSisRlVD9nLmDcVm4lqs/NOdgbYFe/i66VcC9HK0lqpSIjI0hyQgYkogaj+Scv++aOxqfg8LbhuZau9joepl6+jvDlkNzRE0WQ5IRMCQRNU5VGi3OXc+/GZqycfZaPjS3jM2Zm/09NBcW6IJOXg4cmiNqQhiSjIAhiahpUJdVIurmgpaH47KRlFOit9/eygK9ApwRFlC9Cri3E4fmiBozhiQjYEgiappSckpwKL46MB2Jz4a6TH9ozs/FBmE3VwEP9XeGnaWFRJUS0YNgSDIChiSipq9Ko8X5GwW6Z82dTtEfmpObydCtlUN1L1MbF3RqaQ9zuZmEFRPRvTAkGQFDElHzU3hzaO5wfPV8psTsYr39dpbm1XfNtXFB7wBXtHLm0ByRqWFIMgKGJCK6lltyMzBl4Uh8DgpKK/X2+zhbV08AD3DFYwHOUHFojkhyDElGwJBERLfSaAUu3CjAoSvVz5o7nZKHqtuG5rp4OyAswAV92rigs5cDh+aIJMCQZAQMSUR0N0XlVTh28665Q/HZuJp129Cc0hyh/s7o3cYVfQJd4ONsI1GlRM0LQ5IRMCQRUV1czyupngAeX33XXH6J/tCct5NV9bPmAl0Q6u8CeysOzRE1BIYkI2BIIqIHpdEKxKQWVD825UoWTqfkoVLz91+7ZjKgs7cDegdWr83UxdsBFhyaI6oXDElGwJBERPWluLwKxxNzcPBK9STwhNuG5mxrhuZuPjrF19kaMhlXASd6EAxJRsCQREQNJTW/FIdvPpz3SHw28m4bmvNytNIFpsf8neFgrZCoUqLGhyHJCBiSiMgYtFqBmFQ1DsVn4dCVbJxKzq01NBfs5YA+N0NT11YcmiO6G4YkI2BIIiIplFRU4XhiLg7dHJqLyyzS22+jkN8cmquez+TnYsOhOaJbPOz3t6T/BFm0aBEeeeQR2NnZwc3NDcOGDUNsbKxem7KyMkRERMDZ2Rm2trYYOXIkMjIy9NqkpKRgyJAhsLa2hpubG15//XVUVek/g2n//v3o1q0blEolAgICEBkZ2dCnR0T0UKwV5ujf1g1vPx2E3a/2xbGFA/HJqE74R2dPONsoUFyhwZ9/ZeKdLTEY8NkBhH20D2/+ch5bz6cir7hC6vKJGj1Je5KeeOIJjBkzBo888giqqqrwz3/+ExcvXsSlS5dgY1O9jsiMGTOwbds2REZGwt7eHjNnzoSZmRmOHDkCANBoNOjSpQs8PDzwySefIC0tDePGjcOUKVPwwQcfAAASExPRsWNHTJ8+HZMnT8aePXswd+5cbNu2DeHh4feskz1JRGRqtFqBS2lqHIrLxuH4LJxMzEOFRqvbL5MBnVraI+zm0Fy3Vo5QmHNojpqXJjXclpWVBTc3Nxw4cAB9+vRBQUEBXF1d8cMPP2DUqFEAgMuXL6N9+/aIiopCz549sWPHDgwdOhSpqalwd3cHACxduhRvvPEGsrKyoFAo8MYbb2Dbtm24ePGi7rPGjBmD/Px87Ny58551MSQRkakrrdDgeGLOzQf0ZiM2o1Bvv7VCjp6t/75rzt+VQ3PU9D3s97d5A9T0wAoKCgAATk5OAIDo6GhUVlZi0KBBujbt2rVDq1atdCEpKioKwcHBuoAEAOHh4ZgxYwZiYmLQtWtXREVF6R2jps3cuXMN1lFeXo7y8nLdz2q1ur5OkYioQVgp5OjX1g392roBADLUZTcDUxYOx2cju6gCey9nYu/lTABAC3tLXWDqFeACJxveNUd0O5MJSVqtFnPnzkWvXr3QsWNHAEB6ejoUCgUcHBz02rq7uyM9PV3X5taAVLO/Zt/d2qjVapSWlsLKykpv36JFi/D//t//q7dzIyIyNneVJUaGeGFkiBe0WoHL6YXVj02Jy8aJpFykFZRh46nr2HjqOmQyoKOnffUDegNdEOLjCKW5XOpTIJKcyYSkiIgIXLx4EYcPH5a6FCxcuBCvvvqq7me1Wg1vb28JKyIienBmZjIEeaoQ5KnCtL7+KKvU4ERiri40XU4vxIUbBbhwowDf7k+AlYUcPVs7Iezmo1MC3Gw5NEfNkkmEpJkzZ2Lr1q04ePAgvLy8dNs9PDxQUVGB/Px8vd6kjIwMeHh46NqcOHFC73g1d7/d2ub2O+IyMjKgUqlq9SIBgFKphFKprJdzIyIyNZYWcvRp44o+bVwBAJnqMhyOz765qGU2sovKsS82C/tiswAAHirLmxPAXRAW4AJnW/79SM2DpCFJCIFZs2bht99+w/79++Hn56e3PyQkBBYWFtizZw9GjhwJAIiNjUVKSgpCQ0MBAKGhofjPf/6DzMxMuLlVj8Xv3r0bKpUKQUFBujbbt2/XO/bu3bt1xyAias7cVJYY0c0LI7p5QYjqobmaVcBPJOYiXV2Gn6Ov4+fo6wCADp4q3QN6Q3w5NEdNl6R3t73yyiv44Ycf8L///Q9t27bVbbe3t9f18MyYMQPbt29HZGQkVCoVZs2aBQA4evQogL+XAPD09MTHH3+M9PR0vPTSS5g8eXKtJQAiIiLw8ssvY+/evZg9ezaXACAiuoeySg1OJeXhUFwWDsZl4680/RtZLC3M0MPv77vm2rhzaI5MR6NeAuBO/yOtXr0aEyZMAFC9mOT8+fPx448/ory8HOHh4fj22291Q2kAkJycjBkzZmD//v2wsbHB+PHj8eGHH8Lc/O+Osv3792PevHm4dOkSvLy88NZbb+k+414YkoiIqmUVluNIfHUv0+G4bGQWluvtd7NT6lYA7xXgAlc7Ds2RdBp1SGosGJKIiGoTQuBKRpFuAvjxxByUVWr12gS1UOl6mbr7OsLSgkNzZDwMSUbAkEREdG9llRpEJ+fh0M31mWJS9YfmlOZmeNTPCX0CXdG7jQvauttxaI4aFEOSETAkERHVXXZR9dBcTWjKUOsPzbnaKdE7wAW921QPzbnZWUpUKTVVDElGwJBERPRwhBCIzyzCwZuB6fjVXJRWavTatPOwQ582rggLcMGjfk4cmqOHxpBkBAxJRET1q7zq76G5w3HZuJhagFu/jRTmZujh54SwgOr5TO1bcGiO6o4hyQgYkoiIGlZOUTmOJOTg8M1J4GkFZXr7XWyVCAtw1t0556bi0BzdG0OSETAkEREZjxACCVnFurvmjl3NQUmF/tBcW3e76rvm2rjiUV8nWCk4NEe1MSQZAUMSEZF0Kqq0OJ2SpwtNF27cNjQnN8Mjfo7oHVg9nymohQpmZhyaI4Yko2BIIiIyHXnFFTiSkI1DV6ongafeNjTnbKNA2M3nzPUOdIWHPYfmmiuGJCNgSCIiMk1CCFzNLsbhm3fNRSXkoPi2obk27rYIC6hem6mHnxOsFSbxbHcyAoYkI2BIIiJqHCqqtDh7LV/3rLnz1/NrDc2F+DiidxsX9Al05dBcE8eQZAQMSUREjVN+SQWOJuRUh6Yr2biRX6q338lGgV4BLjcfneKCFvZWElVKDYEhyQgYkoiIGj8hBJJySnSB6djVHBSVV+m1CXCz1QWmHn7OsFFyaK4xY0gyAoYkIqKmp1JTMzRXPZ/p3LV8aG/5RrSQy6qH5m6uzdTB0x5yDs01KgxJRsCQRETU9BWUVCLqajYOxmXj4JUsXM/TH5pztLbAYwEu6BPogrBAV7R04NCcqWNIMgKGJCKi5kUIgeScEhyKz8ahK9V3zRXeNjTX2tUGfW72MvVo7QxbDs2ZHIYkI2BIIiJq3qo0Wpy7no+DV7JxOD4bZ6/lQ3PL2Jy5mQzdfBx1vUzBLTk0ZwoYkoyAIYmIiG5VUFqJqIQcHI6vXgU8OadEb7+9lQXCAlwQdnMSuJejtUSVNm8MSUbAkERERHeTklOCQ/FZOHQlG0cSslFYpj805+dic/OuOVf0bO0EO0sLiSptXhiSjIAhiYiI7lf10FyBbhXwMwaG5rq2ctDdNdfJy4FDcw2EIckIGJKIiOhBqcsqcSwhB4fiquczJWYX6+1XWZrfXNCyOjR5O3Forr4wJBkBQxIREdWXa7klNwNTFg7HZUN929Ccr7M1ege6IizQBaH+zlBxaO6BMSQZAUMSERE1BI1W4Pz1/JtDc9k4nZKHqluG5uRmMnTxdtDNZ+rsZQ9zuZmEFTcuDElGwJBERETGUFRedXNorvquuau3Dc3ZWZrjMX9n9A50RZ9AV7Ry5tDc3TAkGQFDEhERSeF6Xomul+lIQjbySyr19rdystb1MoX6O8PeikNzt2JIMgKGJCIikppGK3DxRoGulyk6WX9ozkwGdPF2QFigK/oEuqCztwMsmvnQHEOSETAkERGRqSkqr8Lxqzm6B/QmZN02NKc0R6i/s66nycfZGjJZ81pqgCHJCBiSiIjI1KXml+JwXDYOxmXhSHw28m4bmvN2skJYQHUv02P+LrC3bvpDcwxJRsCQREREjYlWKxCTqsbBuCwcistCdHIeKjX6Q3OdvBx0z5rr2qppDs0xJBkBQxIRETVmxeVVOJGYi4Nx1WszxWUW6e23VZqjZ+uaoTkX+LnYNImhOYYkI2BIIiKipiStoLR6Qcubq4DnFlfo7W/pYKWby9QrwBkO1gqJKn04DElGwJBERERNlVYrcClNrZsAfiopDxUarW6/7ObQXO+A6l6mrq0coTBvHENzDElGwJBERETNRUlF9dBcTWi6kqE/NGetkCP05tBcWKAr/F1Nd2iOIckIGJKIiKi5Si8ow+H46sB0JD4b2UX6Q3Oe9pa6Z831CnCBk43pDM0xJBkBQxIREVH10Nxf6WrdfKYTSbmoqNIfmgtuaY+wgOr5TCE+0g7NMSQZAUMSERFRbaUVGpxIysXhm6uAX04v1NtvrZCjh59T9bPm2rjA39XWqENzDElGwJBERER0b5nqmqG56ld2Ubnefg+VZfVdc21c0cvfGc62ygathyHJCBiSiIiI6kYIgcvphbpnzZ1IzEX5LUNzANCxpQq9A13RO9AFIT6OUJrL67UGhiQjYEgiIiJ6OGWVGpxMytX1Mv2Vptbbb6s0x8n/GwQrRf0FpYf9/javt0qIiIiI7sDSQn6z18gVAJBZWIYjtwzNeTla1WtAqg8MSURERGR0bnaWGN7VC8O7ekEIUeuBvKagcSyZSURERE2WTCYzqfWVajAkERERERnAkERERERkAEMSERERkQEMSUREREQGMCQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZwJBEREREZABDEhEREZEBDElEREREBjAkERERERlgLnUBjYEQAgCgVqslroSIiIjuV833ds33eF0xJN2HwsJCAIC3t7fElRAREVFdFRYWwt7evs7vk4kHjVfNiFarRWpqKuzs7CCTyR7qWGq1Gt7e3rh27RpUKlU9Vdj08bo9GF63B8Pr9uB47R4Mr9uDudd1E0KgsLAQnp6eMDOr+wwj9iTdBzMzM3h5edXrMVUqFf9HeAC8bg+G1+3B8Lo9OF67B8Pr9mDudt0epAepBiduExERERnAkERERERkAEOSkSmVSrzzzjtQKpVSl9Ko8Lo9GF63B8Pr9uB47R4Mr9uDaejrxonbRERERAawJ4mIiIjIAIYkIiIiIgMYkoiIiIgMYEgiIiIiMoAhyci++eYb+Pr6wtLSEj169MCJEyekLklSBw8exNNPPw1PT0/IZDJs3rxZb78QAm+//TZatGgBKysrDBo0CHFxcXptcnNzMXbsWKhUKjg4OGDSpEkoKioy4lkY16JFi/DII4/Azs4Obm5uGDZsGGJjY/XalJWVISIiAs7OzrC1tcXIkSORkZGh1yYlJQVDhgyBtbU13Nzc8Prrr6OqqsqYp2JUS5YsQadOnXSLzoWGhmLHjh26/bxm9+fDDz+ETCbD3Llzddt47Qx79913IZPJ9F7t2rXT7ed1u7MbN27gxRdfhLOzM6ysrBAcHIxTp07p9hvtu0GQ0WzYsEEoFAqxatUqERMTI6ZMmSIcHBxERkaG1KVJZvv27eL//u//xK+//ioAiN9++01v/4cffijs7e3F5s2bxblz58Q//vEP4efnJ0pLS3VtnnjiCdG5c2dx7NgxcejQIREQECCef/55I5+J8YSHh4vVq1eLixcvirNnz4qnnnpKtGrVShQVFenaTJ8+XXh7e4s9e/aIU6dOiZ49e4rHHntMt7+qqkp07NhRDBo0SJw5c0Zs375duLi4iIULF0pxSkaxZcsWsW3bNnHlyhURGxsr/vnPfwoLCwtx8eJFIQSv2f04ceKE8PX1FZ06dRJz5szRbee1M+ydd94RHTp0EGlpabpXVlaWbj+vm2G5ubnCx8dHTJgwQRw/flxcvXpV7Nq1S8THx+vaGOu7gSHJiB599FERERGh+1mj0QhPT0+xaNEiCasyHbeHJK1WKzw8PMQnn3yi25afny+USqX48ccfhRBCXLp0SQAQJ0+e1LXZsWOHkMlk4saNG0arXUqZmZkCgDhw4IAQovoaWVhYiE2bNuna/PXXXwKAiIqKEkJUh1MzMzORnp6ua7NkyRKhUqlEeXm5cU9AQo6OjmLFihW8ZvehsLBQBAYGit27d4u+ffvqQhKv3Z298847onPnzgb38brd2RtvvCHCwsLuuN+Y3w0cbjOSiooKREdHY9CgQbptZmZmGDRoEKKioiSszHQlJiYiPT1d75rZ29ujR48eumsWFRUFBwcHdO/eXddm0KBBMDMzw/Hjx41esxQKCgoAAE5OTgCA6OhoVFZW6l23du3aoVWrVnrXLTg4GO7u7ro24eHhUKvViImJMWL10tBoNNiwYQOKi4sRGhrKa3YfIiIiMGTIEL1rBPD37V7i4uLg6emJ1q1bY+zYsUhJSQHA63Y3W7ZsQffu3fHss8/Czc0NXbt2xXfffafbb8zvBoYkI8nOzoZGo9H7ZQcAd3d3pKenS1SVaau5Lne7Zunp6XBzc9Pbb25uDicnp2ZxXbVaLebOnYtevXqhY8eOAKqviUKhgIODg17b26+boetas6+punDhAmxtbaFUKjF9+nT89ttvCAoK4jW7hw0bNuD06dNYtGhRrX28dnfWo0cPREZGYufOnViyZAkSExPRu3dvFBYW8rrdxdWrV7FkyRIEBgZi165dmDFjBmbPno01a9YAMO53g/nDnAgRSSsiIgIXL17E4cOHpS6lUWjbti3Onj2LgoIC/Pzzzxg/fjwOHDggdVkm7dq1a5gzZw52794NS0tLqctpVJ588kndnzt16oQePXrAx8cHGzduhJWVlYSVmTatVovu3bvjgw8+AAB07doVFy9exNKlSzF+/Hij1sKeJCNxcXGBXC6vdedCRkYGPDw8JKrKtNVcl7tdMw8PD2RmZurtr6qqQm5ubpO/rjNnzsTWrVuxb98+eHl56bZ7eHigoqIC+fn5eu1vv26GrmvNvqZKoVAgICAAISEhWLRoETp37ozFixfzmt1FdHQ0MjMz0a1bN5ibm8Pc3BwHDhzAV199BXNzc7i7u/Pa3ScHBwe0adMG8fHx/J27ixYtWiAoKEhvW/v27XVDlcb8bmBIMhKFQoGQkBDs2bNHt02r1WLPnj0IDQ2VsDLT5efnBw8PD71rplarcfz4cd01Cw0NRX5+PqKjo3Vt9u7dC61Wix49ehi9ZmMQQmDmzJn47bffsHfvXvj5+entDwkJgYWFhd51i42NRUpKit51u3Dhgt5fIrt374ZKpar1l1NTptVqUV5ezmt2FwMHDsSFCxdw9uxZ3at79+4YO3as7s+8dvenqKgICQkJaNGiBX/n7qJXr161ljW5cuUKfHx8ABj5u6Hu887pQW3YsEEolUoRGRkpLl26JKZOnSocHBz07lxobgoLC8WZM2fEmTNnBADx+eefizNnzojk5GQhRPVtng4ODuJ///ufOH/+vHjmmWcM3ubZtWtXcfz4cXH48GERGBjYpJcAmDFjhrC3txf79+/Xu7W4pKRE12b69OmiVatWYu/eveLUqVMiNDRUhIaG6vbX3Fo8ePBgcfbsWbFz507h6urapG8tfvPNN8WBAwdEYmKiOH/+vHjzzTeFTCYTf/zxhxCC16wubr27TQheuzuZP3++2L9/v0hMTBRHjhwRgwYNEi4uLiIzM1MIwet2JydOnBDm5ubiP//5j4iLixPr168X1tbWYt26dbo2xvpuYEgysv/+97+iVatWQqFQiEcffVQcO3ZM6pIktW/fPgGg1mv8+PFCiOpbPd966y3h7u4ulEqlGDhwoIiNjdU7Rk5Ojnj++eeFra2tUKlUYuLEiaKwsFCCszEOQ9cLgFi9erWuTWlpqXjllVeEo6OjsLa2FsOHDxdpaWl6x0lKShJPPvmksLKyEi4uLmL+/PmisrLSyGdjPC+//LLw8fERCoVCuLq6ioEDB+oCkhC8ZnVxe0jitTPsueeeEy1atBAKhUK0bNlSPPfcc3pr/fC63dnvv/8uOnbsKJRKpWjXrp1Yvny53n5jfTfIhBCijj1hRERERE0e5yQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZwJBEREREZABDEhEREZEBDElE1Gykp6fj8ccfh42NTa2nr9eYMGEChg0bZtS6iMg0MSQRkdFMmDABMpkMH374od72zZs3QyaTNfjnf/HFF0hLS8PZs2dx5coVg20WL16MyMhI3c/9+vXD3LlzG7w2IjI9DElEZFSWlpb46KOPkJeXZ/TPTkhIQEhICAIDA+Hm5mawjb29/R17mYioeWFIIiKjGjRoEDw8PLBo0aK7tvvll1/QoUMHKJVK+Pr64rPPPrvnsZcsWQJ/f38oFAq0bdsW33//vW6fr68vfvnlF6xduxYymQwTJkwweIxbh9smTJiAAwcOYPHixZDJZJDJZEhKSgIAXLx4EU8++SRsbW3h7u6Ol156CdnZ2brj9OvXD7NmzcLcuXPh6OgId3d3fPfddyguLsbEiRNhZ2eHgIAA7NixQ/eevLw8jB07Fq6urrCyskJgYCBWr159z/MmoobBkERERiWXy/HBBx/gv//9L65fv26wTXR0NEaPHo0xY8bgwoULePfdd/HWW2/pDYPd7rfffsOcOXMwf/58XLx4EdOmTcPEiROxb98+AMDJkyfxxBNPYPTo0UhLS8PixYvvWevixYsRGhqKKVOmIC0tDWlpafD29kZ+fj4GDBiArl274tSpU9i5cycyMjIwevRovfevWbMGLi4uOHHiBGbNmoUZM2bg2WefxWOPPYbTp09j8ODBeOmll1BSUgIAeOutt3Dp0iXs2LEDf/31F5YsWQIXF5f7vLJEVN/4gFsiMpoJEyYgPz8fmzdvRmhoKIKCgrBy5Ups3rwZw4cPR81fR2PHjkVWVhb++OMP3XsXLFiAbdu2ISYmxuCxe/XqhQ4dOmD58uW6baNHj0ZxcTG2bdsGABg2bBgcHBzuGrZurRGo7hHq0qULvvzyS12bf//73zh06BB27dql23b9+nV4e3sjNjYWbdq0Qb9+/aDRaHDo0CEAgEajgb29PUaMGIG1a9cCqJ5I3qJFC0RFRaFnz574xz/+ARcXF6xater+LyoRNRj2JBGRJD766COsWbMGf/31V619f/31F3r16qW3rVevXoiLi4NGozF4vDu9x9DxH9a5c+ewb98+2Nra6l7t2rUDUD3vqUanTp10f5bL5XB2dkZwcLBum7u7OwAgMzMTADBjxgxs2LABXbp0wYIFC3D06NF6r52I7h9DEhFJok+fPggPD8fChQulLqXOioqK8PTTT+Ps2bN6r7i4OPTp00fXzsLCQu99MplMb1vNHX1arRYA8OSTTyI5ORnz5s1DamoqBg4ciNdee80IZ0REhjAkEZFkPvzwQ/z++++IiorS296+fXscOXJEb9uRI0fQpk0byOVyg8e603uCgoIeqkaFQlGr96pbt26IiYmBr68vAgIC9F42NjYP9Xmurq4YP3481q1bhy+//FJv+JCIjIshiYgkExwcjLFjx+Krr77S2z5//nzs2bMH77//Pq5cuYI1a9bg66+/vmuvyuuvv47IyEgsWbIEcXFx+Pzzz/Hrr78+dE+Mr68vjh8/jqSkJGRnZ0Or1SIiIgK5ubl4/vnncfLkSSQkJGDXrl2YOHHiHYcD78fbb7+N//3vf4iPj0dMTAy2bt2K9u3bP1T9RPTgGJKISFLvvfeebripRrdu3bBx40Zs2LABHTt2xNtvv4333nvvjrftA9WTshcvXoxPP/0UHTp0wLJly7B69Wr069fvoep77bXXIJfLERQUBFdXV6SkpMDT0xNHjhyBRqPB4MGDERwcjLlz58LBwQFmZg/+16pCocDChQvRqVMn9OnTB3K5HBs2bHio+onowfHuNiIiIiID2JNEREREZABDEhEREZEBDElEREREBjAkERERERnAkERERERkAEMSERERkQEMSUREREQGMCQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZ8P8BtZITKLvbaEYAAAAASUVORK5CYII=\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.MaxPFGrowth(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['MaxPFGrowth', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "383114d5-196f-4c9a-a40b-4e84202f9d68" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n", - "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n", - "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n", - "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n", - "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Maximal Periodic Frequent patterns using MaxPFGrowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.periodicFrequentPattern.maximal import MaxPFGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.MaxPFGrowth(iFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('maximalPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d17348da-bd62-4a78-b7e0-170722552dae" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n", + "Total No of patterns: 3443\n", + "Runtime: 13.052407026290894\n", + "Memory (RSS): 606658560\n", + "Memory (USS): 559308800\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'maximalPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3cd989ab-db62-4b04-c41b-a3c017074adb" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "330:102:4598 \n", + "102:109:4201 \n", + "906\t490\t856:103:4431 \n", + "914\t90\t339\t191:102:4449 \n", + "914\t825\t276\t339\t191:100:4449 \n", + "914\t825\t276\t90\t191:101:4449 \n", + "914\t90\t339\t426:101:4525 \n", + "914\t825\t276\t90\t426:100:4525 \n", + "914\t825\t276\t339\t426:101:4525 \n", + "146:113:3550 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _maximalPeriodicFrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the MaxPFGrowth algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.periodicFrequentPattern.maximal import MaxPFGrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 5000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of MaxPFGrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of MaxPFGrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.MaxPFGrowth(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['MaxPFGrowth', minSupCount, maximumPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "383114d5-196f-4c9a-a40b-4e84202f9d68" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n", + "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n", + "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n", + "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n", + "Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "67e2f852-ddcf-40bc-a51f-d7e960549227" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximumPeriodCount patterns runtime memory\n", + "0 MaxPFGrowth 100 5000 3443 14.754199 609312768\n", + "1 MaxPFGrowth 150 5000 2367 12.909081 607485952\n", + "2 MaxPFGrowth 200 5000 1954 13.010225 606203904\n", + "3 MaxPFGrowth 250 5000 1601 12.284047 603185152\n", + "4 MaxPFGrowth 300 5000 1268 11.556020 599805952\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "9505c28a-3ba0-4480-8eea-cdab0a9852b4" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "67e2f852-ddcf-40bc-a51f-d7e960549227" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximumPeriodCount patterns runtime memory\n", - "0 MaxPFGrowth 100 5000 3443 14.754199 609312768\n", - "1 MaxPFGrowth 150 5000 2367 12.909081 607485952\n", - "2 MaxPFGrowth 200 5000 1954 13.010225 606203904\n", - "3 MaxPFGrowth 250 5000 1601 12.284047 603185152\n", - "4 MaxPFGrowth 300 5000 1268 11.556020 599805952\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "9505c28a-3ba0-4480-8eea-cdab0a9852b4" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/periodicFrequentPattern/topk/TopKPFP.ipynb b/notebooks/periodicFrequentPattern/topk/TopKPFP.ipynb index 7db483ec..328c4861 100644 --- a/notebooks/periodicFrequentPattern/topk/TopKPFP.ipynb +++ b/notebooks/periodicFrequentPattern/topk/TopKPFP.ipynb @@ -1,701 +1,701 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Top-K Periodic Frequent patterns in Temporal Databases using k-PFPMiner" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Top-K Periodic Frequent patterns in a temporal database using the kPFPMiner algorithm. In the final part, we describe an advanced approach, where we evaluate the kPFPMiner algorithm on a dataset at different K values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "6b3180ca-9166-4973-aa7a-2f0321666538" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/835.0 kB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K \u001B[91m━━\u001B[0m\u001B[91m╸\u001B[0m\u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m61.4/835.0 kB\u001B[0m 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python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Top-K Periodic Frequent patterns in Temporal Databases using k-PFPMiner" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Top-K Periodic Frequent patterns in a temporal database using the kPFPMiner algorithm. In the final part, we describe an advanced approach, where we evaluate the kPFPMiner algorithm on a dataset at different K values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "6b3180ca-9166-4973-aa7a-2f0321666538" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/835.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/835.0 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m829.4/835.0 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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" Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", - "Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "79b7c047-dc1a-4797-d6b9-78f87b2d19e8" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 09:39:50-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.10MB/s in 6.0s \n", - "\n", - "2023-08-28 09:39:57 (753 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "8577a7fa-c806-4822-8077-76b913bee116" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Top-K Periodic Frequent patterns using kPFPMiner" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (K) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "bdffb1dc-e0c3-4e6e-db8b-b2b11debb4ae" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "33102149-d6c5-4a99-fd1c-e2795dbdee82" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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t27dHRkYGVq5cCbVafce1pojozhiSiIhMyJIlS7Bu3Tp06dIFkZGRdX7/U089hZ9//hnLly+HTCZDt27dsHLlSvTp06f+iyVq4jgniYiIiMgAzkkiIiIiMoAhiYiIiMgAzkm6D1qtFqmpqbCzs7uvxdiIiIhIekIIFBYWwtPTE2Zmde8XYki6D6mpqbWelk5ERESNw7Vr1+Dl5VXn9zEk3Qc7OzsA1RdZpVJJXA0RERHdD7VaDW9vb933eF0xJN2HmiE2lUrFkERERNTIPOhUGU7cJiIiIjKAIYmIiIjIAIYkIiIiIgMYkoiIiIgMYEgiIiIiMoAhiYiIiMgAhiQiIiIiAxiSiIiIiAxgSCIiIiIygCGJiIiIyACGJCIiIiIDGJKIiIiIDGBIklh+SQVi0wulLoOIiIhuw5AkofjMQnR5bzdGLTkKIYTU5RAREdEtGJIk5OVoDZkMKCyvQl5JpdTlEBER0S0YkiRkaSGHh8oSAJCUUyxxNURERHQrhiSJ+ThbAwCSGZKIiIhMCkOSxHydbQAAyTklEldCREREt2JIkpgPQxIREZFJYkiSWM1wG+ckERERmRaGJIn9PSeJPUlERESmhCFJYjXDbbnFFVCXcRkAIiIiU8GQJDFbpTlcbBUAgBT2JhEREZkMhiQTUNObxHlJREREpoMhyQRwXhIREZHpYUgyAX+vlcSeJCIiIlPBkGQC/l4GgD1JREREpoIhyQT4sCeJiIjI5DAkmQDfmz1JGepylFZoJK6GiIiIAIYkk+BgrYDK0hwAkJLLITciIiJTwJBkInxduAwAERGRKWFIMhGcl0RERGRaGJJMhC/XSiIiIjIpDEkmopUTQxIREZEpYUgyEZyTREREZFoYkkxEzYKSqfmlqKjSSlwNERERMSSZCFdbJawVcmgFcD2PQ25ERERSY0gyETKZjPOSiIiITAhDkgmpedAt5yURERFJjyHJhPi4sCeJiIjIVDAkmRAfJy4oSUREZCoYkkwIF5QkIiIyHQxJJsTn5lpJ1/JKoNEKiashIiJq3iQPSTdu3MCLL74IZ2dnWFlZITg4GKdOndLtF0Lg7bffRosWLWBlZYVBgwYhLi5O7xi5ubkYO3YsVCoVHBwcMGnSJBQVFem1OX/+PHr37g1LS0t4e3vj448/Nsr51YWHyhIKuRkqNQKp+aVSl0NERNSsSRqS8vLy0KtXL1hYWGDHjh24dOkSPvvsMzg6OurafPzxx/jqq6+wdOlSHD9+HDY2NggPD0dZWZmuzdixYxETE4Pdu3dj69atOHjwIKZOnarbr1arMXjwYPj4+CA6OhqffPIJ3n33XSxfvtyo53svcjMZvJ2sAHDIjYiISHJCQm+88YYICwu7436tVis8PDzEJ598otuWn58vlEql+PHHH4UQQly6dEkAECdPntS12bFjh5DJZOLGjRtCCCG+/fZb4ejoKMrLy/U+u23btvdVZ0FBgQAgCgoK6nR+D+Ll1SeEzxtbxfdRSQ3+WURERE3Zw35/S9qTtGXLFnTv3h3PPvss3Nzc0LVrV3z33Xe6/YmJiUhPT8egQYN02+zt7dGjRw9ERUUBAKKiouDg4IDu3bvr2gwaNAhmZmY4fvy4rk2fPn2gUCh0bcLDwxEbG4u8vLxadZWXl0OtVuu9jMXHmXe4ERERmQJJQ9LVq1exZMkSBAYGYteuXZgxYwZmz56NNWvWAADS09MBAO7u7nrvc3d31+1LT0+Hm5ub3n5zc3M4OTnptTF0jFs/41aLFi2Cvb297uXt7V0PZ3t/fHiHGxERkUmQNCRptVp069YNH3zwAbp27YqpU6diypQpWLp0qZRlYeHChSgoKNC9rl27ZrTPZkgiIiIyDZKGpBYtWiAoKEhvW/v27ZGSkgIA8PDwAABkZGTotcnIyNDt8/DwQGZmpt7+qqoq5Obm6rUxdIxbP+NWSqUSKpVK72UsNY8mSc4thhBcBoCIiEgqkoakXr16ITY2Vm/blStX4OPjAwDw8/ODh4cH9uzZo9uvVqtx/PhxhIaGAgBCQ0ORn5+P6OhoXZu9e/dCq9WiR48eujYHDx5EZWWlrs3u3bvRtm1bvTvpTEFLRyvIzWQoq9Qis7Bc6nKIiIiaLUlD0rx583Ds2DF88MEHiI+Pxw8//IDly5cjIiICACCTyTB37lz8+9//xpYtW3DhwgWMGzcOnp6eGDZsGIDqnqcnnngCU6ZMwYkTJ3DkyBHMnDkTY8aMgaenJwDghRdegEKhwKRJkxATE4OffvoJixcvxquvvirVqd+RhdwMLR2qlwFIyubkbSIiIqmYS/nhjzzyCH777TcsXLgQ7733Hvz8/PDll19i7NixujYLFixAcXExpk6divz8fISFhWHnzp2wtLTUtVm/fj1mzpyJgQMHwszMDCNHjsRXX32l229vb48//vgDERERCAkJgYuLC95++229tZRMiY+zNVJyS5CcU4IerZ2lLoeIiKhZkglOfLkntVoNe3t7FBQUGGV+0lubL+L7Y8l4pZ8/FjzRrsE/j4iIqCl62O9vyR9LQrXp7nDL5R1uREREUmFIMkFcUJKIiEh6DEkmyLemJym7hMsAEBERSYQhyQR5O1WHpMLyKuQWV0hcDRERUfPEkGSCLC3kaGFfffce5yURERFJgyHJRP39eBLOSyIiIpICQ5KJqnk8SVI2e5KIiIikwJBkolrd7ElK4XAbERGRJBiSTJSuJ4nDbURERJJgSDJRf89JYk8SERGRFBiSTFTNgpK5xRVQl1VKXA0REVHzw5BkomyV5nCxVQAAUtibREREZHQMSSbMh/OSiIiIJMOQZMI4L4mIiEg6DEkmzMepZq0k9iQREREZG0OSCfN1udmTxLWSiIiIjI4hyYTVzEnio0mIiIiMjyHJhPnenJOUoS5HaYVG4mqIiIiaF4YkE+ZgrYDK0hwAH09CRERkbAxJJs7XhcsAEBERSYEhycRxXhIREZE0GJJMnI9T9bykJK6VREREZFQMSSauZkFJPpqEiIjIuBiSTBznJBEREUmDIcnE1fQkpeaXoryKywAQEREZC0OSiXO1VcJaIYdWANfzSqUuh4iIqNlgSDJxMpkMrZw4L4mIiMjYGJIaAV9nzksiIiIyNoakRqBmXlIye5KIiIiMhiGpEeCCkkRERMbHkNQI+LIniYiIyOgYkhoBn5trJV3LK0GVRitxNURERM0DQ1Ij4KGyhEJuhkqNQFpBmdTlEBERNQsMSY2A3EwGbycrABxyIyIiMhaGpEaCywAQEREZF0NSI9FKN3mbIYmIiMgYGJIaib97kjjcRkREZAwMSY1EzYKSfDQJERGRcTAkNRI1PUnJucXQaoXE1RARETV9DEmNREtHK8jNZCir1CKzsFzqcoiIiJo8hqRGwkJuhpYONcsAcPI2ERFRQ2NIakT4oFsiIiLjYUhqRGpCEtdKIiIiangMSY1IzeTtKxlFEldCRETU9DEkNSI9/JwBAPtiMzkviYiIqIExJDUiwV726NPGFRqtwLf7EqQuh4iIqEljSGpk5gwMBAD8cvo6ruVyAjcREVFDYUhqZEJ8HBEW4IIqrcCSA+xNIiIiaigMSY3Q7Ju9SZtOXUNqfqnE1RARETVNDEmN0KN+TujZ2gmVGoEl+9mbRERE1BAYkhqpOQPbAAB+OnkN6QVlEldDRETU9DAkNVI9WzvhUV8nVGi0WMq5SURERPWOIamRkslkurlJP55IQaaavUlERET1SdKQ9O6770Imk+m92rVrp9tfVlaGiIgIODs7w9bWFiNHjkRGRobeMVJSUjBkyBBYW1vDzc0Nr7/+OqqqqvTa7N+/H926dYNSqURAQAAiIyONcXoNrleAM7q1ckB5lRbLDl6VuhwiIqImRfKepA4dOiAtLU33Onz4sG7fvHnz8Pvvv2PTpk04cOAAUlNTMWLECN1+jUaDIUOGoKKiAkePHsWaNWsQGRmJt99+W9cmMTERQ4YMQf/+/XH27FnMnTsXkydPxq5du4x6ng3h1t6k9ceTkVVYLnFFRERETYdMCCGk+vB3330XmzdvxtmzZ2vtKygogKurK3744QeMGjUKAHD58mW0b98eUVFR6NmzJ3bs2IGhQ4ciNTUV7u7uAIClS5fijTfeQFZWFhQKBd544w1s27YNFy9e1B17zJgxyM/Px86dO++rTrVaDXt7exQUFEClUj38idcjIQSGfXsU567lY1qf1lj4VHupSyIiIjIJD/v9LXlPUlxcHDw9PdG6dWuMHTsWKSkpAIDo6GhUVlZi0KBBurbt2rVDq1atEBUVBQCIiopCcHCwLiABQHh4ONRqNWJiYnRtbj1GTZuaYzR2MpkMcwYGAADWRiUjp4i9SURERPVB0pDUo0cPREZGYufOnViyZAkSExPRu3dvFBYWIj09HQqFAg4ODnrvcXd3R3p6OgAgPT1dLyDV7K/Zd7c2arUapaWGF2IsLy+HWq3We5my/m3d0LGlCqWVGqw4nCh1OURERE2CpCHpySefxLPPPotOnTohPDwc27dvR35+PjZu3ChlWVi0aBHs7e11L29vb0nruReZTIbZA6rnJq09moS84gqJKyIiImr8JB9uu5WDgwPatGmD+Ph4eHh4oKKiAvn5+XptMjIy4OHhAQDw8PCodbdbzc/3aqNSqWBlZWWwjoULF6KgoED3unbtWn2cXoN6PMgd7VuoUFyhwaoj7E0iIiJ6WCYVkoqKipCQkIAWLVogJCQEFhYW2LNnj25/bGwsUlJSEBoaCgAIDQ3FhQsXkJmZqWuze/duqFQqBAUF6drceoyaNjXHMESpVEKlUum9TN2tc5MijyShoKRS4oqIiIgaN0lD0muvvYYDBw4gKSkJR48exfDhwyGXy/H888/D3t4ekyZNwquvvop9+/YhOjoaEydORGhoKHr27AkAGDx4MIKCgvDSSy/h3Llz2LVrF/71r38hIiICSqUSADB9+nRcvXoVCxYswOXLl/Htt99i48aNmDdvnpSn3iAGB3mgrbsdCsursPooe5OIiIgehqQh6fr163j++efRtm1bjB49Gs7Ozjh27BhcXV0BAF988QWGDh2KkSNHok+fPvDw8MCvv/6qe79cLsfWrVshl8sRGhqKF198EePGjcN7772na+Pn54dt27Zh9+7d6Ny5Mz777DOsWLEC4eHhRj/fhmZmJsOsm71Jqw4nQl3G3iQiIqIHJek6SY2FKa+TdDuNViD8y4OIzyzC/MfbYNbNxSaJiIiam0a/ThLVL7mZDLMGVPcmrTiciKLyqnu8g4iIiAxhSGqChnbyRGsXGxSUVmJtVJLU5RARETVKDElNkNxMhpk3e5O+O3gVxexNIiIiqjOGpCbqH5094etsjbySSqw7lix1OURERI0OQ1ITZS43Q0T/6t6k5QevorRCI3FFREREjQtDUhM2rGtLeDtZIae4AuuPszeJiIioLhiSmjALuRki+lX3Ji07eBVllexNIiIiul8MSU3ciG5eaOlghazCcvx4IkXqcoiIiBoNhqQmTmFuhlf6+wMAlh5IYG8SERHRfWJIagZGhXihhb0lMtTl2HTqmtTlEBERNQoMSc2A0lyOGf2qe5O+3Z+A8ir2JhEREd0LQ1IzMbq7N9zslEgrKMPP0delLoeIiMjkMSQ1E5YWckzve7M3aV8CKqq0EldERERk2hiSmpEXerSCi60SN/JL8dsZ9iYRERHdDUNSM1Ldm9QaAPD1vnhUatibREREdCcMSc3MCz1awdlGgWu5pdh85obU5RAREZkshqRmxlphjil9qnuTvtkXjyr2JhERERnEkNQMvdTTB47WFkjKKcHv51OlLoeIiMgkMSQ1QzZKc0zuXd2b9N+98dBohcQVERERmR6GpGZqXKgP7K0scDWrGFvZm0RERFQLQ1IzZWdpgUlhfgCqe5O07E0iIiLSw5DUjI1/zBd2luaIzyzCjovpUpdDRERkUhiSmjF7Kwu83KumNymOvUlERES3YEhq5l7u5QdbpTkupxfij0sZUpdDRERkMhiSmjl7awtMeMwXAPDVnjgIwd4kIiIigCGJAEwK84ONQo5LaWr8+Vem1OUQERGZBIYkgqONAuPYm0RERKSHIYkAAJPD/GBlIceFGwXYH5sldTlERESSY0giAICzrRIvhfoAAL5kbxIRERFDEv1tSu/WUJqb4dy1fByMy5a6HCIiIkkxJJGOq50SY3tU9yYt/vMKe5OIiKhZq3NIunr1akPUQSZiet/WUJib4XRKPo4m5EhdDhERkWTqHJICAgLQv39/rFu3DmVlZQ1RE0nITWWJFx5tBQBY/CfnJhERUfNV55B0+vRpdOrUCa+++io8PDwwbdo0nDhxoiFqI4lM69saCrkZTiTl4tjVXKnLISIikkSdQ1KXLl2wePFipKamYtWqVUhLS0NYWBg6duyIzz//HFlZvH28sWthb4XRj3gBqF43iYiIqDl64Inb5ubmGDFiBDZt2oSPPvoI8fHxeO211+Dt7Y1x48YhLS2tPuskI5vRLwAWchmirubgRCJ7k4iIqPl54JB06tQpvPLKK2jRogU+//xzvPbaa0hISMDu3buRmpqKZ555pj7rJCNr6WCFUSHeANibREREzVOdQ9Lnn3+O4OBgPPbYY0hNTcXatWuRnJyMf//73/Dz80Pv3r0RGRmJ06dPN0S9ZESv9POHuZkMh+OzEZ3M3iQiImpe6hySlixZghdeeAHJycnYvHkzhg4dCjMz/cO4ublh5cqV9VYkScPbyRojurUEAHy1J17iaoiIiIxLJniP9z2p1WrY29ujoKAAKpVK6nKMKjmnGAM+OwCNVmBzRC908XaQuiQiIqL78rDf33XuSVq9ejU2bdpUa/umTZuwZs2aOhdAps3H2QbDulT3Jv2Xc5OIiKgZqXNIWrRoEVxcXGptd3NzwwcffFAvRZFpiejvDzMZsOdyJi5cL5C6HCIiIqOoc0hKSUmBn59fre0+Pj5ISUmpl6LItLR2tcU/OnsCAL7ay94kIiJqHuocktzc3HD+/Pla28+dOwdnZ+d6KYpMz8wBAZDJgN2XMhCTyt4kIiJq+uockp5//nnMnj0b+/btg0ajgUajwd69ezFnzhyMGTOmIWokExDgZoehnap7k77eyzvdiIio6TOv6xvef/99JCUlYeDAgTA3r367VqvFuHHjOCepiZs1IAC/n0vFjovpuJyuRjuP5nWnHxERNS8PvATAlStXcO7cOVhZWSE4OBg+Pj71XZvJaM5LANzulfXR2H4hHUM6tcA3L3STuhwiIqI7etjv7zr3JNVo06YN2rRp86Bvp0Zq1oBAbL+Qju0X0hCXUYhAdzupSyIiImoQdQ5JGo0GkZGR2LNnDzIzM6HVavX27927t96KI9PTvoUK4R3csSsmA1/vi8fiMV2lLomIiKhB1DkkzZkzB5GRkRgyZAg6duwImUzWEHWRCZs1IBC7YjLw+7lUzB4YCH9XW6lLIiIiqnd1DkkbNmzAxo0b8dRTTzVEPdQIdGxpj0Ht3fDnX5n4Zm88Pn+ui9QlERER1bs6LwGgUCgQEBDQELVQIzJrQCAAYPPZG0jKLpa4GiIiovpX55A0f/58LF68GHwubvPW2dsB/dq6QiuAb/Zx3SQiImp66jzcdvjwYezbtw87duxAhw4dYGFhobf/119/rbfiyLTNHhiI/bFZ+PXMDcweGAhvJ2upSyIiIqo3de5JcnBwwPDhw9G3b1+4uLjA3t5e7/WgPvzwQ8hkMsydO1e3raysDBEREXB2doatrS1GjhyJjIwMvfelpKRgyJAhsLa2hpubG15//XVUVVXptdm/fz+6desGpVKJgIAAREZGPnCd9LdurRzRO9AFGq1gbxIRETU5de5JWr16db0XcfLkSSxbtgydOnXS2z5v3jxs27YNmzZtgr29PWbOnIkRI0bgyJEjAKqXIxgyZAg8PDxw9OhRpKWlYdy4cbCwsNCt/p2YmIghQ4Zg+vTpWL9+Pfbs2YPJkyejRYsWCA8Pr/dzaW7mDAzEobhs/Bx9HTMHBMDLkb1JRETUNNS5JwkAqqqq8Oeff2LZsmUoLCwEAKSmpqKoqKjOxyoqKsLYsWPx3XffwdHRUbe9oKAAK1euxOeff44BAwYgJCQEq1evxtGjR3Hs2DEAwB9//IFLly5h3bp16NKlC5588km8//77+Oabb1BRUQEAWLp0Kfz8/PDZZ5+hffv2mDlzJkaNGoUvvvjiQU6dbtPd1wmP+TujSiuwZH+C1OUQERHVmzqHpOTkZAQHB+OZZ55BREQEsrKyAAAfffQRXnvttToXEBERgSFDhmDQoEF626Ojo1FZWam3vV27dmjVqhWioqIAAFFRUQgODoa7u7uuTXh4ONRqNWJiYnRtbj92eHi47hiGlJeXQ61W673ozuYMrL7TbeOpa0jNL5W4GiIiovpR55A0Z84cdO/eHXl5ebCystJtHz58OPbs2VOnY23YsAGnT5/GokWLau1LT0+HQqGAg4OD3nZ3d3ekp6fr2twakGr21+y7Wxu1Wo3SUsNf6IsWLdKbZ+Xt7V2n82puerR2Rg8/J1RqBJYeYG8SERE1DXUOSYcOHcK//vUvKBQKve2+vr64cePGfR/n2rVrmDNnDtavXw9LS8u6ltGgFi5ciIKCAt3r2rVrUpdk8mp6kzacuIb0gjKJqyEiInp4dQ5JWq0WGo2m1vbr16/Dzu7+H3YaHR2NzMxMdOvWDebm5jA3N8eBAwfw1VdfwdzcHO7u7qioqEB+fr7e+zIyMuDh4QEA8PDwqHW3W83P92qjUqn0esJupVQqoVKp9F50d6H+zuju44gKjRbLDrI3iYiIGr86h6TBgwfjyy+/1P0sk8lQVFSEd955p06PKhk4cCAuXLiAs2fP6l7du3fH2LFjdX+2sLDQG8KLjY1FSkoKQkNDAQChoaG4cOECMjMzdW12794NlUqFoKAgXZvbhwF3796tOwbVD5lMhjmDqnuTfjiegsxC9iYREVHjJhN1XDr7+vXrCA8PhxACcXFx6N69O+Li4uDi4oKDBw/Czc3tgYvp168funTpogthM2bMwPbt2xEZGQmVSoVZs2YBAI4ePQqgegmALl26wNPTEx9//DHS09Px0ksvYfLkyXpLAHTs2BERERF4+eWXsXfvXsyePRvbtm277yUA1Go17O3tUVBQwF6luxBCYMSSoziTko/JYX7419AgqUsiIqJm7GG/v+u8TpKXlxfOnTuHDRs24Pz58ygqKsKkSZMwduzYOw5fPagvvvgCZmZmGDlyJMrLyxEeHo5vv/1Wt18ul2Pr1q2YMWMGQkNDYWNjg/Hjx+O9997TtfHz88O2bdswb948LF68GF5eXlixYgXXSGoAMpkMswcGYuLqk1h3PBlPBnsgxMdJ6rKIiIgeSJ17kpoj9iTdPyEExq06gUNx2bCykOO7cd0RFugidVlERNQMPez3d51D0tq1a++6f9y4cXUuwtQxJNVNaYUG09ZF4+CVLCjkZvjvC10R3sFD6rKIiKiZMXpIunVVbACorKxESUkJFAoFrK2tkZubW+ciTB1DUt2VV2kwd8NZ7LiYDrmZDJ8+2wnDu3pJXRYRETUjD/v9Xee72/Ly8vReRUVFiI2NRVhYGH788cc6F0BNk9Jcjv8+3xUju3lBoxWY99M5fH8sWeqyiIiI7tsDPbvtdoGBgfjwww8xZ86c+jgcNRHmcjN8MqoTJjzmCwB4a/NFfLs/XtqiiIiI7lO9hCQAMDc3R2pqan0djpoIMzMZ3nk6CDP7BwAAPt4Zi493XgbvFyAiIlNX5yUAtmzZovezEAJpaWn4+uuv0atXr3orjJoOmUyG18LbwtbSHB/uuIxv9yegqLwK7z7dAWZmMqnLIyIiMqjOIWnYsGF6P8tkMri6umLAgAH47LPP6qsuaoKm9/WHrdIcb/3vItZGJaOorAofj+oEc3m9dWgSERHVmzqHJK1W2xB1UDPxYk8f2Fma49WN5/DrmRsorqjCV893hdJcLnVpREREevhPeDK6Z7q0xNIXQ6AwN8OumAxMXnMKJRVVUpdFRESkp87rJL366qv33fbzzz+vc0GmiOskNYwj8dmYsvYUSio06O7jiJUTHoG9lYXUZRERURNh9Ge3nTlzBmfOnEFlZSXatm0LALhy5Qrkcjm6deumayeTcUIu3V2vABd8P6kHJq4+gVPJeXh++TF8P+lRONsqpS6NiIio7sNtTz/9NPr06YPr16/j9OnTOH36NK5du4b+/ftj6NCh2LdvH/bt24e9e/c2RL3UxIT4OGLD1FC42CpwKU2N0cuikFZQKnVZREREdR9ua9myJf744w906NBBb/vFixcxePDgJrlWEofbGt7VrCK8uOI4UgvK0NLBCj9M6QEfZxupyyIiokbM6I8lUavVyMrKqrU9KysLhYWFdS6ACABau9pi04zH4OtsjRv5pXh2aRRi0/n7RERE0qlzSBo+fDgmTpyIX3/9FdevX8f169fxyy+/YNKkSRgxYkRD1EjNREsHK2ycHop2HnbILCzHc8ujcO5avtRlERFRM1Xn4baSkhK89tprWLVqFSorKwFUP5Jk0qRJ+OSTT2Bj0/SGSDjcZlz5JRWYsPokzl7Lh63SHCvGd0fP1s5Sl0VERI3Mw35/1zkk1SguLkZCQgIAwN/fv0mGoxoMScZXVF6FKWtOIepqDpTmZlj6Ygj6t3OTuiwiImpEjD4nqUZaWhrS0tIQGBgIGxsbPrCU6pWt0hyrJz6Cge3cUF6lxZS1p7D1fNO7KYCIiExXnUNSTk4OBg4ciDZt2uCpp55CWloaAGDSpEmYP39+vRdIzZelhRxLXwrB0509UaUVmP3jGWw8eU3qsoiIqJmoc0iaN28eLCwskJKSAmtra9325557Djt37qzX4ogs5Gb48rkueP5Rb2gFsOCX81h5OFHqsoiIqBmo84rbf/zxB3bt2gUvLy+97YGBgUhOTq63wohqyM1k+GB4MOwsLbD84FW8v/USisqqMHtgAFd2JyKiBlPnnqTi4mK9HqQaubm5UCr5OAlqGDKZDAufbIf5j7cBAHzx5xX8Z9tfnAtHREQNps4hqXfv3li7dq3uZ5lMBq1Wi48//hj9+/ev1+KIbiWTyTBrYCDeHhoEAFhxOBELf70AjZZBiYiI6l+dh9s+/vhjDBw4EKdOnUJFRQUWLFiAmJgY5Obm4siRIw1RI5Gel8P8YGtpjjd/OY8NJ6+hqLwKXzzXBRbyB75Zk4iIqJY6f6t07NgRV65cQVhYGJ555hkUFxdjxIgROHPmDPz9/RuiRqJaRnf3xn+f7wYLuQxbz6dh2vfRKKvUSF0WERE1IXVaTLKyshJPPPEEli5disDAwIasy6RwMUnTtS82E9O/j0Z5lRY9WzthxfhHYKuscwcpERE1QUZdTNLCwgLnz5+v84cQNZT+bd2w9uVHYas0x7GruRi74jjySyqkLouIiJqAOg+3vfjii1i5cmVD1EL0QHq0dsYPU3rA0doC567l47llx5CpLpO6LCIiauTqPC5RVVWFVatW4c8//0RISEitZ7Z9/vnn9VYc0f3q5OWAn6aF4sUVxxGbUYjRy6KwbnIPeDnWXq6CiIjoftzXnKTz58+jY8eOMDMzu+tt/jKZDHv37q3XAk0B5yQ1Hik5JXhhxTFczytFC3tLrJvcA/6utlKXRUREEnjY7+/7CklyuRxpaWlwc3ND69atcfLkSTg7Oz9QwY0RQ1Ljkl5QhhdXHkd8ZhGcbRRYO+lRdPC0l7osIiIyMqNM3HZwcEBiYvXzspKSkqDVauv8QUTG4mFviZ+m9kQHTxVyiiswZvkxRCfnSl0WERE1Mvc1J2nkyJHo27cvWrRoAZlMhu7du0Mulxtse/Xq1XotkOhBONsq8ePUnpgUeRInk/Lw4ooT+G5cd4QFukhdGhERNRL3vU7Szp07ER8fj9mzZ+O9996DnZ2dwXZz5syp1wJNAYfbGq/SCg2mfn8Kh+KyoZCb4esXumJwBw+pyyIiIiMwypykW02cOBFfffXVHUNSU8SQ1LiVV2kw58ez2BmTDrmZDJ8+2wnDu3pJXRYRETUwoy4mCQCrV69uVgGJGj+luRxfv9AVI7t5QaMVeHXjOXx/LFnqsoiIyMTxiaDULJjLzfDJqE4YH+oDIYC3Nl/Ekv0JUpdFREQmjCGJmg0zMxne/UcHzOwfAAD4aOdlfLzzMuo44kxERM0EQxI1KzKZDK+Ft8WbT7YDAHy7PwHvbImBVsugRERE+hiSqFma3tcf/x7WETIZsDYqGa/9fA5VGq7/RUREf2NIombrxZ4++GJ0F8jNZPj19A1E/HAa5VUaqcsiIiITwZBEzdqwri2xZGw3KORm2BWTgclrTqGkokrqsoiIyAQwJFGzN7iDB1ZPfATWCjkOxWVj3MoTKCitlLosIiKSGEMSEYBeAS74flIPqCzNcSo5Dy98dww5ReVSl0VERBJiSCK6KcTHERumhsLFVoGYVDVGL4tCWkGp1GUREZFEGJKIbhHkqcJP00LRwt4SCVnFeHZpFJJziqUui4iIJMCQRHQbf1dbbJoeCl9na1zPK8WzS6NwJaNQ6rKIiMjIGJKIDPBytMbG6aFo52GHzMJyjF4WhXPX8qUui4iIjIghiegO3OwssWFqT3T2dkB+SSXGrjiO41dzpC6LiIiMhCGJ6C4crBVYP7kHQls7o6i8CuNWncC+y5lSl0VEREbAkER0D7ZKc6ye+AgGtnNDeZUWU9aewrbzaVKXRUREDYwhieg+WFrIsfSlEDzd2RNVWoFZP57GxpPXpC6LiIgaEEMS0X2ykJvhy+e64PlHvaEVwIJfzmPl4USpyyIiogYiaUhasmQJOnXqBJVKBZVKhdDQUOzYsUO3v6ysDBEREXB2doatrS1GjhyJjIwMvWOkpKRgyJAhsLa2hpubG15//XVUVek/e2v//v3o1q0blEolAgICEBkZaYzToyZIbibDB8ODMaW3HwDg/a2XsPjPOAghJK6MiIjqm6QhycvLCx9++CGio6Nx6tQpDBgwAM888wxiYmIAAPPmzcPvv/+OTZs24cCBA0hNTcWIESN079doNBgyZAgqKipw9OhRrFmzBpGRkXj77bd1bRITEzFkyBD0798fZ8+exdy5czF58mTs2rXL6OdLTYNMJsM/n2qPVx9vAwD44s8r+GD7XwxKRERNjEyY2N/sTk5O+OSTTzBq1Ci4urrihx9+wKhRowAAly9fRvv27REVFYWePXtix44dGDp0KFJTU+Hu7g4AWLp0Kd544w1kZWVBoVDgjTfewLZt23Dx4kXdZ4wZMwb5+fnYuXPnfdWkVqthb2+PgoICqFSq+j9parRWHU7Ee1svAQCef9Qb/x4WDLmZTOKqiIgIePjvb5OZk6TRaLBhwwYUFxcjNDQU0dHRqKysxKBBg3Rt2rVrh1atWiEqKgoAEBUVheDgYF1AAoDw8HCo1Wpdb1RUVJTeMWra1ByD6GG8HOaHj0d2gpkM+PHENczZcAaVGq3UZRERUT0wl7qACxcuIDQ0FGVlZbC1tcVvv/2GoKAgnD17FgqFAg4ODnrt3d3dkZ6eDgBIT0/XC0g1+2v23a2NWq1GaWkprKysatVUXl6O8vK/nwCvVqsf+jyp6Rr9iDdslOaY+9MZbD2fhpIKDb4d2w2WFnKpSyMioocgeU9S27ZtcfbsWRw/fhwzZszA+PHjcenSJUlrWrRoEezt7XUvb29vSesh0zekUwssH9cdSnMz7L2ciQmrT6CovOrebyQiIpMleUhSKBQICAhASEgIFi1ahM6dO2Px4sXw8PBARUUF8vPz9dpnZGTAw8MDAODh4VHrbrean+/VRqVSGexFAoCFCxeioKBA97p2jevh0L31b+uGtS8/ClulOY5dzcXYFceRX1IhdVlERPSAJA9Jt9NqtSgvL0dISAgsLCywZ88e3b7Y2FikpKQgNDQUABAaGooLFy4gM/Pvx0Ts3r0bKpUKQUFBuja3HqOmTc0xDFEqlbplCWpeRPejR2tn/DClBxysLXDuWj6eW3YMmYVlUpdFREQPQNKQtHDhQhw8eBBJSUm4cOECFi5ciP3792Ps2LGwt7fHpEmT8Oqrr2Lfvn2Ijo7GxIkTERoaip49ewIABg8ejKCgILz00ks4d+4cdu3ahX/961+IiIiAUqkEAEyfPh1Xr17FggULcPnyZXz77bfYuHEj5s2bJ+WpUxPWycsBG6eFws1OidiMQoxeGoXreSVSl0VERHUkaUjKzMzEuHHj0LZtWwwcOBAnT57Erl278PjjjwMAvvjiCwwdOhQjR45Enz594OHhgV9//VX3frlcjq1bt0IulyM0NBQvvvgixo0bh/fee0/Xxs/PD9u2bcPu3bvRuXNnfPbZZ1ixYgXCw8ONfr7UfLRxt8Om6aHwcrRCUk4Jnl0ahYSsIqnLIiKiOjC5dZJMEddJogeVXlCGsSuOISGrGM42Cqyd9Cg6eNpLXRYRUbPQZNZJImqKPOwtsXFaKDp4qpBTXIExy48hOjlP6rKIiOg+MCQRNTBnWyV+nNoT3X0cUVhWhZdWHsfhuGypyyIiontgSCIyApWlBdZOehS9A11QUqHBy5En8UdMutRlERHRXTAkERmJtcIcK8Z3xxMdPFCh0WLG+tPYfOaG1GUREdEdMCQRGZHSXI6vX+iKEd1aQqMVmLfxLNYdS5a6LCIiMoAhicjIzOVm+HRUZ4wP9YEQwL82X8SS/QlSl0VERLdhSCKSgJmZDO/+owMi+vsDAD7aeRmf7LoMrshBRGQ6GJKIJCKTyfB6eDu8+WQ7AMA3+xLw7pYYaLUMSkREpoAhiUhi0/v64/1hHSGTAWuikvHaz+dQpdFKXRYRUbPHkERkAl7q6YMvRneB3EyGX0/fwMwfzqC8SiN1WUREzRpDEpGJGNa1JZaM7QaF3Aw7Y9Ixec0plFRUSV0WEVGzxZBEZEIGd/DAqgmPwMpCjkNx2Ri38gTUZZVSl0VE1CwxJBGZmLBAF6yb3AMqS3OcSs7D88uPIaeoXOqyiIiaHYYkIhMU4uOIH6f2hLONAjGpaoxeFoX0gjKpyyIialYYkohMVAdPe2ycHooW9pZIyCrGqKVHkZxTLHVZRETNBkMSkQnzd7XFpumh8HW2xvW8Ujy7NApXMgqlLouIqFlgSCIycV6O1tg4PRRt3e2QWViO0cuicP56vtRlERE1eQxJRI2Am50lfprWE529HZBfUokXvjuO41dzpC6LiKhJkwk+LOqe1Go17O3tUVBQAJVKJXU51IwVlVdh8pqTOHY1FzIZ0MFThd6Brugd6IIQH0cozeVSl0hEZDIe9vubIek+MCSRKSmr1GD+pnPYdj5Nb7uVhRw9WjvpQlOgmy1kMplEVRIRSY8hyQgYksgUZRaW4Uh8Ng5dycah+GxkFeqvpeSuUuoCU68AF7jYKiWqlIhIGgxJRsCQRKZOCIHYjEJdYDp+NQflVfoPye3gqUJYoAv6BLoixMcRlhYcmiOipo0hyQgYkqixKavU4FRSHg7FZ+HQlWxcSlPr7be0MMOjfs7oE+iC3oGuaOPOoTkianoYkoyAIYkau6zCchxNyMbBK9k4FJeFzNuG5tzslAgLdEHvQBeEBbjC1Y5Dc0TU+DEkGQFDEjUlQgjEZRbh4JUsHIrLxvHEHJRV6g/NtW+hQp9AF4QFuuARXycOzRFRo8SQZAQMSdSUlVVqcDo5DwfjsnE4PgsXb+gPzSnNzfConxN63xyaa+dhx6E5ImoUGJKMgCGJmpOconIcjs/G4bhsHIrLRrpa/8G6rnZKhAXUDM25wE1lKVGlRER3x5BkBAxJ1FwJIRCfWYRDcdVzmY5dzUVppUavTTsPO10v06N+HJojItPBkGQEDElE1cqrNDidnI9DcdXzmS6mFuDWv0EU5mZ41Ld6aC4s0AXtPVQwM+PQHBFJgyHJCBiSiAzLLa6oXtDyZmhKK9AfmnOxVSAswAVhNxe1dOfQHBEZEUOSETAkEd2bEAIJWcU4FJeFw3HZiLqag5IK/aG5Nu62ulXAe/g5w0rBoTkiajgMSUbAkERUdxVVWpxOybs5ATwL52/cNjQnN0N3X0ddaApqwaE5IqpfDElGwJBE9PDyiitwNCFHNzR3I79Ub7+zjQK9bt411zvQFR72HJojoofDkGQEDElE9UsIgcTsYt1dc1EJOSi+bWgu0M1W96y5Hq2dYK0wl6haImqsGJKMgCGJqGFVarQ4k/L3XXPnr+dDe8vfTBZyGUJ8qofm+gS6ooMnh+aI6N4YkoyAIYnIuPJLaobmsnHwSlatoTlHawv0CqjuZQoLdIGng5VElRKRKWNIMgKGJCLpCCGQlFOCw3FZOBiXjaiEHBSVV+m18Xe1qe5lalN915yNkkNzRMSQZBQMSUSmo1Kjxblr+Th4cz7TuWu1h+a6tXLUTQDv2NIecg7NETVLDElGwJBEZLoKSisRlVD9nLmDcVm4lqs/NOdgbYFe/i66VcC9HK0lqpSIjI0hyQgYkogaj+Scv++aOxqfg8LbhuZau9joepl6+jvDlkNzRE0WQ5IRMCQRNU5VGi3OXc+/GZqycfZaPjS3jM2Zm/09NBcW6IJOXg4cmiNqQhiSjIAhiahpUJdVIurmgpaH47KRlFOit9/eygK9ApwRFlC9Cri3E4fmiBozhiQjYEgiappSckpwKL46MB2Jz4a6TH9ozs/FBmE3VwEP9XeGnaWFRJUS0YNgSDIChiSipq9Ko8X5GwW6Z82dTtEfmpObydCtlUN1L1MbF3RqaQ9zuZmEFRPRvTAkGQFDElHzU3hzaO5wfPV8psTsYr39dpbm1XfNtXFB7wBXtHLm0ByRqWFIMgKGJCK6lltyMzBl4Uh8DgpKK/X2+zhbV08AD3DFYwHOUHFojkhyDElGwJBERLfSaAUu3CjAoSvVz5o7nZKHqtuG5rp4OyAswAV92rigs5cDh+aIJMCQZAQMSUR0N0XlVTh28665Q/HZuJp129Cc0hyh/s7o3cYVfQJd4ONsI1GlRM0LQ5IRMCQRUV1czyupngAeX33XXH6J/tCct5NV9bPmAl0Q6u8CeysOzRE1BIYkI2BIIqIHpdEKxKQWVD825UoWTqfkoVLz91+7ZjKgs7cDegdWr83UxdsBFhyaI6oXDElGwJBERPWluLwKxxNzcPBK9STwhNuG5mxrhuZuPjrF19kaMhlXASd6EAxJRsCQREQNJTW/FIdvPpz3SHw28m4bmvNytNIFpsf8neFgrZCoUqLGhyHJCBiSiMgYtFqBmFQ1DsVn4dCVbJxKzq01NBfs5YA+N0NT11YcmiO6G4YkI2BIIiIplFRU4XhiLg7dHJqLyyzS22+jkN8cmquez+TnYsOhOaJbPOz3t6T/BFm0aBEeeeQR2NnZwc3NDcOGDUNsbKxem7KyMkRERMDZ2Rm2trYYOXIkMjIy9NqkpKRgyJAhsLa2hpubG15//XVUVek/g2n//v3o1q0blEolAgICEBkZ2dCnR0T0UKwV5ujf1g1vPx2E3a/2xbGFA/HJqE74R2dPONsoUFyhwZ9/ZeKdLTEY8NkBhH20D2/+ch5bz6cir7hC6vKJGj1Je5KeeOIJjBkzBo888giqqqrwz3/+ExcvXsSlS5dgY1O9jsiMGTOwbds2REZGwt7eHjNnzoSZmRmOHDkCANBoNOjSpQs8PDzwySefIC0tDePGjcOUKVPwwQcfAAASExPRsWNHTJ8+HZMnT8aePXswd+5cbNu2DeHh4feskz1JRGRqtFqBS2lqHIrLxuH4LJxMzEOFRqvbL5MBnVraI+zm0Fy3Vo5QmHNojpqXJjXclpWVBTc3Nxw4cAB9+vRBQUEBXF1d8cMPP2DUqFEAgMuXL6N9+/aIiopCz549sWPHDgwdOhSpqalwd3cHACxduhRvvPEGsrKyoFAo8MYbb2Dbtm24ePGi7rPGjBmD/Px87Ny58551MSQRkakrrdDgeGLOzQf0ZiM2o1Bvv7VCjp6t/75rzt+VQ3PU9D3s97d5A9T0wAoKCgAATk5OAIDo6GhUVlZi0KBBujbt2rVDq1atdCEpKioKwcHBuoAEAOHh4ZgxYwZiYmLQtWtXREVF6R2jps3cuXMN1lFeXo7y8nLdz2q1ur5OkYioQVgp5OjX1g392roBADLUZTcDUxYOx2cju6gCey9nYu/lTABAC3tLXWDqFeACJxveNUd0O5MJSVqtFnPnzkWvXr3QsWNHAEB6ejoUCgUcHBz02rq7uyM9PV3X5taAVLO/Zt/d2qjVapSWlsLKykpv36JFi/D//t//q7dzIyIyNneVJUaGeGFkiBe0WoHL6YXVj02Jy8aJpFykFZRh46nr2HjqOmQyoKOnffUDegNdEOLjCKW5XOpTIJKcyYSkiIgIXLx4EYcPH5a6FCxcuBCvvvqq7me1Wg1vb28JKyIienBmZjIEeaoQ5KnCtL7+KKvU4ERiri40XU4vxIUbBbhwowDf7k+AlYUcPVs7Iezmo1MC3Gw5NEfNkkmEpJkzZ2Lr1q04ePAgvLy8dNs9PDxQUVGB/Px8vd6kjIwMeHh46NqcOHFC73g1d7/d2ub2O+IyMjKgUqlq9SIBgFKphFKprJdzIyIyNZYWcvRp44o+bVwBAJnqMhyOz765qGU2sovKsS82C/tiswAAHirLmxPAXRAW4AJnW/79SM2DpCFJCIFZs2bht99+w/79++Hn56e3PyQkBBYWFtizZw9GjhwJAIiNjUVKSgpCQ0MBAKGhofjPf/6DzMxMuLlVj8Xv3r0bKpUKQUFBujbbt2/XO/bu3bt1xyAias7cVJYY0c0LI7p5QYjqobmaVcBPJOYiXV2Gn6Ov4+fo6wCADp4q3QN6Q3w5NEdNl6R3t73yyiv44Ycf8L///Q9t27bVbbe3t9f18MyYMQPbt29HZGQkVCoVZs2aBQA4evQogL+XAPD09MTHH3+M9PR0vPTSS5g8eXKtJQAiIiLw8ssvY+/evZg9ezaXACAiuoeySg1OJeXhUFwWDsZl4680/RtZLC3M0MPv77vm2rhzaI5MR6NeAuBO/yOtXr0aEyZMAFC9mOT8+fPx448/ory8HOHh4fj22291Q2kAkJycjBkzZmD//v2wsbHB+PHj8eGHH8Lc/O+Osv3792PevHm4dOkSvLy88NZbb+k+414YkoiIqmUVluNIfHUv0+G4bGQWluvtd7NT6lYA7xXgAlc7Ds2RdBp1SGosGJKIiGoTQuBKRpFuAvjxxByUVWr12gS1UOl6mbr7OsLSgkNzZDwMSUbAkEREdG9llRpEJ+fh0M31mWJS9YfmlOZmeNTPCX0CXdG7jQvauttxaI4aFEOSETAkERHVXXZR9dBcTWjKUOsPzbnaKdE7wAW921QPzbnZWUpUKTVVDElGwJBERPRwhBCIzyzCwZuB6fjVXJRWavTatPOwQ582rggLcMGjfk4cmqOHxpBkBAxJRET1q7zq76G5w3HZuJhagFu/jRTmZujh54SwgOr5TO1bcGiO6o4hyQgYkoiIGlZOUTmOJOTg8M1J4GkFZXr7XWyVCAtw1t0556bi0BzdG0OSETAkEREZjxACCVnFurvmjl3NQUmF/tBcW3e76rvm2rjiUV8nWCk4NEe1MSQZAUMSEZF0Kqq0OJ2SpwtNF27cNjQnN8Mjfo7oHVg9nymohQpmZhyaI4Yko2BIIiIyHXnFFTiSkI1DV6ongafeNjTnbKNA2M3nzPUOdIWHPYfmmiuGJCNgSCIiMk1CCFzNLsbhm3fNRSXkoPi2obk27rYIC6hem6mHnxOsFSbxbHcyAoYkI2BIIiJqHCqqtDh7LV/3rLnz1/NrDc2F+DiidxsX9Al05dBcE8eQZAQMSUREjVN+SQWOJuRUh6Yr2biRX6q338lGgV4BLjcfneKCFvZWElVKDYEhyQgYkoiIGj8hBJJySnSB6djVHBSVV+m1CXCz1QWmHn7OsFFyaK4xY0gyAoYkIqKmp1JTMzRXPZ/p3LV8aG/5RrSQy6qH5m6uzdTB0x5yDs01KgxJRsCQRETU9BWUVCLqajYOxmXj4JUsXM/TH5pztLbAYwEu6BPogrBAV7R04NCcqWNIMgKGJCKi5kUIgeScEhyKz8ahK9V3zRXeNjTX2tUGfW72MvVo7QxbDs2ZHIYkI2BIIiJq3qo0Wpy7no+DV7JxOD4bZ6/lQ3PL2Jy5mQzdfBx1vUzBLTk0ZwoYkoyAIYmIiG5VUFqJqIQcHI6vXgU8OadEb7+9lQXCAlwQdnMSuJejtUSVNm8MSUbAkERERHeTklOCQ/FZOHQlG0cSslFYpj805+dic/OuOVf0bO0EO0sLiSptXhiSjIAhiYiI7lf10FyBbhXwMwaG5rq2ctDdNdfJy4FDcw2EIckIGJKIiOhBqcsqcSwhB4fiquczJWYX6+1XWZrfXNCyOjR5O3Forr4wJBkBQxIREdWXa7klNwNTFg7HZUN929Ccr7M1ege6IizQBaH+zlBxaO6BMSQZAUMSERE1BI1W4Pz1/JtDc9k4nZKHqluG5uRmMnTxdtDNZ+rsZQ9zuZmEFTcuDElGwJBERETGUFRedXNorvquuau3Dc3ZWZrjMX9n9A50RZ9AV7Ry5tDc3TAkGQFDEhERSeF6Xomul+lIQjbySyr19rdystb1MoX6O8PeikNzt2JIMgKGJCIikppGK3DxRoGulyk6WX9ozkwGdPF2QFigK/oEuqCztwMsmvnQHEOSETAkERGRqSkqr8Lxqzm6B/QmZN02NKc0R6i/s66nycfZGjJZ81pqgCHJCBiSiIjI1KXml+JwXDYOxmXhSHw28m4bmvN2skJYQHUv02P+LrC3bvpDcwxJRsCQREREjYlWKxCTqsbBuCwcistCdHIeKjX6Q3OdvBx0z5rr2qppDs0xJBkBQxIRETVmxeVVOJGYi4Nx1WszxWUW6e23VZqjZ+uaoTkX+LnYNImhOYYkI2BIIiKipiStoLR6Qcubq4DnFlfo7W/pYKWby9QrwBkO1gqJKn04DElGwJBERERNlVYrcClNrZsAfiopDxUarW6/7ObQXO+A6l6mrq0coTBvHENzDElGwJBERETNRUlF9dBcTWi6kqE/NGetkCP05tBcWKAr/F1Nd2iOIckIGJKIiKi5Si8ow+H46sB0JD4b2UX6Q3Oe9pa6Z831CnCBk43pDM0xJBkBQxIREVH10Nxf6WrdfKYTSbmoqNIfmgtuaY+wgOr5TCE+0g7NMSQZAUMSERFRbaUVGpxIysXhm6uAX04v1NtvrZCjh59T9bPm2rjA39XWqENzDElGwJBERER0b5nqmqG56ld2Ubnefg+VZfVdc21c0cvfGc62ygathyHJCBiSiIiI6kYIgcvphbpnzZ1IzEX5LUNzANCxpQq9A13RO9AFIT6OUJrL67UGhiQjYEgiIiJ6OGWVGpxMytX1Mv2Vptbbb6s0x8n/GwQrRf0FpYf9/javt0qIiIiI7sDSQn6z18gVAJBZWIYjtwzNeTla1WtAqg8MSURERGR0bnaWGN7VC8O7ekEIUeuBvKagcSyZSURERE2WTCYzqfWVajAkERERERnAkERERERkAEMSERERkQEMSUREREQGMCQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZwJBEREREZABDEhEREZEBDElEREREBjAkERERERlgLnUBjYEQAgCgVqslroSIiIjuV833ds33eF0xJN2HwsJCAIC3t7fElRAREVFdFRYWwt7evs7vk4kHjVfNiFarRWpqKuzs7CCTyR7qWGq1Gt7e3rh27RpUKlU9Vdj08bo9GF63B8Pr9uB47R4Mr9uDudd1E0KgsLAQnp6eMDOr+wwj9iTdBzMzM3h5edXrMVUqFf9HeAC8bg+G1+3B8Lo9OF67B8Pr9mDudt0epAepBiduExERERnAkERERERkAEOSkSmVSrzzzjtQKpVSl9Ko8Lo9GF63B8Pr9uB47R4Mr9uDaejrxonbRERERAawJ4mIiIjIAIYkIiIiIgMYkoiIiIgMYEgiIiIiMoAhyci++eYb+Pr6wtLSEj169MCJEyekLklSBw8exNNPPw1PT0/IZDJs3rxZb78QAm+//TZatGgBKysrDBo0CHFxcXptcnNzMXbsWKhUKjg4OGDSpEkoKioy4lkY16JFi/DII4/Azs4Obm5uGDZsGGJjY/XalJWVISIiAs7OzrC1tcXIkSORkZGh1yYlJQVDhgyBtbU13Nzc8Prrr6OqqsqYp2JUS5YsQadOnXSLzoWGhmLHjh26/bxm9+fDDz+ETCbD3Llzddt47Qx79913IZPJ9F7t2rXT7ed1u7MbN27gxRdfhLOzM6ysrBAcHIxTp07p9hvtu0GQ0WzYsEEoFAqxatUqERMTI6ZMmSIcHBxERkaG1KVJZvv27eL//u//xK+//ioAiN9++01v/4cffijs7e3F5s2bxblz58Q//vEP4efnJ0pLS3VtnnjiCdG5c2dx7NgxcejQIREQECCef/55I5+J8YSHh4vVq1eLixcvirNnz4qnnnpKtGrVShQVFenaTJ8+XXh7e4s9e/aIU6dOiZ49e4rHHntMt7+qqkp07NhRDBo0SJw5c0Zs375duLi4iIULF0pxSkaxZcsWsW3bNnHlyhURGxsr/vnPfwoLCwtx8eJFIQSv2f04ceKE8PX1FZ06dRJz5szRbee1M+ydd94RHTp0EGlpabpXVlaWbj+vm2G5ubnCx8dHTJgwQRw/flxcvXpV7Nq1S8THx+vaGOu7gSHJiB599FERERGh+1mj0QhPT0+xaNEiCasyHbeHJK1WKzw8PMQnn3yi25afny+USqX48ccfhRBCXLp0SQAQJ0+e1LXZsWOHkMlk4saNG0arXUqZmZkCgDhw4IAQovoaWVhYiE2bNuna/PXXXwKAiIqKEkJUh1MzMzORnp6ua7NkyRKhUqlEeXm5cU9AQo6OjmLFihW8ZvehsLBQBAYGit27d4u+ffvqQhKv3Z298847onPnzgb38brd2RtvvCHCwsLuuN+Y3w0cbjOSiooKREdHY9CgQbptZmZmGDRoEKKioiSszHQlJiYiPT1d75rZ29ujR48eumsWFRUFBwcHdO/eXddm0KBBMDMzw/Hjx41esxQKCgoAAE5OTgCA6OhoVFZW6l23du3aoVWrVnrXLTg4GO7u7ro24eHhUKvViImJMWL10tBoNNiwYQOKi4sRGhrKa3YfIiIiMGTIEL1rBPD37V7i4uLg6emJ1q1bY+zYsUhJSQHA63Y3W7ZsQffu3fHss8/Czc0NXbt2xXfffafbb8zvBoYkI8nOzoZGo9H7ZQcAd3d3pKenS1SVaau5Lne7Zunp6XBzc9Pbb25uDicnp2ZxXbVaLebOnYtevXqhY8eOAKqviUKhgIODg17b26+boetas6+punDhAmxtbaFUKjF9+nT89ttvCAoK4jW7hw0bNuD06dNYtGhRrX28dnfWo0cPREZGYufOnViyZAkSExPRu3dvFBYW8rrdxdWrV7FkyRIEBgZi165dmDFjBmbPno01a9YAMO53g/nDnAgRSSsiIgIXL17E4cOHpS6lUWjbti3Onj2LgoIC/Pzzzxg/fjwOHDggdVkm7dq1a5gzZw52794NS0tLqctpVJ588kndnzt16oQePXrAx8cHGzduhJWVlYSVmTatVovu3bvjgw8+AAB07doVFy9exNKlSzF+/Hij1sKeJCNxcXGBXC6vdedCRkYGPDw8JKrKtNVcl7tdMw8PD2RmZurtr6qqQm5ubpO/rjNnzsTWrVuxb98+eHl56bZ7eHigoqIC+fn5eu1vv26GrmvNvqZKoVAgICAAISEhWLRoETp37ozFixfzmt1FdHQ0MjMz0a1bN5ibm8Pc3BwHDhzAV199BXNzc7i7u/Pa3ScHBwe0adMG8fHx/J27ixYtWiAoKEhvW/v27XVDlcb8bmBIMhKFQoGQkBDs2bNHt02r1WLPnj0IDQ2VsDLT5efnBw8PD71rplarcfz4cd01Cw0NRX5+PqKjo3Vt9u7dC61Wix49ehi9ZmMQQmDmzJn47bffsHfvXvj5+entDwkJgYWFhd51i42NRUpKit51u3Dhgt5fIrt374ZKpar1l1NTptVqUV5ezmt2FwMHDsSFCxdw9uxZ3at79+4YO3as7s+8dvenqKgICQkJaNGiBX/n7qJXr161ljW5cuUKfHx8ABj5u6Hu887pQW3YsEEolUoRGRkpLl26JKZOnSocHBz07lxobgoLC8WZM2fEmTNnBADx+eefizNnzojk5GQhRPVtng4ODuJ///ufOH/+vHjmmWcM3ubZtWtXcfz4cXH48GERGBjYpJcAmDFjhrC3txf79+/Xu7W4pKRE12b69OmiVatWYu/eveLUqVMiNDRUhIaG6vbX3Fo8ePBgcfbsWbFz507h6urapG8tfvPNN8WBAwdEYmKiOH/+vHjzzTeFTCYTf/zxhxCC16wubr27TQheuzuZP3++2L9/v0hMTBRHjhwRgwYNEi4uLiIzM1MIwet2JydOnBDm5ubiP//5j4iLixPr168X1tbWYt26dbo2xvpuYEgysv/+97+iVatWQqFQiEcffVQcO3ZM6pIktW/fPgGg1mv8+PFCiOpbPd966y3h7u4ulEqlGDhwoIiNjdU7Rk5Ojnj++eeFra2tUKlUYuLEiaKwsFCCszEOQ9cLgFi9erWuTWlpqXjllVeEo6OjsLa2FsOHDxdpaWl6x0lKShJPPvmksLKyEi4uLmL+/PmisrLSyGdjPC+//LLw8fERCoVCuLq6ioEDB+oCkhC8ZnVxe0jitTPsueeeEy1atBAKhUK0bNlSPPfcc3pr/fC63dnvv/8uOnbsKJRKpWjXrp1Yvny53n5jfTfIhBCijj1hRERERE0e5yQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZwJBEREREZABDEhEREZEBDElE1Gykp6fj8ccfh42NTa2nr9eYMGEChg0bZtS6iMg0MSQRkdFMmDABMpkMH374od72zZs3QyaTNfjnf/HFF0hLS8PZs2dx5coVg20WL16MyMhI3c/9+vXD3LlzG7w2IjI9DElEZFSWlpb46KOPkJeXZ/TPTkhIQEhICAIDA+Hm5mawjb29/R17mYioeWFIIiKjGjRoEDw8PLBo0aK7tvvll1/QoUMHKJVK+Pr64rPPPrvnsZcsWQJ/f38oFAq0bdsW33//vW6fr68vfvnlF6xduxYymQwTJkwweIxbh9smTJiAAwcOYPHixZDJZJDJZEhKSgIAXLx4EU8++SRsbW3h7u6Ol156CdnZ2brj9OvXD7NmzcLcuXPh6OgId3d3fPfddyguLsbEiRNhZ2eHgIAA7NixQ/eevLw8jB07Fq6urrCyskJgYCBWr159z/MmoobBkERERiWXy/HBBx/gv//9L65fv26wTXR0NEaPHo0xY8bgwoULePfdd/HWW2/pDYPd7rfffsOcOXMwf/58XLx4EdOmTcPEiROxb98+AMDJkyfxxBNPYPTo0UhLS8PixYvvWevixYsRGhqKKVOmIC0tDWlpafD29kZ+fj4GDBiArl274tSpU9i5cycyMjIwevRovfevWbMGLi4uOHHiBGbNmoUZM2bg2WefxWOPPYbTp09j8ODBeOmll1BSUgIAeOutt3Dp0iXs2LEDf/31F5YsWQIXF5f7vLJEVN/4gFsiMpoJEyYgPz8fmzdvRmhoKIKCgrBy5Ups3rwZw4cPR81fR2PHjkVWVhb++OMP3XsXLFiAbdu2ISYmxuCxe/XqhQ4dOmD58uW6baNHj0ZxcTG2bdsGABg2bBgcHBzuGrZurRGo7hHq0qULvvzyS12bf//73zh06BB27dql23b9+nV4e3sjNjYWbdq0Qb9+/aDRaHDo0CEAgEajgb29PUaMGIG1a9cCqJ5I3qJFC0RFRaFnz574xz/+ARcXF6xater+LyoRNRj2JBGRJD766COsWbMGf/31V619f/31F3r16qW3rVevXoiLi4NGozF4vDu9x9DxH9a5c+ewb98+2Nra6l7t2rUDUD3vqUanTp10f5bL5XB2dkZwcLBum7u7OwAgMzMTADBjxgxs2LABXbp0wYIFC3D06NF6r52I7h9DEhFJok+fPggPD8fChQulLqXOioqK8PTTT+Ps2bN6r7i4OPTp00fXzsLCQu99MplMb1vNHX1arRYA8OSTTyI5ORnz5s1DamoqBg4ciNdee80IZ0REhjAkEZFkPvzwQ/z++++IiorS296+fXscOXJEb9uRI0fQpk0byOVyg8e603uCgoIeqkaFQlGr96pbt26IiYmBr68vAgIC9F42NjYP9Xmurq4YP3481q1bhy+//FJv+JCIjIshiYgkExwcjLFjx+Krr77S2z5//nzs2bMH77//Pq5cuYI1a9bg66+/vmuvyuuvv47IyEgsWbIEcXFx+Pzzz/Hrr78+dE+Mr68vjh8/jqSkJGRnZ0Or1SIiIgK5ubl4/vnncfLkSSQkJGDXrl2YOHHiHYcD78fbb7+N//3vf4iPj0dMTAy2bt2K9u3bP1T9RPTgGJKISFLvvfeebripRrdu3bBx40Zs2LABHTt2xNtvv4333nvvjrftA9WTshcvXoxPP/0UHTp0wLJly7B69Wr069fvoep77bXXIJfLERQUBFdXV6SkpMDT0xNHjhyBRqPB4MGDERwcjLlz58LBwQFmZg/+16pCocDChQvRqVMn9OnTB3K5HBs2bHio+onowfHuNiIiIiID2JNEREREZABDEhEREZEBDElEREREBjAkERERERnAkERERERkAEMSERERkQEMSUREREQGMCQRERERGcCQRERERGQAQxIRERGRAQxJRERERAYwJBEREREZ8P8BtZITKLvbaEYAAAAASUVORK5CYII=\n" 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zM3z++ecoKSnB4sWLa3U81fnPf/6D0aNHo0+fPnjqqaeQnZ2NFStWICgoSK+gupuRI0diw4YNGDt2LEaMGIHExESsWrUKgYGB1T5Hu3bt0LdvX7zwwgsoKSnBRx99BBcXF7z22mtGOy4iAJyqgKiu3n77bdGiRQuhUCj0Lt8HcMfpA7766ivRvn17oVKpRMeOHcXq1avFggULqlxufqfnuPVS+5KSEvHqq6+KkJAQYWdnJ2xsbERISIhYuXKl3mNOnz4tIiIihK2trXB1dRXPPvusbtqDWy97F0KIU6dOibFjxwpHR0dhaWkp/P39xRtvvFGj4759GgAhhLh48aJ48MEHdc/Xs2dPsWnTJr02lZfmr1+/Xm97dZfm38m+fftEaGiosLCwEG3atBGrVq2q9n29PeM777wjevbsKRwdHYWVlZXo2LGjePfdd0VpaamujVqtFi+++KJwc3MTkiTpnrMy35IlS6rkudNUBTY2NuLixYtiyJAhwtraWnh4eIgFCxbopm2olJGRIcaPHy+sra2Fk5OTeO6558SpU6eqPOedsglRdaoCIYQ4ceKEiIyMFLa2tsLa2lrcd9994tChQ3ptKqcqOHbsmN72O02hUJ2ffvpJdOzYUahUKhEUFCQ2btwoxo8fLzp27FjlParu/dNqteI///mP8PX1FSqVSnTt2lVs2rRJTJ48udrpDpYsWSKWLl0qfHx8hEqlEv369ROxsbF6z1n5/t+uus8J0Z1IQtTjKEwiIqJbdOnSBW5ublXG29XF5cuX4efnhyVLlmDOnDlGe16iO+GYJyIiMrqysjKo1Wq9bXv37kVsbCwGDhwoTygiI+GYJyIiMrrr168jIiICjz/+OLy9vXH27FmsWrUKnp6eVSaoJGpsWDwREZHROTk5ITQ0FF9++SUyMjJgY2ODESNG4L333tOtyUjUWHHMExEREZEBOOaJiIiIyAAsnoiIiIgMwDFPRqLVapGcnAw7OzujLuNARERE9UcIgfz8fHh7e9d40l4WT0aSnJxslDWhiIiIqOFdvXoVLVu2rFFbFk9GYmdnB6D8zTfG2lBERERU//Ly8uDj46P7Hq8JFk9GUtlVZ29vz+KJiIiokTFkyA0HjBMREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBE1A8VlGrkjEBE1GSyeiJowrVZgWdQ5BC3Yjn//GgchhNyRiIgaPTO5AxBR/SgoUWPW2hhEnU4DAPx4JAnt3GzxdF8/mZMRETVuPPNE1ARdySzEuJUHEXU6DRZKBUZ29gIAvLvlDA5duCFzOiKixo3FE1ETc+D8DTyw4iDOpRXA3U6Ftc/1wvLHumJc1xbQaAWm/3gCV7OK5I5JRNRosXgiaiKEEPjqQCImfX0EuTfL0MXHEX+82BddWzlBkiT8Z1wwgls4ILuoDFP/F42bpRxETkRUGyyeiJqA4jIN5qw/ibc3nYZWAOO7tcRPU3vBw95S18bSXInPnwiFi40FzqTk4bVfTnIAORFRLbB4Imrk0vKK8cgXf+GXE9egkIA3Rgbig4c6w9JcWaWtt6MVVk7sBjOFhD9ik/HF/ksyJCYiatxYPBE1YieSsjFq+QHEXs2Bg5U5vns6DFP6+kGSpDs+JqyNCxaMCgQAvL/tLPady2iouERETQKLJ6JGav3xq3j087+Qnl+CDh622DijD/q2d63RYx/v5YtHuvtAK4AXfzyByzcK6zktEVHTweKJqJFRa7R48494vPrzSZRqtBgS6IEN0/rA18Wmxs8hSRLeGtMJXVs5Iq9Yjan/O47CEnU9piYiajpYPBE1ItmFpZi8+ihWH7wMAHh5cHusejwUtirD57tVmSmx6vFQuNmpcC6tAK+si+UAciKiGmDxRNRIJKTmY/SnB3HwQiasLZT4bGI3zLq/AxSKO49vuhcPe0usejwU5koJ2+JT8emeC0ZMTETUNLF4ImoEtp1KxdiVB5GUVYSWTlb45YXeGBbsZZTnDvV1wtujgwAAS6POYdeZNKM8LxFRU8XiicjE/Rx9Dc9/H42iUg3C27hg44y+CPCyN+prPNqzFR7v1QpCADN/isHFjAKjPj8RUVPC4onIhJ1Ly8frv8UBAB7v1QrfTekJZxuLenmt+SM7oUdrJ+SXqPHsd8eRV1xWL69DRNTYsXgiMlHFZRrM+PEEisu06NfeFW89EARzZf39lbUwU2DlxFB4OVjiUkYhZq+NgVbLAeRERLdj8URkot784zTOpRXA1VaFZQ93qdPA8Jpys1Ph8ydCYWGmwM4z6fho1/l6f00iosaGxRORCdp0MhlrjiZBkoCPHukCNztVg71255aOWDQ2GADwya7z2HYqtcFem4ioMWDxRGRikjKLMO+X8nFO0wa2rfGs4cY0PrQlnu7jBwB4ZV0MzqXlN3gGIiJTxeKJyISUqrV48ae/kV+iRqivE2ZFdJAty7+Hd0Tvti4oLNVg6nfHkVvEAeRERACLJyKT8sGOBN0iv5881hVm9ThA/F7MlAqsmNANLRytcDmzCPM3npItCxGRKWHxRGQi9iSk44v9lwAAix/sjBaOVjInApxtLLB8QlcAwNa4VOTe5NknIiIWT0QmIC2vGK+siwUATA73RWQnT5kT/aNbKyd08LBFqUaLqNOcfZyIiMUTkcw0WoGZP8Ugq7AUgV72mDc8QO5IVYzs7A0A+CM2WeYkRETyk7V42r9/P0aNGgVvb29IkoTffvtNb78QAvPnz4eXlxesrKwQERGB8+f1553JysrCxIkTYW9vD0dHR0yZMgUFBfpLS5w8eRL9+vWDpaUlfHx8sHjx4ipZ1q9fj44dO8LS0hLBwcHYsmWL0Y+XqDqf7rmAw5fKF/tdMaErLM2VckeqYmTn8nX0Dly4gazCUpnTEBHJS9biqbCwECEhIfj000+r3b948WJ88sknWLVqFY4cOQIbGxtERkaiuLhY12bixImIj49HVFQUNm3ahP3792Pq1Km6/Xl5eRgyZAh8fX0RHR2NJUuWYOHChfjiiy90bQ4dOoTHHnsMU6ZMwd9//40xY8ZgzJgxOHWKA2Spfh25lImPdp4DALwzJght3GxlTlS9Nm626ORtD41WcN4nIiJhIgCIX3/9VXdfq9UKT09PsWTJEt22nJwcoVKpxJo1a4QQQpw+fVoAEMeOHdO12bp1q5AkSVy/fl0IIcTKlSuFk5OTKCkp0bWZO3eu8Pf3191/+OGHxYgRI/TyhIWFieeee67G+XNzcwUAkZubW+PHUPOWVVAiwt7dKXznbhKz1v4td5x7+mzvBeE7d5N49PPDckchIjKa2nx/m+yYp8TERKSmpiIiIkK3zcHBAWFhYTh8+DAA4PDhw3B0dET37t11bSIiIqBQKHDkyBFdm/79+8PC4p/FVCMjI5GQkIDs7Gxdm1tfp7JN5etUp6SkBHl5eXo3opoSQmDO+lik5hWjjZsN3h4dJHekexoRXN5191diJtLziu/Rmoio6TLZ4ik1tbxrwMPDQ2+7h4eHbl9qairc3d319puZmcHZ2VmvTXXPcetr3KlN5f7qLFq0CA4ODrqbj4+PoYdIzdjXBy9j19l0WJgpsPyxrrBRmckd6Z58nK3RtZUjhAC2xKXIHYeISDYmWzyZunnz5iE3N1d3u3r1qtyRqJGIu5aL97aeAQC8PiIAnbwdZE5Uc6Mqr7o7yeKJiJovky2ePD3L57lJS9OfVyYtLU23z9PTE+np6Xr71Wo1srKy9NpU9xy3vsad2lTur45KpYK9vb3ejehe8ovLMGPNCZRpBCI7eeCJXr5yRzLIiM5ekCQg+ko2rufclDsOEZEsTLZ48vPzg6enJ3bt2qXblpeXhyNHjiA8PBwAEB4ejpycHERHR+va7N69G1qtFmFhYbo2+/fvR1nZPzMjR0VFwd/fH05OTro2t75OZZvK1yEyBiEE/u/XU7iSWYQWjlZYPD4EkiTJHcsgHvaW6NnaGQCw+STnfCKi5knW4qmgoAAxMTGIiYkBUD5IPCYmBklJSZAkCTNnzsQ777yDjRs3Ii4uDpMmTYK3tzfGjBkDAAgICMDQoUPx7LPP4ujRozh48CBmzJiBRx99FN7e5d0LEyZMgIWFBaZMmYL4+HisXbsWH3/8MWbPnq3L8fLLL2Pbtm1YunQpzp49i4ULF+L48eOYMWNGQ78l1IStP34NG2OToVRI+OSxLnCwNpc7Uq2MCin/u7WJXXdE1FzV27V/NbBnzx4BoMpt8uTJQojy6QreeOMN4eHhIVQqlRg8eLBISEjQe47MzEzx2GOPCVtbW2Fvby+eeuopkZ+fr9cmNjZW9O3bV6hUKtGiRQvx3nvvVcmybt060aFDB2FhYSE6deokNm/ebNCxcKoCuptzqXnC//UtwnfuJrFi93m549TJjfxi0WbeZuE7d5NIzCiQOw4RUZ3U5vtbEkIIGWu3JiMvLw8ODg7Izc3l+CfSU1ymwegVB5GQlo9+7V3x7VM9oVA0ru662z3x1RH8ef4G5gzpgBmD2ssdh4io1mrz/W2yY56ImoqVey4gIS0frrYqLHu4S6MvnIB/rrpj1x0RNUcsnojqWWWB8fqIALjZqWROYxyRnTxhrpRwNjUf59Py5Y5DRNSgWDwR1aPEG4W4dKMQ5koJgwPc7/2ARsLB2hwDOrgB4JxPRNT8sHgiqke7z5bPQ9bTzxl2lo3z6ro7GVnZdRebDA6dJKLmhMUTUT3afbZ88tX7/JvOWadKEYEeUJkpcOlGIeKTubYjETUfLJ6I6kl+cRmOJmYBAAYHeNyjdeNjqzLDoI7lRSEHjhNRc8LiiaieHDh/A2UaAT9XG/i52sgdp178M2Emu+6IqPlg8URUTyrHO1WenWmK7vN3h42FEteybyLmao7ccYiIGgSLJ6J6oNUK7EkoL54GN+HiycpCiYjA8i7JP2LZdUdEzQOLJ6J6EHc9FzcKSmGrMkP3ioV0m6rKCTM3xyVDq2XXHRE1fSyeiOrBroouu/4dXGFh1rT/mvXr4Ao7SzOk5ZXg2OUsueMQEdW7pv2vOpFMmvIUBbdTmSkxtJMnAOCPk8kypyEiqn8snoiMLC2vGKeu50GSgIHNoHgC/rnqbmtcKtQarcxpiIjqF4snIiPbU9FlF9LSscmsZXcvvdu6wNnGApmFpTh8KVPuOERE9YrFE5GRNYcpCm5nplRgWFBF110su+6IqGlj8URkRCVqDQ5cuAGgeRVPwD9r3W07lYpSNbvuiKjpYvFEZERHLmWhqFQDD3sVOnnbyx2nQfX0c4a7nQp5xWr8eT5D7jhERPWGxROREd3aZSdJksxpGpZSIWF4sBcArnVHRE0biyciIxFCYFczmqKgOpVX3e2IT0VxmUbmNERE9YPFE5GRXMwowNWsm7AwU6BPO1e548iiWytHtHC0QmGpRnfVIRFRU8PiichIdp0pLxbC27jARmUmcxp5SJKEkZ3ZdUdETRuLJyIjaY5TFFSnsutu19k0FJaoZU5DRGR8LJ6IjCC3qAzHr2QDYPHUydserV2sUVymxc4zaXLHISIyOhZPREaw73wGNFqB9u628HG2ljuOrCRJ0p19+iOWXXdE1PSweCIygsrB0YMCmvdZp0qVxdO+c+nIvVkmcxoiIuNi8URURxqtwN6EiuKpmU5RcLsOHnbo4GGLMo3AjvhUueMQERkViyeiOoq5mo3sojLYW5oh1NdJ7jgmY1TFci1/8Ko7ImpiWDwR1VHlFAUD/d1hpuRfqUojK7ruDl64gazCUpnTEBEZD/+lJ6ojTlFQPT9XGwS1sIdGK7D1FM8+EVHTweKJqA6u59zE2dR8KCRgQAc3ueOYHF3XXWyyzEmIiIyHxRNRHVSederWyglONhYypzE9IypmGz+SmIX0vGKZ0xARGQeLJ6I64BQFd9fSyRrdWjlCCGBzHLvuiKhpYPFEVEs3SzU4eOEGAGBwRw+Z05iukRVddxvZdUdETQSLJ6JaOnzpBkrUWrRwtEIHD1u545iskZ29oFRI+DspBxfSC+SOQ0RUZyyeiGqpcoqCQR3dIUmSzGlMl7u9JQZWDKZff/yqzGmIiOqOxRNRLQghOEWBAR7u4QMA+OXENZRptDKnISKqGxZPRLVwNjUfKbnFsDRXILyti9xxTN6gju5wtVXhRkGprugkImqsWDwR1UJlAdCnrSsszZUypzF95koFxndrAYBdd0TU+LF4IqqF3ZyiwGAPdS/vutuTkME5n4ioUWPxRGSgrMJSnEjKBsDxToZo526L7r5O0GgFfj5xTe44RES1xuKJyED7zqVDCCDAyx5eDlZyx2lUKgeOrz9+DUIImdMQEdUOiyciA1VOUTCYZ50MNiLYCzYWSiTeKMTRxCy54xAR1QqLJyIDlGm02HcuAwBwH4sng9mozHQzjq/lwHEiaqRYPBEZIPpKNvKL1XC2sUAXH0e54zRKlV13W+JSkFdcJnMaIiLDsXgiMkDlVXYD/d2gVHBW8dro1soR7dxtUVymxR9c746IGiEWT0QG4KzidSdJEh6pmLZg3XFedUdEjQ+LJ6IaSsoswoX0ApgpJPRr7yZ3nEZtbLcWMFNIiL2ag4TUfLnjEBEZhMUTUQ3tPpsGAOje2gkOVuYyp2ncXG1ViAjwAACsPcaB40TUuLB4IqqhXWcrpyjwkDlJ0/BIxcDxX/++hhK1RuY0REQ1x+KJqAYKS9Q4cql8XiJOUWAc/dq7wsNeheyiMuw8zcWCiajxYPFEVAMHLtxAqUYLXxdrtHWzkTtOk2CmVODB0JYAOOcTETUuJl08aTQavPHGG/Dz84OVlRXatm2Lt99+W29ZByEE5s+fDy8vL1hZWSEiIgLnz5/Xe56srCxMnDgR9vb2cHR0xJQpU1BQUKDX5uTJk+jXrx8sLS3h4+ODxYsXN8gxUuOw+8w/V9lJEqcoMJaHK666+/N8Bq7n3JQ5DRFRzZh08fT+++/js88+w4oVK3DmzBm8//77WLx4MZYvX65rs3jxYnzyySdYtWoVjhw5AhsbG0RGRqK4+J9V2ydOnIj4+HhERUVh06ZN2L9/P6ZOnarbn5eXhyFDhsDX1xfR0dFYsmQJFi5ciC+++KJBj5dMk1YrsCeBUxTUB18XG/Rq4wwhgJ85bQERNRbChI0YMUI8/fTTetvGjRsnJk6cKIQQQqvVCk9PT7FkyRLd/pycHKFSqcSaNWuEEEKcPn1aABDHjh3Ttdm6dauQJElcv35dCCHEypUrhZOTkygpKdG1mTt3rvD3969x1tzcXAFA5ObmGn6gZNJOXs0RvnM3icA3toriMrXccZqcDSeuCt+5m0Sf93YJjUYrdxwiamZq8/1t0meeevfujV27duHcuXMAgNjYWBw4cADDhg0DACQmJiI1NRURERG6xzg4OCAsLAyHDx8GABw+fBiOjo7o3r27rk1ERAQUCgWOHDmia9O/f39YWFjo2kRGRiIhIQHZ2dnVZispKUFeXp7ejZqmXRVTFPRt7wqVmVLmNE3PsCAv2Fma4Vr2TRy+lCl3HCKiezLp4ulf//oXHn30UXTs2BHm5ubo2rUrZs6ciYkTJwIAUlNTAQAeHvqXjnt4eOj2paamwt1dv6vFzMwMzs7Oem2qe45bX+N2ixYtgoODg+7m4+NTx6MlU7XtVPlngFMU1A9LcyVGd6lYLJhzPhFRI2DSxdO6devwww8/4Mcff8SJEyfw7bff4oMPPsC3334rdzTMmzcPubm5utvVq/xHvylKSM3H2dR8mCslDOnE4qm+PNK9FQBgW3wqcou4WDARmTaTLp5effVV3dmn4OBgPPHEE5g1axYWLVoEAPD09AQApKWl6T0uLS1Nt8/T0xPp6fpzyKjVamRlZem1qe45bn2N26lUKtjb2+vdqOn5LeY6AGCgvzscrS3u0ZpqK6iFPQK87FGq1urecyIiU2XSxVNRUREUCv2ISqUSWq0WAODn5wdPT0/s2rVLtz8vLw9HjhxBeHg4ACA8PBw5OTmIjo7Wtdm9eze0Wi3CwsJ0bfbv34+ysn/+xxsVFQV/f384OTnV2/GRadNqBTbGJAMAxnZtIXOapk2SJDzcvWLOJ3bdEZGJM+niadSoUXj33XexefNmXL58Gb/++iuWLVuGsWPHAij/B3fmzJl45513sHHjRsTFxWHSpEnw9vbGmDFjAAABAQEYOnQonn32WRw9ehQHDx7EjBkz8Oijj8Lbu3ycxYQJE2BhYYEpU6YgPj4ea9euxccff4zZs2fLdehkAo5fycb1nJuwU5lxioIGMKZLC1goFTidkodT13PljkNEdEdmcge4m+XLl+ONN97AtGnTkJ6eDm9vbzz33HOYP3++rs1rr72GwsJCTJ06FTk5Oejbty+2bdsGS0tLXZsffvgBM2bMwODBg6FQKDB+/Hh88sknuv0ODg7YsWMHpk+fjtDQULi6umL+/Pl6c0FR8/Pr3+XdR0ODPGFpzqvs6puTjQWGdPLAppMpWHvsKoJaOMgdiYioWpIQt0zXTbWWl5cHBwcH5ObmcvxTE1Cq1qLHuzuRe7MMPzwThj7tXOWO1Cz8eT4DT3x1FHaWZjj2fxEsWomo3tXm+9uku+2I5LI3IR25N8vgbqdCrzYucsdpNvq0dUULRyvkF6uxPb76aUKIiOTG4omoGr9XDBR/IMQbSgXXsmsoCoWEhzhwnIhMHIsnotvkF5dh55nyqSrG8Cq7BvdQdx9IEnDoYiaSMovkjkNEVAWLJ6LbbDuVihK1Fu3cbdHJm+PXGloLRyv0rRhjtj6aZ5+IyPSweCK6TeUkjWO6eEOS2GUnh4e7ly939HP0NWi0vKaFiEwLiyeiW6TlFePQxfLFaUd3YZedXIZ08oCjtTlScoux/3yG3HGIiPSweCK6xR+xyRACCPV1go+ztdxxmi2VmRJjKorXdRw4TkQmhsUT0S10XXYcKC67R3qUd93tPJOGzIISmdMQEf2DxRNRhQvp+Th1PQ9mCgkjgr3kjtPsBXjZo3NLB5RphG62dyIiU8DiiajCb3+Xz+00oIMbnG0sZE5DwD8Dx9cdvwouhkBEpoLFExEAIQR+jy0/uzGaXXYm44Eu3rA0V+BcWgFirubIHYeICACLJyIAwImkbFzNugkbCyXuD/CQOw5VsLc0x/Cg8i7Udcc5cJyITAOLJyL802UX2ckTVhZcjNaUPFwxcPyP2BQUl2lkTkNExOKJCGUaLTadLC+eeJWd6Qnzc4aHvQoFJWocvpQpdxwiIhZPRPvPZSC7qAyutir0busidxy6jSRJuD+wvCt1R3yazGmIiFg8EeG3mPKzTqNCvGCm5F8JU3R/oCeA8jmftFyuhYhkxm8KatYKStSIOp0KALoZrcn09GrjDFuVGTLySxB7LUfuOETUzLF4omZtR3wqisu08HO1QeeWDnLHoTtQmSkx0N8NALDjNLvuiEheLJ6oWavsshvdxRuSJMmchu6mctxTFIsnIpIZiydqtjLyS3DgfAYAdtk1Bvd1dIe5UsKF9AJcyiiQOw4RNWMsnqjZ+iM2GVoBdPFxRGtXG7nj0D3YW5qjV5vyqyF59omI5MTiiZqt32PKl2MZ08Vb5iRUU+y6IyJTwOKJmqXEG4WIvZYLpULCyBAWT41FRMXSOdFJ2cjIL5E5DRE1VyyeqFn67e/ys05927nC1VYlcxqqKW9HKwS3cIAQwO6zPPtERPJg8UTNjhBC12U3lsuxNDrsuiMiubF4omYn5moOLmcWwcpcqfsipsZjSKfy39mf52+gqFQtcxoiao5YPFGz83vF3E5DOnnARmUmcxoylL+HHXycrVCi1mL/uRtyxyGiZojFEzUrao0Wm06WF0+c26lxkiQJ9weUr3XHrjsikgOLJ2pWDly4gRsFpXC2sUDf9q5yx6Faquy623U2DWqNVuY0RNTcsHiiZqWyy25kZy+YK/nxb6y6+zrB0docOUVlOH4lW+44RNTM8NuDmo2iUjW2x6cCAMbwKrtGzUypwKCO7gCAHfHsuiOihsXiiZqNqNNpKCrVwNfFGl19HOWOQ3U0JLBi3NOZVAghZE5DRM0JiydqNionxhwd4g1JkmROQ3XVv4MrVGYKXM26iYS0fLnjEFEzwuKJmoXMghLsP19+Wftodtk1CdYWZuhXMeifXXdE1JBYPFGzsDkuBRqtQHALB7R1s5U7DhkJZxsnIjmweKJm4deKLjsOFG9aBnX0gCQBcddzkZxzU+44RNRMsHiiJu9KZiH+TsqBQgJGhXjJHYeMyM1OhdBWTgCAnWd49omIGgaLJ2ryKud26tPOFe52ljKnIWNj1x0RNTQWT9Tk/RFbXjyN5nIsTVJl8XT4YiZyb5bJnIaImgODi6dLly7VRw6ienEpowDn0wtgppB0X7LUtLRxs0U7d1uotQJ7E9LljkNEzYDBxVO7du1w33334fvvv0dxcXF9ZCIymsqunPC2LnCwMpc5DdUXdt0RUUMyuHg6ceIEOnfujNmzZ8PT0xPPPfccjh49Wh/ZiOpsR8WX6RCedWrSKounvQkZKFFrZE5DRE2dwcVTly5d8PHHHyM5ORlff/01UlJS0LdvXwQFBWHZsmXIyMioj5xEBsvIL8GJpPJFYyNYPDVpXVo6ws1OhYISNf66lCV3HCJq4mo9YNzMzAzjxo3D+vXr8f777+PChQuYM2cOfHx8MGnSJKSkpBgzJ5HBdp1JgxBA55YO8HKwkjsO1SOFQkJEQGXXXarMaYioqat18XT8+HFMmzYNXl5eWLZsGebMmYOLFy8iKioKycnJGD16tDFzEhmMXXbNy5Bbxj1ptVwomIjqj5mhD1i2bBlWr16NhIQEDB8+HN999x2GDx8OhaK8DvPz88M333yD1q1bGzsrUY0VlKhx4EL5WnZDOnnKnIYaQnhbF9hYKJGWV4K467kI8XGUOxIRNVEGn3n67LPPMGHCBFy5cgW//fYbRo4cqSucKrm7u+Orr74yWkgiQ+0/l4FStRatXazR3p1r2TUHluZKDPB3A8Cr7oiofhl85un8+fP3bGNhYYHJkyfXKhCRMVR+ed4f6AFJkmROQw1lSKAntsSlYsfpVMyJ9Jc7DhE1UQafeVq9ejXWr19fZfv69evx7bffGiUUUV2UabTYVbHOGbvsmpf7/N2hVEg4l1aAK5mFcschoibK4OJp0aJFcHV1rbLd3d0d//nPf4wSiqgujiZmIa9YDRcbC3SrWDSWmgcHa3OE+TkDYNcdEdUfg4unpKQk+Pn5Vdnu6+uLpKQko4S61fXr1/H444/DxcUFVlZWCA4OxvHjx3X7hRCYP38+vLy8YGVlhYiIiCpdi1lZWZg4cSLs7e3h6OiIKVOmoKCgQK/NyZMn0a9fP1haWsLHxweLFy82+rFQw9gRX36pekSAB5QKdtk1N5VX3e2IZ/FERPXD4OLJ3d0dJ0+erLI9NjYWLi4uRglVKTs7G3369IG5uTm2bt2K06dPY+nSpXBy+udswuLFi/HJJ59g1apVOHLkCGxsbBAZGam3dMzEiRMRHx+PqKgobNq0Cfv378fUqVN1+/Py8jBkyBD4+voiOjoaS5YswcKFC/HFF18Y9Xio/gkh9MY7UfNTOSHq8StZyCwokTkNETVJwkCvvfaa8PX1Fbt37xZqtVqo1Wqxa9cu4evrK1555RVDn+6u5s6dK/r27XvH/VqtVnh6eoolS5botuXk5AiVSiXWrFkjhBDi9OnTAoA4duyYrs3WrVuFJEni+vXrQgghVq5cKZycnERJSYnea/v7+9c4a25urgAgcnNza/wYMr64aznCd+4m0fH1reJmqVruOCSTYR/tF75zN4m1x5LkjkJEJq42398Gn3l6++23ERYWhsGDB8PKygpWVlYYMmQIBg0aZPQxTxs3bkT37t3x0EMPwd3dHV27dsV///tf3f7ExESkpqYiIiJCt83BwQFhYWE4fPgwAODw4cNwdHRE9+7ddW0iIiKgUChw5MgRXZv+/fvDwsJC1yYyMhIJCQnIzs426jFR/arsshvQwQ2W5kqZ05BchnTiQsFEVH8MLp4sLCywdu1anD17Fj/88AM2bNiAixcv4uuvv9YrPozh0qVL+Oyzz9C+fXts374dL7zwAl566SXdVX2pqeVflB4e+t0zHh4eun2pqalwd3fX229mZgZnZ2e9NtU9x62vcbuSkhLk5eXp3Uh+ulnFO7HLrjmr7LL983wGbpZyoWAiMi6D53mq1KFDB3To0MGYWarQarXo3r277oxW165dcerUKaxatUr2eaQWLVqEN998U9YMpC8pswhnU/OhVEgY1NH93g+gJivQyx4tHK1wPecm/jyfwSkriMioDD7zpNFo8NVXX2HChAmIiIjAoEGD9G7G5OXlhcDAQL1tAQEBuqv6PD3L/0FMS9M/NZ+Wlqbb5+npifT0dL39arUaWVlZem2qe45bX+N28+bNQ25uru529erV2hwiGdGOigVhe7Z2hqO1cc+CUuMiSZLu7BO77ojI2Awunl5++WW8/PLL0Gg0CAoKQkhIiN7NmPr06YOEhAS9befOnYOvry+A8nX0PD09sWvXLt3+vLw8HDlyBOHh4QCA8PBw5OTkIDo6Wtdm9+7d0Gq1CAsL07XZv38/ysrKdG2ioqLg7++vd2XfrVQqFezt7fVuJC922dGtKqcs2HU2HRouFExExmToqHQXFxexefNmQx9WK0ePHhVmZmbi3XffFefPnxc//PCDsLa2Ft9//72uzXvvvSccHR3F77//Lk6ePClGjx4t/Pz8xM2bN3Vthg4dKrp27SqOHDkiDhw4INq3by8ee+wx3f6cnBzh4eEhnnjiCXHq1Cnx008/CWtra/H555/XOCuvtpNXZkGJ8PvXJuE7d5O4mlUodxwyAaVqjQhesE34zt0kjlzKlDsOEZmoBrnazsLCAu3atTN+FVeNHj164Ndff8WaNWsQFBSEt99+Gx999BEmTpyoa/Paa6/hxRdfxNSpU9GjRw8UFBRg27ZtsLS01LX54Ycf0LFjRwwePBjDhw9H37599eZwcnBwwI4dO5CYmIjQ0FC88sormD9/vt5cUGTadp1Jg1aUj3Vp6WQtdxwyAeZKBQYHVHbdVX/hBxFRbUhCCIPOZy9duhSXLl3CihUruODqLfLy8uDg4IDc3Fx24cng2e+OI+p0GmZGtMfMiPq9kIEajy1xKZj2wwn4ulhj75yB/DeLiKqozfe3wVfbHThwAHv27MHWrVvRqVMnmJub6+3fsGGDoU9JVCc3SzX483wGAGBIIK+qon/07+AGCzMFrmQW4Xx6ATp42MkdiYiaAIOLJ0dHR4wdO7Y+shDVyv7zGSgu06KlkxUCvPjlSP+wVZmhT1sX7EnIQNTpNBZPRGQUBhdPq1evro8cRLV261p27Jah290f6Ik9CRnYEZ+K6fc1zHhNImraDB4wDpTPk7Rz5058/vnnyM/PBwAkJyejoKDAqOGI7kWt0WLXmYopCthlR9WICHSHJAGx13KRnHNT7jhE1AQYXDxduXIFwcHBGD16NKZPn46MjPKxJu+//z7mzJlj9IBEd3P8Sjayi8rgaG2OHq2rn5OLmjd3O0v08HUGAGw6mSxzGiJqCmo1SWb37t2RnZ0NKysr3faxY8fqTVZJ1BB2xJefdRrc0QNmylqdSKVm4IEu3gCA32NYPBFR3Rn8bfPnn3/i9ddfr7IIcOvWrXH9+nWjBSO6FyEEos6Uz99TuRQHUXWGB3vBTCEhPjkPFzM4vICI6sbg4kmr1UKjqbpK+bVr12BnxytZqOGcTc3H1aybUJkp0L+Dq9xxyIQ521igb/vyz8hGnn0iojoyuHgaMmQIPvroI919SZJQUFCABQsWYPjw4cbMRnRXlV12/dq7wdrC4AtHqZkZXdF1tzE2GQbODUxEpMfg4mnp0qU4ePAgAgMDUVxcjAkTJui67N5///36yEhUrR0VS25wIWCqifsDPaEyUyDxRiFOXc+TOw4RNWIG/3e9ZcuWiI2NxU8//YSTJ0+ioKAAU6ZMwcSJE/UGkBPVp+s5NxGfnAeFBAzu6C53HGoEbFVmiAj0wOaTKdgYex3BLR3kjkREjVSt+jrMzMzw+OOPGzsLUY1FxZefderu6wwXW5XMaaixeCDEG5tPpuCP2BTMGxYAhYKTqhKR4Qwunr777ru77p80aVKtwxDV1I6KWcXZZUeGGOjvBjtLM6TmFePo5Sz0auMidyQiaoQMLp5efvllvftlZWUoKiqChYUFrK2tWTxRvcspKsWRxCwAnKKADKMyU2JYkCfWHb+GjbHJLJ6IqFYMHjCenZ2tdysoKEBCQgL69u2LNWvW1EdGIj27z6ZDoxXw97CDr4uN3HGokXkgpAUAYEtcCkrVWpnTEFFjZJQpmdu3b4/33nuvylkpovoQxS47qoPwti5wtVUhp6gMBy5kyB2HiBoho61nYWZmhuRkTj5H9au4TIN958q/8LgQMNWGUiFhZGcvAJwwk4hqx+AxTxs3btS7L4RASkoKVqxYgT59+hgtGFF1Dl64gaJSDbwcLBHUwl7uONRIPdDFG98cuowdp9Nws1QDKwul3JGIqBExuHgaM2aM3n1JkuDm5oZBgwZh6dKlxspFVK3KWcXvD/SAJPEyc6qdrj6O8HG2wtWsm9h5Jg2jQrzljkREjYjBxZNWywGWJA+NVmDX2YrxTuyyozqQJAkPhHjj0z0XsTE2mcUTERnEaGOeiOrb30nZuFFQCjtLM4S1cZY7DjVylVfd7U1IR25RmcxpiKgxMfjM0+zZs2vcdtmyZYY+PdEdVU6MObijO8yVrPupbvw97eDvYYeEtHxsi0/BIz1ayR2JiBoJg4unv//+G3///TfKysrg7+8PADh37hyUSiW6deuma8fxKGRMQgjsqFiS5X522ZGRPNDFG0u2J2BjbDKLJyKqMYOLp1GjRsHOzg7ffvstnJycAJRPnPnUU0+hX79+eOWVV4wekuhCegEuZxbBQqnAAH83ueNQE/FASHnxdOhiJtLziuFubyl3JCJqBAzu+1i6dCkWLVqkK5wAwMnJCe+88w6vtqN6U9ll16edC2xVtVrPmqgKH2drdGvlCCGATSdT5I5DRI2EwcVTXl4eMjKqzsqbkZGB/Px8o4Qiul1ll92QTuyyI+N6oOJKu42xnDCTiGrG4OJp7NixeOqpp7BhwwZcu3YN165dwy+//IIpU6Zg3Lhx9ZGRmrnU3GLEXsuFJAGDA9zljkNNzIjO3lBIQMzVHFzJLJQ7DhE1AgYXT6tWrcKwYcMwYcIE+Pr6wtfXFxMmTMDQoUOxcuXK+shIzVzUmfIuu64+jnC345gUMi43OxX6tHMFAPzBs09EVAMGF0/W1tZYuXIlMjMzdVfeZWVlYeXKlbCx4Qr3ZHzssqP6VjlJ5u8xyRBCyJyGiExdrSfLSUlJQUpKCtq3bw8bGxv+g0P1IqeoFIcvZgIoX5KFqD5EdvKEhVKB8+kFOJvKsZtEdHcGF0+ZmZkYPHgwOnTogOHDhyMlpfwKlSlTpnCaAjK6HafToNYKdPS0Q1s3W7njUBPlYGWO+zqWT4HBgeNEdC8GF0+zZs2Cubk5kpKSYG1trdv+yCOPYNu2bUYNR7S54vLxEcFeMiehpq5yuZaN7LojonsweMKcHTt2YPv27WjZsqXe9vbt2+PKlStGC0aUU1SKgxduAACGd2bxRPVrcIA7bCyUuJ5zEyeSshHqy/UTiah6Bp95Kiws1DvjVCkrKwsqlcoooYgAYEc8u+yo4ViaKxFZcVHCxhh23RHRnRlcPPXr1w/fffed7r4kSdBqtVi8eDHuu+8+o4aj5m1zHLvsqGGN6lJ+1d3muBSoNVqZ0xCRqTK4227x4sUYPHgwjh8/jtLSUrz22muIj49HVlYWDh48WB8ZqRlilx3JoW87VzjbWOBGQSkOXcxE/w5cR5GIqjL4zFNQUBDOnTuHvn37YvTo0SgsLMS4cePw999/o23btvWRkZohdtmRHMyVCgwPrui641V3RHQHBp15Kisrw9ChQ7Fq1Sr83//9X31lIsKmii67kTzrRA3sgZAW+P6vJGw/lYp3xgTB0lwpdyQiMjEGnXkyNzfHyZMn6ysLEQAgu7AUhyq77DjeiRpYd18neDtYIr9Ejb0J6XLHISITZHC33eOPP46vvvqqPrIQAQB2nE6FWisQ4GWPNuyyowamUEi65VrYdUdE1TF4wLharcbXX3+NnTt3IjQ0tMp6dsuWLTNaOGqeNseVr2U3Iphr2ZE8RoV44/P9l7DrTDryi8tgZ2kudyQiMiE1Kp5OnjyJoKAgKBQKnDp1Ct26dQMAnDt3Tq+dJEnGT0jNSnbhLVfZscuOZNLJ2x5t3WxwMaMQO+LTMD605b0fRETNRo2Kp65duyIlJQXu7u64cuUKjh07BhcXl/rORs3QjtOp0LDLjmQmSRIeCGmBD3eew8bYZBZPRKSnRmOeHB0dkZiYCAC4fPkytFpOHkf1Y9NJXmVHpuGBigkzD1y4gcyCEpnTEJEpqdGZp/Hjx2PAgAHw8vKCJEno3r07lMrqL9+9dOmSUQNS85FdWD4xIcAuO5Kfn6sNOrd0wMlrudgSl4InwlvLHYmITESNiqcvvvgC48aNw4ULF/DSSy/h2WefhZ2dXX1no2Zme3x5l12glz38XG3u/QCievZAiDdOXsvFxthkFk9EpFPjq+2GDh0KAIiOjsbLL7/M4omMTreWHbvsyESM7OyNd7ecwbHL2biecxMtHK3kjkREJsDgeZ5Wr17NwomMLuuWLjsuBEymwtPBEmF+zgCAPzjnExFVMLh4IqoPOyq67Dp526M1u+zIhDwQ0gIAsDGGxRMRlWPxRCahssuOA8XJ1AwL8oS5UsLplDwkpObLHYeITACLJ5Idu+zIlDnZWGBwRw8AwA9HrsichohMQaMqnt577z1IkoSZM2fqthUXF2P69OlwcXGBra0txo8fj7S0NL3HJSUlYcSIEbC2toa7uzteffVVqNVqvTZ79+5Ft27doFKp0K5dO3zzzTcNcEQE/HOVHbvsyFQ9Ee4LANhw4joKStT3aE1ETV2jKZ6OHTuGzz//HJ07d9bbPmvWLPzxxx9Yv3499u3bh+TkZIwbN063X6PRYMSIESgtLcWhQ4fw7bff4ptvvsH8+fN1bRITEzFixAjcd999iImJwcyZM/HMM89g+/btDXZ8zdkWXmVHJq53Wxe0cbNBQYkav/59Xe44RCSzRlE8FRQUYOLEifjvf/8LJycn3fbc3Fx89dVXWLZsGQYNGoTQ0FCsXr0ahw4dwl9//QUA2LFjB06fPo3vv/8eXbp0wbBhw/D222/j008/RWlpKQBg1apV8PPzw9KlSxEQEIAZM2bgwQcfxIcffijL8TYn7LKjxkCSJDzRq/zs0/eHr0AIIXMiIpJToyiepk+fjhEjRiAiIkJve3R0NMrKyvS2d+zYEa1atcLhw4cBAIcPH0ZwcDA8PDx0bSIjI5GXl4f4+Hhdm9ufOzIyUvcc1SkpKUFeXp7ejQxX2WUX1MIevi7ssiPTNa5bS1iZK5GQlo+jiVlyxyEiGZl88fTTTz/hxIkTWLRoUZV9qampsLCwgKOjo952Dw8PpKam6trcWjhV7q/cd7c2eXl5uHnzZrW5Fi1aBAcHB93Nx8enVsfX3G0+yavsqHFwsDLHmK7l69397y8OHCdqzky6eLp69Spefvll/PDDD7C0tJQ7jp558+YhNzdXd7t69arckRqdzIISHL7ELjtqPB6v6LrbdioV6fnFMqchIrmYdPEUHR2N9PR0dOvWDWZmZjAzM8O+ffvwySefwMzMDB4eHigtLUVOTo7e49LS0uDp6QkA8PT0rHL1XeX9e7Wxt7eHlVX1yzGoVCrY29vr3cgw2+PT2GVHjUonbweE+jpBrRVYe5T/YSJqrky6eBo8eDDi4uIQExOju3Xv3h0TJ07U/Wxubo5du3bpHpOQkICkpCSEh4cDAMLDwxEXF4f09HRdm6ioKNjb2yMwMFDX5tbnqGxT+RxUPzbHlc/YPCLYW+YkRDVXOXD8x6NJUGu0MqchIjnUeGFgOdjZ2SEoKEhvm42NDVxcXHTbp0yZgtmzZ8PZ2Rn29vZ48cUXER4ejl69egEAhgwZgsDAQDzxxBNYvHgxUlNT8frrr2P69OlQqVQAgOeffx4rVqzAa6+9hqeffhq7d+/GunXrsHnz5oY94GYks6AEh3mVHTVCw4I98fYmC6TkFmPnmXQMDfKUOxIRNTCTPvNUEx9++CFGjhyJ8ePHo3///vD09MSGDRt0+5VKJTZt2gSlUonw8HA8/vjjmDRpEt566y1dGz8/P2zevBlRUVEICQnB0qVL8eWXXyIyMlKOQ2oWtsWnQiuA4BYOaOViLXccohpTmSnxSI/yC0S+58BxomZJEpywxCjy8vLg4OCA3Nxcjn+qgYlf/oWDFzIxd2hHvDCwrdxxiAxyLbsI/RbvgRDArlcGoK2brdyRiKiWavP93ejPPFHjc4NddtTItXSyxuCO7gB49omoOWLxRA1uO7vsqAl4Irw1AODn6GsoKuV6d0TNCYsnanCVE2NyLTtqzPq1c4WvizXyi9X4PSZZ7jhE1IBYPFGDulFQgr84MSY1AQqFhMfDyqct+B/XuyNqVlg8UYPadqq8y65zSwf4OLPLjhq3h7q3hMpMgdMpeTiRlCN3HCJqICyeqEFtiavosuNZJ2oCHK0t8EBI+SSvHDhO1HyweKIGc2uXHRcCpqbiifDyrrvNJ1Nwo6BE5jRE1BBYPFGDqeyyC2GXHTUhnVs6IqSlA0o1Wqw7zvXuiJoDFk/UYCqvsuNZJ2pqKqct+OGvJGi0HDhO1NSxeKIGkZFfgiOJ7LKjpmlkZy84Wpvjes5N7Dmbfu8HEFGjxuKJGkTlWnbssqOmyNJciYe7l6939z8OHCdq8lg8UYPYwokxqYmbGNYKkgTsO5eBK5mFcschonrE4onq3a1ddsOCWDxR0+TrYoMBHdwAcNoCoqaOxRPVO12XnY8ju+yoSXuiV/m0BeuOX0NxmUbmNERUX1g8Ub3bfLJ83a8RwZ4yJyGqXwP93dHC0Qq5N8vwRyzXuyNqqlg8Ub26UVCCo4lZANhlR02fUiFhYq9WANh1R9SUsXiierX7TDq0AghuwavsqHl4pLsPLJQKxF7LRezVHLnjEFE9YPFE9WrH6VQAwP2BHjInIWoYLrYq3VWlnLaAqGli8UT1pqhUjT/P3wAADOnE4omaj8crBo7/EZuM7MJSmdMQkbGxeKJ6s//cDZSotfBxtoK/h53ccYgaTLdWjgj0skeJWov10VzvjqipYfFE9SbqdBoAYEigJyRJkjkNUcORJAmTwsvPPn3/VxK0XO+OqElh8UT1Qq3RYtfZ8uKJ452oOXqgizfsLM2QlFWE/ecz5I5DREbE4onqxfEr2cgpKoOTtTm6+zrJHYeowVlbmOHB0JYAgP8d5sBxoqaExRPVix3x5WedBnX0gJmSHzNqnioHju9OSMfVrCKZ0xCRsfBbjYxOCKGbooBX2VFz1tbNFn3buUII4MejSXLHISIjYfFERnc2NR/Xsm9CZaZAv/aucschklXl2ae1x66iRM317oiaAhZPZHSVXXb92rvB2sJM5jRE8ooIcIeXgyWyCkux7vg1ueMQkRGweCKjizrDLjuiSmZKBZ4f0BYA8PHO8ygsUcuciIjqisUTGdX1nJs4dT0PCgkY3NFd7jhEJuGxnq3QytkaNwpK8PWBRLnjEFEdsXgio9pZMTFmd19nuNiqZE5DZBoszBSYE+kPAPh8/yVkFpTInIiI6oLFExkVFwImqt7IYC8Et3BAQYkay3dfkDsOEdUBiycymtyiMvx1KQsAiyei2ykUEv41rCMA4IcjV5CUyXmfiBorFk9kNHsS0qHRCnTwsEVrVxu54xCZnD7tXNGvvSvKNAJLoxLkjkNEtcTiiYxGNzFmoKfMSYhM19yh5Weffo9JxqnruTKnIaLaYPFERlFcpsG+hPLFT9llR3RnQS0cMLqLNwDg/W1nZU5DRLXB4omM4vDFTBSWauBpb4ngFg5yxyEyaXOG+MNcKeHP8zdw4PwNueMQkYFYPJFR7KiYoiAi0B0KhSRzGiLT5uNsrVu25b1tZ6DVCpkTEZEhWDxRnWm1AjvPlBdPHO9EVDMz7msHW5UZTl3Pw6a4FLnjEJEBWDxRncVcy0FGfgnsVGbo1cZF7jhEjYKLrQrP9W8DAPhgewJK1VqZExFRTbF4ojqrXAh4YEd3WJjxI0VUU1P6+cHNToWkrCKsOZokdxwiqiF+01Gd/TNFAa+yIzKEtYUZZka0BwB8sus88ovLZE5ERDXB4onq5EJ6AS5lFMJcKWGgv5vccYganYe7+6CNqw0yC0vx3z+5aDBRY8DiieokquIqu/C2rrCzNJc5DVHjY65U4NWKRYO//PMS0vOLZU5ERPfC4onqJIoLARPV2dAgT4T4OKKoVIPlu7hoMJGpY/FEtZaeX4y/r+YAAO4PYPFEVFuSJGFexaLBa44mIfFGocyJiOhuWDxRre06kw4hgJCWDvB0sJQ7DlGj1quNCwZ1dIdaK/DBDi4aTGTKWDxRre2Ir7jKrhMnxiQyhteG+kOSgM0nUxBbcVaXiEwPiyeqlYISNQ5eyATA8U5ExtLR0x7jurYEACzaegZCcNkWIlPE4olqZf+5DJRqtGjtYo327rZyxyFqMmYP6QALMwX+upSFfecy5I5DRNVg8US1Utlld3+gBySJCwETGUsLRytMDq9YNHjrWS4aTGSCWDyRwco0Wuw+mw6A452I6sP0+9rBztIMZ1Pz8XvsdbnjENFtTLp4WrRoEXr06AE7Ozu4u7tjzJgxSEjQvwqluLgY06dPh4uLC2xtbTF+/HikpaXptUlKSsKIESNgbW0Nd3d3vPrqq1Cr1Xpt9u7di27dukGlUqFdu3b45ptv6vvwGq2jiVnIK1bDxcYC3Vo5yR2HqMlxtLbAtIHtAAAfbD+HErVG5kREdCuTLp727duH6dOn46+//kJUVBTKysowZMgQFBb+MwfKrFmz8Mcff2D9+vXYt28fkpOTMW7cON1+jUaDESNGoLS0FIcOHcK3336Lb775BvPnz9e1SUxMxIgRI3DfffchJiYGM2fOxDPPPIPt27c36PE2FpWzig8OcIdSwS47ovrwVJ/W8LS3xPWcm/jf4StyxyGiW0iiEV3OkZGRAXd3d+zbtw/9+/dHbm4u3Nzc8OOPP+LBBx8EAJw9exYBAQE4fPgwevXqha1bt2LkyJFITk6Gh0f5VWGrVq3C3LlzkZGRAQsLC8ydOxebN2/GqVOndK/16KOPIicnB9u2batRtry8PDg4OCA3Nxf29vbGP3gTIYRAn/d2Izm3GF9O6o4IXmlHVG/WHkvC3F/i4Ghtjv2v3Qd7LoFEZHS1+f426TNPt8vNzQUAODs7AwCio6NRVlaGiIgIXZuOHTuiVatWOHz4MADg8OHDCA4O1hVOABAZGYm8vDzEx8fr2tz6HJVtKp+jOiUlJcjLy9O7NQfxyXlIzi2GlbkSfdu7yh2HqEkb360l2rvbIqeoDJ/vuyh3HCKq0GiKJ61Wi5kzZ6JPnz4ICgoCAKSmpsLCwgKOjo56bT08PJCamqprc2vhVLm/ct/d2uTl5eHmzZvV5lm0aBEcHBx0Nx8fnzofY2Owo6LLrn8HV1iaK2VOQ9S0mSkVeG1o+bItXx1IRGouFw0mMgWNpniaPn06Tp06hZ9++knuKACAefPmITc3V3e7evWq3JEaxD9TFPAqO6KGEBHgju6+Tigu0+LVn2M5dQGRCWgUxdOMGTOwadMm7NmzBy1bttRt9/T0RGlpKXJycvTap6WlwdPTU9fm9qvvKu/fq429vT2srKyqzaRSqWBvb693a+quZhXhbGo+FBIwuKO73HGImgVJkrBoXDAszRX48/wNfL7/ktyRiJo9ky6ehBCYMWMGfv31V+zevRt+fn56+0NDQ2Fubo5du3bptiUkJCApKQnh4eEAgPDwcMTFxSE9PV3XJioqCvb29ggMDNS1ufU5KttUPgeVq+yy69HaGU42FjKnIWo+2nvY4c0HOgEAPtiRgOgr2TInImreTLp4mj59Or7//nv8+OOPsLOzQ2pqKlJTU3XjkBwcHDBlyhTMnj0be/bsQXR0NJ566imEh4ejV69eAIAhQ4YgMDAQTzzxBGJjY7F9+3a8/vrrmD59OlQqFQDg+eefx6VLl/Daa6/h7NmzWLlyJdatW4dZs2bJduymKOo0FwImksvD3X0wKsQbGq3AS2v+Rm5RmdyRiJotky6ePvvsM+Tm5mLgwIHw8vLS3dauXatr8+GHH2LkyJEYP348+vfvD09PT2zYsEG3X6lUYtOmTVAqlQgPD8fjjz+OSZMm4a233tK18fPzw+bNmxEVFYWQkBAsXboUX375JSIjIxv0eE1ZdmEpjiZmAQCGcHoCogYnSRL+MzYIrZytcT3nJv614SQXDiaSSaOa58mUNfV5nn6OvoY562PR0dMO22b2lzsOUbN18loOxn92CGUagXfGBOHxXr5yRyJq1Jr8PE8kH3bZEZmGzi0dMbdi+oK3Np3GmZTmMccckSlh8UT3VFymwf5zNwCwy47IFDzdxw/3+buhVK3FjB9PoKhUfe8HEZHRsHiiezpw/gZulmng7WCJTt5Nr0uSqLFRKCR88FAIPOxVuJhRiIUb4+WORNSssHiie9pxunJiTA9IEhcCJjIFLrYqfPRIV0gSsO74Nfwec13uSETNBosnuiuNVmDXmfI5sjjeici0hLd1wYuD2gMA/r0hDpdvFMqciKh5YPFEd7U9PhWZhaVwsDJHTz9nueMQ0W1eGtQOPVs7o7BUgxlrTqBErZE7ElGTx+KJ7kirFfhk13kAwOTerWGu5MeFyNSYKRX4+LEucLQ2x6nreVi8LUHuSERNHr8N6Y6izqThbGo+bFVmeLpPa7njENEdeDlY4YMHQwAAXx1IxK4zafd4BBHVBYsnqpYQt5518oWjNdeyIzJlEYEeeKriPzlz1sciJfemvIGImjAWT1StPQnpiE/Og7WFElP6tpE7DhHVwL+GdURQC3tkF5Xh5Z9ioNFyAQmi+sDiiaoQQuDjXRcAAE/08oWzDc86ETUGKjMllj/WDTYWShxNzNKdPSYi42LxRFXsP38DsVdzYGmuwDP9eNaJqDHxc7XBu2ODAQDLd5/H4YuZMicianpYPJGeW8c6TejpCzc7lcyJiMhQY7q2wEOhLaEVwMy1fyOrsFTuSERNCosn0nP4Yiair2TDwkyB5wbwrBNRY/Xm6E5o42aDtLwSzFkfCyE4/onIWFg8kZ6PK846PdbDBx72ljKnIaLasrYww6cTusHCTIHdZ9Px1YFEuSMRNRksnkjnyKVMHEnMgrlSwnMD2sodh4jqKMDLHm+MDAQAvL/tLI5fzpI5EVHTwOKJdJbvLr/C7qHuPvB2tJI5DREZw+NhrTAsyBNlGoHJXx/FX5c4gJyorlg8EQAg+ko2Dly4ATOFhBd41omoyZAkCUsfDkGfdi4oLNVg8tdHsTchXe5YRI0aiycCUH5JMwCM69YCPs7WMqchImOytjDDV5N7YFBHd5SotXj2u+PYdipV7lhEjRaLJ0Ls1RzsTciAUiFh+n3t5I5DRPXA0lyJVY+HYkSwF8o0AtN/PIHf/r4udyyiRonFE+nOOo3u4g1fFxuZ0xBRfbEwU+DjR7tgfLeW0GgFZq2LwZqjSXLHImp0WDw1c6eu52LnmXRIEnjWiagZMFMqsOTBzni8VysIAczbEMdpDIgMxOKpmVtRcYXdqM7eaOtmK3MaImoICoWEt0cHYWr/8olw3950Git2cx08oppi8dSMnU3Nw7b4VEgSMGMQzzoRNSeSJGHesI6YGdEeAPDBjnNYvO0sZyInqgEWT81Y5bxOw4O80MHDTuY0RNTQJEnCzIgO+PfwjgCAlXsv4s0/TkOrZQFFdDcsnpqpC+n52BKXAoBnnYiau6n92+LtMUEAgG8OXca8DXHQsIAiuiMWT83Uit0XIAQwJNADAV72cschIpk90csXHzwUAoUErD1+FbPWxqBMo5U7FpFJYvHUDCXeKMTG2GQAwEuD28uchohMxYOhLbH8sW4wU0jYGJuMaT+cQIlaI3csIpPD4qkZ+nTPBWgFMKijO4JaOMgdh4hMyIjOXvj8iVBYmCkQdToNz3x7HDdLWUAR3YrFUzOTlFmEXytmFX6RY52IqBqDAzyw+skesDJX4s/zNzD566PILy6TOxaRyWDx1Mys3HsBGq1A/w5u6NrKSe44RGSi+rRzxf+m9ISdygxHL2fh8S+PILuwVO5YRCaBxVMzci27CL+cuAYAeIlnnYjoHrq3dsaPz/aCo7U5Yq/lYvgnf+LQxRtyxyKSHYunZmTVvoso0wj0buuC7q2d5Y5DRI1AcEsHrHsuHH6uNkjJLcbEL49g0ZYzHEhOzRqLp2YiJfcm1h2rOOvEK+yIyAAdPOyw6cW+eKynD4QAPt9/CWM/PYTzaflyRyOSBYunZuLzfZdQqtGiZ2tn9GrjInccImpkbFRmWDSuM754IhRO1uY4nZKHkcsP4NtDl7mkCzU7LJ6agfS8Yqw5mgSAZ52IqG6GdPLE9pn9MaCDG0rUWizYGI8nVx9Den6x3NGIGgyLp2bgi/2XUKLWolsrR/Rpx7NORFQ37vaW+OapHnjzgU5QmSmw71wGhn70J6JOp8kdjahBsHhq4uKTc/H9kSsAgBcHt4ckSTInIqKmQJIkTO7dGn+82BcBXvbIKizFs98dx7wNcSgqVcsdj6hesXhqwn6PuY7xnx1CcZkWob5OGNjBTe5IRNTEdPCww2/Te2Nq/zaQJGDN0SSM+OQAYq/myB2NqN6weGqC1Bot3t18Gi//FIPiMi36d3DD15N78KwTEdULlZkS/x4egB+mhMHLwRKJNwox/rNDWLH7PDRaDianpofFUxOTXViKyauP4r9/JgIApg1si9VP9oCDtbnMyYioqevdzhXbXu6PEZ29oNYKfLDjHB75/DCuZhXJHY3IqFg8NSHxybkYteIADl7IhLWFEisndsNrQztCqeAZJyJqGA7W5ljxWFcsezgEtiozHL+SjWEf/4kNJ65xSgNqMiTBT7NR5OXlwcHBAbm5ubC3t2/w1/895jrm/nISxWVa+LpY44snusPf067BcxARVbqaVYRZa2Nw/Eo2ACC4hQNGdPbC8CAvtHKxljkdUbnafH+zeDISuYontUaL97ed1XXT9e/ghuWPdmU3HRGZBLVGi1X7LuLjXedRpvnn6yaohT2GBXlheLAX/FxtZExIzR2LJxnJUTxlF5ZixpoTOHghE0D5+KZXhvizm46ITM6NghJsj0/F1rhUHL6UqTeQPMDLHsODPDG8sxfautnKmJKaIxZPMmro4ik+ORfP/S8a17JvwtpCiQ8eCsHwYK96f10iorrKLChB1Ok0bI5LwaGL+oWUv4cdhgV7YkSwF9p7cOgB1T8WTzJqyOKJ45uIqKnILixF1Ok0bDmVgoMXbuh17bVzt8XwYC8MD/aEv4cdp1uhesHiSUYNUTypNVq8t/UsvjzA8U1E1PTkFpUh6kwatsal4M/zN1Cq0er2tXG1QZ92rgj0tkeglz38Pe1gaa6UMS01FSyeZFTfxVNWYSle5PgmImom8orLsOtMGrbEpWLfuQyUqrV6+xUS0NbNFp287SsKKgcEetvD2cZCpsTUWLF4klF9Fk/xybmY+l00rudwfBMRNT/5xWXYdy4DcddycTolD/HJecgqLK22rae9JQK97cuLKq/ywsrHyRoK/keT7oDFk4zqq3jafDIFr6yP4fgmIqIKQgik55fgdHIeTqfk4XRyHuKTc3E5s/qZzG1VZgjwskNrFxu426vgbmcJdzuV7mc3OxW7AJux2nx/m9Vzpkbn008/xZIlS5CamoqQkBAsX74cPXv2lC2PnaUZStVaDOjghk84vomICJIkwcPeEh72lrivo7tue0GJGmdT/imoTqfk4WxqPgpK1Dh2ORvHLmff8TntLc3gbl9RVNmpdD+72VUUW/YquNqoYGdpxrNYxDNPt1q7di0mTZqEVatWISwsDB999BHWr1+PhIQEuLu73/Wx9dlt99elTPRo7czxTUREBlJrtLh0oxCnk/NwPecm0vOKkZ5fUnErRlpeSZXxVHejVEhwtDKHo7U5nKwt4GhtASdrczjb/PNz5Z9ONhYVbcxhruRqaKaK3XZ1FBYWhh49emDFihUAAK1WCx8fH7z44ov417/+ddfHyr08CxERGU4IgbybaqTnF+sKqvS8kn8KrLxiZFT8XFCirvXr2KnMYGtpBjOlBHOFAmZKCUqFAuZKCWYKCWbKyp//+dNMKcFcqdDtN1NIUCokKCQJSgWgqPxZkqBQlP9Zub3858q2/+w3q3g9paL8ucv/LM9ipqjMUv76ylt+rnyM3LNFWJkr4WKrMupzstuuDkpLSxEdHY158+bptikUCkRERODw4cNV2peUlKCkpER3Py8vr0FyEhGR8UiSBAdrczhYm99zUs7iMg1yisqQXVSK7KJS3c85RWXIKqy6LbuoFLk3yyAEkF+iRn4dii8q90CINz55rKvcMVg8Vbpx4wY0Gg08PDz0tnt4eODs2bNV2i9atAhvvvlmQ8UjIiKZWZor4emghKeDZY0fo9EK5N4sL6SKSjQo02qh1gioNVqUaSv+1AhotAJqbfnPt+5Ta8Q/j9EKaLUCGlHx560/CwGNFvr7RXkbbcWfGi2g0Wqh1gqoK16zTKst/1Mjqu7TaCtyVWTRyt9RZaY0jeErLJ5qad68eZg9e7bufl5eHnx8fGRMREREpkapkOBsY8H5p5oYFk8VXF1doVQqkZaWprc9LS0Nnp6eVdqrVCqoVMbtdyUiIiLTx+H/FSwsLBAaGopdu3bptmm1WuzatQvh4eEyJiMiIiJTwjNPt5g9ezYmT56M7t27o2fPnvjoo49QWFiIp556Su5oREREZCJYPN3ikUceQUZGBubPn4/U1FR06dIF27ZtqzKInIiIiJovzvNkJJzniYiIqPGpzfc3xzwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDLsxhJ5UTteXl5MichIiKimqr83jZkwRUWT0aSn58PAPDx8ZE5CRERERkqPz8fDg4ONWrLte2MRKvVIjk5GXZ2dpAkSW9fXl4efHx8cPXqVa57Vwt8/+qO72Hd8P2rO76HdcP3r+7u9B4KIZCfnw9vb28oFDUbzcQzT0aiUCjQsmXLu7axt7fnh74O+P7VHd/DuuH7V3d8D+uG71/dVfce1vSMUyUOGCciIiIyAIsnIiIiIgOweGoAKpUKCxYsgEqlkjtKo8T3r+74HtYN37+643tYN3z/6s6Y7yEHjBMREREZgGeeiIiIiAzA4omIiIjIACyeiIiIiAzA4omIiIjIACye6tmnn36K1q1bw9LSEmFhYTh69KjckRqNhQsXQpIkvVvHjh3ljmWy9u/fj1GjRsHb2xuSJOG3337T2y+EwPz58+Hl5QUrKytERETg/Pnz8oQ1Ufd6D5988skqn8mhQ4fKE9YELVq0CD169ICdnR3c3d0xZswYJCQk6LUpLi7G9OnT4eLiAltbW4wfPx5paWkyJTYtNXn/Bg4cWOUz+Pzzz8uU2PR89tln6Ny5s24izPDwcGzdulW331ifPxZP9Wjt2rWYPXs2FixYgBMnTiAkJASRkZFIT0+XO1qj0alTJ6SkpOhuBw4ckDuSySosLERISAg+/fTTavcvXrwYn3zyCVatWoUjR47AxsYGkZGRKC4ubuCkpute7yEADB06VO8zuWbNmgZMaNr27duH6dOn46+//kJUVBTKysowZMgQFBYW6trMmjULf/zxB9avX499+/YhOTkZ48aNkzG16ajJ+wcAzz77rN5ncPHixTIlNj0tW7bEe++9h+joaBw/fhyDBg3C6NGjER8fD8CInz9B9aZnz55i+vTpuvsajUZ4e3uLRYsWyZiq8ViwYIEICQmRO0ajBED8+uuvuvtarVZ4enqKJUuW6Lbl5OQIlUol1qxZI0NC03f7eyiEEJMnTxajR4+WJU9jlJ6eLgCIffv2CSHKP3Pm5uZi/fr1ujZnzpwRAMThw4flimmybn//hBBiwIAB4uWXX5YvVCPk5OQkvvzyS6N+/njmqZ6UlpYiOjoaERERum0KhQIRERE4fPiwjMkal/Pnz8Pb2xtt2rTBxIkTkZSUJHekRikxMRGpqal6n0cHBweEhYXx82igvXv3wt3dHf7+/njhhReQmZkpdySTlZubCwBwdnYGAERHR6OsrEzvc9ixY0e0atWKn8Nq3P7+Vfrhhx/g6uqKoKAgzJs3D0VFRXLEM3kajQY//fQTCgsLER4ebtTPHxcGric3btyARqOBh4eH3nYPDw+cPXtWplSNS1hYGL755hv4+/sjJSUFb775Jvr164dTp07Bzs5O7niNSmpqKgBU+3ms3Ef3NnToUIwbNw5+fn64ePEi/v3vf2PYsGE4fPgwlEql3PFMilarxcyZM9GnTx8EBQUBKP8cWlhYwNHRUa8tP4dVVff+AcCECRPg6+sLb29vnDx5EnPnzkVCQgI2bNggY1rTEhcXh/DwcBQXF8PW1ha//vorAgMDERMTY7TPH4snMlnDhg3T/dy5c2eEhYXB19cX69atw5QpU2RMRs3Vo48+qvs5ODgYnTt3Rtu2bbF3714MHjxYxmSmZ/r06Th16hTHKdbSnd6/qVOn6n4ODg6Gl5cXBg8ejIsXL6Jt27YNHdMk+fv7IyYmBrm5ufj5558xefJk7Nu3z6ivwW67euLq6gqlUlllFH9aWho8PT1lStW4OTo6okOHDrhw4YLcURqdys8cP4/G1aZNG7i6uvIzeZsZM2Zg06ZN2LNnD1q2bKnb7unpidLSUuTk5Oi15+dQ353ev+qEhYUBAD+Dt7CwsEC7du0QGhqKRYsWISQkBB9//LFRP38snuqJhYUFQkNDsWvXLt02rVaLXbt2ITw8XMZkjVdBQQEuXrwILy8vuaM0On5+fvD09NT7PObl5eHIkSP8PNbBtWvXkJmZyc9kBSEEZsyYgV9//RW7d++Gn5+f3v7Q0FCYm5vrfQ4TEhKQlJTEzyHu/f5VJyYmBgD4GbwLrVaLkpISo37+2G1Xj2bPno3Jkyeje/fu6NmzJz766CMUFhbiqaeekjtaozBnzhyMGjUKvr6+SE5OxoIFC6BUKvHYY4/JHc0kFRQU6P3vMzExETExMXB2dkarVq0wc+ZMvPPOO2jfvj38/PzwxhtvwNvbG2PGjJEvtIm523vo7OyMN998E+PHj4enpycuXryI1157De3atUNkZKSMqU3H9OnT8eOPP+L333+HnZ2dbhyJg4MDrKys4ODggClTpmD27NlwdnaGvb09XnzxRYSHh6NXr14yp5ffvd6/ixcv4scff8Tw4cPh4uKCkydPYtasWejfvz86d+4sc3rTMG/ePAwbNgytWrVCfn4+fvzxR+zduxfbt2837ufPuBcE0u2WL18uWrVqJSwsLETPnj3FX3/9JXekRuORRx4RXl5ewsLCQrRo0UI88sgj4sKFC3LHMll79uwRAKrcJk+eLIQon67gjTfeEB4eHkKlUonBgweLhIQEeUObmLu9h0VFRWLIkCHCzc1NmJubC19fX/Hss8+K1NRUuWObjOreOwBi9erVujY3b94U06ZNE05OTsLa2lqMHTtWpKSkyBfahNzr/UtKShL9+/cXzs7OQqVSiXbt2olXX31V5ObmyhvchDz99NPC19dXWFhYCDc3NzF48GCxY8cO3X5jff4kIYSoa6VHRERE1FxwzBMRERGRAVg8ERERERmAxRMRERGRAVg8ERERERmAxRMRERGRAVg8ERERERmAxRMRERGRAVg8ERHdZu/evZAkqcoaWMY2cOBAzJw5s15fo6Ya6piJmgIWT0RkEFP6wjeG6o6nd+/eSElJgYODgzyh6llT+x0SNTQWT0RkdEIIqNVquWPUmoWFBTw9PSFJktxRiMgEsXgiohp78sknsW/fPnz88ceQJAmSJOHy5cu6Lp+tW7ciNDQUKpUKBw4cwMWLFzF69Gh4eHjA1tYWPXr0wM6dO/Wes3Xr1vjPf/6Dp59+GnZ2dmjVqhW++OIL3f7S0lLMmDEDXl5esLS0hK+vLxYtWqTbv2zZMgQHB8PGxgY+Pj6YNm0aCgoK9F7j4MGDGDhwIKytreHk5ITIyEhkZ2ff83hu7cL65Zdf0KlTJ6hUKrRu3RpLly416DhqoqSkBHPmzEGLFi1gY2ODsLAw7N27V7f/m2++gaOjI7Zv346AgADY2tpi6NChSElJ0bVRq9V46aWX4OjoCBcXF8ydOxeTJ0/WLQB9p2OuFB0dje7du8Pa2hq9e/dGQkKCQcdA1CwYbTU+ImrycnJyRHh4uHj22WdFSkqKSElJEWq1WregbufOncWOHTvEhQsXRGZmpoiJiRGrVq0ScXFx4ty5c+L1118XlpaW4sqVK7rn9PX1Fc7OzuLTTz8V58+fF4sWLRIKhUKcPXtWCCHEkiVLhI+Pj9i/f7+4fPmy+PPPP8WPP/6oe/yHH34odu/eLRITE8WuXbuEv7+/eOGFF3T7//77b6FSqcQLL7wgYmJixKlTp8Ty5ctFRkbGPY8nOztbCCHE8ePHhUKhEG+99ZZISEgQq1evFlZWVnoL3t7rOKozYMAA8fLLL+vuP/PMM6J3795i//794sKFC2LJkiVCpVKJc+fOCSGEWL16tTA3NxcRERHi2LFjIjo6WgQEBIgJEybonuOdd94Rzs7OYsOGDeLMmTPi+eefF/b29mL06NE1+h2GhYWJvXv3ivj4eNGvXz/Ru3dvgz8nRE0diyciMsjtX/hCCN0X72+//XbPx3fq1EksX75cd9/X11c8/vjjuvtarVa4u7uLzz77TAghxIsvvigGDRoktFptjfKtX79euLi46O4/9thjok+fPrU6nsriacKECeL+++/Xa/Pqq6+KwMDAGh/HvV77ypUrQqlUiuvXr+u1GTx4sJg3b54Qorx4AiAuXLig2//pp58KDw8P3X0PDw+xZMkS3X21Wi1atWqlK57udcw7d+7Ubdu8ebMAIG7evHnHYyBqjthtR0RG0717d737BQUFmDNnDgICAuDo6AhbW1ucOXMGSUlJeu06d+6s+1mSJHh6eiI9PR1AeTdTTEwM/P398dJLL2HHjh16j925cycGDx6MFi1awM7ODk888QQyMzNRVFQEAIiJicHgwYPrdFxnzpxBnz599Lb16dMH58+fh0ajqdFx3EtcXBw0Gg06dOgAW1tb3W3fvn24ePGirp21tTXatm2ru+/l5aV7jdzcXKSlpaFnz566/UqlEqGhoTU+1luPwcvLCwBqfAxEzYWZ3AGIqOmwsbHRuz9nzhxERUXhgw8+QLt27WBlZYUHH3wQpaWleu3Mzc317kuSBK1WCwDo1q0bEhMTsXXrVuzcuRMPP/wwIiIi8PPPP+Py5csYOXIkXnjhBbz77rtwdnbGgQMHMGXKFJSWlsLa2hpWVlb1e9A1PI57KSgogFKpRHR0NJRKpd4+W1vbu76GEKKWiau69fkrB8zX9BiImgueeSIig1hYWOidbbmbgwcP4sknn8TYsWMRHBwMT09PvcHJNWVvb49HHnkE//3vf7F27Vr88ssvyMrKQnR0NLRaLZYuXYpevXqhQ4cOSE5O1nts586dsWvXrjodT0BAAA4ePFjl2Dp06FCl0Kmtrl27QqPRID09He3atdO7eXp61ug5HBwc4OHhgWPHjum2aTQanDhxQq+dIb9DIqqKZ56IyCCtW7fGkSNHcPnyZdja2sLZ2fmObdu3b48NGzZg1KhRkCQJb7zxhsFnMZYtWwYvLy907doVCoUC69evh6enJxwdHdGuXTuUlZVh+fLlGDVqFA4ePIhVq1bpPX7evHkIDg7GtGnT8Pzzz8PCwgJ79uzBQw89BFdX1xodzyuvvIIePXrg7bffxiOPPILDhw9jxYoVWLlypUHHcjcdOnTAxIkTMWnSJCxduhRdu3ZFRkYGdu3ahc6dO2PEiBE1ep4XX3wRixYtQrt27dCxY0csX74c2dnZetMuGPI7JKKqeOaJiAwyZ84cKJVKBAYGws3Nrcr4pVstW7YMTk5O6N27N0aNGoXIyEh069bNoNezs7PD4sWL0b17d/To0QOXL1/Gli1boFAoEBISgmXLluH9999HUFAQfvjhB71pDIDyomTHjh2IjY1Fz549ER4ejt9//x1mZmY1Pp5u3bph3bp1+OmnnxAUFIT58+fjrbfewpNPPmnQsdzL6tWrMWnSJLzyyivw9/fHmDFjcOzYMbRq1arGzzF37lw89thjmDRpEsLDw2Fra4vIyEhYWlrq2hjyOySiqiRhzM5yIiIyKVqtFgEBAXj44Yfx9ttvyx2HqElgtx0RURNy5coV7NixAwMGDEBJSQlWrFiBxMRETJgwQe5oRE0Gu+2IiJoQhUKBb775Bj169ECfPn0QFxeHnTt3IiAgQO5oRE0Gu+2IiIiIDMAzT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQG+H9n6W6W5uqvUwAAAABJRU5ErkJggg==\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *K* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _K_ value of 100 (in count). We can increase or decrease the _K_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "k = 100 #k is specified in count. However, the users can also specify k between 0 and 1.\n" - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Top-K Periodic Frequent patterns using kPFPMiner" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.periodicFrequentPattern.topk.kPFPMiner import kPFPMiner as alg #import the algorithm\n", - "\n", - "obj = alg.kPFPMiner(iFile=inputFile, k=k, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('topkPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "6fe2d050-5f66-43e2-ce94-a4e066dc023b" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "kPFPMiner has successfully generated top-k frequent patterns\n", - "Total No of patterns: 100\n", - "Runtime: 4.695229530334473\n", - "Memory (RSS): 254177280\n", - "Memory (USS): 207020032\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _K_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'topkPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "6c1ec0fa-ca09-42c8-d9f8-aec0171511b3" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "471:526 \n", - "829 789 :462 \n", - "236:441 \n", - "617:408 \n", - "793:407 \n", - "758:381 \n", - "413:360 \n", - "8:355 \n", - "960:351 \n", - "548:342 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "79b7c047-dc1a-4797-d6b9-78f87b2d19e8" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 09:39:50-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.10MB/s in 6.0s \n", + "\n", + "2023-08-28 09:39:57 (753 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "8577a7fa-c806-4822-8077-76b913bee116" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Top-K Periodic Frequent patterns using kPFPMiner" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (K) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "bdffb1dc-e0c3-4e6e-db8b-b2b11debb4ae" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "33102149-d6c5-4a99-fd1c-e2795dbdee82" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _topkPeriodicFrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the kPFPMiner algorithm on a dataset at different K values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.periodicFrequentPattern.topk.kPFPMiner import kPFPMiner as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "k = [100, 200, 300, 400, 500]\n", - "#K can also specified between 0 to 1. E.g., K = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of kPFPMiner" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of kPFPMiner algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different K values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in k:\n", - " obj = alg.kPFPMiner(inputFile, k=minSupCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['kPFPMiner', minSupCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "da25ebd6-08e7-450b-f287-b1e905a2c74c" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "kPFPMiner has successfully generated top-k frequent patterns\n", - "kPFPMiner has successfully generated top-k frequent patterns\n", - "kPFPMiner has successfully generated top-k frequent patterns\n", - "kPFPMiner has successfully generated top-k frequent patterns\n", - "kPFPMiner has successfully generated top-k frequent patterns\n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *K* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _K_ value of 100 (in count). We can increase or decrease the _K_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "k = 100 #k is specified in count. However, the users can also specify k between 0 and 1.\n" + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Top-K Periodic Frequent patterns using kPFPMiner" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.periodicFrequentPattern.topk.kPFPMiner import kPFPMiner as alg #import the algorithm\n", + "\n", + "obj = alg.kPFPMiner(iFile=inputFile, k=k, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('topkPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6fe2d050-5f66-43e2-ce94-a4e066dc023b" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "kPFPMiner has successfully generated top-k frequent patterns\n", + "Total No of patterns: 100\n", + "Runtime: 4.695229530334473\n", + "Memory (RSS): 254177280\n", + "Memory (USS): 207020032\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _K_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'topkPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6c1ec0fa-ca09-42c8-d9f8-aec0171511b3" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "471:526 \n", + "829 789 :462 \n", + "236:441 \n", + "617:408 \n", + "793:407 \n", + "758:381 \n", + "413:360 \n", + "8:355 \n", + "960:351 \n", + "548:342 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _topkPeriodicFrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the kPFPMiner algorithm on a dataset at different K values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.periodicFrequentPattern.topk.kPFPMiner import kPFPMiner as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "k = [100, 200, 300, 400, 500]\n", + "#K can also specified between 0 to 1. E.g., K = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of kPFPMiner" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of kPFPMiner algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different K values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in k:\n", + " obj = alg.kPFPMiner(inputFile, k=minSupCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['kPFPMiner', minSupCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "da25ebd6-08e7-450b-f287-b1e905a2c74c" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "kPFPMiner has successfully generated top-k frequent patterns\n", + "kPFPMiner has successfully generated top-k frequent patterns\n", + "kPFPMiner has successfully generated top-k frequent patterns\n", + "kPFPMiner has successfully generated top-k frequent patterns\n", + "kPFPMiner has successfully generated top-k frequent patterns\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5595ea01-531b-40d8-9865-a55ae65d03b7" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup patterns runtime memory\n", + "0 kPFPMiner 100 100 4.062737 255533056\n", + "1 kPFPMiner 200 200 10.061997 256196608\n", + "2 kPFPMiner 300 300 14.052537 256794624\n", + "3 kPFPMiner 400 400 21.115762 256720896\n", + "4 kPFPMiner 500 500 29.186294 256475136\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "f72ee25f-7575-4bea-8ba8-d62d7024d4d3" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 14 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5595ea01-531b-40d8-9865-a55ae65d03b7" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup patterns runtime memory\n", - "0 kPFPMiner 100 100 4.062737 255533056\n", - "1 kPFPMiner 200 200 10.061997 256196608\n", - "2 kPFPMiner 300 300 14.052537 256794624\n", - "3 kPFPMiner 400 400 21.115762 256720896\n", - "4 kPFPMiner 500 500 29.186294 256475136\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "f72ee25f-7575-4bea-8ba8-d62d7024d4d3" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 14 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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XkfKrVasWP/30E08//TQ9evSgoKCAyMhI4uPjLyt8XEjt2rX5/vvvGTt2LC1btiQ4OJgHH3yQ55577rK27969O+Hh4TRt2pRatWqVu46yKNPdT5WZ7n4Sqfx+3HaMYZ/+AsBnD95Al4Y1LrFFxTMMg2NZBb8FnCxS0nJIOZbFrmM5FBSff6xCkI97qZATU7Nk3E6Al8brlNfF7qaRipGTk0Pt2rWZOnUqAwcOvGhbU+5+EhExy4nsAp6euRmAYZ2jHDLQwG9z3QV6ERboxY1/Gq9zID33nEtY+9NzycgrYu2+U6zdV3q8Tu0gbxr99mydmDA/YmoG0CDUF083jdcRx2Wz2Th58iRvvvkmQUFB9O3bt8LeW6FGRByeYRiM/WYT6bmFNA7zZ2x8jNkllZmri4X6NfyoX8OPXs1/vyMlv8jK7uO/j9c5e+t5WlY+hzPOcDjjDMv+MF7n7LifP17CahzmT0Q1H1w0XkccQGpqKlFRUdSpU4dp06Zd1cHLl6JQIyIO79PVB0hMOYGHmwv/N6S1U52p8HJ3pVntQJrVLj1eJyOvkJ3HckhJyyp163l2fjG7j+ew+3gO8zlqb+/t7kqjmn72hwmeXWr4abyOVKx69eqZNg+bQo2IOLRdx7J59fvtAIzr1ZhGNf1NrqhiBPl4cENUMDdElX6+TlpWfskDBP/wMMHdJ3I4U2Rl06FMNh3KLLWfYF8PGtX0o3FYQKnA4+epP//ifPRTLSIOq6DYyiMzNlJQbKNroxrc17Ge2SWZymKxEB7oTXigN91iQu2vF1tt7E/P+8N4nSx2Hsthf3oup3ILWbP3FGv2njtep/EfHiQYE+ZP/RA/PNzKf7eMI6oi98JUelfrOCnUiIjDenPRTrYfzSLY14M3/tJCl1EuwM21ZGqH6FA/buX38TpnCq3sOn7uLefHswvs43WW7Dj++35cLNSv4fvb1BD+v00C6k+dat6VbrzO2Sfl5uXl2afxEcdVWFgIUO5n6pylUCMiDmnV7pN8tGIvAK8NakFogG7LLStvD1da1AmiRZ2gUq+fzi38/Q6sY79fysouKGbnsRx2Hsth3ubfx+v4eLjSsKY/jf80XifEz7OCe3T5XF1dCQoKss9j5OPjo1DsoGw2GydOnMDHx+eKBxUr1IiIw8nIK+TxrzZhGDDkhrrc0qSm2SU5lWq+HrSvX5329avbXzMMgyOZ+b8/Wycti5RjOew5nkNeoZVNBzPYdDCj1H6q+3qUerbO2WkifB1kvE5YWMlToc8GG3FcLi4u1K1b94qDpx6+JyIOxTAMRk7/le+T06gf4su8Rzo75Xw9lUWR1cb+k7mlnq2Tciyb1FN5XOjTIyLY+w8PEyy5hFW/hu8FJx291qxWK0VFRaa8t1weDw+PCz79uCyf3wo1IuJQvv7lIGO/2Yybi4Vv/97xnEsn4hjyCovZdaz0fFg70rI5mVNw3vburhYa1PjDLee//bd2UOUbryMVS08UFpFK6UB6Li99txWAx25ppEDjwHw83GgZEUTLiKBSr6fnlEwRsfOP43WO5ZBTUMyO34IPm35v7+vhar8D64aoYPq2rK1JP6XcdKZGRBxCsdXGHR+s5tfUDG6ICuaL4e314eYkDMPg0Okz9gcIpvz2MME9J3Iospb+CIoND+ClPk1o94fxPlK16fLTeSjUiDi2t3/cyds/7sLfy40Fj3ahTjUfs0uSa6yw2Mb+9Fx2pGWz9Ugm09emkp1fDMCtLcIZ16uxfg5EoeZ8FGpEHNf6A6e5/f1V2Ax4585W9GtV2+ySxATpOQW8uXgnX6xLxTDA082Fh29swMM3NsDbw3mmxpCyKcvnt3M9OlJEKp2cgmIe+3IjNgP6taqlQFOFVffz5NUBzZk3ujM3RAVTUGzjnSW7uPnNROZuOqKnA8slKdSIiKle+m4rqafyqB3kzYR+zcwuRxxA01qBfPlQe/59V2tqBXpxJDOf0V9sYPCHa9h6JPPSO5AqS6FGREzzffJRvll/CBcLTB7cikBvd7NLEgdhsVi4rUUtljwRx5juDfF0c2HdvlP0+ddKxs9KJv0Ct45L1aZQIyKmOJp5hnHfJgMwIq5BqdmoRc7y9nBlTPdGLH0yjltbhGMzYPraVLq9kcjHK/dRZLWZXaI4EIUaEalwNpvBE19tIvNMES3qBDKmeyOzSxIHVzvIm3fvuo4vH2pPbHgAWfnFTJi3jV7vrGD5zhNmlycOQqFGRCrcf1buZdWedLzdXXl7cCvTHp8vlU+7+tWZN7oz/xjQjGo+7uw+nsPQj9cx7JNf2H8y1+zyxGT6SyIiFWrrkUxe/yEFgOdva0L9Gn4mVySVjauLhbvbRZL4ZDfu71QPVxcLP24/Ro/Jy5m0YAc5BcVmlygmUagRkQqTX2Tl0RkbKbIa3NKkJkNuiDC7JKnEAn3cebFPUxY+2oUuDUMotNp4P2kP3d5I5Jv1h7DZdAt4VaNQIyIVJuH77ew+nkMNf09eG9QCi0XTIMiVa1jTn08fuIGPhl5PZHUfTmQX8OTXmxgwZRUbUk+bXZ5UIIUaEakQy3Yc55PVBwB44/aWBPt6mFyROBOLxcItTWqy6LGuPB3fGF8PVzYdzGDAe6t4/KuNHM/KN7tEqQAKNSJyzZ3MKWDsNyVTM9/XsR43NqphckXirDzdXBkR14BlT8Yx6Lo6AHz762G6vZHIlMQ9FBRbTa5QriWFGhG5pgzD4OlvNnMyp5BGNf14pldjs0uSKiA0wIs372jJrL93pGVEELmFVl5buIMek5ezeNsxTbngpBRqROSa+t/aVJbsOI6Hqwvv3NkaL3dNTCgVp3Xdaswa0ZE3b29JDX9PDqTnMfzTXxj68Tp2Hcs2uzy5yhRqROSa2X08h1fmbwPgqfgYYsMvPsOuyLXg4mJhUJs6LHsyjodvbICHqwsrdp0k/p0VvDx3K5l5RWaXKFeJQo2IXBOFxTbGfLmB/CIbnaNDeKBTlNklSRXn5+nGM70as+ixrnSPrYnVZjD1p/10ezOR/609gFW3gFd6CjUick28tXgnWw5nEeTjzpt3tMTFRbdvi2OoF+LLf+69nk8fuIHoUD9O5Rby7Kwt3Pavlazdm252eXIFFGpE5KpbvSedD5bvAWDSwObUDPAyuSKRc3VtVIMFj3bhhdua4O/lxvajWQz+cA0jp//KodN5Zpcn5aBQIyJXVWZeEU98tRHDgDuur0N8s3CzSxK5IHdXFx7oHEXik3Hc1a4uFgvM33yUm99MYvLinZwp1C3glYlCjYhcNYZh8NycLRzJzKdedR9e7NPU7JJELkt1P09eHdCceaM7c0NUMAXFNt5ZsovubyUxb/MR3QJeSSjUiMhVM3vjYeZuOoKri4XJg1vh6+lmdkkiZdK0ViBfPtSef9/VmlqBXhzOOMOo6RsY/OEath7JNLs8uQSFGhG5Kg6eyuP52VsBePTmhrSuW83kikTKx2KxcFuLWix5Io4x3Rvi5e7Cun2n6POvlYyflUx6ToHZJcoFKNSIyBUrttp47MuN5BQU0yayGn+Pa2B2SSJXzNvDlTHdG7HkiThuaxGOzYDpa1Pp9kYiH6/cR5HVZnaJ8icKNSJyxaYk7uGXA6fx83Tj7cGtcHPVnxZxHrWDvPn3Xdfx5UPtaRIeQFZ+MRPmbaPXOytYvvOE2eXJH+gvj4hckY0HM3h7yS4AJvRrSkSwj8kViVwb7epXZ+7ozrw6oDnBvh7sPp7D0I/XMeyTX9h/Mtfs8gSFGhG5ArkFxYyZsQGrzeC2FuEMaF3b7JJErilXFwt3tavLsifiuL9TPVxdLPy4/Rg9Ji9n0oId5BQUm11ilVamUJOQkEDbtm3x9/cnNDSU/v37k5KSctFtpk2bhsViKbV4eZ37IK7t27fTt29fAgMD8fX1pW3btqSmppZqs3r1am666SZ8fX0JCAiga9eunDlzpixdEJGraOK8bexPz6NWoBf/6N8ci0VPDZaqIdDHnRf7NGXho13o0jCEQquN95P2cNMbicxcfwibplwwRZlCTVJSEiNHjmTNmjUsXryYoqIievToQW7uxU+7BQQEcPToUfty4MCBUuv37NlD586dady4MYmJiWzevJnnn3++VPhZvXo18fHx9OjRg3Xr1vHzzz8zatQoXFx0sknEDAu3pDHj54NYLPDmHa0I9HE3uySRCtewpj+fPnADHw29nsjqPhzPLuCJrzcxcMoqNqSeNru8KsdiXMEThU6cOEFoaChJSUl07dr1vG2mTZvGmDFjyMjIuOB+7rzzTtzd3fnss88u2KZ9+/bccsstTJw4sVy1ZmVlERgYSGZmJgEBmilY5Eocy8qn59vLycgr4m831mdcr1izSxIxXUGxlY9X7uffS3eR+9uTiAddV4en42MI1VQh5VaWz+8rOs2RmVnyIKLg4OCLtsvJySEyMpKIiAj69evH1q1b7etsNhvz58+nUaNG9OzZk9DQUNq1a8fs2bPtbY4fP87atWsJDQ2lY8eO1KxZkxtvvJGVK1deSfkiUg42m8GTX28iI6+IZrUDeOKWGLNLEnEInm6ujIhrwLIn4xh0XR0AZv56iG5vJDIlcQ8FxZpy4Vord6ix2WyMGTOGTp060axZswu2i4mJ4eOPP2bOnDl8/vnn2Gw2OnbsyKFDh4CSwJKTk8OkSZOIj49n0aJFDBgwgIEDB5KUlATA3r17AXjppZcYPnw4Cxcu5LrrruPmm29m165d533fgoICsrKySi0icuWmrtrPil0n8XJ34e3BrfFw0yVgkT8KDfDizTtaMuvvHWkZEURuoZXXFu6gx+TlLN52TFMuXEPlvvw0YsQIFixYwMqVK6lTp85lb1dUVERsbCxDhgxh4sSJHDlyhNq1azNkyBCmT59ub9e3b198fX354osvWLVqFZ06dWLcuHG8+uqr9jYtWrTg1ltvJSEh4Zz3eemll3j55ZfPeV2Xn0TKb/vRLPr9+ycKrTYm9m/GPe0jzS5JxKHZbAazNhxm0sIdnMgueRJxl4YhvHBbExrW9De5usrhml9+GjVqFPPmzWPZsmVlCjQA7u7utG7dmt27dwMQEhKCm5sbTZo0KdUuNjbWfvdTeHjJLL8Xa/Nn48aNIzMz074cPHiwTHWKSGn5RVbGzNhIodXGzY1D+Wu7umaXJOLwXFwsDGpTh2VPxjEirgEeri6s2HWS+HdW8PLcrWTmFZldolMpU6gxDINRo0Yxa9Ysli5dSlRUVJnf0Gq1kpycbA8qHh4etG3b9pxbw3fu3ElkZMn/BdarV49atWpdtM2feXp6EhAQUGoRkfJ7beEOUo5lE+LnwWt/aaHbt0XKwM/TjafjG7Posa7c0qQmVpvB1J/20+3NRP639gBW3QJ+VZRpCt2RI0cyffp05syZg7+/P2lpaQAEBgbi7e0NwNChQ6ldu7b9ktCECRNo37490dHRZGRk8Prrr3PgwAGGDRtm3+/YsWMZPHgwXbt2pVu3bixcuJC5c+eSmJgIlEwuNnbsWF588UVatmxJq1at+OSTT9ixYwfffPPN1fg+iMhFLN95gqk/7Qfg9b+0JMTP09yCRCqpeiG+fDT0elbsOsHLc7ex+3gOz87awv/WpPJinya0q1/d7BIrtTKFmilTpgAQFxdX6vWpU6dy3333AZCamlrq2TGnT59m+PDhpKWlUa1aNdq0acOqVatKXUoaMGAA77//PgkJCTzyyCPExMQwc+ZMOnfubG8zZswY8vPzeeyxxzh16hQtW7Zk8eLFNGigifNErqVTuYU88fUmAIZ2iKRb41CTKxKp/Lo0rMGCR7vw2eoDTP5xJ9uOZjH4wzXc2iKc8b1jqR3kbXaJldIVPaemMtFzakTKzjAMHvpsPYu3HSM61I95ozvj5e5qdlkiTiU9p4A3F+/ki3WpGAZ4ubvw8I0N+FvXBnh76Petwp5TIyLObcbPB1m87RjurhbeubOVAo3INVDdz5NXBzRn3ujO3BAVTH6Rjbd/3EX3t5KYt/mIbgEvA4UaETmvvSdymDB3GwBP9oihaa1AkysScW5NawXy5UPt+fddrakV6MXhjDOMmr6BwR+uYeuRTLPLqxQUakTkHEVWG499uZEzRVY61K/O8C71zS5JpEqwWCzc1qIWS56IY0z3hni5u7Bu3yn6/Gsl42clk55TYHaJDk2hRkTO8c6Pu9h0KJNAb3fevKMlLi66fVukInl7uDKmeyOWPBHHbS3CsRkwfW0q3d5I5OOV+yiy2swu0SEp1IhIKev2neK9xJKHY746oDm1dBeGiGlqB3nz77uu48uH2tMkPICs/GImzNtGr3dWsHznCbPLczgKNSJil5VfxGNfbsRmlMwufGuLcLNLEhGgXf3qzB3dmVcHNCfY14Pdx3MY+vE6hn3yCwfSc80uz2Eo1IiI3Quzt3A44wwRwd681LfJpTcQkQrj6mLhrnZ1WfZEHA90isLNxcKP249xy1vLeW3hDnIKis0u0XQKNSICwJyNh5m98QguFnh7cCv8vdzNLklEziPQx50X+jRh4ZgudGkYQqHVxpTEPdz0RiIz1x/CVoWnXFCoEREOnc7judlbABh1U0PaRAabXJGIXEp0qD+fPnAD/xl6PZHVfTieXcATX29i4JRVbEg9bXZ5plCoEanirDaDx7/aRHZ+Ma3rBvHITdFmlyQil8lisdC9SU0WPdaVp+Mb4+vhysaDGQx4bxVPfLWJ41n5ZpdYoRRqRKq4D5bvYd2+U/h6uPL24Fa4uerPgkhl4+nmyoi4Bix7Mo5B19UBYOavh+j2RiJTEvdQUGw1ucKKob9eIlXY5kMZvLVoJwAv9m1KZHVfkysSkSsRGuDFm3e0ZNbfO9IyIojcQiuvLdxBj8nLWbztmNNPuaBQI1JF5RUWM2bGRoptBr2ahXF7mzpmlyQiV0nrutWYNaIjb97ekhr+nhxIz2P4p78w9ON17D6ebXZ514xCjUgV9cr87ew9mUvNgJLJ9CwWPTVYxJm4uFgY1KYOy56MY0RcAzxcXVix6yQ9317By3O3knmmyOwSrzqFGpEqaPG2Y0xfmwrAW3e0opqvh8kVici14ufpxtPxjVn0WFduaVITq81g6k/76fZGItPXpmJ1olvAFWpEqpjj2fk8PXMzAMO7RNEpOsTkikSkItQL8eWjodfz2YM3EB3qx6ncQsbPSqbPv1aydm+62eVdFQo1IlWIYRiM/Xozp3ILiQ0P4MmeMWaXJCIVrEvDGix4tAsv9mlCgJcb245mMfjDNYyc/iuHM86YXd4VUagRqUI+WbWfpJ0n8HRz4Z07W+Hp5mp2SSJiAndXF+7vFMWyJ+O4u11dXCwwf/NRbn4zkbd/3MmZwsp5C7hCjUgVsfNYNq8u2AHAuF6NaVTT3+SKRMRs1f08+ceA5swd3ZkbooLJL7Lx9o+76P5WEvM2H6l0t4Ar1IhUAQXFVh75YgOFxTZubFSDezvWM7skEXEgTWsF8uVD7fn3Xa2pFejF4YwzjJq+gcEfrmHrkUyzy7tsCjUiVcAbP6SwIy2bYF8PXr+9hW7fFpFzWCwWbmtRiyVPxDGme0O83F1Yt+8Uff61kvGzkjmVW2h2iZekUCPi5FbuOslHK/YB8M9BLQj19zK5IhFxZN4erozp3oglT8RxW4twbAZMX5tK3OvL+HjlPoqsNrNLvCCFGhEndjq3kCe+3gjAXe3q0r1JTXMLEpFKo3aQN/++6zq+fKg9TcIDyMovZsK8bfR+ZwUrdp0wu7zzUqgRcVKGYTB+VjLHsgqoH+LLc7fGml2SiFRC7epXZ+7ozrw6oDnBvh7sOp7DPf9dx7BPfuFAeq7Z5ZWiUCPipL5ef4gFW9Jwc7Hwzp2t8fFwM7skEamkXF0s3NWuLsueiOOBTlG4uVj4cfsxbnlrOa8t3EFOQbHZJQIKNSJO6UB6Li9/txWAx3s0onmdQJMrEhFnEOjjzgt9mrBwTBe6NAyh0GpjSuIebnojkZnrD2EzecoFhRoRJ1NktfHojI3kFlq5ISqYv3VtYHZJIuJkokP9+fSBG/jP0OuJrO7D8ewCnvh6EwOnrCIr37yJMhVqRJzMv5buZuPBDPy93Jg8uBWuLrp9W0SuPovFQvcmNVn0WFee6dUYXw9X/L3c8Pc071K3LrKLOJH1B07x76W7AHilfzNqB3mbXJGIODtPN1cevrEBA1vXpqDYZupzsBRqRJxEdn4RY77ciM2A/q1q0a9VbbNLEpEqJDTA/Gdg6fKTiJN46bttHDx1htpB3kzo38zsckREKpxCjYgTmLf5CDN/PYSLBd6+sxUBXu5mlyQiUuEUakQquSMZZxj/bTIAf4+Lpm29YJMrEhExh0KNSCVmsxk88dUmsvKLaVknkEe7NzS7JBER0yjUiFRiH63Yy+q96Xi7uzJ5cCvcXfUrLSJVl/4CilRSWw5n8saiFABe7NOE+jX8TK5IRMRcCjUildCZQitjvtxIkdWgR5OaDG4bYXZJIiKmU6gRqYRe/X47u4/nEOrvyaRBLUx92JWIiKNQqBGpZJbuOMZnaw4A8MbtLQn29TC5IhERx6BQI1KJnMwp4KlvNgNwf6d6dG1Uw+SKREQch0KNSCVhGAZPfbOZkzmFxNT05+n4xmaXJCLiUBRqRCqJz9emsnTHcTzcXHhnSCu83F3NLklExKEo1IhUAruPZ/OP+dsAeDq+MY3DAkyuSETE8ZQp1CQkJNC2bVv8/f0JDQ2lf//+pKSkXHSbadOmYbFYSi1eXufO5Ll9+3b69u1LYGAgvr6+tG3bltTU1HPaGYZBr169sFgszJ49uyzli1RKhcU2Hp2xkfwiG10ahnB/x3pmlyQi4pDKFGqSkpIYOXIka9asYfHixRQVFdGjRw9yc3Mvul1AQABHjx61LwcOHCi1fs+ePXTu3JnGjRuTmJjI5s2bef75588bft5++23dvipVypuLU9h6JItqPu68cXtLXFz08y8icj5uZWm8cOHCUl9PmzaN0NBQ1q9fT9euXS+4ncViISws7ILrn332WXr37s0///lP+2sNGjQ4p93GjRt58803+eWXXwgPDy9L6SKV0uo96Xy4fC8ACQNbUDPg3KAvIiIlrmhMTWZmJgDBwRefFTgnJ4fIyEgiIiLo168fW7duta+z2WzMnz+fRo0a0bNnT0JDQ2nXrt05l5by8vK46667ePfddy8akM4qKCggKyur1CJSmWTmFfH4VxsxDLizbQTxzS79cy8iUpWVO9TYbDbGjBlDp06daNas2QXbxcTE8PHHHzNnzhw+//xzbDYbHTt25NChQwAcP36cnJwcJk2aRHx8PIsWLWLAgAEMHDiQpKQk+34ee+wxOnbsSL9+/S6rvoSEBAIDA+1LRIQeIy+Vh2EYjJ+dzNHMfOpV9+H525qYXZKIiMOzGIZhlGfDESNGsGDBAlauXEmdOnUue7uioiJiY2MZMmQIEydO5MiRI9SuXZshQ4Ywffp0e7u+ffvi6+vLF198wXfffccTTzzBhg0b8PMrmbTPYrEwa9Ys+vfvf973KSgooKCgwP51VlYWERERZGZmEhCgO0fEsc1cf4gnvt6Eq4uFmSM60ioiyOySRERMkZWVRWBg4GV9fpfrTM2oUaOYN28ey5YtK1OgAXB3d6d169bs3r0bgJCQENzc3GjSpPT/icbGxtrvflq6dCl79uwhKCgINzc33NxKhgINGjSIuLi4876Pp6cnAQEBpRaRyuDgqTxe/K7kEu2Ymxsq0IiIXKYyDRQ2DIPRo0cza9YsEhMTiYqKKvMbWq1WkpOT6d27NwAeHh60bdv2nFvDd+7cSWRkJADPPPMMw4YNK7W+efPmTJ48mT59+pS5BhFHVWy1MebLjeQUFHN9ZDX+3i3a7JJERCqNMoWakSNHMn36dObMmYO/vz9paWkABAYG4u3tDcDQoUOpXbs2CQkJAEyYMIH27dsTHR1NRkYGr7/+OgcOHCgVUsaOHcvgwYPp2rUr3bp1Y+HChcydO5fExEQAwsLCzjs4uG7duuUKViKO6r3EPaw/cBp/TzcmD26Fq27fFhG5bGUKNVOmTAE455LP1KlTue+++wBITU3FxeX3q1qnT59m+PDhpKWlUa1aNdq0acOqVatKXW4aMGAA77//PgkJCTzyyCPExMQwc+ZMOnfuXM5uiVQ+G1JP886SXQBM6N+UiGAfkysSEalcyj1QuLIpy0AjkYqWU1DMrf+3ggPpefRpWYv/u7OVHjIpIkIFDBQWkatrwtytHEjPo1agF6/0b6ZAIyJSDgo1IiZbuOUoX/1yCIsF3hrcikBvd7NLEhGplBRqREyUlpnPM98mA/C3rg1oX7+6yRWJiFReCjUiJrHZDJ78ehMZeUU0qx3A47c0MrskEZFKTaFGxCQf/7SPlbtP4uXuwtuDW+Phpl9HEZErob+iIibYdiSLfy4seeDkc7c2ITrUz+SKREQqP4UakQqWX2RlzJcbKLTa6B4byt3t6ppdkoiIU1CoEalgkxbsYOexHEL8PJg0qIVu3xYRuUoUakQqUNLOE0xbtR+A129vSYifp7kFiYg4EYUakQqSnlPAk19vAuDeDpF0iwk1uSIREeeiUCNSAQzD4OmZyZzILqBhqB/jeseaXZKIiNNRqBGpAF+sO8iP24/h7mrh7Ttb4eXuanZJIiJOR6FG5BrbeyKHifO2ATC2ZwxNawWaXJGIiHNSqBG5hoqsNsZ8uZEzRVY6NqjOsM71zS5JRMRpKdSIXENv/7iTzYcyCfR25807WuLiotu3RUSuFYUakWtk7d503kvcA0DCwOaEB3qbXJGIiHNTqBG5BjLPFPH4V5swDPhLmzr0bh5udkkiIk5PoUbkGnhhzhYOZ5yhbrAPL/VtanY5IiJVgkKNyFU2Z+Nh5mw8gquLhcmDW+Hn6WZ2SSIiVYJCjchVdOh0Hs/N2gLA6JuiaRNZzeSKRESqDoUakavEajN4/MtNZBcUc13dIEZ1iza7JBGRKkWhRuQqeT9pD+v2n8LXw5W3B7fGzVW/XiIiFUl/dUWugs2HMpi8eCcAL/VtSt3qPiZXJCJS9SjUiFyhvMJixszYSLHNoHfzMP7Spo7ZJYmIVEkKNSJXaOK87ew9mUtYgBevDmiOxaKnBouImEGhRuQKLNqaxhfrUrFY4K07WhLk42F2SSIiVZZCjUg5Hc/K5+mZmwEY3qU+HaNDTK5IRKRqU6gRKQebzeDJbzZzOq+IJuEBPNGjkdkliYhUeQo1IuXwyer9LN95Ak83F965sxWebq5mlyQiUuUp1IiUUUpaNgkLdgAwvncsDWv6m1yRiIiAQo1ImeQXWXl0xgYKi210i6nB0A6RZpckIiK/UagRKYPXf0hhR1o21X09+OdfWur2bRERB6JQI3KZVuw6wX9X7gPgn39pQQ1/T5MrEhGRP1KoEbkMp3MLefLrTQDc3a4uN8fWNLkiERH5M4UakUswDINx3yZzLKuA+jV8ee7WJmaXJCIi56FQI3IJX/9yiIVb03B3tfB/d7bG20O3b4uIOCKFGpGL2H8yl5fmbgXg8VtiaFY70OSKRETkQhRqRC6gyGrj0S83kldopV1UMA91rW92SSIichEKNSIX8K8lu9h0MAN/LzfeGtwKVxfdvi0i4sgUakTO45f9p/j3st0A/GNAc2oHeZtckYiIXIpCjcifZOcXMebLjdgMGNi6Nn1b1jK7JBERuQwKNSJ/8uJ3Wzl0+gx1qnnzcr+mZpcjIiKXqUyhJiEhgbZt2+Lv709oaCj9+/cnJSXlottMmzYNi8VSavHy8jqn3fbt2+nbty+BgYH4+vrStm1bUlNTATh16hSjR48mJiYGb29v6tatyyOPPEJmZmZZyhe5pLmbjvDtr4dxscDbg1vh7+VudkkiInKZyhRqkpKSGDlyJGvWrGHx4sUUFRXRo0cPcnNzL7pdQEAAR48etS8HDhwotX7Pnj107tyZxo0bk5iYyObNm3n++eft4efIkSMcOXKEN954gy1btjBt2jQWLlzIgw8+WMbuilzYkYwzPDsrGYCR3aK5vl6wyRWJiEhZWAzDMMq78YkTJwgNDSUpKYmuXbuet820adMYM2YMGRkZF9zPnXfeibu7O5999tllv/fXX3/NX//6V3Jzc3Fzc7tk+6ysLAIDA8nMzCQgIOCy30eqBqvN4O7/rGHN3lO0jAjim4c74O6qq7MiImYry+f3Ff3VPnv5Jzj44v9Hm5OTQ2RkJBEREfTr14+tW7fa19lsNubPn0+jRo3o2bMnoaGhtGvXjtmzZ1/yvQMCAi4YaAoKCsjKyiq1iFzIRyv2smbvKXw8XHl7cCsFGhGRSqjcf7ltNhtjxoyhU6dONGvW7ILtYmJi+Pjjj5kzZw6ff/45NpuNjh07cujQIQCOHz9OTk4OkyZNIj4+nkWLFjFgwAAGDhxIUlLSefd58uRJJk6cyEMPPXTB901ISCAwMNC+RERElLer4uS2HsnkzUUlY8Ne7NOEqBBfkysSEZHyKPflpxEjRrBgwQJWrlxJnTp1Lnu7oqIiYmNjGTJkCBMnTuTIkSPUrl2bIUOGMH36dHu7vn374uvryxdffFFq+6ysLG655RaCg4P57rvvcHc//0DOgoICCgoKSm0XERGhy09SSrHVRr93f2LrkSx6NKnJB/e0wWLRQ/ZERBxFWS4/XXowynmMGjWKefPmsXz58jIFGgB3d3dat27N7t0lDzYLCQnBzc2NJk1Kz3wcGxvLypUrS72WnZ1NfHw8/v7+zJo164KBBsDT0xNPT88y1SZVz8c/7WPrkSwCvNx4ZUAzBRoRkUqsTJefDMNg1KhRzJo1i6VLlxIVFVXmN7RarSQnJxMeHg6Ah4cHbdu2PefW8J07dxIZGWn/Oisrix49euDh4cF333133tvCRcoiNT2PtxbvBODZW2MJ9dfPlIhIZVamMzUjR45k+vTpzJkzB39/f9LS0gAIDAzE27vkMfJDhw6ldu3aJCQkADBhwgTat29PdHQ0GRkZvP766xw4cIBhw4bZ9zt27FgGDx5M165d6datGwsXLmTu3LkkJiYCvweavLw8Pv/881IDf2vUqIGrq+sVfyOkajEMg/GzkskvstGhfnXuuF5jrkREKrsyhZopU6YAEBcXV+r1qVOnct999wGQmpqKi8vvJ4BOnz7N8OHDSUtLo1q1arRp04ZVq1aVutw0YMAA3n//fRISEnjkkUeIiYlh5syZdO7cGYBff/2VtWvXAhAdHV3qvfft20e9evXK0g0Rvv31MCt3n8TDzYVXBzbXZScRESdwRc+pqUz0nBo5Kz2ngO5vJXE6r4ixPWMY2S360huJiIgpKuw5NSKV0cR52zidV0TjMH8e6lrf7HJEROQqUaiRKmVZynFmbzyCiwVeG9RCD9kTEXEi+osuVUZuQTHPzdoCwH0do2gZEWRuQSIiclUp1EiV8dbinRzOOEPtIG+e6NHI7HJEROQqU6iRKmHTwQym/rQPgH8MaIavZ7meOykiIg5MoUacXpHVxtMzN2MzoF+rWsTFhJpdkoiIXAMKNeL0Plqxlx1p2QT5uPP8bU0uvYGIiFRKCjXi1PadzOWdH3cB8PytTQjx03xgIiLOSqFGnJZhGIz/NpmCYhtdGoYw8LraZpckIiLXkEKNOK2vfznE6r3peLm78I/+mgpBRMTZKdSIUzqenc8/vt8OwOO3NKJudR+TKxIRkWtNoUac0stzt5F5pohmtQN4oFOU2eWIiEgFUKgRp7Nk+zHmbz6Kq4uFSQNb4KapEEREqgT9tRenklNQzHOzS6ZCeLBzFM1qB5pckYiIVBSFGnEqb/yQwtHMfOoG+/BYd02FICJSlSjUiNNYf+A0n6zeD5RMheDt4WpuQSIiUqEUasQpFBbbGPftZgwDBl5Xmy4Na5hdkoiIVDCFGnEKHyTtYeexHKr7evD8rZoKQUSkKlKokUpv9/Ec/rV0NwAv9GlCNV8PkysSEREzKNRIpWazlUyFUGi1cWOjGvRtWcvskkRExCQKNVKpzfj5IOv2n8Lb3ZVX+jfTVAgiIlWYQo1UWsey8klYUDIVwpM9Y4gI1lQIIiJVmUKNVFovztlKdn4xLesEcl/HemaXIyIiJlOokUrph61pLNyahquLhYSBLXB10WUnEZGqTqFGKp2s/CJemFMyFcLfutanSa0AkysSERFHoFAjlc5rC3ZwLKuAetV9eOTmhmaXIyIiDkKhRiqVn/ef4n9rUwF4dWBzvNw1FYKIiJRQqJFKo6DYyjMzNwMw+PoIOjYIMbkiERFxJAo1Umm8u2wPe07kEuLnyfjesWaXIyIiDkahRiqFnceymZJYMhXCS32bEOjjbnJFIiLiaBRqxOHZbAbjvk2myGpwc+NQbm0ebnZJIiLigBRqxOH9b+0B1h84ja+HKxM1FYKIiFyAQo04tKOZZ3htYQoAT8U3plaQt8kViYiIo1KoEYdlGAbPz95KTkExresG8df2kWaXJCIiDkyhRhzWgi1p/Lj9GO6uFl4bpKkQRETk4hRqxCFl5hXxwpytAIy4sQGNavqbXJGIiDg6hRpxSAkLtnMyp4D6NXz5e7dos8sREZFKQKFGHM6avenM+PkgAJMGttBUCCIiclkUasSh5BdZGfdtMgB3tavLDVHBJlckIiKVhUKNOJR/Ld3FvpO5hPp78kyvxmaXIyIilYhCjTiM7Uez+CBpLwAT+jUlwEtTIYiIyOVTqBGHYLUZPPNtMsU2g55NaxLfTFMhiIhI2ZQp1CQkJNC2bVv8/f0JDQ2lf//+pKSkXHSbadOmYbFYSi1eXl7ntNu+fTt9+/YlMDAQX19f2rZtS2pqqn19fn4+I0eOpHr16vj5+TFo0CCOHTtWlvLFgX2yaj+bDmbg7+nGhH7NzC5HREQqoTKFmqSkJEaOHMmaNWtYvHgxRUVF9OjRg9zc3ItuFxAQwNGjR+3LgQMHSq3fs2cPnTt3pnHjxiQmJrJ582aef/75UuHnscceY+7cuXz99dckJSVx5MgRBg4cWJbyxUEdOp3HG4tKwvHTvRpTM+Dc0CsiInIpFsMwjPJufOLECUJDQ0lKSqJr167nbTNt2jTGjBlDRkbGBfdz55134u7uzmeffXbe9ZmZmdSoUYPp06fzl7/8BYAdO3YQGxvL6tWrad++/SVrzcrKIjAwkMzMTAICAi7dOakQhmHwwLSfWZZygrb1qvHlQx1w0ZODRUTkN2X5/L6iMTWZmZkABAdf/LbbnJwcIiMjiYiIoF+/fmzdutW+zmazMX/+fBo1akTPnj0JDQ2lXbt2zJ49295m/fr1FBUV0b17d/trjRs3pm7duqxevfpKuiAmm7v5KMtSTuDh6kLCwBYKNCIiUm7lDjU2m40xY8bQqVMnmjW78BiImJgYPv74Y+bMmcPnn3+OzWajY8eOHDp0CIDjx4+Tk5PDpEmTiI+PZ9GiRQwYMICBAweSlJQEQFpaGh4eHgQFBZXad82aNUlLSzvv+xYUFJCVlVVqEcdyOreQl78rCbgju0UTHepnckUiIlKZuZV3w5EjR7JlyxZWrlx50XYdOnSgQ4cO9q87duxIbGwsH3zwARMnTsRmswHQr18/HnvsMQBatWrFqlWreP/997nxxhvLVV9CQgIvv/xyubaVivGP77eTnltIw1A/RsQ1MLscERGp5Mp1pmbUqFHMmzePZcuWUadOnTJt6+7uTuvWrdm9ezcAISEhuLm50aRJk1LtYmNj7Xc/hYWFUVhYeM64nGPHjhEWFnbe9xk3bhyZmZn25eDBg2WqU66tlbtO8s36Q1gsMGlQCzzc9HQBERG5MmX6JDEMg1GjRjFr1iyWLl1KVFRUmd/QarWSnJxMeHjJc0g8PDxo27btObeG79y5k8jISADatGmDu7s7S5Yssa9PSUkhNTW11FmgP/L09CQgIKDUIo7hTKGV8bNKpkK4p30kbSKrmVyRiIg4gzJdfho5ciTTp09nzpw5+Pv728ezBAYG4u3tDcDQoUOpXbs2CQkJAEyYMIH27dsTHR1NRkYGr7/+OgcOHGDYsGH2/Y4dO5bBgwfTtWtXunXrxsKFC5k7dy6JiYn2/T/44IM8/vjjBAcHExAQwOjRo+nQocNl3fkkjuXtJTtJPZVHWIAXY3vGmF2OiIg4iTKFmilTpgAQFxdX6vWpU6dy3333AZCamoqLy+8ngE6fPs3w4cNJS0ujWrVqtGnThlWrVpW63DRgwADef/99EhISeOSRR4iJiWHmzJl07tzZ3mby5Mm4uLgwaNAgCgoK6NmzJ++9915Z+ysm23I4k/+s2AfAxP7N8NdUCCIicpVc0XNqKhM9p8Z8xVYbA95bRfLhTG5tHs67d19ndkkiIuLgKuw5NSJlMfWn/SQfziTAy40X+za59AYiIiJloFAjFeLgqTzeWrwTgPG9Ywn111QIIiJydSnUyDVnGAbjZyVzpshK+/rBDG4bYXZJIiLihBRq5JqbteEwK3adxMOtZCoEi0VTIYiIyNWnUCPXVHpOARPnbQPg0ZsbEhXia3JFIiLirBRq5Jp6Zf52TucV0TjMn4e61je7HBERcWIKNXLNJO08wawNh+1TIbi76sdNRESuHX3KyDWRV1jMs79NhXBfx3q0iggytyAREXF6CjVyTby1aCeHTp+hdpA3T/bQVAgiInLtKdTIVbf5UAYf/1QyFcIrA5rh61mm2ThERETKRaFGrqoiq42nZyZjM6Bvy1p0iwk1uyQREakiFGrkqvrPin1sP5pFkI87L/TRVAgiIlJxFGrkqtl/Mpe3fyyZCuG5W5sQ4udpckUiIlKVKNTIVXF2KoSCYhudo0MYdF1ts0sSEZEqRqFGroqv1x9i1Z50vNxd+MeAZpoKQUREKpxCjVyxE9kF/GP+dgAe696IyOqaCkFERCqeQo1csQnztpF5poimtQJ4sHOU2eWIiEgVpVAjV2TpjmPM3XQEFwtMGtgCN02FICIiJtEnkJRbTkExz83aAsCDnaNoXifQ5IpERKQqU6iRcnvjhxSOZOYTEezNY7c0MrscERGp4hRqpFx+TT3NJ6v3A/DqgOb4eGgqBBERMZdCjZRZYbGNcTOTMQwY2Lo2XRrWMLskERERhRopuw+X7yHlWDbBvh48d5umQhAREcegUCNlsudEDv+3dDcAL9zWhGBfD5MrEhERKaFQI5fNZjMY920yhcU2ujaqQb9WtcwuSURExE6hRi7bl78cZN2+U3i7u/KP/poKQUREHItCjVyW41n5vPp9yVQIT/RoRESwj8kViYiIlKZQI5flxe+2kp1fTIs6gdzfSVMhiIiI41GokUtatDWNBVvScHWxMGlgC1xddNlJREQcj0KNXFR2fhEvzNkKwENd69OkVoDJFYmIiJyfQo1c1D8XppCWlU+96j48enNDs8sRERG5IIUauaBf9p/iszUHgJKpELzcXU2uSERE5MIUauS8CoqtPPNtMgB3XF+HjtEhJlckIiJycQo1cl5TEvew+3gOIX4ejO8da3Y5IiIil6RQI+fYdSybd5eVTIXwYp+mBPloKgQREXF8CjVSis1m8My3yRRZDW5qHMptLcLNLklEROSyKNRIKf9bl8r6A6fx9XBloqZCEBGRSkShRuzSMvN5bcEOAMb2jKF2kLfJFYmIiFw+hRoBwDAMnp+zhZyCYlpFBHFPh3pmlyQiIlImCjUCwMItaSzedgw3FwuvDdJUCCIiUvko1AiZZ4p44buSqRBGxDUgJszf5IpERETKTqFGmLRgOyeyC6hfw5eR3aLNLkdERKRcyhRqEhISaNu2Lf7+/oSGhtK/f39SUlIuus20adOwWCylFi8vr1Jt7rvvvnPaxMfHl2qzc+dO+vXrR0hICAEBAXTu3Jlly5aVpXw5jzV70/li3UEAEjQVgoiIVGJlCjVJSUmMHDmSNWvWsHjxYoqKiujRowe5ubkX3S4gIICjR4/alwMHDpzTJj4+vlSbL774otT62267jeLiYpYuXcr69etp2bIlt912G2lpaWXpgvxBfpGV8b9NhTDkhrq0q1/d5IpERETKz60sjRcuXFjq62nTphEaGsr69evp2rXrBbezWCyEhYVddN+enp4XbHPy5El27drFf//7X1q0aAHApEmTeO+999iyZcsl9y3n9++lu9l7MpdQf0+e6dXY7HJERESuyBWNqcnMzAQgODj4ou1ycnKIjIwkIiKCfv36sXXr1nPaJCYmEhoaSkxMDCNGjCA9Pd2+rnr16sTExPDpp5+Sm5tLcXExH3zwAaGhobRp0+ZKulBl7UjL4v2kPQC83Lcpgd7uJlckIiJyZSyGYRjl2dBms9G3b18yMjJYuXLlBdutXr2aXbt20aJFCzIzM3njjTdYvnw5W7dupU6dOgDMmDEDHx8foqKi2LNnD+PHj8fPz4/Vq1fj6loyxuPQoUP079+fX3/9FRcXF0JDQ5k/fz6tW7c+7/sWFBRQUFBg/zorK4uIiAgyMzMJCAgoT5edhtVmMGjKKjYezKBHk5p8cE8bPTlYREQcUlZWFoGBgZf1+V3uUDNixAgWLFjAypUr7eHkchQVFREbG8uQIUOYOHHiedvs3buXBg0a8OOPP3LzzTdjGAb9+/enqKiIZ599Fm9vb/7zn//w3Xff8fPPPxMefu78RC+99BIvv/zyOa8r1MC0n/bx0txt+Hu6sfjxGwkL9Lr0RiIiIiYoS6gp1+WnUaNGMW/ePJYtW1amQAPg7u5O69at2b179wXb1K9fn5CQEHubpUuXMm/ePGbMmEGnTp247rrreO+99/D29uaTTz457z7GjRtHZmamfTl48GCZ6nRWhzPO8M8fSu5Ye6pXYwUaERFxGmUaKGwYBqNHj2bWrFkkJiYSFRVV5je0Wq0kJyfTu3fvC7Y5dOgQ6enp9jMweXl5ALi4lM5gLi4u2Gy28+7D09MTT0/PMtfnzAzD4PnZW8grtHJ9ZDXuvqGu2SWJiIhcNWU6UzNy5Eg+//xzpk+fjr+/P2lpaaSlpXHmzBl7m6FDhzJu3Dj71xMmTGDRokXs3buXX3/9lb/+9a8cOHCAYcOGASWDiMeOHcuaNWvYv38/S5YsoV+/fkRHR9OzZ08AOnToQLVq1bj33nvZtGkTO3fuZOzYsezbt49bb731anwfqoR5m4+ydMdxPFxdmDSoOS6aCkFERJxImULNlClTyMzMJC4ujvDwcPvy5Zdf2tukpqZy9OhR+9enT59m+PDhxMbG0rt3b7Kysli1ahVNmjQBwNXVlc2bN9O3b18aNWrEgw8+SJs2bVixYoX9TEtISAgLFy4kJyeHm266ieuvv56VK1cyZ84cWrZseTW+D04vI6+Ql+eW3HX2924NiA7VVAgiIuJcyj1QuLIpy0AjZzT26018vf4Q0aF+zH+kM55uenKwiIg4vms+UFgql1W7T/L1+kNYLPDaoOYKNCIi4pQUapxcfpGVcbNKpkL4a7tI2kRe/EGJIiIilZVCjZN7+8ddHEjPIyzAi6fiY8wuR0RE5JpRqHFiW49k8tGKvQBM7N8Mfy9NhSAiIs5LocZJFVttjPs2GavNoHfzMG5pUtPskkRERK4phRonNW3VfjYfysTfy42X+jQ1uxwREZFrTqHGCR08lcebi3YCML53LKEBmgpBREScn0KNkzEMg2dnb+FMkZV2UcEMvj7C7JJEREQqhEKNk5mz8QjLd57Aw82FhIGaCkFERKoOhRonciq3kAnztgHwyE3R1K/hZ3JFIiIiFUehxom8Mm8bp3ILaRzmz0NdG5hdjoiISIVSqHESy3ee4NsNh7FYIGFgczzcdGhFRKRq0SefE8grLGb8b1Mh3NuhHq3rVjO5IhERkYqnUOMEJi/eyaHTZ6gd5M2TPTUVgoiIVE0KNZVc8qFM/rtyHwCv9G+Gn6ebyRWJiIiYQ6GmEiuy2nh65mZsBvRpWYtujUPNLklERMQ0CjWV2H9X7mPb0SwCvd154bYmZpcjIiJiKoWaSupAei6TF5dMhfDcrbHU8Pc0uSIRERFzKdRUQoZhMH5WMgXFNjpFV+cvbeqYXZKIiIjpFGoqoW/WH+Kn3el4urnwj/7NsVg0FYKIiIhCTSVzMqeAf3y/HYDHbmlEvRBfkysSERFxDAo1lcyEudvIyCuiSXgAwzpHmV2OiIiIw1CoqUSW7TjOd5uO4GKB1wa1wM1Vh09EROQsfSpWErkFxTw3ewsAD3SKonmdQJMrEhERcSwKNZXEG4tSOJxxhjrVvHm8RyOzyxEREXE4CjWVwIbU00xbtR+AVwc0x8dDUyGIiIj8mUKNgyuy2hj3bTKGAQNa16ZroxpmlyQiIuKQFGoc3IfL97IjLZtgXw+e11QIIiIiF6RQ48D2nsjhnSW7AHj+tliCfT1MrkhERMRxKdQ4KJvNYNy3yRQW2+jaqAb9W9U2uyQRERGHplDjoL765SBr953C292Vf/RvpqkQRERELkGhxgEdz87n1d+mQniiRyMign1MrkhERMTxKdQ4oJe/20ZWfjHNawdyX8d6ZpcjIiJSKSjUOJjF244xP/kori4WJg1qrqkQRERELpM+MR1Idn4Rz/82FcLwLvVpWktTIYiIiFwuhRoH8voPKaRl5RNZ3Ycx3RuaXY6IiEilolDjINYfOMVnaw4AJVMheLm7mlyRiIhI5aJQ4wAKiq08M7NkKoTb29ShU3SI2SWJiIhUOgo1DuD9xL3sOp5DiJ8Hz94aa3Y5IiIilZJCjcl2H8/m3WW7AXihT1OCfDQVgoiISHko1JjIZjN4ZmYyhVYb3WJq0KdFuNkliYiIVFoKNSaavi6VXw6cxsfDlVcGNNdUCCIiIldAocYkaZn5vLZgBwBje8ZQO8jb5IpEREQqtzKFmoSEBNq2bYu/vz+hoaH079+flJSUi24zbdo0LBZLqcXLy6tUm/vuu++cNvHx8efsa/78+bRr1w5vb2+qVatG//79y1K+Q3lhzhayC4ppFRHE0A71zC5HRESk0nMrS+OkpCRGjhxJ27ZtKS4uZvz48fTo0YNt27bh6+t7we0CAgJKhZ/zXWaJj49n6tSp9q89PT1LrZ85cybDhw/n1Vdf5aabbqK4uJgtW7aUpXyHsXDLURZtO4bbb1MhuLrospOIiMiVKlOoWbhwYamvp02bRmhoKOvXr6dr164X3M5isRAWFnbRfXt6el6wTXFxMY8++iivv/46Dz74oP31Jk2alKF6x5B5pogX5mwF4OEbG9A4LMDkikRERJzDFY2pyczMBCA4OPii7XJycoiMjCQiIoJ+/fqxdevWc9okJiYSGhpKTEwMI0aMID093b7u119/5fDhw7i4uNC6dWvCw8Pp1avXRc/UFBQUkJWVVWpxBK8t3MHx7ALqh/gy6qZos8sRERFxGuUONTabjTFjxtCpUyeaNWt2wXYxMTF8/PHHzJkzh88//xybzUbHjh05dOiQvU18fDyffvopS5Ys4bXXXiMpKYlevXphtVoB2Lt3LwAvvfQSzz33HPPmzaNatWrExcVx6tSp875vQkICgYGB9iUiIqK8Xb1q1u5NZ/raVABeHaipEERERK4mi2EYRnk2HDFiBAsWLGDlypXUqVPnsrcrKioiNjaWIUOGMHHixPO22bt3Lw0aNODHH3/k5ptvZvr06dx999188MEHPPTQQ0DJmZg6derwyiuv8Le//e2cfRQUFFBQUGD/Oisri4iICDIzMwkIqPhLPvlFVnr/3wr2nshlyA0RJAxsUeE1iIiIVDZZWVkEBgZe1ud3uc7UjBo1innz5rFs2bIyBRoAd3d3Wrduze7duy/Ypn79+oSEhNjbhIeXPJTuj2NoPD09qV+/Pqmpqefdh6enJwEBAaUWM723bDd7T+RSw9+TZ3ppKgQREZGrrUyhxjAMRo0axaxZs1i6dClRUVFlfkOr1UpycrI9qJzPoUOHSE9Pt7dp06YNnp6epe6gKioqYv/+/URGRpa5hoqWkpbNe4l7AHi5b1MCvd1NrkhERMT5lOnup5EjRzJ9+nTmzJmDv78/aWlpAAQGBuLtXfLwuKFDh1K7dm0SEhIAmDBhAu3btyc6OpqMjAxef/11Dhw4wLBhw4CSQcQvv/wygwYNIiwsjD179vDUU08RHR1Nz549gZJbwh9++GFefPFFIiIiiIyM5PXXXwfg9ttvvzrfiWvEajN45tvNFNsMbmlSk17NLn4XmIiIiJRPmULNlClTAIiLiyv1+tSpU7nvvvsASE1NxcXl9xNAp0+fZvjw4aSlpVGtWjXatGnDqlWr7JeSXF1d2bx5M5988gkZGRnUqlWLHj16MHHixFLPqnn99ddxc3Pjnnvu4cyZM7Rr146lS5dSrVq18vS7wny+5gAbUjPw83RjYr9mmgpBRETkGin3QOHKpiwDja6WIxlnuOWtJHILrUzs15R79ORgERGRMrnmA4Xl0gzD4PnZW8gttNImshp3t3P8sT8iIiKVmULNNTI/+ShLdhzH3dXCpIHNcdFUCCIiIteUQs01kJFXyEvflTw1+e9x0TSs6W9yRSIiIs5PoeYaePX77ZzMKSQ61I+/d2tgdjkiIiJVgkLNVbZqz0m++qVkCohJA5vj6aapEERERCqCQs1VlF9kZfy3yQD8tX1drq938Yk+RURE5OpRqLmK3lmyi/3pedQM8OSp+MZmlyMiIlKlKNRcJduOZPHh8pLZxCf2a0aAl6ZCEBERqUgKNVeB1WYw7tvNWG0GvZqF0aOppkIQERGpaAo1V8G0VfvZdCgTfy83Xu7b1OxyREREqiSFmit08FQeb/xQMnv4+N6xhAZ4mVyRiIhI1VSmCS3lXOm5hVT386BWkDeDr48wuxwREZEqS6HmCrWKCGLRY13JOlOsqRBERERMpFBzFfh4uOHjoW+liIiImTSmRkRERJyCQo2IiIg4BYUaERERcQoKNSIiIuIUFGpERETEKSjUiIiIiFNQqBERERGnoFAjIiIiTkGhRkRERJyCQo2IiIg4BYUaERERcQoKNSIiIuIUFGpERETEKVSZqaUNwwAgKyvL5EpERETkcp393D77OX4xVSbUZGdnAxAREWFyJSIiIlJW2dnZBAYGXrSNxbic6OMEbDYbR44cwd/fH4vFclX3nZWVRUREBAcPHiQgIOCq7tsROHv/wPn7qP5Vfs7eR/Wv8rtWfTQMg+zsbGrVqoWLy8VHzVSZMzUuLi7UqVPnmr5HQECA0/6wgvP3D5y/j+pf5efsfVT/Kr9r0cdLnaE5SwOFRURExCko1IiIiIhTUKi5Cjw9PXnxxRfx9PQ0u5Rrwtn7B87fR/Wv8nP2Pqp/lZ8j9LHKDBQWERER56YzNSIiIuIUFGpERETEKSjUiIiIiFNQqLmA5cuX06dPH2rVqoXFYmH27Nml1huGwQsvvEB4eDje3t50796dXbt2lWpz6tQp7r77bgICAggKCuLBBx8kJyenAntxcZfq43333YfFYim1xMfHl2rjqH1MSEigbdu2+Pv7ExoaSv/+/UlJSSnVJj8/n5EjR1K9enX8/PwYNGgQx44dK9UmNTWVW2+9FR8fH0JDQxk7dizFxcUV2ZULupw+xsXFnXMMH3744VJtHLWPU6ZMoUWLFvZnXnTo0IEFCxbY11f24weX7mNlPn7nM2nSJCwWC2PGjLG/5gzH8azz9a+yH8OXXnrpnPobN25sX+9wx8+Q8/r++++NZ5991vj2228NwJg1a1ap9ZMmTTICAwON2bNnG5s2bTL69u1rREVFGWfOnLG3iY+PN1q2bGmsWbPGWLFihREdHW0MGTKkgntyYZfq47333mvEx8cbR48etS+nTp0q1cZR+9izZ09j6tSpxpYtW4yNGzcavXv3NurWrWvk5OTY2zz88MNGRESEsWTJEuOXX34x2rdvb3Ts2NG+vri42GjWrJnRvXt3Y8OGDcb3339vhISEGOPGjTOjS+e4nD7eeOONxvDhw0sdw8zMTPt6R+7jd999Z8yfP9/YuXOnkZKSYowfP95wd3c3tmzZYhhG5T9+hnHpPlbm4/dn69atM+rVq2e0aNHCePTRR+2vO8NxNIwL96+yH8MXX3zRaNq0aan6T5w4YV/vaMdPoeYy/PkD32azGWFhYcbrr79ufy0jI8Pw9PQ0vvjiC8MwDGPbtm0GYPz888/2NgsWLDAsFotx+PDhCqv9cl0o1PTr1++C21SmPh4/ftwAjKSkJMMwSo6Xu7u78fXXX9vbbN++3QCM1atXG4ZREvpcXFyMtLQ0e5spU6YYAQEBRkFBQcV24DL8uY+GUfIH9Y9/YP+ssvWxWrVqxn/+8x+nPH5nne2jYTjP8cvOzjYaNmxoLF68uFSfnOU4Xqh/hlH5j+GLL75otGzZ8rzrHPH46fJTOezbt4+0tDS6d+9ufy0wMJB27dqxevVqAFavXk1QUBDXX3+9vU337t1xcXFh7dq1FV5zeSUmJhIaGkpMTAwjRowgPT3dvq4y9TEzMxOA4OBgANavX09RUVGpY9i4cWPq1q1b6hg2b96cmjVr2tv07NmTrKwstm7dWoHVX54/9/Gs//3vf4SEhNCsWTPGjRtHXl6efV1l6aPVamXGjBnk5ubSoUMHpzx+f+7jWc5w/EaOHMmtt95a6niB8/weXqh/Z1X2Y7hr1y5q1apF/fr1ufvuu0lNTQUc8/hVmbmfrqa0tDSAUgfp7Ndn16WlpREaGlpqvZubG8HBwfY2ji4+Pp6BAwcSFRXFnj17GD9+PL169WL16tW4urpWmj7abDbGjBlDp06daNasGVByfDw8PAgKCirV9s/H8HzH+Ow6R3K+PgLcddddREZGUqtWLTZv3szTTz9NSkoK3377LeD4fUxOTqZDhw7k5+fj5+fHrFmzaNKkCRs3bnSa43ehPkLlP34AM2bM4Ndff+Xnn38+Z50z/B5erH9Q+Y9hu3btmDZtGjExMRw9epSXX36ZLl26sGXLFoc8fgo1ckF33nmn/d/NmzenRYsWNGjQgMTERG6++WYTKyubkSNHsmXLFlauXGl2KdfMhfr40EMP2f/dvHlzwsPDufnmm9mzZw8NGjSo6DLLLCYmho0bN5KZmck333zDvffeS1JSktllXVUX6mOTJk0q/fE7ePAgjz76KIsXL8bLy8vscq66y+lfZT+GvXr1sv+7RYsWtGvXjsjISL766iu8vb1NrOz8dPmpHMLCwgDOGeF97Ngx+7qwsDCOHz9ean1xcTGnTp2yt6ls6tevT0hICLt37wYqRx9HjRrFvHnzWLZsWalZ2sPCwigsLCQjI6NU+z8fw/Md47PrHMWF+ng+7dq1Ayh1DB25jx4eHkRHR9OmTRsSEhJo2bIl77zzjlMdvwv18Xwq2/Fbv349x48f57rrrsPNzQ03NzeSkpL4v//7P9zc3KhZs2alPo6X6p/Vaj1nm8p2DP8sKCiIRo0asXv3bof8PVSoKYeoqCjCwsJYsmSJ/bWsrCzWrl1rvxbeoUMHMjIyWL9+vb3N0qVLsdls9h/qyubQoUOkp6cTHh4OOHYfDcNg1KhRzJo1i6VLlxIVFVVqfZs2bXB3dy91DFNSUkhNTS11DJOTk0sFt8WLFxMQEGC/PGCmS/XxfDZu3AhQ6hg6ch//zGazUVBQ4BTH70LO9vF8Ktvxu/nmm0lOTmbjxo325frrr+fuu++2/7syH8dL9c/V1fWcbSrbMfyznJwc9uzZQ3h4uGP+Hl71ocdOIjs729iwYYOxYcMGAzDeeustY8OGDcaBAwcMwyi5pTsoKMiYM2eOsXnzZqNfv37nvaW7devWxtq1a42VK1caDRs2dIjbnc+6WB+zs7ONJ5980li9erWxb98+48cffzSuu+46o2HDhkZ+fr59H47axxEjRhiBgYFGYmJiqVsR8/Ly7G0efvhho27dusbSpUuNX375xejQoYPRoUMH+/qztyL26NHD2Lhxo7Fw4UKjRo0aDnOr5aX6uHv3bmPChAnGL7/8Yuzbt8+YM2eOUb9+faNr1672fThyH5955hkjKSnJ2Ldvn7F582bjmWeeMSwWi7Fo0SLDMCr/8TOMi/exsh+/C/nz3UDOcBz/6I/9c4Zj+MQTTxiJiYnGvn37jJ9++sno3r27ERISYhw/ftwwDMc7fgo1F7Bs2TIDOGe59957DcMoua37+eefN2rWrGl4enoaN998s5GSklJqH+np6caQIUMMPz8/IyAgwLj//vuN7OxsE3pzfhfrY15entGjRw+jRo0ahru7uxEZGWkMHz681G15huG4fTxfvwBj6tSp9jZnzpwx/v73vxvVqlUzfHx8jAEDBhhHjx4ttZ/9+/cbvXr1Mry9vY2QkBDjiSeeMIqKiiq4N+d3qT6mpqYaXbt2NYKDgw1PT08jOjraGDt2bKlnZBiG4/bxgQceMCIjIw0PDw+jRo0axs0332wPNIZR+Y+fYVy8j5X9+F3In0ONMxzHP/pj/5zhGA4ePNgIDw83PDw8jNq1axuDBw82du/ebV/vaMdPs3SLiIiIU9CYGhEREXEKCjUiIiLiFBRqRERExCko1IiIiIhTUKgRERERp6BQIyIiIk5BoUZEREScgkKNiIiIOAWFGhFxOImJiVgslnMmyhMRuRiFGhFxOB07duTo0aMEBgZe9jZ5eXmMGzeOBg0a4OXlRY0aNbjxxhuZM2fONaxURByJm9kFiIj8mYeHB2FhYWXa5uGHH2bt2rX861//okmTJqSnp7Nq1SrS09OvUZUi4mh0pkZErrm4uDhGjx7NmDFjqFatGjVr1uSjjz4iNzeX+++/H39/f6Kjo1mwYAFw7uWnadOmERQUxA8//EBsbCx+fn7Ex8dz9OhR+3t89913jB8/nt69e1OvXj3atGnD6NGjeeCBB+xtLBYLs2fPLlVbUFAQ06ZNA2D//v1YLBZmzJhBx44d8fLyolmzZiQlJV3T74+IXB0KNSJSIT755BNCQkJYt24do0ePZsSIEdx+++107NiRX3/9lR49enDPPfeQl5d33u3z8vJ44403+Oyzz1i+fDmpqak8+eST9vVhYWF8//33ZGdnX3GtY8eO5YknnmDDhg106NCBPn366IyPSCWgUCMiFaJly5Y899xzNGzYkHHjxuHl5UVISAjDhw+nYcOGvPDCC6Snp7N58+bzbl9UVMT777/P9ddfz3XXXceoUaNYsmSJff2HH37IqlWrqF69Om3btuWxxx7jp59+Kleto0aNYtCgQcTGxjJlyhQCAwP573//W659iUjFUagRkQrRokUL+79dXV2pXr06zZs3t79Ws2ZNAI4fP37e7X18fGjQoIH96/Dw8FJtu3btyt69e1myZAl/+ctf2Lp1K126dGHixIllrrVDhw72f7u5uXH99dezffv2Mu9HRCqWQo2IVAh3d/dSX1ssllKvWSwWAGw222VvbxjGOW26dOnC008/zaJFi5gwYQITJ06ksLDwgtsUFRWVr0Mi4nAUakTEaTVp0oTi4mLy8/MBqFGjRqnBxbt27TrvGJ41a9bY/11cXMz69euJjY299gWLyBXRLd0i4hTi4uIYMmQI119/PdWrV2fbtm2MHz+ebt26ERAQAMBNN93Ev//9bzp06IDVauXpp58+5wwQwLvvvkvDhg2JjY1l8uTJnD59utRdVCLimHSmRkScQs+ePfnkk0/o0aMHsbGxjB49mp49e/LVV1/Z27z55ptERETQpUsX7rrrLp588kl8fHzO2dekSZOYNGkSLVu2ZOXKlXz33XeEhIRUZHdEpBwsxp8vMIuIVFH79+8nKiqKDRs20KpVK7PLEZEy0pkaERERcQoKNSIiIuIUdPlJREREnILO1IiIiIhTUKgRERERp6BQIyIiIk5BoUZEREScgkKNiIiIOAWFGhEREXEKCjUiIiLiFBRqRERExCko1IiIiIhT+H/FZqzaaoh5LgAAAABJRU5ErkJggg==\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/periodicFrequentPattern/topk/kPFPMiner.ipynb b/notebooks/periodicFrequentPattern/topk/kPFPMiner.ipynb index 8e0e67e2..0a2cf511 100644 --- a/notebooks/periodicFrequentPattern/topk/kPFPMiner.ipynb +++ b/notebooks/periodicFrequentPattern/topk/kPFPMiner.ipynb @@ -1,701 +1,701 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Top-K Periodic Frequent patterns in Temporal Databases using k-PFPMiner" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Top-K Periodic Frequent patterns in a temporal database using the kPFPMiner algorithm. In the final part, we describe an advanced approach, where we evaluate the kPFPMiner algorithm on a dataset at different K values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "6b3180ca-9166-4973-aa7a-2f0321666538" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/835.0 kB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K \u001B[91m━━\u001B[0m\u001B[91m╸\u001B[0m\u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m61.4/835.0 kB\u001B[0m 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+ " Downloading validators-0.21.2-py3-none-any.whl (25 kB)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.4.4)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages 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python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.21.2\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Top-K Periodic Frequent patterns in Temporal Databases using k-PFPMiner" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Top-K Periodic Frequent patterns in a temporal database using the kPFPMiner algorithm. In the final part, we describe an advanced approach, where we evaluate the kPFPMiner algorithm on a dataset at different K values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "6b3180ca-9166-4973-aa7a-2f0321666538" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/835.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/835.0 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m829.4/835.0 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "79b7c047-dc1a-4797-d6b9-78f87b2d19e8" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-28 09:39:50-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.10MB/s in 6.0s \n", - "\n", - "2023-08-28 09:39:57 (753 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "8577a7fa-c806-4822-8077-76b913bee116" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Top-K Periodic Frequent patterns using kPFPMiner" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (K) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "bdffb1dc-e0c3-4e6e-db8b-b2b11debb4ae" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "33102149-d6c5-4a99-fd1c-e2795dbdee82" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *K* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _K_ value of 100 (in count). We can increase or decrease the _K_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "k = 100 #k is specified in count. However, the users can also specify k between 0 and 1.\n" - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Top-K Periodic Frequent patterns using kPFPMiner" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.periodicFrequentPattern.topk.kPFPMiner import kPFPMiner as alg #import the algorithm\n", - "\n", - "obj = alg.kPFPMiner(iFile=inputFile, k=k, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('topkPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "6fe2d050-5f66-43e2-ce94-a4e066dc023b" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "kPFPMiner has successfully generated top-k frequent patterns\n", - "Total No of patterns: 100\n", - "Runtime: 4.695229530334473\n", - "Memory (RSS): 254177280\n", - "Memory (USS): 207020032\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _K_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'topkPeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "6c1ec0fa-ca09-42c8-d9f8-aec0171511b3" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "471:526 \n", - "829 789 :462 \n", - "236:441 \n", - "617:408 \n", - "793:407 \n", - "758:381 \n", - "413:360 \n", - "8:355 \n", - "960:351 \n", - "548:342 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "79b7c047-dc1a-4797-d6b9-78f87b2d19e8" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-28 09:39:50-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.10MB/s in 6.0s \n", + "\n", + "2023-08-28 09:39:57 (753 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "8577a7fa-c806-4822-8077-76b913bee116" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Top-K Periodic Frequent patterns using kPFPMiner" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (K) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "bdffb1dc-e0c3-4e6e-db8b-b2b11debb4ae" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "33102149-d6c5-4a99-fd1c-e2795dbdee82" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _topkPeriodicFrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the kPFPMiner algorithm on a dataset at different K values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.periodicFrequentPattern.topk.kPFPMiner import kPFPMiner as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "k = [100, 200, 300, 400, 500]\n", - "#K can also specified between 0 to 1. E.g., K = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of kPFPMiner" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of kPFPMiner algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different K values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in k:\n", - " obj = alg.kPFPMiner(inputFile, k=minSupCount, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['kPFPMiner', minSupCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "da25ebd6-08e7-450b-f287-b1e905a2c74c" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "kPFPMiner has successfully generated top-k frequent patterns\n", - "kPFPMiner has successfully generated top-k frequent patterns\n", - "kPFPMiner has successfully generated top-k frequent patterns\n", - "kPFPMiner has successfully generated top-k frequent patterns\n", - "kPFPMiner has successfully generated top-k frequent patterns\n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *K* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _K_ value of 100 (in count). We can increase or decrease the _K_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "k = 100 #k is specified in count. However, the users can also specify k between 0 and 1.\n" + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Top-K Periodic Frequent patterns using kPFPMiner" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.periodicFrequentPattern.topk.kPFPMiner import kPFPMiner as alg #import the algorithm\n", + "\n", + "obj = alg.kPFPMiner(iFile=inputFile, k=k, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('topkPeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6fe2d050-5f66-43e2-ce94-a4e066dc023b" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "kPFPMiner has successfully generated top-k frequent patterns\n", + "Total No of patterns: 100\n", + "Runtime: 4.695229530334473\n", + "Memory (RSS): 254177280\n", + "Memory (USS): 207020032\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _K_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'topkPeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6c1ec0fa-ca09-42c8-d9f8-aec0171511b3" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "471:526 \n", + "829 789 :462 \n", + "236:441 \n", + "617:408 \n", + "793:407 \n", + "758:381 \n", + "413:360 \n", + "8:355 \n", + "960:351 \n", + "548:342 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _topkPeriodicFrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the kPFPMiner algorithm on a dataset at different K values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.periodicFrequentPattern.topk.kPFPMiner import kPFPMiner as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "k = [100, 200, 300, 400, 500]\n", + "#K can also specified between 0 to 1. E.g., K = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of kPFPMiner" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of kPFPMiner algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different K values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in k:\n", + " obj = alg.kPFPMiner(inputFile, k=minSupCount, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['kPFPMiner', minSupCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "da25ebd6-08e7-450b-f287-b1e905a2c74c" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "kPFPMiner has successfully generated top-k frequent patterns\n", + "kPFPMiner has successfully generated top-k frequent patterns\n", + "kPFPMiner has successfully generated top-k frequent patterns\n", + "kPFPMiner has successfully generated top-k frequent patterns\n", + "kPFPMiner has successfully generated top-k frequent patterns\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5595ea01-531b-40d8-9865-a55ae65d03b7" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup patterns runtime memory\n", + "0 kPFPMiner 100 100 4.062737 255533056\n", + "1 kPFPMiner 200 200 10.061997 256196608\n", + "2 kPFPMiner 300 300 14.052537 256794624\n", + "3 kPFPMiner 400 400 21.115762 256720896\n", + "4 kPFPMiner 500 500 29.186294 256475136\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "f72ee25f-7575-4bea-8ba8-d62d7024d4d3" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 14 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5595ea01-531b-40d8-9865-a55ae65d03b7" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup patterns runtime memory\n", - "0 kPFPMiner 100 100 4.062737 255533056\n", - "1 kPFPMiner 200 200 10.061997 256196608\n", - "2 kPFPMiner 300 300 14.052537 256794624\n", - "3 kPFPMiner 400 400 21.115762 256720896\n", - "4 kPFPMiner 500 500 29.186294 256475136\n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "f72ee25f-7575-4bea-8ba8-d62d7024d4d3" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 14 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" 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XkfKrVasWP/30E08//TQ9evSgoKCAyMhI4uPjLyt8XEjt2rX5/vvvGTt2LC1btiQ4OJgHH3yQ55577rK27969O+Hh4TRt2pRatWqVu46yKNPdT5WZ7n4Sqfx+3HaMYZ/+AsBnD95Al4Y1LrFFxTMMg2NZBb8FnCxS0nJIOZbFrmM5FBSff6xCkI97qZATU7Nk3E6Al8brlNfF7qaRipGTk0Pt2rWZOnUqAwcOvGhbU+5+EhExy4nsAp6euRmAYZ2jHDLQwG9z3QV6ERboxY1/Gq9zID33nEtY+9NzycgrYu2+U6zdV3q8Tu0gbxr99mydmDA/YmoG0CDUF083jdcRx2Wz2Th58iRvvvkmQUFB9O3bt8LeW6FGRByeYRiM/WYT6bmFNA7zZ2x8jNkllZmri4X6NfyoX8OPXs1/vyMlv8jK7uO/j9c5e+t5WlY+hzPOcDjjDMv+MF7n7LifP17CahzmT0Q1H1w0XkccQGpqKlFRUdSpU4dp06Zd1cHLl6JQIyIO79PVB0hMOYGHmwv/N6S1U52p8HJ3pVntQJrVLj1eJyOvkJ3HckhJyyp163l2fjG7j+ew+3gO8zlqb+/t7kqjmn72hwmeXWr4abyOVKx69eqZNg+bQo2IOLRdx7J59fvtAIzr1ZhGNf1NrqhiBPl4cENUMDdElX6+TlpWfskDBP/wMMHdJ3I4U2Rl06FMNh3KLLWfYF8PGtX0o3FYQKnA4+epP//ifPRTLSIOq6DYyiMzNlJQbKNroxrc17Ge2SWZymKxEB7oTXigN91iQu2vF1tt7E/P+8N4nSx2Hsthf3oup3ILWbP3FGv2njtep/EfHiQYE+ZP/RA/PNzKf7eMI6oi98JUelfrOCnUiIjDenPRTrYfzSLY14M3/tJCl1EuwM21ZGqH6FA/buX38TpnCq3sOn7uLefHswvs43WW7Dj++35cLNSv4fvb1BD+v00C6k+dat6VbrzO2Sfl5uXl2afxEcdVWFgIUO5n6pylUCMiDmnV7pN8tGIvAK8NakFogG7LLStvD1da1AmiRZ2gUq+fzi38/Q6sY79fysouKGbnsRx2Hsth3ubfx+v4eLjSsKY/jf80XifEz7OCe3T5XF1dCQoKss9j5OPjo1DsoGw2GydOnMDHx+eKBxUr1IiIw8nIK+TxrzZhGDDkhrrc0qSm2SU5lWq+HrSvX5329avbXzMMgyOZ+b8/Wycti5RjOew5nkNeoZVNBzPYdDCj1H6q+3qUerbO2WkifB1kvE5YWMlToc8GG3FcLi4u1K1b94qDpx6+JyIOxTAMRk7/le+T06gf4su8Rzo75Xw9lUWR1cb+k7mlnq2Tciyb1FN5XOjTIyLY+w8PEyy5hFW/hu8FJx291qxWK0VFRaa8t1weDw+PCz79uCyf3wo1IuJQvv7lIGO/2Yybi4Vv/97xnEsn4hjyCovZdaz0fFg70rI5mVNw3vburhYa1PjDLee//bd2UOUbryMVS08UFpFK6UB6Li99txWAx25ppEDjwHw83GgZEUTLiKBSr6fnlEwRsfOP43WO5ZBTUMyO34IPm35v7+vhar8D64aoYPq2rK1JP6XcdKZGRBxCsdXGHR+s5tfUDG6ICuaL4e314eYkDMPg0Okz9gcIpvz2MME9J3Iospb+CIoND+ClPk1o94fxPlK16fLTeSjUiDi2t3/cyds/7sLfy40Fj3ahTjUfs0uSa6yw2Mb+9Fx2pGWz9Ugm09emkp1fDMCtLcIZ16uxfg5EoeZ8FGpEHNf6A6e5/f1V2Ax4585W9GtV2+ySxATpOQW8uXgnX6xLxTDA082Fh29swMM3NsDbw3mmxpCyKcvnt3M9OlJEKp2cgmIe+3IjNgP6taqlQFOFVffz5NUBzZk3ujM3RAVTUGzjnSW7uPnNROZuOqKnA8slKdSIiKle+m4rqafyqB3kzYR+zcwuRxxA01qBfPlQe/59V2tqBXpxJDOf0V9sYPCHa9h6JPPSO5AqS6FGREzzffJRvll/CBcLTB7cikBvd7NLEgdhsVi4rUUtljwRx5juDfF0c2HdvlP0+ddKxs9KJv0Ct45L1aZQIyKmOJp5hnHfJgMwIq5BqdmoRc7y9nBlTPdGLH0yjltbhGMzYPraVLq9kcjHK/dRZLWZXaI4EIUaEalwNpvBE19tIvNMES3qBDKmeyOzSxIHVzvIm3fvuo4vH2pPbHgAWfnFTJi3jV7vrGD5zhNmlycOQqFGRCrcf1buZdWedLzdXXl7cCvTHp8vlU+7+tWZN7oz/xjQjGo+7uw+nsPQj9cx7JNf2H8y1+zyxGT6SyIiFWrrkUxe/yEFgOdva0L9Gn4mVySVjauLhbvbRZL4ZDfu71QPVxcLP24/Ro/Jy5m0YAc5BcVmlygmUagRkQqTX2Tl0RkbKbIa3NKkJkNuiDC7JKnEAn3cebFPUxY+2oUuDUMotNp4P2kP3d5I5Jv1h7DZdAt4VaNQIyIVJuH77ew+nkMNf09eG9QCi0XTIMiVa1jTn08fuIGPhl5PZHUfTmQX8OTXmxgwZRUbUk+bXZ5UIIUaEakQy3Yc55PVBwB44/aWBPt6mFyROBOLxcItTWqy6LGuPB3fGF8PVzYdzGDAe6t4/KuNHM/KN7tEqQAKNSJyzZ3MKWDsNyVTM9/XsR43NqphckXirDzdXBkR14BlT8Yx6Lo6AHz762G6vZHIlMQ9FBRbTa5QriWFGhG5pgzD4OlvNnMyp5BGNf14pldjs0uSKiA0wIs372jJrL93pGVEELmFVl5buIMek5ezeNsxTbngpBRqROSa+t/aVJbsOI6Hqwvv3NkaL3dNTCgVp3Xdaswa0ZE3b29JDX9PDqTnMfzTXxj68Tp2Hcs2uzy5yhRqROSa2X08h1fmbwPgqfgYYsMvPsOuyLXg4mJhUJs6LHsyjodvbICHqwsrdp0k/p0VvDx3K5l5RWaXKFeJQo2IXBOFxTbGfLmB/CIbnaNDeKBTlNklSRXn5+nGM70as+ixrnSPrYnVZjD1p/10ezOR/609gFW3gFd6CjUick28tXgnWw5nEeTjzpt3tMTFRbdvi2OoF+LLf+69nk8fuIHoUD9O5Rby7Kwt3Pavlazdm252eXIFFGpE5KpbvSedD5bvAWDSwObUDPAyuSKRc3VtVIMFj3bhhdua4O/lxvajWQz+cA0jp//KodN5Zpcn5aBQIyJXVWZeEU98tRHDgDuur0N8s3CzSxK5IHdXFx7oHEXik3Hc1a4uFgvM33yUm99MYvLinZwp1C3glYlCjYhcNYZh8NycLRzJzKdedR9e7NPU7JJELkt1P09eHdCceaM7c0NUMAXFNt5ZsovubyUxb/MR3QJeSSjUiMhVM3vjYeZuOoKri4XJg1vh6+lmdkkiZdK0ViBfPtSef9/VmlqBXhzOOMOo6RsY/OEath7JNLs8uQSFGhG5Kg6eyuP52VsBePTmhrSuW83kikTKx2KxcFuLWix5Io4x3Rvi5e7Cun2n6POvlYyflUx6ToHZJcoFKNSIyBUrttp47MuN5BQU0yayGn+Pa2B2SSJXzNvDlTHdG7HkiThuaxGOzYDpa1Pp9kYiH6/cR5HVZnaJ8icKNSJyxaYk7uGXA6fx83Tj7cGtcHPVnxZxHrWDvPn3Xdfx5UPtaRIeQFZ+MRPmbaPXOytYvvOE2eXJH+gvj4hckY0HM3h7yS4AJvRrSkSwj8kViVwb7epXZ+7ozrw6oDnBvh7sPp7D0I/XMeyTX9h/Mtfs8gSFGhG5ArkFxYyZsQGrzeC2FuEMaF3b7JJErilXFwt3tavLsifiuL9TPVxdLPy4/Rg9Ji9n0oId5BQUm11ilVamUJOQkEDbtm3x9/cnNDSU/v37k5KSctFtpk2bhsViKbV4eZ37IK7t27fTt29fAgMD8fX1pW3btqSmppZqs3r1am666SZ8fX0JCAiga9eunDlzpixdEJGraOK8bexPz6NWoBf/6N8ci0VPDZaqIdDHnRf7NGXho13o0jCEQquN95P2cNMbicxcfwibplwwRZlCTVJSEiNHjmTNmjUsXryYoqIievToQW7uxU+7BQQEcPToUfty4MCBUuv37NlD586dady4MYmJiWzevJnnn3++VPhZvXo18fHx9OjRg3Xr1vHzzz8zatQoXFx0sknEDAu3pDHj54NYLPDmHa0I9HE3uySRCtewpj+fPnADHw29nsjqPhzPLuCJrzcxcMoqNqSeNru8KsdiXMEThU6cOEFoaChJSUl07dr1vG2mTZvGmDFjyMjIuOB+7rzzTtzd3fnss88u2KZ9+/bccsstTJw4sVy1ZmVlERgYSGZmJgEBmilY5Eocy8qn59vLycgr4m831mdcr1izSxIxXUGxlY9X7uffS3eR+9uTiAddV4en42MI1VQh5VaWz+8rOs2RmVnyIKLg4OCLtsvJySEyMpKIiAj69evH1q1b7etsNhvz58+nUaNG9OzZk9DQUNq1a8fs2bPtbY4fP87atWsJDQ2lY8eO1KxZkxtvvJGVK1deSfkiUg42m8GTX28iI6+IZrUDeOKWGLNLEnEInm6ujIhrwLIn4xh0XR0AZv56iG5vJDIlcQ8FxZpy4Vord6ix2WyMGTOGTp060axZswu2i4mJ4eOPP2bOnDl8/vnn2Gw2OnbsyKFDh4CSwJKTk8OkSZOIj49n0aJFDBgwgIEDB5KUlATA3r17AXjppZcYPnw4Cxcu5LrrruPmm29m165d533fgoICsrKySi0icuWmrtrPil0n8XJ34e3BrfFw0yVgkT8KDfDizTtaMuvvHWkZEURuoZXXFu6gx+TlLN52TFMuXEPlvvw0YsQIFixYwMqVK6lTp85lb1dUVERsbCxDhgxh4sSJHDlyhNq1azNkyBCmT59ub9e3b198fX354osvWLVqFZ06dWLcuHG8+uqr9jYtWrTg1ltvJSEh4Zz3eemll3j55ZfPeV2Xn0TKb/vRLPr9+ycKrTYm9m/GPe0jzS5JxKHZbAazNhxm0sIdnMgueRJxl4YhvHBbExrW9De5usrhml9+GjVqFPPmzWPZsmVlCjQA7u7utG7dmt27dwMQEhKCm5sbTZo0KdUuNjbWfvdTeHjJLL8Xa/Nn48aNIzMz074cPHiwTHWKSGn5RVbGzNhIodXGzY1D+Wu7umaXJOLwXFwsDGpTh2VPxjEirgEeri6s2HWS+HdW8PLcrWTmFZldolMpU6gxDINRo0Yxa9Ysli5dSlRUVJnf0Gq1kpycbA8qHh4etG3b9pxbw3fu3ElkZMn/BdarV49atWpdtM2feXp6EhAQUGoRkfJ7beEOUo5lE+LnwWt/aaHbt0XKwM/TjafjG7Posa7c0qQmVpvB1J/20+3NRP639gBW3QJ+VZRpCt2RI0cyffp05syZg7+/P2lpaQAEBgbi7e0NwNChQ6ldu7b9ktCECRNo37490dHRZGRk8Prrr3PgwAGGDRtm3+/YsWMZPHgwXbt2pVu3bixcuJC5c+eSmJgIlEwuNnbsWF588UVatmxJq1at+OSTT9ixYwfffPPN1fg+iMhFLN95gqk/7Qfg9b+0JMTP09yCRCqpeiG+fDT0elbsOsHLc7ex+3gOz87awv/WpPJinya0q1/d7BIrtTKFmilTpgAQFxdX6vWpU6dy3333AZCamlrq2TGnT59m+PDhpKWlUa1aNdq0acOqVatKXUoaMGAA77//PgkJCTzyyCPExMQwc+ZMOnfubG8zZswY8vPzeeyxxzh16hQtW7Zk8eLFNGigifNErqVTuYU88fUmAIZ2iKRb41CTKxKp/Lo0rMGCR7vw2eoDTP5xJ9uOZjH4wzXc2iKc8b1jqR3kbXaJldIVPaemMtFzakTKzjAMHvpsPYu3HSM61I95ozvj5e5qdlkiTiU9p4A3F+/ki3WpGAZ4ubvw8I0N+FvXBnh76Petwp5TIyLObcbPB1m87RjurhbeubOVAo3INVDdz5NXBzRn3ujO3BAVTH6Rjbd/3EX3t5KYt/mIbgEvA4UaETmvvSdymDB3GwBP9oihaa1AkysScW5NawXy5UPt+fddrakV6MXhjDOMmr6BwR+uYeuRTLPLqxQUakTkHEVWG499uZEzRVY61K/O8C71zS5JpEqwWCzc1qIWS56IY0z3hni5u7Bu3yn6/Gsl42clk55TYHaJDk2hRkTO8c6Pu9h0KJNAb3fevKMlLi66fVukInl7uDKmeyOWPBHHbS3CsRkwfW0q3d5I5OOV+yiy2swu0SEp1IhIKev2neK9xJKHY746oDm1dBeGiGlqB3nz77uu48uH2tMkPICs/GImzNtGr3dWsHznCbPLczgKNSJil5VfxGNfbsRmlMwufGuLcLNLEhGgXf3qzB3dmVcHNCfY14Pdx3MY+vE6hn3yCwfSc80uz2Eo1IiI3Quzt3A44wwRwd681LfJpTcQkQrj6mLhrnZ1WfZEHA90isLNxcKP249xy1vLeW3hDnIKis0u0XQKNSICwJyNh5m98QguFnh7cCv8vdzNLklEziPQx50X+jRh4ZgudGkYQqHVxpTEPdz0RiIz1x/CVoWnXFCoEREOnc7judlbABh1U0PaRAabXJGIXEp0qD+fPnAD/xl6PZHVfTieXcATX29i4JRVbEg9bXZ5plCoEanirDaDx7/aRHZ+Ma3rBvHITdFmlyQil8lisdC9SU0WPdaVp+Mb4+vhysaDGQx4bxVPfLWJ41n5ZpdYoRRqRKq4D5bvYd2+U/h6uPL24Fa4uerPgkhl4+nmyoi4Bix7Mo5B19UBYOavh+j2RiJTEvdQUGw1ucKKob9eIlXY5kMZvLVoJwAv9m1KZHVfkysSkSsRGuDFm3e0ZNbfO9IyIojcQiuvLdxBj8nLWbztmNNPuaBQI1JF5RUWM2bGRoptBr2ahXF7mzpmlyQiV0nrutWYNaIjb97ekhr+nhxIz2P4p78w9ON17D6ebXZ514xCjUgV9cr87ew9mUvNgJLJ9CwWPTVYxJm4uFgY1KYOy56MY0RcAzxcXVix6yQ9317By3O3knmmyOwSrzqFGpEqaPG2Y0xfmwrAW3e0opqvh8kVici14ufpxtPxjVn0WFduaVITq81g6k/76fZGItPXpmJ1olvAFWpEqpjj2fk8PXMzAMO7RNEpOsTkikSkItQL8eWjodfz2YM3EB3qx6ncQsbPSqbPv1aydm+62eVdFQo1IlWIYRiM/Xozp3ILiQ0P4MmeMWaXJCIVrEvDGix4tAsv9mlCgJcb245mMfjDNYyc/iuHM86YXd4VUagRqUI+WbWfpJ0n8HRz4Z07W+Hp5mp2SSJiAndXF+7vFMWyJ+O4u11dXCwwf/NRbn4zkbd/3MmZwsp5C7hCjUgVsfNYNq8u2AHAuF6NaVTT3+SKRMRs1f08+ceA5swd3ZkbooLJL7Lx9o+76P5WEvM2H6l0t4Ar1IhUAQXFVh75YgOFxTZubFSDezvWM7skEXEgTWsF8uVD7fn3Xa2pFejF4YwzjJq+gcEfrmHrkUyzy7tsCjUiVcAbP6SwIy2bYF8PXr+9hW7fFpFzWCwWbmtRiyVPxDGme0O83F1Yt+8Uff61kvGzkjmVW2h2iZekUCPi5FbuOslHK/YB8M9BLQj19zK5IhFxZN4erozp3oglT8RxW4twbAZMX5tK3OvL+HjlPoqsNrNLvCCFGhEndjq3kCe+3gjAXe3q0r1JTXMLEpFKo3aQN/++6zq+fKg9TcIDyMovZsK8bfR+ZwUrdp0wu7zzUqgRcVKGYTB+VjLHsgqoH+LLc7fGml2SiFRC7epXZ+7ozrw6oDnBvh7sOp7DPf9dx7BPfuFAeq7Z5ZWiUCPipL5ef4gFW9Jwc7Hwzp2t8fFwM7skEamkXF0s3NWuLsueiOOBTlG4uVj4cfsxbnlrOa8t3EFOQbHZJQIKNSJO6UB6Li9/txWAx3s0onmdQJMrEhFnEOjjzgt9mrBwTBe6NAyh0GpjSuIebnojkZnrD2EzecoFhRoRJ1NktfHojI3kFlq5ISqYv3VtYHZJIuJkokP9+fSBG/jP0OuJrO7D8ewCnvh6EwOnrCIr37yJMhVqRJzMv5buZuPBDPy93Jg8uBWuLrp9W0SuPovFQvcmNVn0WFee6dUYXw9X/L3c8Pc071K3LrKLOJH1B07x76W7AHilfzNqB3mbXJGIODtPN1cevrEBA1vXpqDYZupzsBRqRJxEdn4RY77ciM2A/q1q0a9VbbNLEpEqJDTA/Gdg6fKTiJN46bttHDx1htpB3kzo38zsckREKpxCjYgTmLf5CDN/PYSLBd6+sxUBXu5mlyQiUuEUakQquSMZZxj/bTIAf4+Lpm29YJMrEhExh0KNSCVmsxk88dUmsvKLaVknkEe7NzS7JBER0yjUiFRiH63Yy+q96Xi7uzJ5cCvcXfUrLSJVl/4CilRSWw5n8saiFABe7NOE+jX8TK5IRMRcCjUildCZQitjvtxIkdWgR5OaDG4bYXZJIiKmU6gRqYRe/X47u4/nEOrvyaRBLUx92JWIiKNQqBGpZJbuOMZnaw4A8MbtLQn29TC5IhERx6BQI1KJnMwp4KlvNgNwf6d6dG1Uw+SKREQch0KNSCVhGAZPfbOZkzmFxNT05+n4xmaXJCLiUBRqRCqJz9emsnTHcTzcXHhnSCu83F3NLklExKEo1IhUAruPZ/OP+dsAeDq+MY3DAkyuSETE8ZQp1CQkJNC2bVv8/f0JDQ2lf//+pKSkXHSbadOmYbFYSi1eXufO5Ll9+3b69u1LYGAgvr6+tG3bltTU1HPaGYZBr169sFgszJ49uyzli1RKhcU2Hp2xkfwiG10ahnB/x3pmlyQi4pDKFGqSkpIYOXIka9asYfHixRQVFdGjRw9yc3Mvul1AQABHjx61LwcOHCi1fs+ePXTu3JnGjRuTmJjI5s2bef75588bft5++23dvipVypuLU9h6JItqPu68cXtLXFz08y8icj5uZWm8cOHCUl9PmzaN0NBQ1q9fT9euXS+4ncViISws7ILrn332WXr37s0///lP+2sNGjQ4p93GjRt58803+eWXXwgPDy9L6SKV0uo96Xy4fC8ACQNbUDPg3KAvIiIlrmhMTWZmJgDBwRefFTgnJ4fIyEgiIiLo168fW7duta+z2WzMnz+fRo0a0bNnT0JDQ2nXrt05l5by8vK46667ePfddy8akM4qKCggKyur1CJSmWTmFfH4VxsxDLizbQTxzS79cy8iUpWVO9TYbDbGjBlDp06daNas2QXbxcTE8PHHHzNnzhw+//xzbDYbHTt25NChQwAcP36cnJwcJk2aRHx8PIsWLWLAgAEMHDiQpKQk+34ee+wxOnbsSL9+/S6rvoSEBAIDA+1LRIQeIy+Vh2EYjJ+dzNHMfOpV9+H525qYXZKIiMOzGIZhlGfDESNGsGDBAlauXEmdOnUue7uioiJiY2MZMmQIEydO5MiRI9SuXZshQ4Ywffp0e7u+ffvi6+vLF198wXfffccTTzzBhg0b8PMrmbTPYrEwa9Ys+vfvf973KSgooKCgwP51VlYWERERZGZmEhCgO0fEsc1cf4gnvt6Eq4uFmSM60ioiyOySRERMkZWVRWBg4GV9fpfrTM2oUaOYN28ey5YtK1OgAXB3d6d169bs3r0bgJCQENzc3GjSpPT/icbGxtrvflq6dCl79uwhKCgINzc33NxKhgINGjSIuLi4876Pp6cnAQEBpRaRyuDgqTxe/K7kEu2Ymxsq0IiIXKYyDRQ2DIPRo0cza9YsEhMTiYqKKvMbWq1WkpOT6d27NwAeHh60bdv2nFvDd+7cSWRkJADPPPMMw4YNK7W+efPmTJ48mT59+pS5BhFHVWy1MebLjeQUFHN9ZDX+3i3a7JJERCqNMoWakSNHMn36dObMmYO/vz9paWkABAYG4u3tDcDQoUOpXbs2CQkJAEyYMIH27dsTHR1NRkYGr7/+OgcOHCgVUsaOHcvgwYPp2rUr3bp1Y+HChcydO5fExEQAwsLCzjs4uG7duuUKViKO6r3EPaw/cBp/TzcmD26Fq27fFhG5bGUKNVOmTAE455LP1KlTue+++wBITU3FxeX3q1qnT59m+PDhpKWlUa1aNdq0acOqVatKXW4aMGAA77//PgkJCTzyyCPExMQwc+ZMOnfuXM5uiVQ+G1JP886SXQBM6N+UiGAfkysSEalcyj1QuLIpy0AjkYqWU1DMrf+3ggPpefRpWYv/u7OVHjIpIkIFDBQWkatrwtytHEjPo1agF6/0b6ZAIyJSDgo1IiZbuOUoX/1yCIsF3hrcikBvd7NLEhGplBRqREyUlpnPM98mA/C3rg1oX7+6yRWJiFReCjUiJrHZDJ78ehMZeUU0qx3A47c0MrskEZFKTaFGxCQf/7SPlbtP4uXuwtuDW+Phpl9HEZErob+iIibYdiSLfy4seeDkc7c2ITrUz+SKREQqP4UakQqWX2RlzJcbKLTa6B4byt3t6ppdkoiIU1CoEalgkxbsYOexHEL8PJg0qIVu3xYRuUoUakQqUNLOE0xbtR+A129vSYifp7kFiYg4EYUakQqSnlPAk19vAuDeDpF0iwk1uSIREeeiUCNSAQzD4OmZyZzILqBhqB/jeseaXZKIiNNRqBGpAF+sO8iP24/h7mrh7Ttb4eXuanZJIiJOR6FG5BrbeyKHifO2ATC2ZwxNawWaXJGIiHNSqBG5hoqsNsZ8uZEzRVY6NqjOsM71zS5JRMRpKdSIXENv/7iTzYcyCfR25807WuLiotu3RUSuFYUakWtk7d503kvcA0DCwOaEB3qbXJGIiHNTqBG5BjLPFPH4V5swDPhLmzr0bh5udkkiIk5PoUbkGnhhzhYOZ5yhbrAPL/VtanY5IiJVgkKNyFU2Z+Nh5mw8gquLhcmDW+Hn6WZ2SSIiVYJCjchVdOh0Hs/N2gLA6JuiaRNZzeSKRESqDoUakavEajN4/MtNZBcUc13dIEZ1iza7JBGRKkWhRuQqeT9pD+v2n8LXw5W3B7fGzVW/XiIiFUl/dUWugs2HMpi8eCcAL/VtSt3qPiZXJCJS9SjUiFyhvMJixszYSLHNoHfzMP7Spo7ZJYmIVEkKNSJXaOK87ew9mUtYgBevDmiOxaKnBouImEGhRuQKLNqaxhfrUrFY4K07WhLk42F2SSIiVZZCjUg5Hc/K5+mZmwEY3qU+HaNDTK5IRKRqU6gRKQebzeDJbzZzOq+IJuEBPNGjkdkliYhUeQo1IuXwyer9LN95Ak83F965sxWebq5mlyQiUuUp1IiUUUpaNgkLdgAwvncsDWv6m1yRiIiAQo1ImeQXWXl0xgYKi210i6nB0A6RZpckIiK/UagRKYPXf0hhR1o21X09+OdfWur2bRERB6JQI3KZVuw6wX9X7gPgn39pQQ1/T5MrEhGRP1KoEbkMp3MLefLrTQDc3a4uN8fWNLkiERH5M4UakUswDINx3yZzLKuA+jV8ee7WJmaXJCIi56FQI3IJX/9yiIVb03B3tfB/d7bG20O3b4uIOCKFGpGL2H8yl5fmbgXg8VtiaFY70OSKRETkQhRqRC6gyGrj0S83kldopV1UMA91rW92SSIichEKNSIX8K8lu9h0MAN/LzfeGtwKVxfdvi0i4sgUakTO45f9p/j3st0A/GNAc2oHeZtckYiIXIpCjcifZOcXMebLjdgMGNi6Nn1b1jK7JBERuQwKNSJ/8uJ3Wzl0+gx1qnnzcr+mZpcjIiKXqUyhJiEhgbZt2+Lv709oaCj9+/cnJSXlottMmzYNi8VSavHy8jqn3fbt2+nbty+BgYH4+vrStm1bUlNTATh16hSjR48mJiYGb29v6tatyyOPPEJmZmZZyhe5pLmbjvDtr4dxscDbg1vh7+VudkkiInKZyhRqkpKSGDlyJGvWrGHx4sUUFRXRo0cPcnNzL7pdQEAAR48etS8HDhwotX7Pnj107tyZxo0bk5iYyObNm3n++eft4efIkSMcOXKEN954gy1btjBt2jQWLlzIgw8+WMbuilzYkYwzPDsrGYCR3aK5vl6wyRWJiEhZWAzDMMq78YkTJwgNDSUpKYmuXbuet820adMYM2YMGRkZF9zPnXfeibu7O5999tllv/fXX3/NX//6V3Jzc3Fzc7tk+6ysLAIDA8nMzCQgIOCy30eqBqvN4O7/rGHN3lO0jAjim4c74O6qq7MiImYry+f3Ff3VPnv5Jzj44v9Hm5OTQ2RkJBEREfTr14+tW7fa19lsNubPn0+jRo3o2bMnoaGhtGvXjtmzZ1/yvQMCAi4YaAoKCsjKyiq1iFzIRyv2smbvKXw8XHl7cCsFGhGRSqjcf7ltNhtjxoyhU6dONGvW7ILtYmJi+Pjjj5kzZw6ff/45NpuNjh07cujQIQCOHz9OTk4OkyZNIj4+nkWLFjFgwAAGDhxIUlLSefd58uRJJk6cyEMPPXTB901ISCAwMNC+RERElLer4uS2HsnkzUUlY8Ne7NOEqBBfkysSEZHyKPflpxEjRrBgwQJWrlxJnTp1Lnu7oqIiYmNjGTJkCBMnTuTIkSPUrl2bIUOGMH36dHu7vn374uvryxdffFFq+6ysLG655RaCg4P57rvvcHc//0DOgoICCgoKSm0XERGhy09SSrHVRr93f2LrkSx6NKnJB/e0wWLRQ/ZERBxFWS4/XXowynmMGjWKefPmsXz58jIFGgB3d3dat27N7t0lDzYLCQnBzc2NJk1Kz3wcGxvLypUrS72WnZ1NfHw8/v7+zJo164KBBsDT0xNPT88y1SZVz8c/7WPrkSwCvNx4ZUAzBRoRkUqsTJefDMNg1KhRzJo1i6VLlxIVFVXmN7RarSQnJxMeHg6Ah4cHbdu2PefW8J07dxIZGWn/Oisrix49euDh4cF333133tvCRcoiNT2PtxbvBODZW2MJ9dfPlIhIZVamMzUjR45k+vTpzJkzB39/f9LS0gAIDAzE27vkMfJDhw6ldu3aJCQkADBhwgTat29PdHQ0GRkZvP766xw4cIBhw4bZ9zt27FgGDx5M165d6datGwsXLmTu3LkkJiYCvweavLw8Pv/881IDf2vUqIGrq+sVfyOkajEMg/GzkskvstGhfnXuuF5jrkREKrsyhZopU6YAEBcXV+r1qVOnct999wGQmpqKi8vvJ4BOnz7N8OHDSUtLo1q1arRp04ZVq1aVutw0YMAA3n//fRISEnjkkUeIiYlh5syZdO7cGYBff/2VtWvXAhAdHV3qvfft20e9evXK0g0Rvv31MCt3n8TDzYVXBzbXZScRESdwRc+pqUz0nBo5Kz2ngO5vJXE6r4ixPWMY2S360huJiIgpKuw5NSKV0cR52zidV0TjMH8e6lrf7HJEROQqUaiRKmVZynFmbzyCiwVeG9RCD9kTEXEi+osuVUZuQTHPzdoCwH0do2gZEWRuQSIiclUp1EiV8dbinRzOOEPtIG+e6NHI7HJEROQqU6iRKmHTwQym/rQPgH8MaIavZ7meOykiIg5MoUacXpHVxtMzN2MzoF+rWsTFhJpdkoiIXAMKNeL0Plqxlx1p2QT5uPP8bU0uvYGIiFRKCjXi1PadzOWdH3cB8PytTQjx03xgIiLOSqFGnJZhGIz/NpmCYhtdGoYw8LraZpckIiLXkEKNOK2vfznE6r3peLm78I/+mgpBRMTZKdSIUzqenc8/vt8OwOO3NKJudR+TKxIRkWtNoUac0stzt5F5pohmtQN4oFOU2eWIiEgFUKgRp7Nk+zHmbz6Kq4uFSQNb4KapEEREqgT9tRenklNQzHOzS6ZCeLBzFM1qB5pckYiIVBSFGnEqb/yQwtHMfOoG+/BYd02FICJSlSjUiNNYf+A0n6zeD5RMheDt4WpuQSIiUqEUasQpFBbbGPftZgwDBl5Xmy4Na5hdkoiIVDCFGnEKHyTtYeexHKr7evD8rZoKQUSkKlKokUpv9/Ec/rV0NwAv9GlCNV8PkysSEREzKNRIpWazlUyFUGi1cWOjGvRtWcvskkRExCQKNVKpzfj5IOv2n8Lb3ZVX+jfTVAgiIlWYQo1UWsey8klYUDIVwpM9Y4gI1lQIIiJVmUKNVFovztlKdn4xLesEcl/HemaXIyIiJlOokUrph61pLNyahquLhYSBLXB10WUnEZGqTqFGKp2s/CJemFMyFcLfutanSa0AkysSERFHoFAjlc5rC3ZwLKuAetV9eOTmhmaXIyIiDkKhRiqVn/ef4n9rUwF4dWBzvNw1FYKIiJRQqJFKo6DYyjMzNwMw+PoIOjYIMbkiERFxJAo1Umm8u2wPe07kEuLnyfjesWaXIyIiDkahRiqFnceymZJYMhXCS32bEOjjbnJFIiLiaBRqxOHZbAbjvk2myGpwc+NQbm0ebnZJIiLigBRqxOH9b+0B1h84ja+HKxM1FYKIiFyAQo04tKOZZ3htYQoAT8U3plaQt8kViYiIo1KoEYdlGAbPz95KTkExresG8df2kWaXJCIiDkyhRhzWgi1p/Lj9GO6uFl4bpKkQRETk4hRqxCFl5hXxwpytAIy4sQGNavqbXJGIiDg6hRpxSAkLtnMyp4D6NXz5e7dos8sREZFKQKFGHM6avenM+PkgAJMGttBUCCIiclkUasSh5BdZGfdtMgB3tavLDVHBJlckIiKVhUKNOJR/Ld3FvpO5hPp78kyvxmaXIyIilYhCjTiM7Uez+CBpLwAT+jUlwEtTIYiIyOVTqBGHYLUZPPNtMsU2g55NaxLfTFMhiIhI2ZQp1CQkJNC2bVv8/f0JDQ2lf//+pKSkXHSbadOmYbFYSi1eXl7ntNu+fTt9+/YlMDAQX19f2rZtS2pqqn19fn4+I0eOpHr16vj5+TFo0CCOHTtWlvLFgX2yaj+bDmbg7+nGhH7NzC5HREQqoTKFmqSkJEaOHMmaNWtYvHgxRUVF9OjRg9zc3ItuFxAQwNGjR+3LgQMHSq3fs2cPnTt3pnHjxiQmJrJ582aef/75UuHnscceY+7cuXz99dckJSVx5MgRBg4cWJbyxUEdOp3HG4tKwvHTvRpTM+Dc0CsiInIpFsMwjPJufOLECUJDQ0lKSqJr167nbTNt2jTGjBlDRkbGBfdz55134u7uzmeffXbe9ZmZmdSoUYPp06fzl7/8BYAdO3YQGxvL6tWrad++/SVrzcrKIjAwkMzMTAICAi7dOakQhmHwwLSfWZZygrb1qvHlQx1w0ZODRUTkN2X5/L6iMTWZmZkABAdf/LbbnJwcIiMjiYiIoF+/fmzdutW+zmazMX/+fBo1akTPnj0JDQ2lXbt2zJ49295m/fr1FBUV0b17d/trjRs3pm7duqxevfpKuiAmm7v5KMtSTuDh6kLCwBYKNCIiUm7lDjU2m40xY8bQqVMnmjW78BiImJgYPv74Y+bMmcPnn3+OzWajY8eOHDp0CIDjx4+Tk5PDpEmTiI+PZ9GiRQwYMICBAweSlJQEQFpaGh4eHgQFBZXad82aNUlLSzvv+xYUFJCVlVVqEcdyOreQl78rCbgju0UTHepnckUiIlKZuZV3w5EjR7JlyxZWrlx50XYdOnSgQ4cO9q87duxIbGwsH3zwARMnTsRmswHQr18/HnvsMQBatWrFqlWreP/997nxxhvLVV9CQgIvv/xyubaVivGP77eTnltIw1A/RsQ1MLscERGp5Mp1pmbUqFHMmzePZcuWUadOnTJt6+7uTuvWrdm9ezcAISEhuLm50aRJk1LtYmNj7Xc/hYWFUVhYeM64nGPHjhEWFnbe9xk3bhyZmZn25eDBg2WqU66tlbtO8s36Q1gsMGlQCzzc9HQBERG5MmX6JDEMg1GjRjFr1iyWLl1KVFRUmd/QarWSnJxMeHjJc0g8PDxo27btObeG79y5k8jISADatGmDu7s7S5Yssa9PSUkhNTW11FmgP/L09CQgIKDUIo7hTKGV8bNKpkK4p30kbSKrmVyRiIg4gzJdfho5ciTTp09nzpw5+Pv728ezBAYG4u3tDcDQoUOpXbs2CQkJAEyYMIH27dsTHR1NRkYGr7/+OgcOHGDYsGH2/Y4dO5bBgwfTtWtXunXrxsKFC5k7dy6JiYn2/T/44IM8/vjjBAcHExAQwOjRo+nQocNl3fkkjuXtJTtJPZVHWIAXY3vGmF2OiIg4iTKFmilTpgAQFxdX6vWpU6dy3333AZCamoqLy+8ngE6fPs3w4cNJS0ujWrVqtGnThlWrVpW63DRgwADef/99EhISeOSRR4iJiWHmzJl07tzZ3mby5Mm4uLgwaNAgCgoK6NmzJ++9915Z+ysm23I4k/+s2AfAxP7N8NdUCCIicpVc0XNqKhM9p8Z8xVYbA95bRfLhTG5tHs67d19ndkkiIuLgKuw5NSJlMfWn/SQfziTAy40X+za59AYiIiJloFAjFeLgqTzeWrwTgPG9Ywn111QIIiJydSnUyDVnGAbjZyVzpshK+/rBDG4bYXZJIiLihBRq5JqbteEwK3adxMOtZCoEi0VTIYiIyNWnUCPXVHpOARPnbQPg0ZsbEhXia3JFIiLirBRq5Jp6Zf52TucV0TjMn4e61je7HBERcWIKNXLNJO08wawNh+1TIbi76sdNRESuHX3KyDWRV1jMs79NhXBfx3q0iggytyAREXF6CjVyTby1aCeHTp+hdpA3T/bQVAgiInLtKdTIVbf5UAYf/1QyFcIrA5rh61mm2ThERETKRaFGrqoiq42nZyZjM6Bvy1p0iwk1uyQREakiFGrkqvrPin1sP5pFkI87L/TRVAgiIlJxFGrkqtl/Mpe3fyyZCuG5W5sQ4udpckUiIlKVKNTIVXF2KoSCYhudo0MYdF1ts0sSEZEqRqFGroqv1x9i1Z50vNxd+MeAZpoKQUREKpxCjVyxE9kF/GP+dgAe696IyOqaCkFERCqeQo1csQnztpF5poimtQJ4sHOU2eWIiEgVpVAjV2TpjmPM3XQEFwtMGtgCN02FICIiJtEnkJRbTkExz83aAsCDnaNoXifQ5IpERKQqU6iRcnvjhxSOZOYTEezNY7c0MrscERGp4hRqpFx+TT3NJ6v3A/DqgOb4eGgqBBERMZdCjZRZYbGNcTOTMQwY2Lo2XRrWMLskERERhRopuw+X7yHlWDbBvh48d5umQhAREcegUCNlsudEDv+3dDcAL9zWhGBfD5MrEhERKaFQI5fNZjMY920yhcU2ujaqQb9WtcwuSURExE6hRi7bl78cZN2+U3i7u/KP/poKQUREHItCjVyW41n5vPp9yVQIT/RoRESwj8kViYiIlKZQI5flxe+2kp1fTIs6gdzfSVMhiIiI41GokUtatDWNBVvScHWxMGlgC1xddNlJREQcj0KNXFR2fhEvzNkKwENd69OkVoDJFYmIiJyfQo1c1D8XppCWlU+96j48enNDs8sRERG5IIUauaBf9p/iszUHgJKpELzcXU2uSERE5MIUauS8CoqtPPNtMgB3XF+HjtEhJlckIiJycQo1cl5TEvew+3gOIX4ejO8da3Y5IiIil6RQI+fYdSybd5eVTIXwYp+mBPloKgQREXF8CjVSis1m8My3yRRZDW5qHMptLcLNLklEROSyKNRIKf9bl8r6A6fx9XBloqZCEBGRSkShRuzSMvN5bcEOAMb2jKF2kLfJFYmIiFw+hRoBwDAMnp+zhZyCYlpFBHFPh3pmlyQiIlImCjUCwMItaSzedgw3FwuvDdJUCCIiUvko1AiZZ4p44buSqRBGxDUgJszf5IpERETKTqFGmLRgOyeyC6hfw5eR3aLNLkdERKRcyhRqEhISaNu2Lf7+/oSGhtK/f39SUlIuus20adOwWCylFi8vr1Jt7rvvvnPaxMfHl2qzc+dO+vXrR0hICAEBAXTu3Jlly5aVpXw5jzV70/li3UEAEjQVgoiIVGJlCjVJSUmMHDmSNWvWsHjxYoqKiujRowe5ubkX3S4gIICjR4/alwMHDpzTJj4+vlSbL774otT62267jeLiYpYuXcr69etp2bIlt912G2lpaWXpgvxBfpGV8b9NhTDkhrq0q1/d5IpERETKz60sjRcuXFjq62nTphEaGsr69evp2rXrBbezWCyEhYVddN+enp4XbHPy5El27drFf//7X1q0aAHApEmTeO+999iyZcsl9y3n9++lu9l7MpdQf0+e6dXY7HJERESuyBWNqcnMzAQgODj4ou1ycnKIjIwkIiKCfv36sXXr1nPaJCYmEhoaSkxMDCNGjCA9Pd2+rnr16sTExPDpp5+Sm5tLcXExH3zwAaGhobRp0+ZKulBl7UjL4v2kPQC83Lcpgd7uJlckIiJyZSyGYRjl2dBms9G3b18yMjJYuXLlBdutXr2aXbt20aJFCzIzM3njjTdYvnw5W7dupU6dOgDMmDEDHx8foqKi2LNnD+PHj8fPz4/Vq1fj6loyxuPQoUP079+fX3/9FRcXF0JDQ5k/fz6tW7c+7/sWFBRQUFBg/zorK4uIiAgyMzMJCAgoT5edhtVmMGjKKjYezKBHk5p8cE8bPTlYREQcUlZWFoGBgZf1+V3uUDNixAgWLFjAypUr7eHkchQVFREbG8uQIUOYOHHiedvs3buXBg0a8OOPP3LzzTdjGAb9+/enqKiIZ599Fm9vb/7zn//w3Xff8fPPPxMefu78RC+99BIvv/zyOa8r1MC0n/bx0txt+Hu6sfjxGwkL9Lr0RiIiIiYoS6gp1+WnUaNGMW/ePJYtW1amQAPg7u5O69at2b179wXb1K9fn5CQEHubpUuXMm/ePGbMmEGnTp247rrreO+99/D29uaTTz457z7GjRtHZmamfTl48GCZ6nRWhzPO8M8fSu5Ye6pXYwUaERFxGmUaKGwYBqNHj2bWrFkkJiYSFRVV5je0Wq0kJyfTu3fvC7Y5dOgQ6enp9jMweXl5ALi4lM5gLi4u2Gy28+7D09MTT0/PMtfnzAzD4PnZW8grtHJ9ZDXuvqGu2SWJiIhcNWU6UzNy5Eg+//xzpk+fjr+/P2lpaaSlpXHmzBl7m6FDhzJu3Dj71xMmTGDRokXs3buXX3/9lb/+9a8cOHCAYcOGASWDiMeOHcuaNWvYv38/S5YsoV+/fkRHR9OzZ08AOnToQLVq1bj33nvZtGkTO3fuZOzYsezbt49bb731anwfqoR5m4+ydMdxPFxdmDSoOS6aCkFERJxImULNlClTyMzMJC4ujvDwcPvy5Zdf2tukpqZy9OhR+9enT59m+PDhxMbG0rt3b7Kysli1ahVNmjQBwNXVlc2bN9O3b18aNWrEgw8+SJs2bVixYoX9TEtISAgLFy4kJyeHm266ieuvv56VK1cyZ84cWrZseTW+D04vI6+Ql+eW3HX2924NiA7VVAgiIuJcyj1QuLIpy0AjZzT26018vf4Q0aF+zH+kM55uenKwiIg4vms+UFgql1W7T/L1+kNYLPDaoOYKNCIi4pQUapxcfpGVcbNKpkL4a7tI2kRe/EGJIiIilZVCjZN7+8ddHEjPIyzAi6fiY8wuR0RE5JpRqHFiW49k8tGKvQBM7N8Mfy9NhSAiIs5LocZJFVttjPs2GavNoHfzMG5pUtPskkRERK4phRonNW3VfjYfysTfy42X+jQ1uxwREZFrTqHGCR08lcebi3YCML53LKEBmgpBREScn0KNkzEMg2dnb+FMkZV2UcEMvj7C7JJEREQqhEKNk5mz8QjLd57Aw82FhIGaCkFERKoOhRonciq3kAnztgHwyE3R1K/hZ3JFIiIiFUehxom8Mm8bp3ILaRzmz0NdG5hdjoiISIVSqHESy3ee4NsNh7FYIGFgczzcdGhFRKRq0SefE8grLGb8b1Mh3NuhHq3rVjO5IhERkYqnUOMEJi/eyaHTZ6gd5M2TPTUVgoiIVE0KNZVc8qFM/rtyHwCv9G+Gn6ebyRWJiIiYQ6GmEiuy2nh65mZsBvRpWYtujUPNLklERMQ0CjWV2H9X7mPb0SwCvd154bYmZpcjIiJiKoWaSupAei6TF5dMhfDcrbHU8Pc0uSIRERFzKdRUQoZhMH5WMgXFNjpFV+cvbeqYXZKIiIjpFGoqoW/WH+Kn3el4urnwj/7NsVg0FYKIiIhCTSVzMqeAf3y/HYDHbmlEvRBfkysSERFxDAo1lcyEudvIyCuiSXgAwzpHmV2OiIiIw1CoqUSW7TjOd5uO4GKB1wa1wM1Vh09EROQsfSpWErkFxTw3ewsAD3SKonmdQJMrEhERcSwKNZXEG4tSOJxxhjrVvHm8RyOzyxEREXE4CjWVwIbU00xbtR+AVwc0x8dDUyGIiIj8mUKNgyuy2hj3bTKGAQNa16ZroxpmlyQiIuKQFGoc3IfL97IjLZtgXw+e11QIIiIiF6RQ48D2nsjhnSW7AHj+tliCfT1MrkhERMRxKdQ4KJvNYNy3yRQW2+jaqAb9W9U2uyQRERGHplDjoL765SBr953C292Vf/RvpqkQRERELkGhxgEdz87n1d+mQniiRyMign1MrkhERMTxKdQ4oJe/20ZWfjHNawdyX8d6ZpcjIiJSKSjUOJjF244xP/kori4WJg1qrqkQRERELpM+MR1Idn4Rz/82FcLwLvVpWktTIYiIiFwuhRoH8voPKaRl5RNZ3Ycx3RuaXY6IiEilolDjINYfOMVnaw4AJVMheLm7mlyRiIhI5aJQ4wAKiq08M7NkKoTb29ShU3SI2SWJiIhUOgo1DuD9xL3sOp5DiJ8Hz94aa3Y5IiIilZJCjcl2H8/m3WW7AXihT1OCfDQVgoiISHko1JjIZjN4ZmYyhVYb3WJq0KdFuNkliYiIVFoKNSaavi6VXw6cxsfDlVcGNNdUCCIiIldAocYkaZn5vLZgBwBje8ZQO8jb5IpEREQqtzKFmoSEBNq2bYu/vz+hoaH079+flJSUi24zbdo0LBZLqcXLy6tUm/vuu++cNvHx8efsa/78+bRr1w5vb2+qVatG//79y1K+Q3lhzhayC4ppFRHE0A71zC5HRESk0nMrS+OkpCRGjhxJ27ZtKS4uZvz48fTo0YNt27bh6+t7we0CAgJKhZ/zXWaJj49n6tSp9q89PT1LrZ85cybDhw/n1Vdf5aabbqK4uJgtW7aUpXyHsXDLURZtO4bbb1MhuLrospOIiMiVKlOoWbhwYamvp02bRmhoKOvXr6dr164X3M5isRAWFnbRfXt6el6wTXFxMY8++iivv/46Dz74oP31Jk2alKF6x5B5pogX5mwF4OEbG9A4LMDkikRERJzDFY2pyczMBCA4OPii7XJycoiMjCQiIoJ+/fqxdevWc9okJiYSGhpKTEwMI0aMID093b7u119/5fDhw7i4uNC6dWvCw8Pp1avXRc/UFBQUkJWVVWpxBK8t3MHx7ALqh/gy6qZos8sRERFxGuUONTabjTFjxtCpUyeaNWt2wXYxMTF8/PHHzJkzh88//xybzUbHjh05dOiQvU18fDyffvopS5Ys4bXXXiMpKYlevXphtVoB2Lt3LwAvvfQSzz33HPPmzaNatWrExcVx6tSp875vQkICgYGB9iUiIqK8Xb1q1u5NZ/raVABeHaipEERERK4mi2EYRnk2HDFiBAsWLGDlypXUqVPnsrcrKioiNjaWIUOGMHHixPO22bt3Lw0aNODHH3/k5ptvZvr06dx999188MEHPPTQQ0DJmZg6derwyiuv8Le//e2cfRQUFFBQUGD/Oisri4iICDIzMwkIqPhLPvlFVnr/3wr2nshlyA0RJAxsUeE1iIiIVDZZWVkEBgZe1ud3uc7UjBo1innz5rFs2bIyBRoAd3d3Wrduze7duy/Ypn79+oSEhNjbhIeXPJTuj2NoPD09qV+/Pqmpqefdh6enJwEBAaUWM723bDd7T+RSw9+TZ3ppKgQREZGrrUyhxjAMRo0axaxZs1i6dClRUVFlfkOr1UpycrI9qJzPoUOHSE9Pt7dp06YNnp6epe6gKioqYv/+/URGRpa5hoqWkpbNe4l7AHi5b1MCvd1NrkhERMT5lOnup5EjRzJ9+nTmzJmDv78/aWlpAAQGBuLtXfLwuKFDh1K7dm0SEhIAmDBhAu3btyc6OpqMjAxef/11Dhw4wLBhw4CSQcQvv/wygwYNIiwsjD179vDUU08RHR1Nz549gZJbwh9++GFefPFFIiIiiIyM5PXXXwfg9ttvvzrfiWvEajN45tvNFNsMbmlSk17NLn4XmIiIiJRPmULNlClTAIiLiyv1+tSpU7nvvvsASE1NxcXl9xNAp0+fZvjw4aSlpVGtWjXatGnDqlWr7JeSXF1d2bx5M5988gkZGRnUqlWLHj16MHHixFLPqnn99ddxc3Pjnnvu4cyZM7Rr146lS5dSrVq18vS7wny+5gAbUjPw83RjYr9mmgpBRETkGin3QOHKpiwDja6WIxlnuOWtJHILrUzs15R79ORgERGRMrnmA4Xl0gzD4PnZW8gttNImshp3t3P8sT8iIiKVmULNNTI/+ShLdhzH3dXCpIHNcdFUCCIiIteUQs01kJFXyEvflTw1+e9x0TSs6W9yRSIiIs5PoeYaePX77ZzMKSQ61I+/d2tgdjkiIiJVgkLNVbZqz0m++qVkCohJA5vj6aapEERERCqCQs1VlF9kZfy3yQD8tX1drq938Yk+RURE5OpRqLmK3lmyi/3pedQM8OSp+MZmlyMiIlKlKNRcJduOZPHh8pLZxCf2a0aAl6ZCEBERqUgKNVeB1WYw7tvNWG0GvZqF0aOppkIQERGpaAo1V8G0VfvZdCgTfy83Xu7b1OxyREREqiSFmit08FQeb/xQMnv4+N6xhAZ4mVyRiIhI1VSmCS3lXOm5hVT386BWkDeDr48wuxwREZEqS6HmCrWKCGLRY13JOlOsqRBERERMpFBzFfh4uOHjoW+liIiImTSmRkRERJyCQo2IiIg4BYUaERERcQoKNSIiIuIUFGpERETEKSjUiIiIiFNQqBERERGnoFAjIiIiTkGhRkRERJyCQo2IiIg4BYUaERERcQoKNSIiIuIUFGpERETEKVSZqaUNwwAgKyvL5EpERETkcp393D77OX4xVSbUZGdnAxAREWFyJSIiIlJW2dnZBAYGXrSNxbic6OMEbDYbR44cwd/fH4vFclX3nZWVRUREBAcPHiQgIOCq7tsROHv/wPn7qP5Vfs7eR/Wv8rtWfTQMg+zsbGrVqoWLy8VHzVSZMzUuLi7UqVPnmr5HQECA0/6wgvP3D5y/j+pf5efsfVT/Kr9r0cdLnaE5SwOFRURExCko1IiIiIhTUKi5Cjw9PXnxxRfx9PQ0u5Rrwtn7B87fR/Wv8nP2Pqp/lZ8j9LHKDBQWERER56YzNSIiIuIUFGpERETEKSjUiIiIiFNQqLmA5cuX06dPH2rVqoXFYmH27Nml1huGwQsvvEB4eDje3t50796dXbt2lWpz6tQp7r77bgICAggKCuLBBx8kJyenAntxcZfq43333YfFYim1xMfHl2rjqH1MSEigbdu2+Pv7ExoaSv/+/UlJSSnVJj8/n5EjR1K9enX8/PwYNGgQx44dK9UmNTWVW2+9FR8fH0JDQxk7dizFxcUV2ZULupw+xsXFnXMMH3744VJtHLWPU6ZMoUWLFvZnXnTo0IEFCxbY11f24weX7mNlPn7nM2nSJCwWC2PGjLG/5gzH8azz9a+yH8OXXnrpnPobN25sX+9wx8+Q8/r++++NZ5991vj2228NwJg1a1ap9ZMmTTICAwON2bNnG5s2bTL69u1rREVFGWfOnLG3iY+PN1q2bGmsWbPGWLFihREdHW0MGTKkgntyYZfq47333mvEx8cbR48etS+nTp0q1cZR+9izZ09j6tSpxpYtW4yNGzcavXv3NurWrWvk5OTY2zz88MNGRESEsWTJEuOXX34x2rdvb3Ts2NG+vri42GjWrJnRvXt3Y8OGDcb3339vhISEGOPGjTOjS+e4nD7eeOONxvDhw0sdw8zMTPt6R+7jd999Z8yfP9/YuXOnkZKSYowfP95wd3c3tmzZYhhG5T9+hnHpPlbm4/dn69atM+rVq2e0aNHCePTRR+2vO8NxNIwL96+yH8MXX3zRaNq0aan6T5w4YV/vaMdPoeYy/PkD32azGWFhYcbrr79ufy0jI8Pw9PQ0vvjiC8MwDGPbtm0GYPz888/2NgsWLDAsFotx+PDhCqv9cl0o1PTr1++C21SmPh4/ftwAjKSkJMMwSo6Xu7u78fXXX9vbbN++3QCM1atXG4ZREvpcXFyMtLQ0e5spU6YYAQEBRkFBQcV24DL8uY+GUfIH9Y9/YP+ssvWxWrVqxn/+8x+nPH5nne2jYTjP8cvOzjYaNmxoLF68uFSfnOU4Xqh/hlH5j+GLL75otGzZ8rzrHPH46fJTOezbt4+0tDS6d+9ufy0wMJB27dqxevVqAFavXk1QUBDXX3+9vU337t1xcXFh7dq1FV5zeSUmJhIaGkpMTAwjRowgPT3dvq4y9TEzMxOA4OBgANavX09RUVGpY9i4cWPq1q1b6hg2b96cmjVr2tv07NmTrKwstm7dWoHVX54/9/Gs//3vf4SEhNCsWTPGjRtHXl6efV1l6aPVamXGjBnk5ubSoUMHpzx+f+7jWc5w/EaOHMmtt95a6niB8/weXqh/Z1X2Y7hr1y5q1apF/fr1ufvuu0lNTQUc8/hVmbmfrqa0tDSAUgfp7Ndn16WlpREaGlpqvZubG8HBwfY2ji4+Pp6BAwcSFRXFnj17GD9+PL169WL16tW4urpWmj7abDbGjBlDp06daNasGVByfDw8PAgKCirV9s/H8HzH+Ow6R3K+PgLcddddREZGUqtWLTZv3szTTz9NSkoK3377LeD4fUxOTqZDhw7k5+fj5+fHrFmzaNKkCRs3bnSa43ehPkLlP34AM2bM4Ndff+Xnn38+Z50z/B5erH9Q+Y9hu3btmDZtGjExMRw9epSXX36ZLl26sGXLFoc8fgo1ckF33nmn/d/NmzenRYsWNGjQgMTERG6++WYTKyubkSNHsmXLFlauXGl2KdfMhfr40EMP2f/dvHlzwsPDufnmm9mzZw8NGjSo6DLLLCYmho0bN5KZmck333zDvffeS1JSktllXVUX6mOTJk0q/fE7ePAgjz76KIsXL8bLy8vscq66y+lfZT+GvXr1sv+7RYsWtGvXjsjISL766iu8vb1NrOz8dPmpHMLCwgDOGeF97Ngx+7qwsDCOHz9ean1xcTGnTp2yt6ls6tevT0hICLt37wYqRx9HjRrFvHnzWLZsWalZ2sPCwigsLCQjI6NU+z8fw/Md47PrHMWF+ng+7dq1Ayh1DB25jx4eHkRHR9OmTRsSEhJo2bIl77zzjlMdvwv18Xwq2/Fbv349x48f57rrrsPNzQ03NzeSkpL4v//7P9zc3KhZs2alPo6X6p/Vaj1nm8p2DP8sKCiIRo0asXv3bof8PVSoKYeoqCjCwsJYsmSJ/bWsrCzWrl1rvxbeoUMHMjIyWL9+vb3N0qVLsdls9h/qyubQoUOkp6cTHh4OOHYfDcNg1KhRzJo1i6VLlxIVFVVqfZs2bXB3dy91DFNSUkhNTS11DJOTk0sFt8WLFxMQEGC/PGCmS/XxfDZu3AhQ6hg6ch//zGazUVBQ4BTH70LO9vF8Ktvxu/nmm0lOTmbjxo325frrr+fuu++2/7syH8dL9c/V1fWcbSrbMfyznJwc9uzZQ3h4uGP+Hl71ocdOIjs729iwYYOxYcMGAzDeeustY8OGDcaBAwcMwyi5pTsoKMiYM2eOsXnzZqNfv37nvaW7devWxtq1a42VK1caDRs2dIjbnc+6WB+zs7ONJ5980li9erWxb98+48cffzSuu+46o2HDhkZ+fr59H47axxEjRhiBgYFGYmJiqVsR8/Ly7G0efvhho27dusbSpUuNX375xejQoYPRoUMH+/qztyL26NHD2Lhxo7Fw4UKjRo0aDnOr5aX6uHv3bmPChAnGL7/8Yuzbt8+YM2eOUb9+faNr1672fThyH5955hkjKSnJ2Ldvn7F582bjmWeeMSwWi7Fo0SLDMCr/8TOMi/exsh+/C/nz3UDOcBz/6I/9c4Zj+MQTTxiJiYnGvn37jJ9++sno3r27ERISYhw/ftwwDMc7fgo1F7Bs2TIDOGe59957DcMoua37+eefN2rWrGl4enoaN998s5GSklJqH+np6caQIUMMPz8/IyAgwLj//vuN7OxsE3pzfhfrY15entGjRw+jRo0ahru7uxEZGWkMHz681G15huG4fTxfvwBj6tSp9jZnzpwx/v73vxvVqlUzfHx8jAEDBhhHjx4ttZ/9+/cbvXr1Mry9vY2QkBDjiSeeMIqKiiq4N+d3qT6mpqYaXbt2NYKDgw1PT08jOjraGDt2bKlnZBiG4/bxgQceMCIjIw0PDw+jRo0axs0332wPNIZR+Y+fYVy8j5X9+F3In0ONMxzHP/pj/5zhGA4ePNgIDw83PDw8jNq1axuDBw82du/ebV/vaMdPs3SLiIiIU9CYGhEREXEKCjUiIiLiFBRqRERExCko1IiIiIhTUKgRERERp6BQIyIiIk5BoUZEREScgkKNiIiIOAWFGhFxOImJiVgslnMmyhMRuRiFGhFxOB07duTo0aMEBgZe9jZ5eXmMGzeOBg0a4OXlRY0aNbjxxhuZM2fONaxURByJm9kFiIj8mYeHB2FhYWXa5uGHH2bt2rX861//okmTJqSnp7Nq1SrS09OvUZUi4mh0pkZErrm4uDhGjx7NmDFjqFatGjVr1uSjjz4iNzeX+++/H39/f6Kjo1mwYAFw7uWnadOmERQUxA8//EBsbCx+fn7Ex8dz9OhR+3t89913jB8/nt69e1OvXj3atGnD6NGjeeCBB+xtLBYLs2fPLlVbUFAQ06ZNA2D//v1YLBZmzJhBx44d8fLyolmzZiQlJV3T74+IXB0KNSJSIT755BNCQkJYt24do0ePZsSIEdx+++107NiRX3/9lR49enDPPfeQl5d33u3z8vJ44403+Oyzz1i+fDmpqak8+eST9vVhYWF8//33ZGdnX3GtY8eO5YknnmDDhg106NCBPn366IyPSCWgUCMiFaJly5Y899xzNGzYkHHjxuHl5UVISAjDhw+nYcOGvPDCC6Snp7N58+bzbl9UVMT777/P9ddfz3XXXceoUaNYsmSJff2HH37IqlWrqF69Om3btuWxxx7jp59+Kleto0aNYtCgQcTGxjJlyhQCAwP573//W659iUjFUagRkQrRokUL+79dXV2pXr06zZs3t79Ws2ZNAI4fP37e7X18fGjQoIH96/Dw8FJtu3btyt69e1myZAl/+ctf2Lp1K126dGHixIllrrVDhw72f7u5uXH99dezffv2Mu9HRCqWQo2IVAh3d/dSX1ssllKvWSwWAGw222VvbxjGOW26dOnC008/zaJFi5gwYQITJ06ksLDwgtsUFRWVr0Mi4nAUakTEaTVp0oTi4mLy8/MBqFGjRqnBxbt27TrvGJ41a9bY/11cXMz69euJjY299gWLyBXRLd0i4hTi4uIYMmQI119/PdWrV2fbtm2MHz+ebt26ERAQAMBNN93Ev//9bzp06IDVauXpp58+5wwQwLvvvkvDhg2JjY1l8uTJnD59utRdVCLimHSmRkScQs+ePfnkk0/o0aMHsbGxjB49mp49e/LVV1/Z27z55ptERETQpUsX7rrrLp588kl8fHzO2dekSZOYNGkSLVu2ZOXKlXz33XeEhIRUZHdEpBwsxp8vMIuIVFH79+8nKiqKDRs20KpVK7PLEZEy0pkaERERcQoKNSIiIuIUdPlJREREnILO1IiIiIhTUKgRERERp6BQIyIiIk5BoUZEREScgkKNiIiIOAWFGhEREXEKCjUiIiLiFBRqRERExCko1IiIiIhT+H/FZqzaaoh5LgAAAABJRU5ErkJggg==\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/periodicFrequentPatternMiningPollutionDemo.ipynb b/notebooks/periodicFrequentPatternMiningPollutionDemo.ipynb index 66063825..24c6af10 100644 --- a/notebooks/periodicFrequentPatternMiningPollutionDemo.ipynb +++ b/notebooks/periodicFrequentPatternMiningPollutionDemo.ipynb @@ -1,3383 +1,3383 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true, + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Walk through: Discovering Periodic-Frequent Patterns from Big Multiple Time Series Data\n", + "\n", + "\n", + "> Illustration with 5-year nation-wide air pollution data of Japan\n", + "\n" + ], + "metadata": { + "id": "9kxtMcdavLld" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Introduction\n", + "\n", + "Multiple time series data is ubiqutous. Beneficial patterns that can empower the users with competitive knowledge are hidden in this series. This article describe the process to discover frequently occurring patterns (or frequent patterns) in a multiple time series. We use the 5-year nation-wide air pollution (PM2.5) data of Japan for illustration purposes." + ], + "metadata": { + "id": "V_0ZTN7ExFVq" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Download the air pollution dataset [1]\n", + "\n" + ], + "metadata": { + "id": "zABbPAWlzzf-" + } + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "P6B2vwnWu6oa", "colab": { - "provenance": [], - "toc_visible": true, - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "base_uri": "https://localhost:8080/" }, - "language_info": { - "name": "python" + "outputId": "a0130e80-e2e9-4734-b90c-5d1b713875e9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-08-07 12:19:59-- https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.5.18, 2620:100:601d:18::a27d:512\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.5.18|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: /s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv [following]\n", + "--2023-08-07 12:20:00-- https://www.dropbox.com/s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", + "Reusing existing connection to www.dropbox.com:443.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com/cd/0/inline/CBVTD1qCsLwUm19vPFBZR8CsH5Diohh3p1lEX-tw-nwdow-tztlDEdrALFfZUQMgUkcS5wKDxx_58K6T7d5y79HxCHqRd68gNZCrs6ZX3vBkzZme-fcdYRN9ThGHieYo-VZHV6DO3W4f4klK1b5vKGMd/file# [following]\n", + "--2023-08-07 12:20:00-- https://uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com/cd/0/inline/CBVTD1qCsLwUm19vPFBZR8CsH5Diohh3p1lEX-tw-nwdow-tztlDEdrALFfZUQMgUkcS5wKDxx_58K6T7d5y79HxCHqRd68gNZCrs6ZX3vBkzZme-fcdYRN9ThGHieYo-VZHV6DO3W4f4klK1b5vKGMd/file\n", + "Resolving uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com (uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com)... 162.125.5.15, 2620:100:601d:15::a27d:50f\n", + "Connecting to uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com (uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com)|162.125.5.15|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 262918270 (251M) [text/plain]\n", + "Saving to: ‘ETL_DATA_new.csv.1’\n", + "\n", + "ETL_DATA_new.csv.1 100%[===================>] 250.74M 63.9MB/s in 4.1s \n", + "\n", + "2023-08-07 12:20:05 (61.1 MB/s) - ‘ETL_DATA_new.csv.1’ saved [262918270/262918270]\n", + "\n" + ] } + ], + "source": [ + "!wget https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Walk through: Discovering Periodic-Frequent Patterns from Big Multiple Time Series Data\n", - "\n", - "\n", - "> Illustration with 5-year nation-wide air pollution data of Japan\n", - "\n" - ], - "metadata": { - "id": "9kxtMcdavLld" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Introduction\n", - "\n", - "Multiple time series data is ubiqutous. Beneficial patterns that can empower the users with competitive knowledge are hidden in this series. This article describe the process to discover frequently occurring patterns (or frequent patterns) in a multiple time series. We use the 5-year nation-wide air pollution (PM2.5) data of Japan for illustration purposes." - ], - "metadata": { - "id": "V_0ZTN7ExFVq" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Download the air pollution dataset [1]\n", - "\n" - ], - "metadata": { - "id": "zABbPAWlzzf-" - } - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "P6B2vwnWu6oa", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a0130e80-e2e9-4734-b90c-5d1b713875e9" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-08-07 12:19:59-- https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", - "Resolving www.dropbox.com (www.dropbox.com)... 162.125.5.18, 2620:100:601d:18::a27d:512\n", - "Connecting to www.dropbox.com (www.dropbox.com)|162.125.5.18|:443... connected.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: /s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv [following]\n", - "--2023-08-07 12:20:00-- https://www.dropbox.com/s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv\n", - "Reusing existing connection to www.dropbox.com:443.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com/cd/0/inline/CBVTD1qCsLwUm19vPFBZR8CsH5Diohh3p1lEX-tw-nwdow-tztlDEdrALFfZUQMgUkcS5wKDxx_58K6T7d5y79HxCHqRd68gNZCrs6ZX3vBkzZme-fcdYRN9ThGHieYo-VZHV6DO3W4f4klK1b5vKGMd/file# [following]\n", - "--2023-08-07 12:20:00-- https://uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com/cd/0/inline/CBVTD1qCsLwUm19vPFBZR8CsH5Diohh3p1lEX-tw-nwdow-tztlDEdrALFfZUQMgUkcS5wKDxx_58K6T7d5y79HxCHqRd68gNZCrs6ZX3vBkzZme-fcdYRN9ThGHieYo-VZHV6DO3W4f4klK1b5vKGMd/file\n", - "Resolving uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com (uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com)... 162.125.5.15, 2620:100:601d:15::a27d:50f\n", - "Connecting to uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com (uca94ba065f0ebb23c6b0d732829.dl.dropboxusercontent.com)|162.125.5.15|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 262918270 (251M) [text/plain]\n", - "Saving to: ‘ETL_DATA_new.csv.1’\n", - "\n", - "ETL_DATA_new.csv.1 100%[===================>] 250.74M 63.9MB/s in 4.1s \n", - "\n", - "2023-08-07 12:20:05 (61.1 MB/s) - ‘ETL_DATA_new.csv.1’ saved [262918270/262918270]\n", - "\n" - ] - } - ], - "source": [ - "!wget https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv" - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Read the dataset and analyze it" - ], - "metadata": { - "id": "DYJOJKOq3Qr2" - } - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "dataset = pd.read_csv('ETL_DATA_new.csv', index_col=0)\n", - "\n", - "dataset\n", - "# you can notice that dataset is collected from 2018-01-01 01:00:00 hours to 2023-04-25 22:00:00 hours (5+ years)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 693 - }, - "id": "vsonMLxu1CMG", - "outputId": "2f879591-77f2-4d5a-f68d-073e18249b6f" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " TimeStamp Point(139.0794379 36.3727776) \\\n", - " \n", - "0 2018-01-01 01:00:00 NaN \n", - "1 2018-01-01 02:00:00 NaN \n", - "2 2018-01-01 03:00:00 NaN \n", - "3 2018-01-01 04:00:00 NaN \n", - "4 2018-01-01 05:00:00 NaN \n", - "... ... ... \n", - "46000 2023-04-25 18:00:00 NaN \n", - "46001 2023-04-25 19:00:00 NaN \n", - "46002 2023-04-25 20:00:00 NaN \n", - "46003 2023-04-25 21:00:00 NaN \n", - "46004 2023-04-25 22:00:00 NaN \n", - "\n", - " Point(139.1051411 36.3963822) Point(139.0960211 36.4047323) \\\n", - " \n", - "0 NaN 5.0 \n", - "1 NaN 11.0 \n", - "2 NaN 7.0 \n", - "3 NaN 5.0 \n", - "4 NaN 6.0 \n", - "... ... ... \n", - "46000 NaN NaN \n", - "46001 NaN NaN \n", - "46002 NaN NaN \n", - "46003 NaN NaN \n", - "46004 NaN NaN \n", - "\n", - " Point(139.0428727 36.3816035) Point(138.9955116 36.33801589999999) \\\n", - " \n", - "0 13.0 18.0 \n", - "1 12.0 22.0 \n", - "2 12.0 19.0 \n", - "3 11.0 16.0 \n", - "4 11.0 10.0 \n", - "... ... ... \n", - "46000 22.0 3.0 \n", - "46001 21.0 2.0 \n", - "46002 20.0 10.0 \n", - "46003 19.0 2.0 \n", - "46004 19.0 1.0 \n", - "\n", - " Point(139.342672 36.4105658) Point(139.3526243 36.3695416) \\\n", - " \n", - "0 20.0 NaN \n", - "1 15.0 NaN \n", - "2 16.0 NaN \n", - "3 11.0 NaN \n", - "4 8.0 NaN \n", - "... ... ... \n", - "46000 15.0 NaN \n", - "46001 19.0 NaN \n", - "46002 19.0 NaN \n", - "46003 15.0 NaN \n", - "46004 17.0 NaN \n", - "\n", - " Point(139.1945766 36.31351160000001) Point(139.2076974 36.3034767) \\\n", - " \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "46000 NaN NaN \n", - "46001 NaN NaN \n", - "46002 NaN NaN \n", - "46003 NaN NaN \n", - "46004 NaN NaN \n", - "\n", - " ... 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\n" ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.3: Fillup the missing values (NaN) with zero" + ], + "metadata": { + "id": "Ym9fqZJO6FAH" + } + }, + { + "cell_type": "code", + "source": [ + "dataset = dataset.fillna(0)\n", + "dataset.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331 }, + "id": "Qka2TNy86OCx", + "outputId": "096a12dc-8ed1-4997-d3e1-66f6689608a1" + }, + "execution_count": 7, + "outputs": [ { - "cell_type": "code", - "source": [ - "maxValueInEachColumn.plot() #point the maximum values recorded by each sensor." + "output_type": "execute_result", + "data": { + "text/plain": [ + " Point(139.0794379 36.3727776) Point(139.1051411 36.3963822) \\\n", + " \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Point(139.0960211 36.4047323) Point(139.0428727 36.3816035) \\\n", + " \n", + "0 5.0 13.0 \n", + "1 11.0 12.0 \n", + "2 7.0 12.0 \n", + "3 5.0 11.0 \n", + "4 6.0 11.0 \n", + "\n", + " Point(138.9955116 36.33801589999999) Point(139.342672 36.4105658) \\\n", + " \n", + "0 18.0 20.0 \n", + "1 22.0 15.0 \n", + "2 19.0 16.0 \n", + "3 16.0 11.0 \n", + "4 10.0 8.0 \n", + "\n", + " Point(139.3526243 36.3695416) Point(139.1945766 36.31351160000001) \\\n", + " \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Point(139.2076974 36.3034767) Point(139.3817322 36.2909131) ... \\\n", + " ... \n", + "0 0.0 23.0 ... \n", + "1 0.0 32.0 ... \n", + "2 0.0 32.0 ... \n", + "3 0.0 28.0 ... \n", + "4 0.0 27.0 ... \n", + "\n", + " Point(139.9239959 36.8062129) Point(139.9418164 36.7656467) \\\n", + " \n", + "0 1.0 0.0 \n", + "1 0.0 0.0 \n", + "2 2.0 0.0 \n", + "3 3.0 0.0 \n", + "4 5.0 0.0 \n", + "\n", + " Point(140.0549894 36.9688923) Point(139.8775674 36.3847082) \\\n", + " \n", + "0 0.0 0.0 \n", + "1 6.0 0.0 \n", + "2 0.0 0.0 \n", + "3 2.0 0.0 \n", + "4 4.0 0.0 \n", + "\n", + " Point(139.9101767 36.4393022) Point(139.9074816 36.4445767) \\\n", + " \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " Point(140.0934838 36.4673588) Point(139.7422865 36.2305774) \\\n", + " \n", + "0 6.0 0.0 \n", + "1 9.0 0.0 \n", + "2 10.0 0.0 \n", + "3 11.0 0.0 \n", + "4 8.0 0.0 \n", + "\n", + " Point(139.7151723 36.822353) Point(140.1510903 36.6598314) \n", + " \n", + "0 0.0 4.0 \n", + "1 0.0 5.0 \n", + "2 0.0 6.0 \n", + "3 0.0 11.0 \n", + "4 0.0 6.0 \n", + "\n", + "[5 rows x 1764 columns]" ], - "metadata": { - "id": "Wmvv1z6mZwA2", - "outputId": "9c082f58-7ea1-4773-c536-b183b7912c1a", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - } - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 9 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" ] - }, - { - "cell_type": "markdown", - "source": [ - "__Observation:__ we can see many sensors have recorded high PM2.5 values greater than 250. Such values are generally outliers/abnormalities and are not useful for the analysis." - ], - "metadata": { - "id": "tBrwAabsaJmE" - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Step 3.4.2: Replacing the values greater than 250 to zero." - ], - "metadata": { - "id": "6ZAm2FtlaxQT" - } - }, + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.4: Removing abnormal values in the data" + ], + "metadata": { + "id": "MJeJRYFfZRfW" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.4.1: Finding the maximum values recorded by each sensor" + ], + "metadata": { + "id": "oFNpo4dJa3u7" + } + }, + { + "cell_type": "code", + "source": [ + "maxValueInEachColumn = dataset.max() #Gathering the maximum PM2.5 value recorded by each sensor\n", + "maxValueInEachColumn #Printing the maxValue of each sensor" + ], + "metadata": { + "id": "FT2EIM1ZVzRf", + "outputId": "e894c6d4-2769-420f-b606-67813286c65c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 8, + "outputs": [ { - "cell_type": "code", - "source": [ - "dataset.where(dataset <= 250, 0, inplace=True)\n", - "dataset.max().plot()" - ], - "metadata": { - "id": "7Vx27DZ_bG8L", - "outputId": "5c90c23b-482d-4b00-e2d9-705699d011f5", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - } - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 10 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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6f70Zj8zY5lY6yrofKIvTPrUV7H7AMIx2YaWW8SjuWjuVnAQldz/Q2Ixur+3kKMu8XWcBAHvP5dq8Ruo2kRo1tbGYlQ1rIb20zIfLj6Dvl5uk3f0vY5eTOnya2Ofjtd/34aFvt0qatydYvOcCun2yDkcy8pQWhVEJ7FPLMCKxpdSxpdcxctWQ2l1C1C5fdSqsuB9IpWZZfU5kfnZMRzP/8e95PNG5gax5SQ0Rua3kWrt9wa6zMPpb2rPm7z7rVj5KYZL7v/P3okFUsMLSMGqAlVpGlRBZH5BlcT+QIU3GEl9V/JUodnlFJY5fLEBzF050s+Z+4KgIWmhbT8ffdRXz8c3WGGiPi9cibMSFB1r9/VJBCcb+sc91AVVKmUbal5Efdj9gVIPad+raWsrjE5fU33ZKocRq/pgFe9Hni034YdMpp+/1eJxaD3UcDejdACyrw9nwbxWVhFveX41OH6xBcZn1zZWFJd55CIOexx/mGqzUMppF6+OY1pam7aEVpcHzkMctmUv2XgAATN/gfLB+axZN6dwPxH4pPVLmYuvlNiXtCu7+fAM2Hr1Y47d1qVm4+/MN2HcuR3Q+zspcUn5dkb1UUGL1GrX7oLuKnt+qmWuwUsuoEjEDumQTlZsJaWH5VWnm75LHZ0/tVe+KfFcKS/H1mmNWj0qVG3NDrafrVu1t6YgnZu7EsawCDPlxZ40xYdisXTiWVYCnf062m4a50qnkQR22WPjvOaVFsAqvljEmWKllPIoKx2mXOJbpeyebudN2W09kSyeIhqgkcrreXv5tLz775ygGTZdgN7rKnzdPieeJF88CEUv75tZUa5jrZlKIXF3ZE6v7ZeRZP9BlzIK9yLLxm5Kw+wFjgpVaRrMoOY49+v12BXNXH3IYSmTdde8hXMl6y/FLAIDMPOtLyGJxt/7EtqnYMjq6zJuMbbYsh4H+Bg9L4hql5ZU4e9n6SkFecZmHpXGMN/Udxj1YqWVUic3DF2QYvVzxbb1UUCr821WZvMVqDXhXWaSEoJyxlAC3NFtPtKmv6SLOKLVKux9svvZyZYuKSsLw2bs8JI192KeWMcFKLaMoZ7IL8f3GkygqLRe1iUGN/qtqlIlRB4p3DaXzN8Pac+I59wMPZSTkZz1Do5/9KVdy9wP3k7DJ9pPZWHskS8YcxMNKLWOC49QyinL3lI0oLa/E+ZyreOTWROF7MeN5Vn4JKioJBnaosqCkvAJGP20sc3oLto/JVU6rdOapyC0qg5/BNf9LW2TmFSMm1Ai9yOdT8RcAkbhTL0EB9p9LWxvFXH1xllPXK1VRbFieAhgTbKllFKW0vGpg3H5S3Eai6kv9T/2kjuUvNVEhU6xRe6kqZShRvSJE6rfkF5WWo92kVbhxwkoLJdwdsdcdyUKnD9bguTn2d/t7CrWEzwt04mVTDoklfU7VUaUA2FLLXIeVWkY1uBJDcV1qzZiQziKFzpGSdgUPTt+KPWdz3E9Mg6hcb5MdW+VX2i/SHFuinL5kfUPQkr0XMHOz4wMcrCX73caqGLkrD2bavc4cOfUSTzeDLT/76tZwe5jL7Kr8ctSpirq0ACu1jAlWahlVUH2gVOPAaY9B07di95kr0oRhchN36664rAL/23ba5u5nRjz2Nor9vS8dO0SuUMhFSXkF/rf9jPB39RfLd5cecildtT2/coljywovhbuAHBZ+b43n6qXFYlyAfWoZRgJMK/5yLf17ki/XHMP09ScQYDiMf8Z0VVoct/FEi9j0qbWT+ai5/wIATn/YTwaJxPHt+pP4dWea8Lenl+nVpvyKwR39yVF5bW0Uk6qavFX3Y0stY4IttYxHUeMcpljIJZkydnd8Nx2UoKaNIFrFlcMXpEJMP9hxynOWYkf1oEUFV2qZpT5RzFuPxa2OnjUZ5hrcFRhVUN1CpJaNHe8uPYQJiw9U/aHQ/FBUWo7hs3dh/q40xxdDgolW4iVVpVFyWpejxvady8HgH7bjwPlcj+R/LKtAglQc8/rC/ThxUZ68Pl2ZKku6UmOxUc/8exVGP1ATbKllTLBSy6gGMePSqoMZkua5dN8FHEnPE/6evysNV0urjrIsLCnHzM2n8NO2M7iY797pTu4wa8tprD2Shdf+2O+R/FyZPi8Xljq+yEmkmqcUVcVlyPz+aVux5Xg2/u+7bW6LUr2OXdGdpHzZGfnzbsnSMqdcpFvQuiNZ2H36suh0pW5eKTaH+SLe6ivMOA/71DKqwNEAXlhSjlOXCjFnhzhrpRi2n8zG6LkpFt+99sd+7D2Xiw/ub4MKM6GU9JXNvaq+Yymrc7XM/pn2ruANkzpd+5+rnL1chMSoYIvvTH2xqNRxnTs71ctZ52LqQUyZ5CIjtxjDrp2QJZefs6M6MB9nzF8WJPOp9VLdj+PUMibYUsuoEvPJNSuvGDdOWIn+X2+WNA9zC605/xzKrCGDHKjFxYKRD3f7UGZesTSCqBR7z4Cnnw9X6lpql5wHv70ePUWWOLVOvuZo5cWS3Q8YE6zUMvhl+xn859cUlKt0Y9AKiV0OHGFtIPelMVMrE5laSUm7IvybSMwGKXuKnTvoHMeGrabkHLLxoucLiH3GxSx1u9puxWXXx2DLE8VcS0/OYUtNL+XWLLUl5RUYNedfLNh91vMCMYrBSi2D8YsOYMneC/h7f7rsedkanNUzPFrH3qSidtmlwtPl1NKLhLlyeP+069Y2MTvY5XuJUFfPVPvLkiuRAqQq0qKU87hUYOm37059CfdWe4i09Ew5g7UXjfm7zuLv/ekY+/s+BSRilIJ9ahmBvOJypUXwKdQ6yavJAqMV3KmzCiLoPRCjQa7oFd7SW6RU+JxN6qX5e9AoOsTiO4voBwrVslbGAmuW2pwi9e9FYKSHLbWMgKKhj4hs5q8G44K9CU8N8jHaRb6jdB33TG+13LmL2BcAZ5vO3vWnLhVa/F3pxuZUVw4D0TLsU8uYYKWWEVB6XLCMy6iYGNdRgwxgpVksr/2+z2qUCk/2per+e2KeKVX0dYWw69ajYL3Yy1tUm0onSlV6KvSpVROs1DIm2P2AYWrg3AzibTqJVpWs+bvP4s7mMYrkbfLHrO6/J2ajmD1LrVbbQotYHFEr8h45l+fls+C7hxqlYp2WMcGWWkbAE0cq2poECL5jVTAh1+TgarpEhPKKStub+VQ4m1VfJs4rrulH56kJz1WfVQVDIMuGFosk9RG17iKFCDUO13CyZcorlK8HMbClljHBllpGHVD1P82+4AHLIzz6/XYcyyxARLC/0qJIiif0EwJZPQ1Lp3OsSMipQFmcUOWBPKS4TiksLLVulsnWiOVMHShdXSeyCjBuoWdOMXQXPnyBMcFKLSPgCd3RnjXYZv4eng2txqm1c723jKfbT1YdD5otw5G3vsDqw1k1vhPTdUmm8ND8LmgJEYk+TtVdtwIpRiwp4tRWx5l0fth8SppMJcbaiggfk8uYYPcDxqPYPUFIadNENcRObCoTWzNk5hVjy/FLoq7NyBV/rRLYe1lz1K8rFOz4SisDaor/7IqlVk4kcT9wPwmbqKGOGKY6rNQyAoqG9Kr+t/kXDiZeIpItBqerEJHDkDxqk9nTdPpgDQb/sAMbjl50eG3nydavVXsNitEZ7W8Uc9diqJ4aUloWR1Vp/mJiN/qBRPI4wrztXa07uV5a3Ak3xjBywkotgMmTJ+OWW25BWFgYYmNjcd999yE1NdXimuLiYowaNQq1a9dGaGgoBg0ahMzMTItr0tLS0K9fPwQHByM2Nhavvvoqysv5QAOxuDr+3jdtKx6YvlVVSuKIn3aj55QNKC1X59HDLiFT9W49Yd0Ca836aetatSKmS6phUxJTPfqBPHFqncHZpEW5urgkiSXfbzyJdhNX4UhGvgSpSYOaxn5GWVipBbBhwwaMGjUK27dvxz///IOysjL06tULhYXXg2H/97//xV9//YXffvsNGzZswIULF/DAAw8Iv1dUVKBfv34oLS3F1q1b8dNPP2H27Nl4++23lSiSSyi5Ell9UHJmiNp7NgcpaTnIu6qeF4i1R7Jw8mIh9pzNUVoUSfl522l8veaYtInaPDrZiu+cS0eZKmwhdPS7h8Rz1ldcdLoqsgbbwxkppWwT881WztSVxSY/FVXxb8nnkF9Sjk9Wpjq+mGE8DG8UA7BixQqLv2fPno3Y2FgkJyeja9euyM3NxcyZMzF37lzcddddAIBZs2ahZcuW2L59Ozp37oxVq1bh0KFDWL16NeLi4tC+fXu8++67eO211/DOO+8gICBAiaJZsPv0Zeh0OoQYDWgRH17jd0+E9NICVucPGapG9DylkmZZeTADby8+CAB4rFN91A41upTOgl1n8cDNdaUUTXL2ns1BbLgRCRFBbqXjrvuBlJRXVmLL8Uu4uX4tBAUYPJKnOWpSzKxh3lRSivrrzjSX7pOiX1TvfkpZNCsqCTtOZaNN3QiEBboXXUXt/YhRFrbUWiE3NxcAEBUVBQBITk5GWVkZevbsKVzTokUL1K9fH9u2bQMAbNu2DW3atEFcXJxwTe/evZGXl4eDBw9azaekpAR5eXkWH7nYfOwSHvx2GwZN34o+X2zCFQ3tcBer08ltMVJMt1TJID72j+sHC7gj0tg/9uErc2uvjYqV6iXL2XRSM/IxcOoWJE1e63beVYcvOArp5XY2VtHBUgH4eEUqBv+wA6Pn/itPhi5gr+hSK2DOpGfvWlfcFFzBE+HYPMVPW0/jse934OHvtsuWh1ZWDBh5YaW2GpWVlXjppZdw2223oXXr1gCAjIwMBAQEIDIy0uLauLg4ZGRkCNeYK7Sm302/WWPy5MmIiIgQPomJiRKX5jobj1lusEnPLa55kSe0NptLzdX+9sDrOA+ByjHX3Holc0M4O9ntOXtFJkms46lNN3+mnAcArDlyPfQYR0K6jnlduNskUgxf7lhqTbfWOHxBoUFvYco5AMChdHkMN+WVhJUHMx1fyHg9rNRWY9SoUThw4ADmzZsne17jxo1Dbm6u8Dl79qzjmxjZMSnUqlnmclLx8KVNE9WL6mzRT18qxITFB3DuSpFbcthSDqsOX7CPXO4HWfklmLDE+iqRe7imCSvdKx3nb3lO7rYT2Xhv6SEUl1XYTtPJQqlpY5knkXtIOnRBvlVORluwT60Zo0ePxtKlS7Fx40bUq1dP+D4+Ph6lpaXIycmxsNZmZmYiPj5euGbnzp0W6ZmiI5iuqY7RaITR6JpfolSYK0COpqqs/GJcLiy16o8rGruZyG82OpNdCB10qF87WHFXVS3rnlqW3ZyHZ2xDZl4Jdpy6jBUvdXU5HXeOFrZnFXS3mv85JIf1qtqmTi/pC9XdCh79vmqpPCo0AM/f2dTltFzFcqOYc5XMFnhLikrLcTg9HzclRkLPx495NWypRdWAMXr0aPz5559Yu3YtGjVqZPF7hw4d4O/vjzVr1gjfpaamIi0tDUlJSQCApKQk7N+/H1lZ15f2/vnnH4SHh6NVq1aeKYgLOLPMduv7a9Dni004ebHA9QztTv7k6DK36fbJenT9ZJ13hdpiBJydzDPzSgBA0fBESob04un9OhYbxcyaJC3bthVfzpaTZqOY97WwKxt5H/t+BwZN34o5Lm7aY7QDK7Wocjn45ZdfMHfuXISFhSEjIwMZGRm4evUqACAiIgIjRozAmDFjsG7dOiQnJ2PYsGFISkpC586dAQC9evVCq1at8MQTT2Dv3r1YuXIlxo8fj1GjRilujbWH+cApNlC3p8NUiVVUnJkDrpbaWVIUn4xDlLCYeInhzCqO6lNtVkOdzrFM9qxwaiuPO1grpyddZZzJynJclDcvm2nY+LdTeJ9O6xKmOeu33ezi5+2wUgtg+vTpyM3NxZ133omEhAThM3/+fOGaKVOmoH///hg0aBC6du2K+Ph4LFy4UPjdYDBg6dKlMBgMSEpKwuOPP44hQ4Zg0qRJShRJNGoJ/F61mUe9I7A9i4eUVt/Jyw/jsEybKRj5sKX4uOt+4MvIVS2VlYTP/zmKzccsD/Iwf6m3zLt645qfPCZf4zk7NouRRanhXu581TtzMJ6GfWohbjAIDAzE1KlTMXXqVJvXNGjQAMuWLZNSNNkxL/qFnKvIyitGbHigonJY+1sJxIpQ7qpWYuW27zacxHcbTuL0h/1cS9NDSBk+R+uWcYc4KKA95UXu8sh1jKoaMfXZxXvPCyHlbD1n5k0iZRW5egCEq+Oh77SuOLg+vB+21Po45hPq5/8cxa0frLFztbz40PzqFM76xanhhUAuHNWEtbJ7oj5s5SHq8AU7hn7NtqW1dpAxO2fGjrOXr1r93ty4IfqYXAnksZm2DI3vrbFcfenljLEPK7U+jpqWPi3GcBfkUlFRJMVbJyIleHD6VpRXeG6TIJHj9lOLC5B41KdAiKlCIXark+m5UlrJm1RrXURGfClkIeM8rNT6CLYGAqknVCLCD5tOYn1qlvXfbd5nO02xlsqv1hyTxL/VU2MmK6v2ccX4Yu0e8+92n7mCzccv1bzITdwxFGkv+oF2++2eszn4eu1xq79ZvFPbcT+wCP2l0riz1g5fyCsuc0sed9Buj2G0Biu1Pg5Z0QFdeRMuKa/AiYsF2HHqMt77+zCenLXL6TTcXUGavfU0Zm055V4iNtDU6pYnZhCFZqkaJySJiJla/buKa8sTx7PcCE2Hqj7vCFHuBz4y46vhoIL7pm5BqQhLfVb+9RMXlQqLZXlMrohNYFa+M5d9zPw92nVnkQpNDeSMK7BS6yPY8jmSykr08Hfb0eOzDfjftjOSpOeqFTM1U7p4o2pZ5lIy1qSSectZ/SsPZqDn5xvcyvex73c4vIZIw36xNvFOxcC8nQZ8s0XkTc5m4sSlbnQca8P96sPWV888gdxjqXf2SMYVWKn1caRSak1xAP/eny5Jei/8moL//JoiSVrW2HEq2+ZvtgZgX9yMoDUXiT9Tzou6bu4O94OwJ5+54nYajpC7/l3r0hJGvpAoKTHlcDmCgAoee1uyZ+QW454vN2FetUMFfk8+h75fbsKFXMtNcdp6msWjhjZi1AErtT5OYUnNJVQ1WJY2HbuEJXsvIKeo1KkBS6xl8en/JTtvZJHc/1jS5LwOq/6xDtp3x6nLDtN1VO9ST5De3szW6nPnaSvtIKf/qVnaU9cdt3sKmJ1UrH5rrzvI+dIhJuUPlh3GofQ8vL5wv8X3X6w+hsPpeTjjUj1Ij5QGAW9/nhj3YKXWR7ClkH219pjseZ+7UoTyikpcyLlqcyOXLfm8c/lWXnKvluFKYamseSjVJFqzHItBLW4uSiCHhe2Tlam4b1pN9wFX+45SKzSWPrXWKTI7GVHN3UgtfZwNut4PH77g4xw4nytr+huOXsTQH3fC6KdHiQuRCWSNbenktVpxP+j6yToAwJF3+yDQ36CwNI5Ry4RnQmpx1FY+S5Tt0/aqxp0XmMsyvtSZ15gt+W3JrvRLmbr7ouso6fvPqAu21PoIthSyEGPN95r84nL837fbMFuCSAL/23YaAEQptLaGJafcD2Q6/UeLU0FmXrHjixyg9snCS+fo63hR+ZRW6Byhtr5kXl/eqowyjNSwUuvjhFpRamduPomdpy/jnb8OeUwONQ3Z1mSZuyMN+87lOJ2WfX889aM2RcQVJdvZEmjEIK8YlwpK8cOmk8gpct8aKlX/UmqjmM0TxaR4GZTh0VPX08ww0sNKrY/TJCa0xneFpY7jb3oKIlLMVmg+CX7+z1H8LDJcmVqsKnJaWVVSRJfwtOiO8qv+u73rpVAkzXFVgX/v78P47/w9ANTx4iNFf5RVQXUBOVaKpFi9cZcdJ21HnhGDtbYW24/5hdX7YaXWx7khrqZS+6OEBxiInWy0rCQx8sATUE3yi8uVFkFgXepFp663qox4UGGUaogxd+WyucFVCUVfRJZiYivLzensQqVFYLwYVmqZGmhZwVSDHiRekRd3oa8qd2IsMlroqlp+nuTGrvJnaxMWEcbM34PJyw5LK4uN/CT103fq8AXH92lxbODngZETVmoZWXFm/FJbdAE1LKsC2g4ar0ZS0uQ/NEEq5O6BWuwiqZn5WJhyHt9tPOnUfa66BSnlF19BhHk703A8S7pTEtWAOkZVxlthpdZHsBcHVg0QyKqMBPmiH9gsupt1opIqlRW1KPyuMG39CY/m56iu1PIMyo21YrpS9rJyeSpMbX369+RzeH3hfvT8fKPqZHMWtfRxLb7EMc7BSi0jK1JsmlJsQFTLQOyjI7H1E8V8AJX0O7GoRWFxhFRimo9pcpZdzIqCTzwP1bCm4KttlY9RDlZqfQS1P/RKTIxy1YhYRV4juoDsaEUpUoJvN5zAi/NSUFkpTyUpPSwonb85HvGpdeZaB+EPtp/MxqpDmWaXaONBslXP5RWVGDXnX8zcLN1GZcb3YKWWcZo//j2H8zlXPZafU+4HKrNdqGnS9lmUnuvdyH/TsUtYvOcCNhxzLtKAGrHqXiRin9ieszlYe6RKebuYX4Lfks+6mL9Lt9k1CCipSD4yY7tiecvBioMZ+Ht/Ot5d6nx8dB5mGROs1Po4rgzKW45no/sn60Wm7x5yWvGcjR/qSrpKWiHlVKgVK5dXviU4rsxiFcWONuGpprhv6hYMn70bZy8X4aFvt4qOF+0sNi21MuRVWl7p8JRFCzcHGWTwJGLmmcIS+cPVqX3FknEfVmp9BKkPBCitcHzsrTMQ1DNwq0UONaA61wBXBPLAPGbfmuccnvQZVHplw5m6OZ9zFaezi2STRSxk8w+R9xOh8+Q1zuXjRchh3WZdlTHBSi3jMl+uPubwGjUfvuBoHHRVJvP77B6x6a2zltrwQD3LfYqcmidtsSV3t4bcrgJXBZC47kvKK3G50PHJcGLi1Nq63ptx50QxxvthpdZHcNfSc6mgpMZ3U1YfdStNsThjTfLGwc3VIvnCUpsWJnIxMqZlF2H5/nTVHLEsBil6l5p6qC0Lor3xx2krvIbaVy64Chg58VNaAEZZxA4wA7/Z4lr6Lt1lfr/nR0B3Jx6pZeY5wAyNKepixCUCun6yDgAwffDN1tORUijzdBWuTnt9u/pj6O5LmqvPZY1T7Dz0QGolmoEY5K4zselra/RgXIEttYwo5I92oL4BXO5JRcuTlnYllwdbCpezk/mu01dsLK9qfzp2V7GRuwpckc/WPVIocRbuB/zE2UVs3+Ba9H5YqfUR5Fr2cpSuM/lKIaLVgP0eVgikrmol1RmeBNxHCoVEjSqtFM+VM+ODtdw8Mb4oVffe+uzJUS6lNzwy6oGVWkYVyBq6y8XEte77pZVh3lY1W5uoPFGmo5kFTt9jq4+p3cAqhXye8hNVqi5r5mseasu5dnf18AVRG8WcSNvbEPviqPLHkZEAVmp9BJvLo26mK/t8RuCRyEXsKQHlEodkkwOlllylPNGIyPEzIqaUaleOxSBHqDJnxh/Xgx84L6M046J3qKnlFZWyl0TrBghGOlip9RFkcz+QJVXPYEt2cvC77ALIzPGsArSbuAofrTjichrq2cXtWA5PKMdyu7joRab/o8aOGLXXj2q2m7x1LKVPrRRY+tSqH2vPwLYT2Wg2fjmOZ5mtfqhm7GC8EVZqGVVAZv/vHmozaalNHuCNhftRWFqB6etPKC2KXay6H6ivOu2i07ly+IK1hMTdO8nJI0bd8UX0RFOYK73W2t655Xxpoh94Cq2pftbq96X5Kais9rXWysVoCw7p5eu4G76K7PsHqOGl3JYVTa65Sg1lBmwrLGmXxZ/KpB6LrCVXCkux/3xujQnTGlX1oEw5VFp9ymClLhxVjyfrz3ac2mrXuWlBdc0iLGJFwoc7m+iQXhp7KWach5VaRjVYU1CcHaarD1qVleTGRjG5Q3qJQ+qBuLxS/f60jrjnq01Izy0Wda0z7gdKKAZislTzXCxnjVWaW2qt5e3gpVoSJHwAnfIB9mEl1R7WqoVrijHBSq2PIJfPn0NLi8jh5nJhKXpN2Wj1N1cl/+/8PUg+cwV1IgNtyCYPlmV2Pxep57YKMeZNByg135qsz2IVWmeRoGpqIIVyIpvPrhvJSiGSw6OqLfJz9/AFB7/LZEGtKYe4eyxGES/S2uSM7cswrNT6CPLFqXXvdzn5M+U8AOeW24HrMnvrIFsuh+bmJUih8LuLtWdVzZZasVirWXu1XT1yhFVLrZsyiaGG+4EH8gS0N/648tJx9nIRpq0/gafuaCSDRIwvwhvFfByNjZsOcWZY9YxPrXLqiK05plIFipuryB3JoFIGTUJryolYyirEFWzr8UsoczGEnHl7u2sZdvnwBYkfYbFyaM39wBV5h8/ehV93puH/vt3mEX9XPqTB+2GllnEL2Y+S1da47hRyb26wdVuFM6cwuZa1U6ipjV1Vat2ZKsUoA2rd4CLGsv3YDzvw6cpU6z+6udIjZd+xlZQ9RcjZ/MlOPmLlkep6pTDv78euhfrKLiyVLE3Gt2GlllE9nvYHdldRl3p4lXq81vI+MbktLa4aseWeUmUrt5uCl1dWiuqfP2077XY/drcOpHoB95j+ZB5lgXU2AaUOZWG0ASu1PoI7iuHiPedt/qZmn1rGOs5Yan2N6pbH3KIyhSSxRK2WWrGEBNjYvuGgXO521bBA8dtGPHXUsVirotae0s3HL2Hy8sMOTyvUWrkYbcFKrY9gayAVM76+OG+PtMLIiDMTkMOd165a7SRWGqWeVNWwGcqTiPX9BGq2nbOHGUiBNWntdYGM3GKcuFhg5wrlCQowoKDEyguCnaYhVAvpZfXwBWdiZIm/1Byl3ifM+6IWrJPrUy/iuw0n8dof+132oZab8zlXsXx/Onafvqy0KIxMsFLLyIr7S/nqH8xdRXZ/ZCvf5RWrw/IoBmtKjNwWy+oKf2pmnqj77InlcDXDzQw6T16DHp9tQHZBiZiUquXtmecrJMDPZsg+e3jy6bfpU2un7m2Gp3IhH1evUxt//HsOby8+6NE8xdoRzudcxXNz/lX9aYqM67BSy7iFN65ku1smy9ia7qUlNVl5zik+vhZTUq2uGWL8SU9nOxe6DvBcOwYFGFzyV3Z4TK78hlorebqRkjObNM0u/TftipRJy86vO9Ns/iaHnN5s/GCcg5VaRlakGMCcC9Ml/mqHwdi9cKA0+qnvkbdVz0pM0mqYcFX2HmSXjNxiUdpigIv9zuIF0ZWacUbptXGtvf0IYo/WFZOPPf47f6/zN3kpalLeGfWhvhmO8Sju+n86mrB9cfwRH4dSXjms4W/Q9iMvt8Intb/xwQt5Tqdp1af2WsHt9RklVgVG/LRb1vQlDdmlscHIG1+qGUZutD3DMYoj90QhZ/pybRRTC1qR35YFTgklTWyc2oMXckVddz7nKj5YdtjuNdWztCaCmKpw7ZhX5+8x53iWexvU7PqeEllcIH9/EBf9wPwqV3xqxeqqWnl+nUWOYjlbV2pzC2Okg5VahlE5UsYotb3U76w1UbqpSU0WKbExfDcevSQ6zdWHs1yU5jqmJXA11ZUncLgS5MXV4fzhC15cGQ7w3ZIz1WGl1kfw9AEGSqXvTDFtpe12mSSuFEkVSI2P/nJbWKxZaovLKvDl6mM4dOF6JIRAf88OnWLK7UrdqF0RqpTQUuuqgmz3RDGnZXCiztXdNC7Dp38xcsJKrY9gM06tTOlKloGTSKnzqE1hVyZP35qArEU/+GrNMUxZfRT3fLVJ+C7I3+BJsYR+LbU+IEV6cirGFtEPrDzdSvj0e0onU/sLh1JIUyvsf+CtsFLLqBo53+rlGtaknoxcdT+wJofWrSTOiu+0W4WV6/eey6nxXaCESq2Y/iJsFJMsV8t01YqU5XW169uPU+vCa6KP+9Tawr1IaT5WWYxNxJ8hyDBW8ERYLGcm3sPp+ej75SaM7d3c9QyviezqQPnA9K2u5+1BmsWFCv92XlmUWBiZcFZOawchlVs5kUxKpVYcMrkPKdyO9p4xIs/Kp7YurTZ5tMTAqVtw7rLzcZsZ7cNKLSMrbh9k4OT9O68dfzhs9i7HabsikAhOXiyUKWX3Ma9Po5+nFTP3cday6GwbW/OptRaSy9M+tSaktkh5THFyMSMpN4q5Wla7MWdtfH8x3/ohJ9UCOtjF6bbWuBYs5arB3rM5HsuLURfsfuDjeFrp1BKy+9R6c+U5gZqqwZoCW27lu+rxft2aJEWUX65JWE11bxW3xyfxCdg+fKHadR7SHtXeNAyjRlipZdyDR17VYm2S9lY3A1s4++Ig1lJbMx+nsrHgoxVHHF4jbBQTcY1zKNvAjt2XzP9txUf82n/FKP2uvkTajX7gQpJi5dD6s2cL9w0ptvsBw7BSy6gaJQ9f8EbMFQPLfzubjnQ4Y4V0NjSd8+4HNb8rs+ZoKyF7z4k7yEGtyPmMij0MQ5IoDh7SIn1dAZPF0u3rlcoIsFLLuIUnQup42v/JVCYp5jgpzoCXsvxqtP6oSSaxltrql8ndR/WmwxdUVFeewLy81lce3HcvcESNtiWbfyiKeiRRP75o0PAVWKn1cdQ+EMoaA1O2lKVFSkXGYjlXKxXgBlKUUYz7gdz4qk+tJ8XzRF4EUn2dM4yWYaWWcQuHZy9oegTXsuzWpVdLe6g1gL01uaxtFFMuML462s9TOOqvzvjkuoo9lxeVPE6aQo4642ZgTLBSe42NGzfi3nvvRZ06daDT6bBo0SKL35988knodDqLT58+fSyuuXz5MgYPHozw8HBERkZixIgRKCgo8GApPI/ck4oSPrVqm6gkdT+w9b2TJ86pRTn2BGqw1JqQ/EQxaZOzk4+N/uXwpVjcta4+I+b9WPShCK5lZXa/evqT3GTmWQ9tBgBv/rnfpTSleFnnkF7eCyu11ygsLES7du0wdepUm9f06dMH6enpwufXX3+1+H3w4ME4ePAg/vnnHyxduhQbN27E008/LbfoqmXFgQykpOW4nY6rJ2qpAbVNX2KVBG/B+TJaC+lVc6OYWupODS8XSkpgKr6r1SDmPlfi1DL2uZhfgjk70iRL73Q2H7TAVMGHL1yjb9++6Nu3r91rjEYj4uPjrf52+PBhrFixArt27ULHjh0BAF9//TXuuecefPrpp6hTp47kMrtDQUk5yisq3Z4U7d3/7C/JbqUNyDtpOLQye9mMlVtUBrVOw7lXyxBm9INeb/8FRgkLixostblXywA4COmlYvOTKy+m1Z8/dy2cVjeaiUhf8o2aMnUnrYxXBHW/KDLahi21TrB+/XrExsaiefPmeO6555CdnS38tm3bNkRGRgoKLQD07NkTer0eO3bssJpeSUkJ8vLyLD6e4v++24Z7v9kiSVpqsBapFSWrxrxdJi87jHaTVmHZ/ozrvyshlJW8T2cXot3EVXj0++2O75NZaPE+tZ7liZk7sWx/ek05XBTk42uxcT0Xxsq1fByG9HLCfcFVpIhgwliidL1pefWPsQ8rtSLp06cPfv75Z6xZswYfffQRNmzYgL59+6KiogIAkJGRgdjYWIt7/Pz8EBUVhYyMDGtJYvLkyYiIiBA+iYmJspfDnMPp7ivRBOuxPaVCzknX0bDmTfPVdxtPAgA+/+eoJOlJWTfrUy8CAHacuuzwWr2zcWqliH5QcT2R41n57ifoIu//fVgyZWDa+hPSJCQzUvYzq4c3uFCh7rSBTudd44oriD25jWFcgZVakTzyyCMYMGAA2rRpg/vuuw9Lly7Frl27sH79epfTHDduHHJzc4XP2bNnpRPYQxB5n6W2vJJwPueq7PmoqdpsbwjzqBiS43T0AyvflZktlQ6fvbvqOgUqRo6TlJRuXodxrkVu5HJ5o5jNP8zTthP9QILoGr6IO9XAdcjYg5VaF2ncuDGio6Nx/PhxAEB8fDyysrIsrikvL8fly5dt+uEajUaEh4dbfLSI3H6vssXotPNbt4/XeSAXcchlwNDiy4gSO8fNfWpNLzvVpVBDiDKlDF1y9iPH0VXomgyO0yoqrXBJBqnHHw0+di5R7uRJfL5SL4y8sFLrIufOnUN2djYSEhIAAElJScjJyUFy8vXNUWvXrkVlZSU6deqklJiy463BxMsr1VMuV8VQi/zWEKMIWbOQOVsmKa43V2rV9iJgYclUUA5HuFJtUo8tfb/cVDMP82ggNu6reaCY82HAXL3eqbRV1gO6f7be6vdV7aqsrOzq4L1w9INrFBQUCFZXADh16hT27NmDqKgoREVFYeLEiRg0aBDi4+Nx4sQJjB07Fk2bNkXv3r0BAC1btkSfPn0wcuRIfPvttygrK8Po0aPxyCOPqC7ygTPkFZc5vEbs+eyuoDIdQlLUNgmpDSkmPmdSeOHXFIQF1hwSrfqMK9R0ksepVX0XvC7gM/+rGU3F8T4y+xdcKSpFXHig/UQk1oB85bk/e9m6C1eVy1rN71nRZKSALbXX2L17N2666SbcdNNNAIAxY8bgpptuwttvvw2DwYB9+/ZhwIABaNasGUaMGIEOHTpg06ZNMBqNQhpz5sxBixYt0KNHD9xzzz24/fbbMWPGDKWKJAlfrT5m9/c9EsShVQrHG8UkUKpk3n0tFWqyOCnFX3svYK6D2JlqK7a5PM72k7KKSmw4elFKcWzi+uEI1/8th5/7R8uPWM3LHHtR5rzxOWAYLcOW2mvceeeddi1DK1eudJhGVFQU5s6dK6VYsuNoUM4uLLX7+9P/S8b+d3pJKFF1eNZQErmsSmJiqlp3P3B2Y4488levF0/1UimLM+NaRAxPYEtuR+VxFFnF9LNO51rdZOXbPvHKRPXwT+62ASvCbiKFoYCtwl4LW2oZu4h5+MsqtDlKe0JqKfKQS061xNCVNR+p07OpnMlfHoK0LxmrDmVKko68G0XFpe5q9ZuPb64cvuBKdA1vfJ4ZRi2wUsvYRekg1coqXtpOX252iograwutb36rLodSp3mppT7kwJbvpeU17lWAqBUDt3JgrOHN/ZZRFlZqfRxHlgYHp5ZWpaHREUork5VsIb3Md3E7rWYS/u+7bdIKJCpXJ6+3cUNOkX23GmfxmOVZymw08NzKvdRv/mzZunb/+VyH6R+wc031/LQ6XkqJ9YMwFBCE8TpYqWXsIuYEJzlPFKsUYa2RCyny5YFaYWzU/95z4pQQm8mqpF21spPeZcu8o8MZXEzXhJiX9jk70pB79XoUmOp5FpdVoP/Xm0Xlp43W8n6UXoFk5IM3ijF2EbOqKufE+uC3W5FfXC5L2t4+wXijUu50lAYvamWHVkcNztOO3QvEpePqRjFz9wN7t18uLEVEkL/V35w91EFtz5SnIbIef1hs//WmZ5qRHrbUMnYR43NW6dzBMU4hl0IrBrkHT6WHZi36K6tlQlOHFOpQkMTIILe+7fJGMdHp287AGXcCJU9fUwtq6LOM98JKrY/jaIARszwn5+ELcuKZ+K/S1o1sIapUGKdWivbRWtfceMx+3FitlceEq2I7GlscH75gH1c2+Fmc4qbR9lAaa9Umti5PXip0XwANrmow4mCllrGLmDFfq0qtFlmy94Loa9Vi1XQVdyY+e2lIgVwvF8ezCmznqfH2dBaC/EqjRUgve9ZYO2n4VqsozwPTtiotAqNiWKll7CJmo5hWdVpHYssf0ssJh8FrrD6cJV3+kqXkObQos5RIqdhqoS4dPqNulsJdgx3Bun+o3Xu0UPEyUvWyUrMStOgTzqgP3ijG2EWMUlshZ/gDjSNJzSg4C8qVtcuWThWcKPbT1tOIjwislo/k2Vilej7eriCJbT9XN4rpRW4Us5Cpxt9e3ggy4EqNbTl+Cf9IdGAI68/eC1tqfRwphmN2P5APIkLK2RzZ0nb5XgnlsIUkPrUSpFGdCUsOorjMuR3vakSqid0dpc5RH3SYMpnScS1/0TvubaRPBKc62ZWiUqScvSL+BifQUvxba6I6En/wDzswe+tpWeRhvAdWahm30aqh1jMbxdy7f8PRi9h07JIieSuNVZ9aj0thHaWOhrZnJXQ29qYa6lL2k9icCYPmckQO8WTmleDFeXtcy8hLqBqX1ND7GG+ElVrGLuJ8anmAcgUxtbZGQh9ab0At/ou8OiENjkJlPf/Lv/bvdzN/pY439nXciVPLMPZgpZaxi7joB/LLIQdKbxQTQ/X6l/IFQsniiclbGvcDuZyCPZSPeR6k3RdIV+XOyCt26T6xCpKloVZkjAOzf1baOEiAsY+1OcOT9cgvM94LK7U+jhQDCVutbKP07myxqMUC6jBflSxbVpdDy0c5K4m74pvK76qO4q5yU+VSq45G0EpfoGv/Yxg5YKWWsYuYIV+rSq2jsqlx4HVmEha7ycb+JeqqA6e7mmzRG+RJ12G+dn5zVj+TrAwq6CI2N3I5EE5vEafWtXw1OvwpCrsfMHLBSi1jFzEDjauD+tjf97p2o5cg6njRag2g1eVnqSircO5M5ts/WieLHNWXT7eeyJYlH0f4dm+wrbSK1Y/EH5NrK0+1vfapHyLlDSGsP3svrNQydhFjGXR1gFqw+5xL90mFR4ZVnvGs42K9lJQ7p9SWOqkEi0UpVcbb3mnslccjYePM49SKyPBYZj4+XpFqcY+vv2i6gishvRhGDHz4go/jaHIW80brrYcvqHGQlXKDgzvFU0qpKymTR0l1FrX0DVao3MPZx+nuKRst/j55qbDGQRwMwygHW2oZt/FSnVYS3Awt75afmTMKj1YWUUvK1XHogRK1RYrlLB/u9ju3N4qJXIi2JeXY3/fhcHq+a5n7KATlfWrZf9d7YaWWsY8on1ptTrSON4p5N2LaTcmmtTbxOOt+IBeVCr3JfbP2uMXfauijqpDB1kYxB8JZbBRzMe91qRxL2imIFPepZbwXVmoZu4ixZGjVUqtRsb0CMXVvbd5Ti1Kr1IvcutSLkqWlFeu8Pdw/fEESMVTBpYISpUUQjbV2Yz2XkQJWan0cjlMrL+5WTfWXiryrZe4lKBFKNblPux9oeHONLTHtbhTzQNliw677w9o/3cx2Gmppg9FzU5QWQTRaXd1j1A8rtYzbeKtSK/fA60ryG46Kt9Q5St4iMJEKm9CaFa1YJRvFtLo64W24+4zGhBkVl0EqzudcVVoEURCsPz8e9an1XFaMh2GllrGLXsY4tUqj9oGNiPDjllNKi6Eq1PICpRZFRutIVYvVFSJTlBBn0ucW9SQ1a5sfKUYKWKll3EYtiobUSFEqd/wWXT33XguwUigRKqhGJZvSlLerMojth/aeY+7KzsN1xsgFK7WM22g1Tq3apZa7Xt2ZWBQ7JlYljeatL3LqQv46lsIFxxs23HkSIuXHXinjfTPqgpVaxi5yHpOrdqQol5InJjkjv5c2oWyopc9rXaHSwgYsRnqshcRjPZORAlZqGbuIC+mlzdnH18dQJRUiVy0lhSXleHTGdomlcR6FDslVJFcpSEnLUVoEq1gOXdqOfqAVyMbIw/XISAEfk8u4jUa9DxgFcdWn9lhWAY5lFUgsjfOo5UVOJWK4gR1FUszdZieKWQ955u6ZfiJkkCANX0P7/ZZRK2yp9XEcDfrhQY7fe7zXp9b9ctlLQQsDu82Tmtysm8JSdcSbdRUttJ0j1FAGNchgwvXNZtLK4QtYm3c4pBcjBazUMnaJDApweA3vZFcrDqxUZP5vbkNnUOqY3OqYS6GUT6Ia/Hplj35g1zde+fJrCTVsFGO8F1ZqGbuIGbC9dYBiPY+xBXcN+RHz/Nkanzyq33NncBotn47HqBtWahm3UYt/obN4YuJTswVUxaIBUPcSoVr6vJr7l7uIeaE+nV0kYX6evc+XUdy6rebBhXELVmp9HEdzopg5UyUrsU6jtNiKD+zXyC8uc7oNvViXEoUSfd4b69zdIg39cScA264XjtIXW6f2D1/wwoaRETUck8t4Lxz9gLGLmOFaLf6FzjLxr0N2f9f6zmgxc+2Z7EJ0+2Q9bqwTbj0NBUug5l6lFkXGwqdWg+Yne/XoiSo279+uH77AOIu1dlfJI8VoHLbUMm6jlqVYxnn++Pc8AODghTyFJVEnB85brxc1dnlnX0CkKoMa6kINR/Uy4lG6yrT4AsiIg5Vaxi7e7H7gCJ6slEXN044aX+QOedmLiUcsteYRQFw8fEGNfUHNEKlnpYPxPlip9XEc+pyJeKfmQd0OCp5E5NifkFStOKoZtbzImfehV3/fp5wgMiCF64vDPQNu56C81VGLWGsX9qllpICVWsZttOpTyzCuooSlyRufMqnKJIVC5HKTemPDyAiBOKQXIxus1DJ28W33Ay8tmBmOlAEfqAKXUEu1qCWChqvYPdTAjaKJVXKl6N9abwMlULrG2CrsvbBSy7gND+q2cdVPzxNwq7kOr05cRw014XrkArPoBx7O25dhlzVGLlip9XGk8Dnz1vld68USM2/wLmDXUE2fV4scXo5cFmWfhJzzqS0tr5RXHsarYKWWsY+IEZutVrZR84TnjmyeKJdOxWuESliafMEdxhyPRz9wMUNeqXIOMvt/i++tVOOvO9PQbPxyrDyYIakM6h1ZGHdhpZZxm3JWahkZ8DUlzhW0XkNStbGS7z88/DmP2Dobt3A/AOC5X5JllIbxJlip9RGy8kusfu/IyiBm7Cmv8M7lIflDbik/GzrcKOYZMTSHN/gEqqEEapBBDGr2jdcaRNajH6h4YYbREKzU+giL91xw6T4xA3aZlyq1UqDoSUcOX1h4NnYVtSi1ahBDLou6M/3T5Y1iksiugkbQEETKjz2sQHsvrNQyblNW4Z2DutIDrydwdWz3RN2o26dWaQm8H3f1TTEKqyQhvbgvOEWljY1iXI+MFLBSy7hNeSVbahnfQi3+vr7w4uUKrkT1cDXCAbeAc1QSqWalg/E+WKn1cRyG9BIx+HirpVaK2cpeEkqP62LyP5KeJ78gZqRlF+HA+VyP5ukKSrSdWp+yo5n5sqQrtrw7TmbLnoc9Nh27KEEqjIoXZhgN4ae0AIy6ETPos0+tOhEVp9bBRPL6td3HnqLrJ+sAANvH9fBovs7ClqbrDJ+92/WbJajGh2dsdz1785BeLgrjtS/1MlFpY6OYJx8pjs/tvbCllnGbch7UJaeotFz2PNxptaLSCsnksMapS4Wypu8uavGpdS/WsPKFUF4CcWhFTi1QScRuM4xssFLL2EXMvPe/7WfkF0QBJNkX7aLisGy/tMHGbeHqZqzl+9MllsQStS9FsqVWGgpKbL+8uat0i7nd4phcblKP8Mv2NGTkWg8xyTDuwkqtj+NoHN/I/mKMAqhcp1WN6U4lYsiCW2UT2YFYkVWGj1YcqfGdJ19k1f7SzLgOK7WMXdan+q5SK0m4H/eTcD1vh5sAXU9b7nBber26Zx1ljsn1eJaK4uny2o9+4GOVrwBcxYwUsFLLMApwubAU/b/erLQYqkXdKq2afGpVIohG4dpjGO+CldprbNy4Effeey/q1KkDnU6HRYsWWfxORHj77beRkJCAoKAg9OzZE8eOHbO45vLlyxg8eDDCw8MRGRmJESNGoKCgwIOlYLTCD5tOKi0C1Dylq315UBlLrXrbS418u/GE44vM6pRrl2G0Dyu11ygsLES7du0wdepUq79//PHH+Oqrr/Dtt99ix44dCAkJQe/evVFcXCxcM3jwYBw8eBD//PMPli5dio0bN+Lpp5/2VBFcgydKm0ixQ9dW9ZarxNSnXuVR3UF31NF63v74ul640vJKfLwiVQWSMGJhn1pGCjhO7TX69u2Lvn37Wv2NiPDFF19g/PjxGDhwIADg559/RlxcHBYtWoRHHnkEhw8fxooVK7Br1y507NgRAPD111/jnnvuwaeffoo6dep4rCwMA2j7tCm9Tt2KhFqsppuPX1JaBNnwRBWLzUIlze3VWMQMJlL1MdmMemFLrQhOnTqFjIwM9OzZU/guIiICnTp1wrZt2wAA27ZtQ2RkpKDQAkDPnj2h1+uxY8cOq+mWlJQgLy/P4sOohxHuBJV3gBqUIiL12kPVPqGpoPkAAOM8fDiGt1FdkbLFwQu56HbtYBCGYdQLK7UiyMioihkaFxdn8X1cXJzwW0ZGBmJjYy1+9/PzQ1RUlHBNdSZPnoyIiAjhk5iYKIP0jKuUSnJSmvWJUi1Kkav8cyhT1vR1UPdmMY5TKz+eqOFv1h1HoZ1YuSbeXnwQZ7KLPCCR7+LZ91g1jy6MO7BSqyDjxo1Dbm6u8Dl79qzHZeCp2bMcz8pHcVmFKurdHRnO51yVTA5rqNxQq5roB4z7fLqqyveWm1RZLK3msucmdwaMQrBPrQji4+MBAJmZmUhISBC+z8zMRPv27YVrsrKyLO4rLy/H5cuXhfurYzQaYTQa5RGaUQXmg/PaI5kYPns3WtcNR6dGtT2aty3Uqjzq1SrYNZRwH/G1adhTVXzwPLt9MYy3wJZaETRq1Ajx8fFYs2aN8F1eXh527NiBpKQkAEBSUhJycnKQnJwsXLN27VpUVlaiU6dOHpeZUR/zd1VZ4g+cz9O8+4Gvw+0nP556cRA2VHKb+hDqfmlmXIcttdcoKCjA8ePHhb9PnTqFPXv2ICoqCvXr18dLL72E9957DzfccAMaNWqEt956C3Xq1MF9990HAGjZsiX69OmDkSNH4ttvv0VZWRlGjx6NRx55hCMf+DBzd6ahb5uEGt87ikxw/spVfLP2uN1r3IWIVDu0q9xQq4hPbX6xY99Pxnn4BUUdePaZ50b3Vlipvcbu3bvRvXt34e8xY8YAAIYOHYrZs2dj7NixKCwsxNNPP42cnBzcfvvtWLFiBQIDA4V75syZg9GjR6NHjx7Q6/UYNGgQvvrqK4+XxRl4QJeXTccuYcPRi+jQoJZTPmNTVh+VV7BrqFV5VGtUBhP82HgPJPyXW1VJLMZH5cRgNA4rtde488477S536XQ6TJo0CZMmTbJ5TVRUFObOnSuHeIyGGfrjThj0OvRoEev4YgYAoFe5Y5Q3vAyqvQyeEk8N4fUYhpEGlU8dDOMdVFSqzw6kNnnMYUst4yldU7DUcqMyjOZhpZZhFEAN1qGcojLZ4826ilrdIkyoof28HU+9BqqlKdvWiwAA1Ar2V1gSZbD3zHMIPUYsrNT6OOqzH3ovavQZ23X6itIiWEXlOi3jhSj9TN7ZLAbJ43vi04faKSyJMog93Y1h7MFKLcMogEfOtdf4vKBma63W65a5jmqaUqdD7VCjqvs9w6gdVmoZxmNcnz73n89VUA7GXXiFwwN4bqeYhzISh9r9yeWClXlGClipZRgF2HM2R2kRVA1BdbqGBWqWzVvwmE5r+i+3qaKo0T2L0R6s1Po4rg7kT93eSFpBfABPT5psTZQPb1CAuH9UIaYt9WxFZBhNwEot4xLRYUalRWC8GCJ1L0eyQig/ngvpRRb/tcatjaJkl0NX4x8MwzgLK7WMS/C46zysBnkP3mCpZaoQ05bc3vLjyZdYbk/vhZVahmFUxwGVb6TjOdF7MCk49hQdT7a3rxoMnDlGnGFswUqtj+Pq2KHmpWG14unYi1qeGF7+ba/SIthHw3WrFdTk4sFxU70Lnr+8F1ZqGYZhnERNCpe34vFjcu1d4wFZTIqWjjUu2eF3FO+FlVrGJXw1lqI78DjqPXjDpOgNZZACMVZYrir5YV2ekQJWahmX8PUBKKlxbaVFYBSElRz58Xgd21Fu2f1Afizj1Mpb374+f3kzrNT6ODxWu4Yrg67n49QycsFKjvcgKvqB/GIIsL4lP/z4ei+s1DKMC/Cg6Ntw88uPp14crseptSeLR0RhGMZNWKllGBdwZZLjedF7YCVHfjx2TK6oOLXyS2PapyD10vgH97eRNkEPwM8X4yqs1DIu4es7dLW0+/1YZr7SIriIevuYdlqfcYSYttRye9/bLkFpERjGY7BS6+NoSTlTEy5Zaj0ep7Yqv7unbPRovj6BF5iSVF8CT/ug2zt8QfWVxTAMwEot4yLqtaF5hkqe5Xwabn3vQVxIL8+1uNThErWyqqYRMRmVw0qtr8Ozs2gCDNcfF64234bfabwH4fAFuyG95JfD15U6fqYYKWCl1seR45jcJaNvczFV5WlXL0L4d5jRz+I3g/56oXkA9m3YbUd+PFbHojaKyS+GCV9XbhnGHVipZSRHr9FReepjN2PuyM42f7dUajlOrS/DLzXyo6pjcj0hiExoczRmGNdgpdbHcXXzkg7AjCc6WP3NXPnTErc0rIWQatZZE0Y/vaVS6ymh3KSyUiuSagtWar0HUT61HmxwbY6e7qNRWwijMlipZVymcUyI1e+1qtTa487mMfBz0/1AiSXrCta+ZIFrVX48bqlVuFHlGjW1oixaHJPLDxjjIqzU+gh1IgKtfu/q4FG1o9b6aKlZndaB3IM71Rf+PbB9HaeT33I82+l73KWCLbWywMfkeg9impKjncjP6ewij+XFzem9WF9rZXwGdzaK2VJetRJCxln+0+MGtEwIh0GvQ4+WcXjv78NKi2QXIqCclVpGo3jsRDG1HZMr8fApdYgwuTifc1VpERgvgJVaH0GOMdnWhjCtbhSrPvhXrzM/gx5922jrdJ6KCu0qtWruRt5g6VG7tdlT8ok6Jld+MXyei/nFwr8JhCV7L8iWl5rHFsY92P2AcQkdtKu8+hLllZVKi+Ayata5eDna+7Afp1a7hy9ohcKSCou///Nrimx58ePrvbBS6+O483B7m05rrzxanWjYp1YeuFblx2PuByqJUyvXeOpt4zTD2IOVWh/H5R35Op3NwZLHULVAmvapVfNkrPale0ZaPOpSq+J+LyeeLLev1rEvwEot4zLe5n7gXaWpgi218sC1Kj8eC+mlsji1DHDv15tlTZ+b03thpdbHcTmkF2y/7XqZrqtptGypVTVcrR7AQxvFJLqGcQ/zuejExULlBGE0DSu1PoIceqZW/Uxt4Y2hyMortLtRTM14g5KjdsVh//lcj+RjUqbsveB7xqe2avzxvlFIHF44/DIKwEot4xI6HQ9CUvNMt8aSpsdxauWDl6PlZ+q6Ex7JR8y+AiVOA5QKrYzTGhGTUTms1Po47kzOtgYhrVpwq0ttXjeemBhqhwRInqaWdS81y15QLfwQo10ES60dxdWTkfG8ccVIDCp+3BkNwUot4xI66PjVWmLk2HinZQtTQUmZ0iLYJO+qemVjGHO0YmRQ80ssox1YqfVx3Dkm19vwtjJl5pVoeqLYfvKy0iLYpJR9lV1i9rBblBahBnnFZTh3pciBT62GHyQrqHGsU6NMjPZgpdZHsDUku3X4gg0LQCURlr94h+sJM5Iwau6/KCgpV1oMhhEI8FPflFNcVonbP1qH9Nxim9doOU6ttfRYf2S8FfWNMIwmsDcoVhKhZUI4WiaEe0weKVB6mU4OX7q0y0WSp8n4Jp0bR7mdhtLPmD3+Tbti8zd7Ci/DMOqBlVof59edaS7fa0sHM224V+/0pU5kqS/vWjVlFETNCqk3IUctj+7e1DIPFa71e5mHB6MQrNT6CLYGDFdDPul0QEiAn428SLhGUygsrxz1peWNYoy60EswW6h5TPBWpUoH4JXezfHT8FstvmMYb4SVWsZlggIMVr83zQ1qnsDUCFeX9yB1zGE1IIWllvu4bUzjpVzjZnigdSOEWvDkfMEv+94LK7U+gtQPsb0JruKa9Vdry5X2BlVPDLhyLAl6q/VJ7dx/U12lRZAcfknVJsJJZSpvQB6rGClgpZaRnEqtuh8ojDzuB95Plya1lRbBJ5BCKdLpdGgRHyaBNNLjS9Y7Xx+btWZwYcTDSq2PIPlbsJ0xwZSX2i0D1alxopgbaSVEBLojCuMEbOHxDFI8zTod8M1jN0uQkjcj/7ipRqWO3Q8YKWCllpEc4ugHCDE6778mR30prfDVCvZXVgCFUKPS4C56iYokVToA8MOQjpKlpfSzIlef0VX7L8N4M6zU+ggeNNRq1v1ASsuyWk4gUtoiIcfRv+ZMfqCNrOkz15HE/UCidEz0bBUnWVpqwSPjpgrHZk8Omd740slUwUotIzkmpVZuhcaTODsIujQ+e1F9eYrbm0YrLYLPIFXvVGsvV8l7qORo5UQxdj9gpICVWsYl7FlbaocYq67xlDASYU9epwdBlYyZSk/Uck9UlUoX0Abe+H4izUYxCQRhGIaxgboD1zGSIfXcb21uGnN3MyRGBaF+7eCqa3x4AnOl7N5ZXfKWqpLY6uIppHmedaodF5TuRUKcWg/mpSZU+n7KaAxWahnJ6NYsBu0SI4W/tea3ZDdOrRNl6dKkNox+epy4WCiBVO6h9Dzhs5ZapQWQAW8sky9gzcKutbGZYcTC7gc+g8SHL1jz06r+nY+Om7OH3erSUq0sCqBKlT6pELMh79aGUR6QxPuRwkdep1OvQqWWzZ1aC4UoFfvP5yotAuMFsKWWkQ2tDc3VJ1tn57hFo25DvVpBCPBTz7ui0tO03H1AJXqITyCFrqWTKB3Gc3RpUhtbT2QrLQbDiEI9sy8jK5L71FrdUatzeI23svSF29E+MRLRoUaX01CrBcsd5Hc/kDd9V1Gq78uZrzdFM1EzctWyefM505RfPXqT9MIwjEywUsvIhtaUNHfm7NZ1I9zOXw7/UKUtmXL3gUoix2XUVjf0OCEBBnEXSmGpZcXYJp6sGbF5DU1q4NaLOsN4GlZqRfLOO+9Ap9NZfFq0aCH8XlxcjFGjRqF27doIDQ3FoEGDkJmZqaDElkh/+IKVzQfVvnqsU32Jc1UQD8w4cvj0Ke0nKLcOI6Z4vqRGuVLWSQNby5Y2o20MelYRGG3BPdYJbrzxRqSnpwufzZs3C7/997//xV9//YXffvsNGzZswIULF/DAAw8oKK3y9G+bgJUvdcUXD7dXWhSP44oCIIf6qdLVeckQY91WxjiojAoYE+a8VU1s/UiyUcyJ/DyN0qsaJjxRP2It5mptK4axBW8UcwI/Pz/Ex8fX+D43NxczZ87E3LlzcddddwEAZs2ahZYtW2L79u3o3Lmzp0WVHTGDnU6nQ/P4MJy8WCC/QF5ApVodRN3AF+fEv/9zu2J5t4gPh7+hAOeuXBV9j1jFRZKNYjp2QVAD3AKMt8KWWic4duwY6tSpg8aNG2Pw4MFIS0sDACQnJ6OsrAw9e/YUrm3RogXq16+Pbdu22UyvpKQEeXl5Fh+5UHIZWitzmKtyNo0NlSR/L9RpZVdg1Bin9sY67vtXu8Pw2xrJki5vFJOX64cvqKee1SMJw4iDlVqRdOrUCbNnz8aKFSswffp0nDp1CnfccQfy8/ORkZGBgIAAREZGWtwTFxeHjIwMm2lOnjwZERERwicxMVHmUsiL7TlPG0Ojq5PJE50bSJK/IwXt5vqRLqTpojAaIbFWsEMXCyWUBCX1P2ebXGz9SFEknapUNkt86mQ6tTYCw7gJux+IpG/fvsK/27Zti06dOqFBgwZYsGABgoKCXEpz3LhxGDNmjPB3Xl6ebIqt5BvFnJi1vcHA42wRXCmzCo2ObiNX2w9oVwf/vbsZaoUEKCaDGnGlC4muH4ncD9SKWp4/j/jUir1Oxe3FMNZgS62LREZGolmzZjh+/Dji4+NRWlqKnJwci2syMzOt+uCaMBqNCA8Pt/h4I1oZF+0N4M7Od65MkGpcSncXuSbF+IhANIoOUVQGNfDJg209lpdU7gfe3B5qRr02cs/jhUMtcw1Wal2koKAAJ06cQEJCAjp06AB/f3+sWbNG+D01NRVpaWlISkpSUMrrSH74gtXvvGvQ9PRypByuAkqH9PJVPPUkPNTRcy5Leu96vFWH3OOnK+MZb+pjtAYrtSJ55ZVXsGHDBpw+fRpbt27F/fffD4PBgEcffRQREREYMWIExowZg3Xr1iE5ORnDhg1DUlKSV0Y+cBatDIz2pPSE+4EjS60r6qnSOq0UE/V/etyAtS93U1QGb0Z0eCcJ6lGnU297+NLrn1bGZLnw8eJ7NexTK5Jz587h0UcfRXZ2NmJiYnD77bdj+/btiImJAQBMmTIFer0egwYNQklJCXr37o1p06YpLLV8WD0m18ZAweOHOLzRqirV5GF3EjartjCjH/JLyqXJVIMQkWz9SLq2lCYdqfHG588c85cJ0WHcZJJFaby8qX0aVmpFMm/ePLu/BwYGYurUqZg6daqHJHIODunlGKWtF94YqUCqGhWbzp+juqDn5xst7/VQs9aNDMK0wTdfy1MjnR7ObBqS4vAF7dSLUsjVdXwqugPjs7D7AeMSYo7JdfQ9c51mcaG4yUHILt58dh1bL2lNY8M8LMl13ru/NdolRiqWv9xIZqmVJhnJUfpJ8eQ4qdY2YBh3YaXWR1BywNaKdaa6lJ7UB1e+1BUGL9yJI411ryZq1NV1Nv6tdsRWpSRxarVUMV6GpfsBNwTjnbBSy7iEVZ9aW9OeF4yfck8CYtJXoR7nEMncD7ygD2kdqY7J9YbxQE7kMgK45H7AbcVoDFZqfQUFNSKtjIvSKk6unL4gZf6yJekcKmh8T1mltGr9Eiu1ZHFq1dAprKH4w+I5VNoCDOM2rNQyLmE1Tq1Nn1oeQuWiRbx9H1KlfWql2yjmekpK9D4tdXmxPUQKpVa1Cq2KkKvvuBb9gNuL0Ras1PoIyvrUagNXlW81hwJSsWhuQRb/tl9ILSmY7qL29tbp1NseKq86hmFEwEot4xLWfWrFX8u4gNo1FitIslHMzSS4+0kDP8eeQU31rCZZGEYMrNQyLiJ+tPP2JSxripsrk4EjldXa746UxkovCX6rtcnVG/u8NO4H6n3JUPOKi/SotRUYxj1YqfURPDFgc5xa9aG0TitX0/uU/uEkctWNdJEseEBQGm4CxlthpZZxCWcGRa2On57Wm+SoJz5FyLeUKDnbW7KQXipF6SfF1E/VZOVXjyQMIw5Wan0EzwzYXhyn1s5vUlnBXUnFUdUqbdGURBFyUEqly2gNJZU32XbPS5KwmlQ234XbgPFWWKllXMKZQZGnMWlwRXlT2k9QDYHkfa33yeZ+4OUVqZaXI0/Us+iQXl7e5oz3wUqtj+CJAdtXfWrVvLyt9Dzdp3W8JOm4U8Uqbh5NIclGMRWH9PJ2uN6vo/S4yMgHK7WMSzijyPniWOqpMjtqBqUPX4gJMyqavy8il1+tNM4H6kVp/3Ndtf9KjflQwKtnjLfCSq2PoOSArWZLphRI5lPrIBlX2lAtS6ru4u19SCrkbG+pmoAVKuXx9cfJx4vv1bBSy7iE1WNyPS4F4wildVq5dsybK2+Oy+hbPVMupVEa9wOdrzWHanCl+bz1BUTpcZGRD1ZqGdnxBquAGspgzQqndvcDNViKlWg7pfoLkfMWfbErDSp4BGRFDX0VUMdYwzBahZVaH0HqAdvqMbk2RmPNjtEi60yqE8XkQC0Ttbu4U50qaQrtI9WJYtwgiiO2CbitGK3BSi0jOzwwikMOv2fFQ3qpoO09JYN5XWvJB1isrJJsFFNxtSj9Ani9buSvJC31T4ZxBlZqfQSpx2urllrbV0ucu7pQWnG0h4pFY1SC2P4rhU8t4O2jgXfBbcVoDVZqGZdwZgMBGwWkwZru4UgfqfQCpdbakrW5IuZIKfPUZhc1WL/kjHKil+h0ODXUE8Mw3gkrtb6CkocvyJ+16lDLrmGlY2/6EhbuB4rKIU+6ckWyYCxR04li3GCM1mCllnENJ8Y6b7fMqLl8SrsfSKXcu5OOiptHU0jVz7k5rMP1wjDuw0otIxm2FA9vH6w9d/iCK2l6p6XWmVKxUisN3l6PanlWPFHNYtvSy5uc8UJYqfURpF6Gdmaw84bJUKtFUMc07R5a7D9KxqmVCyms7jqdetvTG54VsajFPYphpIaVWkYybPvU8gCqFCoxPrmNPUXIURG5/0kDH5PrGdTkzqQiURhGFKzU+gjSH77A0Q9MKDkJOY5+4CVaLSMauVpcmji1Xj4YuIGpbtTiBsEwWoSVWoaxgTsuG67M3Y5yc2Wy84aQXkBNhcqpqlDimFwvtEZKFqdWpVXjS7qkWtuAYdyFlVofQfLDF5y51ssHUHVbVtQsm2fw8u7nMSQJ6eV+EowEcDsw3gortYxkeLPyqtZlU8fWXY+IYRNpYpuqs+7ViJzNLUU7qLkpOaZzTbxxxQFQflxk5IOVWh9BamuiM5OTNwyM6rbG2sZrfGqrnyhmpoA4KqISSrGalTdXkapIaq0bb3lUxMAvioy3wkot4xLeoKhKhbUJwiWfWhlmVV+aqBnIaqqV6phctaL0o+JJPdPXDxTz1nIxrNQyjCjUYNlwRUFVeqJWA8q3nHcglfuBWhVbtazGqEMK70YlTc3IACu1PoLkG8XUOS8pglSToRyKs7e4H2jtmFxvfDy8/Zn3kkdFHHyiGOOlsFLLyI63T4ZSwe4H4jEvl+PDF3wLudpckji1UO944KWPilVU2gQM4zas1PoIkh++IG1yqkRtCqEru7PVsqTqLtUVIe8olfTIuYNfDS44cqL0s2KqXTU9sl7e5IwXwkotIxm2BmNvHxjVPNkrPT96ombUW/vehSSHL+jU217eclAJw/gyrNQyrqHWmUkm7BXXmoVHjs0w1l4aHFmXfGGiduh+oOKXDi0hjU6rU217KG2p9SRi20CtbcUwtmCllpEdte52dgZnpzuXXAWcvsMx3rBRTOemdU+J3qf9Wq+J9p9i+6inzeSXxNvbkvFdWKllXMIbFFWfQD0ztXw4PH3BM2KoASL5/GqlcD9w9wVFVpR+VmS2irLRlfEFWKn1AZReVvOGwdTZIqhF6Vf66E+52t4LDNDaQx1dWja8YVXDHubF84YxmWGswUot4xI8KF7HU35nrky5lZWSi6EIdsvuoP499YLh3SqRRBvFoN6xw9vbzxy1vHQzjNSwUusDeLkBwjPYmQMks4Q7SMaVfJS21MqHeaBa9ZVRhSK5jVRqkFo3H3ljm5mj0mpnGElhpZZxCWfGR68YSzU64XlD9AOtWJW0IaXreLtS5L0vgFWw+wHjC7BS6wPIMVSr1dqiGlRSPd5ufRKDL3VVgnxtLslGMQnkkAulnxU+fIFh3IeVWkZ2tDowWswtGi2DZk3MEqJMSC/vq3etPsdiUZMyKQfe3n4MA7BS6xPIEf2AB0jP49JGMS+ZqKt3YWe6tKf6qhqq2iW/azUIrgK88UXEXbTi+sMwJlipZTwAD4xicOnABhk2l0mJFJOiI6WUVRHPIE2cWvWOBb6k3Ku5HRjGHVipZVyCh0Rt4C2WWi1g/kxoSUESq994ux6kdJN5sn7FZuXtbc54H6zU+gC+NFh7NS40pNJtLxdqVBpVKJIoxNalVHFq1YpaDl9QhxTeDbuaeC+s1DIu4eXzWw2cXUZXS/Uo7X7AeBY5W1uKPq2W58IavvSoiLbOyysGw0gOK7U+gC8N1nLhiTd7V9rJkVze0vb2yunYr1hiYXwUX3uR9Wa4LRlvhZVaxkV4VNQCii+zSdBNrCWheLl8EN5cJC+m1SA1vYRxkzNag5VaH0BpBUCr46L50r0aQtu4FNKrUnIxGBGoSC+RDOWfAIZhGPuwUsu4BL/B20ctVi2lX2gY70EtfZpxHzW8pDOMHLBS6wPIsZzFQ6Ln4cD6jCPkbG+9FK4kPHAwDCMjrNTKwNSpU9GwYUMEBgaiU6dO2Llzp9IiKQpbeOTDFzZJWes+5uVyuFlOYnl8FX6MtY0r1lm26DJag5VaiZk/fz7GjBmDCRMm4N9//0W7du3Qu3dvZGVlKS2apLCiqg281f3AO0vlPgT5wrjxMy8vpur11meWYTwBK7US8/nnn2PkyJEYNmwYWrVqhW+//RbBwcH48ccflRZNMbxhKvTEfO7Q6ipDmppBY+XwxvjA3vAcM1XwKXKMt8JKrYSUlpYiOTkZPXv2FL7T6/Xo2bMntm3bVuP6kpIS5OXlWXzk4GhmvuRp8linDc5cLlI0f+4n3oMUllpezmYYRk5YqZWQS5cuoaKiAnFxcRbfx8XFISMjo8b1kydPRkREhPBJTEyURa703GLJ0wwN9AMAJDWuLXwXdu276kQG+0uev5y0TAgHAPRqFS981z4x0ub1N8SFWUmj5ne2aHUtv8SoYIvv69UKsvi7S5PaqM7tN0TbTftifonV78NttJXURIca3U4j1OiPYKOlvObtcVvTqjoIDjBYvd9e24mh941xNn/r2fL6b/Uir7dXWOD1Pt8kJsSt/J3htia1Ubdav3FEw+iQGn2tR4vYGtfFhbvflkZ/6aecprGhkqepBKHX+nhUSIDT995UP9Li73vaxNe4Jjr0erq2xurqNLs2tt1lpT8AQJu6ESIlVBfNrIzZjHegI29cJ1OICxcuoG7duti6dSuSkpKE78eOHYsNGzZgx44dFteXlJSgpOS60pGXl4fExETk5uYiPDxcMrlOXCzAwn/PoXaIEfvP52LX6cvIyC3GPW0SAABt60UgIsgfm49fwn963IBdpy7jhrgwfLH6KPaezUGHBrWg1+nQMiEckcH+qBUcgEEd6gEAcopKMfb3feh1YzwevPadNX5PPoc3/tyPhIhADElqiMuFJSgpq4TRX4/Nxy7hYn4JKgloFh+GJjEhOJZZgFCjHy4VlOB8zlXccUM0IoMDkF9cBgCIDA5AdKgR8eGBOHelCB0bRmHnqctISbsCfz89TmQVwKDX4UhGPupEBqKignDHDTFIOXsFRzML0PvGOCREBOHXnWloXTcCb/dvhXbXlJ+L+SVYfiAd991UF1l5Jdh1+jL+r2MiDNW2f+8/l4ujmflCXZhTWFKOX3emISO3GPVqBWHTsUs4e6UIfno93uzXEnHhRqw9koUAgx7929URlL8fNp3E5uOX8PydTREW6Idv1h5HZl4xbmkUhZF3NEZBcTk+WHYYu89cQbdmMXjvvtY4mpmPh2dsQ0l5JW6uXwsRQf4IC/TD1dIK3BAXisuFpdh2Ihsjbm+EikpCsNEPPVvG4YvVR5GVV4IG0cHofWM8Nh29hLziMiSfuYLwIH+0qRuOq6WVyMwrhkGvw9AuDfH3vnScvFSASqp6oQnw06NuZBBmbj6Jfm0SkNQkGpOWHkRlJdC3TTweu7U+0nOLMWbBHkSFBOBKURlOXixEn9ZxMPoZMDSpIdYfzULtECM2H7+EbScuofeN8YgI9kdhSTkigwLwWKf6CDH6YVHKeXy04gj6tI7H+H6thPYoLqvAb8nn0L15DOrVCsaRjDxMXHIIp7MLhWs3HruIHzadxBOdG2D5gQwQAcv2p6NurSCEBPhBpwNaxIejsKQcDaNDcCwzH9tOZmPYbQ0xuvsN2HjsIioqCfvO5WL36csoKCnHmLub4bam0Xjzz/1oWy8Sw29vZNEH1h3JgkGvQ71aQfgz5TxOXizE3/vTcU+bePjp9cgvLoPRz4Cs/GKk5xajYe0Q6PXA8awCFBSX4793NwMA/HMoE01jQxEbFojQQD80jwvDuStF6NCgFm6IC0NadhF+3ZWGmFAjHutUH3qdDj9vO43953PRrl4kDpzPxcKU8wj01+POZrEoq6hEg9ohmLvzDF7s0QzP3dkElwpKMOKn3dh7NgdfPtIe3VvEYnHKeXRrFotVhzIQFx6Ie9vVwYoD6ThwPg/7zudi49GL6NKkNlrEh4NA2HTsEopKytGzVRwKSsrRq1U8FqWcR+fGUVh1KBOP3FofA9rVAQDsPHUZMzefRL1awVi+Px2Z+SWoHxUMP70Ox7IKEBxgQMPaIWgUE4KSsko0jgnBzlOX0bZeBDLzinExvwSf/197NIwOwfGsAkxaeggbj15Ev7YJeOr2Rlh9OBNH0vMREeSPnq3ikJVXjMLSCizbn44Hbq6HsopK6ABsOZGN45n5aB4fhlZ1wlFeQbiQW4wj6Xn4cFBbrE/NwvaT2agVHID29SNh9DPg5vpVdVpSXom1R7JwJrsIRj89br8hGg1rh2DlwQzkFJXhwQ710LFhLaTnFmPxnvNIrBWMs1eKkFgrGFGhAdh7Ngc7Tl1Gg9ohGNiuDh7rVB+B/lUvZvN3peHtxQdxX/u6CDH6ITO/GO3rReLM5UJEBQdgxB2N8UfyOaw/ehHtEyPxZJeG+HVnGg6l5yEm1Ijx/Vrir30X0DQmDG3qXVc8/zmUiZAAA2LCjFiy9wI2HL2I57o1wdojWbhSVIqrZRXYeiIbTWJCMTSpAR7v3AA6nQ65RWVYvPc8ikorkHa5CAv/PYdXejXHve3qYOq641iw+yy+fvRm+Ol1eHvJARh0OhSWViCxVhAOpefhic4NkHa5CEmNayM4wA8l5RVoFB2KCiJMXHIQbepF4NSlQoQF+uHUxUJ0blIbOuhwOD0PBr0OQf4GXMi9iga1g6HX6XDuStWcEBboh+KySuw6fRnpucUINfrhjhuiERceiMKScszcfAol5VXBuhvUDkZIgB86N66Nl3s1Q4hR2hf7vLw8RERESD5/M87BSq2ElJaWIjg4GL///jvuu+8+4fuhQ4ciJycHixcvtns/PxQMwzAMoz14/lYH7H4gIQEBAejQoQPWrFkjfFdZWYk1a9ZYWG4ZhmEYhmEYafGMY50PMWbMGAwdOhQdO3bErbfeii+++AKFhYUYNmyY0qIxDMMwDMN4LazUSszDDz+Mixcv4u2330ZGRgbat2+PFStW1Ng8xjAMwzAMw0gH+9SqCPbJYRiGYRjtwfO3OmCfWoZhGIZhGEbzsFLLMAzDMAzDaB5WahmGYRiGYRjNw0otwzAMwzAMo3lYqWUYhmEYhmE0Dyu1DMMwDMMwjOZhpZZhGIZhGIbRPKzUMgzDMAzDMJqHlVqGYRiGYRhG8/AxuSrCdLhbXl6ewpIwDMMwDCMW07zNh7QqCyu1KiI/Px8AkJiYqLAkDMMwDMM4S35+PiIiIpQWw2fREb9WqIbKykpcuHABYWFh0Ol0kqadl5eHxMREnD171ifPpebyc/m5/L5Zfl8uO8Dl91T5iQj5+fmoU6cO9Hr27FQKttSqCL1ej3r16smaR3h4uE8ObCa4/Fx+Lr9vlt+Xyw5w+T1RfrbQKg+/TjAMwzAMwzCah5VahmEYhmEYRvOwUusjGI1GTJgwAUajUWlRFIHLz+Xn8vtm+X257ACX39fL72vwRjGGYRiGYRhG87CllmEYhmEYhtE8rNQyDMMwDMMwmoeVWoZhGIZhGEbzsFLLMAzDMAzDaB6PKrWzZ89GZGSkZOm99dZbePrppyVLT264/Fx+Z8r/+uuv44UXXrD5u7eX/9tvv8W9995r83dvL3/nzp3xxx9/2Pzd28vvTf3flbHvkUcewWeffWb1Ny2V3VVWrFiB9u3bo7KyUmlRFMHR8+/tuFx+coKhQ4cSAAJA/v7+1KRJE5o4cSKVlZWJur+oqIgyMzOdyZK6detGL774Yo3v09PTKSwsjE6fPi18t2HDBurfvz8lJCQQAPrzzz9r3DdhwgRq3rw5BQcHU2RkJPXo0YO2b99ucU1ycjL17NmTIiIiKCoqikaOHEn5+fl2y19ZWUlvvfUWxcfHU2BgIPXo0YOOHj0qpLlu3Trh3uqfnTt3CtfNnz+f2rVrR0FBQVS/fn1q3LixzfIHBweTXq+ndu3aWZQ/IiKCAFBgYCCFhYVR586dadmyZcK9p06dsinLggUL6L333qOkpCQyGo1kMBgoNDSU4uLiqHXr1nbb/9ChQ1S/fn3S6XQEgPz8/Khnz550+PBhIiK6/fbba+Sn1+spJiaGnn/+eSIiqzL16dOHnn76aWrcuDEFBgZSSEgIBQcHEwBq1KgR/fTTTzRr1iybZQJARqOR/vjjD2rUqBEZDAbhe51OR6GhoUI/iIuLs5lGnTp17OaRkJBAgYGBFBUVZfOaWrVqkV6vJ4PBQAaDgWrVqkWRkZEUEBBAcXFx1LRpU4qKiqLAwEBq2rQp6XQ6evLJJyk1NZUGDBhA4eHhZDAYyM/PjwDQd999R0REe/bsobvuuosCAwOFvPz8/Gr0cVP/NxqNwnUhISE0fPhwys/PJyKib775hmJiYizkjo+Pt+j/pjL4+/tT+/bt6YcffqChQ4eS0WgknU5HOp2O/P39yd/fnyIjI2nkyJGUlZVFmZmZVFFRQU2bNrVIPzw8nEaNGkW5ublERHTp0iWqW7cuAaAuXbrYrM+6devS559/btH/Q0NDhd/N+/+MGTMoMTGR9Hq90Pamf1+5coWILMc4809oaCg1btzY4judTke1atWi8ePHU0lJCRERjRw5Umib6h+DwUBt2rSx24diY2MpPj6egoODqVGjRuTv70//+c9/6N5776XExETh2ar+ueeee2j9+vXUsmVLoX/XqlWLbrnlFgJAU6ZMsWh/Uz8JCQkR2iIlJYWIiPr3708BAQEWcg8cONBm3QCgdu3aUevWrclgMJBeryedTif8u1WrVkRElJiYaLfspue0U6dOFBoaSrVr1yYA1KFDB4f31atXjzp06ED+/v7CdwEBARbtP2HCBKvjj9FopLCwMLrjjjvo8uXLNHz4cKH8pnKYp+vn50dBQUFkNBopIiKC7rjjDofyOfq888479Mcff1DPnj0pOjqawsLCKCQkhPz9/SknJ4eOHz9O9913HwUHBwt9IDIykgYOHEhHjhyhDRs2UFxcnNCfTc9nnTp16IUXXqCcnBx68cUXreYdFRVFY8eOpbKyMkpOTqZmzZoJ6fj7+1NERAQFBARQnTp16L333qNp06ZRmzZtKCwsjMLCwuiWW26h/v37U926dUmv19vs/wAoKCiIDAYDGY1GMhqNFBoaSkFBQRQZGUn169enli1bUkBAgDCndezYkUaOHGkzvX79+tGpU6ds9s2oqChh3vvggw+oY8eOwvwRHBxMAQEB1LZtW1q+fDkREX333XfUrVs3i/5v+jRv3tzuPP7RRx9Rhw4dKCQkRBijTWX55ZdfBDkOHTpE9957L4WHh1NwcLAwrgwcONBijv/rr7+oadOm9Ntvv1GHDh0oIiKCgoODqV27dvTzzz9bXPvHH3/Q3XffLcw9pmfZnG7dutWQ+ZlnnhF+37NnDz3yyCNUr149CgwMpBYtWtAXX3xRI51ffvmF2rZtS0FBQRQfH0/Dhg2jS5cuWVxz5coVev755yk+Pp4CAgLohhtuoL///lv4vby8nMaPH08NGzakwMBAaty4MU2aNIkqKytrlL+ioqKGDPZw2lLbp08fpKen49ixY3j55Zfxzjvv4JNPPhF1b1BQEGJjY53N0io//PADunTpggYNGgjfFRYWol27dpg6darN+5o1a4ZvvvkG+/fvx+bNm9GwYUP06tULFy9eBABcuHABPXv2RNOmTbFjxw6sWLECBw8exJNPPgnAdvk//vhjfPXVV/j222+xY8cOhISEoHfv3iguLgYAdOnSBenp6Rafp556Co0aNULHjh0BAMuXL8fgwYPx7LPP4sCBA5g2bRrOnTuHvXv31ijHN998AyJCz549a5T/+eefBwB89tln2L17N+666y4MHDgQBw8eBAAkJiZi5syZuPHGGxEfH4+JEydi4sSJCA0NRd++fVFaWoouXbqgrKwM/v7+SElJwfz585GWloaGDRtaLf+JEydw++23o0WLFpgxYwbWr1+Pjz/+GHq9Hr169UJFRQUMBgNGjhyJCRMmIC4uDlOnTkVycjJWr16N3r17C+WYNWuWUEdJSUlo0qQJOnTogFmzZuHNN98EUHUcodFoxHvvvYdRo0YhNDQUv/zyC1588UUMHz4cAHDjjTciKSkJ6enpOHPmDDZu3IhOnTrhyy+/xBtvvIG+ffsCACoqKhAXF4devXph5cqV2Lt3L/bu3YuXX34Z7777LgCgdu3aKC0tRUREBPbu3YudO3fiww8/hE6nQ6dOnWAwGPDFF1/gwIED+OWXXxAVFYWYmBgAwI8//oi77roLN9xwA0aPHo0hQ4bg448/htFoRFlZGa5evYo33ngDH374IQYPHoyNGzfi8OHDmDBhAogIa9euRf/+/VFeXo7Jkyfj6aefRqNGjQAA/v7+AIDk5GSEhobiwQcfxLBhw4S6fP755y36eLNmzfDhhx8CAIKDg4VjI9etW4enn34a8+fPx0svvYTCwkJ06NABHTp0gMFgQFxcnND/u3btitatW+Pll19GeXk5oqOj8dRTT2Hfvn147rnnMHXqVPj5+cHPzw8tWrRAYmIiDh48iOeeew6xsbHo1q0bLly4AKDKGvfXX3/hzTffxOrVq/Hss88CAEaMGIG2bdsCgMUzs2rVKkybNg2tWrWC0WjE5MmTMX78eMyYMQOFhYVo3bo1QkNDAQCBgYEW/X/RokXo0aMHpk6dimeffRYtWrQQ6unSpUvCvwMDA/Hqq6/i5Zdfxv/+9z+sWbMGixYtQn5+PkJCQvB///d/+PnnnzFx4kTk5ORgypQpeOONNwAAOTk5aN++PQBgwoQJ6NWrF+rUqYOWLVvihhtuwDPPPAMAqFu3rtDP9u7di+TkZKEv/fHHH9i3bx9eeOEFlJWVYceOHejevTsWLFiAlJQULFmyBHXq1EF4eDgOHDgAg8GAhx56CJ999hkyMzPx6quvAgAGDBiA7Oxs1KlTRyibqf1jYmLQpUsXlJeX4/Tp08Lv8+fPx7JlyzBgwAA0a9YMBoMBOp0OJSUlQvsnJSXhkUceEfr/mDFjUKtWLbz11lt49dVX8d1336F27dqIjo5G7dq1hfT1ej0mTZqE9PR0HD58GK1atUK3bt0QHh6O9PR0NG3aFGVlZbjrrruwZ88erFq1Cn5+fjh06JDQ/qmpqdi7dy8aNmyIiIgInD17FlFRUahbty7atm2Lxx9/XKjj8vJy/Pzzz0L7Z2VloW7dupg/fz6+//57BAcHo23btvD398fKlSsxevRovPbaa5g7dy4SExMxcOBAYb4qLy9Hnz59cPz4cYSEhKBdu3YoLS3Fk08+iZiYGERHRyMtLQ1Tp07FTz/9hJEjRyIoKAgPPfQQwsLC0LRpU/zf//0f0tPT8c477+Dzzz9HvXr10LBhQ/j7+2P27NnYuHEj7r77bixbtgzJycmoXbs2ysrKMHHiRPTq1Qs6nQ7t27fHXXfdJZS5tLQUvXr1QnZ2Ntq0aSNYth9//HG0bdsWkZGRWLFiBUaMGCGMGe+99x78/PzQpk0bhISEYMaMGViyZAlGjx6Nrl274vjx4/joo4/Qr18/6HQ6FBUV4fHHH8eSJUtw6623ol69evjwww+RnJyM3bt3o6ysDEuXLsW7776LN954A//9739hMBjw5ptvomPHjsJHp9PhwQcfxJNPPonGjRujvLwcrVq1QklJCd577z34+/vj3LlzeOihh4T++OSTTyI5ORnp6enYsWMHjEYjXnjhBdxxxx24+eabkZ+fjwceeABffvkl0tPTsXPnTowYMQJTpkyBwWDADTfcIKS1YcMGjBo1Cg8//DBCQkLQqlUrREdHY9iwYbj//vuRkpKCoqIi9OnTB3fccQcA4MiRI0Lf27x5s915/JZbbsGbb76Jp556Cs888wxGjRqFiooKdO7cGUOGDMHSpUst5sn169dj2bJlyMnJQadOnVCdvn37Ij8/H8eOHcObb76Jbdu2Yd++fRg2bBiGDRuGlStXCtcWFhbi9ttvx0cffVQjHXNGjhxpIfvHH38s/JacnIzY2Fj88ssvOHjwIN58802MGzcO33zzjXDNli1bMGTIEIwYMQIHDx7Eb7/9hp07d2LkyJHCNaWlpbj77rtx+vRp/P7770hNTcX333+PunXrCtd89NFHmD59Or755hscPnwYH330ET7++GN8/fXXNcq/fPlyu2WqgTMa8NChQ2u8Tdx9993UuXNnIiK6fPkyPfHEExQZGUlBQUHUp08fC2vlrFmzKCIiQvh7woQJwltHgwYNKDw8nB5++GHKy8sT8kO1N4tTp04REdGNN95I33zzjU1ZAeuW2urk5uYSAFq9ejURVb2pxcbGWrwd7Nu3jwDQ/fffb7P88fHxNHHiRIvy63Q6wYpUvfylpaUUHBxM8fHxQvn9/f2pXr16osofHh5Offr0EerQUflr1apFP/zwAxERnTt3jurWrUsHDhygBg0a0JQpU6h9+/Y0fPhw4fpx48ZRw4YNLdrrrrvuIr1eL8hnXv6HH36YHnrooRrtv2TJEgJAx48fp+bNm1NAQAAFBQXR6tWrrbY/AJo7d67N8t900030yiuvUJMmTYR0x4wZQ7fddptFPeOahan6G211mjdvTgBo8eLFFv3AxOjRo0mv19PEiRPprrvuIgBUWlpqUf7g4GAaPny40P/DwsIEKw8Amj17Nvn7+9PIkSOF+ly+fLnwNt+jRw8KDAx0qv+3bNlSsPBYA6iycHfv3r1GH+/bty8BoJEjRwrW9w8++IB0Oh21a9eOQkJChL5SUVFBQUFB1KpVK+H5DwkJEer17rvvpvbt2xMAeu211+iJJ54QrOj+/v40ePBg0ul0lJycLFhqANCmTZsIADVs2FBo/8DAQAoKCqLPP/+cunXrRr169bJa/tLSUjIYDNSvXz8iIrr//vvp8ccfJyKisWPH0uOPP04AKDg4WKgP8/5vory8XLBYLlq0SKjzoKAgwbJpjq32r1+/PjVq1Ig2bNhAQUFBwkrJTTfdJPR/ADRu3Djht4YNG1r0/1q1ahEAGjRokKj2r1+/PgGgTz/9lMLCwmj37t3k5+dHR44cEdo/KirK4hk38fDDD9P48ePpscces7BIpaSk0K233kqjRo0iouvjVfX2N189szX+m1YCunfvLtRZ7dq1KTAwUGinNm3aUGJiIgUFBQmrazqdjnJycuyWPysriwDQkCFD6M8//ySdTmexYmcqf7169WjixIlC+997773CWNmpUycaP368xbORk5MjWBl37dpFRESHDx8W8u7duzft2rWLAFBaWppQdtP8cN999wljX1hYGA0aNEhIH6iy9pt45ZVXBEuZySr60EMP2W37ffv2EVHV3PfJJ5+QTqej3377jQDQhg0bLMr+559/0uLFi0mn09HcuXMpICCAjh07RgBo+PDh1LFjR4uyL1myRLBAd+/enQ4dOkR+fn7CuHjzzTfXeB5M3HjjjRQYGGjxfN1888305ptvCvfrdDrq3r278LtJ7kWLFtE999xDw4YNE+rxhRdeENrpzJkzwjj/22+/kZ+fH2VkZJC/vz/9/PPPtGTJEtLpdBbPJBHRn3/+SQBo2LBhNeRNSEigb775RuhHGzZsoAceeIAGDx4sXGOqf9MKji1KS0spJiaGJk2aZPX3m266icaPHy+U8eGHHxbGqvLycurSpYuwylVdtyAiGjZsmHC9tXSrY1qFtWWptbbqa4/nn3/eot0++eQTaty4scU1X331FdWtW1f4e/r06dS4ceMabWJOv379LPQNIqrRBkS2y28Pt31qg4KCUFpaCqDqrWr37t1YsmQJtm3bBiLCPffcg7KyMpv3nzhxAosWLcLSpUuxdOlSbNiwQbAkffnll0hKSrJ4u0hMTMTly5dx6NAhwcLpKqWlpZgxYwYiIiLQrl07AEBJSQkCAgKg11+vmqCgIABAVlaW1fLn5+cjIyMDa9eutSh/ZGQkJk6caLX8S5YsQVFREfLz84XyJyUl4dKlSxblb9KkCQBg586dQvm//vpr5OXlYfz48Q7LWFFRgXnz5qGwsBBJSUmorKzEE088gVdffRU33ngjAODs2bPYs2cPRowYIdxXUlIiWAFNGAwGVFZWIjk52aL8paWl+Pvvv7F//34sXLgQRIQmTZrgwoULGDJkCBo2bIjExEShzq9evYqhQ4fio48+wr59+zBu3Dj88MMPWLp0KQDg6aefRnR0NPbt24cmTZrgqaeeEtrfJNuJEyeQkJCAxMREBAUFYefOnTXquaKiAuPGjbOwUptTXFyMjIwMBAYGIiUlxaIfmGSdPXs2iAj3338/Tp48iaioKIt6KSkpQVFREUaMGIEnn3wSu3btQlRUFB5++GH4+fkBANauXYvg4GCL/vrrr7/Cz88PlZWVWLduHXQ6Hfr27Yv169cL7T98+HAhjfr16+Oxxx5DVFQUsrKycOTIEQAQ+oc1ioqKEBkZadHHZ82ahcOHDyMyMhJxcXHIzs5GREQEhg0bBr1ej3379qGwsBB6vR433XST8HadmZkppNulSxfMnz8fly9fRmBgoLDKsXHjRuzevRsjR45EbGws9Ho95s+fj86dOwsW4atXr6JOnTrYsWMHAODMmTN4++238cUXX6B58+aorKzEW2+9hZ9//tnCn/KPP/4Q+v+cOXNQUVGBZ555BikpKdi6dSu6deuGtWvX4rfffrNYqane/83JyckRxq7WrVtb9IsxY8bAYDAgOjoa77zzDrKysqy2v6n/R0ZGol+/fqhduzb++usvIe8hQ4YgLCwMN9xwA5o1aybcd+7cOXz88cfYv38/vvjiC0RFRaF///7YsmWLxfNvNBrh7+9vMf6dOHECaWlpaNu2LWbPno1HHnkEa9asQePGjbF06VI0bNgQQNXqQkJCgkWZZ82ahZMnT+LZZ5/FkiVLEB4ejrCwMABAWVkZkpOTLVZ/AKBOnTq4fPmyxXdz5sxBdHQ0tm/fjlOnTqGoqMhi/P/yyy8BAJs3b0ZQUJBQZyUlJQgPD8cXX3yB2rVrIysrC2VlZbjzzjuRkJAAIsJjjz2GiooKTJo0SfBDNR//vv32WwBVz8fMmTPRs2dPixU7E1evXkVkZKTQ/omJiTh27Bji4uKwY8cO/P3332jVqhV0Oh3eeustzJ49G+Xl5YiIiBCe1RYtWqBWrVoAgCtXrqB58+aoXbs2Zs6cCaPRiOLiYsycOROhoaFITU3FkiVLMHPmTOTn52P79u2YPn06AgICEBMTA4PBIMhmWn1LS0tDQEAAEhISsGnTphpzX2BgIHQ6HfR6PZo0aSLMfZ06dYJer8fGjRsBAFFRURZlz8/Px5w5c9ClSxcUFhYiPDxcGEtMFv927doJVuegoCCUl5fDz88P//77L6ZNm4bGjRtj2bJlAIDDhw/jqaeeqtEPTKtcJSUlaNy4MYgI69atw9GjR9G5c2fMmTNH+P61114T7uvduzf0ej2mTZuGnJwchIeHY+bMmWjZsqWF73H9+vURFxeHTZs2oUOHDtDr9XjhhRcQFBSEu+++G//73//Qs2fPGnPVzJkzUatWLWHcMaekpASBgYHIzc0V6i4oKAibN2+ucW3Lli3RuHFjDB48GGlpaTV+X7JkCbKzsy1WxwCAiLBmzRqkpqaia9euyM3NRa1atfD333+jWbNm6N27N8LDw3H48GHUrl27Rrombr31VmzatMlmus5iem5bt26NcePGoaioyO71ubm5Fn0rKSkJZ8+exbJly0BEyMzMxO+//4577rlHuGbJkiVISkrCqFGjEBcXh9atW+ODDz5ARUWFcE2XLl2wZs0aHD16FEDV87B582Zh9dRW+UXhjAZs/jZRWVlJ//zzDxmNRnrllVfo6NGjBIC2bNkiXH/p0iUKCgqiBQsWEJF1S21wcLCF5e/VV1+lTp06CX9be7tISUkR3pZtATuW2r/++otCQkJIp9NRnTp1LHxaDxw4QH5+fvTxxx9TSUkJXb58mQYNGiS8rVor/6OPPiq8TZuXf8CAAWQwGKyWv2/fvtS0aVOL8n/33Xfk5+dHLVq0oIqKCkpNTRUsW1u3biUioqNHjwpWnbS0NLuWWpNPbEREhODP8sEHH9Ddd98t+K40aNCAunTpQi1btrS4f+XKlaTT6Sg4OJjKy8vp3LlzFBsbK1hSzcv/3HPPCeV/4YUX6PXXXxd80HQ6HX311VdEVNV/AgICyM/PjxISEigoKIj0ej1169aNmjdvTiUlJXTbbbdRq1at6N9//6UPP/yQdDoddevWjYiIpk6dauHbtnHjRtq1a5fgB3vhwgUiItq6dSsBVb6Y69evp/79+1N4eDidPXvWov1N6QCo0Q+IqvybAQiWlOjoaOrbt69F+xsMBoqKihL6/4ABA2jw4MFUv359eumllwSL2XPPPUezZs0if39/oU1Naev1enr33XepQYMG1Lp1a/L396eAgADS6/WCRW7BggXUoUMHCz9Qe33c9Lt5Hz969CjFxsbSwIEDLfyeTeU2+TACoPr169Pvv/9Ou3fvpoiICNLpdPTII4/QwIED6cqVK3T33XcL1/r7+wu+XMuXL69hZfrqq6+EZ0in0wl+k6a/Tdfdc889FB0dTU2aNCGi637oAQEBNHv2bKFst912m5CvXq+nSZMm0aVLlygxMVGwWJnSrN7/q7d/dZ/aX3/9lYYPH04zZ86kKVOmWPQTa+0fEBBAAQEB1LFjR+H5nzp1qkX9BwUF0UcffSQ8/3feeSc1bdqUnn76acHvGADt2LHDYvwz9T8AVFFRQWPHjhWs4ADol19+Ee575plnhHp9+umnBWtw9+7dBUutqf2nT58u+N36+fnRokWLCACtXLnSYqwxyXvjjTdSZGSkMP5/9913tHz5cpoxYwb5+/tTaGio0B+2bNlCY8aMEdpVp9NRvXr1iKjKQhwcHExxcXE0ZswYMhqNpNfrKTw8nGrVqkUfffQRPfLII+Tn5yfIZ1r1WL9+vdB+jRo1IqBqL4LBYKD58+fb7P/m7b9s2TJasGABjRkzxqJ9ZsyYQS+99BL5+fmRXq+nZs2aWaRlyq9t27ZEVLVyZ7IsAxB+N439ppUQoMrvNSwsjB566CGLuc98DAgICKD33nuvxtzXuHFjwZIeFhZGL774ojC2mZ6x+vXrW6xSjR07Vki3c+fOlJqaSvXr16c33niDLl68SCNGjBDSNK1S/Pjjj4JfsMFgoAEDBtTw3X7qqaeoffv2gtVu3759gu9oeHg49ejRQ+hPBoNByKNz587UtWtX0ul0NdqodevWQn/W6XTUvHlzOn36dI057aabbhJWpNavX08Gg0GQLykpqYY19fz582QwGKhVq1ZWLZOPPvootWrViu68807q0qULrVq1ioKCgiggIEC45sMPPySgakVpxYoVlJSURPXr17fQVUxtbRoTiIhycnIoJCSE/Pz8yGg00syZM2n+/PkUEBBA69evJ6BqBWn06NEUExND48ePJ51OR71797ZqqV28eDHp9Xq6fPlyjXStYc9S+91339GKFSto37599Msvv1DdunXp/vvvt5oOEdGWLVvIz8+PVq5cafH9ggULKDQ0VFjVuPfeey2ssqY9G8OHD6fdu3fTvHnzKCoqymJVsaKigl577TXS6XTk5+dHOp2OPvjgA5vld8av1mml1mAwUEhIiKCcDBkyhAoKCmjx4sXk5+dH5eXlFve0b99eWAKyptSaNhGY+Pzzz6lRo0bC39aUWtODnZWVZbtgdib8goICOnbsGG3bto2GDx9ODRs2tNjANmfOHIqLixMezldeeYXi4uLo5ptvtlr+1atXCw+0efkfeughioyMrFH+s2fPkl6vp4ceesii/JWVldS9e3dhc0KtWrWoQYMGBIC2b99O5eXl1LFjR3r11VeF8ttTaqdNm0a7d++m119/naKjo2nBggUUFxdH58+fF66rX78+BQYG0qefflojjYcfflgY6IKDg+nmm28WlGXz8puWtXQ6HZWXl1NOTg4dPXqUunTpIiiwV69epVmzZgkD6cqVK2nIkCHCpKzX62nFihU12r9BgwbCsl1OTg7t27ePOnfuLAy2CQkJwkCekZFh0T9MZSotLaUmTZoIyzUFBQX02WefkdFopDvuuIMSEhLo0UcfrdEPunbtKihlq1atotjYWNLpdBbt7+/vT++//z4tXryYDAYDNWrUiDp27Eh9+vSh0tJSQc7du3fTrFmzKDw8nA4fPkzt2rUT6uzdd98loipnf6Bq2XTfvn00Y8YMYeDo3Lkz9e3blzZv3myxKc7a4LZ//34CQP3797fo4+3ataPp06fThAkTqGHDhjRixAiKiooSym2u1Jo2oBFVuTrodDrq3LmzsGnG1Ef79+9Pb7zxhsUGNYPBQM2bN7fYMGJSpE3KSmpqqvAy8fvvvxMAioiIoOjoaKH9TUptcHAwTZs2jYiIzp49K0xo69evpxkzZlBUVBR17NiRXnvtNYv+HxgYaNH/Dx48KLT/yy+/TGFhYcLmRXM3KXNMy82mDY3V2z80NJS6d+9OCQkJwvOfk5NDAOi9994TlJfTp08Lz7/pmTWNfzfffDP5+/sT0fXxb+3atRQcHCxsrioqKqKLFy9SamoqffHFF0LdtWnThohI2Ezzxx9/CC95n376qXDdZ599Rh07dqTp06fTxx9/TDfffDMNGzaMoqKiqF69eqKVWmvj39KlS4Xx7+rVq9S7d29q1aoV/fnnn8ILWmVlpfBSZ2qnAQMGEFDlkuLn50f79u2j6OhoioiIoH///Zc2bNgguGuYXkpMzzYAeuONN6h27drCJj0Tc+bMIaDqBdta+5vG60ceeYT8/f0pOjqaMjMzhY14tpRavV4vGEN0Oh3dc889tHbtWkpKSiIAlJ+fT0VFRRQREUGvv/463XDDDdSwYUMCQO+++64w95nKUK9ePVq1ahUZjUZq2bIlffbZZ0LfnzNnDun1euFl4fvvv7fYqPjggw9SdHQ0hYaGCi/rREQXL14kADRhwgTq1KkTRURECGORqe8fO3aM/vOf/1gYHiZPnkxAlQuF+Utzjx49BDlMLkRHjhyhkpISOnbsGO3evZu6d+9OBoOBvvnmG9q7dy9NnjyZgoOD6cMPP6SkpCRh05o5RUVFFBgYSDqdjiZMmEDbtm2jQYMG0Y033khvvPGGxZzWpUsXGjt2LKWnpwvPwy+//EIbNmygbt26UY8ePSw2GH3wwQdUu3Zt6tq1q1WlNisrS2gXg8FAzZo1o+eff15wjSG6PvaYFOYrV65QeHi4hYuFaR7//fffhe8qKiro2LFjlJKSQp9++imFhISQ0Wikn376ic6fPy+0XcOGDYXN2/feey81bNjQqlK7atUqAiC0myndiIgIWrduXY3r7Sm11VmzZg0BVa4d1dm/fz9FR0cLc5OJgwcPUkJCAn388ce0d+9eWrFiBbVp08bCleCGG26gxMREC13os88+o/j4eOHvX3/9lerVq0e//vor7du3j37++WeKioqyMF6Yl7+oqMhheUw4rdT27NmTjh07RmfOnLGIeuCqUltdIZsyZQo1aNBA+NuaUmuyiqWmptoumB2ltjpNmza1+paQkZFB+fn5VFBQIFgUrZX/xIkTVpXarl27UnR0dI3yT5o0SXhLs1X+c+fOUUlJibBbOisri65cuSIMrqb/miZ4g8FAa9assVn+Hj16UFJSkqBYmD6mwSsxMbFG+U3ynj9/noqKiui+++4TJk7z8peUlAgWR/PymyxL/v7+NHfuXJo1a5YwyZ89e5bGjRtHAGjFihUUGxtLM2bMqNH+Jp/P4uJi4TtT+wcEBNAvv/xC06ZNo7CwMOFtbvjw4TXK/+CDD9IjjzxCRFUPVFBQEC1dutSi/c37wenTp0mn01FERIQwITz44IMEVFlNz5w5I0zSWVlZwhulqT5NA7np727duln0f9OEDly3MB86dIgAWPgnmQZxvV4vRAYwlR9ADX+jgwcPChZ18/KbJkRzK4e5NWbYsGEWv23atEm4t0mTJuTv709t2rQRrKT//POPxfPftm1b0uv1lJ2dLbxs3nrrrRQZGUkA6IEHHrCw/JjXjclKZB5dwrRz3nwSJyKaNGmSsFJhev7fffddi4gS5mkbDAaaOXMm9ejRg55++mkiqvILi4iIoF27dgmT11tvvVWj/5swlaF3795C++/cuZOaNm1KTzzxBP3nP/+x2u6mZ8Lk12hNqW3Tpo3wTBQXF9OUKVMoLi6OQkJC6LvvvqOWLVuSn5+fhTzm7W8aG99++23y8/OjKVOmCG1YvT9Wr/fqH1P7m/qNSd4mTZoI0S+sjX8FBQXC/QMHDqS2bdsKu6FNUTS2bt1q4eteXQ6dTkf9+/enxMREi+ffFPngxx9/JKKqZ9vkT25aDTHH9GxbG/9M7X/y5EkCQP/73/+oY8eOVKtWLfrggw/ozjvvFF6uzDH1t7Zt29L7779fQ5E2vZTNmTOHfv75Z2FMaN++PTVq1IhCQkJo0KBBQrrDhw+nuLg4Yew3Rfl48cUXqUGDBkIZWrduLRgWLl68SERE27dvJwA0ePBg0uv19Prrr9for0DVapqp7qwpP0RVRpTatWtTaGioMPa0a9eOXnnlFRozZgwZDAYqKCggnU5HAQEBQjuvWrVKSKOoqIj8/f2pXbt2Qv0SEY0YMYJ69+4tvICZK4hEJBhmTM81UdU8EhwcTIMGDbKYF1u0aEGffPIJjR8/nqKjo6l9+/bCb2fPniUAtG3bNqFMTZs2pZdeesmmD+moUaOoXr16dPjwYTp37hxVVlbS2LFjLQxM1ZVaoqpIDOb1bZrHbfmOrl+/nvz8/IR0S0pKyM/Pj55//nmL58B8PDYYDBZK5rx58ygkJKRG2iNGjKBevXrV+N4ZpdbUnitWrLD43jSHvPHGGzXuefzxxy3ajIiE/RGmeaxr167Uo0cPi2uWLVtGAITnpl69ejX2RL377rvUvHlzi+9sld8eTvvUhoSEoGnTpqhfv77gowNU+Z6Ul5cLvnIAkJ2djdTUVLRq1crZbAQCAgIsfDGAKj/C8PBwHDp0yOV0zamsrBR295oTFxeH0NBQzJ8/H4GBgahTp47V8jdq1AjR0dEW5c/Ly8P27duRl5dXo/yzZs3CkCFDLHysqlO3bl0EBATg0qVLiI+PR0xMDMLDw7F//36kpKQgNDQUn3/+OZ599lk0b94ce/bssbqD0ryM9evXx759+7Bnzx7hExAQgGbNmuGff/6xeW+dOnUQFBSEkydPIigoCAMHDrQof0BAANq2bYvKykqL9j9w4ABKSkosdk+b7klNTUVGRgaAql34ly5dsukXZzQaYTQahe9M7Q9U+QHOmzcP/fv3h16vR0FBARYsWGCRRkVFBfbv34+EhAT8+uuvGDZsGH799Vf069dPqJuSkhKLfvDjjz9Cr9dj+PDhNfy1YmJiUL9+fcyePRsDBgxATEwMEhMTUVlZiRYtWmDnzp3Yu3evsGuzU6dOmDVrlnD/wYMHBT9aAEKeR48ehU6ns+gX5eXlwr9Nft7m5Scii3S7d++OoUOH1qhHAHjuueewZ88eLFq0CAAwaNAgNG/eHPXq1cPVq1dRWVmJ1q1bw2AwIDU1VaibCxcuoLKyEqGhoQgICABQ1T/Nn/+wsDBUVlbiyJEjiImJwbFjx7Br1y7BZysuLg5+fn4IDAwEAMHvtG7dunj99dcBAC+//DIAIDY2Fnv27MEPP/wgpP/qq6+CiDBr1iwMGzbM4vmvrKxEfHy8Rb8GqqIY7NmzB/fff7/Qth9//DHeffddrFixwsLH2eRbW51z584hJycHer3eog8OHjwYHTt2xKxZszBu3DjBH/ynn34S8h8zZgwqKythMBisji/Adb/eyMhIGI1GHD9+HFlZWfjoo4/w9NNPo7i4WIiiYaJJkyZCPXbv3h0AcNttt6G8vBy333479u3bBwB45ZVXhPp85ZVXsGTJEiGqwMKFC/F///d/aNiwIaKjowFURT5o164d1qxZY5Ffenq64FdnbfwzlbeiogL79u3D6tWrUbt2bWRnZ+PKlSsArvfxsLAwoY169Ogh5PH111+jfv36wjNhwuQn37BhQ+HZfvbZZxESEoK0tDSLfQDmz3Z1zJ/thg0bok6dOti/fz9OnDgBvV6PkpISZGdnQ6fTITc3V9g3kJqaKpQhISEBYWFh8Pf3txgTTOP70aNHMXPmTAwYMAB6vR5HjhzBqVOnEBYWJjzHpjLcfPPNNWQsLy9HYWGhUIaEhARcvHgR9erVE9qoY8eOCAgIwJIlS1BZWSlE5anOO++8U2Pcqo5Op0NpaSlCQ0Px66+/IjExEXq9Hnq9Hr1790ZFRQW+/vprYY+JaUwwH6fLyspQVlYGIrLo46b9F6a28PPzE/rV+vXr8cUXXwCoekZM6PV66HQ6izGtuLgYJ06cwE033YScnBxcvnzZos1NY6Uplu2GDRtw/Phxi2tMEBFGjx6NP//8E2vXrkWLFi1Qt25dlJeX448//sDAgQNt1lVBQYGwj8OUlmket1bP69evR79+/dCxY0fh+Q0ICMAtt9yCK1euYP/+/cJz0L17d9StWxfdu3fHnj17hP0nQNUcetNNN9VI35bO4gym59bc7958Dnn//fdr3FNUVGSx3wi43gamdrvttttw/Phxi/jCR48eRUJCgjB/2EqnekxiW+W3izMasK0deiYGDhxIrVq1ok2bNtGePXuoT58+1LRpU+FNxhVL7ciRI+mWW26hU6dO0cWLFwVr3AMPPEAvv/yyxb35+fmUkpIi+Nx+/vnnlJKSQmfOnCGiqjeTcePG0bZt2+j06dO0e/duGjZsGBmNRjpw4ICQztdff03JycmUmppK33zzDQUFBdGXX35pUf7mzZvTwoULhXs+/PBD8vPzo8TERJo9ezbdeeedFBQURE2aNLEov8lH7/DhwzXKf/HiRXrwwQepTp06lJKSQv/5z38E3yBb5TdPw1T+YcOGEQAaP348LViwgJ577jnS6XQWb9hEJLgNmL9hE1XtOE1JSRF29v/+++9CGrfeeqvV8i9cuJB0Oh2FhYXR+++/L/jZBgYGUq1atWjHjh10//33C/53iYmJQozC/v37U6tWrWjhwoX08MMPU1xcHDVq1IjGjh1LBoOBYmNj6dVXX6XVq1fTmjVr6O2336aYmBgKCAiggQMHUlRUFJ06dYry8/Pp7bffFqxAb775Js2dO5fuvfdeCgwMpHfffZd0Oh395z//oaFDh9IXX3xB9957L/n7+9N9990n9IOKigrBOrdw4UI6ffo0rVmzhmJiYig4OJhOnDghWD6XL19Oubm51KlTJwoPD6emTZvSokWLaPXq1dSiRQsCqqIf/P333zR8+HAKDg6mqKgowc/MYDDQ+vXrafHixRQTE0PNmjWjmJgYatSoEX3yySek1+spICCAIiMjqV+/frR582ZatGiR4Gv78ssvU0pKCq1atYqio6Opd+/e9MgjjxAAeumll2j69On00EMPkdFopJ07dwr9v1u3blS7dm0KDw+ngIAAql+/Pj366KM0b948wfVl4sSJdPfddwsxZ/v06UNdu3alpk2bCn6ix48fF5a5GzZsSDExMTR69GghhqXRaBR2vT/22GMUERFBN998s2CBj4mJofbt29NNN91ELVu2pEaNGlFCQgI1b96cFixYIFjkT506JbhnTJs2jXr16kXDhw+nH374gcLCwujNN9+s0f+NRqNF/x8yZAjp9Xr64IMPaPny5TR16lQhjutPP/1EW7ZsoUGDBtGLL75In332GT3xxBOUkJBAer2eevToQTExMRQUFEQNGjSg4OBg+uuvvyg9PV349OzZk2JjY+mHH34goMqX2hTLNjMzkz788EMKDAykrl27UsOGDYXlb71eT2+//TatXbuW/P39KSQkhJo0aUL//vsvxcbGUu3atemdd96htWvX0smTJ2nNmjWC64tpBaOiooLatWtHN998M/3666+CJbNz585Ut25dmjJlSo3xb+TIkVSrVi3B/zElJYXmzZtHAQEBNHHiRHrssccEH8l27dpR3759qVOnTjRp0iRq0KABffvtt7R48WJq1KgR1a5dmwICAqh27do0ZcoU+uyzz6h58+ZChI8ffviB+vTpQ6GhoXTixAn65ZdfhFUck9uSaTnUFKM3OTmZwsLCSKfT0eHDh2nKlClkNBrpypUrlJiYaLGcOWfOHDIYDPT6668L1s2XXnqJZs2aJbT/wIEDafDgwbRw4UJ68sknhZiqAQEB9Mwzz1BgYCA9+uijQozozz77jJo0aSL4/N155520cOFCIfbyvHnz6MCBA/T444+Tn5+f4Cr28ssvU7du3Sg8PJwCAwPJaDTSe++9RxEREfTGG2+Qv78/DRkyhBo1akTfffedkF6fPn0IAL3//vvUpEkT6tOnD0VFRdF9991HX375JW3ZsoUee+wxwQXomWeeEfresmXL6LXXXqMff/xR6HstWrSgunXr0unTp+nEiRPUr18/mjhxIq1evZoef/xxweJ91113kb+/P/3555/Ut29fCg4Opk8//ZQSEhKE1aqePXtSp06dhFio7777Lp06dYr27dsnrCaNHj2aJkyYQBMnTiSj0Sj4/SYkJNDIkSOpfv369Nlnn1FgYCDFxcWRTqejoUOH0oYNG2jLli00cOBACg0NpW7dupG/vz+lpKTQ999/TyEhIVRYWEgvv/wyAVVRRI4ePUrJycnUu3dvatCggbA8/fjjj1Pr1q0pJSWFOnToQI899hilpKTQwYMH6bnnnqOIiAiaNm0a/fDDD7R9+3b6888/6c4776RGjRrRlStXKD09nVJSUoTIK/Pnz6dZs2ZRt27dKDo6WliFMrmwmGKwE1W5PaxatYp++eUXCgwMpLvuuov8/Pzo008/pfT0dMrOzhb6z4wZM+jYsWP09ddfk8FgoD59+gi6xRNPPCFYhE2rw6tWraITJ07QoUOH6NNPPyU/Pz/6/vvvhbyzs7MpJSWF/v77bwJA8+bNo5SUFEpPTyciouPHj9OkSZNo9+7ddOrUKVq8eDE1btyYunbtKqSxf/9+iomJoccff9xiXDN385w1axb5+fnRtGnT6MSJE7R582bq2LGjoBcQEaWlpVFYWBiNHj2aUlNTaenSpRQbG0vvvfeecM3QoUOpbt26tHTpUjp16hQtXLiQoqOjaezYsRa6SLdu3WxGlrCFpEqtKaSLKQxM7969RYX0Mqe6UpuamkqdO3cWlpRMIW2WLVtGdevWtXAgthUYeejQoUREdPXqVbr//vupTp06FBAQQAkJCTRgwIAaG4SeeOIJioqKEgIzm8IXmZcfAM2aNUu4p7Kykl555RXBt1Cv19Ptt99eo/z+/v7UpUsXq+W/ePEiNWjQQNig1aNHD1qwYIHd8r/99ttCGrbKn5CQUEOhJaoK22UwGCzCjpnKaS0d841y1ctPVLV8YL6s6O/vT/3796cjR45QWloaNWvWTNgoZDpEwGAw0P33309paWm0fPlyqlOnjrAcYzrc4eabb7ZYPjUpAjqdjgYMGCCEMbJV/rp169K///5rM0i60Wi06Acm38LQ0FCKiooio9FIDRs2pGbNmlHv3r2FJZ6YmBiqqKiwG5Ab15ZB165dK4Qhs/YxGAzUvXt3i8MfbrzxRoqKiqKmTZsKIZKqb3AzfUw+utY+iYmJtHPnzhr93+TzGhISQsOGDRMOX/jiiy+EDTrWPqbl77CwMAoODqa2bdvS448/XuPABlxbUmvZsiX9/PPPFu4s5m1hMBiocePG9Nprr9EHH3wg1IFJOatbt67Q/+vVq0c33nijcMhD+/btadq0aXbbwdT/TS8C9j6NGze28AU2vXg0aNCAmjVrZnEASfXPwYMHLfovAGratKnQP03h46p/nnzySaqoqLD53JkOWQGqFHzTcnVUVJTF+GcKk1T9ExISQlOmTKnR/qGhoRQeHi5sFDMtWd56661268jka+7n5ydseHNUr+afwMBAatmypRAi0Gg0Woy9pjHE9BLZoEEDof0HDBhAOTk5woueqfzWAstXb/8HH3zQ4tAR06Ybo9FISUlJtGnTJrp69SoNHz7cYkNq9Y/JbSokJIRq1apFd911F61cuZJatWpl0W90Oh3dcMMNtHPnTqHvt2vXjuLi4izkcPQx36hq6xqTy5W9T+vWrWscjGAwGKhTp06Cf+fgwYOFujY/QCU2NpaefPJJys7OJgDCS0xMTAzdfvvt1KtXL4qNjRVcbkwbyEJCQigtLY2uXr1Kzz//vNVDDUxtYTIkVP889thjRESUlJREXbp0oZtuuolCQkIoJiaGBgwYICiWOTk5FhvwzD+mFw5rny5dugj7TKwd0AGAbr31Vgu3gEcffVSYx028+eab1LRpU5suPqYNzzNnzqSmTZtSYGAgtWvXjhYtWmShW3Tr1o2GDh1K586dI39/f3rhhReE62vVqkVJSUk0b948i7xtHT40YcIEIqpSNLt27SrMZ02bNqVXX31VcGmzV3ZzfYyoKoRXq1athFB8gwcPpnPnzllcs3XrVurUqRMZjUZq3Lgxvf/++xauiXl5efTiiy8Ke3oaN25Mb775poVbj6n85j7jYnBKqVUTlZWVdMsttwgxTX0NLr/3l3/ZsmXUsmVLqyf2+UL5Dxw4QLGxsULcUnN8ofxjx46lkSNHWv3NF8rv6/1/2rRpdPfdd9f43hfKTlRl5ImKiqKTJ08qLYoi2Hv+fQFXy+92nFql0Ol0mDFjhoXPoS/B5ff+8hcWFmLWrFkWvqsmfKH86enp+PnnnxEREVHjN18of2xsrHCiXXV8ofy+3v/9/f0tTlgy4QtlB4DTp09j2rRpwklovoa9598XcLX8OiIzr2yGYRiGYRiG0SCatdQyDMMwDMMwjAlWahmGYRiGYRjNw0otwzAMwzAMo3lYqWUYhmEYhmE0Dyu1DMMwDMMwjOZhpZZhGIZhGIbRPKzUMgzDMAzDMJqHlVqGYRiGYRhG87BSyzAMwzAMw2ie/wdA1ap4OT0eqgAAAABJRU5ErkJggg==\n" - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "Point(139.0794379 36.3727776) 0.0\n", + "Point(139.1051411 36.3963822) 0.0\n", + "Point(139.0960211 36.4047323) 110.0\n", + "Point(139.0428727 36.3816035) 77.0\n", + "Point(138.9955116 36.33801589999999) 200.0\n", + " ... \n", + "Point(139.9074816 36.4445767) 0.0\n", + "Point(140.0934838 36.4673588) 137.0\n", + "Point(139.7422865 36.2305774) 0.0\n", + "Point(139.7151723 36.822353) 0.0\n", + "Point(140.1510903 36.6598314) 165.0\n", + "Length: 1764, dtype: float64" ] - }, - { - "cell_type": "markdown", - "source": [ - "__Observation:__ We can notice that the maximum values of every sensor are no more than the 250 value.\n", - "\n", - "We will now check for the minimum values recorded by the sensors." - ], - "metadata": { - "id": "opYvY-uQb1fR" - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Step 3.4.3: Finding the minimum values recorded by each sensor" - ], - "metadata": { - "id": "JGSmOe8EcI8l" - } - }, + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "source": [ + "maxValueInEachColumn.plot() #point the maximum values recorded by each sensor." + ], + "metadata": { + "id": "Wmvv1z6mZwA2", + "outputId": "9c082f58-7ea1-4773-c536-b183b7912c1a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + } + }, + "execution_count": 9, + "outputs": [ { - "cell_type": "code", - "source": [ - "minValueInEachColumn = dataset.min() #Reading the minimum PM2.5 value recorded by each sensor\n", - "minValueInEachColumn.plot() #ploting them\n", - "\n", - "#dataset.min().plot() #memory efficient approach" - ], - "metadata": { - "id": "zwbbXYqCZEo2", - "outputId": "a81b1ccb-60cc-4e40-8a2f-5dca3825ed93", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - } - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 11 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 9 }, { - "cell_type": "markdown", - "source": [ - "__Observation:__ We can observe that many sensors have recorded negative PM2.5 values. Thus, we replace the negative PM2.5 values of each sensor with Zero" - ], - "metadata": { - "id": "buCv95fkc2sO" - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Step 3.4.5: Replacing the values less than zero to 0" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "OYlUYzY1dTjW" - } - }, + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ we can see many sensors have recorded high PM2.5 values greater than 250. Such values are generally outliers/abnormalities and are not useful for the analysis." + ], + "metadata": { + "id": "tBrwAabsaJmE" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.4.2: Replacing the values greater than 250 to zero." + ], + "metadata": { + "id": "6ZAm2FtlaxQT" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.where(dataset <= 250, 0, inplace=True)\n", + "dataset.max().plot()" + ], + "metadata": { + "id": "7Vx27DZ_bG8L", + "outputId": "5c90c23b-482d-4b00-e2d9-705699d011f5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + } + }, + "execution_count": 10, + "outputs": [ { - "cell_type": "code", - "source": [ - "dataset.where(dataset > 0, 0, inplace=True)\n", - "dataset.min().plot()" - ], - "metadata": { - "id": "Q0DaB2g_daf8", - "outputId": "d62c98c2-cdf6-4db3-904f-8f02893ffe2f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - } - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 12 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 10 }, { - "cell_type": "markdown", - "source": [ - "__Observation:__ The abnormal values were replaced to 0." + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "U1O0xBeJd8Sl" - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Step 3.5: Create a dataframe of the sensors having pm25 >= 15\n", - "(useful to prune those sensors that do not record any pm2.5 value)" - ], - "metadata": { - "id": "AyJCyA6n97Oq" - } - }, + "image/png": 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6f70Zj8zY5lY6yrofKIvTPrUV7H7AMIx2YaWW8SjuWjuVnAQldz/Q2Ixur+3kKMu8XWcBAHvP5dq8Ruo2kRo1tbGYlQ1rIb20zIfLj6Dvl5uk3f0vY5eTOnya2Ofjtd/34aFvt0qatydYvOcCun2yDkcy8pQWhVEJ7FPLMCKxpdSxpdcxctWQ2l1C1C5fdSqsuB9IpWZZfU5kfnZMRzP/8e95PNG5gax5SQ0Rua3kWrt9wa6zMPpb2rPm7z7rVj5KYZL7v/P3okFUsMLSMGqAlVpGlRBZH5BlcT+QIU3GEl9V/JUodnlFJY5fLEBzF050s+Z+4KgIWmhbT8ffdRXz8c3WGGiPi9cibMSFB1r9/VJBCcb+sc91AVVKmUbal5Efdj9gVIPad+raWsrjE5fU33ZKocRq/pgFe9Hni034YdMpp+/1eJxaD3UcDejdACyrw9nwbxWVhFveX41OH6xBcZn1zZWFJd55CIOexx/mGqzUMppF6+OY1pam7aEVpcHzkMctmUv2XgAATN/gfLB+axZN6dwPxH4pPVLmYuvlNiXtCu7+fAM2Hr1Y47d1qVm4+/MN2HcuR3Q+zspcUn5dkb1UUGL1GrX7oLuKnt+qmWuwUsuoEjEDumQTlZsJaWH5VWnm75LHZ0/tVe+KfFcKS/H1mmNWj0qVG3NDrafrVu1t6YgnZu7EsawCDPlxZ40xYdisXTiWVYCnf062m4a50qnkQR22WPjvOaVFsAqvljEmWKllPIoKx2mXOJbpeyebudN2W09kSyeIhqgkcrreXv5tLz775ygGTZdgN7rKnzdPieeJF88CEUv75tZUa5jrZlKIXF3ZE6v7ZeRZP9BlzIK9yLLxm5Kw+wFjgpVaRrMoOY49+v12BXNXH3IYSmTdde8hXMl6y/FLAIDMPOtLyGJxt/7EtqnYMjq6zJuMbbYsh4H+Bg9L4hql5ZU4e9n6SkFecZmHpXGMN/Udxj1YqWVUic3DF2QYvVzxbb1UUCr821WZvMVqDXhXWaSEoJyxlAC3NFtPtKmv6SLOKLVKux9svvZyZYuKSsLw2bs8JI192KeWMcFKLaMoZ7IL8f3GkygqLRe1iUGN/qtqlIlRB4p3DaXzN8Pac+I59wMPZSTkZz1Do5/9KVdy9wP3k7DJ9pPZWHskS8YcxMNKLWOC49QyinL3lI0oLa/E+ZyreOTWROF7MeN5Vn4JKioJBnaosqCkvAJGP20sc3oLto/JVU6rdOapyC0qg5/BNf9LW2TmFSMm1Ai9yOdT8RcAkbhTL0EB9p9LWxvFXH1xllPXK1VRbFieAhgTbKllFKW0vGpg3H5S3Eai6kv9T/2kjuUvNVEhU6xRe6kqZShRvSJE6rfkF5WWo92kVbhxwkoLJdwdsdcdyUKnD9bguTn2d/t7CrWEzwt04mVTDoklfU7VUaUA2FLLXIeVWkY1uBJDcV1qzZiQziKFzpGSdgUPTt+KPWdz3E9Mg6hcb5MdW+VX2i/SHFuinL5kfUPQkr0XMHOz4wMcrCX73caqGLkrD2bavc4cOfUSTzeDLT/76tZwe5jL7Kr8ctSpirq0ACu1jAlWahlVUH2gVOPAaY9B07di95kr0oRhchN36664rAL/23ba5u5nRjz2Nor9vS8dO0SuUMhFSXkF/rf9jPB39RfLd5cecildtT2/coljywovhbuAHBZ+b43n6qXFYlyAfWoZRgJMK/5yLf17ki/XHMP09ScQYDiMf8Z0VVoct/FEi9j0qbWT+ai5/wIATn/YTwaJxPHt+pP4dWea8Lenl+nVpvyKwR39yVF5bW0Uk6qavFX3Y0stY4IttYxHUeMcpljIJZkydnd8Nx2UoKaNIFrFlcMXpEJMP9hxynOWYkf1oEUFV2qZpT5RzFuPxa2OnjUZ5hrcFRhVUN1CpJaNHe8uPYQJiw9U/aHQ/FBUWo7hs3dh/q40xxdDgolW4iVVpVFyWpejxvady8HgH7bjwPlcj+R/LKtAglQc8/rC/ThxUZ68Pl2ZKku6UmOxUc/8exVGP1ATbKllTLBSy6gGMePSqoMZkua5dN8FHEnPE/6evysNV0urjrIsLCnHzM2n8NO2M7iY797pTu4wa8tprD2Shdf+2O+R/FyZPi8Xljq+yEmkmqcUVcVlyPz+aVux5Xg2/u+7bW6LUr2OXdGdpHzZGfnzbsnSMqdcpFvQuiNZ2H36suh0pW5eKTaH+SLe6ivMOA/71DKqwNEAXlhSjlOXCjFnhzhrpRi2n8zG6LkpFt+99sd+7D2Xiw/ub4MKM6GU9JXNvaq+Yymrc7XM/pn2ruANkzpd+5+rnL1chMSoYIvvTH2xqNRxnTs71ctZ52LqQUyZ5CIjtxjDrp2QJZefs6M6MB9nzF8WJPOp9VLdj+PUMibYUsuoEvPJNSuvGDdOWIn+X2+WNA9zC605/xzKrCGDHKjFxYKRD3f7UGZesTSCqBR7z4Cnnw9X6lpql5wHv70ePUWWOLVOvuZo5cWS3Q8YE6zUMvhl+xn859cUlKt0Y9AKiV0OHGFtIPelMVMrE5laSUm7IvybSMwGKXuKnTvoHMeGrabkHLLxoucLiH3GxSx1u9puxWXXx2DLE8VcS0/OYUtNL+XWLLUl5RUYNedfLNh91vMCMYrBSi2D8YsOYMneC/h7f7rsedkanNUzPFrH3qSidtmlwtPl1NKLhLlyeP+069Y2MTvY5XuJUFfPVPvLkiuRAqQq0qKU87hUYOm37059CfdWe4i09Ew5g7UXjfm7zuLv/ekY+/s+BSRilIJ9ahmBvOJypUXwKdQ6yavJAqMV3KmzCiLoPRCjQa7oFd7SW6RU+JxN6qX5e9AoOsTiO4voBwrVslbGAmuW2pwi9e9FYKSHLbWMgKKhj4hs5q8G44K9CU8N8jHaRb6jdB33TG+13LmL2BcAZ5vO3vWnLhVa/F3pxuZUVw4D0TLsU8uYYKWWEVB6XLCMy6iYGNdRgwxgpVksr/2+z2qUCk/2per+e2KeKVX0dYWw69ajYL3Yy1tUm0onSlV6KvSpVROs1DIm2P2AYWrg3AzibTqJVpWs+bvP4s7mMYrkbfLHrO6/J2ajmD1LrVbbQotYHFEr8h45l+fls+C7hxqlYp2WMcGWWkbAE0cq2poECL5jVTAh1+TgarpEhPKKStub+VQ4m1VfJs4rrulH56kJz1WfVQVDIMuGFosk9RG17iKFCDUO13CyZcorlK8HMbClljHBllpGHVD1P82+4AHLIzz6/XYcyyxARLC/0qJIiif0EwJZPQ1Lp3OsSMipQFmcUOWBPKS4TiksLLVulsnWiOVMHShdXSeyCjBuoWdOMXQXPnyBMcFKLSPgCd3RnjXYZv4eng2txqm1c723jKfbT1YdD5otw5G3vsDqw1k1vhPTdUmm8ND8LmgJEYk+TtVdtwIpRiwp4tRWx5l0fth8SppMJcbaiggfk8uYYPcDxqPYPUFIadNENcRObCoTWzNk5hVjy/FLoq7NyBV/rRLYe1lz1K8rFOz4SisDaor/7IqlVk4kcT9wPwmbqKGOGKY6rNQyAoqG9Kr+t/kXDiZeIpItBqerEJHDkDxqk9nTdPpgDQb/sAMbjl50eG3nydavVXsNitEZ7W8Uc9diqJ4aUloWR1Vp/mJiN/qBRPI4wrztXa07uV5a3Ak3xjBywkotgMmTJ+OWW25BWFgYYmNjcd999yE1NdXimuLiYowaNQq1a9dGaGgoBg0ahMzMTItr0tLS0K9fPwQHByM2Nhavvvoqysv5QAOxuDr+3jdtKx6YvlVVSuKIn3aj55QNKC1X59HDLiFT9W49Yd0Ca836aetatSKmS6phUxJTPfqBPHFqncHZpEW5urgkiSXfbzyJdhNX4UhGvgSpSYOaxn5GWVipBbBhwwaMGjUK27dvxz///IOysjL06tULhYXXg2H/97//xV9//YXffvsNGzZswIULF/DAAw8Iv1dUVKBfv34oLS3F1q1b8dNPP2H27Nl4++23lSiSSyi5Ell9UHJmiNp7NgcpaTnIu6qeF4i1R7Jw8mIh9pzNUVoUSfl522l8veaYtInaPDrZiu+cS0eZKmwhdPS7h8Rz1ldcdLoqsgbbwxkppWwT881WztSVxSY/FVXxb8nnkF9Sjk9Wpjq+mGE8DG8UA7BixQqLv2fPno3Y2FgkJyeja9euyM3NxcyZMzF37lzcddddAIBZs2ahZcuW2L59Ozp37oxVq1bh0KFDWL16NeLi4tC+fXu8++67eO211/DOO+8gICBAiaJZsPv0Zeh0OoQYDWgRH17jd0+E9NICVucPGapG9DylkmZZeTADby8+CAB4rFN91A41upTOgl1n8cDNdaUUTXL2ns1BbLgRCRFBbqXjrvuBlJRXVmLL8Uu4uX4tBAUYPJKnOWpSzKxh3lRSivrrzjSX7pOiX1TvfkpZNCsqCTtOZaNN3QiEBboXXUXt/YhRFrbUWiE3NxcAEBUVBQBITk5GWVkZevbsKVzTokUL1K9fH9u2bQMAbNu2DW3atEFcXJxwTe/evZGXl4eDBw9azaekpAR5eXkWH7nYfOwSHvx2GwZN34o+X2zCFQ3tcBer08ltMVJMt1TJID72j+sHC7gj0tg/9uErc2uvjYqV6iXL2XRSM/IxcOoWJE1e63beVYcvOArp5XY2VtHBUgH4eEUqBv+wA6Pn/itPhi5gr+hSK2DOpGfvWlfcFFzBE+HYPMVPW0/jse934OHvtsuWh1ZWDBh5YaW2GpWVlXjppZdw2223oXXr1gCAjIwMBAQEIDIy0uLauLg4ZGRkCNeYK7Sm302/WWPy5MmIiIgQPomJiRKX5jobj1lusEnPLa55kSe0NptLzdX+9sDrOA+ByjHX3Holc0M4O9ntOXtFJkms46lNN3+mnAcArDlyPfQYR0K6jnlduNskUgxf7lhqTbfWOHxBoUFvYco5AMChdHkMN+WVhJUHMx1fyHg9rNRWY9SoUThw4ADmzZsne17jxo1Dbm6u8Dl79qzjmxjZMSnUqlnmclLx8KVNE9WL6mzRT18qxITFB3DuSpFbcthSDqsOX7CPXO4HWfklmLDE+iqRe7imCSvdKx3nb3lO7rYT2Xhv6SEUl1XYTtPJQqlpY5knkXtIOnRBvlVORluwT60Zo0ePxtKlS7Fx40bUq1dP+D4+Ph6lpaXIycmxsNZmZmYiPj5euGbnzp0W6ZmiI5iuqY7RaITR6JpfolSYK0COpqqs/GJcLiy16o8rGruZyG82OpNdCB10qF87WHFXVS3rnlqW3ZyHZ2xDZl4Jdpy6jBUvdXU5HXeOFrZnFXS3mv85JIf1qtqmTi/pC9XdCh79vmqpPCo0AM/f2dTltFzFcqOYc5XMFnhLikrLcTg9HzclRkLPx495NWypRdWAMXr0aPz5559Yu3YtGjVqZPF7hw4d4O/vjzVr1gjfpaamIi0tDUlJSQCApKQk7N+/H1lZ15f2/vnnH4SHh6NVq1aeKYgLOLPMduv7a9Dni004ebHA9QztTv7k6DK36fbJenT9ZJ13hdpiBJydzDPzSgBA0fBESob04un9OhYbxcyaJC3bthVfzpaTZqOY97WwKxt5H/t+BwZN34o5Lm7aY7QDK7Wocjn45ZdfMHfuXISFhSEjIwMZGRm4evUqACAiIgIjRozAmDFjsG7dOiQnJ2PYsGFISkpC586dAQC9evVCq1at8MQTT2Dv3r1YuXIlxo8fj1GjRilujbWH+cApNlC3p8NUiVVUnJkDrpbaWVIUn4xDlLCYeInhzCqO6lNtVkOdzrFM9qxwaiuPO1grpyddZZzJynJclDcvm2nY+LdTeJ9O6xKmOeu33ezi5+2wUgtg+vTpyM3NxZ133omEhAThM3/+fOGaKVOmoH///hg0aBC6du2K+Ph4LFy4UPjdYDBg6dKlMBgMSEpKwuOPP44hQ4Zg0qRJShRJNGoJ/F61mUe9I7A9i4eUVt/Jyw/jsEybKRj5sKX4uOt+4MvIVS2VlYTP/zmKzccsD/Iwf6m3zLt645qfPCZf4zk7NouRRanhXu581TtzMJ6GfWohbjAIDAzE1KlTMXXqVJvXNGjQAMuWLZNSNNkxL/qFnKvIyitGbHigonJY+1sJxIpQ7qpWYuW27zacxHcbTuL0h/1cS9NDSBk+R+uWcYc4KKA95UXu8sh1jKoaMfXZxXvPCyHlbD1n5k0iZRW5egCEq+Oh77SuOLg+vB+21Po45hPq5/8cxa0frLFztbz40PzqFM76xanhhUAuHNWEtbJ7oj5s5SHq8AU7hn7NtqW1dpAxO2fGjrOXr1r93ty4IfqYXAnksZm2DI3vrbFcfenljLEPK7U+jpqWPi3GcBfkUlFRJMVbJyIleHD6VpRXeG6TIJHj9lOLC5B41KdAiKlCIXark+m5UlrJm1RrXURGfClkIeM8rNT6CLYGAqknVCLCD5tOYn1qlvXfbd5nO02xlsqv1hyTxL/VU2MmK6v2ccX4Yu0e8+92n7mCzccv1bzITdwxFGkv+oF2++2eszn4eu1xq79ZvFPbcT+wCP2l0riz1g5fyCsuc0sed9Buj2G0Biu1Pg5Z0QFdeRMuKa/AiYsF2HHqMt77+zCenLXL6TTcXUGavfU0Zm055V4iNtDU6pYnZhCFZqkaJySJiJla/buKa8sTx7PcCE2Hqj7vCFHuBz4y46vhoIL7pm5BqQhLfVb+9RMXlQqLZXlMrohNYFa+M5d9zPw92nVnkQpNDeSMK7BS6yPY8jmSykr08Hfb0eOzDfjftjOSpOeqFTM1U7p4o2pZ5lIy1qSSectZ/SsPZqDn5xvcyvex73c4vIZIw36xNvFOxcC8nQZ8s0XkTc5m4sSlbnQca8P96sPWV888gdxjqXf2SMYVWKn1caRSak1xAP/eny5Jei/8moL//JoiSVrW2HEq2+ZvtgZgX9yMoDUXiT9Tzou6bu4O94OwJ5+54nYajpC7/l3r0hJGvpAoKTHlcDmCgAoee1uyZ+QW454vN2FetUMFfk8+h75fbsKFXMtNcdp6msWjhjZi1AErtT5OYUnNJVQ1WJY2HbuEJXsvIKeo1KkBS6xl8en/JTtvZJHc/1jS5LwOq/6xDtp3x6nLDtN1VO9ST5De3szW6nPnaSvtIKf/qVnaU9cdt3sKmJ1UrH5rrzvI+dIhJuUPlh3GofQ8vL5wv8X3X6w+hsPpeTjjUj1Ij5QGAW9/nhj3YKXWR7ClkH219pjseZ+7UoTyikpcyLlqcyOXLfm8c/lWXnKvluFKYamseSjVJFqzHItBLW4uSiCHhe2Tlam4b1pN9wFX+45SKzSWPrXWKTI7GVHN3UgtfZwNut4PH77g4xw4nytr+huOXsTQH3fC6KdHiQuRCWSNbenktVpxP+j6yToAwJF3+yDQ36CwNI5Ry4RnQmpx1FY+S5Tt0/aqxp0XmMsyvtSZ15gt+W3JrvRLmbr7ouso6fvPqAu21PoIthSyEGPN95r84nL837fbMFuCSAL/23YaAEQptLaGJafcD2Q6/UeLU0FmXrHjixyg9snCS+fo63hR+ZRW6Byhtr5kXl/eqowyjNSwUuvjhFpRamduPomdpy/jnb8OeUwONQ3Z1mSZuyMN+87lOJ2WfX889aM2RcQVJdvZEmjEIK8YlwpK8cOmk8gpct8aKlX/UmqjmM0TxaR4GZTh0VPX08ww0sNKrY/TJCa0xneFpY7jb3oKIlLMVmg+CX7+z1H8LDJcmVqsKnJaWVVSRJfwtOiO8qv+u73rpVAkzXFVgX/v78P47/w9ANTx4iNFf5RVQXUBOVaKpFi9cZcdJ21HnhGDtbYW24/5hdX7YaXWx7khrqZS+6OEBxiInWy0rCQx8sATUE3yi8uVFkFgXepFp663qox4UGGUaogxd+WyucFVCUVfRJZiYivLzensQqVFYLwYVmqZGmhZwVSDHiRekRd3oa8qd2IsMlroqlp+nuTGrvJnaxMWEcbM34PJyw5LK4uN/CT103fq8AXH92lxbODngZETVmoZWXFm/FJbdAE1LKsC2g4ar0ZS0uQ/NEEq5O6BWuwiqZn5WJhyHt9tPOnUfa66BSnlF19BhHk703A8S7pTEtWAOkZVxlthpdZHsBcHVg0QyKqMBPmiH9gsupt1opIqlRW1KPyuMG39CY/m56iu1PIMyo21YrpS9rJyeSpMbX369+RzeH3hfvT8fKPqZHMWtfRxLb7EMc7BSi0jK1JsmlJsQFTLQOyjI7H1E8V8AJX0O7GoRWFxhFRimo9pcpZdzIqCTzwP1bCm4KttlY9RDlZqfQS1P/RKTIxy1YhYRV4juoDsaEUpUoJvN5zAi/NSUFkpTyUpPSwonb85HvGpdeZaB+EPtp/MxqpDmWaXaONBslXP5RWVGDXnX8zcLN1GZcb3YKWWcZo//j2H8zlXPZafU+4HKrNdqGnS9lmUnuvdyH/TsUtYvOcCNhxzLtKAGrHqXiRin9ieszlYe6RKebuYX4Lfks+6mL9Lt9k1CCipSD4yY7tiecvBioMZ+Ht/Ot5d6nx8dB5mGROs1Po4rgzKW45no/sn60Wm7x5yWvGcjR/qSrpKWiHlVKgVK5dXviU4rsxiFcWONuGpprhv6hYMn70bZy8X4aFvt4qOF+0sNi21MuRVWl7p8JRFCzcHGWTwJGLmmcIS+cPVqX3FknEfVmp9BKkPBCitcHzsrTMQ1DNwq0UONaA61wBXBPLAPGbfmuccnvQZVHplw5m6OZ9zFaezi2STRSxk8w+R9xOh8+Q1zuXjRchh3WZdlTHBSi3jMl+uPubwGjUfvuBoHHRVJvP77B6x6a2zltrwQD3LfYqcmidtsSV3t4bcrgJXBZC47kvKK3G50PHJcGLi1Nq63ptx50QxxvthpdZHcNfSc6mgpMZ3U1YfdStNsThjTfLGwc3VIvnCUpsWJnIxMqZlF2H5/nTVHLEsBil6l5p6qC0Lor3xx2krvIbaVy64Chg58VNaAEZZxA4wA7/Z4lr6Lt1lfr/nR0B3Jx6pZeY5wAyNKepixCUCun6yDgAwffDN1tORUijzdBWuTnt9u/pj6O5LmqvPZY1T7Dz0QGolmoEY5K4zselra/RgXIEttYwo5I92oL4BXO5JRcuTlnYllwdbCpezk/mu01dsLK9qfzp2V7GRuwpckc/WPVIocRbuB/zE2UVs3+Ba9H5YqfUR5Fr2cpSuM/lKIaLVgP0eVgikrmol1RmeBNxHCoVEjSqtFM+VM+ODtdw8Mb4oVffe+uzJUS6lNzwy6oGVWkYVyBq6y8XEte77pZVh3lY1W5uoPFGmo5kFTt9jq4+p3cAqhXye8hNVqi5r5mseasu5dnf18AVRG8WcSNvbEPviqPLHkZEAVmp9BJvLo26mK/t8RuCRyEXsKQHlEodkkwOlllylPNGIyPEzIqaUaleOxSBHqDJnxh/Xgx84L6M046J3qKnlFZWyl0TrBghGOlip9RFkcz+QJVXPYEt2cvC77ALIzPGsArSbuAofrTjichrq2cXtWA5PKMdyu7joRab/o8aOGLXXj2q2m7x1LKVPrRRY+tSqH2vPwLYT2Wg2fjmOZ5mtfqhm7GC8EVZqGVVAZv/vHmozaalNHuCNhftRWFqB6etPKC2KXay6H6ivOu2i07ly+IK1hMTdO8nJI0bd8UX0RFOYK73W2t655Xxpoh94Cq2pftbq96X5Kais9rXWysVoCw7p5eu4G76K7PsHqOGl3JYVTa65Sg1lBmwrLGmXxZ/KpB6LrCVXCkux/3xujQnTGlX1oEw5VFp9ymClLhxVjyfrz3ac2mrXuWlBdc0iLGJFwoc7m+iQXhp7KWach5VaRjVYU1CcHaarD1qVleTGRjG5Q3qJQ+qBuLxS/f60jrjnq01Izy0Wda0z7gdKKAZislTzXCxnjVWaW2qt5e3gpVoSJHwAnfIB9mEl1R7WqoVrijHBSq2PIJfPn0NLi8jh5nJhKXpN2Wj1N1cl/+/8PUg+cwV1IgNtyCYPlmV2Pxep57YKMeZNByg135qsz2IVWmeRoGpqIIVyIpvPrhvJSiGSw6OqLfJz9/AFB7/LZEGtKYe4eyxGES/S2uSM7cswrNT6CPLFqXXvdzn5M+U8AOeW24HrMnvrIFsuh+bmJUih8LuLtWdVzZZasVirWXu1XT1yhFVLrZsyiaGG+4EH8gS0N/648tJx9nIRpq0/gafuaCSDRIwvwhvFfByNjZsOcWZY9YxPrXLqiK05plIFipuryB3JoFIGTUJryolYyirEFWzr8UsoczGEnHl7u2sZdvnwBYkfYbFyaM39wBV5h8/ehV93puH/vt3mEX9XPqTB+2GllnEL2Y+S1da47hRyb26wdVuFM6cwuZa1U6ipjV1Vat2ZKsUoA2rd4CLGsv3YDzvw6cpU6z+6udIjZd+xlZQ9RcjZ/MlOPmLlkep6pTDv78euhfrKLiyVLE3Gt2GlllE9nvYHdldRl3p4lXq81vI+MbktLa4aseWeUmUrt5uCl1dWiuqfP2077XY/drcOpHoB95j+ZB5lgXU2AaUOZWG0ASu1PoI7iuHiPedt/qZmn1rGOs5Yan2N6pbH3KIyhSSxRK2WWrGEBNjYvuGgXO521bBA8dtGPHXUsVirotae0s3HL2Hy8sMOTyvUWrkYbcFKrY9gayAVM76+OG+PtMLIiDMTkMOd165a7SRWGqWeVNWwGcqTiPX9BGq2nbOHGUiBNWntdYGM3GKcuFhg5wrlCQowoKDEyguCnaYhVAvpZfXwBWdiZIm/1Byl3ifM+6IWrJPrUy/iuw0n8dof+132oZab8zlXsXx/Onafvqy0KIxMsFLLyIr7S/nqH8xdRXZ/ZCvf5RWrw/IoBmtKjNwWy+oKf2pmnqj77InlcDXDzQw6T16DHp9tQHZBiZiUquXtmecrJMDPZsg+e3jy6bfpU2un7m2Gp3IhH1evUxt//HsOby8+6NE8xdoRzudcxXNz/lX9aYqM67BSy7iFN65ku1smy9ia7qUlNVl5zik+vhZTUq2uGWL8SU9nOxe6DvBcOwYFGFzyV3Z4TK78hlorebqRkjObNM0u/TftipRJy86vO9Ns/iaHnN5s/GCcg5VaRlakGMCcC9Ml/mqHwdi9cKA0+qnvkbdVz0pM0mqYcFX2HmSXjNxiUdpigIv9zuIF0ZWacUbptXGtvf0IYo/WFZOPPf47f6/zN3kpalLeGfWhvhmO8Sju+n86mrB9cfwRH4dSXjms4W/Q9iMvt8Intb/xwQt5Tqdp1af2WsHt9RklVgVG/LRb1vQlDdmlscHIG1+qGUZutD3DMYoj90QhZ/pybRRTC1qR35YFTgklTWyc2oMXckVddz7nKj5YdtjuNdWztCaCmKpw7ZhX5+8x53iWexvU7PqeEllcIH9/EBf9wPwqV3xqxeqqWnl+nUWOYjlbV2pzC2Okg5VahlE5UsYotb3U76w1UbqpSU0WKbExfDcevSQ6zdWHs1yU5jqmJXA11ZUncLgS5MXV4fzhC15cGQ7w3ZIz1WGl1kfw9AEGSqXvTDFtpe12mSSuFEkVSI2P/nJbWKxZaovLKvDl6mM4dOF6JIRAf88OnWLK7UrdqF0RqpTQUuuqgmz3RDGnZXCiztXdNC7Dp38xcsJKrY9gM06tTOlKloGTSKnzqE1hVyZP35qArEU/+GrNMUxZfRT3fLVJ+C7I3+BJsYR+LbU+IEV6cirGFtEPrDzdSvj0e0onU/sLh1JIUyvsf+CtsFLLqBo53+rlGtaknoxcdT+wJofWrSTOiu+0W4WV6/eey6nxXaCESq2Y/iJsFJMsV8t01YqU5XW169uPU+vCa6KP+9Tawr1IaT5WWYxNxJ8hyDBW8ERYLGcm3sPp+ej75SaM7d3c9QyviezqQPnA9K2u5+1BmsWFCv92XlmUWBiZcFZOawchlVs5kUxKpVYcMrkPKdyO9p4xIs/Kp7YurTZ5tMTAqVtw7rLzcZsZ7cNKLSMrbh9k4OT9O68dfzhs9i7HabsikAhOXiyUKWX3Ma9Po5+nFTP3cday6GwbW/OptRaSy9M+tSaktkh5THFyMSMpN4q5Wla7MWdtfH8x3/ohJ9UCOtjF6bbWuBYs5arB3rM5HsuLURfsfuDjeFrp1BKy+9R6c+U5gZqqwZoCW27lu+rxft2aJEWUX65JWE11bxW3xyfxCdg+fKHadR7SHtXeNAyjRlipZdyDR17VYm2S9lY3A1s4++Ig1lJbMx+nsrHgoxVHHF4jbBQTcY1zKNvAjt2XzP9txUf82n/FKP2uvkTajX7gQpJi5dD6s2cL9w0ptvsBw7BSy6gaJQ9f8EbMFQPLfzubjnQ4Y4V0NjSd8+4HNb8rs+ZoKyF7z4k7yEGtyPmMij0MQ5IoDh7SIn1dAZPF0u3rlcoIsFLLuIUnQup42v/JVCYp5jgpzoCXsvxqtP6oSSaxltrql8ndR/WmwxdUVFeewLy81lce3HcvcESNtiWbfyiKeiRRP75o0PAVWKn1cdQ+EMoaA1O2lKVFSkXGYjlXKxXgBlKUUYz7gdz4qk+tJ8XzRF4EUn2dM4yWYaWWcQuHZy9oegTXsuzWpVdLe6g1gL01uaxtFFMuML462s9TOOqvzvjkuoo9lxeVPE6aQo4642ZgTLBSe42NGzfi3nvvRZ06daDT6bBo0SKL35988knodDqLT58+fSyuuXz5MgYPHozw8HBERkZixIgRKCgo8GApPI/ck4oSPrVqm6gkdT+w9b2TJ86pRTn2BGqw1JqQ/EQxaZOzk4+N/uXwpVjcta4+I+b9WPShCK5lZXa/evqT3GTmWQ9tBgBv/rnfpTSleFnnkF7eCyu11ygsLES7du0wdepUm9f06dMH6enpwufXX3+1+H3w4ME4ePAg/vnnHyxduhQbN27E008/LbfoqmXFgQykpOW4nY6rJ2qpAbVNX2KVBG/B+TJaC+lVc6OYWupODS8XSkpgKr6r1SDmPlfi1DL2uZhfgjk70iRL73Q2H7TAVMGHL1yjb9++6Nu3r91rjEYj4uPjrf52+PBhrFixArt27ULHjh0BAF9//TXuuecefPrpp6hTp47kMrtDQUk5yisq3Z4U7d3/7C/JbqUNyDtpOLQye9mMlVtUBrVOw7lXyxBm9INeb/8FRgkLixostblXywA4COmlYvOTKy+m1Z8/dy2cVjeaiUhf8o2aMnUnrYxXBHW/KDLahi21TrB+/XrExsaiefPmeO6555CdnS38tm3bNkRGRgoKLQD07NkTer0eO3bssJpeSUkJ8vLyLD6e4v++24Z7v9kiSVpqsBapFSWrxrxdJi87jHaTVmHZ/ozrvyshlJW8T2cXot3EVXj0++2O75NZaPE+tZ7liZk7sWx/ek05XBTk42uxcT0Xxsq1fByG9HLCfcFVpIhgwliidL1pefWPsQ8rtSLp06cPfv75Z6xZswYfffQRNmzYgL59+6KiogIAkJGRgdjYWIt7/Pz8EBUVhYyMDGtJYvLkyYiIiBA+iYmJspfDnMPp7ivRBOuxPaVCzknX0bDmTfPVdxtPAgA+/+eoJOlJWTfrUy8CAHacuuzwWr2zcWqliH5QcT2R41n57ifoIu//fVgyZWDa+hPSJCQzUvYzq4c3uFCh7rSBTudd44oriD25jWFcgZVakTzyyCMYMGAA2rRpg/vuuw9Lly7Frl27sH79epfTHDduHHJzc4XP2bNnpRPYQxB5n6W2vJJwPueq7PmoqdpsbwjzqBiS43T0AyvflZktlQ6fvbvqOgUqRo6TlJRuXodxrkVu5HJ5o5jNP8zTthP9QILoGr6IO9XAdcjYg5VaF2ncuDGio6Nx/PhxAEB8fDyysrIsrikvL8fly5dt+uEajUaEh4dbfLSI3H6vssXotPNbt4/XeSAXcchlwNDiy4gSO8fNfWpNLzvVpVBDiDKlDF1y9iPH0VXomgyO0yoqrXBJBqnHHw0+di5R7uRJfL5SL4y8sFLrIufOnUN2djYSEhIAAElJScjJyUFy8vXNUWvXrkVlZSU6deqklJiy463BxMsr1VMuV8VQi/zWEKMIWbOQOVsmKa43V2rV9iJgYclUUA5HuFJtUo8tfb/cVDMP82ggNu6reaCY82HAXL3eqbRV1gO6f7be6vdV7aqsrOzq4L1w9INrFBQUCFZXADh16hT27NmDqKgoREVFYeLEiRg0aBDi4+Nx4sQJjB07Fk2bNkXv3r0BAC1btkSfPn0wcuRIfPvttygrK8Po0aPxyCOPqC7ygTPkFZc5vEbs+eyuoDIdQlLUNgmpDSkmPmdSeOHXFIQF1hwSrfqMK9R0ksepVX0XvC7gM/+rGU3F8T4y+xdcKSpFXHig/UQk1oB85bk/e9m6C1eVy1rN71nRZKSALbXX2L17N2666SbcdNNNAIAxY8bgpptuwttvvw2DwYB9+/ZhwIABaNasGUaMGIEOHTpg06ZNMBqNQhpz5sxBixYt0KNHD9xzzz24/fbbMWPGDKWKJAlfrT5m9/c9EsShVQrHG8UkUKpk3n0tFWqyOCnFX3svYK6D2JlqK7a5PM72k7KKSmw4elFKcWzi+uEI1/8th5/7R8uPWM3LHHtR5rzxOWAYLcOW2mvceeeddi1DK1eudJhGVFQU5s6dK6VYsuNoUM4uLLX7+9P/S8b+d3pJKFF1eNZQErmsSmJiqlp3P3B2Y4488levF0/1UimLM+NaRAxPYEtuR+VxFFnF9LNO51rdZOXbPvHKRPXwT+62ASvCbiKFoYCtwl4LW2oZu4h5+MsqtDlKe0JqKfKQS061xNCVNR+p07OpnMlfHoK0LxmrDmVKko68G0XFpe5q9ZuPb64cvuBKdA1vfJ4ZRi2wUsvYRekg1coqXtpOX252iograwutb36rLodSp3mppT7kwJbvpeU17lWAqBUDt3JgrOHN/ZZRFlZqfRxHlgYHp5ZWpaHREUork5VsIb3Md3E7rWYS/u+7bdIKJCpXJ6+3cUNOkX23GmfxmOVZymw08NzKvdRv/mzZunb/+VyH6R+wc031/LQ6XkqJ9YMwFBCE8TpYqWXsIuYEJzlPFKsUYa2RCyny5YFaYWzU/95z4pQQm8mqpF21spPeZcu8o8MZXEzXhJiX9jk70pB79XoUmOp5FpdVoP/Xm0Xlp43W8n6UXoFk5IM3ijF2EbOqKufE+uC3W5FfXC5L2t4+wXijUu50lAYvamWHVkcNztOO3QvEpePqRjFz9wN7t18uLEVEkL/V35w91EFtz5SnIbIef1hs//WmZ5qRHrbUMnYR43NW6dzBMU4hl0IrBrkHT6WHZi36K6tlQlOHFOpQkMTIILe+7fJGMdHp287AGXcCJU9fUwtq6LOM98JKrY/jaIARszwn5+ELcuKZ+K/S1o1sIapUGKdWivbRWtfceMx+3FitlceEq2I7GlscH75gH1c2+Fmc4qbR9lAaa9Umti5PXip0XwANrmow4mCllrGLmDFfq0qtFlmy94Loa9Vi1XQVdyY+e2lIgVwvF8ezCmznqfH2dBaC/EqjRUgve9ZYO2n4VqsozwPTtiotAqNiWKll7CJmo5hWdVpHYssf0ssJh8FrrD6cJV3+kqXkObQos5RIqdhqoS4dPqNulsJdgx3Bun+o3Xu0UPEyUvWyUrMStOgTzqgP3ijG2EWMUlshZ/gDjSNJzSg4C8qVtcuWThWcKPbT1tOIjwislo/k2Vilej7eriCJbT9XN4rpRW4Us5Cpxt9e3ggy4EqNbTl+Cf9IdGAI68/eC1tqfRwphmN2P5APIkLK2RzZ0nb5XgnlsIUkPrUSpFGdCUsOorjMuR3vakSqid0dpc5RH3SYMpnScS1/0TvubaRPBKc62ZWiUqScvSL+BifQUvxba6I6En/wDzswe+tpWeRhvAdWahm30aqh1jMbxdy7f8PRi9h07JIieSuNVZ9aj0thHaWOhrZnJXQ29qYa6lL2k9icCYPmckQO8WTmleDFeXtcy8hLqBqX1ND7GG+ElVrGLuJ8anmAcgUxtbZGQh9ab0At/ou8OiENjkJlPf/Lv/bvdzN/pY439nXciVPLMPZgpZaxi7joB/LLIQdKbxQTQ/X6l/IFQsniiclbGvcDuZyCPZSPeR6k3RdIV+XOyCt26T6xCpKloVZkjAOzf1baOEiAsY+1OcOT9cgvM94LK7U+jhQDCVutbKP07myxqMUC6jBflSxbVpdDy0c5K4m74pvK76qO4q5yU+VSq45G0EpfoGv/Yxg5YKWWsYuYIV+rSq2jsqlx4HVmEha7ycb+JeqqA6e7mmzRG+RJ12G+dn5zVj+TrAwq6CI2N3I5EE5vEafWtXw1OvwpCrsfMHLBSi1jFzEDjauD+tjf97p2o5cg6njRag2g1eVnqSircO5M5ts/WieLHNWXT7eeyJYlH0f4dm+wrbSK1Y/EH5NrK0+1vfapHyLlDSGsP3svrNQydhFjGXR1gFqw+5xL90mFR4ZVnvGs42K9lJQ7p9SWOqkEi0UpVcbb3mnslccjYePM49SKyPBYZj4+XpFqcY+vv2i6gishvRhGDHz4go/jaHIW80brrYcvqHGQlXKDgzvFU0qpKymTR0l1FrX0DVao3MPZx+nuKRst/j55qbDGQRwMwygHW2oZt/FSnVYS3Awt75afmTMKj1YWUUvK1XHogRK1RYrlLB/u9ju3N4qJXIi2JeXY3/fhcHq+a5n7KATlfWrZf9d7YaWWsY8on1ptTrSON4p5N2LaTcmmtTbxOOt+IBeVCr3JfbP2uMXfauijqpDB1kYxB8JZbBRzMe91qRxL2imIFPepZbwXVmoZu4ixZGjVUqtRsb0CMXVvbd5Ti1Kr1IvcutSLkqWlFeu8Pdw/fEESMVTBpYISpUUQjbV2Yz2XkQJWan0cjlMrL+5WTfWXiryrZe4lKBFKNblPux9oeHONLTHtbhTzQNliw677w9o/3cx2Gmppg9FzU5QWQTRaXd1j1A8rtYzbeKtSK/fA60ryG46Kt9Q5St4iMJEKm9CaFa1YJRvFtLo64W24+4zGhBkVl0EqzudcVVoEURCsPz8e9an1XFaMh2GllrGLXsY4tUqj9oGNiPDjllNKi6Eq1PICpRZFRutIVYvVFSJTlBBn0ucW9SQ1a5sfKUYKWKll3EYtiobUSFEqd/wWXT33XguwUigRKqhGJZvSlLerMojth/aeY+7KzsN1xsgFK7WM22g1Tq3apZa7Xt2ZWBQ7JlYljeatL3LqQv46lsIFxxs23HkSIuXHXinjfTPqgpVaxi5yHpOrdqQol5InJjkjv5c2oWyopc9rXaHSwgYsRnqshcRjPZORAlZqGbuIC+mlzdnH18dQJRUiVy0lhSXleHTGdomlcR6FDslVJFcpSEnLUVoEq1gOXdqOfqAVyMbIw/XISAEfk8u4jUa9DxgFcdWn9lhWAY5lFUgsjfOo5UVOJWK4gR1FUszdZieKWQ955u6ZfiJkkCANX0P7/ZZRK2yp9XEcDfrhQY7fe7zXp9b9ctlLQQsDu82Tmtysm8JSdcSbdRUttJ0j1FAGNchgwvXNZtLK4QtYm3c4pBcjBazUMnaJDApweA3vZFcrDqxUZP5vbkNnUOqY3OqYS6GUT6Ia/Hplj35g1zde+fJrCTVsFGO8F1ZqGbuIGbC9dYBiPY+xBXcN+RHz/Nkanzyq33NncBotn47HqBtWahm3UYt/obN4YuJTswVUxaIBUPcSoVr6vJr7l7uIeaE+nV0kYX6evc+XUdy6rebBhXELVmp9HEdzopg5UyUrsU6jtNiKD+zXyC8uc7oNvViXEoUSfd4b69zdIg39cScA264XjtIXW6f2D1/wwoaRETUck8t4Lxz9gLGLmOFaLf6FzjLxr0N2f9f6zmgxc+2Z7EJ0+2Q9bqwTbj0NBUug5l6lFkXGwqdWg+Yne/XoiSo279+uH77AOIu1dlfJI8VoHLbUMm6jlqVYxnn++Pc8AODghTyFJVEnB85brxc1dnlnX0CkKoMa6kINR/Uy4lG6yrT4AsiIg5Vaxi7e7H7gCJ6slEXN044aX+QOedmLiUcsteYRQFw8fEGNfUHNEKlnpYPxPlip9XEc+pyJeKfmQd0OCp5E5NifkFStOKoZtbzImfehV3/fp5wgMiCF64vDPQNu56C81VGLWGsX9qllpICVWsZttOpTyzCuooSlyRufMqnKJIVC5HKTemPDyAiBOKQXIxus1DJ28W33Ay8tmBmOlAEfqAKXUEu1qCWChqvYPdTAjaKJVXKl6N9abwMlULrG2CrsvbBSy7gND+q2cdVPzxNwq7kOr05cRw014XrkArPoBx7O25dhlzVGLlip9XGk8Dnz1vld68USM2/wLmDXUE2fV4scXo5cFmWfhJzzqS0tr5RXHsarYKWWsY+IEZutVrZR84TnjmyeKJdOxWuESliafMEdxhyPRz9wMUNeqXIOMvt/i++tVOOvO9PQbPxyrDyYIakM6h1ZGHdhpZZxm3JWahkZ8DUlzhW0XkNStbGS7z88/DmP2Dobt3A/AOC5X5JllIbxJlip9RGy8kusfu/IyiBm7Cmv8M7lIflDbik/GzrcKOYZMTSHN/gEqqEEapBBDGr2jdcaRNajH6h4YYbREKzU+giL91xw6T4xA3aZlyq1UqDoSUcOX1h4NnYVtSi1ahBDLou6M/3T5Y1iksiugkbQEETKjz2sQHsvrNQyblNW4Z2DutIDrydwdWz3RN2o26dWaQm8H3f1TTEKqyQhvbgvOEWljY1iXI+MFLBSy7hNeSVbahnfQi3+vr7w4uUKrkT1cDXCAbeAc1QSqWalg/E+WKn1cRyG9BIx+HirpVaK2cpeEkqP62LyP5KeJ78gZqRlF+HA+VyP5ukKSrSdWp+yo5n5sqQrtrw7TmbLnoc9Nh27KEEqjIoXZhgN4ae0AIy6ETPos0+tOhEVp9bBRPL6td3HnqLrJ+sAANvH9fBovs7ClqbrDJ+92/WbJajGh2dsdz1785BeLgrjtS/1MlFpY6OYJx8pjs/tvbCllnGbch7UJaeotFz2PNxptaLSCsnksMapS4Wypu8uavGpdS/WsPKFUF4CcWhFTi1QScRuM4xssFLL2EXMvPe/7WfkF0QBJNkX7aLisGy/tMHGbeHqZqzl+9MllsQStS9FsqVWGgpKbL+8uat0i7nd4phcblKP8Mv2NGTkWg8xyTDuwkqtj+NoHN/I/mKMAqhcp1WN6U4lYsiCW2UT2YFYkVWGj1YcqfGdJ19k1f7SzLgOK7WMXdan+q5SK0m4H/eTcD1vh5sAXU9b7nBber26Zx1ljsn1eJaK4uny2o9+4GOVrwBcxYwUsFLLMApwubAU/b/erLQYqkXdKq2afGpVIohG4dpjGO+CldprbNy4Effeey/q1KkDnU6HRYsWWfxORHj77beRkJCAoKAg9OzZE8eOHbO45vLlyxg8eDDCw8MRGRmJESNGoKCgwIOlYLTCD5tOKi0C1Dylq315UBlLrXrbS418u/GE44vM6pRrl2G0Dyu11ygsLES7du0wdepUq79//PHH+Oqrr/Dtt99ix44dCAkJQe/evVFcXCxcM3jwYBw8eBD//PMPli5dio0bN+Lpp5/2VBFcgydKm0ixQ9dW9ZarxNSnXuVR3UF31NF63v74ul640vJKfLwiVQWSMGJhn1pGCjhO7TX69u2Lvn37Wv2NiPDFF19g/PjxGDhwIADg559/RlxcHBYtWoRHHnkEhw8fxooVK7Br1y507NgRAPD111/jnnvuwaeffoo6dep4rCwMA2j7tCm9Tt2KhFqsppuPX1JaBNnwRBWLzUIlze3VWMQMJlL1MdmMemFLrQhOnTqFjIwM9OzZU/guIiICnTp1wrZt2wAA27ZtQ2RkpKDQAkDPnj2h1+uxY8cOq+mWlJQgLy/P4sOohxHuBJV3gBqUIiL12kPVPqGpoPkAAOM8fDiGt1FdkbLFwQu56HbtYBCGYdQLK7UiyMioihkaFxdn8X1cXJzwW0ZGBmJjYy1+9/PzQ1RUlHBNdSZPnoyIiAjhk5iYKIP0jKuUSnJSmvWJUi1Kkav8cyhT1vR1UPdmMY5TKz+eqOFv1h1HoZ1YuSbeXnwQZ7KLPCCR7+LZ91g1jy6MO7BSqyDjxo1Dbm6u8Dl79qzHZeCp2bMcz8pHcVmFKurdHRnO51yVTA5rqNxQq5roB4z7fLqqyveWm1RZLK3msucmdwaMQrBPrQji4+MBAJmZmUhISBC+z8zMRPv27YVrsrKyLO4rLy/H5cuXhfurYzQaYTQa5RGaUQXmg/PaI5kYPns3WtcNR6dGtT2aty3Uqjzq1SrYNZRwH/G1adhTVXzwPLt9MYy3wJZaETRq1Ajx8fFYs2aN8F1eXh527NiBpKQkAEBSUhJycnKQnJwsXLN27VpUVlaiU6dOHpeZUR/zd1VZ4g+cz9O8+4Gvw+0nP556cRA2VHKb+hDqfmlmXIcttdcoKCjA8ePHhb9PnTqFPXv2ICoqCvXr18dLL72E9957DzfccAMaNWqEt956C3Xq1MF9990HAGjZsiX69OmDkSNH4ttvv0VZWRlGjx6NRx55hCMf+DBzd6ahb5uEGt87ikxw/spVfLP2uN1r3IWIVDu0q9xQq4hPbX6xY99Pxnn4BUUdePaZ50b3Vlipvcbu3bvRvXt34e8xY8YAAIYOHYrZs2dj7NixKCwsxNNPP42cnBzcfvvtWLFiBQIDA4V75syZg9GjR6NHjx7Q6/UYNGgQvvrqK4+XxRl4QJeXTccuYcPRi+jQoJZTPmNTVh+VV7BrqFV5VGtUBhP82HgPJPyXW1VJLMZH5cRgNA4rtde488477S536XQ6TJo0CZMmTbJ5TVRUFObOnSuHeIyGGfrjThj0OvRoEev4YgYAoFe5Y5Q3vAyqvQyeEk8N4fUYhpEGlU8dDOMdVFSqzw6kNnnMYUst4yldU7DUcqMyjOZhpZZhFEAN1qGcojLZ4826ilrdIkyoof28HU+9BqqlKdvWiwAA1Ar2V1gSZbD3zHMIPUYsrNT6OOqzH3ovavQZ23X6itIiWEXlOi3jhSj9TN7ZLAbJ43vi04faKSyJMog93Y1h7MFKLcMogEfOtdf4vKBma63W65a5jmqaUqdD7VCjqvs9w6gdVmoZxmNcnz73n89VUA7GXXiFwwN4bqeYhzISh9r9yeWClXlGClipZRgF2HM2R2kRVA1BdbqGBWqWzVvwmE5r+i+3qaKo0T2L0R6s1Po4rg7kT93eSFpBfABPT5psTZQPb1CAuH9UIaYt9WxFZBhNwEot4xLRYUalRWC8GCJ1L0eyQig/ngvpRRb/tcatjaJkl0NX4x8MwzgLK7WMS/C46zysBnkP3mCpZaoQ05bc3vLjyZdYbk/vhZVahmFUxwGVb6TjOdF7MCk49hQdT7a3rxoMnDlGnGFswUqtj+Pq2KHmpWG14unYi1qeGF7+ba/SIthHw3WrFdTk4sFxU70Lnr+8F1ZqGYZhnERNCpe34vFjcu1d4wFZTIqWjjUu2eF3FO+FlVrGJXw1lqI78DjqPXjDpOgNZZACMVZYrir5YV2ekQJWahmX8PUBKKlxbaVFYBSElRz58Xgd21Fu2f1Afizj1Mpb374+f3kzrNT6ODxWu4Yrg67n49QycsFKjvcgKvqB/GIIsL4lP/z4ei+s1DKMC/Cg6Ntw88uPp14crseptSeLR0RhGMZNWKllGBdwZZLjedF7YCVHfjx2TK6oOLXyS2PapyD10vgH97eRNkEPwM8X4yqs1DIu4es7dLW0+/1YZr7SIriIevuYdlqfcYSYttRye9/bLkFpERjGY7BS6+NoSTlTEy5Zaj0ep7Yqv7unbPRovj6BF5iSVF8CT/ug2zt8QfWVxTAMwEot4yLqtaF5hkqe5Xwabn3vQVxIL8+1uNThErWyqqYRMRmVw0qtr8Ozs2gCDNcfF64234bfabwH4fAFuyG95JfD15U6fqYYKWCl1seR45jcJaNvczFV5WlXL0L4d5jRz+I3g/56oXkA9m3YbUd+PFbHojaKyS+GCV9XbhnGHVipZSRHr9FReepjN2PuyM42f7dUajlOrS/DLzXyo6pjcj0hiExoczRmGNdgpdbHcXXzkg7AjCc6WP3NXPnTErc0rIWQatZZE0Y/vaVS6ymh3KSyUiuSagtWar0HUT61HmxwbY6e7qNRWwijMlipZVymcUyI1e+1qtTa487mMfBz0/1AiSXrCta+ZIFrVX48bqlVuFHlGjW1oixaHJPLDxjjIqzU+gh1IgKtfu/q4FG1o9b6aKlZndaB3IM71Rf+PbB9HaeT33I82+l73KWCLbWywMfkeg9impKjncjP6ewij+XFzem9WF9rZXwGdzaK2VJetRJCxln+0+MGtEwIh0GvQ4+WcXjv78NKi2QXIqCclVpGo3jsRDG1HZMr8fApdYgwuTifc1VpERgvgJVaH0GOMdnWhjCtbhSrPvhXrzM/gx5922jrdJ6KCu0qtWruRt5g6VG7tdlT8ok6Jld+MXyei/nFwr8JhCV7L8iWl5rHFsY92P2AcQkdtKu8+hLllZVKi+Ayata5eDna+7Afp1a7hy9ohcKSCou///Nrimx58ePrvbBS6+O483B7m05rrzxanWjYp1YeuFblx2PuByqJUyvXeOpt4zTD2IOVWh/H5R35Op3NwZLHULVAmvapVfNkrPale0ZaPOpSq+J+LyeeLLev1rEvwEot4zLe5n7gXaWpgi218sC1Kj8eC+mlsji1DHDv15tlTZ+b03thpdbHcTmkF2y/7XqZrqtptGypVTVcrR7AQxvFJLqGcQ/zuejExULlBGE0DSu1PoIceqZW/Uxt4Y2hyMortLtRTM14g5KjdsVh//lcj+RjUqbsveB7xqe2avzxvlFIHF44/DIKwEot4xI6HQ9CUvNMt8aSpsdxauWDl6PlZ+q6Ex7JR8y+AiVOA5QKrYzTGhGTUTms1Po47kzOtgYhrVpwq0ttXjeemBhqhwRInqaWdS81y15QLfwQo10ES60dxdWTkfG8ccVIDCp+3BkNwUot4xI66PjVWmLk2HinZQtTQUmZ0iLYJO+qemVjGHO0YmRQ80ssox1YqfVx3Dkm19vwtjJl5pVoeqLYfvKy0iLYpJR9lV1i9rBblBahBnnFZTh3pciBT62GHyQrqHGsU6NMjPZgpdZHsDUku3X4gg0LQCURlr94h+sJM5Iwau6/KCgpV1oMhhEI8FPflFNcVonbP1qH9Nxim9doOU6ttfRYf2S8FfWNMIwmsDcoVhKhZUI4WiaEe0weKVB6mU4OX7q0y0WSp8n4Jp0bR7mdhtLPmD3+Tbti8zd7Ci/DMOqBlVof59edaS7fa0sHM224V+/0pU5kqS/vWjVlFETNCqk3IUctj+7e1DIPFa71e5mHB6MQrNT6CLYGDFdDPul0QEiAn428SLhGUygsrxz1peWNYoy60EswW6h5TPBWpUoH4JXezfHT8FstvmMYb4SVWsZlggIMVr83zQ1qnsDUCFeX9yB1zGE1IIWllvu4bUzjpVzjZnigdSOEWvDkfMEv+94LK7U+gtQPsb0JruKa9Vdry5X2BlVPDLhyLAl6q/VJ7dx/U12lRZAcfknVJsJJZSpvQB6rGClgpZaRnEqtuh8ojDzuB95Plya1lRbBJ5BCKdLpdGgRHyaBNNLjS9Y7Xx+btWZwYcTDSq2PIPlbsJ0xwZSX2i0D1alxopgbaSVEBLojCuMEbOHxDFI8zTod8M1jN0uQkjcj/7ipRqWO3Q8YKWCllpEc4ugHCDE6778mR30prfDVCvZXVgCFUKPS4C56iYokVToA8MOQjpKlpfSzIlef0VX7L8N4M6zU+ggeNNRq1v1ASsuyWk4gUtoiIcfRv+ZMfqCNrOkz15HE/UCidEz0bBUnWVpqwSPjpgrHZk8Omd740slUwUotIzkmpVZuhcaTODsIujQ+e1F9eYrbm0YrLYLPIFXvVGsvV8l7qORo5UQxdj9gpICVWsYl7FlbaocYq67xlDASYU9epwdBlYyZSk/Uck9UlUoX0Abe+H4izUYxCQRhGIaxgboD1zGSIfXcb21uGnN3MyRGBaF+7eCqa3x4AnOl7N5ZXfKWqpLY6uIppHmedaodF5TuRUKcWg/mpSZU+n7KaAxWahnJ6NYsBu0SI4W/tea3ZDdOrRNl6dKkNox+epy4WCiBVO6h9Dzhs5ZapQWQAW8sky9gzcKutbGZYcTC7gc+g8SHL1jz06r+nY+Om7OH3erSUq0sCqBKlT6pELMh79aGUR6QxPuRwkdep1OvQqWWzZ1aC4UoFfvP5yotAuMFsKWWkQ2tDc3VJ1tn57hFo25DvVpBCPBTz7ui0tO03H1AJXqITyCFrqWTKB3Gc3RpUhtbT2QrLQbDiEI9sy8jK5L71FrdUatzeI23svSF29E+MRLRoUaX01CrBcsd5Hc/kDd9V1Gq78uZrzdFM1EzctWyefM505RfPXqT9MIwjEywUsvIhtaUNHfm7NZ1I9zOXw7/UKUtmXL3gUoix2XUVjf0OCEBBnEXSmGpZcXYJp6sGbF5DU1q4NaLOsN4GlZqRfLOO+9Ap9NZfFq0aCH8XlxcjFGjRqF27doIDQ3FoEGDkJmZqaDElkh/+IKVzQfVvnqsU32Jc1UQD8w4cvj0Ke0nKLcOI6Z4vqRGuVLWSQNby5Y2o20MelYRGG3BPdYJbrzxRqSnpwufzZs3C7/997//xV9//YXffvsNGzZswIULF/DAAw8oKK3y9G+bgJUvdcUXD7dXWhSP44oCIIf6qdLVeckQY91WxjiojAoYE+a8VU1s/UiyUcyJ/DyN0qsaJjxRP2It5mptK4axBW8UcwI/Pz/Ex8fX+D43NxczZ87E3LlzcddddwEAZs2ahZYtW2L79u3o3Lmzp0WVHTGDnU6nQ/P4MJy8WCC/QF5ApVodRN3AF+fEv/9zu2J5t4gPh7+hAOeuXBV9j1jFRZKNYjp2QVAD3AKMt8KWWic4duwY6tSpg8aNG2Pw4MFIS0sDACQnJ6OsrAw9e/YUrm3RogXq16+Pbdu22UyvpKQEeXl5Fh+5UHIZWitzmKtyNo0NlSR/L9RpZVdg1Bin9sY67vtXu8Pw2xrJki5vFJOX64cvqKee1SMJw4iDlVqRdOrUCbNnz8aKFSswffp0nDp1CnfccQfy8/ORkZGBgIAAREZGWtwTFxeHjIwMm2lOnjwZERERwicxMVHmUsiL7TlPG0Ojq5PJE50bSJK/IwXt5vqRLqTpojAaIbFWsEMXCyWUBCX1P2ebXGz9SFEknapUNkt86mQ6tTYCw7gJux+IpG/fvsK/27Zti06dOqFBgwZYsGABgoKCXEpz3LhxGDNmjPB3Xl6ebIqt5BvFnJi1vcHA42wRXCmzCo2ObiNX2w9oVwf/vbsZaoUEKCaDGnGlC4muH4ncD9SKWp4/j/jUir1Oxe3FMNZgS62LREZGolmzZjh+/Dji4+NRWlqKnJwci2syMzOt+uCaMBqNCA8Pt/h4I1oZF+0N4M7Od65MkGpcSncXuSbF+IhANIoOUVQGNfDJg209lpdU7gfe3B5qRr02cs/jhUMtcw1Wal2koKAAJ06cQEJCAjp06AB/f3+sWbNG+D01NRVpaWlISkpSUMrrSH74gtXvvGvQ9PRypByuAkqH9PJVPPUkPNTRcy5Leu96vFWH3OOnK+MZb+pjtAYrtSJ55ZVXsGHDBpw+fRpbt27F/fffD4PBgEcffRQREREYMWIExowZg3Xr1iE5ORnDhg1DUlKSV0Y+cBatDIz2pPSE+4EjS60r6qnSOq0UE/V/etyAtS93U1QGb0Z0eCcJ6lGnU297+NLrn1bGZLnw8eJ7NexTK5Jz587h0UcfRXZ2NmJiYnD77bdj+/btiImJAQBMmTIFer0egwYNQklJCXr37o1p06YpLLV8WD0m18ZAweOHOLzRqirV5GF3EjartjCjH/JLyqXJVIMQkWz9SLq2lCYdqfHG588c85cJ0WHcZJJFaby8qX0aVmpFMm/ePLu/BwYGYurUqZg6daqHJHIODunlGKWtF94YqUCqGhWbzp+juqDn5xst7/VQs9aNDMK0wTdfy1MjnR7ObBqS4vAF7dSLUsjVdXwqugPjs7D7AeMSYo7JdfQ9c51mcaG4yUHILt58dh1bL2lNY8M8LMl13ru/NdolRiqWv9xIZqmVJhnJUfpJ8eQ4qdY2YBh3YaXWR1BywNaKdaa6lJ7UB1e+1BUGL9yJI411ryZq1NV1Nv6tdsRWpSRxarVUMV6GpfsBNwTjnbBSy7iEVZ9aW9OeF4yfck8CYtJXoR7nEMncD7ygD2kdqY7J9YbxQE7kMgK45H7AbcVoDFZqfQUFNSKtjIvSKk6unL4gZf6yJekcKmh8T1mltGr9Eiu1ZHFq1dAprKH4w+I5VNoCDOM2rNQyLmE1Tq1Nn1oeQuWiRbx9H1KlfWql2yjmekpK9D4tdXmxPUQKpVa1Cq2KkKvvuBb9gNuL0Ras1PoIyvrUagNXlW81hwJSsWhuQRb/tl9ILSmY7qL29tbp1NseKq86hmFEwEot4xLWfWrFX8u4gNo1FitIslHMzSS4+0kDP8eeQU31rCZZGEYMrNQyLiJ+tPP2JSxripsrk4EjldXa746UxkovCX6rtcnVG/u8NO4H6n3JUPOKi/SotRUYxj1YqfURPDFgc5xa9aG0TitX0/uU/uEkctWNdJEseEBQGm4CxlthpZZxCWcGRa2On57Wm+SoJz5FyLeUKDnbW7KQXipF6SfF1E/VZOVXjyQMIw5Wan0EzwzYXhyn1s5vUlnBXUnFUdUqbdGURBFyUEqly2gNJZU32XbPS5KwmlQ234XbgPFWWKllXMKZQZGnMWlwRXlT2k9QDYHkfa33yeZ+4OUVqZaXI0/Us+iQXl7e5oz3wUqtj+CJAdtXfWrVvLyt9Dzdp3W8JOm4U8Uqbh5NIclGMRWH9PJ2uN6vo/S4yMgHK7WMSzijyPniWOqpMjtqBqUPX4gJMyqavy8il1+tNM4H6kVp/3Ndtf9KjflQwKtnjLfCSq2PoOSArWZLphRI5lPrIBlX2lAtS6ru4u19SCrkbG+pmoAVKuXx9cfJx4vv1bBSy7iE1WNyPS4F4wildVq5dsybK2+Oy+hbPVMupVEa9wOdrzWHanCl+bz1BUTpcZGRD1ZqGdnxBquAGspgzQqndvcDNViKlWg7pfoLkfMWfbErDSp4BGRFDX0VUMdYwzBahZVaH0HqAdvqMbk2RmPNjtEi60yqE8XkQC0Ttbu4U50qaQrtI9WJYtwgiiO2CbitGK3BSi0jOzwwikMOv2fFQ3qpoO09JYN5XWvJB1isrJJsFFNxtSj9Ani9buSvJC31T4ZxBlZqfQSpx2urllrbV0ucu7pQWnG0h4pFY1SC2P4rhU8t4O2jgXfBbcVoDVZqGZdwZgMBGwWkwZru4UgfqfQCpdbakrW5IuZIKfPUZhc1WL/kjHKil+h0ODXUE8Mw3gkrtb6CkocvyJ+16lDLrmGlY2/6EhbuB4rKIU+6ckWyYCxR04li3GCM1mCllnENJ8Y6b7fMqLl8SrsfSKXcu5OOiptHU0jVz7k5rMP1wjDuw0otIxm2FA9vH6w9d/iCK2l6p6XWmVKxUisN3l6PanlWPFHNYtvSy5uc8UJYqfURpF6Gdmaw84bJUKtFUMc07R5a7D9KxqmVCyms7jqdetvTG54VsajFPYphpIaVWkYybPvU8gCqFCoxPrmNPUXIURG5/0kDH5PrGdTkzqQiURhGFKzU+gjSH77A0Q9MKDkJOY5+4CVaLSMauVpcmji1Xj4YuIGpbtTiBsEwWoSVWoaxgTsuG67M3Y5yc2Wy84aQXkBNhcqpqlDimFwvtEZKFqdWpVXjS7qkWtuAYdyFlVofQfLDF5y51ssHUHVbVtQsm2fw8u7nMSQJ6eV+EowEcDsw3gortYxkeLPyqtZlU8fWXY+IYRNpYpuqs+7ViJzNLUU7qLkpOaZzTbxxxQFQflxk5IOVWh9BamuiM5OTNwyM6rbG2sZrfGqrnyhmpoA4KqISSrGalTdXkapIaq0bb3lUxMAvioy3wkot4xLeoKhKhbUJwiWfWhlmVV+aqBnIaqqV6phctaL0o+JJPdPXDxTz1nIxrNQyjCjUYNlwRUFVeqJWA8q3nHcglfuBWhVbtazGqEMK70YlTc3IACu1PoLkG8XUOS8pglSToRyKs7e4H2jtmFxvfDy8/Zn3kkdFHHyiGOOlsFLLyI63T4ZSwe4H4jEvl+PDF3wLudpckji1UO944KWPilVU2gQM4zas1PoIkh++IG1yqkRtCqEru7PVsqTqLtUVIe8olfTIuYNfDS44cqL0s2KqXTU9sl7e5IwXwkotIxm2BmNvHxjVPNkrPT96ombUW/vehSSHL+jU217eclAJw/gyrNQyrqHWmUkm7BXXmoVHjs0w1l4aHFmXfGGiduh+oOKXDi0hjU6rU217KG2p9SRi20CtbcUwtmCllpEdte52dgZnpzuXXAWcvsMx3rBRTOemdU+J3qf9Wq+J9p9i+6inzeSXxNvbkvFdWKllXMIbFFWfQD0ztXw4PH3BM2KoASL5/GqlcD9w9wVFVpR+VmS2irLRlfEFWKn1AZReVvOGwdTZIqhF6Vf66E+52t4LDNDaQx1dWja8YVXDHubF84YxmWGswUot4xI8KF7HU35nrky5lZWSi6EIdsvuoP499YLh3SqRRBvFoN6xw9vbzxy1vHQzjNSwUusDeLkBwjPYmQMks4Q7SMaVfJS21MqHeaBa9ZVRhSK5jVRqkFo3H3ljm5mj0mpnGElhpZZxCWfGR68YSzU64XlD9AOtWJW0IaXreLtS5L0vgFWw+wHjC7BS6wPIMVSr1dqiGlRSPd5ufRKDL3VVgnxtLslGMQnkkAulnxU+fIFh3IeVWkZ2tDowWswtGi2DZk3MEqJMSC/vq3etPsdiUZMyKQfe3n4MA7BS6xPIEf2AB0jP49JGMS+ZqKt3YWe6tKf6qhqq2iW/azUIrgK88UXEXbTi+sMwJlipZTwAD4xicOnABhk2l0mJFJOiI6WUVRHPIE2cWvWOBb6k3Ku5HRjGHVipZVyCh0Rt4C2WWi1g/kxoSUESq994ux6kdJN5sn7FZuXtbc54H6zU+gC+NFh7NS40pNJtLxdqVBpVKJIoxNalVHFq1YpaDl9QhxTeDbuaeC+s1DIu4eXzWw2cXUZXS/Uo7X7AeBY5W1uKPq2W58IavvSoiLbOyysGw0gOK7U+gC8N1nLhiTd7V9rJkVze0vb2yunYr1hiYXwUX3uR9Wa4LRlvhZVaxkV4VNQCii+zSdBNrCWheLl8EN5cJC+m1SA1vYRxkzNag5VaH0BpBUCr46L50r0aQtu4FNKrUnIxGBGoSC+RDOWfAIZhGPuwUsu4BL/B20ctVi2lX2gY70EtfZpxHzW8pDOMHLBS6wPIsZzFQ6Ln4cD6jCPkbG+9FK4kPHAwDCMjrNTKwNSpU9GwYUMEBgaiU6dO2Llzp9IiKQpbeOTDFzZJWes+5uVyuFlOYnl8FX6MtY0r1lm26DJag5VaiZk/fz7GjBmDCRMm4N9//0W7du3Qu3dvZGVlKS2apLCiqg281f3AO0vlPgT5wrjxMy8vpur11meWYTwBK7US8/nnn2PkyJEYNmwYWrVqhW+//RbBwcH48ccflRZNMbxhKvTEfO7Q6ipDmppBY+XwxvjA3vAcM1XwKXKMt8JKrYSUlpYiOTkZPXv2FL7T6/Xo2bMntm3bVuP6kpIS5OXlWXzk4GhmvuRp8linDc5cLlI0f+4n3oMUllpezmYYRk5YqZWQS5cuoaKiAnFxcRbfx8XFISMjo8b1kydPRkREhPBJTEyURa703GLJ0wwN9AMAJDWuLXwXdu276kQG+0uev5y0TAgHAPRqFS981z4x0ub1N8SFWUmj5ne2aHUtv8SoYIvv69UKsvi7S5PaqM7tN0TbTftifonV78NttJXURIca3U4j1OiPYKOlvObtcVvTqjoIDjBYvd9e24mh941xNn/r2fL6b/Uir7dXWOD1Pt8kJsSt/J3htia1Ubdav3FEw+iQGn2tR4vYGtfFhbvflkZ/6aecprGhkqepBKHX+nhUSIDT995UP9Li73vaxNe4Jjr0erq2xurqNLs2tt1lpT8AQJu6ESIlVBfNrIzZjHegI29cJ1OICxcuoG7duti6dSuSkpKE78eOHYsNGzZgx44dFteXlJSgpOS60pGXl4fExETk5uYiPDxcMrlOXCzAwn/PoXaIEfvP52LX6cvIyC3GPW0SAABt60UgIsgfm49fwn963IBdpy7jhrgwfLH6KPaezUGHBrWg1+nQMiEckcH+qBUcgEEd6gEAcopKMfb3feh1YzwevPadNX5PPoc3/tyPhIhADElqiMuFJSgpq4TRX4/Nxy7hYn4JKgloFh+GJjEhOJZZgFCjHy4VlOB8zlXccUM0IoMDkF9cBgCIDA5AdKgR8eGBOHelCB0bRmHnqctISbsCfz89TmQVwKDX4UhGPupEBqKignDHDTFIOXsFRzML0PvGOCREBOHXnWloXTcCb/dvhXbXlJ+L+SVYfiAd991UF1l5Jdh1+jL+r2MiDNW2f+8/l4ujmflCXZhTWFKOX3emISO3GPVqBWHTsUs4e6UIfno93uzXEnHhRqw9koUAgx7929URlL8fNp3E5uOX8PydTREW6Idv1h5HZl4xbmkUhZF3NEZBcTk+WHYYu89cQbdmMXjvvtY4mpmPh2dsQ0l5JW6uXwsRQf4IC/TD1dIK3BAXisuFpdh2Ihsjbm+EikpCsNEPPVvG4YvVR5GVV4IG0cHofWM8Nh29hLziMiSfuYLwIH+0qRuOq6WVyMwrhkGvw9AuDfH3vnScvFSASqp6oQnw06NuZBBmbj6Jfm0SkNQkGpOWHkRlJdC3TTweu7U+0nOLMWbBHkSFBOBKURlOXixEn9ZxMPoZMDSpIdYfzULtECM2H7+EbScuofeN8YgI9kdhSTkigwLwWKf6CDH6YVHKeXy04gj6tI7H+H6thPYoLqvAb8nn0L15DOrVCsaRjDxMXHIIp7MLhWs3HruIHzadxBOdG2D5gQwQAcv2p6NurSCEBPhBpwNaxIejsKQcDaNDcCwzH9tOZmPYbQ0xuvsN2HjsIioqCfvO5WL36csoKCnHmLub4bam0Xjzz/1oWy8Sw29vZNEH1h3JgkGvQ71aQfgz5TxOXizE3/vTcU+bePjp9cgvLoPRz4Cs/GKk5xajYe0Q6PXA8awCFBSX4793NwMA/HMoE01jQxEbFojQQD80jwvDuStF6NCgFm6IC0NadhF+3ZWGmFAjHutUH3qdDj9vO43953PRrl4kDpzPxcKU8wj01+POZrEoq6hEg9ohmLvzDF7s0QzP3dkElwpKMOKn3dh7NgdfPtIe3VvEYnHKeXRrFotVhzIQFx6Ie9vVwYoD6ThwPg/7zudi49GL6NKkNlrEh4NA2HTsEopKytGzVRwKSsrRq1U8FqWcR+fGUVh1KBOP3FofA9rVAQDsPHUZMzefRL1awVi+Px2Z+SWoHxUMP70Ox7IKEBxgQMPaIWgUE4KSsko0jgnBzlOX0bZeBDLzinExvwSf/197NIwOwfGsAkxaeggbj15Ev7YJeOr2Rlh9OBNH0vMREeSPnq3ikJVXjMLSCizbn44Hbq6HsopK6ABsOZGN45n5aB4fhlZ1wlFeQbiQW4wj6Xn4cFBbrE/NwvaT2agVHID29SNh9DPg5vpVdVpSXom1R7JwJrsIRj89br8hGg1rh2DlwQzkFJXhwQ710LFhLaTnFmPxnvNIrBWMs1eKkFgrGFGhAdh7Ngc7Tl1Gg9ohGNiuDh7rVB+B/lUvZvN3peHtxQdxX/u6CDH6ITO/GO3rReLM5UJEBQdgxB2N8UfyOaw/ehHtEyPxZJeG+HVnGg6l5yEm1Ijx/Vrir30X0DQmDG3qXVc8/zmUiZAAA2LCjFiy9wI2HL2I57o1wdojWbhSVIqrZRXYeiIbTWJCMTSpAR7v3AA6nQ65RWVYvPc8ikorkHa5CAv/PYdXejXHve3qYOq641iw+yy+fvRm+Ol1eHvJARh0OhSWViCxVhAOpefhic4NkHa5CEmNayM4wA8l5RVoFB2KCiJMXHIQbepF4NSlQoQF+uHUxUJ0blIbOuhwOD0PBr0OQf4GXMi9iga1g6HX6XDuStWcEBboh+KySuw6fRnpucUINfrhjhuiERceiMKScszcfAol5VXBuhvUDkZIgB86N66Nl3s1Q4hR2hf7vLw8RERESD5/M87BSq2ElJaWIjg4GL///jvuu+8+4fuhQ4ciJycHixcvtns/PxQMwzAMoz14/lYH7H4gIQEBAejQoQPWrFkjfFdZWYk1a9ZYWG4ZhmEYhmEYafGMY50PMWbMGAwdOhQdO3bErbfeii+++AKFhYUYNmyY0qIxDMMwDMN4LazUSszDDz+Mixcv4u2330ZGRgbat2+PFStW1Ng8xjAMwzAMw0gH+9SqCPbJYRiGYRjtwfO3OmCfWoZhGIZhGEbzsFLLMAzDMAzDaB5WahmGYRiGYRjNw0otwzAMwzAMo3lYqWUYhmEYhmE0Dyu1DMMwDMMwjOZhpZZhGIZhGIbRPKzUMgzDMAzDMJqHlVqGYRiGYRhG8/AxuSrCdLhbXl6ewpIwDMMwDCMW07zNh7QqCyu1KiI/Px8AkJiYqLAkDMMwDMM4S35+PiIiIpQWw2fREb9WqIbKykpcuHABYWFh0Ol0kqadl5eHxMREnD171ifPpebyc/m5/L5Zfl8uO8Dl91T5iQj5+fmoU6cO9Hr27FQKttSqCL1ej3r16smaR3h4uE8ObCa4/Fx+Lr9vlt+Xyw5w+T1RfrbQKg+/TjAMwzAMwzCah5VahmEYhmEYRvOwUusjGI1GTJgwAUajUWlRFIHLz+Xn8vtm+X257ACX39fL72vwRjGGYRiGYRhG87CllmEYhmEYhtE8rNQyDMMwDMMwmoeVWoZhGIZhGEbzsFLLMAzDMAzDaB6PKrWzZ89GZGSkZOm99dZbePrppyVLT264/Fx+Z8r/+uuv44UXXrD5u7eX/9tvv8W9995r83dvL3/nzp3xxx9/2Pzd28vvTf3flbHvkUcewWeffWb1Ny2V3VVWrFiB9u3bo7KyUmlRFMHR8+/tuFx+coKhQ4cSAAJA/v7+1KRJE5o4cSKVlZWJur+oqIgyMzOdyZK6detGL774Yo3v09PTKSwsjE6fPi18t2HDBurfvz8lJCQQAPrzzz9r3DdhwgRq3rw5BQcHU2RkJPXo0YO2b99ucU1ycjL17NmTIiIiKCoqikaOHEn5+fl2y19ZWUlvvfUWxcfHU2BgIPXo0YOOHj0qpLlu3Trh3uqfnTt3CtfNnz+f2rVrR0FBQVS/fn1q3LixzfIHBweTXq+ndu3aWZQ/IiKCAFBgYCCFhYVR586dadmyZcK9p06dsinLggUL6L333qOkpCQyGo1kMBgoNDSU4uLiqHXr1nbb/9ChQ1S/fn3S6XQEgPz8/Khnz550+PBhIiK6/fbba+Sn1+spJiaGnn/+eSIiqzL16dOHnn76aWrcuDEFBgZSSEgIBQcHEwBq1KgR/fTTTzRr1iybZQJARqOR/vjjD2rUqBEZDAbhe51OR6GhoUI/iIuLs5lGnTp17OaRkJBAgYGBFBUVZfOaWrVqkV6vJ4PBQAaDgWrVqkWRkZEUEBBAcXFx1LRpU4qKiqLAwEBq2rQp6XQ6evLJJyk1NZUGDBhA4eHhZDAYyM/PjwDQd999R0REe/bsobvuuosCAwOFvPz8/Gr0cVP/NxqNwnUhISE0fPhwys/PJyKib775hmJiYizkjo+Pt+j/pjL4+/tT+/bt6YcffqChQ4eS0WgknU5HOp2O/P39yd/fnyIjI2nkyJGUlZVFmZmZVFFRQU2bNrVIPzw8nEaNGkW5ublERHTp0iWqW7cuAaAuXbrYrM+6devS559/btH/Q0NDhd/N+/+MGTMoMTGR9Hq90Pamf1+5coWILMc4809oaCg1btzY4judTke1atWi8ePHU0lJCRERjRw5Umib6h+DwUBt2rSx24diY2MpPj6egoODqVGjRuTv70//+c9/6N5776XExETh2ar+ueeee2j9+vXUsmVLoX/XqlWLbrnlFgJAU6ZMsWh/Uz8JCQkR2iIlJYWIiPr3708BAQEWcg8cONBm3QCgdu3aUevWrclgMJBeryedTif8u1WrVkRElJiYaLfspue0U6dOFBoaSrVr1yYA1KFDB4f31atXjzp06ED+/v7CdwEBARbtP2HCBKvjj9FopLCwMLrjjjvo8uXLNHz4cKH8pnKYp+vn50dBQUFkNBopIiKC7rjjDofyOfq888479Mcff1DPnj0pOjqawsLCKCQkhPz9/SknJ4eOHz9O9913HwUHBwt9IDIykgYOHEhHjhyhDRs2UFxcnNCfTc9nnTp16IUXXqCcnBx68cUXreYdFRVFY8eOpbKyMkpOTqZmzZoJ6fj7+1NERAQFBARQnTp16L333qNp06ZRmzZtKCwsjMLCwuiWW26h/v37U926dUmv19vs/wAoKCiIDAYDGY1GMhqNFBoaSkFBQRQZGUn169enli1bUkBAgDCndezYkUaOHGkzvX79+tGpU6ds9s2oqChh3vvggw+oY8eOwvwRHBxMAQEB1LZtW1q+fDkREX333XfUrVs3i/5v+jRv3tzuPP7RRx9Rhw4dKCQkRBijTWX55ZdfBDkOHTpE9957L4WHh1NwcLAwrgwcONBijv/rr7+oadOm9Ntvv1GHDh0oIiKCgoODqV27dvTzzz9bXPvHH3/Q3XffLcw9pmfZnG7dutWQ+ZlnnhF+37NnDz3yyCNUr149CgwMpBYtWtAXX3xRI51ffvmF2rZtS0FBQRQfH0/Dhg2jS5cuWVxz5coVev755yk+Pp4CAgLohhtuoL///lv4vby8nMaPH08NGzakwMBAaty4MU2aNIkqKytrlL+ioqKGDPZw2lLbp08fpKen49ixY3j55Zfxzjvv4JNPPhF1b1BQEGJjY53N0io//PADunTpggYNGgjfFRYWol27dpg6darN+5o1a4ZvvvkG+/fvx+bNm9GwYUP06tULFy9eBABcuHABPXv2RNOmTbFjxw6sWLECBw8exJNPPgnAdvk//vhjfPXVV/j222+xY8cOhISEoHfv3iguLgYAdOnSBenp6Rafp556Co0aNULHjh0BAMuXL8fgwYPx7LPP4sCBA5g2bRrOnTuHvXv31ijHN998AyJCz549a5T/+eefBwB89tln2L17N+666y4MHDgQBw8eBAAkJiZi5syZuPHGGxEfH4+JEydi4sSJCA0NRd++fVFaWoouXbqgrKwM/v7+SElJwfz585GWloaGDRtaLf+JEydw++23o0WLFpgxYwbWr1+Pjz/+GHq9Hr169UJFRQUMBgNGjhyJCRMmIC4uDlOnTkVycjJWr16N3r17C+WYNWuWUEdJSUlo0qQJOnTogFmzZuHNN98EUHUcodFoxHvvvYdRo0YhNDQUv/zyC1588UUMHz4cAHDjjTciKSkJ6enpOHPmDDZu3IhOnTrhyy+/xBtvvIG+ffsCACoqKhAXF4devXph5cqV2Lt3L/bu3YuXX34Z7777LgCgdu3aKC0tRUREBPbu3YudO3fiww8/hE6nQ6dOnWAwGPDFF1/gwIED+OWXXxAVFYWYmBgAwI8//oi77roLN9xwA0aPHo0hQ4bg448/htFoRFlZGa5evYo33ngDH374IQYPHoyNGzfi8OHDmDBhAogIa9euRf/+/VFeXo7Jkyfj6aefRqNGjQAA/v7+AIDk5GSEhobiwQcfxLBhw4S6fP755y36eLNmzfDhhx8CAIKDg4VjI9etW4enn34a8+fPx0svvYTCwkJ06NABHTp0gMFgQFxcnND/u3btitatW+Pll19GeXk5oqOj8dRTT2Hfvn147rnnMHXqVPj5+cHPzw8tWrRAYmIiDh48iOeeew6xsbHo1q0bLly4AKDKGvfXX3/hzTffxOrVq/Hss88CAEaMGIG2bdsCgMUzs2rVKkybNg2tWrWC0WjE5MmTMX78eMyYMQOFhYVo3bo1QkNDAQCBgYEW/X/RokXo0aMHpk6dimeffRYtWrQQ6unSpUvCvwMDA/Hqq6/i5Zdfxv/+9z+sWbMGixYtQn5+PkJCQvB///d/+PnnnzFx4kTk5ORgypQpeOONNwAAOTk5aN++PQBgwoQJ6NWrF+rUqYOWLVvihhtuwDPPPAMAqFu3rtDP9u7di+TkZKEv/fHHH9i3bx9eeOEFlJWVYceOHejevTsWLFiAlJQULFmyBHXq1EF4eDgOHDgAg8GAhx56CJ999hkyMzPx6quvAgAGDBiA7Oxs1KlTRyibqf1jYmLQpUsXlJeX4/Tp08Lv8+fPx7JlyzBgwAA0a9YMBoMBOp0OJSUlQvsnJSXhkUceEfr/mDFjUKtWLbz11lt49dVX8d1336F27dqIjo5G7dq1hfT1ej0mTZqE9PR0HD58GK1atUK3bt0QHh6O9PR0NG3aFGVlZbjrrruwZ88erFq1Cn5+fjh06JDQ/qmpqdi7dy8aNmyIiIgInD17FlFRUahbty7atm2Lxx9/XKjj8vJy/Pzzz0L7Z2VloW7dupg/fz6+//57BAcHo23btvD398fKlSsxevRovPbaa5g7dy4SExMxcOBAYb4qLy9Hnz59cPz4cYSEhKBdu3YoLS3Fk08+iZiYGERHRyMtLQ1Tp07FTz/9hJEjRyIoKAgPPfQQwsLC0LRpU/zf//0f0tPT8c477+Dzzz9HvXr10LBhQ/j7+2P27NnYuHEj7r77bixbtgzJycmoXbs2ysrKMHHiRPTq1Qs6nQ7t27fHXXfdJZS5tLQUvXr1QnZ2Ntq0aSNYth9//HG0bdsWkZGRWLFiBUaMGCGMGe+99x78/PzQpk0bhISEYMaMGViyZAlGjx6Nrl274vjx4/joo4/Qr18/6HQ6FBUV4fHHH8eSJUtw6623ol69evjwww+RnJyM3bt3o6ysDEuXLsW7776LN954A//9739hMBjw5ptvomPHjsJHp9PhwQcfxJNPPonGjRujvLwcrVq1QklJCd577z34+/vj3LlzeOihh4T++OSTTyI5ORnp6enYsWMHjEYjXnjhBdxxxx24+eabkZ+fjwceeABffvkl0tPTsXPnTowYMQJTpkyBwWDADTfcIKS1YcMGjBo1Cg8//DBCQkLQqlUrREdHY9iwYbj//vuRkpKCoqIi9OnTB3fccQcA4MiRI0Lf27x5s915/JZbbsGbb76Jp556Cs888wxGjRqFiooKdO7cGUOGDMHSpUst5sn169dj2bJlyMnJQadOnVCdvn37Ij8/H8eOHcObb76Jbdu2Yd++fRg2bBiGDRuGlStXCtcWFhbi9ttvx0cffVQjHXNGjhxpIfvHH38s/JacnIzY2Fj88ssvOHjwIN58802MGzcO33zzjXDNli1bMGTIEIwYMQIHDx7Eb7/9hp07d2LkyJHCNaWlpbj77rtx+vRp/P7770hNTcX333+PunXrCtd89NFHmD59Or755hscPnwYH330ET7++GN8/fXXNcq/fPlyu2WqgTMa8NChQ2u8Tdx9993UuXNnIiK6fPkyPfHEExQZGUlBQUHUp08fC2vlrFmzKCIiQvh7woQJwltHgwYNKDw8nB5++GHKy8sT8kO1N4tTp04REdGNN95I33zzjU1ZAeuW2urk5uYSAFq9ejURVb2pxcbGWrwd7Nu3jwDQ/fffb7P88fHxNHHiRIvy63Q6wYpUvfylpaUUHBxM8fHxQvn9/f2pXr16osofHh5Offr0EerQUflr1apFP/zwAxERnTt3jurWrUsHDhygBg0a0JQpU6h9+/Y0fPhw4fpx48ZRw4YNLdrrrrvuIr1eL8hnXv6HH36YHnrooRrtv2TJEgJAx48fp+bNm1NAQAAFBQXR6tWrrbY/AJo7d67N8t900030yiuvUJMmTYR0x4wZQ7fddptFPeOahan6G211mjdvTgBo8eLFFv3AxOjRo0mv19PEiRPprrvuIgBUWlpqUf7g4GAaPny40P/DwsIEKw8Amj17Nvn7+9PIkSOF+ly+fLnwNt+jRw8KDAx0qv+3bNlSsPBYA6iycHfv3r1GH+/bty8BoJEjRwrW9w8++IB0Oh21a9eOQkJChL5SUVFBQUFB1KpVK+H5DwkJEer17rvvpvbt2xMAeu211+iJJ54QrOj+/v40ePBg0ul0lJycLFhqANCmTZsIADVs2FBo/8DAQAoKCqLPP/+cunXrRr169bJa/tLSUjIYDNSvXz8iIrr//vvp8ccfJyKisWPH0uOPP04AKDg4WKgP8/5vory8XLBYLlq0SKjzoKAgwbJpjq32r1+/PjVq1Ig2bNhAQUFBwkrJTTfdJPR/ADRu3Djht4YNG1r0/1q1ahEAGjRokKj2r1+/PgGgTz/9lMLCwmj37t3k5+dHR44cEdo/KirK4hk38fDDD9P48ePpscces7BIpaSk0K233kqjRo0iouvjVfX2N189szX+m1YCunfvLtRZ7dq1KTAwUGinNm3aUGJiIgUFBQmrazqdjnJycuyWPysriwDQkCFD6M8//ySdTmexYmcqf7169WjixIlC+997773CWNmpUycaP368xbORk5MjWBl37dpFRESHDx8W8u7duzft2rWLAFBaWppQdtP8cN999wljX1hYGA0aNEhIH6iy9pt45ZVXBEuZySr60EMP2W37ffv2EVHV3PfJJ5+QTqej3377jQDQhg0bLMr+559/0uLFi0mn09HcuXMpICCAjh07RgBo+PDh1LFjR4uyL1myRLBAd+/enQ4dOkR+fn7CuHjzzTfXeB5M3HjjjRQYGGjxfN1888305ptvCvfrdDrq3r278LtJ7kWLFtE999xDw4YNE+rxhRdeENrpzJkzwjj/22+/kZ+fH2VkZJC/vz/9/PPPtGTJEtLpdBbPJBHRn3/+SQBo2LBhNeRNSEigb775RuhHGzZsoAceeIAGDx4sXGOqf9MKji1KS0spJiaGJk2aZPX3m266icaPHy+U8eGHHxbGqvLycurSpYuwylVdtyAiGjZsmHC9tXSrY1qFtWWptbbqa4/nn3/eot0++eQTaty4scU1X331FdWtW1f4e/r06dS4ceMabWJOv379LPQNIqrRBkS2y28Pt31qg4KCUFpaCqDqrWr37t1YsmQJtm3bBiLCPffcg7KyMpv3nzhxAosWLcLSpUuxdOlSbNiwQbAkffnll0hKSrJ4u0hMTMTly5dx6NAhwcLpKqWlpZgxYwYiIiLQrl07AEBJSQkCAgKg11+vmqCgIABAVlaW1fLn5+cjIyMDa9eutSh/ZGQkJk6caLX8S5YsQVFREfLz84XyJyUl4dKlSxblb9KkCQBg586dQvm//vpr5OXlYfz48Q7LWFFRgXnz5qGwsBBJSUmorKzEE088gVdffRU33ngjAODs2bPYs2cPRowYIdxXUlIiWAFNGAwGVFZWIjk52aL8paWl+Pvvv7F//34sXLgQRIQmTZrgwoULGDJkCBo2bIjExEShzq9evYqhQ4fio48+wr59+zBu3Dj88MMPWLp0KQDg6aefRnR0NPbt24cmTZrgqaeeEtrfJNuJEyeQkJCAxMREBAUFYefOnTXquaKiAuPGjbOwUptTXFyMjIwMBAYGIiUlxaIfmGSdPXs2iAj3338/Tp48iaioKIt6KSkpQVFREUaMGIEnn3wSu3btQlRUFB5++GH4+fkBANauXYvg4GCL/vrrr7/Cz88PlZWVWLduHXQ6Hfr27Yv169cL7T98+HAhjfr16+Oxxx5DVFQUsrKycOTIEQAQ+oc1ioqKEBkZadHHZ82ahcOHDyMyMhJxcXHIzs5GREQEhg0bBr1ej3379qGwsBB6vR433XST8HadmZkppNulSxfMnz8fly9fRmBgoLDKsXHjRuzevRsjR45EbGws9Ho95s+fj86dOwsW4atXr6JOnTrYsWMHAODMmTN4++238cUXX6B58+aorKzEW2+9hZ9//tnCn/KPP/4Q+v+cOXNQUVGBZ555BikpKdi6dSu6deuGtWvX4rfffrNYqane/83JyckRxq7WrVtb9IsxY8bAYDAgOjoa77zzDrKysqy2v6n/R0ZGol+/fqhduzb++usvIe8hQ4YgLCwMN9xwA5o1aybcd+7cOXz88cfYv38/vvjiC0RFRaF///7YsmWLxfNvNBrh7+9vMf6dOHECaWlpaNu2LWbPno1HHnkEa9asQePGjbF06VI0bNgQQNXqQkJCgkWZZ82ahZMnT+LZZ5/FkiVLEB4ejrCwMABAWVkZkpOTLVZ/AKBOnTq4fPmyxXdz5sxBdHQ0tm/fjlOnTqGoqMhi/P/yyy8BAJs3b0ZQUJBQZyUlJQgPD8cXX3yB2rVrIysrC2VlZbjzzjuRkJAAIsJjjz2GiooKTJo0SfBDNR//vv32WwBVz8fMmTPRs2dPixU7E1evXkVkZKTQ/omJiTh27Bji4uKwY8cO/P3332jVqhV0Oh3eeustzJ49G+Xl5YiIiBCe1RYtWqBWrVoAgCtXrqB58+aoXbs2Zs6cCaPRiOLiYsycOROhoaFITU3FkiVLMHPmTOTn52P79u2YPn06AgICEBMTA4PBIMhmWn1LS0tDQEAAEhISsGnTphpzX2BgIHQ6HfR6PZo0aSLMfZ06dYJer8fGjRsBAFFRURZlz8/Px5w5c9ClSxcUFhYiPDxcGEtMFv927doJVuegoCCUl5fDz88P//77L6ZNm4bGjRtj2bJlAIDDhw/jqaeeqtEPTKtcJSUlaNy4MYgI69atw9GjR9G5c2fMmTNH+P61114T7uvduzf0ej2mTZuGnJwchIeHY+bMmWjZsqWF73H9+vURFxeHTZs2oUOHDtDr9XjhhRcQFBSEu+++G//73//Qs2fPGnPVzJkzUatWLWHcMaekpASBgYHIzc0V6i4oKAibN2+ucW3Lli3RuHFjDB48GGlpaTV+X7JkCbKzsy1WxwCAiLBmzRqkpqaia9euyM3NRa1atfD333+jWbNm6N27N8LDw3H48GHUrl27Rrombr31VmzatMlmus5iem5bt26NcePGoaioyO71ubm5Fn0rKSkJZ8+exbJly0BEyMzMxO+//4577rlHuGbJkiVISkrCqFGjEBcXh9atW+ODDz5ARUWFcE2XLl2wZs0aHD16FEDV87B582Zh9dRW+UXhjAZs/jZRWVlJ//zzDxmNRnrllVfo6NGjBIC2bNkiXH/p0iUKCgqiBQsWEJF1S21wcLCF5e/VV1+lTp06CX9be7tISUkR3pZtATuW2r/++otCQkJIp9NRnTp1LHxaDxw4QH5+fvTxxx9TSUkJXb58mQYNGiS8rVor/6OPPiq8TZuXf8CAAWQwGKyWv2/fvtS0aVOL8n/33Xfk5+dHLVq0oIqKCkpNTRUsW1u3biUioqNHjwpWnbS0NLuWWpNPbEREhODP8sEHH9Ddd98t+K40aNCAunTpQi1btrS4f+XKlaTT6Sg4OJjKy8vp3LlzFBsbK1hSzcv/3HPPCeV/4YUX6PXXXxd80HQ6HX311VdEVNV/AgICyM/PjxISEigoKIj0ej1169aNmjdvTiUlJXTbbbdRq1at6N9//6UPP/yQdDoddevWjYiIpk6dauHbtnHjRtq1a5fgB3vhwgUiItq6dSsBVb6Y69evp/79+1N4eDidPXvWov1N6QCo0Q+IqvybAQiWlOjoaOrbt69F+xsMBoqKihL6/4ABA2jw4MFUv359eumllwSL2XPPPUezZs0if39/oU1Naev1enr33XepQYMG1Lp1a/L396eAgADS6/WCRW7BggXUoUMHCz9Qe33c9Lt5Hz969CjFxsbSwIEDLfyeTeU2+TACoPr169Pvv/9Ou3fvpoiICNLpdPTII4/QwIED6cqVK3T33XcL1/r7+wu+XMuXL69hZfrqq6+EZ0in0wl+k6a/Tdfdc889FB0dTU2aNCGi637oAQEBNHv2bKFst912m5CvXq+nSZMm0aVLlygxMVGwWJnSrN7/q7d/dZ/aX3/9lYYPH04zZ86kKVOmWPQTa+0fEBBAAQEB1LFjR+H5nzp1qkX9BwUF0UcffSQ8/3feeSc1bdqUnn76acHvGADt2LHDYvwz9T8AVFFRQWPHjhWs4ADol19+Ee575plnhHp9+umnBWtw9+7dBUutqf2nT58u+N36+fnRokWLCACtXLnSYqwxyXvjjTdSZGSkMP5/9913tHz5cpoxYwb5+/tTaGio0B+2bNlCY8aMEdpVp9NRvXr1iKjKQhwcHExxcXE0ZswYMhqNpNfrKTw8nGrVqkUfffQRPfLII+Tn5yfIZ1r1WL9+vdB+jRo1IqBqL4LBYKD58+fb7P/m7b9s2TJasGABjRkzxqJ9ZsyYQS+99BL5+fmRXq+nZs2aWaRlyq9t27ZEVLVyZ7IsAxB+N439ppUQoMrvNSwsjB566CGLuc98DAgICKD33nuvxtzXuHFjwZIeFhZGL774ojC2mZ6x+vXrW6xSjR07Vki3c+fOlJqaSvXr16c33niDLl68SCNGjBDSNK1S/Pjjj4JfsMFgoAEDBtTw3X7qqaeoffv2gtVu3759gu9oeHg49ejRQ+hPBoNByKNz587UtWtX0ul0NdqodevWQn/W6XTUvHlzOn36dI057aabbhJWpNavX08Gg0GQLykpqYY19fz582QwGKhVq1ZWLZOPPvootWrViu68807q0qULrVq1ioKCgiggIEC45sMPPySgakVpxYoVlJSURPXr17fQVUxtbRoTiIhycnIoJCSE/Pz8yGg00syZM2n+/PkUEBBA69evJ6BqBWn06NEUExND48ePJ51OR71797ZqqV28eDHp9Xq6fPlyjXStYc9S+91339GKFSto37599Msvv1DdunXp/vvvt5oOEdGWLVvIz8+PVq5cafH9ggULKDQ0VFjVuPfeey2ssqY9G8OHD6fdu3fTvHnzKCoqymJVsaKigl577TXS6XTk5+dHOp2OPvjgA5vld8av1mml1mAwUEhIiKCcDBkyhAoKCmjx4sXk5+dH5eXlFve0b99eWAKyptSaNhGY+Pzzz6lRo0bC39aUWtODnZWVZbtgdib8goICOnbsGG3bto2GDx9ODRs2tNjANmfOHIqLixMezldeeYXi4uLo5ptvtlr+1atXCw+0efkfeughioyMrFH+s2fPkl6vp4ceesii/JWVldS9e3dhc0KtWrWoQYMGBIC2b99O5eXl1LFjR3r11VeF8ttTaqdNm0a7d++m119/naKjo2nBggUUFxdH58+fF66rX78+BQYG0qefflojjYcfflgY6IKDg+nmm28WlGXz8puWtXQ6HZWXl1NOTg4dPXqUunTpIiiwV69epVmzZgkD6cqVK2nIkCHCpKzX62nFihU12r9BgwbCsl1OTg7t27ePOnfuLAy2CQkJwkCekZFh0T9MZSotLaUmTZoIyzUFBQX02WefkdFopDvuuIMSEhLo0UcfrdEPunbtKihlq1atotjYWNLpdBbt7+/vT++//z4tXryYDAYDNWrUiDp27Eh9+vSh0tJSQc7du3fTrFmzKDw8nA4fPkzt2rUT6uzdd98loipnf6Bq2XTfvn00Y8YMYeDo3Lkz9e3blzZv3myxKc7a4LZ//34CQP3797fo4+3ataPp06fThAkTqGHDhjRixAiKiooSym2u1Jo2oBFVuTrodDrq3LmzsGnG1Ef79+9Pb7zxhsUGNYPBQM2bN7fYMGJSpE3KSmpqqvAy8fvvvxMAioiIoOjoaKH9TUptcHAwTZs2jYiIzp49K0xo69evpxkzZlBUVBR17NiRXnvtNYv+HxgYaNH/Dx48KLT/yy+/TGFhYcLmRXM3KXNMy82mDY3V2z80NJS6d+9OCQkJwvOfk5NDAOi9994TlJfTp08Lz7/pmTWNfzfffDP5+/sT0fXxb+3atRQcHCxsrioqKqKLFy9SamoqffHFF0LdtWnThohI2Ezzxx9/CC95n376qXDdZ599Rh07dqTp06fTxx9/TDfffDMNGzaMoqKiqF69eqKVWmvj39KlS4Xx7+rVq9S7d29q1aoV/fnnn8ILWmVlpfBSZ2qnAQMGEFDlkuLn50f79u2j6OhoioiIoH///Zc2bNgguGuYXkpMzzYAeuONN6h27drCJj0Tc+bMIaDqBdta+5vG60ceeYT8/f0pOjqaMjMzhY14tpRavV4vGEN0Oh3dc889tHbtWkpKSiIAlJ+fT0VFRRQREUGvv/463XDDDdSwYUMCQO+++64w95nKUK9ePVq1ahUZjUZq2bIlffbZZ0LfnzNnDun1euFl4fvvv7fYqPjggw9SdHQ0hYaGCi/rREQXL14kADRhwgTq1KkTRURECGORqe8fO3aM/vOf/1gYHiZPnkxAlQuF+Utzjx49BDlMLkRHjhyhkpISOnbsGO3evZu6d+9OBoOBvvnmG9q7dy9NnjyZgoOD6cMPP6SkpCRh05o5RUVFFBgYSDqdjiZMmEDbtm2jQYMG0Y033khvvPGGxZzWpUsXGjt2LKWnpwvPwy+//EIbNmygbt26UY8ePSw2GH3wwQdUu3Zt6tq1q1WlNisrS2gXg8FAzZo1o+eff15wjSG6PvaYFOYrV65QeHi4hYuFaR7//fffhe8qKiro2LFjlJKSQp9++imFhISQ0Wikn376ic6fPy+0XcOGDYXN2/feey81bNjQqlK7atUqAiC0myndiIgIWrduXY3r7Sm11VmzZg0BVa4d1dm/fz9FR0cLc5OJgwcPUkJCAn388ce0d+9eWrFiBbVp08bCleCGG26gxMREC13os88+o/j4eOHvX3/9lerVq0e//vor7du3j37++WeKioqyMF6Yl7+oqMhheUw4rdT27NmTjh07RmfOnLGIeuCqUltdIZsyZQo1aNBA+NuaUmuyiqWmptoumB2ltjpNmza1+paQkZFB+fn5VFBQIFgUrZX/xIkTVpXarl27UnR0dI3yT5o0SXhLs1X+c+fOUUlJibBbOisri65cuSIMrqb/miZ4g8FAa9assVn+Hj16UFJSkqBYmD6mwSsxMbFG+U3ynj9/noqKiui+++4TJk7z8peUlAgWR/PymyxL/v7+NHfuXJo1a5YwyZ89e5bGjRtHAGjFihUUGxtLM2bMqNH+Jp/P4uJi4TtT+wcEBNAvv/xC06ZNo7CwMOFtbvjw4TXK/+CDD9IjjzxCRFUPVFBQEC1dutSi/c37wenTp0mn01FERIQwITz44IMEVFlNz5w5I0zSWVlZwhulqT5NA7np727duln0f9OEDly3MB86dIgAWPgnmQZxvV4vRAYwlR9ADX+jgwcPChZ18/KbJkRzK4e5NWbYsGEWv23atEm4t0mTJuTv709t2rQRrKT//POPxfPftm1b0uv1lJ2dLbxs3nrrrRQZGUkA6IEHHrCw/JjXjclKZB5dwrRz3nwSJyKaNGmSsFJhev7fffddi4gS5mkbDAaaOXMm9ejRg55++mkiqvILi4iIoF27dgmT11tvvVWj/5swlaF3795C++/cuZOaNm1KTzzxBP3nP/+x2u6mZ8Lk12hNqW3Tpo3wTBQXF9OUKVMoLi6OQkJC6LvvvqOWLVuSn5+fhTzm7W8aG99++23y8/OjKVOmCG1YvT9Wr/fqH1P7m/qNSd4mTZoI0S+sjX8FBQXC/QMHDqS2bdsKu6FNUTS2bt1q4eteXQ6dTkf9+/enxMREi+ffFPngxx9/JKKqZ9vkT25aDTHH9GxbG/9M7X/y5EkCQP/73/+oY8eOVKtWLfrggw/ozjvvFF6uzDH1t7Zt29L7779fQ5E2vZTNmTOHfv75Z2FMaN++PTVq1IhCQkJo0KBBQrrDhw+nuLg4Yew3Rfl48cUXqUGDBkIZWrduLRgWLl68SERE27dvJwA0ePBg0uv19Prrr9for0DVapqp7qwpP0RVRpTatWtTaGioMPa0a9eOXnnlFRozZgwZDAYqKCggnU5HAQEBQjuvWrVKSKOoqIj8/f2pXbt2Qv0SEY0YMYJ69+4tvICZK4hEJBhmTM81UdU8EhwcTIMGDbKYF1u0aEGffPIJjR8/nqKjo6l9+/bCb2fPniUAtG3bNqFMTZs2pZdeesmmD+moUaOoXr16dPjwYTp37hxVVlbS2LFjLQxM1ZVaoqpIDOb1bZrHbfmOrl+/nvz8/IR0S0pKyM/Pj55//nmL58B8PDYYDBZK5rx58ygkJKRG2iNGjKBevXrV+N4ZpdbUnitWrLD43jSHvPHGGzXuefzxxy3ajIiE/RGmeaxr167Uo0cPi2uWLVtGAITnpl69ejX2RL377rvUvHlzi+9sld8eTvvUhoSEoGnTpqhfv77gowNU+Z6Ul5cLvnIAkJ2djdTUVLRq1crZbAQCAgIsfDGAKj/C8PBwHDp0yOV0zamsrBR295oTFxeH0NBQzJ8/H4GBgahTp47V8jdq1AjR0dEW5c/Ly8P27duRl5dXo/yzZs3CkCFDLHysqlO3bl0EBATg0qVLiI+PR0xMDMLDw7F//36kpKQgNDQUn3/+OZ599lk0b94ce/bssbqD0ryM9evXx759+7Bnzx7hExAQgGbNmuGff/6xeW+dOnUQFBSEkydPIigoCAMHDrQof0BAANq2bYvKykqL9j9w4ABKSkosdk+b7klNTUVGRgaAql34ly5dsukXZzQaYTQahe9M7Q9U+QHOmzcP/fv3h16vR0FBARYsWGCRRkVFBfbv34+EhAT8+uuvGDZsGH799Vf069dPqJuSkhKLfvDjjz9Cr9dj+PDhNfy1YmJiUL9+fcyePRsDBgxATEwMEhMTUVlZiRYtWmDnzp3Yu3evsGuzU6dOmDVrlnD/wYMHBT9aAEKeR48ehU6ns+gX5eXlwr9Nft7m5Scii3S7d++OoUOH1qhHAHjuueewZ88eLFq0CAAwaNAgNG/eHPXq1cPVq1dRWVmJ1q1bw2AwIDU1VaibCxcuoLKyEqGhoQgICABQ1T/Nn/+wsDBUVlbiyJEjiImJwbFjx7Br1y7BZysuLg5+fn4IDAwEAMHvtG7dunj99dcBAC+//DIAIDY2Fnv27MEPP/wgpP/qq6+CiDBr1iwMGzbM4vmvrKxEfHy8Rb8GqqIY7NmzB/fff7/Qth9//DHeffddrFixwsLH2eRbW51z584hJycHer3eog8OHjwYHTt2xKxZszBu3DjBH/ynn34S8h8zZgwqKythMBisji/Adb/eyMhIGI1GHD9+HFlZWfjoo4/w9NNPo7i4WIiiYaJJkyZCPXbv3h0AcNttt6G8vBy333479u3bBwB45ZVXhPp85ZVXsGTJEiGqwMKFC/F///d/aNiwIaKjowFURT5o164d1qxZY5Ffenq64FdnbfwzlbeiogL79u3D6tWrUbt2bWRnZ+PKlSsArvfxsLAwoY169Ogh5PH111+jfv36wjNhwuQn37BhQ+HZfvbZZxESEoK0tDSLfQDmz3Z1zJ/thg0bok6dOti/fz9OnDgBvV6PkpISZGdnQ6fTITc3V9g3kJqaKpQhISEBYWFh8Pf3txgTTOP70aNHMXPmTAwYMAB6vR5HjhzBqVOnEBYWJjzHpjLcfPPNNWQsLy9HYWGhUIaEhARcvHgR9erVE9qoY8eOCAgIwJIlS1BZWSlE5anOO++8U2Pcqo5Op0NpaSlCQ0Px66+/IjExEXq9Hnq9Hr1790ZFRQW+/vprYY+JaUwwH6fLyspQVlYGIrLo46b9F6a28PPzE/rV+vXr8cUXXwCoekZM6PV66HQ6izGtuLgYJ06cwE033YScnBxcvnzZos1NY6Uplu2GDRtw/Phxi2tMEBFGjx6NP//8E2vXrkWLFi1Qt25dlJeX448//sDAgQNt1lVBQYGwj8OUlmket1bP69evR79+/dCxY0fh+Q0ICMAtt9yCK1euYP/+/cJz0L17d9StWxfdu3fHnj17hP0nQNUcetNNN9VI35bO4gym59bc7958Dnn//fdr3FNUVGSx3wi43gamdrvttttw/Phxi/jCR48eRUJCgjB/2EqnekxiW+W3izMasK0deiYGDhxIrVq1ok2bNtGePXuoT58+1LRpU+FNxhVL7ciRI+mWW26hU6dO0cWLFwVr3AMPPEAvv/yyxb35+fmUkpIi+Nx+/vnnlJKSQmfOnCGiqjeTcePG0bZt2+j06dO0e/duGjZsGBmNRjpw4ICQztdff03JycmUmppK33zzDQUFBdGXX35pUf7mzZvTwoULhXs+/PBD8vPzo8TERJo9ezbdeeedFBQURE2aNLEov8lH7/DhwzXKf/HiRXrwwQepTp06lJKSQv/5z38E3yBb5TdPw1T+YcOGEQAaP348LViwgJ577jnS6XQWb9hEJLgNmL9hE1XtOE1JSRF29v/+++9CGrfeeqvV8i9cuJB0Oh2FhYXR+++/L/jZBgYGUq1atWjHjh10//33C/53iYmJQozC/v37U6tWrWjhwoX08MMPU1xcHDVq1IjGjh1LBoOBYmNj6dVXX6XVq1fTmjVr6O2336aYmBgKCAiggQMHUlRUFJ06dYry8/Pp7bffFqxAb775Js2dO5fuvfdeCgwMpHfffZd0Oh395z//oaFDh9IXX3xB9957L/n7+9N9990n9IOKigrBOrdw4UI6ffo0rVmzhmJiYig4OJhOnDghWD6XL19Oubm51KlTJwoPD6emTZvSokWLaPXq1dSiRQsCqqIf/P333zR8+HAKDg6mqKgowc/MYDDQ+vXrafHixRQTE0PNmjWjmJgYatSoEX3yySek1+spICCAIiMjqV+/frR582ZatGiR4Gv78ssvU0pKCq1atYqio6Opd+/e9MgjjxAAeumll2j69On00EMPkdFopJ07dwr9v1u3blS7dm0KDw+ngIAAql+/Pj366KM0b948wfVl4sSJdPfddwsxZ/v06UNdu3alpk2bCn6ix48fF5a5GzZsSDExMTR69GghhqXRaBR2vT/22GMUERFBN998s2CBj4mJofbt29NNN91ELVu2pEaNGlFCQgI1b96cFixYIFjkT506JbhnTJs2jXr16kXDhw+nH374gcLCwujNN9+s0f+NRqNF/x8yZAjp9Xr64IMPaPny5TR16lQhjutPP/1EW7ZsoUGDBtGLL75In332GT3xxBOUkJBAer2eevToQTExMRQUFEQNGjSg4OBg+uuvvyg9PV349OzZk2JjY+mHH34goMqX2hTLNjMzkz788EMKDAykrl27UsOGDYXlb71eT2+//TatXbuW/P39KSQkhJo0aUL//vsvxcbGUu3atemdd96htWvX0smTJ2nNmjWC64tpBaOiooLatWtHN998M/3666+CJbNz585Ut25dmjJlSo3xb+TIkVSrVi3B/zElJYXmzZtHAQEBNHHiRHrssccEH8l27dpR3759qVOnTjRp0iRq0KABffvtt7R48WJq1KgR1a5dmwICAqh27do0ZcoU+uyzz6h58+ZChI8ffviB+vTpQ6GhoXTixAn65ZdfhFUck9uSaTnUFKM3OTmZwsLCSKfT0eHDh2nKlClkNBrpypUrlJiYaLGcOWfOHDIYDPT6668L1s2XXnqJZs2aJbT/wIEDafDgwbRw4UJ68sknhZiqAQEB9Mwzz1BgYCA9+uijQozozz77jJo0aSL4/N155520cOFCIfbyvHnz6MCBA/T444+Tn5+f4Cr28ssvU7du3Sg8PJwCAwPJaDTSe++9RxEREfTGG2+Qv78/DRkyhBo1akTfffedkF6fPn0IAL3//vvUpEkT6tOnD0VFRdF9991HX375JW3ZsoUee+wxwQXomWeeEfresmXL6LXXXqMff/xR6HstWrSgunXr0unTp+nEiRPUr18/mjhxIq1evZoef/xxweJ91113kb+/P/3555/Ut29fCg4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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ We can notice that the maximum values of every sensor are no more than the 250 value.\n", + "\n", + "We will now check for the minimum values recorded by the sensors." + ], + "metadata": { + "id": "opYvY-uQb1fR" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.4.3: Finding the minimum values recorded by each sensor" + ], + "metadata": { + "id": "JGSmOe8EcI8l" + } + }, + { + "cell_type": "code", + "source": [ + "minValueInEachColumn = dataset.min() #Reading the minimum PM2.5 value recorded by each sensor\n", + "minValueInEachColumn.plot() #ploting them\n", + "\n", + "#dataset.min().plot() #memory efficient approach" + ], + "metadata": { + "id": "zwbbXYqCZEo2", + "outputId": "a81b1ccb-60cc-4e40-8a2f-5dca3825ed93", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + } + }, + "execution_count": 11, + "outputs": [ { - "cell_type": "code", - "source": [ - "thresholdValue = 15\n", - "pm25 = pd.DataFrame(columns=[\"long\", \"lat\", \"pm25\"])\n", - "for col in dataset[1:]:\n", - " res = [i for i in dataset[col].values if i >= thresholdValue]\n", - " if len(res) == 0 or col == \"Unnamed\":\n", - " dataset = dataset.drop([col], axis = 1)\n", - " else:\n", - " if \"Poi\" in col:\n", - " #print(\"Hey\")\n", - " col = col.strip(\"Point()\")\n", - " col = col.rstrip(\").1\")\n", - " long, lat = col.split()\n", - " pm25 = pm25._append({'long': float(long), 'lat': float(lat), 'pm25': len(res)}, ignore_index=True)\n", - "pm25.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - }, - "id": "51ureCEF_jv6", - "outputId": "b2042a63-df43-4510-c395-d02194cf09d7" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " long lat pm25\n", - "0 139.096021 36.404732 8204.0\n", - "1 139.042873 36.381603 8999.0\n", - "2 138.995512 36.338016 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" ], - "metadata": { - "id": "3_LVZ8q0AXua" - } - }, + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ We can observe that many sensors have recorded negative PM2.5 values. Thus, we replace the negative PM2.5 values of each sensor with Zero" + ], + "metadata": { + "id": "buCv95fkc2sO" + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Step 3.4.5: Replacing the values less than zero to 0" + ], + "metadata": { + "id": "OYlUYzY1dTjW" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.where(dataset > 0, 0, inplace=True)\n", + "dataset.min().plot()" + ], + "metadata": { + "id": "Q0DaB2g_daf8", + "outputId": "d62c98c2-cdf6-4db3-904f-8f02893ffe2f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + } + }, + "execution_count": 12, + "outputs": [ { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "import plotly.express as px\n", - "\n", - "fig = px.density_mapbox(pm25, lat = 'lat', lon = 'long', z = 'pm25',\n", - " radius = 8,\n", - " zoom = 6,\n", - " mapbox_style = 'open-street-map')\n", - "fig.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 542 - }, - "id": "t105pivfBTRh", - "outputId": "fb8edabb-3e70-4a11-d212-49051a9b27f9" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
\n", - "
\n", - "\n", - "" - ] - }, - "metadata": {} - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 12 }, { - "cell_type": "markdown", - "source": [ - "__Inference from the above figure:__ High PM2.5 levels were frequently observed at the south part of Japan, starting from Tokyo. " + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "Z-ppzs3AB7xM" - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Step 3.7: Printing the heat map of maximum PM2.5 value recorded by each sensor" - ], - "metadata": { - "id": "1QDznnGOClCq" - } + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "__Observation:__ The abnormal values were replaced to 0." + ], + "metadata": { + "id": "U1O0xBeJd8Sl" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.5: Create a dataframe of the sensors having pm25 >= 15\n", + "(useful to prune those sensors that do not record any pm2.5 value)" + ], + "metadata": { + "id": "AyJCyA6n97Oq" + } + }, + { + "cell_type": "code", + "source": [ + "thresholdValue = 15\n", + "pm25 = pd.DataFrame(columns=[\"long\", \"lat\", \"pm25\"])\n", + "for col in dataset[1:]:\n", + " res = [i for i in dataset[col].values if i >= thresholdValue]\n", + " if len(res) == 0 or col == \"Unnamed\":\n", + " dataset = dataset.drop([col], axis = 1)\n", + " else:\n", + " if \"Poi\" in col:\n", + " #print(\"Hey\")\n", + " col = col.strip(\"Point()\")\n", + " col = col.rstrip(\").1\")\n", + " long, lat = col.split()\n", + " pm25 = pm25._append({'long': float(long), 'lat': float(lat), 'pm25': len(res)}, ignore_index=True)\n", + "pm25.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "51ureCEF_jv6", + "outputId": "b2042a63-df43-4510-c395-d02194cf09d7" + }, + "execution_count": 13, + "outputs": [ { - "cell_type": "code", - "source": [ - "maxPM25 = pd.DataFrame(columns=[\"long\", \"lat\", \"maxPM25\"])\n", - "for col in dataset[1:]:\n", - " res = [i for i in dataset[col].values if i >= 15]\n", - " if len(res) == 0 or col == \"Unnamed\":\n", - " dataset = dataset.drop([col], axis = 1)\n", - " else:\n", - " if \"Poi\" in col:\n", - " col = col.strip(\"Point()\")\n", - " col = col.rstrip(\").1\")\n", - " long, lat = col.split()\n", - " maxPM25 = maxPM25._append({'long': float(long), 'lat': float(lat), 'maxPM25': max(res)}, ignore_index=True)\n", - "maxPM25.head()\n", - "\n", - "import pandas as pd\n", - "import plotly.express as px\n", - "\n", - "fig = px.density_mapbox(maxPM25, lat = 'lat', lon = 'long', z = 'maxPM25',\n", - " radius = 8,\n", - " zoom = 6,\n", - " mapbox_style = 'open-street-map')\n", - "fig.show()" + "output_type": "execute_result", + "data": { + "text/plain": [ + " long lat pm25\n", + "0 139.096021 36.404732 8204.0\n", + "1 139.042873 36.381603 8999.0\n", + "2 138.995512 36.338016 13929.0\n", + "3 139.342672 36.410566 12667.0\n", + "4 139.381732 36.290913 10391.0" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 542 - }, - "id": "90j1c_T9DVtL", - "outputId": "4bf1b92b-1001-4256-aaf3-bfe3710a0aef" - }, - "execution_count": 15, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
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\n" ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.6: Drawing the frequency heatmap of sensors\n", + "\n", + "The frequency heatmap provides cruical information regarding how frequently a particular sensor has recorded harmful levels of pollution" + ], + "metadata": { + "id": "3_LVZ8q0AXua" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import plotly.express as px\n", + "\n", + "fig = px.density_mapbox(pm25, lat = 'lat', lon = 'long', z = 'pm25',\n", + " radius = 8,\n", + " zoom = 6,\n", + " mapbox_style = 'open-street-map')\n", + "fig.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 }, + "id": "t105pivfBTRh", + "outputId": "fb8edabb-3e70-4a11-d212-49051a9b27f9" + }, + "execution_count": 14, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Step 4: Installing the latest version of PAMI package" - ], - "metadata": { - "id": "eJ_MqM0dpnmM" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Im02B5LdO6cJ", - "outputId": "70d3bbc5-fce6-4b05-e80c-62bfcf17014e" - }, - "execution_count": 16, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: pami in /usr/local/lib/python3.10/dist-packages (2023.8.6.2)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.13.1)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", - "Requirement already satisfied: resource in /usr/local/lib/python3.10/dist-packages (from pami) (0.2.1)\n", - "Requirement already satisfied: validators in /usr/local/lib/python3.10/dist-packages (from pami) (0.20.0)\n", - "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (1.26.16)\n", - "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.22.4)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (0.11.0)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (4.41.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.4.4)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (23.1)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (3.1.0)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pami) (2022.7.1)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->pami) (8.2.2)\n", - "Requirement already satisfied: JsonForm>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.0.2)\n", - "Requirement already satisfied: JsonSir>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.0.2)\n", - "Requirement already satisfied: python-easyconfig>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from resource->pami) (0.1.7)\n", - "Requirement already satisfied: decorator>=3.4.0 in /usr/local/lib/python3.10/dist-packages (from validators->pami) (4.4.2)\n", - "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.3.3)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", - "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", - "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", - "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.19.3)\n" - ] - } + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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" + ], + "metadata": { + "id": "Z-ppzs3AB7xM" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Step 3.7: Printing the heat map of maximum PM2.5 value recorded by each sensor" + ], + "metadata": { + "id": "1QDznnGOClCq" + } + }, + { + "cell_type": "code", + "source": [ + "maxPM25 = pd.DataFrame(columns=[\"long\", \"lat\", \"maxPM25\"])\n", + "for col in dataset[1:]:\n", + " res = [i for i in dataset[col].values if i >= 15]\n", + " if len(res) == 0 or col == \"Unnamed\":\n", + " dataset = dataset.drop([col], axis = 1)\n", + " else:\n", + " if \"Poi\" in col:\n", + " col = col.strip(\"Point()\")\n", + " col = col.rstrip(\").1\")\n", + " long, lat = col.split()\n", + " maxPM25 = maxPM25._append({'long': float(long), 'lat': float(lat), 'maxPM25': max(res)}, ignore_index=True)\n", + "maxPM25.head()\n", + "\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "\n", + "fig = px.density_mapbox(maxPM25, lat = 'lat', lon = 'long', z = 'maxPM25',\n", + " radius = 8,\n", + " zoom = 6,\n", + " mapbox_style = 'open-street-map')\n", + "fig.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 }, + "id": "90j1c_T9DVtL", + "outputId": "4bf1b92b-1001-4256-aaf3-bfe3710a0aef" + }, + "execution_count": 15, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Step 5: Converting the data frame into a temporal database" - ], - "metadata": { - "id": "FsT19X0fp1ux" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.extras.DF2DB import DenseFormatDF as db\n", - "obj = db.DenseFormatDF(dataset, '>=', 35)\n", - "obj.createTemporal('temporalDatabasePM25HeavyPollution.csv')" - ], - "metadata": { - "id": "smUkVkF4O3By" - }, - "execution_count": 17, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 6: Printing new lines of the created temporal database" - ], - "metadata": { - "id": "5q43oauYTlc_" - } - }, - { - "cell_type": "code", - "source": [ - "!head -5 temporalDatabasePM25HeavyPollution.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "D0En4pJ8TtHF", - "outputId": "1ec98ca2-a616-440d-b3fb-903d5762344d" - }, - "execution_count": 18, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "0\tPoint(139.750635 35.7977241)\tPoint(139.8841693 35.8422674)\tPoint(139.3819898 36.2229999)\tPoint(139.9206727 35.684037)\tPoint(139.9785953 35.6880639)\tPoint(139.9033705 35.7876179)\tPoint(139.9123927 35.7995549)\tPoint(139.880097 35.953403)\tPoint(139.9035901 35.8570293)\tPoint(139.9012134 35.6552406)\tPoint(139.8356927 35.6967785)\tPoint(139.7209595 35.6108138)\tPoint(139.7054233 35.7609043)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8773135 35.6821907)\tPoint(137.1418898 34.9626945)\tPoint(136.8905668 35.0122987)\tPoint(136.6548337 35.0051925)\tPoint(139.4949175 36.2914457)\n", - 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"xm6W5dCBPPbo", - "outputId": "f6ea6890-fbb4-457d-a612-78a8164b311f" - }, - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 31462\n", - "Number of items : 1119\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 16.545896637213147\n", - "Maximum Transaction Size : 503\n", - "Minimum Inter Arrival Period : 1\n", - "Average Inter Arrival Period : 1.0\n", - "Maximum Inter Arrival Period : 1\n", - "Minimum periodicity : 590\n", - "Average periodicity : 5948.814119749776\n", - "Maximum periodicicty : 31415\n", - "Standard Deviation Transaction Size : 39.73276427461552\n", - "Variance : 1578.7427362530073\n", - "Sparsity : 0.9852136759274235\n" - ] - }, - { - "output_type": "error", - "ename": "TypeError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprintStats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotGraphs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PAMI/extras/dbStats/temporalDatabaseStats.py\u001b[0m in \u001b[0;36mplotGraphs\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[0mitemPeriods\u001b[0m \u001b[0;34m=\u001b[0m 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'periodicity')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotLineGraphFromDictionary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransactionLength\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'transaction length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'transaction length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'frequency'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PAMI/extras/graph/plotLineGraphFromDictionary.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, end, start, title, xlabel, ylabel)\u001b[0m\n\u001b[1;32m 31\u001b[0m \"\"\"\n\u001b[1;32m 32\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m 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pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.19.3)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Converting the data frame into a temporal database" + ], + "metadata": { + "id": "FsT19X0fp1ux" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.DF2DB import DenseFormatDF as db\n", + "obj = db.DenseFormatDF(dataset, '>=', 35)\n", + "obj.createTemporal('temporalDatabasePM25HeavyPollution.csv')" + ], + "metadata": { + "id": "smUkVkF4O3By" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 6: Printing new lines of the created temporal database" + ], + "metadata": { + "id": "5q43oauYTlc_" + } + }, + { + "cell_type": "code", + "source": [ + "!head -5 temporalDatabasePM25HeavyPollution.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "D0En4pJ8TtHF", + "outputId": "1ec98ca2-a616-440d-b3fb-903d5762344d" + }, + "execution_count": 18, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Step 8: Mining Frequent Patterns using FP-growth algorithm" - ], - "metadata": { - "id": "3Xw24J5sEP3n" - } + "output_type": "stream", + "name": "stdout", + "text": [ + "0\tPoint(139.750635 35.7977241)\tPoint(139.8841693 35.8422674)\tPoint(139.3819898 36.2229999)\tPoint(139.9206727 35.684037)\tPoint(139.9785953 35.6880639)\tPoint(139.9033705 35.7876179)\tPoint(139.9123927 35.7995549)\tPoint(139.880097 35.953403)\tPoint(139.9035901 35.8570293)\tPoint(139.9012134 35.6552406)\tPoint(139.8356927 35.6967785)\tPoint(139.7209595 35.6108138)\tPoint(139.7054233 35.7609043)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8773135 35.6821907)\tPoint(137.1418898 34.9626945)\tPoint(136.8905668 35.0122987)\tPoint(136.6548337 35.0051925)\tPoint(139.4949175 36.2914457)\n", + "1\tPoint(139.7962265 35.8959041)\tPoint(139.8841693 35.8422674)\tPoint(139.3819898 36.2229999)\tPoint(139.9206727 35.684037)\tPoint(139.9785953 35.6880639)\tPoint(139.9033705 35.7876179)\tPoint(139.9123927 35.7995549)\tPoint(139.880097 35.953403)\tPoint(139.9012134 35.6552406)\tPoint(139.8276492 35.689975)\tPoint(139.8356927 35.6967785)\tPoint(139.7054233 35.7609043)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8776074 35.744251)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8859668 35.7099429)\tPoint(139.8773135 35.6821907)\tPoint(139.5257126 35.3986601)\tPoint(139.4898296 35.425629)\tPoint(139.6704215 35.2829123)\tPoint(138.3366179 34.9856239)\tPoint(136.8880309 35.1033076)\tPoint(137.1882268 34.9467181)\tPoint(137.1418898 34.9626945)\tPoint(136.9121266 35.0427414)\tPoint(130.1948336 32.4611967)\n", + "2\tPoint(139.8841693 35.8422674)\tPoint(140.067069 35.6445893)\tPoint(139.9785953 35.6880639)\tPoint(139.9033705 35.7876179)\tPoint(139.9123927 35.7995549)\tPoint(140.0679921 35.5260141)\tPoint(139.9012134 35.6552406)\tPoint(139.8276492 35.689975)\tPoint(139.8356927 35.6967785)\tPoint(139.7054233 35.7609043)\tPoint(139.5990181 35.7377286)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8776074 35.744251)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8859668 35.7099429)\tPoint(139.8773135 35.6821907)\tPoint(139.871021 35.6536708)\tPoint(130.1948336 32.4611967)\tPoint(140.5167529 36.1595964)\n", + "3\tPoint(139.8841693 35.8422674)\tPoint(139.9033705 35.7876179)\tPoint(139.9012134 35.6552406)\tPoint(139.8356927 35.6967785)\tPoint(139.7054233 35.7609043)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8776074 35.744251)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8859668 35.7099429)\tPoint(139.8773135 35.6821907)\tPoint(139.871021 35.6536708)\n", + "4\tPoint(139.9033705 35.7876179)\tPoint(139.880097 35.953403)\tPoint(139.9012134 35.6552406)\tPoint(139.8356927 35.6967785)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8692678 35.7864558)\tPoint(139.871021 35.6536708)\tPoint(139.6849913 35.5003919)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 7: Printing the statistics of the database" + ], + "metadata": { + "id": "rkppthohO2yJ" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.dbStats import TemporalDatabase as tempDS\n", + "obj = tempDS.TemporalDatabase('temporalDatabasePM25HeavyPollution.csv')\n", + "obj.run()\n", + "obj.printStats()\n", + "#obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 613 }, + "id": "xm6W5dCBPPbo", + "outputId": "f6ea6890-fbb4-457d-a612-78a8164b311f" + }, + "execution_count": 21, + "outputs": [ { - "cell_type": "code", - "source": [ - "from PAMI.periodicFrequentPattern.basic import PFPGrowth as alg\n", - "obj = alg.PFPGrowth('temporalDatabasePM25HeavyPollution.csv',maxPer=8000, minSup=100, sep='\\t')\n", - "obj.mine()\n", - "obj.printResults()\n", - "obj.save('soramame_periodicFrequentPatterns.txt')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oGprbukCQFXY", - "outputId": "ca999950-0864-49b4-801c-4e29660d71b5" - }, - "execution_count": 24, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", - "Total number of Periodic Frequent Patterns: 104\n", - "Total Memory in USS: 2632912896\n", - "Total Memory in RSS 2679320576\n", - "Total ExecutionTime in ms: 1.0586721897125244\n" - ] - } - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 31462\n", + "Number of items : 1119\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 16.545896637213147\n", + "Maximum Transaction Size : 503\n", + "Minimum Inter Arrival Period : 1\n", + "Average Inter Arrival Period : 1.0\n", + "Maximum Inter Arrival Period : 1\n", + "Minimum periodicity : 590\n", + "Average periodicity : 5948.814119749776\n", + "Maximum periodicicty : 31415\n", + "Standard Deviation Transaction Size : 39.73276427461552\n", + "Variance : 1578.7427362530073\n", + "Sparsity : 0.9852136759274235\n" + ] }, { - "cell_type": "markdown", - "source": [ - "### Step 9: Printing some of the generated periodic-frequent patterns" - ], - "metadata": { - "id": "xiV9dSvpZd7O" - } + "output_type": "error", + "ename": "TypeError", + "evalue": "ignored", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", + "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[1;32m 3\u001B[0m 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369\u001B[0m \u001B[0;31m#numberOfTransactionPerTimeStamp = self.getNumberOfTransactionsPerTimestamp()\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 370\u001B[0;31m \u001B[0mplt\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mplotLineGraphFromDictionary\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mitemFrequencies\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m80\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'Frequency'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'no of items'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'frequency'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 371\u001B[0m \u001B[0;31m#plt.plotLineGraphFromDictionary(itemPeriods, 50, 'Periodicity', 'no of items', 'periodicity')\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 372\u001B[0m 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"https://localhost:8080/" }, + "id": "oGprbukCQFXY", + "outputId": "ca999950-0864-49b4-801c-4e29660d71b5" + }, + "execution_count": 24, + "outputs": [ { - "cell_type": "code", - "source": [ - "!head soramame_periodicFrequentPatterns.txt" - ], - "metadata": { - "id": "rihfOjuEZl1M", - "outputId": "1cc60d82-9f2f-403b-a86e-a3c92bc4f534", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 23, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Point(140.0776785\t35.4879615):350:4720 \n", - "Point(140.0934838\t36.4673588):366:3935 \n", - "Point(130.5076265\t32.3449429):383:3457 \n", - "Point(136.0929559\t33.8919062):403:4390 \n", - "Point(130.4010097\t33.5909924):622:4909 \n", - "Point(133.7536465\t34.2725045):1149:3818 \n", - "Point(133.7536465\t34.2725045)\tPoint(139.378643\t35.8322468):151:4251 \n", - "Point(133.7536465\t34.2725045)\tPoint(135.5608747\t34.7363332):253:3850 \n", - "Point(135.5608747\t34.7363332):1211:4715 \n", - "Point(139.378643\t35.8322468):1266:4405 \n" - ] - } - ] - }, + "output_type": "stream", + "name": "stdout", + "text": [ + "Periodic Frequent patterns were generated successfully using PFPGrowth algorithm \n", + "Total number of Periodic Frequent Patterns: 104\n", + "Total Memory in USS: 2632912896\n", + "Total Memory in RSS 2679320576\n", + "Total ExecutionTime in ms: 1.0586721897125244\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 9: Printing some of the generated periodic-frequent patterns" + ], + "metadata": { + "id": "xiV9dSvpZd7O" + } + }, + { + "cell_type": "code", + "source": [ + "!head soramame_periodicFrequentPatterns.txt" + ], + "metadata": { + "id": "rihfOjuEZl1M", + "outputId": "1cc60d82-9f2f-403b-a86e-a3c92bc4f534", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 23, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Step 7: Visualization of Generated Patterns" - ], - "metadata": { - "id": "A9X6ymGdQI08" - } + "output_type": "stream", + "name": "stdout", + "text": [ + "Point(140.0776785\t35.4879615):350:4720 \n", + "Point(140.0934838\t36.4673588):366:3935 \n", + "Point(130.5076265\t32.3449429):383:3457 \n", + "Point(136.0929559\t33.8919062):403:4390 \n", + "Point(130.4010097\t33.5909924):622:4909 \n", + "Point(133.7536465\t34.2725045):1149:3818 \n", + "Point(133.7536465\t34.2725045)\tPoint(139.378643\t35.8322468):151:4251 \n", + "Point(133.7536465\t34.2725045)\tPoint(135.5608747\t34.7363332):253:3850 \n", + "Point(135.5608747\t34.7363332):1211:4715 \n", + "Point(139.378643\t35.8322468):1266:4405 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 7: Visualization of Generated Patterns" + ], + "metadata": { + "id": "A9X6ymGdQI08" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.graph import visualizePatterns as fig\n", + "\n", + "obj = fig.visualizePatterns('soramame_periodicFrequentPatterns.txt',10)\n", + "obj.visualize(width=1000,height=900)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 375 }, + "id": "XCX7QBEuQITs", + "outputId": "cab4d36a-39a8-428a-c9e4-fe92844822d7" + }, + "execution_count": 25, + "outputs": [ { - "cell_type": "code", - "source": [ - "from PAMI.extras.graph import visualizePatterns as fig\n", - "\n", - "obj = fig.visualizePatterns('soramame_periodicFrequentPatterns.txt',10)\n", - "obj.visualize(width=1000,height=900)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 375 - }, - "id": "XCX7QBEuQITs", - "outputId": "cab4d36a-39a8-428a-c9e4-fe92844822d7" - }, - "execution_count": 25, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Number \t Pattern\n", - "2\tPoint(130.1934363\t32.4585255)\tPoint(133.7536465\t34.2725045)\tPoint(130.1948336\t32.4611967)\n" - ] - }, - { - "output_type": "error", - "ename": "IndexError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualizePatterns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'soramame_periodicFrequentPatterns.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m 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RAGE, Uday kiran (2023), “5+ Years of Multiple Time Series Data of Hourly PM2.5 Recordings Gathered from Various Sensors Located throughout Japan (1-1-2018 to 25-4-2023)”, Mendeley Data, V1, doi: 10.17632/phgrnvykmr.1\n", - "2. PAMI: PAttern Mining toolkit. https://github.com/UdayLab/PAMI\n", - "\n" - ], - "metadata": { - "id": "NqQ1qbZY2120" - } + "output_type": "error", + "ename": "IndexError", + "evalue": "ignored", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mIndexError\u001B[0m Traceback (most recent call last)", + "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[1;32m 2\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 3\u001B[0m \u001B[0mobj\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mfig\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvisualizePatterns\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'soramame_periodicFrequentPatterns.txt'\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m10\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 4\u001B[0;31m \u001B[0mobj\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvisualize\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mwidth\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m1000\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0mheight\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m900\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m", + "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/PAMI/extras/graph/visualizePatterns.py\u001B[0m in \u001B[0;36mvisualize\u001B[0;34m(self, markerSize, zoom, width, height)\u001B[0m\n\u001B[1;32m 67\u001B[0m \u001B[0mtemp\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpoints\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mi\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msplit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 68\u001B[0m \u001B[0mlat\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mfloat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtemp\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 69\u001B[0;31m \u001B[0mlong\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mfloat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtemp\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 70\u001B[0m \u001B[0mname\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mfreq\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 71\u001B[0m \u001B[0mcolor\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"#\"\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0mRHex\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0mGHex\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0mBHex\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;31mIndexError\u001B[0m: list index out of range" + ] } - ] -} \ No newline at end of file + ] + }, + { + "cell_type": "markdown", + "source": [ + "References:\n", + "\n", + "\n", + "1. RAGE, Uday kiran (2023), “5+ Years of Multiple Time Series Data of Hourly PM2.5 Recordings Gathered from Various Sensors Located throughout Japan (1-1-2018 to 25-4-2023)”, Mendeley Data, V1, doi: 10.17632/phgrnvykmr.1\n", + "2. PAMI: PAttern Mining toolkit. https://github.com/UdayLab/PAMI\n", + "\n" + ], + "metadata": { + "id": "NqQ1qbZY2120" + } + } + ] +} diff --git a/notebooks/recurringPatterns/basic/RPGrowth.ipynb b/notebooks/recurringPatterns/basic/RPGrowth.ipynb index 5604b39b..b6a33132 100644 --- a/notebooks/recurringPatterns/basic/RPGrowth.ipynb +++ b/notebooks/recurringPatterns/basic/RPGrowth.ipynb @@ -1,713 +1,713 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Recurring patterns in Temporal Databases using RPGrowth" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Recurring patterns in a temporal database using the RPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the RPGrowth algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:\n", + "\n" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "adfa3aa0-a5a9-4ec1-b529-ded1435da81b" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m7.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already 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"metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Recurring patterns in a temporal database using the RPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the RPGrowth algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:\n", - "\n" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "adfa3aa0-a5a9-4ec1-b529-ded1435da81b" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "d8741f77-7468-4721-85b0-2523bb9211d7" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-05 07:45:33-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.29MB/s in 3.4s \n", - "\n", - "2023-09-05 07:45:38 (1.29 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "cae70cb0-6bf0-46fa-8689-c9f27bbf4103" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Recurring patterns using RPGrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "c9ae4c75-c829-4350-93ec-2997cd51842a" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "80c05027-3922-484a-904d-3e75db3bdad9" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1.\n", - "minRec = 1.8 #minRec is specified in count. However, the users can also specify minRec between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Recurring patterns using RPGrowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.recurringPattern.basic import RPGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.RPGrowth(iFile=inputFile, minPS=minimumSupportCount, maxPer=maximumPeriodCount, minRec=minRec, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('recurringPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "0deb3d54-4af6-449e-95e5-3ac7058f1f68" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Recurring patterns were generated successfully using RPGrowth algorithm \n", - "Total No of patterns: 25955\n", - "Runtime: 27.390766620635986\n", - "Memory (RSS): 615133184\n", - "Memory (USS): 566452224\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'recurringPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "fc8da638-2c8f-4565-d287-f95031bde6e5" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "3\t3\t0:102:1:{{[146, 98911] : 102}} \n", - "1\t0\t2:109:1:{{[753, 99449] : 109}} \n", - "8\t5\t6:109:1:{{[739, 99814] : 109}} \n", - "8\t5\t6\t\t\t4\t9\t0:103:1:{{[739, 99814] : 103}} \n", - "8\t5\t6\t\t\t4\t9\t0\t\t\t9\t0\t6:103:1:{{[739, 99814] : 103}} \n", - "8\t5\t6\t\t\t9\t0\t6:103:1:{{[739, 99814] : 103}} \n", - "1\t9\t1:111:1:{{[705, 98971] : 111}} \n", - "1\t9\t1\t\t\t3\t3\t9:106:1:{{[705, 98509] : 106}} \n", - "1\t9\t1\t\t\t3\t3\t9\t\t\t9\t0:102:1:{{[705, 98509] : 102}} \n", - "1\t9\t1\t\t\t3\t3\t9\t\t\t9\t0\t\t\t9\t1\t4:102:1:{{[705, 98509] : 102}} \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "d8741f77-7468-4721-85b0-2523bb9211d7" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-05 07:45:33-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.29MB/s in 3.4s \n", + "\n", + "2023-09-05 07:45:38 (1.29 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "cae70cb0-6bf0-46fa-8689-c9f27bbf4103" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Recurring patterns using RPGrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "c9ae4c75-c829-4350-93ec-2997cd51842a" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "80c05027-3922-484a-904d-3e75db3bdad9" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _recurringPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the RPGrowth algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.recurringPattern.basic import RPGrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 5000\n", - "minRec = 1.8\n", - "minimumSupportCountList = [100, 200, 300, 400, 500]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of RPGrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'minRec', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of RPGrowth algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.RPGrowth(inputFile, minPS=minSupCount, maxPer=maximumPeriodCount, minRec=minRec, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['RPGrowth', minSupCount, maximumPeriodCount, minRec, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "91164247-8454-427d-f1eb-08cdca86b918" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Recurring patterns were generated successfully using RPGrowth algorithm \n", - "Recurring patterns were generated successfully using RPGrowth algorithm \n", - "Recurring patterns were generated successfully using RPGrowth algorithm \n", - "Recurring patterns were generated successfully using RPGrowth algorithm \n", - "Recurring patterns were generated successfully using RPGrowth algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1.\n", + "minRec = 1.8 #minRec is specified in count. However, the users can also specify minRec between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Recurring patterns using RPGrowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.recurringPattern.basic import RPGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.RPGrowth(iFile=inputFile, minPS=minimumSupportCount, maxPer=maximumPeriodCount, minRec=minRec, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('recurringPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0deb3d54-4af6-449e-95e5-3ac7058f1f68" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Recurring patterns were generated successfully using RPGrowth algorithm \n", + "Total No of patterns: 25955\n", + "Runtime: 27.390766620635986\n", + "Memory (RSS): 615133184\n", + "Memory (USS): 566452224\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'recurringPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fc8da638-2c8f-4565-d287-f95031bde6e5" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "3\t3\t0:102:1:{{[146, 98911] : 102}} \n", + "1\t0\t2:109:1:{{[753, 99449] : 109}} \n", + "8\t5\t6:109:1:{{[739, 99814] : 109}} \n", + "8\t5\t6\t\t\t4\t9\t0:103:1:{{[739, 99814] : 103}} \n", + "8\t5\t6\t\t\t4\t9\t0\t\t\t9\t0\t6:103:1:{{[739, 99814] : 103}} \n", + "8\t5\t6\t\t\t9\t0\t6:103:1:{{[739, 99814] : 103}} \n", + "1\t9\t1:111:1:{{[705, 98971] : 111}} \n", + "1\t9\t1\t\t\t3\t3\t9:106:1:{{[705, 98509] : 106}} \n", + "1\t9\t1\t\t\t3\t3\t9\t\t\t9\t0:102:1:{{[705, 98509] : 102}} \n", + "1\t9\t1\t\t\t3\t3\t9\t\t\t9\t0\t\t\t9\t1\t4:102:1:{{[705, 98509] : 102}} \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _recurringPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the RPGrowth algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.recurringPattern.basic import RPGrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 5000\n", + "minRec = 1.8\n", + "minimumSupportCountList = [100, 200, 300, 400, 500]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of RPGrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'minRec', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of RPGrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.RPGrowth(inputFile, minPS=minSupCount, maxPer=maximumPeriodCount, minRec=minRec, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['RPGrowth', minSupCount, maximumPeriodCount, minRec, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "91164247-8454-427d-f1eb-08cdca86b918" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Recurring patterns were generated successfully using RPGrowth algorithm \n", + "Recurring patterns were generated successfully using RPGrowth algorithm \n", + "Recurring patterns were generated successfully using RPGrowth algorithm \n", + "Recurring patterns were generated successfully using RPGrowth algorithm \n", + "Recurring patterns were generated successfully using RPGrowth algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cc986379-3a35-46a5-eca0-692098920b22" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximumPeriodCount minRec patterns runtime \\\n", + "0 RPGrowth 100 5000 1.8 25955 27.367340 \n", + "1 RPGrowth 200 5000 1.8 13232 25.191905 \n", + "2 RPGrowth 300 5000 1.8 4529 23.890415 \n", + "3 RPGrowth 400 5000 1.8 1999 20.740788 \n", + "4 RPGrowth 500 5000 1.8 1072 19.778986 \n", + "\n", + " memory \n", + "0 622387200 \n", + "1 615542784 \n", + "2 607285248 \n", + "3 596369408 \n", + "4 583581696 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "fc2ccf58-be9e-40ad-eff5-b6642941e112" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "cc986379-3a35-46a5-eca0-692098920b22" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximumPeriodCount minRec patterns runtime \\\n", - "0 RPGrowth 100 5000 1.8 25955 27.367340 \n", - "1 RPGrowth 200 5000 1.8 13232 25.191905 \n", - "2 RPGrowth 300 5000 1.8 4529 23.890415 \n", - "3 RPGrowth 400 5000 1.8 1999 20.740788 \n", - "4 RPGrowth 500 5000 1.8 1072 19.778986 \n", - "\n", - " memory \n", - "0 622387200 \n", - "1 615542784 \n", - "2 607285248 \n", - "3 596369408 \n", - "4 583581696 \n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "fc2ccf58-be9e-40ad-eff5-b6642941e112" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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VmUfNjiUiIg2cykc91ybUj6QJfYlr2ZiC4nLu/+RnZv28z+xYIiLSgKl8NADBjTyZ8furuPmKZpTbDP4wdwuvLNqmK2FERMQUKh8NhLeHG2/ecQWPD4oB4L3kncTP3MCpUl0JIyIijlWt8pGYmEivXr3w9/cnLCyMkSNHkpGRcc5ya9as4brrrqNRo0YEBATQv39/Tp06VWuhpWYsFgtPXN+ON+7ojqeblUXp2dz5wRpyCorNjiYiIg1ItcpHcnIy8fHxrF27lsWLF1NWVsaQIUMoLCy0L7NmzRqGDRvGkCFD+Pnnn1m/fj0JCQlYrdrJ4ixu6RHJjN9fRZCvB5sO5HHLlNVkZOtKGBERcQyLYRg1PvB/5MgRwsLCSE5Opn///gBcffXVXH/99bz88suXtI6SkhJKSv53D4r8/HyioqLIy8sjICCgptHkEuw+WshD09ez+2ghfl7uTLm7J9e2CzU7loiIuKD8/HwCAwMv6fv7snZH5OXlARAcHAxATk4O69atIywsjD59+tC0aVOuvfZaVq5ced51JCYmEhgYaH9FRUVdTiSphuiQRiRN6MNV0cGcLCnnoenrmbF2r9mxRESknqvxng+bzcaIESPIzc21l4u1a9fSu3dvgoOD+ec//8kVV1zBZ599xtSpU0lPTycmJuac9WjPh/lKy238Ye5m5m44CMDD10QzeXhH3KwWk5OJiIircMiej/j4eNLT05k1a5Z9ms1mA+DRRx/lwQcfpEePHrzxxhu0b9+eTz75pMr1eHl5ERAQUOkljuXpbuW10d15ekg7AD5euZtHP0+lqLTc5GQiIlIf1ah8JCQksHDhQpYtW0ZkZKR9ekREBACdOnWqtHzHjh3Zt083tnJmFouFhOtieHtMDzzdrfz462Fuf38Nh/N1JYyIiNSuapUPwzBISEggKSmJpUuXEh0dXWl+q1ataNas2TmX327fvp2WLVteflqpcyO6N+PLsVcR3MiT9IP5jJyyiq2H8s2OJSIi9Ui1ykd8fDwzZsxg5syZ+Pv7k52dTXZ2tv0eHhaLhWeeeYa3336bOXPmkJmZyZ/+9Ce2bdvGww8/XCcDkNoX2zKYeRP60ia0EVl5xYx+bzVLtx02O5aIiNQT1Trh1GKp+gTEadOm8cADD9j//MorrzBlyhSOHz9O9+7defXVV7nmmmsu6TOqc8KK1K28U2WMn5HK6p3HsFrghZs68UDf6Iu/UUREGpzqfH9f1n0+6oLKh3Mpq7DxfFI6X6XsB+CBPq34002ddCWMiIhU4rD7fEj95+Fm5ZXbuvLssA4ATF+9h7GfpXCyRFfCiIhIzah8yEVZLBbGD2jD1Lt74uVuZem2HEa/t4asPD2vR0REqk/lQy7Z8K4RfPVob0L8vPg1K5+b31nFlgN5ZscSEREXo/Ih1XJFVBDz4vvQrqkfOQUl3P7+Gn74JdvsWCIi4kJUPqTaIhv7Mmd8H/rFhHCqrIJHZ6Ty0U+7cLJzl0VExEmpfEiNBHh7MO2BXtx1VQsMA/76za88Py+d8gqb2dFERMTJqXxIjbm7WfnbyC48f2NHLBb4Yt0+Hvo0hYLiMrOjiYiIE1P5kMtisVj4fb/WvHdPLD4ebqzYfoRR767hwIkis6OJiIiTUvmQWjG0czhfP9qbMH8vMg4XMHLKatL255odS0REnJDKh9SarpGBzIvvS4dwf46eLOGO99ewaEuW2bFERMTJqHxIrWoW5MOc8X0Y2D6UknIb47/YwHvJO3UljIiI2Kl8SK3z83Lnw/viuL93SwBeWbSNP/x7C2W6EkZERFD5kDri7mblpZu78OLvOmG1wFcp+3lg2s/kndKVMCIiDZ3Kh9SpB/tG8+F9cfh6urEq8xi3vbua/cd1JYyISEOm8iF1blDHpswe15vwAG8yc04ycsoqUveeMDuWiIiYROVDHKJzs9NXwnRuFsCxwlLGfLiW/2w6ZHYsERExgcqHOEx4oDdfP9qbwR2bUlpuY+KXG3ln6Q5dCSMi0sCofIhDNfJy5/17Y3n4mmgA/vnDdp6evZnScl0JIyLSUKh8iMO5WS386aZOvDyyC25WC//ecIB7P15HblGp2dFERMQBVD7ENPde3ZKP74/Dz8uddbuPc+vU1ew5Wmh2LBERqWMqH2KqAe3DmDO+N82DfNh1tJCRU1fx8+7jZscSEZE6pPIhpusQHkBSfB+6RwaSW1TGPR+tI2njAbNjiYhIHVH5EKcQ5u/NrEd6M6xzOKUVNp74ahNvLN6uK2FEROohlQ9xGj6ebky9uyePXtsagLeW7OCJr9IoKa8wOZmIiNQmlQ9xKlarhedu6EjirV1xs1qYl3aIez5ax/FCXQkjIlJfqHyIUxpzZQs+ffBK/L3dWb/nBLdMXcXOIyfNjiUiIrVA5UOc1jUxIcwd34fIxj7sPVbErVNXs2bnMbNjiYjIZVL5EKcW09SfefF96dEiiLxTZdz3yTr+tWQHhSXlZkcTEZEaUvkQpxfi58WXY6/mxm4RlFUYvLZ4O/1eXcYHK3ZyqlQno4qIuBqL4WTXMubn5xMYGEheXh4BAQFmxxEnYrMZzN90kLd+3MGeY0UAhPp7MWFAG8Zc2QJvDzeTE4qINFzV+f5W+RCXU15hY+6Gg7y9dAcHTpwCIDzAm/jr2nJHXBSe7tqhJyLiaCof0iCUltuYnbqfd5ZmkpVXDEDzIB8eG9SWW3tG4uGmEiIi4igqH9KgFJdVMOvnfUxZvpMjBSUAtGziy+ODYrj5iua4WS0mJxQRqf+q8/1drX8aJiYm0qtXL/z9/QkLC2PkyJFkZGRUWmbAgAFYLJZKr3HjxlV/FCKXyNvDjQf6RrPimYH8cXhHght5svdYEU9+vYkhbyTzn02HsNmcqmOLiDRo1drzMWzYMO6880569epFeXk5kydPJj09na1bt9KoUSPgdPlo164df/nLX+zv8/X1veS9GNrzIZersKScT9fs4f3kXeSdKgOgfVN/nrg+hqGdw7FYtCdERKS2Oeywy5EjRwgLCyM5OZn+/fsDp8vHFVdcwZtvvlmjdap8SG0pKC7jk5V7+OinXRT8974gnZsF8OT17biuQ5hKiIhILaqzwy5ny8vLAyA4OLjS9C+++IKQkBC6dOnCc889R1FR0XnXUVJSQn5+fqWXSG3w9/bg8cExrHz2OhIGtqWRpxu/HMrn4U9TuGXqalZsP6Kn5oqImKDGez5sNhsjRowgNzeXlStX2qd/8MEHtGzZkmbNmrF582aeffZZrrzySubOnVvlev785z/z0ksvnTNdez6kth0vLOX95J18umYPxWU2AHq1asyT17end5smJqcTEXFtDjnsMn78eBYtWsTKlSuJjIw873JLly5l0KBBZGZm0qZNm3Pml5SUUFJSUil8VFSUyofUmZyCYt5bvosZ6/ZSWn66hPRp04Qnr29HXKvgi7xbRESqUuflIyEhgfnz57NixQqio6MvuGxhYSF+fn589913DB069KLr1jkf4ijZecVMWZbJrPX7KKs4/WvQv10oT13fju5RQeaGExFxMXV2zodhGCQkJJCUlMTSpUsvWjwA0tLSAIiIiKjOR4nUufBAb14e2YVlTw/gzl5RuFktrNh+hJunrOL3n67nl0N5ZkcUEamXqrXnY8KECcycOZP58+fTvn17+/TAwEB8fHzYuXMnM2fOZPjw4TRp0oTNmzfzxBNPEBkZSXJy8iV9hvZ8iFn2HivkrSU7mLfxIGduC3JDl3CeuL4d7Zr6mxtORMTJ1dlhl/Ndmjht2jQeeOAB9u/fzz333EN6ejqFhYVERUVxyy238Pzzz+s+H+IyMnNO8vaSHfxn8yEMAywW+F23Zjw+OIY2oX5mxxMRcUq6vbpILcjILuDNH7ezKD0bAKsFRvZozuODYmjZpJHJ6UREnIvKh0gtSj+Yx5s/bufHX3MAcLdaGBUbScJ1bYls7GtyOhER56DyIVIH0vbn8sbi7SRvPwKAh5uFO3u1IH5gW8IDvU1OJyJiLpUPkTqUsuc4ry/ezuqdxwDwdLdyz1UtGTegNWH+KiEi0jCpfIg4wJqdx3h9cQbr95wAwNvDyv29W/HotW0IbuRpcjoREcdS+RBxEMMw+GnHUV5bvJ1N+3MBaOTpxoN9oxnbrzWBvh7mBhQRcRCVDxEHMwyDpdtyeH3xdn45dPrhiP7e7vz+mtY8dE0r/L1VQkSkflP5EDGJYRh8/8th3li8nYzDBQAE+XrwSP/W3N+7FY283E1OKCJSN1Q+RExmsxl8syWLN3/czs4jhQA0aeTJuGvbcG/vlnh7uJmcUESkdql8iDiJCpvB/LSDvLVkB3uPFQEQ6u9F/IA2jLmqBV7uKiEiUj+ofIg4mbIKG3M3HODtJZkczD0FQESgNwnXtWV0bBSe7tV6xqOIiNNR+RBxUqXlNr5O2c87SzPJzi8GILKxD48NiuHWHs1xd1MJERHXpPIh4uSKyyr48ud9TFm2k6MnSwBo1cSXxwfHMKJ7c9ysVT/EUUTEWal8iLiIU6UVfL52D+8l7+J4YSkAbcP8mDQ4huFdIrCqhIiIi1D5EHExJ0vK+XT1Hj5YsYu8U2UAdAj354nr2zGkU1MsFpUQEXFuKh8iLiq/uIxPVu7m4592U1BSDkDX5oE8eX07BrQPVQkREael8iHi4nKLSvnwp11MW7WHotIKAHq0COLJ69txTdsQlRARcToqHyL1xLGTJby/YhefrdlDcZkNgCujg3ny+nZc3bqJyelERP5H5UOknskpKGbqsp3M/HkfpeWnS0jftk148vr2xLZsbHI6ERGVD5F6KyvvFFOWZfLV+v2UVZz+1R3QPpQnr29Ht8ggc8OJSIOm8iFSz+0/XsQ7SzOZs+EAFbbTv8KDOzblyevb0amZfm9ExPFUPkQaiD1HC3l7yQ7mpR3kvx2E4V3DeWJwO2Ka+psbTkQaFJUPkQYmM+ckb/64nW+2ZGEYYLHAiO7NeHxQDK1D/cyOJyINgMqHSAO1LTufNxfv4LtfsgGwWuDWnpE8dl0MLZr4mpxOROozlQ+RBi79YB5vLN7Okm05ALhbLYyOiyThuhiaB/mYnE5E6iOVDxEBYOO+E7y+eDs/7TgKgKeblTuvjCJ+YFuaBnibnE5E6hOVDxGpZP2e47z2QwZrdx0HwMvdyj1Xt2T8gDaE+HmZnE5E6gOVDxGp0uqdR3n9h+2k7D0BgI+HG/f3acWj/VvTuJGnyelExJWpfIjIeRmGwYodR3n9hww2HcgDwM/LnYf6tuLhfq0J9PEwOaGIuCKVDxG5KMMwWPJrDq8v3s7WrHwA/L3dGduvNQ/2bYW/t0qIiFw6lQ8RuWQ2m8H3v2Tzxo/b2X74JABBvh482r8N9/dpia+nu8kJRcQVqHyISLXZbAYLt2Tx5o/b2XWkEIAQP0/GXduGe65uibeHm8kJRcSZqXyISI2VV9iYn3aIt5bsYN/xIgCaBngRP7Atd/SKwstdJUREzqXyISKXrazCxr9TD/CvpZkczD0FQLNAbxKui2F0XCQeblaTE4qIM6nO93e1/vZITEykV69e+Pv7ExYWxsiRI8nIyKhyWcMwuOGGG7BYLMybN686HyMiTsDDzcqdV7Zg6dPX8vLNnWka4MWhvGImJ23huteWMztlP+UVNrNjiogLqlb5SE5OJj4+nrVr17J48WLKysoYMmQIhYWF5yz75ptvYrFYai2oiJjDy92Ne3u3IvmZgbxwUydC/LzYf/wUz8zZzJA3VjA/7SAVNqfagSoiTu6yDrscOXKEsLAwkpOT6d+/v316WloaN910EykpKURERJCUlMTIkSMvaZ067CLi3IpKy/l8zV7eS97JiaIyAGLC/Hji+nYM6xyO1ap/dIg0RHV22OVseXmnb1AUHBxsn1ZUVMRdd93FlClTCA8Pv+g6SkpKyM/Pr/QSEefl6+nOo9e24adnr+PpIe0I8HZnR85JJnyxgRv/tZIffsnGyU4lExEnU+PyYbPZmDRpEn379qVLly726U888QR9+vTh5ptvvqT1JCYmEhgYaH9FRUXVNJKIOJCflzsJ18Ww8g/X8figGPy83Pk1K59HPk/l5imrWJaRoxIiIlWq8WGX8ePHs2jRIlauXElkZCQACxYs4KmnnmLjxo34+fmd/gCL5YKHXUpKSigpKbH/OT8/n6ioKB12EXExuUWlfLBiF9NX76GotAKAni2CeGpIe/q0aaJzwETquTq/1DYhIYH58+ezYsUKoqOj7dMnTZrE22+/jdX6vx0qFRUVWK1W+vXrx/Lly2s1vIg4n6MnS3g/eSefrdlLSfnpq2Guig7mqSHtuTI6+CLvFhFXVWflwzAMJk6cSFJSEsuXLycmJqbS/OzsbI4ePVppWteuXXnrrbf43e9+V6mo1EZ4EXFeOfnFTF2+k5nr9lH630ty+8WE8MT17ejZorHJ6USkttVZ+ZgwYQIzZ85k/vz5tG/f3j49MDAQHx+fqj/gIoddLie8iDi/Q7mneGdZJl+v30/5fy/JfXVUN26P0/ldIvVJnV3t8u6775KXl8eAAQOIiIiwv7766qvLCiwi9VezIB/+3y1dWfb0AEZ0bwbA80npbNh3wuRkImKWaj2usibnpupsdxEBiAr25c07rqCkvILvfznMuM9T+c/Ea2ga4G12NBFxMD2cQUQcxmq18NrtV9CuqR85BSU8+nkqxWUVZscSEQdT+RARh/LzcufD++II9PEgbX8uf5qXrj2kIg2MyoeIOFzLJo14564eWC0wO/UAn67eY3YkEXEglQ8RMUW/mFCeu6EjAC9/8yurdx69yDtEpL5Q+RAR0/y+XzS39GhOhc0g/osN7D9eZHYkEXEAlQ8RMY3FYiHx1q50aR7AiaIyHvk8laLScrNjiUgdU/kQEVN5e7jx/r1xNGnkya9Z+fzfnM06AVWknlP5EBHTNQ/y4d17YnG3Wli4OYv3kneZHUlE6pDKh4g4hSujg3lxRGcAXv1+G8syckxOJCJ1ReVDRJzGPVe1YMyVURgGPPblRnYdOWl2JBGpAyofIuI0LBYLL43oQmzLxhQUl/PI56kUFJeZHUtEapnKh4g4FU93K+/e05PwAG8yc07yxFdp2Gw6AVWkPlH5EBGnE+bvzfv3xuLpbuXHX3N488ftZkcSkVqk8iEiTql7VBCJt3QF4O2lmXyXnmVyIhGpLSofIuK0bouN5KG+0QA8+fUmtmXnm5xIRGqDyoeIOLXJwzvQt20TikoreOSzVHKLSs2OJCKXSeVDRJyau5uVd8b0JCrYh33Hi0iYuZHyCpvZsUTkMqh8iIjTa9zIkw/ujcPHw42VmUd5ZdE2syOJyGVQ+RARl9AxIoDXbu8OwEcrdzN3wwGTE4lITal8iIjLGN41goSBbQH4w9wtbD6Qa24gEakRlQ8RcSlPXt+OQR3CKC238ejnqRwpKDE7kohUk8qHiLgUq9XCG3deQevQRmTlFTPhi1RKy3UCqogrUfkQEZcT4O3Bh/fF4e/lzvo9J3jpP7+YHUlEqkHlQ0RcUptQP94acwUWC3yxbh8z1+0zO5KIXCKVDxFxWdd1aMrTQ9oD8OKCdFL2HDc5kYhcCpUPEXFpEwa04cauEZRVGIybsYGsvFNmRxKRi1D5EBGXZrFY+MfobnQI9+foyRIe/TyV4rIKs2OJyAWofIiIy/P1dOfD++Jo7OvB5gN5TJ67BcMwzI4lIueh8iEi9UJUsC9T7uqJm9XC3I0H+XjlbrMjich5qHyISL3Rp20IfxzeEYD/9+2vrNxx1OREIlIVlQ8RqVce7NuK23pGYjMg4csN7DtWZHYkETmLyoeI1CsWi4W/3dKF7lFB5BaVMfazFApLys2OJSK/ofIhIvWOt4cb798TS6i/FxmHC3h69iadgCriRFQ+RKReCg/05r17euLhZmFRejbvLM00O5KI/Fe1ykdiYiK9evXC39+fsLAwRo4cSUZGRqVlHn30Udq0aYOPjw+hoaHcfPPNbNu2rVZDi4hcitiWwbx8cxcAXv9xOz9uPWxyIhGBapaP5ORk4uPjWbt2LYsXL6asrIwhQ4ZQWFhoXyY2NpZp06bx66+/8v3332MYBkOGDKGiQjf9ERHHu/PKFtx7dUsMAyZ9lUZmzkmzI4k0eBbjMg6EHjlyhLCwMJKTk+nfv3+Vy2zevJnu3buTmZlJmzZtzplfUlJCSUmJ/c/5+flERUWRl5dHQEBATaOJiNiVVdi4+6N1/Lz7OK1DGpEU35dAHw+zY4nUK/n5+QQGBl7S9/dlnfORl5cHQHBwcJXzCwsLmTZtGtHR0URFRVW5TGJiIoGBgfbX+ZYTEakpDzcrU+/uSbNAb3YdLWTSrI1U2HQCqohZalw+bDYbkyZNom/fvnTp0qXSvKlTp+Ln54efnx+LFi1i8eLFeHp6Vrme5557jry8PPtr//79NY0kInJeIX5evH9vHF7uVpZlHOG1HzIu/iYRqRM1Lh/x8fGkp6cza9asc+bdfffdbNy4keTkZNq1a8ftt99OcXFxlevx8vIiICCg0ktEpC50jQzk1VHdAJi6fCcLNx8yOZFIw1Sj8pGQkMDChQtZtmwZkZGR58wPDAwkJiaG/v37M2fOHLZt20ZSUtJlhxURuVw3X9GcR/q3BuCZ2ZvZeijf5EQiDU+1yodhGCQkJJCUlMTSpUuJjo6+pPcYhlHppFIRETM9O6wD/WJCOFVWwdjPUjheWGp2JJEGpVrlIz4+nhkzZjBz5kz8/f3Jzs4mOzubU6dOAbBr1y4SExNJTU1l3759rF69mtGjR+Pj48Pw4cPrZAAiItXlZrXwzpietGziy8HcU8R/sYGyCpvZsUQajGqVj3fffZe8vDwGDBhARESE/fXVV18B4O3tzU8//cTw4cNp27Ytd9xxB/7+/qxevZqwsLA6GYCISE0E+nrw4X1xNPJ0Y82uY/ztm1/NjiTSYFzWfT7qQnWuExYRuVzf/5LNo5+nAvDqqG7cHqfL/UVqwmH3+RARcXVDO4fz+KAYAJ5PSmfjvhMmJxKp/1Q+RKTBe3xQDEM6NaW0wsa4Gank5Fd9awARqR0qHyLS4FmtFl6/4wpiwvw4nF/CozNSKSnX86hE6orKh4gI4Oflzof3xRHg7c7Gfbm8MO8XnOyUOJF6Q+VDROS/WoU04l939cRqga9S9jNj7V6zI4nUSyofIiK/cW27UJ4d1gGAl/6zlXW7jpmcSKT+UfkQETnLI/1bM6J7M8ptBhO+2MDB3FNmRxKpV1Q+RETOYrFY+Ptt3ejcLIBjhaU88lkKp0p1AqpIbVH5EBGpgo+nG+/fG0twI09+OZTPs//erBNQRWqJyoeIyHlENvZl6t09cbdaWLDpEB+s2GV2JJF6QeVDROQCrm7dhBd+1wmAv3+3jeTtR0xOJOL6VD5ERC7i3qtbckdcFDYDJs7cwJ6jhWZHEnFpKh8iIhdhsVj4y8jO9GwRRH5xOWM/S+FkSbnZsURclsqHiMgl8HJ34717Ymka4MWOnJM88VUaNptOQBWpCZUPEZFLFBbgzXv3xOLpZmXx1sO8tWSH2ZFEXJLKh4hINfRo0Zi/3dIFgLeW7OC79GyTE4m4HpUPEZFqGh0XxQN9WgHw1NdpbD9cYG4gERej8iEiUgN/vLEjvVs3obC0grGfpZBXVGZ2JBGXofIhIlIDHm5Wptzdk8jGPuw9VkTClxuo0AmoIpdE5UNEpIaCG3nywb1x+Hi48dOOo7z63TazI4m4BJUPEZHL0KlZAP8Y3Q2A91fsYn7aQZMTiTg/lQ8Rkct0U7dmTBjQBoD/m7OZ9IN5JicScW4qHyIiteCpIe0Z2D6UknIbj3yWwtGTJWZHEnFaKh8iIrXAzWrhzTt70DqkEYfyipnwxQbKKmxmxxJxSiofIiK1JNDHgw/ui8XPy52fdx/nL//ZanYkEaek8iEiUovahvnz5h1XYLHA52v3MuvnfWZHEnE6Kh8iIrVscKemPDm4HQB/mp9O6t7jJicScS4qHyIidSDhurbc0CWcsgqDcTM2kJ1XbHYkEaeh8iEiUgcsFgv/HN2dDuH+HCko4dHPUyguqzA7lohTUPkQEakjjbzc+eDeOIJ8Pdh0II8/JqVjGLoFu4jKh4hIHWrRxJd3xvTEaoF/bzjA9NV7zI4kYjqVDxGROnZNTAiTh3cE4K/f/MrqzKMmJxIxl8qHiIgDPHxNNLf2aE6FzSB+5gb2Hy8yO5KIaapVPhITE+nVqxf+/v6EhYUxcuRIMjIy7POPHz/OxIkTad++PT4+PrRo0YLHHnuMvDw950BEGjaLxcL/u7Ur3SIDOVFUxtjPUigqLTc7logpqlU+kpOTiY+PZ+3atSxevJiysjKGDBlCYWEhAIcOHeLQoUP885//JD09nenTp/Pdd9/x8MMP10l4ERFX4u3hxvv3xhLi58W27AKemb1ZJ6BKg2QxLuP//CNHjhAWFkZycjL9+/evcpnZs2dzzz33UFhYiLu7+0XXmZ+fT2BgIHl5eQQEBNQ0moiI00rZc5wxH66lrMLgmaHtiR/Y1uxIIpetOt/fl3XOx5nDKcHBwRdcJiAg4LzFo6SkhPz8/EovEZH6LK5VMC+N6ALAP3/IYOm2wyYnEnGsGpcPm83GpEmT6Nu3L126dKlymaNHj/Lyyy/zyCOPnHc9iYmJBAYG2l9RUVE1jSQi4jLuuqoFd1/VAsOAx79MY+eRk2ZHEnGYGh92GT9+PIsWLWLlypVERkaeMz8/P5/rr7+e4OBgFixYgIeHR5XrKSkpoaSkpNL7oqKidNhFROq90nIbd3+0lvV7TtA6tBHz4vsS4F3135Uizq7OD7skJCSwcOFCli1bVmXxKCgoYNiwYfj7+5OUlHTe4gHg5eVFQEBApZeISEPg6W5l6t2xRAR6s+tIIU/MSsNm0wmoUv9Vq3wYhkFCQgJJSUksXbqU6Ojoc5bJz89nyJAheHp6smDBAry9vWstrIhIfRPq78X798bi5W5lybYcXl+83exIInWuWuUjPj6eGTNmMHPmTPz9/cnOziY7O5tTp04B/ysehYWFfPzxx+Tn59uXqajQA5VERKrSLTKIV27rCsA7yzL5dkuWyYlE6la1zvmwWCxVTp82bRoPPPAAy5cvZ+DAgVUus3v3blq1anXRz9CltiLSUP114VY+WrkbHw835k7oQ8cI/R0orqM639+XdZ+PuqDyISINVXmFjQenr+enHUeJCvZhQfw1NG7kaXYskUvisPt8iIhI7XF3s/KvMT1oEezL/uOnSPhyA+UVNrNjidQ6lQ8REScS5OvJh/fF4evpxqrMYyQu2mZ2JJFap/IhIuJk2of78/rt3QH4eOVu/p16wOREIrVL5UNExAkN6xLBY9edfubLc0lb2LQ/19xAIrVI5UNExElNGtyOwR2bUlpu49HPU8kpKDY7kkitUPkQEXFSVquFN+7oTtswP7Lzi5kwYwOl5ToBVVyfyoeIiBPz9/bgg3tj8fd2J2XvCV5c8IvZkUQum8qHiIiTax3qx7/G9MBigS9/3seMtXvNjiRyWVQ+RERcwID2Yfzf0A4A/HnBL/y8+7jJiURqTuVDRMRFjLu2NTd1i6DcZjDhi1QO5Z4yO5JIjah8iIi4CIvFwqujutExIoCjJ0t55PMUisv00E5xPSofIiIuxNfTnQ/ujSW4kSfpB/P5w78342SP6BK5KJUPEREXExXsy5S7euJmtTAv7RAf/bTb7Egi1aLyISLignq3acKfbuwIQOKiX1mx/YjJiUQuncqHiIiLur9PK0bHRmIzYOKXG9l7rNDsSCKXROVDRMRFWSwW/npLF66ICiLvVBmPfJZKYUm52bFELkrlQ0TEhXm5u/H+vbGE+XuRcbiAp77ehM2mE1DFual8iIi4uKYB3rx3byyebla++yWbd5Zlmh1J5IJUPkRE6oGeLRrz15FdAHh98XYWbz1sciKR81P5EBGpJ27vFcX9vVsC8MRXaWTmFJicSKRqKh8iIvXI8zd14urWwZwsKWfsZ6nknSozO5LIOVQ+RETqEQ83K1Pu6knzIB92Hy3ksS83UqETUMXJqHyIiNQzTfy8+OC+WLw9rCRvP8I/vs8wO5JIJSofIiL1UOdmgfxjVHcA3kveyYJNh0xOJPI/Kh8iIvXU77o3Y9y1bQD4vzmbSD+YZ3IikdNUPkRE6rFnhrbn2nahFJfZePTzVI6dLDE7kojKh4hIfeZmtfD2nT1o1cSXg7mnmPDFBsoqbGbHkgZO5UNEpJ4L9PXgw/viaOTpxrrdx/nrwq1mR5IGTuVDRKQBiGnqzxt3XAHAp2v28vX6/eYGkgZN5UNEpIEY0jmcJwa3A+D5eels2HfC5ETSUKl8iIg0IBOva8vQzk0prbAx7vNUDucXmx1JGiCVDxGRBsRqtfDa7VfQrqkfOQUljJuRSkl5hdmxpIFR+RARaWD8vNz58L44An082Lgvlz/NS8cwdAt2cZxqlY/ExER69eqFv78/YWFhjBw5koyMyrft/eCDDxgwYAABAQFYLBZyc3NrM6+IiNSClk0a8c5dPbBa4OuUA3y2Zq/ZkaQBqVb5SE5OJj4+nrVr17J48WLKysoYMmQIhYWF9mWKiooYNmwYkydPrvWwIiJSe/rFhPLcDR0B+MvCrazZeczkRNJQWIzL2Nd25MgRwsLCSE5Opn///pXmLV++nIEDB3LixAmCgoIueZ35+fkEBgaSl5dHQEBATaOJiMglMAyDJ7/eRNLGgwQ38mRBQl8iG/uaHUtcUHW+vy/rnI+8vNPPCQgODq7xOkpKSsjPz6/0EhERx7BYLCTe2pWuzQM5XljKI5+lcqpUJ6BK3apx+bDZbEyaNIm+ffvSpUuXGgdITEwkMDDQ/oqKiqrxukREpPq8Pdx4/95YQvw82ZqVzzNzNukEVKlTNS4f8fHxpKenM2vWrMsK8Nxzz5GXl2d/7d+vu+6JiDhasyAfpt4di7vVwsLNWbyXvMvsSFKP1ah8JCQksHDhQpYtW0ZkZORlBfDy8iIgIKDSS0REHO/K6GD+PKIzAK9+v41lGTkmJ5L6qlrlwzAMEhISSEpKYunSpURHR9dVLhERMcE9V7dkzJUtMAx47MuN7Dpy0uxIUg9Vq3zEx8czY8YMZs6cib+/P9nZ2WRnZ3Pq1Cn7MtnZ2aSlpZGZmQnAli1bSEtL4/jx47WbXERE6sRLIzoT27IxBcXlPPJ5KgXFZWZHknqmWuXj3XffJS8vjwEDBhAREWF/ffXVV/Zl3nvvPXr06MHYsWMB6N+/Pz169GDBggW1m1xEROqEp7uVd+/pSXiAN5k5J3niq03YbDoBVWrPZd3noy7oPh8iIs5h0/5cRr+/htJyG48NiuHJ69uZHUmcmMPu8yEiIvVX96ggEm/pCsDbS3bwXXqWyYmkvlD5EBGR87otNpKH+p6+uODJrzeRkV1gciKpD1Q+RETkgiYP70Dftk0oKq1g7Gcp5BaVmh1JXJzKh4iIXJC7m5V3xvQkKtiHfceL+P2nKazbdUx3QZUa0wmnIiJySX7NyufWqas5VXb62S8tgn0ZFRvJbbGRNA/yMTmdmK06398qHyIicsm2ZeczbeUeFm4+ROF/H0BnsUDfNiGMjotkaOdwvD3cTE4pZlD5EBGROlVUWs536dnMTjnAml3H7NP9vdy5qXszRsdF0iMqCIvFYmJKcSSVDxERcZj9x4v494YDzEk9wIET/7vjdZvQRoyKjeLWns1pGuBtYkJxBJUPERFxOJvNYO3uY8xJPcCiLdn2c0OsFri2XSijYqMY3CkML3cdlqmPVD5ERMRUBcVlfLsli9kpB0jZe8I+PcjXg5u7N2NUbBRdmgfosEw9ovIhIiJOY/fRQuak7uffqQfJzi+2T+8Q7s+o2EhG9mhOiJ+XiQmlNqh8iIiI06mwGazMPMqc1AN8/0s2peU2ANytFgZ2CGN0bCQDO4Th4aZbULkilQ8REXFqeUVlLNh8iDmpB9i0P9c+vUkjT0b2aM7ouEg6hOs7wJWofIiIiMvYfriAOakHmLvhIEdPltind20eyOi4SEZ0b0aQr6eJCeVSqHyIiIjLKa+wkbz9CLNTDrBk22HKKk5/PXm6Wbm+U1NGxUXSr20I7jos45RUPkRExKUdLyxlftpBZqccYGtWvn160wAvbukRyei4SNqE+pmYUM6m8iEiIvXGL4fymJ1ygPlpBzlRVGaf3rNFEKNio7ipewQB3h4mJhRQ+RARkXqotNzG0m2HmZ1ygOXbj1BhO/315e1hZVjncEbFRtGnTROsVt07xAwqHyIiUq/lFBQzb+PpwzI7ck7apzcP8uG2ns0ZFRtFiya+JiZseFQ+RESkQTAMg00H8piTup8FaYfILy63z7syOpjRsZEM7xpBIy93E1M2DCofIiLS4BSXVfDD1sPMST3ATzuOcObbzdfTjeFdIxgdG8mV0cG6pXsdUfkQEZEGLSvvFHM3HGR2yn72HCuyT2/ZxJdRPSO5NTaS5kE+Jiasf1Q+REREOH1YJnXvCWanHGDh5kMUlp5+0q7FAn3bhDA6LpKhncPx9tCTdi+XyoeIiMhZikrLWbQlmzmpB1iz65h9ur+XOzd1b8bouEh6RAXpsEwNqXyIiIhcwP7jRcxJPcCc1AMczD1ln94mtBGjYqO4tWdzmgZ4m5jQ9ah8iIiIXAKbzWDt7mPMSTnAt+lZFJedftKu1QLXtgtlVGwUgzuF4eWuwzIXo/IhIiJSTQXFZXy7JYvZKQdI2XvCPj3I14ObuzdjdFwUnZsF6LDMeah8iIiIXIZdR07y7w0H+HfqQbLzi+3TO4T7Myo2kpE9mhPi52ViQuej8iEiIlILKmwGKzOPMjtlPz9sPUxp+enDMu5WCwM7hDE6NpKBHcLw0JN2VT5ERERqW15RGQs2H2JOyn42HcizTw/x82TkFc0ZFRdJh/CG+72l8iEiIlKHth8uYE7qAeZuOMjRkyX26V2bBzI6LpIR3ZsR5OtpYkLHU/kQERFxgLIKG8kZR5iTeoAl2w5TVnH6K9XTzcr1nZoyKi6S/jGhuDWAJ+2qfIiIiDjYsZMlzE87xOzUA/yalW+f3jTAi1t6RDI6LpI2oX4mJqxbKh8iIiIm+uVQHrNTDjA/7SAnisrs03u2CGJUbBQ3dY8gwNvDxIS1rzrf39U6PTcxMZFevXrh7+9PWFgYI0eOJCMjo9IyxcXFxMfH06RJE/z8/Ljttts4fPhw9UchIiLiojo3C+TPIzqzdvIg3r27J4M6hOFmtbBhXy6Tk7Zw5d9+ZNKsjazKPIrN5lT7AByiWns+hg0bxp133kmvXr0oLy9n8uTJpKens3XrVho1agTA+PHj+eabb5g+fTqBgYEkJCRgtVpZtWrVJX2G9nyIiEh9lJNfTNLGg8xOPUBmzkn79OZBPtzWszmjYqNo0cTXxISXx2GHXY4cOUJYWBjJycn079+fvLw8QkNDmTlzJqNGjQJg27ZtdOzYkTVr1nD11Vefs46SkhJKSv53pnB+fj5RUVEqHyIiUi8ZhsGmA3nMTtnPgk2HKCgut8+7MjqY0bGRDO8aQSMvdxNTVl+dHXY5W17e6eucg4ODAUhNTaWsrIzBgwfbl+nQoQMtWrRgzZo1Va4jMTGRwMBA+ysqKupyIomIiDg1i8XCFVFB/O2Wrqz/42DeHtODfjEhWCzw8+7jPDNnM73+9iPPzN7Eul3HcLJTM2tFjfd82Gw2RowYQW5uLitXrgRg5syZPPjgg5X2ZABceeWVDBw4kL///e/nrEd7PkREROBQ7qnTh2VS9rPnWJF9essmvozqGcmtsZE0D/IxMeGFVWfPR4336cTHx5Oenm4vHjXl5eWFl5fujy8iIg1bsyAf4ge2ZcKANqTsPcHslP18szmLvceKeG3xdl7/cTt924QwOi6SoZ3D8fZw3Sft1qh8JCQksHDhQlasWEFkZKR9enh4OKWlpeTm5hIUFGSffvjwYcLDwy87rIiISH1nsVjo1SqYXq2C+fOIzizaks3s1P2s3XWclZlHWZl5FH8vd27q3ozRcZH0iApyuSftVuuwi2EYTJw4kaSkJJYvX05MTEyl+WdOOP3yyy+57bbbAMjIyKBDhw7nPeH0bLraRURE5Fz7jhXx7w0HmJN6gIO5p+zT24Q2YlRsFLf2bE7TAG/T8tXZ1S4TJkxg5syZzJ8/n/bt29unBwYG4uNz+jjU+PHj+fbbb5k+fToBAQFMnDgRgNWrV9d6eBERkYbGZjNYu+sYc1IP8G16FsVlp5+0a7XAte1CGR0XxaCOYXi5O/awTJ2Vj/Pt1pk2bRoPPPAAcPomY0899RRffvklJSUlDB06lKlTp17yYReVDxERkUtTUFzGN5uzmJ16gNS9J+zTg3w9uLl7M0bHRdG5WYBDDsvo9uoiIiINzK4jJ+1P2s3OL7ZP7xDuz6jYSG7p0ZwmfnV3gYfKh4iISANVYTNYmXmU2Sn7+WHrYUrLTx+WcbdauK5DGKNiIxnYIQwPt8u61dc5VD5ERESEvKIyFmw6yJzUA2w6kGef3jTAi+RnBtbq5boOuc+HiIiIOLdAXw/u7d2Ke3u3IiO7gDmp+0naeJCuzQNNvU+I9nyIiIg0IGUVNnKLygj1r93zPxz2bBcRERFxLR5u1lovHtWl8iEiIiIOpfIhIiIiDqXyISIiIg6l8iEiIiIOpfIhIiIiDqXyISIiIg6l8iEiIiIOpfIhIiIiDqXyISIiIg6l8iEiIiIOpfIhIiIiDqXyISIiIg6l8iEiIiIO5W52gLMZhgGcfjSviIiIuIYz39tnvscvxOnKR0FBAQBRUVEmJxEREZHqKigoIDAw8ILLWIxLqSgOZLPZOHToEP7+/lgsllpdd35+PlFRUezfv5+AgIBaXbczqO/jg/o/Ro3P9dX3MWp8rq+uxmgYBgUFBTRr1gyr9cJndTjdng+r1UpkZGSdfkZAQEC9/Z8K6v/4oP6PUeNzffV9jBqf66uLMV5sj8cZOuFUREREHErlQ0RERByqQZUPLy8vXnzxRby8vMyOUifq+/ig/o9R43N99X2MGp/rc4YxOt0JpyIiIlK/Nag9HyIiImI+lQ8RERFxKJUPERERcSiVDxEREXEoly8fK1as4He/+x3NmjXDYrEwb968SvMNw+CFF14gIiICHx8fBg8ezI4dOyotc/z4ce6++24CAgIICgri4Ycf5uTJkw4cxYVdbIwPPPAAFoul0mvYsGGVlnHWMSYmJtKrVy/8/f0JCwtj5MiRZGRkVFqmuLiY+Ph4mjRpgp+fH7fddhuHDx+utMy+ffu48cYb8fX1JSwsjGeeeYby8nJHDuW8LmWMAwYMOGcbjhs3rtIyzjrGd999l27dutlvWNS7d28WLVpkn+/q2w8uPkZX3n5VeeWVV7BYLEyaNMk+rT5sxzOqGp+rb8M///nP5+Tv0KGDfb7TbT/DxX377bfGH//4R2Pu3LkGYCQlJVWa/8orrxiBgYHGvHnzjE2bNhkjRowwoqOjjVOnTtmXGTZsmNG9e3dj7dq1xk8//WS0bdvWGDNmjINHcn4XG+P9999vDBs2zMjKyrK/jh8/XmkZZx3j0KFDjWnTphnp6elGWlqaMXz4cKNFixbGyZMn7cuMGzfOiIqKMpYsWWKkpKQYV199tdGnTx/7/PLycqNLly7G4MGDjY0bNxrffvutERISYjz33HNmDOkclzLGa6+91hg7dmylbZiXl2ef78xjXLBggfHNN98Y27dvNzIyMozJkycbHh4eRnp6umEYrr/9DOPiY3Tl7Xe2n3/+2WjVqpXRrVs34/HHH7dPrw/b0TDOPz5X34Yvvvii0blz50r5jxw5Yp/vbNvP5cvHb539xWyz2Yzw8HDjH//4h31abm6u4eXlZXz55ZeGYRjG1q1bDcBYv369fZlFixYZFovFOHjwoMOyX6rzlY+bb775vO9xpTHm5OQYgJGcnGwYxunt5eHhYcyePdu+zK+//moAxpo1awzDOF3OrFarkZ2dbV/m3XffNQICAoySkhLHDuASnD1Gwzj9F99v/yI8m6uNsXHjxsZHH31UL7ffGWfGaBj1Z/sVFBQYMTExxuLFiyuNqb5sx/ONzzBcfxu++OKLRvfu3auc54zbz+UPu1zI7t27yc7OZvDgwfZpgYGBXHXVVaxZswaANWvWEBQURFxcnH2ZwYMHY7VaWbduncMz19Ty5csJCwujffv2jB8/nmPHjtnnudIY8/LyAAgODgYgNTWVsrKyStuwQ4cOtGjRotI27Nq1K02bNrUvM3ToUPLz8/nll18cmP7SnD3GM7744gtCQkLo0qULzz33HEVFRfZ5rjLGiooKZs2aRWFhIb17966X2+/sMZ5RH7ZffHw8N954Y6XtBfXn9/B84zvD1bfhjh07aNasGa1bt+buu+9m3759gHNuP6d7sFxtys7OBqj0H/PMn8/My87OJiwsrNJ8d3d3goOD7cs4u2HDhnHrrbcSHR3Nzp07mTx5MjfccANr1qzBzc3NZcZos9mYNGkSffv2pUuXLsDp7ePp6UlQUFClZc/ehlVt4zPznElVYwS46667aNmyJc2aNWPz5s08++yzZGRkMHfuXMD5x7hlyxZ69+5NcXExfn5+JCUl0alTJ9LS0urN9jvfGMH1tx/ArFmz2LBhA+vXrz9nXn34PbzQ+MD1t+FVV13F9OnTad++PVlZWbz00kv069eP9PR0p9x+9bp8NBR33nmn/eeuXbvSrVs32rRpw/Llyxk0aJCJyaonPj6e9PR0Vq5caXaUOnO+MT7yyCP2n7t27UpERASDBg1i586dtGnTxtExq619+/akpaWRl5fHnDlzuP/++0lOTjY7Vq063xg7derk8ttv//79PP744yxevBhvb2+z49S6Sxmfq2/DG264wf5zt27duOqqq2jZsiVff/01Pj4+JiarWr0+7BIeHg5wzhm9hw8fts8LDw8nJyen0vzy8nKOHz9uX8bVtG7dmpCQEDIzMwHXGGNCQgILFy5k2bJlREZG2qeHh4dTWlpKbm5upeXP3oZVbeMz85zF+cZYlauuugqg0jZ05jF6enrStm1bYmNjSUxMpHv37rz11lv1avudb4xVcbXtl5qaSk5ODj179sTd3R13d3eSk5N5++23cXd3p2nTpi69HS82voqKinPe42rb8GxBQUG0a9eOzMxMp/w9rNflIzo6mvDwcJYsWWKflp+fz7p16+zHanv37k1ubi6pqan2ZZYuXYrNZrP/z+dqDhw4wLFjx4iIiACce4yGYZCQkEBSUhJLly4lOjq60vzY2Fg8PDwqbcOMjAz27dtXaRtu2bKlUsFavHgxAQEB9t3iZrrYGKuSlpYGUGkbOvMYz2az2SgpKakX2+98zoyxKq62/QYNGsSWLVtIS0uzv+Li4rj77rvtP7vydrzY+Nzc3M55j6ttw7OdPHmSnTt3EhER4Zy/h7V+CquDFRQUGBs3bjQ2btxoAMbrr79ubNy40di7d69hGKcvtQ0KCjLmz59vbN682bj55purvNS2R48exrp164yVK1caMTExTnEZ6hkXGmNBQYHx9NNPG2vWrDF2795t/Pjjj0bPnj2NmJgYo7i42L4OZx3j+PHjjcDAQGP58uWVLhErKiqyLzNu3DijRYsWxtKlS42UlBSjd+/eRu/eve3zz1wiNmTIECMtLc347rvvjNDQUKe5BO5iY8zMzDT+8pe/GCkpKcbu3buN+fPnG61btzb69+9vX4czj/EPf/iDkZycbOzevdvYvHmz8Yc//MGwWCzGDz/8YBiG628/w7jwGF19+53P2Vd/1Ift+Fu/HV992IZPPfWUsXz5cmP37t3GqlWrjMGDBxshISFGTk6OYRjOt/1cvnwsW7bMAM553X///YZhnL7c9k9/+pPRtGlTw8vLyxg0aJCRkZFRaR3Hjh0zxowZY/j5+RkBAQHGgw8+aBQUFJgwmqpdaIxFRUXGkCFDjNDQUMPDw8No2bKlMXbs2EqXSxmG846xqnEBxrRp0+zLnDp1ypgwYYLRuHFjw9fX17jllluMrKysSuvZs2ePccMNNxg+Pj5GSEiI8dRTTxllZWUOHk3VLjbGffv2Gf379zeCg4MNLy8vo23btsYzzzxT6R4DhuG8Y3zooYeMli1bGp6enkZoaKgxaNAge/EwDNfffoZx4TG6+vY7n7PLR33Yjr/12/HVh214xx13GBEREYanp6fRvHlz44477jAyMzPt851t+1kMwzBqf3+KiIiISNXq9TkfIiIi4nxUPkRERMShVD5ERETEoVQ+RERExKFUPkRERMShVD5ERETEoVQ+RERExKFUPkRERMShVD5EpMaWL1+OxWI554FVIiIXovIhIjXWp08fsrKyCAwMvOT3FBUV8dxzz9GmTRu8vb0JDQ3l2muvZf78+XWYVEScibvZAUTEdXl6elb7cdvjxo1j3bp1/Otf/6JTp04cO3aM1atXc+zYsTpKKSLORns+RMRuwIABTJw4kUmTJtG4cWOaNm3Khx9+SGFhIQ8++CD+/v60bduWRYsWAecedpk+fTpBQUF8//33dOzYET8/P4YNG0ZWVpb9MxYsWMDkyZMZPnw4rVq1IjY2lokTJ/LQQw/Zl7FYLMybN69StqCgIKZPnw7Anj17sFgszJo1iz59+uDt7U2XLl1ITk6u0/8+IlI7VD5EpJJPP/2UkJAQfv75ZyZOnMj48eMZPXo0ffr0YcOGDQwZMoR7772XoqKiKt9fVFTEP//5Tz7//HNWrFjBvn37ePrpp+3zw8PD+fbbbykoKLjsrM888wxPPfUUGzdupHfv3vzud7/THhQRF6DyISKVdO/eneeff56YmBiee+45vL29CQkJYezYscTExPDCCy9w7NgxNm/eXOX7y8rKeO+994iLi6Nnz54kJCSwZMkS+/wPPviA1atX06RJE3r16sUTTzzBqlWrapQ1ISGB2267jY4dO/Luu+8SGBjIxx9/XKN1iYjjqHyISCXdunWz/+zm5kaTJk3o2rWrfVrTpk0ByMnJqfL9vr6+tGnTxv7niIiISsv279+fXbt2sWTJEkaNGsUvv/xCv379ePnll6udtXfv3vaf3d3diYuL49dff632ekTEsVQ+RKQSDw+PSn+2WCyVplksFgBsNtslv98wjHOW6devH88++yw//PADf/nLX3j55ZcpLS0973vKyspqNiARcToqHyJiuk6dOlFeXk5xcTEAoaGhlU5S3bFjR5XnmKxdu9b+c3l5OampqXTs2LHuA4vIZdGltiLiUAMGDGDMmDHExcXRpEkTtm7dyuTJkxk4cCABAQEAXHfddbzzzjv07t2biooKnn322XP2qABMmTKFmJgYOnbsyBtvvMGJEycqXTUjIs5Jez5ExKGGDh3Kp59+ypAhQ+jYsSMTJ05k6NChfP311/ZlXnvtNaKioujXrx933XUXTz/9NL6+vues65VXXuGVV16he/furFy5kgULFhASEuLI4YhIDViMsw+siog4uT179hAdHc3GjRu54oorzI4jItWkPR8iIiLiUCofIiIi4lA67CIiIiIOpT0fIiIi4lAqHyIiIuJQKh8iIiLiUCofIiIi4lAqHyIiIuJQKh8iIiLiUCofIiIi4lAqHyIiIuJQ/x83ksSE7QHBAwAAAABJRU5ErkJggg==\n" 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\n" 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Tpk2Vda1bt0ZaWtoT9920aRMmTpyILVu2VHn7x8rKCnK5HFlZWVXGZ2Vlwc7OrtrjhYaGIi8vT7Wkp6erMxWd0M3NCtunBmD5yE5wszFF3r1yfLH3/lNz10dfQ1kFb/UjIiLdoFZhCQgIQHx8fJV1CQkJcHJyeux+GzduxLhx47Bx40b079+/yjYDAwN06tQJhw4dUq1TKpU4dOgQ/P39qz2moaEhFApFlaUhEgQBfdvZYe/MHlg01AvNGhsju6AUH2y//9TcbWeuo5JPzSUiIi2nVmGZNWsWoqOjsWDBAiQlJWHDhg1YsWIFpk6dqhoTGhqK0aNHq77fsGEDRo8ejUWLFsHX1xeZmZnIzMxEXl6easzs2bOxcuVKrFu3DpcvX8aUKVNQVFSEcePG1cIUGwa5TMArnZrh77d74ZOBbWFlaoi0O8WYtfks+n17DPsvZvIzN4iISGup/eC4v/76C6GhoUhMTISzszNmz56NkJAQ1faxY8ciNTUVR44cAQD06tUL4eHhDx1nzJgxWLt2rer7H374QfXguA4dOuC7776Dr69vjXPp8m3NT6O4rAJrI1Ox/MhV5Jfcf2puB0cLvBvUCl1bWkmcjoiI6D4+mp8AAHnF5Vhx7CpWH0/FvfL7F+N2a2mFOUGt0MHRQtpwRETU4LGwUBXZBSVYdvgqfo25hvLK+6c8qK0t3u7TCu62ZhKnIyKihoqFhR4p/U4xlhxMxLYz16EUAUEABns3xaxAdzg24VNziYiofrGw0GMlZhVg0f4E7L14/+F8+nIBwV2aY9pzLWFjZiRxOiIiaihYWKhGzqbn4uv98TiWeBsAYKQvw7gAZ0zu4QpzE32J0xERka5jYSG1RF69ja/2xeNMWi4AwMxID5N7umJcQAuYGOhJG46IiHQWCwupTRRFHLycja/3xSM+qwAAYGVqiGm9XRHs2xyGenKJExIRka5hYaGnVqkU8efZm1h8IAFpd4oBAE0tjDHrBXcM9m4KuUyQOCEREekKFhZ6ZmUVSmw5lY7vDiUiu6AUANDSxhRz+rgjqK0dBIHFhYiIng0LC9Wae2WVWBeVirAjV5F3rxwA0L6ZOd4JaoVuLa1YXIiI6KmxsFCtyy8px8qjyfjpeAqKy+4/NdfPpQne7euBjs0bS5yOiIi0EQsL1ZnbhaVYejgJv0anoaxSCQAIbG2LOUHu8LDjnz0REdUcCwvVuRu59/DtwQT8Hvu/p+YO9HLArBfc4WTZSOp4RESkBVhYqN4kZRfimwMJ2HU+AwCgJxMwrLMjZjzvBlsFn5pLRETVY2Ghenf+eh6+3h+P8IRbAO4/NXdM1xaY3MMVjRsZSJyOiIg0EQsLSSYmOQdf7otH7LW7AAAzQz1M6uGC8d2c0ciQT80lIqL/YWEhSYmiiMPx2fhqXwIuZ+QDACwbGWBq75YY7tscRvp8ai4REbGwSB2H/p9SKeKv8xlYvD8eqTn3n5rrYG6EmYHuGNKxKfTkMokTEhGRlFhYSKOUVyrxe+x1fHswEZn5JQAAF+tGePuFVnixnR1kfNw/EVGDxMJCGqmkvBK/RF/D0sNJuFt8/6m57ZoqMKdPK/R0t+ZTc4mIGhgWFtJoBSXlWHUsBauOJaPo/5+a28W5Cd4NagWfFk0kTkdERPWFhYW0wp2iMiw7nISfo6+hrOL+U3Of87DBnD6t0MaB55GISNexsJBWyci7h+8OJWLLqeuoVN7/T3KAlwNmv+AOZys+NZeISFexsJBWSrldhMUHEvDn2ZsAALlMwGs+zTDjeTfYmxtLnI6IiGobCwtptYs387BofwL+vpINADDQk2G0nxPe7N0STfjUXCIincHCQjrhZOodfLU3HidS7wAATA31MLG7MyZ0c4aZkb7E6YiI6FmxsJDOEEUR4Qm38NW+eFy8ef+puY1N9DG1d0uM9HPiU3OJiLQYCwvpHKVSxJ4LmVh0IB7Jt4oAAHYKI7wV6IZXOzWDPp+aS0SkdVhYSGdVVCqx9fQNLDmYgJt595+a62zVCLNecMdLnvZ8ai4RkRZhYSGdV1JeiQ0xaVh6OAk5RWUAgNb2CrwT5I7erWz41FwiIi3AwkINRmFpBdYcT8GKo8koKK0AAPg4NcY7Qa3g62IpcToiInocFhZqcO4WlWF5+FWsjUxF6f8/NbenuzXeCWqFdk3NJU5HRESPwsJCDVZWfgm+O5SIzSfTUfH/T80d2MEB8/q3gbWZocTpiIjon2r6+1vt2ypu3LiBkSNHwtLSEsbGxvD09MSpU6eqHZ+RkYHhw4fD3d0dMpkMM2fOfGjM2rVrIQhClcXIyEjdaEQAAFuFEf472BOH3u6JQR0cIAjAjribCFwcji0n06EjHZ2IqEFRq7DcvXsXAQEB0NfXx549e3Dp0iUsWrQIjRs3rnaf0tJSWFtbY968efDy8qp2nEKhQEZGhmq5du2aOtGIHuJk2QhLXvfGzqnd0NZBgbx75Xj3j3MIXhmN5FuFUscjIiI16Kkz+IsvvoCjoyPWrFmjWufs7PzYfVq0aIFvv/0WALB69epqxwmCADs7O3XiENWIZzNz7JgagNURKVh8IAHRyXfQ99tjmPFcS0zq4QoDPT6/hYhI06n1L/XOnTvh4+ODoUOHwsbGBt7e3li5cmWtBCksLISTkxMcHR0xcOBAXLx4sVaOSwQAenIZJvVwxYFZPdHD3RplFUp8vT8BA74/jtNpd6WOR0RET6BWYUlOTkZYWBjc3Nywb98+TJkyBTNmzMC6deueKUSrVq2wevVq7NixA7/88guUSiW6du2K69evV7tPaWkp8vPzqyxET+LYxATrxnXGkmEd0KSRAeKzCvBKWCQ+3HEBBSXlUscjIqJqqHWXkIGBAXx8fBAZGalaN2PGDJw8eRJRUVFP3L9Xr17o0KEDlixZ8thx5eXlaN26NYKDg/Hpp58+csz8+fPx8ccfP7SedwlRTd0tKsN/d1/G77H3i7GdwgifDGyLPm351iQRUX2pk7uE7O3t0aZNmyrrWrdujbS0tKdLWQ19fX14e3sjKSmp2jGhoaHIy8tTLenp6bWagXRf40YG+HqoF36d6AsnSxNk5pdg0vpYTPklFln5JVLHIyKif1CrsAQEBCA+Pr7KuoSEBDg5OdVqqMrKSpw/fx729vbVjjE0NIRCoaiyED2NgJZW2DezB6b0coVcJmDPhUwELgrHL9HXoFTyFmgiIk2gVmGZNWsWoqOjsWDBAiQlJWHDhg1YsWIFpk6dqhoTGhqK0aNHV9kvLi4OcXFxKCwsxK1btxAXF4dLly6ptn/yySfYv38/kpOTcfr0aYwcORLXrl3DxIkTn3F6RDVjpC/He3098Nf0bvBytEBBaQXmbb+A136MQmJWgdTxiIgaPLWfdPvXX38hNDQUiYmJcHZ2xuzZsxESEqLaPnbsWKSmpuLIkSP/+yGP+BA6JycnpKamArhfhLZu3YrMzEw0btwYnTp1wmeffQZvb+8a5+KTbqm2VCpF/ByViq/2xaO4rBL6cgFTerXE1N6uMNSTSx2PiEin8NH8RM/oRu49fLj9Ag5dyQYAuFg3wudD2qOLcxOJkxER6Y46ezQ/UUPR1MIYq8b4YOnwjrAyNUTyrSK89mMUQreeR9493gJNRFSfWFiIHkMQBPRvb49Ds3siuIsjAGDjiTQELg7HrnMZ/FwiIqJ6wsJCVAPmJvpYOKQ9Nk/yg4t1I9wqKMXUDacxcd0p3My9J3U8IiKdx8JCpAZfF0vseas7ZjzvBn25gENXsvHC4nCsiUhBJW+BJiKqMywsRGoy1JNj9gvu2D2jO3ycGqOorBIf/3kJQ8IicTmDHxFBRFQXWFiInpKbrRm2vOGPzwa1g5mhHs6m52LA98fx5d4rKCmvlDoeEZFOYWEhegYymYCRfk44+HZP9G1rhwqliGVHriJoyVFEJN2WOh4Rkc5gYSGqBbYKIywf1Qk/juoEO4URruUUY8SqGLy95SzuFpVJHY+ISOuxsBDVoqC2djgwuwdG+ztBEIA/Tl/H84vDsf3MDd4CTUT0DFhYiGqZmZE+PhnYDr9P7gp3W1PcKSrDzM1xGLPmJNLvFEsdj4hIK7GwENWRTk6N8df07pjTxx0GejIcTbiFF74Jx4qjV1FRqZQ6HhGRVmFhIapDBnoyTHvODXvf6g4/lyYoKVdiwe4rGLg0Auev50kdj4hIa7CwENUDF2tTbAzxw5evtIe5sT4u3szHwKXH8dlfl1BcViF1PCIijcfCQlRPBEHAa50dcXB2TwzwcoBSBFYdT8ELi4/iSHy21PGIiDQaCwtRPbM2M8T3wd5YM7YzmloY40buPYxdcxJvbTqD24WlUscjItJILCxEEuntYYP9s3pgQjdnyARgR9xNPL8oHFtOpfMWaCKif2FhIZJQI0M9fPBSG2yfGoA29grk3SvHu7+fw/CVMUi5XSR1PCIijcHCQqQB2jezwI5pAQh90QNG+jJEJecgaMlRLD2chHLeAk1ExMJCpCn05TK80dMV+2f2RHc3K5RVKPHVvngM+P44zqTdlToeEZGkWFiINExzSxP8PL4LvhnmhSaNDHAlswBDwiIxf+dFFJbyFmgiaphYWIg0kCAIGOzdDAdn98SQjk0hisDayFS8sDgcBy5lSR2PiKjesbAQabAmjQyw+LUO+GWCL5o3MUFGXglCfj6FN3+NRXZ+idTxiIjqDQsLkRbo5maFfTN7YHJPV8hlAnafz8Tzi8Pxa8w1KJW8BZqIdB8LC5GWMDaQY+6LHvhzWjd4NTNHQUkF/rPtAoatiEJSdoHU8YiI6hQLC5GWaeOgwNY3A/DhS21gYiDHydS76PftcSw5mIDSikqp4xER1QkWFiItJJcJGN/NGQdm98RzHjYoq1RiycFE9Pv2GE6m3pE6HhFRrWNhIdJiTS2M8dMYH/ww3BtWpoa4eqsIQ5dH4f1t55F3r1zqeEREtYaFhUjLCYKAl9o74NDsnni9syMAYENMGgIXh2P3+Qx+LhER6QQWFiIdYW6ij89faY9Nk/zgYtUItwpK8eavpxHycyxu5t6TOh4R0TNhYSHSMX4ultj9VnfMeK4l9OUCDl7OwguLw7EuMhWVvAWaiLQUCwuRDjLSl2N2n1bYNaM7Ojk1RlFZJT7aeRGvhEXiSma+1PGIiNTGwkKkw9xtzfDbG/74dFA7mBrqIS49Fy99dxxf7buCknLeAk1E2oOFhUjHyWQCRvk54eDsnghqa4sKpYilh6+i75KjiEy6LXU8IqIaUbuw3LhxAyNHjoSlpSWMjY3h6emJU6dOVTs+IyMDw4cPh7u7O2QyGWbOnPnIcb/99hs8PDxgZGQET09P7N69W91oRPQYduZG+HGUD5aP7ARbhSFSc4oxfFUM3vntLO4WlUkdj4josdQqLHfv3kVAQAD09fWxZ88eXLp0CYsWLULjxo2r3ae0tBTW1taYN28evLy8HjkmMjISwcHBmDBhAs6cOYNBgwZh0KBBuHDhgnqzIaIn6tvODgdm98QoPycIAvBb7HUELg7HjrgbvAWaiDSWIKrxL9TcuXMRERGBY8eOPdUP69WrFzp06IAlS5ZUWT9s2DAUFRXhr7/+Uq3z8/NDhw4dsHz58hodOz8/H+bm5sjLy4NCoXiqfEQNTey1Owjdeh4JWYUAgJ7u1vhsUDs4NjGROBkRNRQ1/f2t1issO3fuhI+PD4YOHQobGxt4e3tj5cqVzxw2KioKgYGBVdYFBQUhKiqq2n1KS0uRn59fZSEi9XRyaoK/pnfH2y+4w0AuQ3jCLfT55ihWHk1GRaVS6nhERCpqFZbk5GSEhYXBzc0N+/btw5QpUzBjxgysW7fumUJkZmbC1ta2yjpbW1tkZmZWu8/ChQthbm6uWhwdHZ8pA1FDZaAnw/Tn3bBnZnd0cW6Ce+WV+O/uyxi0LAIXbuRJHY+ICICahUWpVKJjx45YsGABvL29MWnSJISEhNT4bZvaFBoairy8PNWSnp5e7xmIdImrtSk2hfjh8yGeUBjp4cKNfAxcGoEFuy+juKxC6nhE1MCpVVjs7e3Rpk2bKutat26NtLS0ZwphZ2eHrKysKuuysrJgZ2dX7T6GhoZQKBRVFiJ6NjKZgNe7NMfBt3vipfb2qFSKWHE0GUFLjiI84ZbU8YioAVOrsAQEBCA+Pr7KuoSEBDg5OT1TCH9/fxw6dKjKugMHDsDf3/+ZjktET8fGzAg/DO+I1WN90NTCGOl37mHM6hOYuekMbheWSh2PiBogtQrLrFmzEB0djQULFiApKQkbNmzAihUrMHXqVNWY0NBQjB49usp+cXFxiIuLQ2FhIW7duoW4uDhcunRJtf2tt97C3r17sWjRIly5cgXz58/HqVOnMG3atGecHhE9i+c8bLF/Vg+MD3CGTAC2x91E4OJw/B57nbdAE1G9Uuu2ZgD466+/EBoaisTERDg7O2P27NkICQlRbR87dixSU1Nx5MiR//0QQXjoOE5OTkhNTVV9/9tvv2HevHlITU2Fm5sbvvzyS/Tr16/GuXhbM1HdOpuei7lbz+Nyxv078rq6WmLBYE+0sGokcTIi0mY1/f2tdmHRVCwsRHWvvFKJVcdSsORgAkorlDDUk+GtQDeEdHeBvpyf9EFE6quT57AQUcOmL5dhSi9X7J/VA91aWqG0Qokv98ZjwPfHEZeeK3U8ItJhLCxEpDYny0ZYP6ELFr/mhcYm+riSWYDByyIwf+dFFJbyFmgiqn0sLET0VARBwJCOzXBwdk8M8W4KUQTWRqaiz+JwHLqc9eQDEBGpgYWFiJ6JpakhFg/rgJ/Hd4FjE2PczCvBhHWnMPXX08jOL5E6HhHpCBYWIqoVPdytsX9mT7zRwwVymYBd5zPw/OJwbDyRBqVSJ67tJyIJsbAQUa0xNpAjtF9r7JgaAM+m5igoqUDo1vN4fUU0krILpY5HRFqMhYWIal27pubY9mZXfPBSG5gYyHEi9Q76fXsM3x5MRGlFpdTxiEgLsbAQUZ3Qk8swoZsz9s/qgV6trFFWqcQ3BxPQ/7vjOJV6R+p4RKRlWFiIqE41a2yCNWM747tgb1iZGiApuxCvLo/Cf7adR35JudTxiEhLsLAQUZ0TBAEvezng4OyeeM2nGQDg15g0BC4Kx94LGfxcIiJ6IhYWIqo3FiYG+PJVL2wI8YWzVSNkF5Ri8i+nMWl9LDLy7kkdj4g0GAsLEdW7rq5W2PNWd0x/riX0ZAIOXMrCC4uP4ueoVFTyFmgiegQWFiKShJG+HG/3aYVdM7rDu7kFCksr8OGOi3h1eSTiMwukjkdEGoaFhYgk1crODL9P7opPBraFqaEezqTlov93x/D1vniUlPMWaCK6j4WFiCQnlwkY7d8CB2b3wAttbFGhFPHD4SS8+O0xRF3NkToeEWkAFhYi0hj25sZYMaoTlo/sCBszQ6TcLkLwymi8+/tZ5BXzFmiihoyFhYg0iiAI6NvOHgff7omRfs0BAFtOXcegZRFIvsXH+xM1VCwsRKSRFEb6+GyQJ36f7I+mFsZIuV2EQUsjEJF0W+poRCQBFhYi0mg+LZpg+9QAdGxugfySCoxefQK/xlyTOhYR1TMWFiLSeNZmhtgQ4odBHRxQqRTxn20X8PGfF1FRqZQ6GhHVExYWItIKRvpyfDOsA+b0cQcArIlIxcSfT/HziIgaCBYWItIagiBg2nNuCBvREUb6MhyJv4VXlkUiLadY6mhEVMdYWIhI67zoaY/f3ugKW4UhErMLMWhZBE6k3JE6FhHVIRYWItJKns3MsWNqN3g2NcedojKMWBWN306lSx2LiOoICwsRaS07cyNsecMf/TztUF4p4p3fz2HhnstQ8gMUiXQOCwsRaTVjAzl+CO6I6c+1BAD8GJ6MN36JRVFphcTJiKg2sbAQkdaTyQS83acVvn29Awz0ZDhwKQuvLo/Cjdx7UkcjolrCwkJEOmNgh6bYGOIHK1MDXM7Ix8AfInAm7a7UsYioFrCwEJFO6eTUGNunBsDDzgy3C0sxbEU0dp69KXUsInpGLCxEpHOaNTbB71O6IrC1DcoqlJix8QwWH0jgxbhEWoyFhYh0kqmhHn4c5YM3ergAAL47lIjpm87gXlmlxMmI6GmwsBCRzpLLBIT2a40vX2kPfbmAXecy8PqKKGTnl0gdjYjUpHZhuXHjBkaOHAlLS0sYGxvD09MTp06deuw+R44cQceOHWFoaIiWLVti7dq1VbbPnz8fgiBUWTw8PNSNRkT0SK91dsT6Cb5obKKPs9fz8PIPEbhwI0/qWESkBrUKy927dxEQEAB9fX3s2bMHly5dwqJFi9C4ceNq90lJSUH//v3Ru3dvxMXFYebMmZg4cSL27dtXZVzbtm2RkZGhWo4fP/50MyIiegQ/F0tsnxqAljamyMwvwdDlUdh7IUPqWERUQ3rqDP7iiy/g6OiINWvWqNY5Ozs/dp/ly5fD2dkZixYtAgC0bt0ax48fxzfffIOgoKD/BdHTg52dnTpxiIjU4mTZCFvf7Iqpv57GscTbmPzLabwT1Apv9nKFIAhSxyOix1DrFZadO3fCx8cHQ4cOhY2NDby9vbFy5crH7hMVFYXAwMAq64KCghAVFVVlXWJiIhwcHODi4oIRI0YgLS3tscctLS1Ffn5+lYWI6EkURvpYM7YzxnZtAQD4al883t5yFqUVvBiXSJOpVViSk5MRFhYGNzc37Nu3D1OmTMGMGTOwbt26avfJzMyEra1tlXW2trbIz8/HvXv3n0Lp6+uLtWvXYu/evQgLC0NKSgq6d++OgoKCao+7cOFCmJubqxZHR0d1pkJEDZieXIb5L7fFp4PaQS4TsPXMDQxfGYPbhaVSRyOiagiiKNb4wQQGBgbw8fFBZGSkat2MGTNw8uTJh14xecDd3R3jxo1DaGioat3u3bvRv39/FBcXw9jY+KF9cnNz4eTkhMWLF2PChAmPPG5paSlKS//3j0t+fj4cHR2Rl5cHhUJR0ykRUQN3LPEW3vz1NApKKtCssTF+GtMZrezMpI5F1GDk5+fD3Nz8ib+/1XqFxd7eHm3atKmyrnXr1o99+8bOzg5ZWVlV1mVlZUGhUDyyrACAhYUF3N3dkZSUVO1xDQ0NoVAoqixEROrq7maNbW8GoIWlCa7fvYchyyLw95WsJ+9IRPVKrcISEBCA+Pj4KusSEhLg5ORU7T7+/v44dOhQlXUHDhyAv79/tfsUFhbi6tWrsLe3VyceEdFTaWljim1vBsDPpQmKyioxcd0prDqWDDVegCaiOqZWYZk1axaio6OxYMECJCUlYcOGDVixYgWmTp2qGhMaGorRo0ervp88eTKSk5Px7rvv4sqVK1i2bBm2bNmCWbNmqcbMmTMH4eHhSE1NRWRkJAYPHgy5XI7g4OBamCIR0ZM1bmSAn8f74vXOjlCKwGe7LuP9bedRVqGUOhoRQc3C0rlzZ2zbtg0bN25Eu3bt8Omnn2LJkiUYMWKEakxGRkaVt4icnZ2xa9cuHDhwAF5eXli0aBFWrVpV5Zbm69evIzg4GK1atcJrr70GS0tLREdHw9rauhamSERUMwZ6Miwc4ol5/VtDJgAbT6Rj9OoY3C0qkzoaUYOn1kW3mqymF+0QEdXE31eyMGNjHApLK9DC0gSrxnRGSxtTqWMR6Zw6ueiWiKiheM7DFn9M6YqmFsZIzSnG4GUROJ54W+pYRA0WCwsRUTVa2Zlhx7QAdHJqjIKSCoxZcwLro69JHYuoQWJhISJ6DCtTQ2wI8cUQ76aoVIr4YPsFfLTjAioqeTEuUX1iYSEiegJDPTkWveaFd4JaAQDWRV3D+HWnkF9SLnEyooaDhYWIqAYEQcDU3i2xfGRHGOvLcTThFoYsi8S1nCKpoxE1CCwsRERq6NvOHr9N9oedwghJ2YUYtDQCMck5Usci0nksLEREamrX1Bw7pgWgfTNz3C0ux8ifYrDlVLrUsYh0GgsLEdFTsFUYYfMkf/Rvb4/yShHv/n4OC3ZfRqVSJx5tRaRxWFiIiJ6SsYEcPwR7463n3QAAK44m4431p1BYWiFxMiLdw8JCRPQMBEHArBfc8V2wNwz0ZDh4ORuvhkXi+t1iqaMR6RQWFiKiWvCylwM2T/KDlakhrmQWYNDSCMReuyt1LCKdwcJCRFRLvJs3xs5pAWhtr8DtwjIEr4zG9jM3pI5FpBNYWIiIapGDhTF+n+yPF9rYoqxCiZmb47BofzyUvBiX6JmwsBAR1bJGhnr4cWQnTO7pCgD4/u8kTNt4GvfKKiVORqS9WFiIiOqATCZg7ose+OrV9tCXC9h9PhOv/RiFzLwSqaMRaSUWFiKiOjTUxxG/TvRDYxN9nL+Rh4FLj+P89TypYxFpHRYWIqI61sW5CXZM7QY3G1Nk5Zdi6I+R2H0+Q+pYRFqFhYWIqB40tzTBH292RU93a5SUK/Hmr6fxw9+JEEVejEtUEywsRET1RGGkj5/G+GBcQAsAwNf7EzBrcxxKynkxLtGTsLAQEdUjPbkMHw1oi/8Obgc9mYDtcTcxfGU0bhWUSh2NSKOxsBARSWCErxN+Ht8FCiM9nE7LxaClEbickS91LCKNxcJCRCSRri2tsH1qAJytGuFG7j28GhaJg5eypI5FpJFYWIiIJORibYptb3ZFV1dLFJVVImT9Kaw8msyLcYn+hYWFiEhiFiYGWDe+C4b7NocoAv/dfRnv/XEOZRVKqaMRaQwWFiIiDaAvl+G/g9rhw5faQCYAW05dx6ifYnC3qEzqaEQagYWFiEhDCIKA8d2c8dPYzjA11ENMyh0MWhaBpOwCqaMRSY6FhYhIw/RuZYOtb3aFYxNjXMspxuBlkTiacEvqWESSYmEhItJA7rZm2P5mADq3aIyCkgqMW3sS6yJTpY5FJBkWFiIiDWVpaohfJvrilY7NUKkU8dHOi/hg+wVUVPJiXGp4WFiIiDSYoZ4cXw9tj7kvekAQgPXR1zB2zUnk3SuXOhpRvWJhISLScIIgYHJPVywf2QnG+nIcT7qNwcsikHq7SOpoRPWGhYWISEsEtbXD71P8YW9uhORbRRi0LAJRV3OkjkVUL9QuLDdu3MDIkSNhaWkJY2NjeHp64tSpU4/d58iRI+jYsSMMDQ3RsmVLrF279qExS5cuRYsWLWBkZARfX1+cOHFC3WhERDqvrYM5dkwLgJejBXKLyzHqpxhsOpEmdSyiOqdWYbl79y4CAgKgr6+PPXv24NKlS1i0aBEaN25c7T4pKSno378/evfujbi4OMycORMTJ07Evn37VGM2b96M2bNn46OPPsLp06fh5eWFoKAgZGdnP/3MiIh0lI2ZETZP8sMALwdUKEXM3Xoen/11CZVKPs6fdJcgqvGBFXPnzkVERASOHTtW4x/w3nvvYdeuXbhw4YJq3euvv47c3Fzs3bsXAODr64vOnTvjhx9+AAAolUo4Ojpi+vTpmDt3bo1+Tn5+PszNzZGXlweFQlHjfERE2koURXx3KAnfHEwAADznYYNvX+8AMyN9iZMR1VxNf3+r9QrLzp074ePjg6FDh8LGxgbe3t5YuXLlY/eJiopCYGBglXVBQUGIiooCAJSVlSE2NrbKGJlMhsDAQNUYIiJ6mCAIeCvQDT8M94ahngx/X8nGq2FRSL9TLHU0olqnVmFJTk5GWFgY3NzcsG/fPkyZMgUzZszAunXrqt0nMzMTtra2VdbZ2toiPz8f9+7dw+3bt1FZWfnIMZmZmdUet7S0FPn5+VUWIqKG6KX2Dtj8hj+szQwRn1WAQUsjEHvtjtSxiGqVWoVFqVSiY8eOWLBgAby9vTFp0iSEhIRg+fLldZWvWgsXLoS5ublqcXR0rPcMRESaooOjBXZOC0BbBwVyisoQvCIGW09flzoWUa1Rq7DY29ujTZs2Vda1bt0aaWnVX6FuZ2eHrKysKuuysrKgUChgbGwMKysryOXyR46xs7Or9rihoaHIy8tTLenp6epMhYhI59ibG+O3yf4IamuLskolZm85iy/3XoGSF+OSDlCrsAQEBCA+Pr7KuoSEBDg5OVW7j7+/Pw4dOlRl3YEDB+Dv7w8AMDAwQKdOnaqMUSqVOHTokGrMoxgaGkKhUFRZiIgaOhMDPYSN6IQ3e7kCAJYduYo3fz2N4rIKiZMRPRu1CsusWbMQHR2NBQsWICkpCRs2bMCKFSswdepU1ZjQ0FCMHj1a9f3kyZORnJyMd999F1euXMGyZcuwZcsWzJo1SzVm9uzZWLlyJdatW4fLly9jypQpKCoqwrhx42phikREDYtMJuDdvh5Y/JoXDOQy7L2Yidd+jEJG3j2poxE9NbUKS+fOnbFt2zZs3LgR7dq1w6effoolS5ZgxIgRqjEZGRlV3iJydnbGrl27cODAAXh5eWHRokVYtWoVgoKCVGOGDRuGr7/+Gh9++CE6dOiAuLg47N2796ELcYmIqOaGdGyGDSG+sGxkgAs38jHwhwicTc+VOhbRU1HrOSyajM9hISJ6tPQ7xZiw7iQSsgphqCfD4tc6oH97e6ljEQGoo+ewEBGR9nFsYoI/pnRF71bWKK1QYuqG0/juUCJ05P9XqYFgYSEiagDMjPSxakxnTOjmDABYfCABb22KQ0l5pcTJiGqGhYWIqIGQywR88FIbLBziCT2ZgJ1nb+L1FdHILiiROhrRE7GwEBE1MMFdmuPnCV1gbqyPuPRcDPohApdu8mnhpNlYWIiIGqCurlbYPjUALlaNcDOvBK8uj8SBS1lP3pFIIiwsREQNlLNVI2x7MwDdWlqhuKwSk9afwvLwq7wYlzQSCwsRUQNmbqKPNeM6Y6Rfc4gi8PmeK3jn93MoreDFuKRZWFiIiBo4fbkMnw3yxMcvt4VMAH6PvY5Rq07gTlGZ1NGIVFhYiIgIADCmawusGdcFZoZ6OJF6BwOXHkdiVoHUsYgAsLAQEdE/9HS3xtY3u6J5ExOk37mHIcsicSQ+W+pYRCwsRERUlZutGbZPDUAX5yYoKK3A+LUnsSYihRfjkqRYWIiI6CFNGhnglwm+GNqpGZQi8PGflzBv+wWUVyqljkYNFAsLERE9koGeDF++2h7v9/OAIAC/xqRh7JoTyCsulzoaNUAsLEREVC1BEDCphytWjPKBiYEcEUk5GLwsAim3i6SORg0MCwsRET3RC21s8fvkrnAwN0Ly7SIMWhqByKTbUseiBoSFhYiIaqSNgwLbpwXAu7kF8u6VY/TqE9gQkyZ1LGogWFiIiKjGbMyMsDHEDwM7OKBCKeL9befx8Z8XUankHURUt1hYiIhILUb6ciwZ1gFvv+AOAFgTkYoJ606ioIQX41LdYWEhIiK1CYKA6c+7YdmIjjDSl+FI/C28EhaJ9DvFUkcjHcXCQkRET62fpz22vOEPW4UhErIKMXBpBE6m3pE6FukgFhYiInom7ZtZYMfUbmjXVIE7RWUYsTIGf8RelzoW6RgWFiIiemZ25kbY8oY/Xmxnh7JKJd7+7Sy+2HsFSl6MS7WEhYWIiGqFiYEelg7viGm9WwIAwo5cxeRfYlFUWiFxMtIFLCxERFRrZDIBc4Ja4ZthXjCQy7D/UhaGLo/Czdx7UkcjLcfCQkREtW6wdzNsnOQHK1MDXMrIx8ClEbh0M1/qWKTFWFiIiKhOdHJqjO1TA+BhZ4ZbBaUYvioaF27kSR2LtBQLCxER1ZlmjU2w+Q1/dHC0QG5xOYavjMa567lSxyItxMJCRER1ytxYH+sndEEnp8bIL6nAiFUxOJN2V+pYpGVYWIiIqM6ZGelj3fgu6NKiCQpKKjDqpxM4xQfMkRpYWIiIqF6YGuph7fjO8HexRGFpBUavPoGY5BypY5GWYGEhIqJ6Y2Kgh9VjO6NbSysUl1Vi7JqTiLx6W+pYpAVYWIiIqF4ZG8ixaowPerhb4155JcavPYnjiSwt9HgsLEREVO+M9OVYMaoTnvOwQUm5EuPXncSR+GypY5EGU6uwzJ8/H4IgVFk8PDyqHV9eXo5PPvkErq6uMDIygpeXF/bu3ftMxyQiIt1gpC9H2MiOeKGNLcoqlJj0cyz+vpIldSzSUGq/wtK2bVtkZGSoluPHj1c7dt68efjxxx/x/fff49KlS5g8eTIGDx6MM2fOPPUxiYhIdxjqybF0eEf0bXv/QxPfWB+LA5dYWuhhahcWPT092NnZqRYrK6tqx65fvx7vv/8++vXrBxcXF0yZMgX9+vXDokWLnvqYRESkWwz0ZPh+uDf6e9qjvFLElF9isfdChtSxSMOoXVgSExPh4OAAFxcXjBgxAmlpadWOLS0thZGRUZV1xsbGD72Cos4x/3ns/Pz8KgsREWknfbkM377eAQM7OKBCKWLqhjP469xNqWORBlGrsPj6+mLt2rXYu3cvwsLCkJKSgu7du6OgoOCR44OCgrB48WIkJiZCqVTiwIED2Lp1KzIyMp76mA8sXLgQ5ubmqsXR0VGdqRARkYbRk8uw+LUOGOLdFJVKETM2nsGOuBtSxyINIYiiKD7tzrm5uXBycsLixYsxYcKEh7bfunULISEh+PPPPyEIAlxdXREYGIjVq1fj3r1Hf9T4k475QGlpKUpLS1Xf5+fnw9HREXl5eVAoFE87JSIiklilUsTcP87ht9jrkAnA10O9MKRjM6ljUR3Jz8+Hubn5E39/P9NtzRYWFnB3d0dSUtIjt1tbW2P79u0oKirCtWvXcOXKFZiamsLFxeWpj/mAoaEhFApFlYWIiLSfXCbgi1faI7hLcyhF4O3fzmLLyXSpY5HEnqmwFBYW4urVq7C3t3/sOCMjIzRt2hQVFRX4448/MHDgwGc+JhER6S6ZTMB/B7XDKD8niCLw7h/nsCHmydc3ku5Sq7DMmTMH4eHhSE1NRWRkJAYPHgy5XI7g4GAAwOjRoxEaGqoaHxMTg61btyI5ORnHjh1D3759oVQq8e6779b4mERE1DDJZAI+GdgWY7u2AAC8v+081kelSpqJpKOnzuDr168jODgYOTk5sLa2Rrdu3RAdHQ1ra2sAQFpaGmSy/3WgkpISzJs3D8nJyTA1NUW/fv2wfv16WFhY1PiYRETUcAmCgI8GtIG+XMDKYyn4YMdFVChFjAtwljoa1bNnuuhWk9T0oh0iItI+oijii73xWB5+FQAwr39rTOxe/fWQpD3q5aJbIiKi+iAIAt7r2wrTn2sJAPhs12WEHbkqcSqqTywsRESkFQRBwNt9WmFmoBsA4Iu9V/DD34kSp6L6wsJCRERaZWagO+b0cQcAfL0/AUsOJkBHrm6gx2BhISIirTPtOTfMfdEDALDkYCIW7Wdp0XUsLEREpJUm93TFvP6tAQA/HE7CF3vjWVp0GAsLERFprYndXfDRgDYAgOXhV/HfXZdZWnQUCwsREWm1cQHO+HRQOwDAquMp+PjPSywtOoiFhYiItN4oPycsHOIJAFgbmYoPd1yEUsnSoktYWIiISCcEd2mOL19tD0EA1kdfw3+2n2dp0SEsLEREpDNe83HEoqFekAnAxhPpeO+Pc6hkadEJLCxERKRThnRshm+GdYBMAH6LvY53fjvL0qIDWFiIiEjnDOzQFN8Hd4RcJmDrmRuYtTkOFZVKqWPRM2BhISIindS/vT2WDveGnkzAzrM38damOJSztGgtFhYiItJZfdvZY9mIjtCXC9h1PgPTN5xBWQVLizZiYSEiIp3Wp60dfhzVCQZyGfZezMSbv55GaUWl1LFITSwsRESk857zsMXKMT4w0JPh4OUsTF4fi5JylhZtwsJCREQNQk93a6we0xlG+jIcjr+FSSwtWoWFhYiIGoxublZYM7YLjPXlOJpwCxPXncK9MpYWbcDCQkREDYq/qyXWje8CEwM5jifdxvi1J1FcViF1LHoCFhYiImpwujg3wfoJXWBqqIeo5ByMXX0ShaUsLZqMhYWIiBqkTk73S4uZkR5OpN7BmNUnUFBSLnUsqgYLCxERNVjezRvj14m+UBjpIfbaXYz66QTy7rG0aCIWFiIiatDaN7PAhhA/WJjoIy49F6N+ikFucZnUsehfWFiIiKjBa9fUHBsm+qFJIwOcu56HEaticLeIpUWTsLAQEREBaOOgwMYQP1iZGuDizXwEr4xGTmGp1LHo/7GwEBER/b9WdmbYNMkP1maGuJJZgOCV0bhVwNKiCVhYiIiI/qGlzf3SYqswREJWIYJXRiM7v0TqWA0eCwsREdG/uFqbYvMkf9ibGyEpuxCvr4hGZh5Li5RYWIiIiB6hhVUjbJ7kj6YWxki+XYRhK6JwM/ee1LEaLBYWIiKiajS3NMGmSX5wbGKMaznFGLYiCtfvFksdq0FiYSEiInoMxyYm2DzJH06WJki/cw/DfoxGWg5LS31jYSEiInoCBwtjbJ7kDxerRriRew+vr4hC6u0iqWM1KGoVlvnz50MQhCqLh4dHtePLy8vxySefwNXVFUZGRvDy8sLevXsfGrd06VK0aNECRkZG8PX1xYkTJ9SfCRERUR2yMzfCpkl+cLVuhJt5JRi2IgrJtwqljtVgqP0KS9u2bZGRkaFajh8/Xu3YefPm4ccff8T333+PS5cuYfLkyRg8eDDOnDmjGrN582bMnj0bH330EU6fPg0vLy8EBQUhOzv76WZERERUR2wURtg0yR/utqbIyi/FsBXRSMpmaakPahcWPT092NnZqRYrK6tqx65fvx7vv/8++vXrBxcXF0yZMgX9+vXDokWLVGMWL16MkJAQjBs3Dm3atMHy5cthYmKC1atXP92MiIiI6pC1mSE2hvjBw84MtwpK8fqKKCRkFUgdS+epXVgSExPh4OAAFxcXjBgxAmlpadWOLS0thZGRUZV1xsbGqldlysrKEBsbi8DAwP8FkskQGBiIqKiox+YoLS1Ffn5+lYWIiKg+WJreLy1t7BW4XViG11dE43IGfw/VJbUKi6+vL9auXYu9e/ciLCwMKSkp6N69OwoKHt0sg4KCsHjxYiQmJkKpVOLAgQPYunUrMjIyAAC3b99GZWUlbG1tq+xna2uLzMzMx2ZZuHAhzM3NVYujo6M6UyEiInomjRsZYEOILzybmuNOURmCV0bjwo08qWPpLLUKy4svvoihQ4eiffv2CAoKwu7du5Gbm4stW7Y8cvy3334LNzc3eHh4wMDAANOmTcO4ceMgkz37zUmhoaHIy8tTLenp6c98TCIiInVYmBjgl4m+8HK0QG5xOUasisH56ywtdeGZmoOFhQXc3d2RlJT0yO3W1tbYvn07ioqKcO3aNVy5cgWmpqZwcXEBAFhZWUEulyMrK6vKfllZWbCzs3vszzY0NIRCoaiyEBER1TdzY32sn9AFHZtbIO9eOYavisaZtLtSx9I5z1RYCgsLcfXqVdjb2z92nJGREZo2bYqKigr88ccfGDhwIADAwMAAnTp1wqFDh1RjlUolDh06BH9//2eJRkREVG8URvr4eYIvurRogoKSCoz66QRir92ROpZOUauwzJkzB+Hh4UhNTUVkZCQGDx4MuVyO4OBgAMDo0aMRGhqqGh8TE4OtW7ciOTkZx44dQ9++faFUKvHuu++qxsyePRsrV67EunXrcPnyZUyZMgVFRUUYN25cLU2RiIio7pka6mHt+M7wc2mCwtIKjP7pBE6ksLTUFj11Bl+/fh3BwcHIycmBtbU1unXrhujoaFhbWwMA0tLSqlyfUlJSgnnz5iE5ORmmpqbo168f1q9fDwsLC9WYYcOG4datW/jwww+RmZmJDh06YO/evQ9diEtERKTpTAz0sGZsF0z8+SQiknIwZvUJrB7bGf6ullJH03qCKIqi1CFqQ35+PszNzZGXl8frWYiISFIl5ZWYtD4WRxNuwUhfhp/GdEZAy+qfW9aQ1fT3Nz9LiIiIqJYZ6cuxYlQn9G5ljZJyJcavPYnwhFtSx9JqLCxERER1wEhfjuWjOiGwtS1KK5QI+fkUDl/hx848LRYWIiKiOmKoJ8eyER3Rt60dyiqUeGN9LA5eynryjvQQFhYiIqI6ZKAnw/fDvdHf0x5llUpM/iUWey88/mnu9DAWFiIiojqmL5fh29c74GUvB1QoRUzdcBq7zmVIHUursLAQERHVAz25DN8M64Ah3k1RqRQxY9MZ7Dx7U+pYWoOFhYiIqJ7IZQK+GuqFoZ2aoVIpYuamM9h25rrUsbQCCwsREVE9kssEfPFKewR3cYRSBGZvOYvfTvEDfJ+EhYWIiKieyWQC/jvIEyP9mkMUgXf/OIdNJ9KkjqXRWFiIiIgkIJMJ+HRgO4zt2gKiCMzdeh6/RF+TOpbGYmEhIiKSiCAI+GhAG0zs5gwAmLf9AtZFpkobSkOxsBAREUlIEAT8p39rvNHTBQDw0c6LWHUsWeJUmoeFhYiISGKCIGBuXw9M690SAPDZrsv4MfyqxKk0CwsLERGRBhAEAW/3ccfMQDcAwMI9V7D0cJLEqTQHCwsREZGGEAQBMwPd8fYL7gCAr/bF49uDiRKn0gwsLERERBpm+vNueK+vBwDgm4MJWLw/HqIoSpxKWiwsREREGmhKL1fM698aAPDd30n4cl/DLi0sLERERBpqYncXfDSgDQAg7MhVLNxzpcGWFhYWIiIiDTYuwBmfDmwLAFhxNBmf/HWpQZYWFhYiIiINN8q/BRYM9gQArIlIxUc7L0KpbFilhYWFiIhICwz3bY4vX2kPQQB+jrqGeTsuNKjSwsJCRESkJV7r7IivX/WCTAA2xKQhdOv5BlNaWFiIiIi0yCudmuGbYR0gE4DNp9Ix5/ezqGwApYWFhYiISMsM7NAU3wV7Qy4TsPX0DczeEoeKSqXUseoUCwsREZEWeqm9A34I9oaeTMCOuJuYuTkO5TpcWlhYiIiItNSLnvZYNqIj9OUC/jqXgRkbz+hsaWFhISIi0mJ92tph+chOMJDLsOdCJqb+ehplFbpXWlhYiIiItNzzrW2xYnQnGOjJsP9SFqb8EovSikqpY9UqFhYiIiId0KuVDVaP6QxDPRkOXcnGpJ9jUVKuO6WFhYWIiEhHdHOzwppxnWGsL0d4wi2E/HwK98p0o7SwsBAREemQrq5WWDuuM0wM5DiWeBvj155EcVmF1LGeGQsLERGRjvF1scTP47vA1FAPUck5GLvmJIpKtbu0qFVY5s+fD0EQqiweHh6P3WfJkiVo1aoVjI2N4ejoiFmzZqGkpOSZjklERESP59OiCX6e0AVmhno4kXIHY1afQEFJudSxnpqeuju0bdsWBw8e/N8B9Ko/xIYNGzB37lysXr0aXbt2RUJCAsaOHQtBELB48eKnOiYRERHVTMfmjfHLRF+M+ikGp67dxejVJ7BufBcojPSljqY2tZuBnp4e7OzsajQ2MjISAQEBGD58OACgRYsWCA4ORkxMzFMfk4iIiGrOy9ECG0L8MPKnGJxJy8WoVTH4ebwvzE20q7SofQ1LYmIiHBwc4OLighEjRiAtLa3asV27dkVsbCxOnDgBAEhOTsbu3bvRr1+/pz4mERERqaddU3NsmOiHJo0McPZ6Hkb8FI27RWVSx1KLIIpijT/icc+ePSgsLESrVq2QkZGBjz/+GDdu3MCFCxdgZmb2yH2+++47zJkzB6IooqKiApMnT0ZYWNgzHRMASktLUVpaqvo+Pz8fjo6OyMvLg0KhqOmUiIiIGoz4zAIMXxmNnKIytLZX4NeJvmjSyEDSTPn5+TA3N3/i72+1Csu/5ebmwsnJCYsXL8aECRMe2n7kyBG8/vrr+Oyzz+Dr64ukpCS89dZbCAkJwQcffPBUx3xg/vz5+Pjjjx9az8JCRERUvcSsAgSvjMHtwlJ42Jnhl4m+sDI1lCxPTQvLM93WbGFhAXd3dyQlJT1y+wcffIBRo0Zh4sSJ8PT0xODBg7FgwQIsXLgQSuWjP+fgScd8IDQ0FHl5eaolPT39WaZCRETUILjZmmHzG36wVRjiSmYBgldEI7ug5Mk7SuyZCkthYSGuXr0Ke3v7R24vLi6GTFb1R8jlcgBAdS/sPOmYDxgaGkKhUFRZiIiI6MlcrU2xeZI/7M2NkJhdiNdXRCMrX7NLi1qFZc6cOQgPD0dqaioiIyMxePBgyOVyBAcHAwBGjx6N0NBQ1fgBAwYgLCwMmzZtQkpKCg4cOIAPPvgAAwYMUBWXJx2TiIiIal8Lq0bYPMkfTS2MkXyrCK+viEZG3j2pY1VLrduar1+/juDgYOTk5MDa2hrdunVDdHQ0rK2tAQBpaWlVXlGZN28eBEHAvHnzcOPGDVhbW2PAgAH473//W+NjEhERUd1obmmCTZP8MHxVNFJuF2HYj9HYEOKLZo1NpI72kGe66FaT1PSiHSIiIqrqRu49DF8ZjWs5xWhqYYxNk/zg2KR+Sku9XHRLRERE2q+phTE2T/KHs1Uj3Mi9h2E/RuFaTpHUsapgYSEiIiLYmRth8yQ/uFo3ws28Egz78f7bRJqChYWIiIgAADYKI2ya5A83G1Nk5pdg2I9RSMoulDoWABYWIiIi+gdrM0NsmuQHDzszZBeU4vUV0UjIKpA6FgsLERERVWVpaogNIX5oY6/A7cJSBK+IxpXMfEkzsbAQERHRQ5o0MsCGEF94NjVHTlEZgldE43KGdKWFhYWIiIgeycLEAL9M9IWXowUamxjAUsIPSlTrwXFERETUsJgb62P9hC64V1YJG4WRZDlYWIiIiOixFEb6UBjpS5qBbwkRERGRxmNhISIiIo3HwkJEREQaj4WFiIiINB4LCxEREWk8FhYiIiLSeCwsREREpPFYWIiIiEjjsbAQERGRxmNhISIiIo3HwkJEREQaj4WFiIiINB4LCxEREWk8nfm0ZlEUAQD5+fkSJyEiIqKaevB7+8Hv8eroTGEpKCgAADg6OkqchIiIiNRVUFAAc3PzarcL4pMqjZZQKpW4efMmzMzMIAhCrR03Pz8fjo6OSE9Ph0KhqLXjahJdnyPnp/10fY6cn/bT9TnW5fxEUURBQQEcHBwgk1V/pYrOvMIik8nQrFmzOju+QqHQyf8I/0nX58j5aT9dnyPnp/10fY51Nb/HvbLyAC+6JSIiIo3HwkJEREQaj4XlCQwNDfHRRx/B0NBQ6ih1RtfnyPlpP12fI+en/XR9jpowP5256JaIiIh0F19hISIiIo3HwkJEREQaj4WFiIiINF6DLSxHjx7FgAED4ODgAEEQsH379irbRVHEhx9+CHt7exgbGyMwMBCJiYlVxty5cwcjRoyAQqGAhYUFJkyYgMLCwnqcRfWeNL+xY8dCEIQqS9++fauM0eT5LVy4EJ07d4aZmRlsbGwwaNAgxMfHVxlTUlKCqVOnwtLSEqampnjllVeQlZVVZUxaWhr69+8PExMT2NjY4J133kFFRUV9TuWRajK/Xr16PXQOJ0+eXGWMps4PAMLCwtC+fXvVcx38/f2xZ88e1XZtPn/Ak+en7efv3z7//HMIgoCZM2eq1mn7OfynR81P28/h/PnzH8rv4eGh2q5x509soHbv3i3+5z//Ebdu3SoCELdt21Zl++effy6am5uL27dvF8+ePSu+/PLLorOzs3jv3j3VmL59+4peXl5idHS0eOzYMbFly5ZicHBwPc/k0Z40vzFjxoh9+/YVMzIyVMudO3eqjNHk+QUFBYlr1qwRL1y4IMbFxYn9+vUTmzdvLhYWFqrGTJ48WXR0dBQPHToknjp1SvTz8xO7du2q2l5RUSG2a9dODAwMFM+cOSPu3r1btLKyEkNDQ6WYUhU1mV/Pnj3FkJCQKucwLy9PtV2T5yeKorhz505x165dYkJCghgfHy++//77or6+vnjhwgVRFLX7/Inik+en7efvn06cOCG2aNFCbN++vfjWW2+p1mv7OXyguvlp+zn86KOPxLZt21bJf+vWLdV2TTt/Dbaw/NO/f6ErlUrRzs5O/Oqrr1TrcnNzRUNDQ3Hjxo2iKIripUuXRADiyZMnVWP27NkjCoIg3rhxo96y10R1hWXgwIHV7qNN8xNFUczOzhYBiOHh4aIo3j9f+vr64m+//aYac/nyZRGAGBUVJYri/VInk8nEzMxM1ZiwsDBRoVCIpaWl9TuBJ/j3/ETx/j+W//zH89+0aX4PNG7cWFy1apXOnb8HHsxPFHXn/BUUFIhubm7igQMHqsxJV85hdfMTRe0/hx999JHo5eX1yG2aeP4a7FtCj5OSkoLMzEwEBgaq1pmbm8PX1xdRUVEAgKioKFhYWMDHx0c1JjAwEDKZDDExMfWe+WkcOXIENjY2aNWqFaZMmYKcnBzVNm2bX15eHgCgSZMmAIDY2FiUl5dXOYceHh5o3rx5lXPo6ekJW1tb1ZigoCDk5+fj4sWL9Zj+yf49vwd+/fVXWFlZoV27dggNDUVxcbFqmzbNr7KyEps2bUJRURH8/f117vz9e34P6ML5mzp1Kvr371/lXAG683ewuvk9oO3nMDExEQ4ODnBxccGIESOQlpYGQDPPn858llBtyszMBIAqJ+HB9w+2ZWZmwsbGpsp2PT09NGnSRDVGk/Xt2xdDhgyBs7Mzrl69ivfffx8vvvgioqKiIJfLtWp+SqUSM2fOREBAANq1awfg/vkxMDCAhYVFlbH/PoePOscPtmmKR80PAIYPHw4nJyc4ODjg3LlzeO+99xAfH4+tW7cC0I75nT9/Hv7+/igpKYGpqSm2bduGNm3aIC4uTifOX3XzA3Tj/G3atAmnT5/GyZMnH9qmC38HHzc/QPvPoa+vL9auXYtWrVohIyMDH3/8Mbp3744LFy5o5PljYWmgXn/9ddXXnp6eaN++PVxdXXHkyBE8//zzEiZT39SpU3HhwgUcP35c6ih1orr5TZo0SfW1p6cn7O3t8fzzz+Pq1atwdXWt75hPpVWrVoiLi0NeXh5+//13jBkzBuHh4VLHqjXVza9NmzZaf/7S09Px1ltv4cCBAzAyMpI6Tq2ryfy0/Ry++OKLqq/bt28PX19fODk5YcuWLTA2NpYw2aPxLaFHsLOzA4CHrobOyspSbbOzs0N2dnaV7RUVFbhz545qjDZxcXGBlZUVkpKSAGjP/KZNm4a//voLhw8frvJp3XZ2digrK0Nubm6V8f8+h486xw+2aYLq5vcovr6+AFDlHGr6/AwMDNCyZUt06tQJCxcuhJeXF7799ludOX/Vze9RtO38xcbGIjs7Gx07doSenh709PQQHh6O7777Dnp6erC1tdXqc/ik+VVWVj60j7adw3+zsLCAu7s7kpKSNPLvIAvLIzg7O8POzg6HDh1SrcvPz0dMTIzq/Wd/f3/k5uYiNjZWNebvv/+GUqlU/UerTa5fv46cnBzY29sD0Pz5iaKIadOmYdu2bfj777/h7OxcZXunTp2gr69f5RzGx8cjLS2tyjk8f/58lWJ24MABKBQK1cv2UnnS/B4lLi4OAKqcQ02dX3WUSiVKS0u1/vxV58H8HkXbzt/zzz+P8+fPIy4uTrX4+PhgxIgRqq+1+Rw+aX5yufyhfbTtHP5bYWEhrl69Cnt7e838O1jrl/FqiYKCAvHMmTPimTNnRADi4sWLxTNnzojXrl0TRfH+bc0WFhbijh07xHPnzokDBw585G3N3t7eYkxMjHj8+HHRzc1NY277fdz8CgoKxDlz5ohRUVFiSkqKePDgQbFjx46im5ubWFJSojqGJs9vypQporm5uXjkyJEqt+QVFxerxkyePFls3ry5+Pfff4unTp0S/f39RX9/f9X2B7fk9enTR4yLixP37t0rWltba8Qth0+aX1JSkvjJJ5+Ip06dElNSUsQdO3aILi4uYo8ePVTH0OT5iaIozp07VwwPDxdTUlLEc+fOiXPnzhUFQRD3798viqJ2nz9RfPz8dOH8Pcq/75rR9nP4b/+cny6cw7fffls8cuSImJKSIkZERIiBgYGilZWVmJ2dLYqi5p2/BltYDh8+LAJ4aBkzZowoivdvbf7ggw9EW1tb0dDQUHz++efF+Pj4KsfIyckRg4ODRVNTU1GhUIjjxo0TCwoKJJjNwx43v+LiYrFPnz6itbW1qK+vLzo5OYkhISFVbk0TRc2e36PmBkBcs2aNasy9e/fEN998U2zcuLFoYmIiDh48WMzIyKhynNTUVPHFF18UjY2NRSsrK/Htt98Wy8vL63k2D3vS/NLS0sQePXqITZo0EQ0NDcWWLVuK77zzTpVnQIii5s5PFEVx/PjxopOTk2hgYCBaW1uLzz//vKqsiKJ2nz9RfPz8dOH8Pcq/C4u2n8N/++f8dOEcDhs2TLS3txcNDAzEpk2bisOGDROTkpJU2zXt/PHTmomIiEjj8RoWIiIi0ngsLERERKTxWFiIiIhI47GwEBERkcZjYSEiIiKNx8JCREREGo+FhYiIiDQeCwsRERFpPBYWIqp3R44cgSAID32wGhFRdVhYiKjede3aFRkZGTA3N6/xPsXFxQgNDYWrqyuMjIxgbW2Nnj17YseOHXWYlIg0hZ7UAYio4TEwMFD74+cnT56MmJgYfP/992jTpg1ycnIQGRmJnJycOkpJRJqEr7AQ0TPr1asXpk+fjpkzZ6Jx48awtbXFypUrUVRUhHHjxsHMzAwtW7bEnj17ADz8ltDatWthYWGBffv2oXXr1jA1NUXfvn2RkZGh+hk7d+7E+++/j379+qFFixbo1KkTpk+fjvHjx6vGCIKA7du3V8lmYWGBtWvXAgBSU1MhCAI2bdqErl27wsjICO3atUN4eHid/vkQ0bNjYSGiWrFu3TpYWVnhxIkTmD59OqZMmYKhQ4eia9euOH36NPr06YNRo0ahuLj4kfsXFxfj66+/xvr163H06FGkpaVhzpw5qu12dnbYvXs3CgoKnjnrO++8g7fffhtnzpyBv78/BgwYwFdqiDQcCwsR1QovLy/MmzcPbm5uCA0NhZGREaysrBASEgI3Nzd8+OGHyMnJwblz5x65f3l5OZYvXw4fHx907NgR06ZNw6FDh1TbV6xYgcjISFhaWqJz586YNWsWIiIinirrtGnT8Morr6B169YICwuDubk5fvrpp6c6FhHVDxYWIqoV7du3V30tl8thaWkJT09P1TpbW1sAQHZ29iP3NzExgaurq+p7e3v7KmN79OiB5ORkHDp0CK+++iouXryI7t2749NPP1U7q7+/v+prPT09+Pj44PLly2ofh4jqDwsLEdUKfX39Kt8LglBlnSAIAAClUlnj/UVRfGhM9+7d8d5772H//v345JNP8Omnn6KsrKzafcrLy59uQkSkUVhYiEhrtWnTBhUVFSgpKQEAWFtbV7lQNzEx8ZHXzERHR6u+rqioQGxsLFq3bl33gYnoqfG2ZiLSCr169UJwcDB8fHxgaWmJS5cu4f3330fv3r2hUCgAAM899xx++OEH+Pv7o7KyEu+9995Dr9wAwNKlS+Hm5obWrVvjm2++wd27d6vcbUREmoevsBCRVggKCsK6devQp08ftG7dGtOnT0dQUBC2bNmiGrNo0SI4Ojqie/fuGD58OObMmQMTE5OHjvX555/j888/h5eXF44fP46dO3fCysqqPqdDRGoSxH+/4UtEpKNSU1Ph7OyMM2fOoEOHDlLHISI18BUWIiIi0ngsLERERKTx+JYQERERaTy+wkJEREQaj4WFiIiINB4LCxEREWk8FhYiIiLSeCwsREREpPFYWIiIiEjjsbAQERGRxmNhISIiIo3HwkJEREQa7/8A4G/BFhn+oNsAAAAASUVORK5CYII=\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/relativeFrequentPattern/basic/RSFPGrowth.ipynb b/notebooks/relativeFrequentPattern/basic/RSFPGrowth.ipynb index f2d530b8..acd17990 100644 --- a/notebooks/relativeFrequentPattern/basic/RSFPGrowth.ipynb +++ b/notebooks/relativeFrequentPattern/basic/RSFPGrowth.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/relativeHighUtilityPattern/basic/RHUIM.ipynb b/notebooks/relativeHighUtilityPattern/basic/RHUIM.ipynb index e0dcda8f..1da46a89 100644 --- a/notebooks/relativeHighUtilityPattern/basic/RHUIM.ipynb +++ b/notebooks/relativeHighUtilityPattern/basic/RHUIM.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/stablePeriodicPatterns/basic/SPPECLAT.ipynb b/notebooks/stablePeriodicPatterns/basic/SPPECLAT.ipynb index 6bf4394a..cd703374 100644 --- a/notebooks/stablePeriodicPatterns/basic/SPPECLAT.ipynb +++ b/notebooks/stablePeriodicPatterns/basic/SPPECLAT.ipynb @@ -1,712 +1,712 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Stable Periodic Frequent patterns in Temporal Databases using SPPECLAT" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Stable Periodic Frequent patterns in a temporal database using the SPPECLAT algorithm. In the final part, we describe an advanced approach, where we evaluate the SPPECLAT algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "681b0f61-36f6-4346-ffa0-4e7dc1566478" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[?25l \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/835.0 kB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K \u001B[91m━━━━\u001B[0m\u001B[90m╺\u001B[0m\u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m92.2/835.0 kB\u001B[0m 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+ " Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from 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sha256=f41ff4580b83a0c83dd2f991238708d21846a6f285a43ea9e16611e66f1a9b51\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Stable Periodic Frequent patterns in Temporal Databases using SPPECLAT" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Stable Periodic Frequent patterns in a temporal database using the SPPECLAT algorithm. In the final part, we describe an advanced approach, where we evaluate the SPPECLAT algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "681b0f61-36f6-4346-ffa0-4e7dc1566478" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/835.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/835.0 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m399.4/835.0 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m747.5/835.0 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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" Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", - "Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "2. Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "96b374ea-631a-4364-81f7-1ec3ec6c9b02" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-05 16:50:04-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 919KB/s in 7.5s \n", - "\n", - "2023-09-05 16:50:13 (599 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "61024e00-771b-49ba-be10-e29be445c236" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Stable Periodic Frequent patterns using SPPECLAT" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "c7f22808-6eda-4883-a3c3-8a630624a4fa" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "f2a8fdec-f793-459c-c52c-dcb2aaf0181a" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1.\n", - "maxLaValue = 1000 #maxLaValue is specified in count. However, the users can also specify maxLaValue between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Stable Periodic Frequent patterns using SPPECLAT" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.stablePeriodicFrequentPattern.basic import SPPEclat as alg #import the algorithm\n", - "\n", - "obj = alg.SPPEclat(inputFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, maxLa=maxLaValue, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('stablePeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "1ae0e8d3-bff5-499f-f518-93aa7e6b6fff" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n", - "Total No of patterns: 26959\n", - "Runtime: 750.3077821731567\n", - "Memory (RSS): 2241507328\n", - "Memory (USS): 2195099648\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'stablePeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "3a6d1f7f-5102-44f5-e4b3-1d5d266791a8" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "368:7818:0 \n", - "529\t368:639:0 \n", - "529\t720\t368:181:0 \n", - "529\t676\t720\t368:157:0 \n", - "529\t720\t48\t676\t368:155:0 \n", - "529\t319\t720\t48\t676\t368:153:0 \n", - "529\t319\t527\t720\t48\t676\t368:150:0 \n", - "529\t319\t527\t352\t720\t48\t676\t368:149:0 \n", - "352\t720\t595\t676\t368\t529\t319\t527\t48:147:0 \n", - "529\t319\t527\t595\t720\t48\t676\t368:148:0 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "96b374ea-631a-4364-81f7-1ec3ec6c9b02" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-05 16:50:04-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 919KB/s in 7.5s \n", + "\n", + "2023-09-05 16:50:13 (599 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "61024e00-771b-49ba-be10-e29be445c236" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Stable Periodic Frequent patterns using SPPECLAT" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "c7f22808-6eda-4883-a3c3-8a630624a4fa" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "f2a8fdec-f793-459c-c52c-dcb2aaf0181a" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _stablePeriodicfrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the SPPECLAT algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.stablePeriodicFrequentPattern.basic import SPPEclat as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 5000\n", - "maxLaValue=1000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 10, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of SPPECLAT" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'maxLaValue', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of SPPECLAT algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 11, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.SPPEclat(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, maxLa=maxLaValue, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['SPPEclat', minSupCount, maximumPeriodCount, maxLaValue, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8d938e8d-da69-4681-b0bc-baa6a18d12fa" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n", - "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n", - "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n", - "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n", - "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n" - ] - } + "image/png": 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zM3z++ecoKSnB4sWLa3U81fnPf/6D0aNHo0+fPnjqqaeQnZ2NFStWICgoSK+gupuRI0diw4YNGDt2LEaMGIHExESsWrUKgYGB1T5Hu3bt0LdvX7zwwgsoKSnBRx99BBcXF7z22mtGOy4iAJyqgKiu3n77bdGiRQuhUCj0Lt8HcMfpA7766ivRvn17oVKpRMeOHcXq1avFggULqlxufqfnuPVS+5KSEvHqq6+KkJAQYWdnJ2xsbERISIhYuXKl3mNOnz4tIiIihK2trXB1dRXPPvusbtqDWy97F0KIU6dOibFjxwpHR0dhaWkp/P39xRtvvFGj4759GgAhhLh48aJ48MEHdc/Xs2dPsWnTJr02lZfmr1+/Xm97dZfm38m+fftEaGiosLCwEG3atBGrVq2q9n29PeM777wjevbsKRwdHYWVlZXo2LGjePfdd0VpaamujVqtFi+++KJwc3MTkiTpnrMy35IlS6rkudNUBTY2NuLixYtiyJAhwtraWnh4eIgFCxbopm2olJGRIcaPHy+sra2Fk5OTeO6558SpU6eqPOedsglRdaoCIYQ4ceKEiIyMFLa2tsLa2lrcd9994tChQ3ptKqcqOHbsmN72O02hUJ2ffvpJdOzYUahUKhEUFCQ2btwoxo8fLzp27FjlParu/dNqteI///mP8PX1FSqVSnTt2lVs2rRJTJ48udrpDpYsWSKWLl0qfHx8hEqlEv369ROxsbF6z1n5/t+uus8J0Z1IQtTjKEwiIqJbdOnSBW5ublXG29XF5cuX4efnhyVLlmDOnDlGe16iO+GYJyIiMrqysjKo1Wq9bXv37kVsbCwGDhwoTygiI+GYJyIiMrrr168jIiICjz/+OLy9vXH27FmsWrUKnp6eVSaoJGpsWDwREZHROTk5ITQ0FF9++SUyMjJgY2ODESNG4L333tOtyUjUWHHMExEREZEBOOaJiIiIyAAsnoiIiIgMwDFPRqLVapGcnAw7OzujLuNARERE9UcIgfz8fHh7e9d40l4WT0aSnJxslDWhiIiIqOFdvXoVLVu2rFFbFk9GYmdnB6D8zTfG2lBERERU//Ly8uDj46P7Hq8JFk9GUtlVZ29vz+KJiIiokTFkyA0HjBMREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBEREREZgMUTERERkQFYPBE1A8VlGrkjEBE1GSyeiJowrVZgWdQ5BC3Yjn//GgchhNyRiIgaPTO5AxBR/SgoUWPW2hhEnU4DAPx4JAnt3GzxdF8/mZMRETVuPPNE1ARdySzEuJUHEXU6DRZKBUZ29gIAvLvlDA5duCFzOiKixo3FE1ETc+D8DTyw4iDOpRXA3U6Ftc/1wvLHumJc1xbQaAWm/3gCV7OK5I5JRNRosXgiaiKEEPjqQCImfX0EuTfL0MXHEX+82BddWzlBkiT8Z1wwgls4ILuoDFP/F42bpRxETkRUGyyeiJqA4jIN5qw/ibc3nYZWAOO7tcRPU3vBw95S18bSXInPnwiFi40FzqTk4bVfTnIAORFRLbB4Imrk0vKK8cgXf+GXE9egkIA3Rgbig4c6w9JcWaWtt6MVVk7sBjOFhD9ik/HF/ksyJCYiatxYPBE1YieSsjFq+QHEXs2Bg5U5vns6DFP6+kGSpDs+JqyNCxaMCgQAvL/tLPady2iouERETQKLJ6JGav3xq3j087+Qnl+CDh622DijD/q2d63RYx/v5YtHuvtAK4AXfzyByzcK6zktEVHTweKJqJFRa7R48494vPrzSZRqtBgS6IEN0/rA18Wmxs8hSRLeGtMJXVs5Iq9Yjan/O47CEnU9piYiajpYPBE1ItmFpZi8+ihWH7wMAHh5cHusejwUtirD57tVmSmx6vFQuNmpcC6tAK+si+UAciKiGmDxRNRIJKTmY/SnB3HwQiasLZT4bGI3zLq/AxSKO49vuhcPe0usejwU5koJ2+JT8emeC0ZMTETUNLF4ImoEtp1KxdiVB5GUVYSWTlb45YXeGBbsZZTnDvV1wtujgwAAS6POYdeZNKM8LxFRU8XiicjE/Rx9Dc9/H42iUg3C27hg44y+CPCyN+prPNqzFR7v1QpCADN/isHFjAKjPj8RUVPC4onIhJ1Ly8frv8UBAB7v1QrfTekJZxuLenmt+SM7oUdrJ+SXqPHsd8eRV1xWL69DRNTYsXgiMlHFZRrM+PEEisu06NfeFW89EARzZf39lbUwU2DlxFB4OVjiUkYhZq+NgVbLAeRERLdj8URkot784zTOpRXA1VaFZQ93qdPA8Jpys1Ph8ydCYWGmwM4z6fho1/l6f00iosaGxRORCdp0MhlrjiZBkoCPHukCNztVg71255aOWDQ2GADwya7z2HYqtcFem4ioMWDxRGRikjKLMO+X8nFO0wa2rfGs4cY0PrQlnu7jBwB4ZV0MzqXlN3gGIiJTxeKJyISUqrV48ae/kV+iRqivE2ZFdJAty7+Hd0Tvti4oLNVg6nfHkVvEAeRERACLJyKT8sGOBN0iv5881hVm9ThA/F7MlAqsmNANLRytcDmzCPM3npItCxGRKWHxRGQi9iSk44v9lwAAix/sjBaOVjInApxtLLB8QlcAwNa4VOTe5NknIiIWT0QmIC2vGK+siwUATA73RWQnT5kT/aNbKyd08LBFqUaLqNOcfZyIiMUTkcw0WoGZP8Ugq7AUgV72mDc8QO5IVYzs7A0A+CM2WeYkRETyk7V42r9/P0aNGgVvb29IkoTffvtNb78QAvPnz4eXlxesrKwQERGB8+f1553JysrCxIkTYW9vD0dHR0yZMgUFBfpLS5w8eRL9+vWDpaUlfHx8sHjx4ipZ1q9fj44dO8LS0hLBwcHYsmWL0Y+XqDqf7rmAw5fKF/tdMaErLM2VckeqYmTn8nX0Dly4gazCUpnTEBHJS9biqbCwECEhIfj000+r3b948WJ88sknWLVqFY4cOQIbGxtERkaiuLhY12bixImIj49HVFQUNm3ahP3792Pq1Km6/Xl5eRgyZAh8fX0RHR2NJUuWYOHChfjiiy90bQ4dOoTHHnsMU6ZMwd9//40xY8ZgzJgxOHWKA2Spfh25lImPdp4DALwzJght3GxlTlS9Nm626ORtD41WcN4nIiJhIgCIX3/9VXdfq9UKT09PsWTJEt22nJwcoVKpxJo1a4QQQpw+fVoAEMeOHdO12bp1q5AkSVy/fl0IIcTKlSuFk5OTKCkp0bWZO3eu8Pf3191/+OGHxYgRI/TyhIWFieeee67G+XNzcwUAkZubW+PHUPOWVVAiwt7dKXznbhKz1v4td5x7+mzvBeE7d5N49PPDckchIjKa2nx/m+yYp8TERKSmpiIiIkK3zcHBAWFhYTh8+DAA4PDhw3B0dET37t11bSIiIqBQKHDkyBFdm/79+8PC4p/FVCMjI5GQkIDs7Gxdm1tfp7JN5etUp6SkBHl5eXo3opoSQmDO+lik5hWjjZsN3h4dJHekexoRXN5191diJtLziu/Rmoio6TLZ4ik1tbxrwMPDQ2+7h4eHbl9qairc3d319puZmcHZ2VmvTXXPcetr3KlN5f7qLFq0CA4ODrqbj4+PoYdIzdjXBy9j19l0WJgpsPyxrrBRmckd6Z58nK3RtZUjhAC2xKXIHYeISDYmWzyZunnz5iE3N1d3u3r1qtyRqJGIu5aL97aeAQC8PiIAnbwdZE5Uc6Mqr7o7yeKJiJovky2ePD3L57lJS9OfVyYtLU23z9PTE+np6Xr71Wo1srKy9NpU9xy3vsad2lTur45KpYK9vb3ejehe8ovLMGPNCZRpBCI7eeCJXr5yRzLIiM5ekCQg+ko2rufclDsOEZEsTLZ48vPzg6enJ3bt2qXblpeXhyNHjiA8PBwAEB4ejpycHERHR+va7N69G1qtFmFhYbo2+/fvR1nZPzMjR0VFwd/fH05OTro2t75OZZvK1yEyBiEE/u/XU7iSWYQWjlZYPD4EkiTJHcsgHvaW6NnaGQCw+STnfCKi5knW4qmgoAAxMTGIiYkBUD5IPCYmBklJSZAkCTNnzsQ777yDjRs3Ii4uDpMmTYK3tzfGjBkDAAgICMDQoUPx7LPP4ujRozh48CBmzJiBRx99FN7e5d0LEyZMgIWFBaZMmYL4+HisXbsWH3/8MWbPnq3L8fLLL2Pbtm1YunQpzp49i4ULF+L48eOYMWNGQ78l1IStP34NG2OToVRI+OSxLnCwNpc7Uq2MCin/u7WJXXdE1FzV27V/NbBnzx4BoMpt8uTJQojy6QreeOMN4eHhIVQqlRg8eLBISEjQe47MzEzx2GOPCVtbW2Fvby+eeuopkZ+fr9cmNjZW9O3bV6hUKtGiRQvx3nvvVcmybt060aFDB2FhYSE6deokNm/ebNCxcKoCuptzqXnC//UtwnfuJrFi93m549TJjfxi0WbeZuE7d5NIzCiQOw4RUZ3U5vtbEkIIGWu3JiMvLw8ODg7Izc3l+CfSU1ymwegVB5GQlo9+7V3x7VM9oVA0ru662z3x1RH8ef4G5gzpgBmD2ssdh4io1mrz/W2yY56ImoqVey4gIS0frrYqLHu4S6MvnIB/rrpj1x0RNUcsnojqWWWB8fqIALjZqWROYxyRnTxhrpRwNjUf59Py5Y5DRNSgWDwR1aPEG4W4dKMQ5koJgwPc7/2ARsLB2hwDOrgB4JxPRNT8sHgiqke7z5bPQ9bTzxl2lo3z6ro7GVnZdRebDA6dJKLmhMUTUT3afbZ88tX7/JvOWadKEYEeUJkpcOlGIeKTubYjETUfLJ6I6kl+cRmOJmYBAAYHeNyjdeNjqzLDoI7lRSEHjhNRc8LiiaieHDh/A2UaAT9XG/i52sgdp178M2Emu+6IqPlg8URUTyrHO1WenWmK7vN3h42FEteybyLmao7ccYiIGgSLJ6J6oNUK7EkoL54GN+HiycpCiYjA8i7JP2LZdUdEzQOLJ6J6EHc9FzcKSmGrMkP3ioV0m6rKCTM3xyVDq2XXHRE1fSyeiOrBroouu/4dXGFh1rT/mvXr4Ao7SzOk5ZXg2OUsueMQEdW7pv2vOpFMmvIUBbdTmSkxtJMnAOCPk8kypyEiqn8snoiMLC2vGKeu50GSgIHNoHgC/rnqbmtcKtQarcxpiIjqF4snIiPbU9FlF9LSscmsZXcvvdu6wNnGApmFpTh8KVPuOERE9YrFE5GRNYcpCm5nplRgWFBF110su+6IqGlj8URkRCVqDQ5cuAGgeRVPwD9r3W07lYpSNbvuiKjpYvFEZERHLmWhqFQDD3sVOnnbyx2nQfX0c4a7nQp5xWr8eT5D7jhERPWGxROREd3aZSdJksxpGpZSIWF4sBcArnVHRE0biyciIxFCYFczmqKgOpVX3e2IT0VxmUbmNERE9YPFE5GRXMwowNWsm7AwU6BPO1e548iiWytHtHC0QmGpRnfVIRFRU8PiichIdp0pLxbC27jARmUmcxp5SJKEkZ3ZdUdETRuLJyIjaY5TFFSnsutu19k0FJaoZU5DRGR8LJ6IjCC3qAzHr2QDYPHUydserV2sUVymxc4zaXLHISIyOhZPREaw73wGNFqB9u628HG2ljuOrCRJ0p19+iOWXXdE1PSweCIygsrB0YMCmvdZp0qVxdO+c+nIvVkmcxoiIuNi8URURxqtwN6EiuKpmU5RcLsOHnbo4GGLMo3AjvhUueMQERkViyeiOoq5mo3sojLYW5oh1NdJ7jgmY1TFci1/8Ko7ImpiWDwR1VHlFAUD/d1hpuRfqUojK7ruDl64gazCUpnTEBEZD/+lJ6ojTlFQPT9XGwS1sIdGK7D1FM8+EVHTweKJqA6u59zE2dR8KCRgQAc3ueOYHF3XXWyyzEmIiIyHxRNRHVSederWyglONhYypzE9IypmGz+SmIX0vGKZ0xARGQeLJ6I64BQFd9fSyRrdWjlCCGBzHLvuiKhpYPFEVEs3SzU4eOEGAGBwRw+Z05iukRVddxvZdUdETQSLJ6JaOnzpBkrUWrRwtEIHD1u545iskZ29oFRI+DspBxfSC+SOQ0RUZyyeiGqpcoqCQR3dIUmSzGlMl7u9JQZWDKZff/yqzGmIiOqOxRNRLQghOEWBAR7u4QMA+OXENZRptDKnISKqGxZPRLVwNjUfKbnFsDRXILyti9xxTN6gju5wtVXhRkGprugkImqsWDwR1UJlAdCnrSsszZUypzF95koFxndrAYBdd0TU+LF4IqqF3ZyiwGAPdS/vutuTkME5n4ioUWPxRGSgrMJSnEjKBsDxToZo526L7r5O0GgFfj5xTe44RES1xuKJyED7zqVDCCDAyx5eDlZyx2lUKgeOrz9+DUIImdMQEdUOiyciA1VOUTCYZ50MNiLYCzYWSiTeKMTRxCy54xAR1QqLJyIDlGm02HcuAwBwH4sng9mozHQzjq/lwHEiaqRYPBEZIPpKNvKL1XC2sUAXH0e54zRKlV13W+JSkFdcJnMaIiLDsXgiMkDlVXYD/d2gVHBW8dro1soR7dxtUVymxR9c746IGiEWT0QG4KzidSdJEh6pmLZg3XFedUdEjQ+LJ6IaSsoswoX0ApgpJPRr7yZ3nEZtbLcWMFNIiL2ag4TUfLnjEBEZhMUTUQ3tPpsGAOje2gkOVuYyp2ncXG1ViAjwAACsPcaB40TUuLB4IqqhXWcrpyjwkDlJ0/BIxcDxX/++hhK1RuY0REQ1x+KJqAYKS9Q4cql8XiJOUWAc/dq7wsNeheyiMuw8zcWCiajxYPFEVAMHLtxAqUYLXxdrtHWzkTtOk2CmVODB0JYAOOcTETUuJl08aTQavPHGG/Dz84OVlRXatm2Lt99+W29ZByEE5s+fDy8vL1hZWSEiIgLnz5/Xe56srCxMnDgR9vb2cHR0xJQpU1BQUKDX5uTJk+jXrx8sLS3h4+ODxYsXN8gxUuOw+8w/V9lJEqcoMJaHK666+/N8Bq7n3JQ5DRFRzZh08fT+++/js88+w4oVK3DmzBm8//77WLx4MZYvX65rs3jxYnzyySdYtWoVjhw5AhsbG0RGRqK4+J9V2ydOnIj4+HhERUVh06ZN2L9/P6ZOnarbn5eXhyFDhsDX1xfR0dFYsmQJFi5ciC+++KJBj5dMk1YrsCeBUxTUB18XG/Rq4wwhgJ85bQERNRbChI0YMUI8/fTTetvGjRsnJk6cKIQQQqvVCk9PT7FkyRLd/pycHKFSqcSaNWuEEEKcPn1aABDHjh3Ttdm6dauQJElcv35dCCHEypUrhZOTkygpKdG1mTt3rvD3969x1tzcXAFA5ObmGn6gZNJOXs0RvnM3icA3toriMrXccZqcDSeuCt+5m0Sf93YJjUYrdxwiamZq8/1t0meeevfujV27duHcuXMAgNjYWBw4cADDhg0DACQmJiI1NRURERG6xzg4OCAsLAyHDx8GABw+fBiOjo7o3r27rk1ERAQUCgWOHDmia9O/f39YWFjo2kRGRiIhIQHZ2dnVZispKUFeXp7ejZqmXRVTFPRt7wqVmVLmNE3PsCAv2Fma4Vr2TRy+lCl3HCKiezLp4ulf//oXHn30UXTs2BHm5ubo2rUrZs6ciYkTJwIAUlNTAQAeHvqXjnt4eOj2paamwt1dv6vFzMwMzs7Oem2qe45bX+N2ixYtgoODg+7m4+NTx6MlU7XtVPlngFMU1A9LcyVGd6lYLJhzPhFRI2DSxdO6devwww8/4Mcff8SJEyfw7bff4oMPPsC3334rdzTMmzcPubm5utvVq/xHvylKSM3H2dR8mCslDOnE4qm+PNK9FQBgW3wqcou4WDARmTaTLp5effVV3dmn4OBgPPHEE5g1axYWLVoEAPD09AQApKWl6T0uLS1Nt8/T0xPp6fpzyKjVamRlZem1qe45bn2N26lUKtjb2+vdqOn5LeY6AGCgvzscrS3u0ZpqK6iFPQK87FGq1urecyIiU2XSxVNRUREUCv2ISqUSWq0WAODn5wdPT0/s2rVLtz8vLw9HjhxBeHg4ACA8PBw5OTmIjo7Wtdm9eze0Wi3CwsJ0bfbv34+ysn/+xxsVFQV/f384OTnV2/GRadNqBTbGJAMAxnZtIXOapk2SJDzcvWLOJ3bdEZGJM+niadSoUXj33XexefNmXL58Gb/++iuWLVuGsWPHAij/B3fmzJl45513sHHjRsTFxWHSpEnw9vbGmDFjAAABAQEYOnQonn32WRw9ehQHDx7EjBkz8Oijj8Lbu3ycxYQJE2BhYYEpU6YgPj4ea9euxccff4zZs2fLdehkAo5fycb1nJuwU5lxioIGMKZLC1goFTidkodT13PljkNEdEdmcge4m+XLl+ONN97AtGnTkJ6eDm9vbzz33HOYP3++rs1rr72GwsJCTJ06FTk5Oejbty+2bdsGS0tLXZsffvgBM2bMwODBg6FQKDB+/Hh88sknuv0ODg7YsWMHpk+fjtDQULi6umL+/Pl6c0FR8/Pr3+XdR0ODPGFpzqvs6puTjQWGdPLAppMpWHvsKoJaOMgdiYioWpIQt0zXTbWWl5cHBwcH5ObmcvxTE1Cq1qLHuzuRe7MMPzwThj7tXOWO1Cz8eT4DT3x1FHaWZjj2fxEsWomo3tXm+9uku+2I5LI3IR25N8vgbqdCrzYucsdpNvq0dUULRyvkF6uxPb76aUKIiOTG4omoGr9XDBR/IMQbSgXXsmsoCoWEhzhwnIhMHIsnotvkF5dh55nyqSrG8Cq7BvdQdx9IEnDoYiaSMovkjkNEVAWLJ6LbbDuVihK1Fu3cbdHJm+PXGloLRyv0rRhjtj6aZ5+IyPSweCK6TeUkjWO6eEOS2GUnh4e7ly939HP0NWi0vKaFiEwLiyeiW6TlFePQxfLFaUd3YZedXIZ08oCjtTlScoux/3yG3HGIiPSweCK6xR+xyRACCPV1go+ztdxxmi2VmRJjKorXdRw4TkQmhsUT0S10XXYcKC67R3qUd93tPJOGzIISmdMQEf2DxRNRhQvp+Th1PQ9mCgkjgr3kjtPsBXjZo3NLB5RphG62dyIiU8DiiajCb3+Xz+00oIMbnG0sZE5DwD8Dx9cdvwouhkBEpoLFExEAIQR+jy0/uzGaXXYm44Eu3rA0V+BcWgFirubIHYeICACLJyIAwImkbFzNugkbCyXuD/CQOw5VsLc0x/Cg8i7Udcc5cJyITAOLJyL802UX2ckTVhZcjNaUPFwxcPyP2BQUl2lkTkNExOKJCGUaLTadLC+eeJWd6Qnzc4aHvQoFJWocvpQpdxwiIhZPRPvPZSC7qAyutir0busidxy6jSRJuD+wvCt1R3yazGmIiFg8EeG3mPKzTqNCvGCm5F8JU3R/oCeA8jmftFyuhYhkxm8KatYKStSIOp0KALoZrcn09GrjDFuVGTLySxB7LUfuOETUzLF4omZtR3wqisu08HO1QeeWDnLHoTtQmSkx0N8NALDjNLvuiEheLJ6oWavsshvdxRuSJMmchu6mctxTFIsnIpIZiydqtjLyS3DgfAYAdtk1Bvd1dIe5UsKF9AJcyiiQOw4RNWMsnqjZ+iM2GVoBdPFxRGtXG7nj0D3YW5qjV5vyqyF59omI5MTiiZqt32PKl2MZ08Vb5iRUU+y6IyJTwOKJmqXEG4WIvZYLpULCyBAWT41FRMXSOdFJ2cjIL5E5DRE1VyyeqFn67e/ys05927nC1VYlcxqqKW9HKwS3cIAQwO6zPPtERPJg8UTNjhBC12U3lsuxNDrsuiMiubF4omYn5moOLmcWwcpcqfsipsZjSKfy39mf52+gqFQtcxoiao5YPFGz83vF3E5DOnnARmUmcxoylL+HHXycrVCi1mL/uRtyxyGiZojFEzUrao0Wm06WF0+c26lxkiQJ9weUr3XHrjsikgOLJ2pWDly4gRsFpXC2sUDf9q5yx6Faquy623U2DWqNVuY0RNTcsHiiZqWyy25kZy+YK/nxb6y6+zrB0docOUVlOH4lW+44RNTM8NuDmo2iUjW2x6cCAMbwKrtGzUypwKCO7gCAHfHsuiOihsXiiZqNqNNpKCrVwNfFGl19HOWOQ3U0JLBi3NOZVAghZE5DRM0JiydqNionxhwd4g1JkmROQ3XVv4MrVGYKXM26iYS0fLnjEFEzwuKJmoXMghLsP19+Wftodtk1CdYWZuhXMeifXXdE1JBYPFGzsDkuBRqtQHALB7R1s5U7DhkJZxsnIjmweKJm4deKLjsOFG9aBnX0gCQBcddzkZxzU+44RNRMsHiiJu9KZiH+TsqBQgJGhXjJHYeMyM1OhdBWTgCAnWd49omIGgaLJ2ryKud26tPOFe52ljKnIWNj1x0RNTQWT9Tk/RFbXjyN5nIsTVJl8XT4YiZyb5bJnIaImgODi6dLly7VRw6ienEpowDn0wtgppB0X7LUtLRxs0U7d1uotQJ7E9LljkNEzYDBxVO7du1w33334fvvv0dxcXF9ZCIymsqunPC2LnCwMpc5DdUXdt0RUUMyuHg6ceIEOnfujNmzZ8PT0xPPPfccjh49Wh/ZiOpsR8WX6RCedWrSKounvQkZKFFrZE5DRE2dwcVTly5d8PHHHyM5ORlff/01UlJS0LdvXwQFBWHZsmXIyMioj5xEBsvIL8GJpPJFYyNYPDVpXVo6ws1OhYISNf66lCV3HCJq4mo9YNzMzAzjxo3D+vXr8f777+PChQuYM2cOfHx8MGnSJKSkpBgzJ5HBdp1JgxBA55YO8HKwkjsO1SOFQkJEQGXXXarMaYioqat18XT8+HFMmzYNXl5eWLZsGebMmYOLFy8iKioKycnJGD16tDFzEhmMXXbNy5Bbxj1ptVwomIjqj5mhD1i2bBlWr16NhIQEDB8+HN999x2GDx8OhaK8DvPz88M333yD1q1bGzsrUY0VlKhx4EL5WnZDOnnKnIYaQnhbF9hYKJGWV4K467kI8XGUOxIRNVEGn3n67LPPMGHCBFy5cgW//fYbRo4cqSucKrm7u+Orr74yWkgiQ+0/l4FStRatXazR3p1r2TUHluZKDPB3A8Cr7oiofhl85un8+fP3bGNhYYHJkyfXKhCRMVR+ed4f6AFJkmROQw1lSKAntsSlYsfpVMyJ9Jc7DhE1UQafeVq9ejXWr19fZfv69evx7bffGiUUUV2UabTYVbHOGbvsmpf7/N2hVEg4l1aAK5mFcschoibK4OJp0aJFcHV1rbLd3d0d//nPf4wSiqgujiZmIa9YDRcbC3SrWDSWmgcHa3OE+TkDYNcdEdUfg4unpKQk+Pn5Vdnu6+uLpKQko4S61fXr1/H444/DxcUFVlZWCA4OxvHjx3X7hRCYP38+vLy8YGVlhYiIiCpdi1lZWZg4cSLs7e3h6OiIKVOmoKCgQK/NyZMn0a9fP1haWsLHxweLFy82+rFQw9gRX36pekSAB5QKdtk1N5VX3e2IZ/FERPXD4OLJ3d0dJ0+erLI9NjYWLi4uRglVKTs7G3369IG5uTm2bt2K06dPY+nSpXBy+udswuLFi/HJJ59g1apVOHLkCGxsbBAZGam3dMzEiRMRHx+PqKgobNq0Cfv378fUqVN1+/Py8jBkyBD4+voiOjoaS5YswcKFC/HFF18Y9Xio/gkh9MY7UfNTOSHq8StZyCwokTkNETVJwkCvvfaa8PX1Fbt37xZqtVqo1Wqxa9cu4evrK1555RVDn+6u5s6dK/r27XvH/VqtVnh6eoolS5botuXk5AiVSiXWrFkjhBDi9OnTAoA4duyYrs3WrVuFJEni+vXrQgghVq5cKZycnERJSYnea/v7+9c4a25urgAgcnNza/wYMr64aznCd+4m0fH1reJmqVruOCSTYR/tF75zN4m1x5LkjkJEJq42398Gn3l6++23ERYWhsGDB8PKygpWVlYYMmQIBg0aZPQxTxs3bkT37t3x0EMPwd3dHV27dsV///tf3f7ExESkpqYiIiJCt83BwQFhYWE4fPgwAODw4cNwdHRE9+7ddW0iIiKgUChw5MgRXZv+/fvDwsJC1yYyMhIJCQnIzs426jFR/arsshvQwQ2W5kqZ05BchnTiQsFEVH8MLp4sLCywdu1anD17Fj/88AM2bNiAixcv4uuvv9YrPozh0qVL+Oyzz9C+fXts374dL7zwAl566SXdVX2pqeVflB4e+t0zHh4eun2pqalwd3fX229mZgZnZ2e9NtU9x62vcbuSkhLk5eXp3Uh+ulnFO7HLrjmr7LL983wGbpZyoWAiMi6D53mq1KFDB3To0MGYWarQarXo3r277oxW165dcerUKaxatUr2eaQWLVqEN998U9YMpC8pswhnU/OhVEgY1NH93g+gJivQyx4tHK1wPecm/jyfwSkriMioDD7zpNFo8NVXX2HChAmIiIjAoEGD9G7G5OXlhcDAQL1tAQEBuqv6PD3L/0FMS9M/NZ+Wlqbb5+npifT0dL39arUaWVlZem2qe45bX+N28+bNQ25uru529erV2hwiGdGOigVhe7Z2hqO1cc+CUuMiSZLu7BO77ojI2Awunl5++WW8/PLL0Gg0CAoKQkhIiN7NmPr06YOEhAS9befOnYOvry+A8nX0PD09sWvXLt3+vLw8HDlyBOHh4QCA8PBw5OTkIDo6Wtdm9+7d0Gq1CAsL07XZv38/ysrKdG2ioqLg7++vd2XfrVQqFezt7fVuJC922dGtKqcs2HU2HRouFExExmToqHQXFxexefNmQx9WK0ePHhVmZmbi3XffFefPnxc//PCDsLa2Ft9//72uzXvvvSccHR3F77//Lk6ePClGjx4t/Pz8xM2bN3Vthg4dKrp27SqOHDkiDhw4INq3by8ee+wx3f6cnBzh4eEhnnjiCXHq1Cnx008/CWtra/H555/XOCuvtpNXZkGJ8PvXJuE7d5O4mlUodxwyAaVqjQhesE34zt0kjlzKlDsOEZmoBrnazsLCAu3atTN+FVeNHj164Ndff8WaNWsQFBSEt99+Gx999BEmTpyoa/Paa6/hxRdfxNSpU9GjRw8UFBRg27ZtsLS01LX54Ycf0LFjRwwePBjDhw9H37599eZwcnBwwI4dO5CYmIjQ0FC88sormD9/vt5cUGTadp1Jg1aUj3Vp6WQtdxwyAeZKBQYHVHbdVX/hBxFRbUhCCIPOZy9duhSXLl3CihUruODqLfLy8uDg4IDc3Fx24cng2e+OI+p0GmZGtMfMiPq9kIEajy1xKZj2wwn4ulhj75yB/DeLiKqozfe3wVfbHThwAHv27MHWrVvRqVMnmJub6+3fsGGDoU9JVCc3SzX483wGAGBIIK+qon/07+AGCzMFrmQW4Xx6ATp42MkdiYiaAIOLJ0dHR4wdO7Y+shDVyv7zGSgu06KlkxUCvPjlSP+wVZmhT1sX7EnIQNTpNBZPRGQUBhdPq1evro8cRLV261p27Jah290f6Ik9CRnYEZ+K6fc1zHhNImraDB4wDpTPk7Rz5058/vnnyM/PBwAkJyejoKDAqOGI7kWt0WLXmYopCthlR9WICHSHJAGx13KRnHNT7jhE1AQYXDxduXIFwcHBGD16NKZPn46MjPKxJu+//z7mzJlj9IBEd3P8Sjayi8rgaG2OHq2rn5OLmjd3O0v08HUGAGw6mSxzGiJqCmo1SWb37t2RnZ0NKysr3faxY8fqTVZJ1BB2xJefdRrc0QNmylqdSKVm4IEu3gCA32NYPBFR3Rn8bfPnn3/i9ddfr7IIcOvWrXH9+nWjBSO6FyEEos6Uz99TuRQHUXWGB3vBTCEhPjkPFzM4vICI6sbg4kmr1UKjqbpK+bVr12BnxytZqOGcTc3H1aybUJkp0L+Dq9xxyIQ521igb/vyz8hGnn0iojoyuHgaMmQIPvroI919SZJQUFCABQsWYPjw4cbMRnRXlV12/dq7wdrC4AtHqZkZXdF1tzE2GQbODUxEpMfg4mnp0qU4ePAgAgMDUVxcjAkTJui67N5///36yEhUrR0VS25wIWCqifsDPaEyUyDxRiFOXc+TOw4RNWIG/3e9ZcuWiI2NxU8//YSTJ0+ioKAAU6ZMwcSJE/UGkBPVp+s5NxGfnAeFBAzu6C53HGoEbFVmiAj0wOaTKdgYex3BLR3kjkREjVSt+jrMzMzw+OOPGzsLUY1FxZefderu6wwXW5XMaaixeCDEG5tPpuCP2BTMGxYAhYKTqhKR4Qwunr777ru77p80aVKtwxDV1I6KWcXZZUeGGOjvBjtLM6TmFePo5Sz0auMidyQiaoQMLp5efvllvftlZWUoKiqChYUFrK2tWTxRvcspKsWRxCwAnKKADKMyU2JYkCfWHb+GjbHJLJ6IqFYMHjCenZ2tdysoKEBCQgL69u2LNWvW1EdGIj27z6ZDoxXw97CDr4uN3HGokXkgpAUAYEtcCkrVWpnTEFFjZJQpmdu3b4/33nuvylkpovoQxS47qoPwti5wtVUhp6gMBy5kyB2HiBoho61nYWZmhuRkTj5H9au4TIN958q/8LgQMNWGUiFhZGcvAJwwk4hqx+AxTxs3btS7L4RASkoKVqxYgT59+hgtGFF1Dl64gaJSDbwcLBHUwl7uONRIPdDFG98cuowdp9Nws1QDKwul3JGIqBExuHgaM2aM3n1JkuDm5oZBgwZh6dKlxspFVK3KWcXvD/SAJPEyc6qdrj6O8HG2wtWsm9h5Jg2jQrzljkREjYjBxZNWywGWJA+NVmDX2YrxTuyyozqQJAkPhHjj0z0XsTE2mcUTERnEaGOeiOrb30nZuFFQCjtLM4S1cZY7DjVylVfd7U1IR25RmcxpiKgxMfjM0+zZs2vcdtmyZYY+PdEdVU6MObijO8yVrPupbvw97eDvYYeEtHxsi0/BIz1ayR2JiBoJg4unv//+G3///TfKysrg7+8PADh37hyUSiW6deuma8fxKGRMQgjsqFiS5X522ZGRPNDFG0u2J2BjbDKLJyKqMYOLp1GjRsHOzg7ffvstnJycAJRPnPnUU0+hX79+eOWVV4wekuhCegEuZxbBQqnAAH83ueNQE/FASHnxdOhiJtLziuFubyl3JCJqBAzu+1i6dCkWLVqkK5wAwMnJCe+88w6vtqN6U9ll16edC2xVtVrPmqgKH2drdGvlCCGATSdT5I5DRI2EwcVTXl4eMjKqzsqbkZGB/Px8o4Qiul1ll92QTuyyI+N6oOJKu42xnDCTiGrG4OJp7NixeOqpp7BhwwZcu3YN165dwy+//IIpU6Zg3Lhx9ZGRmrnU3GLEXsuFJAGDA9zljkNNzIjO3lBIQMzVHFzJLJQ7DhE1AgYXT6tWrcKwYcMwYcIE+Pr6wtfXFxMmTMDQoUOxcuXK+shIzVzUmfIuu64+jnC345gUMi43OxX6tHMFAPzBs09EVAMGF0/W1tZYuXIlMjMzdVfeZWVlYeXKlbCx4Qr3ZHzssqP6VjlJ5u8xyRBCyJyGiExdrSfLSUlJQUpKCtq3bw8bGxv+g0P1IqeoFIcvZgIoX5KFqD5EdvKEhVKB8+kFOJvKsZtEdHcGF0+ZmZkYPHgwOnTogOHDhyMlpfwKlSlTpnCaAjK6HafToNYKdPS0Q1s3W7njUBPlYGWO+zqWT4HBgeNEdC8GF0+zZs2Cubk5kpKSYG1trdv+yCOPYNu2bUYNR7S54vLxEcFeMiehpq5yuZaN7LojonsweMKcHTt2YPv27WjZsqXe9vbt2+PKlStGC0aUU1SKgxduAACGd2bxRPVrcIA7bCyUuJ5zEyeSshHqy/UTiah6Bp95Kiws1DvjVCkrKwsqlcoooYgAYEc8u+yo4ViaKxFZcVHCxhh23RHRnRlcPPXr1w/fffed7r4kSdBqtVi8eDHuu+8+o4aj5m1zHLvsqGGN6lJ+1d3muBSoNVqZ0xCRqTK4227x4sUYPHgwjh8/jtLSUrz22muIj49HVlYWDh48WB8ZqRlilx3JoW87VzjbWOBGQSkOXcxE/w5cR5GIqjL4zFNQUBDOnTuHvn37YvTo0SgsLMS4cePw999/o23btvWRkZohdtmRHMyVCgwPrui641V3RHQHBp15Kisrw9ChQ7Fq1Sr83//9X31lIsKmii67kTzrRA3sgZAW+P6vJGw/lYp3xgTB0lwpdyQiMjEGnXkyNzfHyZMn6ysLEQAgu7AUhyq77DjeiRpYd18neDtYIr9Ejb0J6XLHISITZHC33eOPP46vvvqqPrIQAQB2nE6FWisQ4GWPNuyyowamUEi65VrYdUdE1TF4wLharcbXX3+NnTt3IjQ0tMp6dsuWLTNaOGqeNseVr2U3Iphr2ZE8RoV44/P9l7DrTDryi8tgZ2kudyQiMiE1Kp5OnjyJoKAgKBQKnDp1Ct26dQMAnDt3Tq+dJEnGT0jNSnbhLVfZscuOZNLJ2x5t3WxwMaMQO+LTMD605b0fRETNRo2Kp65duyIlJQXu7u64cuUKjh07BhcXl/rORs3QjtOp0LDLjmQmSRIeCGmBD3eew8bYZBZPRKSnRmOeHB0dkZiYCAC4fPkytFpOHkf1Y9NJXmVHpuGBigkzD1y4gcyCEpnTEJEpqdGZp/Hjx2PAgAHw8vKCJEno3r07lMrqL9+9dOmSUQNS85FdWD4xIcAuO5Kfn6sNOrd0wMlrudgSl4InwlvLHYmITESNiqcvvvgC48aNw4ULF/DSSy/h2WefhZ2dXX1no2Zme3x5l12glz38XG3u/QCievZAiDdOXsvFxthkFk9EpFPjq+2GDh0KAIiOjsbLL7/M4omMTreWHbvsyESM7OyNd7ecwbHL2biecxMtHK3kjkREJsDgeZ5Wr17NwomMLuuWLjsuBEymwtPBEmF+zgCAPzjnExFVMLh4IqoPOyq67Dp526M1u+zIhDwQ0gIAsDGGxRMRlWPxRCahssuOA8XJ1AwL8oS5UsLplDwkpObLHYeITACLJ5Idu+zIlDnZWGBwRw8AwA9HrsichohMQaMqnt577z1IkoSZM2fqthUXF2P69OlwcXGBra0txo8fj7S0NL3HJSUlYcSIEbC2toa7uzteffVVqNVqvTZ79+5Ft27doFKp0K5dO3zzzTcNcEQE/HOVHbvsyFQ9Ee4LANhw4joKStT3aE1ETV2jKZ6OHTuGzz//HJ07d9bbPmvWLPzxxx9Yv3499u3bh+TkZIwbN063X6PRYMSIESgtLcWhQ4fw7bff4ptvvsH8+fN1bRITEzFixAjcd999iImJwcyZM/HMM89g+/btDXZ8zdkWXmVHJq53Wxe0cbNBQYkav/59Xe44RCSzRlE8FRQUYOLEifjvf/8LJycn3fbc3Fx89dVXWLZsGQYNGoTQ0FCsXr0ahw4dwl9//QUA2LFjB06fPo3vv/8eXbp0wbBhw/D222/j008/RWlpKQBg1apV8PPzw9KlSxEQEIAZM2bgwQcfxIcffijL8TYn7LKjxkCSJDzRq/zs0/eHr0AIIXMiIpJToyiepk+fjhEjRiAiIkJve3R0NMrKyvS2d+zYEa1atcLhw4cBAIcPH0ZwcDA8PDx0bSIjI5GXl4f4+Hhdm9ufOzIyUvcc1SkpKUFeXp7ejQxX2WUX1MIevi7ssiPTNa5bS1iZK5GQlo+jiVlyxyEiGZl88fTTTz/hxIkTWLRoUZV9qampsLCwgKOjo952Dw8PpKam6trcWjhV7q/cd7c2eXl5uHnzZrW5Fi1aBAcHB93Nx8enVsfX3G0+yavsqHFwsDLHmK7l69397y8OHCdqzky6eLp69Spefvll/PDDD7C0tJQ7jp558+YhNzdXd7t69arckRqdzIISHL7ELjtqPB6v6LrbdioV6fnFMqchIrmYdPEUHR2N9PR0dOvWDWZmZjAzM8O+ffvwySefwMzMDB4eHigtLUVOTo7e49LS0uDp6QkA8PT0rHL1XeX9e7Wxt7eHlVX1yzGoVCrY29vr3cgw2+PT2GVHjUonbweE+jpBrRVYe5T/YSJqrky6eBo8eDDi4uIQExOju3Xv3h0TJ07U/Wxubo5du3bpHpOQkICkpCSEh4cDAMLDwxEXF4f09HRdm6ioKNjb2yMwMFDX5tbnqGxT+RxUPzbHlc/YPCLYW+YkRDVXOXD8x6NJUGu0MqchIjnUeGFgOdjZ2SEoKEhvm42NDVxcXHTbp0yZgtmzZ8PZ2Rn29vZ48cUXER4ejl69egEAhgwZgsDAQDzxxBNYvHgxUlNT8frrr2P69OlQqVQAgOeffx4rVqzAa6+9hqeffhq7d+/GunXrsHnz5oY94GYks6AEh3mVHTVCw4I98fYmC6TkFmPnmXQMDfKUOxIRNTCTPvNUEx9++CFGjhyJ8ePHo3///vD09MSGDRt0+5VKJTZt2gSlUonw8HA8/vjjmDRpEt566y1dGz8/P2zevBlRUVEICQnB0qVL8eWXXyIyMlKOQ2oWtsWnQiuA4BYOaOViLXccohpTmSnxSI/yC0S+58BxomZJEpywxCjy8vLg4OCA3Nxcjn+qgYlf/oWDFzIxd2hHvDCwrdxxiAxyLbsI/RbvgRDArlcGoK2brdyRiKiWavP93ejPPFHjc4NddtTItXSyxuCO7gB49omoOWLxRA1uO7vsqAl4Irw1AODn6GsoKuV6d0TNCYsnanCVE2NyLTtqzPq1c4WvizXyi9X4PSZZ7jhE1IBYPFGDulFQgr84MSY1AQqFhMfDyqct+B/XuyNqVlg8UYPadqq8y65zSwf4OLPLjhq3h7q3hMpMgdMpeTiRlCN3HCJqICyeqEFtiavosuNZJ2oCHK0t8EBI+SSvHDhO1HyweKIGc2uXHRcCpqbiifDyrrvNJ1Nwo6BE5jRE1BBYPFGDqeyyC2GXHTUhnVs6IqSlA0o1Wqw7zvXuiJoDFk/UYCqvsuNZJ2pqKqct+OGvJGi0HDhO1NSxeKIGkZFfgiOJ7LKjpmlkZy84Wpvjes5N7Dmbfu8HEFGjxuKJGkTlWnbssqOmyNJciYe7l6939z8OHCdq8lg8UYPYwokxqYmbGNYKkgTsO5eBK5mFcschonrE4onq3a1ddsOCWDxR0+TrYoMBHdwAcNoCoqaOxRPVO12XnY8ju+yoSXuiV/m0BeuOX0NxmUbmNERUX1g8Ub3bfLJ83a8RwZ4yJyGqXwP93dHC0Qq5N8vwRyzXuyNqqlg8Ub26UVCCo4lZANhlR02fUiFhYq9WANh1R9SUsXiierX7TDq0AghuwavsqHl4pLsPLJQKxF7LRezVHLnjEFE9YPFE9WrH6VQAwP2BHjInIWoYLrYq3VWlnLaAqGli8UT1pqhUjT/P3wAADOnE4omaj8crBo7/EZuM7MJSmdMQkbGxeKJ6s//cDZSotfBxtoK/h53ccYgaTLdWjgj0skeJWov10VzvjqipYfFE9SbqdBoAYEigJyRJkjkNUcORJAmTwsvPPn3/VxK0XO+OqElh8UT1Qq3RYtfZ8uKJ452oOXqgizfsLM2QlFWE/ecz5I5DREbE4onqxfEr2cgpKoOTtTm6+zrJHYeowVlbmOHB0JYAgP8d5sBxoqaExRPVix3x5WedBnX0gJmSHzNqnioHju9OSMfVrCKZ0xCRsfBbjYxOCKGbooBX2VFz1tbNFn3buUII4MejSXLHISIjYfFERnc2NR/Xsm9CZaZAv/aucschklXl2ae1x66iRM317oiaAhZPZHSVXXb92rvB2sJM5jRE8ooIcIeXgyWyCkux7vg1ueMQkRGweCKjizrDLjuiSmZKBZ4f0BYA8PHO8ygsUcuciIjqisUTGdX1nJs4dT0PCgkY3NFd7jhEJuGxnq3QytkaNwpK8PWBRLnjEFEdsXgio9pZMTFmd19nuNiqZE5DZBoszBSYE+kPAPh8/yVkFpTInIiI6oLFExkVFwImqt7IYC8Et3BAQYkay3dfkDsOEdUBiycymtyiMvx1KQsAiyei2ykUEv41rCMA4IcjV5CUyXmfiBorFk9kNHsS0qHRCnTwsEVrVxu54xCZnD7tXNGvvSvKNAJLoxLkjkNEtcTiiYxGNzFmoKfMSYhM19yh5Weffo9JxqnruTKnIaLaYPFERlFcpsG+hPLFT9llR3RnQS0cMLqLNwDg/W1nZU5DRLXB4omM4vDFTBSWauBpb4ngFg5yxyEyaXOG+MNcKeHP8zdw4PwNueMQkYFYPJFR7KiYoiAi0B0KhSRzGiLT5uNsrVu25b1tZ6DVCpkTEZEhWDxRnWm1AjvPlBdPHO9EVDMz7msHW5UZTl3Pw6a4FLnjEJEBWDxRncVcy0FGfgnsVGbo1cZF7jhEjYKLrQrP9W8DAPhgewJK1VqZExFRTbF4ojqrXAh4YEd3WJjxI0VUU1P6+cHNToWkrCKsOZokdxwiqiF+01Gd/TNFAa+yIzKEtYUZZka0BwB8sus88ovLZE5ERDXB4onq5EJ6AS5lFMJcKWGgv5vccYganYe7+6CNqw0yC0vx3z+5aDBRY8DiieokquIqu/C2rrCzNJc5DVHjY65U4NWKRYO//PMS0vOLZU5ERPfC4onqJIoLARPV2dAgT4T4OKKoVIPlu7hoMJGpY/FEtZaeX4y/r+YAAO4PYPFEVFuSJGFexaLBa44mIfFGocyJiOhuWDxRre06kw4hgJCWDvB0sJQ7DlGj1quNCwZ1dIdaK/DBDi4aTGTKWDxRre2Ir7jKrhMnxiQyhteG+kOSgM0nUxBbcVaXiEwPiyeqlYISNQ5eyATA8U5ExtLR0x7jurYEACzaegZCcNkWIlPE4olqZf+5DJRqtGjtYo327rZyxyFqMmYP6QALMwX+upSFfecy5I5DRNVg8US1Utlld3+gBySJCwETGUsLRytMDq9YNHjrWS4aTGSCWDyRwco0Wuw+mw6A452I6sP0+9rBztIMZ1Pz8XvsdbnjENFtTLp4WrRoEXr06AE7Ozu4u7tjzJgxSEjQvwqluLgY06dPh4uLC2xtbTF+/HikpaXptUlKSsKIESNgbW0Nd3d3vPrqq1Cr1Xpt9u7di27dukGlUqFdu3b45ptv6vvwGq2jiVnIK1bDxcYC3Vo5yR2HqMlxtLbAtIHtAAAfbD+HErVG5kREdCuTLp727duH6dOn46+//kJUVBTKysowZMgQFBb+MwfKrFmz8Mcff2D9+vXYt28fkpOTMW7cON1+jUaDESNGoLS0FIcOHcK3336Lb775BvPnz9e1SUxMxIgRI3DfffchJiYGM2fOxDPPPIPt27c36PE2FpWzig8OcIdSwS47ovrwVJ/W8LS3xPWcm/jf4StyxyGiW0iiEV3OkZGRAXd3d+zbtw/9+/dHbm4u3Nzc8OOPP+LBBx8EAJw9exYBAQE4fPgwevXqha1bt2LkyJFITk6Gh0f5VWGrVq3C3LlzkZGRAQsLC8ydOxebN2/GqVOndK/16KOPIicnB9u2batRtry8PDg4OCA3Nxf29vbGP3gTIYRAn/d2Izm3GF9O6o4IXmlHVG/WHkvC3F/i4Ghtjv2v3Qd7LoFEZHS1+f426TNPt8vNzQUAODs7AwCio6NRVlaGiIgIXZuOHTuiVatWOHz4MADg8OHDCA4O1hVOABAZGYm8vDzEx8fr2tz6HJVtKp+jOiUlJcjLy9O7NQfxyXlIzi2GlbkSfdu7yh2HqEkb360l2rvbIqeoDJ/vuyh3HCKq0GiKJ61Wi5kzZ6JPnz4ICgoCAKSmpsLCwgKOjo56bT08PJCamqprc2vhVLm/ct/d2uTl5eHmzZvV5lm0aBEcHBx0Nx8fnzofY2Owo6LLrn8HV1iaK2VOQ9S0mSkVeG1o+bItXx1IRGouFw0mMgWNpniaPn06Tp06hZ9++knuKACAefPmITc3V3e7evWq3JEaxD9TFPAqO6KGEBHgju6+Tigu0+LVn2M5dQGRCWgUxdOMGTOwadMm7NmzBy1bttRt9/T0RGlpKXJycvTap6WlwdPTU9fm9qvvKu/fq429vT2srKyqzaRSqWBvb693a+quZhXhbGo+FBIwuKO73HGImgVJkrBoXDAszRX48/wNfL7/ktyRiJo9ky6ehBCYMWMGfv31V+zevRt+fn56+0NDQ2Fubo5du3bptiUkJCApKQnh4eEAgPDwcMTFxSE9PV3XJioqCvb29ggMDNS1ufU5KttUPgeVq+yy69HaGU42FjKnIWo+2nvY4c0HOgEAPtiRgOgr2TInImreTLp4mj59Or7//nv8+OOPsLOzQ2pqKlJTU3XjkBwcHDBlyhTMnj0be/bsQXR0NJ566imEh4ejV69eAIAhQ4YgMDAQTzzxBGJjY7F9+3a8/vrrmD59OlQqFQDg+eefx6VLl/Daa6/h7NmzWLlyJdatW4dZs2bJduymKOo0FwImksvD3X0wKsQbGq3AS2v+Rm5RmdyRiJotky6ePvvsM+Tm5mLgwIHw8vLS3dauXatr8+GHH2LkyJEYP348+vfvD09PT2zYsEG3X6lUYtOmTVAqlQgPD8fjjz+OSZMm4a233tK18fPzw+bNmxEVFYWQkBAsXboUX375JSIjIxv0eE1ZdmEpjiZmAQCGcHoCogYnSRL+MzYIrZytcT3nJv614SQXDiaSSaOa58mUNfV5nn6OvoY562PR0dMO22b2lzsOUbN18loOxn92CGUagXfGBOHxXr5yRyJq1Jr8PE8kH3bZEZmGzi0dMbdi+oK3Np3GmZTmMccckSlh8UT3VFymwf5zNwCwy47IFDzdxw/3+buhVK3FjB9PoKhUfe8HEZHRsHiiezpw/gZulmng7WCJTt5Nr0uSqLFRKCR88FAIPOxVuJhRiIUb4+WORNSssHiie9pxunJiTA9IEhcCJjIFLrYqfPRIV0gSsO74Nfwec13uSETNBosnuiuNVmDXmfI5sjjeici0hLd1wYuD2gMA/r0hDpdvFMqciKh5YPFEd7U9PhWZhaVwsDJHTz9nueMQ0W1eGtQOPVs7o7BUgxlrTqBErZE7ElGTx+KJ7kirFfhk13kAwOTerWGu5MeFyNSYKRX4+LEucLQ2x6nreVi8LUHuSERNHr8N6Y6izqThbGo+bFVmeLpPa7njENEdeDlY4YMHQwAAXx1IxK4zafd4BBHVBYsnqpYQt5518oWjNdeyIzJlEYEeeKriPzlz1sciJfemvIGImjAWT1StPQnpiE/Og7WFElP6tpE7DhHVwL+GdURQC3tkF5Xh5Z9ioNFyAQmi+sDiiaoQQuDjXRcAAE/08oWzDc86ETUGKjMllj/WDTYWShxNzNKdPSYi42LxRFXsP38DsVdzYGmuwDP9eNaJqDHxc7XBu2ODAQDLd5/H4YuZMicianpYPJGeW8c6TejpCzc7lcyJiMhQY7q2wEOhLaEVwMy1fyOrsFTuSERNCosn0nP4Yiair2TDwkyB5wbwrBNRY/Xm6E5o42aDtLwSzFkfCyE4/onIWFg8kZ6PK846PdbDBx72ljKnIaLasrYww6cTusHCTIHdZ9Px1YFEuSMRNRksnkjnyKVMHEnMgrlSwnMD2sodh4jqKMDLHm+MDAQAvL/tLI5fzpI5EVHTwOKJdJbvLr/C7qHuPvB2tJI5DREZw+NhrTAsyBNlGoHJXx/FX5c4gJyorlg8EQAg+ko2Dly4ATOFhBd41omoyZAkCUsfDkGfdi4oLNVg8tdHsTchXe5YRI0aiycCUH5JMwCM69YCPs7WMqchImOytjDDV5N7YFBHd5SotXj2u+PYdipV7lhEjRaLJ0Ls1RzsTciAUiFh+n3t5I5DRPXA0lyJVY+HYkSwF8o0AtN/PIHf/r4udyyiRonFE+nOOo3u4g1fFxuZ0xBRfbEwU+DjR7tgfLeW0GgFZq2LwZqjSXLHImp0WDw1c6eu52LnmXRIEnjWiagZMFMqsOTBzni8VysIAczbEMdpDIgMxOKpmVtRcYXdqM7eaOtmK3MaImoICoWEt0cHYWr/8olw3950Git2cx08oppi8dSMnU3Nw7b4VEgSMGMQzzoRNSeSJGHesI6YGdEeAPDBjnNYvO0sZyInqgEWT81Y5bxOw4O80MHDTuY0RNTQJEnCzIgO+PfwjgCAlXsv4s0/TkOrZQFFdDcsnpqpC+n52BKXAoBnnYiau6n92+LtMUEAgG8OXca8DXHQsIAiuiMWT83Uit0XIAQwJNADAV72cschIpk90csXHzwUAoUErD1+FbPWxqBMo5U7FpFJYvHUDCXeKMTG2GQAwEuD28uchohMxYOhLbH8sW4wU0jYGJuMaT+cQIlaI3csIpPD4qkZ+nTPBWgFMKijO4JaOMgdh4hMyIjOXvj8iVBYmCkQdToNz3x7HDdLWUAR3YrFUzOTlFmEXytmFX6RY52IqBqDAzyw+skesDJX4s/zNzD566PILy6TOxaRyWDx1Mys3HsBGq1A/w5u6NrKSe44RGSi+rRzxf+m9ISdygxHL2fh8S+PILuwVO5YRCaBxVMzci27CL+cuAYAeIlnnYjoHrq3dsaPz/aCo7U5Yq/lYvgnf+LQxRtyxyKSHYunZmTVvoso0wj0buuC7q2d5Y5DRI1AcEsHrHsuHH6uNkjJLcbEL49g0ZYzHEhOzRqLp2YiJfcm1h2rOOvEK+yIyAAdPOyw6cW+eKynD4QAPt9/CWM/PYTzaflyRyOSBYunZuLzfZdQqtGiZ2tn9GrjInccImpkbFRmWDSuM754IhRO1uY4nZKHkcsP4NtDl7mkCzU7LJ6agfS8Yqw5mgSAZ52IqG6GdPLE9pn9MaCDG0rUWizYGI8nVx9Den6x3NGIGgyLp2bgi/2XUKLWolsrR/Rpx7NORFQ37vaW+OapHnjzgU5QmSmw71wGhn70J6JOp8kdjahBsHhq4uKTc/H9kSsAgBcHt4ckSTInIqKmQJIkTO7dGn+82BcBXvbIKizFs98dx7wNcSgqVcsdj6hesXhqwn6PuY7xnx1CcZkWob5OGNjBTe5IRNTEdPCww2/Te2Nq/zaQJGDN0SSM+OQAYq/myB2NqN6weGqC1Bot3t18Gi//FIPiMi36d3DD15N78KwTEdULlZkS/x4egB+mhMHLwRKJNwox/rNDWLH7PDRaDianpofFUxOTXViKyauP4r9/JgIApg1si9VP9oCDtbnMyYioqevdzhXbXu6PEZ29oNYKfLDjHB75/DCuZhXJHY3IqFg8NSHxybkYteIADl7IhLWFEisndsNrQztCqeAZJyJqGA7W5ljxWFcsezgEtiozHL+SjWEf/4kNJ65xSgNqMiTBT7NR5OXlwcHBAbm5ubC3t2/w1/895jrm/nISxWVa+LpY44snusPf067BcxARVbqaVYRZa2Nw/Eo2ACC4hQNGdPbC8CAvtHKxljkdUbnafH+zeDISuYontUaL97ed1XXT9e/ghuWPdmU3HRGZBLVGi1X7LuLjXedRpvnn6yaohT2GBXlheLAX/FxtZExIzR2LJxnJUTxlF5ZixpoTOHghE0D5+KZXhvizm46ITM6NghJsj0/F1rhUHL6UqTeQPMDLHsODPDG8sxfautnKmJKaIxZPMmro4ik+ORfP/S8a17JvwtpCiQ8eCsHwYK96f10iorrKLChB1Ok0bI5LwaGL+oWUv4cdhgV7YkSwF9p7cOgB1T8WTzJqyOKJ45uIqKnILixF1Ok0bDmVgoMXbuh17bVzt8XwYC8MD/aEv4cdp1uhesHiSUYNUTypNVq8t/UsvjzA8U1E1PTkFpUh6kwatsal4M/zN1Cq0er2tXG1QZ92rgj0tkeglz38Pe1gaa6UMS01FSyeZFTfxVNWYSle5PgmImom8orLsOtMGrbEpWLfuQyUqrV6+xUS0NbNFp287SsKKgcEetvD2cZCpsTUWLF4klF9Fk/xybmY+l00rudwfBMRNT/5xWXYdy4DcddycTolD/HJecgqLK22rae9JQK97cuLKq/ywsrHyRoK/keT7oDFk4zqq3jafDIFr6yP4fgmIqIKQgik55fgdHIeTqfk4XRyHuKTc3E5s/qZzG1VZgjwskNrFxu426vgbmcJdzuV7mc3OxW7AJux2nx/m9Vzpkbn008/xZIlS5CamoqQkBAsX74cPXv2lC2PnaUZStVaDOjghk84vomICJIkwcPeEh72lrivo7tue0GJGmdT/imoTqfk4WxqPgpK1Dh2ORvHLmff8TntLc3gbl9RVNmpdD+72VUUW/YquNqoYGdpxrNYxDNPt1q7di0mTZqEVatWISwsDB999BHWr1+PhIQEuLu73/Wx9dlt99elTPRo7czxTUREBlJrtLh0oxCnk/NwPecm0vOKkZ5fUnErRlpeSZXxVHejVEhwtDKHo7U5nKwt4GhtASdrczjb/PNz5Z9ONhYVbcxhruRqaKaK3XZ1FBYWhh49emDFihUAAK1WCx8fH7z44ov417/+ddfHyr08CxERGU4IgbybaqTnF+sKqvS8kn8KrLxiZFT8XFCirvXr2KnMYGtpBjOlBHOFAmZKCUqFAuZKCWYKCWbKyp//+dNMKcFcqdDtN1NIUCokKCQJSgWgqPxZkqBQlP9Zub3858q2/+w3q3g9paL8ucv/LM9ipqjMUv76ylt+rnyM3LNFWJkr4WKrMupzstuuDkpLSxEdHY158+bptikUCkRERODw4cNV2peUlKCkpER3Py8vr0FyEhGR8UiSBAdrczhYm99zUs7iMg1yisqQXVSK7KJS3c85RWXIKqy6LbuoFLk3yyAEkF+iRn4dii8q90CINz55rKvcMVg8Vbpx4wY0Gg08PDz0tnt4eODs2bNV2i9atAhvvvlmQ8UjIiKZWZor4emghKeDZY0fo9EK5N4sL6SKSjQo02qh1gioNVqUaSv+1AhotAJqbfnPt+5Ta8Q/j9EKaLUCGlHx560/CwGNFvr7RXkbbcWfGi2g0Wqh1gqoK16zTKst/1Mjqu7TaCtyVWTRyt9RZaY0jeErLJ5qad68eZg9e7bufl5eHnx8fGRMREREpkapkOBsY8H5p5oYFk8VXF1doVQqkZaWprc9LS0Nnp6eVdqrVCqoVMbtdyUiIiLTx+H/FSwsLBAaGopdu3bptmm1WuzatQvh4eEyJiMiIiJTwjNPt5g9ezYmT56M7t27o2fPnvjoo49QWFiIp556Su5oREREZCJYPN3ikUceQUZGBubPn4/U1FR06dIF27ZtqzKInIiIiJovzvNkJJzniYiIqPGpzfc3xzwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDFExEREZEBWDwRERERGYDLsxhJ5UTteXl5MichIiKimqr83jZkwRUWT0aSn58PAPDx8ZE5CRERERkqPz8fDg4ONWrLte2MRKvVIjk5GXZ2dpAkSW9fXl4efHx8cPXqVa57Vwt8/+qO72Hd8P2rO76HdcP3r+7u9B4KIZCfnw9vb28oFDUbzcQzT0aiUCjQsmXLu7axt7fnh74O+P7VHd/DuuH7V3d8D+uG71/dVfce1vSMUyUOGCciIiIyAIsnIiIiIgOweGoAKpUKCxYsgEqlkjtKo8T3r+74HtYN37+643tYN3z/6s6Y7yEHjBMREREZgGeeiIiIiAzA4omIiIjIACyeiIiIiAzA4omIiIjIACye6tmnn36K1q1bw9LSEmFhYTh69KjckRqNhQsXQpIkvVvHjh3ljmWy9u/fj1GjRsHb2xuSJOG3337T2y+EwPz58+Hl5QUrKytERETg/Pnz8oQ1Ufd6D5988skqn8mhQ4fKE9YELVq0CD169ICdnR3c3d0xZswYJCQk6LUpLi7G9OnT4eLiAltbW4wfPx5paWkyJTYtNXn/Bg4cWOUz+Pzzz8uU2PR89tln6Ny5s24izPDwcGzdulW331ifPxZP9Wjt2rWYPXs2FixYgBMnTiAkJASRkZFIT0+XO1qj0alTJ6SkpOhuBw4ckDuSySosLERISAg+/fTTavcvXrwYn3zyCVatWoUjR47AxsYGkZGRKC4ubuCkpute7yEADB06VO8zuWbNmgZMaNr27duH6dOn46+//kJUVBTKysowZMgQFBYW6trMmjULf/zxB9avX499+/YhOTkZ48aNkzG16ajJ+wcAzz77rN5ncPHixTIlNj0tW7bEe++9h+joaBw/fhyDBg3C6NGjER8fD8CInz9B9aZnz55i+vTpuvsajUZ4e3uLRYsWyZiq8ViwYIEICQmRO0ajBED8+uuvuvtarVZ4enqKJUuW6Lbl5OQIlUol1qxZI0NC03f7eyiEEJMnTxajR4+WJU9jlJ6eLgCIffv2CSHKP3Pm5uZi/fr1ujZnzpwRAMThw4flimmybn//hBBiwIAB4uWXX5YvVCPk5OQkvvzyS6N+/njmqZ6UlpYiOjoaERERum0KhQIRERE4fPiwjMkal/Pnz8Pb2xtt2rTBxIkTkZSUJHekRikxMRGpqal6n0cHBweEhYXx82igvXv3wt3dHf7+/njhhReQmZkpdySTlZubCwBwdnYGAERHR6OsrEzvc9ixY0e0atWKn8Nq3P7+Vfrhhx/g6uqKoKAgzJs3D0VFRXLEM3kajQY//fQTCgsLER4ebtTPHxcGric3btyARqOBh4eH3nYPDw+cPXtWplSNS1hYGL755hv4+/sjJSUFb775Jvr164dTp07Bzs5O7niNSmpqKgBU+3ms3Ef3NnToUIwbNw5+fn64ePEi/v3vf2PYsGE4fPgwlEql3PFMilarxcyZM9GnTx8EBQUBKP8cWlhYwNHRUa8tP4dVVff+AcCECRPg6+sLb29vnDx5EnPnzkVCQgI2bNggY1rTEhcXh/DwcBQXF8PW1ha//vorAgMDERMTY7TPH4snMlnDhg3T/dy5c2eEhYXB19cX69atw5QpU2RMRs3Vo48+qvs5ODgYnTt3Rtu2bbF3714MHjxYxmSmZ/r06Th16hTHKdbSnd6/qVOn6n4ODg6Gl5cXBg8ejIsXL6Jt27YNHdMk+fv7IyYmBrm5ufj5558xefJk7Nu3z6ivwW67euLq6gqlUlllFH9aWho8PT1lStW4OTo6okOHDrhw4YLcURqdys8cP4/G1aZNG7i6uvIzeZsZM2Zg06ZN2LNnD1q2bKnb7unpidLSUuTk5Oi15+dQ353ev+qEhYUBAD+Dt7CwsEC7du0QGhqKRYsWISQkBB9//LFRP38snuqJhYUFQkNDsWvXLt02rVaLXbt2ITw8XMZkjVdBQQEuXrwILy8vuaM0On5+fvD09NT7PObl5eHIkSP8PNbBtWvXkJmZyc9kBSEEZsyYgV9//RW7d++Gn5+f3v7Q0FCYm5vrfQ4TEhKQlJTEzyHu/f5VJyYmBgD4GbwLrVaLkpISo37+2G1Xj2bPno3Jkyeje/fu6NmzJz766CMUFhbiqaeekjtaozBnzhyMGjUKvr6+SE5OxoIFC6BUKvHYY4/JHc0kFRQU6P3vMzExETExMXB2dkarVq0wc+ZMvPPOO2jfvj38/PzwxhtvwNvbG2PGjJEvtIm523vo7OyMN998E+PHj4enpycuXryI1157De3atUNkZKSMqU3H9OnT8eOPP+L333+HnZ2dbhyJg4MDrKys4ODggClTpmD27NlwdnaGvb09XnzxRYSHh6NXr14yp5ffvd6/ixcv4scff8Tw4cPh4uKCkydPYtasWejfvz86d+4sc3rTMG/ePAwbNgytWrVCfn4+fvzxR+zduxfbt2837ufPuBcE0u2WL18uWrVqJSwsLETPnj3FX3/9JXekRuORRx4RXl5ewsLCQrRo0UI88sgj4sKFC3LHMll79uwRAKrcJk+eLIQon67gjTfeEB4eHkKlUonBgweLhIQEeUObmLu9h0VFRWLIkCHCzc1NmJubC19fX/Hss8+K1NRUuWObjOreOwBi9erVujY3b94U06ZNE05OTsLa2lqMHTtWpKSkyBfahNzr/UtKShL9+/cXzs7OQqVSiXbt2olXX31V5ObmyhvchDz99NPC19dXWFhYCDc3NzF48GCxY8cO3X5jff4kIYSoa6VHRERE1FxwzBMRERGRAVg8ERERERmAxRMRERGRAVg8ERERERmAxRMRERGRAVg8ERERERmAxRMRERGRAVg8ERHdZu/evZAkqcoaWMY2cOBAzJw5s15fo6Ya6piJmgIWT0RkEFP6wjeG6o6nd+/eSElJgYODgzyh6llT+x0SNTQWT0RkdEIIqNVquWPUmoWFBTw9PSFJktxRiMgEsXgiohp78sknsW/fPnz88ceQJAmSJOHy5cu6Lp+tW7ciNDQUKpUKBw4cwMWLFzF69Gh4eHjA1tYWPXr0wM6dO/Wes3Xr1vjPf/6Dp59+GnZ2dmjVqhW++OIL3f7S0lLMmDEDXl5esLS0hK+vLxYtWqTbv2zZMgQHB8PGxgY+Pj6YNm0aCgoK9F7j4MGDGDhwIKytreHk5ITIyEhkZ2ff83hu7cL65Zdf0KlTJ6hUKrRu3RpLly416DhqoqSkBHPmzEGLFi1gY2ODsLAw7N27V7f/m2++gaOjI7Zv346AgADY2tpi6NChSElJ0bVRq9V46aWX4OjoCBcXF8ydOxeTJ0/WLQB9p2OuFB0dje7du8Pa2hq9e/dGQkKCQcdA1CwYbTU+ImrycnJyRHh4uHj22WdFSkqKSElJEWq1WregbufOncWOHTvEhQsXRGZmpoiJiRGrVq0ScXFx4ty5c+L1118XlpaW4sqVK7rn9PX1Fc7OzuLTTz8V58+fF4sWLRIKhUKcPXtWCCHEkiVLhI+Pj9i/f7+4fPmy+PPPP8WPP/6oe/yHH34odu/eLRITE8WuXbuEv7+/eOGFF3T7//77b6FSqcQLL7wgYmJixKlTp8Ty5ctFRkbGPY8nOztbCCHE8ePHhUKhEG+99ZZISEgQq1evFlZWVnoL3t7rOKozYMAA8fLLL+vuP/PMM6J3795i//794sKFC2LJkiVCpVKJc+fOCSGEWL16tTA3NxcRERHi2LFjIjo6WgQEBIgJEybonuOdd94Rzs7OYsOGDeLMmTPi+eefF/b29mL06NE1+h2GhYWJvXv3ivj4eNGvXz/Ru3dvgz8nRE0diyciMsjtX/hCCN0X72+//XbPx3fq1EksX75cd9/X11c8/vjjuvtarVa4u7uLzz77TAghxIsvvigGDRoktFptjfKtX79euLi46O4/9thjok+fPrU6nsriacKECeL+++/Xa/Pqq6+KwMDAGh/HvV77ypUrQqlUiuvXr+u1GTx4sJg3b54Qorx4AiAuXLig2//pp58KDw8P3X0PDw+xZMkS3X21Wi1atWqlK57udcw7d+7Ubdu8ebMAIG7evHnHYyBqjthtR0RG0717d737BQUFmDNnDgICAuDo6AhbW1ucOXMGSUlJeu06d+6s+1mSJHh6eiI9PR1AeTdTTEwM/P398dJLL2HHjh16j925cycGDx6MFi1awM7ODk888QQyMzNRVFQEAIiJicHgwYPrdFxnzpxBnz599Lb16dMH58+fh0ajqdFx3EtcXBw0Gg06dOgAW1tb3W3fvn24ePGirp21tTXatm2ru+/l5aV7jdzcXKSlpaFnz566/UqlEqGhoTU+1luPwcvLCwBqfAxEzYWZ3AGIqOmwsbHRuz9nzhxERUXhgw8+QLt27WBlZYUHH3wQpaWleu3Mzc317kuSBK1WCwDo1q0bEhMTsXXrVuzcuRMPP/wwIiIi8PPPP+Py5csYOXIkXnjhBbz77rtwdnbGgQMHMGXKFJSWlsLa2hpWVlb1e9A1PI57KSgogFKpRHR0NJRKpd4+W1vbu76GEKKWiau69fkrB8zX9BiImgueeSIig1hYWOidbbmbgwcP4sknn8TYsWMRHBwMT09PvcHJNWVvb49HHnkE//3vf7F27Vr88ssvyMrKQnR0NLRaLZYuXYpevXqhQ4cOSE5O1nts586dsWvXrjodT0BAAA4ePFjl2Dp06FCl0Kmtrl27QqPRID09He3atdO7eXp61ug5HBwc4OHhgWPHjum2aTQanDhxQq+dIb9DIqqKZ56IyCCtW7fGkSNHcPnyZdja2sLZ2fmObdu3b48NGzZg1KhRkCQJb7zxhsFnMZYtWwYvLy907doVCoUC69evh6enJxwdHdGuXTuUlZVh+fLlGDVqFA4ePIhVq1bpPX7evHkIDg7GtGnT8Pzzz8PCwgJ79uzBQw89BFdX1xodzyuvvIIePXrg7bffxiOPPILDhw9jxYoVWLlypUHHcjcdOnTAxIkTMWnSJCxduhRdu3ZFRkYGdu3ahc6dO2PEiBE1ep4XX3wRixYtQrt27dCxY0csX74c2dnZetMuGPI7JKKqeOaJiAwyZ84cKJVKBAYGws3Nrcr4pVstW7YMTk5O6N27N0aNGoXIyEh069bNoNezs7PD4sWL0b17d/To0QOXL1/Gli1boFAoEBISgmXLluH9999HUFAQfvjhB71pDIDyomTHjh2IjY1Fz549ER4ejt9//x1mZmY1Pp5u3bph3bp1+OmnnxAUFIT58+fjrbfewpNPPmnQsdzL6tWrMWnSJLzyyivw9/fHmDFjcOzYMbRq1arGzzF37lw89thjmDRpEsLDw2Fra4vIyEhYWlrq2hjyOySiqiRhzM5yIiIyKVqtFgEBAXj44Yfx9ttvyx2HqElgtx0RURNy5coV7NixAwMGDEBJSQlWrFiBxMRETJgwQe5oRE0Gu+2IiJoQhUKBb775Bj169ECfPn0QFxeHnTt3IiAgQO5oRE0Gu+2IiIiIDMAzT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQGYPFEREREZAAWT0REREQG+H9n6W6W5uqvUwAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1.\n", + "maxLaValue = 1000 #maxLaValue is specified in count. However, the users can also specify maxLaValue between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Stable Periodic Frequent patterns using SPPECLAT" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.stablePeriodicFrequentPattern.basic import SPPEclat as alg #import the algorithm\n", + "\n", + "obj = alg.SPPEclat(inputFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, maxLa=maxLaValue, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('stablePeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1ae0e8d3-bff5-499f-f518-93aa7e6b6fff" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n", + "Total No of patterns: 26959\n", + "Runtime: 750.3077821731567\n", + "Memory (RSS): 2241507328\n", + "Memory (USS): 2195099648\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'stablePeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3a6d1f7f-5102-44f5-e4b3-1d5d266791a8" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "368:7818:0 \n", + "529\t368:639:0 \n", + "529\t720\t368:181:0 \n", + "529\t676\t720\t368:157:0 \n", + "529\t720\t48\t676\t368:155:0 \n", + "529\t319\t720\t48\t676\t368:153:0 \n", + "529\t319\t527\t720\t48\t676\t368:150:0 \n", + "529\t319\t527\t352\t720\t48\t676\t368:149:0 \n", + "352\t720\t595\t676\t368\t529\t319\t527\t48:147:0 \n", + "529\t319\t527\t595\t720\t48\t676\t368:148:0 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _stablePeriodicfrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the SPPECLAT algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.stablePeriodicFrequentPattern.basic import SPPEclat as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 5000\n", + "maxLaValue=1000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of SPPECLAT" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'maxLaValue', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of SPPECLAT algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.SPPEclat(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, maxLa=maxLaValue, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['SPPEclat', minSupCount, maximumPeriodCount, maxLaValue, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8d938e8d-da69-4681-b0bc-baa6a18d12fa" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n", + "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n", + "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n", + "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n", + "Stable Periodic Frequent patterns were generated successfully using basic algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "780bdbf0-601b-413e-c8ff-577df3a94edd" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximumPeriodCount maxLaValue patterns runtime \\\n", + "0 SPPEclat 100 5000 1000 26959 743.577161 \n", + "1 SPPEclat 150 5000 1000 19115 556.540653 \n", + "2 SPPEclat 200 5000 1000 13235 417.171257 \n", + "3 SPPEclat 250 5000 1000 7674 283.871248 \n", + "4 SPPEclat 300 5000 1000 4529 198.811954 \n", + "\n", + " memory \n", + "0 4106850304 \n", + "1 3623673856 \n", + "2 3314810880 \n", + "3 3133554688 \n", + "4 3126312960 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "c0f3c51b-65a4-438a-951f-7ffbafaddfa2" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 16 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "780bdbf0-601b-413e-c8ff-577df3a94edd" - }, - "execution_count": 15, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximumPeriodCount maxLaValue patterns runtime \\\n", - "0 SPPEclat 100 5000 1000 26959 743.577161 \n", - "1 SPPEclat 150 5000 1000 19115 556.540653 \n", - "2 SPPEclat 200 5000 1000 13235 417.171257 \n", - "3 SPPEclat 250 5000 1000 7674 283.871248 \n", - "4 SPPEclat 300 5000 1000 4529 198.811954 \n", - "\n", - " memory \n", - "0 4106850304 \n", - "1 3623673856 \n", - "2 3314810880 \n", - "3 3133554688 \n", - "4 3126312960 \n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "c0f3c51b-65a4-438a-951f-7ffbafaddfa2" - }, - "execution_count": 16, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 16 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/stablePeriodicPatterns/basic/SPPGrowth.ipynb b/notebooks/stablePeriodicPatterns/basic/SPPGrowth.ipynb index 4af00662..03982066 100644 --- a/notebooks/stablePeriodicPatterns/basic/SPPGrowth.ipynb +++ b/notebooks/stablePeriodicPatterns/basic/SPPGrowth.ipynb @@ -1,712 +1,712 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Finding Stable Periodic Frequent patterns in Temporal Databases using SPPGrowth" + ], + "metadata": { + "id": "XZ4vrXSQ1yEs" + } + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Stable Periodic Frequent patterns in a temporal database using the SPPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the SPPGrowth algorithm on a dataset at different minimum support threshold values.\n", + "***" + ], + "metadata": { + "id": "roOSCMZX2Eb2" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Prerequisites:" + ], + "metadata": { + "id": "TFlIIF_X2SzU" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Installing the PAMI library" + ], + "metadata": { + "id": "TqMwpaLw2XLu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami #install the pami repository" + ], + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "de257a90-7f0f-4a91-fdfa-30f521d7b07a" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m835.0/835.0 kB\u001B[0m \u001B[31m6.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", + "Collecting resource (from pami)\n", + " Downloading Resource-0.2.1-py2.py3-none-any.whl (25 kB)\n", + "Collecting validators (from pami)\n", + " Downloading validators-0.22.0-py3-none-any.whl (26 kB)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.0.4)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (9.4.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.23.5)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pami) (1.1.0)\n", + "Requirement already 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(8.2.3)\n", + "Collecting JsonForm>=0.0.2 (from resource->pami)\n", + " Downloading JsonForm-0.0.2.tar.gz (2.4 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting JsonSir>=0.0.2 (from resource->pami)\n", + " Downloading JsonSir-0.0.2.tar.gz (2.2 kB)\n", + " Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + "Collecting python-easyconfig>=0.1.0 (from resource->pami)\n", + " Downloading Python_EasyConfig-0.1.7-py2.py3-none-any.whl (5.4 kB)\n", + "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from JsonForm>=0.0.2->resource->pami) (4.19.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pami) (1.16.0)\n", + "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from python-easyconfig>=0.1.0->resource->pami) (6.0.1)\n", + "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (23.1.0)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (2023.7.1)\n", + "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.30.2)\n", + "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->JsonForm>=0.0.2->resource->pami) (0.9.2)\n", + "Building wheels for collected packages: JsonForm, JsonSir\n", + " Building wheel for JsonForm (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonForm: filename=JsonForm-0.0.2-py3-none-any.whl size=3313 sha256=93c2845870eea1f601874f08261a22f736b8ced605ca770566d99cc12daf9a5d\n", + " Stored in directory: /root/.cache/pip/wheels/b6/e5/87/11026246d3bd4ad67c0615682d2d6748bbd9a40ac0490882bd\n", + " Building wheel for JsonSir (setup.py) ... \u001B[?25l\u001B[?25hdone\n", + " Created wheel for JsonSir: filename=JsonSir-0.0.2-py3-none-any.whl size=4750 sha256=37f403f13afee12191f294b03dc3bc1e4ee67c9f59d2447d077263e6592be2f6\n", + " Stored in directory: /root/.cache/pip/wheels/1d/4c/d3/4d9757425983b43eb709be1043d82cd03fb863ce5f56f117e6\n", + "Successfully built JsonForm JsonSir\n", + "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, pami\n", + "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.8.6.8 python-easyconfig-0.1.7 resource-0.2.1 validators-0.22.0\n" + ] } + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Finding Stable Periodic Frequent patterns in Temporal Databases using SPPGrowth" - ], - "metadata": { - "id": "XZ4vrXSQ1yEs" - } - }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Stable Periodic Frequent patterns in a temporal database using the SPPGrowth algorithm. In the final part, we describe an advanced approach, where we evaluate the SPPGrowth algorithm on a dataset at different minimum support threshold values.\n", - "***" - ], - "metadata": { - "id": "roOSCMZX2Eb2" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Prerequisites:" - ], - "metadata": { - "id": "TFlIIF_X2SzU" - } - }, - { - "cell_type": "markdown", - "source": [ - "1. Installing the PAMI library" - ], - "metadata": { - "id": "TqMwpaLw2XLu" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install -U pami #install the pami repository" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "de257a90-7f0f-4a91-fdfa-30f521d7b07a" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.8.6.8-py3-none-any.whl (834 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m835.0/835.0 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - 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Downloading a sample dataset" - ], - "metadata": { - "id": "rYfvWHRN2oBs" - } - }, - { - "cell_type": "code", - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "72bbcd4e-a8b2-4610-ca5a-820ee6acb8ae" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-09-05 17:30:08-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 3.30MB/s in 1.3s \n", - "\n", - "2023-09-05 17:30:11 (3.30 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "3. Printing few lines of a dataset to know its format." - ], - "metadata": { - "id": "USUJbpXu3Gkw" - } - }, - { - "cell_type": "code", - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "f01ad1e2-8081-4efc-bd38-056fc0c50844" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ], - "metadata": { - "id": "oQQdz3qn3Qwz" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 1: Finding Stable Periodic Frequent patterns using SPPGrowth" - ], - "metadata": { - "id": "62Vkqg-C3WVZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ], - "metadata": { - "id": "gaxxPgXv3ecW" - } - }, - { - "cell_type": "code", - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "050452ae-af90-4577-d03e-157a7aa8d7d6" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ], - "metadata": { - "id": "1oJIEK8A3wQS" - } - }, - { - "cell_type": "code", - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "802d6851-3116-4b05-c0f3-23254fa42363" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ], - "metadata": { - "id": "zpLiRCBp39k9" - } - }, - { - "cell_type": "code", - "source": [ - "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1.\n", - "maxLaValue = 1000 #maxLaValue is specified in count. However, the users can also specify maxLaValue between 0 and 1." - ], - "metadata": { - "id": "RP9ynbti4L48" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 4: Mining Stable Periodic Frequent patterns using SPPGrowth" - ], - "metadata": { - "id": "-Yr0r7zw4Q85" - } - }, - { - "cell_type": "code", - "source": [ - "from PAMI.stablePeriodicFrequentPattern.basic import SPPGrowth as alg #import the algorithm\n", - "\n", - "obj = alg.SPPGrowth(inputFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, maxLa=maxLaValue, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('stablePeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ], - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4106936b-9734-46c3-8037-eaf75d383af3" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n", - "Total No of patterns: 26959\n", - "Runtime: 31.285231828689575\n", - "Memory (RSS): 608731136\n", - "Memory (USS): 562049024\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ], - "metadata": { - "id": "3M8FtfKU4bhu" - } - }, - { - "cell_type": "code", - "source": [ - "!head 'stablePeriodicFrequentPatternsAtMinSupCount100.txt'" - ], - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4032c95f-86f5-44bf-af1c-04b0903842d0" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "330:102:0 \n", - "102:109:0 \n", - "199:109:444 \n", - "856:109:0 \n", - "856\t490:103:0 \n", - "856\t490\t906:103:0 \n", - "856\t906:103:0 \n", - "191:111:0 \n", - "191\t339:106:0 \n", - "191\t339\t90:102:0 \n" - ] - } + { + "cell_type": "markdown", + "source": [ + "2. Downloading a sample dataset" + ], + "metadata": { + "id": "rYfvWHRN2oBs" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "72bbcd4e-a8b2-4610-ca5a-820ee6acb8ae" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-09-05 17:30:08-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.95.161.176, 150.31.244.160\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.95.161.176|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 3.30MB/s in 1.3s \n", + "\n", + "2023-09-05 17:30:11 (3.30 MB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Printing few lines of a dataset to know its format." + ], + "metadata": { + "id": "USUJbpXu3Gkw" + } + }, + { + "cell_type": "code", + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "f01ad1e2-8081-4efc-bd38-056fc0c50844" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ], + "metadata": { + "id": "oQQdz3qn3Qwz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Finding Stable Periodic Frequent patterns using SPPGrowth" + ], + "metadata": { + "id": "62Vkqg-C3WVZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ], + "metadata": { + "id": "gaxxPgXv3ecW" + } + }, + { + "cell_type": "code", + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "050452ae-af90-4577-d03e-157a7aa8d7d6" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ], + "metadata": { + "id": "1oJIEK8A3wQS" + } + }, + { + "cell_type": "code", + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "y7pfaeJV34H_", + "outputId": "802d6851-3116-4b05-c0f3-23254fa42363" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "markdown", - "source": [ - "The storage format is: _stablePeriodicfrequentPattern:support_\n", - "***" - ], - "metadata": { - "id": "j4CpTPXw4k9I" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2: Evaluating the SPPGrowth algorithm on a dataset at different minSup values" - ], - "metadata": { - "id": "kC71sBV74qY0" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ], - "metadata": { - "id": "EobpZCdu6G0Z" - } - }, - { - "cell_type": "code", - "source": [ - "#Import the libraries\n", - "from PAMI.stablePeriodicFrequentPattern.basic import SPPGrowth as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "maximumPeriodCount = 5000\n", - "maxLaValue=1000\n", - "minimumSupportCountList = [100, 150, 200, 250, 300]\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ], - "metadata": { - "id": "W96B78JT6KT2" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Step 2: Create a data frame to store the results of SPPGrowth" - ], - "metadata": { - "id": "gS09HRlY6PPR" - } - }, - { - "cell_type": "code", - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'maxLaValue', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of SPPGrowth algorithm" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "0tbQu3re6VGI" - }, - "execution_count": 10, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kn2TtrbW6awD" - } - }, - { - "cell_type": "code", - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.SPPGrowth(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, maxLa=maxLaValue, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['SPPGrowth', minSupCount, maximumPeriodCount, maxLaValue, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ], - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "865beda1-02b1-4098-e01a-2a945b96bb4d" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n", - "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n", - "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n", - "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n", - "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n" - ] - } + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 100 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ], + "metadata": { + "id": "zpLiRCBp39k9" + } + }, + { + "cell_type": "code", + "source": [ + "minimumSupportCount = 100 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "maximumPeriodCount = 5000 #maximumPeriodCount is specified in count. However, the users can also specify maximumPeriodCount between 0 and 1.\n", + "maxLaValue = 1000 #maxLaValue is specified in count. However, the users can also specify maxLaValue between 0 and 1." + ], + "metadata": { + "id": "RP9ynbti4L48" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Mining Stable Periodic Frequent patterns using SPPGrowth" + ], + "metadata": { + "id": "-Yr0r7zw4Q85" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.stablePeriodicFrequentPattern.basic import SPPGrowth as alg #import the algorithm\n", + "\n", + "obj = alg.SPPGrowth(inputFile=inputFile, minSup=minimumSupportCount, maxPer=maximumPeriodCount, maxLa=maxLaValue, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('stablePeriodicFrequentPatternsAtMinSupCount100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ], + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4106936b-9734-46c3-8037-eaf75d383af3" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n", + "Total No of patterns: 26959\n", + "Runtime: 31.285231828689575\n", + "Memory (RSS): 608731136\n", + "Memory (USS): 562049024\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ], + "metadata": { + "id": "3M8FtfKU4bhu" + } + }, + { + "cell_type": "code", + "source": [ + "!head 'stablePeriodicFrequentPatternsAtMinSupCount100.txt'" + ], + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4032c95f-86f5-44bf-af1c-04b0903842d0" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "330:102:0 \n", + "102:109:0 \n", + "199:109:444 \n", + "856:109:0 \n", + "856\t490:103:0 \n", + "856\t490\t906:103:0 \n", + "856\t906:103:0 \n", + "191:111:0 \n", + "191\t339:106:0 \n", + "191\t339\t90:102:0 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The storage format is: _stablePeriodicfrequentPattern:support_\n", + "***" + ], + "metadata": { + "id": "j4CpTPXw4k9I" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2: Evaluating the SPPGrowth algorithm on a dataset at different minSup values" + ], + "metadata": { + "id": "kC71sBV74qY0" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ], + "metadata": { + "id": "EobpZCdu6G0Z" + } + }, + { + "cell_type": "code", + "source": [ + "#Import the libraries\n", + "from PAMI.stablePeriodicFrequentPattern.basic import SPPGrowth as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "maximumPeriodCount = 5000\n", + "maxLaValue=1000\n", + "minimumSupportCountList = [100, 150, 200, 250, 300]\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ], + "metadata": { + "id": "W96B78JT6KT2" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Create a data frame to store the results of SPPGrowth" + ], + "metadata": { + "id": "gS09HRlY6PPR" + } + }, + { + "cell_type": "code", + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'maxLaValue', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of SPPGrowth algorithm" + ], + "metadata": { + "id": "0tbQu3re6VGI" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ], + "metadata": { + "id": "kn2TtrbW6awD" + } + }, + { + "cell_type": "code", + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.SPPGrowth(inputFile, minSup=minSupCount, maxPer=maximumPeriodCount, maxLa=maxLaValue, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['SPPGrowth', minSupCount, maximumPeriodCount, maxLaValue, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ], + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "865beda1-02b1-4098-e01a-2a945b96bb4d" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n", + "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n", + "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n", + "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n", + "Stable Periodic Frequent patterns were generated successfully using topk algorithm \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Print the Result" + ], + "metadata": { + "id": "NCQLc9pn7BYG" + } + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ad8f7fae-2e28-4619-99ba-af95df99513a" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximumPeriodCount maxLaValue patterns runtime \\\n", + "0 SPPGrowth 100 5000 1000 26959 27.198753 \n", + "1 SPPGrowth 150 5000 1000 19115 23.050247 \n", + "2 SPPGrowth 200 5000 1000 13235 20.051610 \n", + "3 SPPGrowth 250 5000 1000 7674 22.288245 \n", + "4 SPPGrowth 300 5000 1000 4529 19.836372 \n", + "\n", + " memory \n", + "0 628781056 \n", + "1 626077696 \n", + "2 623779840 \n", + "3 620969984 \n", + "4 617181184 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Visualizing the results" + ], + "metadata": { + "id": "S-prY3W27U4Z" + } + }, + { + "cell_type": "code", + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ], + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "6228de0a-8bfa-4edc-9267-c0c040b04f9d" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 14 }, { - "cell_type": "markdown", - "source": [ - "### Step 4: Print the Result" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "NCQLc9pn7BYG" - } + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "source": [ - "print(result)" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "ad8f7fae-2e28-4619-99ba-af95df99513a" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximumPeriodCount maxLaValue patterns runtime \\\n", - "0 SPPGrowth 100 5000 1000 26959 27.198753 \n", - "1 SPPGrowth 150 5000 1000 19115 23.050247 \n", - "2 SPPGrowth 200 5000 1000 13235 20.051610 \n", - "3 SPPGrowth 250 5000 1000 7674 22.288245 \n", - "4 SPPGrowth 300 5000 1000 4529 19.836372 \n", - "\n", - " memory \n", - "0 628781056 \n", - "1 626077696 \n", - "2 623779840 \n", - "3 620969984 \n", - "4 617181184 \n" - ] - } - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "### Step 5: Visualizing the results" + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "metadata": { - "id": "S-prY3W27U4Z" - } - }, - { - "cell_type": "code", - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ], - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "6228de0a-8bfa-4edc-9267-c0c040b04f9d" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 14 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" + }, + "metadata": {} } - ] -} \ No newline at end of file + ] + } + ] +} diff --git a/notebooks/stablePeriodicPatterns/topk/TSPIN.ipynb b/notebooks/stablePeriodicPatterns/topk/TSPIN.ipynb index 276a0d92..986a4926 100644 --- a/notebooks/stablePeriodicPatterns/topk/TSPIN.ipynb +++ b/notebooks/stablePeriodicPatterns/topk/TSPIN.ipynb @@ -1,701 +1,701 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XZ4vrXSQ1yEs" - }, - "source": [ - "# Finding Stable Periodic Frequent patterns in Temporal Databases using TSPIN" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "roOSCMZX2Eb2" - }, - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Stable Periodic Frequent patterns in a Temporal database using the TSPIN algorithm. In the final part, we describe an advanced approach, where we evaluate the TSPIN algorithm on a dataset at different K threshold values.\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TFlIIF_X2SzU" - }, - "source": [ - "# Prerequisites:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TqMwpaLw2XLu" - }, - "source": [ - "1. Installing the PAMI library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "df048b50-3215-451e-98ea-bf596a850d62" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: pami in /usr/local/lib/python3.10/dist-packages (2023.11.17.1)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", - "Requirement already satisfied: resource in /usr/local/lib/python3.10/dist-packages (from pami) (0.2.1)\n", - "Requirement already satisfied: validators in /usr/local/lib/python3.10/dist-packages (from pami) (0.22.0)\n", - 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"2. Downloading a sample dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "6168542f-3eca-4330-8ba1-0ae9b6ad25ab" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "File ‘Temporal_T10I4D100K.csv’ already there; not retrieving.\n", - "\n" - ] - } - ], - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "USUJbpXu3Gkw" - }, - "source": [ - "3. Printing few lines of a dataset to know its format." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "549565fb-8d76-48ae-c00f-4842caee4bca" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ], - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oQQdz3qn3Qwz" - }, - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "62Vkqg-C3WVZ" - }, - "source": [ - "## Part 1: Finding Stable Periodic Frequent patterns using TSPIN" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gaxxPgXv3ecW" - }, - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate k value." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "4fe08159-79be-43d1-d520-d072bc4a22c7" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ], - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1oJIEK8A3wQS" - }, - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 938 - }, - "id": "y7pfaeJV34H_", - "outputId": "76417e74-12d9-4f55-a974-b42e537e7ac2" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ], - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zpLiRCBp39k9" - }, - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _k_ value of 100 (in count). We can increase or decrease the _k_ based on the number of patterns being generated." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "RP9ynbti4L48" - }, - "outputs": [], - "source": [ - "k = 100 #Users can also specify this constraint between 0 to 1.\n", - "maxmunPeriodCount = 5000 #Users can also specify different maxmunPeriodCount.\n", - "maxLaValue = 1000 #Users can also specify different maxLaValue.\n", - "seperator='\\t'" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XZ4vrXSQ1yEs" + }, + "source": [ + "# Finding Stable Periodic Frequent patterns in Temporal Databases using TSPIN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "roOSCMZX2Eb2" + }, + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Stable Periodic Frequent patterns in a Temporal database using the TSPIN algorithm. In the final part, we describe an advanced approach, where we evaluate the TSPIN algorithm on a dataset at different K threshold values.\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TFlIIF_X2SzU" + }, + "source": [ + "# Prerequisites:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TqMwpaLw2XLu" + }, + "source": [ + "1. Installing the PAMI library" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "df048b50-3215-451e-98ea-bf596a850d62" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pami in /usr/local/lib/python3.10/dist-packages (2023.11.17.1)\n", + "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", + "Requirement already satisfied: resource in /usr/local/lib/python3.10/dist-packages (from pami) (0.2.1)\n", + "Requirement already 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Downloading a sample dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "6168542f-3eca-4330-8ba1-0ae9b6ad25ab" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "File ‘Temporal_T10I4D100K.csv’ already there; not retrieving.\n", + "\n" + ] + } + ], + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "USUJbpXu3Gkw" + }, + "source": [ + "3. Printing few lines of a dataset to know its format." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "549565fb-8d76-48ae-c00f-4842caee4bca" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ], + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oQQdz3qn3Qwz" + }, + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "62Vkqg-C3WVZ" + }, + "source": [ + "## Part 1: Finding Stable Periodic Frequent patterns using TSPIN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gaxxPgXv3ecW" + }, + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate k value." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "4fe08159-79be-43d1-d520-d072bc4a22c7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ], + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1oJIEK8A3wQS" + }, + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 938 }, + "id": "y7pfaeJV34H_", + "outputId": "76417e74-12d9-4f55-a974-b42e537e7ac2" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "-Yr0r7zw4Q85" - }, - "source": [ - "### Step 4: Mining Stable Periodic Frequent patterns using TSPIN" + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "BZzrC2Pl4XGJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "3de783f8-b665-4225-ca6b-f1743059f7a9" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n", - "Total No of patterns: 394\n", - "Runtime: 22.128568410873413\n", - "Memory (RSS): 509935616\n", - "Memory (USS): 486895616\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "from PAMI.stablePeriodicFrequentPattern.topK import TSPIN as alg #import the algorithm\n", - "\n", - "obj = alg.TSPIN(inputFile, k, maxmunPeriodCount, maxLaValue, seperator) #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('stablePeriodicFrequentPatternsAtK100.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "3M8FtfKU4bhu" - }, - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "b7IvA0IW4hBe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d222ba54-25ac-4c78-b857-83b8b2c9c538" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "484 :969:4752 \n", - "96 :974:3825 \n", - "277 :982:4008 \n", - "432 :983:3516 \n", - "160 :986:3645 \n", - "395 :989:3908 \n", - "608 :997:2562 \n", - "7 :997:2648 \n", - "912 :1008:3739 \n", - "511 :1014:2819 \n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "!head 'stablePeriodicFrequentPatternsAtK100.txt'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j4CpTPXw4k9I" - }, - "source": [ - "The storage format is: _stablePeriodicFrequentPattern:support_\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kC71sBV74qY0" - }, - "source": [ - "## Part 2: Evaluating the TSPIN algorithm on a dataset at different k values\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EobpZCdu6G0Z" - }, - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "W96B78JT6KT2" - }, - "outputs": [], - "source": [ - "#Import the libraries\n", - "from PAMI.stablePeriodicFrequentPattern.topK import TSPIN as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "separator='\\t'\n", - "maxmunPeriodCount = 5000\n", - "maxLaValue = 1000\n", - "kList = [100, 200, 300, 400, 500]\n", - "#k can also specified between 0 to 1. E.g., kList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gS09HRlY6PPR" - }, - "source": [ - "### Step 2: Create a data frame to store the results of TSPIN" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "0tbQu3re6VGI" - }, - "outputs": [], - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of TSPIN algorithm" - ] + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zpLiRCBp39k9" + }, + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _k_ value of 100 (in count). We can increase or decrease the _k_ based on the number of patterns being generated." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "RP9ynbti4L48" + }, + "outputs": [], + "source": [ + "k = 100 #Users can also specify this constraint between 0 to 1.\n", + "maxmunPeriodCount = 5000 #Users can also specify different maxmunPeriodCount.\n", + "maxLaValue = 1000 #Users can also specify different maxLaValue.\n", + "seperator='\\t'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Yr0r7zw4Q85" + }, + "source": [ + "### Step 4: Mining Stable Periodic Frequent patterns using TSPIN" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "BZzrC2Pl4XGJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3de783f8-b665-4225-ca6b-f1743059f7a9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n", + "Total No of patterns: 394\n", + "Runtime: 22.128568410873413\n", + "Memory (RSS): 509935616\n", + "Memory (USS): 486895616\n" + ] + } + ], + "source": [ + "from PAMI.stablePeriodicFrequentPattern.topK import TSPIN as alg #import the algorithm\n", + "\n", + "obj = alg.TSPIN(inputFile, k, maxmunPeriodCount, maxLaValue, seperator) #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('stablePeriodicFrequentPatternsAtK100.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3M8FtfKU4bhu" + }, + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "b7IvA0IW4hBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d222ba54-25ac-4c78-b857-83b8b2c9c538" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "484 :969:4752 \n", + "96 :974:3825 \n", + "277 :982:4008 \n", + "432 :983:3516 \n", + "160 :986:3645 \n", + "395 :989:3908 \n", + "608 :997:2562 \n", + "7 :997:2648 \n", + "912 :1008:3739 \n", + "511 :1014:2819 \n" + ] + } + ], + "source": [ + "!head 'stablePeriodicFrequentPatternsAtK100.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j4CpTPXw4k9I" + }, + "source": [ + "The storage format is: _stablePeriodicFrequentPattern:support_\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kC71sBV74qY0" + }, + "source": [ + "## Part 2: Evaluating the TSPIN algorithm on a dataset at different k values\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EobpZCdu6G0Z" + }, + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "W96B78JT6KT2" + }, + "outputs": [], + "source": [ + "#Import the libraries\n", + "from PAMI.stablePeriodicFrequentPattern.topK import TSPIN as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "separator='\\t'\n", + "maxmunPeriodCount = 5000\n", + "maxLaValue = 1000\n", + "kList = [100, 200, 300, 400, 500]\n", + "#k can also specified between 0 to 1. E.g., kList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gS09HRlY6PPR" + }, + "source": [ + "### Step 2: Create a data frame to store the results of TSPIN" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "0tbQu3re6VGI" + }, + "outputs": [], + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'maximumPeriodCount', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of TSPIN algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kn2TtrbW6awD" + }, + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "cZNXmKqp6ea1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "60cbb7bd-cb19-4087-8af6-ffccb0cee903" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n", + "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n", + "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n", + "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n", + "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n" + ] + } + ], + "source": [ + "for minSupCount in kList:\n", + " obj = alg.TSPIN(iFile=inputFile, k=minSupCount, maxPer = maxmunPeriodCount, maxLa = maxLaValue, sep = separator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['TSPIN', minSupCount, maxmunPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NCQLc9pn7BYG" + }, + "source": [ + "### Step 4: Print the Result" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "kpkdWbyQ6j6M", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bf58bea6-8e97-44ae-8551-f948a6e954d8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup maximumPeriodCount patterns runtime memory\n", + "0 TSPIN 100 5000 43531 457.651955 484859904\n", + "1 TSPIN 200 5000 43531 476.647002 485982208\n", + "2 TSPIN 300 5000 43531 479.507844 487383040\n", + "3 TSPIN 400 5000 43531 496.476079 487313408\n", + "4 TSPIN 500 5000 43531 474.265221 487452672\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S-prY3W27U4Z" + }, + "source": [ + "### Step 5: Visualizing the results" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "zVEtHn5j7aYE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "outputId": "f7bd050a-1c81-4557-d635-ec4aaad1256b" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "kn2TtrbW6awD" - }, - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "cZNXmKqp6ea1", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "60cbb7bd-cb19-4087-8af6-ffccb0cee903" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n", - "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n", - "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n", - "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n", - "Top-K Stable Periodic patterns were generated successfully using TSPIN algorithm \n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "for minSupCount in kList:\n", - " obj = alg.TSPIN(iFile=inputFile, k=minSupCount, maxPer = maxmunPeriodCount, maxLa = maxLaValue, sep = separator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['TSPIN', minSupCount, maxmunPeriodCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "NCQLc9pn7BYG" - }, - "source": [ - "### Step 4: Print the Result" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "kpkdWbyQ6j6M", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "bf58bea6-8e97-44ae-8551-f948a6e954d8" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup maximumPeriodCount patterns runtime memory\n", - "0 TSPIN 100 5000 43531 457.651955 484859904\n", - "1 TSPIN 200 5000 43531 476.647002 485982208\n", - "2 TSPIN 300 5000 43531 479.507844 487383040\n", - "3 TSPIN 400 5000 43531 496.476079 487313408\n", - "4 TSPIN 500 5000 43531 474.265221 487452672\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "print(result)" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "S-prY3W27U4Z" - }, - "source": [ - "### Step 5: Visualizing the results" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "zVEtHn5j7aYE", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "f7bd050a-1c81-4557-d635-ec4aaad1256b" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" - }, - "metadata": {} - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4", - "toc_visible": true, - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "image/png": 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\n" + }, + "metadata": {} } + ], + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4", + "toc_visible": true, + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/uncertainFrequentPatterns/basic/PUFGrowth.ipynb b/notebooks/uncertainFrequentPatterns/basic/PUFGrowth.ipynb index e07dac1e..5774a5be 100644 --- a/notebooks/uncertainFrequentPatterns/basic/PUFGrowth.ipynb +++ b/notebooks/uncertainFrequentPatterns/basic/PUFGrowth.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/uncertainFrequentPatterns/basic/TubeS.ipynb b/notebooks/uncertainFrequentPatterns/basic/TubeS.ipynb index f974900e..9a7f949f 100644 --- a/notebooks/uncertainFrequentPatterns/basic/TubeS.ipynb +++ b/notebooks/uncertainFrequentPatterns/basic/TubeS.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/uncertainPeriodicFrequentPatterns/basic/UPFPGrowth.ipynb b/notebooks/uncertainPeriodicFrequentPatterns/basic/UPFPGrowth.ipynb index 363e05d3..16174480 100644 --- a/notebooks/uncertainPeriodicFrequentPatterns/basic/UPFPGrowth.ipynb +++ b/notebooks/uncertainPeriodicFrequentPatterns/basic/UPFPGrowth.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/uncertainPeriodicFrequentPatterns/basic/UPFPGrowthPlus.ipynb b/notebooks/uncertainPeriodicFrequentPatterns/basic/UPFPGrowthPlus.ipynb index 0bcf36ac..6d12e13a 100644 --- a/notebooks/uncertainPeriodicFrequentPatterns/basic/UPFPGrowthPlus.ipynb +++ b/notebooks/uncertainPeriodicFrequentPatterns/basic/UPFPGrowthPlus.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/uncertainWeightedFrequent/basic/WUFIM.ipynb b/notebooks/uncertainWeightedFrequent/basic/WUFIM.ipynb index b1ab455d..288919ba 100644 --- a/notebooks/uncertainWeightedFrequent/basic/WUFIM.ipynb +++ b/notebooks/uncertainWeightedFrequent/basic/WUFIM.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/weightedFrequentPatterns/basic/WFIM.ipynb b/notebooks/weightedFrequentPatterns/basic/WFIM.ipynb index 4bcd3304..6e97de3b 100644 --- a/notebooks/weightedFrequentPatterns/basic/WFIM.ipynb +++ b/notebooks/weightedFrequentPatterns/basic/WFIM.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/notebooks/weightedFrequentRegularPatterns/basic/WFRI.ipynb b/notebooks/weightedFrequentRegularPatterns/basic/WFRI.ipynb index ffb6668e..2b2864ac 100644 --- a/notebooks/weightedFrequentRegularPatterns/basic/WFRI.ipynb +++ b/notebooks/weightedFrequentRegularPatterns/basic/WFRI.ipynb @@ -1,730 +1,730 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XZ4vrXSQ1yEs" - }, - "source": [ - "# Finding Weighted Frequent Regular Patterns in Temporal Databases using WFRIM" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "roOSCMZX2Eb2" - }, - "source": [ - "This tutorial has two parts. In the first part, we describe the basic approach to find Weighted Frequent Regular Patterns in a temporal database using the WFRIM algorithm. In the final part, we describe an advanced approach, where we evaluate the WFRIM algorithm on a dataset at different minimum support threshold values.\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TFlIIF_X2SzU" - }, - "source": [ - "# Prerequisites:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TqMwpaLw2XLu" - }, - "source": [ - "1. Installing the PAMI library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EubusNOv2Zcv", - "outputId": "7c16881e-0599-466a-b9e7-16737c42e926" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting pami\n", - " Downloading pami-2023.11.15.4-py3-none-any.whl (883 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.9/883.9 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.7.1)\n", - 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"Successfully built JsonForm JsonSir\n", - "Installing collected packages: JsonSir, validators, python-easyconfig, JsonForm, resource, sphinxcontrib-jquery, sphinx-rtd-theme, pami\n", - "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 pami-2023.11.15.4 python-easyconfig-0.1.7 resource-0.2.1 sphinx-rtd-theme-1.3.0 sphinxcontrib-jquery-4.1 validators-0.22.0\n" - ] - } - ], - "source": [ - "!pip install -U pami #install the pami repository" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rYfvWHRN2oBs" - }, - "source": [ - "2. Downloading a sample dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t1-ej6SX2x8q", - "outputId": "37a6e0e4-d2c9-4cee-929f-6a033fc62f10" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-11-16 16:10:02-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", - "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", - "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4606762 (4.4M) [text/csv]\n", - "Saving to: ‘Temporal_T10I4D100K.csv’\n", - "\n", - "Temporal_T10I4D100K 100%[===================>] 4.39M 1.15MB/s in 6.0s \n", - "\n", - "2023-11-16 16:10:10 (749 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", - "\n" - ] - } - ], - "source": [ - "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "USUJbpXu3Gkw" - }, - "source": [ - "3. Printing few lines of a dataset to know its format." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Qcwg4qNE3MQc", - "outputId": "d8474595-3ddf-4c2b-dcd3-b5d62e4337af" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", - "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" - ] - } - ], - "source": [ - "!head -2 Temporal_T10I4D100K.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oQQdz3qn3Qwz" - }, - "source": [ - "format: every row contains items seperated by a seperator.\n", - "\n", - "Example:\n", - "\n", - "item1 item2 item3 item4\n", - "\n", - "item1 item4 item6\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "62Vkqg-C3WVZ" - }, - "source": [ - "## Part 1: Finding Weighted Frequent Regular Patterns using WFRIM" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gaxxPgXv3ecW" - }, - "source": [ - "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_6SDOnvX3pGy", - "outputId": "166f179e-3335-459e-953b-779ccbf7849e" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Database size : 99913\n", - "Total number of items : 870\n", - "Database sparsity : 0.9883887027691103\n", - "Minimum Transaction Size : 1\n", - "Average Transaction Size : 10.10182859087406\n", - "Maximum Transaction Size : 29\n", - "Standard Deviation Transaction Size : 3.667115963877195\n", - "Variance in Transaction Sizes : 13.447874088362232\n" - ] - } - ], - "source": [ - "#import the class file\n", - "import PAMI.extras.dbStats.temporalDatabaseStats as stats\n", - "\n", - "#specify the file name\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "\n", - "#initialize the class\n", - "obj=stats.temporalDatabaseStats(inputFile,sep='\\t')\n", - "\n", - "#execute the class\n", - "obj.run()\n", - "\n", - "#Printing each of the database statistics\n", - "print(f'Database size : {obj.getDatabaseSize()}')\n", - "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", - "print(f'Database sparsity : {obj.getSparsity()}')\n", - "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", - "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", - "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", - "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", - "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", - "\n", - "#saving the distribution of items' frequencies and transactional lengths\n", - "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "obj.save(itemFrequencies, 'itemFrequency.csv')\n", - "obj.save(transactionLength, 'transactionSize.csv')\n", - "\n", - "#Alternative apporach to print all of the database statistics and plot them\n", - "# obj.printStats()\n", - "# obj.plotGraphs()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1oJIEK8A3wQS" - }, - "source": [ - "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 944 - }, - "id": "y7pfaeJV34H_", - "outputId": "693fefa5-3ed4-40f9-9d7a-4f410f56c9ac" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - } - ], - "source": [ - "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", - "\n", - "itemFrequencies = obj.getFrequenciesInRange()\n", - "transactionLength = obj.getTransanctionalLengthDistribution()\n", - "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", - "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zpLiRCBp39k9" - }, - "source": [ - "### Step 3: Choosing an appropriate *minSup* value\n", - "\n", - "_Observations_\n", - "\n", - " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", - " 2. Many items have low frequencies as seen in the items' frequency graph\n", - " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", - "\n", - " Based on the above observations, let us choose a _minSup_ value of 500 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "RP9ynbti4L48" - }, - "outputs": [], - "source": [ - "minimumSupportCount = 500 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", - "weightFile = 'T10_weights.txt'\n", - "regularity=3000" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XZ4vrXSQ1yEs" + }, + "source": [ + "# Finding Weighted Frequent Regular Patterns in Temporal Databases using WFRIM" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "roOSCMZX2Eb2" + }, + "source": [ + "This tutorial has two parts. In the first part, we describe the basic approach to find Weighted Frequent Regular Patterns in a temporal database using the WFRIM algorithm. In the final part, we describe an advanced approach, where we evaluate the WFRIM algorithm on a dataset at different minimum support threshold values.\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TFlIIF_X2SzU" + }, + "source": [ + "# Prerequisites:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TqMwpaLw2XLu" + }, + "source": [ + "1. Installing the PAMI library" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EubusNOv2Zcv", + "outputId": "7c16881e-0599-466a-b9e7-16737c42e926" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2023.11.15.4-py3-none-any.whl (883 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m883.9/883.9 kB\u001B[0m \u001B[31m8.3 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (1.5.3)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.15.0)\n", + "Requirement already satisfied: matplotlib in 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pami-2023.11.15.4 python-easyconfig-0.1.7 resource-0.2.1 sphinx-rtd-theme-1.3.0 sphinxcontrib-jquery-4.1 validators-0.22.0\n" + ] + } + ], + "source": [ + "!pip install -U pami #install the pami repository" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rYfvWHRN2oBs" + }, + "source": [ + "2. Downloading a sample dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t1-ej6SX2x8q", + "outputId": "37a6e0e4-d2c9-4cee-929f-6a033fc62f10" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-11-16 16:10:02-- https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv\n", + "Resolving u-aizu.ac.jp (u-aizu.ac.jp)... 150.31.244.160, 150.95.161.176\n", + "Connecting to u-aizu.ac.jp (u-aizu.ac.jp)|150.31.244.160|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4606762 (4.4M) [text/csv]\n", + "Saving to: ‘Temporal_T10I4D100K.csv’\n", + "\n", + "Temporal_T10I4D100K 100%[===================>] 4.39M 1.15MB/s in 6.0s \n", + "\n", + "2023-11-16 16:10:10 (749 KB/s) - ‘Temporal_T10I4D100K.csv’ saved [4606762/4606762]\n", + "\n" + ] + } + ], + "source": [ + "!wget -nc https://u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/Temporal_T10I4D100K.csv #download a sample temporal database" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "USUJbpXu3Gkw" + }, + "source": [ + "3. Printing few lines of a dataset to know its format." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Qcwg4qNE3MQc", + "outputId": "d8474595-3ddf-4c2b-dcd3-b5d62e4337af" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1\t25\t52\t164\t240\t274\t328\t368\t448\t538\t561\t630\t687\t730\t775\t825\t834\r\n", + "2\t39\t120\t124\t205\t401\t581\t704\t814\t825\t834\r\n" + ] + } + ], + "source": [ + "!head -2 Temporal_T10I4D100K.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oQQdz3qn3Qwz" + }, + "source": [ + "format: every row contains items seperated by a seperator.\n", + "\n", + "Example:\n", + "\n", + "item1 item2 item3 item4\n", + "\n", + "item1 item4 item6\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "62Vkqg-C3WVZ" + }, + "source": [ + "## Part 1: Finding Weighted Frequent Regular Patterns using WFRIM" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gaxxPgXv3ecW" + }, + "source": [ + "### Step 1: Understanding the statistics of a database to choose an appropriate minimum support (minSup) value." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_6SDOnvX3pGy", + "outputId": "166f179e-3335-459e-953b-779ccbf7849e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size : 99913\n", + "Total number of items : 870\n", + "Database sparsity : 0.9883887027691103\n", + "Minimum Transaction Size : 1\n", + "Average Transaction Size : 10.10182859087406\n", + "Maximum Transaction Size : 29\n", + "Standard Deviation Transaction Size : 3.667115963877195\n", + "Variance in Transaction Sizes : 13.447874088362232\n" + ] + } + ], + "source": [ + "#import the class file\n", + "import PAMI.extras.dbStats.TemporalDatabase as stats\n", + "\n", + "#specify the file name\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "\n", + "#initialize the class\n", + "obj=stats.TemporalDatabase(inputFile,sep='\\t')\n", + "\n", + "#execute the class\n", + "obj.run()\n", + "\n", + "#Printing each of the database statistics\n", + "print(f'Database size : {obj.getDatabaseSize()}')\n", + "print(f'Total number of items : {obj.getTotalNumberOfItems()}')\n", + "print(f'Database sparsity : {obj.getSparsity()}')\n", + "print(f'Minimum Transaction Size : {obj.getMinimumTransactionLength()}')\n", + "print(f'Average Transaction Size : {obj.getAverageTransactionLength()}')\n", + "print(f'Maximum Transaction Size : {obj.getMaximumTransactionLength()}')\n", + "print(f'Standard Deviation Transaction Size : {obj.getStandardDeviationTransactionLength()}')\n", + "print(f'Variance in Transaction Sizes : {obj.getVarianceTransactionLength()}')\n", + "\n", + "#saving the distribution of items' frequencies and transactional lengths\n", + "itemFrequencies = obj.getSortedListOfItemFrequencies()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "obj.save(itemFrequencies, 'itemFrequency.csv')\n", + "obj.save(transactionLength, 'transactionSize.csv')\n", + "\n", + "#Alternative apporach to print all of the database statistics and plot them\n", + "# obj.printStats()\n", + "# obj.plotGraphs()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1oJIEK8A3wQS" + }, + "source": [ + "### Step 2: Draw the item's frequency graph and transaction length's distribution graphs for more information" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 }, + "id": "y7pfaeJV34H_", + "outputId": "693fefa5-3ed4-40f9-9d7a-4f410f56c9ac" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "-Yr0r7zw4Q85" - }, - "source": [ - "### Step 4: Mining Weighted Frequent Regular Patterns using WFRIM" + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 5 }, { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BZzrC2Pl4XGJ", - "outputId": "b017968d-b841-47a9-aae3-3cc6248c1791" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n", - "Total No of patterns: 1201\n", - "Runtime: 25.564584493637085\n", - "Memory (RSS): 613384192\n", - "Memory (USS): 590700544\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "from PAMI.weightedFrequentRegularPattern.basic import WFRIMiner as alg #import the algorithm\n", - "\n", - "obj = alg.WFRIMiner(iFile=inputFile, _wFile=weightFile, WS=minimumSupportCount, regularity=regularity, sep='\\t') #initialize\n", - "obj.mine() #start the mining process\n", - "\n", - "obj.save('weightedFrequentRegularPatternsAtMinSupCount500.txt') #save the patterns\n", - "\n", - "\n", - "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", - "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", - "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", - "\n", - "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", - "print('Memory (USS): ' + str(obj.getMemoryUSS()))" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "3M8FtfKU4bhu" - }, - "source": [ - "### Step 5: Investigating the generated patterns\n", - "\n", - "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b7IvA0IW4hBe", - "outputId": "65f48ce8-ca18-4946-ed30-892a45cd5493" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "609:[184, 2638, 14701.594204892775] \n", - "644:[197, 2997, 16404.561009391855] \n", - "452:[205, 2420, 19117.16199693785] \n", - "89:[211, 2673, 12709.875483117761] \n", - "466:[212, 2321, 18160.54317283448] \n", - "734:[217, 1915, 11051.415491503669] \n", - "902:[222, 2562, 9715.457877147284] \n", - "267:[223, 2262, 4833.587471318886] \n", - "313:[223, 2504, 1906.8641614904022] \n", - "179:[236, 2389, 2170.515800953376] \n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "!head 'weightedFrequentRegularPatternsAtMinSupCount500.txt'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j4CpTPXw4k9I" - }, - "source": [ - "The storage format is: _weightedfrequentRegularPattern:support_\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kC71sBV74qY0" - }, - "source": [ - "## Part 2: Evaluating the WFRIM algorithm on a dataset at different minSup values" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EobpZCdu6G0Z" - }, - "source": [ - "### Step 1: Import the libraries and specify the input parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "W96B78JT6KT2" - }, - "outputs": [], - "source": [ - "#Import the libraries\n", - "from PAMI.weightedFrequentRegularPattern.basic import WFRIMiner as alg #import the algorithm\n", - "import pandas as pd\n", - "\n", - "#Specify the input parameters\n", - "inputFile = 'Temporal_T10I4D100K.csv'\n", - "seperator='\\t'\n", - "minimumSupportCountList = [100, 200, 300, 400, 500]\n", - "weightFile = 'T10_weights.txt'\n", - "regularity = 2000\n", - "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gS09HRlY6PPR" - }, - "source": [ - "### Step 2: Create a data frame to store the results of WFRIM" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "0tbQu3re6VGI" - }, - "outputs": [], - "source": [ - "result = pd.DataFrame(columns=['algorithm', 'minSup', 'patterns', 'runtime', 'memory'])\n", - "#initialize a data frame to store the results of WFRIM algorithm" - ] + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "import PAMI.extras.graph.plotLineGraphFromDictionary as plt\n", + "\n", + "itemFrequencies = obj.getFrequenciesInRange()\n", + "transactionLength = obj.getTransanctionalLengthDistribution()\n", + "plt.plotLineGraphFromDictionary(itemFrequencies, end = 100, title = 'Items\\' frequency graph', xlabel = 'No of items', ylabel= 'frequency')\n", + "plt.plotLineGraphFromDictionary(transactionLength, end = 100, title = 'transaction distribution graph', xlabel = 'transaction length', ylabel = 'frequency')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zpLiRCBp39k9" + }, + "source": [ + "### Step 3: Choosing an appropriate *minSup* value\n", + "\n", + "_Observations_\n", + "\n", + " 1. The input dataset is sparse as the sparsity value is 0.988 (=98.8%)\n", + " 2. Many items have low frequencies as seen in the items' frequency graph\n", + " 3. The dataset is not high dimensional as the inverted curve is around 10.\n", + "\n", + " Based on the above observations, let us choose a _minSup_ value of 500 (in count). We can increase or decrease the _minSup_ based on the number of patterns being generated." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "RP9ynbti4L48" + }, + "outputs": [], + "source": [ + "minimumSupportCount = 500 #minSup is specified in count. However, the users can also specify minSup between 0 and 1.\n", + "weightFile = 'T10_weights.txt'\n", + "regularity=3000" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Yr0r7zw4Q85" + }, + "source": [ + "### Step 4: Mining Weighted Frequent Regular Patterns using WFRIM" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BZzrC2Pl4XGJ", + "outputId": "b017968d-b841-47a9-aae3-3cc6248c1791" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n", + "Total No of patterns: 1201\n", + "Runtime: 25.564584493637085\n", + "Memory (RSS): 613384192\n", + "Memory (USS): 590700544\n" + ] + } + ], + "source": [ + "from PAMI.weightedFrequentRegularPattern.basic import WFRIMiner as alg #import the algorithm\n", + "\n", + "obj = alg.WFRIMiner(iFile=inputFile, _wFile=weightFile, WS=minimumSupportCount, regularity=regularity, sep='\\t') #initialize\n", + "obj.mine() #start the mining process\n", + "\n", + "obj.save('weightedFrequentRegularPatternsAtMinSupCount500.txt') #save the patterns\n", + "\n", + "\n", + "frequentPatternsDF= obj.getPatternsAsDataFrame() #get the generated frequent patterns as a dataframe\n", + "print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns\n", + "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n", + "\n", + "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n", + "print('Memory (USS): ' + str(obj.getMemoryUSS()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3M8FtfKU4bhu" + }, + "source": [ + "### Step 5: Investigating the generated patterns\n", + "\n", + "Open the pattern's file and investigate the generated patterns. If the generated patterns were interesting, use them; otherwise, redo the Steps 3 and 4 with a different _minSup_ value." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b7IvA0IW4hBe", + "outputId": "65f48ce8-ca18-4946-ed30-892a45cd5493" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "609:[184, 2638, 14701.594204892775] \n", + "644:[197, 2997, 16404.561009391855] \n", + "452:[205, 2420, 19117.16199693785] \n", + "89:[211, 2673, 12709.875483117761] \n", + "466:[212, 2321, 18160.54317283448] \n", + "734:[217, 1915, 11051.415491503669] \n", + "902:[222, 2562, 9715.457877147284] \n", + "267:[223, 2262, 4833.587471318886] \n", + "313:[223, 2504, 1906.8641614904022] \n", + "179:[236, 2389, 2170.515800953376] \n" + ] + } + ], + "source": [ + "!head 'weightedFrequentRegularPatternsAtMinSupCount500.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j4CpTPXw4k9I" + }, + "source": [ + "The storage format is: _weightedfrequentRegularPattern:support_\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kC71sBV74qY0" + }, + "source": [ + "## Part 2: Evaluating the WFRIM algorithm on a dataset at different minSup values" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EobpZCdu6G0Z" + }, + "source": [ + "### Step 1: Import the libraries and specify the input parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "W96B78JT6KT2" + }, + "outputs": [], + "source": [ + "#Import the libraries\n", + "from PAMI.weightedFrequentRegularPattern.basic import WFRIMiner as alg #import the algorithm\n", + "import pandas as pd\n", + "\n", + "#Specify the input parameters\n", + "inputFile = 'Temporal_T10I4D100K.csv'\n", + "seperator='\\t'\n", + "minimumSupportCountList = [100, 200, 300, 400, 500]\n", + "weightFile = 'T10_weights.txt'\n", + "regularity = 2000\n", + "#minimumSupport can also specified between 0 to 1. E.g., minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gS09HRlY6PPR" + }, + "source": [ + "### Step 2: Create a data frame to store the results of WFRIM" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "0tbQu3re6VGI" + }, + "outputs": [], + "source": [ + "result = pd.DataFrame(columns=['algorithm', 'minSup', 'patterns', 'runtime', 'memory'])\n", + "#initialize a data frame to store the results of WFRIM algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kn2TtrbW6awD" + }, + "source": [ + "### Step 3: Execute the algorithm at different minSup values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cZNXmKqp6ea1", + "outputId": "06fda638-99c5-483a-82bd-dc2a1740bc9d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n", + "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n", + "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n", + "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n", + "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n" + ] + } + ], + "source": [ + "for minSupCount in minimumSupportCountList:\n", + " obj = alg.WFRIMiner(inputFile, _wFile=weightFile, WS=minSupCount, regularity=regularity, sep=seperator)\n", + " obj.mine()\n", + " #store the results in the data frame\n", + " result.loc[result.shape[0]] = ['WFRIM', minSupCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NCQLc9pn7BYG" + }, + "source": [ + "### Step 4: Print the Result" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kpkdWbyQ6j6M", + "outputId": "9e2fe682-4abc-4d4c-b2f1-d65081e78f2d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " algorithm minSup patterns runtime memory\n", + "0 WFRIM 100 4649 24.710214 607920128\n", + "1 WFRIM 200 4643 23.434430 608448512\n", + "2 WFRIM 300 3545 24.234488 609054720\n", + "3 WFRIM 400 1987 24.782321 609419264\n", + "4 WFRIM 500 1153 25.673576 609087488\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S-prY3W27U4Z" + }, + "source": [ + "### Step 5: Visualizing the results" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "zVEtHn5j7aYE", + "outputId": "c9b7aeba-a8b7-4cc6-d2c5-55827655d294" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "kn2TtrbW6awD" - }, - "source": [ - "### Step 3: Execute the algorithm at different minSup values" + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "execution_count": 13 }, { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cZNXmKqp6ea1", - "outputId": "06fda638-99c5-483a-82bd-dc2a1740bc9d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n", - "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n", - "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n", - "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n", - "Weighted Frequent Regular patterns were generated successfully using WFRIM algorithm\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "for minSupCount in minimumSupportCountList:\n", - " obj = alg.WFRIMiner(inputFile, _wFile=weightFile, WS=minSupCount, regularity=regularity, sep=seperator)\n", - " obj.mine()\n", - " #store the results in the data frame\n", - " result.loc[result.shape[0]] = ['WFRIM', minSupCount, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]" - ] + "image/png": 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xw1Q1At1tkFdUhjErEvDnmb+lLouIyCgxwBA1IGsLU6yZ3AO9vexQWFqBiVGJ2JGaKXVZRERGhwGGqIFZKEywYnw3DHzECaUVlQhddwQ/JV2WuiwiIqPCAEPUCJQmcnw1qguGdXVFRaWI1zcdQ9T+dKnLIiIyGgwwRI3ERC7DgqGdMfExDwDAB1tOYsmuM3ySNRFRA2CAIWpEMpmA9wZ3wqygDgCAz2P+wsfRpxhiiIgeEgMMUSMTBAEzg7wwZ3DVozK+/zMdET8dR0UlQwwRUX0xwBA1kYmPe+CzF/whE4CNhy9jxvojKCmvkLosIiKDxABD1ISGdXXFN6MDoZDLsC0lE5NXHUZhabnUZRERGRwGGKImNtDXGd9P6AZzUzn2nfkbY78/hLyiMqnLIiIyKAwwRBLo7WWPNZN7QGVmgqSLNzHy24O4fqtE6rKIiAwGAwyRRLq2aYkfXlbDzkqJk9c0GL48Hldyi6Qui4jIIDDAEEmok7MKm6ap0drGHOl/F+CFpQdw7nq+1GUREek9BhgiiXnYWeLH6Wq0t7fE1bxiDF8Wj9QreVKXRUSk1xhgiPSAs7U5Nr6shm9rFXIKSjHy24NIvHBD6rKIiPQWAwyRnmhlpcS6KT3xaFtb3Copx9jvExCbli11WUREeokBhkiPqMxMsWrio+jrbY/iskpM+e9hRB+/JnVZRER6hwGGSM+YK+T4dmw3DO7sjLIKETPWH8EPiRlSl0VEpFcYYIj0kMJEhsUjumDko+6oFIGIn1Lw3d7zUpdFRKQ3GGCI9JRcJuCT53zx8hPtAAD/3nYKC39P45OsiYjAAEOk1wRBQOSgTnhzoDcA4MvdZ/HBbydQySdZE1EzxwBDZABe6euJuUN8IQjAqviLeH3TMZRXVEpdFhGRZBhgiAzE2J5tsGh4AOQyAZuPXsH0tUdQXFYhdVlERJJggCEyIEO6tMbyMV2hMJEh5mQWJkYlIr+kXOqyiIiaHAMMkYEJ8nFE1EvdYamQ48C5HIxZkYDcwlKpyyIialIMMEQGqFd7O6yb0hM2FqZIvpSLF5cfRLamWOqyiIiaDAMMkYHyd7PBxpfVcGihRFrWLQxbFo9LNwqlLouIqEkwwBAZsA6OLfDjtF5wt7VAxo1CDFt2AGeybkldFhFRo2OAITJw7q0ssGmaGh0crZClKcHw5fE4fjlX6rKIiBoVAwyREXBUmeGHqWr4u9ngZmEZRn2XgPhzOVKXRUTUaBhgiIxES0sF1k7uAXW7VsgvKcf4lYew61SW1GURETUKBhgiI2KlNMHKl7ojqJMjSssr8fLqJPyafEXqsoiIGhwDDJGRMTOVY+mYQDzXpTXKK0WE/5CM1QcvSl0WEVGDYoAhMkKmchkWvuCPceo2EEXgvV9S8U3sWanLIiJqMAwwREZKJhPw4TOPIKyfJwBgwY40zN9+GqLIJ1kTkeFjgCEyYoIg4I1gb7z9VEcAwLK4c3jnl1RUVDLEEJFhe6gAM3/+fAiCgPDwcO264uJihIaGolWrVrCyssLQoUORlaV7JURGRgZCQkJgYWEBBwcHzJ49G+Xlug+ki42NRWBgIJRKJTw9PREVFfUwpRI1a1P7tMe85/0gCMC6hAyE/5CMsopKqcsiIqq3egeYxMRELF++HJ07d9ZZP2vWLGzZsgWbNm1CXFwcrl69iueff167vaKiAiEhISgtLcWBAwewatUqREVFYc6cOdox6enpCAkJQb9+/ZCcnIzw8HBMnjwZO3furG+5RM3eyEfd8eXILjCVC9hy7CpeXp2E4rIKqcsiIqoXQazHF+L5+fkIDAzEN998g48//hgBAQH44osvkJeXB3t7e6xbtw7Dhg0DAJw+fRqdOnVCfHw8evbsie3bt2Pw4MG4evUqHB0dAQDLli1DREQErl+/DoVCgYiICERHRyM1NVX7niNGjEBubi527NhRY00lJSUoKSnRvtZoNHBzc0NeXh5UKlVdp0hktPakZWP6miQUl1XiUQ9bfD++G1qYmUpdFhERgKrf39bW1g/8/V2vT2BCQ0MREhKCoKAgnfVJSUkoKyvTWd+xY0e4u7sjPj4eABAfHw8/Pz9teAGA4OBgaDQanDhxQjvmzmMHBwdrj1GTefPmwdraWru4ubnVZ2pERq+ftwP+O7EHWihNcCj9BkZ9l4AbBaVSl0VEVCd1DjAbNmzAkSNHMG/evLu2ZWZmQqFQwMbGRme9o6MjMjMztWNuDy/V26u33W+MRqNBUVFRjXVFRkYiLy9Pu1y6dKmuUyNqNh71sMX6qT1ha6lAypU8DF8ej2t5Nf/dIiLSR3UKMJcuXcLMmTOxdu1amJmZNVZN9aJUKqFSqXQWIro339bW2PiyGs7WZjibnY9hS+Nx4e8CqcsiIqqVOgWYpKQkZGdnIzAwECYmJjAxMUFcXByWLFkCExMTODo6orS0FLm5uTr7ZWVlwcnJCQDg5OR011VJ1a8fNEalUsHc3LxOEySie/N0sMKmaWp42FniSm4Rhi2Lx6lrGqnLIiJ6oDoFmP79+yMlJQXJycnapVu3bhg9erT2Z1NTU+zatUu7T1paGjIyMqBWqwEAarUaKSkpyM7O1o6JiYmBSqWCj4+Pdsztx6geU30MImo4ri0tsPFlNTo5q/B3fgleXB6PIxk3pS6LiOi+6nUV0u369u2rvQoJAKZPn45t27YhKioKKpUKM2bMAAAcOHAAQNVl1AEBAXBxccGCBQuQmZmJsWPHYvLkyfjkk08AVF1G7evri9DQUEycOBG7d+/Gq6++iujoaAQHB9eqrtp2MRNRlbzCMrwUdQhHMnJhoZDj27Hd8LiXndRlEVEz06hXId3PokWLMHjwYAwdOhR9+vSBk5MTfv75Z+12uVyOrVu3Qi6XQ61WY8yYMRg3bhw++ugj7RgPDw9ER0cjJiYG/v7+WLhwIVasWFHr8EJEdWdtYYo1k3ugt5cdCksrMDEqETtSM6Uui4ioRg/9CYy+4icwRPVTUl6B8A3J2J6aCZkALBjmj2FdXaUui4iaCck+gSEiw6Y0kePLkV3wQldXVIrAG5uOYeX+dKnLIiLSwQBDRHcxkcvwf0M7Y+JjHgCAD7ecxOI/zvBJ1kSkNxhgiKhGMpmA9wZ3wqygDgCARX/8hY+jTzHEEJFeYIAhonsSBAEzg7wwZ3DVLQ6+/zMdET8dR0UlQwwRSYsBhogeaOLjHvjsBX/IBGDj4csIW3cEJeV8kjURSYcBhohqZVhXV3wzOhAKuQzbUzMxedVhFJaWS10WETVTDDBEVGsDfZ3xnwndYW4qx74zf2Ps94eQV1QmdVlE1AwxwBBRnTzuZYc1k3tAZWaCpIs3MeLbg7h+q0TqsoiomWGAIaI669qmJX54WQ07KyVOXdNg+PJ4XMktkrosImpGGGCIqF46OauwaZoarW3Mkf53AV5YegDnrudLXRYRNRMMMERUbx52lvhxuhrt7S1xNa8Yw5fFI/VKntRlEVEzwABDRA/F2docG19Ww7e1CjkFpRj57UEkXrghdVlEZOQYYIjoobWyUmLdlJ54tK0tbpWUY+z3CYhNy5a6LCIyYgwwRNQgVGamWDXxUfTztkdxWSWm/Pcwoo9fk7osIjJSDDBE1GDMFXIsH9sNgzs7o6xCxIz1R7DhUIbUZRGREWKAIaIGpTCRYfGILhj5qDsqReCtn1Pw3d7zUpdFREaGAYaIGpxcJuCT53zx8hPtAAD/3nYKC39P45OsiajBMMAQUaMQBAGRgzrhzYHeAIAvd5/FB7+dQCWfZE1EDYABhoga1St9PTF3iC8EAVgVfxGvbzqG8opKqcsiIgPHAENEjW5szzZYNDwAcpmAzUevYPraIyguq5C6LCIyYAwwRNQkhnRpjeVjukJhIkPMySxMjEpEfkm51GURkYFigCGiJhPk44hVLz0KS4UcB87lYPSKBOQWlkpdFhEZIAYYImpS6vatsG5KT9hYmOLYpVy8uPwgsjXFUpdFRAaGAYaImpy/mw02vqyGQwsl0rJuYdiyeFy6USh1WURkQBhgiEgSHRxb4MdpveBua4GMG4UYtuwAzmTdkrosIjIQDDBEJBn3Vhb4cZoaHRytkKUpwfDl8Th2KVfqsojIADDAEJGkHFRm+GGqGv5uNrhZWIZR3x1E/LkcqcsiIj3HAENEkmtpqcDayT3Qq30rFJRWYPzKQ/jjZJbUZRGRHmOAISK9YKU0wX8mdMe/fBxRWl6Jl9ck4dfkK1KXRUR6igGGiPSGmakc34wOxHNdWqOiUkT4D8lYffCi1GURkR5igCEivWIql2HhC/4Yp24DUQTe+yUVX+85K3VZRKRnGGCISO/IZAI+fOYRhPXzBAB8ujMN87efhijySdZEVIUBhoj0kiAIeCPYG28/1REAsCzuHN75JRUVlQwxRMQAQ0R6bmqf9pj/vB8EAViXkIHwH5JRVlEpdVlEJDEGGCLSeyMedceXI7vAVC5gy7GrmPrfwygqrZC6LCKSEAMMERmEwZ1d8N24bjAzlWFP2nVMXX2YXycRNWMMMERkMPp6O2D1pB6wUMix78zfWLzrjNQlEZFE6hRgli5dis6dO0OlUkGlUkGtVmP79u3a7X379oUgCDrLtGnTdI6RkZGBkJAQWFhYwMHBAbNnz0Z5ebnOmNjYWAQGBkKpVMLT0xNRUVH1nyERGZXubW0x73k/AMCXu89g35nrEldERFKoU4BxdXXF/PnzkZSUhMOHD+PJJ5/Es88+ixMnTmjHTJkyBdeuXdMuCxYs0G6rqKhASEgISktLceDAAaxatQpRUVGYM2eOdkx6ejpCQkLQr18/JCcnIzw8HJMnT8bOnTsbYLpEZAyeDWiNkY+6QxSB8A3JyNIUS10SETUxQXzIGyvY2tri008/xaRJk9C3b18EBATgiy++qHHs9u3bMXjwYFy9ehWOjo4AgGXLliEiIgLXr1+HQqFAREQEoqOjkZqaqt1vxIgRyM3NxY4dO+5ZR0lJCUpKSrSvNRoN3NzckJeXB5VK9TBTJCI9VFxWgee+OYBT1zR41MMW6yb3gImc34oTGTqNRgNra+sH/v6u99/2iooKbNiwAQUFBVCr1dr1a9euhZ2dHXx9fREZGYnCwkLttvj4ePj5+WnDCwAEBwdDo9FoP8WJj49HUFCQznsFBwcjPj7+vvXMmzcP1tbW2sXNza2+UyMiA2BmKsfXo7rAUiHHofQb+OIP9sMQNSd1DjApKSmwsrKCUqnEtGnTsHnzZvj4+AAARo0ahTVr1mDPnj2IjIzE6tWrMWbMGO2+mZmZOuEFgPZ1ZmbmfcdoNBoUFRXds67IyEjk5eVpl0uXLtV1akRkYNrZW2H+0M4AgK9jzyLuL/bDEDUXJnXdwdvbG8nJycjLy8OPP/6I8ePHIy4uDj4+Ppg6dap2nJ+fH5ydndG/f3+cO3cO7du3b9DC76RUKqFUKhv1PYhI/zzt74KE9BysOZiBWT8kI/rVx+FsbS51WUTUyOr8CYxCoYCnpye6du2KefPmwd/fH4sXL65xbI8ePQAAZ89WPYjNyckJWVlZOmOqXzs5Od13jEqlgrk5/1Eioru9G+KDR1xUuFFQilfXH0U579RLZPQeuuOtsrJSp3n2dsnJyQAAZ2dnAIBarUZKSgqys7O1Y2JiYqBSqbRfQ6nVauzatUvnODExMTp9NkREt6vqhwmEldIEiRduYmHMX1KXRESNrE4BJjIyEnv37sWFCxeQkpKCyMhIxMbGYvTo0Th37hzmzp2LpKQkXLhwAb/99hvGjRuHPn36oHPnqu+oBwwYAB8fH4wdOxbHjh3Dzp078e677yI0NFT79c+0adNw/vx5vPnmmzh9+jS++eYbbNy4EbNmzWr42ROR0WhrZ4kFw6r+rVkaew57Tmc/YA8iMmR1CjDZ2dkYN24cvL290b9/fyQmJmLnzp3417/+BYVCgT/++AMDBgxAx44d8frrr2Po0KHYsmWLdn+5XI6tW7dCLpdDrVZjzJgxGDduHD766CPtGA8PD0RHRyMmJgb+/v5YuHAhVqxYgeDg4IabNREZpaf8nDFe3QYAMGtjMq7m3rvxn4gM20PfB0Zf1fY6ciIyLiXlFRi2NB4pV/LQtU1LbJjaE6a8PwyRwWj0+8AQEekjpUlVP0wLMxMkXbyJz3amSV0SETUCBhgiMjrurSzw6TB/AMDyveex61TWA/YgIkPDAENERmmgrxNeeqwtAOC1jcdw+Wbh/XcgIoPCAENERityUCf4u1ojr6gMYeuOorSc94chMhYMMERktBQmMnw1KhAqMxMkX8rFgh2npS6JiBoIAwwRGTU3Wwt89kJVP8yKP9Px+4lMiSsioobAAENERm/AI06Y9LgHAOCNTcdw6Qb7YYgMHQMMETULEQM7IsDNBpricoStZz8MkaFjgCGiZqGqH6YLrM1NcexSLuZtPyV1SUT0EBhgiKjZcG1pgYX/9MOs3H8BO1KvSVwREdUXAwwRNStBPo6Y2qcdAGD2j8eRkcN+GCJDxABDRM3O7GBvdG3TEreKyxG67ghKyiukLomI6ogBhoiaHVO5DF+O7AIbC1OkXMnDJ9HshyEyNAwwRNQsudiYY9HwAADAqviL2JbCfhgiQ8IAQ0TNVr+ODpj2RHsAQMSPx3Exp0DiioiothhgiKhZe2NAB3Rv2xK3SsrxytojKC5jPwyRIWCAIaJmzUQuw5KRXWBrqcCJqxp8HH1S6pKIqBYYYIio2XO2Nsfnw6vuD7PmYAa2HLsqcUVE9CAMMEREAPp6OyC0X1U/TOTPKUj/m/0wRPqMAYaI6B+zgjqgh4ct8tkPQ6T3GGCIiP5R3Q/TylKBU9c0+HAL+2GI9BUDDBHRbRxVZvhiRAAEAVh/KAO/Jl+RuiQiqgEDDBHRHXp72WNGP08AwNs/p+Dc9XyJKyKiOzHAEBHVYGZQB/RsZ4uC0gqErj2ColL2wxDpEwYYIqIayGUClozoAjsrJU5n3sKHW05IXRIR3YYBhojoHhxUZlj8Tz/MhsRL2Hz0stQlEdE/GGCIiO7jMU87zOzvBQB4++dUnM2+JXFFRAQwwBARPdCMJ73wmGcrFJVV4JW1R1BYWi51SUTNHgMMEdEDyGUCvnixC+xbKPFXVj7e/5X9MERSY4AhIqoF+xZKLBnRBTIB2JR0GT8msR+GSEoMMEREtaRu3wqzgjoAAN79JQV/ZbEfhkgqDDBERHXwSj9P9PayQ3FZJfthiCTEAENEVAdymYBFLwbAUaXE2ex8vPtLKkRRlLosomaHAYaIqI7srP7XD/PzkSvYdJj9MERNjQGGiKgeerRrhdcHeAMA3vs1FaczNRJXRNS8MMAQEdXT9Cfa44kO9igpr+qHKShhPwxRU2GAISKqJ9k//TBOKjOcv16AdzansB+GqInUKcAsXboUnTt3hkqlgkqlglqtxvbt27Xbi4uLERoailatWsHKygpDhw5FVlaWzjEyMjIQEhICCwsLODg4YPbs2Sgv1/2/ltjYWAQGBkKpVMLT0xNRUVH1nyERUSOytVTgy1FdIJcJ+CX5KjYkXpK6JKJmoU4BxtXVFfPnz0dSUhIOHz6MJ598Es8++yxOnKi6K+WsWbOwZcsWbNq0CXFxcbh69Sqef/557f4VFRUICQlBaWkpDhw4gFWrViEqKgpz5szRjklPT0dISAj69euH5ORkhIeHY/Lkydi5c2cDTZmIqGF1b2uLN/7ph3n/txM4eZX9MESNTRAf8vNOW1tbfPrppxg2bBjs7e2xbt06DBs2DABw+vRpdOrUCfHx8ejZsye2b9+OwYMH4+rVq3B0dAQALFu2DBEREbh+/ToUCgUiIiIQHR2N1NRU7XuMGDECubm52LFjxz3rKCkpQUlJifa1RqOBm5sb8vLyoFKpHmaKREQPVFkpYtKqROxJu452dpb4bcbjsFKaSF0WkcHRaDSwtrZ+4O/vevfAVFRUYMOGDSgoKIBarUZSUhLKysoQFBSkHdOxY0e4u7sjPj4eABAfHw8/Pz9teAGA4OBgaDQa7ac48fHxOseoHlN9jHuZN28erK2ttYubm1t9p0ZEVGcymYDPhwfAxdoM5/8uQOTP7Ichakx1DjApKSmwsrKCUqnEtGnTsHnzZvj4+CAzMxMKhQI2NjY64x0dHZGZmQkAyMzM1Akv1durt91vjEajQVFR0T3rioyMRF5enna5dInfQxNR02ppqcCXowJhIhOw5dhVrE3IkLokIqNV5wDj7e2N5ORkJCQkYPr06Rg/fjxOnjzZGLXViVKp1DYXVy9ERE2ta5uWeHNgVT/MR1tPIvVKnsQVERmnOgcYhUIBT09PdO3aFfPmzYO/vz8WL14MJycnlJaWIjc3V2d8VlYWnJycAABOTk53XZVU/fpBY1QqFczNzetaLhFRk5vSux2COjmgtLwSYeuO4FZxmdQlERmdh74PTGVlJUpKStC1a1eYmppi165d2m1paWnIyMiAWq0GAKjVaqSkpCA7O1s7JiYmBiqVCj4+Ptoxtx+jekz1MYiI9J0gCPjsBX+0tjHHhZxCvPUT+2GIGlqdAkxkZCT27t2LCxcuICUlBZGRkYiNjcXo0aNhbW2NSZMm4bXXXsOePXuQlJSEl156CWq1Gj179gQADBgwAD4+Phg7diyOHTuGnTt34t1330VoaCiUSiUAYNq0aTh//jzefPNNnD59Gt988w02btyIWbNmNfzsiYgaiY2FAl+N6gITmYDolGtYffCi1CURGZU6BZjs7GyMGzcO3t7e6N+/PxITE7Fz507861//AgAsWrQIgwcPxtChQ9GnTx84OTnh559/1u4vl8uxdetWyOVyqNVqjBkzBuPGjcNHH32kHePh4YHo6GjExMTA398fCxcuxIoVKxAcHNxAUyYiahpd3FvirUEdAQAfbz2FlMvshyFqKA99Hxh9VdvryImIGpMoinh5dRJ+P5kFd1sLbH31cajMTKUui0hvNfp9YIiI6MEEQcCnw/zh2tIcGTcK8eam4+yHIWoADDBERI3M2sIUX48KhKlcwI4TmVh14ILUJREZPAYYIqIm4O9mg7ef6gQA+Pe2Uzh2KVfagogMHAMMEVETmdCrLQb5OqGsQkTouiPIK+T9YYjqiwGGiKiJCIKA/xvWGe62Frh8swhv/HiM/TBE9cQAQ0TUhFRmVf0wCrkMMSez8J/9F6QuicggMcAQETUxP1drvDu4qh9m3rZTOJpxU+KKiAwPAwwRkQTG9myDED9nlFeKCFt3FLmFpVKXRGRQGGCIiCQgCALmDfVDm1YWuJJbhDc2sR+GqC4YYIiIJKLthzGR4Y9T2VixL13qkogMBgMMEZGEfFtbY85gHwDA/+04jaSL7Ichqg0GGCIiiY3u4Y6n/V1QXilixrojuFnAfhiiB2GAISKSmCAI+OQ5X3jYWeJqXjFe33QMlZXshyG6HwYYIiI90OKffhiliQy7T2fj233npS6JSK8xwBAR6QkfFxU+eOYRAMCnO9OQeOGGxBUR6S8GGCIiPTKiuxueDXBBRaWIGeuOIie/ROqSiPQSAwwRkR6p6ofxQzt7S2RqivHaRvbDENWEAYaISM9YKk3wzehAmJnKEPfXdSyNOyd1SUR6hwGGiEgPdXRS4aNnfAEAC39PQ8L5HIkrItIvDDBERHrqhW6ueL5La1SKwKsbjuJv9sMQaTHAEBHpKUEQ8PFzvvB0sEKWpgSzfkhmPwzRPxhgiIj0mIWiqh/G3FSOfWf+xtd7zkpdEpFeYIAhItJzHRxbYO6Qqn6YRX/8hQPn/pa4IiLpMcAQERmAYV1dMayrKypFYOaGZFy/xX4Yat4YYIiIDMTcZ33RwdEK12+VIPyHo6hgPww1YwwwREQGwlwh1/bD7D+bgy93n5G6JCLJMMAQERkQT4cW+PdzVf0wi3edwYGz7Ieh5okBhojIwDwf6IoXu7lBFIFXNyQj+1ax1CURNTkGGCIiA/Ths4+go1ML/J1fgpnrk9kPQ80OAwwRkQEyM5Xj69GBsFDIEX8+B4v/+EvqkoiaFAMMEZGBam9vhXnP+wEAvtxzFvvOXJe4IqKmwwBDRGTAng1ojZGPukMUgfANycjSsB+GmgcGGCIiA/f+0z7o5KxCTkEpZqw/ivKKSqlLImp0DDBERAbOzFSOr0d1gaVCjkPpN7CI/TDUDDDAEBEZgXb2Vpg/tDMA4Os95xD3F/thyLgxwBARGYmn/V0wpqc7AGDWD8m4llckcUVEjYcBhojIiLwb4oNHXFS4UVCKV9kPQ0asTgFm3rx56N69O1q0aAEHBwcMGTIEaWlpOmP69u0LQRB0lmnTpumMycjIQEhICCwsLODg4IDZs2ejvLxcZ0xsbCwCAwOhVCrh6emJqKio+s2QiKgZqeqHCYSV0gSJF25iYQz7Ycg41SnAxMXFITQ0FAcPHkRMTAzKysowYMAAFBQU6IybMmUKrl27pl0WLFig3VZRUYGQkBCUlpbiwIEDWLVqFaKiojBnzhztmPT0dISEhKBfv35ITk5GeHg4Jk+ejJ07dz7kdImIjF9bO0ssGFbVD7M09hz2nM6WuCKihieIoljv+09fv34dDg4OiIuLQ58+fQBUfQITEBCAL774osZ9tm/fjsGDB+Pq1atwdHQEACxbtgwRERG4fv06FAoFIiIiEB0djdTUVO1+I0aMQG5uLnbs2FHjcUtKSlBSUqJ9rdFo4Obmhry8PKhUqvpOkYjIYL3/aypWxV+EjYUptr3aGy425lKXRPRAGo0G1tbWD/z9/VA9MHl5eQAAW1tbnfVr166FnZ0dfH19ERkZicLCQu22+Ph4+Pn5acMLAAQHB0Oj0eDEiRPaMUFBQTrHDA4ORnx8/D1rmTdvHqytrbWLm5vbw0yNiMjgvR3SCX6trZFbWIawdUdQxn4YMiL1DjCVlZUIDw/HY489Bl9fX+36UaNGYc2aNdizZw8iIyOxevVqjBkzRrs9MzNTJ7wA0L7OzMy87xiNRoOiopq76iMjI5GXl6ddLl26VN+pEREZBaVJVT9MCzMTHMnIxWc70x68E5GBMKnvjqGhoUhNTcWff/6ps37q1Knan/38/ODs7Iz+/fvj3LlzaN++ff0rfQClUgmlUtloxyciMkTurSzw6TB/TFuThOV7z+NRD1v07+T44B2J9Fy9PoEJCwvD1q1bsWfPHri6ut53bI8ePQAAZ8+eBQA4OTkhKytLZ0z1aycnp/uOUalUMDfnd7hERHUx0NcJLz3WFgDw2sZjuHyz8P47EBmAOgUYURQRFhaGzZs3Y/fu3fDw8HjgPsnJyQAAZ2dnAIBarUZKSgqys//XFR8TEwOVSgUfHx/tmF27dukcJyYmBmq1ui7lEhHRPyIHdYK/qzXyisoQtu4oSsvZD0OGrU4BJjQ0FGvWrMG6devQokULZGZmIjMzU9uXcu7cOcydOxdJSUm4cOECfvvtN4wbNw59+vRB585Vl/QNGDAAPj4+GDt2LI4dO4adO3fi3XffRWhoqPYroGnTpuH8+fN48803cfr0aXzzzTfYuHEjZs2a1cDTJyJqHhQmMnw1KhAqMxMkX8rFgh2npS6J6KHU6TJqQRBqXL9y5UpMmDABly5dwpgxY5CamoqCggK4ubnhueeew7vvvqtzKdTFixcxffp0xMbGwtLSEuPHj8f8+fNhYvK/lpzY2FjMmjULJ0+ehKurK9577z1MmDCh1hOr7WVYRETNye8nMjF1dRIA4NuxXTHgESeJKyLSVdvf3w91Hxh9xgBDRFSzj7eexIo/06EyM0H0q73hZmshdUlEWk1yHxgiIjI8bw7siAA3G2iKyxG27gj7YcggMcAQETUzVf0wXWBtbopjl/Mwb/spqUsiqjMGGCKiZsi1pQUWvuAPAFi5/wJ2pF6TuCKiumGAISJqpoJ8HDG1TzsAwOwfjyMjh/eHIcPBAENE1IzNDvZG1zYtcau4HKHrjqCkvELqkohqhQGGiKgZM5XL8OXILmhpYYqUK3n4JJr9MGQYGGCIiJo5FxtzfD48AACwKv4ioo+zH4b0HwMMERGhX0cHTHui6oG7ET8dx4W/CySuiOj+GGCIiAgA8MaADujetiXyS6r6YYrL2A9D+osBhoiIAAAmchmWjOwCW0sFTlzV4OPok1KXRHRPDDBERKTlbG2Oz4dX3R9mzcEMbDl2VeKKiGrGAENERDr6ejsgtF9VP8xbPx1HOvthSA8xwBAR0V1mBXVADw9bFJRW4JW17Ich/cMAQ0REd6nuh2llqcCpaxp8uIX9MKRfGGCIiKhGjiozfDEiAIIArD+UgV+Tr0hdEpEWAwwREd1Tby97zOjnCQB4++cUnLueL3FFRFUYYIiI6L5mBnVAz3ZV/TCha4+gqJT9MCQ9BhgiIrovuUzAkhFdYGelxOnMW/jgtxNSl0TEAENERA/moDLD4n/6YX44fAk/H7ksdUnUzDHAEBFRrTzmaYeZ/b0AAO9sTsXZ7FsSV0TNGQMMERHV2ownvfCYZysUlVXdH6awtFzqkqiZYoAhIqJak8sEfPFiF9i3UOKvrHzM+ZX9MCQNBhgiIqoT+xZKLBnRBTIB+DHpMjYdviR1SdQMMcAQEVGdqdu3wqygDgCA935NxV9Z7IehpsUAQ0RE9RLazxO9vexQXFaJV9YeQUEJ+2Go6TDAEBFRvchkAha9GABHlRJns/Px3i+pEEVR6rKomWCAISKierOz+l8/zM9Hr2DTYd4fhpoGAwwRET2UHu1a4fUB3gCq+mFOZ2okroiaAwYYIiJ6aNOfaI8nOtijpLyqHyaf/TDUyBhgiIjooVX3wzipzHD+egHe2ZzCfhhqVAwwRETUIGwtFfhqVBfIZQJ+Tb6KDYm8Pww1HgYYIiJqMN3a2mJ2cFU/zPu/ncDJq+yHocbBAENERA1qau926Odtj9LySoSuYz8MNQ4GGCIialAymYDPhwfAxdoM6X8XIPJn9sNQw2OAISKiBtfSUoEvRwXCRCZgy7GrWJuQIXVJZGQYYIiIqFF0bdMSbw6s6of5aOtJpF7Jk7giMiYMMERE1Gim9G6HoE4O2n6YW8VlUpdERqJOAWbevHno3r07WrRoAQcHBwwZMgRpaWk6Y4qLixEaGopWrVrBysoKQ4cORVZWls6YjIwMhISEwMLCAg4ODpg9ezbKy3WbvGJjYxEYGAilUglPT09ERUXVb4ZERCQZQRDw2Qv+aG1jjos5hXjrJ/bDUMOoU4CJi4tDaGgoDh48iJiYGJSVlWHAgAEoKCjQjpk1axa2bNmCTZs2IS4uDlevXsXzzz+v3V5RUYGQkBCUlpbiwIEDWLVqFaKiojBnzhztmPT0dISEhKBfv35ITk5GeHg4Jk+ejJ07dzbAlImIqCnZWFTdH8ZEJiA65RpWH7wodUlkBATxIaLw9evX4eDggLi4OPTp0wd5eXmwt7fHunXrMGzYMADA6dOn0alTJ8THx6Nnz57Yvn07Bg8ejKtXr8LR0REAsGzZMkREROD69etQKBSIiIhAdHQ0UlNTte81YsQI5ObmYseOHbWqTaPRwNraGnl5eVCpVPWdIhERNZAV+87j4+hTUMhl+Gl6L/i5WktdEumh2v7+fqgemLy8qoYsW1tbAEBSUhLKysoQFBSkHdOxY0e4u7sjPj4eABAfHw8/Pz9teAGA4OBgaDQanDhxQjvm9mNUj6k+Rk1KSkqg0Wh0FiIi0h+THvfAAB9HlFZU4pV1ScgrYj8M1V+9A0xlZSXCw8Px2GOPwdfXFwCQmZkJhUIBGxsbnbGOjo7IzMzUjrk9vFRvr952vzEajQZFRUU11jNv3jxYW1trFzc3t/pOjYiIGoEgCPh0mD9cW5rj0o0iDF8WzydXU73VO8CEhoYiNTUVGzZsaMh66i0yMhJ5eXna5dIlPoODiEjfWFuYYtmYrrCzUiAt6xae+Wo/Vu5PZ2Mv1Vm9AkxYWBi2bt2KPXv2wNXVVbveyckJpaWlyM3N1RmflZUFJycn7Zg7r0qqfv2gMSqVCubm5jXWpFQqoVKpdBYiItI/vq2tsSO8D57sWHV59YdbTmLCykRk3yqWujQyIHUKMKIoIiwsDJs3b8bu3bvh4eGhs71r164wNTXFrl27tOvS0tKQkZEBtVoNAFCr1UhJSUF2drZ2TExMDFQqFXx8fLRjbj9G9ZjqYxARkWGzs1Li+/HdMPfZR6A0kSHur+sY+MU+/HEy68E7E6GOVyG98sorWLduHX799Vd4e3tr11tbW2s/GZk+fTq2bduGqKgoqFQqzJgxAwBw4MABAFWXUQcEBMDFxQULFixAZmYmxo4di8mTJ+OTTz4BUHUZta+vL0JDQzFx4kTs3r0br776KqKjoxEcHFyrWnkVEhGRYTiTdQuvbkjGqWtV/TBjerrjnad8YK6QS1wZSaG2v7/rFGAEQahx/cqVKzFhwgQAVTeye/3117F+/XqUlJQgODgY33zzjfbrIQC4ePEipk+fjtjYWFhaWmL8+PGYP38+TExMtGNiY2Mxa9YsnDx5Eq6urnjvvfe071EbDDBERIajpLwCn+1Mw3f70gEA7e0tsXhEF/i25qXWzU2jBBhDwgBDRGR49p25jtc3HkP2rRKYygXMDvbG5MfbQSar+X+gyfg0yX1giIiIGlJvL3vsCO+DAT6OKKsQ8cm20xjzfQIy89jgS7oYYIiISK/YWiqwfGxXzH/eD+amchw4l4PgL/Zie8o1qUsjPcIAQ0REekcQBIx41B3Rrz4Ov9bWyCsqw/S1RxDx43EUlJQ/+ABk9BhgiIhIb7Wzt8JP03vhlb7tIQjAD4cvIWTJPhy7lCt1aSQxBhgiItJrChMZ3hzYEeun9ISLtRku5BRi6NID+HrPWVRUGuV1KFQLDDBERGQQerZrhe0z+yCkszPKK0V8ujMNI789iMs3C6UujSTAAENERAbD2sIUX43sgoUv+MNSIcehCzcwaPE+/HbsqtSlURNjgCEiIoMiCAKGdnXFtpm90cXdBreKy/Hq+qN47Ydk3Couk7o8aiIMMEREZJDatLLEppfVmNnfCzIB+PnoFTy1ZB+SLt6QujRqAgwwRERksEzkMsz6VwdsfFkN15bmuHSjCC8si8eimL9QXlEpdXnUiBhgiIjI4HVra4ttM3vj+S6tUSkCi3edwfDl8cjIYYOvsWKAISIio6AyM8XnLwZg8YgAtDAzwZGMXDy1ZB9+SroMI33sX7PGAENEREbl2YDW2D6zNx5ta4v8knK8vukYZqw/irxCNvgaEwYYIiIyOq4tLbB+ak+8MaAD5DIBW49fw6DFe3HwfI7UpVEDYYAhIiKjJJcJCHvSCz9N74U2rSxwNa8YI787iE93nkYZG3wNHgMMEREZtQA3G2x7tTeGd3OFKAJf7zmHoUsPIP3vAqlLo4fAAENEREbPUmmCBcP88c3oQFibm+L45Tw8tXgffkjMYIOvgWKAISKiZuMpP2fsCO8NdbtWKCqrQMRPKZi+5ghuFpRKXRrVEQMMERE1K87W5lg7uQciB3WEqVzAjhOZGLh4L/af/Vvq0qgOGGCIiKjZkckEvPxEe2x+5TG0s7dElqYEo1ck4JNtp1BSXiF1eVQLDDBERNRs+ba2RvSM3hjdwx0A8O3e83ju6wM4m31L4sroQRhgiIioWTNXyPHv5/zw3bhusLVU4OQ1DUKW/InVBy+ywVePMcAQEREB+JePI3bM7I3eXnYoKa/Ee7+kYvKqw/g7v0Tq0qgGDDBERET/cFCZYdVLj2LOYB8o5DLsOp2NgV/sQ2xattSl0R0YYIiIiG4jkwmY+LgHfg17DB0crfB3fgkmrEzEB7+dQHEZG3z1BQMMERFRDTo5q/Bb2OOY0KstACDqwAU8+9V+nM7USFsYAWCAISIiuiczUzk+eOYRrHypO+yslEjLuoVnvtqP//yZjspKNvhKiQGGiIjoAfp5O2BHeG/07+iA0vJKfLT1JCZEJSJbUyx1ac0WAwwREVEt2FkpsWJ8N8wd4guliQx7/7qOgYv3IeZkltSlNUsMMERERLUkCALG9myDrTMeRydnFW4UlGLKfw/jnc0pKCplg29TYoAhIiKqIy/HFvgltBem9PYAAKxNyEDIl/uQeiVP4sqaDwYYIiKielCayPFOiA/WTOoBhxZKnL9egOe+2Y/lcefY4NsEGGCIiIgewuNedtgZ3gfBjziirELEvO2nMeb7BFzLK5K6NKPGAENERPSQWloqsGxMV8x/3g/mpnIcOJeDgV/sw/aUa1KXZrQYYIiIiBqAIAgY8ag7ol99HH6trZFXVIbpa4/gzR+PoaCkXOryjA4DDBERUQNqZ2+Fn6b3wit920MQgI2HLyNkyT4kX8qVujSjwgBDRETUwBQmMrw5sCPWT+kJF2szXMgpxNClB/DV7jOoYINvg6hzgNm7dy+efvppuLi4QBAE/PLLLzrbJ0yYAEEQdJaBAwfqjLlx4wZGjx4NlUoFGxsbTJo0Cfn5+Tpjjh8/jt69e8PMzAxubm5YsGBB3WdHREQkoZ7tWmH7zD4I6eyMikoRn/3+F0Z+exCXbxZKXZrBq3OAKSgogL+/P77++ut7jhk4cCCuXbumXdavX6+zffTo0Thx4gRiYmKwdetW7N27F1OnTtVu12g0GDBgANq0aYOkpCR8+umn+OCDD/Dtt9/WtVwiIiJJWVuY4quRXbDwBX9YKuQ4dOEGBi3eh1+Tr0hdmkETRFGs92dZgiBg8+bNGDJkiHbdhAkTkJube9cnM9VOnToFHx8fJCYmolu3bgCAHTt24KmnnsLly5fh4uKCpUuX4p133kFmZiYUCgUA4K233sIvv/yC06dP16o2jUYDa2tr5OXlQaVS1XeKREREDSYjpxDhPxzFkYxcAMBzXVrjw2cfgcrMVNrC9Ehtf383Sg9MbGwsHBwc4O3tjenTpyMnJ0e7LT4+HjY2NtrwAgBBQUGQyWRISEjQjunTp482vABAcHAw0tLScPPmzRrfs6SkBBqNRmchIiLSJ+6tLLDxZTVm9veCTAA2H72Cpxbvw+ELN6QuzeA0eIAZOHAg/vvf/2LXrl34v//7P8TFxWHQoEGoqKh6RkRmZiYcHBx09jExMYGtrS0yMzO1YxwdHXXGVL+uHnOnefPmwdraWru4ubk19NSIiIgemolchln/6oBN09RwbWmOyzeLMHx5PD6P+QvlFZVSl2cwGjzAjBgxAs888wz8/PwwZMgQbN26FYmJiYiNjW3ot9IRGRmJvLw87XLp0qVGfT8iIqKH0bWNLbbP7I3nu7RGpQgs2XUGLyyPR0YOG3xro9Evo27Xrh3s7Oxw9uxZAICTkxOys7N1xpSXl+PGjRtwcnLSjsnK0n08efXr6jF3UiqVUKlUOgsREZE+a2Fmis9fDMDiEQFoYWaCoxm5GLR4L35KuoyHaFFtFho9wFy+fBk5OTlwdnYGAKjVauTm5iIpKUk7Zvfu3aisrESPHj20Y/bu3YuysjLtmJiYGHh7e6Nly5aNXTIREVGTejagNbbP7I1H29qioLQCr286hhnrjyKvsOzBOzdTdQ4w+fn5SE5ORnJyMgAgPT0dycnJyMjIQH5+PmbPno2DBw/iwoUL2LVrF5599ll4enoiODgYANCpUycMHDgQU6ZMwaFDh7B//36EhYVhxIgRcHFxAQCMGjUKCoUCkyZNwokTJ/DDDz9g8eLFeO211xpu5kRERHrEtaUF1k/tidnB3jCRCdh6/BoGLd6Lg+dzHrxzM1Tny6hjY2PRr1+/u9aPHz8eS5cuxZAhQ3D06FHk5ubCxcUFAwYMwNy5c3Wacm/cuIGwsDBs2bIFMpkMQ4cOxZIlS2BlZaUdc/z4cYSGhiIxMRF2dnaYMWMGIiIial0nL6MmIiJDlXwpF+EbjuJCTiEEAZj+RHuEB3WAwsT4b6Bf29/fD3UfGH3GAENERIasoKQcH205iR8OV12U0tnVGl+8GIB29lYP2NOwSXofGCIiIno4lkoT/N+wzlg6OhDW5qY4fjkPIUv+xIZDGWzwBQMMERGRXhvk54wd4b3Rq30rFJVV4K2fUzBtTRJuFpRKXZqkGGCIiIj0nLO1OdZM6oG3n+oIU7mAnSeyMHDxXvx55m+pS5MMAwwREZEBkMkETO3THptfeQzt7S2RpSnBmO8T8O/okygpr5C6vCbHAENERGRAfFtbY+uM3hjT0x0A8N2+dAz5+gDOZN2SuLKmxQBDRERkYMwVcnw8xA8rxnWDraUCp65pMPjLP7E6/kKzafBlgCEiIjJQQT6O2BHeG3062KOkvBLv/XoCk1Ydxt/5JVKX1ugYYIiIiAyYQwszRE3ojvef9oHCRIbdp7Mx8Iu92JOW/eCdDRgDDBERkYGTyQS89JgHfgt7DN6OLfB3fileWpmID347geIy42zwZYAhIiIyEh2dVPg17DFM6NUWABB14AKe+epPnLqmkbawRsAAQ0REZETMTOX44JlHsPKl7rCzUuKvrHw8+9V+fP9nOiorjafBlwGGiIjICPXzdsCO8N7o39EBpRWVmLv1JMavPIRsTbHUpTUIBhgiIiIjZWelxIrx3TB3iC+UJjLsO/M3gr/Yi99PZEpd2kNjgCEiIjJigiBgbM82iH71cfg4q3CzsAxTVyfh7c0pKCwtl7q8emOAISIiagY8HVpgc2gvTO3TDgCwLiEDg7/8E6lX8iSurH4YYIiIiJoJpYkcbz/VCWsm9YCjSonz1wvw3Df7sSzunME1+DLAEBERNTOPe9lhx8w+CH7EEWUVIuZvP43RKxJwLa9I6tJqjQGGiIioGWppqcCyMV3xf0P9YG4qR/z5HAz8Yh+2pVyTurRaYYAhIiJqpgRBwIvd3RH96uPo7GqNvKIyvLL2CGZvOob8Ev1u8GWAISIiauba2Vvhp+m9ENqvPQQB2JR0GSFL9uFoxk2pS7snBhgiIiKCqVyG2cEdsX5KT7hYm+FiTiGGLYvHl7vOoEIPG3wZYIiIiEirZ7tW2D6zDwZ3dkZFpYiFMX9hxLfxuHyzUOrSdDDAEBERkQ5rC1N8ObILPh/uDyulCRIv3MSgL/bh1+QrUpemxQBDREREdxEEAc8HumLbq70R6G6DWyXlmLkhGbN+SIamuEzq8hhgiIiI6N7cW1lg48tqzOzvBZkAbD56BU8t3ofDF25IWhcDDBEREd2XiVyGWf/qgE3T1HBtaY7LN4swfHk8Vh+8KFlNDDBERERUK13b2GL7zN54vktryGUCurjZSFaLIIqi/l0b1QA0Gg2sra2Rl5cHlUoldTlERERG5dz1fLS3t2rw49b29zc/gSEiIqI6a4zwUhcMMERERGRwGGCIiIjI4DDAEBERkcFhgCEiIiKDwwBDREREBocBhoiIiAwOAwwREREZHAYYIiIiMjh1DjB79+7F008/DRcXFwiCgF9++UVnuyiKmDNnDpydnWFubo6goCCcOXNGZ8yNGzcwevRoqFQq2NjYYNKkScjPz9cZc/z4cfTu3RtmZmZwc3PDggUL6j47IiIiMkp1DjAFBQXw9/fH119/XeP2BQsWYMmSJVi2bBkSEhJgaWmJ4OBgFBcXa8eMHj0aJ06cQExMDLZu3Yq9e/di6tSp2u0ajQYDBgxAmzZtkJSUhE8//RQffPABvv3223pMkYiIiIyO+BAAiJs3b9a+rqysFJ2cnMRPP/1Uuy43N1dUKpXi+vXrRVEUxZMnT4oAxMTERO2Y7du3i4IgiFeuXBFFURS/+eYbsWXLlmJJSYl2TEREhOjt7V3r2vLy8kQAYl5eXn2nR0RERE2str+/G7QHJj09HZmZmQgKCtKus7a2Ro8ePRAfHw8AiI+Ph42NDbp166YdExQUBJlMhoSEBO2YPn36QKFQaMcEBwcjLS0NN2/erPG9S0pKoNFodBYiIiIyTg0aYDIzMwEAjo6OOusdHR212zIzM+Hg4KCz3cTEBLa2tjpjajrG7e9xp3nz5sHa2lq7uLm5PfyEiIiISC+ZSF1AQ4mMjMRrr72mfZ2Xlwd3d3d+EkNERGRAqn9vi6J433ENGmCcnJwAAFlZWXB2dtauz8rKQkBAgHZMdna2zn7l5eW4ceOGdn8nJydkZWXpjKl+XT3mTkqlEkqlUvu6+g+An8QQEREZnlu3bsHa2vqe2xs0wHh4eMDJyQm7du3SBhaNRoOEhARMnz4dAKBWq5Gbm4ukpCR07doVALB7925UVlaiR48e2jHvvPMOysrKYGpqCgCIiYmBt7c3WrZsWataXFxccOnSJbRo0QKCIDTYHDUaDdzc3HDp0iWoVKoGO64+MfY5cn6Gz9jnaOzzA4x/jpxf/YmiiFu3bsHFxeW+4+ocYPLz83H27Fnt6/T0dCQnJ8PW1hbu7u4IDw/Hxx9/DC8vL3h4eOC9996Di4sLhgwZAgDo1KkTBg4ciClTpmDZsmUoKytDWFgYRowYoS121KhR+PDDDzFp0iREREQgNTUVixcvxqJFi2pdp0wmg6ura12nV2sqlcoo/6O8nbHPkfMzfMY+R2OfH2D8c+T86ud+n7xUq3OAOXz4MPr166d9Xd13Mn78eERFReHNN99EQUEBpk6ditzcXDz++OPYsWMHzMzMtPusXbsWYWFh6N+/P2QyGYYOHYolS5boFP77778jNDQUXbt2hZ2dHebMmaNzrxgiIiJqvgTxQV0ypEOj0cDa2hp5eXlGm6qNfY6cn+Ez9jka+/wA458j59f4+CykOlIqlXj//fd1GoaNjbHPkfMzfMY+R2OfH2D8c+T8Gh8/gSEiIiKDw09giIiIyOAwwBAREZHBYYAhIiIig8MAQ0RERAaHAeYfe/fuxdNPPw0XFxcIgoBffvlFZ7soipgzZw6cnZ1hbm6OoKAgnDlzRmfMjRs3MHr0aKhUKtjY2GDSpEnIz89vwlnc24PmN2HCBAiCoLMMHDhQZ4w+z2/evHno3r07WrRoAQcHBwwZMgRpaWk6Y4qLixEaGopWrVrBysoKQ4cOveuRFRkZGQgJCYGFhQUcHBwwe/ZslJeXN+VUalSb+fXt2/euczht2jSdMfo6PwBYunQpOnfurL0xllqtxvbt27XbDfn8AQ+en6GfvzvNnz8fgiAgPDxcu87Qz+GdapqjIZ/HDz744K7aO3bsqN2ud+dPJFEURXHbtm3iO++8I/78888iAHHz5s062+fPny9aW1uLv/zyi3js2DHxmWeeET08PMSioiLtmIEDB4r+/v7iwYMHxX379omenp7iyJEjm3gmNXvQ/MaPHy8OHDhQvHbtmna5ceOGzhh9nl9wcLC4cuVKMTU1VUxOThafeuop0d3dXczPz9eOmTZtmujm5ibu2rVLPHz4sNizZ0+xV69e2u3l5eWir6+vGBQUJB49elTctm2baGdnJ0ZGRkoxJR21md8TTzwhTpkyRecc5uXlabfr8/xEURR/++03MTo6Wvzrr7/EtLQ08e233xZNTU3F1NRUURQN+/yJ4oPnZ+jn73aHDh0S27ZtK3bu3FmcOXOmdr2hn8Pb3WuOhnwe33//ffGRRx7Rqf369eva7fp2/hhganDnL/jKykrRyclJ/PTTT7XrcnNzRaVSKa5fv14URVE8efKkCEBMTEzUjtm+fbsoCIJ45cqVJqu9Nu4VYJ599tl77mNI8xNFUczOzhYBiHFxcaIoVp0vU1NTcdOmTdoxp06dEgGI8fHxoihWhTyZTCZmZmZqxyxdulRUqVRiSUlJ007gAe6cnyhW/cN5+z+kdzKk+VVr2bKluGLFCqM7f9Wq5yeKxnP+bt26JXp5eYkxMTE6czKmc3ivOYqiYZ/H999/X/T3969xmz6eP36FVAvp6enIzMxEUFCQdp21tTV69OiB+Ph4AEB8fDxsbGzQrVs37ZigoCDIZDIkJCQ0ec31ERsbCwcHB3h7e2P69OnIycnRbjO0+eXl5QEAbG1tAQBJSUkoKyvTOYcdO3aEu7u7zjn08/ODo6OjdkxwcDA0Gg1OnDjRhNU/2J3zq7Z27VrY2dnB19cXkZGRKCws1G4zpPlVVFRgw4YNKCgogFqtNrrzd+f8qhnD+QsNDUVISIjOuQKM6+/gveZYzZDP45kzZ+Di4oJ27dph9OjRyMjIAKCf569Bn0ZtrDIzMwFA56RUv67elpmZCQcHB53tJiYmsLW11Y7RZwMHDsTzzz8PDw8PnDt3Dm+//TYGDRqE+Ph4yOVyg5pfZWUlwsPD8dhjj8HX1xdA1flRKBSwsbHRGXvnOazpHFdv0xc1zQ+oeghqmzZt4OLiguPHjyMiIgJpaWn4+eefARjG/FJSUqBWq1FcXAwrKyts3rwZPj4+SE5ONorzd6/5AcZx/jZs2IAjR44gMTHxrm3G8nfwfnMEDPs89ujRA1FRUfD29sa1a9fw4Ycfonfv3khNTdXL88cAQwCAESNGaH/28/ND586d0b59e8TGxqJ///4SVlZ3oaGhSE1NxZ9//il1KY3iXvO7/WGnfn5+cHZ2Rv/+/XHu3Dm0b9++qcusF29vbyQnJyMvLw8//vgjxo8fj7i4OKnLajD3mp+Pj4/Bn79Lly5h5syZiImJ0Xl4rzGpzRwN+TwOGjRI+3Pnzp3Ro0cPtGnTBhs3boS5ubmEldWMXyHVgpOTEwDc1W2dlZWl3ebk5ITs7Gyd7eXl5bhx44Z2jCFp164d7OzscPbsWQCGM7+wsDBs3boVe/bsgaurq3a9k5MTSktLkZubqzP+znNY0zmu3qYP7jW/mvTo0QMAdM6hvs9PoVDA09MTXbt2xbx58+Dv74/Fixcbzfm71/xqYmjnLykpCdnZ2QgMDISJiQlMTEwQFxeHJUuWwMTEBI6OjgZ/Dh80x4qKirv2MbTzeDsbGxt06NABZ8+e1cu/gwwwteDh4QEnJyfs2rVLu06j0SAhIUH7/bVarUZubi6SkpK0Y3bv3o3Kykrtf8CG5PLly8jJyYGzszMA/Z+fKIoICwvD5s2bsXv3bnh4eOhs79q1K0xNTXXOYVpaGjIyMnTOYUpKik5Qi4mJgUql0n7ML5UHza8mycnJAKBzDvV1fvdSWVmJkpISgz9/91I9v5oY2vnr378/UlJSkJycrF26deuG0aNHa3829HP4oDnK5fK79jG083i7/Px8nDt3Ds7Ozvr5d7DB24IN1K1bt8SjR4+KR48eFQGIn3/+uXj06FHx4sWLoihWXUZtY2Mj/vrrr+Lx48fFZ599tsbLqLt06SImJCSIf/75p+jl5aU3lxnfb363bt0S33jjDTE+Pl5MT08X//jjDzEwMFD08vISi4uLtcfQ5/lNnz5dtLa2FmNjY3UuASwsLNSOmTZtmuju7i7u3r1bPHz4sKhWq0W1Wq3dXn0J4IABA8Tk5GRxx44dor29vV5c3vig+Z09e1b86KOPxMOHD4vp6enir7/+KrZr107s06eP9hj6PD9RFMW33npLjIuLE9PT08Xjx4+Lb731ligIgvj777+LomjY508U7z8/Yzh/NbnzihxDP4c1uX2Ohn4eX3/9dTE2NlZMT08X9+/fLwYFBYl2dnZidna2KIr6d/4YYP6xZ88eEcBdy/jx40VRrLqU+r333hMdHR1FpVIp9u/fX0xLS9M5Rk5Ojjhy5EjRyspKVKlU4ksvvSTeunVLgtnc7X7zKywsFAcMGCDa29uLpqamYps2bcQpU6boXAonivo9v5rmBkBcuXKldkxRUZH4yiuviC1bthQtLCzE5557Trx27ZrOcS5cuCAOGjRINDc3F+3s7MTXX39dLCsra+LZ3O1B88vIyBD79Okj2traikqlUvT09BRnz56tc/8JUdTf+YmiKE6cOFFs06aNqFAoRHt7e7F///7a8CKKhn3+RPH+8zOG81eTOwOMoZ/Dmtw+R0M/jy+++KLo7OwsKhQKsXXr1uKLL74onj17Vrtd386fIIqi2PCf6xARERE1HvbAEBERkcFhgCEiIiKDwwBDREREBocBhoiIiAwOAwwREREZHAYYIiIiMjgMMERERGRwGGCIiIjI4DDAEJHkYmNjIQjCXQ+KIyK6FwYYIpJcr169cO3aNVhbW9d6n8LCQkRGRqJ9+/YwMzODvb09nnjiCfz666+NWCkR6QsTqQsgIlIoFHBycqrTPtOmTUNCQgK+/PJL+Pj4ICcnBwcOHEBOTk4jVUlE+oSfwBBRg+vbty9mzJiB8PBwtGzZEo6Ojvjuu+9QUFCAl156CS1atICnpye2b98O4O6vkKKiomBjY4OdO3eiU6dOsLKywsCBA3Ht2jXte/z22294++238dRTT6Ft27bo2rUrZsyYgYkTJ2rHCIKAX375Rac2GxsbREVFAQAuXLgAQRCwYcMG9OrVC2ZmZvD19UVcXFyj/vkQ0cNjgCGiRrFq1SrY2dnh0KFDmDFjBqZPn44XXngBvXr1wpEjRzBgwACMHTsWhYWFNe5fWFiIzz77DKtXr8bevXuRkZGBN954Q7vdyckJ27Ztw61btx661tmzZ+P111/H0aNHoVar8fTTT/OTHCI9xwBDRI3C398f7777Lry8vBAZGQkzMzPY2dlhypQp8PLywpw5c5CTk4Pjx4/XuH9ZWRmWLVuGbt26ITAwEGFhYdi1a5d2+7fffosDBw6gVatW6N69O2bNmoX9+/fXq9awsDAMHToUnTp1wtKlS2FtbY3vv/++XscioqbBAENEjaJz587an+VyOVq1agU/Pz/tOkdHRwBAdnZ2jftbWFigffv22tfOzs46Y/v06YPz589j165dGDZsGE6cOIHevXtj7ty5da5VrVZrfzYxMUG3bt1w6tSpOh+HiJoOAwwRNQpTU1Od14Ig6KwTBAEAUFlZWev9RVG8a0zv3r0RERGB33//HR999BHmzp2L0tLSe+5TVlZWvwkRkV5hgCEio+Hj44Py8nIUFxcDAOzt7XUaf8+cOVNjz83Bgwe1P5eXlyMpKQmdOnVq/IKJqN54GTURGaS+ffti5MiR6NatG1q1aoWTJ0/i7bffRr9+/aBSqQAATz75JL766iuo1WpUVFQgIiLirk92AODrr7+Gl5cXOnXqhEWLFuHmzZs6VzMRkf7hJzBEZJCCg4OxatUqDBgwAJ06dcKMGTMQHByMjRs3ascsXLgQbm5u6N27N0aNGoU33ngDFhYWdx1r/vz5mD9/Pvz9/fHnn3/it99+g52dXVNOh4jqSBDv/IKYiKiZuHDhAjw8PHD06FEEBARIXQ4R1QE/gSEiIiKDwwBDREREBodfIREREZHB4ScwREREZHAYYIiIiMjgMMAQERGRwWGAISIiIoPDAENEREQGhwGGiIiIDA4DDBERERkcBhgiIiIyOP8PGiO9sfcR2BwAAAAASUVORK5CYII=\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "NCQLc9pn7BYG" - }, - "source": [ - "### Step 4: Print the Result" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kpkdWbyQ6j6M", - "outputId": "9e2fe682-4abc-4d4c-b2f1-d65081e78f2d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " algorithm minSup patterns runtime memory\n", - "0 WFRIM 100 4649 24.710214 607920128\n", - "1 WFRIM 200 4643 23.434430 608448512\n", - "2 WFRIM 300 3545 24.234488 609054720\n", - "3 WFRIM 400 1987 24.782321 609419264\n", - "4 WFRIM 500 1153 25.673576 609087488\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "print(result)" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "S-prY3W27U4Z" - }, - "source": [ - "### Step 5: Visualizing the results" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "zVEtHn5j7aYE", - "outputId": "c9b7aeba-a8b7-4cc6-d2c5-55827655d294" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" - }, - "metadata": {} - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "result.plot(x='minSup', y='patterns', kind='line')\n", - "result.plot(x='minSup', y='runtime', kind='line')\n", - "result.plot(x='minSup', y='memory', kind='line')\n", - "\n", - "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "image/png": 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\n" + }, + "metadata": {} } + ], + "source": [ + "result.plot(x='minSup', y='patterns', kind='line')\n", + "result.plot(x='minSup', y='runtime', kind='line')\n", + "result.plot(x='minSup', y='memory', kind='line')\n", + "\n", + "#Graphs can be improved further by using additional packages, such as plotly and matplotlib" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/setup.py b/setup.py index b84019c2..54d183c5 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='pami', - version='2024.5.7.1', + version='2024.5.24.1', author='Rage Uday Kiran', author_email='uday.rage@gmail.com', description='This software is being developed at the University of Aizu, Aizu-Wakamatsu, Fukushima, Japan',