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documentation_correction #300

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Feb 6, 2024
Merged

documentation_correction #300

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eaa892e
updated
yerragollahareeshkumar Feb 6, 2024
07a2f9f
Merge branch 'UdayLab:main' into main
yerragollahareeshkumar Feb 6, 2024
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80 changes: 48 additions & 32 deletions PAMI/georeferencedFrequentPattern/basic/FSPGrowth.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
# that satisfy the user-specified minimum support (minSup) and maximum distance (maxDist) constraints
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
# -------------------------------------------------------
#
# from PAMI.georeferencedFrequentPattern.basic import FSPGrowth as alg
#
Expand All @@ -28,6 +28,7 @@
#
# print("Total ExecutionTime in seconds:", run)
#

__copyright__ = """
Copyright (C) 2021 Rage Uday Kiran

Expand Down Expand Up @@ -55,8 +56,9 @@
class _Node:
"""
A class used to represent the node of frequentPatternTree
Attributes
----------

:Attributes:

item : int
Storing item of a node
count : int
Expand All @@ -77,14 +79,16 @@ def __init__(self, item, count, children):
class _Tree:
"""
A class used to represent the frequentPatternGrowth tree structure
Attributes
----------

:Attributes:

root : Node
The first node of the tree set to Null.
nodeLink : dict
Stores the nodes which shares same item
Methods
-------

:Methods:

createTree(transaction,count)
Adding transaction into the tree
linkNode(node)
Expand All @@ -108,6 +112,7 @@ def __init__(self):
def createTree(self, transaction, count):
"""
Create tree or add transaction into yourself.

:param transaction: list
:param count: int
:return: Tree
Expand All @@ -126,6 +131,7 @@ def createTree(self, transaction, count):
def linkNode(self, node):
"""
Maintain link of node by adding node to nodeLink

:param node: Node
:return:
"""
Expand All @@ -138,6 +144,7 @@ def linkNode(self, node):
def createCPB(self, item, neighbour) :
"""
Create conditional pattern base based on item and neighbour

:param item: int
:param neighbour: dict
:return: Tree
Expand All @@ -153,6 +160,7 @@ def createCPB(self, item, neighbour) :
def mergeTree(self, tree, fpList):
"""
Merge tree into yourself

:param tree: Tree
:param fpList: list
:return: Tree
Expand All @@ -165,8 +173,9 @@ def mergeTree(self, tree, fpList):
def createTransactions(self, fpList):
"""
Create transactions that configure yourself

:param fpList: list
:return: list
:return: transaction list
"""
transactions = []
flist = [x for x in fpList if x in self.nodeLink]
Expand All @@ -185,6 +194,7 @@ def createTransactions(self, fpList):
def getPattern(self, item, suffixItem, minSup, neighbour):
"""
Get frequent patterns based on suffixItem

:param item: int
:param suffixItem: tuple
:param minSup: int
Expand All @@ -208,6 +218,7 @@ def getPattern(self, item, suffixItem, minSup, neighbour):
def mining(self, minSup:collable[int, float], neighbourhood: [Dict[int, List[int]]] = None):
"""
Pattern mining on your own

:param minSup: int
:param neighbourhood: function
:param neighbourhood: dict
Expand All @@ -222,17 +233,14 @@ def mining(self, minSup:collable[int, float], neighbourhood: [Dict[int, List[int

class FSPGrowth(_ab._spatialFrequentPatterns):
"""
Description:
-------------
Given a transactional database and a spatial (or neighborhood) file, FSPM aims to discover all of those patterns
that satisfy the user-specified minimum support (minSup) and maximum distance (maxDist) constraints
Reference:
-----------
Rage, Uday & Fournier Viger, Philippe & Zettsu, Koji & Toyoda, Masashi & Kitsuregawa, Masaru. (2020).
Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases.

Attributes:
------------
:Description: Given a transactional database and a spatial (or neighborhood) file, FSPM aims to discover all of those patterns
that satisfy the user-specified minimum support (minSup) and maximum distance (maxDist) constraints

:Reference: Rage, Uday & Fournier Viger, Philippe & Zettsu, Koji & Toyoda, Masashi & Kitsuregawa, Masaru. (2020).
Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases.

:Attributes:

iFile : file
Input file name or path of the input file
nFile : file
Expand All @@ -252,8 +260,8 @@ class FSPGrowth(_ab._spatialFrequentPatterns):
memoryRSS : float
To store the total amount of RSS memory consumed by the program

Methods:
--------
:Methods:

startMine()
This function starts pattern mining.
getPatterns()
Expand Down Expand Up @@ -285,17 +293,17 @@ class FSPGrowth(_ab._spatialFrequentPatterns):
getAllFrequentPatterns(data, fpList, ndata)
This function generates all frequent patterns

Executing the code on terminal :
---------------------------------
Format:
**Executing the code on terminal :**
--------------------------------------

Format:
>>> python3 FSPGrowth.py <inputFile> <outputFile> <neighbourFile> <minSup>
Examples:

>>> python3 FSPGrowth.py sampleTDB.txt output.txt sampleN.txt 0.5 (minSup will be considered in percentage of database transactions)
Examples:
>>> python3 FSPGrowth.py sampleTDB.txt output.txt sampleN.txt 0.5

Sample run of importing the code :
-----------------------------------
**Sample run of importing the code :**
----------------------------------------
.. code-block:: python

from PAMI.georeferencedFrequentPattern.basic import FSPGrowth as alg
Expand All @@ -322,8 +330,8 @@ class FSPGrowth(_ab._spatialFrequentPatterns):

print("Total ExecutionTime in seconds:", run)

Credits:
----------
**Credits:**
--------------
The complete program was written by Yudai Masu under the supervision of Professor Rage Uday Kiran.
"""

Expand Down Expand Up @@ -441,6 +449,7 @@ def _sortTransaction(self):
def _convert(self, value):
"""
To convert the given user specified value

:param value: user specified value
:return: converted value
"""
Expand Down Expand Up @@ -479,6 +488,7 @@ def startMine(self):

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
"""
Expand All @@ -487,6 +497,7 @@ def getMemoryUSS(self):

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
"""
Expand All @@ -495,6 +506,7 @@ def getMemoryRSS(self):

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
"""
Expand All @@ -503,6 +515,7 @@ def getRuntime(self):

def getPatternsAsDataFrame(self):
"""Storing final frequent patterns in a dataframe

:return: returning frequent patterns in a dataframe
:rtype: pd.DataFrame
"""
Expand All @@ -517,8 +530,9 @@ def getPatternsAsDataFrame(self):
def save(self, oFile):
"""
Complete set of frequent patterns will be loaded in to a output file

:param oFile: name of the output file
:type oFile: file
:type oFile: csv file
"""
self._oFile = oFile
writer = open(self._oFile, 'w+')
Expand All @@ -529,14 +543,16 @@ def save(self, oFile):
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

def printResults(self):
""" This function is used to print the results
"""
This function is used to print the results
"""
print("Total number of Spatial Frequent Patterns:", len(self.getPatterns()))
print("Total Memory in USS:", self.getMemoryUSS())
Expand Down
59 changes: 29 additions & 30 deletions PAMI/georeferencedFrequentPattern/basic/SpatialECLAT.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,19 +54,15 @@

class SpatialECLAT(_ab._spatialFrequentPatterns):
"""
Description:
--------------
Spatial Eclat is a Extension of ECLAT algorithm,which stands for Equivalence Class Clustering and bottom-up
Lattice Traversal.It is one of the popular methods of Association Rule mining. It is a more efficient and
scalable version of the Apriori algorithm.

Reference:
-----------
Rage, Uday & Fournier Viger, Philippe & Zettsu, Koji & Toyoda, Masashi & Kitsuregawa, Masaru. (2020).
Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases.

Attributes :
---------------
:Description: Spatial Eclat is a Extension of ECLAT algorithm,which stands for Equivalence Class Clustering and bottom-up
Lattice Traversal.It is one of the popular methods of Association Rule mining. It is a more efficient and
scalable version of the Apriori algorithm.

:Reference: Rage, Uday & Fournier Viger, Philippe & Zettsu, Koji & Toyoda, Masashi & Kitsuregawa, Masaru. (2020).
Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases.

:Attributes:

iFile : str
Input file name or path of the input file
nFile: str
Expand All @@ -91,8 +87,8 @@ class SpatialECLAT(_ab._spatialFrequentPatterns):
Database : list
To store the complete set of transactions available in the input database/file

Methods :
------------
:Methods:

startMine()
Mining process will start from here
getPatterns()
Expand Down Expand Up @@ -124,18 +120,18 @@ class SpatialECLAT(_ab._spatialFrequentPatterns):
mapNeighbours(file):
A function to map items to their neighbours

Executing the code on terminal :
----------------------------------
Format:
**Executing the code on terminal :**
----------------------------------------

Format:
>>> python3 SpatialECLAT.py <inputFile> <outputFile> <neighbourFile> <minSup>

Examples:

>>> python3 SpatialECLAT.py sampleTDB.txt output.txt sampleN.txt 0.5 (minSup will be considered in percentage of database transactions)

Sample run of importing the code :
-----------------------------------


**Sample run of importing the code :**
------------------------------------------
.. code-block:: python

from PAMI.georeferencedFrequentPattern.basic import SpatialECLAT as alg
Expand Down Expand Up @@ -163,8 +159,8 @@ class SpatialECLAT(_ab._spatialFrequentPatterns):
print("Total ExecutionTime in seconds:", run)


Credits:
---------
**Credits:**
----------------
The complete program was written by B.Sai Chitra under the supervision of Professor Rage Uday Kiran.
"""

Expand Down Expand Up @@ -235,6 +231,7 @@ def _frequentOneItem(self):
def _convert(self, value):
"""
To convert the given user specified value

:param value: user specified value
:return: converted value
"""
Expand Down Expand Up @@ -323,11 +320,12 @@ def _generateSpatialFrequentPatterns(self, tidList):

def _getNeighbourItems(self, keySet):
"""
A function to get Neighbours of a item
:param keySet:itemSet
:type keySet:str or tuple
:return: set of common neighbours
:rtype:set
A function to get Neighbours of a item

:param keySet:itemSet
:type keySet:str or tuple
:return: set of common neighbours
:rtype:set
"""
itemNeighbours = self._NeighboursMap.keys()
if isinstance(keySet, str):
Expand All @@ -344,7 +342,8 @@ def _getNeighbourItems(self, keySet):

def _mapNeighbours(self):
"""
A function to map items to their Neighbours

A function to map items to their Neighbours
"""
self._NeighboursMap = {}
if isinstance(self._nFile, _ab._pd.DataFrame):
Expand Down
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