dm-association-rules.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Association rule discovery\n",
    "\n",
    "To complete the exercise we will need the `mlextend` library since `scikit-learn` does not provide any tools for frequent itemset or association rule discovery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T09:02:10.505976Z",
     "start_time": "2021-04-06T09:02:06.042048Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mlxtend in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (0.18.0)\n",
      "Requirement already satisfied: setuptools in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from mlxtend) (49.6.0.post20210108)\n",
      "Requirement already satisfied: numpy>=1.16.2 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from mlxtend) (1.20.1)\n",
      "Requirement already satisfied: pandas>=0.24.2 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from mlxtend) (1.2.3)\n",
      "Requirement already satisfied: joblib>=0.13.2 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from mlxtend) (1.0.1)\n",
      "Requirement already satisfied: scikit-learn>=0.20.3 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from mlxtend) (0.24.1)\n",
      "Requirement already satisfied: scipy>=1.2.1 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from mlxtend) (1.6.1)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from mlxtend) (3.3.4)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from matplotlib>=3.0.0->mlxtend) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from matplotlib>=3.0.0->mlxtend) (1.3.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from matplotlib>=3.0.0->mlxtend) (8.1.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from matplotlib>=3.0.0->mlxtend) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from matplotlib>=3.0.0->mlxtend) (2.4.7)\n",
      "Requirement already satisfied: six in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from cycler>=0.10->matplotlib>=3.0.0->mlxtend) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from pandas>=0.24.2->mlxtend) (2021.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/kacper/anaconda3/envs/dataviz/lib/python3.8/site-packages (from scikit-learn>=0.20.3->mlxtend) (2.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install mlxtend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first step is to download a piece of text from Wikipedia and to parse paragraphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(\"./data/RC_2009-10\", 'r') as file:\n",
    "#     content = file.read().replace(\"\\n\", \",\")\n",
    "#     content = \"[\"+content[:-1]+\"]\"\n",
    "#     print(content[:1000])\n",
    "#     with open(\"./data/RC_2009-10.json\", \"w\") as f2:\n",
    "#         f2.write(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "comments = pd.read_json(\"./data/RC_2009-10.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "subreddit     1000words  2012  2100science  3DMA  3rb  40Plus  411onsoaps  \\\n",
       "author                                                                      \n",
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       "author                                                                 \n",
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      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "# counts = comments[['author', 'subreddit', 'id']].groupby(['author', 'subreddit']).count()\n",
    "counts = comments[['author', 'subreddit']].groupby(['author', 'subreddit']).size().unstack(fill_value=0)\n",
    "counts.to_csv(\"./data/counts.csv\")\n",
    "counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "              1000words   2012  2100science   3DMA    3rb  40Plus  411onsoaps  \\\n",
       "author                                                                          \n",
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       "\n",
       "              4WheelsNews  4chan    538  ...  yoshiler  yospos  yourfav  \\\n",
       "author                                   ...                              \n",
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       "--cough--           False  False  False  ...     False   False    False   \n",
       "-11                 False  False  False  ...     False   False    False   \n",
       "-13-                False  False  False  ...     False   False    False   \n",
       "\n",
       "              youthpassion  youtube  yuen999  zelda    zen     zh  zombies  \n",
       "author                                                                      \n",
       "---eeZurr---         False    False    False  False  False  False    False  \n",
       "---sniff---          False    False    False  False  False  False    False  \n",
       "--cough--            False    False    False  False  False  False    False  \n",
       "-11                  False    False    False  False  False  False    False  \n",
       "-13-                 False    False    False  False  False  False    False  \n",
       "\n",
       "[5 rows x 2071 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_matrix = pd.read_csv(\"./data/counts.csv\", index_col = 0)\n",
    "threshold = 5\n",
    "counts_matrix.head()\n",
    "df = counts_matrix.mask(counts_matrix < threshold,0).astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "from mlxtend.frequent_patterns import apriori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.142598</td>\n",
       "      <td>(AskReddit)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002778</td>\n",
       "      <td>(Christianity)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.014425</td>\n",
       "      <td>(DoesAnybodyElse)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.007422</td>\n",
       "      <td>(Economics)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.064092</td>\n",
       "      <td>(IAmA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>0.002274</td>\n",
       "      <td>(worldnews, pics, AskReddit, politics, reddit....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>0.002059</td>\n",
       "      <td>(worldnews, pics, AskReddit, reddit.com, scien...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>0.002048</td>\n",
       "      <td>(worldnews, pics, AskReddit, politics, science...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>0.002091</td>\n",
       "      <td>(funny, pics, politics, technology, science, W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>0.002338</td>\n",
       "      <td>(worldnews, funny, pics, politics, science, WT...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1345 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       support                                           itemsets\n",
       "0     0.142598                                        (AskReddit)\n",
       "1     0.002778                                     (Christianity)\n",
       "2     0.014425                                  (DoesAnybodyElse)\n",
       "3     0.007422                                        (Economics)\n",
       "4     0.064092                                             (IAmA)\n",
       "...        ...                                                ...\n",
       "1340  0.002274  (worldnews, pics, AskReddit, politics, reddit....\n",
       "1341  0.002059  (worldnews, pics, AskReddit, reddit.com, scien...\n",
       "1342  0.002048  (worldnews, pics, AskReddit, politics, science...\n",
       "1343  0.002091  (funny, pics, politics, technology, science, W...\n",
       "1344  0.002338  (worldnews, funny, pics, politics, science, WT...\n",
       "\n",
       "[1345 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets = apriori(df, min_support=0.002, use_colnames=True)\n",
    "\n",
    "frequent_itemsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T09:04:00.507227Z",
     "start_time": "2021-04-06T09:04:00.473980Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(DoesAnybodyElse)</td>\n",
       "      <td>(AskReddit)</td>\n",
       "      <td>0.014425</td>\n",
       "      <td>0.142598</td>\n",
       "      <td>0.011047</td>\n",
       "      <td>0.765799</td>\n",
       "      <td>5.370329</td>\n",
       "      <td>0.008990</td>\n",
       "      <td>3.660970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(self)</td>\n",
       "      <td>(AskReddit)</td>\n",
       "      <td>0.005845</td>\n",
       "      <td>0.142598</td>\n",
       "      <td>0.004633</td>\n",
       "      <td>0.792661</td>\n",
       "      <td>5.558699</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>4.135256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(todayilearned)</td>\n",
       "      <td>(AskReddit)</td>\n",
       "      <td>0.005041</td>\n",
       "      <td>0.142598</td>\n",
       "      <td>0.003818</td>\n",
       "      <td>0.757447</td>\n",
       "      <td>5.311755</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>3.534902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(offbeat)</td>\n",
       "      <td>(WTF)</td>\n",
       "      <td>0.005899</td>\n",
       "      <td>0.044465</td>\n",
       "      <td>0.004247</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>16.192359</td>\n",
       "      <td>0.003985</td>\n",
       "      <td>3.412624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(business)</td>\n",
       "      <td>(reddit.com)</td>\n",
       "      <td>0.004494</td>\n",
       "      <td>0.089371</td>\n",
       "      <td>0.003196</td>\n",
       "      <td>0.711217</td>\n",
       "      <td>7.958070</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>3.153337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>(worldnews, politics, science, WTF, funny)</td>\n",
       "      <td>(reddit.com, pics)</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.031542</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.882591</td>\n",
       "      <td>27.981529</td>\n",
       "      <td>0.002254</td>\n",
       "      <td>8.248591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>(worldnews, politics, reddit.com, science, funny)</td>\n",
       "      <td>(WTF, pics)</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.024088</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.804428</td>\n",
       "      <td>33.395225</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>4.990040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>(worldnews, funny, science, WTF, reddit.com)</td>\n",
       "      <td>(politics, pics)</td>\n",
       "      <td>0.003314</td>\n",
       "      <td>0.015251</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.705502</td>\n",
       "      <td>46.259969</td>\n",
       "      <td>0.002287</td>\n",
       "      <td>3.343819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>(worldnews, pics, politics, science, WTF)</td>\n",
       "      <td>(reddit.com, funny)</td>\n",
       "      <td>0.003175</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.736486</td>\n",
       "      <td>38.002621</td>\n",
       "      <td>0.002277</td>\n",
       "      <td>3.721328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>(worldnews, science, politics, funny)</td>\n",
       "      <td>(WTF, reddit.com, pics)</td>\n",
       "      <td>0.003014</td>\n",
       "      <td>0.019251</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.775801</td>\n",
       "      <td>40.298849</td>\n",
       "      <td>0.002280</td>\n",
       "      <td>4.374451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2328 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            antecedents  \\\n",
       "0                                     (DoesAnybodyElse)   \n",
       "1                                                (self)   \n",
       "2                                       (todayilearned)   \n",
       "3                                             (offbeat)   \n",
       "4                                            (business)   \n",
       "...                                                 ...   \n",
       "2323         (worldnews, politics, science, WTF, funny)   \n",
       "2324  (worldnews, politics, reddit.com, science, funny)   \n",
       "2325       (worldnews, funny, science, WTF, reddit.com)   \n",
       "2326          (worldnews, pics, politics, science, WTF)   \n",
       "2327              (worldnews, science, politics, funny)   \n",
       "\n",
       "                  consequents  antecedent support  consequent support  \\\n",
       "0                 (AskReddit)            0.014425            0.142598   \n",
       "1                 (AskReddit)            0.005845            0.142598   \n",
       "2                 (AskReddit)            0.005041            0.142598   \n",
       "3                       (WTF)            0.005899            0.044465   \n",
       "4                (reddit.com)            0.004494            0.089371   \n",
       "...                       ...                 ...                 ...   \n",
       "2323       (reddit.com, pics)            0.002649            0.031542   \n",
       "2324              (WTF, pics)            0.002906            0.024088   \n",
       "2325         (politics, pics)            0.003314            0.015251   \n",
       "2326      (reddit.com, funny)            0.003175            0.019380   \n",
       "2327  (WTF, reddit.com, pics)            0.003014            0.019251   \n",
       "\n",
       "       support  confidence       lift  leverage  conviction  \n",
       "0     0.011047    0.765799   5.370329  0.008990    3.660970  \n",
       "1     0.004633    0.792661   5.558699  0.003800    4.135256  \n",
       "2     0.003818    0.757447   5.311755  0.003099    3.534902  \n",
       "3     0.004247    0.720000  16.192359  0.003985    3.412624  \n",
       "4     0.003196    0.711217   7.958070  0.002794    3.153337  \n",
       "...        ...         ...        ...       ...         ...  \n",
       "2323  0.002338    0.882591  27.981529  0.002254    8.248591  \n",
       "2324  0.002338    0.804428  33.395225  0.002268    4.990040  \n",
       "2325  0.002338    0.705502  46.259969  0.002287    3.343819  \n",
       "2326  0.002338    0.736486  38.002621  0.002277    3.721328  \n",
       "2327  0.002338    0.775801  40.298849  0.002280    4.374451  \n",
       "\n",
       "[2328 rows x 9 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.frequent_patterns import association_rules\n",
    "\n",
    "association_rules(frequent_itemsets, \n",
    "                  metric='confidence', \n",
    "                  min_threshold=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T09:04:13.293976Z",
     "start_time": "2021-04-06T09:04:13.267228Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(AskReddit)</td>\n",
       "      <td>(DoesAnybodyElse)</td>\n",
       "      <td>0.142598</td>\n",
       "      <td>0.014425</td>\n",
       "      <td>0.011047</td>\n",
       "      <td>0.077467</td>\n",
       "      <td>5.370329</td>\n",
       "      <td>0.008990</td>\n",
       "      <td>1.068336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(DoesAnybodyElse)</td>\n",
       "      <td>(AskReddit)</td>\n",
       "      <td>0.014425</td>\n",
       "      <td>0.142598</td>\n",
       "      <td>0.011047</td>\n",
       "      <td>0.765799</td>\n",
       "      <td>5.370329</td>\n",
       "      <td>0.008990</td>\n",
       "      <td>3.660970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(AskReddit)</td>\n",
       "      <td>(self)</td>\n",
       "      <td>0.142598</td>\n",
       "      <td>0.005845</td>\n",
       "      <td>0.004633</td>\n",
       "      <td>0.032491</td>\n",
       "      <td>5.558699</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>1.027541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(self)</td>\n",
       "      <td>(AskReddit)</td>\n",
       "      <td>0.005845</td>\n",
       "      <td>0.142598</td>\n",
       "      <td>0.004633</td>\n",
       "      <td>0.792661</td>\n",
       "      <td>5.558699</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>4.135256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(AskReddit)</td>\n",
       "      <td>(todayilearned)</td>\n",
       "      <td>0.142598</td>\n",
       "      <td>0.005041</td>\n",
       "      <td>0.003818</td>\n",
       "      <td>0.026775</td>\n",
       "      <td>5.311755</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>1.022332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21889</th>\n",
       "      <td>(pics)</td>\n",
       "      <td>(worldnews, politics, reddit.com, science, WTF...</td>\n",
       "      <td>0.052649</td>\n",
       "      <td>0.002563</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.044408</td>\n",
       "      <td>17.324970</td>\n",
       "      <td>0.002203</td>\n",
       "      <td>1.043790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21890</th>\n",
       "      <td>(politics)</td>\n",
       "      <td>(worldnews, pics, reddit.com, science, WTF, fu...</td>\n",
       "      <td>0.039082</td>\n",
       "      <td>0.002982</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.059824</td>\n",
       "      <td>20.065050</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>1.060460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21891</th>\n",
       "      <td>(science)</td>\n",
       "      <td>(worldnews, pics, politics, reddit.com, WTF, f...</td>\n",
       "      <td>0.026212</td>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.089198</td>\n",
       "      <td>28.288823</td>\n",
       "      <td>0.002255</td>\n",
       "      <td>1.094472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21892</th>\n",
       "      <td>(WTF)</td>\n",
       "      <td>(worldnews, pics, politics, reddit.com, scienc...</td>\n",
       "      <td>0.044465</td>\n",
       "      <td>0.002595</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.052581</td>\n",
       "      <td>20.259035</td>\n",
       "      <td>0.002223</td>\n",
       "      <td>1.052760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21893</th>\n",
       "      <td>(reddit.com)</td>\n",
       "      <td>(worldnews, pics, politics, science, WTF, funny)</td>\n",
       "      <td>0.089371</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.026161</td>\n",
       "      <td>10.889652</td>\n",
       "      <td>0.002123</td>\n",
       "      <td>1.024397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21894 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             antecedents                                        consequents  \\\n",
       "0            (AskReddit)                                  (DoesAnybodyElse)   \n",
       "1      (DoesAnybodyElse)                                        (AskReddit)   \n",
       "2            (AskReddit)                                             (self)   \n",
       "3                 (self)                                        (AskReddit)   \n",
       "4            (AskReddit)                                    (todayilearned)   \n",
       "...                  ...                                                ...   \n",
       "21889             (pics)  (worldnews, politics, reddit.com, science, WTF...   \n",
       "21890         (politics)  (worldnews, pics, reddit.com, science, WTF, fu...   \n",
       "21891          (science)  (worldnews, pics, politics, reddit.com, WTF, f...   \n",
       "21892              (WTF)  (worldnews, pics, politics, reddit.com, scienc...   \n",
       "21893       (reddit.com)   (worldnews, pics, politics, science, WTF, funny)   \n",
       "\n",
       "       antecedent support  consequent support   support  confidence  \\\n",
       "0                0.142598            0.014425  0.011047    0.077467   \n",
       "1                0.014425            0.142598  0.011047    0.765799   \n",
       "2                0.142598            0.005845  0.004633    0.032491   \n",
       "3                0.005845            0.142598  0.004633    0.792661   \n",
       "4                0.142598            0.005041  0.003818    0.026775   \n",
       "...                   ...                 ...       ...         ...   \n",
       "21889            0.052649            0.002563  0.002338    0.044408   \n",
       "21890            0.039082            0.002982  0.002338    0.059824   \n",
       "21891            0.026212            0.003153  0.002338    0.089198   \n",
       "21892            0.044465            0.002595  0.002338    0.052581   \n",
       "21893            0.089371            0.002402  0.002338    0.026161   \n",
       "\n",
       "            lift  leverage  conviction  \n",
       "0       5.370329  0.008990    1.068336  \n",
       "1       5.370329  0.008990    3.660970  \n",
       "2       5.558699  0.003800    1.027541  \n",
       "3       5.558699  0.003800    4.135256  \n",
       "4       5.311755  0.003099    1.022332  \n",
       "...          ...       ...         ...  \n",
       "21889  17.324970  0.002203    1.043790  \n",
       "21890  20.065050  0.002222    1.060460  \n",
       "21891  28.288823  0.002255    1.094472  \n",
       "21892  20.259035  0.002223    1.052760  \n",
       "21893  10.889652  0.002123    1.024397  \n",
       "\n",
       "[21894 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association_rules(frequent_itemsets, metric='lift', min_threshold=5.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-06T09:04:40.396389Z",
     "start_time": "2021-04-06T09:04:40.342822Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction]\n",
       "Index: []"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.5)\n",
    "\n",
    "capital_idx =  rules['antecedents'].apply(lambda x: x.issuperset({'capital'}))\n",
    "rules[capital_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "con = sqlite3.connect('example.db')\n",
    "cur = con.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur.execute('''\n",
    "CREATE TABLE IF NOT EXISTS Subredit (\n",
    "    id integer PRIMARY KEY,\n",
    "    name text NOT NULL\n",
    ")\n",
    "''')\n",
    "\n",
    "cur.execute('''\n",
    "CREATE TABLE IF NOT EXISTS Rule (\n",
    "    id INTEGER PRIMARY KEY,\n",
    "    support REAL NOT NULL DEFAULT 0,\n",
    "    consequence REAL NOT NULL DEFAULT 0\n",
    ")\n",
    "''')\n",
    "\n",
    "\n",
    "cur.execute('''\n",
    "CREATE TABLE IF NOT EXISTS Ancendents (\n",
    "    subredit_id integer not null,\n",
    "    rule_id integer not null,\n",
    "    FOREIGN KEY(subredit_id) REFERENCES subredit(id),\n",
    "    FOREIGN KEY(rule_id) REFERENCES rule(id),\n",
    "    PRIMARY KEY(subredit_id, rule_id)\n",
    ")\n",
    "''')"
   ]
  }
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