Association_ Rules _groceries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:55:16.532280Z",
     "start_time": "2020-12-30T05:54:55.868284Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mlxtend in c:\\users\\pcs\\anaconda3\\lib\\site-packages (0.17.3)\n",
      "Requirement already satisfied: setuptools in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from mlxtend) (49.2.0.post20200714)\n",
      "Requirement already satisfied: scikit-learn>=0.20.3 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from mlxtend) (0.23.1)\n",
      "Requirement already satisfied: pandas>=0.24.2 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from mlxtend) (1.0.5)\n",
      "Requirement already satisfied: numpy>=1.16.2 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from mlxtend) (1.18.5)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from mlxtend) (3.3.3)\n",
      "Requirement already satisfied: joblib>=0.13.2 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from mlxtend) (0.16.0)\n",
      "Requirement already satisfied: scipy>=1.2.1 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from mlxtend) (1.5.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.3->mlxtend) (2.1.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2020.1)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.2.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (7.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\pcs\\anaconda3\\lib\\site-packages (from python-dateutil>=2.6.1->pandas>=0.24.2->mlxtend) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install mlxtend\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from mlxtend.frequent_patterns import apriori,association_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:58:39.548169Z",
     "start_time": "2020-12-30T05:58:39.521098Z"
    }
   },
   "outputs": [],
   "source": [
    " groceries = []\n",
    "with open(\"groceries.csv\") as f:\n",
    "    groceries = f.read()\n",
    "\n",
    "\n",
    "\n",
    "# splitting the data into separate transactions using separator as \"\\n\"\n",
    "groceries = groceries.split(\"\\n\")\n",
    "groceries_list = []\n",
    "for i in groceries:\n",
    "    groceries_list.append(i.split(\",\"))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:58:40.304656Z",
     "start_time": "2020-12-30T05:58:40.287611Z"
    }
   },
   "outputs": [],
   "source": [
    "all_groceries_list = [i for item in groceries_list for i in item]\n",
    "from collections import Counter\n",
    "\n",
    "item_frequencies = Counter(all_groceries_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:58:41.036713Z",
     "start_time": "2020-12-30T05:58:41.032704Z"
    }
   },
   "outputs": [],
   "source": [
    "# after sorting\n",
    "#item_frequencies = sorted(item_frequencies.items(),key = lambda x:x[1])\n",
    "item_frequencies = sorted(item_frequencies.items(),key = lambda x:x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:58:42.392439Z",
     "start_time": "2020-12-30T05:58:42.386423Z"
    }
   },
   "outputs": [],
   "source": [
    "# Storing frequencies and items in separate variables \n",
    "frequencies = list(reversed([i[1] for i in item_frequencies]))\n",
    "items = list(reversed([i[0] for i in item_frequencies]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:58:44.829589Z",
     "start_time": "2020-12-30T05:58:43.483406Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-2201dfd3438c>:5: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated since 3.2 and will be removed two minor releases later. Use an explicit list instead.\n",
      "  plt.bar(x=list(range(0,11)) ,height=frequencies[0:11],color='rgbkymc');plt.xticks(list(range(0,11),),items[0:11]);plt.xlabel(\"items\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Items')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barplot of top 10 \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.bar(x=list(range(0,11)) ,height=frequencies[0:11],color='rgbkymc');plt.xticks(list(range(0,11),),items[0:11]);plt.xlabel(\"items\")\n",
    "plt.ylabel(\"Count\");plt.xlabel(\"Items\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:20.394326Z",
     "start_time": "2020-12-30T05:59:20.259097Z"
    }
   },
   "outputs": [],
   "source": [
    "#Creating Data Frame for the transactions data \n",
    "\n",
    "# Purpose of converting all list into Series object Coz to treat each list element as entire element not to separate \n",
    "groceries_series  = pd.DataFrame(pd.Series(groceries_list))\n",
    "groceries_series = groceries_series.iloc[:9835,:] # removing the last empty transaction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:20.948592Z",
     "start_time": "2020-12-30T05:59:20.932958Z"
    }
   },
   "outputs": [],
   "source": [
    "groceries_series.columns = [\"transactions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:23.908726Z",
     "start_time": "2020-12-30T05:59:21.547590Z"
    }
   },
   "outputs": [],
   "source": [
    "# creating a dummy columns for the each item in each transactions ... Using column names as item name\n",
    "X = groceries_series['transactions'].str.join(sep='*').str.get_dummies(sep='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:24.897398Z",
     "start_time": "2020-12-30T05:59:24.034157Z"
    }
   },
   "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>0.082766</td>\n",
       "      <td>(citrus fruit)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.058566</td>\n",
       "      <td>(margarine)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.017692</td>\n",
       "      <td>(semi-finished bread)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.139502</td>\n",
       "      <td>(yogurt)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.104931</td>\n",
       "      <td>(tropical fruit)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.005491</td>\n",
       "      <td>(meat, soda)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.005084</td>\n",
       "      <td>(meat, root vegetables)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.005287</td>\n",
       "      <td>(sausage, meat)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.005287</td>\n",
       "      <td>(yogurt, meat)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>0.005186</td>\n",
       "      <td>(mustard, whole milk)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       support                 itemsets\n",
       "0     0.082766           (citrus fruit)\n",
       "1     0.058566              (margarine)\n",
       "2     0.017692    (semi-finished bread)\n",
       "3     0.139502                 (yogurt)\n",
       "4     0.104931         (tropical fruit)\n",
       "...        ...                      ...\n",
       "996   0.005491             (meat, soda)\n",
       "997   0.005084  (meat, root vegetables)\n",
       "998   0.005287          (sausage, meat)\n",
       "999   0.005287           (yogurt, meat)\n",
       "1000  0.005186    (mustard, whole milk)\n",
       "\n",
       "[1001 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.frequent_patterns import apriori, fpmax, fpgrowth\n",
    "frequent_itemsets_a = fpgrowth(X, min_support=0.005, use_colnames=True)\n",
    "frequent_itemsets_a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T15:23:03.230147Z",
     "start_time": "2020-12-29T15:23:02.488169Z"
    }
   },
   "source": [
    "# Similar methods for as seen above\n",
    "#frequent_itemsets = apriori(df, min_support=0.05, use_colnames=True)\n",
    "#frequent_itemsets_b = fpmax(X, min_support=0.005, use_colnames=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Generating Association Rules from Frequent Itemsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:25.107572Z",
     "start_time": "2020-12-30T05:59:25.032988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>(whole milk)</td>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.030503</td>\n",
       "      <td>0.119379</td>\n",
       "      <td>1.442377</td>\n",
       "      <td>0.009355</td>\n",
       "      <td>1.041577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.030503</td>\n",
       "      <td>0.368550</td>\n",
       "      <td>1.442377</td>\n",
       "      <td>0.009355</td>\n",
       "      <td>1.179008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.021657</td>\n",
       "      <td>0.155248</td>\n",
       "      <td>1.875752</td>\n",
       "      <td>0.010111</td>\n",
       "      <td>1.085803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.021657</td>\n",
       "      <td>0.261671</td>\n",
       "      <td>1.875752</td>\n",
       "      <td>0.010111</td>\n",
       "      <td>1.165467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>(tropical fruit)</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.104931</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.240786</td>\n",
       "      <td>2.294702</td>\n",
       "      <td>0.011244</td>\n",
       "      <td>1.178942</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>2957</th>\n",
       "      <td>(meat)</td>\n",
       "      <td>(sausage)</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.093950</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>0.204724</td>\n",
       "      <td>2.179074</td>\n",
       "      <td>0.002861</td>\n",
       "      <td>1.139290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>(meat)</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>0.037901</td>\n",
       "      <td>1.467540</td>\n",
       "      <td>0.001684</td>\n",
       "      <td>1.012550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>(meat)</td>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>0.204724</td>\n",
       "      <td>1.467540</td>\n",
       "      <td>0.001684</td>\n",
       "      <td>1.082013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>(mustard)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.011998</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.432203</td>\n",
       "      <td>1.691492</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.311181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>(mustard)</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.011998</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.020294</td>\n",
       "      <td>1.691492</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.008468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2962 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         antecedents       consequents  antecedent support  \\\n",
       "0       (whole milk)    (citrus fruit)            0.255516   \n",
       "1     (citrus fruit)      (whole milk)            0.082766   \n",
       "2           (yogurt)    (citrus fruit)            0.139502   \n",
       "3     (citrus fruit)          (yogurt)            0.082766   \n",
       "4     (citrus fruit)  (tropical fruit)            0.082766   \n",
       "...              ...               ...                 ...   \n",
       "2957          (meat)         (sausage)            0.025826   \n",
       "2958        (yogurt)            (meat)            0.139502   \n",
       "2959          (meat)          (yogurt)            0.025826   \n",
       "2960       (mustard)      (whole milk)            0.011998   \n",
       "2961    (whole milk)         (mustard)            0.255516   \n",
       "\n",
       "      consequent support   support  confidence      lift  leverage  conviction  \n",
       "0               0.082766  0.030503    0.119379  1.442377  0.009355    1.041577  \n",
       "1               0.255516  0.030503    0.368550  1.442377  0.009355    1.179008  \n",
       "2               0.082766  0.021657    0.155248  1.875752  0.010111    1.085803  \n",
       "3               0.139502  0.021657    0.261671  1.875752  0.010111    1.165467  \n",
       "4               0.104931  0.019929    0.240786  2.294702  0.011244    1.178942  \n",
       "...                  ...       ...         ...       ...       ...         ...  \n",
       "2957            0.093950  0.005287    0.204724  2.179074  0.002861    1.139290  \n",
       "2958            0.025826  0.005287    0.037901  1.467540  0.001684    1.012550  \n",
       "2959            0.139502  0.005287    0.204724  1.467540  0.001684    1.082013  \n",
       "2960            0.255516  0.005186    0.432203  1.691492  0.002120    1.311181  \n",
       "2961            0.011998  0.005186    0.020294  1.691492  0.002120    1.008468  \n",
       "\n",
       "[2962 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.frequent_patterns import association_rules\n",
    "\n",
    "association_rules(frequent_itemsets_a, metric=\"confidence\", min_threshold=0.005)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rule Generation and Selection Criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:25.371132Z",
     "start_time": "2020-12-30T05:59:25.300942Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>(whole milk)</td>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.030503</td>\n",
       "      <td>0.119379</td>\n",
       "      <td>1.442377</td>\n",
       "      <td>0.009355</td>\n",
       "      <td>1.041577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.030503</td>\n",
       "      <td>0.368550</td>\n",
       "      <td>1.442377</td>\n",
       "      <td>0.009355</td>\n",
       "      <td>1.179008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.021657</td>\n",
       "      <td>0.155248</td>\n",
       "      <td>1.875752</td>\n",
       "      <td>0.010111</td>\n",
       "      <td>1.085803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.021657</td>\n",
       "      <td>0.261671</td>\n",
       "      <td>1.875752</td>\n",
       "      <td>0.010111</td>\n",
       "      <td>1.165467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>(tropical fruit)</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.104931</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.240786</td>\n",
       "      <td>2.294702</td>\n",
       "      <td>0.011244</td>\n",
       "      <td>1.178942</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>2957</th>\n",
       "      <td>(meat)</td>\n",
       "      <td>(sausage)</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.093950</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>0.204724</td>\n",
       "      <td>2.179074</td>\n",
       "      <td>0.002861</td>\n",
       "      <td>1.139290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>(meat)</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>0.037901</td>\n",
       "      <td>1.467540</td>\n",
       "      <td>0.001684</td>\n",
       "      <td>1.012550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>(meat)</td>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>0.204724</td>\n",
       "      <td>1.467540</td>\n",
       "      <td>0.001684</td>\n",
       "      <td>1.082013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>(mustard)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.011998</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.432203</td>\n",
       "      <td>1.691492</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.311181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>(mustard)</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.011998</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.020294</td>\n",
       "      <td>1.691492</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.008468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2962 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         antecedents       consequents  antecedent support  \\\n",
       "0       (whole milk)    (citrus fruit)            0.255516   \n",
       "1     (citrus fruit)      (whole milk)            0.082766   \n",
       "2           (yogurt)    (citrus fruit)            0.139502   \n",
       "3     (citrus fruit)          (yogurt)            0.082766   \n",
       "4     (citrus fruit)  (tropical fruit)            0.082766   \n",
       "...              ...               ...                 ...   \n",
       "2957          (meat)         (sausage)            0.025826   \n",
       "2958        (yogurt)            (meat)            0.139502   \n",
       "2959          (meat)          (yogurt)            0.025826   \n",
       "2960       (mustard)      (whole milk)            0.011998   \n",
       "2961    (whole milk)         (mustard)            0.255516   \n",
       "\n",
       "      consequent support   support  confidence      lift  leverage  conviction  \n",
       "0               0.082766  0.030503    0.119379  1.442377  0.009355    1.041577  \n",
       "1               0.255516  0.030503    0.368550  1.442377  0.009355    1.179008  \n",
       "2               0.082766  0.021657    0.155248  1.875752  0.010111    1.085803  \n",
       "3               0.139502  0.021657    0.261671  1.875752  0.010111    1.165467  \n",
       "4               0.104931  0.019929    0.240786  2.294702  0.011244    1.178942  \n",
       "...                  ...       ...         ...       ...       ...         ...  \n",
       "2957            0.093950  0.005287    0.204724  2.179074  0.002861    1.139290  \n",
       "2958            0.025826  0.005287    0.037901  1.467540  0.001684    1.012550  \n",
       "2959            0.139502  0.005287    0.204724  1.467540  0.001684    1.082013  \n",
       "2960            0.255516  0.005186    0.432203  1.691492  0.002120    1.311181  \n",
       "2961            0.011998  0.005186    0.020294  1.691492  0.002120    1.008468  \n",
       "\n",
       "[2962 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules = association_rules(frequent_itemsets_a, metric=\"lift\", min_threshold=0.4)\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:25.553617Z",
     "start_time": "2020-12-30T05:59:25.545596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2962, 9)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:26.030180Z",
     "start_time": "2020-12-30T05:59:25.976033Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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",
       "      <th>antecedent_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.030503</td>\n",
       "      <td>0.119379</td>\n",
       "      <td>1.442377</td>\n",
       "      <td>0.009355</td>\n",
       "      <td>1.041577</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.030503</td>\n",
       "      <td>0.368550</td>\n",
       "      <td>1.442377</td>\n",
       "      <td>0.009355</td>\n",
       "      <td>1.179008</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.021657</td>\n",
       "      <td>0.155248</td>\n",
       "      <td>1.875752</td>\n",
       "      <td>0.010111</td>\n",
       "      <td>1.085803</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.021657</td>\n",
       "      <td>0.261671</td>\n",
       "      <td>1.875752</td>\n",
       "      <td>0.010111</td>\n",
       "      <td>1.165467</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(citrus fruit)</td>\n",
       "      <td>(tropical fruit)</td>\n",
       "      <td>0.082766</td>\n",
       "      <td>0.104931</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.240786</td>\n",
       "      <td>2.294702</td>\n",
       "      <td>0.011244</td>\n",
       "      <td>1.178942</td>\n",
       "      <td>9835</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>(meat)</td>\n",
       "      <td>(sausage)</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.093950</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>0.204724</td>\n",
       "      <td>2.179074</td>\n",
       "      <td>0.002861</td>\n",
       "      <td>1.139290</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>(meat)</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>0.037901</td>\n",
       "      <td>1.467540</td>\n",
       "      <td>0.001684</td>\n",
       "      <td>1.012550</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>(meat)</td>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>0.204724</td>\n",
       "      <td>1.467540</td>\n",
       "      <td>0.001684</td>\n",
       "      <td>1.082013</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>(mustard)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.011998</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.432203</td>\n",
       "      <td>1.691492</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.311181</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>(mustard)</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.011998</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.020294</td>\n",
       "      <td>1.691492</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.008468</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2962 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         antecedents       consequents  antecedent support  \\\n",
       "0       (whole milk)    (citrus fruit)            0.255516   \n",
       "1     (citrus fruit)      (whole milk)            0.082766   \n",
       "2           (yogurt)    (citrus fruit)            0.139502   \n",
       "3     (citrus fruit)          (yogurt)            0.082766   \n",
       "4     (citrus fruit)  (tropical fruit)            0.082766   \n",
       "...              ...               ...                 ...   \n",
       "2957          (meat)         (sausage)            0.025826   \n",
       "2958        (yogurt)            (meat)            0.139502   \n",
       "2959          (meat)          (yogurt)            0.025826   \n",
       "2960       (mustard)      (whole milk)            0.011998   \n",
       "2961    (whole milk)         (mustard)            0.255516   \n",
       "\n",
       "      consequent support   support  confidence      lift  leverage  \\\n",
       "0               0.082766  0.030503    0.119379  1.442377  0.009355   \n",
       "1               0.255516  0.030503    0.368550  1.442377  0.009355   \n",
       "2               0.082766  0.021657    0.155248  1.875752  0.010111   \n",
       "3               0.139502  0.021657    0.261671  1.875752  0.010111   \n",
       "4               0.104931  0.019929    0.240786  2.294702  0.011244   \n",
       "...                  ...       ...         ...       ...       ...   \n",
       "2957            0.093950  0.005287    0.204724  2.179074  0.002861   \n",
       "2958            0.025826  0.005287    0.037901  1.467540  0.001684   \n",
       "2959            0.139502  0.005287    0.204724  1.467540  0.001684   \n",
       "2960            0.255516  0.005186    0.432203  1.691492  0.002120   \n",
       "2961            0.011998  0.005186    0.020294  1.691492  0.002120   \n",
       "\n",
       "      conviction  antecedent_len  \n",
       "0       1.041577            9835  \n",
       "1       1.179008            9835  \n",
       "2       1.085803            9835  \n",
       "3       1.165467            9835  \n",
       "4       1.178942            9835  \n",
       "...          ...             ...  \n",
       "2957    1.139290            9835  \n",
       "2958    1.012550            9835  \n",
       "2959    1.082013            9835  \n",
       "2960    1.311181            9835  \n",
       "2961    1.008468            9835  \n",
       "\n",
       "[2962 rows x 10 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[\"antecedent_len\"] = rules[\"antecedents\"].apply(lambda x: len(X))\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:26.619333Z",
     "start_time": "2020-12-30T05:59:26.528336Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>antecedent_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>(citrus fruit, tropical fruit)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.009049</td>\n",
       "      <td>0.454082</td>\n",
       "      <td>2.346765</td>\n",
       "      <td>0.005193</td>\n",
       "      <td>1.477341</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>(root vegetables, citrus fruit)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.017692</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.010371</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>3.029608</td>\n",
       "      <td>0.006948</td>\n",
       "      <td>1.949059</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>(root vegetables, whole milk, citrus fruit)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.009151</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.005796</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>3.273165</td>\n",
       "      <td>0.004025</td>\n",
       "      <td>2.199566</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>(whole milk, citrus fruit, other vegetables)</td>\n",
       "      <td>(root vegetables)</td>\n",
       "      <td>0.013015</td>\n",
       "      <td>0.108998</td>\n",
       "      <td>0.005796</td>\n",
       "      <td>0.445312</td>\n",
       "      <td>4.085493</td>\n",
       "      <td>0.004377</td>\n",
       "      <td>1.606313</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>(domestic eggs, margarine)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.008338</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.621951</td>\n",
       "      <td>2.434099</td>\n",
       "      <td>0.003055</td>\n",
       "      <td>1.969280</td>\n",
       "      <td>9835</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2894</th>\n",
       "      <td>(onions)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.031012</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.014235</td>\n",
       "      <td>0.459016</td>\n",
       "      <td>2.372268</td>\n",
       "      <td>0.008234</td>\n",
       "      <td>1.490817</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2900</th>\n",
       "      <td>(onions, root vegetables)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.009456</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>0.602151</td>\n",
       "      <td>3.112008</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>2.027167</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2906</th>\n",
       "      <td>(onions, whole milk)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.012100</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.006609</td>\n",
       "      <td>0.546218</td>\n",
       "      <td>2.822942</td>\n",
       "      <td>0.004268</td>\n",
       "      <td>1.777303</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>(herbs)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.016268</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.007728</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>2.454874</td>\n",
       "      <td>0.004580</td>\n",
       "      <td>1.536205</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>(herbs)</td>\n",
       "      <td>(root vegetables)</td>\n",
       "      <td>0.016268</td>\n",
       "      <td>0.108998</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>0.431250</td>\n",
       "      <td>3.956477</td>\n",
       "      <td>0.005243</td>\n",
       "      <td>1.566596</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       antecedents         consequents  \\\n",
       "46                  (citrus fruit, tropical fruit)  (other vegetables)   \n",
       "70                 (root vegetables, citrus fruit)  (other vegetables)   \n",
       "88     (root vegetables, whole milk, citrus fruit)  (other vegetables)   \n",
       "91    (whole milk, citrus fruit, other vegetables)   (root vegetables)   \n",
       "167                     (domestic eggs, margarine)        (whole milk)   \n",
       "...                                            ...                 ...   \n",
       "2894                                      (onions)  (other vegetables)   \n",
       "2900                     (onions, root vegetables)  (other vegetables)   \n",
       "2906                          (onions, whole milk)  (other vegetables)   \n",
       "2912                                       (herbs)  (other vegetables)   \n",
       "2916                                       (herbs)   (root vegetables)   \n",
       "\n",
       "      antecedent support  consequent support   support  confidence      lift  \\\n",
       "46              0.019929            0.193493  0.009049    0.454082  2.346765   \n",
       "70              0.017692            0.193493  0.010371    0.586207  3.029608   \n",
       "88              0.009151            0.193493  0.005796    0.633333  3.273165   \n",
       "91              0.013015            0.108998  0.005796    0.445312  4.085493   \n",
       "167             0.008338            0.255516  0.005186    0.621951  2.434099   \n",
       "...                  ...                 ...       ...         ...       ...   \n",
       "2894            0.031012            0.193493  0.014235    0.459016  2.372268   \n",
       "2900            0.009456            0.193493  0.005694    0.602151  3.112008   \n",
       "2906            0.012100            0.193493  0.006609    0.546218  2.822942   \n",
       "2912            0.016268            0.193493  0.007728    0.475000  2.454874   \n",
       "2916            0.016268            0.108998  0.007016    0.431250  3.956477   \n",
       "\n",
       "      leverage  conviction  antecedent_len  \n",
       "46    0.005193    1.477341            9835  \n",
       "70    0.006948    1.949059            9835  \n",
       "88    0.004025    2.199566            9835  \n",
       "91    0.004377    1.606313            9835  \n",
       "167   0.003055    1.969280            9835  \n",
       "...        ...         ...             ...  \n",
       "2894  0.008234    1.490817            9835  \n",
       "2900  0.003864    2.027167            9835  \n",
       "2906  0.004268    1.777303            9835  \n",
       "2912  0.004580    1.536205            9835  \n",
       "2916  0.005243    1.566596            9835  \n",
       "\n",
       "[102 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[ (rules['antecedent_len'] >= 9835) &\n",
    "       (rules['confidence'] > 0.43) &\n",
    "       (rules['lift'] > 2.2) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:27.305842Z",
     "start_time": "2020-12-30T05:59:27.102467Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "        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",
       "      <th>antecedent_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(citrus fruit, tropical fruit)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.009049</td>\n",
       "      <td>0.454082</td>\n",
       "      <td>1.777116</td>\n",
       "      <td>0.003957</td>\n",
       "      <td>1.363728</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(citrus fruit, tropical fruit)</td>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.006304</td>\n",
       "      <td>0.316327</td>\n",
       "      <td>2.267545</td>\n",
       "      <td>0.003524</td>\n",
       "      <td>1.258639</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>(citrus fruit, tropical fruit)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.009049</td>\n",
       "      <td>0.454082</td>\n",
       "      <td>2.346765</td>\n",
       "      <td>0.005193</td>\n",
       "      <td>1.477341</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>(citrus fruit, tropical fruit)</td>\n",
       "      <td>(root vegetables)</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.108998</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>2.621269</td>\n",
       "      <td>0.003522</td>\n",
       "      <td>1.247402</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>(citrus fruit, tropical fruit)</td>\n",
       "      <td>(pip fruit)</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.075648</td>\n",
       "      <td>0.005592</td>\n",
       "      <td>0.280612</td>\n",
       "      <td>3.709437</td>\n",
       "      <td>0.004085</td>\n",
       "      <td>1.284915</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        antecedents         consequents  antecedent support  \\\n",
       "36   (citrus fruit, tropical fruit)        (whole milk)            0.019929   \n",
       "42   (citrus fruit, tropical fruit)            (yogurt)            0.019929   \n",
       "46   (citrus fruit, tropical fruit)  (other vegetables)            0.019929   \n",
       "78   (citrus fruit, tropical fruit)   (root vegetables)            0.019929   \n",
       "564  (citrus fruit, tropical fruit)         (pip fruit)            0.019929   \n",
       "\n",
       "     consequent support   support  confidence      lift  leverage  conviction  \\\n",
       "36             0.255516  0.009049    0.454082  1.777116  0.003957    1.363728   \n",
       "42             0.139502  0.006304    0.316327  2.267545  0.003524    1.258639   \n",
       "46             0.193493  0.009049    0.454082  2.346765  0.005193    1.477341   \n",
       "78             0.108998  0.005694    0.285714  2.621269  0.003522    1.247402   \n",
       "564            0.075648  0.005592    0.280612  3.709437  0.004085    1.284915   \n",
       "\n",
       "     antecedent_len  \n",
       "36             9835  \n",
       "42             9835  \n",
       "46             9835  \n",
       "78             9835  \n",
       "564            9835  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can select entries based on the \"antecedents\" or \"consequents\" columns:\n",
    "rules[rules['antecedents']=={'citrus fruit','tropical fruit'}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:27.751741Z",
     "start_time": "2020-12-30T05:59:27.727175Z"
    }
   },
   "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",
       "      <th>antecedent_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>(root vegetables, citrus fruit)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.017692</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.010371</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>3.029608</td>\n",
       "      <td>0.006948</td>\n",
       "      <td>1.949059</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>(root vegetables, citrus fruit)</td>\n",
       "      <td>(tropical fruit)</td>\n",
       "      <td>0.017692</td>\n",
       "      <td>0.104931</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>0.321839</td>\n",
       "      <td>3.067139</td>\n",
       "      <td>0.003838</td>\n",
       "      <td>1.319847</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>(root vegetables, citrus fruit)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.017692</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.009151</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>2.024301</td>\n",
       "      <td>0.004630</td>\n",
       "      <td>1.542145</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>(root vegetables, citrus fruit)</td>\n",
       "      <td>(whole milk, other vegetables)</td>\n",
       "      <td>0.017692</td>\n",
       "      <td>0.074835</td>\n",
       "      <td>0.005796</td>\n",
       "      <td>0.327586</td>\n",
       "      <td>4.377460</td>\n",
       "      <td>0.004472</td>\n",
       "      <td>1.375887</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        antecedents                     consequents  \\\n",
       "70  (root vegetables, citrus fruit)              (other vegetables)   \n",
       "76  (root vegetables, citrus fruit)                (tropical fruit)   \n",
       "83  (root vegetables, citrus fruit)                    (whole milk)   \n",
       "93  (root vegetables, citrus fruit)  (whole milk, other vegetables)   \n",
       "\n",
       "    antecedent support  consequent support   support  confidence      lift  \\\n",
       "70            0.017692            0.193493  0.010371    0.586207  3.029608   \n",
       "76            0.017692            0.104931  0.005694    0.321839  3.067139   \n",
       "83            0.017692            0.255516  0.009151    0.517241  2.024301   \n",
       "93            0.017692            0.074835  0.005796    0.327586  4.377460   \n",
       "\n",
       "    leverage  conviction  antecedent_len  \n",
       "70  0.006948    1.949059            9835  \n",
       "76  0.003838    1.319847            9835  \n",
       "83  0.004630    1.542145            9835  \n",
       "93  0.004472    1.375887            9835  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[rules['antecedents']=={'root vegetables','citrus fruit'}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:28.702582Z",
     "start_time": "2020-12-30T05:59:28.681870Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</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",
       "      <th>antecedent_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>(root vegetables, whole milk, citrus fruit)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.009151</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.005796</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>3.273165</td>\n",
       "      <td>0.004025</td>\n",
       "      <td>2.199566</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    antecedents         consequents  \\\n",
       "88  (root vegetables, whole milk, citrus fruit)  (other vegetables)   \n",
       "\n",
       "    antecedent support  consequent support   support  confidence      lift  \\\n",
       "88            0.009151            0.193493  0.005796    0.633333  3.273165   \n",
       "\n",
       "    leverage  conviction  antecedent_len  \n",
       "88  0.004025    2.199566            9835  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[rules['antecedents']=={'root vegetables', 'citrus fruit', 'whole milk'}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:29.240845Z",
     "start_time": "2020-12-30T05:59:29.214205Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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",
       "      <th>antecedent_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>(whole milk, citrus fruit, other vegetables)</td>\n",
       "      <td>(root vegetables)</td>\n",
       "      <td>0.013015</td>\n",
       "      <td>0.108998</td>\n",
       "      <td>0.005796</td>\n",
       "      <td>0.445312</td>\n",
       "      <td>4.085493</td>\n",
       "      <td>0.004377</td>\n",
       "      <td>1.606313</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     antecedents        consequents  \\\n",
       "91  (whole milk, citrus fruit, other vegetables)  (root vegetables)   \n",
       "\n",
       "    antecedent support  consequent support   support  confidence      lift  \\\n",
       "91            0.013015            0.108998  0.005796    0.445312  4.085493   \n",
       "\n",
       "    leverage  conviction  antecedent_len  \n",
       "91  0.004377    1.606313            9835  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[rules['antecedents']=={'other vegetables', 'citrus fruit', 'whole milk'}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:29.659026Z",
     "start_time": "2020-12-30T05:59:29.626941Z"
    }
   },
   "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",
       "      <th>antecedent_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>(domestic eggs, margarine)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.008338</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.621951</td>\n",
       "      <td>2.434099</td>\n",
       "      <td>0.003055</td>\n",
       "      <td>1.96928</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    antecedents   consequents  antecedent support  \\\n",
       "167  (domestic eggs, margarine)  (whole milk)            0.008338   \n",
       "\n",
       "     consequent support   support  confidence      lift  leverage  conviction  \\\n",
       "167            0.255516  0.005186    0.621951  2.434099  0.003055     1.96928   \n",
       "\n",
       "     antecedent_len  \n",
       "167            9835  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[rules['antecedents']=={'domestic eggs', 'margarine'}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T05:59:30.005622Z",
     "start_time": "2020-12-30T05:59:29.986069Z"
    }
   },
   "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",
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       "    }\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",
       "      <th>antecedent_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2900</th>\n",
       "      <td>(onions, root vegetables)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.009456</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>0.602151</td>\n",
       "      <td>3.112008</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>2.027167</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    antecedents         consequents  antecedent support  \\\n",
       "2900  (onions, root vegetables)  (other vegetables)            0.009456   \n",
       "\n",
       "      consequent support   support  confidence      lift  leverage  \\\n",
       "2900            0.193493  0.005694    0.602151  3.112008  0.003864   \n",
       "\n",
       "      conviction  antecedent_len  \n",
       "2900    2.027167            9835  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[rules['antecedents']=={'root vegetables', 'onions'}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  #                         ********** Visualizing results:-\n",
    "\n",
    "# Support vs Confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T06:01:56.962681Z",
     "start_time": "2020-12-30T06:01:56.688993Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(rules['support'],rules['confidence'],alpha=0.5)\n",
    "\n",
    "plt.xlabel('support')\n",
    "plt.ylabel('confidence')\n",
    "plt.title('Support vs Confidence')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support vs Lift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T18:24:29.265691Z",
     "start_time": "2020-12-29T18:24:28.993693Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(rules['support'],rules['lift'], alpha=0.5)\n",
    "plt.xlabel('support')\n",
    "plt.ylabel('lift')\n",
    "plt.title('Support vs Lift')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lift vs Confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T18:25:43.841925Z",
     "start_time": "2020-12-29T18:25:42.928755Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x9702c165e0>,\n",
       " <matplotlib.lines.Line2D at 0x9702c16070>]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit = np.polyfit(rules['lift'], rules['confidence'], 1)\n",
    "fit_fn = np.poly1d(fit)\n",
    "plt.plot(rules['lift'], rules['confidence'], 'yo', rules['lift'], \n",
    " fit_fn(rules['lift']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T20:14:58.871743Z",
     "start_time": "2020-12-29T20:14:58.855947Z"
    }
   },
   "outputs": [],
   "source": [
    "frequent_itemsets.sort_values('support',ascending = False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T06:02:29.519901Z",
     "start_time": "2020-12-30T06:02:29.229061Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-38-6c0224333500>:5: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated since 3.2 and will be removed two minor releases later. Use an explicit list instead.\n",
      "  plt.bar(x=list(range(0,11)) ,height=frequent_itemsets_a.support[0:11],color='rgbkymc');plt.xticks(list(range(0,11),),items[0:11]);plt.xlabel(\"item-sets\");\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'support')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barplot of top 10 \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.bar(x=list(range(0,11)) ,height=frequent_itemsets_a.support[0:11],color='rgbkymc');plt.xticks(list(range(0,11),),items[0:11]);plt.xlabel(\"item-sets\");\n",
    "plt.ylabel(\"support\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T18:55:20.034186Z",
     "start_time": "2020-12-29T18:55:19.986184Z"
    }
   },
   "outputs": [],
   "source": [
    "rules = association_rules(frequent_itemsets_a, metric=\"lift\", min_threshold=1)\n",
    "rules.head(20)\n",
    "rules.sort_values('lift',ascending = False,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#        ###########To eliminate Redudancy in Rules ############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T06:04:37.991715Z",
     "start_time": "2020-12-30T06:04:37.811509Z"
    }
   },
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import association_rules\n",
    "def to_list(i):\n",
    "    return (sorted(list(i)))\n",
    "\n",
    "\n",
    "ma_X = rules.antecedents.apply(to_list)+rules.consequents.apply(to_list)\n",
    "\n",
    "\n",
    "ma_X = ma_X.apply(sorted)\n",
    "\n",
    "rules_sets = list(ma_X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T06:04:38.596969Z",
     "start_time": "2020-12-30T06:04:38.535651Z"
    }
   },
   "outputs": [],
   "source": [
    "unique_rules_sets = [list(m) for m in set(tuple(i) for i in rules_sets)]\n",
    "index_rules = []\n",
    "for i in unique_rules_sets:\n",
    "    index_rules.append(rules_sets.index(i))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T06:04:39.127674Z",
     "start_time": "2020-12-30T06:04:39.108624Z"
    }
   },
   "outputs": [],
   "source": [
    "# getting rules without any redudancy \n",
    "rules_no_redudancy  = rules.iloc[index_rules,:]\n",
    "\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T06:04:39.894811Z",
     "start_time": "2020-12-30T06:04:39.852484Z"
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    {
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       "<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",
       "      <th>antecedent_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2750</th>\n",
       "      <td>(ham)</td>\n",
       "      <td>(white bread)</td>\n",
       "      <td>0.026029</td>\n",
       "      <td>0.042095</td>\n",
       "      <td>0.005084</td>\n",
       "      <td>0.195312</td>\n",
       "      <td>4.639851</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>1.190407</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>(herbs)</td>\n",
       "      <td>(root vegetables)</td>\n",
       "      <td>0.016268</td>\n",
       "      <td>0.108998</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>0.431250</td>\n",
       "      <td>3.956477</td>\n",
       "      <td>0.005243</td>\n",
       "      <td>1.566596</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>(pip fruit, citrus fruit)</td>\n",
       "      <td>(tropical fruit)</td>\n",
       "      <td>0.013828</td>\n",
       "      <td>0.104931</td>\n",
       "      <td>0.005592</td>\n",
       "      <td>0.404412</td>\n",
       "      <td>3.854060</td>\n",
       "      <td>0.004141</td>\n",
       "      <td>1.502831</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2552</th>\n",
       "      <td>(whipped/sour cream)</td>\n",
       "      <td>(berries)</td>\n",
       "      <td>0.071683</td>\n",
       "      <td>0.033249</td>\n",
       "      <td>0.009049</td>\n",
       "      <td>0.126241</td>\n",
       "      <td>3.796886</td>\n",
       "      <td>0.006666</td>\n",
       "      <td>1.106428</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>(yogurt, root vegetables, whole milk)</td>\n",
       "      <td>(tropical fruit)</td>\n",
       "      <td>0.014540</td>\n",
       "      <td>0.104931</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>0.391608</td>\n",
       "      <td>3.732043</td>\n",
       "      <td>0.004168</td>\n",
       "      <td>1.471205</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>(napkins)</td>\n",
       "      <td>(hygiene articles)</td>\n",
       "      <td>0.052364</td>\n",
       "      <td>0.032944</td>\n",
       "      <td>0.006101</td>\n",
       "      <td>0.116505</td>\n",
       "      <td>3.536498</td>\n",
       "      <td>0.004376</td>\n",
       "      <td>1.094580</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>(yogurt, root vegetables)</td>\n",
       "      <td>(whipped/sour cream)</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.071683</td>\n",
       "      <td>0.006406</td>\n",
       "      <td>0.248031</td>\n",
       "      <td>3.460127</td>\n",
       "      <td>0.004554</td>\n",
       "      <td>1.234516</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>(butter, whole milk)</td>\n",
       "      <td>(whipped/sour cream)</td>\n",
       "      <td>0.027555</td>\n",
       "      <td>0.071683</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.243542</td>\n",
       "      <td>3.397503</td>\n",
       "      <td>0.004736</td>\n",
       "      <td>1.227190</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>(yogurt, pip fruit)</td>\n",
       "      <td>(tropical fruit)</td>\n",
       "      <td>0.017997</td>\n",
       "      <td>0.104931</td>\n",
       "      <td>0.006406</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>3.392048</td>\n",
       "      <td>0.004517</td>\n",
       "      <td>1.389712</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>(yogurt, whole milk)</td>\n",
       "      <td>(curd)</td>\n",
       "      <td>0.056024</td>\n",
       "      <td>0.053279</td>\n",
       "      <td>0.010066</td>\n",
       "      <td>0.179673</td>\n",
       "      <td>3.372304</td>\n",
       "      <td>0.007081</td>\n",
       "      <td>1.154078</td>\n",
       "      <td>9835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                antecedents           consequents  \\\n",
       "2750                                  (ham)         (white bread)   \n",
       "2916                                (herbs)     (root vegetables)   \n",
       "562               (pip fruit, citrus fruit)      (tropical fruit)   \n",
       "2552                   (whipped/sour cream)             (berries)   \n",
       "396   (yogurt, root vegetables, whole milk)      (tropical fruit)   \n",
       "2292                              (napkins)    (hygiene articles)   \n",
       "2372              (yogurt, root vegetables)  (whipped/sour cream)   \n",
       "832                    (butter, whole milk)  (whipped/sour cream)   \n",
       "518                     (yogurt, pip fruit)      (tropical fruit)   \n",
       "1104                   (yogurt, whole milk)                (curd)   \n",
       "\n",
       "      antecedent support  consequent support   support  confidence      lift  \\\n",
       "2750            0.026029            0.042095  0.005084    0.195312  4.639851   \n",
       "2916            0.016268            0.108998  0.007016    0.431250  3.956477   \n",
       "562             0.013828            0.104931  0.005592    0.404412  3.854060   \n",
       "2552            0.071683            0.033249  0.009049    0.126241  3.796886   \n",
       "396             0.014540            0.104931  0.005694    0.391608  3.732043   \n",
       "2292            0.052364            0.032944  0.006101    0.116505  3.536498   \n",
       "2372            0.025826            0.071683  0.006406    0.248031  3.460127   \n",
       "832             0.027555            0.071683  0.006711    0.243542  3.397503   \n",
       "518             0.017997            0.104931  0.006406    0.355932  3.392048   \n",
       "1104            0.056024            0.053279  0.010066    0.179673  3.372304   \n",
       "\n",
       "      leverage  conviction  antecedent_len  \n",
       "2750  0.003988    1.190407            9835  \n",
       "2916  0.005243    1.566596            9835  \n",
       "562   0.004141    1.502831            9835  \n",
       "2552  0.006666    1.106428            9835  \n",
       "396   0.004168    1.471205            9835  \n",
       "2292  0.004376    1.094580            9835  \n",
       "2372  0.004554    1.234516            9835  \n",
       "832   0.004736    1.227190            9835  \n",
       "518   0.004517    1.389712            9835  \n",
       "1104  0.007081    1.154078            9835  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting them with respect to list and getting top 10 rules \n",
    "rules_no_redudancy.sort_values('lift',ascending=False).head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  The first rule & the second rule. states that the support for Vegitables and sausage compared to its default sale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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