MMA831Project_AssociationRules.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import seaborn as sns \n",
    "\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "import sklearn.metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.metrics import accuracy_score, cohen_kappa_score, f1_score, log_loss\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "import itertools\n",
    "\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules\n",
    "\n",
    "import scipy\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "from IPython.display import display\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('/System/Volumes/Data/Users/Michael/Library/CloudStorage/iCloud Drive/MMA/MMA 831/allstate-purchase-prediction-challenge/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pandas_profiling.ProfileReport(df, check_correlation=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>customer_ID</th>\n",
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      ],
      "text/plain": [
       "   customer_ID  shopping_pt  record_type  day   time state  location  \\\n",
       "0     10000000            1            0    0  08:35    IN     10001   \n",
       "1     10000000            2            0    0  08:38    IN     10001   \n",
       "2     10000000            3            0    0  08:38    IN     10001   \n",
       "3     10000000            4            0    0  08:39    IN     10001   \n",
       "4     10000000            5            0    0  11:55    IN     10001   \n",
       "5     10000000            6            0    0  11:57    IN     10001   \n",
       "6     10000000            7            0    0  11:58    IN     10001   \n",
       "7     10000000            8            0    0  12:03    IN     10001   \n",
       "8     10000000            9            1    0  12:07    IN     10001   \n",
       "9     10000005            1            0    3  08:56    NY     10006   \n",
       "\n",
       "   group_size  homeowner  car_age car_value  risk_factor  age_oldest  \\\n",
       "0           2          0        2         g          3.0          46   \n",
       "1           2          0        2         g          3.0          46   \n",
       "2           2          0        2         g          3.0          46   \n",
       "3           2          0        2         g          3.0          46   \n",
       "4           2          0        2         g          3.0          46   \n",
       "5           2          0        2         g          3.0          46   \n",
       "6           2          0        2         g          3.0          46   \n",
       "7           2          0        2         g          3.0          46   \n",
       "8           2          0        2         g          3.0          46   \n",
       "9           1          0       10         e          4.0          28   \n",
       "\n",
       "   age_youngest  married_couple  C_previous  duration_previous  A  B  C  D  E  \\\n",
       "0            42               1         1.0                2.0  1  0  2  2  1   \n",
       "1            42               1         1.0                2.0  1  0  2  2  1   \n",
       "2            42               1         1.0                2.0  1  0  2  2  1   \n",
       "3            42               1         1.0                2.0  1  0  2  2  1   \n",
       "4            42               1         1.0                2.0  1  0  2  2  1   \n",
       "5            42               1         1.0                2.0  1  0  2  2  1   \n",
       "6            42               1         1.0                2.0  1  0  2  2  1   \n",
       "7            42               1         1.0                2.0  1  0  2  2  1   \n",
       "8            42               1         1.0                2.0  1  0  2  2  1   \n",
       "9            28               0         3.0               13.0  1  1  3  3  1   \n",
       "\n",
       "   F  G  cost  \n",
       "0  2  2   633  \n",
       "1  2  1   630  \n",
       "2  2  1   630  \n",
       "3  2  1   630  \n",
       "4  2  1   630  \n",
       "5  2  1   638  \n",
       "6  2  1   638  \n",
       "7  2  1   638  \n",
       "8  2  1   634  \n",
       "9  0  2   755  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slice the data and keep only the purchase record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10000007</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>14:26</td>\n",
       "      <td>PA</td>\n",
       "      <td>10008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10000013</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>09:31</td>\n",
       "      <td>WV</td>\n",
       "      <td>10014</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>d</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>10000014</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17:50</td>\n",
       "      <td>MO</td>\n",
       "      <td>10015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>d</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    customer_ID  shopping_pt  record_type  day   time state  location  \\\n",
       "8      10000000            9            1    0  12:07    IN     10001   \n",
       "14     10000005            6            1    3  09:09    NY     10006   \n",
       "22     10000007            8            1    4  14:26    PA     10008   \n",
       "26     10000013            4            1    4  09:31    WV     10014   \n",
       "32     10000014            6            1    1  17:50    MO     10015   \n",
       "\n",
       "    group_size  homeowner  car_age car_value  risk_factor  age_oldest  \\\n",
       "8            2          0        2         g          3.0          46   \n",
       "14           1          0       10         e          4.0          28   \n",
       "22           1          0       11         c          NaN          43   \n",
       "26           2          1        3         d          3.0          62   \n",
       "32           1          0        5         d          3.0          32   \n",
       "\n",
       "    age_youngest  married_couple  C_previous  duration_previous  A  B  C  D  \\\n",
       "8             42               1         1.0                2.0  1  0  2  2   \n",
       "14            28               0         3.0               13.0  0  0  3  2   \n",
       "22            43               0         2.0                4.0  0  0  1  2   \n",
       "26            60               1         3.0                3.0  1  1  3  2   \n",
       "32            28               1         1.0                2.0  1  1  1  1   \n",
       "\n",
       "    E  F  G  cost  \n",
       "8   1  2  1   634  \n",
       "14  0  0  2   731  \n",
       "22  0  0  1   602  \n",
       "26  1  1  3   626  \n",
       "32  0  2  2   614  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 97009 entries, 8 to 665248\n",
      "Data columns (total 25 columns):\n",
      "customer_ID          97009 non-null int64\n",
      "shopping_pt          97009 non-null int64\n",
      "record_type          97009 non-null int64\n",
      "day                  97009 non-null int64\n",
      "time                 97009 non-null object\n",
      "state                97009 non-null object\n",
      "location             97009 non-null int64\n",
      "group_size           97009 non-null int64\n",
      "homeowner            97009 non-null int64\n",
      "car_age              97009 non-null int64\n",
      "car_value            97009 non-null object\n",
      "risk_factor          62663 non-null float64\n",
      "age_oldest           97009 non-null int64\n",
      "age_youngest         97009 non-null int64\n",
      "married_couple       97009 non-null int64\n",
      "C_previous           96173 non-null float64\n",
      "duration_previous    96173 non-null float64\n",
      "A                    97009 non-null int64\n",
      "B                    97009 non-null int64\n",
      "C                    97009 non-null int64\n",
      "D                    97009 non-null int64\n",
      "E                    97009 non-null int64\n",
      "F                    97009 non-null int64\n",
      "G                    97009 non-null int64\n",
      "cost                 97009 non-null int64\n",
      "dtypes: float64(3), int64(19), object(3)\n",
      "memory usage: 19.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_purchased=df[df.record_type==1]\n",
    "df_purchased.head()\n",
    "df_purchased.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create the purchase record table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    A_0  A_1  A_2  B_0  B_1  C_1  C_2  C_3  C_4  D_1  D_2  D_3  E_0  E_1  F_0  \\\n",
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       "26    0    1    0    0    1    0    0    1    0    0    1    0    0    1    0   \n",
       "32    0    1    0    0    1    1    0    0    0    1    0    0    1    0    0   \n",
       "\n",
       "    F_1  F_2  F_3  G_1  G_2  G_3  G_4  \n",
       "8     0    1    0    1    0    0    0  \n",
       "14    0    0    0    0    1    0    0  \n",
       "22    0    0    0    1    0    0    0  \n",
       "26    1    0    0    0    0    1    0  \n",
       "32    0    1    0    0    1    0    0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_product=[\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\"]\n",
    "df_prducct=pd.DataFrame()\n",
    "for i in list_product:\n",
    "    df_prducct=pd.concat([df_prducct,pd.get_dummies(df_purchased[i],prefix=i)],1)\n",
    "df_prducct.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate the support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.4 s, sys: 10.2 s, total: 22.6 s\n",
      "Wall time: 19.8 s\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "    support itemsets  length\n",
       "0  0.220371    (A_0)       1\n",
       "1  0.614190    (A_1)       1\n",
       "2  0.165438    (A_2)       1\n",
       "3  0.523910    (B_0)       1\n",
       "4  0.476090    (B_1)       1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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       "      <th>7993</th>\n",
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       "      <td>7</td>\n",
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      ],
      "text/plain": [
       "       support                             itemsets  length\n",
       "7990  0.003031  (A_2, C_3, G_3, E_1, D_3, F_2, B_1)       7\n",
       "7991  0.001165  (A_2, F_3, C_3, E_1, G_2, D_3, B_1)       7\n",
       "7992  0.001361  (A_2, F_3, C_3, G_3, E_1, D_3, B_1)       7\n",
       "7993  0.001278  (A_2, C_4, E_1, G_2, D_3, F_0, B_1)       7\n",
       "7994  0.001072  (A_2, C_4, G_3, E_1, D_3, F_2, B_1)       7"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time frequent_itemsets = apriori(df_prducct, min_support=0.001, use_colnames=True)\n",
    "frequent_itemsets['length'] = frequent_itemsets['itemsets'].apply(lambda x: len(x))\n",
    "frequent_itemsets.head()\n",
    "frequent_itemsets.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.154841</td>\n",
       "      <td>(B_0, A_0)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.065530</td>\n",
       "      <td>(A_0, B_1)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.105691</td>\n",
       "      <td>(A_0, C_1)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.051892</td>\n",
       "      <td>(A_0, C_2)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.054211</td>\n",
       "      <td>(C_3, A_0)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     support    itemsets  length\n",
       "22  0.154841  (B_0, A_0)       2\n",
       "23  0.065530  (A_0, B_1)       2\n",
       "24  0.105691  (A_0, C_1)       2\n",
       "25  0.051892  (A_0, C_2)       2\n",
       "26  0.054211  (C_3, A_0)       2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets_list=frequent_itemsets[ (frequent_itemsets['length'] == 2) & (frequent_itemsets['support'] >= 0.02) ]\n",
    "frequent_itemsets_list.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate the support and confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.33 s, sys: 68.3 ms, total: 1.4 s\n",
      "Wall time: 916 ms\n"
     ]
    }
   ],
   "source": [
    "%time rules = association_rules(frequent_itemsets, min_threshold=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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",
       "      <th>antecedent_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(B_0)</td>\n",
       "      <td>(A_0)</td>\n",
       "      <td>0.523910</td>\n",
       "      <td>0.220371</td>\n",
       "      <td>0.154841</td>\n",
       "      <td>0.295549</td>\n",
       "      <td>1.341143</td>\n",
       "      <td>0.039387</td>\n",
       "      <td>1.106719</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(A_0)</td>\n",
       "      <td>(B_0)</td>\n",
       "      <td>0.220371</td>\n",
       "      <td>0.523910</td>\n",
       "      <td>0.154841</td>\n",
       "      <td>0.702638</td>\n",
       "      <td>1.341143</td>\n",
       "      <td>0.039387</td>\n",
       "      <td>1.601046</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(A_0)</td>\n",
       "      <td>(B_1)</td>\n",
       "      <td>0.220371</td>\n",
       "      <td>0.476090</td>\n",
       "      <td>0.065530</td>\n",
       "      <td>0.297362</td>\n",
       "      <td>0.624592</td>\n",
       "      <td>-0.039387</td>\n",
       "      <td>0.745633</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(B_1)</td>\n",
       "      <td>(A_0)</td>\n",
       "      <td>0.476090</td>\n",
       "      <td>0.220371</td>\n",
       "      <td>0.065530</td>\n",
       "      <td>0.137642</td>\n",
       "      <td>0.624592</td>\n",
       "      <td>-0.039387</td>\n",
       "      <td>0.904066</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(A_0)</td>\n",
       "      <td>(C_1)</td>\n",
       "      <td>0.220371</td>\n",
       "      <td>0.298869</td>\n",
       "      <td>0.105691</td>\n",
       "      <td>0.479605</td>\n",
       "      <td>1.604733</td>\n",
       "      <td>0.039829</td>\n",
       "      <td>1.347306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  antecedents consequents  antecedent support  consequent support   support  \\\n",
       "0       (B_0)       (A_0)            0.523910            0.220371  0.154841   \n",
       "1       (A_0)       (B_0)            0.220371            0.523910  0.154841   \n",
       "2       (A_0)       (B_1)            0.220371            0.476090  0.065530   \n",
       "3       (B_1)       (A_0)            0.476090            0.220371  0.065530   \n",
       "4       (A_0)       (C_1)            0.220371            0.298869  0.105691   \n",
       "\n",
       "   confidence      lift  leverage  conviction  antecedent_len  \n",
       "0    0.295549  1.341143  0.039387    1.106719               1  \n",
       "1    0.702638  1.341143  0.039387    1.601046               1  \n",
       "2    0.297362  0.624592 -0.039387    0.745633               1  \n",
       "3    0.137642  0.624592 -0.039387    0.904066               1  \n",
       "4    0.479605  1.604733  0.039829    1.347306               1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[\"antecedent_len\"] = rules[\"antecedents\"].apply(lambda x: len(x))\n",
    "rules.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slice the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***let's review what we have learnd in MMA869***\n",
    "🤣"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Support versus Confidence versus Lift*\n",
    "\n",
    "Consider the rule {Sausage} → {Mustard}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Measure           | MeasureMeaning                                                     | Low Value Means…                                                                          | High Value Means…                                                                         |\n",
    "|-------------------|--------------------------------------------------------------------|-------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|\n",
    "| Support[0, 1]     | How often Sausage and Mustard appear together                      | Sausage and Mustard rarely appear together                                                | Sausage and Mustard appear together often                                                 |\n",
    "| Confidence [0, 1] | How often Mustard appears, when Sausage appears                    | when Sausage appears, Mustard is unlikely to appear                                       | when Sausage appears, Mustard is very likely to appear as well                            |\n",
    "| Lift[0, ∞)        | Whether Mustard appears with Sausage more often than random chance | Sausage and Mustard appear together less often than random chance (negatively correlated) | Sausage and Mustard appear together more often than random chance (positively correlated) |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "sliced=rules[ (rules['antecedent_len'] >= 1) &\n",
    "       (rules['confidence'] > 0.85) &\n",
    "       (rules['lift'] > 1.2) & \n",
    "        (rules['support'] > 0.15)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "sliced_sort_by_confidence=sliced.sort_values('lift',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>675</th>\n",
       "      <td>(A_0)</td>\n",
       "      <td>(E_0, F_0)</td>\n",
       "      <td>0.220371</td>\n",
       "      <td>0.232752</td>\n",
       "      <td>0.190477</td>\n",
       "      <td>0.864347</td>\n",
       "      <td>3.713601</td>\n",
       "      <td>0.139185</td>\n",
       "      <td>5.655944</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>(A_0, E_0)</td>\n",
       "      <td>(F_0)</td>\n",
       "      <td>0.214372</td>\n",
       "      <td>0.322094</td>\n",
       "      <td>0.190477</td>\n",
       "      <td>0.888536</td>\n",
       "      <td>2.758626</td>\n",
       "      <td>0.121429</td>\n",
       "      <td>6.081854</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(A_0)</td>\n",
       "      <td>(F_0)</td>\n",
       "      <td>0.220371</td>\n",
       "      <td>0.322094</td>\n",
       "      <td>0.192539</td>\n",
       "      <td>0.873702</td>\n",
       "      <td>2.712570</td>\n",
       "      <td>0.121559</td>\n",
       "      <td>5.367510</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>(A_0, F_0)</td>\n",
       "      <td>(E_0)</td>\n",
       "      <td>0.192539</td>\n",
       "      <td>0.537393</td>\n",
       "      <td>0.190477</td>\n",
       "      <td>0.989292</td>\n",
       "      <td>1.840909</td>\n",
       "      <td>0.087008</td>\n",
       "      <td>43.202827</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>(B_0, A_0)</td>\n",
       "      <td>(E_0)</td>\n",
       "      <td>0.154841</td>\n",
       "      <td>0.537393</td>\n",
       "      <td>0.152934</td>\n",
       "      <td>0.987684</td>\n",
       "      <td>1.837916</td>\n",
       "      <td>0.069724</td>\n",
       "      <td>37.561152</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(A_0)</td>\n",
       "      <td>(E_0)</td>\n",
       "      <td>0.220371</td>\n",
       "      <td>0.537393</td>\n",
       "      <td>0.214372</td>\n",
       "      <td>0.972776</td>\n",
       "      <td>1.810174</td>\n",
       "      <td>0.095946</td>\n",
       "      <td>16.992445</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>(F_2, E_1)</td>\n",
       "      <td>(A_1)</td>\n",
       "      <td>0.205259</td>\n",
       "      <td>0.614190</td>\n",
       "      <td>0.179427</td>\n",
       "      <td>0.874146</td>\n",
       "      <td>1.423250</td>\n",
       "      <td>0.053358</td>\n",
       "      <td>3.065539</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2445</th>\n",
       "      <td>(C_3, B_1)</td>\n",
       "      <td>(D_3)</td>\n",
       "      <td>0.194704</td>\n",
       "      <td>0.646404</td>\n",
       "      <td>0.178839</td>\n",
       "      <td>0.918520</td>\n",
       "      <td>1.420969</td>\n",
       "      <td>0.052982</td>\n",
       "      <td>4.339651</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8704</th>\n",
       "      <td>(C_3, A_1, E_1)</td>\n",
       "      <td>(D_3)</td>\n",
       "      <td>0.173077</td>\n",
       "      <td>0.646404</td>\n",
       "      <td>0.158563</td>\n",
       "      <td>0.916141</td>\n",
       "      <td>1.417288</td>\n",
       "      <td>0.046685</td>\n",
       "      <td>4.216533</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3278</th>\n",
       "      <td>(C_3, E_1)</td>\n",
       "      <td>(D_3)</td>\n",
       "      <td>0.210145</td>\n",
       "      <td>0.646404</td>\n",
       "      <td>0.192271</td>\n",
       "      <td>0.914942</td>\n",
       "      <td>1.415433</td>\n",
       "      <td>0.056432</td>\n",
       "      <td>4.157099</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>(C_3, A_1)</td>\n",
       "      <td>(D_3)</td>\n",
       "      <td>0.274201</td>\n",
       "      <td>0.646404</td>\n",
       "      <td>0.249719</td>\n",
       "      <td>0.910714</td>\n",
       "      <td>1.408893</td>\n",
       "      <td>0.072474</td>\n",
       "      <td>3.960276</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>(C_3)</td>\n",
       "      <td>(D_3)</td>\n",
       "      <td>0.391335</td>\n",
       "      <td>0.646404</td>\n",
       "      <td>0.355895</td>\n",
       "      <td>0.909438</td>\n",
       "      <td>1.406919</td>\n",
       "      <td>0.102934</td>\n",
       "      <td>3.904470</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3272</th>\n",
       "      <td>(C_3, E_0)</td>\n",
       "      <td>(D_3)</td>\n",
       "      <td>0.181189</td>\n",
       "      <td>0.646404</td>\n",
       "      <td>0.163624</td>\n",
       "      <td>0.903055</td>\n",
       "      <td>1.397045</td>\n",
       "      <td>0.046502</td>\n",
       "      <td>3.647393</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>(C_3, B_0)</td>\n",
       "      <td>(D_3)</td>\n",
       "      <td>0.196631</td>\n",
       "      <td>0.646404</td>\n",
       "      <td>0.177056</td>\n",
       "      <td>0.900446</td>\n",
       "      <td>1.393008</td>\n",
       "      <td>0.049953</td>\n",
       "      <td>3.551788</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>(F_2, B_1)</td>\n",
       "      <td>(A_1)</td>\n",
       "      <td>0.201610</td>\n",
       "      <td>0.614190</td>\n",
       "      <td>0.172097</td>\n",
       "      <td>0.853615</td>\n",
       "      <td>1.389821</td>\n",
       "      <td>0.048270</td>\n",
       "      <td>2.635579</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          antecedents consequents  antecedent support  consequent support  \\\n",
       "675             (A_0)  (E_0, F_0)            0.220371            0.232752   \n",
       "672        (A_0, E_0)       (F_0)            0.214372            0.322094   \n",
       "18              (A_0)       (F_0)            0.220371            0.322094   \n",
       "673        (A_0, F_0)       (E_0)            0.192539            0.537393   \n",
       "383        (B_0, A_0)       (E_0)            0.154841            0.537393   \n",
       "16              (A_0)       (E_0)            0.220371            0.537393   \n",
       "1281       (F_2, E_1)       (A_1)            0.205259            0.614190   \n",
       "2445       (C_3, B_1)       (D_3)            0.194704            0.646404   \n",
       "8704  (C_3, A_1, E_1)       (D_3)            0.173077            0.646404   \n",
       "3278       (C_3, E_1)       (D_3)            0.210145            0.646404   \n",
       "1018       (C_3, A_1)       (D_3)            0.274201            0.646404   \n",
       "209             (C_3)       (D_3)            0.391335            0.646404   \n",
       "3272       (C_3, E_0)       (D_3)            0.181189            0.646404   \n",
       "1964       (C_3, B_0)       (D_3)            0.196631            0.646404   \n",
       "880        (F_2, B_1)       (A_1)            0.201610            0.614190   \n",
       "\n",
       "       support  confidence      lift  leverage  conviction  antecedent_len  \n",
       "675   0.190477    0.864347  3.713601  0.139185    5.655944               1  \n",
       "672   0.190477    0.888536  2.758626  0.121429    6.081854               2  \n",
       "18    0.192539    0.873702  2.712570  0.121559    5.367510               1  \n",
       "673   0.190477    0.989292  1.840909  0.087008   43.202827               2  \n",
       "383   0.152934    0.987684  1.837916  0.069724   37.561152               2  \n",
       "16    0.214372    0.972776  1.810174  0.095946   16.992445               1  \n",
       "1281  0.179427    0.874146  1.423250  0.053358    3.065539               2  \n",
       "2445  0.178839    0.918520  1.420969  0.052982    4.339651               2  \n",
       "8704  0.158563    0.916141  1.417288  0.046685    4.216533               3  \n",
       "3278  0.192271    0.914942  1.415433  0.056432    4.157099               2  \n",
       "1018  0.249719    0.910714  1.408893  0.072474    3.960276               2  \n",
       "209   0.355895    0.909438  1.406919  0.102934    3.904470               1  \n",
       "3272  0.163624    0.903055  1.397045  0.046502    3.647393               2  \n",
       "1964  0.177056    0.900446  1.393008  0.049953    3.551788               2  \n",
       "880   0.172097    0.853615  1.389821  0.048270    2.635579               2  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(15, 10)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sliced_sort_by_confidence\n",
    "sliced_sort_by_confidence.shape"
   ]
  }
 ],
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