Association_Rule_Mining_Apriori.ipynb

{
 "cells": [
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   "id": "76834c65-7793-479d-b68b-5725540807af",
   "metadata": {},
   "source": [
    "# Home work no.5\n",
    ">## professor : Dr. Vahid Khazaei nezhad\n",
    "# Team Members :\n",
    "- Saeed Tizkar\n",
    "- Pooriya Amini\n",
    "- Mohammad Elahi\n",
    "\n",
    "## فایل مربوط به online retail درون پوشه دیتاست قرار ندادم چون حجم زیادی داشت و آپلود زمانبر میشد"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dfa4ad1",
   "metadata": {
    "heading_collapsed": true,
    "tags": []
   },
   "source": [
    "# Home work no.1\n",
    ">## professor : Dr. Vahid Khazaei nezhad\n",
    "# Team Members :\n",
    "- Saeed Tizkar\n",
    "- Pooriya Amini\n",
    "- Mohammad Elahi# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17f73055",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries loaded.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "from mlxtend.frequent_patterns import apriori, association_rules\n",
    "\n",
    "print('Libraries loaded.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9113bc61",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Example 1: Simple use of algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca558c74",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Creating Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af6bace5",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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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>Apple</th>\n",
       "      <th>Corn</th>\n",
       "      <th>Dill</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Ice cream</th>\n",
       "      <th>Kidney Beans</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Nutmeg</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Unicorn</th>\n",
       "      <th>Yogurt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Apple   Corn   Dill   Eggs  Ice cream  Kidney Beans   Milk  Nutmeg  Onion  \\\n",
       "0  False  False  False   True      False          True   True    True   True   \n",
       "1  False  False   True   True      False          True  False    True   True   \n",
       "2   True  False  False   True      False          True   True   False  False   \n",
       "3  False   True  False  False      False          True   True   False  False   \n",
       "4  False   True  False   True       True          True  False   False   True   \n",
       "\n",
       "   Unicorn  Yogurt  \n",
       "0    False    True  \n",
       "1    False    True  \n",
       "2    False   False  \n",
       "3     True    True  \n",
       "4    False   False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket_lists = [['Milk', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],\n",
    "                ['Dill', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],\n",
    "                ['Milk', 'Apple', 'Kidney Beans', 'Eggs'],\n",
    "                ['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'],\n",
    "                ['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]\n",
    "\n",
    "trans_encoder = TransactionEncoder()\n",
    "\n",
    "encoded_baskets = trans_encoder.fit(basket_lists).transform(basket_lists)\n",
    "\n",
    "dataset_df = pd.DataFrame(encoded_baskets, columns= trans_encoder.columns_)\n",
    "\n",
    "dataset_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4992c2a",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Computing support values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf06ff50",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(Kidney Beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Milk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Onion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Yogurt)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Kidney Beans, Eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Onion, Eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Milk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Onion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Yogurt)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Onion, Eggs)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                     itemsets\n",
       "0       0.8                       (Eggs)\n",
       "1       1.0               (Kidney Beans)\n",
       "2       0.6                       (Milk)\n",
       "3       0.6                      (Onion)\n",
       "4       0.6                     (Yogurt)\n",
       "5       0.8         (Kidney Beans, Eggs)\n",
       "6       0.6                (Onion, Eggs)\n",
       "7       0.6         (Kidney Beans, Milk)\n",
       "8       0.6        (Kidney Beans, Onion)\n",
       "9       0.6       (Kidney Beans, Yogurt)\n",
       "10      0.6  (Kidney Beans, Onion, Eggs)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apriori(dataset_df, min_support= 0.6, use_colnames= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad3818bd",
   "metadata": {},
   "source": [
    "# Example2: Association rule mining"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2acc00de",
   "metadata": {},
   "source": [
    "## Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "583b8d69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket_df = pd.read_excel('./Online Retail.xlsx')\n",
    "\n",
    "basket_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d20464",
   "metadata": {},
   "source": [
    "### Dataset description"
   ]
  },
  {
   "cell_type": "raw",
   "id": "01b676ee",
   "metadata": {},
   "source": [
    "Detailed overview of dataset\n",
    "\n",
    "    - Records in the dataset = 525461 ROWS\n",
    "\n",
    "    - Columns in the dataset  = 8 COLUMNS\n",
    "\n",
    "    - InvoiceNo: A 6-digit integral number uniquely assigned to each transaction. \n",
    "                 If this code starts with letter 'C', it indicates a cancellation (Nominal)\n",
    "\n",
    "    - StockCode:  A 5-digit integral number uniquely assigned to each distinct product (Nominal)\n",
    "\n",
    "    - Description: Product (item) name. (Nominal)\n",
    "\n",
    "    - Quantity: The quantities of each product (item) per transaction ( Numeric)\n",
    "\n",
    "    - InvoiceDate:  The day and time when each transaction was generated (Numeric)\n",
    "\n",
    "    - UnitPrice: Product price per unit in sterling (Numeric)\n",
    "\n",
    "    - CustomerID:  A 5-digit integral number uniquely assigned to each customer (Nominal)\n",
    "\n",
    "    - Country:  Name of the country where each customer resides (Nominal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c52bd028",
   "metadata": {},
   "source": [
    "## Some basic exploratory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4763a643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1943068f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo              object\n",
       "StockCode              object\n",
       "Description            object\n",
       "Quantity                int64\n",
       "InvoiceDate    datetime64[ns]\n",
       "UnitPrice             float64\n",
       "CustomerID            float64\n",
       "Country                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56f1fa1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135080\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77cf8748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9288, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canceled = basket_df.loc[basket_df['InvoiceNo'].str.contains('C') == True]\n",
    "\n",
    "canceled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde13ff9",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Cleansing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec951465",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9288 has removed from dataset, new dataset size is 532621\n"
     ]
    }
   ],
   "source": [
    "basket_df['Description'] = basket_df['Description'].str.strip()\n",
    "\n",
    "#removing canceled records\n",
    "basket_df.drop(canceled.index, inplace= True)\n",
    "\n",
    "print(f'{canceled.shape[0]} has removed from dataset, new dataset size is {basket_df.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98012d6d-e3a7-4164-82a6-fc5ba51f30c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket_df.loc[basket_df[\"Description\"] == \"x\"][\"Description\"].empty"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3b3694",
   "metadata": {},
   "source": [
    "## Preparing dataset for analysing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e23b2cf",
   "metadata": {},
   "source": [
    "### Selecting one region (Germany)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3bf61cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8408, 8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "france_basket = basket_df.loc[basket_df['Country'] == 'France']\n",
    "\n",
    "france_basket.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e6123e",
   "metadata": {},
   "source": [
    "### Creating transaction, items set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d5a1d198",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans_basket_provider(in_basket):\n",
    "    basket = []\n",
    "    transaction_nom = []\n",
    "\n",
    "    for transaction, items in in_basket.groupby('InvoiceNo').groups.items():\n",
    "\n",
    "        products = []\n",
    "\n",
    "        transaction_nom.append(transaction)\n",
    "        for item in items:\n",
    "            products.append(str(in_basket.loc[item]['StockCode']))        \n",
    "\n",
    "        ###   \n",
    "        basket.append(products)\n",
    "    return (basket, transaction_nom)\n",
    "fr_basket, fr_trans_nom = trans_basket_provider(france_basket)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06bf9394",
   "metadata": {},
   "source": [
    "### creating binary dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "efda0ae6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>392 rows × 1542 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        10002  10120  10125  10135  11001  15036  15039  15044C  15056BL  \\\n",
       "536370   True  False  False  False  False  False  False   False    False   \n",
       "536852  False  False  False  False  False  False  False   False    False   \n",
       "536974  False  False  False  False  False  False  False   False     True   \n",
       "537065  False  False  False  False  False  False  False   False    False   \n",
       "537463  False  False  False  False  False  False  False   False    False   \n",
       "...       ...    ...    ...    ...    ...    ...    ...     ...      ...   \n",
       "580986  False  False  False  False  False  False  False   False    False   \n",
       "581001  False  False  False  False  False  False  False   False    False   \n",
       "581171  False  False  False  False  False  False  False   False    False   \n",
       "581279  False  False  False  False  False  False  False   False    False   \n",
       "581587  False  False  False  False  False  False  False   False    False   \n",
       "\n",
       "        15056N  ...  90030C  90031  90099  90184B  90184C  90201B  90201C  \\\n",
       "536370   False  ...   False  False  False   False   False   False   False   \n",
       "536852   False  ...   False  False  False   False   False   False   False   \n",
       "536974   False  ...   False  False  False   False   False   False   False   \n",
       "537065   False  ...   False  False  False   False   False   False   False   \n",
       "537463   False  ...   False  False  False   False   False   False   False   \n",
       "...        ...  ...     ...    ...    ...     ...     ...     ...     ...   \n",
       "580986   False  ...   False  False  False   False   False   False   False   \n",
       "581001   False  ...   False  False  False   False   False   False   False   \n",
       "581171   False  ...   False  False  False   False   False   False   False   \n",
       "581279   False  ...   False  False  False   False   False   False   False   \n",
       "581587   False  ...   False  False  False   False   False   False   False   \n",
       "\n",
       "           C2      M   POST  \n",
       "536370  False  False   True  \n",
       "536852  False  False   True  \n",
       "536974  False  False   True  \n",
       "537065  False  False   True  \n",
       "537463  False  False   True  \n",
       "...       ...    ...    ...  \n",
       "580986  False  False   True  \n",
       "581001  False  False   True  \n",
       "581171  False  False   True  \n",
       "581279  False  False   True  \n",
       "581587  False  False  False  \n",
       "\n",
       "[392 rows x 1542 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def binary_df_provider(in_basket, trans_nom) :\n",
    "    trans_encoder = TransactionEncoder()\n",
    "\n",
    "    encoded_baskets = trans_encoder.fit(in_basket).transform(in_basket)\n",
    "    binary_df = pd.DataFrame(encoded_baskets, columns= trans_encoder.columns_, index= trans_nom)\n",
    "\n",
    "    return binary_df\n",
    "fr_binary_df = binary_df_provider(fr_basket, fr_trans_nom)\n",
    "fr_binary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb3aef6",
   "metadata": {},
   "source": [
    "### Association extraction(Recommendation based on antecedents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d3fe33d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <th>conviction</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>(20712)</td>\n",
       "      <td>(POST)</td>\n",
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       "      <td>0.765306</td>\n",
       "      <td>0.076531</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.306667</td>\n",
       "      <td>0.017961</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0.012573</td>\n",
       "      <td>inf</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(21731, 22556)</td>\n",
       "      <td>(POST)</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0.765306</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.306667</td>\n",
       "      <td>0.011974</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(21086, 21080)</td>\n",
       "      <td>(21094)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.127551</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.975</td>\n",
       "      <td>7.644000</td>\n",
       "      <td>0.086474</td>\n",
       "      <td>34.897959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(21080, 21094)</td>\n",
       "      <td>(21086)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.137755</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.975</td>\n",
       "      <td>7.077778</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>34.489796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      antecedents consequents  antecedent support  consequent support  \\\n",
       "0         (20712)      (POST)            0.076531            0.765306   \n",
       "1  (21731, 22554)      (POST)            0.053571            0.765306   \n",
       "2  (21731, 22556)      (POST)            0.051020            0.765306   \n",
       "3  (21086, 21080)     (21094)            0.102041            0.127551   \n",
       "4  (21080, 21094)     (21086)            0.102041            0.137755   \n",
       "\n",
       "    support  confidence      lift  leverage  conviction  \n",
       "0  0.076531       1.000  1.306667  0.017961         inf  \n",
       "1  0.053571       1.000  1.306667  0.012573         inf  \n",
       "2  0.051020       1.000  1.306667  0.011974         inf  \n",
       "3  0.099490       0.975  7.644000  0.086474   34.897959  \n",
       "4  0.099490       0.975  7.077778  0.085433   34.489796  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencies_items = apriori(fr_binary_df, min_support= 0.05, use_colnames= True)\n",
    "frequencies_items\n",
    "rules = association_rules(frequencies_items, metric= \"lift\", min_threshold= 1)\n",
    "\n",
    "rules = rules.sort_values(['confidence', 'lift'], ascending= [False, False])\n",
    "\n",
    "rules.reset_index(inplace= True, drop= True)\n",
    "\n",
    "rules.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435c821e",
   "metadata": {},
   "source": [
    "### Filtering results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "63218677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(21086, 21080)</td>\n",
       "      <td>(21094)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.127551</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>7.644000</td>\n",
       "      <td>0.086474</td>\n",
       "      <td>34.897959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(21080, 21094)</td>\n",
       "      <td>(21086)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.137755</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>7.077778</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>34.489796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>(22726, 22728)</td>\n",
       "      <td>(22727)</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>9.133271</td>\n",
       "      <td>0.056793</td>\n",
       "      <td>6.565689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>(22727, 22728)</td>\n",
       "      <td>(22726)</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>8.892922</td>\n",
       "      <td>0.056604</td>\n",
       "      <td>6.547194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>(21086, 21094)</td>\n",
       "      <td>(21080)</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>0.132653</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>6.125000</td>\n",
       "      <td>0.083247</td>\n",
       "      <td>4.625850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>(22727, 22726)</td>\n",
       "      <td>(22728)</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>7.903226</td>\n",
       "      <td>0.055706</td>\n",
       "      <td>4.639456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>(22551, 22556)</td>\n",
       "      <td>(22554)</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>0.170918</td>\n",
       "      <td>0.068878</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>4.513433</td>\n",
       "      <td>0.053617</td>\n",
       "      <td>3.627232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>(22554, 22556)</td>\n",
       "      <td>(22551)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.137755</td>\n",
       "      <td>0.068878</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>0.054821</td>\n",
       "      <td>2.653061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>(22551, 22554)</td>\n",
       "      <td>(22556)</td>\n",
       "      <td>0.104592</td>\n",
       "      <td>0.168367</td>\n",
       "      <td>0.068878</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>3.911308</td>\n",
       "      <td>0.051268</td>\n",
       "      <td>2.435496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        antecedents consequents  antecedent support  consequent support  \\\n",
       "3    (21086, 21080)     (21094)            0.102041            0.127551   \n",
       "4    (21080, 21094)     (21086)            0.102041            0.137755   \n",
       "53   (22726, 22728)     (22727)            0.073980            0.094388   \n",
       "54   (22727, 22728)     (22726)            0.073980            0.096939   \n",
       "81   (21086, 21094)     (21080)            0.122449            0.132653   \n",
       "84   (22727, 22726)     (22728)            0.079082            0.102041   \n",
       "109  (22551, 22556)     (22554)            0.089286            0.170918   \n",
       "143  (22554, 22556)     (22551)            0.102041            0.137755   \n",
       "147  (22551, 22554)     (22556)            0.104592            0.168367   \n",
       "\n",
       "      support  confidence      lift  leverage  conviction  \n",
       "3    0.099490    0.975000  7.644000  0.086474   34.897959  \n",
       "4    0.099490    0.975000  7.077778  0.085433   34.489796  \n",
       "53   0.063776    0.862069  9.133271  0.056793    6.565689  \n",
       "54   0.063776    0.862069  8.892922  0.056604    6.547194  \n",
       "81   0.099490    0.812500  6.125000  0.083247    4.625850  \n",
       "84   0.063776    0.806452  7.903226  0.055706    4.639456  \n",
       "109  0.068878    0.771429  4.513433  0.053617    3.627232  \n",
       "143  0.068878    0.675000  4.900000  0.054821    2.653061  \n",
       "147  0.068878    0.658537  3.911308  0.051268    2.435496  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition0 = rules.antecedents.apply(lambda x: False if 'POST' in x else True)\n",
    "\n",
    "condition1 = rules.consequents.apply(lambda x: False if 'POST' in x else True)\n",
    "\n",
    "condition2 = rules['antecedents'].apply(lambda x: len(x) >= 2)\n",
    "\n",
    "rules.loc[condition0 & condition1 & condition2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26cb690a",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Excersice1"
   ]
  },
  {
   "cell_type": "raw",
   "id": "58f6474e",
   "metadata": {
    "hidden": true
   },
   "source": [
    "در قواعد بدست آمده از کد کالا استفاده شده است. قواعد را با استفاده از توضیح هر کد کالا برای کاربرقابل فهم کنید. به عبارت دیگر بجای کد کالا از توضیح یا نام کالا استفاده کنید. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d77ef5ae-901a-43e8-a49c-1f1147e5aae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_stocks = basket_df.drop_duplicates(subset=[\"StockCode\"])\n",
    "def name_replacement (x) : \n",
    "    items = list(x)\n",
    "    new_names = []\n",
    "    for item in items:\n",
    "        row = unique_stocks.loc[unique_stocks['StockCode'].apply(lambda y: str(y) == str(item))]\n",
    "        if not row[\"Description\"].empty:\n",
    "            new_names.append(list(row[\"Description\"])[0])\n",
    "        else:\n",
    "            new_names.append(item)\n",
    "            \n",
    "    return tuple(new_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4609f996-1521-469e-80c1-27e048593376",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "antecedents column mapped\n"
     ]
    }
   ],
   "source": [
    "rules['antecedents'] = rules['antecedents'].apply(name_replacement)\n",
    "print(\"antecedents column mapped\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a69d42d9-7bba-4517-b84b-d5c7e158125f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "consequents column mapped\n"
     ]
    }
   ],
   "source": [
    "rules['consequents'] = rules['consequents'].apply(name_replacement)\n",
    "print(\"consequents column mapped\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "305b0c11-5d9b-4c8e-b25a-f5bb97d2458d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(JUMBO BAG WOODLAND ANIMALS,)</td>\n",
       "      <td>(POSTAGE,)</td>\n",
       "      <td>0.076531</td>\n",
       "      <td>0.765306</td>\n",
       "      <td>0.076531</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.306667</td>\n",
       "      <td>0.017961</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(RED TOADSTOOL LED NIGHT LIGHT, PLASTERS IN TI...</td>\n",
       "      <td>(POSTAGE,)</td>\n",
       "      <td>0.053571</td>\n",
       "      <td>0.765306</td>\n",
       "      <td>0.053571</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.306667</td>\n",
       "      <td>0.012573</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(RED TOADSTOOL LED NIGHT LIGHT, PLASTERS IN TI...</td>\n",
       "      <td>(POSTAGE,)</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0.765306</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.306667</td>\n",
       "      <td>0.011974</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(SET/6 RED SPOTTY PAPER CUPS, SET/20 RED RETRO...</td>\n",
       "      <td>(SET/6 RED SPOTTY PAPER PLATES,)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.127551</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.975</td>\n",
       "      <td>7.644000</td>\n",
       "      <td>0.086474</td>\n",
       "      <td>34.897959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(SET/20 RED RETROSPOT PAPER NAPKINS, SET/6 RED...</td>\n",
       "      <td>(SET/6 RED SPOTTY PAPER CUPS,)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.137755</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.975</td>\n",
       "      <td>7.077778</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>34.489796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         antecedents  \\\n",
       "0                      (JUMBO BAG WOODLAND ANIMALS,)   \n",
       "1  (RED TOADSTOOL LED NIGHT LIGHT, PLASTERS IN TI...   \n",
       "2  (RED TOADSTOOL LED NIGHT LIGHT, PLASTERS IN TI...   \n",
       "3  (SET/6 RED SPOTTY PAPER CUPS, SET/20 RED RETRO...   \n",
       "4  (SET/20 RED RETROSPOT PAPER NAPKINS, SET/6 RED...   \n",
       "\n",
       "                        consequents  antecedent support  consequent support  \\\n",
       "0                        (POSTAGE,)            0.076531            0.765306   \n",
       "1                        (POSTAGE,)            0.053571            0.765306   \n",
       "2                        (POSTAGE,)            0.051020            0.765306   \n",
       "3  (SET/6 RED SPOTTY PAPER PLATES,)            0.102041            0.127551   \n",
       "4    (SET/6 RED SPOTTY PAPER CUPS,)            0.102041            0.137755   \n",
       "\n",
       "    support  confidence      lift  leverage  conviction  \n",
       "0  0.076531       1.000  1.306667  0.017961         inf  \n",
       "1  0.053571       1.000  1.306667  0.012573         inf  \n",
       "2  0.051020       1.000  1.306667  0.011974         inf  \n",
       "3  0.099490       0.975  7.644000  0.086474   34.897959  \n",
       "4  0.099490       0.975  7.077778  0.085433   34.489796  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fc9b8a8c-96fa-4d06-808a-3b703b3736a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(SET/6 RED SPOTTY PAPER CUPS, SET/20 RED RETRO...</td>\n",
       "      <td>(SET/6 RED SPOTTY PAPER PLATES,)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.127551</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>7.644000</td>\n",
       "      <td>0.086474</td>\n",
       "      <td>34.897959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(SET/20 RED RETROSPOT PAPER NAPKINS, SET/6 RED...</td>\n",
       "      <td>(SET/6 RED SPOTTY PAPER CUPS,)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.137755</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>7.077778</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>34.489796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN, ALARM CLOCK BAKEL...</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED,)</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>9.133271</td>\n",
       "      <td>0.056793</td>\n",
       "      <td>6.565689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED, ALARM CLOCK BAKELIK...</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN,)</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>8.892922</td>\n",
       "      <td>0.056604</td>\n",
       "      <td>6.547194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>(SET/6 RED SPOTTY PAPER CUPS, SET/6 RED SPOTTY...</td>\n",
       "      <td>(SET/20 RED RETROSPOT PAPER NAPKINS,)</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>0.132653</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>6.125000</td>\n",
       "      <td>0.083247</td>\n",
       "      <td>4.625850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED, ALARM CLOCK BAKELIK...</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK,)</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>7.903226</td>\n",
       "      <td>0.055706</td>\n",
       "      <td>4.639456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>(PLASTERS IN TIN SPACEBOY, PLASTERS IN TIN CIR...</td>\n",
       "      <td>(PLASTERS IN TIN WOODLAND ANIMALS,)</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>0.170918</td>\n",
       "      <td>0.068878</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>4.513433</td>\n",
       "      <td>0.053617</td>\n",
       "      <td>3.627232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>(PLASTERS IN TIN WOODLAND ANIMALS, PLASTERS IN...</td>\n",
       "      <td>(PLASTERS IN TIN SPACEBOY,)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.137755</td>\n",
       "      <td>0.068878</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>0.054821</td>\n",
       "      <td>2.653061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>(PLASTERS IN TIN SPACEBOY, PLASTERS IN TIN WOO...</td>\n",
       "      <td>(PLASTERS IN TIN CIRCUS PARADE,)</td>\n",
       "      <td>0.104592</td>\n",
       "      <td>0.168367</td>\n",
       "      <td>0.068878</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>3.911308</td>\n",
       "      <td>0.051268</td>\n",
       "      <td>2.435496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           antecedents  \\\n",
       "3    (SET/6 RED SPOTTY PAPER CUPS, SET/20 RED RETRO...   \n",
       "4    (SET/20 RED RETROSPOT PAPER NAPKINS, SET/6 RED...   \n",
       "53   (ALARM CLOCK BAKELIKE GREEN, ALARM CLOCK BAKEL...   \n",
       "54   (ALARM CLOCK BAKELIKE RED, ALARM CLOCK BAKELIK...   \n",
       "81   (SET/6 RED SPOTTY PAPER CUPS, SET/6 RED SPOTTY...   \n",
       "84   (ALARM CLOCK BAKELIKE RED, ALARM CLOCK BAKELIK...   \n",
       "109  (PLASTERS IN TIN SPACEBOY, PLASTERS IN TIN CIR...   \n",
       "143  (PLASTERS IN TIN WOODLAND ANIMALS, PLASTERS IN...   \n",
       "147  (PLASTERS IN TIN SPACEBOY, PLASTERS IN TIN WOO...   \n",
       "\n",
       "                               consequents  antecedent support  \\\n",
       "3         (SET/6 RED SPOTTY PAPER PLATES,)            0.102041   \n",
       "4           (SET/6 RED SPOTTY PAPER CUPS,)            0.102041   \n",
       "53             (ALARM CLOCK BAKELIKE RED,)            0.073980   \n",
       "54           (ALARM CLOCK BAKELIKE GREEN,)            0.073980   \n",
       "81   (SET/20 RED RETROSPOT PAPER NAPKINS,)            0.122449   \n",
       "84            (ALARM CLOCK BAKELIKE PINK,)            0.079082   \n",
       "109    (PLASTERS IN TIN WOODLAND ANIMALS,)            0.089286   \n",
       "143            (PLASTERS IN TIN SPACEBOY,)            0.102041   \n",
       "147       (PLASTERS IN TIN CIRCUS PARADE,)            0.104592   \n",
       "\n",
       "     consequent support   support  confidence      lift  leverage  conviction  \n",
       "3              0.127551  0.099490    0.975000  7.644000  0.086474   34.897959  \n",
       "4              0.137755  0.099490    0.975000  7.077778  0.085433   34.489796  \n",
       "53             0.094388  0.063776    0.862069  9.133271  0.056793    6.565689  \n",
       "54             0.096939  0.063776    0.862069  8.892922  0.056604    6.547194  \n",
       "81             0.132653  0.099490    0.812500  6.125000  0.083247    4.625850  \n",
       "84             0.102041  0.063776    0.806452  7.903226  0.055706    4.639456  \n",
       "109            0.170918  0.068878    0.771429  4.513433  0.053617    3.627232  \n",
       "143            0.137755  0.068878    0.675000  4.900000  0.054821    2.653061  \n",
       "147            0.168367  0.068878    0.658537  3.911308  0.051268    2.435496  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules.loc[condition0 & condition1 & condition2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6fea408",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Excersize2"
   ]
  },
  {
   "cell_type": "raw",
   "id": "11867aaf",
   "metadata": {
    "hidden": true
   },
   "source": [
    "بررسی کنید قواعد بدست آمده از تحلیل سبد خرید فرانسوی های چقدر مشابه قوانین سبد خرید سوئیس هاست. \n",
    "به عبارت دیگر با چه احتمالی می توان قواعد بدست آمده از تحلیل سبد خرید فرانسویان را به سبد خرید سوئیسی ها تعمیم داد."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fd83b0ad-12aa-4c9a-9b0d-1772dccf1179",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5320</th>\n",
       "      <td>536858</td>\n",
       "      <td>22326</td>\n",
       "      <td>ROUND SNACK BOXES SET OF4 WOODLAND</td>\n",
       "      <td>30</td>\n",
       "      <td>2010-12-03 10:36:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>13520.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5321</th>\n",
       "      <td>536858</td>\n",
       "      <td>22554</td>\n",
       "      <td>PLASTERS IN TIN WOODLAND ANIMALS</td>\n",
       "      <td>36</td>\n",
       "      <td>2010-12-03 10:36:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>13520.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5322</th>\n",
       "      <td>536858</td>\n",
       "      <td>21731</td>\n",
       "      <td>RED TOADSTOOL LED NIGHT LIGHT</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-03 10:36:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>13520.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5323</th>\n",
       "      <td>536858</td>\n",
       "      <td>20677</td>\n",
       "      <td>PINK POLKADOT BOWL</td>\n",
       "      <td>16</td>\n",
       "      <td>2010-12-03 10:36:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13520.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5324</th>\n",
       "      <td>536858</td>\n",
       "      <td>20750</td>\n",
       "      <td>RED RETROSPOT MINI CASES</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-03 10:36:00</td>\n",
       "      <td>7.95</td>\n",
       "      <td>13520.0</td>\n",
       "      <td>Switzerland</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508666</th>\n",
       "      <td>579259</td>\n",
       "      <td>22915</td>\n",
       "      <td>ASSORTED BOTTLE TOP  MAGNETS</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-11-29 08:40:00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>12451.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508667</th>\n",
       "      <td>579259</td>\n",
       "      <td>23178</td>\n",
       "      <td>JAM CLOCK MAGNET</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-11-29 08:40:00</td>\n",
       "      <td>2.89</td>\n",
       "      <td>12451.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508668</th>\n",
       "      <td>579259</td>\n",
       "      <td>23180</td>\n",
       "      <td>MUM'S KITCHEN CLOCK</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-11-29 08:40:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12451.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508669</th>\n",
       "      <td>579259</td>\n",
       "      <td>23079</td>\n",
       "      <td>TOADSTOOL BEDSIDE LIGHT</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-11-29 08:40:00</td>\n",
       "      <td>8.95</td>\n",
       "      <td>12451.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508670</th>\n",
       "      <td>579259</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-29 08:40:00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>12451.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1967 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                         Description  Quantity  \\\n",
       "5320      536858     22326  ROUND SNACK BOXES SET OF4 WOODLAND        30   \n",
       "5321      536858     22554    PLASTERS IN TIN WOODLAND ANIMALS        36   \n",
       "5322      536858     21731       RED TOADSTOOL LED NIGHT LIGHT        24   \n",
       "5323      536858     20677                  PINK POLKADOT BOWL        16   \n",
       "5324      536858     20750            RED RETROSPOT MINI CASES         2   \n",
       "...          ...       ...                                 ...       ...   \n",
       "508666    579259     22915        ASSORTED BOTTLE TOP  MAGNETS        12   \n",
       "508667    579259     23178                    JAM CLOCK MAGNET         6   \n",
       "508668    579259     23180                 MUM'S KITCHEN CLOCK         4   \n",
       "508669    579259     23079             TOADSTOOL BEDSIDE LIGHT         2   \n",
       "508670    579259      POST                             POSTAGE         1   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID      Country  \n",
       "5320   2010-12-03 10:36:00       2.95     13520.0  Switzerland  \n",
       "5321   2010-12-03 10:36:00       1.65     13520.0  Switzerland  \n",
       "5322   2010-12-03 10:36:00       1.65     13520.0  Switzerland  \n",
       "5323   2010-12-03 10:36:00       1.25     13520.0  Switzerland  \n",
       "5324   2010-12-03 10:36:00       7.95     13520.0  Switzerland  \n",
       "...                    ...        ...         ...          ...  \n",
       "508666 2011-11-29 08:40:00       0.42     12451.0  Switzerland  \n",
       "508667 2011-11-29 08:40:00       2.89     12451.0  Switzerland  \n",
       "508668 2011-11-29 08:40:00       4.95     12451.0  Switzerland  \n",
       "508669 2011-11-29 08:40:00       8.95     12451.0  Switzerland  \n",
       "508670 2011-11-29 08:40:00      40.00     12451.0  Switzerland  \n",
       "\n",
       "[1967 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swit_basket = basket_df.loc[basket_df['Country'] == 'Switzerland']\n",
    "swit_basket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c108f4f9-0909-4958-99fe-932f11d5e579",
   "metadata": {},
   "outputs": [],
   "source": [
    "sw_basket, sw_trans_nom = trans_basket_provider(swit_basket)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "335b2294-3308-46b7-bad3-b0875f6043eb",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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      "text/plain": [
       "        10002  10125  10133  15056BL  15056P  15058A  15058B  15060B  16008  \\\n",
       "536858  False  False  False    False   False   False   False   False  False   \n",
       "539488  False  False  False    False   False   False   False   False  False   \n",
       "541518   True  False  False    False   False   False   False   False  False   \n",
       "541569  False  False  False    False   False   False   False   False  False   \n",
       "542586  False  False  False    False   False   False   False   False  False   \n",
       "\n",
       "        16012  ...  85174  85175  85176  85178  85206A  85227  85232D  90108  \\\n",
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       "539488  False  ...  False  False  False  False   False  False   False  False   \n",
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       "542586   True  ...  False  False  False  False    True  False   False  False   \n",
       "\n",
       "        90114   POST  \n",
       "536858  False   True  \n",
       "539488  False  False  \n",
       "541518   True  False  \n",
       "541569  False   True  \n",
       "542586  False   True  \n",
       "\n",
       "[5 rows x 978 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw_binary_df = binary_df_provider(sw_basket, sw_trans_nom)\n",
    "sw_binary_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a27a0be6-b6a7-4d50-96ae-9f8dbc30baf9",
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   "outputs": [
    {
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      "text/plain": [
       "      antecedents consequents  antecedent support  consequent support  \\\n",
       "0         (22544)     (22539)            0.074074            0.074074   \n",
       "1         (22539)     (22544)            0.074074            0.074074   \n",
       "2         (23292)     (23290)            0.074074            0.074074   \n",
       "3         (23290)     (23292)            0.074074            0.074074   \n",
       "4  (22551, 20724)     (20717)            0.074074            0.074074   \n",
       "\n",
       "    support  confidence  lift  leverage  conviction  \n",
       "0  0.074074         1.0  13.5  0.068587         inf  \n",
       "1  0.074074         1.0  13.5  0.068587         inf  \n",
       "2  0.074074         1.0  13.5  0.068587         inf  \n",
       "3  0.074074         1.0  13.5  0.068587         inf  \n",
       "4  0.074074         1.0  13.5  0.068587         inf  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "sw_frequencies_items = apriori(sw_binary_df, min_support= 0.07, use_colnames= True)\n",
    "\n",
    "sw_rules = association_rules(sw_frequencies_items, metric= \"lift\", min_threshold= 1)\n",
    "\n",
    "sw_rules = sw_rules.sort_values(['confidence', 'lift'], ascending= [False, False])\n",
    "\n",
    "sw_rules.reset_index(inplace= True, drop= True)\n",
    "\n",
    "sw_rules.head()"
   ]
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  {
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   "execution_count": 25,
   "id": "0b688fde-ade0-4c28-a841-3d6b11468c0b",
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       "      <td>0.033951</td>\n",
       "      <td>1.203704</td>\n",
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       "      <td>0.025034</td>\n",
       "      <td>1.150206</td>\n",
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       "      <td>0.240741</td>\n",
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       "      <td>0.074074</td>\n",
       "      <td>0.307692</td>\n",
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       "      <td>0.020576</td>\n",
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      ],
      "text/plain": [
       "                antecedents     consequents  antecedent support  \\\n",
       "4            (22551, 20724)         (20717)            0.074074   \n",
       "6            (23388, 22554)         (23389)            0.074074   \n",
       "8     (22554, 22551, 20724)         (20717)            0.074074   \n",
       "9            (20717, 22554)  (22551, 20724)            0.074074   \n",
       "10           (22551, 20724)  (20717, 22554)            0.074074   \n",
       "...                     ...             ...                 ...   \n",
       "2792         (22551, 22554)         (23199)            0.240741   \n",
       "2793         (22551, 22554)  (22326, 22029)            0.240741   \n",
       "2794         (22551, 22554)         (20685)            0.240741   \n",
       "2795         (22551, 22554)         (22029)            0.240741   \n",
       "2796         (22551, 22554)         (22423)            0.240741   \n",
       "\n",
       "      consequent support   support  confidence       lift  leverage  \\\n",
       "4               0.074074  0.074074    1.000000  13.500000  0.068587   \n",
       "6               0.074074  0.074074    1.000000  13.500000  0.068587   \n",
       "8               0.074074  0.074074    1.000000  13.500000  0.068587   \n",
       "9               0.074074  0.074074    1.000000  13.500000  0.068587   \n",
       "10              0.074074  0.074074    1.000000  13.500000  0.068587   \n",
       "...                  ...       ...         ...        ...       ...   \n",
       "2792            0.129630  0.074074    0.307692   2.373626  0.042867   \n",
       "2793            0.129630  0.074074    0.307692   2.373626  0.042867   \n",
       "2794            0.166667  0.074074    0.307692   1.846154  0.033951   \n",
       "2795            0.203704  0.074074    0.307692   1.510490  0.025034   \n",
       "2796            0.222222  0.074074    0.307692   1.384615  0.020576   \n",
       "\n",
       "      conviction  \n",
       "4            inf  \n",
       "6            inf  \n",
       "8            inf  \n",
       "9            inf  \n",
       "10           inf  \n",
       "...          ...  \n",
       "2792    1.257202  \n",
       "2793    1.257202  \n",
       "2794    1.203704  \n",
       "2795    1.150206  \n",
       "2796    1.123457  \n",
       "\n",
       "[1571 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond0 = sw_rules.antecedents.apply(lambda x: False if 'POST' in x else True)\n",
    "\n",
    "cond1 = sw_rules.consequents.apply(lambda x: False if 'POST' in x else True)\n",
    "\n",
    "cond2 = sw_rules['antecedents'].apply(lambda x: len(x) >= 2)\n",
    "\n",
    "sw_rules.loc[cond0 & cond1 & cond2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "092f777d-8a79-48c6-9a9b-cebe23df8513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "antecedent support   -0.085105\n",
       "consequent support   -0.644130\n",
       "support              -0.110587\n",
       "confidence                 NaN\n",
       "lift                  0.178165\n",
       "leverage              0.070309\n",
       "conviction                 NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw_rules.corrwith(rules, numeric_only=True)"
   ]
  }
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