Associative Rule Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Associative Rule Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The types of classification models we will look at here are the following\n",
    "\n",
    "<ul>\n",
    "    <li>Apriori</li>\n",
    "    <li>Eclat</li>\n",
    "</ul>\n",
    "\n",
    "The required dataset can be found at <b>datasets/associative_rule_learning.csv</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Import and Preprocessing"
   ]
  },
  {
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   "execution_count": 8,
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       "      <th>0</th>\n",
       "      <td>shrimp</td>\n",
       "      <td>almonds</td>\n",
       "      <td>avocado</td>\n",
       "      <td>vegetables mix</td>\n",
       "      <td>green grapes</td>\n",
       "      <td>whole weat flour</td>\n",
       "      <td>yams</td>\n",
       "      <td>cottage cheese</td>\n",
       "      <td>energy drink</td>\n",
       "      <td>tomato juice</td>\n",
       "      <td>low fat yogurt</td>\n",
       "      <td>green tea</td>\n",
       "      <td>honey</td>\n",
       "      <td>salad</td>\n",
       "      <td>mineral water</td>\n",
       "      <td>salmon</td>\n",
       "      <td>antioxydant juice</td>\n",
       "      <td>frozen smoothie</td>\n",
       "      <td>spinach</td>\n",
       "      <td>olive oil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>burgers</td>\n",
       "      <td>meatballs</td>\n",
       "      <td>eggs</td>\n",
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       "      <td>chutney</td>\n",
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       "      <th>3</th>\n",
       "      <td>turkey</td>\n",
       "      <td>avocado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mineral water</td>\n",
       "      <td>milk</td>\n",
       "      <td>energy bar</td>\n",
       "      <td>whole wheat rice</td>\n",
       "      <td>green tea</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>5</th>\n",
       "      <td>low fat yogurt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>6</th>\n",
       "      <td>whole wheat pasta</td>\n",
       "      <td>french fries</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>soup</td>\n",
       "      <td>light cream</td>\n",
       "      <td>shallot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>frozen vegetables</td>\n",
       "      <td>spaghetti</td>\n",
       "      <td>green tea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>french fries</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                  0             1           2                 3   \\\n",
       "0             shrimp       almonds     avocado    vegetables mix   \n",
       "1            burgers     meatballs        eggs               NaN   \n",
       "2            chutney           NaN         NaN               NaN   \n",
       "3             turkey       avocado         NaN               NaN   \n",
       "4      mineral water          milk  energy bar  whole wheat rice   \n",
       "5     low fat yogurt           NaN         NaN               NaN   \n",
       "6  whole wheat pasta  french fries         NaN               NaN   \n",
       "7               soup   light cream     shallot               NaN   \n",
       "8  frozen vegetables     spaghetti   green tea               NaN   \n",
       "9       french fries           NaN         NaN               NaN   \n",
       "\n",
       "             4                 5     6               7             8   \\\n",
       "0  green grapes  whole weat flour  yams  cottage cheese  energy drink   \n",
       "1           NaN               NaN   NaN             NaN           NaN   \n",
       "2           NaN               NaN   NaN             NaN           NaN   \n",
       "3           NaN               NaN   NaN             NaN           NaN   \n",
       "4     green tea               NaN   NaN             NaN           NaN   \n",
       "5           NaN               NaN   NaN             NaN           NaN   \n",
       "6           NaN               NaN   NaN             NaN           NaN   \n",
       "7           NaN               NaN   NaN             NaN           NaN   \n",
       "8           NaN               NaN   NaN             NaN           NaN   \n",
       "9           NaN               NaN   NaN             NaN           NaN   \n",
       "\n",
       "             9               10         11     12     13             14  \\\n",
       "0  tomato juice  low fat yogurt  green tea  honey  salad  mineral water   \n",
       "1           NaN             NaN        NaN    NaN    NaN            NaN   \n",
       "2           NaN             NaN        NaN    NaN    NaN            NaN   \n",
       "3           NaN             NaN        NaN    NaN    NaN            NaN   \n",
       "4           NaN             NaN        NaN    NaN    NaN            NaN   \n",
       "5           NaN             NaN        NaN    NaN    NaN            NaN   \n",
       "6           NaN             NaN        NaN    NaN    NaN            NaN   \n",
       "7           NaN             NaN        NaN    NaN    NaN            NaN   \n",
       "8           NaN             NaN        NaN    NaN    NaN            NaN   \n",
       "9           NaN             NaN        NaN    NaN    NaN            NaN   \n",
       "\n",
       "       15                 16               17       18         19  \n",
       "0  salmon  antioxydant juice  frozen smoothie  spinach  olive oil  \n",
       "1     NaN                NaN              NaN      NaN        NaN  \n",
       "2     NaN                NaN              NaN      NaN        NaN  \n",
       "3     NaN                NaN              NaN      NaN        NaN  \n",
       "4     NaN                NaN              NaN      NaN        NaN  \n",
       "5     NaN                NaN              NaN      NaN        NaN  \n",
       "6     NaN                NaN              NaN      NaN        NaN  \n",
       "7     NaN                NaN              NaN      NaN        NaN  \n",
       "8     NaN                NaN              NaN      NaN        NaN  \n",
       "9     NaN                NaN              NaN      NaN        NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline \n",
    "plt.rcParams['figure.figsize'] = (6,6)\n",
    "\n",
    "dataset = pd.read_csv('datasets/associative_rule_learning.csv', header=None)\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Apriori"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This dataset contains a list of items purchased in a single transaction for a store. The apriori method will tell us given the purchase of item_1, which other items were most likely purchased in the same transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions = dataset.values.tolist()\n",
    "filtered_transactions = list()\n",
    "for t in transactions:\n",
    "    filtered_transactions.append([_t for _t in t if str(_t) != 'nan'])\n",
    "from dependencies.apyori import apriori\n",
    "\n",
    "rules = apriori(filtered_transactions, min_support=0.003, min_confidence=0.2, min_lift=3, max_length=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RelationRecord(items=frozenset({'chicken', 'light cream'}), support=0.004532728969470737, ordered_statistics=[OrderedStatistic(items_base=frozenset({'light cream'}), items_add=frozenset({'chicken'}), confidence=0.29059829059829057, lift=4.84395061728395)]) \n",
      "\n",
      "RelationRecord(items=frozenset({'mushroom cream sauce', 'escalope'}), support=0.005732568990801226, ordered_statistics=[OrderedStatistic(items_base=frozenset({'mushroom cream sauce'}), items_add=frozenset({'escalope'}), confidence=0.3006993006993007, lift=3.790832696715049)]) \n",
      "\n",
      "RelationRecord(items=frozenset({'pasta', 'escalope'}), support=0.005865884548726837, ordered_statistics=[OrderedStatistic(items_base=frozenset({'pasta'}), items_add=frozenset({'escalope'}), confidence=0.3728813559322034, lift=4.700811850163794)]) \n",
      "\n",
      "RelationRecord(items=frozenset({'fromage blanc', 'honey'}), support=0.003332888948140248, ordered_statistics=[OrderedStatistic(items_base=frozenset({'fromage blanc'}), items_add=frozenset({'honey'}), confidence=0.2450980392156863, lift=5.164270764485569)]) \n",
      "\n",
      "RelationRecord(items=frozenset({'ground beef', 'herb & pepper'}), support=0.015997866951073192, ordered_statistics=[OrderedStatistic(items_base=frozenset({'herb & pepper'}), items_add=frozenset({'ground beef'}), confidence=0.3234501347708895, lift=3.2919938411349285)]) \n",
      "\n",
      "RelationRecord(items=frozenset({'ground beef', 'tomato sauce'}), support=0.005332622317024397, ordered_statistics=[OrderedStatistic(items_base=frozenset({'tomato sauce'}), items_add=frozenset({'ground beef'}), confidence=0.3773584905660377, lift=3.840659481324083)]) \n",
      "\n",
      "RelationRecord(items=frozenset({'olive oil', 'light cream'}), support=0.003199573390214638, ordered_statistics=[OrderedStatistic(items_base=frozenset({'light cream'}), items_add=frozenset({'olive oil'}), confidence=0.20512820512820515, lift=3.1147098515519573)]) \n",
      "\n",
      "RelationRecord(items=frozenset({'olive oil', 'whole wheat pasta'}), support=0.007998933475536596, ordered_statistics=[OrderedStatistic(items_base=frozenset({'whole wheat pasta'}), items_add=frozenset({'olive oil'}), confidence=0.2714932126696833, lift=4.122410097642296)]) \n",
      "\n",
      "RelationRecord(items=frozenset({'pasta', 'shrimp'}), support=0.005065991201173177, ordered_statistics=[OrderedStatistic(items_base=frozenset({'pasta'}), items_add=frozenset({'shrimp'}), confidence=0.3220338983050847, lift=4.506672147735896)]) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for r in rules:\n",
    "    print(r, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The above rules tell us that, chicken and light-cream were present in 0.45% (support) of the given transaction set. Also, if a txn contained the purchase of light-cream, there was a 29% chance (confidence) that the user also purchased chicken. This 29% chance for this population is 4.84 times the chance of a transaction containing chicken in the entire population of transactions."
   ]
  }
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