12.3 Market Basket Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取交易数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chutney</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>turkey</td>\n",
       "      <td>avocado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "              0          1           2                 3             4   \\\n",
       "0         shrimp    almonds     avocado    vegetables mix  green grapes   \n",
       "1        burgers  meatballs        eggs               NaN           NaN   \n",
       "2        chutney        NaN         NaN               NaN           NaN   \n",
       "3         turkey    avocado         NaN               NaN           NaN   \n",
       "4  mineral water       milk  energy bar  whole wheat rice     green tea   \n",
       "\n",
       "                 5     6               7             8             9   \\\n",
       "0  whole weat flour  yams  cottage cheese  energy drink  tomato juice   \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",
       "\n",
       "               10         11     12     13             14      15  \\\n",
       "0  low fat yogurt  green tea  honey  salad  mineral water  salmon   \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",
       "\n",
       "                  16               17       18         19  \n",
       "0  antioxydant juice  frozen smoothie  spinach  olive oil  \n",
       "1                NaN              NaN      NaN        NaN  \n",
       "2                NaN              NaN      NaN        NaN  \n",
       "3                NaN              NaN      NaN        NaN  \n",
       "4                NaN              NaN      NaN        NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('Data\\Market_Basket.csv', header = None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 增添交易纪录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "transactions = []\n",
    "for i in range(0, 7501):\n",
    "    transactions.append([str(df.values[i,j]) for j in range(0, 20)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#transactions[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用Apriori 产生关联规则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from apyori import apriori\n",
    "rules = apriori(transactions, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2)\n",
    "\n",
    "# Visualising the results\n",
    "results = list(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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)])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 产生关联规则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for rec in results:\n",
    "    left_hands = rec.ordered_statistics[0].items_base\n",
    "    right_hands = rec.ordered_statistics[0].items_add\n",
    "    l = ';'.join([item for item in left_hands])\n",
    "    r = ';'.join([item for item in right_hands])\n",
    "    #print('{} => {}'.format(l,r))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 产生频繁交易集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('milk', 'spaghetti')\n",
      "('milk', 'avocado')\n",
      "('spaghetti', 'avocado')\n"
     ]
    }
   ],
   "source": [
    "# 'milk', 'spaghetti', 'avocado'\n",
    "#'milk', 'spaghetti'\n",
    "#'milk',  'avocado'\n",
    "#'spaghetti', 'avocado'\n",
    "import itertools\n",
    "for ele in itertools.combinations(['milk', 'spaghetti', 'avocado'], 2):\n",
    "    print(ele)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "itemsets = []\n",
    "for rec in results:    \n",
    "    #print(rec.items)\n",
    "    for ele in itertools.combinations(rec.items, 2):\n",
    "        itemsets.append(ele)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#itemsets[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "df2 = pandas.DataFrame(itemsets)\n",
    "df2.columns = ['Source','Target']\n",
    "df2['Type'] = 'undirected'\n",
    "df2.to_csv('transactions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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