Market Basket Analysis Using mlxtend package.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:06:31.995850Z",
     "start_time": "2019-05-31T07:06:31.991848Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset = [['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']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load require packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:06:33.420200Z",
     "start_time": "2019-05-31T07:06:32.872045Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from mlxtend.preprocessing import TransactionEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:07:40.308048Z",
     "start_time": "2019-05-31T07:07:40.305017Z"
    }
   },
   "outputs": [],
   "source": [
    "te = TransactionEncoder()\n",
    "te_try = te.fit(dataset).transform(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:07:52.796813Z",
     "start_time": "2019-05-31T07:07:52.793823Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(te_try, columns=te.columns_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:07:53.406448Z",
     "start_time": "2019-05-31T07:07:53.392417Z"
    }
   },
   "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",
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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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:08:10.253153Z",
     "start_time": "2019-05-31T07:08:10.248152Z"
    }
   },
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import apriori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:08:54.321311Z",
     "start_time": "2019-05-31T07:08:54.304298Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>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>(3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(3, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(8, 3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(5, 6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(8, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(10, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(8, 3, 5)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support   itemsets\n",
       "0       0.8        (3)\n",
       "1       1.0        (5)\n",
       "2       0.6        (6)\n",
       "3       0.6        (8)\n",
       "4       0.6       (10)\n",
       "5       0.8     (3, 5)\n",
       "6       0.6     (8, 3)\n",
       "7       0.6     (5, 6)\n",
       "8       0.6     (8, 5)\n",
       "9       0.6    (10, 5)\n",
       "10      0.6  (8, 3, 5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apriori(df,min_support=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training with Column Result return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:10:31.442464Z",
     "start_time": "2019-05-31T07:10:31.424494Z"
    }
   },
   "outputs": [
    {
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       "  <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>(Eggs, Kidney Beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Onion)</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>(Yogurt, Kidney Beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Kidney Beans, Onion)</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         (Eggs, Kidney Beans)\n",
       "6       0.6                (Eggs, Onion)\n",
       "7       0.6         (Kidney Beans, Milk)\n",
       "8       0.6        (Kidney Beans, Onion)\n",
       "9       0.6       (Yogurt, Kidney Beans)\n",
       "10      0.6  (Eggs, Kidney Beans, Onion)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apriori(df,min_support=0.5, use_colnames=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the length of Itemset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:10:38.892160Z",
     "start_time": "2019-05-31T07:10:38.872161Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "      <th>length</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(Kidney Beans)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Milk)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Onion)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Yogurt)</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs, Kidney Beans)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Onion)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Milk)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Onion)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Yogurt, Kidney Beans)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Kidney Beans, Onion)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                     itemsets  length\n",
       "0       0.8                       (Eggs)       1\n",
       "1       1.0               (Kidney Beans)       1\n",
       "2       0.6                       (Milk)       1\n",
       "3       0.6                      (Onion)       1\n",
       "4       0.6                     (Yogurt)       1\n",
       "5       0.8         (Eggs, Kidney Beans)       2\n",
       "6       0.6                (Eggs, Onion)       2\n",
       "7       0.6         (Kidney Beans, Milk)       2\n",
       "8       0.6        (Kidney Beans, Onion)       2\n",
       "9       0.6       (Yogurt, Kidney Beans)       2\n",
       "10      0.6  (Eggs, Kidney Beans, Onion)       3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets = apriori(df, min_support=0.6, use_colnames=True)\n",
    "frequent_itemsets['length'] = frequent_itemsets['itemsets'].apply(lambda x: len(x))\n",
    "frequent_itemsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Length is 2 and Support is > 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:19:32.894284Z",
     "start_time": "2019-05-31T07:19:32.882283Z"
    }
   },
   "outputs": [
    {
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       "<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",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs, Kidney Beans)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   support              itemsets  length\n",
       "5      0.8  (Eggs, Kidney Beans)       2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "frequent_itemsets[ (frequent_itemsets['length'] == 2) & (frequent_itemsets['support'] >= 0.8) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:20:32.354382Z",
     "start_time": "2019-05-31T07:20:32.344415Z"
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   "outputs": [
    {
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       "   support       itemsets  length\n",
       "6      0.6  (Eggs, Onion)       2"
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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>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>4</th>\n",
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       "      <td>True</td>\n",
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      "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": 18,
     "metadata": {},
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    }
   ],
   "source": [
    "oht_ary = te.fit(dataset).transform(dataset, sparse=True)\n",
    "sparse_df = pd.SparseDataFrame(te_try, columns=te.columns_, default_fill_value=False)\n",
    "sparse_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verbose return the number of iteration and itemset default size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:21:32.834307Z",
     "start_time": "2019-05-31T07:21:32.808308Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Iteration: 1 | Sampling itemset size 2\r",
      "Iteration: 2 | Sampling itemset size 2\r",
      "Iteration: 3 | Sampling itemset size 2\r",
      "Iteration: 4 | Sampling itemset size 2\r",
      "Iteration: 5 | Sampling itemset size 2\r",
      "Iteration: 6 | Sampling itemset size 2\r",
      "Iteration: 7 | Sampling itemset size 2\r",
      "Iteration: 8 | Sampling itemset size 2\r",
      "Iteration: 9 | Sampling itemset size 2\r",
      "Iteration: 10 | Sampling itemset size 2\r",
      "Iteration: 11 | Sampling itemset size 3\r",
      "Iteration: 12 | Sampling itemset size 3\r",
      "Iteration: 13 | Sampling itemset size 3\r",
      "Iteration: 14 | Sampling itemset size 3\r",
      "Iteration: 15 | Sampling itemset size 3\r",
      "Iteration: 16 | Sampling itemset size 3\r",
      "Iteration: 17 | Sampling itemset size 3\n"
     ]
    },
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       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Milk)</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Onion)</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>(Yogurt, Kidney Beans)</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Kidney Beans, Onion)</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         (Eggs, Kidney Beans)\n",
       "6       0.6                (Eggs, Onion)\n",
       "7       0.6         (Kidney Beans, Milk)\n",
       "8       0.6        (Kidney Beans, Onion)\n",
       "9       0.6       (Yogurt, Kidney Beans)\n",
       "10      0.6  (Eggs, Kidney Beans, Onion)"
      ]
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   "metadata": {},
   "source": [
    "## Using Max_len set the itemset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T07:23:00.978675Z",
     "start_time": "2019-05-31T07:23:00.953698Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Iteration: 1 | Sampling itemset size 2\r",
      "Iteration: 2 | Sampling itemset size 2\r",
      "Iteration: 3 | Sampling itemset size 2\r",
      "Iteration: 4 | Sampling itemset size 2\r",
      "Iteration: 5 | Sampling itemset size 2\r",
      "Iteration: 6 | Sampling itemset size 2\r",
      "Iteration: 7 | Sampling itemset size 2\r",
      "Iteration: 8 | Sampling itemset size 2\r",
      "Iteration: 9 | Sampling itemset size 2\r",
      "Iteration: 10 | Sampling itemset size 2\r",
      "Iteration: 11 | Sampling itemset size 3\r",
      "Iteration: 12 | Sampling itemset size 3\r",
      "Iteration: 13 | Sampling itemset size 3\r",
      "Iteration: 14 | Sampling itemset size 3\r",
      "Iteration: 15 | Sampling itemset size 3\r",
      "Iteration: 16 | Sampling itemset size 3\r",
      "Iteration: 17 | Sampling itemset size 3\n"
     ]
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       "    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         (Eggs, Kidney Beans)\n",
       "6       0.6                (Eggs, Onion)\n",
       "7       0.6         (Kidney Beans, Milk)\n",
       "8       0.6        (Kidney Beans, Onion)\n",
       "9       0.6       (Yogurt, Kidney Beans)\n",
       "10      0.6  (Eggs, Kidney Beans, Onion)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
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   ],
   "source": [
    "apriori(sparse_df, min_support=0.6, use_colnames=True, verbose=1, max_len=3)"
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