1_example.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM1DGTohBihmjW/D2dfLEvH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sudo343/Foundations-of-ML-MA722/blob/main/1_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqrbPI7VrWNd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRcBZlXptneN"
      },
      "source": [
        "## **Apriori Algorithm for Market basket analysis.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Wl_tC4et4mO"
      },
      "source": [
        "install, then import concerned libraries.\n",
        "\" arules \""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OblcIYHLtx_V",
        "outputId": "11062e6d-fc1c-46ff-f51b-eb06cd9d2b19"
      },
      "source": [
        "install.packages(\"arules\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mk-U_UyBuGTx",
        "outputId": "dba87725-970f-4b1e-f205-933804cf43f3"
      },
      "source": [
        "library(arules)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading required package: Matrix\n",
            "\n",
            "\n",
            "Attaching package: ‘arules’\n",
            "\n",
            "\n",
            "The following objects are masked from ‘package:base’:\n",
            "\n",
            "    abbreviate, write\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r829SSIGu0BS"
      },
      "source": [
        "Load the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUcj4VC1u7UT"
      },
      "source": [
        "mydata = read.csv(\"/content/Example1.csv\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EOny4cGvIUd"
      },
      "source": [
        "Let's examine the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "wUBYUD8MvNK6",
        "outputId": "72106a1f-e739-4b9f-9f3e-30be19c8b5dd"
      },
      "source": [
        "View(mydata)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "\nA data.frame: 5 × 6\n\n| Bread &lt;chr&gt; | Cheese &lt;chr&gt; | Eggs &lt;chr&gt; | Juice &lt;chr&gt; | Milk &lt;chr&gt; | Yogurt &lt;chr&gt; |\n|---|---|---|---|---|---|\n| Yes | Yes | Yes | Yes | No  | No  |\n| Yes | Yes | No  | Yes | No  | No  |\n| Yes | No  | No  | No  | Yes | Yes |\n| Yes | No  | No  | No  | Yes | No  |\n| No  | Yes | No  | Yes | Yes | No  |\n\n",
            "text/latex": "A data.frame: 5 × 6\n\\begin{tabular}{llllll}\n Bread & Cheese & Eggs & Juice & Milk & Yogurt\\\\\n <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n\\hline\n\t Yes & Yes & Yes & Yes & No  & No \\\\\n\t Yes & Yes & No  & Yes & No  & No \\\\\n\t Yes & No  & No  & No  & Yes & Yes\\\\\n\t Yes & No  & No  & No  & Yes & No \\\\\n\t No  & Yes & No  & Yes & Yes & No \\\\\n\\end{tabular}\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 5 × 6</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Bread</th><th scope=col>Cheese</th><th scope=col>Eggs</th><th scope=col>Juice</th><th scope=col>Milk</th><th scope=col>Yogurt</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>Yes</td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>Yes</td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>No </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "  Bread Cheese Eggs Juice Milk Yogurt\n",
              "1 Yes   Yes    Yes  Yes   No   No    \n",
              "2 Yes   Yes    No   Yes   No   No    \n",
              "3 Yes   No     No   No    Yes  Yes   \n",
              "4 Yes   No     No   No    Yes  No    \n",
              "5 No    Yes    No   Yes   Yes  No    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPz-82FmvlP7"
      },
      "source": [
        "### Apply apriori on the given data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYG1Kz_Xv_aa",
        "outputId": "fb378a35-b677-42d6-ec09-9d632ed01e0b"
      },
      "source": [
        "rules = apriori(mydata)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“Column(s) 1, 2, 3, 4, 5, 6 not logical or factor. Applying default discretization (see '? discretizeDF').”\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apriori\n",
            "\n",
            "Parameter specification:\n",
            " confidence minval smax arem  aval originalSupport maxtime support minlen\n",
            "        0.8    0.1    1 none FALSE            TRUE       5     0.1      1\n",
            " maxlen target  ext\n",
            "     10  rules TRUE\n",
            "\n",
            "Algorithmic control:\n",
            " filter tree heap memopt load sort verbose\n",
            "    0.1 TRUE TRUE  FALSE TRUE    2    TRUE\n",
            "\n",
            "Absolute minimum support count: 0 \n",
            "\n",
            "set item appearances ...[0 item(s)] done [0.00s].\n",
            "set transactions ...[12 item(s), 5 transaction(s)] done [0.00s].\n",
            "sorting and recoding items ... [12 item(s)] done [0.00s].\n",
            "creating transaction tree ... done [0.00s].\n",
            "checking subsets of size 1 2 3 4 5 6 done [0.00s].\n",
            "writing ... [495 rule(s)] done [0.00s].\n",
            "creating S4 object  ... done [0.00s].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkMIbQ4KwILz"
      },
      "source": [
        "examine the \" rules \" variable we just created"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "v-msXjG_wNjK",
        "outputId": "6f1dceed-c352-464f-cdc8-b43fbba34e84"
      },
      "source": [
        "rules"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 495 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKGbhzL7wUFO"
      },
      "source": [
        "so we have 495 rules from the dataset.\n",
        "lets use inspect on rules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bhDIaNZ6wh9F",
        "outputId": "40d690dd-de74-4d3c-b58a-72d095ac622b"
      },
      "source": [
        "inspect(rules)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      lhs             rhs          support confidence coverage     lift count\n",
            "[1]   {}           => {Bread=Yes}      0.8        0.8      1.0 1.000000     4\n",
            "[2]   {}           => {Yogurt=No}      0.8        0.8      1.0 1.000000     4\n",
            "[3]   {}           => {Eggs=No}        0.8        0.8      1.0 1.000000     4\n",
            "[4]   {Eggs=Yes}   => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[5]   {Eggs=Yes}   => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[6]   {Eggs=Yes}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[7]   {Eggs=Yes}   => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[8]   {Eggs=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[9]   {Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[10]  {Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[11]  {Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[12]  {Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[13]  {Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[14]  {Bread=No}   => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[15]  {Bread=No}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[16]  {Bread=No}   => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[17]  {Bread=No}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[18]  {Bread=No}   => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[19]  {Milk=No}    => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[20]  {Milk=No}    => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[21]  {Milk=No}    => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[22]  {Milk=No}    => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[23]  {Cheese=No}  => {Juice=No}       0.4        1.0      0.4 2.500000     2\n",
            "[24]  {Juice=No}   => {Cheese=No}      0.4        1.0      0.4 2.500000     2\n",
            "[25]  {Cheese=No}  => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[26]  {Cheese=No}  => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[27]  {Cheese=No}  => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[28]  {Juice=No}   => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[29]  {Juice=No}   => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[30]  {Juice=No}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[31]  {Cheese=Yes} => {Juice=Yes}      0.6        1.0      0.6 1.666667     3\n",
            "[32]  {Juice=Yes}  => {Cheese=Yes}     0.6        1.0      0.6 1.666667     3\n",
            "[33]  {Cheese=Yes} => {Yogurt=No}      0.6        1.0      0.6 1.250000     3\n",
            "[34]  {Juice=Yes}  => {Yogurt=No}      0.6        1.0      0.6 1.250000     3\n",
            "[35]  {Milk=Yes}   => {Eggs=No}        0.6        1.0      0.6 1.250000     3\n",
            "[36]  {Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[37]  {Cheese=Yes,                                                           \n",
            "       Eggs=Yes}   => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[38]  {Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[39]  {Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[40]  {Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[41]  {Bread=Yes,                                                            \n",
            "       Eggs=Yes}   => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[42]  {Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[43]  {Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[44]  {Cheese=Yes,                                                           \n",
            "       Eggs=Yes}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[45]  {Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[46]  {Cheese=Yes,                                                           \n",
            "       Eggs=Yes}   => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[47]  {Bread=Yes,                                                            \n",
            "       Eggs=Yes}   => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[48]  {Cheese=Yes,                                                           \n",
            "       Eggs=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[49]  {Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[50]  {Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[51]  {Bread=Yes,                                                            \n",
            "       Eggs=Yes}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[52]  {Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[53]  {Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[54]  {Bread=Yes,                                                            \n",
            "       Eggs=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[55]  {Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[56]  {Cheese=No,                                                            \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[57]  {Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[58]  {Cheese=No,                                                            \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[59]  {Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[60]  {Cheese=No,                                                            \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[61]  {Bread=Yes,                                                            \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[62]  {Cheese=No,                                                            \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[63]  {Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[64]  {Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[65]  {Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[66]  {Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[67]  {Bread=Yes,                                                            \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[68]  {Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[69]  {Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[70]  {Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[71]  {Bread=Yes,                                                            \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[72]  {Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[73]  {Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[74]  {Bread=Yes,                                                            \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[75]  {Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[76]  {Bread=No,                                                             \n",
            "       Cheese=Yes} => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[77]  {Bread=No,                                                             \n",
            "       Juice=Yes}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[78]  {Bread=No,                                                             \n",
            "       Cheese=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[79]  {Bread=No,                                                             \n",
            "       Milk=Yes}   => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[80]  {Cheese=Yes,                                                           \n",
            "       Milk=Yes}   => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[81]  {Bread=No,                                                             \n",
            "       Cheese=Yes} => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[82]  {Bread=No,                                                             \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[83]  {Bread=No,                                                             \n",
            "       Cheese=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[84]  {Bread=No,                                                             \n",
            "       Eggs=No}    => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[85]  {Bread=No,                                                             \n",
            "       Juice=Yes}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[86]  {Bread=No,                                                             \n",
            "       Milk=Yes}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[87]  {Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[88]  {Bread=No,                                                             \n",
            "       Juice=Yes}  => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[89]  {Bread=No,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[90]  {Bread=No,                                                             \n",
            "       Juice=Yes}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[91]  {Bread=No,                                                             \n",
            "       Eggs=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[92]  {Bread=No,                                                             \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[93]  {Bread=No,                                                             \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[94]  {Bread=No,                                                             \n",
            "       Milk=Yes}   => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[95]  {Bread=No,                                                             \n",
            "       Eggs=No}    => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[96]  {Bread=No,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[97]  {Bread=No,                                                             \n",
            "       Eggs=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[98]  {Cheese=Yes,                                                           \n",
            "       Milk=No}    => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[99]  {Juice=Yes,                                                            \n",
            "       Milk=No}    => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[100] {Cheese=Yes,                                                           \n",
            "       Milk=No}    => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[101] {Bread=Yes,                                                            \n",
            "       Milk=No}    => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[102] {Bread=Yes,                                                            \n",
            "       Cheese=Yes} => {Milk=No}        0.4        1.0      0.4 2.500000     2\n",
            "[103] {Cheese=Yes,                                                           \n",
            "       Milk=No}    => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[104] {Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[105] {Eggs=No,                                                              \n",
            "       Milk=No}    => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[106] {Juice=Yes,                                                            \n",
            "       Milk=No}    => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[107] {Bread=Yes,                                                            \n",
            "       Milk=No}    => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[108] {Bread=Yes,                                                            \n",
            "       Juice=Yes}  => {Milk=No}        0.4        1.0      0.4 2.500000     2\n",
            "[109] {Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[110] {Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[111] {Eggs=No,                                                              \n",
            "       Milk=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[112] {Bread=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[113] {Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[114] {Eggs=No,                                                              \n",
            "       Milk=No}    => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[115] {Eggs=No,                                                              \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[116] {Cheese=No,                                                            \n",
            "       Juice=No}   => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[117] {Cheese=No,                                                            \n",
            "       Milk=Yes}   => {Juice=No}       0.4        1.0      0.4 2.500000     2\n",
            "[118] {Juice=No,                                                             \n",
            "       Milk=Yes}   => {Cheese=No}      0.4        1.0      0.4 2.500000     2\n",
            "[119] {Cheese=No,                                                            \n",
            "       Juice=No}   => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[120] {Bread=Yes,                                                            \n",
            "       Cheese=No}  => {Juice=No}       0.4        1.0      0.4 2.500000     2\n",
            "[121] {Bread=Yes,                                                            \n",
            "       Juice=No}   => {Cheese=No}      0.4        1.0      0.4 2.500000     2\n",
            "[122] {Cheese=No,                                                            \n",
            "       Yogurt=No}  => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[123] {Juice=No,                                                             \n",
            "       Yogurt=No}  => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[124] {Cheese=No,                                                            \n",
            "       Juice=No}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[125] {Cheese=No,                                                            \n",
            "       Eggs=No}    => {Juice=No}       0.4        1.0      0.4 2.500000     2\n",
            "[126] {Eggs=No,                                                              \n",
            "       Juice=No}   => {Cheese=No}      0.4        1.0      0.4 2.500000     2\n",
            "[127] {Cheese=No,                                                            \n",
            "       Milk=Yes}   => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[128] {Bread=Yes,                                                            \n",
            "       Cheese=No}  => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[129] {Bread=Yes,                                                            \n",
            "       Milk=Yes}   => {Cheese=No}      0.4        1.0      0.4 2.500000     2\n",
            "[130] {Cheese=No,                                                            \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[131] {Cheese=No,                                                            \n",
            "       Milk=Yes}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[132] {Cheese=No,                                                            \n",
            "       Eggs=No}    => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[133] {Cheese=No,                                                            \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[134] {Bread=Yes,                                                            \n",
            "       Cheese=No}  => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[135] {Cheese=No,                                                            \n",
            "       Eggs=No}    => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[136] {Cheese=No,                                                            \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[137] {Juice=No,                                                             \n",
            "       Milk=Yes}   => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[138] {Bread=Yes,                                                            \n",
            "       Juice=No}   => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[139] {Bread=Yes,                                                            \n",
            "       Milk=Yes}   => {Juice=No}       0.4        1.0      0.4 2.500000     2\n",
            "[140] {Juice=No,                                                             \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[141] {Juice=No,                                                             \n",
            "       Milk=Yes}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[142] {Eggs=No,                                                              \n",
            "       Juice=No}   => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[143] {Juice=No,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[144] {Bread=Yes,                                                            \n",
            "       Juice=No}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[145] {Eggs=No,                                                              \n",
            "       Juice=No}   => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[146] {Juice=No,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[147] {Cheese=Yes,                                                           \n",
            "       Milk=Yes}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[148] {Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[149] {Bread=Yes,                                                            \n",
            "       Cheese=Yes} => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[150] {Bread=Yes,                                                            \n",
            "       Juice=Yes}  => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[151] {Cheese=Yes,                                                           \n",
            "       Juice=Yes}  => {Yogurt=No}      0.6        1.0      0.6 1.250000     3\n",
            "[152] {Cheese=Yes,                                                           \n",
            "       Yogurt=No}  => {Juice=Yes}      0.6        1.0      0.6 1.666667     3\n",
            "[153] {Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.6        1.0      0.6 1.666667     3\n",
            "[154] {Cheese=Yes,                                                           \n",
            "       Eggs=No}    => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[155] {Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[156] {Cheese=Yes,                                                           \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[157] {Cheese=Yes,                                                           \n",
            "       Milk=Yes}   => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[158] {Bread=Yes,                                                            \n",
            "       Cheese=Yes} => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[159] {Cheese=Yes,                                                           \n",
            "       Eggs=No}    => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[160] {Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[161] {Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[162] {Bread=Yes,                                                            \n",
            "       Juice=Yes}  => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[163] {Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[164] {Bread=Yes,                                                            \n",
            "       Milk=Yes}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[165] {Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[166] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[167] {Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[168] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[169] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[170] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[171] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes}   => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[172] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[173] {Eggs=Yes,                                                             \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[174] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[175] {Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[176] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[177] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[178] {Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[179] {Eggs=Yes,                                                             \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[180] {Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[181] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[182] {Eggs=Yes,                                                             \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[183] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[184] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[185] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[186] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[187] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[188] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[189] {Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[190] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[191] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[192] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[193] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[194] {Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[195] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[196] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[197] {Cheese=No,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[198] {Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[199] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[200] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[201] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[202] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[203] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[204] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[205] {Cheese=No,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[206] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[207] {Bread=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[208] {Cheese=No,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[209] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[210] {Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[211] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[212] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[213] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[214] {Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[215] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[216] {Bread=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[217] {Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[218] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[219] {Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[220] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[221] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[222] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[223] {Bread=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[224] {Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[225] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[226] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[227] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Milk=Yes}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[228] {Bread=No,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[229] {Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[230] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes}  => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[231] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[232] {Bread=No,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[233] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[234] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[235] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[236] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[237] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[238] {Bread=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[239] {Cheese=Yes,                                                           \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[240] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Milk=Yes}   => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[241] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No}    => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[242] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[243] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[244] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[245] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[246] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[247] {Bread=No,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[248] {Bread=No,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[249] {Bread=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[250] {Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[251] {Bread=No,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[252] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[253] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[254] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[255] {Bread=No,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[256] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[257] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[258] {Bread=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[259] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[260] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[261] {Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[262] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Milk=No}    => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[263] {Bread=Yes,                                                            \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[264] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes}  => {Milk=No}        0.4        1.0      0.4 2.500000     2\n",
            "[265] {Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[266] {Cheese=Yes,                                                           \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[267] {Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[268] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[269] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[270] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Milk=No}    => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[271] {Cheese=Yes,                                                           \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[272] {Bread=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[273] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Yogurt=No}  => {Milk=No}        0.4        1.0      0.4 2.500000     2\n",
            "[274] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No}    => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[275] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No}    => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[276] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No}    => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[277] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[278] {Eggs=No,                                                              \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[279] {Bread=Yes,                                                            \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[280] {Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[281] {Bread=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[282] {Bread=Yes,                                                            \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=No}        0.4        1.0      0.4 2.500000     2\n",
            "[283] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[284] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[285] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[286] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[287] {Eggs=No,                                                              \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[288] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[289] {Eggs=No,                                                              \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[290] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes}   => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[291] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Juice=No}   => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[292] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Milk=Yes}   => {Juice=No}       0.4        1.0      0.4 2.500000     2\n",
            "[293] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes}   => {Cheese=No}      0.4        1.0      0.4 2.500000     2\n",
            "[294] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[295] {Cheese=No,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[296] {Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[297] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[298] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No}   => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[299] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Juice=No}       0.4        1.0      0.4 2.500000     2\n",
            "[300] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes}   => {Cheese=No}      0.4        1.0      0.4 2.500000     2\n",
            "[301] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[302] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Yogurt=No}  => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[303] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[304] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Juice=No}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[305] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No}   => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[306] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No}    => {Juice=No}       0.4        1.0      0.4 2.500000     2\n",
            "[307] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No}   => {Cheese=No}      0.4        1.0      0.4 2.500000     2\n",
            "[308] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[309] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[310] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[311] {Cheese=No,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[312] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[313] {Bread=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[314] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Milk=Yes}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[315] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[316] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No}    => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[317] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Cheese=No}      0.4        1.0      0.4 2.500000     2\n",
            "[318] {Cheese=No,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[319] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[320] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[321] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[322] {Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[323] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[324] {Bread=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[325] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[326] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes}   => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[327] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No}   => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[328] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Juice=No}       0.4        1.0      0.4 2.500000     2\n",
            "[329] {Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[330] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[331] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[332] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[333] {Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[334] {Cheese=Yes,                                                           \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[335] {Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[336] {Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[337] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[338] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[339] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes}  => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[340] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Yogurt=No}  => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[341] {Bread=Yes,                                                            \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[342] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[343] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[344] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[345] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[346] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[347] {Cheese=Yes,                                                           \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[348] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[349] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[350] {Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[351] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[352] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[353] {Bread=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[354] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[355] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[356] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[357] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[358] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[359] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[360] {Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[361] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[362] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[363] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[364] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[365] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[366] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[367] {Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[368] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[369] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[370] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes}  => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[371] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[372] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[373] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[374] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[375] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[376] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[377] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[378] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[379] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[380] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[381] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[382] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[383] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[384] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[385] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[386] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[387] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[388] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[389] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[390] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[391] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[392] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[393] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[394] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[395] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[396] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[397] {Bread=No,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[398] {Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[399] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[400] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[401] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[402] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[403] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[404] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[405] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[406] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[407] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[408] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[409] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[410] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[411] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[412] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[413] {Bread=No,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[414] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[415] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[416] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[417] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[418] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.4        1.0      0.4 1.250000     2\n",
            "[419] {Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[420] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.4        1.0      0.4 1.666667     2\n",
            "[421] {Bread=Yes,                                                            \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.4        1.0      0.4 1.666667     2\n",
            "[422] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=No}        0.4        1.0      0.4 2.500000     2\n",
            "[423] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[424] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No}    => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[425] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[426] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[427] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[428] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[429] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[430] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[431] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[432] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[433] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[434] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[435] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[436] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[437] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[438] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[439] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[440] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[441] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[442] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes}   => {Eggs=No}        0.4        1.0      0.4 1.250000     2\n",
            "[443] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes}   => {Bread=Yes}      0.4        1.0      0.4 1.250000     2\n",
            "[444] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No}   => {Milk=Yes}       0.4        1.0      0.4 1.666667     2\n",
            "[445] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes}   => {Juice=No}       0.4        1.0      0.4 2.500000     2\n",
            "[446] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes}   => {Cheese=No}      0.4        1.0      0.4 2.500000     2\n",
            "[447] {Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[448] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[449] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[450] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[451] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[452] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[453] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[454] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[455] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[456] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[457] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[458] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[459] {Bread=Yes,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[460] {Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[461] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[462] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[463] {Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[464] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[465] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[466] {Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[467] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes}  => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[468] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[469] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[470] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[471] {Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[472] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[473] {Bread=Yes,                                                            \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[474] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=Yes,                                                             \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[475] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[476] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[477] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=Yes} => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[478] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[479] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=Yes} => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n",
            "[480] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[481] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes}   => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[482] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[483] {Bread=No,                                                             \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[484] {Bread=No,                                                             \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[485] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=No}       0.2        1.0      0.2 5.000000     1\n",
            "[486] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No}    => {Yogurt=No}      0.2        1.0      0.2 1.250000     1\n",
            "[487] {Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[488] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Juice=Yes}      0.2        1.0      0.2 1.666667     1\n",
            "[489] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Milk=No,                                                              \n",
            "       Yogurt=No}  => {Cheese=Yes}     0.2        1.0      0.2 1.666667     1\n",
            "[490] {Bread=Yes,                                                            \n",
            "       Cheese=Yes,                                                           \n",
            "       Eggs=No,                                                              \n",
            "       Juice=Yes,                                                            \n",
            "       Yogurt=No}  => {Milk=No}        0.2        1.0      0.2 2.500000     1\n",
            "[491] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Eggs=No}        0.2        1.0      0.2 1.250000     1\n",
            "[492] {Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Bread=Yes}      0.2        1.0      0.2 1.250000     1\n",
            "[493] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Yogurt=No}  => {Milk=Yes}       0.2        1.0      0.2 1.666667     1\n",
            "[494] {Bread=Yes,                                                            \n",
            "       Cheese=No,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Juice=No}       0.2        1.0      0.2 2.500000     1\n",
            "[495] {Bread=Yes,                                                            \n",
            "       Eggs=No,                                                              \n",
            "       Juice=No,                                                             \n",
            "       Milk=Yes,                                                             \n",
            "       Yogurt=No}  => {Cheese=No}      0.2        1.0      0.2 2.500000     1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pB0X_m1FwtiU"
      },
      "source": [
        "### lets determine rules with constraints of support, confidence."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92HpHiaJw4jF"
      },
      "source": [
        "itemsets and maximum 3 itemsets with a support of 0.5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TY_bLd6Mw-LE",
        "outputId": "0a5c3ffb-3a76-418a-d8a0-3c3b02e0f2f6"
      },
      "source": [
        "rules = apriori(mydata, \n",
        "                parameter=list(minlen=2,\n",
        "                               maxlen=3, \n",
        "                               support=0.5))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“Column(s) 1, 2, 3, 4, 5, 6 not logical or factor. Applying default discretization (see '? discretizeDF').”\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apriori\n",
            "\n",
            "Parameter specification:\n",
            " confidence minval smax arem  aval originalSupport maxtime support minlen\n",
            "        0.8    0.1    1 none FALSE            TRUE       5     0.5      2\n",
            " maxlen target  ext\n",
            "      3  rules TRUE\n",
            "\n",
            "Algorithmic control:\n",
            " filter tree heap memopt load sort verbose\n",
            "    0.1 TRUE TRUE  FALSE TRUE    2    TRUE\n",
            "\n",
            "Absolute minimum support count: 2 \n",
            "\n",
            "set item appearances ...[0 item(s)] done [0.00s].\n",
            "set transactions ...[12 item(s), 5 transaction(s)] done [0.00s].\n",
            "sorting and recoding items ... [6 item(s)] done [0.00s].\n",
            "creating transaction tree ... done [0.00s].\n",
            "checking subsets of size 1 2 3"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message in apriori(mydata, parameter = list(minlen = 2, maxlen = 3, support = 0.5)):\n",
            "“Mining stopped (maxlen reached). Only patterns up to a length of 3 returned!”\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " done [0.00s].\n",
            "writing ... [8 rule(s)] done [0.00s].\n",
            "creating S4 object  ... done [0.00s].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJkh1WkSxNAR"
      },
      "source": [
        "examine the rules now"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "eyvgKq5-xSBo",
        "outputId": "e896cf64-c7b7-4e0b-b561-0dee06e6d551"
      },
      "source": [
        "rules"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 8 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78SY6D-WxUTu"
      },
      "source": [
        "so we brought it down to 8 rules.\n",
        "lets inspect them..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oSGuXjvfxeEG",
        "outputId": "db975773-ab8b-4d73-fd14-44d831ec90cc"
      },
      "source": [
        "inspect(rules)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs                       rhs          support confidence coverage lift    \n",
            "[1] {Cheese=Yes}           => {Juice=Yes}  0.6     1          0.6      1.666667\n",
            "[2] {Juice=Yes}            => {Cheese=Yes} 0.6     1          0.6      1.666667\n",
            "[3] {Cheese=Yes}           => {Yogurt=No}  0.6     1          0.6      1.250000\n",
            "[4] {Juice=Yes}            => {Yogurt=No}  0.6     1          0.6      1.250000\n",
            "[5] {Milk=Yes}             => {Eggs=No}    0.6     1          0.6      1.250000\n",
            "[6] {Cheese=Yes,Juice=Yes} => {Yogurt=No}  0.6     1          0.6      1.250000\n",
            "[7] {Cheese=Yes,Yogurt=No} => {Juice=Yes}  0.6     1          0.6      1.666667\n",
            "[8] {Juice=Yes,Yogurt=No}  => {Cheese=Yes} 0.6     1          0.6      1.666667\n",
            "    count\n",
            "[1] 3    \n",
            "[2] 3    \n",
            "[3] 3    \n",
            "[4] 3    \n",
            "[5] 3    \n",
            "[6] 3    \n",
            "[7] 3    \n",
            "[8] 3    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Roa-KRFgx3LV"
      },
      "source": [
        "### now Call apriori algorithm with default values with minimum 2\n",
        "itemsets and maximum 3 itemsets with a support of 0.5 and confidence of 0.75"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x7mBXeo1yDVd",
        "outputId": "7bd505b7-80da-440e-817d-37ab3fb450ff"
      },
      "source": [
        "rules = apriori(mydata, \n",
        "                parameter=list(minlen=2,\n",
        "                               maxlen=2,\n",
        "                               support=0.5, \n",
        "                               confidence = 0.75))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“Column(s) 1, 2, 3, 4, 5, 6 not logical or factor. Applying default discretization (see '? discretizeDF').”\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apriori\n",
            "\n",
            "Parameter specification:\n",
            " confidence minval smax arem  aval originalSupport maxtime support minlen\n",
            "       0.75    0.1    1 none FALSE            TRUE       5     0.5      2\n",
            " maxlen target  ext\n",
            "      2  rules TRUE\n",
            "\n",
            "Algorithmic control:\n",
            " filter tree heap memopt load sort verbose\n",
            "    0.1 TRUE TRUE  FALSE TRUE    2    TRUE\n",
            "\n",
            "Absolute minimum support count: 2 \n",
            "\n",
            "set item appearances ...[0 item(s)] done [0.00s].\n",
            "set transactions ...[12 item(s), 5 transaction(s)] done [0.00s].\n",
            "sorting and recoding items ... [6 item(s)] done [0.00s].\n",
            "creating transaction tree ... done [0.00s].\n",
            "checking subsets of size 1 2"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message in apriori(mydata, parameter = list(minlen = 2, maxlen = 2, support = 0.5, :\n",
            "“Mining stopped (maxlen reached). Only patterns up to a length of 2 returned!”\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " done [0.00s].\n",
            "writing ... [14 rule(s)] done [0.00s].\n",
            "creating S4 object  ... done [0.00s].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKlpN8PLyY66"
      },
      "source": [
        "check the new rules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBgFuzwOydZE",
        "outputId": "c75345aa-942c-4109-d0af-c058228c8f46"
      },
      "source": [
        "inspect(rules)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     lhs             rhs          support confidence coverage lift     count\n",
            "[1]  {Cheese=Yes} => {Juice=Yes}  0.6     1.00       0.6      1.666667 3    \n",
            "[2]  {Juice=Yes}  => {Cheese=Yes} 0.6     1.00       0.6      1.666667 3    \n",
            "[3]  {Cheese=Yes} => {Yogurt=No}  0.6     1.00       0.6      1.250000 3    \n",
            "[4]  {Yogurt=No}  => {Cheese=Yes} 0.6     0.75       0.8      1.250000 3    \n",
            "[5]  {Juice=Yes}  => {Yogurt=No}  0.6     1.00       0.6      1.250000 3    \n",
            "[6]  {Yogurt=No}  => {Juice=Yes}  0.6     0.75       0.8      1.250000 3    \n",
            "[7]  {Milk=Yes}   => {Eggs=No}    0.6     1.00       0.6      1.250000 3    \n",
            "[8]  {Eggs=No}    => {Milk=Yes}   0.6     0.75       0.8      1.250000 3    \n",
            "[9]  {Bread=Yes}  => {Yogurt=No}  0.6     0.75       0.8      0.937500 3    \n",
            "[10] {Yogurt=No}  => {Bread=Yes}  0.6     0.75       0.8      0.937500 3    \n",
            "[11] {Bread=Yes}  => {Eggs=No}    0.6     0.75       0.8      0.937500 3    \n",
            "[12] {Eggs=No}    => {Bread=Yes}  0.6     0.75       0.8      0.937500 3    \n",
            "[13] {Yogurt=No}  => {Eggs=No}    0.6     0.75       0.8      0.937500 3    \n",
            "[14] {Eggs=No}    => {Yogurt=No}  0.6     0.75       0.8      0.937500 3    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SS9Qj4pOykvN"
      },
      "source": [
        "use summary() on rules "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "68SB7yLnyqnT",
        "outputId": "976d981a-0bf7-4594-8aa8-da4c5885a1bf"
      },
      "source": [
        "summary(rules)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 14 rules\n",
              "\n",
              "rule length distribution (lhs + rhs):sizes\n",
              " 2 \n",
              "14 \n",
              "\n",
              "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
              "      2       2       2       2       2       2 \n",
              "\n",
              "summary of quality measures:\n",
              "    support      confidence        coverage           lift            count  \n",
              " Min.   :0.6   Min.   :0.7500   Min.   :0.6000   Min.   :0.9375   Min.   :3  \n",
              " 1st Qu.:0.6   1st Qu.:0.7500   1st Qu.:0.6000   1st Qu.:0.9375   1st Qu.:3  \n",
              " Median :0.6   Median :0.7500   Median :0.8000   Median :1.2500   Median :3  \n",
              " Mean   :0.6   Mean   :0.8393   Mean   :0.7286   Mean   :1.1756   Mean   :3  \n",
              " 3rd Qu.:0.6   3rd Qu.:1.0000   3rd Qu.:0.8000   3rd Qu.:1.2500   3rd Qu.:3  \n",
              " Max.   :0.6   Max.   :1.0000   Max.   :0.8000   Max.   :1.6667   Max.   :3  \n",
              "\n",
              "mining info:\n",
              "   data ntransactions support confidence\n",
              " mydata             5     0.5       0.75"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ua1-19RJyvqE"
      },
      "source": [
        "### Let's start visualizing the rules\n",
        "for plotting, \n",
        "use library \" arulesViz \""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooUS8p0EzLgM",
        "outputId": "edd599bf-836c-4156-8e83-543dfcf7256d"
      },
      "source": [
        "install.packages(\"arulesViz\")\n",
        "library(arulesViz)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependencies ‘foreach’, ‘TSP’, ‘crosstalk’, ‘promises’, ‘seriation’, ‘igraph’, ‘scatterplot3d’, ‘ggnetwork’, ‘DT’, ‘plotly’\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "4NwC3pevzkTG",
        "outputId": "4aec5899-52ca-45bf-d453-e96cda137b1f"
      },
      "source": [
        "plot(rules)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\njBQHAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "HiXHNFAO1nW4",
        "outputId": "7c4a0cb7-ab3b-4e10-d038-24c153e59cec"
      },
      "source": [
        "plot(rules,\"grouped\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4fsmA9U1_Qa"
      },
      "source": [
        "### Since we are only interested in \" Yes \" rules,\n",
        "set the constraints in the function,\n",
        "set lhs, rhs etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqC5jeOi2HhH",
        "outputId": "7f62103a-ba87-4fca-b50c-40673c4c3761"
      },
      "source": [
        "rules <- apriori(mydata,\n",
        "                 parameter = list(minlen=2,\n",
        "                                  maxlen=3,\n",
        "                                  supp=.01,\n",
        "                                  conf=.5),\n",
        "                 appearance = list(rhs=c(\"Bread=Yes\"),\n",
        "                                   lhs=c(\"Eggs=Yes\"),\n",
        "                                   default=\"none\"))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“Column(s) 1, 2, 3, 4, 5, 6 not logical or factor. Applying default discretization (see '? discretizeDF').”\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apriori\n",
            "\n",
            "Parameter specification:\n",
            " confidence minval smax arem  aval originalSupport maxtime support minlen\n",
            "        0.5    0.1    1 none FALSE            TRUE       5    0.01      2\n",
            " maxlen target  ext\n",
            "      3  rules TRUE\n",
            "\n",
            "Algorithmic control:\n",
            " filter tree heap memopt load sort verbose\n",
            "    0.1 TRUE TRUE  FALSE TRUE    2    TRUE\n",
            "\n",
            "Absolute minimum support count: 0 \n",
            "\n",
            "set item appearances ...[2 item(s)] done [0.00s].\n",
            "set transactions ...[2 item(s), 5 transaction(s)] done [0.00s].\n",
            "sorting and recoding items ... [2 item(s)] done [0.00s].\n",
            "creating transaction tree ... done [0.00s].\n",
            "checking subsets of size 1 2 done [0.00s].\n",
            "writing ... [1 rule(s)] done [0.00s].\n",
            "creating S4 object  ... done [0.00s].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQF0Qh-A2jXQ",
        "outputId": "d2b4757a-939a-4945-d8c0-7b0b87bc6afd"
      },
      "source": [
        "inspect(rules)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs           rhs         support confidence coverage lift count\n",
            "[1] {Eggs=Yes} => {Bread=Yes} 0.2     1          0.2      1.25 1    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "2xAZsN-a2zjp",
        "outputId": "69a7b8c7-882d-48ea-b2ee-784e261c67f8"
      },
      "source": [
        "rules"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 1 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DkIsTcVk3C2L"
      },
      "source": [
        "from above we see that those who buy eggs are more likely to buy bread.\n",
        "let's plot these rules."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "O8B9HSPQ22uj",
        "outputId": "07461415-e6ab-4004-86c9-7407ca1ea043"
      },
      "source": [
        "plot(rules)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "oMJMgrLk3TbY",
        "outputId": "c62cc18d-46e7-42fe-9fd9-44327207a311"
      },
      "source": [
        "plot(rules,\"grouped\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ERROR",
          "evalue": "ignored",
          "traceback": [
            "Error in stats::hclust(stats::dist(m_clust)): must have n >= 2 objects to cluster\nTraceback:\n",
            "1. plot(rules, \"grouped\")",
            "2. plot.rules(rules, \"grouped\")",
            "3. grouped_matrix_plot(x, measure = measure, shading = shading, \n .     control = control, ...)",
            "4. grouped_matrix_ggplot2(rules, measure, shading, control, ...)",
            "5. rules2groupedMatrix(x, shading, measure, k = control$k, aggr.fun = control$aggr.fun, \n .     lhs_label_items = control$lhs_label_items)",
            "6. stats::cutree(stats::hclust(stats::dist(m_clust)), h = 0)",
            "7. nrow(tree$merge)",
            "8. stats::hclust(stats::dist(m_clust))",
            "9. stop(\"must have n >= 2 objects to cluster\")"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "2SXpkQqt3cmS",
        "outputId": "4e954034-3279-4b0d-a253-a0ba744aa679"
      },
      "source": [
        "plot(rules,\"graph\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nRU5ErkJggg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tuO3rhL23tPB"
      },
      "source": [
        "### Let's explore for more rules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkdZFazD3y14",
        "outputId": "a77611ed-fe78-4da6-9c1f-e7e1c814bfa7"
      },
      "source": [
        "rules <- apriori(mydata,\n",
        "                 parameter = list(minlen=2,\n",
        "                                  maxlen=3,\n",
        "                                  supp=.01, \n",
        "                                  conf=.5),\n",
        "                 appearance=list(rhs=c(\"Bread=Yes\"),\n",
        "                                 lhs=c(\"Eggs=Yes\", \"Cheese=Yes\"),\n",
        "                                 default=\"none\"))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“Column(s) 1, 2, 3, 4, 5, 6 not logical or factor. Applying default discretization (see '? discretizeDF').”\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apriori\n",
            "\n",
            "Parameter specification:\n",
            " confidence minval smax arem  aval originalSupport maxtime support minlen\n",
            "        0.5    0.1    1 none FALSE            TRUE       5    0.01      2\n",
            " maxlen target  ext\n",
            "      3  rules TRUE\n",
            "\n",
            "Algorithmic control:\n",
            " filter tree heap memopt load sort verbose\n",
            "    0.1 TRUE TRUE  FALSE TRUE    2    TRUE\n",
            "\n",
            "Absolute minimum support count: 0 \n",
            "\n",
            "set item appearances ...[3 item(s)] done [0.00s].\n",
            "set transactions ...[3 item(s), 5 transaction(s)] done [0.00s].\n",
            "sorting and recoding items ... [3 item(s)] done [0.00s].\n",
            "creating transaction tree ... done [0.00s].\n",
            "checking subsets of size 1 2 3 done [0.00s].\n",
            "writing ... [3 rule(s)] done [0.00s].\n",
            "creating S4 object  ... done [0.00s].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZ7V0aVY4E2O",
        "outputId": "ead382f4-108c-4cd1-890e-27335cbfb941"
      },
      "source": [
        "inspect(rules)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs                      rhs         support confidence coverage lift     \n",
            "[1] {Eggs=Yes}            => {Bread=Yes} 0.2     1.0000000  0.2      1.2500000\n",
            "[2] {Cheese=Yes}          => {Bread=Yes} 0.4     0.6666667  0.6      0.8333333\n",
            "[3] {Cheese=Yes,Eggs=Yes} => {Bread=Yes} 0.2     1.0000000  0.2      1.2500000\n",
            "    count\n",
            "[1] 1    \n",
            "[2] 2    \n",
            "[3] 1    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "GwmoI8_p4IiX",
        "outputId": "ce9d2d37-fbb9-4b62-84e5-bb136a43cb0b"
      },
      "source": [
        "rules"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 3 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYrIVRT44RRQ"
      },
      "source": [
        "truncating the rules to get desired results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5SyFDge4WYo",
        "outputId": "c579abce-f230-40b4-ea1c-aced09ca3b48"
      },
      "source": [
        "quality(rules)= round (quality(rules), digits =3)\n",
        "inspect(rules)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs                      rhs         support confidence coverage lift \n",
            "[1] {Eggs=Yes}            => {Bread=Yes} 0.2     1.000      0.2      1.250\n",
            "[2] {Cheese=Yes}          => {Bread=Yes} 0.4     0.667      0.6      0.833\n",
            "[3] {Cheese=Yes,Eggs=Yes} => {Bread=Yes} 0.2     1.000      0.2      1.250\n",
            "    count\n",
            "[1] 1    \n",
            "[2] 2    \n",
            "[3] 1    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nIdlbF7v4hhX"
      },
      "source": [
        "if rules need to be sorted"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTEBPZnh4etp",
        "outputId": "809483af-42fb-4b57-b20a-96455c742441"
      },
      "source": [
        "rules.sorted = sort(rules, by =\"confidence\")\n",
        "inspect(rules.sorted)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs                      rhs         support confidence coverage lift \n",
            "[1] {Eggs=Yes}            => {Bread=Yes} 0.2     1.000      0.2      1.250\n",
            "[2] {Cheese=Yes,Eggs=Yes} => {Bread=Yes} 0.2     1.000      0.2      1.250\n",
            "[3] {Cheese=Yes}          => {Bread=Yes} 0.4     0.667      0.6      0.833\n",
            "    count\n",
            "[1] 1    \n",
            "[2] 1    \n",
            "[3] 2    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bko0IHsS4n1a"
      },
      "source": [
        "prune the rules, remove redundancy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "OXuDZu2Z4Y23",
        "outputId": "e05cd1b7-d3a4-4b98-b6a9-8665904e8128"
      },
      "source": [
        "redundant = is.redundant(rules,measure =\"confidence\")\n",
        "which(redundant)\n",
        "rules.pruned=rules[!redundant]\n",
        "inspect(rules.pruned)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 3"
            ],
            "text/latex": "3",
            "text/markdown": "3",
            "text/html": [
              "3"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs             rhs         support confidence coverage lift  count\n",
            "[1] {Eggs=Yes}   => {Bread=Yes} 0.2     1.000      0.2      1.250 1    \n",
            "[2] {Cheese=Yes} => {Bread=Yes} 0.4     0.667      0.6      0.833 2    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-b9Qnh6T6SB9"
      },
      "source": [
        "## Exercise on Cosmetics dataset\n",
        "check dependencies,\n",
        "load data,\n",
        "get rules."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKtQ04L96c4K"
      },
      "source": [
        "#library(arules)\n",
        "cosmetic_data = read.csv(\"/content/Cosmetics.csv\",\n",
        "                          header = T,\n",
        "                          colClasses = \"factor\")"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "d3YBYm9d9a15",
        "outputId": "efd60130-121a-41c0-8ea9-cb76614806ac"
      },
      "source": [
        "cosmetic_data"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     Bag Blush Nail.Polish Brushes Concealer Eyebrow.Pencils Bronzer Lip.liner\n",
              "1    No  Yes   Yes         Yes     Yes       No              Yes     Yes      \n",
              "2    No  No    Yes         No      Yes       No              Yes     Yes      \n",
              "3    No  Yes   No          No      Yes       Yes             Yes     Yes      \n",
              "4    No  No    Yes         Yes     Yes       No              Yes     No       \n",
              "5    No  Yes   No          No      Yes       No              Yes     Yes      \n",
              "6    No  No    No          No      Yes       No              No      No       \n",
              "7    No  Yes   Yes         Yes     Yes       No              Yes     Yes      \n",
              "8    No  No    Yes         Yes     No        No              Yes     No       \n",
              "9    No  No    No          No      Yes       No              No      No       \n",
              "10   Yes Yes   Yes         Yes     No        No              No      No       \n",
              "11   No  No    Yes         No      No        No              Yes     No       \n",
              "12   No  No    Yes         Yes     Yes       No              Yes     No       \n",
              "13   No  Yes   No          No      Yes       No              No      Yes      \n",
              "14   No  Yes   Yes         Yes     Yes       No              Yes     Yes      \n",
              "15   No  Yes   No          No      Yes       No              Yes     Yes      \n",
              "16   No  No    Yes         Yes     Yes       No              Yes     Yes      \n",
              "17   Yes Yes   Yes         Yes     Yes       No              Yes     Yes      \n",
              "18   No  No    No          No      Yes       No              No      No       \n",
              "19   No  Yes   Yes         No      Yes       Yes             No      No       \n",
              "20   No  Yes   No          No      Yes       No              Yes     No       \n",
              "21   No  Yes   No          No      Yes       Yes             Yes     Yes      \n",
              "22   No  No    No          No      No        No              No      No       \n",
              "23   No  Yes   No          No      Yes       No              Yes     Yes      \n",
              "24   No  No    No          No      No        No              Yes     No       \n",
              "25   No  Yes   No          No      No        No              Yes     Yes      \n",
              "26   No  No    No          No      No        No              Yes     No       \n",
              "27   No  No    Yes         No      Yes       No              No      No       \n",
              "28   Yes Yes   Yes         Yes     Yes       No              Yes     Yes      \n",
              "29   No  No    Yes         Yes     No        No              Yes     Yes      \n",
              "30   No  Yes   Yes         Yes     Yes       No              Yes     No       \n",
              "⋮    ⋮   ⋮     ⋮           ⋮       ⋮         ⋮               ⋮       ⋮        \n",
              "971  No  No    No          No      No        No              No      No       \n",
              "972  No  No    No          No      No        No              No      No       \n",
              "973  No  No    No          No      No        No              No      No       \n",
              "974  No  No    No          No      No        No              No      No       \n",
              "975  No  No    No          No      No        No              No      Yes      \n",
              "976  No  Yes   No          No      No        No              No      No       \n",
              "977  No  No    Yes         Yes     No        No              No      No       \n",
              "978  No  No    No          No      No        No              No      No       \n",
              "979  No  No    No          No      No        No              No      Yes      \n",
              "980  No  No    No          No      No        No              No      No       \n",
              "981  No  No    Yes         No      No        No              Yes     No       \n",
              "982  No  No    No          No      No        No              No      No       \n",
              "983  No  No    Yes         Yes     No        No              Yes     Yes      \n",
              "984  No  No    Yes         Yes     No        No              No      No       \n",
              "985  No  Yes   No          No      No        No              No      Yes      \n",
              "986  No  No    No          No      Yes       No              No      No       \n",
              "987  Yes Yes   No          No      Yes       No              No      No       \n",
              "988  No  No    Yes         No      No        No              No      Yes      \n",
              "989  No  No    No          No      No        No              No      No       \n",
              "990  No  No    No          No      No        No              No      No       \n",
              "991  Yes No    Yes         No      No        No              No      No       \n",
              "992  No  No    No          No      No        No              No      No       \n",
              "993  No  No    No          No      No        No              No      No       \n",
              "994  No  No    No          No      Yes       No              No      Yes      \n",
              "995  No  Yes   No          No      No        No              No      No       \n",
              "996  No  No    No          No      No        No              No      No       \n",
              "997  No  No    No          No      No        No              No      No       \n",
              "998  No  Yes   Yes         Yes     Yes       No              No      Yes      \n",
              "999  Yes Yes   No          No      Yes       No              No      No       \n",
              "1000 No  No    No          No      Yes       No              Yes     Yes      \n",
              "     Mascara Eye.shadow Foundation Lip.Gloss Lipstick Eyeliner\n",
              "1    Yes     No         No         No        No       Yes     \n",
              "2    No      No         Yes        Yes       No       No      \n",
              "3    Yes     Yes        Yes        Yes       Yes      No      \n",
              "4    No      No         Yes        No        No       Yes     \n",
              "5    Yes     Yes        No         Yes       Yes      No      \n",
              "6    No      No         No         No        No       Yes     \n",
              "7    Yes     Yes        Yes        Yes       No       No      \n",
              "8    Yes     Yes        No         Yes       No       No      \n",
              "9    No      No         Yes        No        Yes      No      \n",
              "10   Yes     Yes        No         No        No       No      \n",
              "11   No      No         No         Yes       Yes      Yes     \n",
              "12   Yes     Yes        Yes        No        No       No      \n",
              "13   Yes     Yes        No         Yes       No       No      \n",
              "14   Yes     Yes        Yes        Yes       Yes      No      \n",
              "15   Yes     Yes        Yes        Yes       Yes      Yes     \n",
              "16   Yes     Yes        Yes        Yes       Yes      Yes     \n",
              "17   Yes     Yes        No         No        No       Yes     \n",
              "18   No      No         Yes        Yes       Yes      Yes     \n",
              "19   Yes     Yes        No         No        Yes      No      \n",
              "20   Yes     No         Yes        No        Yes      Yes     \n",
              "21   Yes     Yes        Yes        Yes       Yes      Yes     \n",
              "22   No      No         Yes        Yes       Yes      No      \n",
              "23   No      No         No         No        No       No      \n",
              "24   Yes     Yes        Yes        No        Yes      No      \n",
              "25   No      No         No         No        Yes      Yes     \n",
              "26   No      No         No         Yes       Yes      No      \n",
              "27   Yes     No         Yes        Yes       Yes      Yes     \n",
              "28   Yes     Yes        Yes        Yes       Yes      Yes     \n",
              "29   No      Yes        No         No        Yes      No      \n",
              "30   Yes     Yes        Yes        No        Yes      Yes     \n",
              "⋮    ⋮       ⋮          ⋮          ⋮         ⋮        ⋮       \n",
              "971  No      No         No         No        No       No      \n",
              "972  No      No         Yes        No        No       Yes     \n",
              "973  No      No         No         No        No       Yes     \n",
              "974  No      No         Yes        Yes       No       No      \n",
              "975  No      Yes        No         Yes       No       Yes     \n",
              "976  No      No         Yes        Yes       No       No      \n",
              "977  Yes     Yes        No         No        No       Yes     \n",
              "978  No      No         Yes        Yes       No       No      \n",
              "979  Yes     No         No         Yes       No       No      \n",
              "980  No      No         No         No        Yes      No      \n",
              "981  Yes     Yes        No         Yes       No       No      \n",
              "982  No      No         No         No        Yes      No      \n",
              "983  No      No         No         No        No       No      \n",
              "984  No      No         No         No        No       No      \n",
              "985  No      No         No         Yes       No       No      \n",
              "986  No      No         No         No        No       Yes     \n",
              "987  Yes     No         Yes        Yes       No       No      \n",
              "988  Yes     Yes        Yes        No        Yes      No      \n",
              "989  No      No         Yes        Yes       No       No      \n",
              "990  Yes     Yes        Yes        Yes       No       No      \n",
              "991  Yes     Yes        No         No        No       Yes     \n",
              "992  No      No         No         No        Yes      Yes     \n",
              "993  No      No         No         No        Yes      No      \n",
              "994  Yes     Yes        Yes        No        No       Yes     \n",
              "995  Yes     Yes        Yes        Yes       No       No      \n",
              "996  No      No         No         No        No       No      \n",
              "997  No      No         Yes        Yes       No       No      \n",
              "998  No      No         Yes        No        Yes      Yes     \n",
              "999  Yes     Yes        No         No        Yes      Yes     \n",
              "1000 No      Yes        No         No        No       Yes     "
            ],
            "text/latex": "A data.frame: 1000 × 14\n\\begin{tabular}{llllllllllllll}\n Bag & Blush & Nail.Polish & Brushes & Concealer & Eyebrow.Pencils & Bronzer & Lip.liner & Mascara & Eye.shadow & Foundation & Lip.Gloss & Lipstick & Eyeliner\\\\\n <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct>\\\\\n\\hline\n\t No  & Yes & Yes & Yes & Yes & No  & Yes & Yes & Yes & No  & No  & No  & No  & Yes\\\\\n\t No  & No  & Yes & No  & Yes & No  & Yes & Yes & No  & No  & Yes & Yes & No  & No \\\\\n\t No  & Yes & No  & No  & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & No \\\\\n\t No  & No  & Yes & Yes & Yes & No  & Yes & No  & No  & No  & Yes & No  & No  & Yes\\\\\n\t No  & Yes & No  & No  & Yes & No  & Yes & Yes & Yes & Yes & No  & Yes & Yes & No \\\\\n\t No  & No  & No  & No  & Yes & No  & No  & No  & No  & No  & No  & No  & No  & Yes\\\\\n\t No  & Yes & Yes & Yes & Yes & No  & Yes & Yes & Yes & Yes & Yes & Yes & No  & No \\\\\n\t No  & No  & Yes & Yes & No  & No  & Yes & No  & Yes & Yes & No  & Yes & No  & No \\\\\n\t No  & No  & No  & No  & Yes & No  & No  & No  & No  & No  & Yes & No  & Yes & No \\\\\n\t Yes & Yes & Yes & Yes & No  & No  & No  & No  & Yes & Yes & No  & No  & No  & No \\\\\n\t No  & No  & Yes & No  & No  & No  & Yes & No  & No  & No  & No  & Yes & Yes & Yes\\\\\n\t No  & No  & Yes & Yes & Yes & No  & Yes & No  & Yes & Yes & Yes & No  & No  & No \\\\\n\t No  & Yes & No  & No  & Yes & No  & No  & Yes & Yes & Yes & No  & Yes & No  & No \\\\\n\t No  & Yes & Yes & Yes & Yes & No  & Yes & Yes & Yes & Yes & Yes & Yes & Yes & No \\\\\n\t No  & Yes & No  & No  & Yes & No  & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\\\\n\t No  & No  & Yes & Yes & Yes & No  & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\\\\n\t Yes & Yes & Yes & Yes & Yes & No  & Yes & Yes & Yes & Yes & No  & No  & No  & Yes\\\\\n\t No  & No  & No  & No  & Yes & No  & No  & No  & No  & No  & Yes & Yes & Yes & Yes\\\\\n\t No  & Yes & Yes & No  & Yes & Yes & No  & No  & Yes & Yes & No  & No  & Yes & No \\\\\n\t No  & Yes & No  & No  & Yes & No  & Yes & No  & Yes & No  & Yes & No  & Yes & Yes\\\\\n\t No  & Yes & No  & No  & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\\\\n\t No  & No  & No  & No  & No  & No  & No  & No  & No  & No  & Yes & Yes & Yes & No \\\\\n\t No  & Yes & No  & No  & Yes & No  & Yes & Yes & No  & No  & No  & No  & No  & No \\\\\n\t No  & No  & No  & No  & No  & No  & Yes & No  & Yes & Yes & Yes & No  & Yes & No \\\\\n\t No  & Yes & No  & No  & No  & No  & Yes & Yes & No  & No  & No  & No  & Yes & Yes\\\\\n\t No  & No  & No  & No  & No  & No  & Yes & No  & No  & No  & No  & Yes & Yes & No \\\\\n\t No  & No  & Yes & No  & Yes & No  & No  & No  & Yes & No  & Yes & Yes & Yes & Yes\\\\\n\t Yes & Yes & Yes & Yes & Yes & No  & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\\\\n\t No  & No  & Yes & Yes & No  & No  & Yes & Yes & No  & Yes & No  & No  & Yes & No \\\\\n\t No  & Yes & Yes & Yes & Yes & No  & Yes & No  & Yes & Yes & Yes & No  & Yes & Yes\\\\\n\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & No  & No  & No  & No \\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & Yes & No  & No  & Yes\\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & No  & No  & No  & Yes\\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & Yes & Yes & No  & No \\\\\n\t No  & No  & No  & No  & No  & No & No  & Yes & No  & Yes & No  & Yes & No  & Yes\\\\\n\t No  & Yes & No  & No  & No  & No & No  & No  & No  & No  & Yes & Yes & No  & No \\\\\n\t No  & No  & Yes & Yes & No  & No & No  & No  & Yes & Yes & No  & No  & No  & Yes\\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & Yes & Yes & No  & No \\\\\n\t No  & No  & No  & No  & No  & No & No  & Yes & Yes & No  & No  & Yes & No  & No \\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & No  & No  & Yes & No \\\\\n\t No  & No  & Yes & No  & No  & No & Yes & No  & Yes & Yes & No  & Yes & No  & No \\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & No  & No  & Yes & No \\\\\n\t No  & No  & Yes & Yes & No  & No & Yes & Yes & No  & No  & No  & No  & No  & No \\\\\n\t No  & No  & Yes & Yes & No  & No & No  & No  & No  & No  & No  & No  & No  & No \\\\\n\t No  & Yes & No  & No  & No  & No & No  & Yes & No  & No  & No  & Yes & No  & No \\\\\n\t No  & No  & No  & No  & Yes & No & No  & No  & No  & No  & No  & No  & No  & Yes\\\\\n\t Yes & Yes & No  & No  & Yes & No & No  & No  & Yes & No  & Yes & Yes & No  & No \\\\\n\t No  & No  & Yes & No  & No  & No & No  & Yes & Yes & Yes & Yes & No  & Yes & No \\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & Yes & Yes & No  & No \\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & Yes & Yes & Yes & Yes & No  & No \\\\\n\t Yes & No  & Yes & No  & No  & No & No  & No  & Yes & Yes & No  & No  & No  & Yes\\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & No  & No  & Yes & Yes\\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & No  & No  & Yes & No \\\\\n\t No  & No  & No  & No  & Yes & No & No  & Yes & Yes & Yes & Yes & No  & No  & Yes\\\\\n\t No  & Yes & No  & No  & No  & No & No  & No  & Yes & Yes & Yes & Yes & No  & No \\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & No  & No  & No  & No \\\\\n\t No  & No  & No  & No  & No  & No & No  & No  & No  & No  & Yes & Yes & No  & No \\\\\n\t No  & Yes & Yes & Yes & Yes & No & No  & Yes & No  & No  & Yes & No  & Yes & Yes\\\\\n\t Yes & Yes & No  & No  & Yes & No & No  & No  & Yes & Yes & No  & No  & Yes & Yes\\\\\n\t No  & No  & No  & No  & Yes & No & Yes & Yes & No  & Yes & No  & No  & No  & Yes\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 1000 × 14\n\n| Bag &lt;fct&gt; | Blush &lt;fct&gt; | Nail.Polish &lt;fct&gt; | Brushes &lt;fct&gt; | Concealer &lt;fct&gt; | Eyebrow.Pencils &lt;fct&gt; | Bronzer &lt;fct&gt; | Lip.liner &lt;fct&gt; | Mascara &lt;fct&gt; | Eye.shadow &lt;fct&gt; | Foundation &lt;fct&gt; | Lip.Gloss &lt;fct&gt; | Lipstick &lt;fct&gt; | Eyeliner &lt;fct&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| No  | Yes | Yes | Yes | Yes | No  | Yes | Yes | Yes | No  | No  | No  | No  | Yes |\n| No  | No  | Yes | No  | Yes | No  | Yes | Yes | No  | No  | Yes | Yes | No  | No  |\n| No  | Yes | No  | No  | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | No  |\n| No  | No  | Yes | Yes | Yes | No  | Yes | No  | No  | No  | Yes | No  | No  | Yes |\n| No  | Yes | No  | No  | Yes | No  | Yes | Yes | Yes | Yes | No  | Yes | Yes | No  |\n| No  | No  | No  | No  | Yes | No  | No  | No  | No  | No  | No  | No  | No  | Yes |\n| No  | Yes | Yes | Yes | Yes | No  | Yes | Yes | Yes | Yes | Yes | Yes | No  | No  |\n| No  | No  | Yes | Yes | No  | No  | Yes | No  | Yes | Yes | No  | Yes | No  | No  |\n| No  | No  | No  | No  | Yes | No  | No  | No  | No  | No  | Yes | No  | Yes | No  |\n| Yes | Yes | Yes | Yes | No  | No  | No  | No  | Yes | Yes | No  | No  | No  | No  |\n| No  | No  | Yes | No  | No  | No  | Yes | No  | No  | No  | No  | Yes | Yes | Yes |\n| No  | No  | Yes | Yes | Yes | No  | Yes | No  | Yes | Yes | Yes | No  | No  | No  |\n| No  | Yes | No  | No  | Yes | No  | No  | Yes | Yes | Yes | No  | Yes | No  | No  |\n| No  | Yes | Yes | Yes | Yes | No  | Yes | Yes | Yes | Yes | Yes | Yes | Yes | No  |\n| No  | Yes | No  | No  | Yes | No  | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |\n| No  | No  | Yes | Yes | Yes | No  | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |\n| Yes | Yes | Yes | Yes | Yes | No  | Yes | Yes | Yes | Yes | No  | No  | No  | Yes |\n| No  | No  | No  | No  | Yes | No  | No  | No  | No  | No  | Yes | Yes | Yes | Yes |\n| No  | Yes | Yes | No  | Yes | Yes | No  | No  | Yes | Yes | No  | No  | Yes | No  |\n| No  | Yes | No  | No  | Yes | No  | Yes | No  | Yes | No  | Yes | No  | Yes | Yes |\n| No  | Yes | No  | No  | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |\n| No  | No  | No  | No  | No  | No  | No  | No  | No  | No  | Yes | Yes | Yes | No  |\n| No  | Yes | No  | No  | Yes | No  | Yes | Yes | No  | No  | No  | No  | No  | No  |\n| No  | No  | No  | No  | No  | No  | Yes | No  | Yes | Yes | Yes | No  | Yes | No  |\n| No  | Yes | No  | No  | No  | No  | Yes | Yes | No  | No  | No  | No  | Yes | Yes |\n| No  | No  | No  | No  | No  | No  | Yes | No  | No  | No  | No  | Yes | Yes | No  |\n| No  | No  | Yes | No  | Yes | No  | No  | No  | Yes | No  | Yes | Yes | Yes | Yes |\n| Yes | Yes | Yes | Yes | Yes | No  | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |\n| No  | No  | Yes | Yes | No  | No  | Yes | Yes | No  | Yes | No  | No  | Yes | No  |\n| No  | Yes | Yes | Yes | Yes | No  | Yes | No  | Yes | Yes | Yes | No  | Yes | Yes |\n| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | No  | No  | No  | No  |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | Yes | No  | No  | Yes |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | No  | No  | No  | Yes |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | Yes | Yes | No  | No  |\n| No  | No  | No  | No  | No  | No | No  | Yes | No  | Yes | No  | Yes | No  | Yes |\n| No  | Yes | No  | No  | No  | No | No  | No  | No  | No  | Yes | Yes | No  | No  |\n| No  | No  | Yes | Yes | No  | No | No  | No  | Yes | Yes | No  | No  | No  | Yes |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | Yes | Yes | No  | No  |\n| No  | No  | No  | No  | No  | No | No  | Yes | Yes | No  | No  | Yes | No  | No  |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | No  | No  | Yes | No  |\n| No  | No  | Yes | No  | No  | No | Yes | No  | Yes | Yes | No  | Yes | No  | No  |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | No  | No  | Yes | No  |\n| No  | No  | Yes | Yes | No  | No | Yes | Yes | No  | No  | No  | No  | No  | No  |\n| No  | No  | Yes | Yes | No  | No | No  | No  | No  | No  | No  | No  | No  | No  |\n| No  | Yes | No  | No  | No  | No | No  | Yes | No  | No  | No  | Yes | No  | No  |\n| No  | No  | No  | No  | Yes | No | No  | No  | No  | No  | No  | No  | No  | Yes |\n| Yes | Yes | No  | No  | Yes | No | No  | No  | Yes | No  | Yes | Yes | No  | No  |\n| No  | No  | Yes | No  | No  | No | No  | Yes | Yes | Yes | Yes | No  | Yes | No  |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | Yes | Yes | No  | No  |\n| No  | No  | No  | No  | No  | No | No  | No  | Yes | Yes | Yes | Yes | No  | No  |\n| Yes | No  | Yes | No  | No  | No | No  | No  | Yes | Yes | No  | No  | No  | Yes |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | No  | No  | Yes | Yes |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | No  | No  | Yes | No  |\n| No  | No  | No  | No  | Yes | No | No  | Yes | Yes | Yes | Yes | No  | No  | Yes |\n| No  | Yes | No  | No  | No  | No | No  | No  | Yes | Yes | Yes | Yes | No  | No  |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | No  | No  | No  | No  |\n| No  | No  | No  | No  | No  | No | No  | No  | No  | No  | Yes | Yes | No  | No  |\n| No  | Yes | Yes | Yes | Yes | No | No  | Yes | No  | No  | Yes | No  | Yes | Yes |\n| Yes | Yes | No  | No  | Yes | No | No  | No  | Yes | Yes | No  | No  | Yes | Yes |\n| No  | No  | No  | No  | Yes | No | Yes | Yes | No  | Yes | No  | No  | No  | Yes |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 1000 × 14</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Bag</th><th scope=col>Blush</th><th scope=col>Nail.Polish</th><th scope=col>Brushes</th><th scope=col>Concealer</th><th scope=col>Eyebrow.Pencils</th><th scope=col>Bronzer</th><th scope=col>Lip.liner</th><th scope=col>Mascara</th><th scope=col>Eye.shadow</th><th scope=col>Foundation</th><th scope=col>Lip.Gloss</th><th scope=col>Lipstick</th><th scope=col>Eyeliner</th></tr>\n",
              "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No</td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No</td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No</td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No</td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>No </td><td>No</td><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td></tr>\n",
              "\t<tr><td>No </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No </td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>No</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td><td>No </td><td>No </td><td>Yes</td><td>Yes</td></tr>\n",
              "\t<tr><td>No </td><td>No </td><td>No </td><td>No </td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>No </td><td>Yes</td><td>No </td><td>No </td><td>No </td><td>Yes</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NClhWo7F9ol0",
        "outputId": "12a1ec77-cb81-4dc2-ea28-60d5412c8306"
      },
      "source": [
        "Rule_cosmetic = apriori( cosmetic_data,\n",
        "                         parameter = list( support = 0.25,\n",
        "                                           confidence = 0.7))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apriori\n",
            "\n",
            "Parameter specification:\n",
            " confidence minval smax arem  aval originalSupport maxtime support minlen\n",
            "        0.7    0.1    1 none FALSE            TRUE       5    0.25      1\n",
            " maxlen target  ext\n",
            "     10  rules TRUE\n",
            "\n",
            "Algorithmic control:\n",
            " filter tree heap memopt load sort verbose\n",
            "    0.1 TRUE TRUE  FALSE TRUE    2    TRUE\n",
            "\n",
            "Absolute minimum support count: 250 \n",
            "\n",
            "set item appearances ...[0 item(s)] done [0.00s].\n",
            "set transactions ...[28 item(s), 1000 transaction(s)] done [0.00s].\n",
            "sorting and recoding items ... [24 item(s)] done [0.00s].\n",
            "creating transaction tree ... done [0.00s].\n",
            "checking subsets of size 1 2 3 4 5 6 7 8 done [0.01s].\n",
            "writing ... [7483 rule(s)] done [0.00s].\n",
            "creating S4 object  ... done [0.00s].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qH9WIbFWC7W0"
      },
      "source": [
        "for bakery"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rdQa7kW6C84r",
        "outputId": "d892ab71-de29-472b-ceac-e5078a35b255"
      },
      "source": [
        "Rule_bakery = apriori( mydata,\n",
        "                         parameter = list( support = 0.25,\n",
        "                                           confidence = 0.7))"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“Column(s) 1, 2, 3, 4, 5, 6 not logical or factor. Applying default discretization (see '? discretizeDF').”\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apriori\n",
            "\n",
            "Parameter specification:\n",
            " confidence minval smax arem  aval originalSupport maxtime support minlen\n",
            "        0.7    0.1    1 none FALSE            TRUE       5    0.25      1\n",
            " maxlen target  ext\n",
            "     10  rules TRUE\n",
            "\n",
            "Algorithmic control:\n",
            " filter tree heap memopt load sort verbose\n",
            "    0.1 TRUE TRUE  FALSE TRUE    2    TRUE\n",
            "\n",
            "Absolute minimum support count: 1 \n",
            "\n",
            "set item appearances ...[0 item(s)] done [0.00s].\n",
            "set transactions ...[12 item(s), 5 transaction(s)] done [0.00s].\n",
            "sorting and recoding items ... [9 item(s)] done [0.00s].\n",
            "creating transaction tree ... done [0.00s].\n",
            "checking subsets of size 1 2 3 4 5 done [0.00s].\n",
            "writing ... [130 rule(s)] done [0.00s].\n",
            "creating S4 object  ... done [0.00s].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ckgl5AfQ-jYJ",
        "outputId": "af57d426-f45f-435c-fb0b-b8adecbd5ffd"
      },
      "source": [
        "Rule_cosmetic"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 7483 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "9Q43cLs9DLMY",
        "outputId": "2d29e3d6-febe-41f0-9718-91388376c741"
      },
      "source": [
        "Rule_bakery"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 130 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBfoYcuc_DJB"
      },
      "source": [
        "get rules with lift < 1 in cosmetics data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "rhTc2cii-sjw",
        "outputId": "449cedcb-bf6e-43df-e300-532529f2e514"
      },
      "source": [
        "Rule_cosmetic[ \"lift\" < 1]"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 0 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddGrtd2AAnVR"
      },
      "source": [
        "get rules with lift < 1 in bakery data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "_7T-JwsW_Vep",
        "outputId": "28406285-5b4e-4923-c6a2-f7e4d5bb7bc9"
      },
      "source": [
        "Rule_bakery[ \"lift\" < 1]"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 0 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7uVT99SAukG"
      },
      "source": [
        "lets examine the rules. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Azf04pkCUiY"
      },
      "source": [
        "in Cosmetic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMwcMUQdA_5j",
        "outputId": "7a72f229-6d83-4767-bbb7-b2f9e6e83a05"
      },
      "source": [
        "Rule_cosmetic.sorted <- sort(Rule_cosmetic, by = \"lift\", decreasing = TRUE )\n",
        "inspect( head(Rule_cosmetic.sorted) )"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs                     rhs              support confidence coverage     lift count\n",
            "[1] {Bag=No,                                                                           \n",
            "     Mascara=Yes}        => {Eye.shadow=Yes}   0.290  0.9090909    0.319 2.386065   290\n",
            "[2] {Bag=No,                                                                           \n",
            "     Eyebrow.Pencils=No,                                                               \n",
            "     Mascara=Yes}        => {Eye.shadow=Yes}   0.270  0.9030100    0.299 2.370105   270\n",
            "[3] {Mascara=Yes}        => {Eye.shadow=Yes}   0.321  0.8991597    0.357 2.359999   321\n",
            "[4] {Eye.shadow=Yes}     => {Mascara=Yes}      0.321  0.8425197    0.381 2.359999   321\n",
            "[5] {Eyebrow.Pencils=No,                                                               \n",
            "     Eye.shadow=Yes}     => {Mascara=Yes}      0.298  0.8370787    0.356 2.344758   298\n",
            "[6] {Eyebrow.Pencils=No,                                                               \n",
            "     Mascara=Yes}        => {Eye.shadow=Yes}   0.298  0.8922156    0.334 2.341773   298\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yRRc-Fp8CIYU"
      },
      "source": [
        "get rules with lift > 1 in bakery data and examine them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVuZg5-7CMT8",
        "outputId": "1fa7c27d-1d8d-422b-b195-b41f6ce19a29"
      },
      "source": [
        "Rule_bakery.sorted <- sort(Rule_bakery, by = \"lift\", decreasing = TRUE )\n",
        "inspect( head(Rule_bakery.sorted) )"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs                       rhs         support confidence coverage lift\n",
            "[1] {Cheese=No}            => {Juice=No}  0.4     1          0.4      2.5 \n",
            "[2] {Juice=No}             => {Cheese=No} 0.4     1          0.4      2.5 \n",
            "[3] {Bread=Yes,Cheese=Yes} => {Milk=No}   0.4     1          0.4      2.5 \n",
            "[4] {Bread=Yes,Juice=Yes}  => {Milk=No}   0.4     1          0.4      2.5 \n",
            "[5] {Cheese=No,Milk=Yes}   => {Juice=No}  0.4     1          0.4      2.5 \n",
            "[6] {Juice=No,Milk=Yes}    => {Cheese=No} 0.4     1          0.4      2.5 \n",
            "    count\n",
            "[1] 2    \n",
            "[2] 2    \n",
            "[3] 2    \n",
            "[4] 2    \n",
            "[5] 2    \n",
            "[6] 2    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETXAEuY5DhGi"
      },
      "source": [
        "so in cosmetics rules, get rules with good support and then with high confidence,\n",
        " first get ones with high confidence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGERUxnHDvTn",
        "outputId": "ecc0af1f-e079-4705-c5a7-6bcb29c39df6"
      },
      "source": [
        "Rule_hiConf <- sort(Rule_cosmetic.sorted, by = \"confidence\", decreasing = TRUE )\n",
        "inspect( head(Rule_hiConf) )"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs                     rhs              support confidence coverage     lift count\n",
            "[1] {Bronzer=No,                                                                       \n",
            "     Lip.liner=No,                                                                     \n",
            "     Foundation=No}      => {Lip.Gloss=No}     0.253          1    0.253 1.960784   253\n",
            "[2] {Bronzer=No,                                                                       \n",
            "     Lip.liner=No,                                                                     \n",
            "     Lip.Gloss=Yes}      => {Foundation=Yes}   0.295          1    0.295 1.865672   295\n",
            "[3] {Brushes=No,                                                                       \n",
            "     Bronzer=No,                                                                       \n",
            "     Lip.liner=No,                                                                     \n",
            "     Lip.Gloss=Yes}      => {Foundation=Yes}   0.275          1    0.275 1.865672   275\n",
            "[4] {Bag=No,                                                                           \n",
            "     Bronzer=No,                                                                       \n",
            "     Lip.liner=No,                                                                     \n",
            "     Lip.Gloss=Yes}      => {Foundation=Yes}   0.282          1    0.282 1.865672   282\n",
            "[5] {Eyebrow.Pencils=No,                                                               \n",
            "     Bronzer=No,                                                                       \n",
            "     Lip.liner=No,                                                                     \n",
            "     Lip.Gloss=Yes}      => {Foundation=Yes}   0.288          1    0.288 1.865672   288\n",
            "[6] {Bag=No,                                                                           \n",
            "     Brushes=No,                                                                       \n",
            "     Bronzer=No,                                                                       \n",
            "     Lip.liner=No,                                                                     \n",
            "     Lip.Gloss=Yes}      => {Foundation=Yes}   0.265          1    0.265 1.865672   265\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "gd20axZ7EKoF",
        "outputId": "2200b7ef-8798-4479-fa58-38a2d227624f"
      },
      "source": [
        "Rule_hiConf"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 7483 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lojkHAh-E4xH"
      },
      "source": [
        "on removing redundancy "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "SC07cBqkErHX",
        "outputId": "d612b536-fd34-4be6-f125-b7d7efe49c7a"
      },
      "source": [
        "redundant <- is.redundant(Rule_bakery, measure=\"confidence\")\n",
        "which(redundant)\n",
        "Rule_bakery.pruned <- Rule_bakery[!redundant]\n",
        "Rule_bakery"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  [1]  24  25  26  27  28  29  30  31  32  33  35  36  37  38  40  41  42  43\n",
              " [19]  44  45  46  47  48  49  50  51  52  53  54  56  57  58  59  60  61  63\n",
              " [37]  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81\n",
              " [55]  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99\n",
              " [73] 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117\n",
              " [91] 118 119 120 121 122 123 124 125 126 127 128 129 130"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 24\n\\item 25\n\\item 26\n\\item 27\n\\item 28\n\\item 29\n\\item 30\n\\item 31\n\\item 32\n\\item 33\n\\item 35\n\\item 36\n\\item 37\n\\item 38\n\\item 40\n\\item 41\n\\item 42\n\\item 43\n\\item 44\n\\item 45\n\\item 46\n\\item 47\n\\item 48\n\\item 49\n\\item 50\n\\item 51\n\\item 52\n\\item 53\n\\item 54\n\\item 56\n\\item 57\n\\item 58\n\\item 59\n\\item 60\n\\item 61\n\\item 63\n\\item 64\n\\item 65\n\\item 66\n\\item 67\n\\item 68\n\\item 69\n\\item 70\n\\item 71\n\\item 72\n\\item 73\n\\item 74\n\\item 75\n\\item 76\n\\item 77\n\\item 78\n\\item 79\n\\item 80\n\\item 81\n\\item 82\n\\item 83\n\\item 84\n\\item 85\n\\item 86\n\\item 87\n\\item 88\n\\item 89\n\\item 90\n\\item 91\n\\item 92\n\\item 93\n\\item 94\n\\item 95\n\\item 96\n\\item 97\n\\item 98\n\\item 99\n\\item 100\n\\item 101\n\\item 102\n\\item 103\n\\item 104\n\\item 105\n\\item 106\n\\item 107\n\\item 108\n\\item 109\n\\item 110\n\\item 111\n\\item 112\n\\item 113\n\\item 114\n\\item 115\n\\item 116\n\\item 117\n\\item 118\n\\item 119\n\\item 120\n\\item 121\n\\item 122\n\\item 123\n\\item 124\n\\item 125\n\\item 126\n\\item 127\n\\item 128\n\\item 129\n\\item 130\n\\end{enumerate*}\n",
            "text/markdown": "1. 24\n2. 25\n3. 26\n4. 27\n5. 28\n6. 29\n7. 30\n8. 31\n9. 32\n10. 33\n11. 35\n12. 36\n13. 37\n14. 38\n15. 40\n16. 41\n17. 42\n18. 43\n19. 44\n20. 45\n21. 46\n22. 47\n23. 48\n24. 49\n25. 50\n26. 51\n27. 52\n28. 53\n29. 54\n30. 56\n31. 57\n32. 58\n33. 59\n34. 60\n35. 61\n36. 63\n37. 64\n38. 65\n39. 66\n40. 67\n41. 68\n42. 69\n43. 70\n44. 71\n45. 72\n46. 73\n47. 74\n48. 75\n49. 76\n50. 77\n51. 78\n52. 79\n53. 80\n54. 81\n55. 82\n56. 83\n57. 84\n58. 85\n59. 86\n60. 87\n61. 88\n62. 89\n63. 90\n64. 91\n65. 92\n66. 93\n67. 94\n68. 95\n69. 96\n70. 97\n71. 98\n72. 99\n73. 100\n74. 101\n75. 102\n76. 103\n77. 104\n78. 105\n79. 106\n80. 107\n81. 108\n82. 109\n83. 110\n84. 111\n85. 112\n86. 113\n87. 114\n88. 115\n89. 116\n90. 117\n91. 118\n92. 119\n93. 120\n94. 121\n95. 122\n96. 123\n97. 124\n98. 125\n99. 126\n100. 127\n101. 128\n102. 129\n103. 130\n\n\n",
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>24</li><li>25</li><li>26</li><li>27</li><li>28</li><li>29</li><li>30</li><li>31</li><li>32</li><li>33</li><li>35</li><li>36</li><li>37</li><li>38</li><li>40</li><li>41</li><li>42</li><li>43</li><li>44</li><li>45</li><li>46</li><li>47</li><li>48</li><li>49</li><li>50</li><li>51</li><li>52</li><li>53</li><li>54</li><li>56</li><li>57</li><li>58</li><li>59</li><li>60</li><li>61</li><li>63</li><li>64</li><li>65</li><li>66</li><li>67</li><li>68</li><li>69</li><li>70</li><li>71</li><li>72</li><li>73</li><li>74</li><li>75</li><li>76</li><li>77</li><li>78</li><li>79</li><li>80</li><li>81</li><li>82</li><li>83</li><li>84</li><li>85</li><li>86</li><li>87</li><li>88</li><li>89</li><li>90</li><li>91</li><li>92</li><li>93</li><li>94</li><li>95</li><li>96</li><li>97</li><li>98</li><li>99</li><li>100</li><li>101</li><li>102</li><li>103</li><li>104</li><li>105</li><li>106</li><li>107</li><li>108</li><li>109</li><li>110</li><li>111</li><li>112</li><li>113</li><li>114</li><li>115</li><li>116</li><li>117</li><li>118</li><li>119</li><li>120</li><li>121</li><li>122</li><li>123</li><li>124</li><li>125</li><li>126</li><li>127</li><li>128</li><li>129</li><li>130</li></ol>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 130 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vdZ9VVBrFBLT",
        "outputId": "03acad31-b735-4a80-ab74-e083919a2297"
      },
      "source": [
        "inspect( head(Rule_bakery))"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs          rhs          support confidence coverage lift     count\n",
            "[1] {}        => {Bread=Yes}  0.8     0.8        1.0      1.000000 4    \n",
            "[2] {}        => {Yogurt=No}  0.8     0.8        1.0      1.000000 4    \n",
            "[3] {}        => {Eggs=No}    0.8     0.8        1.0      1.000000 4    \n",
            "[4] {Milk=No} => {Cheese=Yes} 0.4     1.0        0.4      1.666667 2    \n",
            "[5] {Milk=No} => {Juice=Yes}  0.4     1.0        0.4      1.666667 2    \n",
            "[6] {Milk=No} => {Bread=Yes}  0.4     1.0        0.4      1.250000 2    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCAZwtjJGPLl"
      },
      "source": [
        "lets get rules with more constraints"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M07Y1rMoFVe2",
        "outputId": "d5f3a554-cfaa-4815-e53c-7d658d64089a"
      },
      "source": [
        "Rule_cosmetic <- apriori(cosmetic_data ,\n",
        "                         parameter = list(minlen=2, \n",
        "                                          maxlen=5,\n",
        "                                          supp=.1, \n",
        "                                          conf=.5),\n",
        "                         appearance=list(rhs=c(\"Foundation=Yes\"),\n",
        "                                         lhs=c(\"Bag=Yes\", \"Blush=Yes\",\n",
        "                                               \"Nail.Polish=Yes\", \"Brushes=Yes\", \n",
        "                                               \"Concealer=Yes\", \"Eyebrow.Pencils=Yes\",\n",
        "                                                \"Bronzer=Yes\", \"Lip.liner=Yes\", \"Mascara=Yes\", \n",
        "                                                \"Eye.shadow=Yes\",\"Lip.Gloss=Yes\", \n",
        "                                                \"Lipstick=Yes\", \"Eyeliner=Yes\"),\n",
        "                                         default=\"none\"))\n"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apriori\n",
            "\n",
            "Parameter specification:\n",
            " confidence minval smax arem  aval originalSupport maxtime support minlen\n",
            "        0.5    0.1    1 none FALSE            TRUE       5     0.1      2\n",
            " maxlen target  ext\n",
            "      5  rules TRUE\n",
            "\n",
            "Algorithmic control:\n",
            " filter tree heap memopt load sort verbose\n",
            "    0.1 TRUE TRUE  FALSE TRUE    2    TRUE\n",
            "\n",
            "Absolute minimum support count: 100 \n",
            "\n",
            "set item appearances ...[14 item(s)] done [0.00s].\n",
            "set transactions ...[14 item(s), 1000 transaction(s)] done [0.00s].\n",
            "sorting and recoding items ... [12 item(s)] done [0.00s].\n",
            "creating transaction tree ... done [0.00s].\n",
            "checking subsets of size 1 2 3 4 done [0.00s].\n",
            "writing ... [22 rule(s)] done [0.00s].\n",
            "creating S4 object  ... done [0.00s].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQfP3cpNG8aN",
        "outputId": "a5486e21-6db2-400c-a0e5-717ddd6ab400"
      },
      "source": [
        "Rule_cosmetic.sorted <- sort(Rule_cosmetic, by = \"lift\", decreasing = TRUE )\n",
        "inspect( head(Rule_cosmetic.sorted) )"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    lhs                 rhs              support confidence coverage     lift count\n",
            "[1] {Lip.Gloss=Yes,                                                                \n",
            "     Lipstick=Yes}   => {Foundation=Yes}   0.116  0.7341772    0.158 1.369734   116\n",
            "[2] {Lip.Gloss=Yes}  => {Foundation=Yes}   0.356  0.7265306    0.490 1.355468   356\n",
            "[3] {Eye.shadow=Yes,                                                               \n",
            "     Lip.Gloss=Yes}  => {Foundation=Yes}   0.146  0.7263682    0.201 1.355164   146\n",
            "[4] {Mascara=Yes,                                                                  \n",
            "     Lip.Gloss=Yes}  => {Foundation=Yes}   0.130  0.7182320    0.181 1.339985   130\n",
            "[5] {Mascara=Yes,                                                                  \n",
            "     Eye.shadow=Yes,                                                               \n",
            "     Lip.Gloss=Yes}  => {Foundation=Yes}   0.111  0.7025316    0.158 1.310693   111\n",
            "[6] {Lip.Gloss=Yes,                                                                \n",
            "     Eyeliner=Yes}   => {Foundation=Yes}   0.156  0.6872247    0.227 1.282136   156\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "YqZKIEOqHHF8",
        "outputId": "083180bc-2156-44db-ac86-8fa49a2034b3"
      },
      "source": [
        "redundant <- is.redundant(Rule_cosmetic, measure=\"confidence\")\n",
        "which(redundant)\n",
        "Rule_cosmetic.pruned <- Rule_cosmetic[!redundant]\n",
        "Rule_cosmetic"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              " [1] 11 12 13 14 15 16 17 18 19 20 21 22"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 11\n\\item 12\n\\item 13\n\\item 14\n\\item 15\n\\item 16\n\\item 17\n\\item 18\n\\item 19\n\\item 20\n\\item 21\n\\item 22\n\\end{enumerate*}\n",
            "text/markdown": "1. 11\n2. 12\n3. 13\n4. 14\n5. 15\n6. 16\n7. 17\n8. 18\n9. 19\n10. 20\n11. 21\n12. 22\n\n\n",
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>11</li><li>12</li><li>13</li><li>14</li><li>15</li><li>16</li><li>17</li><li>18</li><li>19</li><li>20</li><li>21</li><li>22</li></ol>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 22 rules "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 733
        },
        "id": "arDNwYImHORN",
        "outputId": "c4860621-95e6-48d6-e55d-6e23b876bd98"
      },
      "source": [
        "Rule_cosmetic[\"lift\" > 1]\n",
        "inspect( Rule_cosmetic[\"lift\" > 1] )"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "set of 22 rules "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     lhs                  rhs              support confidence coverage      lift count\n",
            "[1]  {Lipstick=Yes}    => {Foundation=Yes}   0.167  0.5186335    0.322 0.9675999   167\n",
            "[2]  {Nail.Polish=Yes} => {Foundation=Yes}   0.143  0.5107143    0.280 0.9528252   143\n",
            "[3]  {Blush=Yes}       => {Foundation=Yes}   0.192  0.5289256    0.363 0.9868015   192\n",
            "[4]  {Mascara=Yes}     => {Foundation=Yes}   0.192  0.5378151    0.357 1.0033864   192\n",
            "[5]  {Eye.shadow=Yes}  => {Foundation=Yes}   0.211  0.5538058    0.381 1.0332197   211\n",
            "[6]  {Eyeliner=Yes}    => {Foundation=Yes}   0.238  0.5207877    0.457 0.9716189   238\n",
            "[7]  {Lip.Gloss=Yes}   => {Foundation=Yes}   0.356  0.7265306    0.490 1.3554676   356\n",
            "[8]  {Concealer=Yes}   => {Foundation=Yes}   0.231  0.5226244    0.442 0.9750456   231\n",
            "[9]  {Lip.Gloss=Yes,                                                                  \n",
            "      Lipstick=Yes}    => {Foundation=Yes}   0.116  0.7341772    0.158 1.3697336   116\n",
            "[10] {Blush=Yes,                                                                      \n",
            "      Mascara=Yes}     => {Foundation=Yes}   0.101  0.5489130    0.184 1.0240915   101\n",
            "[11] {Blush=Yes,                                                                      \n",
            "      Eye.shadow=Yes}  => {Foundation=Yes}   0.100  0.5494505    0.182 1.0250943   100\n",
            "[12] {Blush=Yes,                                                                      \n",
            "      Lip.Gloss=Yes}   => {Foundation=Yes}   0.119  0.6685393    0.178 1.2472749   119\n",
            "[13] {Blush=Yes,                                                                      \n",
            "      Concealer=Yes}   => {Foundation=Yes}   0.115  0.5227273    0.220 0.9752374   115\n",
            "[14] {Mascara=Yes,                                                                    \n",
            "      Eye.shadow=Yes}  => {Foundation=Yes}   0.166  0.5171340    0.321 0.9648022   166\n",
            "[15] {Mascara=Yes,                                                                    \n",
            "      Lip.Gloss=Yes}   => {Foundation=Yes}   0.130  0.7182320    0.181 1.3399852   130\n",
            "[16] {Concealer=Yes,                                                                  \n",
            "      Mascara=Yes}     => {Foundation=Yes}   0.107  0.5245098    0.204 0.9785631   107\n",
            "[17] {Eye.shadow=Yes,                                                                 \n",
            "      Lip.Gloss=Yes}   => {Foundation=Yes}   0.146  0.7263682    0.201 1.3551645   146\n",
            "[18] {Concealer=Yes,                                                                  \n",
            "      Eye.shadow=Yes}  => {Foundation=Yes}   0.104  0.5174129    0.201 0.9653226   104\n",
            "[19] {Lip.Gloss=Yes,                                                                  \n",
            "      Eyeliner=Yes}    => {Foundation=Yes}   0.156  0.6872247    0.227 1.2821356   156\n",
            "[20] {Concealer=Yes,                                                                  \n",
            "      Eyeliner=Yes}    => {Foundation=Yes}   0.152  0.5117845    0.297 0.9548219   152\n",
            "[21] {Concealer=Yes,                                                                  \n",
            "      Lip.Gloss=Yes}   => {Foundation=Yes}   0.141  0.6467890    0.218 1.2066959   141\n",
            "[22] {Mascara=Yes,                                                                    \n",
            "      Eye.shadow=Yes,                                                                 \n",
            "      Lip.Gloss=Yes}   => {Foundation=Yes}   0.111  0.7025316    0.158 1.3106934   111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "n-_2cYgRIT0e",
        "outputId": "c88e7380-44be-4dcd-f2db-46fe28545830"
      },
      "source": [
        "plot(Rule_cosmetic)\n"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "MPA9g5kuIegG",
        "outputId": "d5173682-609d-4f28-823a-abbefd1eddad"
      },
      "source": [
        "plot(Rule_cosmetic,\"grouped\")\n"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nRU5ErkJggg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "jm_zTPlpIf7Z",
        "outputId": "856e5887-6a36-44ea-ae87-d709d57c2362"
      },
      "source": [
        "plot(Rule_cosmetic,\"graph\")"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    }
  ]
}