FPgrowth.ipynb

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        "FP-Growth algorithm -- Lê Tuấn Kiệt"
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        "#  install the required module\n",
        "%pip install pandas\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.7/dist-packages (0.20.0)\n",
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            "Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (1.1.0)\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0.2->mlxtend) (3.1.0)\n"
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        "pip install mlxtend --upgrade --no-deps\n"
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.7/dist-packages (0.20.0)\n"
          ]
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              "(7500, 20)"
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          "metadata": {},
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      ],
      "source": [
        "# import module\n",
        "import pandas as pd\n",
        "\n",
        "# dataset\n",
        "dataset = pd.read_csv('https://raw.githubusercontent.com/RinzCSGO/machine-learning/main/data/ml-basics/Market_Basket_Optimisation.csv')\n",
        "\n",
        "# printing the shape of the dataset\n",
        "dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print the columns and few rows using head\n",
        "dataset.head()"
      ],
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          "height": 320
        },
        "id": "uyWtfxHl8f6n",
        "outputId": "32e910db-e50e-4295-f5eb-0ea7d4a00219"
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      "execution_count": null,
      "outputs": [
        {
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              "           shrimp    almonds     avocado    vegetables mix green grapes  \\\n",
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              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import module\n",
        "import numpy as np\n",
        "\n",
        "# Gather all items of each Transactions into Numpy Array\n",
        "transaction = []\n",
        "for i in range(0, dataset.shape[0]):\n",
        "  for j in range(0,dataset.shape[1]):\n",
        "    transaction.append(dataset.values[i,j])\n",
        "\n",
        "# convert to numpy array\n",
        "transaction = np.array(transaction)\n",
        "print(transaction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "budDb1MQ8nVJ",
        "outputId": "4bc5a00f-18ee-48fc-95fd-a28775a32284"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['burgers' 'meatballs' 'eggs' ... 'nan' 'nan' 'nan']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  Transform Them a Pandas DataFrame\n",
        "df = pd.DataFrame(transaction, columns=[\"items\"]) \n",
        "\n",
        "# Put 1 to Each Item For Making Countable Table, to be able to perform Group By\n",
        "df[\"incident_count\"] = 1 \n",
        "\n",
        "#  Delete NaN Items from Dataset\n",
        "indexNames = df[df['items'] == \"nan\" ].index\n",
        "df.drop(indexNames , inplace=True)\n",
        "\n",
        "# Making a New Appropriate Pandas DataFrame for Visualizations  \n",
        "df_table = df.groupby(\"items\").sum().sort_values(\"incident_count\", ascending=False).reset_index()\n",
        "\n",
        "#  Initial Visualizations\n",
        "df_table.head(5).style.background_gradient(cmap='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "p_BISYbl935-",
        "outputId": "5a1f2da0-29af-480b-e3d9-9b7e75a2694c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f9b7bfd1fd0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_5baef_row0_col1 {\n",
              "  background-color: #08306b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_5baef_row1_col1 {\n",
              "  background-color: #cde0f1;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_5baef_row2_col1 {\n",
              "  background-color: #dceaf6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_5baef_row3_col1 {\n",
              "  background-color: #e5eff9;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_5baef_row4_col1 {\n",
              "  background-color: #f7fbff;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_5baef_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >items</th>\n",
              "      <th class=\"col_heading level0 col1\" >incident_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_5baef_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_5baef_row0_col0\" class=\"data row0 col0\" >mineral water</td>\n",
              "      <td id=\"T_5baef_row0_col1\" class=\"data row0 col1\" >1787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5baef_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_5baef_row1_col0\" class=\"data row1 col0\" >eggs</td>\n",
              "      <td id=\"T_5baef_row1_col1\" class=\"data row1 col1\" >1348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5baef_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_5baef_row2_col0\" class=\"data row2 col0\" >spaghetti</td>\n",
              "      <td id=\"T_5baef_row2_col1\" class=\"data row2 col1\" >1306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5baef_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_5baef_row3_col0\" class=\"data row3 col0\" >french fries</td>\n",
              "      <td id=\"T_5baef_row3_col1\" class=\"data row3 col1\" >1282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5baef_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_5baef_row4_col0\" class=\"data row4 col0\" >chocolate</td>\n",
              "      <td id=\"T_5baef_row4_col1\" class=\"data row4 col1\" >1230</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing required module\n",
        "import plotly.express as px\n",
        "\n",
        "# to have a same origin\n",
        "df_table[\"all\"] = \"Top 50 items\" \n",
        "\n",
        "# creating tree map using plotly\n",
        "fig = px.treemap(df_table.head(50), path=['all', \"items\"], values='incident_count',\n",
        "                  color=df_table[\"incident_count\"].head(50), hover_data=['items'],\n",
        "                  color_continuous_scale='Blues',\n",
        "                )\n",
        "# ploting the treemap\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "yFCMJc1vDUwI",
        "outputId": "143c7bda-aa02-4032-fac9-369377a6726d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<html>\n",
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    {
      "cell_type": "code",
      "source": [
        "# Transform Every Transaction to Seperate List & Gather Them into Numpy Array\n",
        "transaction = []\n",
        "for i in range(dataset.shape[0]):\n",
        "    transaction.append([str(dataset.values[i,j]) for j in range(dataset.shape[1])])\n",
        "\n",
        "# creating the numpy array of the transactions\n",
        "transaction = np.array(transaction)\n",
        "\n",
        "# importing the required module\n",
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "\n",
        "# initializing the transactionEncoder\n",
        "te = TransactionEncoder()\n",
        "te_ary = te.fit(transaction).transform(transaction)\n",
        "dataset = pd.DataFrame(te_ary, columns=te.columns_)\n",
        "\n",
        "# dataset after encoded\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "nRbqrjKhGuRk",
        "outputId": "4ad4c068-f677-4a28-db4f-53443af13b9d"
      },
      "execution_count": null,
      "outputs": [
        {
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              "    asparagus  almonds  antioxydant juice  asparagus  avocado  babies food  \\\n",
              "0       False    False              False      False    False        False   \n",
              "1       False    False              False      False    False        False   \n",
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              "3       False    False              False      False    False        False   \n",
              "4       False    False              False      False    False        False   \n",
              "\n",
              "   bacon  barbecue sauce  black tea  blueberries  ...  turkey  vegetables mix  \\\n",
              "0  False           False      False        False  ...   False           False   \n",
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              "3  False           False      False        False  ...   False           False   \n",
              "4  False           False      False        False  ...   False           False   \n",
              "\n",
              "   water spray  white wine  whole weat flour  whole wheat pasta  \\\n",
              "0        False       False             False              False   \n",
              "1        False       False             False              False   \n",
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              "4        False       False             False              False   \n",
              "\n",
              "   whole wheat rice   yams  yogurt cake  zucchini  \n",
              "0             False  False        False     False  \n",
              "1             False  False        False     False  \n",
              "2             False  False        False     False  \n",
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              "4             False  False        False     False  \n",
              "\n",
              "[5 rows x 121 columns]"
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-86be33b4-9f8f-4b4d-91a0-d239397203f2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-86be33b4-9f8f-4b4d-91a0-d239397203f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# select top 30 items\n",
        "first30 = df_table[\"items\"].head(30).values \n",
        "\n",
        "# Extract Top 30\n",
        "dataset = dataset.loc[:,first30] \n",
        "\n",
        "# shape of the dataset\n",
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AiSVEbLdJMoz",
        "outputId": "cdd5ff89-7bf2-4fb1-f954-82778ebed53e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7500, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing Libraries\n",
        "from mlxtend.frequent_patterns import fpgrowth\n",
        "\n",
        "#running the fpgrowth algorithm\n",
        "res=fpgrowth(dataset,min_support=0.05, use_colnames=True)\n",
        "\n",
        "# printing top 10\n",
        "res.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "ik4FWo92J7Ty",
        "outputId": "79299522-5617-43b3-b110-78beb2edc1fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    support            itemsets\n",
              "0  0.179733              (eggs)\n",
              "1  0.087200           (burgers)\n",
              "2  0.062533            (turkey)\n",
              "3  0.238267     (mineral water)\n",
              "4  0.132000         (green tea)\n",
              "5  0.129600              (milk)\n",
              "6  0.058533  (whole wheat rice)\n",
              "7  0.076400    (low fat yogurt)\n",
              "8  0.170933      (french fries)\n",
              "9  0.050533              (soup)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb5838b0-c9a9-49ce-ad91-525c8c58204a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>support</th>\n",
              "      <th>itemsets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.179733</td>\n",
              "      <td>(eggs)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.087200</td>\n",
              "      <td>(burgers)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.062533</td>\n",
              "      <td>(turkey)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.238267</td>\n",
              "      <td>(mineral water)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.132000</td>\n",
              "      <td>(green tea)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.129600</td>\n",
              "      <td>(milk)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.058533</td>\n",
              "      <td>(whole wheat rice)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.076400</td>\n",
              "      <td>(low fat yogurt)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.170933</td>\n",
              "      <td>(french fries)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.050533</td>\n",
              "      <td>(soup)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb5838b0-c9a9-49ce-ad91-525c8c58204a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eb5838b0-c9a9-49ce-ad91-525c8c58204a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eb5838b0-c9a9-49ce-ad91-525c8c58204a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing required module\n",
        "from mlxtend.frequent_patterns import association_rules\n",
        "\n",
        "# creating asssociation rules\n",
        "res=association_rules(res, metric=\"lift\", min_threshold=1)\n",
        "\n",
        "# printing association rules\n",
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "keGa6Tv_QECD",
        "outputId": "7091b760-2309-4423-c612-c1c8b32730d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       antecedents      consequents  antecedent support  consequent support  \\\n",
              "0           (eggs)  (mineral water)            0.179733            0.238267   \n",
              "1  (mineral water)           (eggs)            0.238267            0.179733   \n",
              "2  (mineral water)      (spaghetti)            0.238267            0.174133   \n",
              "3      (spaghetti)  (mineral water)            0.174133            0.238267   \n",
              "4  (mineral water)      (chocolate)            0.238267            0.163867   \n",
              "5      (chocolate)  (mineral water)            0.163867            0.238267   \n",
              "\n",
              "    support  confidence      lift  leverage  conviction  \n",
              "0  0.050933    0.283383  1.189351  0.008109    1.062957  \n",
              "1  0.050933    0.213766  1.189351  0.008109    1.043286  \n",
              "2  0.059733    0.250699  1.439698  0.018243    1.102184  \n",
              "3  0.059733    0.343032  1.439698  0.018243    1.159468  \n",
              "4  0.052667    0.221041  1.348907  0.013623    1.073398  \n",
              "5  0.052667    0.321400  1.348907  0.013623    1.122506  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-31c19efa-2ee6-440e-9f43-5b53933f871c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(eggs)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.179733</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.050933</td>\n",
              "      <td>0.283383</td>\n",
              "      <td>1.189351</td>\n",
              "      <td>0.008109</td>\n",
              "      <td>1.062957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(eggs)</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.179733</td>\n",
              "      <td>0.050933</td>\n",
              "      <td>0.213766</td>\n",
              "      <td>1.189351</td>\n",
              "      <td>0.008109</td>\n",
              "      <td>1.043286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.174133</td>\n",
              "      <td>0.059733</td>\n",
              "      <td>0.250699</td>\n",
              "      <td>1.439698</td>\n",
              "      <td>0.018243</td>\n",
              "      <td>1.102184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.174133</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.059733</td>\n",
              "      <td>0.343032</td>\n",
              "      <td>1.439698</td>\n",
              "      <td>0.018243</td>\n",
              "      <td>1.159468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.163867</td>\n",
              "      <td>0.052667</td>\n",
              "      <td>0.221041</td>\n",
              "      <td>1.348907</td>\n",
              "      <td>0.013623</td>\n",
              "      <td>1.073398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.163867</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.052667</td>\n",
              "      <td>0.321400</td>\n",
              "      <td>1.348907</td>\n",
              "      <td>0.013623</td>\n",
              "      <td>1.122506</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31c19efa-2ee6-440e-9f43-5b53933f871c')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-31c19efa-2ee6-440e-9f43-5b53933f871c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort values based on confidence\n",
        "res.sort_values(\"confidence\",ascending=False)"
      ],
      "metadata": {
        "id": "tkIU_1-CQV1E",
        "outputId": "22393c3d-543e-4ad1-f865-a8d613e6813e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       antecedents      consequents  antecedent support  consequent support  \\\n",
              "3      (spaghetti)  (mineral water)            0.174133            0.238267   \n",
              "5      (chocolate)  (mineral water)            0.163867            0.238267   \n",
              "0           (eggs)  (mineral water)            0.179733            0.238267   \n",
              "2  (mineral water)      (spaghetti)            0.238267            0.174133   \n",
              "4  (mineral water)      (chocolate)            0.238267            0.163867   \n",
              "1  (mineral water)           (eggs)            0.238267            0.179733   \n",
              "\n",
              "    support  confidence      lift  leverage  conviction  \n",
              "3  0.059733    0.343032  1.439698  0.018243    1.159468  \n",
              "5  0.052667    0.321400  1.348907  0.013623    1.122506  \n",
              "0  0.050933    0.283383  1.189351  0.008109    1.062957  \n",
              "2  0.059733    0.250699  1.439698  0.018243    1.102184  \n",
              "4  0.052667    0.221041  1.348907  0.013623    1.073398  \n",
              "1  0.050933    0.213766  1.189351  0.008109    1.043286  "
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.174133</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.059733</td>\n",
              "      <td>0.343032</td>\n",
              "      <td>1.439698</td>\n",
              "      <td>0.018243</td>\n",
              "      <td>1.159468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.163867</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.052667</td>\n",
              "      <td>0.321400</td>\n",
              "      <td>1.348907</td>\n",
              "      <td>0.013623</td>\n",
              "      <td>1.122506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(eggs)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.179733</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.050933</td>\n",
              "      <td>0.283383</td>\n",
              "      <td>1.189351</td>\n",
              "      <td>0.008109</td>\n",
              "      <td>1.062957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.174133</td>\n",
              "      <td>0.059733</td>\n",
              "      <td>0.250699</td>\n",
              "      <td>1.439698</td>\n",
              "      <td>0.018243</td>\n",
              "      <td>1.102184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.163867</td>\n",
              "      <td>0.052667</td>\n",
              "      <td>0.221041</td>\n",
              "      <td>1.348907</td>\n",
              "      <td>0.013623</td>\n",
              "      <td>1.073398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(eggs)</td>\n",
              "      <td>0.238267</td>\n",
              "      <td>0.179733</td>\n",
              "      <td>0.050933</td>\n",
              "      <td>0.213766</td>\n",
              "      <td>1.189351</td>\n",
              "      <td>0.008109</td>\n",
              "      <td>1.043286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9ccd09a-5c15-480c-b451-10eed6d4ac20')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9ccd09a-5c15-480c-b451-10eed6d4ac20 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9ccd09a-5c15-480c-b451-10eed6d4ac20');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    }
  ]
}