Project 3/AssociationRules.ipynb

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      "name": "AssociationRules.ipynb",
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "<div dir=rtl >\n",
        "<h1>قوانین انجمنی</h1>\n",
        "\n",
        "قوانین انجمنی روابط و وابستگی‌های متقابل بین مجموعه بزرگی از اقلام داده‌ای را نشان می‌دهند. \n",
        "\n",
        "مثال متداول در رابطه با کشف قوانین انجمنی \"تحلیل سبد خرید\" است.  در این فرایند با توجه به اقلام مختلفی که مشتریان در سبد خریدشان قرار می‌دهند، عادات و رفتار خرید مشتریان مورد تحلیل قرار می‌گیرد و می‌توان با شناسایی ارتباط بین محصولات، الگوهای تکرار شونده در حین خرید را بدست آورد.\n",
        "\n",
        "سه پارامتر مهم:\n",
        "- Support  میزان محبوبیت یک مجموعه آیتم را با توجه به دفعات حضور در تراکنش‌ها نشان می‌دهد.\n",
        "- Confidence  احتمال خرید آیتم y در صورت خرید آیتم x را نشان می‌دهد. x -> y\n",
        "- Lift   ترکیب دو پارامتر بالا می‌باشد.\n",
        "\n",
        "برای پیاده سازی قوانین انجمنی در این تمرین، از الگوریتم Apriori که یکی از محبوب‌ترین و کارآمدترین الگوریتم‌های این حوزه است استفاده می‌کنیم.\n",
        "\n",
        "\n",
        "<font color='Green'> پرسش:  تاثیر مقادیر مختلف پارامتر Lift را در احتمال وقوع نتیجه بررسی کنید.  </font>\n",
        "\n",
        "\n",
        "</div>\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "LqmgsmWjUkAZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "<div dir=rtl >\n",
        "<h1>الگوریتم Apriori</h1>\n",
        "\n",
        "روش کار الگوریتم به این صورت است که یک  حداقل مقدار پشتیبانی در نظر گرفته    می‌شود و تکرارها با مجموعه آیتم‌های مکرر اتفاق می‌افتند. در صورتیکه  مجموعه‌ها و زیرمجموعه‌ها مقدار پشتیبانی کمتر از حد آستانه داشته باشند حذف می‌شوند. این روند تا زمانیکه امکان حذف وجود نداشته باشد ادامه می‌یابد. \n",
        "\n",
        "در این بخش از تمرین می‌خواهیم الگوریتم apriori را بر روی مجموعه داده Hypermarket_dataset که شامل سفارشات خرید افراد از فروشگاه های مواد غذایی است اعمال کنیم.\n",
        "\n",
        "\n",
        "</div>\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Ts9-4Z3GowPg"
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "<div dir=rtl >\n",
        "<h1>آماده‌سازی داده</h1>\n",
        "\n",
        "<font color='Green'>پرسش: برای شروع کار باید داده‌های دیتاست را در قالب ماتریس پراکنده که محصولات  خریداری شده در ستون‌ و شماره سفارشات خرید به عنوان شاخص (index) هستند آماده کنید. \n",
        "\n",
        "\n",
        "برای راحتی کار محصولات خریداری شده در هر سفارش را با اعداد 0,1 کدگذاری کنید.\n",
        "\n",
        " نمونه ماتریس خروجی شما:\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1eD0jan1ZbeYqSklgK--ks7oeY-MyTA3p\"></img>\n",
        "\n",
        "</div>\n",
        "\n",
        "\n"
      ],
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        "<div dir=\"rtl\">\n",
        "  <font color=\"yellow\">\n",
        "    معیار lift میزان خوب بودن ارتباط دو آیتم را نشان میدهد. این معیار نسبت فرکانس دو آیتم که باهم خریداری شده اند بر فرکانس مورد انتظار ما برای اینکه باهم خریداری شوند به شرطی که آیتم ها مستقل از هم باشند است. مقدار بالای lift میزان ارتباط زیاد دو آیتم را نشان میدهد در حالی که مقدار کم lift میزان ارتباط کم دو آیتم را بیان میکند.\n",
        "  </font>\n",
        "</div>"
      ],
      "metadata": {
        "id": "H3sKRmeUHVsW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        " \n",
        "from mlxtend.frequent_patterns import apriori\n",
        "from mlxtend.frequent_patterns import association_rules"
      ],
      "metadata": {
        "id": "7JZqfYl3onM2"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# your code here\n",
        "main_columns = []\n",
        "df = pd.read_csv(\"Hypermarket_dataset.csv\", header = None)\n",
        "\n",
        "df = df.iloc[1: , :]\n",
        "for i in df.iloc[:, 2]:\n",
        "  main_columns.append(i)\n",
        "main_columns = list(set(main_columns))\n",
        "\n",
        "data_dict = {}\n",
        "data = []\n",
        "indexes = []\n",
        "for index, row in df.iterrows():\n",
        "  index = row[0]\n",
        "  items = row[2]\n",
        "  indexes.append(index)\n",
        "  if index in data_dict:\n",
        "    data_dict[index].append(items)\n",
        "  else:\n",
        "    data_dict[index] = [items]\n",
        "\n",
        "for key, value in data_dict.items():\n",
        "  items_list = [0]*len(main_columns)\n",
        "  for v in value:\n",
        "    items_list[main_columns.index(v)] = 1\n",
        "  data.append(items_list)\n",
        "\n",
        "indexes = list(set(indexes))\n",
        "final_df = pd.DataFrame(data, index=indexes, columns=main_columns)\n",
        "final_df"
      ],
      "metadata": {
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        "outputId": "206ba2d4-5d7a-4940-d984-4d52fae9fc4e"
      },
      "execution_count": 18,
      "outputs": [
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3898 rows × 167 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29b13773-5fff-4f33-8f15-76e019554fc0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
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              "          const element = document.querySelector('#df-29b13773-5fff-4f33-8f15-76e019554fc0');\n",
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              "            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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "zqj05tKbLNjA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "<div dir=rtl >\n",
        "<h1>شناسایی الگوهای پر تکرار</h1>\n",
        "\n",
        "<font color='Green'> پرسش:  با اعمال الگوریتم apriori و به ازای مقدار min_support = 0.07 تمامی الگوهای پر تکرار را تولید نمایید. \n",
        "\n",
        "</div>\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "xMcroZSNxy8D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# your code here\n",
        "frequent_itemsets = apriori(final_df, min_support=0.07)\n",
        "frequent_itemsets['length'] = frequent_itemsets['itemsets'].apply(lambda x: len(x))\n",
        "frequent_itemsets"
      ],
      "metadata": {
        "id": "tyVIYwa640Xk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "5d8db795-f727-45d1-ea43-0c0ddf737463"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     support        itemsets  length\n",
              "0   0.088507             (1)       1\n",
              "1   0.165213             (8)       1\n",
              "2   0.230631            (10)       1\n",
              "3   0.086455            (13)       1\n",
              "4   0.349666            (15)       1\n",
              "..       ...             ...     ...\n",
              "78  0.075423      (164, 124)       2\n",
              "79  0.071575      (156, 142)       2\n",
              "80  0.120318      (164, 156)       2\n",
              "81  0.082093   (156, 46, 15)       3\n",
              "82  0.071832  (164, 156, 46)       3\n",
              "\n",
              "[83 rows x 3 columns]"
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              "      <th></th>\n",
              "      <th>support</th>\n",
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              "      <td>0.230631</td>\n",
              "      <td>(10)</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.086455</td>\n",
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              "      <td>1</td>\n",
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              "      <th>80</th>\n",
              "      <td>0.120318</td>\n",
              "      <td>(164, 156)</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>0.082093</td>\n",
              "      <td>(156, 46, 15)</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>0.071832</td>\n",
              "      <td>(164, 156, 46)</td>\n",
              "      <td>3</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>83 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c85e4694-ed4d-4b24-aab6-367404919bb9')\"\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",
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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",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-c85e4694-ed4d-4b24-aab6-367404919bb9');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
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          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "<div dir=rtl >\n",
        "<h1>استخراج قوانین انجمنی</h1>\n",
        "\n",
        "\n",
        "<font color='Green'> پرسش: تابعی بنویسید که با گرفتن دو ورودی confidence و lift قوانین انجمنی حاصل را در خروجی نشان دهد. </br>\n",
        "برای دو حالت خروجی خود را در گزارش ثبت کنید.\n",
        "\n",
        "\n",
        "</div>\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "8gKO4jR1zhVU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# your code here\n",
        "rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1.2)\n",
        "rules[\"antecedents_length\"] = rules[\"antecedents\"].apply(lambda x: len(x))\n",
        "rules[\"consequents_length\"] = rules[\"consequents\"].apply(lambda x: len(x))\n",
        "rules.sort_values(\"lift\",ascending=False)"
      ],
      "metadata": {
        "id": "gys0v5Yh5dXK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "outputId": "00b2b5fb-7c94-467d-b4a7-8011aca7d96d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   antecedents consequents  antecedent support  consequent support   support  \\\n",
              "11       (164)   (156, 46)            0.282966            0.191380  0.071832   \n",
              "10   (156, 46)       (164)            0.191380            0.282966  0.071832   \n",
              "13        (46)  (156, 164)            0.458184            0.120318  0.071832   \n",
              "8   (156, 164)        (46)            0.120318            0.458184  0.071832   \n",
              "1        (124)       (164)            0.206003            0.282966  0.075423   \n",
              "0        (164)       (124)            0.282966            0.206003  0.075423   \n",
              "12       (156)   (164, 46)            0.376603            0.150590  0.071832   \n",
              "9    (164, 46)       (156)            0.150590            0.376603  0.071832   \n",
              "2    (156, 46)        (15)            0.191380            0.349666  0.082093   \n",
              "7         (15)   (156, 46)            0.349666            0.191380  0.082093   \n",
              "3    (156, 15)        (46)            0.146742            0.458184  0.082093   \n",
              "6         (46)   (156, 15)            0.458184            0.146742  0.082093   \n",
              "4     (46, 15)       (156)            0.178553            0.376603  0.082093   \n",
              "5        (156)    (46, 15)            0.376603            0.178553  0.082093   \n",
              "\n",
              "    confidence      lift  leverage  conviction  antecedents_length  \\\n",
              "11    0.253853  1.326434  0.017678    1.083727                   1   \n",
              "10    0.375335  1.326434  0.017678    1.147870                   2   \n",
              "13    0.156775  1.303003  0.016704    1.043235                   1   \n",
              "8     0.597015  1.303003  0.016704    1.344507                   2   \n",
              "1     0.366127  1.293892  0.017132    1.131196                   1   \n",
              "0     0.266546  1.293892  0.017132    1.082545                   1   \n",
              "12    0.190736  1.266589  0.015119    1.049608                   1   \n",
              "9     0.477002  1.266589  0.015119    1.191967                   2   \n",
              "2     0.428954  1.226753  0.015174    1.138847                   2   \n",
              "7     0.234776  1.226753  0.015174    1.056710                   1   \n",
              "3     0.559441  1.220996  0.014859    1.229837                   2   \n",
              "6     0.179171  1.220996  0.014859    1.039508                   1   \n",
              "4     0.459770  1.220834  0.014850    1.153947                   2   \n",
              "5     0.217984  1.220834  0.014850    1.050422                   1   \n",
              "\n",
              "    consequents_length  \n",
              "11                   2  \n",
              "10                   1  \n",
              "13                   2  \n",
              "8                    1  \n",
              "1                    1  \n",
              "0                    1  \n",
              "12                   2  \n",
              "9                    1  \n",
              "2                    1  \n",
              "7                    2  \n",
              "3                    1  \n",
              "6                    2  \n",
              "4                    1  \n",
              "5                    2  "
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
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              "      <th>11</th>\n",
              "      <td>(164)</td>\n",
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              "      <th>13</th>\n",
              "      <td>(46)</td>\n",
              "      <td>(156, 164)</td>\n",
              "      <td>0.458184</td>\n",
              "      <td>0.120318</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.156775</td>\n",
              "      <td>1.303003</td>\n",
              "      <td>0.016704</td>\n",
              "      <td>1.043235</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(156, 164)</td>\n",
              "      <td>(46)</td>\n",
              "      <td>0.120318</td>\n",
              "      <td>0.458184</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.597015</td>\n",
              "      <td>1.303003</td>\n",
              "      <td>0.016704</td>\n",
              "      <td>1.344507</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(124)</td>\n",
              "      <td>(164)</td>\n",
              "      <td>0.206003</td>\n",
              "      <td>0.282966</td>\n",
              "      <td>0.075423</td>\n",
              "      <td>0.366127</td>\n",
              "      <td>1.293892</td>\n",
              "      <td>0.017132</td>\n",
              "      <td>1.131196</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(164)</td>\n",
              "      <td>(124)</td>\n",
              "      <td>0.282966</td>\n",
              "      <td>0.206003</td>\n",
              "      <td>0.075423</td>\n",
              "      <td>0.266546</td>\n",
              "      <td>1.293892</td>\n",
              "      <td>0.017132</td>\n",
              "      <td>1.082545</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(156)</td>\n",
              "      <td>(164, 46)</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.150590</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.190736</td>\n",
              "      <td>1.266589</td>\n",
              "      <td>0.015119</td>\n",
              "      <td>1.049608</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(164, 46)</td>\n",
              "      <td>(156)</td>\n",
              "      <td>0.150590</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.477002</td>\n",
              "      <td>1.266589</td>\n",
              "      <td>0.015119</td>\n",
              "      <td>1.191967</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(156, 46)</td>\n",
              "      <td>(15)</td>\n",
              "      <td>0.191380</td>\n",
              "      <td>0.349666</td>\n",
              "      <td>0.082093</td>\n",
              "      <td>0.428954</td>\n",
              "      <td>1.226753</td>\n",
              "      <td>0.015174</td>\n",
              "      <td>1.138847</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(15)</td>\n",
              "      <td>(156, 46)</td>\n",
              "      <td>0.349666</td>\n",
              "      <td>0.191380</td>\n",
              "      <td>0.082093</td>\n",
              "      <td>0.234776</td>\n",
              "      <td>1.226753</td>\n",
              "      <td>0.015174</td>\n",
              "      <td>1.056710</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(156, 15)</td>\n",
              "      <td>(46)</td>\n",
              "      <td>0.146742</td>\n",
              "      <td>0.458184</td>\n",
              "      <td>0.082093</td>\n",
              "      <td>0.559441</td>\n",
              "      <td>1.220996</td>\n",
              "      <td>0.014859</td>\n",
              "      <td>1.229837</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(46)</td>\n",
              "      <td>(156, 15)</td>\n",
              "      <td>0.458184</td>\n",
              "      <td>0.146742</td>\n",
              "      <td>0.082093</td>\n",
              "      <td>0.179171</td>\n",
              "      <td>1.220996</td>\n",
              "      <td>0.014859</td>\n",
              "      <td>1.039508</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(46, 15)</td>\n",
              "      <td>(156)</td>\n",
              "      <td>0.178553</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.082093</td>\n",
              "      <td>0.459770</td>\n",
              "      <td>1.220834</td>\n",
              "      <td>0.014850</td>\n",
              "      <td>1.153947</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(156)</td>\n",
              "      <td>(46, 15)</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.178553</td>\n",
              "      <td>0.082093</td>\n",
              "      <td>0.217984</td>\n",
              "      <td>1.220834</td>\n",
              "      <td>0.014850</td>\n",
              "      <td>1.050422</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.07)\n",
        "rules[\"antecedents_length\"] = rules[\"antecedents\"].apply(lambda x: len(x))\n",
        "rules[\"consequents_length\"] = rules[\"consequents\"].apply(lambda x: len(x))\n",
        "rules.sort_values(\"lift\",ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "GNw8YaCenyAv",
        "outputId": "cb47c504-6e13-4db6-f774-4dcd25b18df8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   antecedents consequents  antecedent support  consequent support   support  \\\n",
              "95       (164)   (156, 46)            0.282966            0.191380  0.071832   \n",
              "94   (156, 46)       (164)            0.191380            0.282966  0.071832   \n",
              "97        (46)  (156, 164)            0.458184            0.120318  0.071832   \n",
              "92  (156, 164)        (46)            0.120318            0.458184  0.071832   \n",
              "81       (124)       (164)            0.206003            0.282966  0.075423   \n",
              "..         ...         ...                 ...                 ...       ...   \n",
              "45        (46)        (43)            0.458184            0.313494  0.151103   \n",
              "51       (156)        (43)            0.376603            0.313494  0.124166   \n",
              "50        (43)       (156)            0.313494            0.376603  0.124166   \n",
              "35        (28)       (156)            0.233710            0.376603  0.091329   \n",
              "34       (156)        (28)            0.376603            0.233710  0.091329   \n",
              "\n",
              "    confidence      lift  leverage  conviction  antecedents_length  \\\n",
              "95    0.253853  1.326434  0.017678    1.083727                   1   \n",
              "94    0.375335  1.326434  0.017678    1.147870                   2   \n",
              "97    0.156775  1.303003  0.016704    1.043235                   1   \n",
              "92    0.597015  1.303003  0.016704    1.344507                   2   \n",
              "81    0.366127  1.293892  0.017132    1.131196                   1   \n",
              "..         ...       ...       ...         ...                 ...   \n",
              "45    0.329787  1.051973  0.007465    1.024310                   1   \n",
              "51    0.329700  1.051695  0.006103    1.024178                   1   \n",
              "50    0.396072  1.051695  0.006103    1.032237                   1   \n",
              "35    0.390779  1.037642  0.003313    1.023269                   1   \n",
              "34    0.242507  1.037642  0.003313    1.011614                   1   \n",
              "\n",
              "    consequents_length  \n",
              "95                   2  \n",
              "94                   1  \n",
              "97                   2  \n",
              "92                   1  \n",
              "81                   1  \n",
              "..                 ...  \n",
              "45                   1  \n",
              "51                   1  \n",
              "50                   1  \n",
              "35                   1  \n",
              "34                   1  \n",
              "\n",
              "[98 rows x 11 columns]"
            ],
            "text/html": [
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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",
              "      <th>antecedents_length</th>\n",
              "      <th>consequents_length</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>(164)</td>\n",
              "      <td>(156, 46)</td>\n",
              "      <td>0.282966</td>\n",
              "      <td>0.191380</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.253853</td>\n",
              "      <td>1.326434</td>\n",
              "      <td>0.017678</td>\n",
              "      <td>1.083727</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>(156, 46)</td>\n",
              "      <td>(164)</td>\n",
              "      <td>0.191380</td>\n",
              "      <td>0.282966</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.375335</td>\n",
              "      <td>1.326434</td>\n",
              "      <td>0.017678</td>\n",
              "      <td>1.147870</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>(46)</td>\n",
              "      <td>(156, 164)</td>\n",
              "      <td>0.458184</td>\n",
              "      <td>0.120318</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.156775</td>\n",
              "      <td>1.303003</td>\n",
              "      <td>0.016704</td>\n",
              "      <td>1.043235</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>(156, 164)</td>\n",
              "      <td>(46)</td>\n",
              "      <td>0.120318</td>\n",
              "      <td>0.458184</td>\n",
              "      <td>0.071832</td>\n",
              "      <td>0.597015</td>\n",
              "      <td>1.303003</td>\n",
              "      <td>0.016704</td>\n",
              "      <td>1.344507</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>(124)</td>\n",
              "      <td>(164)</td>\n",
              "      <td>0.206003</td>\n",
              "      <td>0.282966</td>\n",
              "      <td>0.075423</td>\n",
              "      <td>0.366127</td>\n",
              "      <td>1.293892</td>\n",
              "      <td>0.017132</td>\n",
              "      <td>1.131196</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>(46)</td>\n",
              "      <td>(43)</td>\n",
              "      <td>0.458184</td>\n",
              "      <td>0.313494</td>\n",
              "      <td>0.151103</td>\n",
              "      <td>0.329787</td>\n",
              "      <td>1.051973</td>\n",
              "      <td>0.007465</td>\n",
              "      <td>1.024310</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>(156)</td>\n",
              "      <td>(43)</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.313494</td>\n",
              "      <td>0.124166</td>\n",
              "      <td>0.329700</td>\n",
              "      <td>1.051695</td>\n",
              "      <td>0.006103</td>\n",
              "      <td>1.024178</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>(43)</td>\n",
              "      <td>(156)</td>\n",
              "      <td>0.313494</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.124166</td>\n",
              "      <td>0.396072</td>\n",
              "      <td>1.051695</td>\n",
              "      <td>0.006103</td>\n",
              "      <td>1.032237</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>(28)</td>\n",
              "      <td>(156)</td>\n",
              "      <td>0.233710</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.091329</td>\n",
              "      <td>0.390779</td>\n",
              "      <td>1.037642</td>\n",
              "      <td>0.003313</td>\n",
              "      <td>1.023269</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>(156)</td>\n",
              "      <td>(28)</td>\n",
              "      <td>0.376603</td>\n",
              "      <td>0.233710</td>\n",
              "      <td>0.091329</td>\n",
              "      <td>0.242507</td>\n",
              "      <td>1.037642</td>\n",
              "      <td>0.003313</td>\n",
              "      <td>1.011614</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>98 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64861aca-6eba-4422-b70b-e73fc0e10636')\"\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",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      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-64861aca-6eba-4422-b70b-e73fc0e10636 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-64861aca-6eba-4422-b70b-e73fc0e10636');\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": 21
        }
      ]
    }
  ]
}