Regla_de_asociacion_apriori.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Naren8520/Python/blob/main/Regla_de_asociacion_apriori.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLxbm2nBto2H",
        "papermill": {
          "duration": 0.02305,
          "end_time": "2020-09-26T15:47:26.136882",
          "exception": false,
          "start_time": "2020-09-26T15:47:26.113832",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "<center> <h1 style=\"background-color:seagreen; color:white\" >Regla de Asociación Apriori</h1> \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "9H6P_rJ1znIA",
        "outputId": "711db863-c0a4-4fe9-b865-6af69dc10968"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 32,
          "metadata": {
            "image/png": {
              "width": 900
            },
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@title\n",
        "from IPython.display import Image\n",
        "Image(\"/content/drive/MyDrive/Market basket analysis/imagen/marke_basket.png\", width=900)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qGyAE4fQMI2B"
      },
      "source": [
        "**Naren Castellón**\n",
        "\n",
        "**05 de Julio 2021**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-96a5AFmto2Y",
        "papermill": {
          "duration": 0.022518,
          "end_time": "2020-09-26T15:47:26.180656",
          "exception": false,
          "start_time": "2020-09-26T15:47:26.158138",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## **¿Reglas de asociación? Qué es eso!**\n",
        "\n",
        "El aprendizaje de reglas de asociación es un método de aprendizaje automático basado en reglas para descubrir relaciones interesantes entre variables en grandes bases de datos. Su objetivo es identificar reglas sólidas descubiertas en bases de datos utilizando algunas medidas de interés.\n",
        "\n",
        "Basándose en el concepto de reglas estrictas, **Rakesh Agrawal, Tomasz Imieliński y Arun Swam** introdujeron reglas de asociación para descubrir regularidades entre productos en datos de transacciones a gran escala registrados por sistemas de punto de venta (POS) en los supermercados. Por ejemplo, la regla **{cebollas, patatas} -> {hamburguesa}** que se encuentra en los datos de ventas de un supermercado indicaría que si un cliente compra cebollas y patatas juntas, es probable que también compre carne de hamburguesa. Dicha información se puede utilizar como base para decisiones sobre actividades de marketing como, por ejemplo, precios promocionales o colocación de productos.\n",
        "\n",
        "Además del ejemplo anterior del análisis de la canasta de mercado, las reglas de asociación se emplean hoy en muchas áreas de aplicación, incluida la minería del uso de la web, la detección de intrusiones, la producción continua y la bioinformática. A diferencia de la minería de secuencias, el aprendizaje de reglas de asociación generalmente no considera el orden de los elementos dentro de una transacción o entre transacciones. [Wikipedia]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fhe5aif1vLJH"
      },
      "source": [
        "**Reglas de asociación**\n",
        "\n",
        "Si bien estamos interesados en extraer conjuntos frecuentes de elementos, esta información a menudo se presenta como una colección de reglas **si-entonces**, llamadas **reglas de asociación**.\n",
        "\n",
        "La forma de una regla de asociación es $\\{X\\rightarrow Y\\}$, donde $\\{X\\}$ es un conjunto de elementos y $\\{Y\\}$ es un elemento. La implicación de esta regla de asociación es que si todos los elementos de $\\{X\\}$ aparecen en alguna canasta, entonces $\\{Y\\}$ es “probable” que también aparezcan en esa canasta.\n",
        "\n",
        "*\t$\\{X\\}$ también se llama antecedente o lado izquierdo (LHS) y\n",
        "\n",
        "* $\\{Y\\}$ se llama consecuente o lado derecho (RHS) .\n",
        "\n",
        "Un ejemplo de regla de asociación para productos de Apple podría ser $\\{Apple iPad, Apple,iPad, Cover\\}\\rightarrow \\{Apple, Pencil\\}$, lo que significa que si $\\{X\\}$ se compran la funda para iPad y iPad de Apple , es probable que los clientes también compren el lápiz de Apple $\\{Y\\}$. Observe que el símbolo de implicación lógica \n",
        "$\\rightarrow$ no indica una relación causal entre $\\{X\\}$ e $\\{Y\\}$. Es simplemente una estimación de la probabilidad condicional de $\\{Y\\}$ dado $\\{X\\}$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26mggLDmwS4X"
      },
      "source": [
        "Ahora imagine una tienda de comestibles con decenas de miles de productos diferentes. No querríamos calcular todas las asociaciones entre todas las combinaciones posibles de productos. En su lugar, querríamos seleccionar solo reglas potencialmente \"relevantes\" del conjunto de todas las reglas posibles. Por eso, utilizamos las medidas de \n",
        "\n",
        "* **Soport (Soporte,apoyo)**\n",
        "\n",
        "* **Confidence (Confianza)**\n",
        "\n",
        "* **Lift (Levantamiento)**\n",
        "\n",
        "para reducir el número de relaciones que necesitamos analizar:\n",
        " \n",
        "* **El soporte:** es una indicación de la frecuencia con la que aparece un conjunto de artículos en las cestas.\n",
        "\n",
        "*\t**La confianza:** es una indicación de la frecuencia con la que se ha encontrado que la regla de apoyo es cierta.\n",
        "\n",
        "*\t**El levantamiento:** es una medida de asociación que utiliza tanto apoyo como confianza."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tla2er7KwzI6"
      },
      "source": [
        "Si buscamos reglas de asociación $\\{X\\rightarrow Y\\}$ que se apliquen a una fracción razonable de las canastas, entonces el soporte de X debe ser razonablemente alto. En la práctica, como en el caso de la comercialización en tiendas físicas, \"razonablemente alto\" suele ser entre el 1% y el 10% de las canastas. También queremos que la confianza de la regla sea razonablemente alta, quizás el 50%, o de lo contrario la regla tiene poco efecto práctico. (Leskovec, Rajaraman y Ullman, 2020)\n",
        "\n",
        "Además, se debe suponer que no hay demasiados conjuntos de elementos frecuentes y, por lo tanto, no hay demasiados candidatos para las reglas de asociación de alto apoyo y alta confianza. La razón de esto es que si les damos a las empresas muchas reglas de asociación que cumplen con nuestros umbrales de apoyo y confianza, ni siquiera pueden leerlas, y mucho menos actuar en consecuencia. Por lo tanto, es normal ajustar los umbrales de soporte y confianza para que no obtengamos demasiados conjuntos de elementos frecuentes. (Leskovec, Rajaraman y Ullman, 2020)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDygLSz5xA7Y"
      },
      "source": [
        "**2 Medidas de asociación**\n",
        "\n",
        "#### **2.1 Soporte**\n",
        "\n",
        "El soporte métrico nos dice qué tan popular es un conjunto de elementos, medido por la proporción de transacciones en las que aparece el conjunto de elementos.\n",
        "\n",
        "Según nuestros datos, los soporte son de.\n",
        "El soporte **{manzana}** es 4 de 8, o 50%. \n",
        "El soporte de **{manzana, cerveza, arroz}** es 2 de 8, o 25%.\n",
        "El soporte **{leche, cerveza}** es 3 de 8, o 37.5%.\n",
        "\n",
        "$$support(manzana)=\\frac{4}{8} $$\n",
        "\n",
        "O en general, para un conjunto de elementos $X$:\n",
        "\n",
        "$$support(X)=\\frac{Frecuencia(X)}{n} $$\n",
        "\n",
        "con n = número de todas las transacciones (cestas).\n",
        "\n",
        "Por lo general, se utiliza un umbral de soporte específico para reducir la cantidad de conjuntos de elementos que necesitamos analizar. Al comienzo del análisis, podríamos establecer nuestro umbral de soporte al 10%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eqcLPbuexP34"
      },
      "source": [
        "**2.2 Confianza**\n",
        "\n",
        "La confianza nos dice la probabilidad de que se compre un artículo $Y$ dado que se compra el artículo $X$, expresado como $\\{X\\rightarrow Y\\}$. Se mide por la proporción de transacciones con el artículo $X$, en el que también aparece el artículo $Y$. La confianza de una regla se define como:\n",
        "\n",
        "$$\\ support(X\\rightarrow Y)=\\frac{support(X\\cup Y)}{support(X)} $$\n",
        "\n",
        "Por tanto, la confianza se puede interpretar como una estimación de la probabilidad $P(Y/X)$. En otras palabras, esta es la probabilidad de encontrar el RHS (Y) de la regla en transacciones con la condición de que estas transacciones también contengan el LHS (X) (Hornik, Grün y Hahsler, 2005). La confianza está dirigida y por lo tanto suele dar valores diferentes a las reglas $X\\rightarrow Y$ y $Y\\rightarrow X$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ureZQDWexWsg"
      },
      "source": [
        "Tenga en cuenta que $support(X\\cup Y)$ significa el apoyo de la unión de los elementos en $X$ e $Y$. Dado que generalmente declaramos probabilidades de eventos y no conjuntos de elementos, podemos reescribir $support(X\\cup Y)$ como la probabilidad $P(E_X\\cap E_Y )$, dónde $E_X$ y $E_Y$, son los eventos en los que una transacción contiene el conjunto de elementos $X$ e $Y$, respectivamente.\n",
        "\n",
        "En nuestro ejemplo, la confianza en que se compra cerveza dado que se compra manzana $(\\{manzana\\rightarrow cerveza\\})$   es 3 de 4, o 75%.  Esto significa que la  probabilidad condicional $P(cerveza | manzana) = 75%$. manazan es el antecedente o el lado izquierdo (LHS) y la cerveza es el consecuente o el lado derecho (RHS).\n",
        "\n",
        "$$confidene(apple->beer)=\\frac{support(apple\\cup beer)}{support(apple)} $$\n",
        "\n",
        "$$=\\frac{(2/8)}{(4/8)} =0.5$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VU9iNMYgxfmD"
      },
      "source": [
        "Tenga en cuenta que la medida de confianza podría tergiversar la importancia de una asociación. Esto se debe a que solo tiene en cuenta lo popular que es el artículo $X$ (en nuestro caso, la manzana) pero no $Y$ (en nuestro caso, la cerveza).\n",
        "\n",
        "Si la cerveza también es muy popular en general, habrá una mayor probabilidad de que una transacción que contenga manzana también contenga cerveza, lo que inflará la medida de confianza. Para tener en cuenta la popularidad base de ambos elementos, utilizamos una tercera medida denominada incremento."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jkLnXfly_ys"
      },
      "source": [
        "**Elevación o lift**\n",
        "\n",
        "**Lift** nos dice qué tan probable es que se compre el artículo $Y$ cuando se compra el artículo $X$, mientras controla qué tan populares son los artículos $Y$ y $X$. Mide cuántas veces más $X$ e $Y$ ocurren juntos de lo esperado si fueran estadísticamente independientes.\n",
        "\n",
        "En nuestro ejemplo, la elevación se calcula como:\n",
        "\n",
        "$$Lift(manzana→cerveza)=\\frac {support(manzana\\cup  cerveza)} {(support(manzana)support(cerveza)} $$\n",
        "\n",
        "$$=\\frac{(2/8)}{(4/8)(5/8)}=0.8$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TW_Ge2W4zMM-"
      },
      "source": [
        "Un valor de elevación de:\n",
        "\n",
        "*\t**elevación = 1:** implica que no hay asociación entre elementos.\n",
        "\n",
        "*\t**elevación> 1:** mayor que 1 significa que es probable que se compre el artículo $Y$ si se compra el artículo $X$, es decir,  $P(Y/X)$\n",
        "\n",
        "*\t**elevación <1:** menos de 1 significa que es poco probable que se compre el artículo $Y$ si se compra el artículo $X$.\n",
        "\n",
        "La elevación de $\\{manzana\\rightarrow cerveza\\}$ es 0.8, lo que implica que es poco probable que se compre el artículo $Y$ si se compra el artículo $X$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HTXcEeNzs77F"
      },
      "source": [
        "- **Conviction:** La razón de la frecuencia esperada en la que X ocurre sin Y (es decir, la frecuencia con la que la regla hace una predicción incorrecta) si X e Y fueran independientes dividida por la frecuencia observada de predicciones incorrectas.\n",
        "\n",
        "Conviction compara la probabilidad de que X aparezca sin Y si fueran dependientes con la frecuencia real de aparición de X sin Y. En ese sentido, es similar a lift (ver sección sobre lift en esta página), sin embargo, contrasta levantarlo. es una medida dirigida. Además, la convicción es monótona en la confianza y la elevación.\n",
        "\n",
        "- **Leverage:** Conviction compara la probabilidad de que X aparece sin Y si fueron dependientes con la frecuencia real de aparición de X sin Y. En sentido, es similar a lift (ver sección sobre lift en esta página), sin embargo, contrasta levantarlo. es una medida dirigida. Además, la convicción es monótona en la confianza y la elevación."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D50ME3kOtEiw"
      },
      "source": [
        "Lo racional en un entorno de ventas es averiguar cuántas unidades más (artículos $X$ e $Y$ juntos) se venden de lo esperado de las ventas independientes. Usando min. los umbrales de apalancamiento incorporan al mismo tiempo una restricción de frecuencia implícita. Por ejemplo, para establecer un min. umbrales de apalancamiento al 0,01% (corresponde a 10 casos en un conjunto de datos con 100.000 transacciones), primero se puede utilizar un algoritmo para encontrar todos los conjuntos de elementos con mín. soporte de 0.01% y luego filtrar los conjuntos de elementos encontrados usando la restricción de apalancamiento. Debido a esta propiedad, el apalancamiento también puede sufrir el problema de los artículos raros. [Michael Hahsler]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZWb1tSGto2j",
        "papermill": {
          "duration": 0.020481,
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          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "<a id='libraries'></a>\n",
        "<h1 style=\"color:forestgreen\" >1. Packages</h1> "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_kg_hide-input": true,
        "_kg_hide-output": true,
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "execution": {
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        "tags": []
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      "outputs": [],
      "source": [
        "# ----------------------------\n",
        "# BASE\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime as dt\n",
        "\n",
        "# ----------------------------\n",
        "# INSTALL\n",
        "# !pip install mlxtend\n",
        "\n",
        "# ----------------------------\n",
        "# TRANSACTION ENCODER\n",
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "\n",
        "# ----------------------------\n",
        "# APRIORI FUNCTION \n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "# ----------------------------\n",
        "# ITERTOOLS \n",
        "import itertools\n",
        "\n",
        "# ----------------------------\n",
        "# CONFIGURATION\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.options.display.float_format = '{:.2f}'.format"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3pEmmlSTto2_",
        "papermill": {
          "duration": 0.021661,
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          "exception": false,
          "start_time": "2020-09-26T15:47:27.531080",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "<a id='load'></a>\n",
        "<h1 style=\"color:forestgreen\" >2. Data</h1> "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvfK9yZ4u3E1",
        "outputId": "78850582-3eea-48e7-854b-dba586707509"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_kg_hide-input": true,
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
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        "execution": {
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        },
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        "outputId": "767058f8-3c45-430b-c425-2086ad50e7d0",
        "papermill": {
          "duration": 0.059706,
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          "status": "completed"
        },
        "tags": []
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      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>products</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MILK,BREAD,BISCUIT</td>\n",
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              "    <tr>\n",
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              "      <td>BREAD,TEA,BOURNVITA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>JAM,MAGGI,BREAD,MILK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MAGGI,TEA,BISCUIT</td>\n",
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              "      <th>5</th>\n",
              "      <td>BREAD,TEA,BOURNVITA</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>BREAD,COFFEE,SUGER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>TEA,MILK,COFFEE,CORNFLAKES</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "</div>"
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              "                          products\n",
              "0               MILK,BREAD,BISCUIT\n",
              "1    BREAD,MILK,BISCUIT,CORNFLAKES\n",
              "2              BREAD,TEA,BOURNVITA\n",
              "3             JAM,MAGGI,BREAD,MILK\n",
              "4                MAGGI,TEA,BISCUIT\n",
              "5              BREAD,TEA,BOURNVITA\n",
              "6             MAGGI,TEA,CORNFLAKES\n",
              "7          MAGGI,BREAD,TEA,BISCUIT\n",
              "8              JAM,MAGGI,BREAD,TEA\n",
              "9                       BREAD,MILK\n",
              "10  COFFEE,COCK,BISCUIT,CORNFLAKES\n",
              "11  COFFEE,COCK,BISCUIT,CORNFLAKES\n",
              "12          COFFEE,SUGER,BOURNVITA\n",
              "13               BREAD,COFFEE,COCK\n",
              "14             BREAD,SUGER,BISCUIT\n",
              "15         COFFEE,SUGER,CORNFLAKES\n",
              "16           BREAD,SUGER,BOURNVITA\n",
              "17              BREAD,COFFEE,SUGER\n",
              "18              BREAD,COFFEE,SUGER\n",
              "19      TEA,MILK,COFFEE,CORNFLAKES"
            ]
          },
          "execution_count": 4,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Market basket analysis/data/GroceryStoreDataSet.csv\",names=['products'],header=None)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "tags": []
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "(20, 1)"
            ]
          },
          "execution_count": 5,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6lhmOwbGto3F",
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      },
      "source": [
        "<a id='tidydata'></a>\n",
        "<h1 style=\"color:forestgreen\" >3. Datos ordenados para las reglas de la asociación</h1> "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    BISCUIT  BOURNVITA  BREAD  COCK  COFFEE  CORNFLAKES  JAM  MAGGI  MILK  \\\n",
              "0         1          0      1     0       0           0    0      0     1   \n",
              "1         1          0      1     0       0           1    0      0     1   \n",
              "2         0          1      1     0       0           0    0      0     0   \n",
              "3         0          0      1     0       0           0    1      1     1   \n",
              "4         1          0      0     0       0           0    0      1     0   \n",
              "5         0          1      1     0       0           0    0      0     0   \n",
              "6         0          0      0     0       0           1    0      1     0   \n",
              "7         1          0      1     0       0           0    0      1     0   \n",
              "8         0          0      1     0       0           0    1      1     0   \n",
              "9         0          0      1     0       0           0    0      0     1   \n",
              "10        1          0      0     1       1           1    0      0     0   \n",
              "11        1          0      0     1       1           1    0      0     0   \n",
              "12        0          1      0     0       1           0    0      0     0   \n",
              "13        0          0      1     1       1           0    0      0     0   \n",
              "14        1          0      1     0       0           0    0      0     0   \n",
              "15        0          0      0     0       1           1    0      0     0   \n",
              "16        0          1      1     0       0           0    0      0     0   \n",
              "17        0          0      1     0       1           0    0      0     0   \n",
              "18        0          0      1     0       1           0    0      0     0   \n",
              "19        0          0      0     0       1           1    0      0     1   \n",
              "\n",
              "    SUGER  TEA  \n",
              "0       0    0  \n",
              "1       0    0  \n",
              "2       0    1  \n",
              "3       0    0  \n",
              "4       0    1  \n",
              "5       0    1  \n",
              "6       0    1  \n",
              "7       0    1  \n",
              "8       0    1  \n",
              "9       0    0  \n",
              "10      0    0  \n",
              "11      0    0  \n",
              "12      1    0  \n",
              "13      0    0  \n",
              "14      1    0  \n",
              "15      1    0  \n",
              "16      1    0  \n",
              "17      1    0  \n",
              "18      1    0  \n",
              "19      0    1  "
            ]
          },
          "execution_count": 6,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = list(df[\"products\"].apply(lambda x:x.split(',')))\n",
        "\n",
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "te = TransactionEncoder()\n",
        "te_data = te.fit(data).transform(df)\n",
        "df = pd.DataFrame(te_data,columns=te.columns_).astype(int)\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_kg_hide-input": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "execution": {
          "iopub.execute_input": "2020-09-26T15:47:27.867563Z",
          "iopub.status.busy": "2020-09-26T15:47:27.866707Z",
          "iopub.status.idle": "2020-09-26T15:47:28.299757Z",
          "shell.execute_reply": "2020-09-26T15:47:28.299010Z"
        },
        "id": "3E68bemXto3N",
        "outputId": "eff1436b-f241-4862-ef53-dd75d0070383",
        "papermill": {
          "duration": 0.467358,
          "end_time": "2020-09-26T15:47:28.299973",
          "exception": false,
          "start_time": "2020-09-26T15:47:27.832615",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "p = df.copy()\n",
        "for i in range(1, len(p.columns)+1):\n",
        "    p[\"Cat\"] = np.where(p[p.columns[i]] == 1, 1, 0)\n",
        "    p[p.columns[i]]= i\n",
        "    g =sns.scatterplot(p.index, p[p.columns[i]], hue = p.Cat, legend = False)\n",
        "    g.yaxis.set_label_text('Products')\n",
        "    g.set_yticks(np.arange(1, len(p.columns)))\n",
        "    g.set_xticks(df.index)\n",
        "    g.set_yticklabels(df.columns)\n",
        "    plt.title(\"Data Structure\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y50Qsp55to3P",
        "papermill": {
          "duration": 0.025809,
          "end_time": "2020-09-26T15:47:28.351632",
          "exception": false,
          "start_time": "2020-09-26T15:47:28.325823",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "<a id='step'></a>\n",
        "<h1 style=\"color:forestgreen\" >4. Paso a paso</h1> \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jjkFOgJato3Q",
        "papermill": {
          "duration": 0.025157,
          "end_time": "2020-09-26T15:47:28.402051",
          "exception": false,
          "start_time": "2020-09-26T15:47:28.376894",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "**Primera Iteration:** Encuentre valores de soporte para cada producto.\n",
        "- n: 20 (df.shape[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2020-09-26T15:47:28.461329Z",
          "iopub.status.busy": "2020-09-26T15:47:28.459900Z",
          "iopub.status.idle": "2020-09-26T15:47:28.464671Z",
          "shell.execute_reply": "2020-09-26T15:47:28.465169Z"
        },
        "id": "Yelqgpncto3R",
        "outputId": "8ee1c342-51b3-41df-b5d6-36fa47503be5",
        "papermill": {
          "duration": 0.037379,
          "end_time": "2020-09-26T15:47:28.465320",
          "exception": false,
          "start_time": "2020-09-26T15:47:28.427941",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "BISCUIT        7\n",
              "BOURNVITA      4\n",
              "BREAD         13\n",
              "COCK           3\n",
              "COFFEE         8\n",
              "CORNFLAKES     6\n",
              "JAM            2\n",
              "MAGGI          5\n",
              "MILK           5\n",
              "SUGER          6\n",
              "TEA            7\n",
              "dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Encontrar la frecuencia de los elementos\n",
        "df.sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7V0DRdZto3T",
        "papermill": {
          "duration": 0.025141,
          "end_time": "2020-09-26T15:47:28.515955",
          "exception": false,
          "start_time": "2020-09-26T15:47:28.490814",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "Si dividimos todos los elementos con el número de fila, podemos encontrar el valor de Soporte. Nuestro valor de umbral es 0,2 para el valor de soporte."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "execution": {
          "iopub.execute_input": "2020-09-26T15:47:28.579590Z",
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          "iopub.status.idle": "2020-09-26T15:47:28.584276Z",
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        "papermill": {
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          "exception": false,
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          "status": "completed"
        },
        "tags": []
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      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BREAD</th>\n",
              "      <td>0.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COFFEE</th>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BISCUIT</th>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TEA</th>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CORNFLAKES</th>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SUGER</th>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAGGI</th>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MILK</th>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOURNVITA</th>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COCK</th>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JAM</th>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Support\n",
              "BREAD          0.65\n",
              "COFFEE         0.40\n",
              "BISCUIT        0.35\n",
              "TEA            0.35\n",
              "CORNFLAKES     0.30\n",
              "SUGER          0.30\n",
              "MAGGI          0.25\n",
              "MILK           0.25\n",
              "BOURNVITA      0.20\n",
              "COCK           0.15\n",
              "JAM            0.10"
            ]
          },
          "execution_count": 13,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Product Frequency / Total Sales\n",
        "first = pd.DataFrame(df.sum() / df.shape[0], columns = [\"Support\"]).sort_values(\"Support\", ascending = False)\n",
        "first"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BREAD</th>\n",
              "      <td>0.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COFFEE</th>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BISCUIT</th>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TEA</th>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CORNFLAKES</th>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SUGER</th>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAGGI</th>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MILK</th>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOURNVITA</th>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COCK</th>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Support\n",
              "BREAD          0.65\n",
              "COFFEE         0.40\n",
              "BISCUIT        0.35\n",
              "TEA            0.35\n",
              "CORNFLAKES     0.30\n",
              "SUGER          0.30\n",
              "MAGGI          0.25\n",
              "MILK           0.25\n",
              "BOURNVITA      0.20\n",
              "COCK           0.15"
            ]
          },
          "execution_count": 14,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Eliminación por valor de soporte\n",
        "first[first.Support >= 0.15]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8afhsUxRto3V",
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          "start_time": "2020-09-26T15:47:28.678709",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "**Segunda Iteration:** Encuentre valores de soporte para combinaciones de productos de pares."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[['BREAD', 'COFFEE'],\n",
              " ['BREAD', 'BISCUIT'],\n",
              " ['BREAD', 'TEA'],\n",
              " ['BREAD', 'CORNFLAKES'],\n",
              " ['BREAD', 'SUGER'],\n",
              " ['BREAD', 'MAGGI'],\n",
              " ['BREAD', 'MILK'],\n",
              " ['BREAD', 'BOURNVITA'],\n",
              " ['BREAD', 'COCK'],\n",
              " ['BREAD', 'JAM']]"
            ]
          },
          "execution_count": 15,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "second = list(itertools.combinations(first.index, 2))\n",
        "second = [list(i) for i in second]\n",
        "# Muestra de combinaciones\n",
        "second[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "execution": {
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          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>length</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>index</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(TEA, MAGGI)</th>\n",
              "      <td>0.20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(BREAD, TEA)</th>\n",
              "      <td>0.20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(BREAD, SUGER)</th>\n",
              "      <td>0.20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(BREAD, MILK)</th>\n",
              "      <td>0.20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(BREAD, BISCUIT)</th>\n",
              "      <td>0.20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(COFFEE, CORNFLAKES)</th>\n",
              "      <td>0.20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(COFFEE, SUGER)</th>\n",
              "      <td>0.20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(BREAD, COFFEE)</th>\n",
              "      <td>0.15</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(BREAD, MAGGI)</th>\n",
              "      <td>0.15</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(BREAD, BOURNVITA)</th>\n",
              "      <td>0.15</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(COFFEE, COCK)</th>\n",
              "      <td>0.15</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(BISCUIT, CORNFLAKES)</th>\n",
              "      <td>0.15</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       Support  length\n",
              "index                                 \n",
              "(TEA, MAGGI)              0.20       2\n",
              "(BREAD, TEA)              0.20       2\n",
              "(BREAD, SUGER)            0.20       2\n",
              "(BREAD, MILK)             0.20       2\n",
              "(BREAD, BISCUIT)          0.20       2\n",
              "(COFFEE, CORNFLAKES)      0.20       2\n",
              "(COFFEE, SUGER)           0.20       2\n",
              "(BREAD, COFFEE)           0.15       2\n",
              "(BREAD, MAGGI)            0.15       2\n",
              "(BREAD, BOURNVITA)        0.15       2\n",
              "(COFFEE, COCK)            0.15       2\n",
              "(BISCUIT, CORNFLAKES)     0.15       2"
            ]
          },
          "execution_count": 16,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Encontrar valores de apoyo\n",
        "value = []\n",
        "for i in range(0, len(second)):\n",
        "    temp = df.T.loc[second[i]].sum() \n",
        "    temp = len(temp[temp == df.T.loc[second[i]].shape[0]]) / df.shape[0]\n",
        "    value.append(temp)\n",
        "# Create a data frame            \n",
        "secondIteration = pd.DataFrame(value, columns = [\"Support\"])\n",
        "secondIteration[\"index\"] = [tuple(i) for i in second]\n",
        "secondIteration['length'] = secondIteration['index'].apply(lambda x:len(x))\n",
        "secondIteration = secondIteration.set_index(\"index\").sort_values(\"Support\", ascending = False)\n",
        "# Elimination by Support Value\n",
        "secondIteration = secondIteration[secondIteration.Support > 0.1]\n",
        "secondIteration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CIwnzwzYto3Y",
        "papermill": {
          "duration": 0.027405,
          "end_time": "2020-09-26T15:47:29.024669",
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          "start_time": "2020-09-26T15:47:28.997264",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "<a id='myfunction'></a>\n",
        "<h1 style=\"color:forestgreen\" >5. Mi función</h1> \n",
        "\n",
        "El paquete mlxtend nos ayuda a analizar los problemas de las reglas de asociación. En este contenido, intenté crear mi propia función para las reglas de asociación."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-26T15:47:29.099184Z",
          "iopub.status.busy": "2020-09-26T15:47:29.098071Z",
          "iopub.status.idle": "2020-09-26T15:47:29.100574Z",
          "shell.execute_reply": "2020-09-26T15:47:29.101092Z"
        },
        "id": "QqiSzdmMto3e",
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          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "def ar_iterations(data, num_iter = 1, support_value = 0.1, iterationIndex = None):\n",
        "    \n",
        "    # Next Iterations\n",
        "    def ar_calculation(iterationIndex = iterationIndex): \n",
        "        # Calculation of support value\n",
        "        value = []\n",
        "        for i in range(0, len(iterationIndex)):\n",
        "            result = data.T.loc[iterationIndex[i]].sum() \n",
        "            result = len(result[result == data.T.loc[iterationIndex[i]].shape[0]]) / data.shape[0]\n",
        "            value.append(result)\n",
        "        # Bind results\n",
        "        result = pd.DataFrame(value, columns = [\"Support\"])\n",
        "        result[\"index\"] = [tuple(i) for i in iterationIndex]\n",
        "        result['length'] = result['index'].apply(lambda x:len(x))\n",
        "        result = result.set_index(\"index\").sort_values(\"Support\", ascending = False)\n",
        "        # Elimination by Support Value\n",
        "        result = result[result.Support > support_value]\n",
        "        return result    \n",
        "    \n",
        "    # First Iteration\n",
        "    first = pd.DataFrame(df.T.sum(axis = 1) / df.shape[0], columns = [\"Support\"]).sort_values(\"Support\", ascending = False)\n",
        "    first = first[first.Support > support_value]\n",
        "    first[\"length\"] = 1\n",
        "    \n",
        "    if num_iter == 1:\n",
        "        res = first.copy()\n",
        "        \n",
        "    # Second Iteration\n",
        "    elif num_iter == 2:\n",
        "        \n",
        "        second = list(itertools.combinations(first.index, 2))\n",
        "        second = [list(i) for i in second]\n",
        "        res = ar_calculation(second)\n",
        "        \n",
        "    # All Iterations > 2\n",
        "    else:\n",
        "        nth = list(itertools.combinations(set(list(itertools.chain(*iterationIndex))), num_iter))\n",
        "        nth = [list(i) for i in nth]\n",
        "        res = ar_calculation(nth)\n",
        "    \n",
        "    return res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "execution": {
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          "shell.execute_reply": "2020-09-26T15:47:29.173797Z"
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          "duration": 0.044956,
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      "outputs": [
        {
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Support</th>\n",
              "      <th>length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BREAD</th>\n",
              "      <td>0.65</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COFFEE</th>\n",
              "      <td>0.40</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BISCUIT</th>\n",
              "      <td>0.35</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TEA</th>\n",
              "      <td>0.35</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CORNFLAKES</th>\n",
              "      <td>0.30</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SUGER</th>\n",
              "      <td>0.30</td>\n",
              "      <td>1</td>\n",
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              "      <th>MILK</th>\n",
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              "      <td>1</td>\n",
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              "      <th>COCK</th>\n",
              "      <td>0.15</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
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              "            Support  length\n",
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              "BISCUIT        0.35       1\n",
              "TEA            0.35       1\n",
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              "MILK           0.25       1\n",
              "BOURNVITA      0.20       1\n",
              "COCK           0.15       1"
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        "<a id='ar'></a>\n",
        "<h1 style=\"color:forestgreen\" >6. Regla de Asociación</h1> \n",
        "\n",
        "Aquí hay dos funciones principales.\n",
        "- La función apriori () evalúa el valor de soporte para cada producto.\n",
        "- La función association_rules () nos ayuda a comprender la relación entre antecedentes y productos de consecuencias. Proporciona información notable sobre los productos.\n",
        "\n",
        "Especialmente, las variables de \"soporte antecedente\", \"soporte consecuente\", \"soporte\", \"confianza\" y \"elevación\" son muy importantes para tomar algunas decisiones comerciales."
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              "      <th></th>\n",
              "      <th>support</th>\n",
              "      <th>itemsets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.65</td>\n",
              "      <td>(BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.40</td>\n",
              "      <td>(COFFEE)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.35</td>\n",
              "      <td>(BISCUIT)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.35</td>\n",
              "      <td>(TEA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.30</td>\n",
              "      <td>(CORNFLAKES)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.30</td>\n",
              "      <td>(SUGER)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.25</td>\n",
              "      <td>(MAGGI)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.25</td>\n",
              "      <td>(MILK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.20</td>\n",
              "      <td>(COFFEE, SUGER)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0.20</td>\n",
              "      <td>(MAGGI, TEA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.20</td>\n",
              "      <td>(BISCUIT, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.20</td>\n",
              "      <td>(COFFEE, CORNFLAKES)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.20</td>\n",
              "      <td>(TEA, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.20</td>\n",
              "      <td>(SUGER, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.20</td>\n",
              "      <td>(MILK, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.20</td>\n",
              "      <td>(BOURNVITA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.15</td>\n",
              "      <td>(COFFEE, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.15</td>\n",
              "      <td>(COFFEE, COCK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.15</td>\n",
              "      <td>(MAGGI, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.15</td>\n",
              "      <td>(COCK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.15</td>\n",
              "      <td>(BREAD, BOURNVITA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.15</td>\n",
              "      <td>(BISCUIT, CORNFLAKES)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(SUGER, BOURNVITA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(COFFEE, COCK, CORNFLAKES)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(MAGGI, TEA, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(MAGGI, JAM, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(COFFEE, SUGER, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(TEA, BREAD, BOURNVITA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, MAGGI, TEA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, COFFEE, CORNFLAKES)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, CORNFLAKES, COCK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, COFFEE, COCK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, MILK, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(CORNFLAKES, TEA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(MAGGI, JAM)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(TEA, BOURNVITA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(CORNFLAKES, MILK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(JAM)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, COCK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(CORNFLAKES, COCK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, COFFEE)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, MAGGI)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, MILK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, TEA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(JAM, BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(BISCUIT, COFFEE, COCK, CORNFLAKES)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    support                             itemsets\n",
              "2      0.65                              (BREAD)\n",
              "4      0.40                             (COFFEE)\n",
              "0      0.35                            (BISCUIT)\n",
              "10     0.35                                (TEA)\n",
              "5      0.30                         (CORNFLAKES)\n",
              "9      0.30                              (SUGER)\n",
              "7      0.25                              (MAGGI)\n",
              "8      0.25                               (MILK)\n",
              "30     0.20                      (COFFEE, SUGER)\n",
              "34     0.20                         (MAGGI, TEA)\n",
              "11     0.20                     (BISCUIT, BREAD)\n",
              "29     0.20                 (COFFEE, CORNFLAKES)\n",
              "26     0.20                         (TEA, BREAD)\n",
              "25     0.20                       (SUGER, BREAD)\n",
              "24     0.20                        (MILK, BREAD)\n",
              "1      0.20                          (BOURNVITA)\n",
              "21     0.15                      (COFFEE, BREAD)\n",
              "27     0.15                       (COFFEE, COCK)\n",
              "23     0.15                       (MAGGI, BREAD)\n",
              "3      0.15                               (COCK)\n",
              "18     0.15                   (BREAD, BOURNVITA)\n",
              "14     0.15                (BISCUIT, CORNFLAKES)\n",
              "19     0.10                   (SUGER, BOURNVITA)\n",
              "44     0.10           (COFFEE, COCK, CORNFLAKES)\n",
              "43     0.10                  (MAGGI, TEA, BREAD)\n",
              "42     0.10                  (MAGGI, JAM, BREAD)\n",
              "41     0.10               (COFFEE, SUGER, BREAD)\n",
              "40     0.10              (TEA, BREAD, BOURNVITA)\n",
              "39     0.10                (BISCUIT, MAGGI, TEA)\n",
              "38     0.10        (BISCUIT, COFFEE, CORNFLAKES)\n",
              "37     0.10          (BISCUIT, CORNFLAKES, COCK)\n",
              "36     0.10              (BISCUIT, COFFEE, COCK)\n",
              "35     0.10               (BISCUIT, MILK, BREAD)\n",
              "32     0.10                    (CORNFLAKES, TEA)\n",
              "33     0.10                         (MAGGI, JAM)\n",
              "20     0.10                     (TEA, BOURNVITA)\n",
              "31     0.10                   (CORNFLAKES, MILK)\n",
              "6      0.10                                (JAM)\n",
              "12     0.10                      (BISCUIT, COCK)\n",
              "28     0.10                   (CORNFLAKES, COCK)\n",
              "13     0.10                    (BISCUIT, COFFEE)\n",
              "15     0.10                     (BISCUIT, MAGGI)\n",
              "16     0.10                      (BISCUIT, MILK)\n",
              "17     0.10                       (BISCUIT, TEA)\n",
              "22     0.10                         (JAM, BREAD)\n",
              "45     0.10  (BISCUIT, COFFEE, COCK, CORNFLAKES)"
            ]
          },
          "execution_count": 23,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Apriori\n",
        "freq_items = apriori(df, min_support = 0.1, use_colnames = True)\n",
        "freq_items.sort_values(\"support\", ascending = False)"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### El valor de soporte nos brinda esta información:\n",
        "\n",
        "**Head 5**\n",
        "- 65 por ciento de 100 compras son \"PAN\"\n",
        "- El 40 por ciento de las 100 compras son \"CAFÉ\"\n",
        "- El 35 por ciento de las 100 compras son \"BISCUIT\"\n",
        "- El 35 por ciento de las 100 compras son \"TEA\"\n",
        "- El 30 por ciento de las 100 compras son \"CORNFLAKES\"\n",
        "\n",
        "**Tail 5**\n",
        "- El 10 por ciento de 100 compras son \"MAGGI\" y \"BISCUIT\"\n",
        "- El 10 por ciento de las 100 compras son \"LECHE\" y \"GALLETAS\"\n",
        "- El 10 por ciento de las 100 compras son \"TÉ\" y \"BISCUIT\"\n",
        "- El 10 por ciento de las 100 compras son \"JAM\" y \"BISCUIT\"\n",
        "- El 10 por ciento de las 100 compras son \"COCK\", \"CAFÉ\", \"GALLETAS\" y \"CORNFLAKES\""
      ]
    },
    {
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              "    support      itemsets\n",
              "2      0.65       (BREAD)\n",
              "4      0.40      (COFFEE)\n",
              "0      0.35     (BISCUIT)\n",
              "10     0.35         (TEA)\n",
              "5      0.30  (CORNFLAKES)"
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        "freq_items.sort_values(\"support\", ascending = False).head(5)"
      ]
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              "      <td>0.10</td>\n",
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              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.10</td>\n",
              "      <td>(JAM, BREAD)</td>\n",
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              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0.10</td>\n",
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              "    support                             itemsets\n",
              "15     0.10                     (BISCUIT, MAGGI)\n",
              "16     0.10                      (BISCUIT, MILK)\n",
              "17     0.10                       (BISCUIT, TEA)\n",
              "22     0.10                         (JAM, BREAD)\n",
              "45     0.10  (BISCUIT, COFFEE, COCK, CORNFLAKES)"
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        "freq_items.sort_values(\"support\", ascending = False).tail(5)"
      ]
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
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              "      <td>(BREAD)</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.88</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(COCK)</td>\n",
              "      <td>(BISCUIT)</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1.90</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(CORNFLAKES)</td>\n",
              "      <td>(BISCUIT)</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.04</td>\n",
              "      <td>1.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(BOURNVITA)</td>\n",
              "      <td>(BREAD)</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.15</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(BOURNVITA)</td>\n",
              "      <td>(SUGER)</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.67</td>\n",
              "      <td>0.04</td>\n",
              "      <td>1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>(BISCUIT, CORNFLAKES)</td>\n",
              "      <td>(COFFEE, COCK)</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.67</td>\n",
              "      <td>4.44</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>(COFFEE, COCK)</td>\n",
              "      <td>(BISCUIT, CORNFLAKES)</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.67</td>\n",
              "      <td>4.44</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>(COFFEE, CORNFLAKES)</td>\n",
              "      <td>(BISCUIT, COCK)</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.50</td>\n",
              "      <td>5.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>(CORNFLAKES, COCK)</td>\n",
              "      <td>(BISCUIT, COFFEE)</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>(COCK)</td>\n",
              "      <td>(BISCUIT, COFFEE, CORNFLAKES)</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.67</td>\n",
              "      <td>6.67</td>\n",
              "      <td>0.09</td>\n",
              "      <td>2.70</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>66 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              antecedents                    consequents  antecedent support  \\\n",
              "0               (BISCUIT)                        (BREAD)                0.35   \n",
              "1                  (COCK)                      (BISCUIT)                0.15   \n",
              "2            (CORNFLAKES)                      (BISCUIT)                0.30   \n",
              "3             (BOURNVITA)                        (BREAD)                0.20   \n",
              "4             (BOURNVITA)                        (SUGER)                0.20   \n",
              "..                    ...                            ...                 ...   \n",
              "61  (BISCUIT, CORNFLAKES)                 (COFFEE, COCK)                0.15   \n",
              "62         (COFFEE, COCK)          (BISCUIT, CORNFLAKES)                0.15   \n",
              "63   (COFFEE, CORNFLAKES)                (BISCUIT, COCK)                0.20   \n",
              "64     (CORNFLAKES, COCK)              (BISCUIT, COFFEE)                0.10   \n",
              "65                 (COCK)  (BISCUIT, COFFEE, CORNFLAKES)                0.15   \n",
              "\n",
              "    consequent support  support  confidence  lift  leverage  conviction  \n",
              "0                 0.65     0.20        0.57  0.88     -0.03        0.82  \n",
              "1                 0.35     0.10        0.67  1.90      0.05        1.95  \n",
              "2                 0.35     0.15        0.50  1.43      0.04        1.30  \n",
              "3                 0.65     0.15        0.75  1.15      0.02        1.40  \n",
              "4                 0.30     0.10        0.50  1.67      0.04        1.40  \n",
              "..                 ...      ...         ...   ...       ...         ...  \n",
              "61                0.15     0.10        0.67  4.44      0.08        2.55  \n",
              "62                0.15     0.10        0.67  4.44      0.08        2.55  \n",
              "63                0.10     0.10        0.50  5.00      0.08        1.80  \n",
              "64                0.10     0.10        1.00 10.00      0.09         inf  \n",
              "65                0.10     0.10        0.67  6.67      0.09        2.70  \n",
              "\n",
              "[66 rows x 9 columns]"
            ]
          },
          "execution_count": 26,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Association Rules & Info\n",
        "df_ar = association_rules(freq_items, metric = \"confidence\", min_threshold = 0.5)\n",
        "df_ar"
      ]
    },
    {
      "cell_type": "markdown",
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      },
      "source": [
        "- La variable de soporte antecedente nos dice la probabilidad de productos antecedentes solamente\n",
        "- La variable de soporte de consecuentes nos dice la probabilidad de productos consecuentes solamente\n",
        "- El valor de soporte es el valor de los dos productos (antecedentes y consecuencias)\n",
        "- La confianza es una indicación de la frecuencia con la que se ha determinado que la regla es cierta.\n",
        "- La relación entre el apoyo observado y el esperado si X e Y fueran independientes."
      ]
    },
    {
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        {
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              "<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>8</th>\n",
              "      <td>(MILK)</td>\n",
              "      <td>(BREAD)</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.04</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(MAGGI)</td>\n",
              "      <td>(TEA)</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.80</td>\n",
              "      <td>2.29</td>\n",
              "      <td>0.11</td>\n",
              "      <td>3.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(SUGER)</td>\n",
              "      <td>(BREAD)</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(CORNFLAKES)</td>\n",
              "      <td>(COFFEE)</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1.67</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(SUGER)</td>\n",
              "      <td>(COFFEE)</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1.67</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(BISCUIT)</td>\n",
              "      <td>(BREAD)</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.88</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(TEA)</td>\n",
              "      <td>(BREAD)</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.88</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(TEA)</td>\n",
              "      <td>(MAGGI)</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.57</td>\n",
              "      <td>2.29</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     antecedents consequents  antecedent support  consequent support  support  \\\n",
              "8         (MILK)     (BREAD)                0.25                0.65     0.20   \n",
              "18       (MAGGI)       (TEA)                0.25                0.35     0.20   \n",
              "9        (SUGER)     (BREAD)                0.30                0.65     0.20   \n",
              "14  (CORNFLAKES)    (COFFEE)                0.30                0.40     0.20   \n",
              "16       (SUGER)    (COFFEE)                0.30                0.40     0.20   \n",
              "0      (BISCUIT)     (BREAD)                0.35                0.65     0.20   \n",
              "10         (TEA)     (BREAD)                0.35                0.65     0.20   \n",
              "19         (TEA)     (MAGGI)                0.35                0.25     0.20   \n",
              "\n",
              "    confidence  lift  leverage  conviction  \n",
              "8         0.80  1.23      0.04        1.75  \n",
              "18        0.80  2.29      0.11        3.25  \n",
              "9         0.67  1.03      0.01        1.05  \n",
              "14        0.67  1.67      0.08        1.80  \n",
              "16        0.67  1.67      0.08        1.80  \n",
              "0         0.57  0.88     -0.03        0.82  \n",
              "10        0.57  0.88     -0.03        0.82  \n",
              "19        0.57  2.29      0.11        1.75  "
            ]
          },
          "execution_count": 27,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ar[(df_ar.support > 0.15) & (df_ar.confidence > 0.5)].sort_values(\"confidence\", ascending = False)"
      ]
    }
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