Asignacion_03_randomdatabayes.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c5NVPua1CrpP",
        "jp-MarkdownHeadingCollapsed": true
      },
      "source": [
        "\n",
        "# Ejemplo Clasificador Naive de Bayes\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6ZJ6-evweTN"
      },
      "source": [
        "Crear un ndarray con numpy como estructura del dataset\n",
        "\n",
        "Se acomodan en X los valores de entrada del arreglo\n",
        "\n",
        "y se clasifica manualmente 0 o 1, dado que tenemos solo dos clases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scvpdANC8M1o"
      },
      "source": [
        "Utilizar sklean para generar algo como lo anterior pero aleatorio   \n",
        "<u> Ej. Ilustrativo del prof <u/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sr4VP7VZq-k9",
        "outputId": "a6fcc994-a1db-4230-e4b9-12024a311f7b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.49292831  0.64624352 -0.34768547  0.79572758  1.17111337 -0.76830354]\n",
            " [ 1.53374582  0.23621852 -0.4299839   0.75571356  0.95793018 -1.09830157]\n",
            " [ 0.59649887  0.9529053   0.20024028  1.87129138  0.4797375   0.79114941]\n",
            " ...\n",
            " [-0.47508001  0.97502456 -0.26154305  0.97089938  1.30680547 -0.44930856]\n",
            " [ 0.45597946 -1.64127058 -0.4049783   0.24117475 -0.7211819  -1.52483034]\n",
            " [ 0.08503145  3.89917344  0.48885418 -2.51287265  2.54118265  2.34530902]]\n",
            "[0 0 2 ... 0 1 2]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.datasets import make_classification\n",
        "\n",
        "X, y = make_classification(\n",
        "    n_features=6,\n",
        "    n_classes=3,\n",
        "    n_samples=1200,\n",
        "    n_informative=2,\n",
        "    random_state=10,\n",
        "    n_clusters_per_class=1,\n",
        ")\n",
        "print(X)\n",
        "print(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jMPWT-wrG_G"
      },
      "source": [
        "X contiene las n-tuplas y 'y' a que clase pertenece las clases son numeradas de 0 a n_classes-1\n",
        "Ahora graficamos el dataset generado"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "bBPQLssZrVVS",
        "outputId": "f14fc30e-7084-4c35-ee88-b48dbd063a94"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, marker=\"*\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHE783qOtAPe"
      },
      "source": [
        "Ahora se divide el dataset en dos grupos, test y modelo de evaluacion. usando el valor de 0.33 como cantidad o 33% del dataset como training. 125 es el valor semilla del generador de numeros aleatorios para seleccionar los casos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3snsphUGrjsP",
        "outputId": "2e884c1c-3793-41a2-be13-d4dae2a35f9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train: [[-0.14881365 -1.64303529 -0.59735313  0.68025733 -0.38619623 -2.04448308]\n",
            " [-1.67043832  0.41038049 -0.36290825 -1.99475682  0.9922964  -0.87144889]\n",
            " [ 0.83606208 -2.63393989 -0.31293152 -0.93172501 -1.74685551 -1.53760745]\n",
            " ...\n",
            " [-1.90437247 -1.92263649 -0.04365379  0.88541579 -1.59833088 -0.62370874]\n",
            " [ 0.32035501  1.79465429  0.1936812  -0.93159007  1.22440904  0.99493297]\n",
            " [ 0.36841263  2.28881348  0.32449266  1.26984983  1.42601234  1.4779976 ]]\n",
            "X_test: [[-2.08289499 -2.04915652 -0.94887125 -1.0501085  -0.12503099 -3.10003408]\n",
            " [ 0.14036849  0.08675889 -0.44960118 -0.20471307  0.8620614  -1.19057202]\n",
            " [-0.48310646  1.67702762  0.32595841 -0.35431677  0.89055786  1.3209771 ]\n",
            " ...\n",
            " [-2.58555255  1.00541272 -0.17588446  0.01673353  1.18343156 -0.21013407]\n",
            " [ 1.25847953 -0.59584668 -0.20634814 -0.26273008 -0.15804034 -0.71368241]\n",
            " [-0.60125997  1.90423525  0.25990857  0.68593469  1.20400647  1.20250339]]\n",
            "y_train: [1 0 1 0 0 1 0 1 0 0 1 0 0 2 1 2 2 1 0 0 1 0 2 2 0 2 2 0 0 2 0 1 0 0 2 2 0\n",
            " 2 0 2 1 0 1 2 2 0 2 1 0 1 1 0 2 1 1 1 2 1 0 0 2 1 2 0 0 2 1 1 2 2 0 1 2 0\n",
            " 0 1 0 1 2 0 1 1 2 2 1 2 0 0 0 0 2 1 0 0 0 2 0 1 0 1 1 1 1 1 2 2 1 1 2 0 1\n",
            " 1 1 2 1 2 1 0 2 2 0 2 2 2 1 2 1 0 1 0 2 2 2 2 2 2 2 2 2 2 0 1 2 2 2 1 0 2\n",
            " 1 1 2 1 1 2 2 2 1 0 0 2 0 2 1 0 1 2 0 0 1 2 1 1 1 1 2 1 1 0 2 0 1 0 2 0 1\n",
            " 0 2 0 0 2 0 1 2 0 2 2 2 0 1 2 2 2 1 0 1 1 2 0 1 2 0 2 1 0 0 2 0 2 2 1 0 2\n",
            " 0 1 1 0 0 0 0 2 1 0 2 2 0 0 2 0 0 2 2 0 1 2 0 1 1 1 0 2 1 2 2 0 1 0 1 2 0\n",
            " 2 2 0 2 2 2 1 1 1 1 0 1 1 1 0 0 0 2 1 1 0 1 1 1 2 1 1 1 2 1 0 1 2 2 0 0 2\n",
            " 2 2 0 2 2 0 1 2 1 2 1 1 2 1 2 1 0 0 0 1 0 1 0 1 1 0 1 2 1 2 0 0 1 0 1 1 1\n",
            " 2 2 0 0 1 2 2 2 1 0 1 2 2 1 2 0 1 2 0 2 2 2 1 0 2 1 2 0 2 2 1 1 1 2 1 1 2\n",
            " 2 1 1 2 0 0 0 1 1 2 1 1 2 1 2 0 1 2 1 2 1 2 0 0 2 0 0 0 2 2 0 1 2 1 0 0 0\n",
            " 1 0 1 2 0 2 1 1 2 0 1 1 2 1 0 1 2 0 2 2 2 0 1 2 0 0 0 0 1 0 1 2 2 2 0 0 1\n",
            " 0 0 0 0 2 1 1 2 0 0 0 1 0 1 1 0 0 1 2 2 1 1 1 0 0 0 2 1 0 0 0 0 0 2 2 1 0\n",
            " 2 2 0 1 0 1 0 1 1 2 2 2 1 1 1 1 2 1 1 0 0 1 2 1 2 1 0 0 1 1 0 1 2 0 0 1 1\n",
            " 0 2 1 2 0 1 0 0 2 1 0 2 2 1 0 0 1 2 0 1 0 0 2 2 2 2 0 1 2 0 2 2 0 0 1 1 0\n",
            " 2 2 2 1 2 0 2 2 1 0 1 1 2 1 0 2 2 2 2 2 0 2 0 1 2 0 0 1 2 2 0 2 0 0 0 1 1\n",
            " 2 1 0 2 0 2 2 2 2 0 0 0 2 2 1 0 2 0 1 0 2 0 0 1 2 1 0 2 0 2 0 0 1 0 2 2 0\n",
            " 0 1 2 2 2 2 2 1 0 2 0 1 2 2 0 1 1 0 1 1 0 0 2 1 2 1 0 0 0 2 2 0 0 2 2 0 2\n",
            " 0 1 0 1 0 0 1 0 2 1 0 0 1 0 2 0 1 2 1 2 1 0 1 1 1 2 1 2 2 1 0 0 2 0 0 1 2\n",
            " 2 2 2 1 1 1 0 0 2 2 1 2 0 1 0 2 2 1 1 2 1 1 1 0 1 0 2 2 0 0 0 2 2 2 1 1 2\n",
            " 2 2 0 1 0 0 0 0 1 2 2 1 2 0 0 2 0 1 1 2 2 2 1 2 2 0 1 0 0 0 0 1 1 0 0 2 1\n",
            " 2 1 1 1 2 2 0 1 1 1 1 1 1 2 1 0 1 2 0 0 1 1 0 2 2 2 2]\n",
            "y_test: [1 0 2 0 2 2 0 2 0 2 2 2 1 0 0 2 0 0 1 1 0 1 1 1 1 0 2 2 0 1 2 1 2 2 2 2 2\n",
            " 0 1 1 2 2 0 2 0 1 2 2 2 2 0 1 2 1 2 0 2 0 1 0 0 1 2 2 1 1 2 0 1 2 0 0 1 2\n",
            " 1 0 1 0 0 0 0 1 0 0 2 1 2 1 1 2 2 1 1 1 2 1 1 1 1 2 0 0 0 0 2 2 0 1 2 2 0\n",
            " 2 1 1 0 2 2 1 2 1 1 0 0 1 2 0 0 1 0 2 2 1 0 1 0 2 0 1 2 1 1 2 1 0 2 1 0 1\n",
            " 0 0 1 0 1 0 1 2 0 2 2 1 1 0 1 1 0 0 2 2 1 2 1 1 2 1 1 0 2 0 2 2 0 1 0 1 2\n",
            " 1 0 1 0 1 0 2 1 2 1 2 0 2 0 2 0 0 1 1 1 1 2 0 0 1 1 2 0 0 1 1 1 0 1 1 1 2\n",
            " 1 2 0 0 1 2 0 2 0 2 2 1 2 2 1 0 2 2 0 0 2 2 0 1 1 0 0 0 2 0 1 0 0 0 2 0 2\n",
            " 0 2 0 1 0 1 0 1 1 2 2 1 0 1 0 0 2 1 0 0 1 1 2 2 0 1 1 0 0 0 0 0 1 1 1 0 1\n",
            " 0 2 1 2 2 0 1 0 0 1 0 1 2 0 1 0 2 2 0 1 0 0 1 2 2 2 2 2 0 2 0 2 1 0 0 2 1\n",
            " 1 1 1 2 2 1 1 0 0 0 2 0 1 0 2 1 1 0 0 1 2 2 0 2 1 2 1 1 1 0 0 1 1 2 0 2 2\n",
            " 2 1 2 0 2 2 0 0 1 0 2 0 0 2 2 1 1 1 2 2 2 1 2 0 1 2]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.33, random_state=125\n",
        ")\n",
        "print(\"X_train:\",X_train)\n",
        "print(\"X_test:\",X_test)\n",
        "print(\"y_train:\",y_train)\n",
        "print(\"y_test:\",y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jglUPUE6SHG"
      },
      "source": [
        "Contruccion del Modelo y Entrenamiento\n",
        "\n",
        "Se verifica que el caso de prueba t_test[6] el cual fue ya marcado coincida con el valor estimado por el modelo: predicted[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4SdHNziprt9x",
        "outputId": "5264020a-7ddf-4520-e401-011f229988db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 2 0 2 2 0 2 0 2 2 2 1 0 0 2 0 0 1 1 0 1 1 1 1 0 2 2 0 1 2 1 2 2 2 2 2\n",
            " 0 1 1 2 2 0 2 0 1 2 2 2 2 0 1 2 1 2 0 2 0 1 0 0 1 2 2 1 1 2 0 1 2 0 0 1 2\n",
            " 1 0 1 0 0 0 0 1 0 0 2 1 2 1 1 2 2 1 1 1 2 1 1 1 1 2 0 0 0 0 2 2 0 1 2 2 0\n",
            " 2 1 1 0 2 2 1 2 1 1 0 0 1 2 0 0 1 0 2 2 1 0 1 0 2 0 1 2 1 1 2 1 0 2 1 0 1\n",
            " 0 0 1 0 1 0 1 2 0 2 2 1 1 0 1 1 0 0 2 2 1 2 1 1 2 1 1 0 2 0 2 2 0 1 0 1 2\n",
            " 1 0 1 0 1 0 2 1 2 1 2 0 2 0 2 0 0 1 1 1 1 2 0 0 1 1 2 0 0 1 1 1 0 1 1 1 2\n",
            " 1 2 0 0 1 2 0 2 0 2 2 1 2 2 1 0 2 2 0 0 2 2 0 1 1 0 0 0 2 0 1 0 0 0 2 0 2\n",
            " 0 2 0 1 0 1 0 1 1 2 2 1 0 1 0 0 2 1 0 0 1 1 2 2 0 1 1 0 0 0 0 0 1 1 1 0 1\n",
            " 0 2 1 2 2 0 1 0 0 1 0 1 2 0 1 0 2 2 0 1 0 0 1 2 2 2 2 2 0 2 0 2 1 0 0 2 1\n",
            " 1 1 1 2 2 1 1 0 0 0 2 0 1 0 2 1 1 0 0 1 2 2 0 2 1 2 1 1 1 0 0 1 1 2 0 2 2\n",
            " 2 1 2 0 2 2 0 0 1 0 2 0 0 2 2 1 1 1 2 2 2 1 2 0 1 2]\n",
            "Actual Value: 0\n",
            "Predicted Value: 1\n"
          ]
        }
      ],
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "#Shows\n",
        "# Build a Gaussian Classifier\n",
        "model = GaussianNB()\n",
        "\n",
        "# Model training\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Predict Output\n",
        "predicted = model.predict([X_test[6]])\n",
        "\n",
        "print(y_test)\n",
        "print(\"Actual Value:\", y_test[6])\n",
        "print(\"Predicted Value:\", predicted[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MB0nFGMM6_AN"
      },
      "source": [
        "Evaluación del Modelo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYDxVyIFr1mE",
        "outputId": "3df7f85d-d31a-4656-a985-363e65dd3df3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9343434343434344\n",
            "F1 Score: 0.9341791865603047\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    confusion_matrix,\n",
        "    ConfusionMatrixDisplay,\n",
        "    f1_score,\n",
        ")\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "accuray = accuracy_score(y_pred, y_test)\n",
        "f1 = f1_score(y_pred, y_test, average=\"weighted\")\n",
        "\n",
        "print(\"Accuracy:\", accuray)\n",
        "print(\"F1 Score:\", f1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jn-cavT7TTC"
      },
      "source": [
        "Visualización de la Matrix de Confusion:\n",
        "\n",
        "Esta matrix nos permite ver cuantos casos no fueron exitosos en la prediccion.\n",
        "\n",
        "La posicion CF(2,0) = 4  se interpreta como que el valor esperado era 2 y predijo 0 4 veces. Lo cual es un falso positivo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "pzQXGJCWr6QX",
        "outputId": "882254fc-889a-4c93-8c83-00502700a3e2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "labels = [0,1,2]\n",
        "cm = confusion_matrix(y_test, y_pred, labels=labels)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_a4_UOujTmyZ"
      },
      "source": [
        "# Tarea no.3\n",
        "Aplicar la plantilla al data set de Peliculas"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# para no 'upload' data\n",
        "# importando archivo de datos desde el drive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from google.colab import drive\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "download = drive.CreateFile({'id': 'xxxxxxxxxx'})\n",
        "download.GetContentFile('archive.zip')"
      ],
      "metadata": {
        "id": "uGhgl3nOL1Qr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c23aca9b-0aa3-4c18-b4ba-26afa865e26a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:pydrive is deprecated and no longer maintained. We recommend that you migrate your projects to pydrive2, the maintained fork of pydrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "F0AhM-saTmya",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "42bfc77b-c850-4f94-f692-19cf1bd43aca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  show_id   type                 title        director  \\\n",
              "0      s1  Movie   The Grand Seduction    Don McKellar   \n",
              "1      s2  Movie  Take Care Good Night    Girish Joshi   \n",
              "2      s3  Movie  Secrets of Deception     Josh Webber   \n",
              "3      s4  Movie    Pink: Staying True  Sonia Anderson   \n",
              "4      s5  Movie         Monster Maker    Giles Foster   \n",
              "\n",
              "                                                cast         country  \\\n",
              "0     Brendan Gleeson, Taylor Kitsch, Gordon Pinsent          Canada   \n",
              "1   Mahesh Manjrekar, Abhay Mahajan, Sachin Khedekar           India   \n",
              "2  Tom Sizemore, Lorenzo Lamas, Robert LaSardo, R...   United States   \n",
              "3  Interviews with: Pink, Adele, Beyoncé, Britney...   United States   \n",
              "4  Harry Dean Stanton, Kieran O'Brien, George Cos...  United Kingdom   \n",
              "\n",
              "       date_added  release_year rating duration                listed_in  \\\n",
              "0  March 30, 2021          2014    NaN  113 min            Comedy, Drama   \n",
              "1  March 30, 2021          2018    13+  110 min     Drama, International   \n",
              "2  March 30, 2021          2017    NaN   74 min  Action, Drama, Suspense   \n",
              "3  March 30, 2021          2014    NaN   69 min              Documentary   \n",
              "4  March 30, 2021          1989    NaN   45 min           Drama, Fantasy   \n",
              "\n",
              "                                         description  \n",
              "0  A small fishing village must procure a local d...  \n",
              "1  A Metro Family decides to fight a Cyber Crimin...  \n",
              "2  After a man discovers his wife is cheating on ...  \n",
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              "summary": "{\n  \"name\": \"df_peliculas\",\n  \"rows\": 9668,\n  \"fields\": [\n    {\n      \"column\": \"show_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9668,\n        \"samples\": [\n          \"s5193\",\n          \"s2389\",\n          \"s7681\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"TV Show\",\n          \"Movie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9668,\n        \"samples\": [\n          \"Arpo: The Robot for All Kids\",\n          \"Gangstars (Telugu)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"director\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5773,\n        \"samples\": [\n          \"Tester\",\n          \"Jerry Kramer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cast\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7927,\n        \"samples\": [\n          \"1, 2, 3\",\n          \"Barton MacLane, Bobby Larsen, Jan Wiley, Charlotte Wynters, Conrad Binyon, Elizabeth Valentine\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 86,\n        \"samples\": [\n          \"United States, Germany, Italy\",\n          \"Canada\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date_added\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 84,\n        \"samples\": [\n          \"September 29, 2021\",\n          \"March 30, 2021\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"release_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 1920,\n        \"max\": 2021,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          1933,\n          1961\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"TV-PG\",\n          \"PG\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 219,\n        \"samples\": [\n          \"76 min\",\n          \"43 min\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"listed_in\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 518,\n        \"samples\": [\n          \"Documentary, Unscripted\",\n          \"Drama, Kids\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9414,\n        \"samples\": [\n          \"Set in the Bronx, I LIKE IT LIKE THAT is about love, family and a young woman's coming of age. After her husband is arrested for looting during a blackout, Lisette, a young Latina, struggles for independence while dealing with her gender-confused brother, her critical mother-in-law and assorted neighborhood eccentrics.\",\n          \"Roy Rogers meets up with a western writer who he despises because he resents her writing style. But, together they team up to break a silver smuggling gang across the Mexican border. Meanwhile, one of Roy's friends is being hunted by Scotland Yard, but not for the reasons they suspect. One of the best action-packed Rogers films.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "df_peliculas = pd.read_csv('archive.zip', compression='zip')\n",
        "df_peliculas.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNUULWosTmya"
      },
      "source": [
        "### Clasificación “automática”\n",
        "* Nuestra clasificación consistirá en asignar un puntaje (score) que adjudique si la película en cuestión está orientada al público **masculino** o **femenino**.\n",
        "\n",
        "* Cuanto más alto se cifre el puntaje en la escala **positiva**, cuanto mayor en escala la película estará más orientada al público **masculino**. Y por tanto, la película se clasificará con **+1**.\n",
        "\n",
        "* Cuanto más alto se cifre el puntaje en la escala **negativa**, denotara que la película en cuestión estará más orientada al público **femenino**. La película se clasificará con **-1**, para el público femenino.\n",
        "\n",
        "* Cuanto más cercano a **0** en el puntaje en la escala implicará que la película estará clasificada como de orientación neutra; *ie*. La película está orientada tanto para el género masculino como para el femenino.\n",
        "\n",
        "### Para calcular el puntaje (score):\n",
        "* Tomaremos los campos **cast** y **listed_in**:\n",
        "\n",
        "* En **cast** lo que pretendemos es identificar el genero (masc ó fem) del elenco **cast** en cada una de las peliculas.\n",
        "* Sí los **nombres** en **cast** son masculinos o femeninos se le adjudicará un puntaje de **+1/-1** respectivamente. Para adjudicar el género se tomará la última letra del nombre de los participantes. Las terminaciones en: e, o, d, h, n, m, l, g, ó sus combinaciones pares, por ej. 'en', 'hn','oh','on', etc. en cada uno de los nombres serán clasificadas con un puntaje de **+1 (masculino)**. Todas las terminaciones distintas a las antes mencionadas !(e, o, h, n, m, l, g) serán clasificadas con un puntaje **-1 (femenino)**.\n",
        "\n",
        "_Nota: Ciertamente las reglas de pre-procesamiento para indentificar el genero del elenco de en la variable **cast** necesita una elaboración más rigurosa. No obstante, para fines ilustrativos las consideraremos en esta asignación._  \n",
        "\n",
        "* El listado de puntajes  en **listed_in** será:\n",
        "\n",
        "<div align=\"center\">\n",
        "\n",
        "**genero**               |**puntaje**\n",
        ":------------------------|:---------:\n",
        "Action|2.5,\n",
        "Adventure|1.5,\n",
        "and Culture|-0.5,\n",
        "Animation|-0.5,\n",
        "Anime|1,\n",
        "Arthouse|-1.5,\n",
        "Arts|-1.5,\n",
        "Comedy|1.5,\n",
        "Documentary|0,\n",
        "Drama|-3,\n",
        "Entertainment|-2.0,\n",
        "Faith and Spirituality|-1.5,\n",
        "Fantasy|-1,\n",
        "Fitness|-2.5,\n",
        "Historical|2,\n",
        "Horror|2.5,\n",
        "International|0,\n",
        "Kids |0,\n",
        "LGBTQ|-3.5,\n",
        "Military and War|3.5,\n",
        "Music Videos and Concerts|0,\n",
        "Romance|-5.5,\n",
        "Science Fiction|2,\n",
        "Special Interest|0,\n",
        "Sports|2.5,\n",
        "Suspense|1.5,\n",
        "Talk Show and Variety|0,\n",
        "TV Shows|0,\n",
        "Unscripted|0,\n",
        "Western|+2,\n",
        "Young Adult Audience|1.0\n",
        "\n",
        "</div>\n",
        "\n",
        "\n",
        "* Se aplicará la siguente expresión en cada uno de los registros para optener el puntaje final clasificatorio:\n",
        "\\begin{align}\n",
        "target\\,predict\\, y=\\sum_{i=1}^{n=9667} cast\\,feature\\,masc_i + cast\\,feature\\, fem_i +  \tgenero\\,film \\,feature\\, masc\\,fem_i\n",
        "\\end{align}\n",
        "\n",
        "\n",
        "* La clasificación final será de acuerdo a la siguiente regla:\n",
        "\\begin{align}\n",
        "        target\\,predict\\, y(x) = \\left\\{\n",
        "        \\begin{array}{ccl}\n",
        "        \\text{masculino} & \\text{si} & x > 1 \\\\\n",
        "        \\text{neutro} &\\text{si} & -1 \\le x \\le 1 \\\\\n",
        "        \\text{femenino} &\\text{si} & x < -1.\n",
        "        \\end{array}\n",
        "        \\right.\n",
        "    \\end{align}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SBuAenjbTmya",
        "outputId": "436db802-4e7f-413d-e303-6c456843f2a2"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  title                                               cast  \\\n",
              "0   The Grand Seduction     Brendan Gleeson, Taylor Kitsch, Gordon Pinsent   \n",
              "1  Take Care Good Night   Mahesh Manjrekar, Abhay Mahajan, Sachin Khedekar   \n",
              "2  Secrets of Deception  Tom Sizemore, Lorenzo Lamas, Robert LaSardo, R...   \n",
              "3    Pink: Staying True  Interviews with: Pink, Adele, Beyoncé, Britney...   \n",
              "4         Monster Maker  Harry Dean Stanton, Kieran O'Brien, George Cos...   \n",
              "\n",
              "                 listed_in  \n",
              "0            Comedy, Drama  \n",
              "1     Drama, International  \n",
              "2  Action, Drama, Suspense  \n",
              "3              Documentary  \n",
              "4           Drama, Fantasy  "
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "        document.querySelector('#' + key + ' button');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_clasificacion",
              "summary": "{\n  \"name\": \"df_clasificacion\",\n  \"rows\": 9668,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9668,\n        \"samples\": [\n          \"Arpo: The Robot for All Kids\",\n          \"Gangstars (Telugu)\",\n          \"Sankarabharanam\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cast\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7927,\n        \"samples\": [\n          \"1, 2, 3\",\n          \"Barton MacLane, Bobby Larsen, Jan Wiley, Charlotte Wynters, Conrad Binyon, Elizabeth Valentine\",\n          \"Maryam Hassouni, Mike Beckingham, Dougie Poynter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"listed_in\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 518,\n        \"samples\": [\n          \"Documentary, Unscripted\",\n          \"Drama, Kids\",\n          \"Documentary, Special Interest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df_clasificacion = df_peliculas.loc[:,['title', 'cast','listed_in']]\n",
        "df_clasificacion.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preprocesamiento de datos"
      ],
      "metadata": {
        "id": "3M2ABw8A7sjA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Clasificacion en **cast**\n",
        "# eleminacion del apellido (no es perfecto)\n",
        "nombres_cast = df_clasificacion['cast'].str.split(\",|-|:\", expand = True).replace(\"(\\w\\'\\w+|\\w+)$\", \"\", regex=True)\n",
        "# la regla < No es perfecta > y se nesecita mas pre-procesamiento. Pero es una buena en 1ra aproximacion\n",
        "lex_masc = \"(e|o|d|f|h|n|m|l|s|t|k|w|en|hn|oh)\"\n",
        "lex_fem = \"[^\" + lex_masc + \"]\" # aunque no es necesario\n",
        "\n",
        "def f_reg(lex):\n",
        "    return \".+\" + lex + \"$\"\n",
        "\n",
        "df_clasificacion['cast_feature_masc'] = nombres_cast.apply(lambda x: x.str.strip().str.match(f_reg(lex_masc), case=False).sum(), axis=1)\n",
        "df_clasificacion['cast_feature_fem'] = nombres_cast.apply(lambda x: x.str.strip().str.match(f_reg(lex_fem), case=False).sum(), axis=1)\n",
        "df_clasificacion['cast_feature_fem'] = df_clasificacion['cast_feature_fem'] * (-1)\n",
        "df_genero_film = df_clasificacion['listed_in'].str.split(', ', expand = True)\n",
        "\n",
        "# Asignacion de puntaje en pelicula segun el tipo o genero/o tipo de pelicula:\n",
        "gen_score = {\n",
        "    'Action':2.5,\n",
        "    'Adventure':1.5,\n",
        "    'and Culture':-0.5,\n",
        "    'Animation':-0.5,\n",
        "    'Anime':1,\n",
        "    'Arthouse':-1.5,\n",
        "    'Arts':-1.5,\n",
        "    'Comedy':1.5,\n",
        "    'Documentary':0,\n",
        "    'Drama':-3,\n",
        "    'Entertainment':-2.0,\n",
        "    'Faith and Spirituality':-1.5,\n",
        "    'Fantasy':-1,\n",
        "    'Fitness':-2.5,\n",
        "    'Historical':2,\n",
        "    'Horror':2.5,\n",
        "    'International':0,\n",
        "    'Kids' :0,\n",
        "    'LGBTQ':-3.5,\n",
        "    'Military and War':3.5,\n",
        "    'Music Videos and Concerts':0,\n",
        "    'Romance':-5.5,\n",
        "    'Science Fiction':2,\n",
        "    'Special Interest':0,\n",
        "    'Sports':2.5,\n",
        "    'Suspense':1.5,\n",
        "    'Talk Show and Variety':0,\n",
        "    'TV Shows':0,\n",
        "    'Unscripted':0,\n",
        "    'Western':+2,\n",
        "    'Young Adult Audience':1.0\n",
        "    }\n",
        "df_clasificacion['genero_film_feature_masc_fem'] = df_genero_film.applymap(lambda x: gen_score[x], na_action='ignore').sum(axis=1)\n",
        "df_clasificacion['target_predict_y'] = df_clasificacion['cast_feature_masc'] + df_clasificacion['cast_feature_fem'] + df_clasificacion['genero_film_feature_masc_fem']\n",
        "def f_categ_genero(scr):\n",
        "    if -1.0 <=scr <=1:\n",
        "        return \"Neutro\"\n",
        "    elif scr < -1.0:\n",
        "        return \"Femenino\"\n",
        "    elif scr > 1.0:\n",
        "        return \"Masculino\"\n",
        "\n",
        "\n",
        "df_clasificacion[\"clasif_genero_film_y\"] = df_clasificacion['target_predict_y'].apply(f_categ_genero)"
      ],
      "metadata": {
        "id": "EjPtMfyhXEx7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67d5dd04-b0e8-4f32-fe58-04d728f26ebe"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-aca77f3e7c6f>:50: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
            "  df_clasificacion['genero_film_feature_masc_fem'] = df_genero_film.applymap(lambda x: gen_score[x], na_action='ignore').sum(axis=1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Visualización de las observaciones"
      ],
      "metadata": {
        "id": "r2vqdgFiZ2cV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_clasificacion.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "eWdwJNU-Ziow",
        "outputId": "ab07889a-8cc5-4480-866c-a4bf19c72048"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  title                                               cast  \\\n",
              "0   The Grand Seduction     Brendan Gleeson, Taylor Kitsch, Gordon Pinsent   \n",
              "1  Take Care Good Night   Mahesh Manjrekar, Abhay Mahajan, Sachin Khedekar   \n",
              "2  Secrets of Deception  Tom Sizemore, Lorenzo Lamas, Robert LaSardo, R...   \n",
              "3    Pink: Staying True  Interviews with: Pink, Adele, Beyoncé, Britney...   \n",
              "4         Monster Maker  Harry Dean Stanton, Kieran O'Brien, George Cos...   \n",
              "\n",
              "                 listed_in  cast_feature_masc  cast_feature_fem  \\\n",
              "0            Comedy, Drama                  2                -1   \n",
              "1     Drama, International                  2                -1   \n",
              "2  Action, Drama, Suspense                  5                -1   \n",
              "3              Documentary                  1                -3   \n",
              "4           Drama, Fantasy                  7                -1   \n",
              "\n",
              "   genero_film_feature_masc_fem  target_predict_y clasif_genero_film_y  \n",
              "0                          -1.5              -0.5               Neutro  \n",
              "1                          -3.0              -2.0             Femenino  \n",
              "2                           1.0               5.0            Masculino  \n",
              "3                           0.0              -2.0             Femenino  \n",
              "4                          -4.0               2.0            Masculino  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9eb2840f-7851-4c46-b7f9-d71dec2a84f5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>cast</th>\n",
              "      <th>listed_in</th>\n",
              "      <th>cast_feature_masc</th>\n",
              "      <th>cast_feature_fem</th>\n",
              "      <th>genero_film_feature_masc_fem</th>\n",
              "      <th>target_predict_y</th>\n",
              "      <th>clasif_genero_film_y</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The Grand Seduction</td>\n",
              "      <td>Brendan Gleeson, Taylor Kitsch, Gordon Pinsent</td>\n",
              "      <td>Comedy, Drama</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>Neutro</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Take Care Good Night</td>\n",
              "      <td>Mahesh Manjrekar, Abhay Mahajan, Sachin Khedekar</td>\n",
              "      <td>Drama, International</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>Femenino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Secrets of Deception</td>\n",
              "      <td>Tom Sizemore, Lorenzo Lamas, Robert LaSardo, R...</td>\n",
              "      <td>Action, Drama, Suspense</td>\n",
              "      <td>5</td>\n",
              "      <td>-1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Masculino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Pink: Staying True</td>\n",
              "      <td>Interviews with: Pink, Adele, Beyoncé, Britney...</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>1</td>\n",
              "      <td>-3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>Femenino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Monster Maker</td>\n",
              "      <td>Harry Dean Stanton, Kieran O'Brien, George Cos...</td>\n",
              "      <td>Drama, Fantasy</td>\n",
              "      <td>7</td>\n",
              "      <td>-1</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Masculino</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9eb2840f-7851-4c46-b7f9-d71dec2a84f5')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-9eb2840f-7851-4c46-b7f9-d71dec2a84f5 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-9eb2840f-7851-4c46-b7f9-d71dec2a84f5');\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",
              "\n",
              "\n",
              "<div id=\"df-da22d9b1-1ebb-4863-aa8b-fb5cc8a7925b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da22d9b1-1ebb-4863-aa8b-fb5cc8a7925b')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-da22d9b1-1ebb-4863-aa8b-fb5cc8a7925b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_clasificacion",
              "summary": "{\n  \"name\": \"df_clasificacion\",\n  \"rows\": 9668,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9668,\n        \"samples\": [\n          \"Arpo: The Robot for All Kids\",\n          \"Gangstars (Telugu)\",\n          \"Sankarabharanam\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cast\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7927,\n        \"samples\": [\n          \"1, 2, 3\",\n          \"Barton MacLane, Bobby Larsen, Jan Wiley, Charlotte Wynters, Conrad Binyon, Elizabeth Valentine\",\n          \"Maryam Hassouni, Mike Beckingham, Dougie Poynter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"listed_in\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 518,\n        \"samples\": [\n          \"Documentary, Unscripted\",\n          \"Drama, Kids\",\n          \"Documentary, Special Interest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cast_feature_masc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 57,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          57,\n          15,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cast_feature_fem\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": -29,\n        \"max\": 0,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          -6,\n          -17,\n          -4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genero_film_feature_masc_fem\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9991459187958713,\n        \"min\": -13.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          -8.5,\n          -5.5,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_predict_y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.1239416619443094,\n        \"min\": -15.5,\n        \"max\": 43.0,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          1.0,\n          -10.0,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clasif_genero_film_y\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Neutro\",\n          \"Femenino\",\n          \"Masculino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "n3p-Hg9X8ASG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Aplicación del Modelo 'Naive' Bayesiano (de acuerdo a la plantilla)"
      ],
      "metadata": {
        "id": "kWkqzf6s8LYn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Adaptacion a la plantilla de ejemplo\n",
        "X = df_clasificacion.loc[:, ['cast_feature_masc', 'cast_feature_fem', 'genero_film_feature_masc_fem']].to_numpy()\n",
        "\n",
        "# Transformando los clasif a un numero entero; Femenino=-1, Neutro=0, Masculino=+1\n",
        "y = df_clasificacion['clasif_genero_film_y']\n",
        "\n",
        "y =(y\n",
        "    .replace(\"Neutro\", 0)\n",
        "    .replace(\"Femenino\",-1)\n",
        "    .replace(\"Masculino\", 1)\n",
        "    .to_numpy()\n",
        "    )\n"
      ],
      "metadata": {
        "id": "2xJipER2VRwy"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Graf. de Clasificación de peliculas\n",
        "\n"
      ],
      "metadata": {
        "id": "IJVRvMJ4gENi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, marker=\"*\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "KiDOAaGnf6mZ",
        "outputId": "ce58ddce-ccb4-442c-e805-b448b50f653a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7d79b0aef340>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## División del dataset Train (Entrenamiento) y Test (Prueba)    \n",
        "Dividimos el set en dos grupos, test y modelo de evaluacion. usando el valor de 0.30 (30%) del dataset como training. El valor semilla del generador de numeros aleatorios para seleccionar los casos es 123."
      ],
      "metadata": {
        "id": "nM9n3EiJjSBW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.30, random_state=123\n",
        ")\n",
        "\n",
        "print(\"X_train:\",X_train)\n",
        "print(\"X_test:\",X_test)\n",
        "print(\"y_train:\",y_train)\n",
        "print(\"y_test:\",y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-NyggOCieQ9",
        "outputId": "06009bea-2aba-4705-f8a6-850c8639ba76"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train: [[ 7.  -1.   5.5]\n",
            " [ 2.  -1.   3.5]\n",
            " [ 2.  -2.  -3. ]\n",
            " ...\n",
            " [ 2.  -1.  -2.5]\n",
            " [ 1.   0.   1.5]\n",
            " [ 5.  -5.  -1.5]]\n",
            "X_test: [[  1.   -2.    2.5]\n",
            " [  3.    0.    2.5]\n",
            " [  6.   -5.    1. ]\n",
            " ...\n",
            " [ 15.  -10.   -4. ]\n",
            " [  3.    0.   -1.5]\n",
            " [  0.    0.    0. ]]\n",
            "y_train: [ 1  1 -1 ... -1  1 -1]\n",
            "y_test: [1 1 1 ... 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Construcción del Modelo + Verificación de Valores de prueba\n",
        "\n",
        "Se verifica que el caso de prueba t_test[6] el cual fue ya marcado coincida con el valor estimado por el modelo: predicted[0]"
      ],
      "metadata": {
        "id": "c_91hA1Km-sM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "#Shows\n",
        "# Build a Gaussian Classifier\n",
        "model = GaussianNB()\n",
        "\n",
        "# Model training\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Predict Output\n",
        "predicted = model.predict([X_test[6]])\n",
        "\n",
        "print(y_test)\n",
        "print(\"Actual Value:\", y_test[6])\n",
        "print(\"Predicted Value:\", predicted[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24hljL9Yiu2-",
        "outputId": "7b85612a-93da-4ead-c641-f9d5f3b02d9c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 1 ... 0 1 0]\n",
            "Actual Value: 1\n",
            "Predicted Value: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluación del Modelo"
      ],
      "metadata": {
        "id": "liukm_30qbuT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay, f1_score\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = accuracy_score(y_pred, y_test)\n",
        "f1 = f1_score(y_pred, y_test, average=\"weighted\")\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"F1 Score:\", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJglFsoFpV0l",
        "outputId": "1a60d13c-b84c-4fc6-8811-ffa2e2b97256"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7094105480868665\n",
            "F1 Score: 0.7028310097060774\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualización de la Matriz de Confusion   \n",
        "**Recordatorio: -1 representa 'Femenino', 0 'Neutro', y +1 'Masculino'*\n"
      ],
      "metadata": {
        "id": "8cJglI7utKHx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [-1,0,1]\n",
        "cm = confusion_matrix(y_test, y_pred, labels=labels)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "nHj4A-7HqZ7x",
        "outputId": "11dfa074-e480-44ea-afda-c6b966e5c843"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7d79a8092590>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "c5NVPua1CrpP",
        "3M2ABw8A7sjA"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}