{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Validación con datos generados sintéticamente\n",
        "=============================================\n",
        "\n",
        "<div class=\"admonition warning\">PRECAUCIÓN 😱: El tema presentado en esta sección está clasificado como avanzado. El entendimiento de este contenido es totalmente opcional.</div>\n",
        "\n"
      ],
      "metadata": {
        "id": "ko9DnAUnfPdj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "No siempre es sencillo tener acceso a repositorios de datos que sean lo suficientemente grandes y con valores realistas (como ser nombre, edad, dirección, fecha de nacimiento, sexo, etc). Disponer de estos conjuntos de datos es útil no solo para aquellas personas que desean probar diferentes técnicas o modelos de aprendizaje automático, sino que también son muy interesantes para validadores de software (testers) que les gustaría tener un conjuntos de datos arbitrariamente grande (aunque con datos ficticios) sobre los cuales ejecutar pruebas de calidad de los modelos de aprendizaje que otros desarrolladores crearon. Esto facilita la posibilidad de probar determinados casos de borde sin necesidad de modificar datos manualmente ni tener que crear de forma artesanal conjuntos de datos para las validaciones."
      ],
      "metadata": {
        "id": "AxFpukOeh_ed"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sobre los datos sintéticos\n",
        "\n",
        "Los conjuntos de datos sintéticos son conjuntos de datos generados a traves de procesos artificiales con el proposito de mantener la privacidad de los datos originales, realizar validaciones de un sistema o pruebas de carga o simplemente verificar un modelo de aprendizaje automático. En general, los datos sintéticos se generan de tal forma que \"imitan\" la distribución o representación de un conjunto de datos real, pero sin necesidad de revelar ningún detalle del conjunto de datos original. En algunos casos puede ser que los datos generados sintéticamente no tengan el mismo valor que los datos reales, pero existen muchos otros casos donde pueden tener incluso el mismo valor. Muchos modelos de aprendizaje automático son entrenados con datos generados sintéticamente.\n",
        "\n",
        "Las generación de datos sintéticos se utiliza incluso durante el proceso de entrenamiento de modelos, con técnicas como `SMOTE` que permiten correjir el balance de clases en conjuntos de datos desbalanceados. Sin embargo, en esta sección no nos centraremos en tales típos de técnicas."
      ],
      "metadata": {
        "id": "jbch3-M2fPds"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Técnicas para la generación de datos sintéticos\n",
        "\n",
        "Existen multiples técnicas para la generación de datos sintéticos dependiendo de los requerimientos de negocio y de las características de los conjuntos de datos que estamos buscando.\n",
        "\n",
        "### Generarlos desde distribuciones conocidas\n",
        "\n",
        "Aunque en la práctica no es el caso más habitual, puede ser que conoscamos la distribución de los datos que necesitamos, como ser una distribución normal, exponencial, binomial, etc. En tales casos, muchas librerías de aprendizaje automático nos permiten generar conjuntos de datos de tamaños arbitrales que se condicen con estas distribuciones.\n",
        "\n",
        "### Utilizando librerías espécificas\n",
        "\n",
        "Existen librerías específicas para generar conjuntos de datos sintéticos en múltiples lenguajes. En `Python`, librerías como las siguientes pueden ser utilizadas:\n",
        "\n",
        " - **faker**: Es una librería que no solo permite la generación de tipos de datos simples, sino que también datos como nombres, direcciones, teléfonos o correos electrónicos.\n",
        " - **SymPy:** Es una libería de alta performance que permite generar datos a traves de expresiones simbólicas.\n",
        " - **PyDBGen:** Es una librería que permite generar datos categóricos localizados (localized) para diferentes paises e idiomas. Puede generar conjuntos de datos tanto en archivos como csv o txt como directamente en tablas de bases de datos.\n",
        " - **mimesis:** Es una librería similar a faker, aunque provee multiples formas de extender sus funcionalidades.\n",
        "\n",
        "### Utilizando modelos generativos\n",
        "\n",
        "Modelos generativos, como son `Variational Autoencoders (VAE)` o `Generative Adversarial Networks (GAN)` puede generar datos sintéticos con un nivel de detallé importante. Muchas técnicas de aumento de datos (data augmentation) descansan en este tipo de modelos."
      ],
      "metadata": {
        "id": "iI8FYXEJfPdt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejemplos"
      ],
      "metadata": {
        "id": "h66mNxo-fPdu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mimesis\n",
        "\n",
        "`Mimesis` es una libería de Python que permite generar conjuntos de datos sintéticos. `Mimesis` no solo permite la generación de tipos de datos simples, sino que también puede generar datos como nombres, direcciones, teléfonos o direcciones de correo electrónico. Esta información la puede generar para diferentes `locations` incluyendo el idioma Español. Lamentablemente, no dispone de una implementación específica para Argentina, siendo las variaciones más aproximadas las de Mexico (`es-mx`) o España (`es`). `Mimesis` es extensible, lo que significa que si existe un tipo de dato que no es soportado actualmente, se lo puede implementar para que la librería lo genere. Es ampliamente utilizado por la comunida de código abierto por lo que existen multiples extensiones ya creadas.\n",
        "\n",
        "Para más información sobre `Mimesis` puede [revisar la documentación oficial](https://mimesis.name/getting_started.html)."
      ],
      "metadata": {
        "id": "WhZ2lk2HfPdv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install mimesis==18.0.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZSHIDftFg8t",
        "outputId": "2975c017-4eeb-46cb-f792-eca4bef5f5ba"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mimesis==18.0.0 in /usr/local/lib/python3.11/dist-packages (18.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "source": [
        "import mimesis\n",
        "import mimesis.schema\n",
        "\n",
        "person = mimesis.Person(locale=\"es\")\n",
        "numbers = mimesis.Numeric()"
      ],
      "outputs": [],
      "metadata": {
        "id": "oshs2P57fPdw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Por ejemplo, si quisieramos generar un nombre de una persona generalmente asociado a sexo femenino, podríamos hacer los siguiente:"
      ],
      "metadata": {
        "id": "Z7cwTsldfPdy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "print(person.full_name(gender=mimesis.enums.Gender.FEMALE))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nieves Molina\n"
          ]
        }
      ],
      "metadata": {
        "id": "8s4Yg1mtfPdz",
        "outputId": "e9aaff65-8032-4777-99f1-8e55b5331d03"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "De forma similar, podriamos generar la edad de una persona mayor a 21 años como sigue:"
      ],
      "metadata": {
        "id": "aipWhENjfPd2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "source": [
        "print(numbers.integer_number(start=21, end=100))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "38\n"
          ]
        }
      ],
      "metadata": {
        "id": "3x9mex7ofPd4",
        "outputId": "1875276c-7f8e-4abe-9628-cd959dc472ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Esta libreria nos permite generar conjuntos completos de datos de forma rápida indicando un **esquema de datos**. En el siguiente ejemplo, crearemos un conjunto de datos con varios campos, para luego construir un `DataFrame` de `Pandas`:"
      ],
      "metadata": {
        "id": "75K7WjngfPd5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "source": [
        "field = mimesis.schema.Field(locale=\"es\")\n",
        "description = (\n",
        "    lambda: {\n",
        "        'id': field('uuid'),\n",
        "        'creator': field('full_name', gender=mimesis.enums.Gender.FEMALE),\n",
        "        'age': field('integer_number', start=21, end=100),\n",
        "        'timestamp': field('datetime.datetime'),\n",
        "        'word': field('text.word'),\n",
        "        'email': field('person.email', domains=['outlook.com', 'gmail.com', 'yahoo.com'], key=str.lower),\n",
        "    }\n",
        ")\n",
        "\n",
        "sample_schema = mimesis.schema.Schema(schema=description)"
      ],
      "outputs": [],
      "metadata": {
        "id": "M1i2gF20fPd5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creamos un `Pandas.DataFrame` con 100 muestras:"
      ],
      "metadata": {
        "id": "fwT5QYTcfPd6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "source": [
        "import pandas as pd\n",
        "\n",
        "sample_schema.iterations = 100\n",
        "\n",
        "pd.DataFrame(sample_schema.create())"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      id          creator  age  \\\n",
              "0   33fb7ea0-5a17-433a-a0e3-fcf5cedd8542   Narcisa Medina   87   \n",
              "1   df6a5130-949a-48b5-86ac-4fcb1f438613    Rosalía Lopez   83   \n",
              "2   55d77079-0370-4831-861f-30345bd474d3     Jessica Moya   38   \n",
              "3   67596f2d-5640-4073-bb3b-c40b3a36a130    Amalia Suarez   32   \n",
              "4   2273bf27-27c4-48bf-9915-f34420279a3d   Meagens Santos   86   \n",
              "..                                   ...              ...  ...   \n",
              "95  339bdc41-0cc3-4598-bf46-57cdbb548732   Begoña Fuentes   86   \n",
              "96  adf7546c-759c-43c6-823b-24f1b9571d8d   Patricia Roman   42   \n",
              "97  4294fdac-5faa-45a3-a18a-f6d85e8ded14  Froilana Mendez   25   \n",
              "98  c76a3f9c-51c0-4d13-99c7-ebe6b3dbebda      Teresa Diaz   76   \n",
              "99  b5617d1e-b148-4fa0-8116-1d26f003e794        Noe Vidal   47   \n",
              "\n",
              "                    timestamp        word                     email  \n",
              "0  2025-11-09 05:55:49.599516      viendo   watershed1805@yahoo.com  \n",
              "1  2025-10-28 04:21:49.607131      rápido      wife1990@outlook.com  \n",
              "2  2025-12-18 21:34:31.034018      toques       rings2053@yahoo.com  \n",
              "3  2025-11-24 13:27:43.362711    medicina    portugal1850@gmail.com  \n",
              "4  2025-12-16 05:53:38.754638       damas      rack2022@outlook.com  \n",
              "..                        ...         ...                       ...  \n",
              "95 2025-04-26 07:33:08.491777   respuesta     declare2001@yahoo.com  \n",
              "96 2025-04-22 03:30:03.276388      parque      kernel2053@yahoo.com  \n",
              "97 2025-03-10 03:57:08.649930  pantalones    senate1969@outlook.com  \n",
              "98 2025-07-28 17:15:43.511954    esperaba     replace1830@yahoo.com  \n",
              "99 2025-04-25 20:24:44.440473       apoyo  determined1945@gmail.com  \n",
              "\n",
              "[100 rows x 6 columns]"
            ],
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>creator</th>\n",
              "      <th>age</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>word</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33fb7ea0-5a17-433a-a0e3-fcf5cedd8542</td>\n",
              "      <td>Narcisa Medina</td>\n",
              "      <td>87</td>\n",
              "      <td>2025-11-09 05:55:49.599516</td>\n",
              "      <td>viendo</td>\n",
              "      <td>watershed1805@yahoo.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>df6a5130-949a-48b5-86ac-4fcb1f438613</td>\n",
              "      <td>Rosalía Lopez</td>\n",
              "      <td>83</td>\n",
              "      <td>2025-10-28 04:21:49.607131</td>\n",
              "      <td>rápido</td>\n",
              "      <td>wife1990@outlook.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>55d77079-0370-4831-861f-30345bd474d3</td>\n",
              "      <td>Jessica Moya</td>\n",
              "      <td>38</td>\n",
              "      <td>2025-12-18 21:34:31.034018</td>\n",
              "      <td>toques</td>\n",
              "      <td>rings2053@yahoo.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>67596f2d-5640-4073-bb3b-c40b3a36a130</td>\n",
              "      <td>Amalia Suarez</td>\n",
              "      <td>32</td>\n",
              "      <td>2025-11-24 13:27:43.362711</td>\n",
              "      <td>medicina</td>\n",
              "      <td>portugal1850@gmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2273bf27-27c4-48bf-9915-f34420279a3d</td>\n",
              "      <td>Meagens Santos</td>\n",
              "      <td>86</td>\n",
              "      <td>2025-12-16 05:53:38.754638</td>\n",
              "      <td>damas</td>\n",
              "      <td>rack2022@outlook.com</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>339bdc41-0cc3-4598-bf46-57cdbb548732</td>\n",
              "      <td>Begoña Fuentes</td>\n",
              "      <td>86</td>\n",
              "      <td>2025-04-26 07:33:08.491777</td>\n",
              "      <td>respuesta</td>\n",
              "      <td>declare2001@yahoo.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>adf7546c-759c-43c6-823b-24f1b9571d8d</td>\n",
              "      <td>Patricia Roman</td>\n",
              "      <td>42</td>\n",
              "      <td>2025-04-22 03:30:03.276388</td>\n",
              "      <td>parque</td>\n",
              "      <td>kernel2053@yahoo.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>4294fdac-5faa-45a3-a18a-f6d85e8ded14</td>\n",
              "      <td>Froilana Mendez</td>\n",
              "      <td>25</td>\n",
              "      <td>2025-03-10 03:57:08.649930</td>\n",
              "      <td>pantalones</td>\n",
              "      <td>senate1969@outlook.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>c76a3f9c-51c0-4d13-99c7-ebe6b3dbebda</td>\n",
              "      <td>Teresa Diaz</td>\n",
              "      <td>76</td>\n",
              "      <td>2025-07-28 17:15:43.511954</td>\n",
              "      <td>esperaba</td>\n",
              "      <td>replace1830@yahoo.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>b5617d1e-b148-4fa0-8116-1d26f003e794</td>\n",
              "      <td>Noe Vidal</td>\n",
              "      <td>47</td>\n",
              "      <td>2025-04-25 20:24:44.440473</td>\n",
              "      <td>apoyo</td>\n",
              "      <td>determined1945@gmail.com</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 6 columns</p>\n",
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              "\n",
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              "        document.querySelector('#df-0fd1c4af-96ad-4593-923c-9c6101eff9ba button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-0fd1c4af-96ad-4593-923c-9c6101eff9ba');\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",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"4d719f80-1201-4497-be77-bdd52e544f4c\",\n          \"d67a5e00-9cf9-40e4-9978-de6dd0086e09\",\n          \"19fc660d-933a-4ef5-b761-9735c8354d07\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"creator\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"Luciana Ortiz\",\n          \"Soledad Gil\",\n          \"Piedad Moreno\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 21,\n        \"max\": 100,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          87,\n          56,\n          78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"timestamp\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2025-01-02 00:10:00.688886\",\n        \"max\": \"2025-12-28 01:33:25.969389\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"2025-03-27 23:45:13.869743\",\n          \"2025-12-28 01:33:25.969389\",\n          \"2025-12-14 06:32:25.631934\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"word\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 97,\n        \"samples\": [\n          \"asiento\",\n          \"termin\\u00f3\",\n          \"parque\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"grades2082@yahoo.com\",\n          \"taken1909@yahoo.com\",\n          \"highly2080@yahoo.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "metadata": {
        "id": "5n4RN0oofPd6",
        "outputId": "5e8a828b-aca4-4968-b87a-b0448ab89ba1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CTGAN\n",
        "\n",
        "CTGAN es un técnica que ha ganado popularidad ya que se basa en la arquitectura GAN para modelar datos tabulares y muestrear filas condicionalmente a partir de un modelo generado especificamente para crear datos sintéticos. Al momento, esta representa una de las técnicas más avanzadas para la generación de datos sintéticos.\n",
        "\n",
        "[Xu, Lei and Skoularidou, Maria and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan. Modeling Tabular data using Conditional GAN. Advances in Neural Information Processing Systems. 2019](https://arxiv.org/abs/1907.00503)\n",
        "\n",
        "Para demostrar como realizar validaciones utilizando conjunto de datos sintéticos, trabajaremos con el conjunto de datos de 'Pima Indians Diabetes Database' (https://www.openml.org/d/37), proveniente originalmente del National Institute of Diabetes and Digestive and Kidney Diseases. El objetivo del conjunto de datos es el de predecir si un paciente tiene o no diabetes, basado en ciertas mediciones que se incluyen. Este conjunto es interesante para esta demostración porque tiene varias restricciones en cuanto a como se recolectaron los datos. En particular, todos los pacientes son de sexo femenino, de al menos 21 años y de ascendencia india Prima."
      ],
      "metadata": {
        "id": "rhfw4mtTfPd7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install ctgan"
      ],
      "metadata": {
        "id": "nwGryNHyHSg2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "source": [
        "import pandas as pd\n",
        "import sklearn.datasets as datasets\n",
        "\n",
        "data = datasets.fetch_openml(name='diabetes', version=1)\n",
        "df = pd.DataFrame(data['data'], columns=data['feature_names'])\n",
        "df['target'] = data['target'].astype('object')"
      ],
      "outputs": [],
      "metadata": {
        "id": "7V9Odk8MfPd7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Veamos como luce el conjunto de datos"
      ],
      "metadata": {
        "id": "W3Ivh2htfPd8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "source": [
        "df.head(10)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   preg  plas  pres  skin  insu  mass   pedi  age           target\n",
              "0     6   148    72    35     0  33.6  0.627   50  tested_positive\n",
              "1     1    85    66    29     0  26.6  0.351   31  tested_negative\n",
              "2     8   183    64     0     0  23.3  0.672   32  tested_positive\n",
              "3     1    89    66    23    94  28.1  0.167   21  tested_negative\n",
              "4     0   137    40    35   168  43.1  2.288   33  tested_positive\n",
              "5     5   116    74     0     0  25.6  0.201   30  tested_negative\n",
              "6     3    78    50    32    88  31.0  0.248   26  tested_positive\n",
              "7    10   115     0     0     0  35.3  0.134   29  tested_negative\n",
              "8     2   197    70    45   543  30.5  0.158   53  tested_positive\n",
              "9     8   125    96     0     0   0.0  0.232   54  tested_positive"
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\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-59dec210-6eb3-4448-bf51-129e8ff19283 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"preg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"plas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pres\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"insu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.8841603203754405,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pedi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33132859501277484,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"tested_negative\",\n          \"tested_positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "metadata": {
        "id": "2q7a87S_fPd8",
        "outputId": "e0d42dda-151f-495b-c928-cae9ae527cd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entrenemos un modelo basado en la arquitectura de CTGAN para generar sintéticamente instancias pertenecientes a este conjunto de datos. Preste especial atención a los valores especificados en la variable `discrete_columns` donde indicamos aquellas variables que son categoricas. Las restantes variables serán consideradas como continuas."
      ],
      "metadata": {
        "id": "T3gdAa-xfPd9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "source": [
        "from ctgan import CTGAN\n",
        "\n",
        "discrete_columns = ['preg', 'age', 'target']\n",
        "\n",
        "ctgan = CTGAN(epochs=5)\n",
        "ctgan.fit(df, discrete_columns)"
      ],
      "outputs": [],
      "metadata": {
        "id": "KJNBnR5jfPd-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "source": [
        "ctgan.sample(10)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   preg  plas  pres  skin  insu       mass      pedi  age           target\n",
              "0     1   135    46    37    98  31.233694  2.772864   45  tested_positive\n",
              "1    11   180    40     3   482  24.726927  0.578021   25  tested_negative\n",
              "2     2   128    68     7    79  48.442813  0.135262   54  tested_negative\n",
              "3     4   132    81    37    68  32.928613  0.582547   70  tested_positive\n",
              "4     9   133    80    -7   489  34.210912  0.551240   59  tested_negative\n",
              "5     1    97    61    32   364  51.610659  0.807465   21  tested_positive\n",
              "6    13   151    84     3   111  45.342234  0.391592   34  tested_positive\n",
              "7     1   218    76    14   154  29.559014  0.173615   38  tested_negative\n",
              "8     1   161    25     4   572  34.164945  0.340733   27  tested_negative\n",
              "9    12   100    77    21   499 -20.287110  0.568273   33  tested_negative"
            ],
            "text/html": [
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              "  <div id=\"df-32c44e76-ccae-416b-828e-63e8e2ec2542\" class=\"colab-df-container\">\n",
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              "        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>preg</th>\n",
              "      <th>plas</th>\n",
              "      <th>pres</th>\n",
              "      <th>skin</th>\n",
              "      <th>insu</th>\n",
              "      <th>mass</th>\n",
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              "      <th>age</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>31.233694</td>\n",
              "      <td>2.772864</td>\n",
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              "      <td>tested_positive</td>\n",
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              "    <tr>\n",
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              "      <td>34.210912</td>\n",
              "      <td>0.551240</td>\n",
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              "    <tr>\n",
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              "      <td>0.807465</td>\n",
              "      <td>21</td>\n",
              "      <td>tested_positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>13</td>\n",
              "      <td>151</td>\n",
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              "      <td>3</td>\n",
              "      <td>111</td>\n",
              "      <td>45.342234</td>\n",
              "      <td>0.391592</td>\n",
              "      <td>34</td>\n",
              "      <td>tested_positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
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              "    <tr>\n",
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              "      <td>100</td>\n",
              "      <td>77</td>\n",
              "      <td>21</td>\n",
              "      <td>499</td>\n",
              "      <td>-20.287110</td>\n",
              "      <td>0.568273</td>\n",
              "      <td>33</td>\n",
              "      <td>tested_negative</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-32c44e76-ccae-416b-828e-63e8e2ec2542')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </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-32c44e76-ccae-416b-828e-63e8e2ec2542 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-32c44e76-ccae-416b-828e-63e8e2ec2542');\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",
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              "\n",
              "\n",
              "    <div id=\"df-d24290b1-ef55-42cc-b2f9-db9a99acb60b\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d24290b1-ef55-42cc-b2f9-db9a99acb60b')\"\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",
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              "    </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",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "  }\n",
              "\n",
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              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    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",
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            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
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        ".. note:: Note que variables como `age` y `preg` que representan la edad de la persona y la cantidad de veces que ha estado embarazada fueron configuradas como variables categoricas. Esto permite que nuestro modelo 1) no genere valores continuos, como ser 20.5 para la edad, y 2) que no utilice valores que se encuentren fuera de los valores que ya existen en el conjunto de datos. En algunos casos, sobre todo considerando escenarios de testing, este comportamiento podría no ser el deseado."
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        "id": "IerX5sadfPd_"
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