nueva_resolucion_tp.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "UYoanQYD3110"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "8vLARohJ3113"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('data/data.csv')\n",
        "#df = pd.read_csv('data.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umloQw_fYX1J"
      },
      "source": [
        "# Ejercicio 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cantidades</th>\n",
              "      <th>percentajes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>343</td>\n",
              "      <td>68.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>157</td>\n",
              "      <td>31.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        cantidades  percentajes\n",
              "target                         \n",
              "0              343         68.6\n",
              "1              157         31.4"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "counts = df['target'].value_counts()\n",
        "percentages = df['target'].value_counts(normalize=True) * 100\n",
        "pd.DataFrame({'cantidades': counts, 'percentajes': percentages})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QcnXhR5U3114"
      },
      "source": [
        "Notamos que estamos ante un dataset algo desbalanceado, por lo que separaremos los datos de forma estratificada."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "U1ZJrEf53115"
      },
      "outputs": [],
      "source": [
        "# Separación train-test estratificada\n",
        "\n",
        "def train_test_split(df,train_size=0.8): \n",
        "    df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
        "    n = df.shape[0]  #cantidad de filas\n",
        "    cantPos = df['target'].sum() #positivas son 1, negativas son 0\n",
        "    #columna 'target' tiene valores 0 y 1\n",
        "    proporcion = cantPos / n\n",
        "\n",
        "    cant_en_train = int(n*train_size)\n",
        "    cant_en_validacion = n - cant_en_train\n",
        "\n",
        "    res = []\n",
        "    cant_positivas = int(proporcion * cant_en_validacion)\n",
        "    cant_negativas = cant_en_validacion - cant_positivas\n",
        "\n",
        "    contador_positivos, contador_negativos = 0, 0\n",
        "    for index, value in enumerate(df['target']):\n",
        "        if value == 0:\n",
        "            contador_negativos += 1\n",
        "            res.append(index)\n",
        "\n",
        "        if contador_negativos == cant_negativas:\n",
        "            break\n",
        "    for index, value in enumerate(df['target']):\n",
        "        if value == 1:\n",
        "            contador_positivos += 1\n",
        "            res.append(index)\n",
        "\n",
        "        if contador_positivos == cant_positivas:\n",
        "            break\n",
        "\n",
        "    df_dev = df.loc[~df.index.isin(res)].reset_index(drop=True)\n",
        "    df_eval = df.loc[res].reset_index(drop=True)\n",
        "    return df_dev,df_eval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "yqtG83Nm3116"
      },
      "outputs": [],
      "source": [
        "# Separamos 70% para entrenamiento y 30% para evaluación\n",
        "\n",
        "df_dev , df_eval =  train_test_split(df,0.7)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2i7y0nMQ3116"
      },
      "source": [
        "# Ejercicio 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "IkwadpMP3116"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import cross_validate\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score , average_precision_score\n",
        "from sklearn.model_selection import cross_val_predict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "SOFAvQ913117"
      },
      "outputs": [],
      "source": [
        "# Datos de entrenamiento\n",
        "X_train = df_dev.drop(columns=['target']).values\n",
        "\n",
        "y_train = df_dev['target'].values\n",
        "\n",
        "\n",
        "\n",
        "# Lo usamos al final para predecir el AUC_ROC\n",
        "X_eval   = df_eval.drop(columns=['target']).values\n",
        "\n",
        "y_eval   =  df_eval['target'].values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ra6DJoh7YX1N"
      },
      "source": [
        "## 2.1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "JO0suEey3117",
        "outputId": "e4b38f6a-10f5-49fa-aa5f-c40efb9a468e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-5 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-5 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-5 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-5 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-5 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-5 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-5 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-5 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=51)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=3, random_state=51)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "DecisionTreeClassifier(max_depth=3, random_state=51)"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tree_clf = DecisionTreeClassifier(max_depth=3, random_state=51)\n",
        "tree_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTVKFdeKYX1N"
      },
      "source": [
        "## 2.2\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "3bdVIWb6YX1O",
        "outputId": "9aeafc56-e95d-41b7-e9ec-f53b326f81f6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>test_accuracy</th>\n",
              "      <th>train_accuracy</th>\n",
              "      <th>test_roc_auc</th>\n",
              "      <th>train_roc_auc</th>\n",
              "      <th>test_average_precision</th>\n",
              "      <th>train_average_precision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.671429</td>\n",
              "      <td>0.832143</td>\n",
              "      <td>0.612689</td>\n",
              "      <td>0.862305</td>\n",
              "      <td>0.449379</td>\n",
              "      <td>0.745010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.657143</td>\n",
              "      <td>0.817857</td>\n",
              "      <td>0.656723</td>\n",
              "      <td>0.787435</td>\n",
              "      <td>0.460012</td>\n",
              "      <td>0.662661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.657143</td>\n",
              "      <td>0.846429</td>\n",
              "      <td>0.623580</td>\n",
              "      <td>0.871656</td>\n",
              "      <td>0.416415</td>\n",
              "      <td>0.717895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.628571</td>\n",
              "      <td>0.846429</td>\n",
              "      <td>0.562973</td>\n",
              "      <td>0.859967</td>\n",
              "      <td>0.350321</td>\n",
              "      <td>0.771605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.853571</td>\n",
              "      <td>0.518939</td>\n",
              "      <td>0.816170</td>\n",
              "      <td>0.371775</td>\n",
              "      <td>0.691897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Promedio</th>\n",
              "      <td>0.662857</td>\n",
              "      <td>0.839286</td>\n",
              "      <td>0.594981</td>\n",
              "      <td>0.839506</td>\n",
              "      <td>0.409580</td>\n",
              "      <td>0.717814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Global</th>\n",
              "      <td>0.662857</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.584280</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.366941</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          test_accuracy  train_accuracy  test_roc_auc  train_roc_auc  \\\n",
              "1              0.671429        0.832143      0.612689       0.862305   \n",
              "2              0.657143        0.817857      0.656723       0.787435   \n",
              "3              0.657143        0.846429      0.623580       0.871656   \n",
              "4              0.628571        0.846429      0.562973       0.859967   \n",
              "5              0.700000        0.853571      0.518939       0.816170   \n",
              "Promedio       0.662857        0.839286      0.594981       0.839506   \n",
              "Global         0.662857             NaN      0.584280            NaN   \n",
              "\n",
              "          test_average_precision  train_average_precision  \n",
              "1                       0.449379                 0.745010  \n",
              "2                       0.460012                 0.662661  \n",
              "3                       0.416415                 0.717895  \n",
              "4                       0.350321                 0.771605  \n",
              "5                       0.371775                 0.691897  \n",
              "Promedio                0.409580                 0.717814  \n",
              "Global                  0.366941                      NaN  "
            ]
          },
          "execution_count": 113,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scoring = ['accuracy', 'roc_auc', 'average_precision']\n",
        "\n",
        "#Dado que el dataset está algo desbalanceado, usamos StratifiedKFold\n",
        "skf = StratifiedKFold(n_splits=5)\n",
        "\n",
        "cv_results = cross_validate(tree_clf, X_train, y_train, cv = skf, scoring = scoring, return_train_score = True)\n",
        "\n",
        "predicted_labels = cross_val_predict(tree_clf, X_train, y_train, cv = skf)\n",
        "\n",
        "df_resultados_ej_2_2 = pd.DataFrame({key: cv_results[key] for key in list(cv_results.keys())[2:]})\n",
        "df_resultados_ej_2_2.index = df_resultados_ej_2_2.index + 1 # asi el indice del df se correponde con el k-Fold\n",
        "metricas_globales = [accuracy_score(y_train, predicted_labels), np.nan, roc_auc_score(y_train, predicted_labels), np.nan,average_precision_score(y_train, predicted_labels), np.nan]\n",
        "\n",
        "\n",
        "df_resultados_ej_2_2.loc['Promedio'] = df_resultados_ej_2_2.mean()\n",
        "df_resultados_ej_2_2.loc['Global'] = metricas_globales\n",
        "\n",
        "df_resultados_ej_2_2\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uZTON8y-YX1O"
      },
      "source": [
        "## 2.3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "eXYrnxvP3118",
        "outputId": "4b1fe8c6-39f5-4d1c-e631-3364618d8201"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Accuracy (training)</th>\n",
              "      <th>Accuracy (testing)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>gini</th>\n",
              "      <td>0.839286</td>\n",
              "      <td>0.662857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>gini</th>\n",
              "      <td>0.939286</td>\n",
              "      <td>0.654286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <th>gini</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.637143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>entropy</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.645714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>entropy</th>\n",
              "      <td>0.912143</td>\n",
              "      <td>0.642857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <th>entropy</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.625714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Accuracy (training)  Accuracy (testing)\n",
              "3   gini                0.839286            0.662857\n",
              "5   gini                0.939286            0.654286\n",
              "NaN gini                1.000000            0.637143\n",
              "3   entropy             0.800000            0.645714\n",
              "5   entropy             0.912143            0.642857\n",
              "NaN entropy             1.000000            0.625714"
            ]
          },
          "execution_count": 114,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "alturas = [3, 5, None]\n",
        "criterios = [\"gini\", \"entropy\"]\n",
        "resultados = {}\n",
        "\n",
        "for criterio in criterios:\n",
        "    for altura in alturas:\n",
        "\n",
        "        tree = DecisionTreeClassifier(max_depth = altura, criterion = criterio, random_state = 51)\n",
        "\n",
        "        cv_results_grid = cross_validate(tree, X_train, y_train, cv = skf, scoring = 'accuracy', return_train_score = True)\n",
        "\n",
        "        accuracy_train = cv_results_grid['train_score'].mean()\n",
        "\n",
        "        accuracy_test =  cv_results_grid['test_score'].mean()\n",
        "\n",
        "\n",
        "\n",
        "        resultados[(altura, criterio)] = (accuracy_train,accuracy_test)\n",
        "\n",
        "results_df = pd.DataFrame(resultados.values(), index=resultados.keys(), columns=['Accuracy (training)', 'Accuracy (testing)'])\n",
        "\n",
        "results_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qb9c4XKCYX1O"
      },
      "source": [
        "## 2.4\n",
        "Notamos que en Test el criterio de corte Gini da siempre mejor que entropy para cualquier altura.\n",
        "Sera porque entropy tiende a producir arboles mas equilibrados y nuestro data set esta desbalanceado?\n",
        "\n",
        "Asimismo tambien vemos que a medida que aumenta la altura del arbol, decae el rendimiento en el test set y aumenta en el train set, independientemente del criterio de corte utilizado."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lgg2Etw93119"
      },
      "source": [
        "# Ejercicio 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "Wv_AHLd3Y5kY"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import scipy\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import preprocessing\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.naive_bayes import GaussianNB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {},
      "outputs": [],
      "source": [
        "n_iter_search = 50    # Esto es la cantidad de configuraciones que pruebo en cada modelo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2H_oZ5wYX1P"
      },
      "source": [
        "## Árboles de decisión"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4yq7Jfh3119",
        "outputId": "24f5e017-94c1-4005-a104-d75d69d9677b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mejor modelo: DecisionTreeClassifier(max_depth=12, max_features=38, max_leaf_nodes=55,\n",
            "                       min_samples_leaf=6, min_samples_split=5,\n",
            "                       random_state=42) \n",
            "\n",
            "Mejor score AUCROC: 0.6683712121212122\n"
          ]
        }
      ],
      "source": [
        "# Hay que revisar en la documentacion del modelo cada parametro y usar una distribucion con los valores mas probables\n",
        "# Las listas son interpretadas como distribuciones uniformes discretas\n",
        "\n",
        "# Comento los parámetros que generan errores\n",
        "dist_params = {\n",
        "    'criterion': ['gini', 'entropy', 'log_loss'],\n",
        "    'splitter': ['best', 'random'],\n",
        "    'max_depth': scipy.stats.randint(1,40), #puede ser float\n",
        "    'min_samples_split': scipy.stats.randint(2,20), #puede ser float\n",
        "    'min_samples_leaf': scipy.stats.randint(1,20),\n",
        "    #'min_weight_fraction_leaf':scipy.stats.uniform(0,1) ,\n",
        "    'max_features': scipy.stats.randint(1,50),\n",
        "    'random_state': [42],\n",
        "    'max_leaf_nodes': scipy.stats.randint(2,len(X_train)),\n",
        "    #'min_impurity_decrease': scipy.stats.uniform(0,0.1),\n",
        "    #'class_weight': {i: scipy.stats.uniform(0,1) for i in range(len(X_train[0]))},\n",
        "    #'ccp_alpha': scipy.stats.uniform(0,0.1),\n",
        "}\n",
        "\n",
        "tree = DecisionTreeClassifier()\n",
        "random_search = RandomizedSearchCV(estimator=tree, param_distributions=dist_params, scoring='roc_auc', cv=skf,  n_iter=n_iter_search)\n",
        "random_search.fit(X_train, y_train)\n",
        "\n",
        "mejor_modelo_arbol = random_search.best_estimator_\n",
        "mejor_score_arbol = random_search.best_score_\n",
        "print(\"Mejor modelo:\", mejor_modelo_arbol,'\\n')\n",
        "print(\"Mejor score AUCROC:\", mejor_score_arbol)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i7oH8WeGYX1P"
      },
      "source": [
        "## KNN (k-vecinos más cercanos)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULK3jcaX3119",
        "outputId": "71ca3f74-1f0c-445d-e731-223048579b79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mejor modelo: KNeighborsClassifier(algorithm='brute', leaf_size=35, n_neighbors=28, p=3,\n",
            "                     weights='distance') \n",
            "\n",
            "Mejor score AUCROC: 0.7623106060606061\n"
          ]
        }
      ],
      "source": [
        "dist_params = {\n",
        "    'n_neighbors': scipy.stats.randint(1, 30),\n",
        "    'weights': ['uniform', 'distance'],\n",
        "    'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
        "    'leaf_size': scipy.stats.randint(1, 50),\n",
        "    'p': scipy.stats.randint(1, 5),\n",
        "    'metric': ['euclidean', 'manhattan', 'chebyshev', 'minkowski'],\n",
        "    #'metric_params':,\n",
        "    #'n_jobs':,\n",
        "}\n",
        "\n",
        "knn = KNeighborsClassifier()\n",
        "random_search = RandomizedSearchCV(estimator=knn, param_distributions=dist_params, scoring='roc_auc', cv=skf, n_iter=n_iter_search)\n",
        "\n",
        "# Normalizo los datos para knn\n",
        "X_train_normalized = preprocessing.normalize(X_train.T).T\n",
        "random_search.fit(X_train_normalized, y_train)\n",
        "\n",
        "mejor_modelo_knn = random_search.best_estimator_\n",
        "mejor_score_knn = random_search.best_score_\n",
        "print(\"Mejor modelo:\", mejor_modelo_knn,'\\n')\n",
        "print(\"Mejor score AUCROC:\", mejor_score_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gvUJ5J0YX1Q"
      },
      "source": [
        "## SVM (Support vector machine)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9xm8t0gI3119",
        "outputId": "7e4d17ff-0e0c-4797-e0b6-3aa973f417c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mejor modelo: SVC(C=3.2267423532765003, cache_size=112, coef0=0.44297096375666045, degree=2,\n",
            "    probability=True, tol=0.09221931902266695) \n",
            "\n",
            "Mejor score AUCROC: 0.8871212121212121\n"
          ]
        }
      ],
      "source": [
        "dist_params = {\n",
        "    'C': scipy.stats.uniform(0, 10),\n",
        "    'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
        "    'degree': scipy.stats.randint(1, 6),\n",
        "    'gamma': ['scale', 'auto' ],\n",
        "    'coef0': scipy.stats.uniform(0, 1),\n",
        "    'shrinking': [True, False],\n",
        "    'probability': [True, False],\n",
        "    'tol': scipy.stats.uniform(0, 0.1),\n",
        "    'cache_size': scipy.stats.randint(1, 1000),\n",
        "    #'class_weight': [{0: x, 1: 1 - x} for x in np.linspace(0, 1, 10)],\n",
        "}\n",
        "\n",
        "svm = SVC()\n",
        "random_search = RandomizedSearchCV(estimator=svm, param_distributions=dist_params, scoring='roc_auc', cv=skf, n_iter=n_iter_search)\n",
        "random_search.fit(X_train, y_train)\n",
        "\n",
        "mejor_modelo_svm = random_search.best_estimator_\n",
        "mejor_score_svm = random_search.best_score_\n",
        "print(\"Mejor modelo:\", mejor_modelo_svm,'\\n')\n",
        "print(\"Mejor score AUCROC:\", mejor_score_svm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3LCHsIJQYX1Q"
      },
      "source": [
        "## LDA (Linear discriminant analysis)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SU3rWYZu3119",
        "outputId": "d751fc46-9d54-4115-85ca-b8bd497f4cc2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mejor modelo: LinearDiscriminantAnalysis(tol=0.058146679228891954) \n",
            "\n",
            "Mejor score AUCROC: 0.7227272727272727\n"
          ]
        }
      ],
      "source": [
        "# Hubo bastantes errores con los parámetros\n",
        "dist_params = {\n",
        "    #'solver': ['svd','lsqr', 'eigen'],\n",
        "    #'shrinkage': ['auto',None], #scipy.stats.uniform(0, 1),\n",
        "    #'priors': [],\n",
        "    #'n_components': scipy.stats.randint(1, min(len(X_train[0,:]), len(X_train[0]-1))),\n",
        "    'tol': scipy.stats.uniform(0, 0.1)\n",
        "    #'covariance_estimator':\n",
        "}\n",
        "\n",
        "lda = LinearDiscriminantAnalysis()\n",
        "random_search = RandomizedSearchCV(estimator=lda, param_distributions=dist_params, scoring='roc_auc', cv=skf, n_iter=n_iter_search)\n",
        "random_search.fit(X_train, y_train)\n",
        "\n",
        "mejor_modelo_lda = random_search.best_estimator_\n",
        "mejor_score_lda = random_search.best_score_\n",
        "print(\"Mejor modelo:\", mejor_modelo_lda,'\\n')\n",
        "print(\"Mejor score AUCROC:\", mejor_score_lda)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O5TADYOcYX1Q"
      },
      "source": [
        "## Naïve Bayes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFhOQ_FH311-",
        "outputId": "ae8265ae-a3d5-4614-9247-6cf3accfe89b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mejor modelo: GaussianNB(var_smoothing=0.024283567515172577) \n",
            "\n",
            "Mejor score AUCROC: 0.8496212121212121\n"
          ]
        }
      ],
      "source": [
        "dist_params = {\n",
        "    'var_smoothing': scipy.stats.uniform(0, 1),\n",
        "    'priors': [None, [0.5, 0.5]],\n",
        "}\n",
        "\n",
        "nb = GaussianNB()\n",
        "random_search = RandomizedSearchCV(estimator=nb, param_distributions=dist_params, scoring='roc_auc', cv=skf, n_iter=n_iter_search)\n",
        "random_search.fit(X_train, y_train)\n",
        "\n",
        "mejor_modelo_naive = random_search.best_estimator_\n",
        "mejor_score_naive = random_search.best_score_\n",
        "print(\"Mejor modelo:\", mejor_modelo_naive,'\\n')\n",
        "print(\"Mejor score AUCROC:\", mejor_score_naive)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Ejercicio 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4.1. Curvas de compejidad"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.base import clone\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def graficar_curva_complejidad (model, X, y, param_name, param_values, model_name, ax = None):\n",
        "    \n",
        "    # Listas para almacenar los puntajes medios de entrenamiento y prueba\n",
        "    mean_train_scores = []\n",
        "    mean_test_scores = []\n",
        "\n",
        "    for param_value in param_values:\n",
        "        # Crea una copia del modelo con los mismos valores de parámetro \n",
        "        model_new = clone(model)\n",
        "\n",
        "        # Establece el valor del parámetro\n",
        "        model_new.set_params(**{param_name: param_value})\n",
        "\n",
        "        # Realiza la validación cruzada\n",
        "        cv_results = cross_validate(model_new, X, y, cv=skf, scoring=['roc_auc'], return_train_score=True)\n",
        "\n",
        "        # Calcula y almacena los puntajes medios de entrenamiento y prueba\n",
        "        mean_train_scores.append(cv_results['train_roc_auc'].mean())\n",
        "        mean_test_scores.append(cv_results['test_roc_auc'].mean())\n",
        "\n",
        "    if ax is None:\n",
        "        fig, ax = plt.subplots()\n",
        "    \n",
        "    mean_train_error = [1 - score for score in mean_train_scores]\n",
        "    mean_test_error = [1 - score for score in mean_test_scores]\n",
        "\n",
        "    #plt.figure(figsize=(10, 6))\n",
        "    #ax.subplots(figsize=(8, 6))\n",
        "    ax.plot(param_values, mean_train_error, color='tab:blue', marker='o', label='Training score')\n",
        "    ax.plot(param_values, mean_test_error, color='tab:orange', marker='o', label='Test score')\n",
        "    ax.set_title(f\"Curva de complejidad de {model_name}\")\n",
        "    ax.set_xlabel(param_name)\n",
        "    ax.set_ylabel('1 - ROC AUC Mean Score')\n",
        "    ax.legend()\n",
        "    #plt.show()\n",
        "\n",
        "    return ax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {},
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Graficamos las curvas de complejidad\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 8), sharey=True)\n",
        "#fig.suptitle('Curvas de complejidad', fontsize=16)\n",
        "graficar_curva_complejidad (mejor_modelo_arbol, X_train, y_train, 'max_depth', range(1, 51), \"Árboles de decisión\", ax[0])\n",
        "graficar_curva_complejidad (mejor_modelo_svm, X_train, y_train, 'C', np.linspace(0.01, 15, 70), \"SVM\", ax[1])\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "C\n",
        "* hay un valor (optimo) a partir del cual aprende ruido (minimo gap)\n",
        "* aumenta training score, disminuyte el test score\n",
        "* aprende ruido, más varianza\n",
        "* mas sesgo\n",
        "* con mayor C, mas posibilidad de \"error\", margenes más anchos\n",
        "\n",
        "\n",
        "\n",
        "max_depth\n",
        "* se vuelven constantes la de train y set, a partir de ...\n",
        "* poca varianza de un modelo a otro (?)\n",
        "gap entre performance entre train y set (15 puntos): sesgado. gap constante para cada modelo\n",
        "* aumento max_depth, baja varianza, aumenta sesgo (hasta un\n",
        "* no aprende nada nuevo respecto al anterior\n",
        "\n",
        "\n",
        "Notar que los máximos deberían coincidir máso menos con los hallados en el 3."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import LearningCurveDisplay\n",
        "#from sklearn.model_selection import learning_curve\n",
        "\n",
        "def plot_learning_curves(models,modelParams):\n",
        "\n",
        "        fig, ax = plt.subplots(nrows=1, ncols=len(models), figsize=(16, 8), sharey=True)\n",
        "        \n",
        "        if not isinstance(ax, np.ndarray):\n",
        "            ax = np.array([ax])\n",
        "            \n",
        "        common_params = {\n",
        "            \"X\": X_train,\n",
        "            \"y\": y_train,\n",
        "            \"train_sizes\": np.linspace(0.1, 1.0, 50),\n",
        "            \"cv\": skf,\n",
        "            \"score_type\": \"both\",\n",
        "            \"n_jobs\": 4,\n",
        "            \"line_kw\": {\"marker\": \"o\"},\n",
        "            \"std_display_style\": \"fill_between\",\n",
        "            \"score_name\": \"roc_auc\",\n",
        "        }\n",
        "        for ax_idx, estimator in enumerate(models):\n",
        "            LearningCurveDisplay.from_estimator(estimator, **common_params, ax=ax[ax_idx])\n",
        "            handles, label = ax[ax_idx].get_legend_handles_labels()\n",
        "            ax[ax_idx].legend(handles[:2], [\"Training score\", \"Test score\"])\n",
        "            ax[ax_idx].set_title(f\"Learning Curve for {estimator.__class__.__name__}{modelParams[ax_idx]}\")\n",
        "\n",
        "        plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x800 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Graficamos el mejor encontrado para cada uno\n",
        "mejores = [mejor_modelo_arbol, mejor_modelo_lda, mejor_modelo_svm]\n",
        "params = [\"\" , \"\", \"\"]\n",
        "\n",
        "plot_learning_curves (mejores, params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Tree: parece que hay lugar para mejorar el modelo con más datos de entrenamiento\n",
        "LDA: parece haber alcanzado una cota, aunque ...\n",
        "SVM: parece haber alcanzado una cota de forma asintótica, por lo que es poco probable que se consigan mejoras significativas con más datos de entrenamiento (por suerte nos quedamos con el 30% para validacion, porque más datos de entrenamiento no parecen mejorar el roc_auc significativamente)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### extra"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {},
      "outputs": [],
      "source": [
        "svm_1 = SVC(C = 1,kernel = 'poly')\n",
        "\n",
        "svm_5 = SVC(C = 5, kernel = 'poly')\n",
        "\n",
        "svm_10 = SVC(C = 10 , kernel = 'poly')\n",
        "\n",
        "svms = [svm_1,svm_5,svm_10]\n",
        "\n",
        "Svm_params = [\" C = 1\",\" C = 5 \",\" C = 10\"]\n",
        "\n",
        "\n",
        "\n",
        "tree_3 = DecisionTreeClassifier(max_depth=3, random_state=51)\n",
        "\n",
        "tree_7 = DecisionTreeClassifier(max_depth=7, random_state=51)\n",
        "\n",
        "tree_inf = DecisionTreeClassifier(max_depth= None, random_state=51)\n",
        "\n",
        "arboles = [tree_3,tree_7,tree_inf]\n",
        "\n",
        "arbol_params = [\" h = 3\",\" h = 7\",\" h = None\"]\n",
        "\n",
        "\n",
        "\n",
        "rnd_200 = RandomForestClassifier(n_estimators=200)\n",
        "\n",
        "rnd_10 = RandomForestClassifier(n_estimators=10)\n",
        "\n",
        "rnd_20 = RandomForestClassifier(n_estimators = 20)\n",
        "\n",
        "bosques = [rnd_200,rnd_10,rnd_20]\n",
        "\n",
        "bosque_params = [\" 200 arboles\",\" 10 arboles\",\" 20 arboles\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x800 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x800 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[128], line 5\u001b[0m\n\u001b[0;32m      1\u001b[0m plot_learning_curves(svms, Svm_params)\n\u001b[0;32m      3\u001b[0m plot_learning_curves(arboles, arbol_params)\n\u001b[1;32m----> 5\u001b[0m \u001b[43mplot_learning_curves\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbosques\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbosque_params\u001b[49m\u001b[43m)\u001b[49m\n",
            "Cell \u001b[1;32mIn[124], line 24\u001b[0m, in \u001b[0;36mplot_learning_curves\u001b[1;34m(models, modelParams)\u001b[0m\n\u001b[0;32m     12\u001b[0m common_params \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m     13\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m: X_train,\n\u001b[0;32m     14\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: y_train,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscore_name\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mroc_auc\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     22\u001b[0m }\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax_idx, estimator \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(models):\n\u001b[1;32m---> 24\u001b[0m     \u001b[43mLearningCurveDisplay\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_estimator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcommon_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m[\u001b[49m\u001b[43max_idx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     25\u001b[0m     handles, label \u001b[38;5;241m=\u001b[39m ax[ax_idx]\u001b[38;5;241m.\u001b[39mget_legend_handles_labels()\n\u001b[0;32m     26\u001b[0m     ax[ax_idx]\u001b[38;5;241m.\u001b[39mlegend(handles[:\u001b[38;5;241m2\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining Score\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTest Score\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\model_selection\\_plot.py:499\u001b[0m, in \u001b[0;36mLearningCurveDisplay.from_estimator\u001b[1;34m(cls, estimator, X, y, groups, train_sizes, cv, scoring, exploit_incremental_learning, n_jobs, pre_dispatch, verbose, shuffle, random_state, error_score, fit_params, ax, negate_score, score_name, score_type, log_scale, std_display_style, line_kw, fill_between_kw, errorbar_kw)\u001b[0m\n\u001b[0;32m    495\u001b[0m check_matplotlib_support(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.from_estimator\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    497\u001b[0m score_name \u001b[38;5;241m=\u001b[39m _validate_score_name(score_name, scoring, negate_score)\n\u001b[1;32m--> 499\u001b[0m train_sizes, train_scores, test_scores \u001b[38;5;241m=\u001b[39m \u001b[43mlearning_curve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    500\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    501\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    502\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    504\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_sizes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_sizes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    505\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    506\u001b[0m \u001b[43m    \u001b[49m\u001b[43mscoring\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscoring\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    507\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexploit_incremental_learning\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexploit_incremental_learning\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    508\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    509\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpre_dispatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpre_dispatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    510\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    511\u001b[0m \u001b[43m    \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshuffle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    512\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    513\u001b[0m \u001b[43m    \u001b[49m\u001b[43merror_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    514\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    515\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfit_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    516\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    518\u001b[0m viz \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(\n\u001b[0;32m    519\u001b[0m     train_sizes\u001b[38;5;241m=\u001b[39mtrain_sizes,\n\u001b[0;32m    520\u001b[0m     train_scores\u001b[38;5;241m=\u001b[39mtrain_scores,\n\u001b[0;32m    521\u001b[0m     test_scores\u001b[38;5;241m=\u001b[39mtest_scores,\n\u001b[0;32m    522\u001b[0m     score_name\u001b[38;5;241m=\u001b[39mscore_name,\n\u001b[0;32m    523\u001b[0m )\n\u001b[0;32m    524\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m viz\u001b[38;5;241m.\u001b[39mplot(\n\u001b[0;32m    525\u001b[0m     ax\u001b[38;5;241m=\u001b[39max,\n\u001b[0;32m    526\u001b[0m     negate_score\u001b[38;5;241m=\u001b[39mnegate_score,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    532\u001b[0m     errorbar_kw\u001b[38;5;241m=\u001b[39merrorbar_kw,\n\u001b[0;32m    533\u001b[0m )\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    211\u001b[0m         )\n\u001b[0;32m    212\u001b[0m     ):\n\u001b[1;32m--> 213\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m    219\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m    220\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    221\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    222\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m    223\u001b[0m     )\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py:1965\u001b[0m, in \u001b[0;36mlearning_curve\u001b[1;34m(estimator, X, y, groups, train_sizes, cv, scoring, exploit_incremental_learning, n_jobs, pre_dispatch, verbose, shuffle, random_state, error_score, return_times, fit_params)\u001b[0m\n\u001b[0;32m   1962\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m n_train_samples \u001b[38;5;129;01min\u001b[39;00m train_sizes_abs:\n\u001b[0;32m   1963\u001b[0m         train_test_proportions\u001b[38;5;241m.\u001b[39mappend((train[:n_train_samples], test))\n\u001b[1;32m-> 1965\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1966\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1967\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1968\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1969\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1970\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscorer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscorer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1971\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1972\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1973\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1974\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1975\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfit_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1976\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# TODO(SLEP6): support score params here\u001b[39;49;00m\n\u001b[0;32m   1977\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscore_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1978\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_train_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1979\u001b[0m \u001b[43m        \u001b[49m\u001b[43merror_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1980\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1981\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1982\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrain_test_proportions\u001b[49m\n\u001b[0;32m   1983\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1984\u001b[0m _warn_or_raise_about_fit_failures(results, error_score)\n\u001b[0;32m   1985\u001b[0m results \u001b[38;5;241m=\u001b[39m _aggregate_score_dicts(results)\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\utils\\parallel.py:67\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m     62\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[0;32m     63\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     64\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[0;32m     65\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[0;32m     66\u001b[0m )\n\u001b[1;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\joblib\\parallel.py:2007\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   2001\u001b[0m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[0;32m   2002\u001b[0m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[0;32m   2003\u001b[0m \u001b[38;5;66;03m# reach the first `yield` statement. This starts the aynchronous\u001b[39;00m\n\u001b[0;32m   2004\u001b[0m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[0;32m   2005\u001b[0m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[1;32m-> 2007\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(output)\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\joblib\\parallel.py:1650\u001b[0m, in \u001b[0;36mParallel._get_outputs\u001b[1;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[0;32m   1647\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[0;32m   1649\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[1;32m-> 1650\u001b[0m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retrieve()\n\u001b[0;32m   1652\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[0;32m   1653\u001b[0m     \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[0;32m   1654\u001b[0m     \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[0;32m   1655\u001b[0m     \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[0;32m   1656\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\joblib\\parallel.py:1762\u001b[0m, in \u001b[0;36mParallel._retrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1757\u001b[0m \u001b[38;5;66;03m# If the next job is not ready for retrieval yet, we just wait for\u001b[39;00m\n\u001b[0;32m   1758\u001b[0m \u001b[38;5;66;03m# async callbacks to progress.\u001b[39;00m\n\u001b[0;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ((\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[0;32m   1760\u001b[0m     (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mget_status(\n\u001b[0;32m   1761\u001b[0m         timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout) \u001b[38;5;241m==\u001b[39m TASK_PENDING)):\n\u001b[1;32m-> 1762\u001b[0m     time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m0.01\u001b[39m)\n\u001b[0;32m   1763\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m   1765\u001b[0m \u001b[38;5;66;03m# We need to be careful: the job list can be filling up as\u001b[39;00m\n\u001b[0;32m   1766\u001b[0m \u001b[38;5;66;03m# we empty it and Python list are not thread-safe by\u001b[39;00m\n\u001b[0;32m   1767\u001b[0m \u001b[38;5;66;03m# default hence the use of the lock\u001b[39;00m\n",
            "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "data": {
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IExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgDRhEQAAAABIExYBAAAAgLRBhcV169bFzJkzo7KyMhYtWhQ7duw47vk/+MEPYs6cOVFZWRkXXnhhbN26dVCLBQAAAABGh3RY3Lx5czQ2NkZzc3Ps2rUr5s6dG/X19fHWW28NeP4LL7wQ1157bdxwww2xe/fuuOaaa+Kaa66JX/7yl5948QAAAADAyEiHxTVr1sSKFSuioaEhzj///Fi/fn1MnDgxNm7cOOD5//RP/xR/+Zd/GV//+tfjvPPOi/vvvz8uueSS+Od//udPvHgAAAAAYGRMyJzc3d0dO3fujKampr5j48ePj7q6umhraxvwmra2tmhsbOx3rL6+Pp555pljPk9XV1d0dXX1fX3w4MGIiOjs7MwsF4Bh9uH7dFEUI7yS0cNMAxi7zLWjmWsAY9dQzLVUWDxw4ED09PREdXV1v+PV1dXxyiuvDHhNe3v7gOe3t7cf83laWlri3nvvPep4TU1NZrkAjJD//u//jqqqqpFexqhgpgGMfebaR8w1gLHvZM61VFgcLk1NTf3ucnz33Xfj05/+dOzbt89AP4bOzs6oqamJ/fv3x6RJk0Z6OaOSPSrNHh2f/Snt4MGDcc4558TZZ5890ksZNcy0PP/WSrNHpdmj0uxRaeba0cy1PP/WSrNHpdmj0uxRaUMx11JhcfLkyVFWVhYdHR39jnd0dMTUqVMHvGbq1Kmp8yMiKioqoqKi4qjjVVVVfjhKmDRpkj0qwR6VZo+Oz/6UNn58+k/4nrLMtMHzb600e1SaPSrNHpVmrn3EXBs8/9ZKs0el2aPS7FFpJ3Oupb5TeXl5zJ8/P1pbW/uO9fb2Rmtra9TW1g54TW1tbb/zIyKee+65Y54PAAAAAIx+6V+FbmxsjOXLl8eCBQti4cKFsXbt2jh8+HA0NDRERMSyZctixowZ0dLSEhERt956a1xxxRXx7W9/O66++urYtGlT/OIXv4hHHnnk5L4SAAAAAGDYpMPikiVL4u23345Vq1ZFe3t7zJs3L7Zt29b3AS379u3rd0vlpZdeGk8++WTcfffdceedd8af/dmfxTPPPBMXXHDBCT9nRUVFNDc3D3jLPX9gj0qzR6XZo+OzP6XZo9LsUWn2qDR7VJo9Ks0elWaPSrNHpdmj0uxRafaoNHtU2lDs0bjiZH7GNAAAAADw/4K/QgwAAAAApAmLAAAAAECasAgAAAAApAmLAAAAAEDaqAmL69ati5kzZ0ZlZWUsWrQoduzYcdzzf/CDH8ScOXOisrIyLrzwwti6deswrXTkZPZow4YNcfnll8dZZ50VZ511VtTV1ZXc01NB9ufoQ5s2bYpx48bFNddcM7QLHGHZ/Xn33Xdj5cqVMW3atKioqIjZs2ef8v/Wsnu0du3a+OxnPxunn3561NTUxG233Ra///3vh2m1w+9nP/tZLF68OKZPnx7jxo2LZ555puQ127dvj0suuSQqKiriM5/5TDz++ONDvs6RZqaVZqaVZqaVZq6VZq4dn7l2Ysy10sy10sy10sy10sy1YxuxmVaMAps2bSrKy8uLjRs3Fv/5n/9ZrFixojjzzDOLjo6OAc//+c9/XpSVlRUPPvhg8dJLLxV33313cdpppxUvvvjiMK98+GT36LrrrivWrVtX7N69u3j55ZeLv/3bvy2qqqqK//qv/xrmlQ+f7B596I033ihmzJhRXH755cVf/dVfDc9iR0B2f7q6uooFCxYUV111VfH8888Xb7zxRrF9+/Ziz549w7zy4ZPdoyeeeKKoqKgonnjiieKNN94onn322WLatGnFbbfdNswrHz5bt24t7rrrruKpp54qIqJ4+umnj3v+3r17i4kTJxaNjY3FSy+9VHznO98pysrKim3btg3PgkeAmVaamVaamVaauVaauVaauVaauVaauVaauVaauVaauXZ8IzXTRkVYXLhwYbFy5cq+r3t6eorp06cXLS0tA57/5S9/ubj66qv7HVu0aFHxd3/3d0O6zpGU3aOP++CDD4ozzjij+N73vjdUSxxxg9mjDz74oLj00kuLRx99tFi+fPkpPayy+/Pd7363OPfcc4vu7u7hWuKIy+7RypUriz//8z/vd6yxsbG47LLLhnSdo8WJDKtvfOMbxec+97l+x5YsWVLU19cP4cpGlplWmplWmplWmrlWmrmWY64NzFwrzVwrzVwrzVwrzVw7ccM500b8V6G7u7tj586dUVdX13ds/PjxUVdXF21tbQNe09bW1u/8iIj6+vpjnj/WDWaPPu69996L999/P84+++yhWuaIGuwe3XfffTFlypS44YYbhmOZI2Yw+/OjH/0oamtrY+XKlVFdXR0XXHBBrF69Onp6eoZr2cNqMHt06aWXxs6dO/tuv9+7d29s3bo1rrrqqmFZ81jg/dpM+zgzrTQzrTRzrTRzbWh4zzbXPs5cK81cK81cK81cO/lO1vv1hJO5qME4cOBA9PT0RHV1db/j1dXV8corrwx4TXt7+4Dnt7e3D9k6R9Jg9ujjbr/99pg+ffpRPzSnisHs0fPPPx+PPfZY7NmzZxhWOLIGsz979+6Nn/zkJ3H99dfH1q1b4/XXX4+bb7453n///Whubh6OZQ+rwezRddddFwcOHIjPf/7zURRFfPDBB/GVr3wl7rzzzuFY8phwrPfrzs7O+N3vfhenn376CK1saJhppZlppZlppZlrpZlrQ8Nc+wNz7SPmWmnmWmnmWmnm2sl3smbaiN+xyNB74IEHYtOmTfH0009HZWXlSC9nVDh06FAsXbo0NmzYEJMnTx7p5YxKvb29MWXKlHjkkUdi/vz5sWTJkrjrrrti/fr1I720UWP79u2xevXqePjhh2PXrl3x1FNPxZYtW+L+++8f6aXBKctMO5qZdmLMtdLMNRh+5trRzLUTY66VZq4NjxG/Y3Hy5MlRVlYWHR0d/Y53dHTE1KlTB7xm6tSpqfPHusHs0YceeuiheOCBB+LHP/5xXHTRRUO5zBGV3aNf/epX8eabb8bixYv7jvX29kZExIQJE+LVV1+NWbNmDe2ih9FgfoamTZsWp512WpSVlfUdO++886K9vT26u7ujvLx8SNc83AazR/fcc08sXbo0brzxxoiIuPDCC+Pw4cNx0003xV133RXjx/t/N8d6v540adIpd1dHhJl2Isy00sy00sy10sy1oWGu/YG59hFzrTRzrTRzrTRz7eQ7WTNtxHexvLw85s+fH62trX3Hent7o7W1NWprawe8pra2tt/5ERHPPffcMc8f6wazRxERDz74YNx///2xbdu2WLBgwXAsdcRk92jOnDnx4osvxp49e/oeX/rSl+LKK6+MPXv2RE1NzXAuf8gN5mfosssui9dff71viEdEvPbaazFt2rRTbkhFDG6P3nvvvaOG0YeD/Q9/Lxfv12bax5lppZlppZlrpZlrQ8N7trn2ceZaaeZaaeZaaebayXfS3q9TH/UyRDZt2lRUVFQUjz/+ePHSSy8VN910U3HmmWcW7e3tRVEUxdKlS4s77rij7/yf//znxYQJE4qHHnqoePnll4vm5ubitNNOK1588cWReglDLrtHDzzwQFFeXl788Ic/LH7729/2PQ4dOjRSL2HIZffo4071TxrL7s++ffuKM844o7jllluKV199tfi3f/u3YsqUKcU//MM/jNRLGHLZPWpubi7OOOOM4l//9V+LvXv3Fv/+7/9ezJo1q/jyl788Ui9hyB06dKjYvXt3sXv37iIiijVr1hS7d+8ufv3rXxdFURR33HFHsXTp0r7z9+7dW0ycOLH4+te/Xrz88svFunXrirKysmLbtm0j9RKGnJlWmplWmplWmrlWmrlWmrlWmrlWmrlWmrlWmrlWmrl2fCM100ZFWCyKovjOd75TnHPOOUV5eXmxcOHC4j/+4z/6/tsVV1xRLF++vN/53//+94vZs2cX5eXlxec+97liy5Ytw7zi4ZfZo09/+tNFRBz1aG5uHv6FD6Psz9GR/j8Mq+z+vPDCC8WiRYuKioqK4txzzy2+9a1vFR988MEwr3p4Zfbo/fffL775zW8Ws2bNKiorK4uampri5ptvLv7nf/5n+Bc+TH76058O+N7y4b4sX768uOKKK466Zt68eUV5eXlx7rnnFv/yL/8y7OsebmZaaWZaaWZaaeZaaeba8ZlrJ8ZcK81cK81cK81cK81cO7aRmmnjisL9nwAAAABAzoj/jUUAAAAAYOwRFgEAAACANGERAAAAAEgTFgEAAACANGERAAAAAEgTFgEAAACANGERAAAAAEgTFgEAAACANGERAAAAAEgTFgEAAACANGERAAAAAEgTFgEAAACAtP8DStOZXutrLCIAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1600x800 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_learning_curves(svms, Svm_params)\n",
        "\n",
        "plot_learning_curves(arboles, arbol_params)\n",
        "\n",
        "plot_learning_curves(bosques, bosque_params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "def valores_curva_aprendizaje (model):\n",
        "    print(model)\n",
        "    # Divide los datos en train y set\n",
        "    df_train_curva , df_test_curva =  train_test_split(df_dev,train_size=0.8)\n",
        "\n",
        "    X_train_curva = df_train_curva.drop(columns=['target']).values\n",
        "    y_train_curva = df_train_curva['target'].values\n",
        "    X_test_curva   = df_test_curva.drop(columns=['target']).values\n",
        "    y_test_curva   =  df_test_curva['target'].values\n",
        "\n",
        "    train_sizes = (np.linspace(0.1, 1.0, 50) * len(X_train_curva)).round().astype(int)\n",
        "    train_scores = []\n",
        "    test_scores = []\n",
        "\n",
        "    for train_size in train_sizes:\n",
        "        # Nos quedamos con las primeras train_size filas\n",
        "        X_train_subset = X_train_curva[:train_size]\n",
        "        y_train_subset = y_train_curva[:train_size]\n",
        "\n",
        "        # Entrenamos el modelo en el subconjunto\n",
        "        model.fit(X_train_subset, y_train_subset)\n",
        "\n",
        "        # Calculamos los scores en ambos subjconjuntos y los agregamos a la lista\n",
        "        train_score = roc_auc_score(y_train_subset, model.predict(X_train_subset))\n",
        "        test_score = roc_auc_score(y_test_curva, model.predict(X_test_curva))\n",
        "\n",
        "        train_scores.append(train_score)\n",
        "        test_scores.append(test_score)\n",
        "    \n",
        "    return train_scores, test_scores, train_sizes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {},
      "outputs": [],
      "source": [
        "def plot_learning_curves2(models,modelParams):\n",
        "\n",
        "        fig, ax = plt.subplots(nrows=1, ncols=len(models), figsize=(16, 8), sharey=True)\n",
        "\n",
        "        if not isinstance(ax, np.ndarray):\n",
        "            ax = np.array([ax])\n",
        "        \n",
        "        for ax_idx, model in enumerate(models):\n",
        "          \n",
        "            train_scores, test_scores, train_sizes = valores_curva_aprendizaje (model)\n",
        "            \n",
        "            ax[ax_idx].plot(train_sizes, train_scores, label='Training score')\n",
        "            ax[ax_idx].plot(train_sizes, test_scores, label='Test score')\n",
        "            \n",
        "            ax[ax_idx].legend([\"Training Score\", \"Test Score\"])\n",
        "            ax[ax_idx].set_title(f\"Learning Curve for {model.__class__.__name__}{modelParams[ax_idx]}\")\n",
        "\n",
        "        plt.show()\n",
        "\n",
        "\n",
        "\n",
        "#FALTA TUNEAR, REFERENCIAS EN EJES, ETC\n",
        "#Y=\"ROC_auc\"\n",
        "#x=\"train size\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# plot_learning_curves2(svms, Svm_params)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4.3 Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-6 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-6 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-6 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-6 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-6 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-6 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-6 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-6 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=200, random_state=51)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_estimators=200, random_state=51)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(n_estimators=200, random_state=51)"
            ]
          },
          "execution_count": 132,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Entrenamos un random forest de 200 árboles\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators = 200, random_state=51)\n",
        "rf.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Curva de complejidad de Random Forrest (n=200)'}, xlabel='max_features', ylabel='1 - ROC AUC Mean Score'>"
            ]
          },
          "execution_count": 167,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Curva de complejidad para el Random Forest\n",
        "\n",
        "graficar_curva_complejidad (rf, X_train, y_train, \"max_features\", range(1,30), \"Random Forrest (n=200)\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Training siempre constante en 0, disiminuye test error hasta aprox 13, a aprtir de ahi queda constante.\n",
        "Aunque aumente max features, el gap queda constante.\n",
        "\n",
        "heuristica indica que el optimo es cercano a np.sqrt(200 = features) = 14\n",
        "0\n",
        "más de 13 features, puede ser que ...\n",
        "\n",
        "cuando hacemos boosting, una hipotesdis era que son modelos independientes. para eso, cada modelo ve cierta cantidad de features. con sqrt (features)\n",
        "\n",
        "a prtir de ahi ruido"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Learning curve \n",
        "\n",
        "bosques = [rf]\n",
        "\n",
        "bosque_params = [\" 200 arboles, max_features=13\"]\n",
        "\n",
        "plot_learning_curves(bosques, bosque_params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Aprox en 110 alcanza el máximo y a partir de ahi parece quedar constante, por lo que más datos de entrnamiento no parecen mejorar el score. \n",
        "\n",
        "más datos queda igual es sesgo, pero la varianza parece disminuir un poco (se ve en la sombra marcada)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 5. Predicciones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8711010121875646"
            ]
          },
          "execution_count": 145,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Obtenemos una predicción del roc_auc a partir de X_eval e y_eval\n",
        "\n",
        "modelo_predicciones = clone(mejor_modelo_svm)\n",
        "modelo_predicciones.set_params(probability = True)\n",
        "\n",
        "roc_auc_score(y_eval, mejor_modelo_svm.predict_proba(X_eval)[:, 1])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Obtenemos una predicción del roc_auc con Bootstrap  a partir de X_eval e y_eval\n",
        "\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.utils import resample\n",
        "\n",
        "np.random.seed(42)\n",
        "n_bootstrap = 10000\n",
        "roc_auc_scores = []\n",
        "\n",
        "for _ in range(n_bootstrap):\n",
        "    X_bootstrap, y_bootstrap = resample(X_eval, y_eval)\n",
        "\n",
        "    y_pred = mejor_modelo_svm.predict_proba(X_bootstrap)[:, 1]\n",
        "    score = roc_auc_score(y_bootstrap, y_pred)\n",
        "    roc_auc_scores.append(score)\n",
        "\n",
        "roc_auc_scores = np.array(roc_auc_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8713"
            ]
          },
          "execution_count": 157,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "roc_auc_scores.mean().round(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.21884414, 0.09454296, 0.59209606, 0.2112988 , 0.19719696,\n",
              "       0.05791243, 0.01771283, 0.21199661, 0.22750925, 0.53389712,\n",
              "       0.02660434, 0.22176158, 0.02706617, 0.08603608, 0.08417062,\n",
              "       0.02727051, 0.18533276, 0.18966561, 0.30600323, 0.05474331,\n",
              "       0.02943834, 0.01821003, 0.03319443, 0.45763627, 0.02729969,\n",
              "       0.99160747, 0.25912651, 0.0389441 , 0.14098068, 0.9576635 ,\n",
              "       0.13908085, 0.80145095, 0.86666668, 0.15591136, 0.71739503,\n",
              "       0.7220659 , 0.36927924, 0.0381169 , 0.71164136, 0.25786746,\n",
              "       0.95731584, 0.23014624, 0.34230689, 0.18263888, 0.04714121,\n",
              "       0.3686383 , 0.01851592, 0.01848952, 0.09429732, 0.07851645,\n",
              "       0.82584884, 0.16341222, 0.31243178, 0.05780524, 0.348381  ,\n",
              "       0.56971416, 0.57003853, 0.08649118, 0.93107194, 0.67928078,\n",
              "       0.17925441, 0.06469066, 0.01771772, 0.10144723, 0.26676223,\n",
              "       0.45818406, 0.0410474 , 0.3557556 , 0.75910326, 0.07787968,\n",
              "       0.29338365, 0.05049417, 0.03449035, 0.33004625, 0.4851312 ,\n",
              "       0.85375679, 0.13554085, 0.25040765, 0.52438059, 0.4929744 ,\n",
              "       0.25274788, 0.8978235 , 0.05900192, 0.01928585, 0.00757973,\n",
              "       0.25209584, 0.57793571, 0.4150894 , 0.03871469, 0.52432752,\n",
              "       0.09143816, 0.43507611, 0.08113663, 0.04125125, 0.04975955,\n",
              "       0.92045551, 0.06167196, 0.33018274, 0.43153645, 0.01489167,\n",
              "       0.06026241, 0.23061103, 0.89923849, 0.03044109, 0.91252442,\n",
              "       0.02474808, 0.33555449, 0.25152663, 0.03955085, 0.10331423,\n",
              "       0.0616158 , 0.46946861, 0.0062422 , 0.41773082, 0.86638027,\n",
              "       0.04267632, 0.96062593, 0.43595999, 0.06165265, 0.2229715 ,\n",
              "       0.01851498, 0.62043318, 0.40618878, 0.28169168, 0.03721185,\n",
              "       0.00466056, 0.15819054, 0.77037092, 0.09120032, 0.81918244,\n",
              "       0.69948991, 0.08000262, 0.08029512, 0.36224267, 0.43643172,\n",
              "       0.10731952, 0.96707469, 0.43279987, 0.95649142, 0.02601536,\n",
              "       0.359534  , 0.14444492, 0.34584485, 0.19240291, 0.35675776,\n",
              "       0.88828869, 0.24968137, 0.68882095, 0.06892015, 0.30296401,\n",
              "       0.48982931, 0.0305123 , 0.67467906, 0.9777599 , 0.14473784,\n",
              "       0.10721282, 0.70868326, 0.12779914, 0.11113238, 0.04659312,\n",
              "       0.34316869, 0.9382963 , 0.05257365, 0.13165929, 0.01595656,\n",
              "       0.80834671, 0.43222471, 0.15992017, 0.04145442, 0.71712972,\n",
              "       0.75448641, 0.22198515, 0.90119899, 0.04201041, 0.00861196,\n",
              "       0.69660524, 0.01070969, 0.02843513, 0.3946584 , 0.60048475,\n",
              "       0.1236685 , 0.03889727, 0.33235704, 0.85590169, 0.6335119 ,\n",
              "       0.01670996, 0.80764293, 0.58351397, 0.47099355, 0.07291082,\n",
              "       0.46562916, 0.87198797, 0.79555793, 0.01644586, 0.51239554,\n",
              "       0.1016426 , 0.20636741, 0.2099239 , 0.00732564, 0.36445734,\n",
              "       0.34600364, 0.06068086, 0.25209354, 0.38635088, 0.95949737,\n",
              "       0.53992331, 0.02060192, 0.04955197, 0.06967705, 0.57411051,\n",
              "       0.00543677, 0.58811105, 0.74984919, 0.15790397, 0.884978  ,\n",
              "       0.93936494, 0.44076996, 0.17179986, 0.23764817, 0.04215424,\n",
              "       0.45521793, 0.00376072, 0.44214355, 0.2299909 , 0.8947046 ,\n",
              "       0.05810865, 0.06055313, 0.78014163, 0.18055864, 0.67975949,\n",
              "       0.07392142, 0.09815997, 0.80492939, 0.26289746, 0.35733522,\n",
              "       0.0757111 , 0.02045059, 0.03128496, 0.03347469, 0.91116101,\n",
              "       0.72574229, 0.19206565, 0.30894899, 0.03214501, 0.01899116,\n",
              "       0.95002196, 0.17237596, 0.03922165, 0.15967596, 0.35270376,\n",
              "       0.11764911, 0.35825718, 0.08825487, 0.94111189, 0.36849599,\n",
              "       0.15703189, 0.04281435, 0.02646452, 0.00709391, 0.84709595,\n",
              "       0.49464611, 0.02362105, 0.37657332, 0.78482442, 0.15358113,\n",
              "       0.01503782, 0.45922283, 0.00811989, 0.54515154, 0.37411077,\n",
              "       0.05098204, 0.08480194, 0.59037863, 0.48814949, 0.04846467,\n",
              "       0.33913898, 0.02208389, 0.27917009, 0.39716048, 0.86418983,\n",
              "       0.22745572, 0.11490917, 0.06828838, 0.10776631, 0.18602327,\n",
              "       0.12883376, 0.51247864, 0.12350154, 0.18559764, 0.19473751,\n",
              "       0.08229417, 0.14399277, 0.1044039 , 0.01770365, 0.38449694,\n",
              "       0.29814029, 0.73344628, 0.00909966, 0.01389621, 0.02041609,\n",
              "       0.38942483, 0.09712551, 0.99315565, 0.61290572, 0.01674096,\n",
              "       0.75107197, 0.15458337, 0.89179666, 0.43165882, 0.53267866,\n",
              "       0.59476979, 0.78421808, 0.02621142, 0.04860053, 0.79011497,\n",
              "       0.01599064, 0.48871022, 0.97861359, 0.66980954, 0.01911386,\n",
              "       0.03073353, 0.10953478, 0.5505321 , 0.29337459, 0.81033504,\n",
              "       0.22988216, 0.99401692, 0.2750274 , 0.08477486, 0.10694888,\n",
              "       0.29747769, 0.33541226, 0.02737959, 0.03545763, 0.94090772,\n",
              "       0.08434205, 0.02646316, 0.38797752, 0.06947117, 0.23622408,\n",
              "       0.09995139, 0.1119118 , 0.42313461, 0.31988758, 0.56551707,\n",
              "       0.16797716, 0.36637258, 0.08494247, 0.01099895, 0.0873378 ,\n",
              "       0.13510902, 0.78043668, 0.10344117, 0.30262934, 0.27881259,\n",
              "       0.09878864, 0.09631397, 0.05352257, 0.13202886, 0.30678112,\n",
              "       0.7143897 , 0.0200996 , 0.23331417, 0.05519471, 0.06015856,\n",
              "       0.71265382, 0.04156355, 0.06135938, 0.19318061, 0.05371393,\n",
              "       0.6876023 , 0.08293805, 0.11376744, 0.10965519, 0.92546674,\n",
              "       0.5707509 , 0.74101202, 0.52242665, 0.31733648, 0.23701839,\n",
              "       0.19687677, 0.01825219, 0.31520013, 0.02412282, 0.64759127,\n",
              "       0.06787498, 0.1875405 , 0.37642592, 0.39851568, 0.92383813,\n",
              "       0.05572424, 0.97021569, 0.0132549 , 0.07961664, 0.18570815,\n",
              "       0.45844421, 0.27554157, 0.41777049, 0.89796083, 0.0785048 ,\n",
              "       0.02682135, 0.11126977, 0.15303848, 0.40112084, 0.18652348,\n",
              "       0.07035437, 0.82342699, 0.06445355, 0.7876114 , 0.02771305,\n",
              "       0.81706274, 0.1242079 , 0.04818692, 0.98483367, 0.3033903 ,\n",
              "       0.35124785, 0.97309409, 0.03111091, 0.22906316, 0.0424986 ,\n",
              "       0.5357585 , 0.10873398, 0.06010427, 0.08117959, 0.06912313,\n",
              "       0.06013665, 0.24424162, 0.13222326, 0.00991876, 0.61682032,\n",
              "       0.33941964, 0.01898158, 0.82186483, 0.93495463, 0.34896171,\n",
              "       0.14975724, 0.04519627, 0.52479893, 0.16790454, 0.16445997,\n",
              "       0.82565976, 0.41408679, 0.02346506, 0.43145149, 0.00949206,\n",
              "       0.17923842, 0.92222833, 0.06586128, 0.65945744, 0.45825252,\n",
              "       0.28843705, 0.71090864, 0.32745563, 0.06755846, 0.89187465,\n",
              "       0.05579231, 0.01884962, 0.06814806, 0.12112013, 0.00344234,\n",
              "       0.7723401 , 0.16611976, 0.03278115, 0.91500831, 0.17613926,\n",
              "       0.33757918, 0.13949278, 0.14439963, 0.06807582, 0.77463637,\n",
              "       0.02692021, 0.02066851, 0.13467809, 0.86178902, 0.01134974,\n",
              "       0.81442543, 0.28511792, 0.0445232 , 0.40580867, 0.12326838,\n",
              "       0.0634055 , 0.61002879, 0.23700778, 0.64507095, 0.01976698,\n",
              "       0.01900326, 0.87772396, 0.21246491, 0.09102232, 0.02954767,\n",
              "       0.09434129, 0.73960082, 0.06790112, 0.13839944, 0.10265938,\n",
              "       0.01321783, 0.93303397, 0.43396385, 0.06385148, 0.25813083])"
            ]
          },
          "execution_count": 158,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Obtenemos las predicciones del held_out\n",
        "\n",
        "df_held_out = pd.read_csv('data/X_held_out.csv').values\n",
        "\n",
        "probabilidades_clase_1 = mejor_modelo_svm.predict_proba(df_held_out)[:, 1]\n",
        "\n",
        "probabilidades_clase_1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Guardamos en un archivo CSV con 4 decimales\n",
        "\n",
        "df_output = pd.DataFrame(probabilidades_clase_1, columns=['output'])\n",
        "\n",
        "df_output.to_csv('output.csv', index=False, float_format='%.4f')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "APRENDIZAJE_SUPERVISADO",
      "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": "3.11.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}