XGBoost.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Importación de librerías y lectura del Data Frame"
      ],
      "metadata": {
        "id": "2IKijoxVmtHq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rj2DZW4dyB6M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dafa9389-38b5-4144-fcb3-14d20f9b300d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: category_encoders in /usr/local/lib/python3.10/dist-packages (2.6.1)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.10.1)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (0.13.5)\n",
            "Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.5.3)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (0.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2022.7.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.1->category_encoders) (1.16.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.1.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.9.0->category_encoders) (23.1)\n"
          ]
        }
      ],
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import tree\n",
        "!pip install category_encoders\n",
        "from category_encoders import TargetEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.metrics import confusion_matrix, mean_squared_error, roc_auc_score\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_secundario = pd.read_csv(\"/content/drive/MyDrive/secondary_data.csv\", delimiter=';')"
      ],
      "metadata": {
        "id": "_VhzrFZ8PDcL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploración de los datos\n",
        "\n",
        "Al igual que en la parte 3, no se van a utilizar todas las columnas y se van a ir probando de a poco hasta alcanzar buenos resultados. Por enunciado si o si debo tener por lo menos 40 features numéricas.\n"
      ],
      "metadata": {
        "id": "D8FVlwy7JwR0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Elimino las columnas que no voy a usar."
      ],
      "metadata": {
        "id": "bWNA1_rAFqBF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_secundario = df_secundario.drop(\"stem-root\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"stem-surface\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"veil-type\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"veil-color\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"spore-print-color\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"season\", axis = 1)\n",
        "df_secundario = df_secundario.drop(\"does-bruise-or-bleed\", axis = 1)"
      ],
      "metadata": {
        "id": "pZGpHRFXpiKh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Imputa los Nans dedos un df, la nombre de la features y un caracter.\n",
        "def imputar_nans(df, nombre_feature, caracter):\n",
        "\n",
        "  df[nombre_feature] = df[nombre_feature].fillna(caracter)"
      ],
      "metadata": {
        "id": "BpvsPO7wPMkR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Reemplaza la d por o en esta categoria.\n",
        "df_secundario['cap-surface'] = df_secundario[\"cap-surface\"].map(lambda x: 'o' if x == 'd' else x)"
      ],
      "metadata": {
        "id": "BfA6RLM6P-aq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imputar_nans(df_secundario, 'cap-surface', 'o')\n",
        "imputar_nans(df_secundario, 'gill-attachment', 'o')\n",
        "imputar_nans(df_secundario, 'gill-spacing', 'o')\n",
        "imputar_nans(df_secundario, 'ring-type', 'o')"
      ],
      "metadata": {
        "id": "_j1qlUptQAPU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparación de los datos\n",
        "\n",
        "En esta sección se preparan los datos para realizar un XGBoost de clasificación.\n"
      ],
      "metadata": {
        "id": "ivXIC9lKNNVv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Separo el target del resto del Data Frame."
      ],
      "metadata": {
        "id": "pdDd8CPsE3dp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_secundario.drop('class', axis=1)"
      ],
      "metadata": {
        "id": "34_sAwjGM0Eh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df_secundario['class']"
      ],
      "metadata": {
        "id": "nnIFgLB9M-LM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creo un set de entrenamiento y uno de medición."
      ],
      "metadata": {
        "id": "u1KOgzISE_0M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_med, y_train, y_med = train_test_split(X, y, test_size=0.2, random_state=13)"
      ],
      "metadata": {
        "id": "5YgWFGVbM_Nu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creo un set de testeo y uno de validación a partir del de medición."
      ],
      "metadata": {
        "id": "qYDaSNgUFICC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test, X_validation, y_test, y_validation = train_test_split(X_med, y_med, test_size=0.5, random_state=13)"
      ],
      "metadata": {
        "id": "7LVv5aY-NChG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kiY8HtVtqnCp"
      },
      "source": [
        "Voy a utilizar una variable binaria {0, 1} para representar al target (\"class\") en donde 1 es para indicar que el hongo es venenoso ('p') y 0 si el hongo es comestible ('e')."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train =  y_train.map(lambda x: 1 if x == 'p' else 0)"
      ],
      "metadata": {
        "id": "nrdaiGsiNJG0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_validation =  y_validation.map(lambda x: 1 if x == 'p' else 0)"
      ],
      "metadata": {
        "id": "uOPEh5ixN11C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test =  y_test.map(lambda x: 1 if x == 'p' else 0)"
      ],
      "metadata": {
        "id": "q4pXPUM1OBQe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HBc5FyxVTxm"
      },
      "source": [
        "Aplico un encoding a todas las columnas. Por enunciado, necesito aplicar mean coding y OHE por lo menos una vez."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Aplica OHE a cada columna dada una feature.\n",
        "def aplicar_ohe(nombre_feature, columnas):\n",
        "\n",
        "  global X_train, X_test, X_validation\n",
        "  ohe = OneHotEncoder()\n",
        "  encoded_train = ohe.fit_transform(X_train[[nombre_feature]]).todense().astype(int)\n",
        "  encoded_test = ohe.fit_transform(X_test[[nombre_feature]]).todense().astype(int)\n",
        "  encoded_validation = ohe.transform(X_validation[[nombre_feature]]).todense().astype(int)\n",
        "  X_train = X_train.reset_index().drop(['index', nombre_feature],axis=1).join(pd.DataFrame(encoded_train))\n",
        "  X_test = X_test.reset_index().drop(['index', nombre_feature],axis=1).join(pd.DataFrame(encoded_test))\n",
        "  X_validation = X_validation.reset_index().drop(['index', nombre_feature],axis=1).join(pd.DataFrame(encoded_validation))\n",
        "  X_train = X_train.rename(columns=columnas)\n",
        "  X_test = X_test.rename(columns=columnas)\n",
        "  X_validation = X_validation.rename(columns=columnas)"
      ],
      "metadata": {
        "id": "ck05vhP5QH4A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Aplica Mean encoding por cada columa.\n",
        "def aplicar_mean_encoding(nombre_feature):\n",
        "\n",
        "  global X_train, X_test, X_validation\n",
        "  encoder = TargetEncoder()\n",
        "  X_train[nombre_feature+ \"-mean\"] = encoder.fit_transform(X_train[nombre_feature], y_train)\n",
        "  X_test[nombre_feature+ \"-mean\"] = encoder.transform(X_test[nombre_feature], y_test)\n",
        "  X_validation[nombre_feature+ \"-mean\"] = encoder.transform(X_validation[nombre_feature], y_validation)\n",
        "  X_train = X_train.drop([nombre_feature],axis=1)\n",
        "  X_test = X_test.drop([nombre_feature],axis=1)\n",
        "  X_validation = X_validation.drop([nombre_feature],axis=1)\n"
      ],
      "metadata": {
        "id": "S09257e-RAdE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Aplicación de Mean Encoding.\n"
      ],
      "metadata": {
        "id": "BgSBOMMKn3qC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con \"cap-surface\":"
      ],
      "metadata": {
        "id": "iKxW5j3jFxl6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_mean_encoding('cap-surface')"
      ],
      "metadata": {
        "id": "kk5x1aHUF9Kz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wg7UsPOo85D"
      },
      "source": [
        "Con \"gill-spacing\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_mean_encoding('gill-spacing')"
      ],
      "metadata": {
        "id": "F1Y9kCZuGw93"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "by1Uz3RIuxmf"
      },
      "source": [
        "Con \"ring-type\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_mean_encoding('ring-type')"
      ],
      "metadata": {
        "id": "fz9XyQVMHv7Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDOleFR7yGgZ"
      },
      "source": [
        "Con \"habitat\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_mean_encoding('habitat')"
      ],
      "metadata": {
        "id": "XWDj_7MQH_pp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Aplicación de OHE."
      ],
      "metadata": {
        "id": "V8k6KkQPoXmp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con \"cap-shape\":"
      ],
      "metadata": {
        "id": "ZEf8p-yKFp1O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('cap-shape', {0:'b0',1:'c0',2:'f0',3:'o0',4:'p0',5:'s0',6:'x0'})"
      ],
      "metadata": {
        "id": "J3KrDH5WFr5V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con \"cap-color\":"
      ],
      "metadata": {
        "id": "C3D7ZEZfGRWE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('cap-color', {0:'b2',1:'e2',2:'g2',3:'k2',4:'l2',5:'n2',6:'o2', 7:'p2', 8:'r2', 9:'u2', 10:'w2', 11:'y2'})"
      ],
      "metadata": {
        "id": "oW1-2Z8QGWS1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-kFw7zvUm9Mc"
      },
      "source": [
        "Con \"gill-attachment\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('gill-attachment', {0:'a4',1:'d4',2:'e4',3:'f4',4:'o4',5:'p4',6:'s4', 7:'x4'})"
      ],
      "metadata": {
        "id": "GXKLZfybGqFO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XEhrQKlCpoua"
      },
      "source": [
        "Con \"gill-color\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('gill-color', {0:'b6',1:'e6',2:'f6',3:'g6',4:'k6',5:'n6',6:'o6', 7:'p6', 8:'r6', 9:'u6', 10:'w6', 11:'y6'})"
      ],
      "metadata": {
        "id": "sXOV_2M_G7Iq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLwKgt4ir9KL"
      },
      "source": [
        "Con \"stem-color\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('stem-color', {0:'b9',1:'e9',2:'f9',3:'g9',4:'k9',5:'l9',6:'n9', 7:'o9', 8:'p9', 9:'r9', 10:'u9', 11:'w9', 12:'y9'})"
      ],
      "metadata": {
        "id": "0RFuDi2WHSt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXditg8Oo7gZ"
      },
      "source": [
        "Con \"has-ring\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('has-ring', {0:'f12',1:'t12'})"
      ],
      "metadata": {
        "id": "4Uh0Kt0zHo_c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "FOuPyXkgKTED",
        "outputId": "65805d67-3bdb-4dfb-c7ca-f231b9a2022b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       cap-diameter  stem-height  stem-width  cap-surface-mean  \\\n",
              "0             15.74         5.02       28.73          0.925151   \n",
              "1              1.27         3.79        5.63          0.431833   \n",
              "2             12.05         6.03       23.98          0.479898   \n",
              "3              8.08         6.04       14.95          0.652791   \n",
              "4              6.44        12.23       12.77          0.473605   \n",
              "...             ...          ...         ...               ...   \n",
              "48850          8.53         3.63       25.28          0.529736   \n",
              "48851         10.66         5.10       19.52          0.521801   \n",
              "48852          1.62         3.73        2.63          0.830604   \n",
              "48853          1.29         5.00        2.45          0.529736   \n",
              "48854          2.46         3.41        2.38          0.830604   \n",
              "\n",
              "       gill-spacing-mean  ring-type-mean  habitat-mean  b0  c0  f0  ...  l9  \\\n",
              "0               0.585689        0.555199      0.544301   0   0   1  ...   0   \n",
              "1               0.603135        0.555199      0.544301   0   0   1  ...   0   \n",
              "2               0.563700        0.566876      0.544301   0   0   1  ...   0   \n",
              "3               0.563700        0.423019      0.544301   0   0   0  ...   0   \n",
              "4               0.563700        0.555199      0.544301   0   0   0  ...   0   \n",
              "...                  ...             ...           ...  ..  ..  ..  ...  ..   \n",
              "48850           0.603135        0.555199      0.544301   0   0   0  ...   0   \n",
              "48851           0.585689        0.555199      0.544301   0   0   1  ...   0   \n",
              "48852           0.563700        0.555199      0.686006   0   0   0  ...   0   \n",
              "48853           0.563700        0.555199      0.686006   0   0   0  ...   0   \n",
              "48854           0.563700        0.555199      0.686006   0   0   1  ...   0   \n",
              "\n",
              "       n9  o9  p9  r9  u9  w9  y9  f12  t12  \n",
              "0       1   0   0   0   0   0   0    1    0  \n",
              "1       0   0   0   0   0   0   1    1    0  \n",
              "2       1   0   0   0   0   0   0    0    1  \n",
              "3       0   0   0   0   0   0   1    0    1  \n",
              "4       0   0   0   0   0   1   0    1    0  \n",
              "...    ..  ..  ..  ..  ..  ..  ..  ...  ...  \n",
              "48850   1   0   0   0   0   0   0    1    0  \n",
              "48851   0   0   0   0   0   1   0    0    1  \n",
              "48852   0   0   0   0   0   1   0    1    0  \n",
              "48853   1   0   0   0   0   0   0    1    0  \n",
              "48854   0   0   0   0   0   1   0    1    0  \n",
              "\n",
              "[48855 rows x 61 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f1d8a261-5b1a-430f-bce5-1a416669373c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>cap-diameter</th>\n",
              "      <th>stem-height</th>\n",
              "      <th>stem-width</th>\n",
              "      <th>cap-surface-mean</th>\n",
              "      <th>gill-spacing-mean</th>\n",
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    {
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      "execution_count": null,
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              "      cap-diameter  stem-height  stem-width  cap-surface-mean  \\\n",
              "0             2.38         8.13        2.59          0.652791   \n",
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              "...            ...          ...         ...               ...   \n",
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              "6104          8.51         4.52       13.00          0.479898   \n",
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              "6106          6.26         6.09       14.68          0.529736   \n",
              "\n",
              "      gill-spacing-mean  ring-type-mean  habitat-mean  b0  c0  f0  ...  l9  \\\n",
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              "      <th>2</th>\n",
              "      <td>7.70</td>\n",
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              "      <th>3</th>\n",
              "      <td>5.64</td>\n",
              "      <td>6.76</td>\n",
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              "      <td>6.860063e-01</td>\n",
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              "      <th>4</th>\n",
              "      <td>5.69</td>\n",
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              "      <td>14.56</td>\n",
              "      <td>0.652791</td>\n",
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              "      <td>0.555199</td>\n",
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              "      <th>6106</th>\n",
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          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
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        "id": "4uSZ_iGOqeoC",
        "outputId": "02462947-8b88-4656-8cb3-b19205538cb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      cap-diameter  stem-height  stem-width  cap-surface-mean  \\\n",
              "0             7.81         6.27       18.18          0.652791   \n",
              "1            10.96         3.89       26.20          0.529736   \n",
              "2             9.27        11.77       18.76          0.830604   \n",
              "3             7.93         7.56       13.68          0.479898   \n",
              "4             9.96         8.33       15.72          0.652791   \n",
              "...            ...          ...         ...               ...   \n",
              "6102         13.53         6.19       16.40          0.652791   \n",
              "6103          3.55         4.84        6.47          0.652791   \n",
              "6104          4.16         5.16       10.51          0.431833   \n",
              "6105          4.51         6.44        9.02          0.473605   \n",
              "6106          4.10         5.69        7.02          0.652791   \n",
              "\n",
              "      gill-spacing-mean  ring-type-mean  habitat-mean  b0  c0  f0  ...  l9  \\\n",
              "0              0.585689        0.555199      0.544301   0   0   0  ...   0   \n",
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              "...                 ...             ...           ...  ..  ..  ..  ...  ..   \n",
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              "6106           0.585689        0.555199      0.686006   0   0   0  ...   0   \n",
              "\n",
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              "      <th>cap-diameter</th>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.96</td>\n",
              "      <td>8.33</td>\n",
              "      <td>15.72</td>\n",
              "      <td>0.652791</td>\n",
              "      <td>0.563700</td>\n",
              "      <td>0.423019</td>\n",
              "      <td>0.544301</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6102</th>\n",
              "      <td>13.53</td>\n",
              "      <td>6.19</td>\n",
              "      <td>16.40</td>\n",
              "      <td>0.652791</td>\n",
              "      <td>0.585689</td>\n",
              "      <td>0.555199</td>\n",
              "      <td>0.544301</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6103</th>\n",
              "      <td>3.55</td>\n",
              "      <td>4.84</td>\n",
              "      <td>6.47</td>\n",
              "      <td>0.652791</td>\n",
              "      <td>0.585689</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.544301</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6104</th>\n",
              "      <td>4.16</td>\n",
              "      <td>5.16</td>\n",
              "      <td>10.51</td>\n",
              "      <td>0.431833</td>\n",
              "      <td>0.563700</td>\n",
              "      <td>0.555199</td>\n",
              "      <td>0.544301</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6105</th>\n",
              "      <td>4.51</td>\n",
              "      <td>6.44</td>\n",
              "      <td>9.02</td>\n",
              "      <td>0.473605</td>\n",
              "      <td>0.585689</td>\n",
              "      <td>0.566876</td>\n",
              "      <td>0.686006</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6106</th>\n",
              "      <td>4.10</td>\n",
              "      <td>5.69</td>\n",
              "      <td>7.02</td>\n",
              "      <td>0.652791</td>\n",
              "      <td>0.585689</td>\n",
              "      <td>0.555199</td>\n",
              "      <td>0.686006</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6107 rows × 61 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0947c9e8-ece6-4413-803c-e7756deb6a5f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0947c9e8-ece6-4413-803c-e7756deb6a5f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0947c9e8-ece6-4413-803c-e7756deb6a5f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Entrenamiento y validacion del modelo\n",
        "\n",
        "En esta sección se encuentra el entrenamiento y búsqueda de híper parámetros del modelo mediante los set de entrenamiento y validación."
      ],
      "metadata": {
        "id": "-1sNwpHWGBpN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entreno un XGboost de clasificación."
      ],
      "metadata": {
        "id": "o6jy1twxGHX8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xg_model = xgb.XGBClassifier(random_state = 16)\n",
        "\n",
        "params = {\"learning_rate\": [0, 0.1, 0.2, 1],\n",
        "           \"max_depth\": [1,3, None],\n",
        "           \"subsample\": [0.1, 0.3, 1],\n",
        "           \"n_estimators\": [1, 2, 10, 100]}\n",
        "\n",
        "rs = RandomizedSearchCV(xg_model, param_distributions=params, n_iter=10, random_state=13, scoring='roc_auc')"
      ],
      "metadata": {
        "id": "IpHxKEJc4zsu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rs.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "7jBimotMI8Lz",
        "outputId": "fc5f2789-ac85-4a03-d2bc-d8d5b99d74e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           callbacks=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None,\n",
              "                                           early_stopping_rounds=None,\n",
              "                                           enable_categorical=False,\n",
              "                                           eval_metric=None, feature_types=None,\n",
              "                                           gamma=None, gpu_id=None,\n",
              "                                           grow_policy=None,\n",
              "                                           importance_type=None,\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate=None...\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           max_leaves=None,\n",
              "                                           min_child_weight=None, missing=nan,\n",
              "                                           monotone_constraints=None,\n",
              "                                           n_estimators=100, n_jobs=None,\n",
              "                                           num_parallel_tree=None,\n",
              "                                           predictor=None, random_state=16, ...),\n",
              "                   param_distributions={'learning_rate': [0, 0.1, 0.2, 1],\n",
              "                                        'max_depth': [1, 3, None],\n",
              "                                        'n_estimators': [1, 2, 10, 100],\n",
              "                                        'subsample': [0.1, 0.3, 1]},\n",
              "                   random_state=13, scoring='roc_auc')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           callbacks=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None,\n",
              "                                           early_stopping_rounds=None,\n",
              "                                           enable_categorical=False,\n",
              "                                           eval_metric=None, feature_types=None,\n",
              "                                           gamma=None, gpu_id=None,\n",
              "                                           grow_policy=None,\n",
              "                                           importance_type=None,\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate=None...\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           max_leaves=None,\n",
              "                                           min_child_weight=None, missing=nan,\n",
              "                                           monotone_constraints=None,\n",
              "                                           n_estimators=100, n_jobs=None,\n",
              "                                           num_parallel_tree=None,\n",
              "                                           predictor=None, random_state=16, ...),\n",
              "                   param_distributions={&#x27;learning_rate&#x27;: [0, 0.1, 0.2, 1],\n",
              "                                        &#x27;max_depth&#x27;: [1, 3, None],\n",
              "                                        &#x27;n_estimators&#x27;: [1, 2, 10, 100],\n",
              "                                        &#x27;subsample&#x27;: [0.1, 0.3, 1]},\n",
              "                   random_state=13, scoring=&#x27;roc_auc&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           callbacks=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None,\n",
              "                                           early_stopping_rounds=None,\n",
              "                                           enable_categorical=False,\n",
              "                                           eval_metric=None, feature_types=None,\n",
              "                                           gamma=None, gpu_id=None,\n",
              "                                           grow_policy=None,\n",
              "                                           importance_type=None,\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate=None...\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           max_leaves=None,\n",
              "                                           min_child_weight=None, missing=nan,\n",
              "                                           monotone_constraints=None,\n",
              "                                           n_estimators=100, n_jobs=None,\n",
              "                                           num_parallel_tree=None,\n",
              "                                           predictor=None, random_state=16, ...),\n",
              "                   param_distributions={&#x27;learning_rate&#x27;: [0, 0.1, 0.2, 1],\n",
              "                                        &#x27;max_depth&#x27;: [1, 3, None],\n",
              "                                        &#x27;n_estimators&#x27;: [1, 2, 10, 100],\n",
              "                                        &#x27;subsample&#x27;: [0.1, 0.3, 1]},\n",
              "                   random_state=13, scoring=&#x27;roc_auc&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=16, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=16, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rFy1nyANJBea",
        "outputId": "b02d87c1-dda8-41f4-f9ff-c85a47e7bc8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'subsample': 0.3, 'n_estimators': 100, 'max_depth': 3, 'learning_rate': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = rs.best_estimator_"
      ],
      "metadata": {
        "id": "KRzc25zWJLNd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entreno el modelo con los mejores parámetros."
      ],
      "metadata": {
        "id": "fV07AO7fVSpI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "0LZwpuDUJPAM",
        "outputId": "89b6f186-ba52-4fac-c375-6c9fe03a0724"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=16, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=16, ...)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=16, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_validation = model.predict(X_validation)"
      ],
      "metadata": {
        "id": "1tAsPSlyreLJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_validation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhyMKufUrqki",
        "outputId": "7f2c3acf-5ab7-4a02-fc42-a8bd882f8900"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Realizo una matriz de confusión con el set de validación."
      ],
      "metadata": {
        "id": "C7h6TVChs-La"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matriz_confusion_validation = confusion_matrix(y_validation, y_pred_validation)"
      ],
      "metadata": {
        "id": "6CwV8ScnsHRc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(matriz_confusion_validation, annot=True, cmap='Greens', fmt='g')\n",
        "plt.title('Matriz de Confusión')\n",
        "plt.xlabel('Predicción')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "2daQJAccbSy8",
        "outputId": "d164f8c4-6dcd-4a97-af83-e50bd0e0220b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Métricas del set de validación"
      ],
      "metadata": {
        "id": "DfKw3SMPX6M9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc = roc_auc_score(y_validation, model.predict_proba(X_validation)[:, 1])\n",
        "print(\"ROC AUC SCORE:\", (roc_auc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_nqIkSMr4h-",
        "outputId": "1717f7bf-d369-4be7-9e62-e037e08bcdc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC AUC SCORE: 0.9998069374881169\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rmse = np.sqrt(mean_squared_error(y_validation, y_pred_validation))\n",
        "print(\"RMSE: %f\" % (rmse))"
      ],
      "metadata": {
        "id": "CtaVG4gqsuyK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95d3c3f5-addf-4b8c-ce84-1498401d5cd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 0.036194\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pruebas realizadas\n",
        "\n",
        "En esta sección están las pruebas realizados con el set de validación en busca de los híper parámetros."
      ],
      "metadata": {
        "id": "Q_sNE_eOHz7d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pruebas:\n",
        "\n",
        "* Prueba 1: Con \"subsample\" = 0.5, \"n_estimators\" = 4, \"max_depth\" = \"None\", \"learning_rate\" = 1 y los demás parámetros por defecto, con features numéricas, cap-shape, cap-color, cap-surface, habitat, stem-color y season.\n",
        "ROC AUC SCORE: 0.9520915756592341, RMSE: 0.350442, Matriz de confusión:([[2384,339],[411, 2973]]).\n",
        "\n",
        "* Prueba 2: Con \"subsample\" = 0.5, \"n_estimators\" = 4, \"max_depth\" = \"None\", \"learning_rate\" = 1 y los demás parámetros por defecto, con features numéricas, cap-shape, cap-color, cap-surface, habitat, stem-color, season, gill-spacing y gill-attachment.\n",
        "ROC AUC SCORE: 0.9862181148416996, RMSE: 0.241102, Matriz de confusión:([[2531,192],[163, 3221]]).\n",
        "\n",
        "* Prueba 3: Con \"subsample\" = 0.5, \"n_estimators\" = 4, \"max_depth\" = \"None\", \"learning_rate\" = 1 y los demás parámetros por defecto, con features numéricas, cap-shape, cap-color, cap-surface, habitat, stem-color, gill-spacing, gill-attac y gill-color:\n",
        "ROC AUC SCORE: 0.9856791350973104, RMSE: 0.242794, Matriz de confusión: ([[2531,192],[168, 3216]]).\n",
        "\n",
        "* Prueba 4: Con \"subsample\" = 0.5, \"n_estimators\" = 4, \"max_depth\" = \"None\", \"learning_rate\" = 1 y los demás parámetros por defecto, con features numéricas, cap-shape, cap-color, cap-surface, habitat, stem-color, gill-spacing, gill-attachment, gill-color, ring-type y has-ring:\n",
        "ROC AUC SCORE: 0.9868512925963838, RMSE: 0.214124, Matriz de confusión: ([[2570,153],[127, 3257]]).\n",
        "\n",
        "* Prueba 5: Con \"subsample\" = 0.3, \"n_estimators\" = 100, \"max_depth\" = 3, \"learning_rate\" = 1 y los demás parámetros por defecto, con features numéricas, cap-shape, cap-color, cap-surface, habitat, stem-color, gill-spacing, gill-attachment, gill-color, ring-type, has-ring:\n",
        "ROC AUC SCORE: 0.9985668988191824, RMSE: 0.036194, Matriz de confusión: ([[2716,7],[1, 3383]]).\n",
        "\n",
        "Si bien es cierto que el modelo no llega a ser igual de bueno que el Random Forest de la parte 3, logra predecir de manera bastante eficaz si un hongo es venenoso o comestible, dando un resultado similar.\n",
        "Una explicación que le puedo dar a esto es que dada la información del data set, es muy probable que su información pueda moldearse de forma eficaz mediante el uso de árboles de decisión, tal y como sucede con este modelo y el de la parte 3, permitiendo que el entrenamiento de dichos modelos sea efectivo.\n",
        "Dado que se pide trabajar el modelo hasta conseguir un Auc Roc Score de por lo menos 0.9, decidí mejorarlo un poco  desde la prueba 1, pero no lo suficiente para que sea igual de eficaz que el de la parte 3; esto último me parece importante aclararlo ya que muy probablemente ambas métricas serian iguales si hiciera un par de pruebas más o si hubiera utilizado un encoding similar.\n"
      ],
      "metadata": {
        "id": "Gtve2nfRLOGE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importancias de las Features por prueba\n",
        "\n",
        "En esta sección se encuentra la evolución de la importancia de las features dependiendo de la prueba.\n",
        "Al igual que en la parte 3, decidi incluirlo mas alla de que no este pedido en el enunciado con la diferencia de que decidi dejar las importancias indiviudales.\n"
      ],
      "metadata": {
        "id": "UTkgEP-hIYY1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 1"
      ],
      "metadata": {
        "id": "As2h5KJrlLWD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = [12, 7]\n",
        "xgb.plot_importance(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "outputId": "fb18d30a-43c3-4dab-e8a5-81de5990710d",
        "id": "NtxqSTFMVdnv"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature importance'}, xlabel='F score', ylabel='Features'>"
            ]
          },
          "metadata": {},
          "execution_count": 300
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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EBGDBggUICgrCt99+Cx0dHSxbtkzxBVceR0dHjBgxAnPmzAEA+Pj4YP/+/Xj8+DGaNWuGf/75B+bm5rh69So6d+4MCwsLzJw5EzNnzgQAXL16Ff7+/jh79iw6duyItWvXKo3foUMHAICDgwMAwMXFBWKxWHF+9erViIiIwLNnzzBixAhERkZCQ0PjlT7bXmFHUNxE95WuoYZJU12OVT0B25CDkJSI6jscqiXMq/Awp8LEvAoPcyo8zKkw1XVeM8O9a33MypSUlCA2NhYFBQVwcnLCvXv3kJKSgtGjR8PZ2RnXr1+HtbU1VqxYgXfeeafcMZ49e4bU1FQEBQUp2tTU1ODm5obk5GRVTeU/YQGlhrKzszFy5EisWrUKQ4YMwdOnT3Hy5En4+fkhKysLeXl52Lp1KwDA0NAQUqkUnp6ecHJywsmTJ9GkSRMsX74cXl5e+P3339G0aVMAwNGjR9G2bVucOHECp0+fhr+/P5KSktC3b1+kpKRg165d+OSTT+Du7o62bduWG1tpQWPOnDmQy+U4efIkmjdvjlOnTsHLywvHjx9HmzZt0Llz5zLXymQyDB06FCYmJkhJSUFubq6isFLq7Nmz6NmzJw4fPow333xTETvwvFhkamqKY8eO4dq1axg+fDi6deuGiRMnlhurRCKBRCJRHOfl5QEANNXkUFeXVz8h1GBpqsmV/kvCwLwKD3MqTMyr8DCnwsOcClNd51UqldbJuC+7ePEi+vbti6KiIjRr1gyxsbGwtLRESkoKACAkJAQrV66EnZ0ddu7cif79+yMtLQ2WlpZlxsrOzkZJSQmMjIyU4m/ZsiX+/PNPlc2pPNW9NwsoNZSdnY3i4mIMHToU7du3BwB07doVAKCtrQ2JRILWrVsr+u/YsQMymQxbtmxRrEzZunUrmjdvDrFYDA8PDwDPiy3r1q2DmpoarKyssGrVKhQWFmLBggUAgKCgIISHh+PUqVMYMWJEubG5urri22+/RUlJCTIyMtC0aVMMHz4cYrEYXl5eEIvFcHFxKffaw4cP4/Llyzh48CDMzMwAAJ999hkGDBig6FP6yI+RkZHSHAGgRYsWWL9+PdTV1WFtbQ1vb28cOXKkwgJKWFgYQkNDy7QvcpBBR6ek3GuocVrmKKvvEKgOMK/Cw5wKE/MqPMyp8DCnwlRXeY2Pj6+TcV8mlUqxevVqFBQUIDk5GR9//DFWrFiBgoICAMC7774LY2NjZGdno1+/fti3bx+Cg4Px8ccflxnr0aNHAICkpCTF7wHgxo0bePLkicrmVJ7CwsJq9WMBpYbs7e3Rv39/dO3aFZ6envDw8MCHH36IFi1alNv/woULuHbtGvT09JTai4qKcP36dcXxm2++qbTpjomJCWxtbRXH6urqMDIyUmyyM2DAAJw8eRIA0L59e1y6dAl9+vTB06dPkZaWhqSkJLi4uMDV1RXh4eEAgOPHjyMwMLDcOP/880+Ym5sriicA4OTkVO3P5c0334S6urri2NTUFBcvXqywf1BQEAICAhTHeXl5MDc3x/I0NRRrqFd4HTUemmpyLHOUYfF5NUhkXJYqFMyr8DCnwsS8Cg9zKjzMqTDVdV4zQjxrfcyqTJ8+HV5eXrhw4QICAwMxf/58vPfeexg4cKCiz44dO9CkSROltlLPnj3DxIkT0alTJ6Xze/bsgZWVVbnXqErpkxBVYQGlhtTV1ZGYmIikpCQcOnQIX375JRYuXKhYyvSy/Px8vPXWW9i5c2eZcy9u4vryXiEikajcNpnseSVzy5YtitdElfZr3rw57O3tIRaLkZycDHd3d/Tt2xfDhw/HX3/9hatXr1a4AuW/qizW8mhqakJTU7NM+4l5bjAyMqr1+Ej1pFIp4uPjkRrs9cp74VDDxbwKD3MqTMyr8DCnwsOcCpNQ8yqXyyGVSmFpaQkzMzNcv35daX7Xrl3DgAEDyp2zhoYG3nrrLRw/fhwffvghgOdbSBw7dgzTpk2r18+puvdmAeU/EIlE6N27N3r37o3g4GC0b98ee/fuRdOmTVFSovz4Sffu3bFr1y60atUK+vr6tRZDmzZtym13cXHBsWPHcPbsWaxYsQKGhoawsbHBihUrYGpqii5dupR7nY2NDf7++29kZ2fD1NQUAHDmzBmlPqV7nrw8RyIiIiIiIhKGoKAgDBgwAO3atcPTp08RHR0NsViMgwcPQiQSITAwEEuWLIG9vT26deuGbdu24fLly9izZ49ijP79+2PIkCGYNm0aACAgIABjxoyBo6MjevbsicjISBQUFFT6kpSGhAWUGkpJScGRI0fg4eGBVq1aISUlBffv34eNjQ2Kiopw8OBBXLlyBUZGRjAwMMDo0aPx+eefY/DgwVi6dCnatm2LW7du4aeffsLcuXMr3BC2plxdXfHll1/C2NgY1tbWirb169fD19e3wuvc3NzQpUsXjBkzBp9//jny8vKwcOFCpT6tWrWCtrY2EhIS0LZtW2hpacHAwKBW4yciIiIiIqL6c+/ePfj5+SE7OxsGBgaws7PDwYMH4e7uDgCYOXMmioqKMGvWLDx69Aj29vZITExEp06dFGNcv34dDx48UBwPHz4c9+/fR3BwMHJyctCtWzckJCTAxMRE5fOrCRZQakhfXx8nTpxAZGQk8vLy0L59e0RERGDAgAFwdHSEWCyGo6Mj8vPzFa8xPnHiBObNm4ehQ4fi6dOnaNOmDfr371+rK1JK9enTBzKZTOlRHVdXV6xduxaurq4VXqempoa9e/fC398fPXv2hIWFBdatWwcvLy9FnyZNmmDdunVYunQpgoOD0adPH6XXGBMREREREVHj9u2331bZZ/78+Zg/f36F5zMzM8u0TZs2TbEipbFhAaWGbGxskJCQUO45Y2NjHDp0qEx769atsW3btgrHjIqKKtNWXmGivC/ClxkaGpbZe8THxwdyednXaL08XpcuXRQb05Z6+boJEyZgwoQJSm3lxR8ZGVllrEREREREREQNnVrVXYiIiIiIiIiIXm8soBARERERERERVYEFFCIiIiIiIiKiKrCAQkRERERERERUBRZQiIiIiIiIiIiqwAIKERERERERUSOzYcMG2NnZQV9fH/r6+nBycsKBAwcU511dXSESiZR+TZ48udIx5XI5goODYWpqCm1tbbi5ueHq1at1PZVGgwWUWnL58mW8/fbb0NLSQrdu3eo7HCIiIiIiIhKwtm3bIjw8HKmpqTh//jz69euHwYMH49KlS4o+EydORHZ2tuLXqlWrKh1z1apVWLduHTZu3IiUlBTo6urC09MTRUVFdT2dRqFJfQcgFEuWLIGuri6uXLmCZs2a1Xc4REREREREJGCDBg1SOl6xYgU2bNiAM2fO4M033wQA6OjooHXr1tUaTy6XIzIyEosWLcLgwYMBANu3b4eJiQni4uIwYsSI2p1AI8QVKP/Rs2fPAADXr1/HO++8g/bt28PIyKieoyIiIiIiIqLXRUlJCWJiYlBQUAAnJydF+86dO9GyZUvY2toiKCgIhYWFFY5x8+ZN5OTkwM3NTdFmYGCAXr16ITk5uU7jbyzqdQWKTCbD6tWrsWnTJvz9998wMTHBJ598goULF2LevHnYu3cv/vnnH7Ru3RqjR49GcHAwNDQ0AAAhISGIi4vDp59+iuXLl+Phw4d47733sHnzZhgYGFR4zz179iA0NBTXrl2Djo4OHBwcsG/fPujq6sLV1RXdunVDZGSkor+Pjw+aN2+OqKgoAICFhQX8/f1x9epVxMXFYejQodi2bRsAIDU1FUuXLsWSJUsQEhJS5RwA4JdffsHSpUtx8eJFNGvWDH369MHevXsBABKJBAsXLsQPP/yAJ0+ewNbWFitXroSrq2uF8yv9XKZPn46QkBA8evQIfn5++PLLLxEREYEvvvgCMpkMM2bMwMKFCxXXPXnyBHPmzMG+ffsgkUjg6OiINWvWwN7eHsDzAlFAQADOnDmDgoIC2NjYICwsTOkPl4WFBSZNmoRr164hNjYWLVq0wKJFizBp0qQK45VIJJBIJIrjvLw8AEDflYdRrKFb4XXUeGiqybHMEXhraQIkMlF9h0O1hHkVHuZUmJhX4WFOhYc5FabSvEql0jq9z8WLF9G3b18UFRWhWbNmiI2NhaWlJaRSKYYPH4527drB1NQUFy9exMKFC/Hnn38iNja23LH++ecfAIChoaFS3MbGxrhz506dz6U+VXdu9VpACQoKwubNm7FmzRq88847yM7OxuXLlwEAenp6iIqKgpmZGS5evIiJEydCT08Pc+fOVVx/7do17N69G7/88gvy8vLg7++PKVOmYOfOneXeLzs7GyNHjsSqVaswZMgQPH36FCdPnoRcLn+luFevXo3g4GAsWbIEABAeHg43Nzd4eXlhzpw5ikd4qprD/v37MWTIECxcuBDbt2/Hs2fPEB8fr7jPtGnT8McffyAmJgZmZmbYu3cvvLy8cPHiRVhaWlYY3/Xr13HgwAEkJCTg+vXr+PDDD3Hjxg106dIFx48fR1JSEsaPHw83Nzf06tULAODr6wttbW0cOHAABgYG+Oabb9C/f3/89ddfMDQ0RH5+PgYOHIgVK1ZAU1MT27dvx6BBg3DlyhW0a9dOce+IiAgsW7YMCxYswJ49e/Dpp5/CxcUFVlZW5cYaFhaG0NDQMu2LHGTQ0Sl5pbxQw7bMUVbfIVAdYF6FhzkVJuZVeJhT4WFOhSkxMbFOx5dKpVi9ejUKCgqQnJyMjz/+GCtWrIC5uTnMzMxQXFyMv//+G82bN8cnn3yC4OBgfPvttzA1NS0zVun34keOHIGhoaGiPTs7GyKRSOl7VaGpbGXOi0TyV60e1JKnT5/C2NgY69evx4QJE6rsv3r1asTExOD8+fMAnq+0WL58OW7duoU2bdoAABISEuDt7Y3bt2+X+5zXb7/9hrfeeguZmZlo3759mfPVXYHi4OCgWCVSqlu3bvDx8UFISEi15+Ds7IyOHTtix44dZfpmZWWhY8eOyMrKgpmZmaLdzc0NPXv2xGeffVbuPUJCQvD5558jJycHenp6AAAvLy9cuXIF169fh5ra86e2rK2tMXbsWMyfPx+nTp2Ct7c37t27B01NTcVYnTt3xty5cytcQWJra4vJkydj2rRpis+mT58++P777wE8f4audevWCA0NrXC35/JWoJibm+ONwBiuQBGI59V3GRafV+NPVQSEeRUe5lSYmFfhYU6FhzkVptK8uru7Kz2BUNe8vLzQsWNHfP3112XOFRQUoEWLFvj111/h4eFR5vyNGzdgbW2Ns2fPKr0YpX///rC3t8cXX3xRl6HXq7y8PLRs2RK5ubnQ19evsF+9rUD5888/IZFI0L9//3LP79q1C+vWrcP169eRn5+P4uLiMhNp166dongCAE5OTpDJZLhy5QquXr2KAQMGKM598803GDFiBPr374+uXbvC09MTHh4e+PDDD9GiRYtXit3R0bFa/aqaQ3p6OiZOnFjutRcvXkRJSQm6dOmi1C6RSBR7rLy4We1HH32EjRs3AnheyCgtngCAiYkJ1NXVFcWT0rZ79+4BAC5cuID8/Pwye7f8+++/uH79OgAgPz8fISEh2L9/P7Kzs1FcXIx///0XWVlZStfY2dkpfi8SidC6dWvFfcqjqampVLQpdWKeG/eSEQipVIr4+HikBnup9B8PqlvMq/Awp8LEvAoPcyo8zKkwleZVQ0NDpXmVy+WQSqXl3rP07Tzm5ublnu/SpQtat26NEydOoEePHgCeFxbOnj2LKVOmCPrrs7pzq7cCira2doXnkpOTMXr0aISGhsLT0xMGBgaIiYlBREREtcd3dHREenq64ri0iJCYmIikpCQcOnQIX375JRYuXIiUlBR06NABampqZR7nKe9ZKF3dqldGVGcOlX0G+fn5UFdXR2pqKtTV1ZXOlRZOXpzfi4WZl5MvEonKbZPJZIp7mZqaQiwWl4mjefPmAIA5c+YgMTERq1evRufOnaGtrY0PP/xQsYluZfcuvQ8RERERERHVjqCgIAwYMADt2rXD06dPER0dDbFYjIMHD+L69euIjo7GwIEDYWRkhN9//x2zZs1C3759lX7obW1tjbCwMAwZMgQikQgzZ87E8uXLYWlpiQ4dOmDx4sUwMzODj49P/U20Aam3AoqlpSW0tbVx5MiRMo/wJCUloX379kqbnN66davMGFlZWbhz547iEZczZ85ATU0NVlZW0NbWRufOnctcIxKJ0Lt3b/Tu3RvBwcFo37499u7di4CAABgbGyM7O1vRt6SkBBkZGXj33XdfeX7VmYOdnR2OHDmCcePGlbnewcEBJSUluHfvHvr06VPuPcqbX010794dOTk5aNKkCSwsLMrtc/r0aYwdOxZDhgwB8LzokpmZWSv3JyIiIiIioldz7949+Pn5ITs7GwYGBrCzs8PBgwfh7u6Ov//+G4cPH0ZkZCQKCgpgbm6ODz74AIsWLVIa48qVK8jNzVUcz507FwUFBZg0aRKePHmCd955BwkJCdDS0lL19BqkeiugaGlpYd68eZg7dy6aNm2K3r174/79+7h06RIsLS2RlZWFmJgY9OjRA/v37y+z50jpGGPGjMHq1auRl5eH6dOnY9iwYRW+5zolJQVHjhyBh4cHWrVqhZSUFNy/fx82NjYAgH79+iEgIAD79+9Hp06d8MUXX+DJkyc1ml915rBkyRL0798fnTp1wogRI1BcXIz4+HjMmzcPXbp0wejRo+Hn54eIiAg4ODjg/v37OHLkCOzs7ODt7V2juMrj5uYGJycn+Pj4YNWqVejSpQvu3Lmj2OTW0dERlpaW+OmnnzBo0CCIRCIsXryYK0uIiIiIiIjqybffflvhOXNzcxw/frzKMV5+AkMkEmHp0qVYunTpf45PiNSq7lJ3Fi9ejNmzZyM4OBg2NjYYPnw47t27h/fffx+zZs3CtGnT0K1bNyQlJWHx4sVlru/cuTOGDh2KgQMHwsPDA3Z2duVullNKX18fJ06cwMCBA9GlSxcsWrQIERERir1Sxo8fjzFjxsDPzw8uLi7o2LFjjVafAKjWHFxdXREbG4uff/4Z3bp1Q79+/XD27FnF+a1bt8LPzw+zZ8+GlZUVfHx8cO7cOaW33tSG0h2V+/bti3HjxqFLly4YMWIEbt26BRMTEwDAF198gRYtWsDZ2RmDBg2Cp6cnunfvXqtxEBERERERETVU9fYWnv8qJCQEcXFxSvuAUOOXl5cHAwMDPHjwgJvICkTpBloDBw4U9MZTrxvmVXiYU2FiXoWHORUe5lSYmNfGpfT70KrewlOvK1CIiIiIiIiIiBoDFlCIiIiIiIiIiKrQaAsoISEhfHyHiIiIiIiIiFSi0RZQiIiIiIiIiIhUhQUUIiIiIiIShLCwMPTo0QN6enpo1aoVfHx8cOXKFcX5zMxMiESicn/FxsZWOK5cLkdwcDBMTU2hra0NNzc3XL16VRVTIqIGhAWURkwsFkMkEuHJkyf/aRyRSIS4uDiV35eIiIiIqDYdP34cU6dOxZkzZ5CYmAipVAoPDw8UFBQAAMzNzZGdna30KzQ0FM2aNcOAAQMqHHfVqlVYt24dNm7ciJSUFOjq6sLT0xNFRUWqmhoRNQBN6juAxmzs2LF48uTJKxUfGqLs7Gy0aNGiVsfka6aJiIiISNUSEhKUjqOiotCqVSukpqaib9++UFdXR+vWrZX67N27F8OGDUOzZs3KHVMulyMyMhKLFi3C4MGDAQDbt2+HiYkJ4uLiMGLEiLqZDBE1OFyBQmjdujU0NTXrOwwiIiIiolqVm5sLADA0NCz3fGpqKtLT0+Hv71/hGDdv3kROTg7c3NwUbQYGBujVqxeSk5NrN2AiatC4AqUa9uzZg9DQUFy7dg06OjpwcHCAg4MDtm3bBuD5IzAAcOzYMbi6uuLvv//G7NmzcejQIaipqaFPnz5Yu3YtLCwsAPzfypWePXti7dq1kEgkCAgIwIIFCxAUFIRvv/0WOjo6WLZsGcaNG1dlfKmpqZg3bx7++OMPdOvWDVu3boWVlZXi/L59+xAaGoo//vgDZmZmGDNmDBYuXIgmTZoo4t+7dy98fHwAAElJSZgyZQouX74MW1tbLFq0CEOGDEFaWhq6detW5X2joqIQGhqq9Nls3boVY8eOrfZn3ivsCIqb6Fa7PzVcmupyrOoJ2IYchKREVN/hUC1hXoWHORUm5lV4GntOM8O9VXYvmUyGmTNnonfv3rC1tS23z7fffgsbGxs4OztXOE5OTg4AwMTERKndxMREcY6IXg8soFQhOzsbI0eOxKpVqzBkyBA8ffoUJ0+ehJ+fH7KyspCXl4etW7cCeF7Zlkql8PT0hJOTE06ePIkmTZpg+fLl8PLywu+//46mTZsCAI4ePYq2bdvixIkTOH36NPz9/ZGUlIS+ffsiJSUFu3btwieffAJ3d3e0bdu20hgXLlyIiIgIGBsbY/LkyRg/fjxOnz4NAIpY161bhz59+uD69euYNGkSAGDJkiVlxsrLy8OgQYMwcOBAREdH49atW5g5c+Yr3Xf48OHIyMhAQkICDh8+DOB5lb48EokEEolE6f4AoKkmh7q6vNJ5U+OgqSZX+i8JA/MqPMypMDGvwtPYcyqVSlV2r2nTpiEjIwPHjh0r977//vsvoqOjsWDBgkrjKi4uBvA89hf7yWQyiESi/zyn0utV+dlQ3WNeG5fq5okFlCpkZ2ejuLgYQ4cORfv27QEAXbt2BQBoa2tDIpEoPUe5Y8cOyGQybNmyRWn1RfPmzSEWi+Hh4QHgebFl3bp1UFNTg5WVFVatWoXCwkIsWLAAABAUFITw8HCcOnWqyucqV6xYARcXFwDA/Pnz4e3tjaKiImhpaSE0NBTz58/HmDFjAAAdO3bEsmXLMHfu3HILKNHR0RCJRNi8eTO0tLTwxhtv4Pbt25g4cWK176utrY1mzZqhSZMmZZ4xfVlYWJhitcqLFjnIoKNTUum11Lgsc5TVdwhUB5hX4WFOhYl5FZ7GmtP4+HiV3GfTpk1ISUnBZ599ht9//x2///57mT7Hjh1DQUEBWrduXWlcpatMfvzxR3Ts2FHRfvnyZXTo0KHW5pSYmFgr41DDwrw2DoWFhdXqxwJKFezt7dG/f3907doVnp6e8PDwwIcffljhpqsXLlzAtWvXoKenp9ReVFSE69evK47ffPNNqKn93xY0JiYmSksL1dXVYWRkhHv37gEABgwYgJMnTwIA2rdvj0uXLin62tnZKX5vamoKALh37x7atWuHCxcu4PTp01ixYoWiT0lJCYqKilBYWAgdHR2lOK9cuQI7OztoaWkp2nr27FnuXCu7b3UFBQUhICBAcZyXlwdzc3MsT1NDsYZ6tcehhktTTY5ljjIsPq8GiazxLTWm8jGvwsOcChPzKjyNPacZIZ51Or5cLsfMmTORnp6OEydOwNLSssK+X3zxBQYNGoSRI0dWOWZISAikUikGDhwI4Pn/s167dg3z589XtNWUVCpFYmIi3N3doaGh8Z/GooaDeW1cSp+EqAoLKFVQV1dHYmIikpKScOjQIXz55ZdYuHAhUlJSyu2fn5+Pt956Czt37ixzztjYWPH7l/8QiUSicttksuc/XdiyZQv+/fffcq998bh01Uvpdfn5+QgNDcXQoUPLxPNikaQmKrtvdWlqapa7ge2JeW4wMjL6T/FRwyCVShEfH4/UYC/+4yEgzKvwMKfCxLwKD3NauSlTpiA6Ohr79u2DoaEhHj58COD54+Ta2tqKfteuXcPJkycRHx9f7udobW2NsLAwDBkyBAAwc+ZMhIWFwdraGh06dMDixYthZmaGDz/8sNbyoKGhwZwKEPPaOFQ3RyygVINIJELv3r3Ru3dvBAcHo3379ti7dy+aNm2KkhLlx0y6d++OXbt2oVWrVtDX16+1GNq0aVOj67p3744rV66gc+fO1epvZWWFHTt2QCKRKAob586de+X7lvfZEBERERHVpQ0bNgAAXF1dldpffqHBd999h7Zt2yoer3/ZlStXFG/wAYC5c+eioKAAkyZNwpMnT/DOO+8gISHhP/9AkogaF77GuAqlz06eP38eWVlZ+Omnn3D//n3Y2NjAwsICv//+O65cuYIHDx5AKpVi9OjRaNmyJQYPHoyTJ0/i5s2bEIvFmD59Ov755x+Vxx8cHIzt27cjNDQUly5dwp9//omYmBgsWrSo3P6jRo2CTCbDpEmT8Oeff+LgwYNYvXo1gP9bZVIdFhYWuHnzJtLT0/HgwQOljWKJiIiIiOqCXC4v99fLb4P87LPPkJWVpfRI/cvjvHiNSCTC0qVLkZOTg6KiIhw+fBhdunSpw5kQUUPEAkoV9PX1ceLECQwcOBBdunTBokWLEBERgQEDBmDixImwsrKCo6MjjI2Ncfr0aejo6ODEiRNo164dhg4dChsbG/j7+6OoqKhWV6RUl6enJ3799VccOnQIPXr0wNtvv401a9YoNsR9mb6+Pn755Rekp6ejW7duWLhwIYKDgwG82iM/H3zwAby8vPDuu+/C2NgYP/zwQ63Mh4iIiIiIiKg+8BGeKtjY2CAhIaHcc8bGxjh06FCZ9tatW2Pbtm0VjhkVFVWmTSwWl2nLzMysNDZXV1fI5cqvsOvWrVuZNk9PT3h6Vrxh18v9nZ2dceHCBcXxzp07oaGhodgctjr31dTUxJ49eyqNn4iIiIiIiKixYAGFyti+fTs6duyINm3a4MKFC5g3bx6GDRumtPEWERERERER0euEBRQqIycnB8HBwcjJyYGpqSl8fX2VXoNMRERERERE9LphAYXKmDt3LubOnVvfYRARERERERE1GNxEloiIiIiIiIioCiygEBERERERERFVgQWUBsLV1RUzZ85UHFtYWCAyMrLe4iEiIiKi/yYsLAw9evSAnp4eWrVqBR8fH1y5ckWpzyeffIJOnTpBW1sbxsbGGDx4MC5fvlzpuHK5HCEhITA1NYW2tjbc3Nxw9erVupwKERGBBZQG69y5c5g0aVJ9h4GoqCg0b968vsMgIiIianSOHz+OqVOn4syZM0hMTIRUKoWHhwcKCgoUfd566y1s3boVf/75Jw4ePAi5XA4PDw+UlJRUOO7evXvx1VdfYePGjUhJSYGuri48PT1RVFSkimkREb22uIlsA2VsbFzfIdSqkpISiEQiqKmxZkdERESvh4SEBKXjqKgotGrVCqmpqejbty8AKP3AzMLCAsuXL4e9vT0yMzPRqVOnMmPK5XL88ssvCAoKwuDBgwEA27dvh4mJCeLi4jBixIg6nBER0evttf9uViaTYdWqVejcuTM0NTXRrl07xSt7582bhy5dukBHRwcdO3bE4sWLIZVKFdeGhISgW7du+Oabb2Bubg4dHR0MGzYMubm5ld6zoKAAfn5+aNasGUxNTREREVGmz8uP8HzxxRfo2rUrdHV1YW5ujilTpiA/P19xvnSlyK+//gorKyvo6Ojgww8/RGFhIbZt2wYLCwu0aNEC06dPV/qJhkQiwZw5c9CmTRvo6uqiV69eEIvFAACxWIxx48YhNzcXIpEIIpEIISEhVV73Yjw///wz3njjDWhqaiIrK6u6aSEiIiISnNL/RzQ0NCz3fEFBAbZu3YoOHTrA3Ny83D43b97E48eP0a9fP0WbgYEBevXqheTk5NoPmoiIFF77FShBQUHYvHkz1qxZg3feeQfZ2dmK50719PQQFRUFMzMzXLx4ERMnToSenp7SK36vXbuG3bt345dffkFeXh78/f0xZcoU7Ny5s8J7BgYG4vjx49i3bx9atWqFBQsW4LfffkO3bt0qvEZNTQ3r1q1Dhw4dcOPGDUyZMgVz587F119/rehTWFiIdevWISYmBk+fPsXQoUMxZMgQNG/eHPHx8bhx4wY++OAD9O7dG8OHDwcATJs2DX/88QdiYmJgZmaGvXv3wsvLCxcvXoSzszMiIyMRHByseF63WbNmVV5naWmpiGflypXYsmULjIyM0KpVqzLzkkgkkEgkiuO8vDwAQN+Vh1GsoVtp7qhx0FSTY5kj8NbSBEhkovoOh2oJ8yo8zKkwMa8VywjxVOn9ZDIZZsyYAWdnZ1hZWSn9UG7jxo0ICgpCQUEBunTpgvj4eIhEIqU+pW7fvg3geRHmxfPGxsa4c+dOuddQw1aaM+ZOWJjXxqW6eRLJ5XJ5HcfSYD19+hTGxsZYv349JkyYUGX/1atXIyYmBufPnwfwfAXK8uXLcevWLbRp0wbA86Wa3t7euH37Nlq3bl1mjPz8fBgZGWHHjh3w9fUFADx69Aht27bFpEmTFKtOLCwsMHPmTKWNZV+0Z88eTJ48GQ8ePADwfMXHuHHjcO3aNcVyz8mTJ+P777/H3bt3FYUPLy8vWFhYYOPGjcjKykLHjh2RlZUFMzMzxdhubm7o2bMnPvvsM0RFRWHmzJl48uSJ4nx1rxs3bhzS09Nhb29f4WcaEhKC0NDQMu3R0dHQ0dGp8DoiIiKixmTjxo1ITU1FWFgYWrZsqXSuoKAAubm5ePz4MeLi4vDw4UOEh4ejadOmZca5fPky5s+fj++++05pJcuqVasgEokQGBhY53MhIhKawsJCjBo1Crm5udDX16+w32u9AuXPP/+ERCJB//79yz2/a9curFu3DtevX0d+fj6Ki4vLfJjt2rVTFE8AwMnJCTKZDFeuXMHVq1cxYMAAxblvvvkGtra2ePbsGXr16qVoNzQ0hJWVVaWxHj58GGFhYbh8+TLy8vJQXFyMoqIiFBYWKgoNOjo6Ss/KmpiYwMLCQlE8KW27d+8eAODixYsoKSlBly5dlO4lkUhgZGRUYSzVva5p06aws7OrdF5BQUEICAhQHOfl5cHc3BzL09RQrKFe6bXUODz/6acMi8+r8aefAsK8Cg9zKkzMa8VUuQJlxowZyMjIwKlTp9ChQ4cq+7Zq1QpFRUXw8fEpc75z586YP38+rK2t4ejoqGiPiIiAvb09Bg4cWNvhUx2TSqVITEyEu7s7NDQ06jscqiXMa+NS+iREVV7rAoq2tnaF55KTkzF69GiEhobC09MTBgYGiImJKXe/koo4OjoiPT1dcWxiYoIbN268cpyZmZl477338Omnn2LFihUwNDTEqVOn4O/vj2fPnikKKC//wRSJROW2yWQyAM9Xw6irqyM1NRXq6srFiheLLi+r7nXa2toQiSr/nzVNTU1oamqWaT8xz63SIg41HlKpFPHx8UgN9uI/HgLCvAoPcypMzGv9ksvl+N///od9+/ZBLBYrHnOujEwmg1wuR0lJSbk5s7S0RIsWLXDy5Ek4OTkBeP4//mfPnsWUKVOY50ZMQ0OD+RMg5rVxqG6OXusCiqWlJbS1tXHkyJEyj/AkJSWhffv2WLhwoaLt1q1bZcbIysrCnTt3FI+ynDlzBmpqarCysoK2tjY6d+6s1L9Tp07Q0NBASkoK2rVrBwB4/Pgx/vrrL7i4uJQbZ2pqKmQyGSIiIhRvsdm9e3fNJ/7/OTg4oKSkBPfu3UOfPn3K7dO0adMyr9GrznVEREREr7upU6ciOjoa+/btg56eHnJycgA83/RVW1sbN27cwK5du+Dh4QFjY2P8888/CA8Ph7a2ttJKEmtra4SFhWHIkCEQiUQYNGgQwsLCYG1tjQ4dOmDx4sUwMzMrd8UKERHVnte6gKKlpYV58+Zh7ty5aNq0KXr37o379+/j0qVLsLS0RFZWFmJiYtCjRw/s378fe/fuLXeMMWPGYPXq1cjLy8P06dMxbNiwcvc/AZ6v0PD390dgYKBiY9WFCxdW+nrfzp07QyqV4ssvv8SgQYNw+vRpbNy48T/Pv0uXLhg9ejT8/PwQEREBBwcH3L9/H0eOHIGdnR28vb1hYWGB/Px8HDlyBPb29tDR0anWdURERESvuw0bNgAAXF1dldq3bt2KsWPHQktLCydPnkRkZCQeP34MExMT9O3bF0lJSUqb71+5ckXpLY9DhgxR7J/35MkTvPPOO0hISICWlpZK5kVE9Lp6rQsoALB48WI0adIEwcHBuHPnDkxNTTF58mT4+/tj1qxZmDZtGiQSCby9vbF48WLFa3xLde7cGUOHDsXAgQPx6NEjvPfee0pvxinP559/jvz8fAwaNAh6enqYPXt2pa8+tre3xxdffIGVK1ciKCgIffv2RVhYGPz8/P7z/Ldu3Yrly5dj9uzZuH37Nlq2bIm3334b7733HgDA2dkZkydPxvDhw/Hw4UMsWbIEISEhVV5HRERE9Lqr6l0NZmZmiI+Pf+VxRCIRQkJCsGLFiv8UHxERvZrX+i08/1VISAji4uKU9jmh/yYvLw8GBgZ48OAB90ARiNLn7wcOHMjnPwWEeRUe5lSYmFfhYU6FhzkVJua1cSn9PrSqt/BU/NwIEREREREREREBYAGFiIiIiIiIiKhKLKD8ByEhIXx8h4iIiIiIiOg1wAIKEREREREREVEVWEAhIiIiIiIiIqoCCyhERPSfPH36FDNnzkT79u2hra0NZ2dnnDt3rtJrxGIxunfvDk1NTXTu3BlRUVGqCZaIiIiIqIZe2wKKq6srZs6cWePro6Ki0Lx580r7hISEoFu3bjW+BxFRYzBhwgQkJibi+++/x8WLF+Hh4QE3Nzfcvn273P43b96Et7c33n33XaSnp2PmzJmYMGECDh48qOLIiYiIiIiq77UtoKjCnDlzcOTIEcXx2LFj4ePj88rjsBBDRA3Vv//+ix9//BGrVq1C37590blzZ4SEhKBz587YsGFDudds3LgRHTp0QEREBGxsbDBt2jR8+OGHWLNmjYqjJyIiIiKqPhZQ6lCzZs1gZGRU32EQEdWZ4uJilJSUQEtLS6ldW1sbp06dKvea5ORkuLm5KbV5enoiOTm5zuIkIiIiIvqvmtR3APVJJpNh7ty52LJlC5o2bYrJkycjJCQEAPDFF19g69atuHHjBgwNDTFo0CCsWrUKzZo1UxojLi4OgYGB+Pvvv+Hi4oItW7bA3NwcwPOVI3FxcUhPT0dISAi2bdsGABCJRACAY8eOwdXVFfPmzcPevXvxzz//oHXr1hg9ejSCg4OhoaGBqKgohIaGKl23detWjB07tsx8MjMz0aFDB+zatQtffvklzp8/D1tbW+zcuRO5ubn49NNPcfnyZfTp0wfbt2+HsbGx4totW7YgIiICN2/ehIWFBaZPn44pU6YozlcW44tznT17NhYvXozHjx9jwIAB2Lx5M/T09F45N73CjqC4ie4rX0cNj6a6HKt6ArYhByEpEdV3OK+VzHDvOr+Hnp4enJycsGzZMtjY2MDExAQ//PADkpOT0blz53KvycnJgYmJiVKbiYkJ8vLy8O+//0JbW7vO4yYiIiIielWvdQFl27ZtCAgIQEpKCpKTkzF27Fj07t0b7u7uUFNTw7p169ChQwfcuHEDU6ZMwdy5c/H1118rri8sLMSKFSuwfft2NG3aFFOmTMGIESNw+vTpMveaM2cO/vzzT+Tl5WHr1q0AAENDQwDPvwGJioqCmZkZLl68iIkTJ0JPTw9z587F8OHDkZGRgYSEBBw+fBgAYGBgUOm8lixZgsjISLRr1w7jx4/HqFGjoKenh7Vr10JHRwfDhg1DcHCwYnn9zp07ERwcjPXr18PBwQFpaWmYOHEidHV1MWbMmCpjLHX9+nXExcXh119/xePHjzFs2DCEh4djxYoVFcYqkUggkUgUx3l5eQAATTU51NXllc6TGgdNNbnSf0l1pFJpnY8tlUrx3XffYdKkSWjTpg3U1dXh4OCA4cOH47fffis3BrlcjpKSEqVzxcXFivGaNHmt/2mqNy/mlISDeRUe5lR4mFNhYl4bl+rmSSSXy1/L72pcXV1RUlKCkydPKtp69uyJfv36ITw8vEz/PXv2YPLkyXjw4AGA55vIjhs3DmfOnEGvXr0AAJcvX4aNjQ1SUlLQs2dPpRUowPM9UJ48eYK4uLhKY1u9ejViYmJw/vx5ACgzTkVKV6Bs2bIF/v7+AICYmBiMHDkSR44cQb9+/QAA4eHhiIqKwuXLlwEAnTt3xrJlyzBy5EjFWMuXL0d8fDySkpKqHePnn3+OnJwcxYqTuXPn4sSJEzhz5kyFMYeEhChW2LwoOjoaOjo6lc6XiBqWoqIiFBYWwtDQEJ9//jmKioqwePHiMv0WLFiAjh07YsKECYq2I0eO4Ntvv0V0dLQqQyYiIiIiQmFhIUaNGoXc3Fzo6+tX2O+1/jGfnZ2d0rGpqSnu3bsHADh8+DDCwsJw+fJl5OXlobi4WPHNQek39k2aNEGPHj0U11tbW6N58+b4888/0bNnz2rHsWvXLqxbtw7Xr19Hfn4+iouLK00aAEyePBk7duxQHOfn55c7r9Jl8l27dlVqK51nQUEBrl+/Dn9/f0ycOFHRp7i4WGmlS3VitLCwUHpc58XPsyJBQUEICAhQHOfl5cHc3Bzvvvsu948RCKlUisTERLi7uyse+aLGr7K8Pn78GBkZGQgLC8PAgQPLXHvy5EkkJCQonfvhhx/wzjvvlNufVIN/VoWJeRUe5lR4mFNhYl4bl9InIaryWhdQXv5CFolEkMlkyMzMxHvvvYdPP/0UK1asgKGhIU6dOgV/f388e/asVldGJCcnY/To0QgNDYWnpycMDAwQExODiIiISq9bunQp5syZU+W8SvdNeblNJpMB+L/Cy+bNmxUraUqpq6u/UowVfZ6V0dTUhKamZrlz4F80wsKcCpOGhgaOHj0KuVwOKysrXLt2DYGBgbC2tsaECROgoaGBoKAg3L59G9u3bwcATJ06FRs2bMDChQsxfvx4HD16FHv27MH+/fv5NdIA8M+qMDGvwsOcCg9zKkzMa+NQ3Ry91gWUiqSmpkImkyEiIgJqas9fVLR79+4y/YqLi3H+/HnFapMrV67gyZMnsLGxKXfcpk2boqSkRKktKSkJ7du3x8KFCxVtt27dqvK6Vq1aoVWrVq8+uZeYmJjAzMwMN27cwOjRo8vtU50Yiej1lZubi6CgIPzzzz8wNDTEBx98gBUrVij+IcrOzkZWVpaif4cOHbB//37MmjULa9euRdu2bbFlyxZ4enrW1xSIiIiIiKrEAko5OnfuDKlUii+//BKDBg3C6dOnsXHjxjL9NDQ08L///Q/r1q1DkyZNMG3aNLz99tsVPr5jYWGBgwcP4sqVKzAyMoKBgQEsLS2RlZWFmJgY9OjRA/v378fevXvLXHfz5k2kp6ejbdu20NPTK3fVRk2FhoZi+vTpMDAwgJeXFyQSCc6fP4/Hjx8jICCgWjES0etr2LBhGDZsWIXno6KiyrS5uroiLS2tDqMiIiIiIqpdavUdQENkb2+PL774AitXrlS8BjgsLKxMPx0dHcybNw+jRo1C79690axZM+zatavCcSdOnAgrKys4OjrC2NgYp0+fxvvvv49Zs2Zh2rRp6NatG5KSkspsuvjBBx/Ay8sL7777LoyNjfHDDz/U6nwnTJiALVu2YOvWrejatStcXFwQFRWFDh06AEC1YiQiIiIiIiISstf2LTzUMOXl5cHAwAAPHjzgJrICIZVKER8fj4EDB/L5TwFhXoWHORUm5lV4mFPhYU6FiXltXEq/D63qLTxcgUJEREREREREVAUWUIiIiIiIiIiIqsACChERERERERFRFVhAISIiIiIiIiKqAgsoRERERERERERVYAGFiKieWVhYQCQSlfk1derUCq+JjY2FtbU1tLS00LVrV8THx6swYiIiIiKi1w8LKFSrdu/ejW7dukFHRwft27fH559/Xt8hETV4586dQ3Z2tuJXYmIiAMDX17fc/klJSRg5ciT8/f2RlpYGHx8f+Pj4ICMjQ5VhExERERG9VlhAoVpz4MABjB49GpMnT0ZGRga+/vprrFmzBuvXr6/v0IgaNGNjY7Ru3Vrx69dff0WnTp3g4uJSbv+1a9fCy8sLgYGBsLGxwbJly9C9e3f+WSMiIiIiqkNN6jsAath+/fVXfPTRR3j48CHU1dWRnp4OBwcHzJs3D+Hh4QCACRMmoKioCDKZDD4+Ppg8eTIAoGPHjggKCsLKlSsxdepUiESiat+3V9gRFDfRrZM5kWppqsuxqidgG3IQkpLqfw00FJnh3iq937Nnz7Bjxw4EBARU+GcmOTkZAQEBSm2enp6Ii4tTQYRERERERK8nFlCoUn369MHTp0+RlpYGR0dHHD9+HC1btoRYLFb0OX78OObNm4cDBw5AR0dH6XptbW38888/uHXrFiwsLMqML5FIIJFIFMd5eXkAAE01OdTV5XUyJ1ItTTW50n8bG6lUqtL77dmzB0+ePMHo0aMrvHdOTg6MjIyUzrds2RI5OTkqi7f0Pqr+fKjuMKfCxLwKD3MqPMypMDGvjUt188QCClXKwMAA3bp1g1gshqOjI8RiMWbNmoXQ0FDk5+cjNzcX165dg4uLC2QyGWbNmoWxY8fi3XffxbVr1xAREQEAyM7OLreAEhYWhtDQ0DLtixxk0NEpqevpkQotc5TVdwg1ourNWT///HM4ODggPT0d6enp5faRy+VIT0+Hvr6+oi0jIwMSiUTl8Zbu10LCwZwKE/MqPMyp8DCnwsS8Ng6FhYXV6scCClXJxcUFYrEYs2fPxsmTJxEWFobdu3fj1KlTePToEczMzGBpaYnOnTvj+vXreO+99yCVSqGvr48ZM2YgJCQEamrlb7cTFBSk9ChCXl4ezM3NsTxNDcUa6qqaItUhTTU5ljnKsPi8GiSyxvcIT0aIp8rudevWLfz+++/YvXs3Bg4cWGE/U1NTmJmZKfU5d+4c2rVrV+l1tUkqlSIxMRHu7u7Q0NBQyT2pbjGnwsS8Cg9zKjzMqTAxr41L6ZMQVWEBhark6uqK7777DhcuXICGhgasra3h6uoKsViMx48fKza6FIlEWLlyJT777DPk5OTA2NgYR44cAfB8P5TyaGpqQlNTs0z7iXluMDIyqrtJkcpIpVLEx8cjNdiL/3hUYceOHWjVqhUGDx6MJk0q/uvZyclJUdQsdfToUTg7O6v8M9bQ0GBeBYY5FSbmVXiYU+FhToWJeW0cqpsjvoWHqlS6D8qaNWsUxZLSAopYLIarq6tSf3V1dbRp0wZNmzbFDz/8ACcnJxgbG9dD5ESNh0wmw9atWzFmzJgyxRM/Pz8EBQUpjmfMmIGEhARERETg8uXLCAkJwfnz5zFt2jRVh01ERERE9NrgChSqUosWLWBnZ4edO3cqXpPat29fDBs2DFKpVFFUefDgAfbs2QNXV1cUFRVh69atiI2NxfHjx+szfKJG4fDhw8jKysL48ePLnMvKylJ6DM7Z2RnR0dFYtGgRFixYAEtLS8TFxcHW1laVIRMRERERvVZYQKFqcXFxQXp6umK1iaGhId544w3cvXsXVlZWin7btm3DnDlzIJfLFY8Z9OzZs56iJmo8PDw8IJeX/6aiF996VcrX1xe+vr51HBUREREREZViAYWqJTIyEpGRkUptL78hpGXLlkhOTlZdUEREREREREQqwj1QiIiIiIiIiIiqwAIKEREREREREVEVWEAhIiIiIiIiIqoCCyhERERERERERFVgAYWIiIiIiIiIqAosoBBRvQgPD4dIJMLMmTMr7RcbGwtra2toaWmha9euiI+PV02AREREREREL2ABhYhU7ty5c/jmm29gZ2dXab+kpCSMHDkS/v7+SEtLg4+PD3x8fJCRkaGiSImIiIiIiJ5jAYVqzfXr1zFkyBAYGxtDX18fw4YNw927d+s7LGpg8vPzMXr0aGzevBktWrSotO/atWvh5eWFwMBA2NjYYNmyZejevTvWr1+vomiJiIiIiIieYwGFakVBQQE8PDwgEolw9OhRnD59Gs+ePcOgQYMgk8nqOzxqQKZOnQpvb2+4ublV2Tc5OblMP09PTyQnJ9dVeEREREREROVqUt8BUOPh6uoKOzs7aGlpYcuWLWjatCkmT56MkJAQnD59GpmZmUhLS4O+vj4AYNu2bWjRogWOHj1a4TfLEokEEolEcZyXlwcA6LvyMIo1dOt+UqSQEeJZJ+NKpVLFf3ft2oXU1FQkJydDKpVCLpdDJpMp+rwsJycHRkZGSudbtmyJnJycCq8h1XgxryQMzKkwMa/Cw5wKD3MqTMxr41LdPLGAQq9k27ZtCAgIQEpKCpKTkzF27Fj07t0bEokEIpEImpqair5aWlpQU1PDqVOnKiyghIWFITQ0tEz7IgcZdHRK6mweVFZdb84aHR2NOXPmIDQ0FEePHgUAPHz4EDdv3qzw3nK5HOnp6YqiHABkZGRAIpFwM9kGIjExsb5DoFrGnAoT8yo8zKnwMKfCxLw2DoWFhdXqxwIKvRI7OzssWbIEAGBpaYn169fjyJEjmD17NnR1dTFv3jx89tlnkMvlmD9/PkpKSpCdnV3heEFBQQgICFAc5+XlwdzcHMvT1FCsoV7n86H/U5crUBITE6Gnp4fc3FzMnj1bca6kpAR//PEHDhw4gPz8fKirK+fc1NQUZmZmGDhwoKLt3LlzaNeunVIbqV5pXt3d3aGhoVHf4VAtYE6FiXkVHuZUeJhTYWJeG5fSJyGqwgIKvZKX35piamqKe/fuwdjYGLGxsfj000+xbt06qKmpYeTIkejevTvU1CreakdTU1Np1UqpE/PcYGRkVOvxU/1xd3fHxYsXldrGjRsHa2trzJs3D1paWmWucXJyglgsViq6HD16FM7OzvyHqIHQ0NBgLgSGORUm5lV4mFPhYU6FiXltHKqbIxZQ6JW8/IUlEokUm8R6eHjg+vXrePDgAZo0aYLmzZujdevW6NixY32ESg2Mnp4ebG1tldp0dXVhZGSkaPfz80ObNm0QFhYGAJgxYwZcXFwQEREBb29vxMTE4Pz589i0aZPK4yciIiIiotcb38JDta5ly5Zo3rw5jh49inv37uH999+v75CokcjKylJ65MvZ2RnR0dHYtGkT7O3tsWfPHsTFxZUpxBAREREREdU1rkChWrN161bY2NjA2NgYycnJmDFjBmbNmgUrK6v6Do0aKLFYXOkxAPj6+sLX11c1AREREREREVWABRSqNVeuXEFQUBAePXoECwsLLFy4ELNmzarvsIiIiIiIiIj+MxZQqNrKWx0QFxen+H14eDjCw8NVFxARERERERGRinAPFCIiIiIiIiKiKrCAQkRERERERERUBRZQiIiIiIiIiIiqwAIKEREREREREVEVWEAhonoRHh4OkUiEmTNnVtovNjYW1tbW0NLSQteuXREfH6+aAImIiIiIiF7AAgoRqdy5c+fwzTffwM7OrtJ+SUlJGDlyJPz9/ZGWlgYfHx/4+PggIyNDRZESERERERE9xwIK1ZqffvoJ7u7uMDY2hr6+PpycnHDw4MH6DosamPz8fIwePRqbN29GixYtKu27du1aeHl5ITAwEDY2Nli2bBm6d++O9evXqyhaIiIiIiKi51hAoVpz4sQJuLu7Iz4+HqmpqXj33XcxaNAgpKWl1Xdo1IBMnToV3t7ecHNzq7JvcnJymX6enp5ITk6uq/CIiIiIiIjK1aS+A6DGY/v27Zg1axbu3LkDTU1NRbuPjw/09PTw/fffK/X/7LPPsG/fPvzyyy9wcHB4pXv1CjuC4ia6tRI3VU9muHed3yMmJga//fYbzp07V63+OTk5MDExUWozMTFBTk5OXYRHRERERERUIRZQqNp8fX0xffp0/Pzzz/D19QUA3Lt3D/v378ehQ4fK9JfJZHj69CkMDQ0rHFMikUAikSiO8/LyAACaanKoq8treQZUGalUWqfj3rx5EzNmzEB8fDzU1dUhlUohl8shk8kqvXdxcbHS+ZKSkjqNl6qn9PNnHoSDORUm5lV4mFPhYU6FiXltXKqbJ5FcLud3qVRtU6ZMQWZmpuJNKF988QW++uorXLt2DSKRSKnvqlWrEB4ejsuXL6NVq1bljhcSEoLQ0NAy7dHR0dDR0an9CVC9OXPmDMLDw6Gm9n9PDspkMohEIohEIsTGxkJdXV3pmgkTJuD999/H+++/r2j74YcfkJKSgsjISFWFTkREREREAlZYWIhRo0YhNzcX+vr6FfZjAYVeSVpaGnr06IFbt26hTZs2sLOzg6+vLxYvXqzULzo6GhMnTsS+ffsq3euivBUo5ubmeCMwBsUafIRHlTJCPOtkXKlUisTERLz99tu4c+eO0rmJEyfCysoKc+bMga2tbZlrR40ahcLCQsTFxSna+vbti65du+Krr76qk3ipekrz6u7uDg0NjfoOh2oBcypMzKvwMKfCw5wKE/PauOTl5aFly5ZVFlD4CA+9EgcHB9jb22P79u3w8PDApUuXsH//fqU+MTExmDBhAmJjY6vcKFRTU1NpP5VSJ+a5wcjIqFZjp/plaGhYZj+TZs2awdjYWLFHjp+fH9q0aYOwsDAAwKxZs+Di4oJ169bB29sbMTExSE1NxebNm/kPUQOhoaHBXAgMcypMzKvwMKfCw5wKE/PaOFQ3Ryyg0CubMGECIiMjcfv2bbi5ucHc3Fxx7ocffsD48eMRExMDb++635SUhCUrK0vpER9nZ2dER0dj0aJFWLBgASwtLREXF1fuahUiIiIiIqK6xAIKvbJRo0Zhzpw52Lx5M7Zv365oj46OxpgxY7B27Vr06tVL8aYUbW1tGBgY1Fe41ICJxeJKj4HnmxeXblpMRERERERUX9Sq7kKkzMDAAB988AGaNWsGHx8fRfumTZtQXFyMqVOnwtTUVPFrxowZ9RcsERERERERUS3gChSqkdu3b2P06NFK+5eUt3qAiIiIiIiISAhYQKFX8vjxY4jFYojFYnz99df1HQ4RERERERGRSrCAQq/EwcEBjx8/xsqVK2FlZVXf4RARERERERGpBAso9EoyMzPrOwQiIiIiIiIileMmskREREREREREVWABhYiIiIiIiIioCiygEFG9CA8Ph0gkwsyZMyvtFxsbC2tra2hpaaFr166Ij49XTYBEREREREQvYAGFao1YLMbgwYNhamoKXV1ddOvWDTt37qzvsKgBOnfuHL755hvY2dlV2i8pKQkjR46Ev78/0tLS4OPjAx8fH2RkZKgoUiIiIiIioudYQKFak5SUBDs7O/z444/4/fffMW7cOPj5+eHXX3+t79CoAcnPz8fo0aOxefNmtGjRotK+a9euhZeXFwIDA2FjY4Nly5ahe/fuWL9+vYqiJSIiIiIieo5v4aFK/frrr/joo4/w8OFDqKurIz09HQ4ODpg3bx7Cw8MBABMmTEBRURF27NihdO2MGTNw6NAh/PTTT3jvvfde6b69wo6guIlurc2DqpYZ7q2S+0ydOhXe3t5wc3PD8uXLK+2bnJyMgIAApTZPT0/ExcXVYYRERERERERlsYBClerTpw+ePn2KtLQ0ODo64vjx42jZsiXEYrGiz/HjxzFv3rxyr8/NzYWNjU2F40skEkgkEsVxXl4eAEBTTQ51dXntTIKqRSqV1um4UqkUu3btQmpqKpKTkyGVSiGXyyGTySq8d05ODoyMjJTOt2zZEjk5OXUWL1XPi3klYWBOhYl5FR7mVHiYU2FiXhuX6uaJBRSqlIGBAbp16waxWAxHR0eIxWLMmjULoaGhyM/PR25uLq5duwYXF5cy1+7evVux10VFwsLCEBoaWqZ9kYMMOjoltToXqlxdb84aHR2NOXPmIDQ0FEePHgUAPHz4EDdv3qzw3nK5HOnp6dDX11e0ZWRkQCKRcDPZBiIxMbG+Q6BaxpwKE/MqPMyp8DCnwsS8Ng6FhYXV6ieSy+X8MT9VKiAgAH/99Rd++eUXGBsb49SpUxgxYgTCw8Px6NEjBAYG4vbt20rXHDt2DO+99x42bNgAPz+/CscubwWKubk53giMQbEGH+FRpYwQzzoZVyqVIjExEUVFRRgxYgTU1dUV50pKSiASiaCmpob8/HylcwDQqVMnzJgxA9OnT1e0hYaG4ueff0ZqamqdxEvVU5pXd3d3aGho1Hc4VAuYU2FiXoWHORUe5lSYmNfGJS8vDy1btkRubq7SD29fxhUoVCVXV1d89913uHDhAjQ0NGBtbQ1XV1eIxWI8fvy4zOqT48ePY9CgQVizZk2lxRMA0NTUhKamZpn2E/PcYGRkVKvzoPrl7u6OixcvKrWNGzcO1tbWmDdvHrS0tMpc4+TkBLFYjNmzZyvajh49CmdnZ/5D1EBoaGgwFwLDnAoT8yo8zKnwMKfCxLw2DtXNEQsoVKXSfVDWrFmjKJa4uroiPDwcjx8/VvrmViwW47333sPKlSsxadKk+gqZGiA9PT3Y2toqtenq6sLIyEjR7ufnhzZt2iAsLAzA842IXVxcEBERAW9vb8TExOD8+fPYtGmTyuMnIiIiIqLXG19jTFVq0aIF7OzssHPnTri6ugIA+vbti99++w1//fWXoqhy7NgxeHt7Y/r06fjggw+Qk5ODnJwcPHr0qB6jp8YkKysL2dnZimNnZ2dER0dj06ZNsLe3x549exAXF1emEENERERERFTXuAKFqsXFxQXp6emKAoqhoSHeeOMN3L17F1ZWVgCAbdu2obCwEGFhYYoVBKXXvvjWHqJSL39dlPd14uvrC19fX9UEREREREREVAGuQKFqiYyMhFwuh7W1taItPT1dabVAVFQU5HJ5mV8snhAREREREVFjxwIKEREREREREVEVWEAhIiIiIiIiIqoCCyhERERERERERFVgAYWIiIiIiIiIqAosoBARERERERERVYEFFCKqF+Hh4RCJRJg5c2al/WJjY2FtbQ0tLS107doV8fHxqgmQiIiIiIjoBSygEJHKnTt3Dt988w3s7Owq7ZeUlISRI0fC398faWlp8PHxgY+PDzIyMlQUKRERERER0XMsoFCtePToEf73v//BysoK2traaNeuHaZPn47c3Nz6Do0amPz8fIwePRqbN29GixYtKu27du1aeHl5ITAwEDY2Nli2bBm6d++O9evXqyhaIiIiIiKi51hAoVpx584d3LlzB6tXr0ZGRgaioqKQkJAAf3//+g6NGpipU6fC29sbbm5uVfZNTk4u08/T0xPJycl1FR4REREREVG5mtR3ANR4uLq6ws7ODlpaWtiyZQuaNm2KyZMnIyQkBLa2tvjxxx8VfTt16oQVK1bgo48+QnFxMZo0ebUvtV5hR1DcRLe2p0CVyAz3rvN7xMTE4LfffsO5c+eq1T8nJwcmJiZKbSYmJsjJyamL8IiIiIiIiCrEAgq9km3btiEgIAApKSlITk7G2LFj0bt3b7i7u5fpm5ubC319/UqLJxKJBBKJRHGcl5cHANBUk0NdXV77E6AKSaXSOh335s2bmDFjBuLj46Gurg6pVAq5XA6ZTFbpvYuLi5XOl5SU1Gm8VD2lnz/zIBzMqTAxr8LDnAoPcypMzGvjUt08ieRyOb9LpWpxdXVFSUkJTp48qWjr2bMn+vXrh/DwcKW+Dx48wFtvvYWPPvoIK1asqHDMkJAQhIaGlmmPjo6Gjo5O7QVP9e7MmTMIDw+Hmtr/PTkok8kgEokgEokQGxsLdXV1pWsmTJiA999/H++//76i7YcffkBKSgoiIyNVFToREREREQlYYWEhRo0apVgEUBEWUKjaXF1d8eabb+Krr75StA0ePBhGRkb47rvvFG15eXlwd3eHoaEhfv75Z2hoaFQ4ZnkrUMzNzZGdnQ0jI6O6mQiplFQqRWJiIt5++23cuXNH6dzEiRNhZWWFOXPmwNbWtsy1o0aNQmFhIeLi4hRtffv2RdeuXZW+Dkn1SvPq7u5e6Z9xajyYU2FiXoWHORUe5lSYmNfGJS8vDy1btqyygMJHeOiVvPyHXyQSQSaTKY6fPn0KLy8v6OnpYe/evVX+ZaGpqQlNTc1y78O/aITF0NCwzH4mzZo1g7GxMRwcHAAAfn5+aNOmDcLCwgAAs2bNgouLC9atWwdvb2/ExMQgNTUVmzdv5tdHA8E/q8LDnAoT8yo8zKnwMKfCxLw2DtXNEd/CQ7UmLy8PHh4eaNq0KX7++WdoaWnVd0jUyGRlZSE7O1tx7OzsjOjoaGzatAn29vbYs2cP4uLiyl2tQkREREREVJe4AoVqRWnxpLCwEDt27EBeXp5iQ1hjY+Mye1sQAYBYLK70GAB8fX3h6+urmoCIiIiIiIgqwAIK1YrffvsNKSkpAIDOnTsrnbt58yYsLCzqISoiIiIiIiKi2sECClVbeasDXtzck/sRExERERERkVBxDxQiIiIiIiIioiqwgEJEREREREREVAUWUIiIiIiIiIiIqsACChERERERERFRFVhAISIiIiIiIiKqAgsoRFQvwsPDIRKJMHPmzEr7xcbGwtraGlpaWujatSvi4+NVEyAREREREdELWEChWiUWi9G9e3doamqic+fOiIqKqu+QqAE6d+4cvvnmG9jZ2VXaLykpCSNHjoS/vz/S0tLg4+MDHx8fZGRkqChSIiIiIiKi51hAoVpz8+ZNeHt7491330V6ejpmzpyJCRMm4ODBg/UdGjUg+fn5GD16NDZv3owWLVpU2nft2rXw8vJCYGAgbGxssGzZMnTv3h3r169XUbRERERERETPNanvAKjxuH//Prp27Yrp06djwYIFAJ6vEHB1dcWBAwdw6NAhdOjQAREREQAAGxsbnDp1CmvWrIGnp+cr3atX2BEUN9Gt9TlQxTLDvVVyn6lTp8Lb2xtubm5Yvnx5pX2Tk5MREBCg1Obp6Ym4uLg6jJCIiIiIiKgsFlCo2oyNjfHdd9/Bx8cHHh4esLKywscff4xp06ahf//+CA0NhZubm9I1np6ele5xIZFIIJFIFMd5eXkAAE01OdTV5XUyDyqfVCqt03GlUil27dqF1NRUJCcnQyqVQi6XQyaTVXjvnJwcGBkZKZ1v2bIlcnJy6ixeqp4X80rCwJwKE/MqPMyp8DCnwsS8Ni7VzRMLKPRKBg4ciIkTJ2L06NFwdHSErq4uwsLCADz/ZtfExESpv4mJCfLy8vDvv/9CW1u7zHhhYWEIDQ0t077IQQYdnZK6mQSVq643Z42OjsacOXMQGhqKo0ePAgAePnyImzdvVnhvuVyO9PR06OvrK9oyMjIgkUi4mWwDkZiYWN8hUC1jToWJeRUe5lR4mFNhYl4bh8LCwmr1YwGFXtnq1atha2uL2NhYpKamQlNTs8ZjBQUFKT2ikZeXB3NzcyxPU0OxhnpthEvVlBHyao9ZVZdUKkViYiL09PSQm5uL2bNnK86VlJTgjz/+wIEDB5Cfnw91deWcm5qawszMDAMHDlS0nTt3Du3atVNqI9Urzau7uzs0NDTqOxyqBcypMDGvwsOcCg9zKkzMa+NS+iREVVhAoVd2/fp13LlzBzKZDJmZmejatSsAoHXr1rh7965S37t370JfX7/c1ScAoKmpWW4B5sQ8NxgZGdV+8FRv3N3dcfHiRaW2cePGwdraGvPmzYOWllaZa5ycnCAWi5WKLkePHoWzszP/IWogNDQ0mAuBYU6FiXkVHuZUeJhTYWJeG4fq5ogFFHolz549w0cffYThw4fDysoKEyZMwMWLF9GqVSs4OTmVeawiMTERTk5O9RQtNSR6enqwtbVVatPV1YWRkZGi3c/PD23atFE8FjZjxgy4uLggIiIC3t7eiImJwfnz57Fp0yaVx09ERERERK83vsaYXsnChQuRm5uLdevWYd68eejSpQvGjx8PAJg8eTJu3LiBuXPn4vLly/j666+xe/duzJo1q56jpsYiKysL2dnZimNnZ2dER0dj06ZNsLe3x549exAXF1emEENERERERFTXuAKFqk0sFiMyMhLHjh1TbOr5/fffw97eHhs2bMCnn36K/fv3Y9asWVi7di3atm2LLVu2vPIrjOn1IRaLKz0GAF9fX/j6+qomICIiIiIiogqwgELV5urqWub1ThYWFsjNzVXqk5aWpurQiIiIiIiIiOoUH+EhIiIiIiIiIqoCCyhERERERERERFVgAYWIiIiIiIiIqAosoBARERERERERVYEFFCIiIiIiIiKiKrCAQkQKGzZsgJ2dHfT19aGvrw8nJyccOHCg0mtiY2NhbW0NLS0tdO3aFfHx8SqKloiIiIiISHVYQKFac+HCBYwcORLm5ubQ1taGjY0N1q5dW99h0Sto27YtwsPDkZqaivPnz6Nfv34YPHgwLl26VG7/pKQkjBw5Ev7+/khLS4OPjw98fHyQkZGh4siJiIiIiIjqVpP6DoCEIzU1Fa1atcKOHTtgbm6OpKQkTJo0Cerq6pg2bVp9h0fVMGjQIKXjFStWYMOGDThz5gzefPPNMv3Xrl0LLy8vBAYGAgCWLVuGxMRErF+/Hhs3blRJzERERERERKrAFSj0SmQyGcLCwtChQwdoa2vD3t4ee/bsAQCMHz8ea9euhYuLCzp27IiPPvoI48aNw08//VTPUVNNlJSUICYmBgUFBXByciq3T3JyMtzc3JTaPD09kZycrIoQiYiIiIiIVIYrUOiVhIWFYceOHdi4cSMsLS1x4sQJfPTRRzA2NoaLi0uZ/rm5uTA0NKxwPIlEAolEojjOy8sDAPRdeRjFGrq1P4FGLCPEUyX3uXjxIvr27YuioiI0a9YMsbGxsLS0hFQqLdM3JycHRkZGSudatmyJnJwcRdvL/yVhYF6FhzkVJuZVeJhT4WFOhYl5bVyqmyeRXC6X13EsJBASiQSGhoY4fPiw0oqECRMmoLCwENHR0Ur9k5KS4OLigv3798PDw6PcMUNCQhAaGlqmPTo6Gjo6OrU7AaoWqVSKBw8eoKCgAMnJyUhMTMSKFStgbm5epu+HH36I6dOno2/fvoq2+Ph47Nq1C9u2bVNl2ERERERERDVSWFiIUaNGITc3F/r6+hX24woUqrZr166hsLAQ7u7uSu3Pnj2Dg4ODUltGRgYGDx6MJUuWVFg8AYCgoCAEBAQojvPy8mBubo7laWoo1lCv3Qk0cqpagfKi6dOnw8vLCxcuXMAnn3xS5rypqSnMzMwwcOBARdu5c+fQrl07RZtUKkViYiLc3d2hoaGhstipbjGvwsOcChPzKjzMqfAwp8LEvDYupU9CVIUFFKq2/Px8AMD+/fvRpk0bpXOampqK3//xxx/o378/Jk2ahEWLFlU6pqamptK1pU7Mc4ORkVEtRE3/lVwuh1QqLfcvficnJ4jFYsyePVvRdvToUTg7O5fpr6GhwX88BIh5FR7mVJiYV+FhToWHORUm5rVxqG6OWEChanvjjTegqamJrKyscvc7AYBLly6hX79+GDNmDFasWKHiCOm/CgoKwoABA9CuXTs8ffoU0dHREIvFOHjwIADAz88Pbdq0QVhYGABgxowZcHFxQUREBLy9vRETE4Pz589j06ZN9TkNIiIiIiKiWscCClWbnp4e5syZg1mzZkEmk+Gdd95Bbm4uTp8+DX19fbz11lvo168fPD09ERAQgJycHACAuro6jI2N6zl6qo579+7Bz88P2dnZMDAwgJ2dHQ4ePKh4bCsrKwtqav/38i5nZ2dER0dj0aJFWLBgASwtLREXFwdbW9v6mgIREREREVGdYAGFXsmyZctgbGyMsLAw3LhxA82bN0f37t2xYMEC7NmzB/fv38eOHTuwY8cOxTXt27dHZmZm/QVN1fbtt99Wel4sFpdp8/X1ha+vbx1FRERERERE1DCwgEKvRCQSYcaMGZgxY0aZc3379kVISIjqgyIiIiIiIiKqY2pVdyEiIiIiIiIier2xgEJEREREREREVAUWUIiIiIiIiIiIqsACChERERERERFRFVhAISIiIiIiIiKqAgsoRKSwYcMG2NnZQV9fH/r6+nBycsKBAwcqvSY2NhbW1tbQ0tJC165dER8fr6JoiYiIiIiIVIcFFKoxuVyOSZMmwdDQECKRCOnp6fUdEv1Hbdu2RXh4OFJTU3H+/Hn069cPgwcPxqVLl8rtn5SUhJEjR8Lf3x9paWnw8fGBj48PMjIyVBw5ERERERFR3WIBhWosISEBUVFR+PXXX5GdnQ1bW1t89dVXsLCwgJaWFnr16oWzZ8/Wd5j0CgYNGoSBAwfC0tISXbp0wYoVK9CsWTOcOXOm3P5r166Fl5cXAgMDYWNjg2XLlqF79+5Yv369iiMnIiIiIiKqWyygUI1dv34dpqamcHZ2RuvWrfHjjz8iICAAS5YswW+//QZ7e3t4enri3r179R0q1UBJSQliYmJQUFAAJyencvskJyfDzc1Nqc3T0xPJycmqCJGIiIiIiEhlmtR3ANQ4jR07Ftu2bQMAiEQitG/fHiYmJpg4cSLGjRsHANi4cSP279+P7777DvPnz3+l8XuFHUFxE91aj7sxywz3Vsl9Ll68CCcnJxQVFaFZs2bYu3cv3njjjXL75uTkwMTERKnNxMQEOTk5qgiViIiIiIhIZVhAoRpZu3YtOnXqhE2bNuHcuXMQiURo06YNgoKCFH3U1NTg5uZW6WoEiUQCiUSiOM7LywMAaKrJoa4ur7sJNEJSqVQl9+nYsSPOnTuHvLw8/PjjjxgzZgwOHz5cYRGluLhYKbaSkhKleF/+LwkD8yo8zKkwMa/Cw5wKD3MqTMxr41LdPLGAQjViYGAAPT09qKuro3Xr1rhz5w5KSkrKXY1w+fLlCscJCwtDaGhomfZFDjLo6JTUetyNWX283aZ37944ePAg5s6diylTppQ5b2BgALFYDH19fUXb6dOnoaOjUybexMTEOo+XVI95FR7mVJiYV+FhToWHORUm5rVxKCwsrFY/FlCoXgUFBSEgIEBxnJeXB3Nzc7z77rswMjKqx8ioVGRkJExMTDBw4MAy51xdXZGTk6N0Ljw8HO7u7oo2qVSKxMREuLu7Q0NDQ2VxU91iXoWHORUm5lV4mFPhYU6FiXltXEqfhKgKCyhUK1q2bAl1dXXcvXtXqf3u3bto3bp1hddpampCU1OzTLuGhgb/oqkHQUFBGDBgANq1a4enT58iOjoax48fx8GDB6GhoQE/Pz+0adMGYWFhAIBZs2bBxcUF69atg7e3N2JiYpCamorNmzeXyR9zKkzMq/Awp8LEvAoPcyo8zKkwMa+NQ3VzxLfwUK1o2rQp3nrrLRw5ckTRJpPJcOTIkQrf4EINz7179+Dn5wcrKyv0798f586dw8GDB+Hu7g4AyMrKQnZ2tqK/s7MzoqOjsWnTJtjb22PPnj2Ii4uDra1tfU2BiIiIiIioTnAFCtWagIAAjBkzBo6OjujZsyciIyNRUFCgeCsPNXzffvttpefFYnGZNl9fX/j6+tZRRERERERERA0DCyhUa4YPH4779+8jODgYOTk56NatGxISEspsLEtERERERETU2PARHqqxmTNnIjMzU6lt2rRpuHXrFiQSCVJSUtCrV6/6CY6IiIiIiIioFrGAQkRERERERERUBRZQiIiIiIiIiIiqwAIKEREREREREVEVWEAhIiIiIiIiIqoCCyhERERERERERFVgAYWqzdXVFTNnzqzvMKgObdiwAXZ2dtDX14e+vj6cnJxw4MCBSq+JjY2FtbU1tLS00LVrV8THx6soWiIiIiIiItVhAYVqjVwuR3BwMExNTaGtrQ03NzdcvXq1vsOiV9C2bVuEh4cjNTUV58+fR79+/TB48GBcunSp3P5JSUkYOXIk/P39kZaWBh8fH/j4+CAjI0PFkRMREREREdUtFlCo1qxatQrr1q3Dxo0bkZKSAl1dXXh6eqKoqKi+Q6NqGjRoEAYOHAhLS0t06dIFK1asQLNmzXDmzJly+69duxZeXl4IDAyEjY0Nli1bhu7du2P9+vUqjpyIiIiIiKhuNanvAKhxKS4uxrRp0/D9999DQ0MDn376KZYuXQoAiIyMxKJFizB48GAAwPbt22FiYoK4uDiMGDHile7TK+wIipvo1nr8jVlmuLdK71dSUoLY2FgUFBTAycmp3D7JyckICAhQavP09ERcXJwKIiQiIiIiIlIdFlDolWzbtg3+/v44e/Yszp8/j0mTJqFdu3bo378/cnJy4ObmpuhrYGCAXr16ITk5ucICikQigUQiURzn5eUBADTV5FBXl9ftZBoZqVSqkvtcvHgRffv2RVFREZo1a4bY2FhYWlqWe/+cnBwYGRkpnWvZsiVycnIUbS//l4SBeRUe5lSYmFfhYU6FhzkVJua1calunmqtgPLkyRM0b968toajBsrc3Bxr1qyBSCSClZUVLl68iDVr1uDNN98EAJiYmCj1NzExQU5OToXjhYWFITQ0tEz7IgcZdHRKajf4Rk5Vm7NKpVKsXr0aBQUFSE5Oxscff4wVK1bA3Ny8TF+5XI709HTo6+sr2jIyMiCRSMrEm5iYWOexk+oxr8LDnAoT8yo8zKnwMKfCxLw2DoWFhdXqV6MCysqVK2FhYYHhw4cDAIYNG4Yff/wRrVu3Rnx8POzt7WsyLDUCb7/9NkQikeLYyckJERERkMtrtlokKChI6RGQvLw8mJubY3maGoo11P9zvEKSEeKp8ntOnz4dXl5euHDhAj755JMy501NTWFmZoaBAwcq2s6dO4d27dop2qRSKRITE+Hu7g4NDQ2VxU51i3kVHuZUmJhX4WFOhYc5FSbmtXEpfRKiKjUqoGzcuBE7d+4E8LyilpiYiAMHDmD37t0IDAzEoUOHajIsNWKtW7cGANy9exempqaK9rt376Jbt24VXqepqQlNTc0y7SfmucHIyKjW46RXJ5fLIZVKy/2L38nJCWKxGLNnz1a0HT16FM7OzmX6a2ho8B8PAWJehYc5FSbmVXiYU+FhToWJeW0cqpujGhVQcnJyFMv5f/31VwwbNgweHh6wsLBAr169ajIkNRIpKSlKx2fOnIGlpSU6duyI1q1b48iRI4qCSV5eHlJSUvDpp5/WQ6RUE0FBQRgwYADatWuHp0+fIjo6GmKxGAcPHgQA+Pn5oU2bNggLCwMAzJgxAy4uLoiIiIC3tzdiYmJw/vx5bNq0qT6nQUREREREVOtq9BrjFi1a4O+//wYAJCQkKDYOlcvlKCnhvhVClpWVhYCAAFy5cgU//PADvvzyS8yYMQMikQgzZ87E8uXL8fPPP+PixYvw8/ODmZkZfHx86jtsqqZ79+7Bz88PVlZW6N+/P86dO4eDBw/C3d0dwPP8Z2dnK/o7OzsjOjoamzZtgr29Pfbs2YO4uDjY2trW1xSIiIiIiIjqRI1WoAwdOhSjRo2CpaUlHj58iAEDBgAA0tLS0Llz51oNkBoWPz8//Pvvv+jZsyfU1dUxY8YMTJo0CQAwd+5cFBQUYNKkSXjy5AneeecdJCQkQEtLq56jpur69ttvKz0vFovLtPn6+sLX17eOIiIiIiIiImoYalRAWbNmDSwsLPD3339j1apVaNasGQAgOzsbU6ZMqdUAqeF48ZvnDRs2lDkvEomwdOlSLF26VIVREREREREREdW9GhVQNDQ0MGfOnDLts2bN+s8BERERERERERE1NDXaAwUAvv/+e7zzzjswMzPDrVu3AACRkZHYt29frQVHRERERERERNQQ1KiAsmHDBgQEBGDAgAF48uSJYuPY5s2bIzIysjbjIyIiIiIiIiKqdzUqoHz55ZfYvHkzFi5cCHV1dUW7o6MjLl68WGvBERERERERERE1BDUqoNy8eRMODg5l2jU1NVFQUPCfgyIiIiIiIiIiakhqVEDp0KED0tPTy7QnJCTAxsbmv8ZERBUICwtDjx49oKenh1atWsHHxwdXrlyp8rrY2FhYW1tDS0sLXbt2RXx8vAqiJSIiIiIiEo4aFVACAgIwdepU7Nq1C3K5HGfPnsWKFSsQFBSEuXPn1naMRPT/HT9+HFOnTsWZM2eQmJgIqVQKDw+PSld+JSUlYeTIkfD390daWhp8fHzg4+ODjIwMFUZORERERETUuNWogDJhwgSsXLkSixYtQmFhIUaNGoUNGzZg7dq1GDFiRG3HSA1AUVERxo4di65du6JJkybw8fEpt59EIsHChQvRvn17aGpqwsLCAt99951qgxWwhIQEjB07Fm+++Sbs7e0RFRWFrKwspKamVnjN2rVr4eXlhcDAQNjY2GDZsmXo3r071q9fr8LIiYiIiIiIGrcmr3pBcXExoqOj4enpidGjR6OwsBD5+flo1apVXcRHDURJSQm0tbUxffp0/PjjjxX2GzZsGO7evYtvv/0WnTt3RnZ2NmQymQojfb3k5uYCAAwNDSvsk5ycjICAAKU2T09PxMXF1WVoREREREREgvLKBZQmTZpg8uTJ+PPPPwEAOjo60NHRqfXAqO79+uuv+Oijj/Dw4UOoq6sjPT0dDg4OmDdvHsLDwwE8X21UVFSEHTt2YMOGDQCA06dP48mTJ2XGS0hIwPHjx3Hjxg3FN/QWFhY1iq1X2BEUN9Gt0bX1KTPcW2X3kslkmDlzJnr37g1bW9sK++Xk5MDExESpzcTEBDk5OXUdIhERERERkWC8cgEFAHr27Im0tDS0b9++tuMhFerTpw+ePn2KtLQ0ODo64vjx42jZsiXEYrGiz/HjxzFv3rxqjffzzz/D0dERq1atwvfffw9dXV28//77WLZsGbS1tcu9RiKRQCKRKI7z8vIAAJpqcqiry2s+uXoilUpVdq9p06YhIyMDx44dq/K+xcXFSn1KSkoAqCbe0nuo8rOhuse8Cg9zKkzMq/Awp8LDnAoT89q4VDdPNSqgTJkyBbNnz8Y///yDt956C7q6yisF7OzsajIsqZiBgQG6desGsVgMR0dHiMVizJo1C6GhocjPz0dubi6uXbsGFxeXao1348YNnDp1ClpaWti7dy8ePHiAKVOm4OHDh9i6dWu514SFhSE0NLRM+yIHGXR0Sv7T/OqDqt5us2nTJqSkpOCzzz7D77//jt9//73CvgYGBhCLxdDX11e0nT59Gjo6Oip9G09iYqLK7kWqw7wKD3MqTMyr8DCnwsOcChPz2jgUFhZWq59ILpe/8o/51dTK7j0rEokgl8shEokUP92mhi8gIAB//fUXfvnlFxgbG+PUqVMYMWIEwsPD8ejRIwQGBuL27dtK14wdOxZPnjwps4eGh4cHTp48iZycHBgYGAAAfvrpJ3z44YcoKCgodxVKeStQzM3NkZ2dDSMjo9qfcCMnl8sxc+ZM7Nu3D4mJibC0tKzymlGjRqGwsFApX3379kXXrl3x1Vdf1WG0z0mlUiQmJsLd3R0aGhp1fj9SDeZVeJhTYWJehYc5FR7mVJiY18YlLy8PLVu2RG5urtIPnl9WoxUoN2/erHFg1LC4urriu+++w4ULF6ChoQFra2u4urpCLBbj8ePH1V59AgCmpqZo06aNongCADY2NpDL5fjnn3/K/WZfU1MTmpqaZdo1NDT4F005pkyZgujoaOzbtw+GhoZ4+PAhgOerTEoLVH5+fmjTpg3CwsIAALNmzYKLiwvWrVsHb29vxMTEIDU1FZs3b1bpZ8ycChPzKjzMqTAxr8LDnAoPcypMzGvjUN0c1aiAwr1PhKN0H5Q1a9YoiiWurq4IDw/H48ePMXv27GqP1bt3b8TGxiI/Px/NmjUDAPz1119QU1ND27Zt6yT+103pRr6urq5K7Vu3bsXYsWMBAFlZWUqrxJydnREdHY1FixZhwYIFsLS0RFxcXKUbzxIREREREZGyGhVQtm/fXul5Pz+/GgVDqteiRQvY2dlh586dWL9+PYDnj3cMGzYMUqlUaQXKH3/8gWfPnuHRo0d4+vQp0tPTAQDdunUD8PxRkWXLlmHcuHEIDQ3FgwcPEBgYiPHjx1e4iSy9muo8cffiJsClfH194evrWwcRERERERERvR5qVECZMWOG0rFUKkVhYSGaNm0KHR0dFlAaGRcXF6SnpytWNRgaGuKNN97A3bt3YWVlpeg3cOBA3Lp1S3Hs4OAA4P++qW/WrBkSExPxv//9D46OjjAyMsKwYcOwfPly1U2GiIiIiIiIqA7UqIDy+PHjMm1Xr17Fp59+isDAwP8cFKlWZGQkIiMjldpKV5e8KDMzs8qxrK2tudM0ERERERERCU7Z1+nUkKWlJcLDw8usTiEiIiIiIiIiauxqrYACAE2aNMGdO3dqc0giIiIiIiIionpXo0d4fv75Z6VjuVyO7OxsrF+/Hr17966VwIiIiIiIiIiIGooaFVB8fHyUjkUiEYyNjdGvXz9ERETURlxERERERERERA1GjQooMpmstuMgIiIiIiIiImqwarQHytKlS1FYWFim/d9//8XSpUv/c1BEVL6wsDD06NEDenp6aNWqFXx8fHDlypUqr4uNjYW1tTW0tLTQtWtXxMfHqyBaIiIiIiIi4ahRASU0NBT5+fll2gsLCxEaGvqfg6LG6auvvoKNjQ20tbVhZWWF7du313dIgnP8+HFMnToVZ86cQWJiIqRSKTw8PFBQUFDhNUlJSRg5ciT8/f2RlpYGHx8f+Pj4ICMjQ4WRExERERERNW41eoRHLpdDJBKVab9w4QIMDQ3/c1DU+GzYsAFBQUHYvHkzevTogbNnz2LixIlo0aIFBg0aVN/hCUZCQoLScVRUFFq1aoXU1FT07du33GvWrl0LLy8vBAYGAgCWLVuGxMRErF+/Hhs3bqzzmImIiIiIiITglQooLVq0gEgkgkgkQpcuXZSKKCUlJcjPz8fkyZNrPUiqf9u3b8esWbNw584daGpqKtp9fHygp6eH69ev45NPPsHw4cMBAB07dsS5c+ewcuXKGhVQeoUdQXET3VqLX1Uyw71Ver/c3FwAqLRwmZycjICAAKU2T09PxMXF1WVoREREREREgvJKBZTIyEjI5XKMHz8eoaGhMDAwUJxr2rQpLCws4OTkVOtBUv3z9fXF9OnT8fPPP8PX1xcAcO/ePezfvx+HDh3CnDlzoKWlpXSNtrY2zp49C6lUCg0NjXLHlUgkkEgkiuO8vDwAgKaaHOrq8jqaTd2RSqUqu5dMJsOMGTPg7OwMKyurCu+dk5MDIyMjpfMtW7ZETk6OSuItvYcqPxuqe8yr8DCnwsS8Cg9zKjzMqTAxr41LdfMkksvlr/xd6vHjx+Hs7FzhN8UkTFOmTEFmZqZiA9IvvvgCX331Fa5du4aFCxdi69at+PXXX9G9e3ekpqbivffew927d3Hnzh2YmpqWO2ZISEi5++ZER0dDR0enTufT2G3cuBGpqakICwtDy5YtK+z34YcfYvr06UqP+MTHx2PXrl3Ytm2bKkIlIiIiIiJqsAoLCzFq1Cjk5uZCX1+/wn412gPFxcVF8fuioiI8e/ZM6XxlN6TGa+LEiejRowdu376NNm3aICoqCmPHjoVIJMLixYuRk5ODt99+G3K5HCYmJhgzZgxWrVoFNbWK9yoOCgpSerwkLy8P5ubmWJ6mhmINdVVMq1ZlhHiq5D4zZsxARkYGTp06hQ4dOlTa19TUFGZmZhg4cKCi7dy5c2jXrp1SW12RSqVITEyEu7s7i64CwrwKD3MqTMyr8DCnwsOcChPz2riUPglRlRoVUAoLCzF37lzs3r0bDx8+LHO+pKSkJsNSA+fg4AB7e3ts374dHh4euHTpEvbv3w/g+eM63333Hb755hvcvXsXpqam2LRpE/T09GBsbFzhmJqamkp7qpQ6Mc8NRkZGdTaXxkoul+N///sf9u3bB7FYDEtLyyqvcXJyglgsxuzZsxVtR48eVfkqMg0NDf7jIUDMq/Awp8LEvAoPcyo8zKkwMa+NQ3VzVKPXGAcGBuLo0aPYsGEDNDU1sWXLFoSGhsLMzIyvrhW4CRMmICoqClu3boWbmxvMzc2VzmtoaKBt27ZQV1dHTEwM3nvvvUpXoNCrmTp1Knbs2IHo6Gjo6ekhJycHOTk5+PfffxV9/Pz8EBQUpDieMWMGEhISEBERgcuXLyMkJATnz5/HtGnT6mMKREREREREjVKNvrP95Zdf8PXXX+ODDz5AkyZN0KdPHyxatAifffYZdu7cWdsxUgMyatQo/PPPP9i8eTPGjx+vaP/rr7+wY8cOXL16FWfPnsWIESOQkZGBzz77rB6jFZ4NGzYgNzcXrq6uMDU1VfzatWuXok9WVhays7MVx87OzoiOjsamTZtgb2+PPXv2IC4uDra2tvUxBSIiIiIiokapRo/wPHr0CB07dgTwfL+TR48eAQDeeecdfPrpp7UXHTU4BgYG+OCDD7B//374+Pgo2ktKShAREYErV65AQ0MD7777LpKSkmBhYVFvsQpRdfZ8FovFZdp8fX0Vb08iIiIiIiKiV1ejAkrHjh1x8+ZNtGvXDtbW1ti9ezd69uyJX375Bc2bN6/lEKmhuX37NkaPHq20d4mNjQ3S0tLqMSoiIiIiIiKiulOjR3jGjRuHCxcuAADmz5+Pr776ClpaWpg1axYCAwNrNUBqOB4/foy9e/dCLBZj6tSp9R0OERERERERkcrUaAXKrFmzFL93c3PD5cuXkZqais6dO8POzq7WgqOGxcHBAY8fP8bKlSthZWVV3+EQERERERERqUyNCigvKioqQvv27dG+ffvaiIcasMzMzPoOgYiIiIiIiKhe1OgRnpKSEixbtgxt2rRBs2bNcOPGDQDA4sWL8e2339ZqgERERERERERE9a1GBZQVK1YgKioKq1atQtOmTRXttra22LJlS60FR0RERERERETUENSogLJ9+3Zs2rQJo0ePhrq6uqLd3t4ely9frrXgiEhZWFgYevToAT09PbRq1Qo+Pj64cuVKldfFxsbC2toaWlpa6Nq1K+Lj41UQLRERERERkXDUqIBy+/ZtdO7cuUy7TCaDVCr9z0FR43T37l2MHTsWZmZm0NHRgZeXF65evVrfYQnK8ePHMXXqVJw5cwaJiYmQSqXw8PBAQUFBhdckJSVh5MiR8Pf3R1paGnx8fODj44OMjAwVRk5ERERERNS41aiA8sYbb+DkyZNl2vfs2QMHB4f/HBQ1PnK5HD4+Prhx4wb27duHtLQ0tG/fHm5ubpV+c0+vJiEhAWPHjsWbb74Je3t7REVFISsrC6mpqRVes3btWnh5eSEwMBA2NjZYtmwZunfvjvXr16swciIiIiIiosatRm/hCQ4OxpgxY3D79m3IZDL89NNPuHLlCrZv345ff/21tmOkBuLp06eYPHky4uLioK+vj7lz52Lfvn3o1q0bpkyZgjNnziAjIwNvvvkmAGDDhg1o3bo1fvjhB0yYMKGeoxem3NxcAIChoWGFfZKTkxEQEKDU5unpibi4uLoMjYiIiIiISFBeqYBy48YNdOjQAYMHD8Yvv/yCpUuXQldXF8HBwejevTt++eUXuLu711Ws/6+9uw+Lqs7/P/4CHAY0MUFFKTC8WcNUwtvA3cIVRTRLt+y7taX+VvOrqwmyXy3ylqxoLVatTNfcDdvS7rbUXV1sUsksZINgN2z1m2lLa2C3yygYDjK/P7yc784KzFBz45yej+vyujyf8zln3oeXZ7p4d27gZ9nZ2Xr77be1Y8cORUdHa9myZXrvvfd07bXXqqGhQZIUFhbmmB8cHCyz2awDBw602EBpaGhwbCtJVqtVknT9r95Qo6mDF4/GOypXpPvss5qampSZmamUlBT169evxdvnampqFBUV5bS+S5cuqqmp8cktdxc+g9v7jIVcjYdMjYlcjYdMjYdMjYlcA4u7ObWpgdK3b19VV1erW7du+tGPfqTIyEi9//77io6O/lZFInCcOnVKmzdv1pYtWzR69GhJ0jPPPKOYmBhJ0tVXX624uDjl5OToN7/5jTp06KDVq1frn//8p6qrq1vcb15ennJzcy8aX5LUpPbtz3nnYLzIlw9n3bBhg8rKypSXl9fq59rtdlVUVCgiIsIxVllZqYaGBp/Wa7FYfPZZ8B1yNR4yNSZyNR4yNR4yNSZyDQz19fVuzWtTA8Vutzst//nPf+b5Ft8Tx44dk81m0/Dhwx1jnTp1Ur9+/SRJJpNJr776qmbMmKHIyEiFhIQoLS1NGRkZF/27+Xc5OTlOt5dYrVbFxsbqwfJgNZpCWtzuUuWrK1AyMzNVWVmpAwcOKD4+vtW5PXr0UExMjMaPH+8Ye/fddxUXF+c05i02m00Wi0VjxoyRyWTy+ufBN8jVeMjUmMjVeMjUeMjUmMg1sFy4E8KVb/UMlAta+8UY3z9DhgxRRUWFamtrdfbsWXXt2lUjRozQ0KFDW9zGbDbLbDZfNL7/3jRFRUV5s9yAZLfbdc8992j79u0qKipS3759XW6TnJysoqIi/fKXv3SM7d27VykpKT79MjeZTPzHw4DI1XjI1JjI1XjI1HjI1JjINTC4m1Gb3sITFBSkoKCgi8ZgfL169ZLJZNK7777rGKutrdX//u//XjS3U6dO6tq1qz788EOVlpbq5ptv9mWphjZ37lw999xz2rJlizp27KiamhrV1NTozJkzjjlTp05VTk6OYzkzM1OFhYXKz8/X4cOHtWLFCpWWlmrevHn+OAQAAAAACEhtvoVn+vTpjisGvvnmG82ePVsdOjg/7PPVV1/1XIW4JHTs2FHTpk3TwoULFRkZqW7dumn58uUKDg52NNFefvllde3aVXFxcXr//feVmZmpSZMmaezYsX6u3jjWr18vSUpNTXUaf+aZZzR9+nRJUlVVlYKD/683mpKSoi1btmjJkiW6//771bdvX23btk0DBgzwVdkAAAAAEPDa1ECZNm2a0/Kdd97p0WJwafv1r3+t2bNn68Ybb3S8xviTTz5xvHmnurpa2dnZOnnypHr06KGpU6dq6dKlfq7aWNy5ba6oqOiisSlTpmjKlCleqAgAAAAAvh/a1EB55plnvFUHAkDHjh31/PPPO5br6uqUm5urWbNmSZLmz5+v+fPn+6s8AAAAAAC85js9RBbfL+Xl5Tp8+LCGDx+u2tpaPfDAA5LEM04AAAAAAIZHAwVt8thjj+nIkSMKDQ3VkCFD9NZbb6lLly7+LgsAAAAAAK+igQK3JSUlqayszN9lAAAAAADgc216jTEAAAAAAMD3EQ0UAAAAAAAAF2igAAEkLy9Pw4YNU8eOHdWtWzdNmjRJR44ccbndyy+/rKuvvlphYWEaOHCgdu3a5YNqAQAAAMA4aKAAAeTNN9/U3LlzdfDgQVksFtlsNo0dO1Z1dXUtbvPOO+/o9ttv14wZM1ReXq5JkyZp0qRJqqys9GHlAAAAABDYaKDAI7766ivdc8896tevn8LDwxUXF6f58+ertrbW36UZSmFhoaZPn65rrrlGiYmJKigoUFVVVasP9127dq3GjRunhQsXKiEhQStXrtTgwYP15JNP+rByAAAAAAhsNFDgEf/85z/16aef6rHHHlNlZaUKCgpUWFioGTNm+Ls0Q7vQoIqMjGxxTnFxsdLS0pzG0tPTVVxc7NXaAAAAAMBIeI0xvpXU1FQNGDBA7dq103PPPaeBAwdq3759jvW9e/fWQw89pDvvvFONjY1q165t/9RG5O1RY7sOni7b6z5+ZILPPqupqUlZWVkaOXKkBgwY0OK8mpoaRUdHO41FR0erpqbG2yUCAAAAgGHQQMG3tnnzZs2ZM0dvv/12s+tra2sVERHRavOkoaFBDQ0NjmWr1SpJMgfbFRJi92zBPmCz2Xz2WfPmzVNlZaX27dvn8nMbGxud5pw7d06Sb+q98Bm+/NnA+8jVeMjUmMjVeMjUeMjUmMg1sLibU5Ddbg+831Lhd6mpqbJarXrvvfeaXf/FF19oyJAhuvPOO/XQQw+1uJ8VK1YoNzf3ovEtW7aoffv2HqvXaDZu3KiSkhI9/PDDF11d8p9mzpypm266STfddJNjbOvWrSopKdGaNWu8XCkAAAAAXNrq6+t1xx13OC4CaAkNFHwrqamp6tu3r55++umL1lmtVo0ZM0aRkZHasWOHTCZTi/tp7gqU2NhYVVdXKyoqyiu1BzK73a6srCxt375dFotFffv2dbnNHXfcofr6em3bts0xdv3112vgwIFat26dF6s9z2azyWKxaMyYMa3+W0BgIVfjIVNjIlfjIVPjIVNjItfAYrVa1aVLF5cNFG7hwbfWocPFzyg5deqUxo0bp44dO+q1115z+WVhNptlNpsvGjeZTHzRNOMXv/iFtmzZou3btysyMlJffvmlJKlTp04KDw+XJE2dOlVXXHGF8vLyJEkLFizQDTfcoMcff1wTJkzQCy+8oLKyMj399NM+/RmTqTGRq/GQqTGRq/GQqfGQqTGRa2BwNyPewgOPsVqtGjt2rEJDQ7Vjxw6FhYX5uyTDWb9+vWpra5WamqoePXo4/rz44ouOOVVVVaqurnYsp6SkaMuWLdq4caMSExP1yiuvaNu2ba0+eBYAAAAA4IwrUOARF5on9fX1eu6552S1Wh0PhO3atatCQkL8XKExuHPHXVFR0UVjU6ZM0ZQpU7xQEQAAAAB8P9BAgUe89957KikpkST16dPHad3x48d11VVX+aEqAAAAAAA8gwYKvpX/vMohNTXVrasjAAAAAAAIRDwDBQAAAAAAwAUaKAAAAAAAAC7QQAEAAAAAAHCBBgoAAAAAAIALNFAAAAAAAABcoIECBJC8vDwNGzZMHTt2VLdu3TRp0iQdOXLE5XYvv/yyrr76aoWFhWngwIHatWuXD6oFAAAAAOOggQKP4hd173rzzTc1d+5cHTx4UBaLRTabTWPHjlVdXV2L27zzzju6/fbbNWPGDJWXl2vSpEmaNGmSKisrfVg5AAAAAAQ2GijwGH5R977CwkJNnz5d11xzjRITE1VQUKCqqiqVlZW1uM3atWs1btw4LVy4UAkJCVq5cqUGDx6sJ5980oeVAwAAAEBga+fvAhB4XnnlFeXm5uro0aNq3769kpKStH37dqdf1CVp5cqVslgsevLJJ7Vhw4Y2fcaIvD1qbNfBG+V71cePTPDp59XW1kqSIiMjW5xTXFys7Oxsp7H09HRt27bNm6UBAAAAgKHQQEGbVFdX6/bbb9eqVas0efJknTp1Sm+99Zbsdvu3+kW9oaFBDQ0NjmWr1SpJMgfbFRJi98oxeJPNZvPZZzU1NSkzM1MpKSnq169fi59dU1OjqKgop/VdunRRTU2NT+q98Bm+/NnA+8jVeMjUmMjVeMjUeMjUmMg1sLibEw0UtEl1dbUaGxv1k5/8RD179pQkDRw4UNL5X9Sjo6Od5kdHR6umpqbF/eXl5Sk3N/ei8SVJTWrf/pwHK/cNXz7zZcOGDSorK1NeXl6rn2u321VRUaGIiAjHWGVlpRoaGnxar8Vi8dlnwXfI1XjI1JjI1XjI1HjI1JjINTDU19e7NY8GCtokMTFRo0eP1sCBA5Wenq6xY8fq1ltvVefOnb/V/nJycpyuWrFarYqNjdWD5cFqNIV4qmyfqVyR7pPPyczMVGVlpQ4cOKD4+PhW5/bo0UMxMTEaP368Y+zdd99VXFyc05i32Gw2WSwWjRkzRiaTyeufB98gV+MhU2MiV+MhU+MhU2Mi18By4U4IV2igoE1CQkJksVj0zjvv6PXXX9cTTzyhxYsXq6SkRN27d9fJkyed5p88eVLdu3dvcX9ms1lms/mi8f33pikqKsrj9Qc6u92ue+65R9u3b1dRUZH69u3rcpvk5GQVFRXpl7/8pWNs7969SklJ8emXuclk4j8eBkSuxkOmxkSuxkOmxkOmxkSugcHdjHgLD9osKChII0eOVG5ursrLyxUaGqrXXntNycnJ2rNnj9Nci8Wi5ORkP1VqPHPnztVzzz2nLVu2qGPHjqqpqVFNTY3OnDnjmDN16lTl5OQ4ljMzM1VYWKj8/HwdPnxYK1asUGlpqebNm+ePQwAAAACAgMQVKGiTkpIS7dmzR2PHjlW3bt1UUlKizz//XAkJCbruuut0ww03KD8/XxMmTNALL7yg0tJSbdy40d9lG8b69eslSampqU7jzzzzjKZPny5JqqqqUnDw//VGU1JStGXLFi1ZskT333+/+vbtq23btmnAgAG+KhsAAAAAAh4NFLRJRESE9u/frzVr1shqtapnz57Kz89XRkaGJPGLupfZ7a7fTFRUVHTR2JQpUzRlyhQvVAQAAAAA3w80UNAmCQkJKiwsbHE9v6gDAAAAAIyIZ6AAAAAAAAC4QAMFAAAAAADABRooAAAAAAAALtBAAQAAAAAAcIEGCgAAAAAAgAs0UIAAkpeXp2HDhqljx47q1q2bJk2apCNHjrjc7uWXX9bVV1+tsLAwDRw4ULt27fJBtQAAAABgHDRQgADy5ptvau7cuTp48KAsFotsNpvGjh2rurq6Frd55513dPvtt2vGjBkqLy/XpEmTNGnSJFVWVvqwcgAAAAAIbDRQ4FFVVVWaMGGC2rdvr27dumnhwoVqbGz0d1mGUVhYqOnTp+uaa65RYmKiCgoKVFVVpbKysha3Wbt2rcaNG6eFCxcqISFBK1eu1ODBg/Xkk0/6sHIAAAAACGw0UOAx586d04QJE3T27Fm988472rx5swoKCrRs2TJ/l2ZYtbW1kqTIyMgW5xQXFystLc1pLD09XcXFxV6tDQAAAACMpJ2/C0BgaWho0MKFC/XCCy/IarVq6NChWr16tYYNG6bXX39dH3zwgd544w1FR0fr2muv1cqVK3XvvfdqxYoVCg0NbXZ/DQ0NjmWr1SpJuv5Xb6jR1MFnx+UplSvSffZZTU1NyszMVEpKivr16yebzdbsvJqaGkVFRTmt79Kli2pqalrcxpMufIYvPgu+Q67GQ6bGRK7GQ6bGQ6bGRK6Bxd2cgux2u93LtcBAMjMz9corr2jTpk3q2bOnVq1apR07dujo0aNas2aNduzYoYqKCsf848ePq1evXnrvvfeUlJR00f5WrFih3Nzci8a3bNmi9u3be/NQAt6GDRtUVlamvLw8denSpcV5t956q+bPn6/rr7/eMbZr1y69+OKL2rx5sy9KBQAAAIBLVn19ve644w7V1tYqIiKixXlcgQK31dXVaf369SooKFBGRoYk6emnn5bFYtFvf/tb1dTUKDo62mmbC8s1NTXN7jMnJ0fZ2dmOZavVqtjYWD1YHqxGU4iXjsR7fHUFSmZmpiorK3XgwAHFx8e3OrdHjx6KiYnR+PHjHWPvvvuu4uLinMa8xWazyWKxaMyYMTKZTF7/PPgGuRoPmRoTuRo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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 2"
      ],
      "metadata": {
        "id": "KQjf3dRCmRPy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = [12, 7]\n",
        "xgb.plot_importance(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "VmPAwSlcOk0I",
        "outputId": "25e3f80d-0bb4-4880-dc85-98ac56123937"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature importance'}, xlabel='F score', ylabel='Features'>"
            ]
          },
          "metadata": {},
          "execution_count": 153
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 3"
      ],
      "metadata": {
        "id": "L8Y2JfDSpRhB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = [12, 7]\n",
        "xgb.plot_importance(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "eiTU6d3QmUAj",
        "outputId": "336d5b63-7657-40a9-e8c2-9ec8118ed7ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature importance'}, xlabel='F score', ylabel='Features'>"
            ]
          },
          "metadata": {},
          "execution_count": 305
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 4"
      ],
      "metadata": {
        "id": "n5YjKGQqvpjk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = [12, 7]\n",
        "xgb.plot_importance(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "aJjRpUezpUfc",
        "outputId": "2a137d3f-b6af-4041-bbb3-eaed0a6acae1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature importance'}, xlabel='F score', ylabel='Features'>"
            ]
          },
          "metadata": {},
          "execution_count": 343
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prueba 5"
      ],
      "metadata": {
        "id": "ekchPxXk2E-l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = [12, 7]\n",
        "xgb.plot_importance(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "WPx7cWC0vrtq",
        "outputId": "e74e30fd-ccb0-47ee-f03b-126023306aa4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature importance'}, xlabel='F score', ylabel='Features'>"
            ]
          },
          "metadata": {},
          "execution_count": 401
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testeo del modelo con el set de testeo\n",
        "\n",
        "La idea de esta sección es ver algunas métricas del modelo final."
      ],
      "metadata": {
        "id": "TwUfA1N42OUT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test = model.predict(X_test)"
      ],
      "metadata": {
        "id": "nNhpkbsO2IGK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0_1m9112iUL",
        "outputId": "535ed67e-a01b-476c-f62a-829c04fe913f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Realizo una matriz de confusión."
      ],
      "metadata": {
        "id": "EzwK5wDNU9MH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matriz_confusion_test = confusion_matrix(y_test, y_pred_test)"
      ],
      "metadata": {
        "id": "7xCUx0ll2q2g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(matriz_confusion_test, annot=True, cmap='Greens', fmt='g')\n",
        "plt.title('Matriz de Confusión')\n",
        "plt.xlabel('Predicción')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "8opUEJUtb3lM",
        "outputId": "aa9b3571-a1fc-4e08-e5bb-3b4d5cdd90de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculo de las metricas."
      ],
      "metadata": {
        "id": "VXKC0OS0VFuf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rmse = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
        "print(\"RMSE: %f\" % (rmse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAvWkYj23NVE",
        "outputId": "8f0667bf-5cd8-40f7-8958-12978a02f9ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 0.042441\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])\n",
        "print(\"ROC AUC SCORE:\", (roc_auc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMPhi9ne2jk6",
        "outputId": "f234d6c7-07c8-4809-91f8-c5bffc693293"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC AUC SCORE: 0.9999789613911161\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Guardo las predicciones en un archivo.csv."
      ],
      "metadata": {
        "id": "honSv5j4VLnn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_predicciones = pd.DataFrame(y_test)"
      ],
      "metadata": {
        "id": "a5vkXA1Z3U36"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_predicciones.columns = ['Actual']"
      ],
      "metadata": {
        "id": "lnwpNNN3V-aE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_predicciones[\"Prediccion\"] = y_pred_test"
      ],
      "metadata": {
        "id": "xNGrETv1WCdT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_predicciones"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-PlkcLEuWR3W",
        "outputId": "25b0ea5b-0c02-459e-96a7-034819a54ae4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Actual  Prediccion\n",
              "22594       1           1\n",
              "60018       1           1\n",
              "38408       1           1\n",
              "43407       1           1\n",
              "52538       0           0\n",
              "...       ...         ...\n",
              "53557       1           1\n",
              "35871       1           1\n",
              "51464       0           0\n",
              "37312       1           1\n",
              "19368       1           1\n",
              "\n",
              "[6107 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-96475921-4dc1-40dc-807b-8600dc7fbc09\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Actual</th>\n",
              "      <th>Prediccion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22594</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60018</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38408</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43407</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52538</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53557</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35871</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51464</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37312</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19368</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6107 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96475921-4dc1-40dc-807b-8600dc7fbc09')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-96475921-4dc1-40dc-807b-8600dc7fbc09 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-96475921-4dc1-40dc-807b-8600dc7fbc09');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_predicciones.to_csv('test.csv')"
      ],
      "metadata": {
        "id": "TzG0BZUCWUE4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se generar un archivo.csv en la notebook."
      ],
      "metadata": {
        "id": "1swTuwp573An"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z3_UWtge76tm"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}