Prediccion Modelo Regresion Logistica.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Evaluación de resultados de un algoritmo de Regresion Logistica con scikit Learning",
   "id": "562771bf9235a9ae"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "En este notebook se muestran técnicas para la evaluación de los resultados de una predicción con un algoritmo de Regresion Logistica",
   "id": "5d9324ac3dfb418b"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Conjunto de datos",
   "id": "eedfad1c27c05d88"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Descripción\n",
    "NSL-KDD is a data set suggested to solve some of the inherent problems of the KDD'99 data set which are mentioned in. Although, this new version of the KDD data set still suffers from some of the problems discussed by McHugh and may not be a perfect representative of existing real networks, because of the lack of public data sets for network-based IDSs, we believe it still can be applied as an effective benchmark data set to help researchers compare different intrusion detection methods. Furthermore, the number of records in the NSL-KDD train and test sets are reasonable. This advantage makes it affordable to run the experiments on the complete set without the need to randomly select a small portion. Consequently, evaluation results of different research work will be consistent and comparable.\n",
    "\n",
    "### Ficheros de datos\n",
    "* <span style=\"color:green\">**KDDTrain+.ARFF**: The full NSL-KDD train set with binary labels in ARFF format</span>\n",
    "* KDDTrain+.TXT: The full NSL-KDD train set including attack-type labels and difficulty level in CSV format\n",
    "* KDDTrain+_20Percent.ARFF:\tA 20% subset of the KDDTrain+.arff file\n",
    "* KDDTrain+_20Percent.TXT:\tA 20% subset of the KDDTrain+.txt file\n",
    "* KDDTest+.ARFF:\tThe full NSL-KDD test set with binary labels in ARFF format\n",
    "* KDDTest+.TXT:\tThe full NSL-KDD test set including attack-type labels and difficulty level in CSV format\n",
    "* KDDTest-21.ARFF:\tA subset of the KDDTest+.arff file which does not include records with difficulty level of 21 out of 21\n",
    "* KDDTest-21.TXT:\tA subset of the KDDTest+.txt file which does not include records with difficulty level of 21 out of 21\n",
    "\n",
    "### Descarga de los ficheros de datos\n",
    "https://iscxdownloads.cs.unb.ca/iscxdownloads/NSL-KDD/#NSL-KDD\n",
    "\n",
    "### Referencias adicionales sobre el conjunto de datos\n",
    "_M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, “A Detailed Analysis of the KDD CUP 99 Data Set,” Submitted to Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), 2009._"
   ],
   "id": "cab7d5cfc15528df"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Imports",
   "id": "b7fe1bed5dd28b4"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-06-16T21:30:30.127874Z",
     "start_time": "2024-06-16T21:30:30.125226Z"
    }
   },
   "source": [
    "import arff\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.base import BaseEstimator, TransformerMixin"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Funciones auxiliares",
   "id": "f3566c2e2c9bef66"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:30:53.495097Z",
     "start_time": "2024-06-16T21:30:53.492057Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def load_kdd_dataset(data_path):\n",
    "    with open(data_path, 'r') as train_set:\n",
    "        dataset = arff.load(train_set)\n",
    "    attributes = [attr[0] for attr in dataset['attributes']]\n",
    "    return pd.DataFrame(dataset['data'], columns=attributes)"
   ],
   "id": "ad53004ec89ba75a",
   "outputs": [],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:30:30.644474Z",
     "start_time": "2024-06-16T21:30:30.641460Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def train_val_test_split(df, rstate=42, shuffle=True, stratify=None):\n",
    "    strat = df[stratify] if stratify else None\n",
    "    train_set, test_set = train_test_split(df, test_size=0.4, random_state=rstate, shuffle = shuffle , stratify = strat)\n",
    "    strat = test_set[stratify] if stratify else None\n",
    "    val_set, test_set = train_test_split(test_set, test_size=0.5, random_state=rstate, shuffle=shuffle, stratify=strat)\n",
    "    return train_set, val_set, test_set"
   ],
   "id": "8f2463ebd2d02c65",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:30:30.897964Z",
     "start_time": "2024-06-16T21:30:30.894694Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# COnstruccion de un pipeline para los atributos numéricos\n",
    "num_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler', RobustScaler())\n",
    "])"
   ],
   "id": "48e413017dfaec23",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:57:18.960383Z",
     "start_time": "2024-06-16T21:57:18.956261Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Transformador para codificar unicamnete las columnas categoricas y devolver el df\n",
    "class CustomOneHotEncoder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        self._oh = OneHotEncoder()\n",
    "        self._columns = None\n",
    "    def fit(self, X, y=None):\n",
    "        X_cat = X.select_dtypes(include=['object'])\n",
    "        self._columns = pd.get_dummies(X_cat).columns\n",
    "        self._oh.fit(X_cat)\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        X_copy = X.copy()\n",
    "        X_cat = X_copy.select_dtypes(include=['object'])\n",
    "        X_num = X_copy.select_dtypes(exclude=['object'])\n",
    "        X_cat_oh = self._oh.transform(X_cat)\n",
    "        print(X_cat_oh)\n",
    "        X_cat_oh = pd.DataFrame(X_cat_oh.toarray(), columns=self._columns, index=X_copy.index)\n",
    "        X_copy.drop(list(X_cat), axis = 1, inplace=True)\n",
    "        return X_copy.join(X_cat_oh)"
   ],
   "id": "e9cc9dbd83063e7b",
   "outputs": [],
   "execution_count": 65
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:55:07.644120Z",
     "start_time": "2024-06-16T21:55:07.640035Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Contruccion que prepara todo el conjunto de datos llamando pipelines y transformadores personalizados\n",
    "class DataFramePreparer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        self._full_pipeline = None\n",
    "        self._columns = None\n",
    "    def fit(self, X, y=None):\n",
    "        num_attribs = list(X.select_dtypes(exclude=['object']))\n",
    "        cat_attribs = list(X.select_dtypes(include=['object']))\n",
    "        self._full_pipeline = ColumnTransformer([\n",
    "            ('num', num_pipeline, num_attribs),\n",
    "            ('cat', CustomOneHotEncoder(), cat_attribs)\n",
    "        ])\n",
    "        self._full_pipeline.fit(X)\n",
    "        self._columns = pd.get_dummies(X).columns\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        X_copy = X.copy()\n",
    "        X_prep = self._full_pipeline.transform(X_copy)\n",
    "        return pd.DataFrame(X_prep, columns=self._columns, index=X_copy.index)"
   ],
   "id": "f8719e71feb9d44b",
   "outputs": [],
   "execution_count": 62
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Lectura del conjunto de datos",
   "id": "f3ccd215b6cd47c2"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:30:58.402753Z",
     "start_time": "2024-06-16T21:30:56.337888Z"
    }
   },
   "cell_type": "code",
   "source": "df = load_kdd_dataset(r\"C:\\Users\\crisb\\Documents\\Curso Data Science Udemy\\datasets\\NSL-KDD\\KDDTrain+.arff\")\n",
   "id": "71a23e93a4cc7700",
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:30:58.879108Z",
     "start_time": "2024-06-16T21:30:58.860040Z"
    }
   },
   "cell_type": "code",
   "source": "df.head(10)",
   "id": "22dd2ad8dcffd39c",
   "outputs": [
    {
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       "   duration protocol_type     service flag  src_bytes  dst_bytes land  \\\n",
       "0       0.0           tcp    ftp_data   SF      491.0        0.0    0   \n",
       "1       0.0           udp       other   SF      146.0        0.0    0   \n",
       "2       0.0           tcp     private   S0        0.0        0.0    0   \n",
       "3       0.0           tcp        http   SF      232.0     8153.0    0   \n",
       "4       0.0           tcp        http   SF      199.0      420.0    0   \n",
       "5       0.0           tcp     private  REJ        0.0        0.0    0   \n",
       "6       0.0           tcp     private   S0        0.0        0.0    0   \n",
       "7       0.0           tcp     private   S0        0.0        0.0    0   \n",
       "8       0.0           tcp  remote_job   S0        0.0        0.0    0   \n",
       "9       0.0           tcp     private   S0        0.0        0.0    0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
       "0             0.0     0.0  0.0  ...                25.0   \n",
       "1             0.0     0.0  0.0  ...                 1.0   \n",
       "2             0.0     0.0  0.0  ...                26.0   \n",
       "3             0.0     0.0  0.0  ...               255.0   \n",
       "4             0.0     0.0  0.0  ...               255.0   \n",
       "5             0.0     0.0  0.0  ...                19.0   \n",
       "6             0.0     0.0  0.0  ...                 9.0   \n",
       "7             0.0     0.0  0.0  ...                15.0   \n",
       "8             0.0     0.0  0.0  ...                23.0   \n",
       "9             0.0     0.0  0.0  ...                13.0   \n",
       "\n",
       "  dst_host_same_srv_rate  dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                   0.17                    0.03                         0.17   \n",
       "1                   0.00                    0.60                         0.88   \n",
       "2                   0.10                    0.05                         0.00   \n",
       "3                   1.00                    0.00                         0.03   \n",
       "4                   1.00                    0.00                         0.00   \n",
       "5                   0.07                    0.07                         0.00   \n",
       "6                   0.04                    0.05                         0.00   \n",
       "7                   0.06                    0.07                         0.00   \n",
       "8                   0.09                    0.05                         0.00   \n",
       "9                   0.05                    0.06                         0.00   \n",
       "\n",
       "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "0                         0.00                  0.00   \n",
       "1                         0.00                  0.00   \n",
       "2                         0.00                  1.00   \n",
       "3                         0.04                  0.03   \n",
       "4                         0.00                  0.00   \n",
       "5                         0.00                  0.00   \n",
       "6                         0.00                  1.00   \n",
       "7                         0.00                  1.00   \n",
       "8                         0.00                  1.00   \n",
       "9                         0.00                  1.00   \n",
       "\n",
       "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
       "0                      0.00                  0.05                      0.00   \n",
       "1                      0.00                  0.00                      0.00   \n",
       "2                      1.00                  0.00                      0.00   \n",
       "3                      0.01                  0.00                      0.01   \n",
       "4                      0.00                  0.00                      0.00   \n",
       "5                      0.00                  1.00                      1.00   \n",
       "6                      1.00                  0.00                      0.00   \n",
       "7                      1.00                  0.00                      0.00   \n",
       "8                      1.00                  0.00                      0.00   \n",
       "9                      1.00                  0.00                      0.00   \n",
       "\n",
       "     class  \n",
       "0   normal  \n",
       "1   normal  \n",
       "2  anomaly  \n",
       "3   normal  \n",
       "4   normal  \n",
       "5  anomaly  \n",
       "6  anomaly  \n",
       "7  anomaly  \n",
       "8  anomaly  \n",
       "9  anomaly  \n",
       "\n",
       "[10 rows x 42 columns]"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
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       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>ftp_data</td>\n",
       "      <td>SF</td>\n",
       "      <td>491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>anomaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>232.0</td>\n",
       "      <td>8153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>normal</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>199.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>REJ</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>anomaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>anomaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>anomaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>remote_job</td>\n",
       "      <td>S0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>anomaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>anomaly</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 42 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## División del conjunto de datos",
   "id": "600102c3c4d3444"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:32:15.537637Z",
     "start_time": "2024-06-16T21:32:15.324021Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Division del conjunto en los distintos subconjuntos\n",
    "train_set, val_set, test_set = train_val_test_split(df, stratify='class')"
   ],
   "id": "c2f8fa0f6d7ff320",
   "outputs": [],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:33:54.682373Z",
     "start_time": "2024-06-16T21:33:54.679604Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"Longitud del conjunto de entrenamiento: \", len(train_set))\n",
    "print(\"Longitud del conjunto de validación: \", len(val_set))\n",
    "print(\"longitud del conjunto de test: \", len(test_set))"
   ],
   "id": "41aec157a4a75f0c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longitud del conjunto de entrenamiento:  75583\n",
      "Longitud del conjunto de validación:  25195\n",
      "longitud del conjunto de test:  25195\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Para cada uno de los subconjuntos, separamos las etiquetas de las características de entrada.",
   "id": "b7507e1df134e56d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:34:36.027554Z",
     "start_time": "2024-06-16T21:34:36.007002Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Conjunto de datos general\n",
    "X_df = df.drop(\"class\", axis=1)\n",
    "y_df = df[\"class\"].copy()"
   ],
   "id": "47ee2430e5b240d9",
   "outputs": [],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:35:18.867330Z",
     "start_time": "2024-06-16T21:35:18.849460Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Conjunto de datos de entrenamiento\n",
    "X_train = train_set.drop(\"class\", axis=1)\n",
    "y_train = train_set[\"class\"].copy()"
   ],
   "id": "30e13d8fc4fdb0a9",
   "outputs": [],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:35:19.039261Z",
     "start_time": "2024-06-16T21:35:19.032251Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Conjunto de datos de validación\n",
    "X_val = val_set.drop(\"class\", axis=1)\n",
    "y_val = val_set[\"class\"].copy()"
   ],
   "id": "c971be44ae65ac2c",
   "outputs": [],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:35:19.219896Z",
     "start_time": "2024-06-16T21:35:19.211585Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Conjunto de datos de pruebas\n",
    "X_test = test_set.drop(\"class\", axis=1)\n",
    "y_test = test_set[\"class\"].copy()"
   ],
   "id": "95605543bdcfc876",
   "outputs": [],
   "execution_count": 23
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Preparación del conjunto de datos",
   "id": "f4fbaa783bdf4268"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:55:11.807723Z",
     "start_time": "2024-06-16T21:55:11.805057Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Instanciamos el primer transformador personalizado\n",
    "data_preparer = DataFramePreparer()"
   ],
   "id": "8fcac5d42ad453e6",
   "outputs": [],
   "execution_count": 63
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T21:55:12.982302Z",
     "start_time": "2024-06-16T21:55:12.105217Z"
    }
   },
   "cell_type": "code",
   "source": "data_preparer.fit(X_df)",
   "id": "12899f24e550709f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFramePreparer()"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DataFramePreparer()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;DataFramePreparer<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>DataFramePreparer()</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:00:29.618147Z",
     "start_time": "2024-06-16T22:00:29.410073Z"
    }
   },
   "cell_type": "code",
   "source": "X_train_prep = data_preparer.transform(X_train)",
   "id": "efce16fee9f78347",
   "outputs": [],
   "execution_count": 66
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:00:36.619077Z",
     "start_time": "2024-06-16T22:00:36.606026Z"
    }
   },
   "cell_type": "code",
   "source": "X_train_prep.head()",
   "id": "553b1cbb51da8577",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        duration  src_bytes  dst_bytes  wrong_fragment  urgent  hot  \\\n",
       "79609        1.0   2.938406   0.633721             0.0     0.0  0.0   \n",
       "79887        0.0  -0.159420   0.000000             0.0     0.0  0.0   \n",
       "108746       0.0  -0.003623   0.250000             0.0     0.0  0.0   \n",
       "57514        0.0   0.637681   0.571705             0.0     0.0  0.0   \n",
       "50720        0.0   0.721014  20.738372             0.0     0.0  0.0   \n",
       "\n",
       "        num_failed_logins  num_compromised  root_shell  su_attempted  ...  \\\n",
       "79609                 0.0              0.0         0.0           0.0  ...   \n",
       "79887                 0.0              0.0         0.0           0.0  ...   \n",
       "108746                0.0              0.0         0.0           0.0  ...   \n",
       "57514                 0.0              0.0         0.0           0.0  ...   \n",
       "50720                 0.0              0.0         0.0           0.0  ...   \n",
       "\n",
       "        flag_SF  flag_SH  land_0  land_1  logged_in_0  logged_in_1  \\\n",
       "79609       1.0      0.0     1.0     0.0          0.0          1.0   \n",
       "79887       0.0      0.0     1.0     0.0          1.0          0.0   \n",
       "108746      1.0      0.0     1.0     0.0          1.0          0.0   \n",
       "57514       1.0      0.0     1.0     0.0          0.0          1.0   \n",
       "50720       1.0      0.0     1.0     0.0          0.0          1.0   \n",
       "\n",
       "        is_host_login_0  is_host_login_1  is_guest_login_0  is_guest_login_1  \n",
       "79609               1.0              0.0               1.0               0.0  \n",
       "79887               1.0              0.0               1.0               0.0  \n",
       "108746              1.0              0.0               1.0               0.0  \n",
       "57514               1.0              0.0               1.0               0.0  \n",
       "50720               1.0              0.0               1.0               0.0  \n",
       "\n",
       "[5 rows x 126 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>num_failed_logins</th>\n",
       "      <th>num_compromised</th>\n",
       "      <th>root_shell</th>\n",
       "      <th>su_attempted</th>\n",
       "      <th>...</th>\n",
       "      <th>flag_SF</th>\n",
       "      <th>flag_SH</th>\n",
       "      <th>land_0</th>\n",
       "      <th>land_1</th>\n",
       "      <th>logged_in_0</th>\n",
       "      <th>logged_in_1</th>\n",
       "      <th>is_host_login_0</th>\n",
       "      <th>is_host_login_1</th>\n",
       "      <th>is_guest_login_0</th>\n",
       "      <th>is_guest_login_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79609</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.938406</td>\n",
       "      <td>0.633721</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79887</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.159420</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108746</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003623</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57514</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.637681</td>\n",
       "      <td>0.571705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50720</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.721014</td>\n",
       "      <td>20.738372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 126 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 67
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:01:09.828560Z",
     "start_time": "2024-06-16T22:01:09.760013Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Transformamos el subconjunto de datos de validacion\n",
    "X_val_prep = data_preparer.transform(X_val)"
   ],
   "id": "5c3ea8e46e71ead8",
   "outputs": [],
   "execution_count": 68
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Entrenamiento de un algoritmo de Regresión Logística",
   "id": "c4738298176d0b9"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "La instanciación de un algoritmo de Machine Learning utilizando Sklearn se realiza utilizando los métodos expuestos por la API de sklearnm en este caso con 1000 iteraciones",
   "id": "d74773033529c5a0"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:02:33.660250Z",
     "start_time": "2024-06-16T22:02:16.892569Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf = LogisticRegression(max_iter=1000, solver='newton-cg')\n",
    "clf.fit(X_train_prep, y_train)"
   ],
   "id": "14b569cade363f4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000, solver='newton-cg')"
      ],
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;newton-cg&#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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;newton-cg&#x27;)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 69
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Predicción de nuevos ejemplos",
   "id": "85927791ccb406c7"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Realizamos una predicción con el modelo generado anteriormente tras el entrenamiento del algoritmo de Regresión Logística. Utilizamos el subconjunto de validación.",
   "id": "3ddf4a5ab7bdbbfa"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:02:46.935234Z",
     "start_time": "2024-06-16T22:02:46.925168Z"
    }
   },
   "cell_type": "code",
   "source": "y_pred = clf.predict(X_val_prep)",
   "id": "36222ec7f74c138a",
   "outputs": [],
   "execution_count": 70
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1. Matriz de Confusión",
   "id": "10cddc4588439d1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:03:25.436486Z",
     "start_time": "2024-06-16T22:03:25.354658Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_val, y_pred)"
   ],
   "id": "85f01c2c79acb433",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11333,   393],\n",
       "       [  226, 13243]], dtype=int64)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 71
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:04:06.936196Z",
     "start_time": "2024-06-16T22:04:05.928265Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "ConfusionMatrixDisplay.from_estimator(clf, X_val_prep, y_val, values_format='d')"
   ],
   "id": "6fe9b7162828a0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1c23af8ddf0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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eLZiYAo5ORXptjJt7C/uvnMeXF36Bk1shIsc3raa7ocoqnfMjZQNKgo1Ht4KCJ/9D75VXXsGiRYswcODAMucEQcCqVaswf/58BAYGonXr1ti2bRvS0tLEDFFCQgIOHz6MzZs3o1OnTujatSvWrl2LXbt2IS0tDQCwfft2FBYW4tNPP0XLli0xbNgwTJkyBStWrBCvtXr1avTp0wczZ86Et7c3oqKi0K5dO6xbt67cn1+1Bj95eXmYPn06YmNjceTIESiVSgwcOBA6nU4sM2/ePMyYMQPx8fFo1qwZhg8fjuLiksmpcXFxGDp0KIYNG4YLFy4gMjIS4eHh2Lp1q951Vq5ciS5duuD8+fPo168fRo0ahdGjR2PkyJE4d+4cmjRpgtGjR0MQStK++fn5aN++PQ4dOoSLFy9i0qRJGDVqFM6cOfPY+xg3bhwSEhJw9uxZ8dj58+fx66+/YuzYsY+tU1BQUOZLR8+HR/+H6LP3Ll76v9+hbqTFyTAHaP/2/3u7uRr02fs7un30B+6nmuDc+3Zl2vHf/jsCvryLDpE5SPzMBjcPWj6nOyBjduuGCpP7eGNqYAsc+m89vL3iJhp6PUROlhnee7MJOvlnY9+V89h7KR42dlpcu2CFR/5KBQB8ucEFIa94Y26QF3Q6YObKZAAc9jJG7u7usLOzE7clS5ZUqp3k5GSkp6fD399fPGZnZ4dOnTqJSYuYmBjY29ujQ4cOYhl/f38olUqcPn1aLNO9e3eYm//1L9KAgAAkJibi3r17YplHr1Na5u/Jkaep1qe9Bg8erLf/6aefol69erh8+TJsbGwAADNmzEC/fv0AAAsWLEDLli2RlJSEFi1aYMWKFXjppZfE7EqzZs1w+fJlLFu2DGPGjBHb7du3L15//XUAQEREBNavX4+OHTvi3//+NwBg9uzZ8PPzQ0ZGBlxcXFC/fn3MmDFDrP/WW2/h22+/xe7du/GPf/yjzH00aNAAAQEB2LJlCzp27AgA2LJlC3r06IHGjRs/9t6XLFmCBQsWVOZjI4nMbQWY22ph20iLOm2ysKeTC1KjLdHo1YdiGct6OljW00HduBgquxx8H1QXrd68D0unv35FbBqUDJXZNy9G/h9KXFxnq9cGUVUoLlLizm8WAICkC9Zo1iYPA8ZlYs1cD5z7SY1x3XyhdiiGVgvkaUyxI/YXpKc46LWhuWcKzT1T3E62QOo1C/z3zAV4t8tDwjmb6rglqgQdJL7b688Jz6mpqVCr1eJxlUr1pCpPlZ6eDgBwdnbWO+7s7CyeS09Ph5OTk955U1NTODo66pXx9PQs00bpOQcHB6Snpz/1OuVRrZmfa9euYfjw4WjcuDHUajUaNWoEAEhJSRHLtG7dWvyzq6srACAzMxNASQqtS5cuem126dIF165dg1arfWwbpR+Yr69vmWOl7Wq1WkRFRcHX1xeOjo6wsbHBt99+q9evv5s4cSJ27tyJ/Px8FBYWYseOHRg3btwTy8+dOxc5OTnilpqa+sSyVMUEQPeU6TrCn/GOtvApf9Hont4GUVVRKAAzc/2sjeaeKfI0pmjTWQP7usX4Odr+yfX//BX4extUswkSn/QS/gx+1Gq13lbZ4EduqjXz079/f3h4eGDTpk1wc3ODTqdDq1atUFj416+Imdlf61MoFCX/sXR/z+E+w+PaeFq7y5Ytw+rVq7Fq1Sr4+vrC2toaYWFhev163L2oVCrs27cP5ubmKCoqwpAhQ55YXqVSGc2XzFCK8hTITTER93NvmeBeginM7QRYu2lRkK3AgzsmeJhZUkaTXPL1tqhbksXJTTXBb99YwrVLAVSOOjxIVyJhky1MVIBbj5Jxr7RjKuT/roSjbxFMrQTkJJkifpkaddsViJmeq9utYO2qFdcGyoxVIeFTGzQfxXV+qGqNnX0bZ39Q426aOSytdeg1IAut/e5j3igvAMDL//4dqUkWyMkyg3e7XLwRmYp9m51w60ZJpqh52zw0a5OHS2dtkJtjAlePAoyekYa0myoknLOuzlujCqppb3V3cXEBAGRkZIiJitL9tm3bimVKkwyliouLkZWVJdZ3cXFBRkaGXpnS/WeVKT1fHtUW/Pzxxx9ITEzEpk2b0K1bNwDAiRMnKtSGt7c3Tp48qXfs5MmTaNasGUxMTJ5Q69lOnjyJwMBAjBw5EkBJUHT16lX4+Pg8sY6pqSmCg4OxZcsWmJubY9iwYbC05BwQQ8q6aIajwXXF/fN/zsPxHPAAL76fjdtHLXD6nb/S+6emOwIAWoXch+9b96E0F3A3zhyJ26xRpClZwLBehwK8vPMuLOqUBL4mKgHXv7DGufdNoStUwMpFiwa9H8Jn4iNPy+gU+GWlGrm3TKA0AWwaFqPtDA2avsYJz1S17OsUYebKm3BwKsKD+yZIvmKJeaO8cP6nkmGLBk3yMXb2bdjaa5Fxyxy71rpi7+a/hhkKHirRpU82Rk1Pg4WlDlmZZog9psbiNa4oKuTDv1R5np6ecHFxwZEjR8RgR6PR4PTp03jzzTcBAH5+fsjOzkZcXBzat28PADh69Ch0Oh06deoklpk3bx6KiorEJEV0dDSaN28OBwcHscyRI0f01tqLjo6Gn59fuftbbcGPg4MD6tSpg40bN8LV1RUpKSmYM2dOhdp4++230bFjR0RFReG1115DTEwM1q1bh48++khS37y8vPDll1/i1KlTcHBwwIoVK5CRkfHU4AcAJkyYAG9vbwAoE5SRdM6dCjH8StoTzzce9BCNBz15zo2Vsw49N2Y9/RovFuLlXb8/tUyzUXloxiwPVYOVsxo99fyW9xtgy/sNnnj+ZqIl5gxvZuBeUXWojhWec3NzkZSUJO4nJycjPj4ejo6OaNiwIcLCwrBo0SJ4eXnB09MT4eHhcHNzw4ABAwCUJCz69OmDiRMnYsOGDSgqKkJoaCiGDRsGNzc3AMCIESOwYMECjB8/HrNnz8bFixexevVqrFy5Urzu1KlT0aNHDyxfvhz9+vXDrl27EBsbq/c4/LNUW/CjVCqxa9cuTJkyBa1atULz5s2xZs0a9OzZs9xttGvXDrt370ZERASioqLg6uqKhQsX6k12roz58+fjxo0bCAgIgJWVFSZNmoQBAwYgJ+fxKwuX8vLyQufOnZGVlSVGsURERIZWHcNesbGx6NWrl7g/ffp0AEBwcDC2bt2KWbNmIS8vD5MmTUJ2dja6du2Kw4cPw8LCQqyzfft2hIaG4qWXXoJSqcTgwYOxZs0a8bydnR2+++47hISEoH379qhbty4iIiL01gLq3LkzduzYgfnz5+Odd96Bl5cX9u/f/9glZ55EIZQ+302SCYIALy8vTJ48WfxSlJdGo4GdnR3Wxf0DljZ85RrVTrtaNazuLhBVmWKhCD8U70FOTo7eE1SGVPpbEfjdOJhZV35V+aK8QnzV+9Mq7WtNxl9ZA7l79y527dqF9PT0J67tQ0REZAhS389l7O/2YvBjIE5OTqhbty42btwoTsoiIiKqCjXtaS+5YfBjIBw9JCIikgcGP0RERDLDzI80DH6IiIhkhsGPNFzVioiIiIwKMz9EREQyw8yPNAx+iIiIZEaAtMfVjf0RHQY/REREMsPMjzSc80NERERGhZkfIiIimWHmRxoGP0RERDLD4EcaDnsRERGRUWHmh4iISGaY+ZGGwQ8REZHMCIICgoQARkrd2oDDXkRERGRUmPkhIiKSGR0UkhY5lFK3NmDwQ0REJDOc8yMNh72IiIjIqDDzQ0REJDOc8CwNgx8iIiKZ4bCXNAx+iIiIZIaZH2k454eIiIiMCjM/REREMiNIHPYy9swPgx8iIiKZEQAIgrT6xozDXkRERGRUmPkhIiKSGR0UUHCF50pj8ENERCQzfNpLGg57ERERkVFh5oeIiEhmdIICCi5yWGkMfoiIiGRGECQ+7WXkj3tx2IuIiIiMCjM/REREMsMJz9Iw+CEiIpIZBj/SMPghIiKSGU54loZzfoiIiMioMPNDREQkM3zaSxoGP0RERDJTEvxImfNjwM7IEIe9iIiIyKgw80NERCQzfNpLGgY/REREMiP8uUmpb8w47EVERERGhZkfIiIimeGwlzQMfoiIiOSG416SMPghIiKSG4mZHxh55odzfoiIiOiptFotwsPD4enpCUtLSzRp0gRRUVEQHlkwSBAEREREwNXVFZaWlvD398e1a9f02snKykJQUBDUajXs7e0xfvx45Obm6pX59ddf0a1bN1hYWMDd3R1Lly41+P0w+CEiIpKZ0hWepWwV8Z///Afr16/HunXrkJCQgP/85z9YunQp1q5dK5ZZunQp1qxZgw0bNuD06dOwtrZGQEAA8vPzxTJBQUG4dOkSoqOjcfDgQRw/fhyTJk0Sz2s0GvTu3RseHh6Ii4vDsmXLEBkZiY0bN0r+zB7FYS8iIiKZed4Tnk+dOoXAwED069cPANCoUSPs3LkTZ86c+bM9AatWrcL8+fMRGBgIANi2bRucnZ2xf/9+DBs2DAkJCTh8+DDOnj2LDh06AADWrl2Lvn374oMPPoCbmxu2b9+OwsJCfPrppzA3N0fLli0RHx+PFStW6AVJUjHzQ0REZKQ0Go3eVlBQ8NhynTt3xpEjR3D16lUAwC+//IITJ07glVdeAQAkJycjPT0d/v7+Yh07Ozt06tQJMTExAICYmBjY29uLgQ8A+Pv7Q6lU4vTp02KZ7t27w9zcXCwTEBCAxMRE3Lt3z2D3zcwPERGR3AgKaZOW/6zr7u6ud/jdd99FZGRkmeJz5syBRqNBixYtYGJiAq1Wi/feew9BQUEAgPT0dACAs7OzXj1nZ2fxXHp6OpycnPTOm5qawtHRUa+Mp6dnmTZKzzk4OFTmbstg8ENERCQzhnqre2pqKtRqtXhcpVI9tvzu3buxfft27NixQxyKCgsLg5ubG4KDgyvfkWrC4IeIiMhIqdVqveDnSWbOnIk5c+Zg2LBhAABfX1/89ttvWLJkCYKDg+Hi4gIAyMjIgKurq1gvIyMDbdu2BQC4uLggMzNTr93i4mJkZWWJ9V1cXJCRkaFXpnS/tIwhcM4PERGR3AgG2CrgwYMHUCr1QwYTExPodDoAgKenJ1xcXHDkyBHxvEajwenTp+Hn5wcA8PPzQ3Z2NuLi4sQyR48ehU6nQ6dOncQyx48fR1FRkVgmOjoazZs3N9iQF8Dgh4iISHZKn/aSslVE//798d577+HQoUO4efMm9u3bhxUrVmDgwIEAAIVCgbCwMCxatAgHDhzAhQsXMHr0aLi5uWHAgAEAAG9vb/Tp0wcTJ07EmTNncPLkSYSGhmLYsGFwc3MDAIwYMQLm5uYYP348Ll26hM8//xyrV6/G9OnTDfr5lWvY68CBA+Vu8F//+lelO0NEREQ1z9q1axEeHo7JkycjMzMTbm5ueP311xERESGWmTVrFvLy8jBp0iRkZ2eja9euOHz4MCwsLMQy27dvR2hoKF566SUolUoMHjwYa9asEc/b2dnhu+++Q0hICNq3b4+6desiIiLCoI+5A4BCEJ49Zervqa4nNqZQQKvVSu6UMdJoNLCzs8O6uH/A0oZTsah22tWqYXV3gajKFAtF+KF4D3Jycso1j6YySn8rGm6MgNLS4tkVnkD3MB8pkxZWaV9rsnL9ypaO6REREVH141vdpZE05+fRJauJiIjoOXnOE55rmwoHP1qtFlFRUahfvz5sbGxw48YNAEB4eDg++eQTg3eQiIiIyJAqHPy899572Lp1K5YuXaq3/HSrVq2wefNmg3aOiIiIHkdhgM14VTj42bZtGzZu3IigoCCYmJiIx9u0aYMrV64YtHNERET0GBz2kqTCwc/t27fRtGnTMsd1Op3eokRERERENVGFgx8fHx/89NNPZY5/+eWXeOGFFwzSKSIiInoKZn4kqfCCMhEREQgODsbt27eh0+mwd+9eJCYmYtu2bTh48GBV9JGIiIgeZaC3uhurCmd+AgMD8fXXX+P777+HtbU1IiIikJCQgK+//hovv/xyVfSRiIiIyGAqtZRwt27dEB0dbei+EBERUTkIQskmpb4xq/R7FGJjY5GQkACgZB5Q+/btDdYpIiIiegqp83YY/FTMrVu3MHz4cJw8eRL29vYAgOzsbHTu3Bm7du1CgwYNDN1HIiIiIoOp8JyfCRMmoKioCAkJCcjKykJWVhYSEhKg0+kwYcKEqugjERERPap0wrOUzYhVOPNz7NgxnDp1Cs2bNxePNW/eHGvXrkW3bt0M2jkiIiIqSyGUbFLqG7MKBz/u7u6PXcxQq9XCzc3NIJ0iIiKip+CcH0kqPOy1bNkyvPXWW4iNjRWPxcbGYurUqfjggw8M2jkiIiIiQytX5sfBwQEKxV/jg3l5eejUqRNMTUuqFxcXw9TUFOPGjcOAAQOqpKNERET0Jy5yKEm5gp9Vq1ZVcTeIiIio3DjsJUm5gp/g4OCq7gcRERHRc1HpRQ4BID8/H4WFhXrH1Gq1pA4RERHRMzDzI0mFJzzn5eUhNDQUTk5OsLa2hoODg95GREREVYxvdZekwsHPrFmzcPToUaxfvx4qlQqbN2/GggUL4Obmhm3btlVFH4mIiIgMpsLDXl9//TW2bduGnj17YuzYsejWrRuaNm0KDw8PbN++HUFBQVXRTyIiIirFp70kqXDmJysrC40bNwZQMr8nKysLANC1a1ccP37csL0jIiKiMkpXeJayGbMKBz+NGzdGcnIyAKBFixbYvXs3gJKMUOmLTomIiIhqqgoHP2PHjsUvv/wCAJgzZw4+/PBDWFhYYNq0aZg5c6bBO0hERER/wwnPklR4zs+0adPEP/v7++PKlSuIi4tD06ZN0bp1a4N2joiIiMjQJK3zAwAeHh7w8PAwRF+IiIioHBSQ+FZ3g/VEnsoV/KxZs6bcDU6ZMqXSnSEiIiKqauUKflauXFmuxhQKBYMfib5s7wpThVl1d4OoSnybFlvdXSCqMpr7Ojg0e04X46PukpQr+Cl9uouIiIhqAL7eQpIKP+1FREREJGeSJzwTERHRc8bMjyQMfoiIiGRG6irNXOGZiIiIyIgw80NERCQ3HPaSpFKZn59++gkjR46En58fbt++DQD4v//7P5w4ccKgnSMiIqLH4OstJKlw8LNnzx4EBATA0tIS58+fR0FBAQAgJycHixcvNngHiYiIiAypwsHPokWLsGHDBmzatAlmZn8txtelSxecO3fOoJ0jIiKiskonPEvZjFmF5/wkJiaie/fuZY7b2dkhOzvbEH0iIiKip+EKz5JUOPPj4uKCpKSkMsdPnDiBxo0bG6RTRERE9BSc8yNJhYOfiRMnYurUqTh9+jQUCgXS0tKwfft2zJgxA2+++WZV9JGIiIjIYCo87DVnzhzodDq89NJLePDgAbp37w6VSoUZM2bgrbfeqoo+EhER0SO4yKE0FQ5+FAoF5s2bh5kzZyIpKQm5ubnw8fGBjY1NVfSPiIiI/o7r/EhS6UUOzc3N4ePjY8i+EBEREVW5Cgc/vXr1gkLx5FniR48eldQhIiIiegapj6sbeeanwhOe27ZtizZt2oibj48PCgsLce7cOfj6+lZFH4mIiOhR1fC01+3btzFy5EjUqVMHlpaW8PX1RWxs7F9dEgRERETA1dUVlpaW8Pf3x7Vr1/TayMrKQlBQENRqNezt7TF+/Hjk5ubqlfn111/RrVs3WFhYwN3dHUuXLq14Z5+hwpmflStXPvZ4ZGRkmRsgIiIi+bt37x66dOmCXr164X//+x/q1auHa9euwcHBQSyzdOlSrFmzBp999hk8PT0RHh6OgIAAXL58GRYWFgCAoKAg3LlzB9HR0SgqKsLYsWMxadIk7NixAwCg0WjQu3dv+Pv7Y8OGDbhw4QLGjRsHe3t7TJo0yWD3oxAEwSDJr6SkJPzjH/9AVlaWIZozOhqNBnZ2duiJQJgqzJ5dgUiGvk2Lr+4uEFUZzX0dHJrdQE5ODtRqddVc48/fisbzFsPkz4CiMrT5+bjx3jvl7uucOXNw8uRJ/PTTT489LwgC3Nzc8Pbbb2PGjBkASl575ezsjK1bt2LYsGFISEiAj48Pzp49iw4dOgAADh8+jL59++LWrVtwc3PD+vXrMW/ePKSnp8Pc3Fy89v79+3HlypVK3+/fVerFpo8TExMjRnZERERUdQz1eguNRqO3lb6v8+8OHDiADh064N///jecnJzwwgsvYNOmTeL55ORkpKenw9/fXzxmZ2eHTp06ISYmBkBJnGBvby8GPgDg7+8PpVKJ06dPi2W6d+8uBj4AEBAQgMTERNy7d89gn1+Fh70GDRqkty8IAu7cuYPY2FiEh4cbrGNERERUtdzd3fX23333XURGRpYpd+PGDaxfvx7Tp0/HO++8g7Nnz2LKlCkwNzdHcHAw0tPTAQDOzs569ZydncVz6enpcHJy0jtvamoKR0dHvTKenp5l2ig99+gwmxQVDn7s7Oz09pVKJZo3b46FCxeid+/eBukUERERVb3U1FS9YS+VSvXYcjqdDh06dMDixYsBAC+88AIuXryIDRs2IDg4+Ln01ZAqFPxotVqMHTsWvr6+Bou+iIiIqIIMtMihWq0u15wfV1fXMmv7eXt7Y8+ePQBK3vsJABkZGXB1dRXLZGRkoG3btmKZzMxMvTaKi4uRlZUl1ndxcUFGRoZemdL90jKGUKE5PyYmJujduzff3k5ERFSNDDXnp7y6dOmCxMREvWNXr16Fh4cHAMDT0xMuLi44cuSIeF6j0eD06dPw8/MDAPj5+SE7OxtxcXFimaNHj0Kn06FTp05imePHj6OoqEgsEx0djebNmxs06VLhCc+tWrXCjRs3DNYBIiIiqtmmTZuGn3/+GYsXL0ZSUhJ27NiBjRs3IiQkBEDJq6/CwsKwaNEiHDhwABcuXMDo0aPh5uaGAQMGACjJFPXp0wcTJ07EmTNncPLkSYSGhmLYsGFwc3MDAIwYMQLm5uYYP348Ll26hM8//xyrV6/G9OnTDXo/FZ7zs2jRIsyYMQNRUVFo3749rK2t9c5X1eN9RERE9IjnuEpzx44dsW/fPsydOxcLFy6Ep6cnVq1ahaCgILHMrFmzkJeXh0mTJiE7Oxtdu3bF4cOH9Z4E3759O0JDQ/HSSy9BqVRi8ODBWLNmjXjezs4O3333HUJCQtC+fXvUrVsXERERBl3jB6jAOj8LFy7E22+/DVtb278qP/KaC0EQoFAooNVqDdpBY8F1fsgYcJ0fqs2e5zo/TWcvholKwjo/BflI+k/51/mpbcqd+VmwYAHeeOMN/PDDD1XZHyIiIqIqVe7gpzRB1KNHjyrrDBERET1bZSYt/72+MavQnJ+nvc2diIiInhMDPepurCoU/DRr1uyZARDf7UVEREQ1WYWCnwULFpRZ4ZmIiIieLw57SVOh4GfYsGFl3stBREREzxmHvSQp9yKHnO9DREREtUGFn/YiIiKiasbMjyTlDn50Ol1V9oOIiIjKiXN+pKnw6y2IiIiomjHzI0mFX2xKREREJGfM/BAREckNMz+SMPghIiKSGc75kYbDXkRERGRUmPkhIiKSGw57ScLgh4iISGY47CUNh72IiIjIqDDzQ0REJDcc9pKEwQ8REZHcMPiRhMNeREREZFSY+SEiIpIZxZ+blPrGjMEPERGR3HDYSxIGP0RERDLDR92l4ZwfIiIiMirM/BAREckNh70kYfBDREQkR0YewEjBYS8iIiIyKsz8EBERyQwnPEvD4IeIiEhuOOdHEg57ERERkVFh5oeIiEhmOOwlDYMfIiIiueGwlyQc9iIiIiKjwswPERGRzHDYSxoGP0RERHLDYS9JGPwQERHJDYMfSTjnh4iIiIwKMz9EREQywzk/0jD4ISIikhsOe0nCYS8iIiIyKsz8EBERyYxCEKAQKp++kVK3NmDwQ0REJDcc9pKEw15ERERkVJj5ISIikhk+7SUNgx8iIiK54bCXJBz2IiIiIqPC4IeIiEhmSoe9pGxSvP/++1AoFAgLCxOP5efnIyQkBHXq1IGNjQ0GDx6MjIwMvXopKSno168frKys4OTkhJkzZ6K4uFivzI8//oh27dpBpVKhadOm2Lp1q7TOPgaDHyIiIrkRDLBV0tmzZ/Hxxx+jdevWesenTZuGr7/+Gl988QWOHTuGtLQ0DBo0SDyv1WrRr18/FBYW4tSpU/jss8+wdetWREREiGWSk5PRr18/9OrVC/Hx8QgLC8OECRPw7bffVr7Dj8Hgh4iISGaqK/OTm5uLoKAgbNq0CQ4ODuLxnJwcfPLJJ1ixYgX++c9/on379tiyZQtOnTqFn3/+GQDw3Xff4fLly/jvf/+Ltm3b4pVXXkFUVBQ+/PBDFBYWAgA2bNgAT09PLF++HN7e3ggNDcWQIUOwcuVKyZ/Zoxj8EBERGSmNRqO3FRQUPLV8SEgI+vXrB39/f73jcXFxKCoq0jveokULNGzYEDExMQCAmJgY+Pr6wtnZWSwTEBAAjUaDS5cuiWX+3nZAQIDYhqEw+CEiIpIbAw17ubu7w87OTtyWLFnyxEvu2rUL586de2yZ9PR0mJubw97eXu+4s7Mz0tPTxTKPBj6l50vPPa2MRqPBw4cPn/qRVAQfdSciIpIhQ6zVk5qaCrVaLe6rVKonlps6dSqio6NhYWEh/cLVjJkfIiIiI6VWq/W2JwU/cXFxyMzMRLt27WBqagpTU1McO3YMa9asgampKZydnVFYWIjs7Gy9ehkZGXBxcQEAuLi4lHn6q3T/WWXUajUsLS0NccsAGPwQERHJjyBI3yrgpZdewoULFxAfHy9uHTp0QFBQkPhnMzMzHDlyRKyTmJiIlJQU+Pn5AQD8/Pxw4cIFZGZmimWio6OhVqvh4+Mjlnm0jdIypW0YCoe9iIiIZOZ5v97C1tYWrVq10jtmbW2NOnXqiMfHjx+P6dOnw9HREWq1Gm+99Rb8/Pzw4osvAgB69+4NHx8fjBo1CkuXLkV6ejrmz5+PkJAQMeP0xhtvYN26dZg1axbGjRuHo0ePYvfu3Th06FDlb/YxGPwQERGRZCtXroRSqcTgwYNRUFCAgIAAfPTRR+J5ExMTHDx4EG+++Sb8/PxgbW2N4OBgLFy4UCzj6emJQ4cOYdq0aVi9ejUaNGiAzZs3IyAgwKB9VQhCBXNfVCU0Gg3s7OzQE4EwVZhVd3eIqsS3afHV3QWiKqO5r4NDsxvIycnRm0Rs0Gv8+VvRYfAimJpVfuJxcVE+YvfMr9K+1mTM/BAREcmMQleySalvzDjhmYiIiIwKMz9Uq7wWmoEufXPg3rQAhflKXI61wifvueLW9ZL0sK19MUbNSEe7HrlwcitETpYpTh22w2dLXfDgvoleWy8PzcKgSXfRoHEBHuSa4PhBO3z4ToPquC0yEhd+tsYXHznh2gUrZGWY4d1PktH5lRzx/P994IIfv7LH3TQzmJkLaOr7EGPn3EGLdg8AAOmp5tix0hnxJ21w764Z6jgX4Z+D7mH41AyYmZed4XA72RwhvZtDaQLsvXJBPH7iGzvsWuOMtJsqFBcB9T0LMfiNTPgPuVf1HwKVj8T3c0mqWwsw+KkijRo1QlhYmN4bb6nqtfbLw9db6+JqvBVMTAWMmXMHi3fewMQezVHw0ASOzkWo41yMTQtdkXLVAk4NCjHl/Vuo41yERZMaie0MmnQXg1/PxOZFbrhyzgoWVjo4uxdW342RUch/oETjlg8RMDwLC8d7ljlfv3E+Qt67BVePQhTkK7FvYz3MHd4EW05dhn0dLVKTVNDpgKn/uQU3zwLcvGKBVTPdkf9AiUnvpum1VVwEvD+5EVp1ysPlWGu9c7b2WgyfmgH3pvkwNRNw+ns1lk9rCPu6xejQ836VfgZUPs/7aa/ahsEP1Srzghrr7S8Pa4jdFy/Bq/VDXDxtg98SLRE1sZF4/s5vKmz9jytmrU2B0kSATquAjV0xgmffwbvBnog/YSuWTU4w3AJbRI/T8Z/30fGfTw4u/jkoW29/UuRtHN5ZB8mXLfFCt1x07HUfHXv9Vd/VoxC3rmfi4La6ZYKfrf9xhXvTfLTtmlsm+GnTOVdvf+CE3/H9bkdcOmPN4KemqMRaPWXqGzGjnfNT+gZZqt2s1VoAwP1sk6eWeZCrhE6rAAC0654LpQKo61KETceu4L+xlzFvw03Uc+N3hmqOokIFvvlvHVirtWjs8+R3HuXdN4GtvVbvWPwJG/x00B4hi2898zqCAJz/yQap11Vo1Sn3meWJ5EA2wU/Pnj0xZcoUzJo1C46OjnBxcUFkZKR4PiUlBYGBgbCxsYFarcbQoUP1lsiOjIxE27ZtsXnzZnh6eorvJlEoFPj444/x6quvwsrKCt7e3oiJiUFSUhJ69uwJa2trdO7cGdevXxfbun79OgIDA+Hs7AwbGxt07NgR33//fYXup6CgoMzbdMmwFAoBbyy4jYtnrPBb4uOzNmrHYowIy8D//ltHPObiUQCFEhg2JRMbItywaJIHbB20WLLrBkzNjPwRCap2P0erEdjUF/09W2PfpnpYsisJdnW0jy17O9kcX31aD31H/S4e02SZ4IOwhpixKgXWtk/+PudplAhs6ot+Hm0QProxQhbdRvseDH5qitJhLymbMZNN8AMAn332GaytrXH69GksXboUCxcuRHR0NHQ6HQIDA5GVlYVjx44hOjoaN27cwGuvvaZXPykpCXv27MHevXsRHx8vHo+KisLo0aMRHx+PFi1aYMSIEXj99dcxd+5cxMbGQhAEhIaGiuVzc3PRt29fHDlyBOfPn0efPn3Qv39/pKSklPtelixZovcmXXd3d8mfD+kLXXwbHi3yseRNj8eet7LRImpbMlKuWuD/lruIx5UKwMxcwEfh9RF3TI0r56yx5E0PuHkWlBkOIHre2nbJxUfRiVh54Bo69LyP915vhOzfy85g+P2OGeYFNUH3V7PRNyhLPL5qpjt6DbwH3xfznnodSxsdPopOxNpvrmLM7Dv4eEF9/HLKxuD3Q5VkoLe6GytZzflp3bo13n33XQCAl5cX1q1bJ74D5MKFC0hOThaDiG3btqFly5Y4e/YsOnbsCKBkqGvbtm2oV6+eXrtjx47F0KFDAQCzZ8+Gn58fwsPDxRUlp06dirFjx4rl27RpgzZt2oj7UVFR2LdvHw4cOKAXJD3N3LlzMX36dHFfo9EwADKgkPduodPLGrw9sAl+v2Ne5ryltRbv7biBh3lKLBjfCNpihXguK7NkkcmUq3+94C8nyxSaLFM41S+q+s4TPYWFlQ71PQtR37MQ3u0fYGwXbxze6Yhhb/31vqQ/0k0x699N4NMhD1OXperVjz9pi5jv7PDlBqeSAwKg0ynwinsbhC1NRcDwkkBJqSx5ygsAmrR6iNRrFvh8rRP/AUC1guyCn0e5uroiMzMTCQkJcHd31wsefHx8YG9vj4SEBDH48fDwKBP4/L1dZ2dnAICvr6/esfz8fGg0GqjVauTm5iIyMhKHDh3CnTt3UFxcjIcPH1Yo86NSqZ749lySQkDIe7fRuU8OZg5piozUsp+xlU1J4FNUqMC7YzxRVKCfAL10tmTyZ4MmBWLgZGtfDLVjMTJulw2kiKqToIPed/j3O2aY9e8m8PJ9iLdXpkD5t/z+qq+vivPbAODUt3b44kMnrDxwDXVcnhzc63RAUaGsBgtqNT7tJY2sgh8zM/3XPigUCuh05Z+DYW1t/djjj7arUCieeKz0WjNmzEB0dDQ++OADNG3aFJaWlhgyZAgnUdcAoYtvo9fAe4gc64mHuUo41Cv5yzzvvgkK85WwstFi8c4bUFnqsPStRrCy0cLKpmS+RM4fptDpFLh9Q4VTh9V4c2EaVs9qgLz7Sox7Jx23klT45STT/lR1HuYpkZb8V8CenmqO6xct/wy+tdix2hl+vXPg6FwETZYpDmypi9/TzdCtfzaAksBn5pCmcKpfiIkRacj546+/4h2digEADb0K9K559RcrKJRAoxb54rFda53g1foB3BoVoqhQgTNH1DiyxxFvLdHPIlE14tNeksgq+HkSb29vpKamIjU1Vcz+XL58GdnZ2fDx8TH49U6ePIkxY8Zg4MCBAErmAN28edPg16GK6z/mDwDAB3uv6x3/IMwd0bsd0dT3IbzblywItzXmil6Z0f/wRsatkszOsikN8fqCNCzclgxBB/z6sw3mBTXWGx4jMrSrv1hh1pCm4v7HkfUBlCy4OeX9VNxKUiHqi0bQZJnC1kGLZm0eYPm+a2jUvCRwOXfcFmnJKqQlqxDUvqVe2xV5r1r+AyXWveOO3++YwdxCB/cmBZi19jf0DMyWfI9ENUGtCH78/f3h6+uLoKAgrFq1CsXFxZg8eTJ69OiBDh06GPx6Xl5e2Lt3L/r37w+FQoHw8PAKZaCo6gS4tXnq+V9jbJ5ZBgAe5Jpg5dvuWPk252HR89Omc+5Tg5SIT24+tX7v17LQ+7Wsp5YpT50xs9MxZnZ6hdqh54vDXtLUigFchUKBr776Cg4ODujevTv8/f3RuHFjfP7551VyvRUrVsDBwQGdO3dG//79ERAQgHbt2lXJtYiIiMrg016SKATByAf+agiNRgM7Ozv0RCBMFWbPrkAkQxUZeiGSG819HRya3UBOTg7UanXVXOPP3wq/PgthamZR6XaKi/IRcziiSvtak9WKYS8iIiJjwmEvaRj8EBERyY1OKNmk1DdiDH6IiIjkRuq8HeOOfWrHhGciIiKi8mLmh4iISGYUkDjnx2A9kScGP0RERHLDFZ4l4bAXERERGRVmfoiIiGSGj7pLw+CHiIhIbvi0lyQc9iIiIiKjwswPERGRzCgEAQoJk5al1K0NGPwQERHJje7PTUp9I8ZhLyIiIjIqzPwQERHJDIe9pGHwQ0REJDd82ksSBj9ERERywxWeJeGcHyIiIjIqzPwQERHJDFd4lobBDxERkdxw2EsSDnsRERGRUWHmh4iISGYUupJNSn1jxuCHiIhIbjjsJQmHvYiIiMioMPNDREQkN1zkUBIGP0RERDLD11tIw2EvIiIiMirM/BAREckNJzxLwuCHiIhIbgQAUh5XN+7Yh8EPERGR3HDOjzSc80NERERGhZkfIiIiuREgcc6PwXoiS8z8EBERyU3phGcpWwUsWbIEHTt2hK2tLZycnDBgwAAkJibqlcnPz0dISAjq1KkDGxsbDB48GBkZGXplUlJS0K9fP1hZWcHJyQkzZ85EcXGxXpkff/wR7dq1g0qlQtOmTbF169ZKfURPw+CHiIiInurYsWMICQnBzz//jOjoaBQVFaF3797Iy8sTy0ybNg1ff/01vvjiCxw7dgxpaWkYNGiQeF6r1aJfv34oLCzEqVOn8Nlnn2Hr1q2IiIgQyyQnJ6Nfv37o1asX4uPjERYWhgkTJuDbb7816P0oBMHIZz3VEBqNBnZ2duiJQJgqzKq7O0RV4tu0+OruAlGV0dzXwaHZDeTk5ECtVlfNNf78rfin72yYmqgq3U6xtgBHL/wHqampen1VqVRQqZ7d7t27d+Hk5IRjx46he/fuyMnJQb169bBjxw4MGTIEAHDlyhV4e3sjJiYGL774Iv73v//h1VdfRVpaGpydnQEAGzZswOzZs3H37l2Ym5tj9uzZOHToEC5evChea9iwYcjOzsbhw4crfb9/x8wPERGRzJQ+7SVlAwB3d3fY2dmJ25IlS8p1/ZycHACAo6MjACAuLg5FRUXw9/cXy7Ro0QINGzZETEwMACAmJga+vr5i4AMAAQEB0Gg0uHTpkljm0TZKy5S2YSic8ExERGSkHpf5eRadToewsDB06dIFrVq1AgCkp6fD3Nwc9vb2emWdnZ2Rnp4ulnk08Ck9X3ruaWU0Gg0ePnwIS0vLit3gEzD4ISIikhsDrfCsVqsrPEQXEhKCixcv4sSJE5W/fjXjsBcREZHcPOenvUqFhobi4MGD+OGHH9CgQQPxuIuLCwoLC5Gdna1XPiMjAy4uLmKZvz/9Vbr/rDJqtdpgWR+AwQ8RERE9gyAICA0Nxb59+3D06FF4enrqnW/fvj3MzMxw5MgR8VhiYiJSUlLg5+cHAPDz88OFCxeQmZkplomOjoZarYaPj49Y5tE2SsuUtmEoHPYiIiKSm+f8YtOQkBDs2LEDX331FWxtbcU5OnZ2drC0tISdnR3Gjx+P6dOnw9HREWq1Gm+99Rb8/Pzw4osvAgB69+4NHx8fjBo1CkuXLkV6ejrmz5+PkJAQca7RG2+8gXXr1mHWrFkYN24cjh49it27d+PQoUOVv9fHYPBDREQkNzoACon1K2D9+vUAgJ49e+od37JlC8aMGQMAWLlyJZRKJQYPHoyCggIEBATgo48+EsuamJjg4MGDePPNN+Hn5wdra2sEBwdj4cKFYhlPT08cOnQI06ZNw+rVq9GgQQNs3rwZAQEBlbrNJ+E6PzUE1/khY8B1fqg2e57r/Pg3my55nZ/vr66o0r7WZJzzQ0REREaFw15ERERy85zn/NQ2DH6IiIjkRicACgkBjM64gx8OexEREZFRYeaHiIhIbjjsJQmDHyIiItmRGPzAuIMfDnsRERGRUWHmh4iISG447CUJgx8iIiK50QmQNHTFp72IiIiIjAczP0RERHIj6Eo2KfWNGIMfIiIiueGcH0kY/BAREckN5/xIwjk/REREZFSY+SEiIpIbDntJwuCHiIhIbgRIDH4M1hNZ4rAXERERGRVmfoiIiOSGw16SMPghIiKSG50OgIS1enTGvc4Ph72IiIjIqDDzQ0REJDcc9pKEwQ8REZHcMPiRhMNeREREZFSY+SEiIpIbvt5CEgY/REREMiMIOggS3swupW5twOCHiIhIbgRBWvaGc36IiIiIjAczP0RERHIjSJzzY+SZHwY/REREcqPTAQoJ83aMfM4Ph72IiIjIqDDzQ0REJDcc9pKEwQ8REZHMCDodBAnDXsb+qDuHvYiIiMioMPNDREQkNxz2koTBDxERkdzoBEDB4KeyOOxFRERERoWZHyIiIrkRBABS1vkx7swPgx8iIiKZEXQCBAnDXgKDHyIiIpIVQQdpmR8+6k5ERERkNJj5ISIikhkOe0nD4IeIiEhuOOwlCYOfGqI0Ci9GkaR1q4hqMs194/4Ll2o3TW7J9/t5ZFWk/lYUo8hwnZEhBj81xP379wEAJ/BNNfeEqOo4NKvuHhBVvfv378POzq5K2jY3N4eLiwtOpEv/rXBxcYG5ubkBeiU/CsHYB/5qCJ1Oh7S0NNja2kKhUFR3d4yCRqOBu7s7UlNToVarq7s7RAbH7/jzJQgC7t+/Dzc3NyiVVfc8UX5+PgoLCyW3Y25uDgsLCwP0SH6Y+akhlEolGjRoUN3dMEpqtZo/DFSr8Tv+/FRVxudRFhYWRhu0GAofdSciIiKjwuCHiIiIjAqDHzJaKpUK7777LlQqVXV3hahK8DtO9Hic8ExERERGhZkfIiIiMioMfoiIiMioMPghIiIio8Lgh8hAevbsibCwsOruBlGVatSoEVatWlXd3SCShMEPERERGRUGP0REtYghXntAVNsx+KEa5fDhw+jatSvs7e1Rp04dvPrqq7h+/ToA4ObNm1AoFNi7dy969eoFKysrtGnTBjExMXpt7NmzBy1btoRKpUKjRo2wfPlyvfONGjXCokWLMHr0aNjY2MDDwwMHDhzA3bt3ERgYCBsbG7Ru3RqxsbFinT/++APDhw9H/fr1YWVlBV9fX+zcufOJ97Fw4UK0atWqzPG2bdsiPDxcykdEtUzPnj0xZcoUzJo1C46OjnBxcUFkZKR4PiUlRfxeqtVqDB06FBkZGeL5yMhItG3bFps3b4anp6f42gOFQoGPP/4Yr776KqysrODt7Y2YmBgkJSWhZ8+esLa2RufOncX/vwDg+vXrCAwMhLOzM2xsbNCxY0d8//33z+2zIHpeGPxQjZKXl4fp06cjNjYWR44cgVKpxMCBA6HT6cQy8+bNw4wZMxAfH49mzZph+PDhKC4uBgDExcVh6NChGDZsGC5cuIDIyEiEh4dj69atetdZuXIlunTpgvPnz6Nfv34YNWoURo8ejZEjR+LcuXNo0qQJRo8ejdJlsPLz89G+fXscOnQIFy9exKRJkzBq1CicOXPmsfcxbtw4JCQk4OzZs+Kx8+fP49dff8XYsWMN/KmR3H322WewtrbG6dOnsXTpUixcuBDR0dHQ6XQIDAxEVlYWjh07hujoaNy4cQOvvfaaXv2kpCTs2bMHe/fuRXx8vHg8KioKo0ePRnx8PFq0aIERI0bg9ddfx9y5cxEbGwtBEBAaGiqWz83NRd++fXHkyBGcP38effr0Qf/+/ZGSkvK8Pgqi50MgqsHu3r0rABAuXLggJCcnCwCEzZs3i+cvXbokABASEhIEQRCEESNGCC+//LJeGzNnzhR8fHzEfQ8PD2HkyJHi/p07dwQAQnh4uHgsJiZGACDcuXPniX3r16+f8Pbbb4v7PXr0EKZOnSruv/LKK8Kbb74p7r/11ltCz549K3D3ZAx69OghdO3aVe9Yx44dhdmzZwvfffedYGJiIqSkpIjnSr/zZ86cEQRBEN59913BzMxMyMzM1GsDgDB//nxxv/Q7/cknn4jHdu7cKVhYWDy1fy1bthTWrl0r7nt4eAgrV66s8H0S1STM/FCNcu3aNQwfPhyNGzeGWq1Go0aNAEDvX56tW7cW/+zq6goAyMzMBAAkJCSgS5cuem126dIF165dg1arfWwbzs7OAABfX98yx0rb1Wq1iIqKgq+vLxwdHWFjY4Nvv/32qf8injhxInbu3In8/HwUFhZix44dGDduXPk/DDIaj34fgZLvdWZmJhISEuDu7g53d3fxnI+PD+zt7ZGQkCAe8/DwQL169Z7a7pO+5/n5+dBoNABKMj8zZsyAt7c37O3tYWNjg4SEBGZ+qNYxre4OED2qf//+8PDwwKZNm+Dm5gadTodWrVrpTeI0MzMT/6xQKABAb1isPB7XxtPaXbZsGVavXo1Vq1bB19cX1tbWCAsLe+rk0v79+0OlUmHfvn0wNzdHUVERhgwZUqF+knF49LsHlHz/KvKdtra2fma75fmez5gxA9HR0fjggw/QtGlTWFpaYsiQIZxETbUOgx+qMf744w8kJiZi06ZN6NatGwDgxIkTFWrD29sbJ0+e1Dt28uRJNGvWDCYmJpXu28mTJxEYGIiRI0cCKPmxuHr1Knx8fJ5Yx9TUFMHBwdiyZQvMzc0xbNgwWFpaVroPZHy8vb2RmpqK1NRUMftz+fJlZGdnP/W7V1knT57EmDFjMHDgQAAlmaCbN28a/DpE1Y3BD9UYDg4OqFOnDjZu3AhXV1ekpKRgzpw5FWrj7bffRseOHREVFYXXXnsNMTExWLduHT766CNJffPy8sKXX36JU6dOwcHBAStWrEBGRsYzf4AmTJgAb29vACgTlBE9i7+/P3x9fREUFIRVq1ahuLgYkydPRo8ePdChQweDX8/Lywt79+5F//79oVAoEB4eXuGsKpEccM4P1RhKpRK7du1CXFwcWrVqhWnTpmHZsmUVaqNdu3bYvXs3du3ahVatWiEiIgILFy7EmDFjJPVt/vz5aNeuHQICAtCzZ0+4uLhgwIABz6zn5eWFzp07o0WLFujUqZOkPpDxUSgU+Oqrr+Dg4IDu3bvD398fjRs3xueff14l11uxYgUcHBzQuXNn9O/fHwEBAWjXrl2VXIuoOikE4c9neYnI4ARBgJeXFyZPnozp06dXd3eIiAgc9iKqMnfv3sWuXbuQnp7OtX2IiGoQBj9EVcTJyQl169bFxo0b4eDgUN3dISKiPzH4IaoiHFEmIqqZOOGZiIiIjAqDHyIiIjIqDH6IiIjIqDD4ISIiIqPC4IeIiIiMCoMfIhKNGTNGb+Xqnj17Iiws7Ln348cff4RCoUB2dvYTyygUCuzfv7/cbUZGRqJt27aS+nXz5k0oFArEx8dLaoeIqheDH6IabsyYMVAoFFAoFDA3N0fTpk2xcOFCFBcXV/m19+7di6ioqHKVLU/AQkRUE3CdHyIZ6NOnD7Zs2YKCggJ88803CAkJgZmZGebOnVumbGFhIczNzQ1yXUdHR4O0Q0RUkzDzQyQDKpUKLi4u8PDwwJtvvgl/f38cOHAAwF9DVe+99x7c3NzQvHlzAEBqaiqGDh0Ke3t7ODo6IjAwEDdv3hTb1Gq1mD59Ouzt7VGnTh3MmjWrzMKMfx/2KigowOzZs+Hu7g6VSoWmTZvik08+wc2bN9GrVy8AgIODAxQKhfgyWZ1OhyVLlsDT0xOWlpZo06YNvvzyS73rfPPNN2jWrBksLS3Rq1cvvX6W1+zZs9GsWTNYWVmhcePGCA8PR1FRUZlyH3/8Mdzd3WFlZYWhQ4ciJydH7/zmzZvh7e0NCwsLtGjRAh999FGF+0JENRuDHyIZsrS0RGFhobh/5MgRJCYmIjo6GgcPHkRRURECAgJga2uLn376CSdPnoSNjQ369Okj1lu+fDm2bt2KTz/9FCdOnEBWVhb27dv31OuOHj0aO3fuxJo1a5CQkICPP/4YNjY2cHd3x549ewAAiYmJuHPnDlavXg0AWLJkCbZt24YNGzbg0qVLmDZtGkaOHIljx44BKAnSBg0ahP79+yM+Ph4TJkzAnDlzKvyZ2NraYuvWrbh8+TJWr16NTZs2YeXKlXplkpKSsHv3bnz99dc4fPgwzp8/j8mTJ4vnt2/fjoiICLz33ntISEjA4sWLER4ejs8++6zC/SGiGkwgohotODhYCAwMFARBEHQ6nRAdHS2oVCphxowZ4nlnZ2ehoKBArPN///d/QvPmzQWdTiceKygoECwtLYVvv/1WEARBcHV1FZYuXSqeLyoqEho0aCBeSxAEoUePHsLUqVMFQRCExMREAYAQHR392H7+8MMPAgDh3r174rH8/HzByspKOHXqlF7Z8ePHC8OHDxcEQRDmzp0r+Pj46J2fPXt2mbb+DoCwb9++J55ftmyZ0L59e3H/3XffFUxMTIRbt26Jx/73v/8JSqVSuHPnjiAIgtCkSRNhx44deu1ERUUJfn5+giAIQnJysgBAOH/+/BOvS0Q1H+f8EMnAwYMHYWNjg6KiIuh0OowYMQKRkZHieV9fX715Pr/88guSkpJga2ur105+fj6uX7+OnJwc3LlzB506dRLPmZqaokOHDk98J1l8fDxMTEzQo0ePcvc7KSkJDx48wMsvv6x3vLCwEC+88AIAICEhQa8fAODn51fua5T6/PPPsWbNGly/fh25ubkoLi6GWq3WK9OwYUPUr19f7zo6nQ6JiYmwtbXF9evXMX78eEycOFEsU1xcDDs7uwr3h4hqLgY/RDLQq1cvrF+/Hubm5nBzc4Opqf7/utbW1nr7ubm5aN++PbZv316mrXr16lWqD5aWlhWuk5ubCwA4dOiQXtABlMxjMpSYmBgEBQVhwYIFCAgIgJ2dHXbt2oXly5dXuK+bNm0qE4yZmJgYrK9EVP0Y/BDJgLW1NZo2bVru8u3atcPnn38OJyenMtmPUq6urjh9+jS6d+8OoCTDERcXh3bt2j22vK+vL3Q6HY4dOwZ/f/8y50szT1qtVjzm4+MDlUqFlJSUJ2aMvL29xcnbpX7++edn3+QjTp06BQ8PD8ybN0889ttvv5Upl5KSgrS0NLi5uYnXUSqVaN68OZydneHm5oYbN24gKCioQtcnInnhhGeiWigoKAh169ZFYGAgfvrpJyQnJ+PHH3/ElClTcOvWLQDA1KlT8f7772P//v24cuUKJk+e/NQ1eho1aoTg4GCMGzcO+/fvF9vcvXs3AMDDwwMKhQIHDx7E3bt3kZubC1tbW8yYMQPTpk3DZ599huvXr+PcuXNYu3atOIn4jTfewLVr1zBz5kwkJiZix44d2Lp1a4Xu18vLCykpKdi1axeuX7+ONWvWPHbytoWFBYKDg/HLL7/gp59+wpQpUzB06FC4uLgAABYsWIAlS5ZgzZo1uHr1Ki5cuIAtW7ZgxYoVFeoPEdVsDH6IaiErKyscP34cDRs2xKBBg+Dt7Y3x48cjPz9fzAS9/fbbGDVqFIKDg+Hn5wdbW1sMHDjwqe2uX78eQ4YMweTJk9GiRQtMnDgReXl5AID69etjwYIFmDNnDpydnREaGgoAiIqKQnh4OJYsWQJvb2/06dMHhw4dgqenJ4CSeTh79uzB/v370aZNG2zYsAGLFy+u0P3+61//wrRp0xAaGoq2bdvi1KlTCA8PL1OuadOmGDRoEPr27YvevXujdevWeo+yT5gwAZs3b8aWLVvg6+uLHj16YOvWrWJfiah2UAhPmt1IREREVAsx80NERERGhcEPERERGRUGP0RERGRUGPwQERGRUWHwQ0REREaFwQ8REREZFQY/REREZFQY/BAREZFRYfBDRERERoXBDxERERkVBj9ERERkVP4fm3KoCab5zegAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 72
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 2. Métricas derivadas de la matriz de confusión",
   "id": "5ae92082cbdb9b1"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Precisión",
   "id": "333e7616905dba0d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:06:06.029356Z",
     "start_time": "2024-06-16T22:06:05.871707Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import precision_score\n",
    "print(\"Precision: \", precision_score(y_val, y_pred, pos_label='anomaly'))"
   ],
   "id": "66154a2785035605",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  0.980448135651873\n"
     ]
    }
   ],
   "execution_count": 73
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Recall",
   "id": "97dfde524e1eee2f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:06:35.073095Z",
     "start_time": "2024-06-16T22:06:34.911446Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from  sklearn.metrics import recall_score\n",
    "print(\"Recall: \", recall_score(y_val, y_pred, pos_label='anomaly'))"
   ],
   "id": "fabe0740af56dd9e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall:  0.9664847347774177\n"
     ]
    }
   ],
   "execution_count": 74
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### F1 Score",
   "id": "712524fb3fca3b62"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:07:17.839418Z",
     "start_time": "2024-06-16T22:07:17.682881Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print(\"F1 Score: \", f1_score(y_val, y_pred, pos_label='anomaly'))"
   ],
   "id": "87a7d1b8538e8f19",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score:  0.9734163624651063\n"
     ]
    }
   ],
   "execution_count": 75
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 3. Curvas ROC y PR",
   "id": "f85d1ec69b277a07"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Curva ROC",
   "id": "3484059de02c957d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:08:14.572489Z",
     "start_time": "2024-06-16T22:08:14.435230Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import RocCurveDisplay\n",
    "RocCurveDisplay.from_estimator(clf, X_val_prep, y_val)"
   ],
   "id": "7faa4057abb1c135",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1c2334088f0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 76
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Curva PR",
   "id": "b8e8a516a06c5067"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:08:39.984172Z",
     "start_time": "2024-06-16T22:08:39.724224Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import PrecisionRecallDisplay\n",
    "PrecisionRecallDisplay.from_estimator(clf, X_val_prep, y_val)"
   ],
   "id": "f6946728d87c27a9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x1c23cc0b8f0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 77
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 4. Evaluación del modelo con el conjunto de datos de pruebas",
   "id": "b326ca5baddeab43"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:09:08.522726Z",
     "start_time": "2024-06-16T22:09:08.428287Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Transformamos el subconjunto de datos de validacion\n",
    "X_test_prep = data_preparer.transform(X_test)"
   ],
   "id": "29b29ff7c078307d",
   "outputs": [],
   "execution_count": 78
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:09:29.602166Z",
     "start_time": "2024-06-16T22:09:29.501403Z"
    }
   },
   "cell_type": "code",
   "source": "y_pred_prep = data_preparer.transform(X_test)",
   "id": "d3ae7bbe73640074",
   "outputs": [],
   "execution_count": 79
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:09:40.787426Z",
     "start_time": "2024-06-16T22:09:40.778885Z"
    }
   },
   "cell_type": "code",
   "source": "y_pred = clf.predict(X_test_prep)",
   "id": "b1c842f84acd7a9a",
   "outputs": [],
   "execution_count": 80
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:10:01.872304Z",
     "start_time": "2024-06-16T22:10:01.619668Z"
    }
   },
   "cell_type": "code",
   "source": "ConfusionMatrixDisplay.from_estimator(clf, X_test_prep, y_test, values_format='d')",
   "id": "b2bed1b27fd47d7b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1c23cb97770>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 81
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-16T22:10:39.196120Z",
     "start_time": "2024-06-16T22:10:39.041005Z"
    }
   },
   "cell_type": "code",
   "source": "print('F1 score:', f1_score(y_test, y_pred, pos_label='anomaly'))",
   "id": "6be4e67709140549",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: 0.9723614860521917\n"
     ]
    }
   ],
   "execution_count": 82
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "15901c561532b954"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}