10_Evaluación de resultados.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/drive/11qkRR_YcEN-C8GGlfFcgFGWkdVUIPziP\">Abre este Jupyter en Google Colab</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluación de resultados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este notebook se muestran técnicas para la evaluación de los resultados de una predicción con un algoritmo de Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conjunto de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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.preprocessing import RobustScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.base import BaseEstimator, TransformerMixin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones auxiliares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_kdd_dataset(data_path):\n",
    "    \"\"\"Lectura del conjunto de datos NSL-KDD.\"\"\"\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "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(\n",
    "        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(\n",
    "        test_set, test_size=0.5, random_state=rstate, shuffle=shuffle, stratify=strat)\n",
    "    return (train_set, val_set, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construcción de un pipeline para los atributos numéricos\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('rbst_scaler', RobustScaler()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transormador para codificar únicamente las columnas categoricas y devolver un df\n",
    "class CustomOneHotEncoder(BaseEstimator, TransformerMixin):\n",
    "    \n",
    "    def __init__(self):\n",
    "        self._oh = OneHotEncoder()\n",
    "        self._columns = None\n",
    "        \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",
    "        \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",
    "        X_cat_oh = pd.DataFrame(X_cat_oh.toarray(), \n",
    "                                columns=self._columns, \n",
    "                                index=X_copy.index)\n",
    "        X_copy.drop(list(X_cat), axis=1, inplace=True)\n",
    "        return X_copy.join(X_cat_oh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transofrmador que prepara todo el conjunto de datos llamando pipelines y transformadores personalizados\n",
    "class DataFramePreparer(BaseEstimator, TransformerMixin):\n",
    "    \n",
    "    def __init__(self):\n",
    "        self._full_pipeline = None\n",
    "        self._columns = None\n",
    "        \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",
    "        \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, \n",
    "                            columns=self._columns, \n",
    "                            index=X_copy.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lectura del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = load_kdd_dataset(\"datasets/NSL-KDD/KDDTrain+.arff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <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",
       "      <th>dst_host_serror_rate</th>\n",
       "      <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",
       "    </tr>\n",
       "  </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",
       "      <td>0.00</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "      <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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>REJ</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>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",
       "      <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>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>"
      ],
      "text/plain": [
       "   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]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## División del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Division del conjunto en los diferentes subconjuntos\n",
    "train_set, val_set, test_set = train_val_test_split(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longitud del Training Set: 75583\n",
      "Longitud del Validation Set: 25195\n",
      "Longitud del Test Set: 25195\n"
     ]
    }
   ],
   "source": [
    "print(\"Longitud del Training Set:\", len(train_set))\n",
    "print(\"Longitud del Validation Set:\", len(val_set))\n",
    "print(\"Longitud del Test Set:\", len(test_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para cada uno de los subconjuntos, separamos las etiquetas de las características de entrada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conjunto de datos general\n",
    "X_df = df.drop(\"class\", axis=1)\n",
    "y_df = df[\"class\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conjunto de datos de entrenamiento\n",
    "X_train = train_set.drop(\"class\", axis=1)\n",
    "y_train = train_set[\"class\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conjunto de datos de validación\n",
    "X_val = val_set.drop(\"class\", axis=1)\n",
    "y_val = val_set[\"class\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conjunto de datos de pruebas\n",
    "X_test = test_set.drop(\"class\", axis=1)\n",
    "y_test = test_set[\"class\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparación del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instanciamos nuestro transformador personalizado\n",
    "data_preparer = DataFramePreparer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "DataFramePreparer()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hacemos el fit con el conjunto de datos general para que adquiera todos los valores posibles\n",
    "data_preparer.fit(X_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformamos el subconjunto de datos de entrenamiento\n",
    "X_train_prep = data_preparer.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_count</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",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98320</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>SF</td>\n",
       "      <td>1032.0</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>210.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8590</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>smtp</td>\n",
       "      <td>SF</td>\n",
       "      <td>1762.0</td>\n",
       "      <td>331.0</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>30.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.73</td>\n",
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       "      <td>0.02</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>91385</th>\n",
       "      <td>0.0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>eco_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>8.0</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>2.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54349</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>csnet_ns</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>255.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69568</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>smtp</td>\n",
       "      <td>SF</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>342.0</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>83.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration protocol_type   service flag  src_bytes  dst_bytes land  \\\n",
       "98320       0.0          icmp     ecr_i   SF     1032.0        0.0    0   \n",
       "8590        0.0           tcp      smtp   SF     1762.0      331.0    0   \n",
       "91385       0.0          icmp     eco_i   SF        8.0        0.0    0   \n",
       "54349       0.0           tcp  csnet_ns   S0        0.0        0.0    0   \n",
       "69568       0.0           tcp      smtp   SF     1518.0      342.0    0   \n",
       "\n",
       "       wrong_fragment  urgent  hot  ...  dst_host_count dst_host_srv_count  \\\n",
       "98320             0.0     0.0  0.0  ...           210.0               65.0   \n",
       "8590              0.0     0.0  0.0  ...            30.0              122.0   \n",
       "91385             0.0     0.0  0.0  ...             2.0              126.0   \n",
       "54349             0.0     0.0  0.0  ...           255.0               18.0   \n",
       "69568             0.0     0.0  0.0  ...            83.0              125.0   \n",
       "\n",
       "       dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "98320                    0.31                    0.01   \n",
       "8590                     0.73                    0.07   \n",
       "91385                    1.00                    0.00   \n",
       "54349                    0.07                    0.07   \n",
       "69568                    0.66                    0.05   \n",
       "\n",
       "       dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "98320                         0.31                         0.00   \n",
       "8590                          0.03                         0.02   \n",
       "91385                         1.00                         0.25   \n",
       "54349                         0.00                         0.00   \n",
       "69568                         0.01                         0.02   \n",
       "\n",
       "       dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "98320                   0.0                       0.0                   0.0   \n",
       "8590                    0.0                       0.0                   0.0   \n",
       "91385                   0.0                       0.0                   0.0   \n",
       "54349                   1.0                       1.0                   0.0   \n",
       "69568                   0.0                       0.0                   0.0   \n",
       "\n",
       "       dst_host_srv_rerror_rate  \n",
       "98320                       0.0  \n",
       "8590                        0.0  \n",
       "91385                       0.0  \n",
       "54349                       0.0  \n",
       "69568                       0.0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <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",
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       "      <th>8590</th>\n",
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       "      <th>91385</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>54349</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>69568</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.340580</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration  src_bytes  dst_bytes  wrong_fragment  urgent  hot  \\\n",
       "98320       0.0   3.579710   0.000000             0.0     0.0  0.0   \n",
       "8590        0.0   6.224638   0.641473             0.0     0.0  0.0   \n",
       "91385       0.0  -0.130435   0.000000             0.0     0.0  0.0   \n",
       "54349       0.0  -0.159420   0.000000             0.0     0.0  0.0   \n",
       "69568       0.0   5.340580   0.662791             0.0     0.0  0.0   \n",
       "\n",
       "       num_failed_logins  num_compromised  root_shell  su_attempted  ...  \\\n",
       "98320                0.0              0.0         0.0           0.0  ...   \n",
       "8590                 0.0              0.0         0.0           0.0  ...   \n",
       "91385                0.0              0.0         0.0           0.0  ...   \n",
       "54349                0.0              0.0         0.0           0.0  ...   \n",
       "69568                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",
       "98320      1.0      0.0     1.0     0.0          1.0          0.0   \n",
       "8590       1.0      0.0     1.0     0.0          0.0          1.0   \n",
       "91385      1.0      0.0     1.0     0.0          1.0          0.0   \n",
       "54349      0.0      0.0     1.0     0.0          1.0          0.0   \n",
       "69568      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",
       "98320              1.0              0.0               1.0               0.0  \n",
       "8590               1.0              0.0               1.0               0.0  \n",
       "91385              1.0              0.0               1.0               0.0  \n",
       "54349              1.0              0.0               1.0               0.0  \n",
       "69568              1.0              0.0               1.0               0.0  \n",
       "\n",
       "[5 rows x 126 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_prep.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformamos el subconjunto de datos de validacion\n",
    "X_val_prep = data_preparer.transform(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entrenamiento de un algoritmo de Regresión Logística"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La instanciación de un algoritmo de Machine Learning utilizando Sklearn se realiza utilizando los métodos expuestos por la API de sklearn tal y como se ha presentado en cuadernos anteriores. En este caso vamos a pedirle al modelo que se entrene durante más tiempo, concretamente 1.000 iteraciones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, solver='newton-cg')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Entrenamos un algoritmo basado en regresión logística\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf = LogisticRegression(solver=\"newton-cg\", max_iter=1000)\n",
    "clf.fit(X_train_prep, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicción de nuevos ejemplos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realizamos una predicción con el modelo generado anteriormente tras el entrenamiento del algoritmo de Regresión Logística. Utilizamos el subconjunto de validación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_val_prep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Matriz de Confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11457,   417],\n",
       "       [  255, 13066]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x297f719e5d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "ConfusionMatrixDisplay.from_estimator(clf, X_val_prep, y_val, values_format='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Métricas derivadas de la matriz de confusión"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precisión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión: 0.9782274590163934\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "\n",
    "print(\"Precisión:\", precision_score(y_val, y_pred, pos_label='anomaly'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.9648812531581606\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "\n",
    "print(\"Recall:\", recall_score(y_val, y_pred, pos_label='anomaly'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F1 Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: 0.9715085220045789\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "print(\"F1 score:\", f1_score(y_val, y_pred, pos_label='anomaly'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Curvas ROC y PR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Curva ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x297fb64a9d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "RocCurveDisplay.from_estimator(clf, X_val_prep, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Curva PR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x297fbeece90>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import PrecisionRecallDisplay\n",
    "\n",
    "PrecisionRecallDisplay.from_estimator(clf, X_val_prep, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Evaluación del modelo con el conjunto de datos de pruebas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformamos el subconjunto de datos de validacion\n",
    "X_test_prep = data_preparer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test_prep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x297fc920ed0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_estimator(clf, X_test_prep, y_test, values_format='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: 0.9691633739907233\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 score:\", f1_score(y_test, y_pred, pos_label='anomaly'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}