random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e6873ff1",
   "metadata": {},
   "source": [
    "### 0. Importar librerías"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b4341cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Se define la semilla para que cuando se vuelva a ejecutar el split train-test\n",
    "# los datos que contiene cada conjunto sean los mismos y para que la inicialización\n",
    "# del modelo creado sea la misma\n",
    "semilla = 1234"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6011d883",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 1. Cargar el dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85764757",
   "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>AF365</th>\n",
       "      <th>AF450</th>\n",
       "      <th>35 deg</th>\n",
       "      <th>42 deg</th>\n",
       "      <th>Grupo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.320158</td>\n",
       "      <td>1.229614</td>\n",
       "      <td>13.01</td>\n",
       "      <td>19.39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.529274</td>\n",
       "      <td>0.616514</td>\n",
       "      <td>8.18</td>\n",
       "      <td>24.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.908257</td>\n",
       "      <td>0.955847</td>\n",
       "      <td>7.75</td>\n",
       "      <td>16.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.590625</td>\n",
       "      <td>1.018495</td>\n",
       "      <td>4.10</td>\n",
       "      <td>16.30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.617071</td>\n",
       "      <td>1.242961</td>\n",
       "      <td>5.76</td>\n",
       "      <td>21.05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>3.034884</td>\n",
       "      <td>2.051768</td>\n",
       "      <td>4.55</td>\n",
       "      <td>8.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>4.093525</td>\n",
       "      <td>3.037288</td>\n",
       "      <td>3.97</td>\n",
       "      <td>5.35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>4.730000</td>\n",
       "      <td>3.040000</td>\n",
       "      <td>7.04</td>\n",
       "      <td>14.44</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>6.70</td>\n",
       "      <td>10.57</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>3.885714</td>\n",
       "      <td>2.403073</td>\n",
       "      <td>6.61</td>\n",
       "      <td>14.30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AF365     AF450  35 deg  42 deg  Grupo\n",
       "0    2.320158  1.229614   13.01   19.39      0\n",
       "1    1.529274  0.616514    8.18   24.78      0\n",
       "2    1.908257  0.955847    7.75   16.90      0\n",
       "3    1.590625  1.018495    4.10   16.30      0\n",
       "4    1.617071  1.242961    5.76   21.05      0\n",
       "..        ...       ...     ...     ...    ...\n",
       "115  3.034884  2.051768    4.55    8.50      2\n",
       "116  4.093525  3.037288    3.97    5.35      2\n",
       "117  4.730000  3.040000    7.04   14.44      2\n",
       "118  3.200000  2.360000    6.70   10.57      2\n",
       "119  3.885714  2.403073    6.61   14.30      2\n",
       "\n",
       "[120 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ruta del fichero\n",
    "path = 'datos/datos_filtrados.csv'\n",
    "\n",
    "# Cargar el dataset\n",
    "datos = pd.read_csv(path)\n",
    "\n",
    "# Al ser un dataset pequeño se muestra completo\n",
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12156f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    61\n",
       "0    46\n",
       "2    13\n",
       "Name: Grupo, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contar el número de instancias de cada grupo\n",
    "datos['Grupo'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f716f6",
   "metadata": {},
   "source": [
    "## Preparar conjuntos de entrenamiento (80%) y test (20%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9fc8f7bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se separa la variable a predecir (Grupo)\n",
    "X = datos.drop('Grupo', axis=1)\n",
    "y = datos['Grupo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "793c7098",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar librería de scikit-learn para separar los datos\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Separar datos en conjunto de entrenamiento (80%) y test (20%) de manera estratificada\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2,\n",
    "                                                    shuffle=True,\n",
    "                                                    stratify=y,\n",
    "                                                    random_state=semilla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34696ba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape: (96, 4)\n",
      "Training Labels Shape: (96,)\n",
      "Testing Features Shape: (24, 4)\n",
      "Testing Labels Shape: (24,)\n"
     ]
    }
   ],
   "source": [
    "# Visualizar la dimensión de los datos separados\n",
    "print('Training Features Shape:', X_train.shape)\n",
    "print('Training Labels Shape:', y_train.shape)\n",
    "print('Testing Features Shape:', X_test.shape)\n",
    "print('Testing Labels Shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f900cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    49\n",
       "0    37\n",
       "2    10\n",
       "Name: Grupo, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribución de los grupos en el conjunto de entrenamiento\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c1cd37d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.510417\n",
       "0    0.385417\n",
       "2    0.104167\n",
       "Name: Grupo, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Valores normalizados\n",
    "y_train.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09a26af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    12\n",
       "0     9\n",
       "2     3\n",
       "Name: Grupo, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribución de los grupos en el conjunto de test\n",
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f3bd577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.500\n",
       "0    0.375\n",
       "2    0.125\n",
       "Name: Grupo, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Valores normalizados\n",
    "y_test.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c55e6fd7",
   "metadata": {},
   "source": [
    "### Visualizar distribución de cada conjunto (entrenamiento y test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3c640b68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(15,5))\n",
    "\n",
    "#fig.suptitle(\"Distribución de los grupos en los conjuntos de entrenamiento y test\")\n",
    "\n",
    "# Representación del conjunto de entrenamiento\n",
    "plt.subplot(121)\n",
    "y_train.value_counts().plot(kind='bar')\n",
    "plt.title('Distribución del conjunto de entrenamiento')\n",
    "plt.xlabel('Grupo')\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Representación del conjunto de test\n",
    "plt.subplot(122)\n",
    "y_test.value_counts().plot(kind='bar', color='seagreen')\n",
    "plt.title('Distribución del conjunto de test')\n",
    "plt.xlabel('Grupo')\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9bd050",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 2. Crear el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6ec7ed1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=8, random_state=1234, shuffle=True),\n",
       "             estimator=RandomForestClassifier(random_state=1234), n_jobs=7,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33,\n",
       "       35, 37, 39, 41, 43, 45, 47, 49]),\n",
       "                         'max_features': [1, 2, 4],\n",
       "                         'n_estimators': array([ 50,  75, 100, 125, 150])},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
    "import multiprocessing\n",
    "\n",
    "# Definición del modelo\n",
    "modelo = RandomForestClassifier(random_state=semilla)\n",
    "\n",
    "# Definición de los parámetros de búsqueda\n",
    "parametros = {'n_estimators': np.arange(50,151,25),\n",
    "              'max_features': [1, 2, 4],\n",
    "              'max_depth'   : np.arange(1,50,2),\n",
    "              'criterion'   : ['gini', 'entropy']}\n",
    "\n",
    "# Definición de los folds estratificados\n",
    "folds = StratifiedKFold(n_splits=8, shuffle=True, random_state=semilla)\n",
    "\n",
    "# Búsqueda de parámetros\n",
    "grid = GridSearchCV(\n",
    "    estimator  = modelo,\n",
    "    param_grid = parametros,\n",
    "    scoring    = 'accuracy',\n",
    "    n_jobs     = multiprocessing.cpu_count() - 1,\n",
    "    cv         = folds, \n",
    "    verbose    = 0,\n",
    "    return_train_score = True\n",
    "    )\n",
    "\n",
    "# Entrenamiento\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9c9202f",
   "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>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 3, 'max_fea...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.110240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 3, 'max_fea...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.110240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 3, 'max_fea...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.110240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 3, 'max_fea...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.822917</td>\n",
       "      <td>0.113632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 3, 'max_fea...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.822917</td>\n",
       "      <td>0.113632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               params  rank_test_score  \\\n",
       "22  {'criterion': 'gini', 'max_depth': 3, 'max_fea...                1   \n",
       "21  {'criterion': 'gini', 'max_depth': 3, 'max_fea...                1   \n",
       "20  {'criterion': 'gini', 'max_depth': 3, 'max_fea...                1   \n",
       "24  {'criterion': 'gini', 'max_depth': 3, 'max_fea...                4   \n",
       "23  {'criterion': 'gini', 'max_depth': 3, 'max_fea...                4   \n",
       "\n",
       "    mean_test_score  std_test_score  \n",
       "22         0.833333        0.110240  \n",
       "21         0.833333        0.110240  \n",
       "20         0.833333        0.110240  \n",
       "24         0.822917        0.113632  \n",
       "23         0.822917        0.113632  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualización de los resultados de la búsqueda ordenados de mejor a peor\n",
    "df_resultados = pd.DataFrame(grid.cv_results_)\n",
    "df_resultados = df_resultados.sort_values(by=['rank_test_score'])\n",
    "df_resultados[['params', 'rank_test_score', 'mean_test_score', 'std_test_score']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9248b40a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejores parámetros:  {'criterion': 'gini', 'max_depth': 3, 'max_features': 2, 'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "# Se muestran los mejores parámetros\n",
    "print('Mejores parámetros: ', grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "414d4db8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8333333333333333\n"
     ]
    }
   ],
   "source": [
    "# Se visualiza la exactitud (accuracy) del entrenamiento para compararlo con el que se obtenga en la evaluación\n",
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "30d6457a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se toma el modelo que ha obtenido el mejor resultado\n",
    "modelo_rf = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b420ecef",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 3. Evaluar el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b93ca4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se lleva a cabo la predicción para evaluar el modelo\n",
    "y_pred = modelo_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39f54714",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 0 0 0 1 1 1 1 1 0 2 1 0 1 0 0 2 0 0 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "# Visualización de las predicciones\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b10bc9",
   "metadata": {},
   "source": [
    "## Matriz de confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1e7a81e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "# Filas: valores reales\n",
    "# Columnas: predicción\n",
    "\n",
    "clases = ['Control', 'Diabetes 1', 'Diabetes 2']\n",
    "\n",
    "# Visualización de la matriz de confusión\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clases)\n",
    "\n",
    "disp.plot(cmap='Blues', colorbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6a0eae",
   "metadata": {},
   "source": [
    "## Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "34d743ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Control     0.7778    0.7778    0.7778         9\n",
      "  Diabetes 1     0.7692    0.8333    0.8000        12\n",
      "  Diabetes 2     1.0000    0.6667    0.8000         3\n",
      "\n",
      "    accuracy                         0.7917        24\n",
      "   macro avg     0.8490    0.7593    0.7926        24\n",
      "weighted avg     0.8013    0.7917    0.7917        24\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clases = ['Control', 'Diabetes 1', 'Diabetes 2']\n",
    "\n",
    "print(classification_report(y_test,\n",
    "                            y_pred,\n",
    "                            target_names=clases,\n",
    "                            digits=4,\n",
    "                            zero_division=0))\n",
    "\n",
    "# El parámetro zero_division no muestra warnings cuando se vaya a realizar una división por 0 al calcular alguna métrica"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65e84f9",
   "metadata": {},
   "source": [
    "Se calcula la __especificidad__ a partir de la clase _multilabel_confusion_matrix_. Se van a obtener también el resto de métricas con las obtenidas en el _classification report_ para comprobar que son correctas.\n",
    "\n",
    "Código basado en la documentación de scikit-learn: https://scikit-learn.org/stable/modules/model_evaluation.html#multilabel-confusion-matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "08f93963",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------\n",
      "Exactitud: \t0.7917\n",
      "----------------------\n",
      "\n",
      "----------------------\n",
      "      Precisión       \n",
      "----------------------\n",
      "Control: \t0.7778\n",
      "Diabetes 1: \t0.7692\n",
      "Diabetes 2: \t1.0000\n",
      "MACRO AVG: \t0.8490\n",
      "\n",
      "----------------------\n",
      "     Sensibilidad     \n",
      "----------------------\n",
      "Control: \t0.7778\n",
      "Diabetes 1: \t0.8333\n",
      "Diabetes 2: \t0.6667\n",
      "MACRO AVG: \t0.7593\n",
      "\n",
      "----------------------\n",
      "    Especificidad     \n",
      "----------------------\n",
      "Control: \t0.8667\n",
      "Diabetes 1: \t0.7500\n",
      "Diabetes 2: \t1.0000\n",
      "MACRO AVG: \t0.8722\n",
      "\n",
      "----------------------\n",
      "       F1-score       \n",
      "----------------------\n",
      "Control: \t0.7778\n",
      "Diabetes 1: \t0.8000\n",
      "Diabetes 2: \t0.8000\n",
      "MACRO AVG: \t0.7926\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score\n",
    "\n",
    "# Se obtiene la matriz de confusión de cada clase\n",
    "cm_multi = multilabel_confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Se obtienen los valores de TN, TP, FN y FP de cada clase\n",
    "tn = cm_multi[:, 0, 0]\n",
    "tp = cm_multi[:, 1, 1]\n",
    "fn = cm_multi[:, 1, 0]\n",
    "fp = cm_multi[:, 0, 1]\n",
    "\n",
    "# Se calculan las métricas (array de dimensión 3 con la métrica para cada clase)\n",
    "accuracy    = accuracy_score(y_test, y_pred)\n",
    "precision   = tp / (tp + fp)\n",
    "recall      = tp / (tp + fn)\n",
    "specificity = tn / (tn + fp)\n",
    "f1          = (2 * precision * recall) / (precision + recall)\n",
    "\n",
    "# Visualizar la exactitud del modelo\n",
    "print(\"----------------------\")\n",
    "print(\"Exactitud: \\t{:.4f}\".format(accuracy))\n",
    "print(\"----------------------\")\n",
    "print(\"\")\n",
    "\n",
    "# Visualizar la métrica precision para cada clase y la global (macro avg)\n",
    "print(\"----------------------\")\n",
    "print(\"      Precisión       \")\n",
    "print(\"----------------------\")\n",
    "print(\"Control: \\t{:.4f}\".format(precision[0]))\n",
    "print(\"Diabetes 1: \\t{:.4f}\".format(precision[1]))\n",
    "print(\"Diabetes 2: \\t{:.4f}\".format(precision[2]))\n",
    "print(\"MACRO AVG: \\t{:.4f}\".format(precision.mean()))\n",
    "print(\"\")\n",
    "\n",
    "# Visualizar la sensibilidad para cada clase y la global (macro avg)\n",
    "print(\"----------------------\")\n",
    "print(\"     Sensibilidad     \")\n",
    "print(\"----------------------\")\n",
    "print(\"Control: \\t{:.4f}\".format(recall[0]))\n",
    "print(\"Diabetes 1: \\t{:.4f}\".format(recall[1]))\n",
    "print(\"Diabetes 2: \\t{:.4f}\".format(recall[2]))\n",
    "print(\"MACRO AVG: \\t{:.4f}\".format(recall.mean()))\n",
    "print(\"\")\n",
    "\n",
    "# Visualizar la especificidad para cada clase y la global (macro avg)\n",
    "print(\"----------------------\")\n",
    "print(\"    Especificidad     \")\n",
    "print(\"----------------------\")\n",
    "print(\"Control: \\t{:.4f}\".format(specificity[0]))\n",
    "print(\"Diabetes 1: \\t{:.4f}\".format(specificity[1]))\n",
    "print(\"Diabetes 2: \\t{:.4f}\".format(specificity[2]))\n",
    "print(\"MACRO AVG: \\t{:.4f}\".format(specificity.mean()))\n",
    "print(\"\")\n",
    "\n",
    "# Visualizar la mátrica F1 para cada clase y la global (macro avg)\n",
    "print(\"----------------------\")\n",
    "print(\"       F1-score       \")\n",
    "print(\"----------------------\")\n",
    "print(\"Control: \\t{:.4f}\".format(f1[0]))\n",
    "print(\"Diabetes 1: \\t{:.4f}\".format(f1[1]))\n",
    "print(\"Diabetes 2: \\t{:.4f}\".format(f1[2]))\n",
    "print(\"MACRO AVG: \\t{:.4f}\".format(f1.mean()))\n",
    "print(\"\")"
   ]
  }
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