red_neuronal.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": "effe4666",
   "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": "087867a9",
   "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": "25406dbd",
   "metadata": {},
   "source": [
    "### Escalar los datos\n",
    "\n",
    "Información sobre esto: https://datascience.stackexchange.com/questions/78489/feature-scaling-for-mlp-neural-network-sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6c989c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# El escalado se ajusta con la media y la desviación estandar del conjunto de entrenamiento\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "951340de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se aplica la transformación a los datos\n",
    "X_train_norm = scaler.transform(X_train)\n",
    "X_test_norm = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "919a795e",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 2. Crear el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4dfe0c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=8, random_state=1234, shuffle=True),\n",
       "             estimator=MLPClassifier(random_state=1234), n_jobs=7,\n",
       "             param_grid={'activation': ['logistic', 'tanh', 'relu'],\n",
       "                         'alpha': array([1.e-01, 1.e-02, 1.e-03, 1.e-04, 1.e-05, 1.e-06, 1.e-07, 1.e-08,\n",
       "       1.e-09]),\n",
       "                         'early_stopping': [True, False],\n",
       "                         'hidden_layer_sizes': [(8, 8), (16, 8), (32, 16)],\n",
       "                         'learning_rate': ['constant'],\n",
       "                         'learning_rate_init': [0.001],\n",
       "                         'max_iter': [1000, 1500, 2000],\n",
       "                         'n_iter_no_change': [10], 'solver': ['sgd', 'adam'],\n",
       "                         'verbose': [False]},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
    "import multiprocessing\n",
    "\n",
    "# Definición del modelo\n",
    "modelo = MLPClassifier(random_state=semilla)\n",
    "\n",
    "# Definición de parámetros de búsqueda\n",
    "parametros = {'hidden_layer_sizes': [(8,8), (16,8), (32,16)],\n",
    "              'activation'        : ['logistic','tanh','relu'],\n",
    "              'solver'            : ['sgd','adam'],\n",
    "              'alpha'             : 10.0 ** -np.arange(1, 10),\n",
    "              'learning_rate'     : ['constant'],\n",
    "              'learning_rate_init': [0.001],\n",
    "              'max_iter'          : [1000,1500,2000],\n",
    "              'verbose'           : [False],\n",
    "              'early_stopping'    : [True,False],\n",
    "              'n_iter_no_change'  : [10]}\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_norm, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1ac876e5",
   "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>502</th>\n",
       "      <td>{'activation': 'tanh', 'alpha': 1e-05, 'early_...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.117851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>{'activation': 'tanh', 'alpha': 0.0001, 'early...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.117851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>{'activation': 'tanh', 'alpha': 1e-09, 'early_...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.117851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>{'activation': 'tanh', 'alpha': 0.01, 'early_s...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.117851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>{'activation': 'tanh', 'alpha': 1e-05, 'early_...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.117851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                params  rank_test_score  \\\n",
       "502  {'activation': 'tanh', 'alpha': 1e-05, 'early_...                1   \n",
       "462  {'activation': 'tanh', 'alpha': 0.0001, 'early...                1   \n",
       "646  {'activation': 'tanh', 'alpha': 1e-09, 'early_...                1   \n",
       "394  {'activation': 'tanh', 'alpha': 0.01, 'early_s...                1   \n",
       "500  {'activation': 'tanh', 'alpha': 1e-05, 'early_...                1   \n",
       "\n",
       "     mean_test_score  std_test_score  \n",
       "502         0.833333        0.117851  \n",
       "462         0.833333        0.117851  \n",
       "646         0.833333        0.117851  \n",
       "394         0.833333        0.117851  \n",
       "500         0.833333        0.117851  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualización de los resultados de la búsqueda\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": 16,
   "id": "36750ee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejores parámetros:  {'activation': 'tanh', 'alpha': 0.1, 'early_stopping': False, 'hidden_layer_sizes': (32, 16), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_iter': 1000, 'n_iter_no_change': 10, 'solver': 'sgd', 'verbose': False}\n"
     ]
    }
   ],
   "source": [
    "# Se muestran los mejores parámetros\n",
    "print('Mejores parámetros: ', grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b3bf752",
   "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": 18,
   "id": "59dae82a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se toma el modelo que ha obtenido el mejor resultado\n",
    "modelo_rn = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c785c0",
   "metadata": {},
   "source": [
    "### Visualización del entrenamiento\n",
    "\n",
    "A continuación, se van a visualizar una serie de parámetros del resultado del entrenamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ccc9794b",
   "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": [
    "# Evolución de la pérdida (loss) en cada iteración del entrenamiento\n",
    "plt.plot(modelo_rn.loss_curve_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "db177dce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "690"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Número de iteraciones en las que ha finalizado el entrenamiento\n",
    "# Si se han alcanzado 'n_iter_no_change' sin que varíe la pérdida un valor mayor de 'tol'\n",
    "modelo_rn.n_iter_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "233ff3eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtención de la matriz de los pesos\n",
    "weights = modelo_rn.coefs_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491c3d79",
   "metadata": {},
   "source": [
    "La variable _weights_ contiene una lista de arrays con todos los pesos de la red neuronal. Esta lista tiene 3 elementos:\n",
    "\n",
    "- ___weights[0]___: lista de 4 arrays de longitud 32. El elemento _weights[0][0]_ es un array de tamaño 32 correspondiente a los pesos de entrada a la capa oculta 1 de la variable _AF365_. De manera análoga, _weights[0][1]_ se corresponde a los pesos de la variable _AF450_, _weights[0][2]_ a los pesos de la variable _35 deg_ y _weights[0][3]_ a los pesos de la variable _42 deg_.\n",
    "- ___weights[1]___: lista de 32 arrays de longitud 16 correspondientes a los pesos de entrada a la capa oculta 2 de cada una de las neuronas de la capa oculta 1.\n",
    "- ___weights[2]___: lista de 16 arrays de longitud 3 correspondientes a los pesos de entrada a la capa de salida de cada una de las neuronas de la capa oculta 2.\n",
    "\n",
    "Estos pesos se pueden visualizar en las celdas siguientes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "74511b88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Pesos de entrada a la capa oculta 1 de la variable AF365:\n",
      "--------------------------------------------------------------------------\n",
      "[-0.27289189  0.0817475   0.01442929  0.1750549   0.12838667 -0.07386082\n",
      " -0.20585071  0.29785135  0.26310921  0.30284277 -0.09295085 -0.16399638\n",
      "  0.18533125  0.16914131 -0.10677528  0.05237521 -0.12474937 -0.40119067\n",
      "  0.18590536  0.34877566 -0.11189354  0.08685324 -0.37705224 -0.11222746\n",
      "  0.41163546  0.17760871 -0.1890666   0.20541195 -0.11593207  0.08766819\n",
      "  0.39102177 -0.12839009]\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------\n",
      "Pesos de entrada a la capa oculta 1 de la variable AF450:\n",
      "--------------------------------------------------------------------------\n",
      "[ 0.1800954  -0.32590719  0.22989853  0.08699651 -0.32506173  0.43489453\n",
      " -0.09307614  0.38772192 -0.44454737 -0.24806849 -0.30836125 -0.0482107\n",
      "  0.12566014  0.04727859 -0.36797456  0.07767315 -0.27307781  0.02645977\n",
      " -0.35299776  0.12063341  0.03955445 -0.39503082  0.05682078  0.31206649\n",
      "  0.31560007  0.44404535  0.24476436  0.20952301 -0.09430602  0.16376344\n",
      "  0.07337559 -0.32537477]\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------\n",
      "Pesos de entrada a la capa oculta 1 de la variable 35 deg:\n",
      "--------------------------------------------------------------------------\n",
      "[-0.13442496 -0.36915876 -0.00168267  0.39724139 -0.24631157 -0.33848231\n",
      "  0.1817079   0.03175781 -0.00289381 -0.32076677 -0.20158232  0.34408058\n",
      " -0.05689691  0.03024218 -0.41832615 -0.07689827 -0.02003461  0.09541592\n",
      "  0.31649226  0.05870849  0.18779827 -0.26872565  0.24520607  0.24848679\n",
      "  0.07782042 -0.0967072  -0.21036028  0.0554777   0.06336158  0.34599999\n",
      " -0.04907725  0.01120277]\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------\n",
      "Pesos de entrada a la capa oculta 1 de la variable 42 deg:\n",
      "--------------------------------------------------------------------------\n",
      "[ 0.06531937  0.31038458 -0.47352935  0.24750779  0.307382    0.10576598\n",
      "  0.26736936  0.05957123  0.4661511  -0.25299499 -0.448739    0.23340209\n",
      " -0.33879198  0.3113914  -0.17218528 -0.24705062  0.03851253  0.31966866\n",
      "  0.34504583 -0.19819509 -0.09727379 -0.22831998  0.29653965  0.22967474\n",
      "  0.09040033  0.26877758  0.41395955  0.05689088 -0.27989402 -0.36304129\n",
      " -0.23688197  0.26229529]\n"
     ]
    }
   ],
   "source": [
    "# Pesos de entrada a la capa oculta 1\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(\"Pesos de entrada a la capa oculta 1 de la variable AF365:\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(weights[0][0])\n",
    "print(\"\\n\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(\"Pesos de entrada a la capa oculta 1 de la variable AF450:\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(weights[0][1])\n",
    "print(\"\\n\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(\"Pesos de entrada a la capa oculta 1 de la variable 35 deg:\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(weights[0][2])\n",
    "print(\"\\n\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(\"Pesos de entrada a la capa oculta 1 de la variable 42 deg:\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(weights[0][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ee0c062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.25595309  0.13734121  0.35188756  0.29090087  0.3456477   0.24438147\n",
      "  -0.00658934 -0.03389478  0.21803673  0.32143763  0.08136357 -0.10850359\n",
      "  -0.18606934  0.24016938 -0.2163052   0.17631323]\n",
      " [-0.16091885  0.22717283  0.21031156  0.13508846  0.14064902  0.09879298\n",
      "   0.19578658  0.20531105 -0.0711001  -0.36458896 -0.10427619  0.16863881\n",
      "  -0.18405325 -0.1293073   0.14628587 -0.20875229]\n",
      " [-0.24706588  0.14113094 -0.20726708 -0.04937664 -0.00190405 -0.38600149\n",
      "   0.21487916  0.24100409  0.29012407  0.2834264   0.14347436  0.25674623\n",
      "   0.27567188  0.12779072 -0.43274141 -0.08854417]\n",
      " [-0.12063375 -0.10944269 -0.31955763  0.17294763  0.01585423  0.1066215\n",
      "   0.08413809 -0.09344262 -0.03246499 -0.05229963 -0.03667732  0.34992779\n",
      "  -0.05518177 -0.33366273  0.29815466  0.25675259]\n",
      " [-0.07971247  0.10307707 -0.13294325 -0.22956367 -0.15940805 -0.01207549\n",
      "  -0.26546619  0.14653863  0.24356875  0.29899108 -0.1130963  -0.11163392\n",
      "  -0.12818799  0.25177042  0.33858531 -0.02983508]\n",
      " [-0.40128103 -0.32784837 -0.21108799  0.09717333 -0.28693164 -0.14391416\n",
      "  -0.34694313  0.0669243  -0.41359358  0.23170539 -0.10375969  0.13109404\n",
      "   0.02091934 -0.36601049  0.01135357  0.11631503]\n",
      " [-0.2464796   0.22945155  0.05168758  0.01738975  0.15322755  0.23105785\n",
      "  -0.22404979  0.25851246  0.23633729  0.13755831  0.09994543  0.0029504\n",
      "   0.11699989  0.11787369 -0.04148265  0.05702217]\n",
      " [-0.14192679 -0.33953226  0.10631714  0.28952773  0.29052822  0.09786699\n",
      "  -0.36340804 -0.10114644 -0.43565552 -0.24605312  0.08845709 -0.29543997\n",
      "   0.06028791 -0.04664575  0.17832142  0.14783658]\n",
      " [ 0.33309898 -0.13601005 -0.23562078  0.09353916  0.33611059  0.40425907\n",
      "   0.13654054 -0.06508292  0.16625537  0.224541    0.03724865 -0.21464634\n",
      "  -0.19148955  0.3814466   0.04416611  0.07840256]\n",
      " [-0.05647178 -0.2245981  -0.17395771  0.03545902  0.31237252 -0.09018789\n",
      "   0.1284814  -0.23913023 -0.35715739 -0.25636987 -0.19246979 -0.18801371\n",
      "   0.00920145  0.15394671  0.20525068  0.09722226]\n",
      " [ 0.21419584  0.05872026  0.17109717  0.04017618  0.24792391 -0.32535713\n",
      "   0.20661152 -0.25485319 -0.3093868  -0.3086246  -0.2880311   0.01116626\n",
      "  -0.08181188  0.34958538 -0.29568449 -0.12643771]\n",
      " [ 0.40607252 -0.05898774  0.22880271  0.02260082  0.34172278  0.28372681\n",
      "   0.20660715 -0.31305596  0.36780193  0.02919513 -0.24333588  0.30462268\n",
      "  -0.19017254 -0.22319823  0.1938074   0.33350756]\n",
      " [-0.06069208 -0.05802875 -0.2612082   0.1277065   0.27292853  0.27134899\n",
      "   0.25537362 -0.26870799 -0.01919774 -0.0736214  -0.03439434 -0.39015556\n",
      "   0.28847093  0.06890436 -0.40119216  0.25429605]\n",
      " [ 0.24473013 -0.11770481  0.17603042 -0.0190552  -0.30684387 -0.03259393\n",
      "  -0.01745469 -0.16879036  0.27404226  0.17202747  0.03182485  0.00482128\n",
      "   0.01190401 -0.15123892  0.11789007  0.18441344]\n",
      " [-0.13731745  0.01343637 -0.29697776  0.13360718  0.26636122  0.04240766\n",
      "   0.40600385 -0.21491128 -0.26370904 -0.02053173  0.30013121  0.26795158\n",
      "  -0.00584166  0.31407308 -0.16563786 -0.14151463]\n",
      " [ 0.15125579  0.14014984 -0.30517389 -0.12626369  0.00725634  0.22897292\n",
      "  -0.09375452 -0.30919149  0.00204927  0.1522921  -0.10681126  0.22767131\n",
      "   0.37121764  0.17832303 -0.23468186  0.27691507]\n",
      " [ 0.27078248  0.17756598  0.30397406 -0.18457546  0.01522821  0.26420217\n",
      "   0.29694188  0.12484849  0.27102907  0.12303383 -0.32349084  0.24731124\n",
      "  -0.17964272  0.30417427 -0.00093997 -0.23977441]\n",
      " [ 0.33826254 -0.31443395  0.07360862 -0.29038715 -0.16471342  0.21758869\n",
      "   0.31967198  0.20495192  0.21059163 -0.06920719 -0.30443026 -0.16224279\n",
      "   0.0502818  -0.29395752  0.10770382 -0.17651928]\n",
      " [ 0.13678562  0.05747927 -0.01613936  0.17976032  0.36375528 -0.14933806\n",
      "   0.16030001  0.18664062 -0.10225618  0.29136142 -0.03383011  0.09565284\n",
      "  -0.17476835 -0.07659341 -0.03155131  0.36100123]\n",
      " [-0.28791073  0.101779    0.1159275  -0.26574389 -0.01325543 -0.25439128\n",
      "  -0.3425011   0.21571405 -0.43943973  0.3148314  -0.06808716 -0.12529605\n",
      "  -0.22716362 -0.31378369 -0.16448096  0.06789466]\n",
      " [-0.23235007  0.33156292 -0.23384406  0.17842565 -0.02128241  0.17031578\n",
      "  -0.12925997 -0.34957924 -0.00972201  0.15636031 -0.31194006  0.13154934\n",
      "  -0.07801869  0.07908492 -0.18972835 -0.17622953]\n",
      " [ 0.26562542  0.22513962 -0.21847746  0.11180775  0.26248463  0.18474874\n",
      "   0.01030004 -0.24374593 -0.3227898   0.22776967 -0.25167121  0.02175798\n",
      "   0.21325257 -0.33014562  0.12821549 -0.00738293]\n",
      " [-0.1235539  -0.29025955 -0.4088831  -0.23420009 -0.22694847 -0.03727291\n",
      "  -0.27055656  0.32794295  0.41533433  0.24122805 -0.28069165  0.12547066\n",
      "   0.29227471  0.38656632 -0.20511176 -0.12457945]\n",
      " [ 0.17752801  0.10364355  0.06469612  0.21993524  0.15962054 -0.12148236\n",
      "  -0.23456978  0.18003362 -0.09440043 -0.28034069  0.14029644  0.27335873\n",
      "  -0.29143352 -0.21784227 -0.04278535 -0.11837343]\n",
      " [-0.37036291 -0.27938694 -0.00939086 -0.2670816   0.20065305  0.22511871\n",
      "  -0.31283072 -0.25132141 -0.13464458  0.31574895  0.22461029 -0.31623984\n",
      "   0.33264307  0.18108532 -0.14236518 -0.26041231]\n",
      " [-0.37546809 -0.28855841  0.31017395 -0.20127188 -0.0996473   0.11183648\n",
      "   0.00805931 -0.09345413 -0.19391889 -0.32978204 -0.02221035  0.09635541\n",
      "   0.16382046 -0.2688913   0.20994061 -0.32246336]\n",
      " [ 0.32882081 -0.17963935 -0.28233279 -0.14184276  0.26408934  0.23966952\n",
      "  -0.08489843 -0.17139799  0.05326167 -0.15477878 -0.34273399  0.09465196\n",
      "   0.01936779 -0.31384043  0.2084558   0.34712253]\n",
      " [ 0.24727138 -0.11809601 -0.11964071 -0.09571764 -0.06857085 -0.0135451\n",
      "   0.10095736 -0.26444841  0.26381967  0.16223425 -0.22167858  0.2304652\n",
      "  -0.2236168   0.09431515 -0.28123425 -0.19980992]\n",
      " [-0.06990042 -0.34985147 -0.06374908  0.19558352  0.11183244  0.00974395\n",
      "   0.08679757 -0.34642823  0.13311175  0.35967852 -0.20140921  0.08119778\n",
      "   0.2803507   0.23284394 -0.10059493 -0.11569865]\n",
      " [ 0.38211622 -0.20915483 -0.21191669 -0.23581107 -0.05706016 -0.26847442\n",
      "   0.07984238 -0.35416555 -0.05293292  0.00534287  0.20535735  0.14812545\n",
      "  -0.02506844 -0.31727632 -0.21949764 -0.11475227]\n",
      " [-0.35090737 -0.03793822  0.27875083  0.32493263 -0.24632839  0.25651831\n",
      "  -0.33605499 -0.17261178  0.18005686 -0.27021445 -0.00321599 -0.11701114\n",
      "   0.34833894  0.13118453 -0.18638252 -0.37150588]\n",
      " [ 0.14217001 -0.03428459  0.21064848  0.01658315  0.37350854  0.36596608\n",
      "  -0.0277105  -0.26970644 -0.25890864 -0.14005423  0.020603    0.1219693\n",
      "  -0.11141685 -0.2244604   0.3617853   0.20985694]]\n"
     ]
    }
   ],
   "source": [
    "# Pesos de entrada a la capa oculta 2\n",
    "print(weights[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ad3c660b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.11458697  0.36154629 -0.68895528]\n",
      " [-0.12681867 -0.21528441 -0.26086234]\n",
      " [-0.3145334  -0.21201718  0.29173124]\n",
      " [-0.50804423 -0.40195089 -0.16616978]\n",
      " [ 0.21650946 -0.25860921 -0.00193136]\n",
      " [ 0.43506325 -0.44929217 -0.20533533]\n",
      " [ 0.21295601  0.44630665 -0.20018865]\n",
      " [ 0.57746741 -0.00432726  0.1793752 ]\n",
      " [ 0.42791286  0.61648723 -0.53611803]\n",
      " [-0.46617847 -0.24931578 -0.58414306]\n",
      " [ 0.14852107  0.33384747  0.6049037 ]\n",
      " [ 0.03293727  0.18772507 -0.50396415]\n",
      " [-0.63907912  0.37138236 -0.24502989]\n",
      " [ 0.22861048  0.50273203  0.03320646]\n",
      " [ 0.77479207 -0.28058492 -0.46403403]\n",
      " [ 0.25341015 -0.12222055  0.04475931]]\n"
     ]
    }
   ],
   "source": [
    "# Pesos de entrada a la capa de salida\n",
    "print(weights[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d2cc8efe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtención de la matriz de los sesgos (bias)\n",
    "bias = modelo_rn.intercepts_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861bf0dc",
   "metadata": {},
   "source": [
    "La variable _bias_ contiene una lista de arrays con todos los sesgos o _bias_ de cada una de las neuronas de la red. Esta lista tiene 3 elementos:\n",
    "\n",
    "- ___bias[0]___: array de longitud 32 correspondiente a los sesgos de las neuronas de la capa oculta 1.\n",
    "- ___bias[1]___: array de longitud 16 correspondiente a los sesgos de las neuronas de la capa oculta 2.\n",
    "- ___bias[2]___: array de longitud 3 correspondiente a los sesgos de las neuronas de la capa de salida.\n",
    "\n",
    "Estos sesgos se pueden visualizar en la celda siguiente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "df74a3ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Sesgos de las neuronas de la capa oculta 1\n",
      "--------------------------------------------------------------------------\n",
      "[-0.41413274 -0.38940823  0.02272226  0.04008685  0.31058181 -0.28824859\n",
      " -0.27993085 -0.44210753  0.42029243 -0.2634877  -0.12573868  0.11622634\n",
      " -0.35165611  0.38433776  0.39571563  0.1555285  -0.18682765  0.00115547\n",
      "  0.15445841 -0.46615805 -0.00677073 -0.16355215  0.49883043  0.03882313\n",
      " -0.2254147  -0.36374361  0.27056332  0.33070776  0.28461791  0.36582201\n",
      "  0.05041448 -0.06265722]\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------\n",
      "Sesgos de las neuronas de la capa oculta 2\n",
      "--------------------------------------------------------------------------\n",
      "[ 0.07648703  0.23466612 -0.38481114 -0.3586019   0.00469205  0.24586089\n",
      " -0.12532385  0.04356266 -0.14249306 -0.19059593 -0.34894764  0.33007593\n",
      "  0.12693226 -0.21244704  0.18523738 -0.03459558]\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------\n",
      "Sesgos de las neuronas de la capa de salida\n",
      "--------------------------------------------------------------------------\n",
      "[0.03890156 0.27520489 0.22745199]\n"
     ]
    }
   ],
   "source": [
    "# Visualización de los sesgos (bias) de la red neuronal\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(\"Sesgos de las neuronas de la capa oculta 1\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(bias[0])\n",
    "print(\"\\n\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(\"Sesgos de las neuronas de la capa oculta 2\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(bias[1])\n",
    "print(\"\\n\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(\"Sesgos de las neuronas de la capa de salida\")\n",
    "print(\"--------------------------------------------------------------------------\")\n",
    "print(bias[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "de16dc71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de capas de la red neuronal: 4\n",
      "Número de características (features) de la red neuronal: 4\n",
      "Número de neuronas en la capa de salida: 3\n",
      "Función de activación de la capa de salida de la red neuronal: softmax\n"
     ]
    }
   ],
   "source": [
    "# Características de la red neuronal\n",
    "print(\"Número de capas de la red neuronal:\",                            modelo_rn.n_layers_)\n",
    "print(\"Número de características (features) de la red neuronal:\",       modelo_rn.n_features_in_)\n",
    "print(\"Número de neuronas en la capa de salida:\",                       modelo_rn.n_outputs_)\n",
    "print(\"Función de activación de la capa de salida de la red neuronal:\", modelo_rn.out_activation_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1d774e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'activation': 'tanh',\n",
       " 'alpha': 0.1,\n",
       " 'batch_size': 'auto',\n",
       " 'beta_1': 0.9,\n",
       " 'beta_2': 0.999,\n",
       " 'early_stopping': False,\n",
       " 'epsilon': 1e-08,\n",
       " 'hidden_layer_sizes': (32, 16),\n",
       " 'learning_rate': 'constant',\n",
       " 'learning_rate_init': 0.001,\n",
       " 'max_fun': 15000,\n",
       " 'max_iter': 1000,\n",
       " 'momentum': 0.9,\n",
       " 'n_iter_no_change': 10,\n",
       " 'nesterovs_momentum': True,\n",
       " 'power_t': 0.5,\n",
       " 'random_state': 1234,\n",
       " 'shuffle': True,\n",
       " 'solver': 'sgd',\n",
       " 'tol': 0.0001,\n",
       " 'validation_fraction': 0.1,\n",
       " 'verbose': False,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hiperparámetros del modelo seleccionado\n",
    "modelo_rn.get_params(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e38a29",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 3. Evaluar el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b93ca4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se lleva a cabo la predicción para evaluar el modelo\n",
    "y_pred = modelo_rn.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "39f54714",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 0 1 0 0 2 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": 31,
   "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": "38845ca4",
   "metadata": {},
   "source": [
    "## Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "34d743ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Control     0.8889    0.8889    0.8889         9\n",
      "  Diabetes 1     0.9167    0.9167    0.9167        12\n",
      "  Diabetes 2     1.0000    1.0000    1.0000         3\n",
      "\n",
      "    accuracy                         0.9167        24\n",
      "   macro avg     0.9352    0.9352    0.9352        24\n",
      "weighted avg     0.9167    0.9167    0.9167        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": "83ad7a82",
   "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": 33,
   "id": "13776c3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------\n",
      "Exactitud: \t0.9167\n",
      "----------------------\n",
      "\n",
      "----------------------\n",
      "      Precisión       \n",
      "----------------------\n",
      "Control: \t0.8889\n",
      "Diabetes 1: \t0.9167\n",
      "Diabetes 2: \t1.0000\n",
      "MACRO AVG: \t0.9352\n",
      "\n",
      "----------------------\n",
      "     Sensibilidad     \n",
      "----------------------\n",
      "Control: \t0.8889\n",
      "Diabetes 1: \t0.9167\n",
      "Diabetes 2: \t1.0000\n",
      "MACRO AVG: \t0.9352\n",
      "\n",
      "----------------------\n",
      "    Especificidad     \n",
      "----------------------\n",
      "Control: \t0.9333\n",
      "Diabetes 1: \t0.9167\n",
      "Diabetes 2: \t1.0000\n",
      "MACRO AVG: \t0.9500\n",
      "\n",
      "----------------------\n",
      "       F1-score       \n",
      "----------------------\n",
      "Control: \t0.8889\n",
      "Diabetes 1: \t0.9167\n",
      "Diabetes 2: \t1.0000\n",
      "MACRO AVG: \t0.9352\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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