arboles_decision.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": "9c9bd050",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 2. Crear el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a0dbe10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=8, random_state=1234, shuffle=True),\n",
       "             estimator=DecisionTreeClassifier(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",
       "                         'min_samples_leaf': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),\n",
       "                         'splitter': ['best', 'random']},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
    "import multiprocessing\n",
    "\n",
    "# Definición del modelo\n",
    "modelo = DecisionTreeClassifier(random_state=semilla)\n",
    "\n",
    "# Definición de parámetros de búsqueda\n",
    "parametros = {'criterion'       : ['gini','entropy'],\n",
    "              'splitter'        : ['best','random'],\n",
    "              'max_depth'       : np.arange(1,50,2),\n",
    "              'min_samples_leaf': np.arange(2,15)}\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": "a5048b60",
   "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>676</th>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 3, 'min_...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.072169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 21, 'min...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.822917</td>\n",
       "      <td>0.097160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 15, 'min...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.822917</td>\n",
       "      <td>0.097160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 39, 'min...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.822917</td>\n",
       "      <td>0.097160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 33, 'min...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.822917</td>\n",
       "      <td>0.097160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 params  rank_test_score  \\\n",
       "676   {'criterion': 'entropy', 'max_depth': 3, 'min_...                1   \n",
       "916   {'criterion': 'entropy', 'max_depth': 21, 'min...                2   \n",
       "840   {'criterion': 'entropy', 'max_depth': 15, 'min...                2   \n",
       "1152  {'criterion': 'entropy', 'max_depth': 39, 'min...                2   \n",
       "1072  {'criterion': 'entropy', 'max_depth': 33, 'min...                2   \n",
       "\n",
       "      mean_test_score  std_test_score  \n",
       "676          0.833333        0.072169  \n",
       "916          0.822917        0.097160  \n",
       "840          0.822917        0.097160  \n",
       "1152         0.822917        0.097160  \n",
       "1072         0.822917        0.097160  "
      ]
     },
     "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": "e5d2c2c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejores parámetros:  {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 2, 'splitter': 'best'}\n"
     ]
    }
   ],
   "source": [
    "# Se muestran los mejores parámetros\n",
    "print('Mejores parámetros: ', grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e5857a30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8333333333333334\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": "0feb214f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se toma el modelo que ha obtenido el mejor resultado\n",
    "modelo_ad = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e38a29",
   "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_ad.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39f54714",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 0 0 0 1 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": 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": "38845ca4",
   "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     0.8000    0.8889    0.8421         9\n",
      "  Diabetes 1     0.9091    0.8333    0.8696        12\n",
      "  Diabetes 2     1.0000    1.0000    1.0000         3\n",
      "\n",
      "    accuracy                         0.8750        24\n",
      "   macro avg     0.9030    0.9074    0.9039        24\n",
      "weighted avg     0.8795    0.8750    0.8756        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": "ee2a4802",
   "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": "f020573b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------\n",
      "Exactitud: \t0.8750\n",
      "----------------------\n",
      "\n",
      "----------------------\n",
      "      Precisión       \n",
      "----------------------\n",
      "Control: \t0.8000\n",
      "Diabetes 1: \t0.9091\n",
      "Diabetes 2: \t1.0000\n",
      "MACRO AVG: \t0.9030\n",
      "\n",
      "----------------------\n",
      "     Sensibilidad     \n",
      "----------------------\n",
      "Control: \t0.8889\n",
      "Diabetes 1: \t0.8333\n",
      "Diabetes 2: \t1.0000\n",
      "MACRO AVG: \t0.9074\n",
      "\n",
      "----------------------\n",
      "    Especificidad     \n",
      "----------------------\n",
      "Control: \t0.8667\n",
      "Diabetes 1: \t0.9167\n",
      "Diabetes 2: \t1.0000\n",
      "MACRO AVG: \t0.9278\n",
      "\n",
      "----------------------\n",
      "       F1-score       \n",
      "----------------------\n",
      "Control: \t0.8421\n",
      "Diabetes 1: \t0.8696\n",
      "Diabetes 2: \t1.0000\n",
      "MACRO AVG: \t0.9039\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(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0272922",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 4. Visualizar el árbol de decisión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4c542e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "\n",
    "feature_names = ['AF365', 'AF450', '35 deg', '42 deg']\n",
    "class_names = ['Control', 'Diabetes 1', 'Diabetes 2']\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "# Configuración de la visualización del árbol\n",
    "plot_tree(modelo_ad,\n",
    "          feature_names = feature_names,\n",
    "          class_names   = class_names,\n",
    "          filled        = True,\n",
    "          node_ids      = True,\n",
    "          rounded       = True,\n",
    "          precision     = 2,\n",
    "          fontsize      = 12)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}