naive_bayes.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\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": "7d4f21c0",
   "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": "b0da9540",
   "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": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# Se crea el modelo\n",
    "modelo_nb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b56e6576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se entrena el modelo\n",
    "modelo_nb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "099c7b70",
   "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(modelo_nb.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e38a29",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 3. Evaluar el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b93ca4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se lleva a cabo la predicción para evaluar el modelo\n",
    "y_pred = modelo_nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "39f54714",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 0 1 0 0 2 2 1 1 0 1 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": "8e06555b",
   "metadata": {},
   "source": [
    "## Matriz de confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8d4f5577",
   "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": "e2776884",
   "metadata": {},
   "source": [
    "## Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5ab61a46",
   "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     0.8333    0.8333    0.8333        12\n",
      "  Diabetes 2     0.6667    0.6667    0.6667         3\n",
      "\n",
      "    accuracy                         0.8333        24\n",
      "   macro avg     0.7963    0.7963    0.7963        24\n",
      "weighted avg     0.8333    0.8333    0.8333        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": "ef8290b1",
   "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": 19,
   "id": "4c041445",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------\n",
      "Exactitud: \t0.8333\n",
      "----------------------\n",
      "\n",
      "----------------------\n",
      "      Precisión       \n",
      "----------------------\n",
      "Control: \t0.8889\n",
      "Diabetes 1: \t0.8333\n",
      "Diabetes 2: \t0.6667\n",
      "MACRO AVG: \t0.7963\n",
      "\n",
      "----------------------\n",
      "     Sensibilidad     \n",
      "----------------------\n",
      "Control: \t0.8889\n",
      "Diabetes 1: \t0.8333\n",
      "Diabetes 2: \t0.6667\n",
      "MACRO AVG: \t0.7963\n",
      "\n",
      "----------------------\n",
      "    Especificidad     \n",
      "----------------------\n",
      "Control: \t0.9333\n",
      "Diabetes 1: \t0.8333\n",
      "Diabetes 2: \t0.9524\n",
      "MACRO AVG: \t0.9063\n",
      "\n",
      "----------------------\n",
      "       F1-score       \n",
      "----------------------\n",
      "Control: \t0.8889\n",
      "Diabetes 1: \t0.8333\n",
      "Diabetes 2: \t0.6667\n",
      "MACRO AVG: \t0.7963\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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