Naive Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb7bfcfa",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f325bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# En este caso usamos un naive bayes que puede usar datos continuos aproximando una distribución gaussiana para cada variable, \n",
    "# pero en la documentación existen otros tipos de naive bayes\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31f674d",
   "metadata": {},
   "source": [
    "De forma esquemática Naive Bayes funciona de la siguiente forma:\n",
    "1. Convertir el conjunto de datos en una tabla de frecuencias.\n",
    "2. Crear una tabla de probabilidad calculando las correspondientes a que ocurran los diversos eventos.\n",
    "3. La ecuación Naive Bayes se usa para calcular la probabilidad posterior de cada clase.\n",
    "4. La clase con la probabilidad posterior más alta es el resultado de la predicción."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd11566e",
   "metadata": {},
   "source": [
    "Se va a usar para esta práctica un dataset implícito en la librería de scikit-learn sobre una vinoteca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96c859d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  target  \n",
       "0                          3.92   1065.0       0  \n",
       "1                          3.40   1050.0       0  \n",
       "2                          3.17   1185.0       0  \n",
       "3                          3.45   1480.0       0  \n",
       "4                          2.93    735.0       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = datasets.load_wine()\n",
    "df = pd.DataFrame(data=wine.data, columns=wine.feature_names)\n",
    "df['target'] = wine.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96eab063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9257ebe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.000618</td>\n",
       "      <td>2.336348</td>\n",
       "      <td>2.366517</td>\n",
       "      <td>19.494944</td>\n",
       "      <td>99.741573</td>\n",
       "      <td>2.295112</td>\n",
       "      <td>2.029270</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>1.590899</td>\n",
       "      <td>5.058090</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>2.611685</td>\n",
       "      <td>746.893258</td>\n",
       "      <td>0.938202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.811827</td>\n",
       "      <td>1.117146</td>\n",
       "      <td>0.274344</td>\n",
       "      <td>3.339564</td>\n",
       "      <td>14.282484</td>\n",
       "      <td>0.625851</td>\n",
       "      <td>0.998859</td>\n",
       "      <td>0.124453</td>\n",
       "      <td>0.572359</td>\n",
       "      <td>2.318286</td>\n",
       "      <td>0.228572</td>\n",
       "      <td>0.709990</td>\n",
       "      <td>314.907474</td>\n",
       "      <td>0.775035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.030000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.362500</td>\n",
       "      <td>1.602500</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.742500</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.782500</td>\n",
       "      <td>1.937500</td>\n",
       "      <td>500.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.050000</td>\n",
       "      <td>1.865000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2.355000</td>\n",
       "      <td>2.135000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>673.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.677500</td>\n",
       "      <td>3.082500</td>\n",
       "      <td>2.557500</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>3.170000</td>\n",
       "      <td>985.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.830000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          alcohol  malic_acid         ash  alcalinity_of_ash   magnesium  \\\n",
       "count  178.000000  178.000000  178.000000         178.000000  178.000000   \n",
       "mean    13.000618    2.336348    2.366517          19.494944   99.741573   \n",
       "std      0.811827    1.117146    0.274344           3.339564   14.282484   \n",
       "min     11.030000    0.740000    1.360000          10.600000   70.000000   \n",
       "25%     12.362500    1.602500    2.210000          17.200000   88.000000   \n",
       "50%     13.050000    1.865000    2.360000          19.500000   98.000000   \n",
       "75%     13.677500    3.082500    2.557500          21.500000  107.000000   \n",
       "max     14.830000    5.800000    3.230000          30.000000  162.000000   \n",
       "\n",
       "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
       "count     178.000000  178.000000            178.000000       178.000000   \n",
       "mean        2.295112    2.029270              0.361854         1.590899   \n",
       "std         0.625851    0.998859              0.124453         0.572359   \n",
       "min         0.980000    0.340000              0.130000         0.410000   \n",
       "25%         1.742500    1.205000              0.270000         1.250000   \n",
       "50%         2.355000    2.135000              0.340000         1.555000   \n",
       "75%         2.800000    2.875000              0.437500         1.950000   \n",
       "max         3.880000    5.080000              0.660000         3.580000   \n",
       "\n",
       "       color_intensity         hue  od280/od315_of_diluted_wines      proline  \\\n",
       "count       178.000000  178.000000                    178.000000   178.000000   \n",
       "mean          5.058090    0.957449                      2.611685   746.893258   \n",
       "std           2.318286    0.228572                      0.709990   314.907474   \n",
       "min           1.280000    0.480000                      1.270000   278.000000   \n",
       "25%           3.220000    0.782500                      1.937500   500.500000   \n",
       "50%           4.690000    0.965000                      2.780000   673.500000   \n",
       "75%           6.200000    1.120000                      3.170000   985.000000   \n",
       "max          13.000000    1.710000                      4.000000  1680.000000   \n",
       "\n",
       "           target  \n",
       "count  178.000000  \n",
       "mean     0.938202  \n",
       "std      0.775035  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      1.000000  \n",
       "75%      2.000000  \n",
       "max      2.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426f38a0",
   "metadata": {},
   "source": [
    "Comprobamos el balance de la variable objetivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "844b8b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.factorplot('target', data=df, kind=\"count\", aspect=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d6b02638",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('target', axis=1)\n",
    "y = df['target']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=109)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e313df1",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "\n",
    "# entrenamos el modelo\n",
    "gnb.fit(X_train, y_train)\n",
    "\n",
    "# predecimos usando el modelo y los datos de test, para una validación honesta\n",
    "y_pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9d426a34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9074074074074074\n"
     ]
    }
   ],
   "source": [
    "# comprobamos la métrica de exactitud\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "630c0e99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.95      0.93        21\n",
      "           1       0.94      0.79      0.86        19\n",
      "           2       0.88      1.00      0.93        14\n",
      "\n",
      "    accuracy                           0.91        54\n",
      "   macro avg       0.91      0.91      0.91        54\n",
      "weighted avg       0.91      0.91      0.91        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0c50d38",
   "metadata": {},
   "source": [
    "### Ejercicio\n",
    "\n",
    "Aplica lo aprendido sobre Naive Bayes en el dataset de bank.csv. ¿Cómo de bien se ajusta el modelo?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3af2c8c5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}