notebooks/naive-bayes-classifier/naive-bayes-classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clasificador Naive Bayes\n",
    "\n",
    "En este notebook se entrena un clasificador Naive Bayes para el set de datos `house-votes-84.data` que sea capaz de generar predicciones correctas para un conjunto de datos no visto en entrenamiento.\n",
    "\n",
    "### Tabla de Contenidos\n",
    "\n",
    "- [Carga de datos](#Carga-de-datos)\n",
    "- [Análisis descriptivo de los datos](#Análisis-descriptivo-de-los-datos)\n",
    "- [Preparación del dataframe](#Preparación-del-dataframe)\n",
    "- [Conjuntos de entrenamiento y validación](#Conjuntos-de-entrenamiento-y-validación)\n",
    "- [Entrenamiento del clasificador](#Entrenamiento-del-clasificador)\n",
    "- [Validación del clasificador](#Validación-del-clasificador)\n",
    "- [Otros clasificadores Naive Bayes](#Otros-clasificadores-Naive-Bayes)\n",
    "  - [Bernoulli NB](#Bernoulli-NB)\n",
    "  - [Multinomial NB](#Multinomial-NB)\n",
    "- [Extra - Binarización de variables categóricas](#Extra---Binarización-de-variables-categóricas)\n",
    "- [Conclusiones](#Conclusiones)\n",
    "\n",
    "### Funciones auxiliares\n",
    "\n",
    "Funciones auxiliares para facilitar la comprensión y limpieza del código."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import errno\n",
    "\n",
    "# Función para validar que el fichero existe\n",
    "def assert_fichero(nombre):\n",
    "    if (not os.path.isfile(nombre)):\n",
    "        raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), nombre)\n",
    "\n",
    "# Separa una variable categórica en tantas columnas como categorías tiene\n",
    "# marcando a 1 la columna asociada al valor de la variable y el resto a 0\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "def separar_categorias(df, feature):\n",
    "    \"\"\"\n",
    "    Separa la feature en tantas columnas como categorías hay marcando\n",
    "    a 1 la columna asociada al valor de la feature y el resto a 0\n",
    "    Por ejemplo: c1 -> c1_y | c1_n | c1_?\n",
    "                  y      1      0      0\n",
    "    \"\"\"\n",
    "    lb_style = LabelBinarizer()\n",
    "    lb_results = lb_style.fit_transform(df[feature])\n",
    "    \n",
    "    # Concatena el nombre de la feature con el valor de la categoría\n",
    "    columnas = list(map(lambda x: '{}_{}'.format(feature, x), lb_style.classes_))\n",
    "    \n",
    "    return pd.DataFrame(lb_results, columns=columnas)\n",
    "\n",
    "# Función para representar la curva ROC\n",
    "def representar_curva_roc(fpr, tpr, roc_auc, nombre_imagen=None):\n",
    "    plt.figure()\n",
    "    lw = 2\n",
    "    \n",
    "    if isinstance(fpr, dict):\n",
    "        for clave in fpr.keys():\n",
    "            plt.plot(fpr[clave], tpr[clave], lw=lw,\n",
    "                     label='Curva ROC %s (area = %0.2f)' % (clave, roc_auc[clave]))\n",
    "    else:\n",
    "        plt.plot(fpr, tpr, color='darkorange', lw=lw,\n",
    "                 label='Curva ROC (area = %0.2f)' % roc_auc)\n",
    "                     \n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.0])\n",
    "    plt.xlabel('Tasa de Falsos Positivos')\n",
    "    plt.ylabel('Tasa de Verdaderos Positivos')\n",
    "    plt.title('Receiver Operating Characteristic (ROC)')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "        \n",
    "    if (nombre_imagen):\n",
    "        plt.savefig('{}.pdf'.format(nombre_imagen), format='pdf')\n",
    "        \n",
    "    plt.show()\n",
    "\n",
    "\"\"\"\n",
    "Calcula y representa la curva ROC y el área bajo la curva (AUC)\n",
    "Devuelve\n",
    "roc_auc => área bajo la curva\n",
    "fpr => tasa de falsos positivos\n",
    "tpr => tasa de verdaderos positivos\n",
    "\"\"\"\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "def calcular_y_representar_curva_roc(esperado, predicho, representar_curva=True, nombre_imagen=None):\n",
    "    fpr, tpr, _ = roc_curve(esperado, predicho)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    if (representar_curva):\n",
    "        representar_curva_roc(fpr, tpr, roc_auc, nombre_imagen)\n",
    "    \n",
    "    return roc_auc, fpr, tpr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carga de datos\n",
    "\n",
    "El primer paso será cargar los módulos necesarios de Python y leer el archivo de datos `house-votes-84.data` que se almacenará en el dataframe `df`.\n",
    "\n",
    "Para hacer más legible el dataframe, se asignan nombres a las columnas para diferenciar la variable a predecir o *target*: `democrat` y las variables independientes o *features*: `cN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n",
    "\n",
    "# Comprobación de que el fichero existe\n",
    "nombre_dataset = 'house-votes-84.data'\n",
    "assert_fichero(nombre_dataset)\n",
    "\n",
    "# Se carga el archivo con el conjunto de datos como un dataframe de pandas\n",
    "df = pd.read_csv(nombre_dataset,\n",
    "                 header=None # El archivo de lectura no contiene cabecera\n",
    "                )\n",
    "\n",
    "# Asigna nombres a las columnas: democrat, c1, c2, c3, ..., c16\n",
    "clave_predecir = 'democrat'\n",
    "claves_predictoras = list(map(lambda x: 'c%d' % x, range(1, 17)))\n",
    "                          \n",
    "df.columns = [clave_predecir] + claves_predictoras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis descriptivo de los datos\n",
    "\n",
    "El conjunto de datos está compuesto por 435 registros y 17 columnas.\n",
    "\n",
    "La primera columna hace referencia al bando del votante y puede tomar los valores `democrat` o `republican`. Es decir, se trata de una variable categórica y binaria, porque el votante sólo puede ser de uno de los dos grupos. Esta será la variable *target* para la que se entrenará el clasificador.\n",
    "\n",
    "Por otro lado, el resto de columnas, de la 2 a la 17, son las votaciones de cada uno de los votantes a distintas votaciones. Cada una de estas columnas puede tener 3 valores diferentes: `y`, `n` o `?`. Estas serán las variable independientes o *features* que servirán para determinar el bando del votante.\n",
    "\n",
    "Según se indica en el documento `house-votes-84.names`, `?` no indica que se desconozca el valor de la votación sino que el votante no voto ni `y` ni `n`, pero votó algo, por ejemplo: *voto nulo*. Por lo que este valor también puede tener asociado un peso que ayude al sistema predictor a determinar si el votante es demócrata o republicano.\n",
    "\n",
    "> NOTE: It is important to recognize that \"?\" in this database does \n",
    "         not mean that the value of the attribute is unknown.  It \n",
    "         means simply, that the value is not \"yea\" or \"nay\" (see \n",
    "         \"Relevant Information\" section above).\n",
    "\n",
    "Por esto, `?` no se va a tratar como un dato desconocido sino como un tercer valor posible.\n",
    "\n",
    "Por tanto, este dataset dispone de un *target* con dos categorías disponibles, `democrat` o `republican`, y de 16 *features* donde cada una de ellas puede tomar el valor `y`, `n` o `?`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "      <th>top</th>\n",
       "      <td>democrat</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
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       "      <td>267</td>\n",
       "      <td>236</td>\n",
       "      <td>195</td>\n",
       "      <td>253</td>\n",
       "      <td>247</td>\n",
       "      <td>212</td>\n",
       "      <td>272</td>\n",
       "      <td>239</td>\n",
       "      <td>242</td>\n",
       "      <td>207</td>\n",
       "      <td>216</td>\n",
       "      <td>264</td>\n",
       "      <td>233</td>\n",
       "      <td>209</td>\n",
       "      <td>248</td>\n",
       "      <td>233</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        democrat   c1   c2   c3   c4   c5   c6   c7   c8   c9  c10  c11  c12  \\\n",
       "count        435  435  435  435  435  435  435  435  435  435  435  435  435   \n",
       "unique         2    3    3    3    3    3    3    3    3    3    3    3    3   \n",
       "top     democrat    n    y    y    n    y    y    y    y    y    y    n    n   \n",
       "freq         267  236  195  253  247  212  272  239  242  207  216  264  233   \n",
       "\n",
       "        c13  c14  c15  c16  \n",
       "count   435  435  435  435  \n",
       "unique    3    3    3    3  \n",
       "top       y    y    n    y  \n",
       "freq    209  248  233  269  "
      ]
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    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aunque `?` se va a tratar como un valor posible, es interesante conocer más datos acerca de esta categoría, como el número de veces que aparece."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>c1</th>\n",
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       "      <th>?</th>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>11</td>\n",
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       "      <td>15</td>\n",
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       "      <td>201</td>\n",
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       "      <th>y</th>\n",
       "      <td>187</td>\n",
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       "      <td>212</td>\n",
       "      <td>272</td>\n",
       "      <td>239</td>\n",
       "      <td>242</td>\n",
       "      <td>207</td>\n",
       "      <td>216</td>\n",
       "      <td>150</td>\n",
       "      <td>171</td>\n",
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      ],
      "text/plain": [
       "    c1   c2   c3   c4   c5   c6   c7   c8   c9  c10  c11  c12  c13  c14  c15  \\\n",
       "?   12   48   11   11   15   11   14   15   22    7   21   31   25   17   28   \n",
       "n  236  192  171  247  208  152  182  178  206  212  264  233  201  170  233   \n",
       "y  187  195  253  177  212  272  239  242  207  216  150  171  209  248  174   \n",
       "\n",
       "   c16  \n",
       "?  104  \n",
       "n   62  \n",
       "y  269  "
      ]
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   ],
   "source": [
    "df.iloc[:,1:].apply(pd.value_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como puede verse, el valor `?` aparece varias veces, por lo que no sería muy práctico eliminar las registros en los que aparece este valor en alguna de las votaciones.\n",
    "\n",
    "Tampoco tiene sentido sustituir el valor `?` por la moda o la media de cada columna, dado que como ya se ha comentado, no se trata de un valor desconocido, sino de un valor distinto de `y` o `n` por lo que se estaría sesgando el conjunto de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En total hay 392 registros con alguna votación nula.\n"
     ]
    }
   ],
   "source": [
    "# Número de registros que al menos tienen una votación con el valor ?\n",
    "numero_registros_nulos = df[df.values == '?'].shape[0]\n",
    "print(\"En total hay %d registros con alguna votación nula.\" % numero_registros_nulos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si se optase por eliminar estos registros del dataset, se estaría eliminando un grandísimo porcentaje de los registros totales.\n",
    "\n",
    "Esto se traduciría en perder muchos datos valiosos de cara al entrenamiento del clasificador."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Un 90.11% de los registros tiene algún voto '?'.\n"
     ]
    }
   ],
   "source": [
    "# Porcentaje de registros con algún voto ?\n",
    "print(\"Un {:05.02f}% de los registros tiene algún voto '?'.\".format(100 * numero_registros_nulos / df.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparación del dataframe\n",
    "\n",
    "Como la variable a predecir es una variable categórica es necesario convertirla a numérica.\n",
    "\n",
    "El criterio por el que se ha optado es, dado que sólo hay dos categorías, predecir si el votante es *demócrata* o no, por tanto, `1` se asignará a la categoría `democrat` y `0` a la categoría `republican`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convierte los valores de la columna 'democrat' que son categóricos a numéricos\n",
    "# 1 => democrat\n",
    "# 0 => republican\n",
    "df['democrat'] = np.where(df['democrat'] == 'democrat', 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El siguiente paso consiste en convertir las variables independientes categóricas en variables numéricas, tal y como se ha hecho para la variable dependiente, sólo que en este caso se asignará `1` cuando el valor sea `y`, `2` cuando sea `n` y `3` cuando sea `?`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>democrat</th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
       "      <th>c5</th>\n",
       "      <th>c6</th>\n",
       "      <th>c7</th>\n",
       "      <th>c8</th>\n",
       "      <th>c9</th>\n",
       "      <th>c10</th>\n",
       "      <th>c11</th>\n",
       "      <th>c12</th>\n",
       "      <th>c13</th>\n",
       "      <th>c14</th>\n",
       "      <th>c15</th>\n",
       "      <th>c16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   democrat  c1  c2  c3  c4  c5  c6  c7  c8  c9  c10  c11  c12  c13  c14  c15  \\\n",
       "0         0   2   1   2   1   1   1   2   2   2    1    3    1    1    1    2   \n",
       "1         0   2   1   2   1   1   1   2   2   2    2    2    1    1    1    2   \n",
       "2         1   3   1   1   3   1   1   2   2   2    2    1    2    1    1    2   \n",
       "3         1   2   1   1   2   3   1   2   2   2    2    1    2    1    2    2   \n",
       "4         1   1   1   1   2   1   1   2   2   2    2    1    3    1    1    1   \n",
       "\n",
       "   c16  \n",
       "0    1  \n",
       "1    3  \n",
       "2    2  \n",
       "3    1  \n",
       "4    1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convierte los valores de las columnas 'cN' que son categóricos a numéricos\n",
    "# 1 => y\n",
    "# 2 => n\n",
    "# 3 => ?\n",
    "df.iloc[:, 1:] = np.where(df.iloc[:, 1:] == 'y', 1,\n",
    "                          np.where(df.iloc[:, 1:] == 'n', 2, 3))\n",
    "\n",
    "# Muestra los primeros registros de la tabla tras aplicar las transformaciones\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conjuntos de entrenamiento y validación\n",
    "\n",
    "Una vez los datos están normalizados y preparados para su tratamiento, se procede a separarlos en dos conjuntos, uno de *entrenamiento* `df_train` y otro de *validación* `df_test` que servirá para validar el clasificador.\n",
    "\n",
    "Del total de datos del que se dispone, un 70% se va a emplear para el entrenamiento del clasificador, mientras que el 30% restante se empleará para la posterior validación del mismo.\n",
    "\n",
    "Para facilitar la reproducibilidad de los resultados se ha desactivado la aleatoriedad del reparto, pero lo correcto sería usar por ejemplo el método **k-fold** para entrenar y validar los clasificadores con distintas muestras de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(df,\n",
    "                                     test_size=0.3, # Porcentaje en tanto por 1 de entradas para validación\n",
    "                                     shuffle=False, # No desordena los registros para facilitar la reproducibilidad\n",
    "                                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrenamiento del clasificador\n",
    "\n",
    "Para entrenar el clasificador se hará uso de la clase `GaussianNB` del paquete `scikit-learn`.\n",
    "\n",
    "Para ello se crea el objeto `clasificador_nb` y se entrena con el método `fit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instancia del clasificador\n",
    "clasificador_nb = GaussianNB()\n",
    "\n",
    "# Entrenamiento del clasificador, donde:\n",
    "# X => son los valores de las features (columnas c1, ..., c16)\n",
    "# y => los valores de la variable target (columna democrat)\n",
    "clasificador_nb.fit(df_train[claves_predictoras].values,\n",
    "                    df_train[clave_predecir])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validación del clasificador\n",
    "\n",
    "Por último, una vez que el clasificador está listo, se usa el set de validación `df_test` para predecir en base a las variables independientes la categoría de cada una de las entradas, es decir, si es o no es `democrat`.\n",
    "\n",
    "Con los valores predichos `predicho` se puede obtener un indicador sobre lo bueno o malo que es el clasificador entrenado comparando estos valores predichos con los conocidos del set de validación `df_test.democrat` o `esperado`.\n",
    "\n",
    "La manera de compararlos será hacer uso de la métrica *Area Under the Curve (**AUC**)* obtenida a partir de las curvas *Receiver Operating Characteristic (**ROC**)* que se aplica para clasificadores binarios. Un valor próximo a `1.0` de esta métrica indicará que el clasificador es bueno mientras que un valor próximo a `0.5` indicará que el clasificador es puramente aleatorio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se han predicho correctamente 119 registros de un total de 131.\n",
      "Área bajo la curva (AUC): 0.91\n"
     ]
    }
   ],
   "source": [
    "# Predicción del clasificador\n",
    "predicho = clasificador_nb.predict(df_test[claves_predictoras])\n",
    "esperado = df_test.democrat\n",
    "\n",
    "roc_auc, _, _ = calcular_y_representar_curva_roc(esperado, predicho)\n",
    "\n",
    "print('Se han predicho correctamente {} registros de un total de {}.'\n",
    "      .format(sum(predicho == esperado), esperado.count()))\n",
    "print('Área bajo la curva (AUC): {:0.2f}'.format(roc_auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Otros clasificadores Naive Bayes\n",
    "\n",
    "A modo de prueba, se van a entrenar otros dos clasificadores: `BernoulliNB` y `MultinomialNB`, y a comparar los resultados obtenidos para los tres clasificadores.\n",
    "\n",
    "#### Bernoulli NB\n",
    "\n",
    "Según la [documentación oficial](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html) de *scikit-learn* para el clasificador `BernoulliNB`, este clasificador es bueno cuando se manejan variables independientes discretas binarias.\n",
    "\n",
    "Este no es el caso, dado que cada feature puede tener el valor `y`, `n` o `?`, por lo que se espera que los resultados no sean especialmente buenos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Área bajo la curva (AUC): 0.50\n"
     ]
    }
   ],
   "source": [
    "bernoulli_nb = BernoulliNB()\n",
    "bernoulli_nb.fit(df_train[claves_predictoras].values,\n",
    "                 df_train[clave_predecir])\n",
    "\n",
    "predicho_bernoulli = bernoulli_nb.predict(df_test[claves_predictoras])\n",
    "\n",
    "roc_auc, _, _ = calcular_y_representar_curva_roc(esperado, predicho_bernoulli)\n",
    "\n",
    "print('Área bajo la curva (AUC): {:0.2f}'.format(roc_auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multinomial NB\n",
    "\n",
    "Según la [documentación oficial](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html) de *scikit-learn* para el clasificador `MultinomialNB`, este clasificador es bueno cuando se manejan variables independientes discretas.\n",
    "\n",
    "Se espera que este clasificador se comporte mejor que el clasificador de `BernoulliNB` dado que `MultinomialNB` no está especializado en valores binarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Área bajo la curva (AUC): 0.87\n"
     ]
    }
   ],
   "source": [
    "multinomial_nb = MultinomialNB()\n",
    "multinomial_nb.fit(df_train[claves_predictoras].values,\n",
    "                   df_train[clave_predecir])\n",
    "\n",
    "predicho_multinomial = multinomial_nb.predict(df_test[claves_predictoras])\n",
    "\n",
    "roc_auc, _, _ = calcular_y_representar_curva_roc(esperado, predicho_multinomial)\n",
    "\n",
    "print('Área bajo la curva (AUC): {:0.2f}'.format(roc_auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra - Binarización de variables categóricas\n",
    "\n",
    "En lugar de asignar los valores numéricos `1`, `2` y `3` a los resultados `y`, `n` y `?`, se va a proceder a separar cada una de las columnas en tres columnas binarias cada una de ellas asociada a un valor distinto, es decir, `c1` se separará en `c1_y`, `c1_n` y `c1_?`.\n",
    "\n",
    "Cada una de estas nuevas columnas sólo podrá tener los valores `1` o `0` en función de si el valor original era `y`, `n` o `?` y serán mutuamente excluyentes.\n",
    "\n",
    "En este caso, es de esperar que el clasificador `BernoulliNB` ofrezca mejores resultados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>democrat</th>\n",
       "      <th>c1_?</th>\n",
       "      <th>c1_n</th>\n",
       "      <th>c1_y</th>\n",
       "      <th>c2_?</th>\n",
       "      <th>c2_n</th>\n",
       "      <th>c2_y</th>\n",
       "      <th>c3_?</th>\n",
       "      <th>c3_n</th>\n",
       "      <th>c3_y</th>\n",
       "      <th>...</th>\n",
       "      <th>c13_y</th>\n",
       "      <th>c14_?</th>\n",
       "      <th>c14_n</th>\n",
       "      <th>c14_y</th>\n",
       "      <th>c15_?</th>\n",
       "      <th>c15_n</th>\n",
       "      <th>c15_y</th>\n",
       "      <th>c16_?</th>\n",
       "      <th>c16_n</th>\n",
       "      <th>c16_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   democrat  c1_?  c1_n  c1_y  c2_?  c2_n  c2_y  c3_?  c3_n  c3_y  ...  c13_y  \\\n",
       "0         0     0     1     0     0     0     1     0     1     0  ...      1   \n",
       "1         0     0     1     0     0     0     1     0     1     0  ...      1   \n",
       "2         1     1     0     0     0     0     1     0     0     1  ...      1   \n",
       "3         1     0     1     0     0     0     1     0     0     1  ...      1   \n",
       "4         1     0     0     1     0     0     1     0     0     1  ...      1   \n",
       "\n",
       "   c14_?  c14_n  c14_y  c15_?  c15_n  c15_y  c16_?  c16_n  c16_y  \n",
       "0      0      0      1      0      1      0      0      0      1  \n",
       "1      0      0      1      0      1      0      1      0      0  \n",
       "2      0      0      1      0      1      0      0      1      0  \n",
       "3      0      1      0      0      1      0      0      0      1  \n",
       "4      0      0      1      0      0      1      0      0      1  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se carga de nuevo el archivo\n",
    "df = pd.read_csv(nombre_dataset,\n",
    "                 names=[clave_predecir] + claves_predictoras,\n",
    "                 header=None)\n",
    "\n",
    "# De nuevo se predecirá 1 si es demócrata o 0 si no lo es\n",
    "df['democrat'] = np.where(df['democrat'] == 'democrat', 1, 0)\n",
    "\n",
    "# Se separan las columnas por categorías\n",
    "series = [df['democrat']]\n",
    "for predictor in claves_predictoras:\n",
    "    series.append(separar_categorias(df, predictor))\n",
    "\n",
    "# Se crea el dataframe por categorías\n",
    "df_categorias = pd.concat(series, axis=1, sort=False)\n",
    "\n",
    "# Se obtienen los conjuntos de entrenamiento y validación como se hizo previamente\n",
    "df_categorias_train, df_categorias_test = train_test_split(df_categorias, test_size=0.3, shuffle=False)\n",
    "\n",
    "# Muestra el nuevo esquema del dataframe\n",
    "df_categorias.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez están los datos preparados se vuelven a probar los distintos clasificadores, es decir: `GaussianNB`, `BernoulliNB` y `MultinomialNB` para ver si los resultados han mejorado o empeorado con este sistema de conversión de variables categóricas a numéricas, y para ver si el ranking de clasificadores ha cambiado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Clasificador GaussianNB: 0.91\n",
      "AUC Clasificador BernoulliNB: 0.90\n",
      "AUC Clasificador MultinomialNB: 0.90\n"
     ]
    },
    {
     "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": [
    "# Se obtienen los nombres de las columnas de las nuevas features\n",
    "predictores = list(df_categorias_train.columns[1:])\n",
    "\n",
    "# Se obtienen los valores esperados\n",
    "esperado = df_categorias_test.democrat\n",
    "\n",
    "# Valores de métrica\n",
    "roc_auc = dict()\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "\n",
    "# Clasificador GaussianNB\n",
    "gaussian_nb = GaussianNB()\n",
    "## Entrenamiento\n",
    "gaussian_nb.fit(df_categorias_train[predictores].values,\n",
    "                df_categorias_train[clave_predecir])\n",
    "## Predicción\n",
    "predicho_gaussian = gaussian_nb.predict(df_categorias_test[predictores])\n",
    "## Validación\n",
    "roc_auc['GaussianNB'], fpr['GaussianNB'], tpr['GaussianNB'] = \\\n",
    "    calcular_y_representar_curva_roc(esperado, predicho_gaussian, representar_curva=False)\n",
    "print('AUC Clasificador GaussianNB: {:0.2f}'.format(roc_auc['GaussianNB']))\n",
    "\n",
    "# Clasificador BernoulliNB\n",
    "bernoulli_nb = BernoulliNB()\n",
    "## Entrenamiento\n",
    "bernoulli_nb.fit(df_categorias_train[predictores].values,\n",
    "                 df_categorias_train[clave_predecir])\n",
    "## Predicción\n",
    "predicho_bernoulli = bernoulli_nb.predict(df_categorias_test[predictores])\n",
    "## Validación\n",
    "roc_auc['BernoulliNB'], fpr['BernoulliNB'], tpr['BernoulliNB'] = \\\n",
    "    calcular_y_representar_curva_roc(esperado, predicho_bernoulli, representar_curva=False)\n",
    "print('AUC Clasificador BernoulliNB: {:0.2f}'.format(roc_auc['BernoulliNB']))\n",
    "\n",
    "# Clasificador MultinomialNB\n",
    "multinomial_nb = MultinomialNB()\n",
    "## Entrenamiento\n",
    "multinomial_nb.fit(df_categorias_train[predictores].values,\n",
    "                   df_categorias_train[clave_predecir])\n",
    "## Predicción\n",
    "predicho_multinomial = multinomial_nb.predict(df_categorias_test[predictores])\n",
    "## Validación\n",
    "roc_auc['MultinomialNB'], fpr['MultinomialNB'], tpr['MultinomialNB'] = \\\n",
    "    calcular_y_representar_curva_roc(esperado, predicho_multinomial, representar_curva=False)\n",
    "print('AUC Clasificador MultinomialNB: {:0.2f}'.format(roc_auc['MultinomialNB']))\n",
    "\n",
    "# Rrepresentación de las tres curvas juntas\n",
    "representar_curva_roc(fpr, tpr, roc_auc)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusiones\n",
    "\n",
    "- Tras comparar los tres clasificadores resulta curioso ver que el que mejor resultados ofrece es el clasificador `GaussianNB` (`0.91`), cuando según la [documentación oficial](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html) este clasificador está diseñado para variables contínuas que sigan una distribución gaussiana.\n",
    "\n",
    "- No resulta tan extraño comprobar que el clasificador `BernoulliNB` (`0.50`) es el que peor resultados ofrece dado que las features empleadas no son binarias. De hecho, un valor de `0.50` indica para la métrica AUC que se trata de un clasificador puramente aleatorio.\n",
    "\n",
    "- Por otro lado, cabría esperar que el clasificador que debería dar mejores resultados es el `MultinomialNB` (`0.87`) dado que los datos siguen una distribución discreta, pero se ha visto que el que mejor resultados ofrece es el `GaussianNB` (`0.91`). No obstante, no queda lejos del resultado obtenido por el clasificador gaussiano.\n",
    "\n",
    "- Es interesante ver que con el segundo sistema para pasar las variables categóricas a numéricas ([Binarización de variables categóricas](#Extra---Binarización-de-variables-categóricas)) los tres clasificadores tienen un porcentaje de aciertos mucho más parejo habiendo mejorado significativamente el clasificador `BernoulliNB`\n",
    "\n",
    "  - GaussianNB: `0.91`\n",
    "  - BernoulliNB: `0.90`\n",
    "  - MultinomialNB: `0.90`\n",
    "\n",
    "- Igual de interesante es que con el segundo sistema los clasificadores `BernoulliNB` y `MultinomialNB` tienen el mismo porcentaje de acierto cuando cabría esperar que el de `BernoulliNB` fuese mejor dado que se trata de variables binarias. Y no deja de llamar la atención que el clasificador `GaussianNB` sigue siendo el que mejor resultados ofrece.\n",
    "\n",
    "- La desventaja de este segundo sistema para convertir variables categóricas a numéricas es que tiene un alto coste en lo que a consumo de memoria se refiere. Sobre todo cuando los posibles valores de cada categoría son muchos."
   ]
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