09_Scikit_Clasificacion_Textos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 09 - Clasificación de textos con Scikit-Learn\n",
    "\n",
    "* En este notebook vamos a ver como clasificar una serie de Tweets en Ingles sobre críticas a los productos de Apple.\n",
    "\n",
    "\n",
    "* Estos tweets estan clasificados como: *positivos*, *neutros* o *negativos*\n",
    "\n",
    "\n",
    "* El este notebook realizaremos los siguientes pasos:\n",
    "    \n",
    "    1. Carga de los datos (tweets)\n",
    "    2. Normalización (en ingles) de los tweets\n",
    "    3. Creacción de la Bolsa de Palabras\n",
    "    4. Particionado de Datos\n",
    "    5. Creacción de modelos\n",
    "        - Multinomial Naive Bayes\n",
    "        - Bernoulli Naive Bayes\n",
    "        - Regresion Logistica\n",
    "        - Support Vector Machine\n",
    "        - Random Forest <sup>(*)Meta-Modelo</sup>\n",
    "    6. Evaluación de los modelos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Carga de Datos\n",
    "\n",
    "\n",
    "* El primer paso que vamos a realizar es el de cargar los datos. Para ello ***leeremos el csv con pandas*** (pasandolo a un dataframe) y posteriormente lo transformaremos en una lista de tuplas (*tweets*) donde cada tupla esta formada por:\n",
    "    - **Posición 0**: Tweet\n",
    "    - **Posición 1**: Polaridad (Positivo | Neutro | Negativo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de Tweets Cargados: 3804\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "tweets_file = './data/Apple_Tweets.csv'\n",
    "df = pd.read_csv(tweets_file, header=None)\n",
    "tweets = [tuple(x) for x in df.values]\n",
    "print('Número de Tweets Cargados: {num}'.format(num=len(tweets)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Normalización\n",
    "\n",
    "* Para este ejemplo haremos uso de ***spaCy***, pero en este caso tenemos que utilizar (e importar) el modelo para Inglés. Para ello: debemos de abrir un terminal en python y ejecutar lo siguiente para descargar el modelo en Ingles (*NOTA: los que uséis conda, tener activado el entorno*).\n",
    "\n",
    "\n",
    "```\n",
    ">> python3 -m spacy download en_core_web_sm\n",
    "```\n",
    "\n",
    "\n",
    "<img src=\"./imgs/010_spacy_en_download.png\" style=\"width: 600px;\"/>\n",
    "\n",
    "\n",
    "* Para ***normalizar*** los tweets realizaremos las siguientes acciones:\n",
    "    1. Pasamos las frases a minúsculas.\n",
    "    2. Eliminamos los signos de puntuación.\n",
    "    3. Eliminamos las palabras con menos de 3 caracteres.\n",
    "    4. Eliminamos las Stop-Words.\n",
    "    5. Eliminamos las palabras que empiecen por '@' o 'http'.\n",
    "    6. Pasamos la palabra a su lema\n",
    "\n",
    "\n",
    "* Todos estos pasos los vamos a realizar en una misma función.\n",
    "\n",
    "\n",
    "* ***NOTA***: *De cara a la normalización de textos se pueden realizar más acciones que las que vamos a realizar, pero con estas serán más que suficientes para realizar un ejercicio con fines didácticos*\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!python -m spacy download en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 3804/3804 [00:27<00:00, 140.63it/s]\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "# Divido los datos en dos listas \n",
    "#     X: los tweets\n",
    "#     y: target (polaridad)\n",
    "\n",
    "X = [doc[0] for doc in tweets]\n",
    "y = [doc[1] for doc in tweets]\n",
    "\n",
    "def normalize(sentenses):\n",
    "    \"\"\"normalizamos la lista de frases y devolvemos la misma lista de frases normalizada\"\"\"\n",
    "    for index, sentense in enumerate(tqdm(sentenses)):\n",
    "        sentense = nlp(sentense.lower()) # Paso la frase a minúsculas y a un objeto de la clase Doc de Spacy\n",
    "        sentenses[index] = \" \".join([word.lemma_ for word in sentense if (not word.is_punct)\n",
    "                                     and (len(word.text) > 2) and (not word.is_stop) \n",
    "                                     and (not word.text.startswith('@')) and (not word.text.startswith('http'))])\n",
    "    return sentenses\n",
    "\n",
    "# Normalizamos las frases\n",
    "X = normalize(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Bolsa de Palabras\n",
    "\n",
    "\n",
    "* El siguiente paso es transformar los tweets a una bolsa de palabras de frecuencias para que sirva de entrada al modelo.\n",
    "\n",
    "\n",
    "* Dado que ya estamos trabajando con un corpus relativamente grande, es necesario reducir el diccionario de palabras (no confundir con un diccionario Python) para trabajar solo con las palabras más relevantes.\n",
    "\n",
    "\n",
    "* La implementación de la clase \"*CountVectorizer*\" de scikit, permite quedarnos con las palabras más relevante. Para ello podemos utilizar dos parámetros que son:\n",
    "    - **max_features**: Con este parámetro le indicamos que nos seleccione la '*X*' palabras más frecuentes del corpus. En este ejemplo **seleccionaremos las 1000 más frecuentes**.\n",
    "    - **min_df**: Con este parámetro le indicamos el número mínimo de documentos en la que tiene que aparecer la palabra para que se incluya en la bolsa de palabras. En este ejemplo **seleccionaremos 3 documentos** (tweets).\n",
    "    \n",
    "\n",
    "* ***NOTA***: para más información podéis mirar la documentación de la clase \"*CountVectorizer*\" en: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "vectorizer = CountVectorizer(max_features=1000, min_df=3)\n",
    "\n",
    "# Pasamos los tweets normalizados a Bolsa de palabras\n",
    "X = vectorizer.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Particionado de Datos (Train y Test)\n",
    "\n",
    "* Vamos a particionar los datos en conjunto de Train y Test.\n",
    "\n",
    "\n",
    "* Para este ejemplo nos vamos a quedar con:\n",
    "    - 80% de datos de entrenamiento\n",
    "    - 20% de datos de test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de Tweets para el entrenamiento: 3043\n",
      "Número de Tweets para el test: 761\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "print('Número de Tweets para el entrenamiento: {num}'.format(num=X_train.shape[0]))\n",
    "print('Número de Tweets para el test: {num}'.format(num=X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Creacción del Modelo\n",
    "\n",
    "\n",
    "* Una vez tenemos creada la bolsa de palabras, podemos usar cualquier algoritmo de aprendizaje para la clasificación.\n",
    "\n",
    "\n",
    "* Para este ejemplo vamos a usar los siguientes algoritmos de aprendizaje:\n",
    "\n",
    "    - Multinomial Naive Bayes: https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html\n",
    "    - Bernoulli Naive Bayes: https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html\n",
    "    - Regresion Logistica: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
    "    - Support Vector Machine Classifier: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html\n",
    "    - Random Forest (ensemble): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
    "    \n",
    "\n",
    "* Para esta caso vamos a crear el modelo y calcular el accuracy para los datos de entrenamiento y tener una idea de que modelo puede funciona mejor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREANDO MODELO: Multinomial NB\n",
      "CREANDO MODELO: Bernoulli NB\n",
      "CREANDO MODELO: Regresion Logistica\n",
      "CREANDO MODELO: SVM lineal\n",
      "CREANDO MODELO: SVM polinomico\n",
      "CREANDO MODELO: SVM Kernel rbf\n",
      "CREANDO MODELO: SVM Kernel Sigmoid\n",
      "CREANDO MODELO: Random Forest d_20\n",
      "CREANDO MODELO: Random Forest d_50\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB, BernoulliNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()\n",
    "lr = LogisticRegression(solver='lbfgs', multi_class='multinomial', max_iter=1000)\n",
    "svm_lin = SVC(kernel='linear')\n",
    "svm_poly = SVC(kernel='poly')\n",
    "svm_rbf = SVC(kernel='rbf')\n",
    "svm_sig = SVC(kernel='sigmoid')\n",
    "rf_20 = RandomForestClassifier(n_estimators=500, bootstrap=True, criterion='gini', max_depth=20, random_state=0)\n",
    "rf_50 = RandomForestClassifier(n_estimators=500, bootstrap=True, criterion='gini', max_depth=50, random_state=0)\n",
    "\n",
    "clasificadores = {'Multinomial NB': mnb,\n",
    "                  'Bernoulli NB': bnb,\n",
    "                  'Regresion Logistica': lr,\n",
    "                  'SVM lineal': svm_lin,\n",
    "                  'SVM polinomico': svm_poly,\n",
    "                  'SVM Kernel rbf': svm_rbf,\n",
    "                  'SVM Kernel Sigmoid': svm_sig,\n",
    "                  'Random Forest d_20': rf_20,\n",
    "                  'Random Forest d_50': rf_50}\n",
    "\n",
    "\n",
    "# Ajustamos los modelos y calculamos el accuracy para los datos de entrenamiento\n",
    "for k, v in clasificadores.items():\n",
    "    print ('CREANDO MODELO: {clas}'.format(clas=k))\n",
    "    v.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Evaluación del Modelo\n",
    "\n",
    "\n",
    "* Para cada uno de los modelos vamos a calcular las siguientes métricas de evaluación:\n",
    "\n",
    "    1. **Accuracy**\n",
    "    2. **Precision**\n",
    "    3. **Recall**\n",
    "    4. **F1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy_train</th>\n",
       "      <th>accuracy_tests</th>\n",
       "      <th>precision_train</th>\n",
       "      <th>precision_tests</th>\n",
       "      <th>recall_train</th>\n",
       "      <th>recall_tests</th>\n",
       "      <th>f1_train</th>\n",
       "      <th>f1_tests</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Multinomial NB</th>\n",
       "      <td>0.780151</td>\n",
       "      <td>0.721419</td>\n",
       "      <td>0.783864</td>\n",
       "      <td>0.727190</td>\n",
       "      <td>0.780151</td>\n",
       "      <td>0.721419</td>\n",
       "      <td>0.781169</td>\n",
       "      <td>0.723901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bernoulli NB</th>\n",
       "      <td>0.755176</td>\n",
       "      <td>0.716163</td>\n",
       "      <td>0.766085</td>\n",
       "      <td>0.726448</td>\n",
       "      <td>0.755176</td>\n",
       "      <td>0.716163</td>\n",
       "      <td>0.755911</td>\n",
       "      <td>0.718362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regresion Logistica</th>\n",
       "      <td>0.867236</td>\n",
       "      <td>0.743758</td>\n",
       "      <td>0.872877</td>\n",
       "      <td>0.736569</td>\n",
       "      <td>0.867236</td>\n",
       "      <td>0.743758</td>\n",
       "      <td>0.863562</td>\n",
       "      <td>0.733259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM lineal</th>\n",
       "      <td>0.876438</td>\n",
       "      <td>0.743758</td>\n",
       "      <td>0.880638</td>\n",
       "      <td>0.738571</td>\n",
       "      <td>0.876438</td>\n",
       "      <td>0.743758</td>\n",
       "      <td>0.873497</td>\n",
       "      <td>0.736051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM polinomico</th>\n",
       "      <td>0.818600</td>\n",
       "      <td>0.668857</td>\n",
       "      <td>0.859948</td>\n",
       "      <td>0.702568</td>\n",
       "      <td>0.818600</td>\n",
       "      <td>0.668857</td>\n",
       "      <td>0.804748</td>\n",
       "      <td>0.616968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM Kernel rbf</th>\n",
       "      <td>0.852120</td>\n",
       "      <td>0.751643</td>\n",
       "      <td>0.867852</td>\n",
       "      <td>0.753920</td>\n",
       "      <td>0.852120</td>\n",
       "      <td>0.751643</td>\n",
       "      <td>0.843071</td>\n",
       "      <td>0.730173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM Kernel Sigmoid</th>\n",
       "      <td>0.760762</td>\n",
       "      <td>0.738502</td>\n",
       "      <td>0.761450</td>\n",
       "      <td>0.733404</td>\n",
       "      <td>0.760762</td>\n",
       "      <td>0.738502</td>\n",
       "      <td>0.738734</td>\n",
       "      <td>0.716475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest d_20</th>\n",
       "      <td>0.747289</td>\n",
       "      <td>0.714849</td>\n",
       "      <td>0.810116</td>\n",
       "      <td>0.757355</td>\n",
       "      <td>0.747289</td>\n",
       "      <td>0.714849</td>\n",
       "      <td>0.713381</td>\n",
       "      <td>0.672328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest d_50</th>\n",
       "      <td>0.850148</td>\n",
       "      <td>0.737188</td>\n",
       "      <td>0.879406</td>\n",
       "      <td>0.756099</td>\n",
       "      <td>0.850148</td>\n",
       "      <td>0.737188</td>\n",
       "      <td>0.843052</td>\n",
       "      <td>0.713749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                     accuracy_train  accuracy_tests  precision_train  \\\n",
       "name                                                                   \n",
       "Multinomial NB             0.780151        0.721419         0.783864   \n",
       "Bernoulli NB               0.755176        0.716163         0.766085   \n",
       "Regresion Logistica        0.867236        0.743758         0.872877   \n",
       "SVM lineal                 0.876438        0.743758         0.880638   \n",
       "SVM polinomico             0.818600        0.668857         0.859948   \n",
       "SVM Kernel rbf             0.852120        0.751643         0.867852   \n",
       "SVM Kernel Sigmoid         0.760762        0.738502         0.761450   \n",
       "Random Forest d_20         0.747289        0.714849         0.810116   \n",
       "Random Forest d_50         0.850148        0.737188         0.879406   \n",
       "\n",
       "                     precision_tests  recall_train  recall_tests  f1_train  \\\n",
       "name                                                                         \n",
       "Multinomial NB              0.727190      0.780151      0.721419  0.781169   \n",
       "Bernoulli NB                0.726448      0.755176      0.716163  0.755911   \n",
       "Regresion Logistica         0.736569      0.867236      0.743758  0.863562   \n",
       "SVM lineal                  0.738571      0.876438      0.743758  0.873497   \n",
       "SVM polinomico              0.702568      0.818600      0.668857  0.804748   \n",
       "SVM Kernel rbf              0.753920      0.852120      0.751643  0.843071   \n",
       "SVM Kernel Sigmoid          0.733404      0.760762      0.738502  0.738734   \n",
       "Random Forest d_20          0.757355      0.747289      0.714849  0.713381   \n",
       "Random Forest d_50          0.756099      0.850148      0.737188  0.843052   \n",
       "\n",
       "                     f1_tests  \n",
       "name                           \n",
       "Multinomial NB       0.723901  \n",
       "Bernoulli NB         0.718362  \n",
       "Regresion Logistica  0.733259  \n",
       "SVM lineal           0.736051  \n",
       "SVM polinomico       0.616968  \n",
       "SVM Kernel rbf       0.730173  \n",
       "SVM Kernel Sigmoid   0.716475  \n",
       "Random Forest d_20   0.672328  \n",
       "Random Forest d_50   0.713749  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,precision_score, recall_score, f1_score\n",
    "\n",
    "def evaluation(model, name, X_train, y_train, X_test, y_test):\n",
    "    \"\"\"\n",
    "    Función de devuelve en un diccionario las métricas de evaluación de \n",
    "    Accuracy, Precision, Recall y F1 para los conjuntos de datos de entrenamiento y test\n",
    "        model: modelo a evaluar\n",
    "        name: nombre del modelo\n",
    "        X_train: Variables de entrada del conjunto de datos de entrenamiento\n",
    "        y_train: Variable de salida del conjunto de datos de entrenamiento\n",
    "        X_test: Variables de entrada del conjunto de datos de test\n",
    "        y_test: Variable de salida del conjunto de datos de test\n",
    "        return: diccionario con el nombre del modelo y el valor de las métricas\n",
    "    \"\"\"\n",
    "    model_dict = {}\n",
    "    model_dict['name'] = name\n",
    "    y_pred_train = model.predict(X_train)\n",
    "    y_pred_test = model.predict(X_test)\n",
    "    model_dict['accuracy_train'] = accuracy_score(y_true=y_train, y_pred=y_pred_train)\n",
    "    model_dict['accuracy_tests'] = accuracy_score(y_true=y_test, y_pred=y_pred_test)\n",
    "    model_dict['precision_train'] = precision_score(y_true=y_train, y_pred=y_pred_train, average='weighted')\n",
    "    model_dict['precision_tests'] = precision_score(y_true=y_test, y_pred=y_pred_test, average='weighted')\n",
    "    model_dict['recall_train'] = recall_score(y_true=y_train, y_pred=y_pred_train, average='weighted')\n",
    "    model_dict['recall_tests'] = recall_score(y_true=y_test, y_pred=y_pred_test, average='weighted')\n",
    "    model_dict['f1_train'] = f1_score(y_true=y_train, y_pred=y_pred_train, average='weighted')\n",
    "    model_dict['f1_tests'] = f1_score(y_true=y_test, y_pred=y_pred_test, average='weighted')\n",
    "    \n",
    "    return model_dict\n",
    "\n",
    "\n",
    "# Calculamos las métricas de los modelos por separado\n",
    "evaluacion = list()\n",
    "for key, model in clasificadores.items():\n",
    "    evaluacion.append(evaluation(model=model, name=key, \n",
    "                                 X_train=X_train, y_train=y_train,\n",
    "                                 X_test=X_test, y_test=y_test))\n",
    "\n",
    "# Pasamos los resultados a un DataFrame para visualizarlos mejor\n",
    "df = pd.DataFrame.from_dict(evaluacion)\n",
    "df.set_index(\"name\", inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Representamos las métricas para los diferentes modelos en un gráfico de barras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Métricas a pintar\n",
    "METRICS = [\"accuracy\", \"precision\", \"recall\", \"f1\"]\n",
    "\n",
    "# Transformamos el dataframe para pintar las gráficas con seaborn\n",
    "df_plot = df.reset_index().melt(id_vars='name').rename(columns=str.title)\n",
    "\n",
    "plt.figure(figsize=(25, 12))\n",
    "pos = 1\n",
    "for metric in METRICS:\n",
    "    # Filtramos la métrica a pintar\n",
    "    df_aux = df_plot[df_plot['Variable'].str.contains(metric)]\n",
    "    \n",
    "    # Pintamos la gráfica en su posición 2x2\n",
    "    plt.subplot(2, 2, pos)\n",
    "    sns.barplot(x='Name', y='Value', hue='Variable', data=df_aux)\n",
    "    plt.title(metric.upper())\n",
    "    plt.grid()\n",
    "    plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)\n",
    "    plt.xticks(rotation=20)\n",
    "    pos += 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Veamos las matrices de confusión de los modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obteniendo Matriz de Confusión de: Multinomial NB\n",
      "Obteniendo Matriz de Confusión de: Bernoulli NB\n",
      "Obteniendo Matriz de Confusión de: Regresion Logistica\n",
      "Obteniendo Matriz de Confusión de: SVM lineal\n",
      "Obteniendo Matriz de Confusión de: SVM polinomico\n",
      "Obteniendo Matriz de Confusión de: SVM Kernel rbf\n",
      "Obteniendo Matriz de Confusión de: SVM Kernel Sigmoid\n",
      "Obteniendo Matriz de Confusión de: Random Forest d_20\n",
      "Obteniendo Matriz de Confusión de: Random Forest d_50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x3240 with 35 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Obtenemos las Matrices de confusión\n",
    "msc = list()\n",
    "for k, v in clasificadores.items():\n",
    "    print ('Obteniendo Matriz de Confusión de: {model}'.format(model=k))\n",
    "    model = {}\n",
    "    model['name'] = k\n",
    "    y_pred_train = v.predict(X_train)\n",
    "    y_pred_test = v.predict(X_test)\n",
    "    model['confusion_matrix_train'] = confusion_matrix(y_true=y_train, y_pred=y_pred_train)\n",
    "    model['confusion_matrix_test'] = confusion_matrix(y_true=y_test, y_pred=y_pred_test)\n",
    "    msc.append(model)\n",
    "\n",
    "    \n",
    "# Definimos el heatmap de la matriz de confusión\n",
    "def plot_confusion_matrix(cm, classes, title, cmap=plt.cm.Greens):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], 'd'), horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "\n",
    "# Pintamos las matrices de confusión\n",
    "plt.figure(figsize=(20, 45))\n",
    "pos = 0\n",
    "for mc in msc:\n",
    "    pos += 1\n",
    "    plt.subplot(9, 2, pos)\n",
    "    plot_confusion_matrix(mc['confusion_matrix_train'], classes=['positive', 'neutral', 'negative'], \n",
    "                          title='{}\\nMatriz de Confusión Datos Entrenamiento'.format(mc['name']))\n",
    "    pos += 1\n",
    "    plt.subplot(9, 2, pos)\n",
    "    plot_confusion_matrix(mc['confusion_matrix_test'], classes=['positive', 'neutral', 'negative'], \n",
    "                          title='{}\\nMatriz de Confusión Datos Tests'.format(mc['name'] ))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "# Bonus Track I - Técnicas de Evaluación\n",
    "\n",
    "\n",
    "* Para evaluar los modelos obtenidos tras la aplicación de alguna de las técnicas de ML, es necesario disponer de un ***conjunto de datos*** (etiquetados o no) para ***generar el mejor modelo  posible y minimizar el error empírico***.\n",
    "\n",
    "\n",
    "* Dado un conjunto de datos, podemos enumerar los siguientes ***métodos de evaluación*** en función de cómo se dividen los datos de entrenamiento y de test:\n",
    "<span></span><br><br>\n",
    "    - **Resustitución**: Todos los datos disponibles se utilizan como datos de test y de entrenamiento.\n",
    "<span></span><br><br>\n",
    "    - **Partición (Hold Out)**: Divide los datos en dos subconjuntos: uno de entrenamiento y uno de test.\n",
    "<span></span><br><br>\n",
    "<img src=\"./imgs/021_tec_eval.png\" style=\"width: 500px;\"/>\n",
    "<span></span><br><br>\n",
    "    - **Validación cruzada (Cross Validation)**: Divide los datos aleatoriamente en ‘N’ bloques. Cada bloque se utiliza como test para un sistema entrenado por el resto de bloques.\n",
    "<span></span><br><br>\n",
    "<img src=\"./imgs/022_tec_eval.png\" style=\"width: 500px;\"/>\n",
    "<span></span><br><br>\n",
    "    - **Exclusión individual (Leave One Out)**: Este método utiliza cada dato individual como dato único de test de un sistema entrenado con todos los datos excepto el de test.\n",
    "<span></span><br><br>\n",
    "<img src=\"./imgs/023_tec_eval.png\" style=\"width: 500px;\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "# Bonus Track II - Evaluacion de Modelos de Clasificación\n",
    "\n",
    "\n",
    "* Para los problemas de Clasificación se utilizan (entre otros) las siguientes métricas de evaluación:\n",
    "\n",
    "    - ***Accuracy***\n",
    "    - ***Precision***\n",
    "    - ***Recall***\n",
    "    - ***Specificity***\n",
    "    - ***F-score***\n",
    "    - ***Curva ROC***\n",
    "    -  ***ROC_AUC: Área sobre la curva ROC***\n",
    "\n",
    "\n",
    "* Para ver como clasifica el modelo generado usamos la denominada ***“Matriz de Confusión”*** en la cual se muestra el número de elementos que clasifica como verdaderos y falsos positivos y verdaderos y falsos negativos.\n",
    "\n",
    "\n",
    "* Veamos a continuación los detalles de las métricas de evaluación y la matriz de confusión:\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "## - Matriz de confusión\n",
    "\n",
    "* Cuando hablamos de clasificación tenemos que medir cuanto hemos acertado y como lo hemos acertado, por ello las ***matrices de confusión nos ayudan a ver el acierto o fallo que tiene nuestro modelo a la hora de clasificar***. La matriz de confusión tiene la siguiente forma:\n",
    "\n",
    "    - **True Positive (TP)**: Valores positivos clasificados como positivos\n",
    "    - **True Negative (TN)**: Valores negativos clasificados como negativos\n",
    "    - **False Positive (FP)**: Valores negativos clasificados como positivos\n",
    "    - **False Negative (FN)**: Valore positivos clasificados como negativos\n",
    "    \n",
    "<img src=\"./imgs/011_Matriz_Confusion.png\" style=\"width: 400px;\"/>\n",
    "\n",
    "\n",
    "## - Accuracy\n",
    "\n",
    "* Indica el porcentaje de acierto:\n",
    "\n",
    "$$Acuracy = \\frac{TP + TN}{TP + FN + FP + TN}$$\n",
    "\n",
    "## - Precision\n",
    "\n",
    "* Los positivos correctamente pronosticados entre todos los ejemplos pronosticados como positivos. \n",
    "\n",
    "\n",
    "* \"***De lo que hemos pronosticado, cuanto hemos acertado***\"\n",
    "\n",
    "\n",
    "$$Precision = \\frac{TP}{TP + FP}$$\n",
    "\n",
    "\n",
    "<img src=\"./imgs/012_Matriz_Confusion_precision.png\" style=\"width: 400px;\"/>\n",
    "                                                               \n",
    "                                                               \n",
    "\n",
    "## - Recall\n",
    "\n",
    "\n",
    "* Los positivos correctamente pronosticados, entre todos los ejemplos que son positivos. \n",
    "\n",
    "\n",
    "* \"***De lo que hay en realidad, cuanto hemos acertado***\"\n",
    "\n",
    "\n",
    "$$Recall = \\frac{TP}{TP + FN}$$\n",
    "\n",
    "\n",
    "<img src=\"./imgs/013_Matriz_Confusion_recall.png\" style=\"width: 400px;\"/>\n",
    "\n",
    "\n",
    "## - Specificity\n",
    "\n",
    "\n",
    "* Los negativos correctamente pronosticados, entre todos los ejemplos que son negativos. \n",
    "\n",
    "\n",
    "$$Specificity = \\frac{TN}{TN + FP} = \\frac{TN}{Negative}$$\n",
    "\n",
    "\n",
    "<img src=\"./imgs/021_eval.png\" style=\"width: 400px;\"/>\n",
    "\n",
    "\n",
    "## - F-Score (Precision-Recall)\n",
    "\n",
    "\n",
    "* Es una media que ***combina la precisión y el recall con la media armonica de ambas medidas***. \n",
    "\n",
    "\n",
    "* Si es cercano a ***1*** significa que la clasificación realizada es buena. \n",
    "\n",
    "\n",
    "* Si es cercano a ***0*** la clasificación realizada es mala.\n",
    "\n",
    "\n",
    "* Se utiliza la media armonica, porque esta penaliza el hecho de que uno de los dos valores(el Precision o el Recall) tenga un valor bajo.\n",
    "\n",
    "\n",
    "* El ***F1*** o ***F-score*** (siendo $score=1$) se calcula de la siguiente manera:\n",
    "\n",
    "\n",
    "$$F1 = 2 \\cdot \\frac{Precision \\cdot Recall}{Precision + Recall}$$\n",
    "\n",
    "\n",
    "* Esta medida en realidad debería de llamarse $F_\\beta$ calculandose de la siguiente manera:\n",
    "\n",
    "$$F_\\beta =  \\frac{(1+\\beta^2) \\cdot Precision \\cdot Recall}{\\beta^2 \\cdot Precision + Recall}$$\n",
    "\n",
    "\n",
    "* En función del valor que le demos a $F_\\beta$ la media armónica dará más importancia a la Precision o al Recall; por ejemplo:\n",
    "\n",
    "    + $F_{0}$: Es el Recall.\n",
    "    + $F_{0.5}$: Da más peso a la Precision que al Recall.\n",
    "    + $F_{1}$: Asigna el mismo peso a la Precision que al Recall.\n",
    "    + $F_{2}$: Da más peso a la Recall que a la Precision.\n",
    "    + $F_{\\infty}$: Es la Precision.\n",
    "    \n",
    "    \n",
    "* ***La medida del F-Score es conocida como la medida F1 porque de esta manera se calcula la media armónica entre la Precision y el Recall otorgando a ambas el mismo peso.***\n",
    "\n",
    "\n",
    "## Curva ROC\n",
    "\n",
    "\n",
    "* La ***curva ROC*** (***R***eceiver ***O***perating ***C***haracteristic) es una gráfica que ***representa la Tasa de Verdaderos Positivos (TPR) en el eje vertical frente a la Tasa de Falsos Positivos (FPR) en el eje horizontal***.\n",
    "\n",
    "    <img src=\"./imgs/022_eval.png\" style=\"width: 400px;\"/>\n",
    "\n",
    "* Siendo:\n",
    "<br>\n",
    "\n",
    "    + ***TPR***: Recall\n",
    "        $$TPR = Recall = \\frac{TP}{TP + FN} = \\frac{TP}{Positive}$$\n",
    "    + ***FPR***: Fall-out\n",
    "        $$FPR = Fall-Out = \\frac{FP}{FP + TN} = \\frac{FP}{Negative}$$\n",
    "\n",
    "* Para ***construir esta curva***:\n",
    "\n",
    "    1. Nuestro ***modelo de clasificación nos tiene que devolver una probabilidad o score de pertenencia a la clase positiva y debemos de ordenar esa probabilidad o score de mayor a menor valor*** (de manera decreciente).\n",
    "<br>\n",
    "\n",
    "    2. ***Al recorrer los elementos ordenados*** vemos:\n",
    "        + Si nos encontramos un elemento que ***pertenece a la clase positiva (TPR) nos movemos verticalmente hacia arriba***.\n",
    "        + Si nos encontremos un elemento que ***pertenece a la clase negativa (FPR) nos movemos hacia la derecha***.\n",
    "        \n",
    "* Por otro lado, en la curva ROC conecta siempre los puntos (0,0) y (1,1) con una recta, normalmente representada de manera punteada.\n",
    "\n",
    "\n",
    "## - ROC_AUC: Área bajo la curva ROC\n",
    "\n",
    "\n",
    "* El ***Área Bajo la Curva ROC (Area Under Curve), es el área que queda bajo la curva ROC*** y nos vale como una medida de precisión del clasificador.\n",
    "\n",
    "\n",
    "* Un modelo que clasifique todos los elementos perfectamente, tendrá un ROC_AUC de 1.\n",
    "\n",
    "\n",
    "* Cuando se representa la curva ROC se suele dibujar una curva diagonal (discontinua) que representa un clasificador cuyos elementos tienen todos la misma probabilidad o score y esta diagonal tiene un ROC_AUC de 0,5.\n",
    "\n",
    "\n",
    "* Cuanto más cercana este nuestra curva ROC de la diagonal, significará que nuestro modelo es menos preciso a la hora de clasificar y cuanto más 'alejado' este de la diagonal, más preciso será nuestro modelo.\n",
    "\n",
    "\n",
    "        \n",
    "## - Métricas en Sckit-Learn:\n",
    "\n",
    "* Scikit-Learn tiene implementados todos estos métodos de evaluación.\n",
    "\n",
    "\n",
    "* Como norma general se le pasa como parámetros un array con la clasificación verdadera y otro array con la predicción:\n",
    "\n",
    "    - **metrics.confusion_matrix(y_true, y_pred[, …])**: Compute confusion matrix to evaluate the accuracy of a classification\n",
    "    - **metrics.accuracy_score(y_true, y_pred[, …])**: Accuracy classification score\n",
    "    - **metrics.precision_score(y_true, y_pred[, …])**: Compute the precision\n",
    "    - **metrics.recall_score(y_true, y_pred[, …])**: Compute the recall\n",
    "    - **metrics.f1_score(y_true, y_pred[, labels, …])**: Compute the F1 score, also known as balanced F-score or F-measure\n",
    "    - **metrics.classification_report(y_true, y_pred)**: Build a text report showing the main classification metrics\n",
    "    - **metrics.roc_auc_score(y_true, y_score[, …])**: Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.\n",
    "    - **metrics.roc_curve(y_true, y_score[, …])**: Compute Receiver operating characteristic (ROC)\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "## Ejemplo: Clasificación Binaria\n",
    "\n",
    "\n",
    "* Supongamos que tenemos que clasificar 10 elementos de dos clases y la realizadad y la predicción vienen dados por:\n",
    "\n",
    "    - y_real =    [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]\n",
    "    - y_predict = [1, 1, 0, 1, 1, 0, 0, 1, 1, 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_real =    [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]\n",
    "y_predict = [1, 1, 0, 1, 1, 0, 0, 1, 1, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Matriz de confusión\n",
    "\n",
    "* En el ejemplo tenemos:\n",
    "\n",
    "    - 5 Elementos que son de la clase 0\n",
    "    - 5 Elementos que son de la clase 0\n",
    "    - True Positive (TP): De los que son de la clase 1, predigo 4 correctamente.\n",
    "    - False Negative (FN): De los que son de la clase 1, predigo 1 incorrectamente.\n",
    "    - True Negative (TN): De los que son de la clase 0, predigo 3 correctamente.\n",
    "    - False Positive (FP): De los que son de la clase 0, predigo 2 incorrectamente.\n",
    "    \n",
    "    \n",
    "\n",
    "* Podemos ver este resultado en la matriz de confusión:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 1],\n",
       "       [2, 3]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_true=y_real, y_pred=y_predict, labels=[1, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Accuracy\n",
    "\n",
    "* Basandonos en el ejemplo, calculamos el accuracy como:\n",
    "\n",
    "$$Acuracy = \\frac{TP + TN}{TP + FN + FP + TN} = \\frac{4 + 3}{4 + 1 + 2 + 3} = \\frac{7}{10} = 0.7$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_true=y_real, y_pred=y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Precision\n",
    "\n",
    "* Basandonos en el ejemplo, calculamos la precision como:\n",
    "\n",
    "$$Precision = \\frac{TP}{TP + FP} = \\frac{4}{4 + 2} = \\frac{4}{6} = 0.67$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6666666666666666"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "\n",
    "precision_score(y_true=y_real, y_pred=y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Recall\n",
    "\n",
    "* Basandonos en el ejemplo, calculamos el recall como:\n",
    "\n",
    "$$Recall = \\frac{TP}{TP + FN} = \\frac{4}{4 + 1} = \\frac{4}{5} = 0.8$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "\n",
    "recall_score(y_true=y_real, y_pred=y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - F1\n",
    "\n",
    "* Basandonos en el ejemplo, calculamos el F1 como:\n",
    "\n",
    "$$F1 = 2 \\cdot \\frac{Precision \\cdot Recall}{Precision + Recall} = 2 \\cdot \\frac{0.67 \\cdot 0.8}{0.67 + 0.8} = 2 \\cdot \\frac{0.54}{1.47} = 0.73$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7272727272727272"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(y_true=y_real, y_pred=y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Curva ROC - Área bajo la curva"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Generamos un conjunto de datos de prueba con 2 clases\n",
    "X, y = make_classification(n_samples=1000, n_classes=2, random_state=1)\n",
    "\n",
    "# Creamos un Clasificador y calculamos las probabilidades de cada elemento\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X, y)\n",
    "X_probs = clf.predict_proba(X)\n",
    "\n",
    "# Clasificador base: Clasificador con probabilidad de todos los elementos a cero\n",
    "X_base_prob = [0 for i in range(len(y))]\n",
    "\n",
    "\n",
    "# Obtenemos Tasa de Verdaderos Positivos (TPR), Tasa de Falsos Positivos (FPR) y el umbral\n",
    "base_fpr, base_tpr, base_thresholds = roc_curve(y, X_base_prob)\n",
    "clf_fpr, clf_tpr, clf_thresholds = roc_curve(y, X_probs[:, 1])\n",
    "\n",
    "# Calculamos el Área bajo la curva\n",
    "base_auc = roc_auc_score(y, X_base_prob)\n",
    "clf_auc = roc_auc_score(y, X_probs[:, 1])\n",
    "\n",
    "# Pintamos la curva ROC\n",
    "plt.plot(clf_fpr, clf_tpr, label='Clasificador - ROC AUC = {:0.2f}'.format(clf_auc))\n",
    "plt.plot(base_fpr, base_tpr, linestyle='--', label='Clasificador Base - ROC AUC = {:0.2f}'.format(base_auc))\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## - Heatmap - Matriz de Confusión\n",
    "\n",
    "* Para representar de una forma visual más agradable y legible la matriz de confusión, podemos usar el siguiente código que tiene la función \"plot_confusion_matrix()\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "%matplotlib inline\n",
    "\n",
    "def plot_confusion_matrix(cm, classes, title, cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], 'd'), horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "plt.figure()\n",
    "matriz_confusion = confusion_matrix(y_true=y_real, y_pred=y_predict, labels=[1, 0])\n",
    "plot_confusion_matrix(matriz_confusion, classes=['Clase 1', 'Clase 0'], title='Matriz de Confusión')\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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}