{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regresión logística\n",
    "\n",
    "Es un algoritmo para obtener un clasificador binario. \n",
    "\n",
    "La regresión logística es bastante efectiva en situaciones en las que la relación entre la **probabilidad** de lograr una meta/objetivo (Y) está vinculada a los recursos necesarios (X) de manera no lineal donde una disminución/aumento de cierto recurso más allá de cierto umbral disminuye/aumenta drásticamente la probabilidad de lograr el objetivo.\n",
    "\n",
    "\n",
    "<img src=\"./img/01-lineal-vs-logistica.png\" width=\"600px\"/>\n",
    "\n",
    "\n",
    "<img src=\"./img/02-regresion-logistica.png\" width=\"600px\"/>\n",
    "\n",
    "\n",
    "Los clasificadores binaros basados en regresión logística clasifican las observaciones de acuerdo a un umbral típicamente 0.5 (50%).\n",
    "\n",
    "Hay dos técnicas comunmente empleadas para obtener los coeficientes de regresión. __[MLE](https://es.wikipedia.org/wiki/M%C3%A1xima_verosimilitud)__ y __[mínimos cuadrados](https://es.wikipedia.org/wiki/M%C3%ADnimos_cuadrados)__ (luego de convertir la relación establecida por la curva \"S\" a una relación lineal)\n",
    "\n",
    "\n",
    "__[Scikit Learn - Regresión logística](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_entrenamiento = pd.read_csv(os.path.join(\"csv\", \"train.csv\"), index_col='PassengerId')"
   ]
  },
  {
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_entrenamiento.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hacemos limpieza de las columnas que no son necesarias para este ejercicio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_entrenamiento = df_entrenamiento.drop(['Ticket', 'Embarked', 'Cabin'], axis=1)\n",
    "df_entrenamiento = df_entrenamiento.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_entrenamiento.loc[:,'Age':].to_numpy().astype('float')\n",
    "y = df_entrenamiento['Survived'].ravel() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(571, 4) (571,)\n",
      "(143, 4) (143,)\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",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# crear el clasificador\n",
    "clasificador_reg_log = LogisticRegression(random_state=0, solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0, solver='liblinear')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# entrenar el clasificador\n",
    "clasificador_reg_log.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy del clasificador - version 1 : 0.64\n"
     ]
    }
   ],
   "source": [
    "print('accuracy del clasificador - version 1 : {0:.2f}'.format(clasificador_reg_log.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### El hiperparámetro 'penalty'\n",
    "__[L1 Norms versus L2 Norms](https://www.kaggle.com/residentmario/l1-norms-versus-l2-norms)__\n",
    "\n",
    "__[L1 and L2 Regularization Methods](https://towardsdatascience.com/l1-and-l2-regularization-methods-ce25e7fc831c)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#evaluar el desempeño\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy del clasificador - version 1 : 0.64\n",
      "matriz de confusión del clasificador - version 1: \n",
      " [[69 10]\n",
      " [41 23]]\n",
      "precision del clasificador - version 1 : 0.70\n",
      "recall del clasificador - version 1 : 0.36\n",
      "f1 del clasificador - version 1 : 0.47\n"
     ]
    }
   ],
   "source": [
    "# accuracy\n",
    "print('accuracy del clasificador - version 1 : {0:.2f}'.format(accuracy_score(y_test, clasificador_reg_log.predict(X_test))))\n",
    "# confusion matrix\n",
    "print('matriz de confusión del clasificador - version 1: \\n {0}'.format(confusion_matrix(y_test, clasificador_reg_log.predict(X_test))))\n",
    "# precision \n",
    "print('precision del clasificador - version 1 : {0:.2f}'.format(precision_score(y_test, clasificador_reg_log.predict(X_test))))\n",
    "# recall \n",
    "print('recall del clasificador - version 1 : {0:.2f}'.format(recall_score(y_test, clasificador_reg_log.predict(X_test))))\n",
    "# f1\n",
    "print('f1 del clasificador - version 1 : {0:.2f}'.format(f1_score(y_test, clasificador_reg_log.predict(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02455679, -0.3946987 ,  0.1850001 ,  0.02286013]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# coeficientes del modelo\n",
    "clasificador_reg_log.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'SibSp', 'Parch', 'Fare'], dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_entrenamiento.loc[:,'Age':].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Age', -0.024556791350753466),\n",
       " ('SibSp', -0.3946987047411627),\n",
       " ('Parch', 0.1850001010903358),\n",
       " ('Fare', 0.02286012707033761)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(df_entrenamiento.loc[:,'Age':].columns, clasificador_reg_log.coef_[0]))"
   ]
  }
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