semana06/5-1_SVR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regresión"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por qué utilizar regresión\n",
    "\n",
    "Los datos que deseamos predecir no siempre se encuentran limitados a un conjunto de clases predeterminado. Para esos casos, no se puede utilizar un problema de clasificación puesto que la cantidad de clases tendría que ser infinita.\n",
    "\n",
    "Por ejemplo, si quisiéramos establecer las posibilidades de respuesta para el país de procedencia de una persona, podemos crear un conjunto de todos los países que existen en el mundo. Si bien el listado puede ser un poco largo, tenemos una cantidad finita de opciones de las cuales elegir una. Sin embargo, si quisiéramos hacer lo mismo con su peso, sería imposible definir cada una de las opciones de respuesta. ¿Las opciones serían 50kg, 51kg, 53kg; hasta qué límite? Y sería aún más complejo si empezáramos a incluir decimales. ¿50.1kg, 50.2kg, 50.3kg?\n",
    "\n",
    "Este tipo de opciones de respuesta se conoce como **continuo**, pues no pertenecen a un conjunto prestablecido de opciones. Cuando tratamos de predecir un dato de esta naturaleza, nos enfrentamos a un problema de regresión."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Empecemos utilizando el set de datos sobre la compañía de telefonía."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>gender</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>29.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>0</td>\n",
       "      <td>56.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>0</td>\n",
       "      <td>53.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>42.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>70.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  Partner  Dependents  PhoneService  MultipleLines  InternetService  \\\n",
       "0       0        1           0             0              1                0   \n",
       "1       1        0           0             1              0                0   \n",
       "2       1        0           0             1              0                0   \n",
       "3       1        0           0             0              1                0   \n",
       "4       0        0           0             1              0                1   \n",
       "\n",
       "   SeniorCitizen  MonthlyCharges  \n",
       "0              0           29.85  \n",
       "1              0           56.95  \n",
       "2              0           53.85  \n",
       "3              0           42.30  \n",
       "4              0           70.70  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datos_limpios.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Empezamos definiendo cuál será nuestra columna objetivo (`y`), que en este caso es `MonthlyCharges`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, 0:len(df.columns) - 1]\n",
    "y = df[['MonthlyCharges']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como podemos ver, los datos de la columna objetivo son continuos, pues no pertenecen a ningún conjunto de clases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MonthlyCharges\n",
       "0           29.85\n",
       "1           56.95\n",
       "2           53.85\n",
       "3           42.30\n",
       "4           70.70"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separamos un set de datos de entrenamiento y de prueba\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entrenamiento finalizado en 1.0302033424377441 segundos\n"
     ]
    }
   ],
   "source": [
    "# Definimos nuestro algoritmo de regresión\n",
    "regresor = SVR(kernel='linear')\n",
    "hora_inicio = time()\n",
    "\n",
    "# Entrenamiento del algoritmo\n",
    "regresor.fit(X_train.values, y_train.values.ravel())\n",
    "print(f'Entrenamiento finalizado en {time() - hora_inicio} segundos')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluando el rendimiento del regresor\n",
    "\n",
    "Al hacer una predicción con un algoritmo de regresión, no podemos hablar de fallos o aciertos, pues no hay un dato específico que intentemos predecir. Este concepto se entiended más fáci con un ejemplo: si un algoritmo que predice `10.2999999` cuando la respuesta correcta era `10.3`, podemos decir que se está desempeñando bastante bien. Sabemos que esto es cierto porque se aproxima mucho a la respuesta correcta. Sin embargo, en un problema de clasificación, diríamos que la respuesta es categóricamente errónea, pues no obtuvimos la respuesta _correcta_. Debido a esta diferencia entre la definición sobre qué es una respuesta correcta, no podemos utilizar la misma medida de rendimiento para un problema de regresión y uno de clasificación.\n",
    "\n",
    "Si bien la métrica estándar para un problema de clasificación es la precisión (`accuracy`), un problema de regresión tiene muchas formas de medir su rendimiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hagamos una serie de predicciones con los datos de prueba\n",
    "y_pred = regresor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cantidad_probar = 1000\n",
    "X_axis = np.arange(cantidad_probar)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(X_axis, y_test.iloc[0:cantidad_probar].values)\n",
    "ax.scatter(X_axis, y_pred[0:cantidad_probar])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3040057750385706"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  }
 ],
 "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",
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