3. Tabular data (regr)/3.4 Regularizated Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularización de la regresión lineal\n",
    "\n",
    "La regularización de los modelos lineales es una técnica que limita los valores de los coeficientes de la regresión, haciendo que tiendan a cero. Con ello se mejora la precisión del modelo y se reduce la varianza de los coeficientes, disminuyendo el riesto de *sobreajuste*. Las técnicas de regularización más frecuentes son la regresión *ridge* y la regresión *lasso*.\n",
    "\n",
    "Os dejamos una breve explicación de la mátemática que subyace a los procedimientos de regularización. En la regresión lineal por mínimos cuadrados (la técnica sin regularizar) se busca minimizar la suma de cuadrados de los residuos (*residual sum of squares*, RSS):\n",
    "\n",
    "$ RSS = \\displaystyle \\sum^{n}_{i=1} (y_i - \\beta_0 - \\sum^{p}_{j=1} \\beta_j x_{ij})^2 $\n",
    "\n",
    "En la regresión regularizada se añade un término al valor a minimizar. En la regresión *ridge* se minimiza\n",
    "\n",
    "$ RSS + \\lambda \\displaystyle \\sum^{p}_{j=1} \\beta_j^2 $\n",
    "\n",
    "Y en la *lasso* se minimiza\n",
    "\n",
    "$ RSS + \\lambda \\displaystyle \\sum^{p}_{j=1} |\\beta_j| $\n",
    "\n",
    "El parámetro $\\lambda$ es un valor que se elige arbitrariamente y determina la *fuerza* del efecto de regularización. Lo habitual es repetir el ajuste *ridge* o *lasso* usando distintos valores de $\\lambda$ para ver cómo se ven afectados los coeficientes del modelo. Aquellos tiendan a cero mas rápidamente serán los que deberemos eliminar de los modelos porque tendrán un menor poder predictivo. Para mayor información se puede visitar este [link](https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-ridge-lasso-regression-python/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veremos un ejemplo de regularización. Comenzamos cargando como siempre las librerías que necesitaremos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load python packages\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "plt.rcParams['figure.figsize'] = (12, 8)\n",
    "sns.set()\n",
    "sns.set(font_scale=1.5)\n",
    "\n",
    "# packages for checking assumptions\n",
    "from scipy import stats as stats\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as sm2\n",
    "\n",
    "# packages for regularization\n",
    "from sklearn.linear_model import Lasso\n",
    "from math import pow, sqrt\n",
    "\n",
    "np.random.seed(1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos los datos desde el repositorio y lo cargamos en el espacio de trabajo de Colab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-02-14 14:54:17--  https://raw.githubusercontent.com/CenticMurcia/curso-ciencia-datos/master/data/loans.csv\n",
      "Resolviendo raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.192.133, 151.101.128.133, 151.101.64.133, ...\n",
      "Conectando con raw.githubusercontent.com (raw.githubusercontent.com)[151.101.192.133]:443... conectado.\n",
      "Petición HTTP enviada, esperando respuesta... 200 OK\n",
      "Longitud: 4947180 (4,7M) [text/plain]\n",
      "Guardando como: “./data/loans.csv”\n",
      "\n",
      "loans.csv           100%[===================>]   4,72M  2,77MB/s    en 1,7s    \n",
      "\n",
      "2020-02-14 14:54:20 (2,77 MB/s) - “./data/loans.csv” guardado [4947180/4947180]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "##### DOWNLOAD DATASET. ONLY IN COLAB !!!\n",
    "!wget -P ./data/ https://raw.githubusercontent.com/CenticMurcia/curso-ciencia-datos/master/data/loans.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/loans.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Echamos un vistazo a las variables que tenemos en el dataset. Se trata de datos sobre micro-préstamos concedidos en Africa usando crowdfunding. Para más información sobre las variables puede consultarse este [link](https://www.kaggle.com/aks709/enhanced-analysis-for-kiva-crowdfunding)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_number                 int64\n",
       "loan_amount               int64\n",
       "lender_count              int64\n",
       "status                   object\n",
       "funded_date              object\n",
       "funded_amount             int64\n",
       "repayment_term            int64\n",
       "location_country_code    object\n",
       "sector                   object\n",
       "description              object\n",
       "use                      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tenemos dos variables categóricas que vamos a recodificar usando *one-hot-encoding* para poder usarlas en el modelo de regresión:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create indicator variables for country\n",
    "for country in df['location_country_code'].unique():\n",
    "    if country is not np.nan:\n",
    "        df['country_'+country] = np.where(df.location_country_code == country, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create indicator variables for sector\n",
    "for sect in df['sector'].unique():\n",
    "    df['sector_'+sect] = np.where(df.sector == sect, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_number                 int64\n",
       "loan_amount               int64\n",
       "lender_count              int64\n",
       "status                   object\n",
       "funded_date              object\n",
       "funded_amount             int64\n",
       "repayment_term            int64\n",
       "location_country_code    object\n",
       "sector                   object\n",
       "description              object\n",
       "use                      object\n",
       "country_BJ                int64\n",
       "country_BW                int64\n",
       "country_BF                int64\n",
       "country_BI                int64\n",
       "country_CM                int64\n",
       "country_CG                int64\n",
       "country_CD                int64\n",
       "country_EG                int64\n",
       "country_GH                int64\n",
       "country_CI                int64\n",
       "country_KE                int64\n",
       "country_LS                int64\n",
       "country_LR                int64\n",
       "country_MG                int64\n",
       "country_MW                int64\n",
       "country_ML                int64\n",
       "country_MR                int64\n",
       "country_MZ                int64\n",
       "country_NG                int64\n",
       "country_RW                int64\n",
       "country_SN                int64\n",
       "country_SL                int64\n",
       "country_SO                int64\n",
       "country_ZA                int64\n",
       "country_SS                int64\n",
       "country_TZ                int64\n",
       "country_TG                int64\n",
       "country_UG                int64\n",
       "country_ZM                int64\n",
       "country_ZW                int64\n",
       "sector_Retail             int64\n",
       "sector_Food               int64\n",
       "sector_Services           int64\n",
       "sector_Clothing           int64\n",
       "sector_Arts               int64\n",
       "sector_Agriculture        int64\n",
       "sector_Manufacturing      int64\n",
       "sector_Construction       int64\n",
       "sector_Wholesale          int64\n",
       "sector_Health             int64\n",
       "sector_Transportation     int64\n",
       "sector_Education          int64\n",
       "sector_Housing            int64\n",
       "sector_Personal Use       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El objetivo del ejercicio es intentar predecir la cantidad a prestar (*loan_amount*) en función de algunos parámetros que a priori nos resultan interesantes: *lender_count*, *sector_Education*, *sector_Clothing*, *sector_Personal Use*, *sector_Retail*, *sector_Transportation*, y *sector_Agriculture*. Comenzamos haciendo una regresión lineal ordinaria sin regularización:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            loan_amount   R-squared:                       0.815\n",
      "Model:                            OLS   Adj. R-squared:                  0.815\n",
      "Method:                 Least Squares   F-statistic:                     3031.\n",
      "Date:                Fri, 14 Feb 2020   Prob (F-statistic):               0.00\n",
      "Time:                        14:54:21   Log-Likelihood:                -40379.\n",
      "No. Observations:                4815   AIC:                         8.077e+04\n",
      "Df Residuals:                    4807   BIC:                         8.083e+04\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "lender_count             31.1105      0.215    144.506      0.000      30.688      31.533\n",
      "sector_Education       -551.4964     77.258     -7.138      0.000    -702.957    -400.035\n",
      "sector_Clothing         100.8504     57.152      1.765      0.078     -11.193     212.893\n",
      "sector_Personal Use    -520.0337     76.627     -6.787      0.000    -670.258    -369.809\n",
      "sector_Retail           -55.9741     40.412     -1.385      0.166    -135.201      23.252\n",
      "sector_Transportation  -211.1112    138.110     -1.529      0.126    -481.871      59.648\n",
      "sector_Agriculture     -251.0753     43.238     -5.807      0.000    -335.842    -166.308\n",
      "cnst                    487.5247     25.064     19.452      0.000     438.389     536.661\n",
      "==============================================================================\n",
      "Omnibus:                     3731.810   Durbin-Watson:                   2.006\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            68788.669\n",
      "Skew:                           3.673   Prob(JB):                         0.00\n",
      "Kurtosis:                      19.998   Cond. No.                         720.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n",
    "# Define the dependent variable\n",
    "y = df['loan_amount']\n",
    "# Define the independent variables\n",
    "X = df[['lender_count', 'sector_Education', 'sector_Clothing', \n",
    "        'sector_Personal Use', 'sector_Retail', 'sector_Transportation', 'sector_Agriculture']]\n",
    "# Add an intercept term to the independent variables\n",
    "X['cnst'] = 1\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "model1 = sm.OLS(endog = y_train,exog = X_train).fit()\n",
    "print(model1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora repetimos el proceso usando regresión regularizada *lasso* con distintos valores de $\\alpha$ oscilando entre 0.001 y 0.999:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = np.arange(0.001, 0.502, 0.002)\n",
    "lasso_coefs = []\n",
    "X_train_lasso= X_train[X_train.columns.tolist()] # Select columns / features for model\n",
    "\n",
    "for a in alphas:\n",
    "    lassoreg = Lasso(alpha=a, copy_X=True, normalize=True)\n",
    "    lassoreg.fit(X_train_lasso, y_train)\n",
    "    lasso_coefs.append(lassoreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso_coefs = np.asarray(lasso_coefs).T\n",
    "\n",
    "plt.figure(figsize=(14,10))\n",
    "for coefs, feature in zip(lasso_coefs, X_train_lasso.columns):\n",
    "    plt.plot(alphas, coefs, label = feature)\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que *Retail* y *Transportation* tienden a 0 para valores de $\\alpha$ mayores de 0.3. Vamos a eliminar estas variables del modelo de regresión lineal ordinaria:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            loan_amount   R-squared:                       0.815\n",
      "Model:                            OLS   Adj. R-squared:                  0.815\n",
      "Method:                 Least Squares   F-statistic:                     4241.\n",
      "Date:                Fri, 14 Feb 2020   Prob (F-statistic):               0.00\n",
      "Time:                        14:54:23   Log-Likelihood:                -40381.\n",
      "No. Observations:                4815   AIC:                         8.077e+04\n",
      "Df Residuals:                    4809   BIC:                         8.081e+04\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "lender_count           31.1189      0.215    144.571      0.000      30.697      31.541\n",
      "sector_Education     -528.2291     75.939     -6.956      0.000    -677.103    -379.355\n",
      "sector_Clothing       124.0076     55.350      2.240      0.025      15.497     232.518\n",
      "sector_Personal Use  -496.7328     75.296     -6.597      0.000    -644.347    -349.119\n",
      "sector_Agriculture   -227.8834     40.819     -5.583      0.000    -307.907    -147.860\n",
      "cnst                  464.0338     20.567     22.562      0.000     423.714     504.354\n",
      "==============================================================================\n",
      "Omnibus:                     3739.232   Durbin-Watson:                   2.007\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            69185.801\n",
      "Skew:                           3.682   Prob(JB):                         0.00\n",
      "Kurtosis:                      20.048   Cond. No.                         412.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n",
    "# Define the dependent variable\n",
    "y = df['loan_amount']\n",
    "# Define the independent variables\n",
    "X = df[['lender_count', 'sector_Education', 'sector_Clothing', \n",
    "        'sector_Personal Use', 'sector_Agriculture']]\n",
    "# Add an intercept term to the independent variables\n",
    "X['cnst'] = 1\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "model2 = sm.OLS(endog = y_train,exog = X_train).fit()\n",
    "print(model2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aunque hemos eliminado dos parámetros el valor de $R^2$ sigue siendo el mismo, lo que indica que las variables eliminadas no aportaban ninguna información a la predicción. Este es un buen ejemplo de cómo usar la regularización como herramienta de selección de características (*feature selection*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algunos detalles importantes sobre la regularización\n",
    "\n",
    "Hay que tener en cuenta que en la regresión lineal los coeficientes son insensibles a la escala: si se multiplica el valor de una variable por una constante $c$, el valor del coeficiente correspondiente en un modelo de regresión lineal se multiplicaría por un factor $1/c$, el resto de parámetros de calidad del modelo permanecerían iguales. Ilustraremos esto con un ejemplo usando un dataset con tres variables: la caja que hace un restaurante determinado en un día, la distancia en metros del restaurante a la universidad mas cercana, y la distancia en kilómetros al hospital mas cercano. Los datos serán inventados, sacados de una distribución normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1234)\n",
    "\n",
    "earnings = np.random.normal(2000, 300, 50)\n",
    "university_distances = np.random.normal(7000,2000,50)\n",
    "hospital_distances = np.random.normal(7,2,50)\n",
    "\n",
    "earnings = [a if a > 0 else -a for a in earnings]\n",
    "university_distances = [a if a > 0 else -a for a in university_distances]\n",
    "hospital_distances = [a if a > 0 else -a for a in hospital_distances]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"earnings\": sorted(earnings), \"university\": sorted(university_distances, reverse=True), \n",
    "                   'hospital' : sorted(hospital_distances, reverse=True)})"
   ]
  },
  {
   "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>earnings</th>\n",
       "      <th>university</th>\n",
       "      <th>hospital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1327.194514</td>\n",
       "      <td>11061.207242</td>\n",
       "      <td>11.731537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1377.506720</td>\n",
       "      <td>11015.685902</td>\n",
       "      <td>10.860494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1393.623554</td>\n",
       "      <td>10091.317609</td>\n",
       "      <td>10.787069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1454.891832</td>\n",
       "      <td>9783.972387</td>\n",
       "      <td>9.726963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1535.928334</td>\n",
       "      <td>9642.211231</td>\n",
       "      <td>9.449149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1642.707292</td>\n",
       "      <td>9067.601465</td>\n",
       "      <td>9.420767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1730.852965</td>\n",
       "      <td>8969.839684</td>\n",
       "      <td>9.353625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1783.823380</td>\n",
       "      <td>8682.017590</td>\n",
       "      <td>8.968376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1803.209197</td>\n",
       "      <td>8633.187853</td>\n",
       "      <td>8.750951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1809.042949</td>\n",
       "      <td>8534.737472</td>\n",
       "      <td>8.632254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      earnings    university   hospital\n",
       "0  1327.194514  11061.207242  11.731537\n",
       "1  1377.506720  11015.685902  10.860494\n",
       "2  1393.623554  10091.317609  10.787069\n",
       "3  1454.891832   9783.972387   9.726963\n",
       "4  1535.928334   9642.211231   9.449149\n",
       "5  1642.707292   9067.601465   9.420767\n",
       "6  1730.852965   8969.839684   9.353625\n",
       "7  1783.823380   8682.017590   8.968376\n",
       "8  1803.209197   8633.187853   8.750951\n",
       "9  1809.042949   8534.737472   8.632254"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Representamos la relación entre la caja (*earnings*) y la distancia a la universidad y al hospital más cercanos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatter plot of distance to nearest university (in meters) vs earnings')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# plot distance to nearest university (in meters) vs. earnings\n",
    "ax = sns.regplot(x='earnings', y='university', data=df, fit_reg=False)\n",
    "ax.set_title('Scatter plot of distance to nearest university (in meters) vs earnings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatter plot of distance to nearest hospital (in kilometers) vs earnings')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# plot distance to nearest hospital (in kilometers) vs. earnings\n",
    "ax = sns.regplot(x='earnings', y='hospital', data=df, fit_reg=False)\n",
    "ax.set_title('Scatter plot of distance to nearest hospital (in kilometers) vs earnings')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora crearemos dos modelo de regresión lineal multivariante: uno sin escalar las variables (dejando una distancia en metros y otra en kilómetros). y otro en el que ambas distancias estén en kilómetros:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               earnings   R-squared:                       0.938\n",
      "Model:                            OLS   Adj. R-squared:                  0.935\n",
      "Method:                 Least Squares   F-statistic:                     353.0\n",
      "Date:                Fri, 14 Feb 2020   Prob (F-statistic):           4.90e-29\n",
      "Time:                        14:54:24   Log-Likelihood:                -284.91\n",
      "No. Observations:                  50   AIC:                             575.8\n",
      "Df Residuals:                      47   BIC:                             581.6\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   3024.1009     40.095     75.423      0.000    2943.440    3104.762\n",
      "university    -0.0643      0.021     -3.046      0.004      -0.107      -0.022\n",
      "hospital     -76.3083     21.703     -3.516      0.001    -119.969     -32.648\n",
      "==============================================================================\n",
      "Omnibus:                       13.332   Durbin-Watson:                   0.197\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               14.480\n",
      "Skew:                          -1.284   Prob(JB):                     0.000717\n",
      "Kurtosis:                       3.595   Cond. No.                     2.84e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.84e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model1 = sm2.ols(formula = 'earnings ~ university + hospital', data = df).fit()\n",
    "print(model1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>earnings</th>\n",
       "      <th>university</th>\n",
       "      <th>hospital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1327.194514</td>\n",
       "      <td>11.061207</td>\n",
       "      <td>11.731537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1377.506720</td>\n",
       "      <td>11.015686</td>\n",
       "      <td>10.860494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1393.623554</td>\n",
       "      <td>10.091318</td>\n",
       "      <td>10.787069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1454.891832</td>\n",
       "      <td>9.783972</td>\n",
       "      <td>9.726963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1535.928334</td>\n",
       "      <td>9.642211</td>\n",
       "      <td>9.449149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1642.707292</td>\n",
       "      <td>9.067601</td>\n",
       "      <td>9.420767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1730.852965</td>\n",
       "      <td>8.969840</td>\n",
       "      <td>9.353625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1783.823380</td>\n",
       "      <td>8.682018</td>\n",
       "      <td>8.968376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1803.209197</td>\n",
       "      <td>8.633188</td>\n",
       "      <td>8.750951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1809.042949</td>\n",
       "      <td>8.534737</td>\n",
       "      <td>8.632254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      earnings  university   hospital\n",
       "0  1327.194514   11.061207  11.731537\n",
       "1  1377.506720   11.015686  10.860494\n",
       "2  1393.623554   10.091318  10.787069\n",
       "3  1454.891832    9.783972   9.726963\n",
       "4  1535.928334    9.642211   9.449149\n",
       "5  1642.707292    9.067601   9.420767\n",
       "6  1730.852965    8.969840   9.353625\n",
       "7  1783.823380    8.682018   8.968376\n",
       "8  1803.209197    8.633188   8.750951\n",
       "9  1809.042949    8.534737   8.632254"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled = df.copy()\n",
    "df_scaled['university'] = df_scaled['university']/1000\n",
    "\n",
    "df_scaled.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               earnings   R-squared:                       0.938\n",
      "Model:                            OLS   Adj. R-squared:                  0.935\n",
      "Method:                 Least Squares   F-statistic:                     353.0\n",
      "Date:                Fri, 14 Feb 2020   Prob (F-statistic):           4.90e-29\n",
      "Time:                        14:54:24   Log-Likelihood:                -284.91\n",
      "No. Observations:                  50   AIC:                             575.8\n",
      "Df Residuals:                      47   BIC:                             581.6\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   3024.1009     40.095     75.423      0.000    2943.440    3104.762\n",
      "university   -64.3473     21.122     -3.046      0.004    -106.839     -21.856\n",
      "hospital     -76.3083     21.703     -3.516      0.001    -119.969     -32.648\n",
      "==============================================================================\n",
      "Omnibus:                       13.332   Durbin-Watson:                   0.197\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               14.480\n",
      "Skew:                          -1.284   Prob(JB):                     0.000717\n",
      "Kurtosis:                       3.595   Cond. No.                         41.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model2 = sm2.ols(formula = 'earnings ~ university + hospital', data = df_scaled).fit()\n",
    "print(model2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que en ambos casos el valor de $R^2$ es 0.938 y el de $R^2$ ajustada es de 0.935. Los coeficientes para la ordenada en el origen, la universidad y el hospital del primer modelo (sin escalar) son 3024.1009, -0.0643, y -76.3083, y para el segundo modelo (escalado a kilómetros) son 3024.1009, -64.3473, y -76.3083, tal y como esperábamos. El resto de parámetros estadísticos del modelo, como los p-valores, no cambian. Vamos a ver qué ocurre si en lugar de regresión lineal ordinaria usamos regresión regularizada *lasso*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regresión *lasso* con datos sin escalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df[['university', 'hospital']]\n",
    "y = df['earnings']\n",
    "\n",
    "alphas = np.arange(0.001, 1, 0.002)\n",
    "lasso_coefs = []\n",
    "X_lasso= X[X.columns.tolist()] # Select columns / features for model\n",
    "\n",
    "for a in alphas:\n",
    "    lassoreg = Lasso(alpha=a, copy_X=True, normalize=True)\n",
    "    lassoreg.fit(X_lasso, y)\n",
    "    lasso_coefs.append(lassoreg.coef_)\n",
    "    \n",
    "lasso_coefs = np.asarray(lasso_coefs).T\n",
    "\n",
    "plt.figure(figsize=(14,10))\n",
    "for coefs, feature in zip(lasso_coefs, X_lasso.columns):\n",
    "    plt.plot(alphas, coefs, label = feature)\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos en el gráfico que los coeficientes para la universidad y el hospital son aproximadamente 0 y -75."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regresión *lasso* con datos escalados a kilómetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_scaled[['university', 'hospital']]\n",
    "y = df_scaled['earnings']\n",
    "\n",
    "alphas = np.arange(0.001, 1, 0.002)\n",
    "lasso_coefs = []\n",
    "X_lasso= X[X.columns.tolist()] # Select columns / features for model\n",
    "\n",
    "for a in alphas:\n",
    "    lassoreg = Lasso(alpha=a, copy_X=True, normalize=True)\n",
    "    lassoreg.fit(X_lasso, y)\n",
    "    lasso_coefs.append(lassoreg.coef_)\n",
    "    \n",
    "lasso_coefs = np.asarray(lasso_coefs).T\n",
    "\n",
    "plt.figure(figsize=(14,10))\n",
    "for coefs, feature in zip(lasso_coefs, X_lasso.columns):\n",
    "    plt.plot(alphas, coefs, label = feature)\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que ahora los coeficientes para universidad y hospital son -64 and -76, respectivamente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hemos comprobado que, efectivamente, el escalado sí que afecta al resultado de la regresión *lasso*. Por ello, al usar técnicas de regularización es conveniente *estandarizar* los predictores, lo que puede hacerse empleando la siguiente fórmula:\n",
    "\n",
    "$ \\tilde{x}_{ij} = \\frac{x_{ij}}{\\sqrt{\\frac{1}{n} \\sum_{i=1}^{n} (x_{ij} - \\bar{x}_{j})^2}} $\n",
    "\n",
    "Vamos a repetir el ejercicio usando los datos sin escalar y estandarizándolos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_standardized = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "university_mean = df_standardized['university'].mean()\n",
    "university_denom = sqrt(sum((df_standardized['university']-university_mean)**2)/len(df_standardized['university']))\n",
    "\n",
    "hospital_mean = df_standardized['hospital'].mean()\n",
    "hospital_denom = sqrt(sum((df_standardized['hospital']-hospital_mean)**2)/len(df_standardized['hospital']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_standardized['university'] = df_standardized['university']/university_denom\n",
    "df_standardized['hospital'] = df_standardized['hospital']/hospital_denom\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>earnings</th>\n",
       "      <th>university</th>\n",
       "      <th>hospital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1327.194514</td>\n",
       "      <td>5.438299</td>\n",
       "      <td>5.926591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1377.506720</td>\n",
       "      <td>5.415918</td>\n",
       "      <td>5.486554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1393.623554</td>\n",
       "      <td>4.961448</td>\n",
       "      <td>5.449461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1454.891832</td>\n",
       "      <td>4.810340</td>\n",
       "      <td>4.913911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1535.928334</td>\n",
       "      <td>4.740642</td>\n",
       "      <td>4.773564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      earnings  university  hospital\n",
       "0  1327.194514    5.438299  5.926591\n",
       "1  1377.506720    5.415918  5.486554\n",
       "2  1393.623554    4.961448  5.449461\n",
       "3  1454.891832    4.810340  4.913911\n",
       "4  1535.928334    4.740642  4.773564"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_standardized.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_standardized[['university', 'hospital']]\n",
    "y = df_standardized['earnings']\n",
    "\n",
    "alphas = np.arange(0.001, 1, 0.002)\n",
    "lasso_coefs = []\n",
    "X_lasso= X[X.columns.tolist()] # Select columns / features for model\n",
    "\n",
    "for a in alphas:\n",
    "    lassoreg = Lasso(alpha=a, copy_X=True, normalize=True)\n",
    "    lassoreg.fit(X_lasso, y)\n",
    "    lasso_coefs.append(lassoreg.coef_)\n",
    "    \n",
    "lasso_coefs = np.asarray(lasso_coefs).T\n",
    "\n",
    "plt.figure(figsize=(14,10))\n",
    "for coefs, feature in zip(lasso_coefs, X_lasso.columns):\n",
    "    plt.plot(alphas, coefs, label = feature)\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al estandarizar los predictores los coeficientes tienen ahora el mismo orden de magnitud. Es muy importante tener esto en cuenta antes de sacar conclusiones de un modelo de regresión regularizado."
   ]
  },
  {
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