2_polinomial_regresion.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Fundamentals\n",
    "## Hesus Garcia Cobos\n",
    "### Lección 2 - Regresión parte 1\n",
    "## Regresión lineal múltiple\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # Para manejar matrices\n",
    "import pandas as pd # Para leer y explorar datos\n",
    "import matplotlib.pyplot as plt # Para dibujar\n",
    "\n",
    "from sklearn.linear_model import LinearRegression # Modelo de regresión lineal\n",
    "from sklearn.preprocessing import StandardScaler # Escalador estandar\n",
    "from sklearn.pipeline import Pipeline # Constructor de Pipelines\n",
    "from sklearn.model_selection import train_test_split # divide y permuta el dataset\n",
    "from sklearn.preprocessing import PolynomialFeatures # Mapeo polinomial\n",
    "\n",
    "\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se lee el archivo de las casas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date   price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000         3       2.25         2570   \n",
       "2  5631500400  20150225T000000  180000         2       1.00          770   \n",
       "3  2487200875  20141209T000000  604000         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/Dr-Carlos-Villasenor/ReconocimientoPatrones/master/Datasets/home_data.csv'\n",
    "df = pd.read_csv(url,parse_dates=[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se eligen las variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21613, 18) (21613, 1)\n"
     ]
    }
   ],
   "source": [
    "#x = np.asanyarray(df.drop(columns=['id','date','price','zipcode']))\n",
    "x = np.asanyarray(df.drop(columns=['id','date','price']))\n",
    "y = np.asanyarray(df[['price']])\n",
    "print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se entrena el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.6997471876839377\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "print('Train: ', model.score(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se crean dos conjuntos de datos, uno para entrenar y otro para probar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into a training set and a test set\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16209, 16209)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train), len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5404, 5404)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_test), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.8315316224669166\n",
      "Test:  0.8153348034452199\n"
     ]
    }
   ],
   "source": [
    "# Definir grado del mapeo polinomico\n",
    "d = 2\n",
    "model = Pipeline([\n",
    "          ('poly_features', PolynomialFeatures(degree=d, include_bias=False)),\n",
    "          ('scaler', StandardScaler()),\n",
    "          ('Regressor', LinearRegression())])\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "print('Train: ', model.score(x_train,y_train))\n",
    "print('Test: ', model.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.6997471876839377\n"
     ]
    }
   ],
   "source": [
    "model = Pipeline([\n",
    "                  ('scaler', StandardScaler()),\n",
    "                  ('Regressor', LinearRegression())])\n",
    "model.fit(x, y)\n",
    "print('Train: ', model.score(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explicación de variables con regresión lineal\n",
    "Se extraen los coheficientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15']\n",
      "[-0.1522137   0.1449964   0.37436456  0.02437329  0.016529    0.23078444\n",
      "  0.18539192  0.07856739  0.51576007  0.34916523  0.12357486 -0.3521743\n",
      "  0.03641543 -0.14259219  0.38216458 -0.13837175  0.06799718 -0.04780646]\n"
     ]
    }
   ],
   "source": [
    "labels = list(df.drop(columns=['id','date','price']).columns)\n",
    "coeff = list(model.named_steps['Regressor'].coef_[0])\n",
    "coeff = coeff / np.linalg.norm(coeff)\n",
    "print(labels)\n",
    "print(coeff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se crea un nuevo dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               importance  positive\n",
      "Features                           \n",
      "yr_built        -0.352174     False\n",
      "bedrooms        -0.152214     False\n",
      "zipcode         -0.142592     False\n",
      "long            -0.138372     False\n",
      "sqft_lot15      -0.047806     False\n",
      "floors           0.016529      True\n",
      "sqft_lot         0.024373      True\n",
      "yr_renovated     0.036415      True\n",
      "sqft_living15    0.067997      True\n",
      "condition        0.078567      True\n",
      "sqft_basement    0.123575      True\n",
      "bathrooms        0.144996      True\n",
      "view             0.185392      True\n",
      "waterfront       0.230784      True\n",
      "sqft_above       0.349165      True\n",
      "sqft_living      0.374365      True\n",
      "lat              0.382165      True\n",
      "grade            0.515760      True\n"
     ]
    }
   ],
   "source": [
    "features = pd.DataFrame()\n",
    "features['Features'] = labels\n",
    "features['importance'] = coeff\n",
    "features.sort_values(by=['importance'], ascending=True, inplace=True)\n",
    "features['positive'] = features['importance'] > 0\n",
    "features.set_index('Features', inplace=True)\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se dibujan las variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Variable importance')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.importance.plot(kind='barh')\n",
    "plt.xlabel('Importance')\n",
    "plt.title('Variable importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21613, 12) (21613, 1)\n"
     ]
    }
   ],
   "source": [
    "#x = np.asanyarray(df.drop(columns=['id','date','price','zipcode']))\n",
    "new_x = np.asanyarray(df.drop(columns=['id','date','price', 'yr_built', 'zipcode', 'sqft_lot15', 'bedrooms', 'long', 'floors']))\n",
    "new_y = np.asanyarray(df[['price']])\n",
    "print(new_x.shape, new_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into a training set and a test set\n",
    "x_train, x_test, y_train, y_test = train_test_split(new_x, new_y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.8421215156796309\n",
      "Test:  0.8071770542673025\n"
     ]
    }
   ],
   "source": [
    "# Definir grado del mapeo polinomico\n",
    "d = 3\n",
    "model = Pipeline([\n",
    "          ('poly_features', PolynomialFeatures(degree=d, include_bias=False)),\n",
    "          ('scaler', StandardScaler()),\n",
    "          ('Regressor', LinearRegression())])\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "\n",
    "print('Train: ', model.score(x_train,y_train))\n",
    "print('Test: ', model.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.7947468847921964\n",
      "Test:  0.7628310917292711\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "d = 3\n",
    "model = Pipeline([\n",
    "  ('poly', PolynomialFeatures(degree=d, include_bias=False)),\n",
    "  ('scaler', StandardScaler()),\n",
    "  ('Regressor', Ridge(alpha=2))])\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "print('Train: ', model.score(x_train, y_train))\n",
    "print('Test: ', model.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.7878908468151274\n",
      "Test:  0.7603124076524121\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "d = 3\n",
    "model = Pipeline([\n",
    "  ('poly', PolynomialFeatures(degree=d, include_bias=False)),\n",
    "  ('scaler', StandardScaler()),\n",
    "  ('Regressor', Lasso(alpha=0.001))])\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "print('Train: ', model.score(x_train, y_train))\n",
    "print('Test: ', model.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regresión polinomoal\n",
    "Se crean unos datos sinteticos oara entender la regresión polinomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-3, 3, 0, 10]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "m = 150\n",
    "x = 6 * np.random.rand(m, 1) - 3\n",
    "y = 0.5 * x**2 + x + 2 + np.random.randn(m, 1)\n",
    "plt.plot(x, y, \"b.\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", rotation=0, fontsize=18)\n",
    "plt.axis([-3, 3, 0, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se aplica una regresión lineal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.8341495167619667\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Definir grado del mapeo polinomico\n",
    "d = 5\n",
    "model = Pipeline([\n",
    "          ('poly_features', PolynomialFeatures(degree=d, include_bias=False)),\n",
    "          ('std_scaler', StandardScaler()),\n",
    "          ('lin_reg', LinearRegression())])\n",
    "model.fit(x,y)\n",
    "\n",
    "# Crear nuevos puntos para dibujar puntos\n",
    "xnew=np.linspace(-3, 3, 100).reshape(100, 1)\n",
    "ynew = model.predict(xnew)\n",
    "plt.plot(xnew,ynew,'-k')\n",
    "\n",
    "# Dibujar datos\n",
    "plt.plot(x, y, \"b.\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", rotation=0, fontsize=18)\n",
    "plt.axis([-3, 3, 0, 10])\n",
    "\n",
    "print('Train: ', model.score(x,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sobreentrenamiento y subentrenamiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(112, 1) (38, 1) (112, 1) (38, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y)\n",
    "print(xtrain.shape, xtest.shape, ytrain.shape, ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.8946125265150975\n",
      "Test:  -5855960023366.4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Definir grado del mapeo polinomico\n",
    "d = 100\n",
    "model = Pipeline([\n",
    "          ('poly_features', PolynomialFeatures(degree=d, include_bias=False)),\n",
    "          ('std_scaler', StandardScaler()),\n",
    "          ('lin_reg', LinearRegression())])\n",
    "model.fit(xtrain, ytrain)\n",
    "\n",
    "# Crear nuevos puntos para dibujar puntos\n",
    "xnew=np.linspace(-3, 3, 100).reshape(100, 1)\n",
    "ynew = model.predict(xnew)\n",
    "plt.plot(xnew,ynew,'-k')\n",
    "\n",
    "# Dibujar datos\n",
    "plt.plot(xtrain, ytrain, \"b.\")\n",
    "plt.plot(xtest, ytest, \"r.\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", rotation=0, fontsize=18)\n",
    "plt.axis([-3, 3, 0, 10])\n",
    "\n",
    "print('Train: ', model.score(xtrain,ytrain))\n",
    "print('Test: ', model.score(xtest, ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularización\n",
    "Se utiliza el método de Ridge, que es una regresión lineal regularizada con L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.8354579771055644\n",
      "Test:  0.8103722024331894\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "d = 20\n",
    "model = Pipeline([\n",
    "  ('poly', PolynomialFeatures(degree=d, include_bias=False)),\n",
    "  ('scaler', StandardScaler()),\n",
    "  ('Regressor', Ridge(alpha=2))])\n",
    "\n",
    "model.fit(xtrain, ytrain)\n",
    "\n",
    "# Crear nuevos puntos para dibujar puntos\n",
    "xnew=np.linspace(-3, 3, 100).reshape(100, 1)\n",
    "ynew = model.predict(xnew)\n",
    "plt.plot(xnew,ynew,'--r')\n",
    "\n",
    "# Dibujar datos\n",
    "plt.plot(xtrain, ytrain, \"b.\")\n",
    "plt.plot(xtest, ytest, \"g.\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", rotation=0, fontsize=18)\n",
    "plt.axis([-3, 3, 0, 10])\n",
    "\n",
    "model.fit(xtrain, ytrain)\n",
    "print('Train: ', model.score(xtrain, ytrain))\n",
    "print('Test: ', model.score(xtest, ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede usar la norma L1 también, en la técnica es conocida como Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aldac\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.001624411113, tolerance: 0.0674359748539385\n",
      "  positive)\n",
      "C:\\Users\\aldac\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.001624411113, tolerance: 0.0674359748539385\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.8383736428639003\n",
      "Test:  0.8148340758714152\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "d = 10\n",
    "model = Pipeline([\n",
    "  ('poly', PolynomialFeatures(degree=d, include_bias=False)),\n",
    "  ('scaler', StandardScaler()),\n",
    "  ('Regressor', Lasso(alpha=0.001))])\n",
    "\n",
    "model.fit(xtrain, ytrain)\n",
    "\n",
    "# Crear nuevos puntos para dibujar puntos\n",
    "xnew=np.linspace(-3, 3, 100).reshape(100, 1)\n",
    "ynew = model.predict(xnew)\n",
    "plt.plot(xnew,ynew,'--r')\n",
    "\n",
    "# Dibujar datos\n",
    "plt.plot(xtrain, ytrain, \"b.\")\n",
    "plt.plot(xtest, ytest, \"g.\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", rotation=0, fontsize=18)\n",
    "plt.axis([-3, 3, 0, 10])\n",
    "\n",
    "model.fit(xtrain, ytrain)\n",
    "print('Train: ', model.score(xtrain, ytrain))\n",
    "print('Test: ', model.score(xtest, ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  }
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