RFE GBReg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>exterior</th>\n",
       "      <th>hasLift</th>\n",
       "      <th>size</th>\n",
       "      <th>floor</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>price</th>\n",
       "      <th>hasParkingSpace</th>\n",
       "      <th>...</th>\n",
       "      <th>Sant Pere - Santa Caterina i la Ribera</th>\n",
       "      <th>Sants</th>\n",
       "      <th>Sants - Badal</th>\n",
       "      <th>Sarrià</th>\n",
       "      <th>Vallcarca i els Penitents</th>\n",
       "      <th>Vallvidrera - El Tibidabo i les Planes</th>\n",
       "      <th>Verdun</th>\n",
       "      <th>Vila de Gràcia</th>\n",
       "      <th>Vilapicina i la Torre Llobeta</th>\n",
       "      <th>Zona Franca - Port</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.157496</td>\n",
       "      <td>41.396466</td>\n",
       "      <td>595.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.216528</td>\n",
       "      <td>41.408302</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.211622</td>\n",
       "      <td>41.406239</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.177979</td>\n",
       "      <td>41.384283</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.165862</td>\n",
       "      <td>41.397875</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   rooms  bathrooms  exterior  hasLift   size  floor  longitude   latitude  \\\n",
       "0    1.0        1.0       0.0      1.0   25.0    1.0   2.157496  41.396466   \n",
       "1    4.0        2.0       1.0      1.0  125.0    6.0   2.216528  41.408302   \n",
       "2    3.0        2.0       1.0      1.0  107.0    6.0   2.211622  41.406239   \n",
       "3    4.0        2.0       1.0      1.0  130.0    1.0   2.177979  41.384283   \n",
       "4    2.0        1.0       0.0      1.0   70.0    2.0   2.165862  41.397875   \n",
       "\n",
       "    price  hasParkingSpace         ...          \\\n",
       "0   595.0              0.0         ...           \n",
       "1  2600.0              1.0         ...           \n",
       "2  2400.0              0.0         ...           \n",
       "3  2000.0              0.0         ...           \n",
       "4  1000.0              0.0         ...           \n",
       "\n",
       "   Sant Pere - Santa Caterina i la Ribera  Sants  Sants - Badal  Sarrià  \\\n",
       "0                                       0      0              0       0   \n",
       "1                                       0      0              0       0   \n",
       "2                                       0      0              0       0   \n",
       "3                                       0      0              0       0   \n",
       "4                                       0      0              0       0   \n",
       "\n",
       "   Vallcarca i els Penitents  Vallvidrera - El Tibidabo i les Planes  Verdun  \\\n",
       "0                          0                                       0       0   \n",
       "1                          0                                       0       0   \n",
       "2                          0                                       0       0   \n",
       "3                          0                                       0       0   \n",
       "4                          0                                       0       0   \n",
       "\n",
       "   Vila de Gràcia  Vilapicina i la Torre Llobeta  Zona Franca - Port  \n",
       "0               0                              0                   0  \n",
       "1               0                              0                   0  \n",
       "2               0                              0                   0  \n",
       "3               0                              0                   0  \n",
       "4               0                              0                   0  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "\n",
    "# Build a classification task using 3 informative features\n",
    "data = pd.read_csv('version4_district+neighborhood.csv')\n",
    "data.head()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd9cf7f0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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B7pd/13cZVe4ABalyByhIlTtAMTwn0J6xioCka4H3Ap8dab4V2F5f3w7cVl/fCDwQEaci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\np48a6QNjhtMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd9d5908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Remove outliers\n",
    "\n",
    "\n",
    "per=np.percentile(np.array(data['price']), [2.5, 89])\n",
    "data=data[data['price']>per[0]]\n",
    "data=data[data['price']<per[1]]\n",
    "\n",
    "data['price'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98\n"
     ]
    }
   ],
   "source": [
    "y=np.array(data['price'])\n",
    "X=np.array(data.drop(['price'], axis=1))\n",
    "n_features=X.shape[1]\n",
    "print n_features\n",
    "Facc=np.zeros(n_features)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=44)\n",
    "scaler = StandardScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test= scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
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      "[ 433.22607927  373.43301355  283.38245727  266.63558113  261.89228528\n",
      "  257.80063228  250.7069364   248.80940789  250.03582482  249.86887008\n",
      "  250.84539337  252.45049761  252.25581439  255.09641259  254.2704897\n",
      "  250.40340073  250.635522    250.75235658  253.25159675  251.23438673\n",
      "  255.02536533  254.03728463  254.24324511  252.59565802  250.75403749\n",
      "  253.44157832  253.24753894  251.98706035  254.50358635  252.81027542\n",
      "  254.1760766   254.62427584  249.59253907  248.63668061  252.26565694\n",
      "  251.58947328  254.0854006   255.36373755  252.01648989  253.05942603\n",
      "  253.19038391  255.3363349   252.90887724  250.14410038  249.33327351\n",
      "  249.28505385  248.99136314  248.45777489  253.09622956  250.57269116\n",
      "  250.70126907  250.26049323  251.63002675  251.66035922  254.67055187\n",
      "  251.6894186   249.57572557  251.20695866  252.49794984  247.79106078\n",
      "  252.87551604  251.17799108  250.85282194  250.36857183  251.25549293\n",
      "  251.68494835  250.39388618  249.68833147  249.64354987  249.04586318\n",
      "  249.16378269  250.06104409  250.05272778  251.1646361   251.51766002\n",
      "  250.51645349  251.30678094  251.2886523   250.70445824  250.81316973\n",
      "  252.18637819  251.88163203  251.59982709  252.22476146  252.50727404\n",
      "  251.8180127   252.66859201  252.28990717  251.67218908  251.99209529\n",
      "  252.61906092  252.54978381  252.19892132  252.08797284  252.33286114\n",
      "  251.89748051  252.4232633   251.59798338]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "cannot concatenate 'str' and 'int' objects",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-61-8434d0094845>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mFacc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Optimal number of features : %d\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mFacc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: cannot concatenate 'str' and 'int' objects"
     ]
    }
   ],
   "source": [
    "# Create the RFE object and compute a cross-validated score.\n",
    "Facc=np.zeros(n_features)\n",
    "# Create the RFE object and compute a cross-validated score.\n",
    "gbr=GradientBoostingRegressor(n_estimators=200, max_depth=5,random_state=44)\n",
    "scaler = StandardScaler()\n",
    "# The \"accuracy\" scoring is proportional to the number of correct\n",
    "# classifications\n",
    "for i in range(n_features):\n",
    "    print i\n",
    "    rfe = RFE(gbr,i+1)\n",
    "    rfe.fit(X_train,y_train)\n",
    "    y_hat=rfe.predict(X_test)\n",
    "    Facc[i]=metrics.mean_absolute_error(y_test, y_hat)\n",
    "   \n",
    "\n",
    "print Facc    \n",
    "print(\"Optimal number of features : %d\" % Facc.argmin()+1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bre4+pcn8w4DfACcCA4CngCObFvD3uqbfYPZs+MY34K234ueY90R9fYTl3LnQs2fcli+H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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc6bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(Facc,'r')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}