eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('data/Train_O4UPEyW.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['ZONEID'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#convert timestamp to datetime\n",
    "train['TIMESTAMP'] = pd.to_datetime(train['TIMESTAMP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "deg_per_rad = 180/math.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#calc wind direction --> atan2(-Ugeo,-Vgeo) * DperR\n",
    "#calculate wind direction and speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['wind_dir_10'] = 270 - (np.arctan2(-train['U10'], -train['V10']) * deg_per_rad)\n",
    "train['wind_dir_100'] = 270 - (np.arctan2(-train['U100'], -train['V100']) * deg_per_rad)\n",
    "train['wind_dir_10'] = [dir - 360 if dir > 180 else dir for dir in train['wind_dir_10']]\n",
    "train['wind_dir_100'] = [dir - 360 if dir > 180 else dir for dir in train['wind_dir_100']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['wind_spd_10'] = np.sqrt(train['U10']**2 + train['V10']**2)\n",
    "train['wind_spd_100'] = np.sqrt(train['U100']**2 + train['V100']**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['U10_sqr'] = train['U10'] **2\n",
    "train['V10_sqr'] = train['V10'] **2\n",
    "train['U100_sqr'] = train['U100'] **2\n",
    "train['V100_sqr'] = train['V100'] **2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create month column to user for getting q1-4 groupings\n",
    "train['month'] = train['TIMESTAMP'].apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['q1'] = [1 if month < 4 else 0 for month in train['month']]\n",
    "train['q2'] = [1 if 3 < month < 7 else 0 for month in train['month']]\n",
    "train['q3'] = [1 if 6 < month < 10 else 0 for month in train['month']]\n",
    "train['q4'] = [1 if 9 < month else 0 for month in train['month']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#creat dummy columns for grouped time of day\n",
    "train['day_q1'] = [1 if time < 6 else 0 for time in train['TIMESTAMP'].apply(lambda x: x.hour)]\n",
    "train['day_q2'] = [1 if 5 < time < 12 else 0 for time in train['TIMESTAMP'].apply(lambda x: x.hour)]\n",
    "train['day_q3'] = [1 if 11 < time < 18 else 0 for time in train['TIMESTAMP'].apply(lambda x: x.hour)]\n",
    "train['day_q4'] = [1 if time > 17 else 0 for time in train['TIMESTAMP'].apply(lambda x: x.hour)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.drop('month', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def feature_eng(df):\n",
    "    deg_per_rad = 180/math.pi\n",
    "    #convert timestamp to datetime\n",
    "    df['TIMESTAMP'] = pd.to_datetime(df['TIMESTAMP'])\n",
    "    \n",
    "    #get geographic wind direction\n",
    "    df['wind_dir_10'] = 270 - (np.arctan2(-df['U10'], -df['V10']) * deg_per_rad)\n",
    "    df['wind_dir_100'] = 270 - (np.arctan2(-df['U100'], -df['V100']) * deg_per_rad)\n",
    "    df['wind_dir_10'] = [dir - 360 if dir > 180 else dir for dir in df['wind_dir_10']]\n",
    "    df['wind_dir_100'] = [dir - 360 if dir > 180 else dir for dir in df['wind_dir_100']]\n",
    "    \n",
    "    #get wind speeds\n",
    "    df['wind_spd_10'] = np.sqrt(df['U10']**2 + df['V10']**2)\n",
    "    df['wind_spd_100'] = np.sqrt(df['U100']**2 + df['V100']**2)\n",
    "    \n",
    "    #create ^2 values\n",
    "    df['U10_sqr'] = df['U10'] **2\n",
    "    df['V10_sqr'] = df['V10'] **2\n",
    "    df['U100_sqr'] = df['U100'] **2\n",
    "    df['V100_sqr'] = df['V100'] **2\n",
    "    \n",
    "    #create month column to user for getting q1-4 groupings\n",
    "    df['month'] = df['TIMESTAMP'].apply(lambda x: x.month)\n",
    "    \n",
    "    df['q1'] = [1 if month < 4 else 0 for month in df['month']]\n",
    "    df['q2'] = [1 if 3 < month < 7 else 0 for month in df['month']]\n",
    "    df['q3'] = [1 if 6 < month < 10 else 0 for month in df['month']]\n",
    "    df['q4'] = [1 if 9 < month else 0 for month in df['month']]\n",
    "    \n",
    "    #creat dummy columns for grouped time of day\n",
    "    df['day_q1'] = [1 if time < 6 else 0 for time in df['TIMESTAMP'].apply(lambda x: x.hour)]\n",
    "    df['day_q2'] = [1 if 5 < time < 12 else 0 for time in df['TIMESTAMP'].apply(lambda x: x.hour)]\n",
    "    df['day_q3'] = [1 if 11 < time < 18 else 0 for time in df['TIMESTAMP'].apply(lambda x: x.hour)]\n",
    "    df['day_q4'] = [1 if time > 17 else 0 for time in df['TIMESTAMP'].apply(lambda x: x.hour)]\n",
    "    \n",
    "    df = df.drop('month', axis = 1)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = feature_eng(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def divide_zones(df):\n",
    "    all_zones = []\n",
    "    for zone in df['ZONEID'].unique():\n",
    "        different_zone = df[df['ZONEID'] == zone]\n",
    "        all_zones.append(different_zone)\n",
    "    return all_zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_zones = divide_zones(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zone_1_targ = my_zones[8]['TARGETVAR']\n",
    "zone_1_X = my_zones[8].drop(['TARGETVAR', 'TIMESTAMP', 'ZONEID', 'ID'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor()\n",
    "gb = GradientBoostingRegressor()\n",
    "lin = Lasso()\n",
    "\n",
    "rf_params = {'n_estimators': [10, 100, 500, 1000], 'n_jobs': [-1], 'max_features':[0.5, 0.6, 0.7, 0.8, 0.9]}\n",
    "gb_params = {'learning_rate': [0.01, 0.1, 0.3, 0.5], 'n_estimators': [50, 100, 250, 500], 'max_depth': [2,3,5,10]}\n",
    "lin_params = {'alpha':[0.001, 0.01, 0.1, 0.25, 0.5, 0.75, 1.0]}\n",
    "\n",
    "regressors = [rf, gb, lin]\n",
    "reg_params = [rf_params, gb_params, lin_params]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_best_regressor(df, regressors, parameters):\n",
    "    score = 100\n",
    "    for i in xrange(len(regressors)):\n",
    "        clf = GridSearchCV(regressors[i], parameters[i], scoring='neg_mean_squared_error', verbose=5, n_jobs=-1)\n",
    "        X = df.drop(['TARGETVAR', 'TIMESTAMP', 'ZONEID', 'ID'], axis=1)\n",
    "        y = df['TARGETVAR']\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)\n",
    "        clf.fit(X_train, y_train)\n",
    "        prediction = clf.predict(X_test)\n",
    "        rmse = np.sqrt(mean_squared_error(y_test, prediction))\n",
    "        print \"root mean squared error: \", rmse\n",
    "        if rmse < score:\n",
    "            score = rmse\n",
    "            estimator = clf.best_estimator_\n",
    "            params = clf.best_params_\n",
    "    \n",
    "    return estimator, params\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n",
      "[CV] max_features=0.5, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV] max_features=0.5, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV] max_features=0.5, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV] max_features=0.5, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=10, n_jobs=-1, score=-0.0286245970411, total=   0.6s\n",
      "[CV] max_features=0.5, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=10, n_jobs=-1, score=-0.0287683521851, total=   0.6s\n",
      "[CV]  max_features=0.5, n_estimators=10, n_jobs=-1, score=-0.0281144851534, total=   0.6s\n",
      "[CV] max_features=0.5, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV] max_features=0.5, n_estimators=1000, n_jobs=-1 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    1.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=0.5, n_estimators=100, n_jobs=-1, score=-0.0259131045218, total=   3.7s\n",
      "[CV] max_features=0.5, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.5, n_estimators=100, n_jobs=-1, score=-0.0263042096476, total=   3.8s\n",
      "[CV] max_features=0.6, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.5, n_estimators=100, n_jobs=-1, score=-0.0256655099503, total=   3.8s\n",
      "[CV] max_features=0.6, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.6, n_estimators=10, n_jobs=-1, score=-0.0282042040898, total=   0.6s\n",
      "[CV]  max_features=0.6, n_estimators=10, n_jobs=-1, score=-0.0290056850247, total=   0.5s\n",
      "[CV] max_features=0.6, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV] max_features=0.6, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=10, n_jobs=-1, score=-0.0278667124929, total=   0.8s\n",
      "[CV] max_features=0.6, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=100, n_jobs=-1, score=-0.0261364174333, total=   6.5s\n",
      "[CV] max_features=0.6, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=100, n_jobs=-1, score=-0.0263806068597, total=   7.4s\n",
      "[CV] max_features=0.6, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=100, n_jobs=-1, score=-0.0256807888672, total=   7.6s\n",
      "[CV] max_features=0.6, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=500, n_jobs=-1, score=-0.0258464881009, total=  28.7s\n",
      "[CV] max_features=0.6, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=500, n_jobs=-1, score=-0.0260208761974, total=  29.0s\n",
      "[CV] max_features=0.6, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.5, n_estimators=500, n_jobs=-1, score=-0.0255670086813, total=  30.0s\n",
      "[CV] max_features=0.6, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=500, n_jobs=-1, score=-0.0257717124969, total=  43.5s\n",
      "[CV] max_features=0.6, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=500, n_jobs=-1, score=-0.0259201091994, total=  45.9s\n",
      "[CV] max_features=0.7, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=10, n_jobs=-1, score=-0.0281328314675, total=   1.7s\n",
      "[CV] max_features=0.7, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=10, n_jobs=-1, score=-0.0280032612596, total=   1.5s\n",
      "[CV] max_features=0.7, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=10, n_jobs=-1, score=-0.0280150731131, total=   1.6s\n",
      "[CV] max_features=0.7, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=1000, n_jobs=-1, score=-0.0258148719174, total= 1.2min\n",
      "[CV] max_features=0.7, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=1000, n_jobs=-1, score=-0.0259854725983, total= 1.2min\n",
      "[CV] max_features=0.7, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=500, n_jobs=-1, score=-0.0255235086991, total=  45.4s\n",
      "[CV] max_features=0.7, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=1000, n_jobs=-1, score=-0.0255051427608, total= 1.2min\n",
      "[CV] max_features=0.7, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.7, n_estimators=100, n_jobs=-1, score=-0.0259687823133, total=   9.1s\n",
      "[CV] max_features=0.7, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.7, n_estimators=100, n_jobs=-1, score=-0.0262635384036, total=   9.1s\n",
      "[CV] max_features=0.7, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.7, n_estimators=100, n_jobs=-1, score=-0.0258936095617, total=   9.1s\n",
      "[CV] max_features=0.7, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=1000, n_jobs=-1, score=-0.0257940110848, total= 1.5min\n",
      "[CV] max_features=0.7, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=1000, n_jobs=-1, score=-0.0259446127819, total= 1.5min\n",
      "[CV] max_features=0.8, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=500, n_jobs=-1, score=-0.0259356268795, total=  48.3s\n",
      "[CV] max_features=0.8, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.8, n_estimators=10, n_jobs=-1, score=-0.0278793415789, total=   1.7s\n",
      "[CV] max_features=0.8, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.8, n_estimators=10, n_jobs=-1, score=-0.0283679649585, total=   1.8s\n",
      "[CV] max_features=0.8, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=10, n_jobs=-1, score=-0.0280077001914, total=   1.5s\n",
      "[CV] max_features=0.8, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.7, n_estimators=500, n_jobs=-1, score=-0.0259826566336, total=  48.8s\n",
      "[CV] max_features=0.8, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.7, n_estimators=500, n_jobs=-1, score=-0.0255696329018, total=  48.0s\n",
      "[CV] max_features=0.8, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=100, n_jobs=-1, score=-0.0260339625506, total=   9.6s\n",
      "[CV] max_features=0.8, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=100, n_jobs=-1, score=-0.0260858469775, total=   9.9s\n",
      "[CV] max_features=0.8, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=100, n_jobs=-1, score=-0.0258858848717, total=   9.8s\n",
      "[CV] max_features=0.8, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=1000, n_jobs=-1, score=-0.0255102896555, total= 1.4min\n",
      "[CV] max_features=0.8, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.7, n_estimators=1000, n_jobs=-1, score=-0.0258713955541, total= 1.6min\n",
      "[CV] max_features=0.8, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.7, n_estimators=1000, n_jobs=-1, score=-0.0259469888858, total= 1.6min\n",
      "[CV] max_features=0.9, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.9, n_estimators=10, n_jobs=-1, score=-0.0280233692255, total=   1.9s\n",
      "[CV] max_features=0.9, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.8, n_estimators=500, n_jobs=-1, score=-0.0259331172481, total=  53.0s\n",
      "[CV] max_features=0.9, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.9, n_estimators=10, n_jobs=-1, score=-0.0282729916686, total=   1.7s\n",
      "[CV] max_features=0.9, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.9, n_estimators=10, n_jobs=-1, score=-0.0283776951202, total=   1.7s\n",
      "[CV] max_features=0.9, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=500, n_jobs=-1, score=-0.0259466228666, total=  51.8s\n",
      "[CV] max_features=0.9, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=500, n_jobs=-1, score=-0.0256883530061, total=  51.8s\n",
      "[CV] max_features=0.9, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.9, n_estimators=100, n_jobs=-1, score=-0.026188251095, total=  11.2s\n",
      "[CV] max_features=0.9, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.9, n_estimators=100, n_jobs=-1, score=-0.0262249482318, total=  11.5s\n",
      "[CV] max_features=0.9, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.9, n_estimators=100, n_jobs=-1, score=-0.0260130994761, total=  10.5s\n",
      "[CV] max_features=0.9, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.7, n_estimators=1000, n_jobs=-1, score=-0.0255952390896, total= 1.5min\n",
      "[CV] max_features=0.9, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.8, n_estimators=1000, n_jobs=-1, score=-0.0259316521231, total= 1.7min\n",
      "[CV] max_features=0.9, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.9, n_estimators=500, n_jobs=-1, score=-0.0259429713795, total=  59.1s\n",
      "[CV]  max_features=0.8, n_estimators=1000, n_jobs=-1, score=-0.0258895029939, total= 1.8min\n",
      "[CV]  max_features=0.9, n_estimators=500, n_jobs=-1, score=-0.0260358275792, total=  59.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=0.9, n_estimators=500, n_jobs=-1, score=-0.025778925224, total=  59.1s\n",
      "[CV]  max_features=0.8, n_estimators=1000, n_jobs=-1, score=-0.0256101804977, total= 1.6min\n",
      "[CV]  max_features=0.9, n_estimators=1000, n_jobs=-1, score=-0.0258685159125, total= 1.5min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  58 out of  60 | elapsed:  4.9min remaining:   10.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=0.9, n_estimators=1000, n_jobs=-1, score=-0.0259644293512, total= 1.4min\n",
      "[CV]  max_features=0.9, n_estimators=1000, n_jobs=-1, score=-0.0256798051132, total=  55.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:  5.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root mean squared error:  0.158594017514\n",
      "Fitting 3 folds for each of 64 candidates, totalling 192 fits\n",
      "[CV] n_estimators=50, learning_rate=0.01, max_depth=2 ................\n",
      "[CV] n_estimators=50, learning_rate=0.01, max_depth=2 ................\n",
      "[CV] n_estimators=50, learning_rate=0.01, max_depth=2 ................\n",
      "[CV] n_estimators=100, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV] n_estimators=100, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV] n_estimators=100, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV] n_estimators=250, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV] n_estimators=250, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV]  n_estimators=50, learning_rate=0.01, max_depth=2, score=-0.0499228992129, total=   0.7s\n",
      "[CV]  n_estimators=50, learning_rate=0.01, max_depth=2, score=-0.0516498786253, total=   0.7s\n",
      "[CV]  n_estimators=50, learning_rate=0.01, max_depth=2, score=-0.0518274509199, total=   0.7s\n",
      "[CV] n_estimators=250, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV] n_estimators=500, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV] n_estimators=500, learning_rate=0.01, max_depth=2 ...............\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.8s\n"
     ]
    },
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     "output_type": "stream",
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      "[CV]  n_estimators=100, learning_rate=0.01, max_depth=2, score=-0.039218825106, total=   1.3s\n",
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      "[CV] n_estimators=500, learning_rate=0.01, max_depth=2 ...............\n",
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      "[CV] n_estimators=250, learning_rate=0.01, max_depth=3 ...............\n",
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      "[CV]  n_estimators=100, learning_rate=0.01, max_depth=3, score=-0.0384815019507, total=   2.3s\n",
      "[CV] n_estimators=250, learning_rate=0.01, max_depth=3 ...............\n",
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      "[CV] n_estimators=500, learning_rate=0.01, max_depth=3 ...............\n",
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      "[CV] n_estimators=500, learning_rate=0.01, max_depth=5 ...............\n",
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      "[CV] n_estimators=50, learning_rate=0.01, max_depth=10 ...............\n",
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      "[CV] n_estimators=100, learning_rate=0.01, max_depth=10 ..............\n",
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      "[CV] n_estimators=50, learning_rate=0.1, max_depth=2 .................\n",
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      "[CV] n_estimators=100, learning_rate=0.1, max_depth=2 ................\n",
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      "[CV] n_estimators=100, learning_rate=0.1, max_depth=2 ................\n",
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      "[CV] n_estimators=250, learning_rate=0.1, max_depth=2 ................\n",
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      "[CV] n_estimators=250, learning_rate=0.1, max_depth=2 ................\n",
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      "[CV] n_estimators=500, learning_rate=0.1, max_depth=2 ................\n",
      "[CV]  n_estimators=250, learning_rate=0.1, max_depth=2, score=-0.0285859091019, total=   3.0s\n",
      "[CV] n_estimators=500, learning_rate=0.1, max_depth=2 ................\n",
      "[CV]  n_estimators=500, learning_rate=0.1, max_depth=2, score=-0.0287973653943, total=   6.0s\n",
      "[CV] n_estimators=500, learning_rate=0.1, max_depth=2 ................\n",
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     ]
    },
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     "output_type": "stream",
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      "[CV] n_estimators=50, learning_rate=0.1, max_depth=3 .................\n",
      "[CV]  n_estimators=50, learning_rate=0.1, max_depth=3, score=-0.029519392551, total=   1.0s\n",
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      "[CV] n_estimators=100, learning_rate=0.1, max_depth=3 ................\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:  1.1min\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[CV]  n_estimators=500, learning_rate=0.1, max_depth=2, score=-0.0283175004903, total=   6.1s\n",
      "[CV] n_estimators=100, learning_rate=0.1, max_depth=3 ................\n",
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      "[CV] n_estimators=250, learning_rate=0.1, max_depth=3 ................\n",
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      "[CV] n_estimators=500, learning_rate=0.1, max_depth=3 ................\n",
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      "[CV] n_estimators=100, learning_rate=0.1, max_depth=5 ................\n",
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      "[CV] n_estimators=100, learning_rate=0.1, max_depth=5 ................\n",
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      "[CV] n_estimators=250, learning_rate=0.1, max_depth=5 ................\n",
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      "[CV] n_estimators=500, learning_rate=0.1, max_depth=5 ................\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=250, learning_rate=0.1, max_depth=10, score=-0.0276527906815, total=  29.0s\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  3.2min\n"
     ]
    },
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      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=2, score=-0.0294082301446, total=   3.4s\n",
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      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=5, score=-0.0337850993739, total=   9.0s\n",
      "[CV] n_estimators=50, learning_rate=0.5, max_depth=10 ................\n",
      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=5, score=-0.0336579815786, total=   9.0s\n",
      "[CV] n_estimators=50, learning_rate=0.5, max_depth=10 ................\n",
      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=5, score=-0.0325383207164, total=   8.9s\n",
      "[CV] n_estimators=50, learning_rate=0.5, max_depth=10 ................\n",
      "[CV]  n_estimators=500, learning_rate=0.3, max_depth=10, score=-0.0297153568507, total=  47.0s\n",
      "[CV] n_estimators=100, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=500, learning_rate=0.3, max_depth=10, score=-0.0301952521682, total=  46.6s\n",
      "[CV] n_estimators=100, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=50, learning_rate=0.5, max_depth=10, score=-0.033961057555, total=   5.7s\n",
      "[CV] n_estimators=100, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=50, learning_rate=0.5, max_depth=10, score=-0.0335734484812, total=   5.8s\n",
      "[CV] n_estimators=250, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=50, learning_rate=0.5, max_depth=10, score=-0.0326776534496, total=   5.9s\n",
      "[CV] n_estimators=250, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=5, score=-0.0345597090139, total=  18.7s\n",
      "[CV] n_estimators=250, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=5, score=-0.033950797174, total=  18.7s\n",
      "[CV] n_estimators=500, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=100, learning_rate=0.5, max_depth=10, score=-0.0338717739336, total=  10.8s\n",
      "[CV] n_estimators=500, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=100, learning_rate=0.5, max_depth=10, score=-0.0335381304644, total=  10.7s\n",
      "[CV] n_estimators=500, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=100, learning_rate=0.5, max_depth=10, score=-0.0330099375783, total=  10.8s\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=5, score=-0.0332470716544, total=  19.0s\n",
      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=10, score=-0.0338836076921, total=  21.5s\n",
      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=10, score=-0.0339055070645, total=  21.2s\n",
      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=10, score=-0.0335867169136, total=  18.6s\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=10, score=-0.0336853669461, total=  19.8s\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=10, score=-0.033479648284, total=  18.8s\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=10, score=-0.0331009258515, total=  18.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 192 out of 192 | elapsed:  4.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root mean squared error:  0.158968135818\n",
      "Fitting 3 folds for each of 7 candidates, totalling 21 fits\n",
      "[CV] alpha=0.001 .....................................................\n",
      "[CV] alpha=0.001 .....................................................\n",
      "[CV] alpha=0.001 .....................................................\n",
      "[CV] alpha=0.01 ......................................................\n",
      "[CV] alpha=0.01 ......................................................\n",
      "[CV] alpha=0.01 ......................................................\n",
      "[CV] alpha=0.1 .......................................................\n",
      "[CV] alpha=0.1 .......................................................\n",
      "[CV] ................ alpha=0.1, score=-0.0368869619119, total=   0.0s\n",
      "[CV] ............... alpha=0.01, score=-0.0359626040919, total=   0.1s\n",
      "[CV] alpha=0.1 .......................................................\n",
      "[CV] ................ alpha=0.1, score=-0.0342573134627, total=   0.0s\n",
      "[CV] alpha=0.25 ......................................................\n",
      "[CV] alpha=0.25 ......................................................\n",
      "[CV] ............... alpha=0.25, score=-0.0367575338078, total=   0.0s\n",
      "[CV] ................ alpha=0.1, score=-0.0342569980022, total=   0.0s\n",
      "[CV] alpha=0.25 ......................................................\n",
      "[CV] alpha=0.5 .......................................................\n",
      "[CV] ............... alpha=0.25, score=-0.0345336452438, total=   0.0s\n",
      "[CV] alpha=0.5 .......................................................\n",
      "[CV] ............... alpha=0.25, score=-0.0346255912726, total=   0.0s\n",
      "[CV] ............... alpha=0.01, score=-0.0332398110688, total=   0.1s\n",
      "[CV] alpha=0.5 .......................................................\n",
      "[CV] alpha=0.75 ......................................................\n",
      "[CV] ................ alpha=0.5, score=-0.0369650335767, total=   0.0s\n",
      "[CV] ................ alpha=0.5, score=-0.0353242670039, total=   0.0s\n",
      "[CV] alpha=0.75 ......................................................\n",
      "[CV] ................ alpha=0.01, score=-0.033231043588, total=   0.2s\n",
      "[CV] ................ alpha=0.5, score=-0.0355525114164, total=   0.0s\n",
      "[CV] ............... alpha=0.75, score=-0.0377015614588, total=   0.0s\n",
      "[CV] alpha=0.75 ......................................................\n",
      "[CV] ............... alpha=0.75, score=-0.0365275214812, total=   0.0s\n",
      "[CV] alpha=1.0 .......................................................\n",
      "[CV] ............... alpha=0.75, score=-0.0368702046715, total=   0.0s\n",
      "[CV] alpha=1.0 .......................................................\n",
      "[CV] alpha=1.0 .......................................................\n",
      "[CV] ................. alpha=1.0, score=-0.038967116199, total=   0.0s\n",
      "[CV] ................ alpha=1.0, score=-0.0381433859845, total=   0.0s\n",
      "[CV] ................ alpha=1.0, score=-0.0385787054539, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  11 out of  21 | elapsed:    0.3s remaining:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  21 | elapsed:    0.4s remaining:    0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .............. alpha=0.001, score=-0.0339429412264, total=   0.4s\n",
      "[CV] .............. alpha=0.001, score=-0.0323954729887, total=   0.4s\n",
      "[CV] .............. alpha=0.001, score=-0.0322657008496, total=   0.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root mean squared error:  0.179128541005\n"
     ]
    }
   ],
   "source": [
    "estimator, params = find_best_regressor(my_zones[0], regressors, reg_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features=0.6, max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 0.6, 'n_estimators': 500, 'n_jobs': -1}"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = pd.read_csv('data/Test_uP7dymh.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def regressor_per_zone(all_zones):\n",
    "    rf = RandomForestRegressor()\n",
    "    gb = GradientBoostingRegressor()\n",
    "    lin = Lasso()\n",
    "\n",
    "    rf_params = {'n_estimators': [10, 100, 500, 1000], 'n_jobs': [-1], 'max_features':[0.5, 0.6, 0.7, 0.8, 0.9]}\n",
    "    gb_params = {'learning_rate': [0.01, 0.1, 0.3, 0.5], 'n_estimators': [50, 100, 250, 500], 'max_depth': [2,3,5,10]}\n",
    "    lin_params = {'alpha':[0.001, 0.01, 0.1, 0.25, 0.5, 0.75, 1.0]}\n",
    "\n",
    "    regressors = [rf, gb, lin]\n",
    "    reg_params = [rf_params, gb_params, lin_params]\n",
    "    \n",
    "    zone_estimators = []\n",
    "    zone_params = []\n",
    "    for i in xrange(len(all_zones)):\n",
    "        estimator, params = find_best_regressor(all_zones[i], regressors, reg_params)\n",
    "        zone_estimators.append(estimator)\n",
    "        zone_params.append(params)\n",
    "    \n",
    "    return zone_estimators, zone_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n",
      "[CV] max_features=0.5, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV] max_features=0.5, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV] max_features=0.5, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV] max_features=0.5, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=10, n_jobs=-1, score=-0.0267776075562, total=   0.5s\n",
      "[CV] max_features=0.5, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=10, n_jobs=-1, score=-0.0280363606483, total=   0.6s\n",
      "[CV]  max_features=0.5, n_estimators=10, n_jobs=-1, score=-0.0265479671051, total=   0.6s\n",
      "[CV] max_features=0.5, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV] max_features=0.5, n_estimators=1000, n_jobs=-1 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    1.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=0.5, n_estimators=100, n_jobs=-1, score=-0.0242316916773, total=   3.3s\n",
      "[CV] max_features=0.5, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.5, n_estimators=100, n_jobs=-1, score=-0.0246127383246, total=   3.4s\n",
      "[CV] max_features=0.6, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.5, n_estimators=100, n_jobs=-1, score=-0.0256108604969, total=   3.4s\n",
      "[CV] max_features=0.6, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.6, n_estimators=10, n_jobs=-1, score=-0.0271076032143, total=   0.5s\n",
      "[CV] max_features=0.6, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.6, n_estimators=10, n_jobs=-1, score=-0.0278137020459, total=   0.5s\n",
      "[CV] max_features=0.6, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=10, n_jobs=-1, score=-0.0263635217565, total=   0.8s\n",
      "[CV] max_features=0.6, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=100, n_jobs=-1, score=-0.0248170709945, total=   5.7s\n",
      "[CV] max_features=0.6, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=100, n_jobs=-1, score=-0.0257432616644, total=   6.3s\n",
      "[CV] max_features=0.6, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=100, n_jobs=-1, score=-0.0243147484849, total=   6.5s\n",
      "[CV] max_features=0.6, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=500, n_jobs=-1, score=-0.0254891029303, total=  23.8s\n",
      "[CV] max_features=0.6, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=500, n_jobs=-1, score=-0.0244705996729, total=  25.1s\n",
      "[CV] max_features=0.6, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.5, n_estimators=500, n_jobs=-1, score=-0.0239725524781, total=  26.7s\n",
      "[CV] max_features=0.6, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=500, n_jobs=-1, score=-0.0245415736562, total=  40.8s\n",
      "[CV] max_features=0.6, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=500, n_jobs=-1, score=-0.0253818337211, total=  43.6s\n",
      "[CV] max_features=0.7, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=10, n_jobs=-1, score=-0.0272444271914, total=   1.7s\n",
      "[CV] max_features=0.7, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=10, n_jobs=-1, score=-0.0272485244429, total=   1.6s\n",
      "[CV] max_features=0.7, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=10, n_jobs=-1, score=-0.0272366950005, total=   2.7s\n",
      "[CV] max_features=0.7, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=1000, n_jobs=-1, score=-0.0245000592179, total= 1.1min\n",
      "[CV]  max_features=0.5, n_estimators=1000, n_jobs=-1, score=-0.0254581408065, total= 1.1min\n",
      "[CV] max_features=0.7, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.7, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=500, n_jobs=-1, score=-0.0239683671872, total=  44.8s\n",
      "[CV] max_features=0.7, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=1000, n_jobs=-1, score=-0.0239585277723, total= 1.2min\n",
      "[CV] max_features=0.7, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.7, n_estimators=100, n_jobs=-1, score=-0.0248043081327, total=   8.8s\n",
      "[CV] max_features=0.7, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.7, n_estimators=100, n_jobs=-1, score=-0.0258349070227, total=   8.9s\n",
      "[CV] max_features=0.7, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.7, n_estimators=100, n_jobs=-1, score=-0.024129675326, total=   8.7s\n",
      "[CV] max_features=0.7, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=1000, n_jobs=-1, score=-0.0244368272382, total= 1.4min\n",
      "[CV] max_features=0.7, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=1000, n_jobs=-1, score=-0.0253957307416, total= 1.5min\n",
      "[CV] max_features=0.8, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=500, n_jobs=-1, score=-0.024519502471, total=  47.2s\n",
      "[CV] max_features=0.8, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.8, n_estimators=10, n_jobs=-1, score=-0.0273976588687, total=   1.8s\n",
      "[CV] max_features=0.8, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.8, n_estimators=10, n_jobs=-1, score=-0.0280743369737, total=   1.8s\n",
      "[CV] max_features=0.8, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=10, n_jobs=-1, score=-0.0265207736162, total=   1.6s\n",
      "[CV] max_features=0.8, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.7, n_estimators=500, n_jobs=-1, score=-0.0254532251087, total=  47.7s\n",
      "[CV] max_features=0.8, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.7, n_estimators=500, n_jobs=-1, score=-0.0239535059762, total=  47.8s\n",
      "[CV] max_features=0.8, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=100, n_jobs=-1, score=-0.0249223116661, total=  10.5s\n",
      "[CV] max_features=0.8, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=100, n_jobs=-1, score=-0.0256734982401, total=  10.4s\n",
      "[CV] max_features=0.8, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=100, n_jobs=-1, score=-0.0242466626886, total=  10.1s\n",
      "[CV] max_features=0.8, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=1000, n_jobs=-1, score=-0.0239253596056, total= 1.4min\n",
      "[CV] max_features=0.8, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.7, n_estimators=1000, n_jobs=-1, score=-0.0245516462671, total= 1.6min\n",
      "[CV] max_features=0.8, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.7, n_estimators=1000, n_jobs=-1, score=-0.0254283737734, total= 1.6min\n",
      "[CV] max_features=0.9, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.8, n_estimators=500, n_jobs=-1, score=-0.0246342369609, total=  53.0s\n",
      "[CV] max_features=0.9, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.9, n_estimators=10, n_jobs=-1, score=-0.0273278652533, total=   3.2s\n",
      "[CV] max_features=0.9, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.9, n_estimators=10, n_jobs=-1, score=-0.0275831306498, total=   1.8s\n",
      "[CV] max_features=0.9, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.9, n_estimators=10, n_jobs=-1, score=-0.0270472095944, total=   1.8s\n",
      "[CV] max_features=0.9, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=500, n_jobs=-1, score=-0.0255921661456, total=  53.0s\n",
      "[CV] max_features=0.9, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=500, n_jobs=-1, score=-0.0240793775574, total=  54.1s\n",
      "[CV] max_features=0.9, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.9, n_estimators=100, n_jobs=-1, score=-0.0246832761581, total=  11.2s\n",
      "[CV] max_features=0.9, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.9, n_estimators=100, n_jobs=-1, score=-0.0257076842339, total=  11.4s\n",
      "[CV] max_features=0.9, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.9, n_estimators=100, n_jobs=-1, score=-0.0241785142722, total=  11.0s\n",
      "[CV] max_features=0.9, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.7, n_estimators=1000, n_jobs=-1, score=-0.0239974362529, total= 1.6min\n",
      "[CV] max_features=0.9, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.8, n_estimators=1000, n_jobs=-1, score=-0.0245514933281, total= 1.8min\n",
      "[CV] max_features=0.9, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.9, n_estimators=500, n_jobs=-1, score=-0.0245321588017, total=  59.2s\n",
      "[CV]  max_features=0.8, n_estimators=1000, n_jobs=-1, score=-0.0255090554571, total= 1.9min\n",
      "[CV]  max_features=0.9, n_estimators=500, n_jobs=-1, score=-0.025607488671, total=  59.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=0.9, n_estimators=500, n_jobs=-1, score=-0.024077420084, total=  59.2s\n",
      "[CV]  max_features=0.8, n_estimators=1000, n_jobs=-1, score=-0.0239720342883, total= 1.6min\n",
      "[CV]  max_features=0.9, n_estimators=1000, n_jobs=-1, score=-0.0246215017191, total= 1.5min\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  58 out of  60 | elapsed:  4.8min remaining:    9.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=0.9, n_estimators=1000, n_jobs=-1, score=-0.0255289020687, total= 1.4min\n",
      "[CV]  max_features=0.9, n_estimators=1000, n_jobs=-1, score=-0.0240036126779, total=  53.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:  5.0min finished\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "root mean squared error:  0.154135287712\n",
      "Fitting 3 folds for each of 64 candidates, totalling 192 fits\n",
      "[CV] n_estimators=50, learning_rate=0.01, max_depth=2 ................\n",
      "[CV] n_estimators=50, learning_rate=0.01, max_depth=2 ................\n",
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      "[CV] n_estimators=100, learning_rate=0.01, max_depth=2 ...............\n",
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      "[CV] n_estimators=100, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV] n_estimators=250, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV] n_estimators=250, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV]  n_estimators=50, learning_rate=0.01, max_depth=2, score=-0.0492918448018, total=   0.7s\n",
      "[CV] n_estimators=250, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV]  n_estimators=50, learning_rate=0.01, max_depth=2, score=-0.052373386321, total=   0.7s\n",
      "[CV] n_estimators=500, learning_rate=0.01, max_depth=2 ...............\n",
      "[CV]  n_estimators=50, learning_rate=0.01, max_depth=2, score=-0.0504392835706, total=   0.7s\n",
      "[CV] n_estimators=500, learning_rate=0.01, max_depth=2 ...............\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.8s\n"
     ]
    },
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     "output_type": "stream",
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      "[CV]  n_estimators=100, learning_rate=0.01, max_depth=2, score=-0.0391593573459, total=   1.4s\n",
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      "[CV] n_estimators=100, learning_rate=0.01, max_depth=3 ...............\n",
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      "[CV] n_estimators=250, learning_rate=0.01, max_depth=3 ...............\n",
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      "[CV] n_estimators=250, learning_rate=0.01, max_depth=3 ...............\n",
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      "[CV] n_estimators=250, learning_rate=0.01, max_depth=3 ...............\n",
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      "[CV] n_estimators=500, learning_rate=0.01, max_depth=5 ...............\n",
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      "[CV] n_estimators=500, learning_rate=0.01, max_depth=5 ...............\n",
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      "[CV] n_estimators=100, learning_rate=0.1, max_depth=2 ................\n",
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      "[CV] n_estimators=250, learning_rate=0.1, max_depth=2 ................\n",
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      "[CV] n_estimators=500, learning_rate=0.1, max_depth=2 ................\n",
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     ]
    },
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     "output_type": "stream",
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      "[CV] n_estimators=50, learning_rate=0.1, max_depth=3 .................\n",
      "[CV]  n_estimators=50, learning_rate=0.1, max_depth=3, score=-0.0290723372481, total=   1.0s\n",
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      "[CV] n_estimators=100, learning_rate=0.1, max_depth=3 ................\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:  1.1min\n"
     ]
    },
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      "[CV]  n_estimators=500, learning_rate=0.1, max_depth=2, score=-0.0276805986809, total=   6.0s\n",
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      "[CV] n_estimators=100, learning_rate=0.3, max_depth=3 ................\n",
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      "[CV] n_estimators=100, learning_rate=0.3, max_depth=3 ................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=250, learning_rate=0.1, max_depth=10, score=-0.0258672747413, total=  26.6s\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  3.1min\n"
     ]
    },
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      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=2, score=-0.0286461610009, total=   3.7s\n",
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      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=5, score=-0.0309145214908, total=   9.0s\n",
      "[CV] n_estimators=50, learning_rate=0.5, max_depth=10 ................\n",
      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=5, score=-0.0308500070912, total=   8.5s\n",
      "[CV] n_estimators=50, learning_rate=0.5, max_depth=10 ................\n",
      "[CV]  n_estimators=500, learning_rate=0.3, max_depth=10, score=-0.0280645698296, total=  45.1s\n",
      "[CV] n_estimators=100, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=500, learning_rate=0.3, max_depth=10, score=-0.028494347928, total=  45.0s\n",
      "[CV] n_estimators=100, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=50, learning_rate=0.5, max_depth=10, score=-0.0328802294223, total=   5.9s\n",
      "[CV] n_estimators=100, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=50, learning_rate=0.5, max_depth=10, score=-0.0312250327653, total=   5.6s\n",
      "[CV] n_estimators=250, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=50, learning_rate=0.5, max_depth=10, score=-0.0323457466132, total=   5.7s\n",
      "[CV] n_estimators=250, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=5, score=-0.0333538403261, total=  18.2s\n",
      "[CV] n_estimators=250, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=5, score=-0.0309241997638, total=  18.2s\n",
      "[CV] n_estimators=500, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=100, learning_rate=0.5, max_depth=10, score=-0.0328529429117, total=  11.2s\n",
      "[CV] n_estimators=500, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=100, learning_rate=0.5, max_depth=10, score=-0.0315728145512, total=  10.8s\n",
      "[CV] n_estimators=500, learning_rate=0.5, max_depth=10 ...............\n",
      "[CV]  n_estimators=100, learning_rate=0.5, max_depth=10, score=-0.0328327617887, total=  10.9s\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=5, score=-0.0316130093161, total=  18.6s\n",
      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=10, score=-0.0331326663747, total=  21.1s\n",
      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=10, score=-0.0318354372103, total=  20.6s\n",
      "[CV]  n_estimators=250, learning_rate=0.5, max_depth=10, score=-0.0333228018369, total=  17.9s\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=10, score=-0.033138465844, total=  18.4s\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=10, score=-0.0313893128155, total=  16.8s\n",
      "[CV]  n_estimators=500, learning_rate=0.5, max_depth=10, score=-0.0328966608044, total=  16.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 192 out of 192 | elapsed:  4.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root mean squared error:  0.153476027341\n",
      "Fitting 3 folds for each of 7 candidates, totalling 21 fits\n",
      "[CV] alpha=0.001 .....................................................\n",
      "[CV] alpha=0.001 .....................................................\n",
      "[CV] alpha=0.001 .....................................................\n",
      "[CV] alpha=0.01 ......................................................\n",
      "[CV] alpha=0.01 ......................................................\n",
      "[CV] alpha=0.01 ......................................................\n",
      "[CV] alpha=0.1 .......................................................\n",
      "[CV] alpha=0.1 .......................................................\n",
      "[CV] ................ alpha=0.1, score=-0.0349008853653, total=   0.0s\n",
      "[CV] alpha=0.1 .......................................................\n",
      "[CV] ................ alpha=0.1, score=-0.0364248614854, total=   0.0s\n",
      "[CV] ............... alpha=0.01, score=-0.0337711356321, total=   0.1s\n",
      "[CV] ................ alpha=0.1, score=-0.0322705205398, total=   0.0s\n",
      "[CV] alpha=0.25 ......................................................\n",
      "[CV] alpha=0.25 ......................................................\n",
      "[CV] alpha=0.25 ......................................................\n",
      "[CV] ............... alpha=0.25, score=-0.0349369721725, total=   0.0s\n",
      "[CV] ............... alpha=0.01, score=-0.0312749120295, total=   0.1s\n",
      "[CV] ............... alpha=0.25, score=-0.0366374725163, total=   0.0s\n",
      "[CV] alpha=0.5 .......................................................\n",
      "[CV] ............... alpha=0.25, score=-0.0325587335425, total=   0.0s\n",
      "[CV] alpha=0.5 .......................................................\n",
      "[CV] alpha=0.5 .......................................................\n",
      "[CV] ................ alpha=0.5, score=-0.0373205034725, total=   0.0s\n",
      "[CV] alpha=0.75 ......................................................\n",
      "[CV] ................ alpha=0.5, score=-0.0353699509994, total=   0.0s\n",
      "[CV] alpha=0.75 ......................................................\n",
      "[CV] ................ alpha=0.5, score=-0.0333751183522, total=   0.0s\n",
      "[CV] alpha=0.75 ......................................................\n",
      "[CV] ............... alpha=0.01, score=-0.0351691836854, total=   0.2s\n",
      "[CV] alpha=1.0 .......................................................\n",
      "[CV] alpha=1.0 .......................................................\n",
      "[CV] ............... alpha=0.75, score=-0.0362689877184, total=   0.0s\n",
      "[CV] alpha=1.0 .......................................................\n",
      "[CV] ............... alpha=0.75, score=-0.0346115712339, total=   0.0s\n",
      "[CV] ................. alpha=1.0, score=-0.039919212463, total=   0.0s\n",
      "[CV] ................ alpha=1.0, score=-0.0376341254583, total=   0.0s\n",
      "[CV] ............... alpha=0.75, score=-0.0384144020706, total=   0.0s\n",
      "[CV] ................ alpha=1.0, score=-0.0362680812039, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  11 out of  21 | elapsed:    0.3s remaining:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  21 | elapsed:    0.4s remaining:    0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .............. alpha=0.001, score=-0.0320270269409, total=   0.4s\n",
      "[CV] .............. alpha=0.001, score=-0.0337356168467, total=   0.4s\n",
      "[CV] ............... alpha=0.001, score=-0.030290004844, total=   0.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root mean squared error:  0.186536127332\n",
      "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n",
      "[CV] max_features=0.5, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV] max_features=0.5, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV] max_features=0.5, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV] max_features=0.5, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV] max_features=0.5, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=10, n_jobs=-1, score=-0.0175784748543, total=   0.6s\n",
      "[CV] max_features=0.5, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=10, n_jobs=-1, score=-0.017315824429, total=   0.6s\n",
      "[CV]  max_features=0.5, n_estimators=10, n_jobs=-1, score=-0.0154450334745, total=   0.6s\n",
      "[CV] max_features=0.5, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV] max_features=0.5, n_estimators=1000, n_jobs=-1 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    1.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=0.5, n_estimators=100, n_jobs=-1, score=-0.0154459311581, total=   3.7s\n",
      "[CV] max_features=0.5, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.5, n_estimators=100, n_jobs=-1, score=-0.0161834799561, total=   3.7s\n",
      "[CV] max_features=0.6, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.5, n_estimators=100, n_jobs=-1, score=-0.0141484793005, total=   3.8s\n",
      "[CV] max_features=0.6, n_estimators=10, n_jobs=-1 ....................\n",
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      "[CV] max_features=0.6, n_estimators=10, n_jobs=-1 ....................\n",
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      "[CV] max_features=0.6, n_estimators=100, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.6, n_estimators=100, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.6, n_estimators=100, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.6, n_estimators=500, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.6, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=500, n_jobs=-1, score=-0.01599579851, total=  25.8s\n",
      "[CV] max_features=0.6, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=500, n_jobs=-1, score=-0.0153997000862, total=  26.9s\n",
      "[CV] max_features=0.6, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.5, n_estimators=500, n_jobs=-1, score=-0.014066266801, total=  27.5s\n",
      "[CV] max_features=0.6, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=500, n_jobs=-1, score=-0.015462385985, total=  39.4s\n",
      "[CV] max_features=0.6, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=500, n_jobs=-1, score=-0.0159695872222, total=  41.6s\n",
      "[CV] max_features=0.7, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=10, n_jobs=-1, score=-0.0174083455421, total=   1.7s\n",
      "[CV] max_features=0.7, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=10, n_jobs=-1, score=-0.0175574718435, total=   1.4s\n",
      "[CV] max_features=0.7, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=10, n_jobs=-1, score=-0.0158653056377, total=   1.5s\n",
      "[CV] max_features=0.7, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=1000, n_jobs=-1, score=-0.0159889354311, total= 1.1min\n",
      "[CV]  max_features=0.5, n_estimators=1000, n_jobs=-1, score=-0.0154770731815, total= 1.1min\n",
      "[CV] max_features=0.7, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV] max_features=0.7, n_estimators=100, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.6, n_estimators=500, n_jobs=-1, score=-0.0140464742926, total=  42.0s\n",
      "[CV] max_features=0.7, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.5, n_estimators=1000, n_jobs=-1, score=-0.0140449095414, total= 1.1min\n",
      "[CV] max_features=0.7, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.7, n_estimators=100, n_jobs=-1, score=-0.0156254752024, total=   8.8s\n",
      "[CV] max_features=0.7, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.7, n_estimators=100, n_jobs=-1, score=-0.0160098613684, total=   8.7s\n",
      "[CV]  max_features=0.7, n_estimators=100, n_jobs=-1, score=-0.0141440329297, total=   8.7s\n",
      "[CV] max_features=0.7, n_estimators=1000, n_jobs=-1 ..................\n",
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      "[CV]  max_features=0.6, n_estimators=1000, n_jobs=-1, score=-0.0154601036565, total= 1.4min\n",
      "[CV] max_features=0.7, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=1000, n_jobs=-1, score=-0.0159643343039, total= 1.4min\n",
      "[CV] max_features=0.8, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.7, n_estimators=500, n_jobs=-1, score=-0.0154470324003, total=  46.7s\n",
      "[CV] max_features=0.8, n_estimators=10, n_jobs=-1 ....................\n",
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      "[CV] max_features=0.8, n_estimators=100, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.8, n_estimators=100, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.8, n_estimators=100, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.8, n_estimators=500, n_jobs=-1 ...................\n",
      "[CV]  max_features=0.8, n_estimators=100, n_jobs=-1, score=-0.0156687951404, total=  10.4s\n",
      "[CV] max_features=0.8, n_estimators=500, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.8, n_estimators=500, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.8, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.6, n_estimators=1000, n_jobs=-1, score=-0.0140102602314, total= 1.4min\n",
      "[CV] max_features=0.8, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.7, n_estimators=1000, n_jobs=-1, score=-0.0154411620718, total= 1.6min\n",
      "[CV]  max_features=0.7, n_estimators=1000, n_jobs=-1, score=-0.0159669740234, total= 1.6min\n",
      "[CV] max_features=0.8, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV] max_features=0.9, n_estimators=10, n_jobs=-1 ....................\n",
      "[CV]  max_features=0.9, n_estimators=10, n_jobs=-1, score=-0.0169448714259, total=   2.0s\n",
      "[CV] max_features=0.9, n_estimators=10, n_jobs=-1 ....................\n",
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      "[CV] max_features=0.9, n_estimators=10, n_jobs=-1 ....................\n",
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      "[CV] max_features=0.9, n_estimators=100, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.9, n_estimators=500, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.9, n_estimators=500, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.9, n_estimators=500, n_jobs=-1 ...................\n",
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      "[CV] max_features=0.9, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.7, n_estimators=1000, n_jobs=-1, score=-0.0139847222298, total= 1.6min\n",
      "[CV] max_features=0.9, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.8, n_estimators=1000, n_jobs=-1, score=-0.0154975723893, total= 1.7min\n",
      "[CV] max_features=0.9, n_estimators=1000, n_jobs=-1 ..................\n",
      "[CV]  max_features=0.9, n_estimators=500, n_jobs=-1, score=-0.0154827234188, total=  56.8s\n",
      "[CV]  max_features=0.8, n_estimators=1000, n_jobs=-1, score=-0.0159193783589, total= 1.8min\n",
      "[CV]  max_features=0.9, n_estimators=500, n_jobs=-1, score=-0.0159430810939, total=  57.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=0.9, n_estimators=500, n_jobs=-1, score=-0.0140541110082, total=  57.3s\n",
      "[CV]  max_features=0.8, n_estimators=1000, n_jobs=-1, score=-0.0139890208733, total= 1.5min\n",
      "[CV]  max_features=0.9, n_estimators=1000, n_jobs=-1, score=-0.0154798984298, total= 1.4min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  58 out of  60 | elapsed:  4.7min remaining:    9.6s\n"
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    },
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     "output_type": "stream",
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      "[CV]  max_features=0.9, n_estimators=1000, n_jobs=-1, score=-0.0159336474796, total= 1.3min\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process PoolWorker-32:\n",
      "Process PoolWorker-31:\n",
      "Process PoolWorker-29:\n",
      "Process PoolWorker-30:\n",
      "Process PoolWorker-27:\n",
      "Process PoolWorker-26:\n",
      "Process PoolWorker-25:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "    self.run()\n",
      "    self.run()\n",
      "    self.run()\n",
      "    self.run()\n",
      "    self.run()\n",
      "    self.run()\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "    task = get()\n",
      "    task = get()\n",
      "    task = get()\n",
      "    task = get()\n",
      "    task = get()\n",
      "    task = get()\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 360, in get\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 360, in get\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 360, in get\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 360, in get\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 360, in get\n",
      "  File \"/Users/npng/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 360, in get\n",
      "    racquire()\n",
      "    racquire()\n",
      "    return recv()\n",
      "    racquire()\n",
      "    racquire()\n",
      "    racquire()\n",
      "    racquire()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-8bd6c54222b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mestimators\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregressor_per_zone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_zones\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-25-dc807d99029b>\u001b[0m in \u001b[0;36mregressor_per_zone\u001b[0;34m(all_zones)\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mzone_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_zones\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_best_regressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_zones\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregressors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m         \u001b[0mzone_estimators\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mzone_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-23-1da160a4f8b8>\u001b[0m in \u001b[0;36mfind_best_regressor\u001b[0;34m(df, regressors, parameters)\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TARGETVAR'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mrmse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/npng/anaconda/lib/python2.7/site-packages/sklearn/model_selection/_search.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    636\u001b[0m                                   error_score=self.error_score)\n\u001b[1;32m    637\u001b[0m           for parameters, (train, test) in product(candidate_params,\n\u001b[0;32m--> 638\u001b[0;31m                                                    cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[0;31m# if one choose to see train score, \"out\" will contain train score info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/npng/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    787\u001b[0m                 \u001b[0;31m# consumption.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    790\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/npng/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    697\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    700\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/npng/anaconda/lib/python2.7/multiprocessing/pool.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 561\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    562\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ready\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/npng/anaconda/lib/python2.7/multiprocessing/pool.pyc\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    554\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    555\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ready\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 556\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    557\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/npng/anaconda/lib/python2.7/threading.pyc\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    338\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    341\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0m__debug__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_note\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s.wait(): got it\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "estimators, params = regressor_per_zone(my_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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