Feature_importance_V2.ipynb
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Feature_importance_V2.ipynb
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	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
	" from ._conv import register_converters as _register_converters\n"
	]
	}
	],
	"source": [
	"import pandas as pd\n",
	"import xarray as xr\n",
	"import numpy as np\n",
	"import itertools\n",
	"\n",
	"from rooftop_handling import link_rooftops_with_pixels\n",
	"import util\n",
	"\n",
	"from sklearn import tree\n",
	"from sklearn.ensemble import RandomForestRegressor\n",
	"from sklearn.linear_model import LinearRegression\n",
	"from sklearn.neighbors import KNeighborsRegressor\n",
	"from sklearn.neural_network import MLPRegressor\n",
	"from sklearn.svm import SVR\n",
	"from sklearn.metrics import mean_squared_error as mse\n",
	"from sklearn.metrics import mean_absolute_error as mae\n",
	"from sklearn.metrics import r2_score as r2\n",
	"from sklearn.model_selection import cross_val_predict\n",
	"from sklearn.preprocessing import Normalizer\n",
	"from sklearn.model_selection import train_test_split\n",
	"from sklearn.base import clone\n",
	"from ELM_ensemble import ELM_Ensemble\n",
	"from scipy.stats import describe\n",
	"from uncertainty import Uncertainty\n",
	"\n",
	"import os\n",
	"import time\n",
	"\n",
	"%matplotlib notebook\n",
	"import matplotlib.pyplot as plt"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"def rescale(vector, training):\n",
	" return (vector * training.std()) + training.mean()\n",
	"\n",
	"def normalize(vector, training):\n",
	" return (vector - training.mean()) / training.std()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [],
	"source": [
	"path = '/Volumes/Seagate Backup Plus Drive/DOKUMENTE/EPFL/data/Results/PV_w_uncertainty/pv_prediction/'"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# Load data"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/numpy/lib/arraysetops.py:569: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
	" mask \|= (ar1 == a)\n"
	]
	}
	],
	"source": [
	"features = pd.read_csv(os.path.join(path, 'features_v2.csv'), index_col = 0)\n",
	"features = features.drop(columns = 'DHI')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {},
	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/numpy/lib/arraysetops.py:569: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
	" mask \|= (ar1 == a)\n"
	]
	}
	],
	"source": [
	"targets = pd.read_csv(os.path.join(path, 'targets_v2.csv'), index_col = 0)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {},
	"outputs": [],
	"source": [
	"data = pd.concat([features, targets], axis = 1)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {},
	"outputs": [
	{
	"data": {
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	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th>NEIGUNG</th>\n",
	" <th>AUSRICHTUNG</th>\n",
	" <th>SIS</th>\n",
	" <th>SISDIR</th>\n",
	" <th>SVF</th>\n",
	" <th>illumination</th>\n",
	" <th>horizon_S</th>\n",
	" <th>horizon_E</th>\n",
	" <th>horizon_NEE</th>\n",
	" <th>horizon_NWW</th>\n",
	" <th>horizon_W</th>\n",
	" <th>horizon_SWW</th>\n",
	" <th>horizon_SSW</th>\n",
	" <th>horizon_SSE</th>\n",
	" <th>horizon_SEE</th>\n",
	" <th>MSTRAHLUNG</th>\n",
	" <th>PV_kWh_m2</th>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>DF_UID</th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
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	" <th></th>\n",
	" <th></th>\n",
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	" <tbody>\n",
	" <tr>\n",
	" <th>5343176</th>\n",
	" <td>16</td>\n",
	" <td>-17.0</td>\n",
	" <td>1350.071526</td>\n",
	" <td>846.491412</td>\n",
	" <td>0.718741</td>\n",
	" <td>0.811781</td>\n",
	" <td>6.092388</td>\n",
	" <td>14.689941</td>\n",
	" <td>17.452604</td>\n",
	" <td>11.537519</td>\n",
	" <td>9.421774</td>\n",
	" <td>4.678150</td>\n",
	" <td>5.391861</td>\n",
	" <td>8.197643</td>\n",
	" <td>9.963911</td>\n",
	" <td>1548</td>\n",
	" <td>210.528</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>5343175</th>\n",
	" <td>17</td>\n",
	" <td>163.0</td>\n",
	" <td>1350.071526</td>\n",
	" <td>846.491412</td>\n",
	" <td>0.759114</td>\n",
	" <td>0.804061</td>\n",
	" <td>11.788798</td>\n",
	" <td>16.118021</td>\n",
	" <td>15.847362</td>\n",
	" <td>2.435227</td>\n",
	" <td>2.435227</td>\n",
	" <td>7.310166</td>\n",
	" <td>11.407730</td>\n",
	" <td>13.261172</td>\n",
	" <td>13.841580</td>\n",
	" <td>1114</td>\n",
	" <td>151.504</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>5343177</th>\n",
	" <td>25</td>\n",
	" <td>-59.0</td>\n",
	" <td>1350.071526</td>\n",
	" <td>846.491412</td>\n",
	" <td>0.706426</td>\n",
	" <td>0.738014</td>\n",
	" <td>12.730241</td>\n",
	" <td>21.675634</td>\n",
	" <td>21.338327</td>\n",
	" <td>11.129818</td>\n",
	" <td>12.766143</td>\n",
	" <td>0.932760</td>\n",
	" <td>11.839274</td>\n",
	" <td>13.979061</td>\n",
	" <td>15.757084</td>\n",
	" <td>545</td>\n",
	" <td>74.120</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>5343178</th>\n",
	" <td>24</td>\n",
	" <td>121.0</td>\n",
	" <td>1350.071526</td>\n",
	" <td>846.491412</td>\n",
	" <td>0.643597</td>\n",
	" <td>0.654795</td>\n",
	" <td>16.819239</td>\n",
	" <td>18.360150</td>\n",
	" <td>19.976019</td>\n",
	" <td>18.210364</td>\n",
	" <td>20.702796</td>\n",
	" <td>20.021681</td>\n",
	" <td>14.710003</td>\n",
	" <td>12.246127</td>\n",
	" <td>15.541741</td>\n",
	" <td>598</td>\n",
	" <td>81.328</td>\n",
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	" <tr>\n",
	" <th>5343214</th>\n",
	" <td>11</td>\n",
	" <td>168.0</td>\n",
	" <td>1350.071526</td>\n",
	" <td>846.491412</td>\n",
	" <td>0.598916</td>\n",
	" <td>0.448989</td>\n",
	" <td>43.407631</td>\n",
	" <td>15.740415</td>\n",
	" <td>5.747942</td>\n",
	" <td>11.909147</td>\n",
	" <td>16.178937</td>\n",
	" <td>21.809173</td>\n",
	" <td>34.131566</td>\n",
	" <td>44.075201</td>\n",
	" <td>35.429473</td>\n",
	" <td>1032</td>\n",
	" <td>140.352</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" NEIGUNG AUSRICHTUNG SIS SISDIR SVF \\\n",
	"DF_UID \n",
	"5343176 16 -17.0 1350.071526 846.491412 0.718741 \n",
	"5343175 17 163.0 1350.071526 846.491412 0.759114 \n",
	"5343177 25 -59.0 1350.071526 846.491412 0.706426 \n",
	"5343178 24 121.0 1350.071526 846.491412 0.643597 \n",
	"5343214 11 168.0 1350.071526 846.491412 0.598916 \n",
	"\n",
	" illumination horizon_S horizon_E horizon_NEE horizon_NWW \\\n",
	"DF_UID \n",
	"5343176 0.811781 6.092388 14.689941 17.452604 11.537519 \n",
	"5343175 0.804061 11.788798 16.118021 15.847362 2.435227 \n",
	"5343177 0.738014 12.730241 21.675634 21.338327 11.129818 \n",
	"5343178 0.654795 16.819239 18.360150 19.976019 18.210364 \n",
	"5343214 0.448989 43.407631 15.740415 5.747942 11.909147 \n",
	"\n",
	" horizon_W horizon_SWW horizon_SSW horizon_SSE horizon_SEE \\\n",
	"DF_UID \n",
	"5343176 9.421774 4.678150 5.391861 8.197643 9.963911 \n",
	"5343175 2.435227 7.310166 11.407730 13.261172 13.841580 \n",
	"5343177 12.766143 0.932760 11.839274 13.979061 15.757084 \n",
	"5343178 20.702796 20.021681 14.710003 12.246127 15.541741 \n",
	"5343214 16.178937 21.809173 34.131566 44.075201 35.429473 \n",
	"\n",
	" MSTRAHLUNG PV_kWh_m2 \n",
	"DF_UID \n",
	"5343176 1548 210.528 \n",
	"5343175 1114 151.504 \n",
	"5343177 545 74.120 \n",
	"5343178 598 81.328 \n",
	"5343214 1032 140.352 "
	]
	},
	"execution_count": 7,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"data.head()"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## Part 1: Define features & normalize"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {},
	"outputs": [],
	"source": [
	"label_name = ['MSTRAHLUNG']\n",
	"identifier = 'DF_UID'\n",
	"\n",
	"all_features = features.columns\n",
	"# feature_labels = ['Roof tilt', 'Roof aspect', 'Annual GHI', 'Horizon (S)', 'Horizon (E)', 'Horizon (W)']\n",
	"\n",
	"feature_names = all_features\n"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"### Part 2.3: Recursive feature importance analysis"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {},
	"outputs": [],
	"source": [
	"# ML MODEL PARAMETERS\n",
	"n_est = 500\n",
	"n_samples_leaf = 3\n",
	"RF_features = 3\n",
	"ELM_nodes = 25\n",
	"ELM_est = 50\n",
	"MLP_layers = (100, 20,) # WORKS RESONABLY WELL\n",
	"SVM_paramC = 1\n",
	"SVM_gamma = 500\n",
	"knn_neighbors = 10\n",
	"\n",
	"k_CV = 5\n",
	"\n",
	"# DEFINITION OF ESTIMATORS\n",
	"def set_estimators():\n",
	" estimator = {\n",
	" 'KNN': KNeighborsRegressor( n_neighbors = knn_neighbors, n_jobs = -1 ),\n",
	" 'LIN': LinearRegression( n_jobs = -1 ),\n",
	" 'RF' : RandomForestRegressor( n_estimators = n_est, min_samples_leaf = n_samples_leaf, n_jobs = -1 ),\n",
	" 'ELM' : ELM_Ensemble( n_estimators = ELM_est, n_nodes = ELM_nodes),\n",
	" 'MLP': MLPRegressor( hidden_layer_sizes = MLP_layers ),\n",
	" 'SVM': SVR( C = SVM_paramC, kernel = 'rbf', gamma = SVM_gamma ),\n",
	"# 'RF_unc' : Uncertainty( RandomForestRegressor( n_estimators = n_est, min_samples_leaf = n_samples_leaf, n_jobs = -1 ) ),\n",
	"# 'ELM_unc': Uncertainty( ELM_Ensemble( n_estimators = ELM_est, n_nodes = ELM_nodes) )\n",
	" }\n",
	" return estimator\n",
	"\n",
	"# 'XGB': XGBRegressor( n_estimators = n_est, max_depth = tree_depth, n_jobs = -1 ),"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {},
	"outputs": [],
	"source": [
	"def get_data( data, feature_names, label_name ):\n",
	" X = data.loc[ : , feature_names ].values\n",
	" T = data.loc[ : , label_name ].values.reshape((-1,))\n",
	"\n",
	" norm = Normalizer()\n",
	" x = norm.fit_transform(X)\n",
	" t = normalize(T, T)\n",
	" \n",
	" return x, t, X, T"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 11,
	"metadata": {},
	"outputs": [],
	"source": [
	"def get_mse( x, t, X, T, model, cv = 3, print_time = True ):\n",
	" \n",
	" est = set_estimators()[ model ]\n",
	"\n",
	" # normalized cross-validation\n",
	" tt = time.time()\n",
	" y = cross_val_predict(est, x, t, n_jobs = -1) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
	" cv_time = time.time()-tt\n",
	" if print_time : \n",
	" print('Crossvalidation time: %.3fs' %cv_time)\n",
	"\n",
	" Y = rescale(y, T)\n",
	" return mse(T, Y), mse(t, y), cv_time"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# BOTTOM-UP RECURSIVE FEATURE SELECTION"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 35,
	"metadata": {
	"scrolled": true
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"\n",
	"Testing 1 features\n",
	"Crossvalidation time: 0.712s\n",
	"Lowest MSE for horizon_SSW (59143.104, 1.005)\n",
	"\n",
	"Testing 2 features\n",
	"Crossvalidation time: 0.301s\n",
	"Lowest MSE for SIS (43659.753, 0.742)\n",
	"\n",
	"Testing 3 features\n",
	"Crossvalidation time: 0.286s\n",
	"Lowest MSE for AUSRICHTUNG (20606.965, 0.350)\n",
	"\n",
	"Testing 4 features\n",
	"Crossvalidation time: 0.287s\n",
	"Lowest MSE for NEIGUNG (14078.853, 0.239)\n",
	"\n",
	"Testing 5 features\n",
	"Crossvalidation time: 0.289s\n",
	"Lowest MSE for horizon_SEE (11888.144, 0.202)\n",
	"\n",
	"Testing 6 features\n",
	"Crossvalidation time: 0.285s\n",
	"Lowest MSE for horizon_W (11265.784, 0.191)\n",
	"\n",
	"Testing 7 features\n",
	"Crossvalidation time: 0.279s\n",
	"Lowest MSE for horizon_S (10923.127, 0.186)\n",
	"\n",
	"Testing 8 features\n",
	"Crossvalidation time: 0.286s\n",
	"Lowest MSE for horizon_E (10751.255, 0.183)\n",
	"\n",
	"Testing 9 features\n",
	"Crossvalidation time: 0.308s\n",
	"Lowest MSE for horizon_SSE (10799.743, 0.183)\n",
	"\n",
	"Testing 10 features\n",
	"Crossvalidation time: 0.302s\n",
	"Lowest MSE for horizon_SWW (10815.454, 0.184)\n",
	"\n",
	"Testing 11 features\n",
	"Crossvalidation time: 0.300s\n",
	"Lowest MSE for horizon_NWW (10882.722, 0.185)\n",
	"\n",
	"Testing 12 features\n",
	"Crossvalidation time: 0.297s\n",
	"Lowest MSE for horizon_NEE (11071.483, 0.188)\n"
	]
	}
	],
	"source": [
	"# choose model with which to perform the feature selection\n",
	"model = 'KNN'\n",
	"training = data.sample(10000)\n",
	"\n",
	"FEATURES = list(feature_names)\n",
	"FTR_LIST = []\n",
	"MSE = pd.DataFrame(data = [], columns = ['mse', 'mse_norm', 'cv_time'])\n",
	"\n",
	"# start from 1 feature, with maximum features equal to the total number of features \n",
	"for k in range( len(feature_names) ):\n",
	" n_features = k+1\n",
	" print('\\nTesting %d features' %n_features)\n",
	" \n",
	" mse_df = pd.DataFrame(data = np.zeros((len(FEATURES), 3)), \n",
	" index = FEATURES, columns = ['mse', 'mse_norm', 'cv_time'])\n",
	" \n",
	" init = True\n",
	" for feature in FEATURES:\n",
	" test_ftrs = FTR_LIST + [feature]\n",
	" # print('Testing:', test_ftrs)\n",
	" \n",
	" x, t, X, T = get_data( training, test_ftrs, label_name)\n",
	" mse_df.loc[feature, :] = get_mse( x, t, X, T, model, print_time = init )\n",
	" if init == True: init = False\n",
	" \n",
	" best_feature = mse_df['mse'].idxmin()\n",
	" FTR_LIST.append( best_feature )\n",
	" FEATURES.remove( best_feature )\n",
	" \n",
	" MSE = MSE.append(mse_df.loc[best_feature,:])\n",
	"\n",
	" print('Lowest MSE for %s (%.3f, %.3f)' \n",
	" %(best_feature, mse_df.loc[best_feature, 'mse'], mse_df.loc[best_feature, 'mse_norm']))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 36,
	"metadata": {},
	"outputs": [
	{
	"data": {
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	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th>mse</th>\n",
	" <th>mse_norm</th>\n",
	" <th>cv_time</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>horizon_SSW</th>\n",
	" <td>59143.104405</td>\n",
	" <td>1.004892</td>\n",
	" <td>0.510537</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>SIS</th>\n",
	" <td>43659.752534</td>\n",
	" <td>0.741817</td>\n",
	" <td>0.286707</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>AUSRICHTUNG</th>\n",
	" <td>20606.965356</td>\n",
	" <td>0.350130</td>\n",
	" <td>0.292405</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>NEIGUNG</th>\n",
	" <td>14078.852855</td>\n",
	" <td>0.239212</td>\n",
	" <td>0.286527</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SEE</th>\n",
	" <td>11888.143608</td>\n",
	" <td>0.201990</td>\n",
	" <td>0.283403</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_W</th>\n",
	" <td>11265.784347</td>\n",
	" <td>0.191415</td>\n",
	" <td>0.287109</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_S</th>\n",
	" <td>10923.126672</td>\n",
	" <td>0.185593</td>\n",
	" <td>0.278921</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_E</th>\n",
	" <td>10751.255112</td>\n",
	" <td>0.182673</td>\n",
	" <td>0.285885</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SSE</th>\n",
	" <td>10799.743072</td>\n",
	" <td>0.183497</td>\n",
	" <td>0.293518</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SWW</th>\n",
	" <td>10815.454443</td>\n",
	" <td>0.183764</td>\n",
	" <td>0.291269</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_NWW</th>\n",
	" <td>10882.721849</td>\n",
	" <td>0.184907</td>\n",
	" <td>0.287005</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_NEE</th>\n",
	" <td>11071.483057</td>\n",
	" <td>0.188114</td>\n",
	" <td>0.297123</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" mse mse_norm cv_time\n",
	"horizon_SSW 59143.104405 1.004892 0.510537\n",
	"SIS 43659.752534 0.741817 0.286707\n",
	"AUSRICHTUNG 20606.965356 0.350130 0.292405\n",
	"NEIGUNG 14078.852855 0.239212 0.286527\n",
	"horizon_SEE 11888.143608 0.201990 0.283403\n",
	"horizon_W 11265.784347 0.191415 0.287109\n",
	"horizon_S 10923.126672 0.185593 0.278921\n",
	"horizon_E 10751.255112 0.182673 0.285885\n",
	"horizon_SSE 10799.743072 0.183497 0.293518\n",
	"horizon_SWW 10815.454443 0.183764 0.291269\n",
	"horizon_NWW 10882.721849 0.184907 0.287005\n",
	"horizon_NEE 11071.483057 0.188114 0.297123"
	]
	},
	"execution_count": 36,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"MSE"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 37,
	"metadata": {},
	"outputs": [],
	"source": [
	"MSE.to_csv('feature_importance_KNN_10k_run4.csv')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 16,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<div>\n",
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	"\n",
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	" text-align: right;\n",
	" }\n",
	"</style>\n",
	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th>mse</th>\n",
	" <th>mse_norm</th>\n",
	" <th>cv_time</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>horizon_S</th>\n",
	" <td>54129.125407</td>\n",
	" <td>0.968591</td>\n",
	" <td>0.991302</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>illumination</th>\n",
	" <td>41940.599048</td>\n",
	" <td>0.750488</td>\n",
	" <td>5.160852</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>AUSRICHTUNG</th>\n",
	" <td>19423.669249</td>\n",
	" <td>0.347569</td>\n",
	" <td>7.757304</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>NEIGUNG</th>\n",
	" <td>13787.965992</td>\n",
	" <td>0.246723</td>\n",
	" <td>8.952484</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>SIS</th>\n",
	" <td>11557.466061</td>\n",
	" <td>0.206810</td>\n",
	" <td>10.904001</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>SISDIR</th>\n",
	" <td>10141.557729</td>\n",
	" <td>0.181474</td>\n",
	" <td>13.081179</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SEE</th>\n",
	" <td>9468.301479</td>\n",
	" <td>0.169427</td>\n",
	" <td>15.023884</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SWW</th>\n",
	" <td>8959.481211</td>\n",
	" <td>0.160322</td>\n",
	" <td>22.211851</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_E</th>\n",
	" <td>8824.834921</td>\n",
	" <td>0.157912</td>\n",
	" <td>23.652409</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_W</th>\n",
	" <td>8697.611396</td>\n",
	" <td>0.155636</td>\n",
	" <td>21.470241</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SSE</th>\n",
	" <td>8645.992412</td>\n",
	" <td>0.154712</td>\n",
	" <td>23.436751</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SSW</th>\n",
	" <td>8602.188165</td>\n",
	" <td>0.153928</td>\n",
	" <td>25.238973</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>SVF</th>\n",
	" <td>8547.802029</td>\n",
	" <td>0.152955</td>\n",
	" <td>27.012909</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_NEE</th>\n",
	" <td>8556.576859</td>\n",
	" <td>0.153112</td>\n",
	" <td>29.132150</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_NWW</th>\n",
	" <td>8572.282956</td>\n",
	" <td>0.153393</td>\n",
	" <td>31.367525</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>DHI</th>\n",
	" <td>8982.478585</td>\n",
	" <td>0.160733</td>\n",
	" <td>33.124487</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" mse mse_norm cv_time\n",
	"horizon_S 54129.125407 0.968591 0.991302\n",
	"illumination 41940.599048 0.750488 5.160852\n",
	"AUSRICHTUNG 19423.669249 0.347569 7.757304\n",
	"NEIGUNG 13787.965992 0.246723 8.952484\n",
	"SIS 11557.466061 0.206810 10.904001\n",
	"SISDIR 10141.557729 0.181474 13.081179\n",
	"horizon_SEE 9468.301479 0.169427 15.023884\n",
	"horizon_SWW 8959.481211 0.160322 22.211851\n",
	"horizon_E 8824.834921 0.157912 23.652409\n",
	"horizon_W 8697.611396 0.155636 21.470241\n",
	"horizon_SSE 8645.992412 0.154712 23.436751\n",
	"horizon_SSW 8602.188165 0.153928 25.238973\n",
	"SVF 8547.802029 0.152955 27.012909\n",
	"horizon_NEE 8556.576859 0.153112 29.132150\n",
	"horizon_NWW 8572.282956 0.153393 31.367525\n",
	"DHI 8982.478585 0.160733 33.124487"
	]
	},
	"execution_count": 16,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"MSE"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# TOP-DOWN feature selection"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {},
	"outputs": [],
	"source": [
	"# remove potentially unavailable features\n",
	"ftrs = feature_names.copy()\n",
	"ftrs = list(ftrs)\n",
	"ftrs.remove('SVF')\n",
	"ftrs.remove('illumination')\n",
	"ftrs.remove('SISDIR')\n",
	"# feature_names.remove('DHI')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 18,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"Crossvalidation time: 0.399s\n",
	"\n",
	"Testing 12 features\n",
	"Crossvalidation time: 0.387s\n",
	"\n",
	"Testing 11 features\n",
	"Crossvalidation time: 0.299s\n",
	"Lowest MSE for removing horizon_NEE (11846.207, 0.198)\n",
	"\n",
	"Testing 10 features\n",
	"Crossvalidation time: 0.296s\n",
	"Lowest MSE for removing horizon_NWW (11755.023, 0.196)\n",
	"\n",
	"Testing 9 features\n",
	"Crossvalidation time: 0.280s\n",
	"Lowest MSE for removing horizon_SEE (11727.268, 0.196)\n",
	"\n",
	"Testing 8 features\n",
	"Crossvalidation time: 0.308s\n",
	"Lowest MSE for removing horizon_SWW (11744.462, 0.196)\n",
	"\n",
	"Testing 7 features\n",
	"Crossvalidation time: 0.273s\n",
	"Lowest MSE for removing horizon_S (11861.679, 0.198)\n",
	"\n",
	"Testing 6 features\n",
	"Crossvalidation time: 0.282s\n",
	"Lowest MSE for removing horizon_W (12264.985, 0.205)\n",
	"\n",
	"Testing 5 features\n",
	"Crossvalidation time: 0.286s\n",
	"Lowest MSE for removing horizon_SSE (12826.366, 0.214)\n",
	"\n",
	"Testing 4 features\n",
	"Crossvalidation time: 0.280s\n",
	"Lowest MSE for removing horizon_E (14754.879, 0.246)\n",
	"\n",
	"Testing 3 features\n",
	"Crossvalidation time: 0.313s\n",
	"Lowest MSE for removing horizon_SSW (18754.774, 0.313)\n",
	"\n",
	"Testing 2 features\n",
	"Crossvalidation time: 0.282s\n",
	"Lowest MSE for removing NEIGUNG (26310.304, 0.439)\n",
	"\n",
	"Testing 1 features\n",
	"Crossvalidation time: 0.593s\n",
	"Lowest MSE for removing SIS (65639.738, 1.095)\n"
	]
	}
	],
	"source": [
	"# choose model with which to perform the feature selection\n",
	"model = 'KNN'\n",
	"training = data.sample(10000)\n",
	"\n",
	"FEATURES = list( ftrs )\n",
	"MSE = pd.DataFrame(data = [], columns = ['mse', 'mse_norm', 'cv_time'])\n",
	"N = len(FEATURES)\n",
	"\n",
	"# get all all features\n",
	"x, t, X, T = get_data( training, all_features, label_name)\n",
	"mse_df = pd.DataFrame(data = np.zeros((len(FEATURES), 3)), \n",
	" index = FEATURES, columns = ['mse', 'mse_norm', 'cv_time'])\n",
	"mse_df.loc['all', :] = get_mse( x, t, X, T, model )\n",
	"MSE = MSE.append(mse_df.loc['all',:])\n",
	"\n",
	"\n",
	"# start from all feature, end with one\n",
	"print('\\nTesting %d features' %N)\n",
	"init = True\n",
	"x, t, X, T = get_data( training, FEATURES, label_name)\n",
	"mse_df = pd.DataFrame(data = np.zeros((len(FEATURES), 3)), \n",
	" index = FEATURES, columns = ['mse', 'mse_norm', 'cv_time'])\n",
	"mse_df.loc['all_reduced', :] = get_mse( x, t, X, T, model, print_time = init )\n",
	"MSE = MSE.append(mse_df.loc['all_reduced',:])\n",
	"\n",
	"for k in range( N-1 ):\n",
	" n_features = N - k - 1\n",
	" print('\\nTesting %d features' %n_features)\n",
	" \n",
	" mse_df = pd.DataFrame(data = np.zeros((len(FEATURES), 3)), \n",
	" index = FEATURES, columns = ['mse', 'mse_norm', 'cv_time'])\n",
	" \n",
	" init = True\n",
	" for feature in FEATURES:\n",
	" test_ftrs = FEATURES.copy()\n",
	" test_ftrs.remove(feature)\n",
	" # print('Testing:', test_ftrs)\n",
	" \n",
	" x, t, X, T = get_data( training, test_ftrs, label_name)\n",
	" mse_df.loc[feature, :] = get_mse( x, t, X, T, model, print_time = init )\n",
	" if init == True: init = False\n",
	" \n",
	" worst_feature = mse_df['mse'].idxmin()\n",
	" FEATURES.remove( worst_feature )\n",
	" \n",
	" MSE = MSE.append(mse_df.loc[worst_feature,:])\n",
	"\n",
	" print('Lowest MSE for removing %s (%.3f, %.3f)' \n",
	" %(worst_feature, mse_df.loc[worst_feature, 'mse'], mse_df.loc[worst_feature, 'mse_norm']))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 19,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<div>\n",
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	"\n",
	" .dataframe thead th {\n",
	" text-align: right;\n",
	" }\n",
	"</style>\n",
	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th>mse</th>\n",
	" <th>mse_norm</th>\n",
	" <th>cv_time</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>all</th>\n",
	" <td>11652.209983</td>\n",
	" <td>0.194469</td>\n",
	" <td>0.399407</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>all_reduced</th>\n",
	" <td>12017.854452</td>\n",
	" <td>0.200571</td>\n",
	" <td>0.387370</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_NEE</th>\n",
	" <td>11846.206693</td>\n",
	" <td>0.197706</td>\n",
	" <td>0.383150</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_NWW</th>\n",
	" <td>11755.023447</td>\n",
	" <td>0.196185</td>\n",
	" <td>0.296947</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SEE</th>\n",
	" <td>11727.267637</td>\n",
	" <td>0.195721</td>\n",
	" <td>0.280106</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SWW</th>\n",
	" <td>11744.462493</td>\n",
	" <td>0.196008</td>\n",
	" <td>0.276042</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_S</th>\n",
	" <td>11861.679242</td>\n",
	" <td>0.197965</td>\n",
	" <td>0.276157</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_W</th>\n",
	" <td>12264.984841</td>\n",
	" <td>0.204696</td>\n",
	" <td>0.283031</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SSE</th>\n",
	" <td>12826.365916</td>\n",
	" <td>0.214065</td>\n",
	" <td>0.287372</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_E</th>\n",
	" <td>14754.879407</td>\n",
	" <td>0.246250</td>\n",
	" <td>0.312895</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SSW</th>\n",
	" <td>18754.774087</td>\n",
	" <td>0.313006</td>\n",
	" <td>0.311631</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>NEIGUNG</th>\n",
	" <td>26310.303630</td>\n",
	" <td>0.439104</td>\n",
	" <td>0.282279</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>SIS</th>\n",
	" <td>65639.737651</td>\n",
	" <td>1.095490</td>\n",
	" <td>0.431207</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" mse mse_norm cv_time\n",
	"all 11652.209983 0.194469 0.399407\n",
	"all_reduced 12017.854452 0.200571 0.387370\n",
	"horizon_NEE 11846.206693 0.197706 0.383150\n",
	"horizon_NWW 11755.023447 0.196185 0.296947\n",
	"horizon_SEE 11727.267637 0.195721 0.280106\n",
	"horizon_SWW 11744.462493 0.196008 0.276042\n",
	"horizon_S 11861.679242 0.197965 0.276157\n",
	"horizon_W 12264.984841 0.204696 0.283031\n",
	"horizon_SSE 12826.365916 0.214065 0.287372\n",
	"horizon_E 14754.879407 0.246250 0.312895\n",
	"horizon_SSW 18754.774087 0.313006 0.311631\n",
	"NEIGUNG 26310.303630 0.439104 0.282279\n",
	"SIS 65639.737651 1.095490 0.431207"
	]
	},
	"execution_count": 19,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"MSE "
	]
	},
	{
	"cell_type": "code",
	"execution_count": 121,
	"metadata": {},
	"outputs": [],
	"source": [
	"MSE.to_csv('feature_select_TD_KNN_100k_allFtrs_run1.csv')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 15,
	"metadata": {},
	"outputs": [
	{
	"data": {
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	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th>mse</th>\n",
	" <th>mse_norm</th>\n",
	" <th>cv_time</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>all</th>\n",
	" <td>10982.248899</td>\n",
	" <td>0.189077</td>\n",
	" <td>0.389041</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>all_reduced</th>\n",
	" <td>10982.248899</td>\n",
	" <td>0.189077</td>\n",
	" <td>0.293694</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_NWW</th>\n",
	" <td>10931.450934</td>\n",
	" <td>0.188202</td>\n",
	" <td>0.379975</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>illumination</th>\n",
	" <td>10931.494254</td>\n",
	" <td>0.188203</td>\n",
	" <td>0.294565</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>SVF</th>\n",
	" <td>10933.103299</td>\n",
	" <td>0.188231</td>\n",
	" <td>0.288811</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_NEE</th>\n",
	" <td>10942.536880</td>\n",
	" <td>0.188393</td>\n",
	" <td>0.388934</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SWW</th>\n",
	" <td>10900.358472</td>\n",
	" <td>0.187667</td>\n",
	" <td>0.392739</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_S</th>\n",
	" <td>10906.303897</td>\n",
	" <td>0.187769</td>\n",
	" <td>0.286313</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SEE</th>\n",
	" <td>10944.184950</td>\n",
	" <td>0.188422</td>\n",
	" <td>0.282786</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>SIS</th>\n",
	" <td>11100.560019</td>\n",
	" <td>0.191114</td>\n",
	" <td>0.281183</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SSE</th>\n",
	" <td>11639.430186</td>\n",
	" <td>0.200391</td>\n",
	" <td>0.282538</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_W</th>\n",
	" <td>12577.760953</td>\n",
	" <td>0.216546</td>\n",
	" <td>0.281616</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_E</th>\n",
	" <td>14515.221880</td>\n",
	" <td>0.249903</td>\n",
	" <td>0.284962</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>NEIGUNG</th>\n",
	" <td>19765.280003</td>\n",
	" <td>0.340291</td>\n",
	" <td>0.289183</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SSW</th>\n",
	" <td>24856.463104</td>\n",
	" <td>0.427943</td>\n",
	" <td>0.293160</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>SISDIR</th>\n",
	" <td>58533.289001</td>\n",
	" <td>1.007743</td>\n",
	" <td>0.408408</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" mse mse_norm cv_time\n",
	"all 10982.248899 0.189077 0.389041\n",
	"all_reduced 10982.248899 0.189077 0.293694\n",
	"horizon_NWW 10931.450934 0.188202 0.379975\n",
	"illumination 10931.494254 0.188203 0.294565\n",
	"SVF 10933.103299 0.188231 0.288811\n",
	"horizon_NEE 10942.536880 0.188393 0.388934\n",
	"horizon_SWW 10900.358472 0.187667 0.392739\n",
	"horizon_S 10906.303897 0.187769 0.286313\n",
	"horizon_SEE 10944.184950 0.188422 0.282786\n",
	"SIS 11100.560019 0.191114 0.281183\n",
	"horizon_SSE 11639.430186 0.200391 0.282538\n",
	"horizon_W 12577.760953 0.216546 0.281616\n",
	"horizon_E 14515.221880 0.249903 0.284962\n",
	"NEIGUNG 19765.280003 0.340291 0.289183\n",
	"horizon_SSW 24856.463104 0.427943 0.293160\n",
	"SISDIR 58533.289001 1.007743 0.408408"
	]
	},
	"execution_count": 15,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"MSE # FOR ALL FEATURES"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 57,
	"metadata": {},
	"outputs": [],
	"source": [
	"MSE_plot = pd.read_csv('feature_select_TD_KNN_100k_reducedFtrs_run1.csv', index_col = 0)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 130,
	"metadata": {},
	"outputs": [],
	"source": [
	"MSE_plot['RMSE'] = np.sqrt(MSE_plot.mse)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 134,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<div>\n",
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	"\n",
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	" text-align: right;\n",
	" }\n",
	"</style>\n",
	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th>mse</th>\n",
	" <th>mse_norm</th>\n",
	" <th>cv_time</th>\n",
	" <th>delta_MSE</th>\n",
	" <th>RMSE</th>\n",
	" <th>delta_RMSE</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>all</th>\n",
	" <td>9516.191579</td>\n",
	" <td>0.164806</td>\n",
	" <td>10.873207</td>\n",
	" <td>0.000000</td>\n",
	" <td>97.550969</td>\n",
	" <td>0.000000</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>all_reduced</th>\n",
	" <td>10157.030298</td>\n",
	" <td>0.175905</td>\n",
	" <td>8.497254</td>\n",
	" <td>0.011098</td>\n",
	" <td>100.782093</td>\n",
	" <td>3.231124</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_NEE</th>\n",
	" <td>10110.068305</td>\n",
	" <td>0.175091</td>\n",
	" <td>6.622710</td>\n",
	" <td>0.010285</td>\n",
	" <td>100.548835</td>\n",
	" <td>2.997866</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_NWW</th>\n",
	" <td>10070.324628</td>\n",
	" <td>0.174403</td>\n",
	" <td>5.392025</td>\n",
	" <td>0.009597</td>\n",
	" <td>100.351007</td>\n",
	" <td>2.800038</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SSE</th>\n",
	" <td>10104.516453</td>\n",
	" <td>0.174995</td>\n",
	" <td>4.897716</td>\n",
	" <td>0.010189</td>\n",
	" <td>100.521224</td>\n",
	" <td>2.970255</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SSW</th>\n",
	" <td>10194.589369</td>\n",
	" <td>0.176555</td>\n",
	" <td>3.756270</td>\n",
	" <td>0.011749</td>\n",
	" <td>100.968259</td>\n",
	" <td>3.417290</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_E</th>\n",
	" <td>10354.968933</td>\n",
	" <td>0.179333</td>\n",
	" <td>2.660432</td>\n",
	" <td>0.014526</td>\n",
	" <td>101.759368</td>\n",
	" <td>4.208399</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_W</th>\n",
	" <td>10621.223454</td>\n",
	" <td>0.183944</td>\n",
	" <td>2.266722</td>\n",
	" <td>0.019138</td>\n",
	" <td>103.059320</td>\n",
	" <td>5.508351</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_S</th>\n",
	" <td>11783.590448</td>\n",
	" <td>0.204074</td>\n",
	" <td>1.229297</td>\n",
	" <td>0.039268</td>\n",
	" <td>108.552248</td>\n",
	" <td>11.001278</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SWW</th>\n",
	" <td>15420.835194</td>\n",
	" <td>0.267066</td>\n",
	" <td>0.798115</td>\n",
	" <td>0.102260</td>\n",
	" <td>124.180655</td>\n",
	" <td>26.629686</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>horizon_SEE</th>\n",
	" <td>19624.714225</td>\n",
	" <td>0.339871</td>\n",
	" <td>1.416270</td>\n",
	" <td>0.175065</td>\n",
	" <td>140.088237</td>\n",
	" <td>42.537268</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>NEIGUNG</th>\n",
	" <td>26818.497085</td>\n",
	" <td>0.464456</td>\n",
	" <td>1.345550</td>\n",
	" <td>0.299650</td>\n",
	" <td>163.763540</td>\n",
	" <td>66.212571</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>AUSRICHTUNG</th>\n",
	" <td>58258.264728</td>\n",
	" <td>1.008946</td>\n",
	" <td>32.625952</td>\n",
	" <td>0.844140</td>\n",
	" <td>241.367489</td>\n",
	" <td>143.816520</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" mse mse_norm cv_time delta_MSE RMSE \\\n",
	"all 9516.191579 0.164806 10.873207 0.000000 97.550969 \n",
	"all_reduced 10157.030298 0.175905 8.497254 0.011098 100.782093 \n",
	"horizon_NEE 10110.068305 0.175091 6.622710 0.010285 100.548835 \n",
	"horizon_NWW 10070.324628 0.174403 5.392025 0.009597 100.351007 \n",
	"horizon_SSE 10104.516453 0.174995 4.897716 0.010189 100.521224 \n",
	"horizon_SSW 10194.589369 0.176555 3.756270 0.011749 100.968259 \n",
	"horizon_E 10354.968933 0.179333 2.660432 0.014526 101.759368 \n",
	"horizon_W 10621.223454 0.183944 2.266722 0.019138 103.059320 \n",
	"horizon_S 11783.590448 0.204074 1.229297 0.039268 108.552248 \n",
	"horizon_SWW 15420.835194 0.267066 0.798115 0.102260 124.180655 \n",
	"horizon_SEE 19624.714225 0.339871 1.416270 0.175065 140.088237 \n",
	"NEIGUNG 26818.497085 0.464456 1.345550 0.299650 163.763540 \n",
	"AUSRICHTUNG 58258.264728 1.008946 32.625952 0.844140 241.367489 \n",
	"\n",
	" delta_RMSE \n",
	"all 0.000000 \n",
	"all_reduced 3.231124 \n",
	"horizon_NEE 2.997866 \n",
	"horizon_NWW 2.800038 \n",
	"horizon_SSE 2.970255 \n",
	"horizon_SSW 3.417290 \n",
	"horizon_E 4.208399 \n",
	"horizon_W 5.508351 \n",
	"horizon_S 11.001278 \n",
	"horizon_SWW 26.629686 \n",
	"horizon_SEE 42.537268 \n",
	"NEIGUNG 66.212571 \n",
	"AUSRICHTUNG 143.816520 "
	]
	},
	"execution_count": 134,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"MSE_plot"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 142,
	"metadata": {},
	"outputs": [
	{
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	" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
	" icon_img.addClass(image);\n",
	" icon_img.addClass('ui-corner-all');\n",
	"\n",
	" var tooltip_span = $('<span/>');\n",
	" tooltip_span.addClass('ui-button-text');\n",
	" tooltip_span.html(tooltip);\n",
	"\n",
	" button.append(icon_img);\n",
	" button.append(tooltip_span);\n",
	"\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" var fmt_picker_span = $('<span/>');\n",
	"\n",
	" var fmt_picker = $('<select/>');\n",
	" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
	" fmt_picker_span.append(fmt_picker);\n",
	" nav_element.append(fmt_picker_span);\n",
	" this.format_dropdown = fmt_picker[0];\n",
	"\n",
	" for (var ind in mpl.extensions) {\n",
	" var fmt = mpl.extensions[ind];\n",
	" var option = $(\n",
	" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
	" fmt_picker.append(option)\n",
	" }\n",
	"\n",
	" // Add hover states to the ui-buttons\n",
	" $( \".ui-button\" ).hover(\n",
	" function() { $(this).addClass(\"ui-state-hover\");},\n",
	" function() { $(this).removeClass(\"ui-state-hover\");}\n",
	" );\n",
	"\n",
	" var status_bar = $('<span class=\"mpl-message\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
	" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
	" // which will in turn request a refresh of the image.\n",
	" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_message = function(type, properties) {\n",
	" properties['type'] = type;\n",
	" properties['figure_id'] = this.id;\n",
	" this.ws.send(JSON.stringify(properties));\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_draw_message = function() {\n",
	" if (!this.waiting) {\n",
	" this.waiting = true;\n",
	" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
	" }\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" var format_dropdown = fig.format_dropdown;\n",
	" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
	" fig.ondownload(fig, format);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
	" var size = msg['size'];\n",
	" if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n",
	" fig._resize_canvas(size[0], size[1]);\n",
	" fig.send_message(\"refresh\", {});\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
	" var x0 = msg['x0'] / mpl.ratio;\n",
	" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
	" var x1 = msg['x1'] / mpl.ratio;\n",
	" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
	" x0 = Math.floor(x0) + 0.5;\n",
	" y0 = Math.floor(y0) + 0.5;\n",
	" x1 = Math.floor(x1) + 0.5;\n",
	" y1 = Math.floor(y1) + 0.5;\n",
	" var min_x = Math.min(x0, x1);\n",
	" var min_y = Math.min(y0, y1);\n",
	" var width = Math.abs(x1 - x0);\n",
	" var height = Math.abs(y1 - y0);\n",
	"\n",
	" fig.rubberband_context.clearRect(\n",
	" 0, 0, fig.canvas.width, fig.canvas.height);\n",
	"\n",
	" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
	" // Updates the figure title.\n",
	" fig.header.textContent = msg['label'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
	" var cursor = msg['cursor'];\n",
	" switch(cursor)\n",
	" {\n",
	" case 0:\n",
	" cursor = 'pointer';\n",
	" break;\n",
	" case 1:\n",
	" cursor = 'default';\n",
	" break;\n",
	" case 2:\n",
	" cursor = 'crosshair';\n",
	" break;\n",
	" case 3:\n",
	" cursor = 'move';\n",
	" break;\n",
	" }\n",
	" fig.rubberband_canvas.style.cursor = cursor;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
	" fig.message.textContent = msg['message'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
	" // Request the server to send over a new figure.\n",
	" fig.send_draw_message();\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
	" fig.image_mode = msg['mode'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Called whenever the canvas gets updated.\n",
	" this.send_message(\"ack\", {});\n",
	"}\n",
	"\n",
	"// A function to construct a web socket function for onmessage handling.\n",
	"// Called in the figure constructor.\n",
	"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
	" return function socket_on_message(evt) {\n",
	" if (evt.data instanceof Blob) {\n",
	" /* FIXME: We get \"Resource interpreted as Image but\n",
	" * transferred with MIME type text/plain:\" errors on\n",
	" * Chrome. But how to set the MIME type? It doesn't seem\n",
	" * to be part of the websocket stream */\n",
	" evt.data.type = \"image/png\";\n",
	"\n",
	" /* Free the memory for the previous frames */\n",
	" if (fig.imageObj.src) {\n",
	" (window.URL \|\| window.webkitURL).revokeObjectURL(\n",
	" fig.imageObj.src);\n",
	" }\n",
	"\n",
	" fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n",
	" evt.data);\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
	" fig.imageObj.src = evt.data;\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	"\n",
	" var msg = JSON.parse(evt.data);\n",
	" var msg_type = msg['type'];\n",
	"\n",
	" // Call the \"handle_{type}\" callback, which takes\n",
	" // the figure and JSON message as its only arguments.\n",
	" try {\n",
	" var callback = fig[\"handle_\" + msg_type];\n",
	" } catch (e) {\n",
	" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
	" return;\n",
	" }\n",
	"\n",
	" if (callback) {\n",
	" try {\n",
	" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
	" callback(fig, msg);\n",
	" } catch (e) {\n",
	" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
	" }\n",
	" }\n",
	" };\n",
	"}\n",
	"\n",
	"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
	"mpl.findpos = function(e) {\n",
	" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
	" var targ;\n",
	" if (!e)\n",
	" e = window.event;\n",
	" if (e.target)\n",
	" targ = e.target;\n",
	" else if (e.srcElement)\n",
	" targ = e.srcElement;\n",
	" if (targ.nodeType == 3) // defeat Safari bug\n",
	" targ = targ.parentNode;\n",
	"\n",
	" // jQuery normalizes the pageX and pageY\n",
	" // pageX,Y are the mouse positions relative to the document\n",
	" // offset() returns the position of the element relative to the document\n",
	" var x = e.pageX - $(targ).offset().left;\n",
	" var y = e.pageY - $(targ).offset().top;\n",
	"\n",
	" return {\"x\": x, \"y\": y};\n",
	"};\n",
	"\n",
	"/*\n",
	" * return a copy of an object with only non-object keys\n",
	" * we need this to avoid circular references\n",
	" * http://stackoverflow.com/a/24161582/3208463\n",
	" */\n",
	"function simpleKeys (original) {\n",
	" return Object.keys(original).reduce(function (obj, key) {\n",
	" if (typeof original[key] !== 'object')\n",
	" obj[key] = original[key]\n",
	" return obj;\n",
	" }, {});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.mouse_event = function(event, name) {\n",
	" var canvas_pos = mpl.findpos(event)\n",
	"\n",
	" if (name === 'button_press')\n",
	" {\n",
	" this.canvas.focus();\n",
	" this.canvas_div.focus();\n",
	" }\n",
	"\n",
	" var x = canvas_pos.x * mpl.ratio;\n",
	" var y = canvas_pos.y * mpl.ratio;\n",
	"\n",
	" this.send_message(name, {x: x, y: y, button: event.button,\n",
	" step: event.step,\n",
	" guiEvent: simpleKeys(event)});\n",
	"\n",
	" /* This prevents the web browser from automatically changing to\n",
	" * the text insertion cursor when the button is pressed. We want\n",
	" * to control all of the cursor setting manually through the\n",
	" * 'cursor' event from matplotlib */\n",
	" event.preventDefault();\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" // Handle any extra behaviour associated with a key event\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.key_event = function(event, name) {\n",
	"\n",
	" // Prevent repeat events\n",
	" if (name == 'key_press')\n",
	" {\n",
	" if (event.which === this._key)\n",
	" return;\n",
	" else\n",
	" this._key = event.which;\n",
	" }\n",
	" if (name == 'key_release')\n",
	" this._key = null;\n",
	"\n",
	" var value = '';\n",
	" if (event.ctrlKey && event.which != 17)\n",
	" value += \"ctrl+\";\n",
	" if (event.altKey && event.which != 18)\n",
	" value += \"alt+\";\n",
	" if (event.shiftKey && event.which != 16)\n",
	" value += \"shift+\";\n",
	"\n",
	" value += 'k';\n",
	" value += event.which.toString();\n",
	"\n",
	" this._key_event_extra(event, name);\n",
	"\n",
	" this.send_message(name, {key: value,\n",
	" guiEvent: simpleKeys(event)});\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
	" if (name == 'download') {\n",
	" this.handle_save(this, null);\n",
	" } else {\n",
	" this.send_message(\"toolbar_button\", {name: name});\n",
	" }\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
	" this.message.textContent = tooltip;\n",
	"};\n",
	"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
	"\n",
	"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
	"\n",
	"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
	" // Create a \"websocket\"-like object which calls the given IPython comm\n",
	" // object with the appropriate methods. Currently this is a non binary\n",
	" // socket, so there is still some room for performance tuning.\n",
	" var ws = {};\n",
	"\n",
	" ws.close = function() {\n",
	" comm.close()\n",
	" };\n",
	" ws.send = function(m) {\n",
	" //console.log('sending', m);\n",
	" comm.send(m);\n",
	" };\n",
	" // Register the callback with on_msg.\n",
	" comm.on_msg(function(msg) {\n",
	" //console.log('receiving', msg['content']['data'], msg);\n",
	" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
	" ws.onmessage(msg['content']['data'])\n",
	" });\n",
	" return ws;\n",
	"}\n",
	"\n",
	"mpl.mpl_figure_comm = function(comm, msg) {\n",
	" // This is the function which gets called when the mpl process\n",
	" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
	"\n",
	" var id = msg.content.data.id;\n",
	" // Get hold of the div created by the display call when the Comm\n",
	" // socket was opened in Python.\n",
	" var element = $(\"#\" + id);\n",
	" var ws_proxy = comm_websocket_adapter(comm)\n",
	"\n",
	" function ondownload(figure, format) {\n",
	" window.open(figure.imageObj.src);\n",
	" }\n",
	"\n",
	" var fig = new mpl.figure(id, ws_proxy,\n",
	" ondownload,\n",
	" element.get(0));\n",
	"\n",
	" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
	" // web socket which is closed, not our websocket->open comm proxy.\n",
	" ws_proxy.onopen();\n",
	"\n",
	" fig.parent_element = element.get(0);\n",
	" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
	" if (!fig.cell_info) {\n",
	" console.error(\"Failed to find cell for figure\", id, fig);\n",
	" return;\n",
	" }\n",
	"\n",
	" var output_index = fig.cell_info[2]\n",
	" var cell = fig.cell_info[0];\n",
	"\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
	" var width = fig.canvas.width/mpl.ratio\n",
	" fig.root.unbind('remove')\n",
	"\n",
	" // Update the output cell to use the data from the current canvas.\n",
	" fig.push_to_output();\n",
	" var dataURL = fig.canvas.toDataURL();\n",
	" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
	" // the notebook keyboard shortcuts fail.\n",
	" IPython.keyboard_manager.enable()\n",
	" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
	" fig.close_ws(fig, msg);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.close_ws = function(fig, msg){\n",
	" fig.send_message('closing', msg);\n",
	" // fig.ws.close()\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
	" // Turn the data on the canvas into data in the output cell.\n",
	" var width = this.canvas.width/mpl.ratio\n",
	" var dataURL = this.canvas.toDataURL();\n",
	" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Tell IPython that the notebook contents must change.\n",
	" IPython.notebook.set_dirty(true);\n",
	" this.send_message(\"ack\", {});\n",
	" var fig = this;\n",
	" // Wait a second, then push the new image to the DOM so\n",
	" // that it is saved nicely (might be nice to debounce this).\n",
	" setTimeout(function () { fig.push_to_output() }, 1000);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items){\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) { continue; };\n",
	"\n",
	" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" // Add the status bar.\n",
	" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"\n",
	" // Add the close button to the window.\n",
	" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
	" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
	" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
	" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
	" buttongrp.append(button);\n",
	" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
	" titlebar.prepend(buttongrp);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(el){\n",
	" var fig = this\n",
	" el.on(\"remove\", function(){\n",
	"\tfig.close_ws(fig, {});\n",
	" });\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(el){\n",
	" // this is important to make the div 'focusable\n",
	" el.attr('tabindex', 0)\n",
	" // reach out to IPython and tell the keyboard manager to turn it's self\n",
	" // off when our div gets focus\n",
	"\n",
	" // location in version 3\n",
	" if (IPython.notebook.keyboard_manager) {\n",
	" IPython.notebook.keyboard_manager.register_events(el);\n",
	" }\n",
	" else {\n",
	" // location in version 2\n",
	" IPython.keyboard_manager.register_events(el);\n",
	" }\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" var manager = IPython.notebook.keyboard_manager;\n",
	" if (!manager)\n",
	" manager = IPython.keyboard_manager;\n",
	"\n",
	" // Check for shift+enter\n",
	" if (event.shiftKey && event.which == 13) {\n",
	" this.canvas_div.blur();\n",
	" event.shiftKey = false;\n",
	" // Send a \"J\" for go to next cell\n",
	" event.which = 74;\n",
	" event.keyCode = 74;\n",
	" manager.command_mode();\n",
	" manager.handle_keydown(event);\n",
	" }\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" fig.ondownload(fig, null);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.find_output_cell = function(html_output) {\n",
	" // Return the cell and output element which can be found uniquely in the notebook.\n",
	" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
	" // IPython event is triggered only after the cells have been serialised, which for\n",
	" // our purposes (turning an active figure into a static one), is too late.\n",
	" var cells = IPython.notebook.get_cells();\n",
	" var ncells = cells.length;\n",
	" for (var i=0; i<ncells; i++) {\n",
	" var cell = cells[i];\n",
	" if (cell.cell_type === 'code'){\n",
	" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
	" var data = cell.output_area.outputs[j];\n",
	" if (data.data) {\n",
	" // IPython >= 3 moved mimebundle to data attribute of output\n",
	" data = data.data;\n",
	" }\n",
	" if (data['text/html'] == html_output) {\n",
	" return [cell, data, j];\n",
	" }\n",
	" }\n",
	" }\n",
	" }\n",
	"}\n",
	"\n",
	"// Register the function which deals with the matplotlib target/channel.\n",
	"// The kernel may be null if the page has been refreshed.\n",
	"if (IPython.notebook.kernel != null) {\n",
	" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
	"}\n"
	],
	"text/plain": [
	"<IPython.core.display.Javascript object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	},
	{
	"data": {
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\" width=\"640\">"
	],
	"text/plain": [
	"<IPython.core.display.HTML object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	},
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
	" warnings.warn(message, mplDeprecation, stacklevel=1)\n",
	"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/matplotlib/figure.py:2299: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
	" warnings.warn(\"This figure includes Axes that are not compatible \"\n"
	]
	}
	],
	"source": [
	"MSE_plot['delta_MSE'] = MSE_plot.mse_norm - MSE_plot.mse_norm[0]\n",
	"MSE_plot['delta_RMSE'] = MSE_plot.RMSE - MSE_plot.RMSE[0]\n",
	"\n",
	"THRESH = 5 # 0.02\n",
	"\n",
	"from mpl_toolkits.axes_grid.inset_locator import inset_axes\n",
	"plt.rc('axes', axisbelow=True)\n",
	"\n",
	"fig, ax = plt.subplots()\n",
	"plt.plot( [-1,14], [THRESH, THRESH], lw = 2, ls = '--', c = 'C1')\n",
	"plt.plot( list(range(len(ftrs), 0, -1)), MSE_plot.delta_RMSE.values[1:], lw = 2, c = 'C0')\n",
	"\n",
	"#plt.grid()\n",
	"plt.xlim((0.5,12.5))\n",
	"plt.xticks(range(1,13))\n",
	"plt.xlabel('Number of features')\n",
	"plt.ylabel('Increase in RMSE w.r.t. all features (in $kWh/m^2$)')\n",
	"\n",
	"# plt.yscale('log')\n",
	"\n",
	"# this is an inset axes over the main axes\n",
	"inset_axes = inset_axes(ax, \n",
	" width=\"60%\", # width = 30% of parent_bbox\n",
	" height=2.0,\n",
	" loc = 1) # height : 1 inch)\n",
	"plt.semilogy( [-1,14], [THRESH, THRESH], lw = 2, ls = '--', c = 'C1', label = 'threshold')\n",
	"plt.semilogy( list(range(len(ftrs), 0, -1)), MSE_plot.delta_RMSE.values[1:], lw = 2, c = 'C0', label = 'RMSE')\n",
	"plt.xlim((0.5,12.5))\n",
	"plt.grid('on')\n",
	"plt.legend()\n",
	"\n",
	"plt.tight_layout()\n",
	"plt.savefig('ftr_selection_topdown_reduced_run1.png', dpi = 300)"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# Get feature importance for final tree (with all features)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 56,
	"metadata": {},
	"outputs": [],
	"source": [
	"sel_ftrs = ['SIS', 'AUSRICHTUNG', 'horizon_S', 'horizon_SWW', 'horizon_SEE', 'NEIGUNG']\n",
	"feature_labels = ['Annual GHI', 'Roof aspect', 'Horizon S (180°)', 'Horizon SWW (240°)', \n",
	" 'Horizon SEE (120°)', 'Roof tilt']"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 48,
	"metadata": {},
	"outputs": [],
	"source": [
	"FEATURES = sel_ftrs"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 49,
	"metadata": {},
	"outputs": [],
	"source": [
	"model = 'RF'\n",
	"training = data.sample(100000)\n",
	"\n",
	"x_all, t_all, X_all, T_all = get_data( training, FEATURES, label_name)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 50,
	"metadata": {
	"scrolled": true
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"0\n",
	"0 10000\n",
	"Fitting time: 6.186s\n",
	"1\n",
	"10000 20000\n",
	"Fitting time: 6.114s\n",
	"2\n",
	"20000 30000\n",
	"Fitting time: 6.019s\n",
	"3\n",
	"30000 40000\n",
	"Fitting time: 6.530s\n",
	"4\n",
	"40000 50000\n",
	"Fitting time: 6.048s\n",
	"5\n",
	"50000 60000\n",
	"Fitting time: 6.112s\n",
	"6\n",
	"60000 70000\n",
	"Fitting time: 6.145s\n",
	"7\n",
	"70000 80000\n",
	"Fitting time: 6.256s\n",
	"8\n",
	"80000 90000\n",
	"Fitting time: 6.037s\n",
	"9\n",
	"90000 100000\n",
	"Fitting time: 6.335s\n"
	]
	}
	],
	"source": [
	"N_batches = 10\n",
	"batch_size = int(len(training) / N_batches)\n",
	"\n",
	"importances = []\n",
	"stds = []\n",
	"inds = []\n",
	"\n",
	"for i in range(N_batches):\n",
	" print(i)\n",
	" start = i * batch_size\n",
	" end = (i+1) * batch_size\n",
	" print(start, end)\n",
	" \n",
	" X = X_all[start:end,:]\n",
	" T = T_all[start:end]\n",
	" \n",
	" tt = time.time()\n",
	" est = set_estimators()[ model ]\n",
	" est.fit(X, T)\n",
	" print('Fitting time: %.3fs' %(time.time()-tt))\n",
	" \n",
	" # for standard estimators\n",
	" n_features = X.shape[1]\n",
	" importance = np.mean([tree.feature_importances_ for tree in est.estimators_],axis=0)\n",
	" std = np.std([tree.feature_importances_ for tree in est.estimators_],axis=0)\n",
	" ind = np.argsort(importance)[-n_features:]\n",
	" \n",
	" importances.append(importance)\n",
	" stds.append(std)\n",
	" inds.append(ind)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 51,
	"metadata": {},
	"outputs": [],
	"source": [
	"importance = np.asarray(importances).mean(axis = 0)\n",
	"std = np.asarray(stds).mean(axis = 0)\n",
	"ind = (np.asarray(inds).mean(axis = 0)).astype(int)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 52,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"[0.06774074 0.36268874 0.32808699 0.04382411 0.05129017 0.14636925]\n",
	"[0.00397842 0.0116344 0.01335585 0.0039962 0.00422148 0.00598959]\n",
	"[3 4 0 5 2 1]\n"
	]
	}
	],
	"source": [
	"print(importance)\n",
	"print(std)\n",
	"print(ind)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 62,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"application/javascript": [
	"/* Put everything inside the global mpl namespace */\n",
	"window.mpl = {};\n",
	"\n",
	"\n",
	"mpl.get_websocket_type = function() {\n",
	" if (typeof(WebSocket) !== 'undefined') {\n",
	" return WebSocket;\n",
	" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
	" return MozWebSocket;\n",
	" } else {\n",
	" alert('Your browser does not have WebSocket support.' +\n",
	" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
	" 'Firefox 4 and 5 are also supported but you ' +\n",
	" 'have to enable WebSockets in about:config.');\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
	" this.id = figure_id;\n",
	"\n",
	" this.ws = websocket;\n",
	"\n",
	" this.supports_binary = (this.ws.binaryType != undefined);\n",
	"\n",
	" if (!this.supports_binary) {\n",
	" var warnings = document.getElementById(\"mpl-warnings\");\n",
	" if (warnings) {\n",
	" warnings.style.display = 'block';\n",
	" warnings.textContent = (\n",
	" \"This browser does not support binary websocket messages. \" +\n",
	" \"Performance may be slow.\");\n",
	" }\n",
	" }\n",
	"\n",
	" this.imageObj = new Image();\n",
	"\n",
	" this.context = undefined;\n",
	" this.message = undefined;\n",
	" this.canvas = undefined;\n",
	" this.rubberband_canvas = undefined;\n",
	" this.rubberband_context = undefined;\n",
	" this.format_dropdown = undefined;\n",
	"\n",
	" this.image_mode = 'full';\n",
	"\n",
	" this.root = $('<div/>');\n",
	" this._root_extra_style(this.root)\n",
	" this.root.attr('style', 'display: inline-block');\n",
	"\n",
	" $(parent_element).append(this.root);\n",
	"\n",
	" this._init_header(this);\n",
	" this._init_canvas(this);\n",
	" this._init_toolbar(this);\n",
	"\n",
	" var fig = this;\n",
	"\n",
	" this.waiting = false;\n",
	"\n",
	" this.ws.onopen = function () {\n",
	" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
	" fig.send_message(\"send_image_mode\", {});\n",
	" if (mpl.ratio != 1) {\n",
	" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
	" }\n",
	" fig.send_message(\"refresh\", {});\n",
	" }\n",
	"\n",
	" this.imageObj.onload = function() {\n",
	" if (fig.image_mode == 'full') {\n",
	" // Full images could contain transparency (where diff images\n",
	" // almost always do), so we need to clear the canvas so that\n",
	" // there is no ghosting.\n",
	" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
	" }\n",
	" fig.context.drawImage(fig.imageObj, 0, 0);\n",
	" };\n",
	"\n",
	" this.imageObj.onunload = function() {\n",
	" fig.ws.close();\n",
	" }\n",
	"\n",
	" this.ws.onmessage = this._make_on_message_function(this);\n",
	"\n",
	" this.ondownload = ondownload;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_header = function() {\n",
	" var titlebar = $(\n",
	" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
	" 'ui-helper-clearfix\"/>');\n",
	" var titletext = $(\n",
	" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
	" 'text-align: center; padding: 3px;\"/>');\n",
	" titlebar.append(titletext)\n",
	" this.root.append(titlebar);\n",
	" this.header = titletext[0];\n",
	"}\n",
	"\n",
	"\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
	"\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_canvas = function() {\n",
	" var fig = this;\n",
	"\n",
	" var canvas_div = $('<div/>');\n",
	"\n",
	" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
	"\n",
	" function canvas_keyboard_event(event) {\n",
	" return fig.key_event(event, event['data']);\n",
	" }\n",
	"\n",
	" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
	" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
	" this.canvas_div = canvas_div\n",
	" this._canvas_extra_style(canvas_div)\n",
	" this.root.append(canvas_div);\n",
	"\n",
	" var canvas = $('<canvas/>');\n",
	" canvas.addClass('mpl-canvas');\n",
	" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
	"\n",
	" this.canvas = canvas[0];\n",
	" this.context = canvas[0].getContext(\"2d\");\n",
	"\n",
	" var backingStore = this.context.backingStorePixelRatio \|\|\n",
	"\tthis.context.webkitBackingStorePixelRatio \|\|\n",
	"\tthis.context.mozBackingStorePixelRatio \|\|\n",
	"\tthis.context.msBackingStorePixelRatio \|\|\n",
	"\tthis.context.oBackingStorePixelRatio \|\|\n",
	"\tthis.context.backingStorePixelRatio \|\| 1;\n",
	"\n",
	" mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n",
	"\n",
	" var rubberband = $('<canvas/>');\n",
	" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
	"\n",
	" var pass_mouse_events = true;\n",
	"\n",
	" canvas_div.resizable({\n",
	" start: function(event, ui) {\n",
	" pass_mouse_events = false;\n",
	" },\n",
	" resize: function(event, ui) {\n",
	" fig.request_resize(ui.size.width, ui.size.height);\n",
	" },\n",
	" stop: function(event, ui) {\n",
	" pass_mouse_events = true;\n",
	" fig.request_resize(ui.size.width, ui.size.height);\n",
	" },\n",
	" });\n",
	"\n",
	" function mouse_event_fn(event) {\n",
	" if (pass_mouse_events)\n",
	" return fig.mouse_event(event, event['data']);\n",
	" }\n",
	"\n",
	" rubberband.mousedown('button_press', mouse_event_fn);\n",
	" rubberband.mouseup('button_release', mouse_event_fn);\n",
	" // Throttle sequential mouse events to 1 every 20ms.\n",
	" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
	"\n",
	" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
	" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
	"\n",
	" canvas_div.on(\"wheel\", function (event) {\n",
	" event = event.originalEvent;\n",
	" event['data'] = 'scroll'\n",
	" if (event.deltaY < 0) {\n",
	" event.step = 1;\n",
	" } else {\n",
	" event.step = -1;\n",
	" }\n",
	" mouse_event_fn(event);\n",
	" });\n",
	"\n",
	" canvas_div.append(canvas);\n",
	" canvas_div.append(rubberband);\n",
	"\n",
	" this.rubberband = rubberband;\n",
	" this.rubberband_canvas = rubberband[0];\n",
	" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
	" this.rubberband_context.strokeStyle = \"#000000\";\n",
	"\n",
	" this._resize_canvas = function(width, height) {\n",
	" // Keep the size of the canvas, canvas container, and rubber band\n",
	" // canvas in synch.\n",
	" canvas_div.css('width', width)\n",
	" canvas_div.css('height', height)\n",
	"\n",
	" canvas.attr('width', width * mpl.ratio);\n",
	" canvas.attr('height', height * mpl.ratio);\n",
	" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
	"\n",
	" rubberband.attr('width', width);\n",
	" rubberband.attr('height', height);\n",
	" }\n",
	"\n",
	" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
	" // upon first draw.\n",
	" this._resize_canvas(600, 600);\n",
	"\n",
	" // Disable right mouse context menu.\n",
	" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
	" return false;\n",
	" });\n",
	"\n",
	" function set_focus () {\n",
	" canvas.focus();\n",
	" canvas_div.focus();\n",
	" }\n",
	"\n",
	" window.setTimeout(set_focus, 100);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items) {\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) {\n",
	" // put a spacer in here.\n",
	" continue;\n",
	" }\n",
	" var button = $('<button/>');\n",
	" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
	" 'ui-button-icon-only');\n",
	" button.attr('role', 'button');\n",
	" button.attr('aria-disabled', 'false');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	"\n",
	" var icon_img = $('<span/>');\n",
	" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
	" icon_img.addClass(image);\n",
	" icon_img.addClass('ui-corner-all');\n",
	"\n",
	" var tooltip_span = $('<span/>');\n",
	" tooltip_span.addClass('ui-button-text');\n",
	" tooltip_span.html(tooltip);\n",
	"\n",
	" button.append(icon_img);\n",
	" button.append(tooltip_span);\n",
	"\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" var fmt_picker_span = $('<span/>');\n",
	"\n",
	" var fmt_picker = $('<select/>');\n",
	" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
	" fmt_picker_span.append(fmt_picker);\n",
	" nav_element.append(fmt_picker_span);\n",
	" this.format_dropdown = fmt_picker[0];\n",
	"\n",
	" for (var ind in mpl.extensions) {\n",
	" var fmt = mpl.extensions[ind];\n",
	" var option = $(\n",
	" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
	" fmt_picker.append(option)\n",
	" }\n",
	"\n",
	" // Add hover states to the ui-buttons\n",
	" $( \".ui-button\" ).hover(\n",
	" function() { $(this).addClass(\"ui-state-hover\");},\n",
	" function() { $(this).removeClass(\"ui-state-hover\");}\n",
	" );\n",
	"\n",
	" var status_bar = $('<span class=\"mpl-message\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
	" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
	" // which will in turn request a refresh of the image.\n",
	" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_message = function(type, properties) {\n",
	" properties['type'] = type;\n",
	" properties['figure_id'] = this.id;\n",
	" this.ws.send(JSON.stringify(properties));\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_draw_message = function() {\n",
	" if (!this.waiting) {\n",
	" this.waiting = true;\n",
	" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
	" }\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" var format_dropdown = fig.format_dropdown;\n",
	" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
	" fig.ondownload(fig, format);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
	" var size = msg['size'];\n",
	" if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n",
	" fig._resize_canvas(size[0], size[1]);\n",
	" fig.send_message(\"refresh\", {});\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
	" var x0 = msg['x0'] / mpl.ratio;\n",
	" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
	" var x1 = msg['x1'] / mpl.ratio;\n",
	" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
	" x0 = Math.floor(x0) + 0.5;\n",
	" y0 = Math.floor(y0) + 0.5;\n",
	" x1 = Math.floor(x1) + 0.5;\n",
	" y1 = Math.floor(y1) + 0.5;\n",
	" var min_x = Math.min(x0, x1);\n",
	" var min_y = Math.min(y0, y1);\n",
	" var width = Math.abs(x1 - x0);\n",
	" var height = Math.abs(y1 - y0);\n",
	"\n",
	" fig.rubberband_context.clearRect(\n",
	" 0, 0, fig.canvas.width, fig.canvas.height);\n",
	"\n",
	" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
	" // Updates the figure title.\n",
	" fig.header.textContent = msg['label'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
	" var cursor = msg['cursor'];\n",
	" switch(cursor)\n",
	" {\n",
	" case 0:\n",
	" cursor = 'pointer';\n",
	" break;\n",
	" case 1:\n",
	" cursor = 'default';\n",
	" break;\n",
	" case 2:\n",
	" cursor = 'crosshair';\n",
	" break;\n",
	" case 3:\n",
	" cursor = 'move';\n",
	" break;\n",
	" }\n",
	" fig.rubberband_canvas.style.cursor = cursor;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
	" fig.message.textContent = msg['message'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
	" // Request the server to send over a new figure.\n",
	" fig.send_draw_message();\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
	" fig.image_mode = msg['mode'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Called whenever the canvas gets updated.\n",
	" this.send_message(\"ack\", {});\n",
	"}\n",
	"\n",
	"// A function to construct a web socket function for onmessage handling.\n",
	"// Called in the figure constructor.\n",
	"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
	" return function socket_on_message(evt) {\n",
	" if (evt.data instanceof Blob) {\n",
	" /* FIXME: We get \"Resource interpreted as Image but\n",
	" * transferred with MIME type text/plain:\" errors on\n",
	" * Chrome. But how to set the MIME type? It doesn't seem\n",
	" * to be part of the websocket stream */\n",
	" evt.data.type = \"image/png\";\n",
	"\n",
	" /* Free the memory for the previous frames */\n",
	" if (fig.imageObj.src) {\n",
	" (window.URL \|\| window.webkitURL).revokeObjectURL(\n",
	" fig.imageObj.src);\n",
	" }\n",
	"\n",
	" fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n",
	" evt.data);\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
	" fig.imageObj.src = evt.data;\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	"\n",
	" var msg = JSON.parse(evt.data);\n",
	" var msg_type = msg['type'];\n",
	"\n",
	" // Call the \"handle_{type}\" callback, which takes\n",
	" // the figure and JSON message as its only arguments.\n",
	" try {\n",
	" var callback = fig[\"handle_\" + msg_type];\n",
	" } catch (e) {\n",
	" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
	" return;\n",
	" }\n",
	"\n",
	" if (callback) {\n",
	" try {\n",
	" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
	" callback(fig, msg);\n",
	" } catch (e) {\n",
	" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
	" }\n",
	" }\n",
	" };\n",
	"}\n",
	"\n",
	"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
	"mpl.findpos = function(e) {\n",
	" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
	" var targ;\n",
	" if (!e)\n",
	" e = window.event;\n",
	" if (e.target)\n",
	" targ = e.target;\n",
	" else if (e.srcElement)\n",
	" targ = e.srcElement;\n",
	" if (targ.nodeType == 3) // defeat Safari bug\n",
	" targ = targ.parentNode;\n",
	"\n",
	" // jQuery normalizes the pageX and pageY\n",
	" // pageX,Y are the mouse positions relative to the document\n",
	" // offset() returns the position of the element relative to the document\n",
	" var x = e.pageX - $(targ).offset().left;\n",
	" var y = e.pageY - $(targ).offset().top;\n",
	"\n",
	" return {\"x\": x, \"y\": y};\n",
	"};\n",
	"\n",
	"/*\n",
	" * return a copy of an object with only non-object keys\n",
	" * we need this to avoid circular references\n",
	" * http://stackoverflow.com/a/24161582/3208463\n",
	" */\n",
	"function simpleKeys (original) {\n",
	" return Object.keys(original).reduce(function (obj, key) {\n",
	" if (typeof original[key] !== 'object')\n",
	" obj[key] = original[key]\n",
	" return obj;\n",
	" }, {});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.mouse_event = function(event, name) {\n",
	" var canvas_pos = mpl.findpos(event)\n",
	"\n",
	" if (name === 'button_press')\n",
	" {\n",
	" this.canvas.focus();\n",
	" this.canvas_div.focus();\n",
	" }\n",
	"\n",
	" var x = canvas_pos.x * mpl.ratio;\n",
	" var y = canvas_pos.y * mpl.ratio;\n",
	"\n",
	" this.send_message(name, {x: x, y: y, button: event.button,\n",
	" step: event.step,\n",
	" guiEvent: simpleKeys(event)});\n",
	"\n",
	" /* This prevents the web browser from automatically changing to\n",
	" * the text insertion cursor when the button is pressed. We want\n",
	" * to control all of the cursor setting manually through the\n",
	" * 'cursor' event from matplotlib */\n",
	" event.preventDefault();\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" // Handle any extra behaviour associated with a key event\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.key_event = function(event, name) {\n",
	"\n",
	" // Prevent repeat events\n",
	" if (name == 'key_press')\n",
	" {\n",
	" if (event.which === this._key)\n",
	" return;\n",
	" else\n",
	" this._key = event.which;\n",
	" }\n",
	" if (name == 'key_release')\n",
	" this._key = null;\n",
	"\n",
	" var value = '';\n",
	" if (event.ctrlKey && event.which != 17)\n",
	" value += \"ctrl+\";\n",
	" if (event.altKey && event.which != 18)\n",
	" value += \"alt+\";\n",
	" if (event.shiftKey && event.which != 16)\n",
	" value += \"shift+\";\n",
	"\n",
	" value += 'k';\n",
	" value += event.which.toString();\n",
	"\n",
	" this._key_event_extra(event, name);\n",
	"\n",
	" this.send_message(name, {key: value,\n",
	" guiEvent: simpleKeys(event)});\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
	" if (name == 'download') {\n",
	" this.handle_save(this, null);\n",
	" } else {\n",
	" this.send_message(\"toolbar_button\", {name: name});\n",
	" }\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
	" this.message.textContent = tooltip;\n",
	"};\n",
	"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
	"\n",
	"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
	"\n",
	"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
	" // Create a \"websocket\"-like object which calls the given IPython comm\n",
	" // object with the appropriate methods. Currently this is a non binary\n",
	" // socket, so there is still some room for performance tuning.\n",
	" var ws = {};\n",
	"\n",
	" ws.close = function() {\n",
	" comm.close()\n",
	" };\n",
	" ws.send = function(m) {\n",
	" //console.log('sending', m);\n",
	" comm.send(m);\n",
	" };\n",
	" // Register the callback with on_msg.\n",
	" comm.on_msg(function(msg) {\n",
	" //console.log('receiving', msg['content']['data'], msg);\n",
	" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
	" ws.onmessage(msg['content']['data'])\n",
	" });\n",
	" return ws;\n",
	"}\n",
	"\n",
	"mpl.mpl_figure_comm = function(comm, msg) {\n",
	" // This is the function which gets called when the mpl process\n",
	" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
	"\n",
	" var id = msg.content.data.id;\n",
	" // Get hold of the div created by the display call when the Comm\n",
	" // socket was opened in Python.\n",
	" var element = $(\"#\" + id);\n",
	" var ws_proxy = comm_websocket_adapter(comm)\n",
	"\n",
	" function ondownload(figure, format) {\n",
	" window.open(figure.imageObj.src);\n",
	" }\n",
	"\n",
	" var fig = new mpl.figure(id, ws_proxy,\n",
	" ondownload,\n",
	" element.get(0));\n",
	"\n",
	" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
	" // web socket which is closed, not our websocket->open comm proxy.\n",
	" ws_proxy.onopen();\n",
	"\n",
	" fig.parent_element = element.get(0);\n",
	" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
	" if (!fig.cell_info) {\n",
	" console.error(\"Failed to find cell for figure\", id, fig);\n",
	" return;\n",
	" }\n",
	"\n",
	" var output_index = fig.cell_info[2]\n",
	" var cell = fig.cell_info[0];\n",
	"\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
	" var width = fig.canvas.width/mpl.ratio\n",
	" fig.root.unbind('remove')\n",
	"\n",
	" // Update the output cell to use the data from the current canvas.\n",
	" fig.push_to_output();\n",
	" var dataURL = fig.canvas.toDataURL();\n",
	" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
	" // the notebook keyboard shortcuts fail.\n",
	" IPython.keyboard_manager.enable()\n",
	" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
	" fig.close_ws(fig, msg);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.close_ws = function(fig, msg){\n",
	" fig.send_message('closing', msg);\n",
	" // fig.ws.close()\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
	" // Turn the data on the canvas into data in the output cell.\n",
	" var width = this.canvas.width/mpl.ratio\n",
	" var dataURL = this.canvas.toDataURL();\n",
	" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Tell IPython that the notebook contents must change.\n",
	" IPython.notebook.set_dirty(true);\n",
	" this.send_message(\"ack\", {});\n",
	" var fig = this;\n",
	" // Wait a second, then push the new image to the DOM so\n",
	" // that it is saved nicely (might be nice to debounce this).\n",
	" setTimeout(function () { fig.push_to_output() }, 1000);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items){\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) { continue; };\n",
	"\n",
	" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" // Add the status bar.\n",
	" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"\n",
	" // Add the close button to the window.\n",
	" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
	" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
	" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
	" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
	" buttongrp.append(button);\n",
	" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
	" titlebar.prepend(buttongrp);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(el){\n",
	" var fig = this\n",
	" el.on(\"remove\", function(){\n",
	"\tfig.close_ws(fig, {});\n",
	" });\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(el){\n",
	" // this is important to make the div 'focusable\n",
	" el.attr('tabindex', 0)\n",
	" // reach out to IPython and tell the keyboard manager to turn it's self\n",
	" // off when our div gets focus\n",
	"\n",
	" // location in version 3\n",
	" if (IPython.notebook.keyboard_manager) {\n",
	" IPython.notebook.keyboard_manager.register_events(el);\n",
	" }\n",
	" else {\n",
	" // location in version 2\n",
	" IPython.keyboard_manager.register_events(el);\n",
	" }\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" var manager = IPython.notebook.keyboard_manager;\n",
	" if (!manager)\n",
	" manager = IPython.keyboard_manager;\n",
	"\n",
	" // Check for shift+enter\n",
	" if (event.shiftKey && event.which == 13) {\n",
	" this.canvas_div.blur();\n",
	" event.shiftKey = false;\n",
	" // Send a \"J\" for go to next cell\n",
	" event.which = 74;\n",
	" event.keyCode = 74;\n",
	" manager.command_mode();\n",
	" manager.handle_keydown(event);\n",
	" }\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" fig.ondownload(fig, null);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.find_output_cell = function(html_output) {\n",
	" // Return the cell and output element which can be found uniquely in the notebook.\n",
	" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
	" // IPython event is triggered only after the cells have been serialised, which for\n",
	" // our purposes (turning an active figure into a static one), is too late.\n",
	" var cells = IPython.notebook.get_cells();\n",
	" var ncells = cells.length;\n",
	" for (var i=0; i<ncells; i++) {\n",
	" var cell = cells[i];\n",
	" if (cell.cell_type === 'code'){\n",
	" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
	" var data = cell.output_area.outputs[j];\n",
	" if (data.data) {\n",
	" // IPython >= 3 moved mimebundle to data attribute of output\n",
	" data = data.data;\n",
	" }\n",
	" if (data['text/html'] == html_output) {\n",
	" return [cell, data, j];\n",
	" }\n",
	" }\n",
	" }\n",
	" }\n",
	"}\n",
	"\n",
	"// Register the function which deals with the matplotlib target/channel.\n",
	"// The kernel may be null if the page has been refreshed.\n",
	"if (IPython.notebook.kernel != null) {\n",
	" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
	"}\n"
	],
	"text/plain": [
	"<IPython.core.display.Javascript object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	},
	{
	"data": {
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\" width=\"640\">"
	],
	"text/plain": [
	"<IPython.core.display.HTML object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.figure()\n",
	"ax = plt.axes()\n",
	"# plt.title(\"Feature Importance for RF (%dk features)\" %(X.shape[0]/1000)) # \\n Testing RMSE: %f ; , np.sqrt(mse(Y_te,T_te))\n",
	"plt.barh(range(n_features),importance[ind],\n",
	" color=\"C1\", xerr=std[ind], alpha=0.5, align=\"center\", capsize = 2)\n",
	"plt.yticks(range(n_features), np.asarray(feature_labels)[ind])\n",
	"plt.ylim([-1, len(ind)])\n",
	"plt.xlabel('Feature importance')\n",
	"plt.tight_layout()\n",
	"plt.show()\n",
	"# plt.savefig('Feature_importance_selFtrs_mean_10k.png', dpi=300)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	},
	{
	"cell_type": "code",
	"execution_count": 118,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"application/javascript": [
	"/* Put everything inside the global mpl namespace */\n",
	"window.mpl = {};\n",
	"\n",
	"\n",
	"mpl.get_websocket_type = function() {\n",
	" if (typeof(WebSocket) !== 'undefined') {\n",
	" return WebSocket;\n",
	" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
	" return MozWebSocket;\n",
	" } else {\n",
	" alert('Your browser does not have WebSocket support.' +\n",
	" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
	" 'Firefox 4 and 5 are also supported but you ' +\n",
	" 'have to enable WebSockets in about:config.');\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
	" this.id = figure_id;\n",
	"\n",
	" this.ws = websocket;\n",
	"\n",
	" this.supports_binary = (this.ws.binaryType != undefined);\n",
	"\n",
	" if (!this.supports_binary) {\n",
	" var warnings = document.getElementById(\"mpl-warnings\");\n",
	" if (warnings) {\n",
	" warnings.style.display = 'block';\n",
	" warnings.textContent = (\n",
	" \"This browser does not support binary websocket messages. \" +\n",
	" \"Performance may be slow.\");\n",
	" }\n",
	" }\n",
	"\n",
	" this.imageObj = new Image();\n",
	"\n",
	" this.context = undefined;\n",
	" this.message = undefined;\n",
	" this.canvas = undefined;\n",
	" this.rubberband_canvas = undefined;\n",
	" this.rubberband_context = undefined;\n",
	" this.format_dropdown = undefined;\n",
	"\n",
	" this.image_mode = 'full';\n",
	"\n",
	" this.root = $('<div/>');\n",
	" this._root_extra_style(this.root)\n",
	" this.root.attr('style', 'display: inline-block');\n",
	"\n",
	" $(parent_element).append(this.root);\n",
	"\n",
	" this._init_header(this);\n",
	" this._init_canvas(this);\n",
	" this._init_toolbar(this);\n",
	"\n",
	" var fig = this;\n",
	"\n",
	" this.waiting = false;\n",
	"\n",
	" this.ws.onopen = function () {\n",
	" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
	" fig.send_message(\"send_image_mode\", {});\n",
	" if (mpl.ratio != 1) {\n",
	" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
	" }\n",
	" fig.send_message(\"refresh\", {});\n",
	" }\n",
	"\n",
	" this.imageObj.onload = function() {\n",
	" if (fig.image_mode == 'full') {\n",
	" // Full images could contain transparency (where diff images\n",
	" // almost always do), so we need to clear the canvas so that\n",
	" // there is no ghosting.\n",
	" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
	" }\n",
	" fig.context.drawImage(fig.imageObj, 0, 0);\n",
	" };\n",
	"\n",
	" this.imageObj.onunload = function() {\n",
	" fig.ws.close();\n",
	" }\n",
	"\n",
	" this.ws.onmessage = this._make_on_message_function(this);\n",
	"\n",
	" this.ondownload = ondownload;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_header = function() {\n",
	" var titlebar = $(\n",
	" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
	" 'ui-helper-clearfix\"/>');\n",
	" var titletext = $(\n",
	" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
	" 'text-align: center; padding: 3px;\"/>');\n",
	" titlebar.append(titletext)\n",
	" this.root.append(titlebar);\n",
	" this.header = titletext[0];\n",
	"}\n",
	"\n",
	"\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
	"\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_canvas = function() {\n",
	" var fig = this;\n",
	"\n",
	" var canvas_div = $('<div/>');\n",
	"\n",
	" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
	"\n",
	" function canvas_keyboard_event(event) {\n",
	" return fig.key_event(event, event['data']);\n",
	" }\n",
	"\n",
	" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
	" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
	" this.canvas_div = canvas_div\n",
	" this._canvas_extra_style(canvas_div)\n",
	" this.root.append(canvas_div);\n",
	"\n",
	" var canvas = $('<canvas/>');\n",
	" canvas.addClass('mpl-canvas');\n",
	" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
	"\n",
	" this.canvas = canvas[0];\n",
	" this.context = canvas[0].getContext(\"2d\");\n",
	"\n",
	" var backingStore = this.context.backingStorePixelRatio \|\|\n",
	"\tthis.context.webkitBackingStorePixelRatio \|\|\n",
	"\tthis.context.mozBackingStorePixelRatio \|\|\n",
	"\tthis.context.msBackingStorePixelRatio \|\|\n",
	"\tthis.context.oBackingStorePixelRatio \|\|\n",
	"\tthis.context.backingStorePixelRatio \|\| 1;\n",
	"\n",
	" mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n",
	"\n",
	" var rubberband = $('<canvas/>');\n",
	" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
	"\n",
	" var pass_mouse_events = true;\n",
	"\n",
	" canvas_div.resizable({\n",
	" start: function(event, ui) {\n",
	" pass_mouse_events = false;\n",
	" },\n",
	" resize: function(event, ui) {\n",
	" fig.request_resize(ui.size.width, ui.size.height);\n",
	" },\n",
	" stop: function(event, ui) {\n",
	" pass_mouse_events = true;\n",
	" fig.request_resize(ui.size.width, ui.size.height);\n",
	" },\n",
	" });\n",
	"\n",
	" function mouse_event_fn(event) {\n",
	" if (pass_mouse_events)\n",
	" return fig.mouse_event(event, event['data']);\n",
	" }\n",
	"\n",
	" rubberband.mousedown('button_press', mouse_event_fn);\n",
	" rubberband.mouseup('button_release', mouse_event_fn);\n",
	" // Throttle sequential mouse events to 1 every 20ms.\n",
	" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
	"\n",
	" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
	" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
	"\n",
	" canvas_div.on(\"wheel\", function (event) {\n",
	" event = event.originalEvent;\n",
	" event['data'] = 'scroll'\n",
	" if (event.deltaY < 0) {\n",
	" event.step = 1;\n",
	" } else {\n",
	" event.step = -1;\n",
	" }\n",
	" mouse_event_fn(event);\n",
	" });\n",
	"\n",
	" canvas_div.append(canvas);\n",
	" canvas_div.append(rubberband);\n",
	"\n",
	" this.rubberband = rubberband;\n",
	" this.rubberband_canvas = rubberband[0];\n",
	" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
	" this.rubberband_context.strokeStyle = \"#000000\";\n",
	"\n",
	" this._resize_canvas = function(width, height) {\n",
	" // Keep the size of the canvas, canvas container, and rubber band\n",
	" // canvas in synch.\n",
	" canvas_div.css('width', width)\n",
	" canvas_div.css('height', height)\n",
	"\n",
	" canvas.attr('width', width * mpl.ratio);\n",
	" canvas.attr('height', height * mpl.ratio);\n",
	" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
	"\n",
	" rubberband.attr('width', width);\n",
	" rubberband.attr('height', height);\n",
	" }\n",
	"\n",
	" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
	" // upon first draw.\n",
	" this._resize_canvas(600, 600);\n",
	"\n",
	" // Disable right mouse context menu.\n",
	" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
	" return false;\n",
	" });\n",
	"\n",
	" function set_focus () {\n",
	" canvas.focus();\n",
	" canvas_div.focus();\n",
	" }\n",
	"\n",
	" window.setTimeout(set_focus, 100);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items) {\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) {\n",
	" // put a spacer in here.\n",
	" continue;\n",
	" }\n",
	" var button = $('<button/>');\n",
	" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
	" 'ui-button-icon-only');\n",
	" button.attr('role', 'button');\n",
	" button.attr('aria-disabled', 'false');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	"\n",
	" var icon_img = $('<span/>');\n",
	" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
	" icon_img.addClass(image);\n",
	" icon_img.addClass('ui-corner-all');\n",
	"\n",
	" var tooltip_span = $('<span/>');\n",
	" tooltip_span.addClass('ui-button-text');\n",
	" tooltip_span.html(tooltip);\n",
	"\n",
	" button.append(icon_img);\n",
	" button.append(tooltip_span);\n",
	"\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" var fmt_picker_span = $('<span/>');\n",
	"\n",
	" var fmt_picker = $('<select/>');\n",
	" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
	" fmt_picker_span.append(fmt_picker);\n",
	" nav_element.append(fmt_picker_span);\n",
	" this.format_dropdown = fmt_picker[0];\n",
	"\n",
	" for (var ind in mpl.extensions) {\n",
	" var fmt = mpl.extensions[ind];\n",
	" var option = $(\n",
	" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
	" fmt_picker.append(option)\n",
	" }\n",
	"\n",
	" // Add hover states to the ui-buttons\n",
	" $( \".ui-button\" ).hover(\n",
	" function() { $(this).addClass(\"ui-state-hover\");},\n",
	" function() { $(this).removeClass(\"ui-state-hover\");}\n",
	" );\n",
	"\n",
	" var status_bar = $('<span class=\"mpl-message\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
	" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
	" // which will in turn request a refresh of the image.\n",
	" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_message = function(type, properties) {\n",
	" properties['type'] = type;\n",
	" properties['figure_id'] = this.id;\n",
	" this.ws.send(JSON.stringify(properties));\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_draw_message = function() {\n",
	" if (!this.waiting) {\n",
	" this.waiting = true;\n",
	" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
	" }\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" var format_dropdown = fig.format_dropdown;\n",
	" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
	" fig.ondownload(fig, format);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
	" var size = msg['size'];\n",
	" if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n",
	" fig._resize_canvas(size[0], size[1]);\n",
	" fig.send_message(\"refresh\", {});\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
	" var x0 = msg['x0'] / mpl.ratio;\n",
	" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
	" var x1 = msg['x1'] / mpl.ratio;\n",
	" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
	" x0 = Math.floor(x0) + 0.5;\n",
	" y0 = Math.floor(y0) + 0.5;\n",
	" x1 = Math.floor(x1) + 0.5;\n",
	" y1 = Math.floor(y1) + 0.5;\n",
	" var min_x = Math.min(x0, x1);\n",
	" var min_y = Math.min(y0, y1);\n",
	" var width = Math.abs(x1 - x0);\n",
	" var height = Math.abs(y1 - y0);\n",
	"\n",
	" fig.rubberband_context.clearRect(\n",
	" 0, 0, fig.canvas.width, fig.canvas.height);\n",
	"\n",
	" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
	" // Updates the figure title.\n",
	" fig.header.textContent = msg['label'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
	" var cursor = msg['cursor'];\n",
	" switch(cursor)\n",
	" {\n",
	" case 0:\n",
	" cursor = 'pointer';\n",
	" break;\n",
	" case 1:\n",
	" cursor = 'default';\n",
	" break;\n",
	" case 2:\n",
	" cursor = 'crosshair';\n",
	" break;\n",
	" case 3:\n",
	" cursor = 'move';\n",
	" break;\n",
	" }\n",
	" fig.rubberband_canvas.style.cursor = cursor;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
	" fig.message.textContent = msg['message'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
	" // Request the server to send over a new figure.\n",
	" fig.send_draw_message();\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
	" fig.image_mode = msg['mode'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Called whenever the canvas gets updated.\n",
	" this.send_message(\"ack\", {});\n",
	"}\n",
	"\n",
	"// A function to construct a web socket function for onmessage handling.\n",
	"// Called in the figure constructor.\n",
	"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
	" return function socket_on_message(evt) {\n",
	" if (evt.data instanceof Blob) {\n",
	" /* FIXME: We get \"Resource interpreted as Image but\n",
	" * transferred with MIME type text/plain:\" errors on\n",
	" * Chrome. But how to set the MIME type? It doesn't seem\n",
	" * to be part of the websocket stream */\n",
	" evt.data.type = \"image/png\";\n",
	"\n",
	" /* Free the memory for the previous frames */\n",
	" if (fig.imageObj.src) {\n",
	" (window.URL \|\| window.webkitURL).revokeObjectURL(\n",
	" fig.imageObj.src);\n",
	" }\n",
	"\n",
	" fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n",
	" evt.data);\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
	" fig.imageObj.src = evt.data;\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	"\n",
	" var msg = JSON.parse(evt.data);\n",
	" var msg_type = msg['type'];\n",
	"\n",
	" // Call the \"handle_{type}\" callback, which takes\n",
	" // the figure and JSON message as its only arguments.\n",
	" try {\n",
	" var callback = fig[\"handle_\" + msg_type];\n",
	" } catch (e) {\n",
	" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
	" return;\n",
	" }\n",
	"\n",
	" if (callback) {\n",
	" try {\n",
	" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
	" callback(fig, msg);\n",
	" } catch (e) {\n",
	" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
	" }\n",
	" }\n",
	" };\n",
	"}\n",
	"\n",
	"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
	"mpl.findpos = function(e) {\n",
	" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
	" var targ;\n",
	" if (!e)\n",
	" e = window.event;\n",
	" if (e.target)\n",
	" targ = e.target;\n",
	" else if (e.srcElement)\n",
	" targ = e.srcElement;\n",
	" if (targ.nodeType == 3) // defeat Safari bug\n",
	" targ = targ.parentNode;\n",
	"\n",
	" // jQuery normalizes the pageX and pageY\n",
	" // pageX,Y are the mouse positions relative to the document\n",
	" // offset() returns the position of the element relative to the document\n",
	" var x = e.pageX - $(targ).offset().left;\n",
	" var y = e.pageY - $(targ).offset().top;\n",
	"\n",
	" return {\"x\": x, \"y\": y};\n",
	"};\n",
	"\n",
	"/*\n",
	" * return a copy of an object with only non-object keys\n",
	" * we need this to avoid circular references\n",
	" * http://stackoverflow.com/a/24161582/3208463\n",
	" */\n",
	"function simpleKeys (original) {\n",
	" return Object.keys(original).reduce(function (obj, key) {\n",
	" if (typeof original[key] !== 'object')\n",
	" obj[key] = original[key]\n",
	" return obj;\n",
	" }, {});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.mouse_event = function(event, name) {\n",
	" var canvas_pos = mpl.findpos(event)\n",
	"\n",
	" if (name === 'button_press')\n",
	" {\n",
	" this.canvas.focus();\n",
	" this.canvas_div.focus();\n",
	" }\n",
	"\n",
	" var x = canvas_pos.x * mpl.ratio;\n",
	" var y = canvas_pos.y * mpl.ratio;\n",
	"\n",
	" this.send_message(name, {x: x, y: y, button: event.button,\n",
	" step: event.step,\n",
	" guiEvent: simpleKeys(event)});\n",
	"\n",
	" /* This prevents the web browser from automatically changing to\n",
	" * the text insertion cursor when the button is pressed. We want\n",
	" * to control all of the cursor setting manually through the\n",
	" * 'cursor' event from matplotlib */\n",
	" event.preventDefault();\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" // Handle any extra behaviour associated with a key event\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.key_event = function(event, name) {\n",
	"\n",
	" // Prevent repeat events\n",
	" if (name == 'key_press')\n",
	" {\n",
	" if (event.which === this._key)\n",
	" return;\n",
	" else\n",
	" this._key = event.which;\n",
	" }\n",
	" if (name == 'key_release')\n",
	" this._key = null;\n",
	"\n",
	" var value = '';\n",
	" if (event.ctrlKey && event.which != 17)\n",
	" value += \"ctrl+\";\n",
	" if (event.altKey && event.which != 18)\n",
	" value += \"alt+\";\n",
	" if (event.shiftKey && event.which != 16)\n",
	" value += \"shift+\";\n",
	"\n",
	" value += 'k';\n",
	" value += event.which.toString();\n",
	"\n",
	" this._key_event_extra(event, name);\n",
	"\n",
	" this.send_message(name, {key: value,\n",
	" guiEvent: simpleKeys(event)});\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
	" if (name == 'download') {\n",
	" this.handle_save(this, null);\n",
	" } else {\n",
	" this.send_message(\"toolbar_button\", {name: name});\n",
	" }\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
	" this.message.textContent = tooltip;\n",
	"};\n",
	"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
	"\n",
	"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
	"\n",
	"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
	" // Create a \"websocket\"-like object which calls the given IPython comm\n",
	" // object with the appropriate methods. Currently this is a non binary\n",
	" // socket, so there is still some room for performance tuning.\n",
	" var ws = {};\n",
	"\n",
	" ws.close = function() {\n",
	" comm.close()\n",
	" };\n",
	" ws.send = function(m) {\n",
	" //console.log('sending', m);\n",
	" comm.send(m);\n",
	" };\n",
	" // Register the callback with on_msg.\n",
	" comm.on_msg(function(msg) {\n",
	" //console.log('receiving', msg['content']['data'], msg);\n",
	" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
	" ws.onmessage(msg['content']['data'])\n",
	" });\n",
	" return ws;\n",
	"}\n",
	"\n",
	"mpl.mpl_figure_comm = function(comm, msg) {\n",
	" // This is the function which gets called when the mpl process\n",
	" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
	"\n",
	" var id = msg.content.data.id;\n",
	" // Get hold of the div created by the display call when the Comm\n",
	" // socket was opened in Python.\n",
	" var element = $(\"#\" + id);\n",
	" var ws_proxy = comm_websocket_adapter(comm)\n",
	"\n",
	" function ondownload(figure, format) {\n",
	" window.open(figure.imageObj.src);\n",
	" }\n",
	"\n",
	" var fig = new mpl.figure(id, ws_proxy,\n",
	" ondownload,\n",
	" element.get(0));\n",
	"\n",
	" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
	" // web socket which is closed, not our websocket->open comm proxy.\n",
	" ws_proxy.onopen();\n",
	"\n",
	" fig.parent_element = element.get(0);\n",
	" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
	" if (!fig.cell_info) {\n",
	" console.error(\"Failed to find cell for figure\", id, fig);\n",
	" return;\n",
	" }\n",
	"\n",
	" var output_index = fig.cell_info[2]\n",
	" var cell = fig.cell_info[0];\n",
	"\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
	" var width = fig.canvas.width/mpl.ratio\n",
	" fig.root.unbind('remove')\n",
	"\n",
	" // Update the output cell to use the data from the current canvas.\n",
	" fig.push_to_output();\n",
	" var dataURL = fig.canvas.toDataURL();\n",
	" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
	" // the notebook keyboard shortcuts fail.\n",
	" IPython.keyboard_manager.enable()\n",
	" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
	" fig.close_ws(fig, msg);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.close_ws = function(fig, msg){\n",
	" fig.send_message('closing', msg);\n",
	" // fig.ws.close()\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
	" // Turn the data on the canvas into data in the output cell.\n",
	" var width = this.canvas.width/mpl.ratio\n",
	" var dataURL = this.canvas.toDataURL();\n",
	" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Tell IPython that the notebook contents must change.\n",
	" IPython.notebook.set_dirty(true);\n",
	" this.send_message(\"ack\", {});\n",
	" var fig = this;\n",
	" // Wait a second, then push the new image to the DOM so\n",
	" // that it is saved nicely (might be nice to debounce this).\n",
	" setTimeout(function () { fig.push_to_output() }, 1000);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items){\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) { continue; };\n",
	"\n",
	" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" // Add the status bar.\n",
	" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"\n",
	" // Add the close button to the window.\n",
	" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
	" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
	" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
	" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
	" buttongrp.append(button);\n",
	" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
	" titlebar.prepend(buttongrp);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(el){\n",
	" var fig = this\n",
	" el.on(\"remove\", function(){\n",
	"\tfig.close_ws(fig, {});\n",
	" });\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(el){\n",
	" // this is important to make the div 'focusable\n",
	" el.attr('tabindex', 0)\n",
	" // reach out to IPython and tell the keyboard manager to turn it's self\n",
	" // off when our div gets focus\n",
	"\n",
	" // location in version 3\n",
	" if (IPython.notebook.keyboard_manager) {\n",
	" IPython.notebook.keyboard_manager.register_events(el);\n",
	" }\n",
	" else {\n",
	" // location in version 2\n",
	" IPython.keyboard_manager.register_events(el);\n",
	" }\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" var manager = IPython.notebook.keyboard_manager;\n",
	" if (!manager)\n",
	" manager = IPython.keyboard_manager;\n",
	"\n",
	" // Check for shift+enter\n",
	" if (event.shiftKey && event.which == 13) {\n",
	" this.canvas_div.blur();\n",
	" event.shiftKey = false;\n",
	" // Send a \"J\" for go to next cell\n",
	" event.which = 74;\n",
	" event.keyCode = 74;\n",
	" manager.command_mode();\n",
	" manager.handle_keydown(event);\n",
	" }\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" fig.ondownload(fig, null);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.find_output_cell = function(html_output) {\n",
	" // Return the cell and output element which can be found uniquely in the notebook.\n",
	" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
	" // IPython event is triggered only after the cells have been serialised, which for\n",
	" // our purposes (turning an active figure into a static one), is too late.\n",
	" var cells = IPython.notebook.get_cells();\n",
	" var ncells = cells.length;\n",
	" for (var i=0; i<ncells; i++) {\n",
	" var cell = cells[i];\n",
	" if (cell.cell_type === 'code'){\n",
	" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
	" var data = cell.output_area.outputs[j];\n",
	" if (data.data) {\n",
	" // IPython >= 3 moved mimebundle to data attribute of output\n",
	" data = data.data;\n",
	" }\n",
	" if (data['text/html'] == html_output) {\n",
	" return [cell, data, j];\n",
	" }\n",
	" }\n",
	" }\n",
	" }\n",
	"}\n",
	"\n",
	"// Register the function which deals with the matplotlib target/channel.\n",
	"// The kernel may be null if the page has been refreshed.\n",
	"if (IPython.notebook.kernel != null) {\n",
	" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
	"}\n"
	],
	"text/plain": [
	"<IPython.core.display.Javascript object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	},
	{
	"data": {
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\" width=\"640\">"
	],
	"text/plain": [
	"<IPython.core.display.HTML object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.figure()\n",
	"ax = plt.axes()\n",
	"plt.title(\"Feature Importance for RF (100k features)\") # \\n Testing RMSE: %f ; , np.sqrt(mse(Y_te,T_te))\n",
	"plt.barh(range(n_features),importance[ind],\n",
	" color=\"r\", xerr=std[ind], alpha=0.4, align=\"center\", capsize = 2)\n",
	"plt.yticks(range(n_features), np.asarray(feature_names)[ind])\n",
	"plt.ylim([-1, len(ind)])\n",
	"plt.xlabel('Feature importance')\n",
	"plt.tight_layout()\n",
	"plt.show()\n",
	"# plt.savefig('Feature_importance_all_100k.png', dpi=300)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 80,
	"metadata": {},
	"outputs": [],
	"source": [
	"sample = data.sample(1000)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 81,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"application/javascript": [
	"/* Put everything inside the global mpl namespace */\n",
	"window.mpl = {};\n",
	"\n",
	"\n",
	"mpl.get_websocket_type = function() {\n",
	" if (typeof(WebSocket) !== 'undefined') {\n",
	" return WebSocket;\n",
	" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
	" return MozWebSocket;\n",
	" } else {\n",
	" alert('Your browser does not have WebSocket support.' +\n",
	" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
	" 'Firefox 4 and 5 are also supported but you ' +\n",
	" 'have to enable WebSockets in about:config.');\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
	" this.id = figure_id;\n",
	"\n",
	" this.ws = websocket;\n",
	"\n",
	" this.supports_binary = (this.ws.binaryType != undefined);\n",
	"\n",
	" if (!this.supports_binary) {\n",
	" var warnings = document.getElementById(\"mpl-warnings\");\n",
	" if (warnings) {\n",
	" warnings.style.display = 'block';\n",
	" warnings.textContent = (\n",
	" \"This browser does not support binary websocket messages. \" +\n",
	" \"Performance may be slow.\");\n",
	" }\n",
	" }\n",
	"\n",
	" this.imageObj = new Image();\n",
	"\n",
	" this.context = undefined;\n",
	" this.message = undefined;\n",
	" this.canvas = undefined;\n",
	" this.rubberband_canvas = undefined;\n",
	" this.rubberband_context = undefined;\n",
	" this.format_dropdown = undefined;\n",
	"\n",
	" this.image_mode = 'full';\n",
	"\n",
	" this.root = $('<div/>');\n",
	" this._root_extra_style(this.root)\n",
	" this.root.attr('style', 'display: inline-block');\n",
	"\n",
	" $(parent_element).append(this.root);\n",
	"\n",
	" this._init_header(this);\n",
	" this._init_canvas(this);\n",
	" this._init_toolbar(this);\n",
	"\n",
	" var fig = this;\n",
	"\n",
	" this.waiting = false;\n",
	"\n",
	" this.ws.onopen = function () {\n",
	" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
	" fig.send_message(\"send_image_mode\", {});\n",
	" if (mpl.ratio != 1) {\n",
	" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
	" }\n",
	" fig.send_message(\"refresh\", {});\n",
	" }\n",
	"\n",
	" this.imageObj.onload = function() {\n",
	" if (fig.image_mode == 'full') {\n",
	" // Full images could contain transparency (where diff images\n",
	" // almost always do), so we need to clear the canvas so that\n",
	" // there is no ghosting.\n",
	" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
	" }\n",
	" fig.context.drawImage(fig.imageObj, 0, 0);\n",
	" };\n",
	"\n",
	" this.imageObj.onunload = function() {\n",
	" fig.ws.close();\n",
	" }\n",
	"\n",
	" this.ws.onmessage = this._make_on_message_function(this);\n",
	"\n",
	" this.ondownload = ondownload;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_header = function() {\n",
	" var titlebar = $(\n",
	" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
	" 'ui-helper-clearfix\"/>');\n",
	" var titletext = $(\n",
	" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
	" 'text-align: center; padding: 3px;\"/>');\n",
	" titlebar.append(titletext)\n",
	" this.root.append(titlebar);\n",
	" this.header = titletext[0];\n",
	"}\n",
	"\n",
	"\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
	"\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_canvas = function() {\n",
	" var fig = this;\n",
	"\n",
	" var canvas_div = $('<div/>');\n",
	"\n",
	" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
	"\n",
	" function canvas_keyboard_event(event) {\n",
	" return fig.key_event(event, event['data']);\n",
	" }\n",
	"\n",
	" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
	" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
	" this.canvas_div = canvas_div\n",
	" this._canvas_extra_style(canvas_div)\n",
	" this.root.append(canvas_div);\n",
	"\n",
	" var canvas = $('<canvas/>');\n",
	" canvas.addClass('mpl-canvas');\n",
	" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
	"\n",
	" this.canvas = canvas[0];\n",
	" this.context = canvas[0].getContext(\"2d\");\n",
	"\n",
	" var backingStore = this.context.backingStorePixelRatio \|\|\n",
	"\tthis.context.webkitBackingStorePixelRatio \|\|\n",
	"\tthis.context.mozBackingStorePixelRatio \|\|\n",
	"\tthis.context.msBackingStorePixelRatio \|\|\n",
	"\tthis.context.oBackingStorePixelRatio \|\|\n",
	"\tthis.context.backingStorePixelRatio \|\| 1;\n",
	"\n",
	" mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n",
	"\n",
	" var rubberband = $('<canvas/>');\n",
	" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
	"\n",
	" var pass_mouse_events = true;\n",
	"\n",
	" canvas_div.resizable({\n",
	" start: function(event, ui) {\n",
	" pass_mouse_events = false;\n",
	" },\n",
	" resize: function(event, ui) {\n",
	" fig.request_resize(ui.size.width, ui.size.height);\n",
	" },\n",
	" stop: function(event, ui) {\n",
	" pass_mouse_events = true;\n",
	" fig.request_resize(ui.size.width, ui.size.height);\n",
	" },\n",
	" });\n",
	"\n",
	" function mouse_event_fn(event) {\n",
	" if (pass_mouse_events)\n",
	" return fig.mouse_event(event, event['data']);\n",
	" }\n",
	"\n",
	" rubberband.mousedown('button_press', mouse_event_fn);\n",
	" rubberband.mouseup('button_release', mouse_event_fn);\n",
	" // Throttle sequential mouse events to 1 every 20ms.\n",
	" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
	"\n",
	" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
	" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
	"\n",
	" canvas_div.on(\"wheel\", function (event) {\n",
	" event = event.originalEvent;\n",
	" event['data'] = 'scroll'\n",
	" if (event.deltaY < 0) {\n",
	" event.step = 1;\n",
	" } else {\n",
	" event.step = -1;\n",
	" }\n",
	" mouse_event_fn(event);\n",
	" });\n",
	"\n",
	" canvas_div.append(canvas);\n",
	" canvas_div.append(rubberband);\n",
	"\n",
	" this.rubberband = rubberband;\n",
	" this.rubberband_canvas = rubberband[0];\n",
	" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
	" this.rubberband_context.strokeStyle = \"#000000\";\n",
	"\n",
	" this._resize_canvas = function(width, height) {\n",
	" // Keep the size of the canvas, canvas container, and rubber band\n",
	" // canvas in synch.\n",
	" canvas_div.css('width', width)\n",
	" canvas_div.css('height', height)\n",
	"\n",
	" canvas.attr('width', width * mpl.ratio);\n",
	" canvas.attr('height', height * mpl.ratio);\n",
	" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
	"\n",
	" rubberband.attr('width', width);\n",
	" rubberband.attr('height', height);\n",
	" }\n",
	"\n",
	" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
	" // upon first draw.\n",
	" this._resize_canvas(600, 600);\n",
	"\n",
	" // Disable right mouse context menu.\n",
	" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
	" return false;\n",
	" });\n",
	"\n",
	" function set_focus () {\n",
	" canvas.focus();\n",
	" canvas_div.focus();\n",
	" }\n",
	"\n",
	" window.setTimeout(set_focus, 100);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items) {\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) {\n",
	" // put a spacer in here.\n",
	" continue;\n",
	" }\n",
	" var button = $('<button/>');\n",
	" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
	" 'ui-button-icon-only');\n",
	" button.attr('role', 'button');\n",
	" button.attr('aria-disabled', 'false');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	"\n",
	" var icon_img = $('<span/>');\n",
	" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
	" icon_img.addClass(image);\n",
	" icon_img.addClass('ui-corner-all');\n",
	"\n",
	" var tooltip_span = $('<span/>');\n",
	" tooltip_span.addClass('ui-button-text');\n",
	" tooltip_span.html(tooltip);\n",
	"\n",
	" button.append(icon_img);\n",
	" button.append(tooltip_span);\n",
	"\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" var fmt_picker_span = $('<span/>');\n",
	"\n",
	" var fmt_picker = $('<select/>');\n",
	" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
	" fmt_picker_span.append(fmt_picker);\n",
	" nav_element.append(fmt_picker_span);\n",
	" this.format_dropdown = fmt_picker[0];\n",
	"\n",
	" for (var ind in mpl.extensions) {\n",
	" var fmt = mpl.extensions[ind];\n",
	" var option = $(\n",
	" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
	" fmt_picker.append(option)\n",
	" }\n",
	"\n",
	" // Add hover states to the ui-buttons\n",
	" $( \".ui-button\" ).hover(\n",
	" function() { $(this).addClass(\"ui-state-hover\");},\n",
	" function() { $(this).removeClass(\"ui-state-hover\");}\n",
	" );\n",
	"\n",
	" var status_bar = $('<span class=\"mpl-message\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
	" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
	" // which will in turn request a refresh of the image.\n",
	" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_message = function(type, properties) {\n",
	" properties['type'] = type;\n",
	" properties['figure_id'] = this.id;\n",
	" this.ws.send(JSON.stringify(properties));\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_draw_message = function() {\n",
	" if (!this.waiting) {\n",
	" this.waiting = true;\n",
	" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
	" }\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" var format_dropdown = fig.format_dropdown;\n",
	" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
	" fig.ondownload(fig, format);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
	" var size = msg['size'];\n",
	" if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n",
	" fig._resize_canvas(size[0], size[1]);\n",
	" fig.send_message(\"refresh\", {});\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
	" var x0 = msg['x0'] / mpl.ratio;\n",
	" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
	" var x1 = msg['x1'] / mpl.ratio;\n",
	" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
	" x0 = Math.floor(x0) + 0.5;\n",
	" y0 = Math.floor(y0) + 0.5;\n",
	" x1 = Math.floor(x1) + 0.5;\n",
	" y1 = Math.floor(y1) + 0.5;\n",
	" var min_x = Math.min(x0, x1);\n",
	" var min_y = Math.min(y0, y1);\n",
	" var width = Math.abs(x1 - x0);\n",
	" var height = Math.abs(y1 - y0);\n",
	"\n",
	" fig.rubberband_context.clearRect(\n",
	" 0, 0, fig.canvas.width, fig.canvas.height);\n",
	"\n",
	" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
	" // Updates the figure title.\n",
	" fig.header.textContent = msg['label'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
	" var cursor = msg['cursor'];\n",
	" switch(cursor)\n",
	" {\n",
	" case 0:\n",
	" cursor = 'pointer';\n",
	" break;\n",
	" case 1:\n",
	" cursor = 'default';\n",
	" break;\n",
	" case 2:\n",
	" cursor = 'crosshair';\n",
	" break;\n",
	" case 3:\n",
	" cursor = 'move';\n",
	" break;\n",
	" }\n",
	" fig.rubberband_canvas.style.cursor = cursor;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
	" fig.message.textContent = msg['message'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
	" // Request the server to send over a new figure.\n",
	" fig.send_draw_message();\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
	" fig.image_mode = msg['mode'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Called whenever the canvas gets updated.\n",
	" this.send_message(\"ack\", {});\n",
	"}\n",
	"\n",
	"// A function to construct a web socket function for onmessage handling.\n",
	"// Called in the figure constructor.\n",
	"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
	" return function socket_on_message(evt) {\n",
	" if (evt.data instanceof Blob) {\n",
	" /* FIXME: We get \"Resource interpreted as Image but\n",
	" * transferred with MIME type text/plain:\" errors on\n",
	" * Chrome. But how to set the MIME type? It doesn't seem\n",
	" * to be part of the websocket stream */\n",
	" evt.data.type = \"image/png\";\n",
	"\n",
	" /* Free the memory for the previous frames */\n",
	" if (fig.imageObj.src) {\n",
	" (window.URL \|\| window.webkitURL).revokeObjectURL(\n",
	" fig.imageObj.src);\n",
	" }\n",
	"\n",
	" fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n",
	" evt.data);\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
	" fig.imageObj.src = evt.data;\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	"\n",
	" var msg = JSON.parse(evt.data);\n",
	" var msg_type = msg['type'];\n",
	"\n",
	" // Call the \"handle_{type}\" callback, which takes\n",
	" // the figure and JSON message as its only arguments.\n",
	" try {\n",
	" var callback = fig[\"handle_\" + msg_type];\n",
	" } catch (e) {\n",
	" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
	" return;\n",
	" }\n",
	"\n",
	" if (callback) {\n",
	" try {\n",
	" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
	" callback(fig, msg);\n",
	" } catch (e) {\n",
	" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
	" }\n",
	" }\n",
	" };\n",
	"}\n",
	"\n",
	"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
	"mpl.findpos = function(e) {\n",
	" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
	" var targ;\n",
	" if (!e)\n",
	" e = window.event;\n",
	" if (e.target)\n",
	" targ = e.target;\n",
	" else if (e.srcElement)\n",
	" targ = e.srcElement;\n",
	" if (targ.nodeType == 3) // defeat Safari bug\n",
	" targ = targ.parentNode;\n",
	"\n",
	" // jQuery normalizes the pageX and pageY\n",
	" // pageX,Y are the mouse positions relative to the document\n",
	" // offset() returns the position of the element relative to the document\n",
	" var x = e.pageX - $(targ).offset().left;\n",
	" var y = e.pageY - $(targ).offset().top;\n",
	"\n",
	" return {\"x\": x, \"y\": y};\n",
	"};\n",
	"\n",
	"/*\n",
	" * return a copy of an object with only non-object keys\n",
	" * we need this to avoid circular references\n",
	" * http://stackoverflow.com/a/24161582/3208463\n",
	" */\n",
	"function simpleKeys (original) {\n",
	" return Object.keys(original).reduce(function (obj, key) {\n",
	" if (typeof original[key] !== 'object')\n",
	" obj[key] = original[key]\n",
	" return obj;\n",
	" }, {});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.mouse_event = function(event, name) {\n",
	" var canvas_pos = mpl.findpos(event)\n",
	"\n",
	" if (name === 'button_press')\n",
	" {\n",
	" this.canvas.focus();\n",
	" this.canvas_div.focus();\n",
	" }\n",
	"\n",
	" var x = canvas_pos.x * mpl.ratio;\n",
	" var y = canvas_pos.y * mpl.ratio;\n",
	"\n",
	" this.send_message(name, {x: x, y: y, button: event.button,\n",
	" step: event.step,\n",
	" guiEvent: simpleKeys(event)});\n",
	"\n",
	" /* This prevents the web browser from automatically changing to\n",
	" * the text insertion cursor when the button is pressed. We want\n",
	" * to control all of the cursor setting manually through the\n",
	" * 'cursor' event from matplotlib */\n",
	" event.preventDefault();\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" // Handle any extra behaviour associated with a key event\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.key_event = function(event, name) {\n",
	"\n",
	" // Prevent repeat events\n",
	" if (name == 'key_press')\n",
	" {\n",
	" if (event.which === this._key)\n",
	" return;\n",
	" else\n",
	" this._key = event.which;\n",
	" }\n",
	" if (name == 'key_release')\n",
	" this._key = null;\n",
	"\n",
	" var value = '';\n",
	" if (event.ctrlKey && event.which != 17)\n",
	" value += \"ctrl+\";\n",
	" if (event.altKey && event.which != 18)\n",
	" value += \"alt+\";\n",
	" if (event.shiftKey && event.which != 16)\n",
	" value += \"shift+\";\n",
	"\n",
	" value += 'k';\n",
	" value += event.which.toString();\n",
	"\n",
	" this._key_event_extra(event, name);\n",
	"\n",
	" this.send_message(name, {key: value,\n",
	" guiEvent: simpleKeys(event)});\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
	" if (name == 'download') {\n",
	" this.handle_save(this, null);\n",
	" } else {\n",
	" this.send_message(\"toolbar_button\", {name: name});\n",
	" }\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
	" this.message.textContent = tooltip;\n",
	"};\n",
	"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
	"\n",
	"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
	"\n",
	"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
	" // Create a \"websocket\"-like object which calls the given IPython comm\n",
	" // object with the appropriate methods. Currently this is a non binary\n",
	" // socket, so there is still some room for performance tuning.\n",
	" var ws = {};\n",
	"\n",
	" ws.close = function() {\n",
	" comm.close()\n",
	" };\n",
	" ws.send = function(m) {\n",
	" //console.log('sending', m);\n",
	" comm.send(m);\n",
	" };\n",
	" // Register the callback with on_msg.\n",
	" comm.on_msg(function(msg) {\n",
	" //console.log('receiving', msg['content']['data'], msg);\n",
	" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
	" ws.onmessage(msg['content']['data'])\n",
	" });\n",
	" return ws;\n",
	"}\n",
	"\n",
	"mpl.mpl_figure_comm = function(comm, msg) {\n",
	" // This is the function which gets called when the mpl process\n",
	" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
	"\n",
	" var id = msg.content.data.id;\n",
	" // Get hold of the div created by the display call when the Comm\n",
	" // socket was opened in Python.\n",
	" var element = $(\"#\" + id);\n",
	" var ws_proxy = comm_websocket_adapter(comm)\n",
	"\n",
	" function ondownload(figure, format) {\n",
	" window.open(figure.imageObj.src);\n",
	" }\n",
	"\n",
	" var fig = new mpl.figure(id, ws_proxy,\n",
	" ondownload,\n",
	" element.get(0));\n",
	"\n",
	" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
	" // web socket which is closed, not our websocket->open comm proxy.\n",
	" ws_proxy.onopen();\n",
	"\n",
	" fig.parent_element = element.get(0);\n",
	" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
	" if (!fig.cell_info) {\n",
	" console.error(\"Failed to find cell for figure\", id, fig);\n",
	" return;\n",
	" }\n",
	"\n",
	" var output_index = fig.cell_info[2]\n",
	" var cell = fig.cell_info[0];\n",
	"\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
	" var width = fig.canvas.width/mpl.ratio\n",
	" fig.root.unbind('remove')\n",
	"\n",
	" // Update the output cell to use the data from the current canvas.\n",
	" fig.push_to_output();\n",
	" var dataURL = fig.canvas.toDataURL();\n",
	" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
	" // the notebook keyboard shortcuts fail.\n",
	" IPython.keyboard_manager.enable()\n",
	" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
	" fig.close_ws(fig, msg);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.close_ws = function(fig, msg){\n",
	" fig.send_message('closing', msg);\n",
	" // fig.ws.close()\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
	" // Turn the data on the canvas into data in the output cell.\n",
	" var width = this.canvas.width/mpl.ratio\n",
	" var dataURL = this.canvas.toDataURL();\n",
	" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Tell IPython that the notebook contents must change.\n",
	" IPython.notebook.set_dirty(true);\n",
	" this.send_message(\"ack\", {});\n",
	" var fig = this;\n",
	" // Wait a second, then push the new image to the DOM so\n",
	" // that it is saved nicely (might be nice to debounce this).\n",
	" setTimeout(function () { fig.push_to_output() }, 1000);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items){\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) { continue; };\n",
	"\n",
	" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" // Add the status bar.\n",
	" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"\n",
	" // Add the close button to the window.\n",
	" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
	" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
	" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
	" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
	" buttongrp.append(button);\n",
	" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
	" titlebar.prepend(buttongrp);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(el){\n",
	" var fig = this\n",
	" el.on(\"remove\", function(){\n",
	"\tfig.close_ws(fig, {});\n",
	" });\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(el){\n",
	" // this is important to make the div 'focusable\n",
	" el.attr('tabindex', 0)\n",
	" // reach out to IPython and tell the keyboard manager to turn it's self\n",
	" // off when our div gets focus\n",
	"\n",
	" // location in version 3\n",
	" if (IPython.notebook.keyboard_manager) {\n",
	" IPython.notebook.keyboard_manager.register_events(el);\n",
	" }\n",
	" else {\n",
	" // location in version 2\n",
	" IPython.keyboard_manager.register_events(el);\n",
	" }\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" var manager = IPython.notebook.keyboard_manager;\n",
	" if (!manager)\n",
	" manager = IPython.keyboard_manager;\n",
	"\n",
	" // Check for shift+enter\n",
	" if (event.shiftKey && event.which == 13) {\n",
	" this.canvas_div.blur();\n",
	" event.shiftKey = false;\n",
	" // Send a \"J\" for go to next cell\n",
	" event.which = 74;\n",
	" event.keyCode = 74;\n",
	" manager.command_mode();\n",
	" manager.handle_keydown(event);\n",
	" }\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" fig.ondownload(fig, null);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.find_output_cell = function(html_output) {\n",
	" // Return the cell and output element which can be found uniquely in the notebook.\n",
	" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
	" // IPython event is triggered only after the cells have been serialised, which for\n",
	" // our purposes (turning an active figure into a static one), is too late.\n",
	" var cells = IPython.notebook.get_cells();\n",
	" var ncells = cells.length;\n",
	" for (var i=0; i<ncells; i++) {\n",
	" var cell = cells[i];\n",
	" if (cell.cell_type === 'code'){\n",
	" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
	" var data = cell.output_area.outputs[j];\n",
	" if (data.data) {\n",
	" // IPython >= 3 moved mimebundle to data attribute of output\n",
	" data = data.data;\n",
	" }\n",
	" if (data['text/html'] == html_output) {\n",
	" return [cell, data, j];\n",
	" }\n",
	" }\n",
	" }\n",
	" }\n",
	"}\n",
	"\n",
	"// Register the function which deals with the matplotlib target/channel.\n",
	"// The kernel may be null if the page has been refreshed.\n",
	"if (IPython.notebook.kernel != null) {\n",
	" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
	"}\n"
	],
	"text/plain": [
	"<IPython.core.display.Javascript object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	},
	{
	"data": {
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\" width=\"640\">"
	],
	"text/plain": [
	"<IPython.core.display.HTML object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	},
	{
	"data": {
	"text/plain": [
	"<matplotlib.colorbar.Colorbar at 0x1c34149d30>"
	]
	},
	"execution_count": 81,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"plt.figure()\n",
	"plt.scatter(sample.SIS, sample.NEIGUNG, c = sample.MSTRAHLUNG)\n",
	"plt.colorbar()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 77,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"application/javascript": [
	"/* Put everything inside the global mpl namespace */\n",
	"window.mpl = {};\n",
	"\n",
	"\n",
	"mpl.get_websocket_type = function() {\n",
	" if (typeof(WebSocket) !== 'undefined') {\n",
	" return WebSocket;\n",
	" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
	" return MozWebSocket;\n",
	" } else {\n",
	" alert('Your browser does not have WebSocket support.' +\n",
	" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
	" 'Firefox 4 and 5 are also supported but you ' +\n",
	" 'have to enable WebSockets in about:config.');\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
	" this.id = figure_id;\n",
	"\n",
	" this.ws = websocket;\n",
	"\n",
	" this.supports_binary = (this.ws.binaryType != undefined);\n",
	"\n",
	" if (!this.supports_binary) {\n",
	" var warnings = document.getElementById(\"mpl-warnings\");\n",
	" if (warnings) {\n",
	" warnings.style.display = 'block';\n",
	" warnings.textContent = (\n",
	" \"This browser does not support binary websocket messages. \" +\n",
	" \"Performance may be slow.\");\n",
	" }\n",
	" }\n",
	"\n",
	" this.imageObj = new Image();\n",
	"\n",
	" this.context = undefined;\n",
	" this.message = undefined;\n",
	" this.canvas = undefined;\n",
	" this.rubberband_canvas = undefined;\n",
	" this.rubberband_context = undefined;\n",
	" this.format_dropdown = undefined;\n",
	"\n",
	" this.image_mode = 'full';\n",
	"\n",
	" this.root = $('<div/>');\n",
	" this._root_extra_style(this.root)\n",
	" this.root.attr('style', 'display: inline-block');\n",
	"\n",
	" $(parent_element).append(this.root);\n",
	"\n",
	" this._init_header(this);\n",
	" this._init_canvas(this);\n",
	" this._init_toolbar(this);\n",
	"\n",
	" var fig = this;\n",
	"\n",
	" this.waiting = false;\n",
	"\n",
	" this.ws.onopen = function () {\n",
	" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
	" fig.send_message(\"send_image_mode\", {});\n",
	" if (mpl.ratio != 1) {\n",
	" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
	" }\n",
	" fig.send_message(\"refresh\", {});\n",
	" }\n",
	"\n",
	" this.imageObj.onload = function() {\n",
	" if (fig.image_mode == 'full') {\n",
	" // Full images could contain transparency (where diff images\n",
	" // almost always do), so we need to clear the canvas so that\n",
	" // there is no ghosting.\n",
	" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
	" }\n",
	" fig.context.drawImage(fig.imageObj, 0, 0);\n",
	" };\n",
	"\n",
	" this.imageObj.onunload = function() {\n",
	" fig.ws.close();\n",
	" }\n",
	"\n",
	" this.ws.onmessage = this._make_on_message_function(this);\n",
	"\n",
	" this.ondownload = ondownload;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_header = function() {\n",
	" var titlebar = $(\n",
	" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
	" 'ui-helper-clearfix\"/>');\n",
	" var titletext = $(\n",
	" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
	" 'text-align: center; padding: 3px;\"/>');\n",
	" titlebar.append(titletext)\n",
	" this.root.append(titlebar);\n",
	" this.header = titletext[0];\n",
	"}\n",
	"\n",
	"\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
	"\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_canvas = function() {\n",
	" var fig = this;\n",
	"\n",
	" var canvas_div = $('<div/>');\n",
	"\n",
	" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
	"\n",
	" function canvas_keyboard_event(event) {\n",
	" return fig.key_event(event, event['data']);\n",
	" }\n",
	"\n",
	" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
	" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
	" this.canvas_div = canvas_div\n",
	" this._canvas_extra_style(canvas_div)\n",
	" this.root.append(canvas_div);\n",
	"\n",
	" var canvas = $('<canvas/>');\n",
	" canvas.addClass('mpl-canvas');\n",
	" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
	"\n",
	" this.canvas = canvas[0];\n",
	" this.context = canvas[0].getContext(\"2d\");\n",
	"\n",
	" var backingStore = this.context.backingStorePixelRatio \|\|\n",
	"\tthis.context.webkitBackingStorePixelRatio \|\|\n",
	"\tthis.context.mozBackingStorePixelRatio \|\|\n",
	"\tthis.context.msBackingStorePixelRatio \|\|\n",
	"\tthis.context.oBackingStorePixelRatio \|\|\n",
	"\tthis.context.backingStorePixelRatio \|\| 1;\n",
	"\n",
	" mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n",
	"\n",
	" var rubberband = $('<canvas/>');\n",
	" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
	"\n",
	" var pass_mouse_events = true;\n",
	"\n",
	" canvas_div.resizable({\n",
	" start: function(event, ui) {\n",
	" pass_mouse_events = false;\n",
	" },\n",
	" resize: function(event, ui) {\n",
	" fig.request_resize(ui.size.width, ui.size.height);\n",
	" },\n",
	" stop: function(event, ui) {\n",
	" pass_mouse_events = true;\n",
	" fig.request_resize(ui.size.width, ui.size.height);\n",
	" },\n",
	" });\n",
	"\n",
	" function mouse_event_fn(event) {\n",
	" if (pass_mouse_events)\n",
	" return fig.mouse_event(event, event['data']);\n",
	" }\n",
	"\n",
	" rubberband.mousedown('button_press', mouse_event_fn);\n",
	" rubberband.mouseup('button_release', mouse_event_fn);\n",
	" // Throttle sequential mouse events to 1 every 20ms.\n",
	" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
	"\n",
	" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
	" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
	"\n",
	" canvas_div.on(\"wheel\", function (event) {\n",
	" event = event.originalEvent;\n",
	" event['data'] = 'scroll'\n",
	" if (event.deltaY < 0) {\n",
	" event.step = 1;\n",
	" } else {\n",
	" event.step = -1;\n",
	" }\n",
	" mouse_event_fn(event);\n",
	" });\n",
	"\n",
	" canvas_div.append(canvas);\n",
	" canvas_div.append(rubberband);\n",
	"\n",
	" this.rubberband = rubberband;\n",
	" this.rubberband_canvas = rubberband[0];\n",
	" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
	" this.rubberband_context.strokeStyle = \"#000000\";\n",
	"\n",
	" this._resize_canvas = function(width, height) {\n",
	" // Keep the size of the canvas, canvas container, and rubber band\n",
	" // canvas in synch.\n",
	" canvas_div.css('width', width)\n",
	" canvas_div.css('height', height)\n",
	"\n",
	" canvas.attr('width', width * mpl.ratio);\n",
	" canvas.attr('height', height * mpl.ratio);\n",
	" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
	"\n",
	" rubberband.attr('width', width);\n",
	" rubberband.attr('height', height);\n",
	" }\n",
	"\n",
	" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
	" // upon first draw.\n",
	" this._resize_canvas(600, 600);\n",
	"\n",
	" // Disable right mouse context menu.\n",
	" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
	" return false;\n",
	" });\n",
	"\n",
	" function set_focus () {\n",
	" canvas.focus();\n",
	" canvas_div.focus();\n",
	" }\n",
	"\n",
	" window.setTimeout(set_focus, 100);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items) {\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) {\n",
	" // put a spacer in here.\n",
	" continue;\n",
	" }\n",
	" var button = $('<button/>');\n",
	" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
	" 'ui-button-icon-only');\n",
	" button.attr('role', 'button');\n",
	" button.attr('aria-disabled', 'false');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	"\n",
	" var icon_img = $('<span/>');\n",
	" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
	" icon_img.addClass(image);\n",
	" icon_img.addClass('ui-corner-all');\n",
	"\n",
	" var tooltip_span = $('<span/>');\n",
	" tooltip_span.addClass('ui-button-text');\n",
	" tooltip_span.html(tooltip);\n",
	"\n",
	" button.append(icon_img);\n",
	" button.append(tooltip_span);\n",
	"\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" var fmt_picker_span = $('<span/>');\n",
	"\n",
	" var fmt_picker = $('<select/>');\n",
	" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
	" fmt_picker_span.append(fmt_picker);\n",
	" nav_element.append(fmt_picker_span);\n",
	" this.format_dropdown = fmt_picker[0];\n",
	"\n",
	" for (var ind in mpl.extensions) {\n",
	" var fmt = mpl.extensions[ind];\n",
	" var option = $(\n",
	" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
	" fmt_picker.append(option)\n",
	" }\n",
	"\n",
	" // Add hover states to the ui-buttons\n",
	" $( \".ui-button\" ).hover(\n",
	" function() { $(this).addClass(\"ui-state-hover\");},\n",
	" function() { $(this).removeClass(\"ui-state-hover\");}\n",
	" );\n",
	"\n",
	" var status_bar = $('<span class=\"mpl-message\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
	" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
	" // which will in turn request a refresh of the image.\n",
	" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_message = function(type, properties) {\n",
	" properties['type'] = type;\n",
	" properties['figure_id'] = this.id;\n",
	" this.ws.send(JSON.stringify(properties));\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_draw_message = function() {\n",
	" if (!this.waiting) {\n",
	" this.waiting = true;\n",
	" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
	" }\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" var format_dropdown = fig.format_dropdown;\n",
	" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
	" fig.ondownload(fig, format);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
	" var size = msg['size'];\n",
	" if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n",
	" fig._resize_canvas(size[0], size[1]);\n",
	" fig.send_message(\"refresh\", {});\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
	" var x0 = msg['x0'] / mpl.ratio;\n",
	" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
	" var x1 = msg['x1'] / mpl.ratio;\n",
	" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
	" x0 = Math.floor(x0) + 0.5;\n",
	" y0 = Math.floor(y0) + 0.5;\n",
	" x1 = Math.floor(x1) + 0.5;\n",
	" y1 = Math.floor(y1) + 0.5;\n",
	" var min_x = Math.min(x0, x1);\n",
	" var min_y = Math.min(y0, y1);\n",
	" var width = Math.abs(x1 - x0);\n",
	" var height = Math.abs(y1 - y0);\n",
	"\n",
	" fig.rubberband_context.clearRect(\n",
	" 0, 0, fig.canvas.width, fig.canvas.height);\n",
	"\n",
	" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
	" // Updates the figure title.\n",
	" fig.header.textContent = msg['label'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
	" var cursor = msg['cursor'];\n",
	" switch(cursor)\n",
	" {\n",
	" case 0:\n",
	" cursor = 'pointer';\n",
	" break;\n",
	" case 1:\n",
	" cursor = 'default';\n",
	" break;\n",
	" case 2:\n",
	" cursor = 'crosshair';\n",
	" break;\n",
	" case 3:\n",
	" cursor = 'move';\n",
	" break;\n",
	" }\n",
	" fig.rubberband_canvas.style.cursor = cursor;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
	" fig.message.textContent = msg['message'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
	" // Request the server to send over a new figure.\n",
	" fig.send_draw_message();\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
	" fig.image_mode = msg['mode'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Called whenever the canvas gets updated.\n",
	" this.send_message(\"ack\", {});\n",
	"}\n",
	"\n",
	"// A function to construct a web socket function for onmessage handling.\n",
	"// Called in the figure constructor.\n",
	"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
	" return function socket_on_message(evt) {\n",
	" if (evt.data instanceof Blob) {\n",
	" /* FIXME: We get \"Resource interpreted as Image but\n",
	" * transferred with MIME type text/plain:\" errors on\n",
	" * Chrome. But how to set the MIME type? It doesn't seem\n",
	" * to be part of the websocket stream */\n",
	" evt.data.type = \"image/png\";\n",
	"\n",
	" /* Free the memory for the previous frames */\n",
	" if (fig.imageObj.src) {\n",
	" (window.URL \|\| window.webkitURL).revokeObjectURL(\n",
	" fig.imageObj.src);\n",
	" }\n",
	"\n",
	" fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n",
	" evt.data);\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
	" fig.imageObj.src = evt.data;\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	"\n",
	" var msg = JSON.parse(evt.data);\n",
	" var msg_type = msg['type'];\n",
	"\n",
	" // Call the \"handle_{type}\" callback, which takes\n",
	" // the figure and JSON message as its only arguments.\n",
	" try {\n",
	" var callback = fig[\"handle_\" + msg_type];\n",
	" } catch (e) {\n",
	" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
	" return;\n",
	" }\n",
	"\n",
	" if (callback) {\n",
	" try {\n",
	" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
	" callback(fig, msg);\n",
	" } catch (e) {\n",
	" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
	" }\n",
	" }\n",
	" };\n",
	"}\n",
	"\n",
	"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
	"mpl.findpos = function(e) {\n",
	" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
	" var targ;\n",
	" if (!e)\n",
	" e = window.event;\n",
	" if (e.target)\n",
	" targ = e.target;\n",
	" else if (e.srcElement)\n",
	" targ = e.srcElement;\n",
	" if (targ.nodeType == 3) // defeat Safari bug\n",
	" targ = targ.parentNode;\n",
	"\n",
	" // jQuery normalizes the pageX and pageY\n",
	" // pageX,Y are the mouse positions relative to the document\n",
	" // offset() returns the position of the element relative to the document\n",
	" var x = e.pageX - $(targ).offset().left;\n",
	" var y = e.pageY - $(targ).offset().top;\n",
	"\n",
	" return {\"x\": x, \"y\": y};\n",
	"};\n",
	"\n",
	"/*\n",
	" * return a copy of an object with only non-object keys\n",
	" * we need this to avoid circular references\n",
	" * http://stackoverflow.com/a/24161582/3208463\n",
	" */\n",
	"function simpleKeys (original) {\n",
	" return Object.keys(original).reduce(function (obj, key) {\n",
	" if (typeof original[key] !== 'object')\n",
	" obj[key] = original[key]\n",
	" return obj;\n",
	" }, {});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.mouse_event = function(event, name) {\n",
	" var canvas_pos = mpl.findpos(event)\n",
	"\n",
	" if (name === 'button_press')\n",
	" {\n",
	" this.canvas.focus();\n",
	" this.canvas_div.focus();\n",
	" }\n",
	"\n",
	" var x = canvas_pos.x * mpl.ratio;\n",
	" var y = canvas_pos.y * mpl.ratio;\n",
	"\n",
	" this.send_message(name, {x: x, y: y, button: event.button,\n",
	" step: event.step,\n",
	" guiEvent: simpleKeys(event)});\n",
	"\n",
	" /* This prevents the web browser from automatically changing to\n",
	" * the text insertion cursor when the button is pressed. We want\n",
	" * to control all of the cursor setting manually through the\n",
	" * 'cursor' event from matplotlib */\n",
	" event.preventDefault();\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" // Handle any extra behaviour associated with a key event\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.key_event = function(event, name) {\n",
	"\n",
	" // Prevent repeat events\n",
	" if (name == 'key_press')\n",
	" {\n",
	" if (event.which === this._key)\n",
	" return;\n",
	" else\n",
	" this._key = event.which;\n",
	" }\n",
	" if (name == 'key_release')\n",
	" this._key = null;\n",
	"\n",
	" var value = '';\n",
	" if (event.ctrlKey && event.which != 17)\n",
	" value += \"ctrl+\";\n",
	" if (event.altKey && event.which != 18)\n",
	" value += \"alt+\";\n",
	" if (event.shiftKey && event.which != 16)\n",
	" value += \"shift+\";\n",
	"\n",
	" value += 'k';\n",
	" value += event.which.toString();\n",
	"\n",
	" this._key_event_extra(event, name);\n",
	"\n",
	" this.send_message(name, {key: value,\n",
	" guiEvent: simpleKeys(event)});\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
	" if (name == 'download') {\n",
	" this.handle_save(this, null);\n",
	" } else {\n",
	" this.send_message(\"toolbar_button\", {name: name});\n",
	" }\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
	" this.message.textContent = tooltip;\n",
	"};\n",
	"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
	"\n",
	"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
	"\n",
	"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
	" // Create a \"websocket\"-like object which calls the given IPython comm\n",
	" // object with the appropriate methods. Currently this is a non binary\n",
	" // socket, so there is still some room for performance tuning.\n",
	" var ws = {};\n",
	"\n",
	" ws.close = function() {\n",
	" comm.close()\n",
	" };\n",
	" ws.send = function(m) {\n",
	" //console.log('sending', m);\n",
	" comm.send(m);\n",
	" };\n",
	" // Register the callback with on_msg.\n",
	" comm.on_msg(function(msg) {\n",
	" //console.log('receiving', msg['content']['data'], msg);\n",
	" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
	" ws.onmessage(msg['content']['data'])\n",
	" });\n",
	" return ws;\n",
	"}\n",
	"\n",
	"mpl.mpl_figure_comm = function(comm, msg) {\n",
	" // This is the function which gets called when the mpl process\n",
	" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
	"\n",
	" var id = msg.content.data.id;\n",
	" // Get hold of the div created by the display call when the Comm\n",
	" // socket was opened in Python.\n",
	" var element = $(\"#\" + id);\n",
	" var ws_proxy = comm_websocket_adapter(comm)\n",
	"\n",
	" function ondownload(figure, format) {\n",
	" window.open(figure.imageObj.src);\n",
	" }\n",
	"\n",
	" var fig = new mpl.figure(id, ws_proxy,\n",
	" ondownload,\n",
	" element.get(0));\n",
	"\n",
	" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
	" // web socket which is closed, not our websocket->open comm proxy.\n",
	" ws_proxy.onopen();\n",
	"\n",
	" fig.parent_element = element.get(0);\n",
	" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
	" if (!fig.cell_info) {\n",
	" console.error(\"Failed to find cell for figure\", id, fig);\n",
	" return;\n",
	" }\n",
	"\n",
	" var output_index = fig.cell_info[2]\n",
	" var cell = fig.cell_info[0];\n",
	"\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
	" var width = fig.canvas.width/mpl.ratio\n",
	" fig.root.unbind('remove')\n",
	"\n",
	" // Update the output cell to use the data from the current canvas.\n",
	" fig.push_to_output();\n",
	" var dataURL = fig.canvas.toDataURL();\n",
	" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
	" // the notebook keyboard shortcuts fail.\n",
	" IPython.keyboard_manager.enable()\n",
	" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
	" fig.close_ws(fig, msg);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.close_ws = function(fig, msg){\n",
	" fig.send_message('closing', msg);\n",
	" // fig.ws.close()\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
	" // Turn the data on the canvas into data in the output cell.\n",
	" var width = this.canvas.width/mpl.ratio\n",
	" var dataURL = this.canvas.toDataURL();\n",
	" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Tell IPython that the notebook contents must change.\n",
	" IPython.notebook.set_dirty(true);\n",
	" this.send_message(\"ack\", {});\n",
	" var fig = this;\n",
	" // Wait a second, then push the new image to the DOM so\n",
	" // that it is saved nicely (might be nice to debounce this).\n",
	" setTimeout(function () { fig.push_to_output() }, 1000);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items){\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) { continue; };\n",
	"\n",
	" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" // Add the status bar.\n",
	" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"\n",
	" // Add the close button to the window.\n",
	" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
	" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
	" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
	" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
	" buttongrp.append(button);\n",
	" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
	" titlebar.prepend(buttongrp);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(el){\n",
	" var fig = this\n",
	" el.on(\"remove\", function(){\n",
	"\tfig.close_ws(fig, {});\n",
	" });\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(el){\n",
	" // this is important to make the div 'focusable\n",
	" el.attr('tabindex', 0)\n",
	" // reach out to IPython and tell the keyboard manager to turn it's self\n",
	" // off when our div gets focus\n",
	"\n",
	" // location in version 3\n",
	" if (IPython.notebook.keyboard_manager) {\n",
	" IPython.notebook.keyboard_manager.register_events(el);\n",
	" }\n",
	" else {\n",
	" // location in version 2\n",
	" IPython.keyboard_manager.register_events(el);\n",
	" }\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" var manager = IPython.notebook.keyboard_manager;\n",
	" if (!manager)\n",
	" manager = IPython.keyboard_manager;\n",
	"\n",
	" // Check for shift+enter\n",
	" if (event.shiftKey && event.which == 13) {\n",
	" this.canvas_div.blur();\n",
	" event.shiftKey = false;\n",
	" // Send a \"J\" for go to next cell\n",
	" event.which = 74;\n",
	" event.keyCode = 74;\n",
	" manager.command_mode();\n",
	" manager.handle_keydown(event);\n",
	" }\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" fig.ondownload(fig, null);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.find_output_cell = function(html_output) {\n",
	" // Return the cell and output element which can be found uniquely in the notebook.\n",
	" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
	" // IPython event is triggered only after the cells have been serialised, which for\n",
	" // our purposes (turning an active figure into a static one), is too late.\n",
	" var cells = IPython.notebook.get_cells();\n",
	" var ncells = cells.length;\n",
	" for (var i=0; i<ncells; i++) {\n",
	" var cell = cells[i];\n",
	" if (cell.cell_type === 'code'){\n",
	" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
	" var data = cell.output_area.outputs[j];\n",
	" if (data.data) {\n",
	" // IPython >= 3 moved mimebundle to data attribute of output\n",
	" data = data.data;\n",
	" }\n",
	" if (data['text/html'] == html_output) {\n",
	" return [cell, data, j];\n",
	" }\n",
	" }\n",
	" }\n",
	" }\n",
	"}\n",
	"\n",
	"// Register the function which deals with the matplotlib target/channel.\n",
	"// The kernel may be null if the page has been refreshed.\n",
	"if (IPython.notebook.kernel != null) {\n",
	" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
	"}\n"
	],
	"text/plain": [
	"<IPython.core.display.Javascript object>"
	]
	},
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	"output_type": "display_data"
	},
	{
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\" width=\"640\">"
	],
	"text/plain": [
	"<IPython.core.display.HTML object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	},
	{
	"data": {
	"text/plain": [
	"<matplotlib.collections.PathCollection at 0x1c2f715c88>"
	]
	},
	"execution_count": 77,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"plt.figure()\n",
	"plt.scatter(sample.SVF, sample.MSTRAHLUNG)"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## Compare to old feature set"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 52,
	"metadata": {},
	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/numpy/lib/arraysetops.py:569: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
	" mask \|= (ar1 == a)\n"
	]
	}
	],
	"source": [
	"old_data = pd.read_csv( os.path.join(path, 'data_all.csv'), index_col = 0,\n",
	" usecols = ['DF_UID', 'SIS', 'NEIGUNG', 'AUSRICHTUNG', \n",
	" 'horizon_S', 'horizon_E', 'horizon_W', 'MSTRAHLUNG'])"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 53,
	"metadata": {},
	"outputs": [],
	"source": [
	"sel_ftrs = ['SIS', 'AUSRICHTUNG', 'horizon_S', 'horizon_W', 'horizon_E', 'NEIGUNG']"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 54,
	"metadata": {},
	"outputs": [],
	"source": [
	"FEATURES = sel_ftrs"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 59,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"Fitting time: 12.310s\n"
	]
	}
	],
	"source": [
	"model = 'RF'\n",
	"training = data.sample(10000)\n",
	"\n",
	"x, t, X, T = get_data( training, FEATURES, label_name)\n",
	"\n",
	"tt = time.time()\n",
	"est = set_estimators()[ model ]\n",
	"est.fit(X, T)\n",
	"print('Fitting time: %.3fs' %(time.time()-tt))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 60,
	"metadata": {},
	"outputs": [],
	"source": [
	"# for standard estimators\n",
	"n_features = X.shape[1]\n",
	"importance = np.mean([tree.feature_importances_ for tree in est.estimators_],axis=0)\n",
	"std = np.std([tree.feature_importances_ for tree in est.estimators_],axis=0)\n",
	"ind = np.argsort(importance)[-n_features:]"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 61,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"[0.07044602 0.38285659 0.30330004 0.04017591 0.04942161 0.15379983]\n",
	"[0.00415838 0.00983228 0.0088024 0.00370167 0.00424183 0.00585539]\n",
	"[3 4 0 5 2 1]\n"
	]
	}
	],
	"source": [
	"print(importance)\n",
	"print(std)\n",
	"print(ind)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 62,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"application/javascript": [
	"/* Put everything inside the global mpl namespace */\n",
	"window.mpl = {};\n",
	"\n",
	"\n",
	"mpl.get_websocket_type = function() {\n",
	" if (typeof(WebSocket) !== 'undefined') {\n",
	" return WebSocket;\n",
	" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
	" return MozWebSocket;\n",
	" } else {\n",
	" alert('Your browser does not have WebSocket support.' +\n",
	" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
	" 'Firefox 4 and 5 are also supported but you ' +\n",
	" 'have to enable WebSockets in about:config.');\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
	" this.id = figure_id;\n",
	"\n",
	" this.ws = websocket;\n",
	"\n",
	" this.supports_binary = (this.ws.binaryType != undefined);\n",
	"\n",
	" if (!this.supports_binary) {\n",
	" var warnings = document.getElementById(\"mpl-warnings\");\n",
	" if (warnings) {\n",
	" warnings.style.display = 'block';\n",
	" warnings.textContent = (\n",
	" \"This browser does not support binary websocket messages. \" +\n",
	" \"Performance may be slow.\");\n",
	" }\n",
	" }\n",
	"\n",
	" this.imageObj = new Image();\n",
	"\n",
	" this.context = undefined;\n",
	" this.message = undefined;\n",
	" this.canvas = undefined;\n",
	" this.rubberband_canvas = undefined;\n",
	" this.rubberband_context = undefined;\n",
	" this.format_dropdown = undefined;\n",
	"\n",
	" this.image_mode = 'full';\n",
	"\n",
	" this.root = $('<div/>');\n",
	" this._root_extra_style(this.root)\n",
	" this.root.attr('style', 'display: inline-block');\n",
	"\n",
	" $(parent_element).append(this.root);\n",
	"\n",
	" this._init_header(this);\n",
	" this._init_canvas(this);\n",
	" this._init_toolbar(this);\n",
	"\n",
	" var fig = this;\n",
	"\n",
	" this.waiting = false;\n",
	"\n",
	" this.ws.onopen = function () {\n",
	" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
	" fig.send_message(\"send_image_mode\", {});\n",
	" if (mpl.ratio != 1) {\n",
	" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
	" }\n",
	" fig.send_message(\"refresh\", {});\n",
	" }\n",
	"\n",
	" this.imageObj.onload = function() {\n",
	" if (fig.image_mode == 'full') {\n",
	" // Full images could contain transparency (where diff images\n",
	" // almost always do), so we need to clear the canvas so that\n",
	" // there is no ghosting.\n",
	" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
	" }\n",
	" fig.context.drawImage(fig.imageObj, 0, 0);\n",
	" };\n",
	"\n",
	" this.imageObj.onunload = function() {\n",
	" fig.ws.close();\n",
	" }\n",
	"\n",
	" this.ws.onmessage = this._make_on_message_function(this);\n",
	"\n",
	" this.ondownload = ondownload;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_header = function() {\n",
	" var titlebar = $(\n",
	" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
	" 'ui-helper-clearfix\"/>');\n",
	" var titletext = $(\n",
	" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
	" 'text-align: center; padding: 3px;\"/>');\n",
	" titlebar.append(titletext)\n",
	" this.root.append(titlebar);\n",
	" this.header = titletext[0];\n",
	"}\n",
	"\n",
	"\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
	"\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_canvas = function() {\n",
	" var fig = this;\n",
	"\n",
	" var canvas_div = $('<div/>');\n",
	"\n",
	" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
	"\n",
	" function canvas_keyboard_event(event) {\n",
	" return fig.key_event(event, event['data']);\n",
	" }\n",
	"\n",
	" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
	" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
	" this.canvas_div = canvas_div\n",
	" this._canvas_extra_style(canvas_div)\n",
	" this.root.append(canvas_div);\n",
	"\n",
	" var canvas = $('<canvas/>');\n",
	" canvas.addClass('mpl-canvas');\n",
	" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
	"\n",
	" this.canvas = canvas[0];\n",
	" this.context = canvas[0].getContext(\"2d\");\n",
	"\n",
	" var backingStore = this.context.backingStorePixelRatio \|\|\n",
	"\tthis.context.webkitBackingStorePixelRatio \|\|\n",
	"\tthis.context.mozBackingStorePixelRatio \|\|\n",
	"\tthis.context.msBackingStorePixelRatio \|\|\n",
	"\tthis.context.oBackingStorePixelRatio \|\|\n",
	"\tthis.context.backingStorePixelRatio \|\| 1;\n",
	"\n",
	" mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n",
	"\n",
	" var rubberband = $('<canvas/>');\n",
	" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
	"\n",
	" var pass_mouse_events = true;\n",
	"\n",
	" canvas_div.resizable({\n",
	" start: function(event, ui) {\n",
	" pass_mouse_events = false;\n",
	" },\n",
	" resize: function(event, ui) {\n",
	" fig.request_resize(ui.size.width, ui.size.height);\n",
	" },\n",
	" stop: function(event, ui) {\n",
	" pass_mouse_events = true;\n",
	" fig.request_resize(ui.size.width, ui.size.height);\n",
	" },\n",
	" });\n",
	"\n",
	" function mouse_event_fn(event) {\n",
	" if (pass_mouse_events)\n",
	" return fig.mouse_event(event, event['data']);\n",
	" }\n",
	"\n",
	" rubberband.mousedown('button_press', mouse_event_fn);\n",
	" rubberband.mouseup('button_release', mouse_event_fn);\n",
	" // Throttle sequential mouse events to 1 every 20ms.\n",
	" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
	"\n",
	" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
	" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
	"\n",
	" canvas_div.on(\"wheel\", function (event) {\n",
	" event = event.originalEvent;\n",
	" event['data'] = 'scroll'\n",
	" if (event.deltaY < 0) {\n",
	" event.step = 1;\n",
	" } else {\n",
	" event.step = -1;\n",
	" }\n",
	" mouse_event_fn(event);\n",
	" });\n",
	"\n",
	" canvas_div.append(canvas);\n",
	" canvas_div.append(rubberband);\n",
	"\n",
	" this.rubberband = rubberband;\n",
	" this.rubberband_canvas = rubberband[0];\n",
	" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
	" this.rubberband_context.strokeStyle = \"#000000\";\n",
	"\n",
	" this._resize_canvas = function(width, height) {\n",
	" // Keep the size of the canvas, canvas container, and rubber band\n",
	" // canvas in synch.\n",
	" canvas_div.css('width', width)\n",
	" canvas_div.css('height', height)\n",
	"\n",
	" canvas.attr('width', width * mpl.ratio);\n",
	" canvas.attr('height', height * mpl.ratio);\n",
	" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
	"\n",
	" rubberband.attr('width', width);\n",
	" rubberband.attr('height', height);\n",
	" }\n",
	"\n",
	" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
	" // upon first draw.\n",
	" this._resize_canvas(600, 600);\n",
	"\n",
	" // Disable right mouse context menu.\n",
	" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
	" return false;\n",
	" });\n",
	"\n",
	" function set_focus () {\n",
	" canvas.focus();\n",
	" canvas_div.focus();\n",
	" }\n",
	"\n",
	" window.setTimeout(set_focus, 100);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items) {\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) {\n",
	" // put a spacer in here.\n",
	" continue;\n",
	" }\n",
	" var button = $('<button/>');\n",
	" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
	" 'ui-button-icon-only');\n",
	" button.attr('role', 'button');\n",
	" button.attr('aria-disabled', 'false');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	"\n",
	" var icon_img = $('<span/>');\n",
	" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
	" icon_img.addClass(image);\n",
	" icon_img.addClass('ui-corner-all');\n",
	"\n",
	" var tooltip_span = $('<span/>');\n",
	" tooltip_span.addClass('ui-button-text');\n",
	" tooltip_span.html(tooltip);\n",
	"\n",
	" button.append(icon_img);\n",
	" button.append(tooltip_span);\n",
	"\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" var fmt_picker_span = $('<span/>');\n",
	"\n",
	" var fmt_picker = $('<select/>');\n",
	" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
	" fmt_picker_span.append(fmt_picker);\n",
	" nav_element.append(fmt_picker_span);\n",
	" this.format_dropdown = fmt_picker[0];\n",
	"\n",
	" for (var ind in mpl.extensions) {\n",
	" var fmt = mpl.extensions[ind];\n",
	" var option = $(\n",
	" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
	" fmt_picker.append(option)\n",
	" }\n",
	"\n",
	" // Add hover states to the ui-buttons\n",
	" $( \".ui-button\" ).hover(\n",
	" function() { $(this).addClass(\"ui-state-hover\");},\n",
	" function() { $(this).removeClass(\"ui-state-hover\");}\n",
	" );\n",
	"\n",
	" var status_bar = $('<span class=\"mpl-message\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
	" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
	" // which will in turn request a refresh of the image.\n",
	" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_message = function(type, properties) {\n",
	" properties['type'] = type;\n",
	" properties['figure_id'] = this.id;\n",
	" this.ws.send(JSON.stringify(properties));\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.send_draw_message = function() {\n",
	" if (!this.waiting) {\n",
	" this.waiting = true;\n",
	" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
	" }\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" var format_dropdown = fig.format_dropdown;\n",
	" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
	" fig.ondownload(fig, format);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
	" var size = msg['size'];\n",
	" if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n",
	" fig._resize_canvas(size[0], size[1]);\n",
	" fig.send_message(\"refresh\", {});\n",
	" };\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
	" var x0 = msg['x0'] / mpl.ratio;\n",
	" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
	" var x1 = msg['x1'] / mpl.ratio;\n",
	" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
	" x0 = Math.floor(x0) + 0.5;\n",
	" y0 = Math.floor(y0) + 0.5;\n",
	" x1 = Math.floor(x1) + 0.5;\n",
	" y1 = Math.floor(y1) + 0.5;\n",
	" var min_x = Math.min(x0, x1);\n",
	" var min_y = Math.min(y0, y1);\n",
	" var width = Math.abs(x1 - x0);\n",
	" var height = Math.abs(y1 - y0);\n",
	"\n",
	" fig.rubberband_context.clearRect(\n",
	" 0, 0, fig.canvas.width, fig.canvas.height);\n",
	"\n",
	" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
	" // Updates the figure title.\n",
	" fig.header.textContent = msg['label'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
	" var cursor = msg['cursor'];\n",
	" switch(cursor)\n",
	" {\n",
	" case 0:\n",
	" cursor = 'pointer';\n",
	" break;\n",
	" case 1:\n",
	" cursor = 'default';\n",
	" break;\n",
	" case 2:\n",
	" cursor = 'crosshair';\n",
	" break;\n",
	" case 3:\n",
	" cursor = 'move';\n",
	" break;\n",
	" }\n",
	" fig.rubberband_canvas.style.cursor = cursor;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
	" fig.message.textContent = msg['message'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
	" // Request the server to send over a new figure.\n",
	" fig.send_draw_message();\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
	" fig.image_mode = msg['mode'];\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Called whenever the canvas gets updated.\n",
	" this.send_message(\"ack\", {});\n",
	"}\n",
	"\n",
	"// A function to construct a web socket function for onmessage handling.\n",
	"// Called in the figure constructor.\n",
	"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
	" return function socket_on_message(evt) {\n",
	" if (evt.data instanceof Blob) {\n",
	" /* FIXME: We get \"Resource interpreted as Image but\n",
	" * transferred with MIME type text/plain:\" errors on\n",
	" * Chrome. But how to set the MIME type? It doesn't seem\n",
	" * to be part of the websocket stream */\n",
	" evt.data.type = \"image/png\";\n",
	"\n",
	" /* Free the memory for the previous frames */\n",
	" if (fig.imageObj.src) {\n",
	" (window.URL \|\| window.webkitURL).revokeObjectURL(\n",
	" fig.imageObj.src);\n",
	" }\n",
	"\n",
	" fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n",
	" evt.data);\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
	" fig.imageObj.src = evt.data;\n",
	" fig.updated_canvas_event();\n",
	" fig.waiting = false;\n",
	" return;\n",
	" }\n",
	"\n",
	" var msg = JSON.parse(evt.data);\n",
	" var msg_type = msg['type'];\n",
	"\n",
	" // Call the \"handle_{type}\" callback, which takes\n",
	" // the figure and JSON message as its only arguments.\n",
	" try {\n",
	" var callback = fig[\"handle_\" + msg_type];\n",
	" } catch (e) {\n",
	" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
	" return;\n",
	" }\n",
	"\n",
	" if (callback) {\n",
	" try {\n",
	" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
	" callback(fig, msg);\n",
	" } catch (e) {\n",
	" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
	" }\n",
	" }\n",
	" };\n",
	"}\n",
	"\n",
	"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
	"mpl.findpos = function(e) {\n",
	" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
	" var targ;\n",
	" if (!e)\n",
	" e = window.event;\n",
	" if (e.target)\n",
	" targ = e.target;\n",
	" else if (e.srcElement)\n",
	" targ = e.srcElement;\n",
	" if (targ.nodeType == 3) // defeat Safari bug\n",
	" targ = targ.parentNode;\n",
	"\n",
	" // jQuery normalizes the pageX and pageY\n",
	" // pageX,Y are the mouse positions relative to the document\n",
	" // offset() returns the position of the element relative to the document\n",
	" var x = e.pageX - $(targ).offset().left;\n",
	" var y = e.pageY - $(targ).offset().top;\n",
	"\n",
	" return {\"x\": x, \"y\": y};\n",
	"};\n",
	"\n",
	"/*\n",
	" * return a copy of an object with only non-object keys\n",
	" * we need this to avoid circular references\n",
	" * http://stackoverflow.com/a/24161582/3208463\n",
	" */\n",
	"function simpleKeys (original) {\n",
	" return Object.keys(original).reduce(function (obj, key) {\n",
	" if (typeof original[key] !== 'object')\n",
	" obj[key] = original[key]\n",
	" return obj;\n",
	" }, {});\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.mouse_event = function(event, name) {\n",
	" var canvas_pos = mpl.findpos(event)\n",
	"\n",
	" if (name === 'button_press')\n",
	" {\n",
	" this.canvas.focus();\n",
	" this.canvas_div.focus();\n",
	" }\n",
	"\n",
	" var x = canvas_pos.x * mpl.ratio;\n",
	" var y = canvas_pos.y * mpl.ratio;\n",
	"\n",
	" this.send_message(name, {x: x, y: y, button: event.button,\n",
	" step: event.step,\n",
	" guiEvent: simpleKeys(event)});\n",
	"\n",
	" /* This prevents the web browser from automatically changing to\n",
	" * the text insertion cursor when the button is pressed. We want\n",
	" * to control all of the cursor setting manually through the\n",
	" * 'cursor' event from matplotlib */\n",
	" event.preventDefault();\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" // Handle any extra behaviour associated with a key event\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.key_event = function(event, name) {\n",
	"\n",
	" // Prevent repeat events\n",
	" if (name == 'key_press')\n",
	" {\n",
	" if (event.which === this._key)\n",
	" return;\n",
	" else\n",
	" this._key = event.which;\n",
	" }\n",
	" if (name == 'key_release')\n",
	" this._key = null;\n",
	"\n",
	" var value = '';\n",
	" if (event.ctrlKey && event.which != 17)\n",
	" value += \"ctrl+\";\n",
	" if (event.altKey && event.which != 18)\n",
	" value += \"alt+\";\n",
	" if (event.shiftKey && event.which != 16)\n",
	" value += \"shift+\";\n",
	"\n",
	" value += 'k';\n",
	" value += event.which.toString();\n",
	"\n",
	" this._key_event_extra(event, name);\n",
	"\n",
	" this.send_message(name, {key: value,\n",
	" guiEvent: simpleKeys(event)});\n",
	" return false;\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
	" if (name == 'download') {\n",
	" this.handle_save(this, null);\n",
	" } else {\n",
	" this.send_message(\"toolbar_button\", {name: name});\n",
	" }\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
	" this.message.textContent = tooltip;\n",
	"};\n",
	"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
	"\n",
	"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
	"\n",
	"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
	" // Create a \"websocket\"-like object which calls the given IPython comm\n",
	" // object with the appropriate methods. Currently this is a non binary\n",
	" // socket, so there is still some room for performance tuning.\n",
	" var ws = {};\n",
	"\n",
	" ws.close = function() {\n",
	" comm.close()\n",
	" };\n",
	" ws.send = function(m) {\n",
	" //console.log('sending', m);\n",
	" comm.send(m);\n",
	" };\n",
	" // Register the callback with on_msg.\n",
	" comm.on_msg(function(msg) {\n",
	" //console.log('receiving', msg['content']['data'], msg);\n",
	" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
	" ws.onmessage(msg['content']['data'])\n",
	" });\n",
	" return ws;\n",
	"}\n",
	"\n",
	"mpl.mpl_figure_comm = function(comm, msg) {\n",
	" // This is the function which gets called when the mpl process\n",
	" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
	"\n",
	" var id = msg.content.data.id;\n",
	" // Get hold of the div created by the display call when the Comm\n",
	" // socket was opened in Python.\n",
	" var element = $(\"#\" + id);\n",
	" var ws_proxy = comm_websocket_adapter(comm)\n",
	"\n",
	" function ondownload(figure, format) {\n",
	" window.open(figure.imageObj.src);\n",
	" }\n",
	"\n",
	" var fig = new mpl.figure(id, ws_proxy,\n",
	" ondownload,\n",
	" element.get(0));\n",
	"\n",
	" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
	" // web socket which is closed, not our websocket->open comm proxy.\n",
	" ws_proxy.onopen();\n",
	"\n",
	" fig.parent_element = element.get(0);\n",
	" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
	" if (!fig.cell_info) {\n",
	" console.error(\"Failed to find cell for figure\", id, fig);\n",
	" return;\n",
	" }\n",
	"\n",
	" var output_index = fig.cell_info[2]\n",
	" var cell = fig.cell_info[0];\n",
	"\n",
	"};\n",
	"\n",
	"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
	" var width = fig.canvas.width/mpl.ratio\n",
	" fig.root.unbind('remove')\n",
	"\n",
	" // Update the output cell to use the data from the current canvas.\n",
	" fig.push_to_output();\n",
	" var dataURL = fig.canvas.toDataURL();\n",
	" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
	" // the notebook keyboard shortcuts fail.\n",
	" IPython.keyboard_manager.enable()\n",
	" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
	" fig.close_ws(fig, msg);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.close_ws = function(fig, msg){\n",
	" fig.send_message('closing', msg);\n",
	" // fig.ws.close()\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
	" // Turn the data on the canvas into data in the output cell.\n",
	" var width = this.canvas.width/mpl.ratio\n",
	" var dataURL = this.canvas.toDataURL();\n",
	" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.updated_canvas_event = function() {\n",
	" // Tell IPython that the notebook contents must change.\n",
	" IPython.notebook.set_dirty(true);\n",
	" this.send_message(\"ack\", {});\n",
	" var fig = this;\n",
	" // Wait a second, then push the new image to the DOM so\n",
	" // that it is saved nicely (might be nice to debounce this).\n",
	" setTimeout(function () { fig.push_to_output() }, 1000);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._init_toolbar = function() {\n",
	" var fig = this;\n",
	"\n",
	" var nav_element = $('<div/>')\n",
	" nav_element.attr('style', 'width: 100%');\n",
	" this.root.append(nav_element);\n",
	"\n",
	" // Define a callback function for later on.\n",
	" function toolbar_event(event) {\n",
	" return fig.toolbar_button_onclick(event['data']);\n",
	" }\n",
	" function toolbar_mouse_event(event) {\n",
	" return fig.toolbar_button_onmouseover(event['data']);\n",
	" }\n",
	"\n",
	" for(var toolbar_ind in mpl.toolbar_items){\n",
	" var name = mpl.toolbar_items[toolbar_ind][0];\n",
	" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
	" var image = mpl.toolbar_items[toolbar_ind][2];\n",
	" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
	"\n",
	" if (!name) { continue; };\n",
	"\n",
	" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
	" button.click(method_name, toolbar_event);\n",
	" button.mouseover(tooltip, toolbar_mouse_event);\n",
	" nav_element.append(button);\n",
	" }\n",
	"\n",
	" // Add the status bar.\n",
	" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
	" nav_element.append(status_bar);\n",
	" this.message = status_bar[0];\n",
	"\n",
	" // Add the close button to the window.\n",
	" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
	" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
	" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
	" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
	" buttongrp.append(button);\n",
	" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
	" titlebar.prepend(buttongrp);\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._root_extra_style = function(el){\n",
	" var fig = this\n",
	" el.on(\"remove\", function(){\n",
	"\tfig.close_ws(fig, {});\n",
	" });\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._canvas_extra_style = function(el){\n",
	" // this is important to make the div 'focusable\n",
	" el.attr('tabindex', 0)\n",
	" // reach out to IPython and tell the keyboard manager to turn it's self\n",
	" // off when our div gets focus\n",
	"\n",
	" // location in version 3\n",
	" if (IPython.notebook.keyboard_manager) {\n",
	" IPython.notebook.keyboard_manager.register_events(el);\n",
	" }\n",
	" else {\n",
	" // location in version 2\n",
	" IPython.keyboard_manager.register_events(el);\n",
	" }\n",
	"\n",
	"}\n",
	"\n",
	"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
	" var manager = IPython.notebook.keyboard_manager;\n",
	" if (!manager)\n",
	" manager = IPython.keyboard_manager;\n",
	"\n",
	" // Check for shift+enter\n",
	" if (event.shiftKey && event.which == 13) {\n",
	" this.canvas_div.blur();\n",
	" event.shiftKey = false;\n",
	" // Send a \"J\" for go to next cell\n",
	" event.which = 74;\n",
	" event.keyCode = 74;\n",
	" manager.command_mode();\n",
	" manager.handle_keydown(event);\n",
	" }\n",
	"}\n",
	"\n",
	"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
	" fig.ondownload(fig, null);\n",
	"}\n",
	"\n",
	"\n",
	"mpl.find_output_cell = function(html_output) {\n",
	" // Return the cell and output element which can be found uniquely in the notebook.\n",
	" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
	" // IPython event is triggered only after the cells have been serialised, which for\n",
	" // our purposes (turning an active figure into a static one), is too late.\n",
	" var cells = IPython.notebook.get_cells();\n",
	" var ncells = cells.length;\n",
	" for (var i=0; i<ncells; i++) {\n",
	" var cell = cells[i];\n",
	" if (cell.cell_type === 'code'){\n",
	" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
	" var data = cell.output_area.outputs[j];\n",
	" if (data.data) {\n",
	" // IPython >= 3 moved mimebundle to data attribute of output\n",
	" data = data.data;\n",
	" }\n",
	" if (data['text/html'] == html_output) {\n",
	" return [cell, data, j];\n",
	" }\n",
	" }\n",
	" }\n",
	" }\n",
	"}\n",
	"\n",
	"// Register the function which deals with the matplotlib target/channel.\n",
	"// The kernel may be null if the page has been refreshed.\n",
	"if (IPython.notebook.kernel != null) {\n",
	" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
	"}\n"
	],
	"text/plain": [
	"<IPython.core.display.Javascript object>"
	]
	},
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	"output_type": "display_data"
	},
	{
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\" width=\"640\">"
	],
	"text/plain": [
	"<IPython.core.display.HTML object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.figure()\n",
	"ax = plt.axes()\n",
	"plt.title(\"Feature Importance for RF (%dk features)\" %(X.shape[0]/1000)) # \\n Testing RMSE: %f ; , np.sqrt(mse(Y_te,T_te))\n",
	"plt.barh(range(n_features),importance[ind],\n",
	" color=\"r\", xerr=std[ind], alpha=0.4, align=\"center\", capsize = 2)\n",
	"plt.yticks(range(n_features), np.asarray(FEATURES)[ind])\n",
	"plt.ylim([-1, len(ind)])\n",
	"plt.xlabel('Feature importance')\n",
	"plt.tight_layout()\n",
	"plt.show()\n",
	"# plt.savefig('Feature_importance_all_50k.png', dpi=300)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3",
	"language": "python",
	"name": "python3"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.6.4"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}

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