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In [1]:

    
import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn.apionly as sns
from sklearn.model_selection import GridSearchCV

import comptools as comp

%matplotlib inline









    



/home/jbourbeau/.virtualenvs/composition/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)



In [2]:

    
# Load simulation data and pipeline
df_sim_train, df_sim_test = comp.load_sim(config='IC86.2012')
feature_list, feature_labels = comp.analysis.get_training_features()
pipeline = comp.get_pipeline('BDT')



In [3]:

    
feature_list









    Out[3]:





['lap_cos_zenith',
 'log_s125',
 'log_dEdX',
 'log_d4r_peak_energy',
 'log_d4r_peak_sigma']



In [4]:

    
pipeline.get_params().keys()









    Out[4]:





['classifier__min_impurity_split',
 'classifier__max_features',
 'classifier__subsample',
 'classifier__max_depth',
 'classifier__min_samples_split',
 'classifier__learning_rate',
 'classifier__min_samples_leaf',
 'classifier__criterion',
 'classifier__loss',
 'classifier__n_estimators',
 'classifier__max_leaf_nodes',
 'steps',
 'classifier__warm_start',
 'classifier__verbose',
 'classifier__presort',
 'classifier__min_weight_fraction_leaf',
 'classifier',
 'classifier__random_state',
 'classifier__init']



In [5]:

    
parameters = {'classifier__max_depth': range(1, 11), 
              'classifier__n_estimators':[5, 10, 20, 50, 100, 200, 300, 400, 500, 600]}

# parameters = {'classifier__max_depth': range(1, 8), 
#               'classifier__n_estimators':[5, 10, 20, 50, 100, 200, 300, 400, 500, 600]}

grid_search = GridSearchCV(pipeline, parameters, n_jobs=25, verbose=2)
grid_search.fit(df_sim_train[feature_list], df_sim_train['target'])









    



Fitting 3 folds for each of 100 candidates, totalling 300 fits
[CV] classifier__n_estimators=5, classifier__max_depth=1 .............
[CV] classifier__n_estimators=5, classifier__max_depth=1 .............
[CV] classifier__n_estimators=5, classifier__max_depth=1 .............
[CV] classifier__n_estimators=10, classifier__max_depth=1 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=1, total=   0.1s
[CV] classifier__n_estimators=10, classifier__max_depth=1 ............
[CV] classifier__n_estimators=10, classifier__max_depth=1 ............
[CV] classifier__n_estimators=20, classifier__max_depth=1 ............
[CV] classifier__n_estimators=20, classifier__max_depth=1 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=1, total=   0.1s
[CV] classifier__n_estimators=20, classifier__max_depth=1 ............
[CV] classifier__n_estimators=50, classifier__max_depth=1 ............
[CV] classifier__n_estimators=50, classifier__max_depth=1 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=1, total=   0.1s
[CV] classifier__n_estimators=50, classifier__max_depth=1 ............
[CV] classifier__n_estimators=100, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=100, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=100, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=200, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=200, classifier__max_depth=1 ...........
[CV]  classifier__n_estimators=10, classifier__max_depth=1, total=   0.2s
[CV] classifier__n_estimators=300, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=200, classifier__max_depth=1 ...........
[CV]  classifier__n_estimators=10, classifier__max_depth=1, total=   0.2s
[CV] classifier__n_estimators=300, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=300, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=400, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=400, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=400, classifier__max_depth=1 ...........
[CV]  classifier__n_estimators=10, classifier__max_depth=1, total=   0.3s
[CV] classifier__n_estimators=500, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=500, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=500, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=600, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=600, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=600, classifier__max_depth=1 ...........
[CV] classifier__n_estimators=5, classifier__max_depth=2 .............
[CV]  classifier__n_estimators=20, classifier__max_depth=1, total=   0.5s
[CV] classifier__n_estimators=5, classifier__max_depth=2 .............
[CV]  classifier__n_estimators=20, classifier__max_depth=1, total=   0.5s
[CV] classifier__n_estimators=5, classifier__max_depth=2 .............
[CV]  classifier__n_estimators=20, classifier__max_depth=1, total=   0.6s
[CV] classifier__n_estimators=10, classifier__max_depth=2 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=2, total=   0.3s
[CV] classifier__n_estimators=10, classifier__max_depth=2 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=2, total=   0.2s
[CV] classifier__n_estimators=10, classifier__max_depth=2 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=2, total=   0.3s
[CV] classifier__n_estimators=20, classifier__max_depth=2 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=2, total=   0.5s
[CV] classifier__n_estimators=20, classifier__max_depth=2 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=2, total=   0.5s
[CV] classifier__n_estimators=20, classifier__max_depth=2 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=2, total=   0.4s
[CV] classifier__n_estimators=50, classifier__max_depth=2 ............
[CV]  classifier__n_estimators=50, classifier__max_depth=1, total=   1.3s
[CV] classifier__n_estimators=50, classifier__max_depth=2 ............
[CV]  classifier__n_estimators=50, classifier__max_depth=1, total=   1.6s
[CV] classifier__n_estimators=50, classifier__max_depth=2 ............
[CV]  classifier__n_estimators=50, classifier__max_depth=1, total=   1.6s
[CV]  classifier__n_estimators=20, classifier__max_depth=2, total=   0.8s
[CV] classifier__n_estimators=100, classifier__max_depth=2 ...........
[CV] classifier__n_estimators=100, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=2, total=   0.9s
[CV] classifier__n_estimators=100, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=2, total=   1.1s
[CV] classifier__n_estimators=200, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=1, total=   2.3s
[CV] classifier__n_estimators=200, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=1, total=   2.7s
[CV] classifier__n_estimators=200, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=1, total=   2.8s
[CV] classifier__n_estimators=300, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=2, total=   1.6s
[CV] classifier__n_estimators=300, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=2, total=   2.0s
[CV] classifier__n_estimators=300, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=1, total=   4.1s
[CV] classifier__n_estimators=400, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=2, total=   2.7s
[CV] classifier__n_estimators=500, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=2, total=   3.3s
[CV] classifier__n_estimators=500, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=1, total=   5.1s
[CV] classifier__n_estimators=600, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=1, total=   5.4s
[CV] classifier__n_estimators=5, classifier__max_depth=3 .............
[CV]  classifier__n_estimators=100, classifier__max_depth=2, total=   3.9s
[CV] classifier__n_estimators=5, classifier__max_depth=3 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=3, total=   0.3s
[CV] classifier__n_estimators=5, classifier__max_depth=3 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=3, total=   0.3s
[CV] classifier__n_estimators=10, classifier__max_depth=3 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=3, total=   0.3s
[CV] classifier__n_estimators=10, classifier__max_depth=3 ............
[CV]  classifier__n_estimators=100, classifier__max_depth=2, total=   4.2s
[CV] classifier__n_estimators=20, classifier__max_depth=3 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=3, total=   0.7s
[CV] classifier__n_estimators=20, classifier__max_depth=3 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=3, total=   0.6s
[CV] classifier__n_estimators=10, classifier__max_depth=3 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=3, total=   0.6s
[CV] classifier__n_estimators=50, classifier__max_depth=3 ............
[CV]  classifier__n_estimators=20, classifier__max_depth=3, total=   1.2s
[CV] classifier__n_estimators=20, classifier__max_depth=3 ............
[CV]  classifier__n_estimators=300, classifier__max_depth=1, total=   7.4s
[CV] classifier__n_estimators=100, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=1, total=   7.6s
[CV] classifier__n_estimators=100, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=3, total=   1.3s
[CV] classifier__n_estimators=50, classifier__max_depth=3 ............
[CV]  classifier__n_estimators=300, classifier__max_depth=1, total=   8.3s
[CV] classifier__n_estimators=200, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=3, total=   1.3s
[CV] classifier__n_estimators=300, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=1, total=   9.5s
[CV] classifier__n_estimators=300, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=1, total=   9.9s
[CV] classifier__n_estimators=400, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=1, total=  10.0s
[CV]  classifier__n_estimators=200, classifier__max_depth=2, total=   7.8s
[CV] classifier__n_estimators=500, classifier__max_depth=3 ...........
[CV] classifier__n_estimators=500, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=2, total=   7.5s
[CV] classifier__n_estimators=600, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=3, total=   3.2s
[CV] classifier__n_estimators=50, classifier__max_depth=3 ............
[CV]  classifier__n_estimators=500, classifier__max_depth=1, total=  10.7s
[CV] classifier__n_estimators=5, classifier__max_depth=4 .............
[CV]  classifier__n_estimators=50, classifier__max_depth=3, total=   3.1s
[CV] classifier__n_estimators=5, classifier__max_depth=4 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=4, total=   0.4s
[CV] classifier__n_estimators=5, classifier__max_depth=4 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=4, total=   0.4s
[CV] classifier__n_estimators=10, classifier__max_depth=4 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=4, total=   0.4s
[CV] classifier__n_estimators=10, classifier__max_depth=4 ............
[CV]  classifier__n_estimators=500, classifier__max_depth=1, total=  11.9s
[CV] classifier__n_estimators=20, classifier__max_depth=4 ............
[CV]  classifier__n_estimators=200, classifier__max_depth=2, total=  10.0s
[CV] classifier__n_estimators=20, classifier__max_depth=4 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=4, total=   0.6s
[CV] classifier__n_estimators=50, classifier__max_depth=4 ............
[CV]  classifier__n_estimators=600, classifier__max_depth=1, total=  12.5s
[CV] classifier__n_estimators=100, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=10, classifier__max_depth=4, total=   1.0s
[CV] classifier__n_estimators=10, classifier__max_depth=4 ............
[CV]  classifier__n_estimators=100, classifier__max_depth=3, total=   5.6s
[CV] classifier__n_estimators=100, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=3, total=   5.5s
[CV] classifier__n_estimators=200, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=4, total=   1.4s
[CV] classifier__n_estimators=20, classifier__max_depth=4 ............
[CV]  classifier__n_estimators=20, classifier__max_depth=4, total=   1.4s
[CV] classifier__n_estimators=50, classifier__max_depth=4 ............
[CV]  classifier__n_estimators=500, classifier__max_depth=1, total=  13.7s
[CV]  classifier__n_estimators=10, classifier__max_depth=4, total=   0.9s
[CV] classifier__n_estimators=100, classifier__max_depth=4 ...........
[CV] classifier__n_estimators=200, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=3, total=   3.5s
[CV] classifier__n_estimators=300, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=2, total=  11.3s
[CV] classifier__n_estimators=300, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=4, total=   1.6s
[CV] classifier__n_estimators=400, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=2, total=  12.6s
[CV] classifier__n_estimators=500, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=2, total=  12.5s
[CV] classifier__n_estimators=400, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=4, total=   3.8s
[CV] classifier__n_estimators=50, classifier__max_depth=4 ............
[CV]  classifier__n_estimators=600, classifier__max_depth=1, total=  16.1s
[CV] classifier__n_estimators=500, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=1, total=  17.0s
[CV] classifier__n_estimators=500, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=3, total=   5.5s
[CV] classifier__n_estimators=600, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=4, total=   5.2s
[CV] classifier__n_estimators=600, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=2, total=  15.6s
[CV] classifier__n_estimators=400, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=3, total=  11.4s
[CV] classifier__n_estimators=200, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=4, total=   3.7s
[CV] classifier__n_estimators=600, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=4, total=   7.6s
[CV] classifier__n_estimators=100, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=4, total=   7.6s
[CV] classifier__n_estimators=200, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=3, total=  10.0s
[CV] classifier__n_estimators=5, classifier__max_depth=5 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=5, total=   0.7s
[CV]  classifier__n_estimators=500, classifier__max_depth=2, total=  19.2s
[CV] classifier__n_estimators=600, classifier__max_depth=2 ...........
[CV] classifier__n_estimators=5, classifier__max_depth=5 .............
[CV]  classifier__n_estimators=500, classifier__max_depth=2, total=  20.5s
[CV] classifier__n_estimators=500, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=5, classifier__max_depth=5, total=   0.7s
[CV] classifier__n_estimators=5, classifier__max_depth=5 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=5, total=   1.0s
[CV]  classifier__n_estimators=300, classifier__max_depth=3, total=  17.3s
[CV] classifier__n_estimators=300, classifier__max_depth=3 ...........
[CV] classifier__n_estimators=10, classifier__max_depth=5 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=5, total=   1.5s
[CV] classifier__n_estimators=10, classifier__max_depth=5 ............
[CV]  classifier__n_estimators=200, classifier__max_depth=4, total=  14.0s
[CV] classifier__n_estimators=200, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=3, total=  18.2s
[CV] classifier__n_estimators=400, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=10, classifier__max_depth=5, total=   1.0s
[CV] classifier__n_estimators=10, classifier__max_depth=5 ............
[CV]  classifier__n_estimators=100, classifier__max_depth=4, total=   8.8s
[CV] classifier__n_estimators=20, classifier__max_depth=5 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=5, total=   1.2s
[CV] classifier__n_estimators=20, classifier__max_depth=5 ............
[CV]  classifier__n_estimators=600, classifier__max_depth=2, total=  25.9s
[CV] classifier__n_estimators=600, classifier__max_depth=2 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=3, total=  11.6s
[CV] classifier__n_estimators=20, classifier__max_depth=5 ............
[CV]  classifier__n_estimators=20, classifier__max_depth=5, total=   2.7s
[CV] classifier__n_estimators=50, classifier__max_depth=5 ............
[CV]  classifier__n_estimators=400, classifier__max_depth=2, total=  16.9s
[CV] classifier__n_estimators=50, classifier__max_depth=5 ............
[CV]  classifier__n_estimators=20, classifier__max_depth=5, total=   2.6s
[CV] classifier__n_estimators=50, classifier__max_depth=5 ............
[CV]  classifier__n_estimators=300, classifier__max_depth=4, total=  19.0s
[CV] classifier__n_estimators=400, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=5, total=   2.6s
[CV] classifier__n_estimators=100, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=3, total=  24.3s
[CV] classifier__n_estimators=400, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=4, total=  14.9s
[CV] classifier__n_estimators=100, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=5, total=   4.4s
[CV] classifier__n_estimators=100, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=5, total=   4.0s
[CV] classifier__n_estimators=200, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=4, total=   9.0s
[CV] classifier__n_estimators=200, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=2, total=  17.6s
[CV] classifier__n_estimators=200, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=4, total=  24.0s
[CV] classifier__n_estimators=300, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=5, total=   5.7s
[CV] classifier__n_estimators=300, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=3, total=  12.2s
[CV] classifier__n_estimators=300, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=3, total=  28.3s
[CV] classifier__n_estimators=600, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=3, total=  29.9s
[CV] classifier__n_estimators=500, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=4, total=  27.2s
[CV] classifier__n_estimators=400, classifier__max_depth=4 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=3, total=  32.9s
[CV] classifier__n_estimators=600, classifier__max_depth=3 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=5, total=  10.9s
[CV] classifier__n_estimators=300, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=5, total=   8.9s
[CV] classifier__n_estimators=400, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=5, total=   9.6s
[CV] classifier__n_estimators=400, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=2, total=  23.5s
[CV] classifier__n_estimators=400, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=5, total=  13.7s
[CV] classifier__n_estimators=500, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=2, total=  27.4s
[CV] classifier__n_estimators=500, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=3, total=  23.9s
[CV] classifier__n_estimators=500, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=3, total=  18.3s
[CV] classifier__n_estimators=600, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=5, total=  16.2s
[CV] classifier__n_estimators=600, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=4, total=  38.6s
[CV] classifier__n_estimators=600, classifier__max_depth=5 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=4, total=  35.7s
[CV] classifier__n_estimators=5, classifier__max_depth=6 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=6, total=   0.8s
[CV] classifier__n_estimators=5, classifier__max_depth=6 .............
[CV]  classifier__n_estimators=500, classifier__max_depth=4, total=  38.8s
[CV] classifier__n_estimators=5, classifier__max_depth=6 .............
[CV]  classifier__n_estimators=500, classifier__max_depth=4, total=  39.8s
[CV] classifier__n_estimators=10, classifier__max_depth=6 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=6, total=   1.0s
[CV] classifier__n_estimators=10, classifier__max_depth=6 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=6, total=   0.7s
[CV] classifier__n_estimators=10, classifier__max_depth=6 ............
[CV]  classifier__n_estimators=600, classifier__max_depth=4, total=  38.3s
[CV] classifier__n_estimators=20, classifier__max_depth=6 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=6, total=   1.6s
[CV]  classifier__n_estimators=10, classifier__max_depth=6, total=   1.3s
[CV] classifier__n_estimators=20, classifier__max_depth=6 ............
[CV] classifier__n_estimators=20, classifier__max_depth=6 ............
[CV]  classifier__n_estimators=600, classifier__max_depth=2, total=  26.9s
[CV] classifier__n_estimators=50, classifier__max_depth=6 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=6, total=   1.3s
[CV] classifier__n_estimators=50, classifier__max_depth=6 ............
[CV]  classifier__n_estimators=200, classifier__max_depth=5, total=  20.8s
[CV] classifier__n_estimators=50, classifier__max_depth=6 ............
[CV]  classifier__n_estimators=600, classifier__max_depth=4, total=  38.5s
[CV] classifier__n_estimators=100, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=6, total=   2.9s
[CV] classifier__n_estimators=100, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=6, total=   2.6s
[CV] classifier__n_estimators=100, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=6, total=   3.1s
[CV] classifier__n_estimators=200, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=4, total=  23.9s
[CV] classifier__n_estimators=200, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=5, total=  23.8s
[CV] classifier__n_estimators=200, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=5, total=  24.2s
[CV] classifier__n_estimators=300, classifier__max_depth=6 ...........






    



[Parallel(n_jobs=25)]: Done 139 tasks      | elapsed:  1.1min






    



[CV]  classifier__n_estimators=500, classifier__max_depth=3, total=  23.0s
[CV] classifier__n_estimators=300, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=6, total=   5.2s
[CV] classifier__n_estimators=300, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=6, total=   5.2s
[CV] classifier__n_estimators=400, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=4, total=  31.0s
[CV] classifier__n_estimators=400, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=6, total=   7.4s
[CV] classifier__n_estimators=400, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=3, total=  27.9s
[CV] classifier__n_estimators=500, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=6, total=  12.1s
[CV] classifier__n_estimators=500, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=4, total=  29.3s
[CV] classifier__n_estimators=500, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=6, total=  12.2s
[CV] classifier__n_estimators=600, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=6, total=  12.7s
[CV] classifier__n_estimators=600, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=3, total=  30.2s
[CV] classifier__n_estimators=600, classifier__max_depth=6 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=5, total=  29.1s
[CV] classifier__n_estimators=5, classifier__max_depth=7 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=7, total=   1.1s
[CV] classifier__n_estimators=5, classifier__max_depth=7 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=7, total=   0.9s
[CV] classifier__n_estimators=5, classifier__max_depth=7 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=7, total=   1.0s
[CV] classifier__n_estimators=10, classifier__max_depth=7 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=7, total=   2.1s
[CV] classifier__n_estimators=10, classifier__max_depth=7 ............
[CV]  classifier__n_estimators=400, classifier__max_depth=5, total=  34.7s
[CV] classifier__n_estimators=10, classifier__max_depth=7 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=7, total=   2.0s
[CV] classifier__n_estimators=20, classifier__max_depth=7 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=7, total=   1.4s
[CV] classifier__n_estimators=20, classifier__max_depth=7 ............
[CV]  classifier__n_estimators=400, classifier__max_depth=5, total=  34.6s
[CV] classifier__n_estimators=20, classifier__max_depth=7 ............
[CV]  classifier__n_estimators=20, classifier__max_depth=7, total=   5.1s
[CV] classifier__n_estimators=50, classifier__max_depth=7 ............
[CV]  classifier__n_estimators=200, classifier__max_depth=6, total=  26.7s
[CV] classifier__n_estimators=50, classifier__max_depth=7 ............
[CV]  classifier__n_estimators=20, classifier__max_depth=7, total=   4.5s
[CV] classifier__n_estimators=50, classifier__max_depth=7 ............
[CV]  classifier__n_estimators=50, classifier__max_depth=7, total=   8.5s
[CV] classifier__n_estimators=100, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=5, total=  43.9s
[CV] classifier__n_estimators=100, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=7, total=   8.7s
[CV] classifier__n_estimators=100, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=6, total=  35.8s
[CV] classifier__n_estimators=200, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=5, total=  49.3s
[CV] classifier__n_estimators=200, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=7, total=  15.8s
[CV] classifier__n_estimators=200, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=6, total=  47.0s
[CV] classifier__n_estimators=300, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=7, total=  22.1s
[CV] classifier__n_estimators=300, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=7, total=  40.7s
[CV] classifier__n_estimators=300, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=5, total= 1.2min
[CV] classifier__n_estimators=400, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=7, total=  42.7s
[CV] classifier__n_estimators=400, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=6, total= 1.1min
[CV] classifier__n_estimators=400, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=6, total= 1.3min
[CV] classifier__n_estimators=500, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=6, total= 1.3min
[CV] classifier__n_estimators=500, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=6, total= 1.4min
[CV] classifier__n_estimators=500, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=6, total= 1.5min
[CV] classifier__n_estimators=600, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=5, total= 1.7min
[CV] classifier__n_estimators=600, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=7, total=  59.5s
[CV] classifier__n_estimators=600, classifier__max_depth=7 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=6, total= 1.6min
[CV] classifier__n_estimators=5, classifier__max_depth=8 .............
[CV]  classifier__n_estimators=600, classifier__max_depth=6, total= 1.5min
[CV] classifier__n_estimators=5, classifier__max_depth=8 .............
[CV]  classifier__n_estimators=500, classifier__max_depth=5, total= 1.9min
[CV] classifier__n_estimators=5, classifier__max_depth=8 .............
[CV]  classifier__n_estimators=5, classifier__max_depth=8, total=   1.1s
[CV] classifier__n_estimators=10, classifier__max_depth=8 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=8, total=   1.2s
[CV] classifier__n_estimators=10, classifier__max_depth=8 ............
[CV]  classifier__n_estimators=200, classifier__max_depth=6, total= 1.7min
[CV] classifier__n_estimators=10, classifier__max_depth=8 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=8, total=   3.5s
[CV] classifier__n_estimators=20, classifier__max_depth=8 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=8, total=   3.0s
[CV] classifier__n_estimators=20, classifier__max_depth=8 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=8, total=   3.0s
[CV] classifier__n_estimators=20, classifier__max_depth=8 ............
[CV]  classifier__n_estimators=20, classifier__max_depth=8, total=   4.8s
[CV] classifier__n_estimators=50, classifier__max_depth=8 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=8, total=   7.6s
[CV] classifier__n_estimators=50, classifier__max_depth=8 ............
[CV]  classifier__n_estimators=200, classifier__max_depth=7, total= 1.2min
[CV] classifier__n_estimators=50, classifier__max_depth=8 ............
[CV]  classifier__n_estimators=20, classifier__max_depth=8, total=   5.0s
[CV] classifier__n_estimators=100, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=5, total= 2.2min
[CV] classifier__n_estimators=100, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=8, total=  14.8s
[CV] classifier__n_estimators=100, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=6, total= 1.8min
[CV] classifier__n_estimators=200, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=8, total=  10.8s
[CV] classifier__n_estimators=200, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=8, total=  10.6s
[CV] classifier__n_estimators=200, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=8, total=  14.5s
[CV] classifier__n_estimators=300, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=7, total= 1.4min
[CV] classifier__n_estimators=300, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=8, total=  23.2s
[CV] classifier__n_estimators=300, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=6, total= 2.3min
[CV] classifier__n_estimators=400, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=7, total= 1.6min
[CV] classifier__n_estimators=400, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=5, total= 2.6min
[CV]  classifier__n_estimators=200, classifier__max_depth=7, total= 1.8min
[CV] classifier__n_estimators=400, classifier__max_depth=8 ...........
[CV] classifier__n_estimators=500, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=7, total= 1.5min
[CV] classifier__n_estimators=500, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=7, total= 1.6min
[CV] classifier__n_estimators=500, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=8, total=  45.0s
[CV] classifier__n_estimators=600, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=8, total=  38.0s
[CV] classifier__n_estimators=600, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=7, total= 1.4min
[CV] classifier__n_estimators=600, classifier__max_depth=8 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=7, total= 1.6min
[CV] classifier__n_estimators=5, classifier__max_depth=9 .............
[CV]  classifier__n_estimators=200, classifier__max_depth=8, total=  50.0s
[CV] classifier__n_estimators=5, classifier__max_depth=9 .............
[CV]  classifier__n_estimators=500, classifier__max_depth=7, total= 1.5min
[CV] classifier__n_estimators=5, classifier__max_depth=9 .............
[CV]  classifier__n_estimators=300, classifier__max_depth=7, total= 2.0min
[CV] classifier__n_estimators=10, classifier__max_depth=9 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=9, total=   1.5s
[CV] classifier__n_estimators=10, classifier__max_depth=9 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=9, total=   1.8s
[CV] classifier__n_estimators=10, classifier__max_depth=9 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=9, total=   3.8s
[CV] classifier__n_estimators=20, classifier__max_depth=9 ............
[CV]  classifier__n_estimators=600, classifier__max_depth=6, total= 2.6min
[CV] classifier__n_estimators=20, classifier__max_depth=9 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=9, total=   4.1s
[CV] classifier__n_estimators=20, classifier__max_depth=9 ............
[CV]  classifier__n_estimators=400, classifier__max_depth=7, total= 1.9min
[CV] classifier__n_estimators=50, classifier__max_depth=9 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=9, total=   3.7s
[CV] classifier__n_estimators=50, classifier__max_depth=9 ............
[CV]  classifier__n_estimators=10, classifier__max_depth=9, total=   6.9s
[CV] classifier__n_estimators=50, classifier__max_depth=9 ............
[CV]  classifier__n_estimators=600, classifier__max_depth=7, total= 1.5min
[CV] classifier__n_estimators=100, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=9, total=   8.0s
[CV] classifier__n_estimators=100, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=9, total=   7.9s
[CV] classifier__n_estimators=100, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=8, total= 1.1min
[CV] classifier__n_estimators=200, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=8, total= 1.2min
[CV] classifier__n_estimators=200, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=9, total=  16.6s
[CV] classifier__n_estimators=200, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=9, total=  15.3s
[CV] classifier__n_estimators=300, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=8, total= 1.0min
[CV] classifier__n_estimators=300, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=200, classifier__max_depth=8, total= 1.3min
[CV] classifier__n_estimators=300, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=9, total=  28.7s
[CV] classifier__n_estimators=400, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=50, classifier__max_depth=9, total=  35.6s
[CV] classifier__n_estimators=400, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=7, total= 2.0min
[CV] classifier__n_estimators=400, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=300, classifier__max_depth=8, total= 1.4min
[CV]  classifier__n_estimators=100, classifier__max_depth=9, total=  31.3s
[CV] classifier__n_estimators=500, classifier__max_depth=9 ...........
[CV] classifier__n_estimators=500, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=7, total= 2.4min
[CV] classifier__n_estimators=500, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=9, total=  49.3s
[CV] classifier__n_estimators=600, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=7, total= 2.5min
[CV] classifier__n_estimators=600, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=6, total= 3.8min
[CV] classifier__n_estimators=600, classifier__max_depth=9 ...........
[CV]  classifier__n_estimators=100, classifier__max_depth=9, total=  58.7s
[CV] classifier__n_estimators=5, classifier__max_depth=10 ............
[CV]  classifier__n_estimators=200, classifier__max_depth=9, total=  54.6s
[CV] classifier__n_estimators=5, classifier__max_depth=10 ............
[CV]  classifier__n_estimators=5, classifier__max_depth=10, total=   3.2s
[CV] classifier__n_estimators=5, classifier__max_depth=10 ............
[CV]  classifier__n_estimators=200, classifier__max_depth=9, total= 1.0min
[CV] classifier__n_estimators=10, classifier__max_depth=10 ...........
[CV]  classifier__n_estimators=400, classifier__max_depth=8, total= 1.8min
[CV] classifier__n_estimators=10, classifier__max_depth=10 ...........
[CV]  classifier__n_estimators=5, classifier__max_depth=10, total=   2.7s
[CV] classifier__n_estimators=10, classifier__max_depth=10 ...........
[CV]  classifier__n_estimators=500, classifier__max_depth=8, total= 1.6min
[CV] classifier__n_estimators=20, classifier__max_depth=10 ...........
[CV]  classifier__n_estimators=5, classifier__max_depth=10, total=  12.4s
[CV] classifier__n_estimators=20, classifier__max_depth=10 ...........
[CV]  classifier__n_estimators=10, classifier__max_depth=10, total=   6.1s
[CV] classifier__n_estimators=20, classifier__max_depth=10 ...........
[CV]  classifier__n_estimators=10, classifier__max_depth=10, total=   5.1s
[CV] classifier__n_estimators=50, classifier__max_depth=10 ...........
[CV]  classifier__n_estimators=20, classifier__max_depth=10, total=  10.9s
[CV]  classifier__n_estimators=200, classifier__max_depth=9, total= 1.2min
[CV] classifier__n_estimators=50, classifier__max_depth=10 ...........
[CV] classifier__n_estimators=50, classifier__max_depth=10 ...........
[CV]  classifier__n_estimators=600, classifier__max_depth=8, total= 1.8min
[CV] classifier__n_estimators=100, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=10, classifier__max_depth=10, total=  19.5s
[CV] classifier__n_estimators=100, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=500, classifier__max_depth=8, total= 2.1min
[CV] classifier__n_estimators=100, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=600, classifier__max_depth=8, total= 1.9min
[CV] classifier__n_estimators=200, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=20, classifier__max_depth=10, total=  17.1s
[CV] classifier__n_estimators=200, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=20, classifier__max_depth=10, total=  22.8s
[CV] classifier__n_estimators=200, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=500, classifier__max_depth=8, total= 2.0min
[CV] classifier__n_estimators=300, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=300, classifier__max_depth=9, total= 1.4min
[CV] classifier__n_estimators=300, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=50, classifier__max_depth=10, total=  23.4s
[CV] classifier__n_estimators=300, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=50, classifier__max_depth=10, total=  19.5s
[CV] classifier__n_estimators=400, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=300, classifier__max_depth=9, total= 1.5min
[CV] classifier__n_estimators=400, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=50, classifier__max_depth=10, total=  26.4s
[CV] classifier__n_estimators=400, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=400, classifier__max_depth=9, total= 1.5min
[CV] classifier__n_estimators=500, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=400, classifier__max_depth=8, total= 2.5min
[CV] classifier__n_estimators=500, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=400, classifier__max_depth=9, total= 1.5min
[CV] classifier__n_estimators=500, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=400, classifier__max_depth=8, total= 2.7min
[CV] classifier__n_estimators=600, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=100, classifier__max_depth=10, total=  41.5s
[CV] classifier__n_estimators=600, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=600, classifier__max_depth=8, total= 2.4min
[CV] classifier__n_estimators=600, classifier__max_depth=10 ..........
[CV]  classifier__n_estimators=100, classifier__max_depth=10, total=  48.4s
[CV]  classifier__n_estimators=400, classifier__max_depth=9, total= 1.8min
[CV]  classifier__n_estimators=100, classifier__max_depth=10, total=  56.6s
[CV]  classifier__n_estimators=300, classifier__max_depth=9, total= 2.2min
[CV]  classifier__n_estimators=200, classifier__max_depth=10, total= 1.1min
[CV]  classifier__n_estimators=200, classifier__max_depth=10, total= 1.3min
[CV]  classifier__n_estimators=200, classifier__max_depth=10, total= 1.5min
[CV]  classifier__n_estimators=500, classifier__max_depth=9, total= 2.5min
[CV]  classifier__n_estimators=300, classifier__max_depth=10, total= 1.6min
[CV]  classifier__n_estimators=300, classifier__max_depth=10, total= 1.8min
[CV]  classifier__n_estimators=300, classifier__max_depth=10, total= 1.8min
[CV]  classifier__n_estimators=600, classifier__max_depth=9, total= 2.5min
[CV]  classifier__n_estimators=500, classifier__max_depth=9, total= 2.8min
[CV]  classifier__n_estimators=600, classifier__max_depth=9, total= 2.5min
[CV]  classifier__n_estimators=500, classifier__max_depth=9, total= 3.0min
[CV]  classifier__n_estimators=400, classifier__max_depth=10, total= 1.8min
[CV]  classifier__n_estimators=400, classifier__max_depth=10, total= 1.9min
[CV]  classifier__n_estimators=600, classifier__max_depth=9, total= 2.9min
[CV]  classifier__n_estimators=400, classifier__max_depth=10, total= 2.1min
[CV]  classifier__n_estimators=500, classifier__max_depth=10, total= 2.0min
[CV]  classifier__n_estimators=500, classifier__max_depth=10, total= 2.0min
[CV]  classifier__n_estimators=600, classifier__max_depth=10, total= 2.2min
[CV]  classifier__n_estimators=500, classifier__max_depth=10, total= 2.4min
[CV]  classifier__n_estimators=600, classifier__max_depth=10, total= 2.4min
[CV]  classifier__n_estimators=600, classifier__max_depth=10, total= 2.5min






    



[Parallel(n_jobs=25)]: Done 300 out of 300 | elapsed:  8.8min finished






    Out[5]:





GridSearchCV(cv=None, error_score='raise',
       estimator=Pipeline(steps=[('classifier', GradientBoostingClassifier(criterion='friedman_mse', init=None,
              learning_rate=0.1, loss='exponential', max_depth=3,
              max_features=None, max_leaf_nodes=None,
              min_impurity_split=1e-07, min_samples_leaf=1,
              min_samples_split=2, min_weight_fraction_leaf=0.0,
              n_estimators=100, presort='auto', random_state=2,
              subsample=1.0, verbose=0, warm_start=False))]),
       fit_params={}, iid=True, n_jobs=25,
       param_grid={'classifier__n_estimators': [5, 10, 20, 50, 100, 200, 300, 400, 500, 600], 'classifier__max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,
       scoring=None, verbose=2)



In [20]:

    
grid_search.best_params_









    Out[20]:





{'classifier__max_depth': 5, 'classifier__n_estimators': 100}



In [6]:

    
cv_results = pd.DataFrame(grid_search.cv_results_)



In [7]:

    
cv_results









    Out[7]:







  
    
      
      mean_fit_time
      mean_score_time
      mean_test_score
      mean_train_score
      param_classifier__max_depth
      param_classifier__n_estimators
      params
      rank_test_score
      split0_test_score
      split0_train_score
      split1_test_score
      split1_train_score
      split2_test_score
      split2_train_score
      std_fit_time
      std_score_time
      std_test_score
      std_train_score
    
  
  
    
      0
      0.124350
      0.003587
      0.624893
      0.625878
      1
      5
      {u'classifier__n_estimators': 5, u'classifier_...
      99
      0.626134
      0.627407
      0.620045
      0.626541
      0.628500
      0.623687
      0.003914
      0.000559
      0.003561
      0.001589
    
    
      1
      0.199901
      0.005151
      0.624873
      0.626813
      1
      10
      {u'classifier__n_estimators': 10, u'classifier...
      100
      0.624164
      0.626302
      0.621299
      0.628959
      0.629156
      0.625179
      0.040196
      0.000582
      0.003247
      0.001585
    
    
      2
      0.515962
      0.007888
      0.631420
      0.634225
      1
      20
      {u'classifier__n_estimators': 20, u'classifier...
      98
      0.631507
      0.633585
      0.627806
      0.635406
      0.634947
      0.633686
      0.046533
      0.000130
      0.002916
      0.000836
    
    
      3
      1.510248
      0.014570
      0.659337
      0.662034
      1
      50
      {u'classifier__n_estimators': 50, u'classifier...
      96
      0.659682
      0.662568
      0.655802
      0.659822
      0.662528
      0.663712
      0.137765
      0.000598
      0.002756
      0.001632
    
    
      4
      2.579062
      0.019430
      0.712924
      0.716048
      1
      100
      {u'classifier__n_estimators': 100, u'classifie...
      94
      0.716452
      0.721846
      0.714721
      0.717488
      0.707600
      0.708811
      0.231105
      0.003755
      0.003831
      0.005418
    
    
      5
      4.828316
      0.045182
      0.736922
      0.742473
      1
      200
      {u'classifier__n_estimators': 200, u'classifie...
      89
      0.734300
      0.741665
      0.735852
      0.743277
      0.740613
      0.742479
      0.522091
      0.018151
      0.002686
      0.000658
    
    
      6
      7.708707
      0.057503
      0.744165
      0.748692
      1
      300
      {u'classifier__n_estimators': 300, u'classifie...
      84
      0.742180
      0.747903
      0.742359
      0.748679
      0.747955
      0.749493
      0.418122
      0.000038
      0.002681
      0.000649
    
    
      7
      9.728296
      0.072701
      0.746572
      0.752144
      1
      400
      {u'classifier__n_estimators': 400, u'classifie...
      79
      0.746060
      0.750828
      0.743971
      0.753336
      0.749687
      0.752268
      0.195547
      0.006477
      0.002361
      0.001027
    
    
      8
      12.004228
      0.083074
      0.748403
      0.753408
      1
      500
      {u'classifier__n_estimators': 500, u'classifie...
      71
      0.747970
      0.752410
      0.745463
      0.755007
      0.751776
      0.752806
      1.205721
      0.011171
      0.002595
      0.001142
    
    
      9
      15.092670
      0.097635
      0.748682
      0.753636
      1
      600
      {u'classifier__n_estimators': 600, u'classifie...
      68
      0.748687
      0.752679
      0.745642
      0.755097
      0.751716
      0.753134
      1.912546
      0.004584
      0.002480
      0.001049
    
    
      10
      0.269011
      0.005947
      0.648314
      0.652134
      2
      5
      {u'classifier__n_estimators': 5, u'classifier_...
      97
      0.652459
      0.653076
      0.638312
      0.647614
      0.654170
      0.655713
      0.023341
      0.001035
      0.007107
      0.003373
    
    
      11
      0.430955
      0.006842
      0.661526
      0.664352
      2
      10
      {u'classifier__n_estimators': 10, u'classifier...
      95
      0.664160
      0.666985
      0.658429
      0.663612
      0.661990
      0.662458
      0.033832
      0.001040
      0.002362
      0.001921
    
    
      12
      0.936188
      0.011190
      0.722376
      0.726067
      2
      20
      {u'classifier__n_estimators': 20, u'classifier...
      91
      0.720869
      0.727368
      0.716153
      0.718682
      0.730106
      0.732151
      0.128106
      0.000296
      0.005795
      0.005575
    
    
      13
      2.092021
      0.018744
      0.745319
      0.750244
      2
      50
      {u'classifier__n_estimators': 50, u'classifier...
      83
      0.745045
      0.750201
      0.742120
      0.750589
      0.748791
      0.749940
      0.424441
      0.005292
      0.002730
      0.000267
    
    
      14
      3.765199
      0.029933
      0.750811
      0.756144
      2
      100
      {u'classifier__n_estimators': 100, u'classifie...
      56
      0.749761
      0.755843
      0.749164
      0.757604
      0.753507
      0.754984
      0.358646
      0.005573
      0.001922
      0.001090
    
    
      15
      8.361417
      0.059305
      0.752542
      0.759725
      2
      200
      {u'classifier__n_estimators': 200, u'classifie...
      46
      0.753462
      0.759156
      0.750179
      0.761156
      0.753985
      0.758865
      1.091169
      0.003030
      0.001684
      0.001018
    
    
      16
      12.050471
      0.074266
      0.753119
      0.761377
      2
      300
      {u'classifier__n_estimators': 300, u'classifie...
      40
      0.754716
      0.760141
      0.750657
      0.763663
      0.753985
      0.760327
      0.610963
      0.013129
      0.001767
      0.001618
    
    
      17
      16.599201
      0.100075
      0.753318
      0.762601
      2
      400
      {u'classifier__n_estimators': 400, u'classifie...
      38
      0.754895
      0.761902
      0.750955
      0.764051
      0.754104
      0.761849
      0.824339
      0.007804
      0.001702
      0.001026
    
    
      18
      20.927085
      0.132801
      0.754512
      0.763815
      2
      500
      {u'classifier__n_estimators': 500, u'classifie...
      19
      0.755731
      0.763633
      0.752388
      0.764887
      0.755418
      0.762924
      1.831645
      0.010141
      0.001507
      0.000812
    
    
      19
      26.569623
      0.151930
      0.754214
      0.765138
      2
      600
      {u'classifier__n_estimators': 600, u'classifie...
      24
      0.756566
      0.764887
      0.751970
      0.765126
      0.754104
      0.765401
      0.576737
      0.009644
      0.001878
      0.000210
    
    
      20
      0.293598
      0.006047
      0.713422
      0.718307
      3
      5
      {u'classifier__n_estimators': 5, u'classifier_...
      93
      0.704453
      0.712056
      0.711318
      0.717190
      0.724494
      0.725675
      0.014422
      0.001097
      0.008316
      0.005616
    
    
      21
      0.636483
      0.014102
      0.721520
      0.725301
      3
      10
      {u'classifier__n_estimators': 10, u'classifier...
      92
      0.724570
      0.727577
      0.716273
      0.721488
      0.723718
      0.726839
      0.041986
      0.007846
      0.003727
      0.002713
    
    
      22
      1.260826
      0.012650
      0.743608
      0.748552
      3
      20
      {u'classifier__n_estimators': 20, u'classifier...
      85
      0.742240
      0.748411
      0.742299
      0.748202
      0.746284
      0.749045
      0.082956
      0.002898
      0.001892
      0.000359
    
    
      23
      3.225238
      0.025809
      0.750572
      0.758313
      3
      50
      {u'classifier__n_estimators': 50, u'classifier...
      60
      0.750298
      0.758290
      0.750239
      0.759335
      0.751179
      0.757313
      0.174540
      0.008014
      0.000430
      0.000826
    
    
      24
      5.498497
      0.045763
      0.753696
      0.762163
      3
      100
      {u'classifier__n_estimators': 100, u'classifie...
      32
      0.754298
      0.760977
      0.751970
      0.763812
      0.754821
      0.761700
      0.029007
      0.000354
      0.001239
      0.001203
    
    
      25
      10.937695
      0.075684
      0.754631
      0.766292
      3
      200
      {u'classifier__n_estimators': 200, u'classifie...
      17
      0.756745
      0.764349
      0.751851
      0.768021
      0.755298
      0.766505
      0.708142
      0.003200
      0.002053
      0.001506
    
    
      26
      15.777434
      0.105495
      0.754631
      0.769884
      3
      300
      {u'classifier__n_estimators': 300, u'classifie...
      17
      0.756268
      0.768797
      0.751791
      0.771811
      0.755835
      0.769043
      2.631624
      0.006071
      0.002016
      0.001367
    
    
      27
      22.047911
      0.133459
      0.754353
      0.772719
      3
      400
      {u'classifier__n_estimators': 400, u'classifie...
      22
      0.755611
      0.772319
      0.751851
      0.773901
      0.755597
      0.771938
      2.758621
      0.007156
      0.001769
      0.000850
    
    
      28
      26.871284
      0.192877
      0.754651
      0.775296
      3
      500
      {u'classifier__n_estimators': 500, u'classifie...
      15
      0.755432
      0.774856
      0.753343
      0.775871
      0.755179
      0.775161
      2.917275
      0.074893
      0.000931
      0.000425
    
    
      29
      30.162273
      0.179199
      0.754870
      0.778390
      3
      600
      {u'classifier__n_estimators': 600, u'classifie...
      12
      0.756328
      0.777781
      0.752507
      0.778826
      0.755776
      0.778564
      2.035969
      0.027476
      0.001686
      0.000444
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      70
      1.909468
      0.016826
      0.747468
      0.778738
      8
      5
      {u'classifier__n_estimators': 5, u'classifier_...
      76
      0.747612
      0.778915
      0.745404
      0.779423
      0.749388
      0.777877
      1.087663
      0.009151
      0.001630
      0.000643
    
    
      71
      4.502956
      0.014476
      0.751507
      0.785584
      8
      10
      {u'classifier__n_estimators': 10, u'classifier...
      52
      0.750000
      0.787213
      0.752686
      0.784437
      0.751836
      0.785100
      2.151204
      0.001851
      0.001121
      0.001184
    
    
      72
      8.161859
      0.048048
      0.754054
      0.793483
      8
      20
      {u'classifier__n_estimators': 20, u'classifier...
      27
      0.753880
      0.794406
      0.754537
      0.793511
      0.753746
      0.792532
      4.653636
      0.028919
      0.000346
      0.000765
    
    
      73
      11.941450
      0.054067
      0.755407
      0.806935
      8
      50
      {u'classifier__n_estimators': 50, u'classifier...
      6
      0.756387
      0.808495
      0.755134
      0.806435
      0.754701
      0.805874
      1.807463
      0.001958
      0.000715
      0.001127
    
    
      74
      46.855635
      0.128020
      0.754910
      0.822326
      8
      100
      {u'classifier__n_estimators': 100, u'classifie...
      11
      0.756328
      0.822762
      0.754955
      0.823359
      0.753448
      0.820857
      20.292094
      0.062670
      0.001176
      0.001067
    
    
      75
      55.133797
      0.152512
      0.753398
      0.847140
      8
      200
      {u'classifier__n_estimators': 200, u'classifie...
      37
      0.756208
      0.847894
      0.752985
      0.847387
      0.751000
      0.846138
      16.680031
      0.022353
      0.002146
      0.000738
    
    
      76
      68.503322
      0.248098
      0.752821
      0.871764
      8
      300
      {u'classifier__n_estimators': 300, u'classifie...
      44
      0.755850
      0.871474
      0.752746
      0.873743
      0.749866
      0.870075
      8.729810
      0.013165
      0.002444
      0.001511
    
    
      77
      140.476567
      0.834749
      0.752005
      0.893573
      8
      400
      {u'classifier__n_estimators': 400, u'classifie...
      50
      0.754835
      0.892935
      0.753044
      0.896069
      0.748134
      0.891714
      23.718944
      0.709707
      0.002833
      0.001834
    
    
      78
      114.292887
      0.382478
      0.750413
      0.913979
      8
      500
      {u'classifier__n_estimators': 500, u'classifie...
      61
      0.751254
      0.915023
      0.753761
      0.915530
      0.746224
      0.911384
      11.450745
      0.009491
      0.003134
      0.001847
    
    
      79
      122.391986
      0.459652
      0.747786
      0.928913
      8
      600
      {u'classifier__n_estimators': 600, u'classifie...
      75
      0.749821
      0.929409
      0.751194
      0.929439
      0.742344
      0.927889
      16.473637
      0.051602
      0.003889
      0.000724
    
    
      80
      2.369117
      0.010380
      0.747388
      0.794200
      9
      5
      {u'classifier__n_estimators': 5, u'classifier_...
      77
      0.745941
      0.796168
      0.746597
      0.793601
      0.749627
      0.792831
      1.027374
      0.001830
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      81
      4.887409
      0.019177
      0.751050
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      9
      10
      {u'classifier__n_estimators': 10, u'classifier...
      54
      0.751492
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      0.749090
      0.802830
      1.429738
      0.000362
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      0.001955
    
    
      82
      10.788364
      0.033367
      0.754054
      0.814416
      9
      20
      {u'classifier__n_estimators': 20, u'classifier...
      27
      0.752806
      0.817001
      0.755611
      0.813569
      0.753746
      0.812679
      4.066792
      0.000524
      0.001166
      0.001864
    
    
      83
      26.453939
      0.095450
      0.753975
      0.831111
      9
      50
      {u'classifier__n_estimators': 50, u'classifier...
      29
      0.755014
      0.832970
      0.755193
      0.831508
      0.751716
      0.828856
      8.403338
      0.038773
      0.001599
      0.001703
    
    
      84
      46.333774
      0.109222
      0.753477
      0.848851
      9
      100
      {u'classifier__n_estimators': 100, u'classifie...
      35
      0.753701
      0.849267
      0.754955
      0.848849
      0.751776
      0.848436
      11.372983
      0.009423
      0.001307
      0.000339
    
    
      85
      63.032850
      0.190216
      0.752025
      0.880559
      9
      200
      {u'classifier__n_estimators': 200, u'classifie...
      49
      0.754000
      0.878518
      0.753462
      0.881085
      0.748612
      0.882074
      7.547722
      0.025919
      0.002423
      0.001498
    
    
      86
      100.539346
      0.432756
      0.750890
      0.907711
      9
      300
      {u'classifier__n_estimators': 300, u'classifie...
      55
      0.752806
      0.906188
      0.751970
      0.908068
      0.747896
      0.908877
      23.701010
      0.259841
      0.002145
      0.001126
    
    
      87
      95.553143
      0.318526
      0.750114
      0.931241
      9
      400
      {u'classifier__n_estimators': 400, u'classifie...
      63
      0.752865
      0.931469
      0.750776
      0.930544
      0.746702
      0.931710
      9.425880
      0.042967
      0.002559
      0.000503
    
    
      88
      163.953341
      0.425673
      0.748602
      0.948174
      9
      500
      {u'classifier__n_estimators': 500, u'classifie...
      70
      0.748926
      0.948602
      0.751194
      0.947109
      0.745687
      0.948812
      11.871646
      0.017295
      0.002260
      0.000758
    
    
      89
      157.235956
      0.469430
      0.747866
      0.962989
      9
      600
      {u'classifier__n_estimators': 600, u'classifie...
      73
      0.748448
      0.962899
      0.751074
      0.962660
      0.744075
      0.963407
      9.564446
      0.057301
      0.002887
      0.000312
    
    
      90
      6.099879
      0.011410
      0.743409
      0.814894
      10
      5
      {u'classifier__n_estimators': 5, u'classifier_...
      86
      0.739733
      0.814852
      0.746180
      0.816136
      0.744314
      0.813694
      4.461479
      0.002716
      0.002709
      0.000997
    
    
      91
      10.227669
      0.023562
      0.747866
      0.826724
      10
      10
      {u'classifier__n_estimators': 10, u'classifier...
      73
      0.746299
      0.828762
      0.750179
      0.825836
      0.747120
      0.825573
      6.569716
      0.001857
      0.001670
      0.001445
    
    
      92
      16.809419
      0.144257
      0.750791
      0.840086
      10
      20
      {u'classifier__n_estimators': 20, u'classifier...
      57
      0.749045
      0.842223
      0.752567
      0.839208
      0.750761
      0.838825
      4.851196
      0.105029
      0.001438
      0.001520
    
    
      93
      23.020543
      0.069602
      0.753517
      0.859526
      10
      50
      {u'classifier__n_estimators': 50, u'classifier...
      34
      0.752030
      0.859535
      0.756805
      0.859416
      0.751716
      0.859629
      2.821898
      0.010969
      0.002329
      0.000087
    
    
      94
      48.698386
      0.163558
      0.752443
      0.879634
      10
      100
      {u'classifier__n_estimators': 100, u'classifie...
      47
      0.751433
      0.878817
      0.754776
      0.878906
      0.751119
      0.881178
      6.103183
      0.058687
      0.001655
      0.001093
    
    
      95
      79.078454
      0.223173
      0.751826
      0.914138
      10
      200
      {u'classifier__n_estimators': 200, u'classifie...
      51
      0.750418
      0.912515
      0.754716
      0.913709
      0.750343
      0.916189
      9.703191
      0.011646
      0.002044
      0.001530
    
    
      96
      103.566639
      0.267204
      0.749955
      0.943866
      10
      300
      {u'classifier__n_estimators': 300, u'classifie...
      65
      0.748746
      0.942722
      0.751970
      0.944065
      0.749149
      0.944813
      5.281808
      0.031177
      0.001434
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      97
      116.578203
      0.311290
      0.748682
      0.964014
      10
      400
      {u'classifier__n_estimators': 400, u'classifie...
      68
      0.748806
      0.962660
      0.751074
      0.963287
      0.746164
      0.966094
      8.222602
      0.034864
      0.002006
      0.001493
    
    
      98
      127.079094
      0.455388
      0.747209
      0.977614
      10
      500
      {u'classifier__n_estimators': 500, u'classifie...
      78
      0.746299
      0.977644
      0.749403
      0.976539
      0.745926
      0.978659
      10.908134
      0.048461
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      99
      140.428828
      0.537423
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      10
      600
      {u'classifier__n_estimators': 600, u'classifie...
      81
      0.746180
      0.986180
      0.749463
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      0.742165
      0.987524
      7.595946
      0.030691
      0.002984
      0.000620
    
  

100 rows × 18 columns



In [8]:

    
pivot_test_score = cv_results.pivot("param_classifier__max_depth", "param_classifier__n_estimators", "mean_test_score")
pivot_test_score









    Out[8]:







  
    
      param_classifier__n_estimators
      5
      10
      20
      50
      100
      200
      300
      400
      500
      600
    
    
      param_classifier__max_depth
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      0.624893
      0.624873
      0.631420
      0.659337
      0.712924
      0.736922
      0.744165
      0.746572
      0.748403
      0.748682
    
    
      2
      0.648314
      0.661526
      0.722376
      0.745319
      0.750811
      0.752542
      0.753119
      0.753318
      0.754512
      0.754214
    
    
      3
      0.713422
      0.721520
      0.743608
      0.750572
      0.753696
      0.754631
      0.754631
      0.754353
      0.754651
      0.754870
    
    
      4
      0.729917
      0.741061
      0.749637
      0.752781
      0.754870
      0.755746
      0.755487
      0.755049
      0.753915
      0.753975
    
    
      5
      0.738673
      0.746373
      0.750154
      0.754651
      0.755865
      0.755208
      0.754810
      0.754393
      0.753577
      0.753219
    
    
      6
      0.745458
      0.750095
      0.753437
      0.755228
      0.755666
      0.755547
      0.754074
      0.753000
      0.752144
      0.750672
    
    
      7
      0.748363
      0.752841
      0.754114
      0.754412
      0.755348
      0.754333
      0.752900
      0.751249
      0.750731
      0.749557
    
    
      8
      0.747468
      0.751507
      0.754054
      0.755407
      0.754910
      0.753398
      0.752821
      0.752005
      0.750413
      0.747786
    
    
      9
      0.747388
      0.751050
      0.754054
      0.753975
      0.753477
      0.752025
      0.750890
      0.750114
      0.748602
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      10
      0.743409
      0.747866
      0.750791
      0.753517
      0.752443
      0.751826
      0.749955
      0.748682
      0.747209
      0.745936



In [9]:

    
# Draw a heatmap with the numeric values in each cell
fig, ax = plt.subplots()
sns.heatmap(pivot_test_score, annot=True, fmt='0.1%', ax=ax,
            cbar_kws={"label": "Testing set accuracy [\%]"}, robust=False, square=False)

# for _, spine in ax.spines.items():
#     spine.set_visible(True)
    
ax.set_xlabel('Number estimators')
ax.set_ylabel('Maximum depth')
ax.invert_yaxis()

outfile = os.path.join(comp.paths.figures_dir, 'model_evaluation',
                       'grid-search-max_depth-n_estimators-heatmap.png')
# plt.savefig(outfile)
plt.show()









    



/home/jbourbeau/.virtualenvs/composition/lib/python2.7/site-packages/matplotlib/figure.py:1743: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
  warnings.warn("This figure includes Axes that are not "



In [10]:

    
pivot_train_score = cv_results.pivot("param_classifier__max_depth", "param_classifier__n_estimators", "mean_train_score")
pivot_train_score









    Out[10]:







  
    
      param_classifier__n_estimators
      5
      10
      20
      50
      100
      200
      300
      400
      500
      600
    
    
      param_classifier__max_depth
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      0.625878
      0.626813
      0.634225
      0.662034
      0.716048
      0.742473
      0.748692
      0.752144
      0.753408
      0.753636
    
    
      2
      0.652134
      0.664352
      0.726067
      0.750244
      0.756144
      0.759725
      0.761377
      0.762601
      0.763815
      0.765138
    
    
      3
      0.718307
      0.725301
      0.748552
      0.758313
      0.762163
      0.766292
      0.769884
      0.772719
      0.775296
      0.778390
    
    
      4
      0.737210
      0.748373
      0.756999
      0.762999
      0.767446
      0.774580
      0.780470
      0.785633
      0.790727
      0.795543
    
    
      5
      0.747826
      0.756004
      0.761228
      0.769316
      0.775316
      0.785912
      0.794946
      0.803631
      0.812257
      0.821033
    
    
      6
      0.757556
      0.764143
      0.770481
      0.778609
      0.787305
      0.801562
      0.814765
      0.827082
      0.840593
      0.852184
    
    
      7
      0.766620
      0.773028
      0.779435
      0.790240
      0.800786
      0.821640
      0.840742
      0.858601
      0.875734
      0.890399
    
    
      8
      0.778738
      0.785584
      0.793483
      0.806935
      0.822326
      0.847140
      0.871764
      0.893573
      0.913979
      0.928913
    
    
      9
      0.794200
      0.804238
      0.814416
      0.831111
      0.848851
      0.880559
      0.907711
      0.931241
      0.948174
      0.962989
    
    
      10
      0.814894
      0.826724
      0.840086
      0.859526
      0.879634
      0.914138
      0.943866
      0.964014
      0.977614
      0.986648



In [11]:

    
overfitting_pivot = (pivot_train_score - pivot_test_score)/pivot_test_score * 100
overfitting_pivot









    Out[11]:







  
    
      param_classifier__n_estimators
      5
      10
      20
      50
      100
      200
      300
      400
      500
      600
    
    
      param_classifier__max_depth
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      0.157627
      0.310482
      0.444347
      0.408931
      0.438215
      0.753362
      0.608320
      0.746288
      0.668688
      0.661795
    
    
      2
      0.589295
      0.427135
      0.510969
      0.660776
      0.710274
      0.954548
      1.096493
      1.232237
      1.232925
      1.448434
    
    
      3
      0.684730
      0.523991
      0.664971
      1.031284
      1.123374
      1.545195
      2.021149
      2.434715
      2.735663
      3.115774
    
    
      4
      0.999132
      0.986790
      0.982137
      1.357353
      1.665964
      2.492101
      3.306819
      4.050600
      4.882813
      5.513182
    
    
      5
      1.239164
      1.290356
      1.476166
      1.943308
      2.573316
      4.065555
      5.317269
      6.526957
      7.786961
      9.003252
    
    
      6
      1.622932
      1.872879
      2.262045
      3.095854
      4.186856
      6.090335
      8.048344
      9.838276
      11.759570
      13.522864
    
    
      7
      2.439575
      2.681450
      3.357696
      4.749029
      6.015542
      8.922683
      11.667150
      14.289877
      16.650764
      18.789986
    
    
      8
      4.183528
      4.534383
      5.228923
      6.821117
      8.930362
      12.442556
      15.799701
      18.825415
      21.796779
      24.221662
    
    
      9
      6.263314
      7.081922
      8.005016
      10.230665
      12.657794
      17.091787
      20.884565
      24.146483
      26.659312
      28.764899
    
    
      10
      9.615901
      10.544382
      11.893405
      14.068607
      16.903790
      21.589072
      25.856349
      28.761459
      30.835396
      32.269852



In [12]:

    
# Draw a heatmap with the numeric values in each cell
fig, ax = plt.subplots()
sns.heatmap(overfitting_pivot, annot=True, ax=ax,
            cbar_kws={"label": "Overfitting [\%]"}, robust=True, square=False)

# for _, spine in ax.spines.items():
#     spine.set_visible(True)
    
ax.set_xlabel('Number estimators')
ax.set_ylabel('Maximum depth')
ax.invert_yaxis()

outfile = os.path.join(comp.paths.figures_dir, 'model_evaluation',
                       'grid-search-max_depth-n_estimators-heatmap-overfitting.png')
# plt.savefig(outfile)
plt.show()



In [13]:

    
pivot_test_std = cv_results.pivot("param_classifier__max_depth", "param_classifier__n_estimators", "std_test_score")
pivot_test_std









    Out[13]:







  
    
      param_classifier__n_estimators
      5
      10
      20
      50
      100
      200
      300
      400
      500
      600
    
    
      param_classifier__max_depth
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      0.003561
      0.003247
      0.002916
      0.002756
      0.003831
      0.002686
      0.002681
      0.002361
      0.002595
      0.002480
    
    
      2
      0.007107
      0.002362
      0.005795
      0.002730
      0.001922
      0.001684
      0.001767
      0.001702
      0.001507
      0.001878
    
    
      3
      0.008316
      0.003727
      0.001892
      0.000430
      0.001239
      0.002053
      0.002016
      0.001769
      0.000931
      0.001686
    
    
      4
      0.004255
      0.002422
      0.001742
      0.000617
      0.000561
      0.000834
      0.001202
      0.000935
      0.001029
      0.000985
    
    
      5
      0.001709
      0.003150
      0.002410
      0.001267
      0.001345
      0.001087
      0.000697
      0.000441
      0.000718
      0.001189
    
    
      6
      0.002992
      0.001697
      0.001193
      0.000525
      0.000975
      0.001170
      0.000786
      0.001299
      0.001325
      0.001517
    
    
      7
      0.002151
      0.001317
      0.000868
      0.001170
      0.001669
      0.001299
      0.001845
      0.001498
      0.002226
      0.001724
    
    
      8
      0.001630
      0.001121
      0.000346
      0.000715
      0.001176
      0.002146
      0.002444
      0.002833
      0.003134
      0.003889
    
    
      9
      0.001605
      0.001454
      0.001166
      0.001599
      0.001307
      0.002423
      0.002145
      0.002559
      0.002260
      0.002887
    
    
      10
      0.002709
      0.001670
      0.001438
      0.002329
      0.001655
      0.002044
      0.001434
      0.002006
      0.001559
      0.002984



In [19]:

    
# Draw a heatmap with the numeric values in each cell
fig, ax = plt.subplots()
sns.heatmap(pivot_test_std*100, annot=True, fmt='0.2f', linewidths=.5, ax=ax,
            cbar_kws={"label": "Std"}, robust=True, square=False)
ax.set_xlabel('Number estimators')
ax.set_ylabel('Maximum depth')
plt.show()



In [15]:

    
pivot_test_rank = cv_results.pivot("param_classifier__max_depth", "param_classifier__n_estimators", "rank_test_score")
pivot_test_rank









    Out[15]:







  
    
      param_classifier__n_estimators
      5
      10
      20
      50
      100
      200
      300
      400
      500
      600
    
    
      param_classifier__max_depth
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      99
      100
      98
      96
      94
      89
      84
      79
      71
      68
    
    
      2
      97
      95
      91
      83
      56
      46
      40
      38
      19
      24
    
    
      3
      93
      92
      85
      60
      32
      17
      17
      22
      15
      12
    
    
      4
      90
      87
      66
      45
      12
      2
      5
      10
      31
      29
    
    
      5
      88
      80
      62
      15
      1
      9
      14
      21
      33
      39
    
    
      6
      82
      64
      36
      8
      3
      4
      26
      41
      48
      59
    
    
      7
      72
      43
      25
      20
      7
      23
      42
      53
      58
      67
    
    
      8
      76
      52
      27
      6
      11
      37
      44
      50
      61
      75
    
    
      9
      77
      54
      27
      29
      35
      49
      55
      63
      70
      73
    
    
      10
      86
      73
      57
      34
      47
      51
      65
      68
      78
      81



In [16]:

    
# Draw a heatmap with the numeric values in each cell
fig, ax = plt.subplots()
sns.heatmap(pivot_test_rank, annot=True, fmt='d', linewidths=.5, ax=ax,
            cbar_kws={"label": "Test score rank"}, robust=True, square=False)
ax.set_xlabel('Number estimators')
ax.set_ylabel('Maximum depth')
ax.invert_yaxis()

outfile = os.path.join(comp.paths.figures_dir, 'model_evaluation',
                       'grid-search-max_depth-n_estimators-heatmap-test-rank.png')
# plt.savefig(outfile)
plt.show()



In [16]:

    
pivot_fit_time = cv_results.pivot("param_classifier__max_depth", "param_classifier__n_estimators", "mean_fit_time")
pivot_fit_time









    Out[16]:







  
    
      param_classifier__n_estimators
      5
      10
      20
      50
      100
      200
      300
      400
      500
      600
    
    
      param_classifier__max_depth
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      0.118298
      0.209823
      0.424922
      1.067445
      2.326791
      3.973685
      5.449798
      6.460932
      8.501271
      11.071048
    
    
      2
      0.226728
      0.394157
      0.777675
      1.811893
      3.038147
      6.127894
      8.540042
      11.151531
      13.852913
      16.218600
    
    
      3
      0.209464
      0.420541
      0.861163
      2.001204
      3.800102
      8.846494
      12.297432
      14.067836
      19.236447
      24.484771
    
    
      4
      0.336374
      0.604984
      1.235185
      3.104119
      5.119169
      9.678869
      15.730774
      18.360161
      23.640411
      27.256437
    
    
      5
      0.404802
      0.706598
      1.183219
      3.298988
      6.213872
      11.399018
      17.786696
      24.940043
      54.227860
      59.289663
    
    
      6
      0.491018
      1.063702
      1.855955
      4.426094
      8.132892
      16.700048
      20.557351
      67.781456
      83.113592
      91.269366
    
    
      7
      0.698670
      1.219647
      2.862616
      11.854092
      34.097424
      12.419558
      19.483242
      24.667472
      71.512716
      36.189908
    
    
      8
      4.632594
      1.209142
      2.245910
      4.815686
      32.550906
      51.969422
      22.415552
      31.409309
      46.906632
      71.876275
    
    
      9
      0.840302
      1.667498
      3.427363
      8.029299
      15.329603
      32.917098
      50.195497
      85.439795
      96.674725
      113.502156
    
    
      10
      2.485992
      5.046360
      11.946008
      19.333625
      35.845129
      98.449756
      104.557402
      136.698006
      160.922844
      191.006499
    
    
      11
      4.815252
      22.271014
      20.515157
      47.504083
      85.621035
      72.296619
      136.622310
      139.268169
      193.004344
      228.055230
    
    
      12
      12.154978
      31.955266
      31.745574
      84.945010
      59.340447
      115.329009
      117.266832
      169.903285
      179.132893
      198.152193



In [19]:

    
# Draw a heatmap with the numeric values in each cell
fig, ax = plt.subplots()
sns.heatmap(pivot_fit_time, annot=True, linewidths=.5, ax=ax,
            cbar_kws={"label": "Mean fit time [s]"}, robust=True, square=False)
ax.set_xlabel('Number estimators')
ax.set_ylabel('Maximum depth')
plt.show()



In [ ]:

	mean_fit_time	mean_score_time	mean_test_score	mean_train_score	param_classifier__max_depth	param_classifier__n_estimators	params	rank_test_score	split0_test_score	split0_train_score	split1_test_score	split1_train_score	split2_test_score	split2_train_score	std_fit_time	std_score_time	std_test_score	std_train_score
0	0.124350	0.003587	0.624893	0.625878	1	5	{u'classifier__n_estimators': 5, u'classifier_...	99	0.626134	0.627407	0.620045	0.626541	0.628500	0.623687	0.003914	0.000559	0.003561	0.001589
1	0.199901	0.005151	0.624873	0.626813	1	10	{u'classifier__n_estimators': 10, u'classifier...	100	0.624164	0.626302	0.621299	0.628959	0.629156	0.625179	0.040196	0.000582	0.003247	0.001585
2	0.515962	0.007888	0.631420	0.634225	1	20	{u'classifier__n_estimators': 20, u'classifier...	98	0.631507	0.633585	0.627806	0.635406	0.634947	0.633686	0.046533	0.000130	0.002916	0.000836
3	1.510248	0.014570	0.659337	0.662034	1	50	{u'classifier__n_estimators': 50, u'classifier...	96	0.659682	0.662568	0.655802	0.659822	0.662528	0.663712	0.137765	0.000598	0.002756	0.001632
4	2.579062	0.019430	0.712924	0.716048	1	100	{u'classifier__n_estimators': 100, u'classifie...	94	0.716452	0.721846	0.714721	0.717488	0.707600	0.708811	0.231105	0.003755	0.003831	0.005418
5	4.828316	0.045182	0.736922	0.742473	1	200	{u'classifier__n_estimators': 200, u'classifie...	89	0.734300	0.741665	0.735852	0.743277	0.740613	0.742479	0.522091	0.018151	0.002686	0.000658
6	7.708707	0.057503	0.744165	0.748692	1	300	{u'classifier__n_estimators': 300, u'classifie...	84	0.742180	0.747903	0.742359	0.748679	0.747955	0.749493	0.418122	0.000038	0.002681	0.000649
7	9.728296	0.072701	0.746572	0.752144	1	400	{u'classifier__n_estimators': 400, u'classifie...	79	0.746060	0.750828	0.743971	0.753336	0.749687	0.752268	0.195547	0.006477	0.002361	0.001027
8	12.004228	0.083074	0.748403	0.753408	1	500	{u'classifier__n_estimators': 500, u'classifie...	71	0.747970	0.752410	0.745463	0.755007	0.751776	0.752806	1.205721	0.011171	0.002595	0.001142
9	15.092670	0.097635	0.748682	0.753636	1	600	{u'classifier__n_estimators': 600, u'classifie...	68	0.748687	0.752679	0.745642	0.755097	0.751716	0.753134	1.912546	0.004584	0.002480	0.001049
10	0.269011	0.005947	0.648314	0.652134	2	5	{u'classifier__n_estimators': 5, u'classifier_...	97	0.652459	0.653076	0.638312	0.647614	0.654170	0.655713	0.023341	0.001035	0.007107	0.003373
11	0.430955	0.006842	0.661526	0.664352	2	10	{u'classifier__n_estimators': 10, u'classifier...	95	0.664160	0.666985	0.658429	0.663612	0.661990	0.662458	0.033832	0.001040	0.002362	0.001921
12	0.936188	0.011190	0.722376	0.726067	2	20	{u'classifier__n_estimators': 20, u'classifier...	91	0.720869	0.727368	0.716153	0.718682	0.730106	0.732151	0.128106	0.000296	0.005795	0.005575
13	2.092021	0.018744	0.745319	0.750244	2	50	{u'classifier__n_estimators': 50, u'classifier...	83	0.745045	0.750201	0.742120	0.750589	0.748791	0.749940	0.424441	0.005292	0.002730	0.000267
14	3.765199	0.029933	0.750811	0.756144	2	100	{u'classifier__n_estimators': 100, u'classifie...	56	0.749761	0.755843	0.749164	0.757604	0.753507	0.754984	0.358646	0.005573	0.001922	0.001090
15	8.361417	0.059305	0.752542	0.759725	2	200	{u'classifier__n_estimators': 200, u'classifie...	46	0.753462	0.759156	0.750179	0.761156	0.753985	0.758865	1.091169	0.003030	0.001684	0.001018
16	12.050471	0.074266	0.753119	0.761377	2	300	{u'classifier__n_estimators': 300, u'classifie...	40	0.754716	0.760141	0.750657	0.763663	0.753985	0.760327	0.610963	0.013129	0.001767	0.001618
17	16.599201	0.100075	0.753318	0.762601	2	400	{u'classifier__n_estimators': 400, u'classifie...	38	0.754895	0.761902	0.750955	0.764051	0.754104	0.761849	0.824339	0.007804	0.001702	0.001026
18	20.927085	0.132801	0.754512	0.763815	2	500	{u'classifier__n_estimators': 500, u'classifie...	19	0.755731	0.763633	0.752388	0.764887	0.755418	0.762924	1.831645	0.010141	0.001507	0.000812
19	26.569623	0.151930	0.754214	0.765138	2	600	{u'classifier__n_estimators': 600, u'classifie...	24	0.756566	0.764887	0.751970	0.765126	0.754104	0.765401	0.576737	0.009644	0.001878	0.000210
20	0.293598	0.006047	0.713422	0.718307	3	5	{u'classifier__n_estimators': 5, u'classifier_...	93	0.704453	0.712056	0.711318	0.717190	0.724494	0.725675	0.014422	0.001097	0.008316	0.005616
21	0.636483	0.014102	0.721520	0.725301	3	10	{u'classifier__n_estimators': 10, u'classifier...	92	0.724570	0.727577	0.716273	0.721488	0.723718	0.726839	0.041986	0.007846	0.003727	0.002713
22	1.260826	0.012650	0.743608	0.748552	3	20	{u'classifier__n_estimators': 20, u'classifier...	85	0.742240	0.748411	0.742299	0.748202	0.746284	0.749045	0.082956	0.002898	0.001892	0.000359
23	3.225238	0.025809	0.750572	0.758313	3	50	{u'classifier__n_estimators': 50, u'classifier...	60	0.750298	0.758290	0.750239	0.759335	0.751179	0.757313	0.174540	0.008014	0.000430	0.000826
24	5.498497	0.045763	0.753696	0.762163	3	100	{u'classifier__n_estimators': 100, u'classifie...	32	0.754298	0.760977	0.751970	0.763812	0.754821	0.761700	0.029007	0.000354	0.001239	0.001203
25	10.937695	0.075684	0.754631	0.766292	3	200	{u'classifier__n_estimators': 200, u'classifie...	17	0.756745	0.764349	0.751851	0.768021	0.755298	0.766505	0.708142	0.003200	0.002053	0.001506
26	15.777434	0.105495	0.754631	0.769884	3	300	{u'classifier__n_estimators': 300, u'classifie...	17	0.756268	0.768797	0.751791	0.771811	0.755835	0.769043	2.631624	0.006071	0.002016	0.001367
27	22.047911	0.133459	0.754353	0.772719	3	400	{u'classifier__n_estimators': 400, u'classifie...	22	0.755611	0.772319	0.751851	0.773901	0.755597	0.771938	2.758621	0.007156	0.001769	0.000850
28	26.871284	0.192877	0.754651	0.775296	3	500	{u'classifier__n_estimators': 500, u'classifie...	15	0.755432	0.774856	0.753343	0.775871	0.755179	0.775161	2.917275	0.074893	0.000931	0.000425
29	30.162273	0.179199	0.754870	0.778390	3	600	{u'classifier__n_estimators': 600, u'classifie...	12	0.756328	0.777781	0.752507	0.778826	0.755776	0.778564	2.035969	0.027476	0.001686	0.000444
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
70	1.909468	0.016826	0.747468	0.778738	8	5	{u'classifier__n_estimators': 5, u'classifier_...	76	0.747612	0.778915	0.745404	0.779423	0.749388	0.777877	1.087663	0.009151	0.001630	0.000643
71	4.502956	0.014476	0.751507	0.785584	8	10	{u'classifier__n_estimators': 10, u'classifier...	52	0.750000	0.787213	0.752686	0.784437	0.751836	0.785100	2.151204	0.001851	0.001121	0.001184
72	8.161859	0.048048	0.754054	0.793483	8	20	{u'classifier__n_estimators': 20, u'classifier...	27	0.753880	0.794406	0.754537	0.793511	0.753746	0.792532	4.653636	0.028919	0.000346	0.000765
73	11.941450	0.054067	0.755407	0.806935	8	50	{u'classifier__n_estimators': 50, u'classifier...	6	0.756387	0.808495	0.755134	0.806435	0.754701	0.805874	1.807463	0.001958	0.000715	0.001127
74	46.855635	0.128020	0.754910	0.822326	8	100	{u'classifier__n_estimators': 100, u'classifie...	11	0.756328	0.822762	0.754955	0.823359	0.753448	0.820857	20.292094	0.062670	0.001176	0.001067
75	55.133797	0.152512	0.753398	0.847140	8	200	{u'classifier__n_estimators': 200, u'classifie...	37	0.756208	0.847894	0.752985	0.847387	0.751000	0.846138	16.680031	0.022353	0.002146	0.000738
76	68.503322	0.248098	0.752821	0.871764	8	300	{u'classifier__n_estimators': 300, u'classifie...	44	0.755850	0.871474	0.752746	0.873743	0.749866	0.870075	8.729810	0.013165	0.002444	0.001511
77	140.476567	0.834749	0.752005	0.893573	8	400	{u'classifier__n_estimators': 400, u'classifie...	50	0.754835	0.892935	0.753044	0.896069	0.748134	0.891714	23.718944	0.709707	0.002833	0.001834
78	114.292887	0.382478	0.750413	0.913979	8	500	{u'classifier__n_estimators': 500, u'classifie...	61	0.751254	0.915023	0.753761	0.915530	0.746224	0.911384	11.450745	0.009491	0.003134	0.001847
79	122.391986	0.459652	0.747786	0.928913	8	600	{u'classifier__n_estimators': 600, u'classifie...	75	0.749821	0.929409	0.751194	0.929439	0.742344	0.927889	16.473637	0.051602	0.003889	0.000724
80	2.369117	0.010380	0.747388	0.794200	9	5	{u'classifier__n_estimators': 5, u'classifier_...	77	0.745941	0.796168	0.746597	0.793601	0.749627	0.792831	1.027374	0.001830	0.001605	0.001427
81	4.887409	0.019177	0.751050	0.804238	9	10	{u'classifier__n_estimators': 10, u'classifier...	54	0.751492	0.807002	0.752567	0.802883	0.749090	0.802830	1.429738	0.000362	0.001454	0.001955
82	10.788364	0.033367	0.754054	0.814416	9	20	{u'classifier__n_estimators': 20, u'classifier...	27	0.752806	0.817001	0.755611	0.813569	0.753746	0.812679	4.066792	0.000524	0.001166	0.001864
83	26.453939	0.095450	0.753975	0.831111	9	50	{u'classifier__n_estimators': 50, u'classifier...	29	0.755014	0.832970	0.755193	0.831508	0.751716	0.828856	8.403338	0.038773	0.001599	0.001703
84	46.333774	0.109222	0.753477	0.848851	9	100	{u'classifier__n_estimators': 100, u'classifie...	35	0.753701	0.849267	0.754955	0.848849	0.751776	0.848436	11.372983	0.009423	0.001307	0.000339
85	63.032850	0.190216	0.752025	0.880559	9	200	{u'classifier__n_estimators': 200, u'classifie...	49	0.754000	0.878518	0.753462	0.881085	0.748612	0.882074	7.547722	0.025919	0.002423	0.001498
86	100.539346	0.432756	0.750890	0.907711	9	300	{u'classifier__n_estimators': 300, u'classifie...	55	0.752806	0.906188	0.751970	0.908068	0.747896	0.908877	23.701010	0.259841	0.002145	0.001126
87	95.553143	0.318526	0.750114	0.931241	9	400	{u'classifier__n_estimators': 400, u'classifie...	63	0.752865	0.931469	0.750776	0.930544	0.746702	0.931710	9.425880	0.042967	0.002559	0.000503
88	163.953341	0.425673	0.748602	0.948174	9	500	{u'classifier__n_estimators': 500, u'classifie...	70	0.748926	0.948602	0.751194	0.947109	0.745687	0.948812	11.871646	0.017295	0.002260	0.000758
89	157.235956	0.469430	0.747866	0.962989	9	600	{u'classifier__n_estimators': 600, u'classifie...	73	0.748448	0.962899	0.751074	0.962660	0.744075	0.963407	9.564446	0.057301	0.002887	0.000312
90	6.099879	0.011410	0.743409	0.814894	10	5	{u'classifier__n_estimators': 5, u'classifier_...	86	0.739733	0.814852	0.746180	0.816136	0.744314	0.813694	4.461479	0.002716	0.002709	0.000997
91	10.227669	0.023562	0.747866	0.826724	10	10	{u'classifier__n_estimators': 10, u'classifier...	73	0.746299	0.828762	0.750179	0.825836	0.747120	0.825573	6.569716	0.001857	0.001670	0.001445
92	16.809419	0.144257	0.750791	0.840086	10	20	{u'classifier__n_estimators': 20, u'classifier...	57	0.749045	0.842223	0.752567	0.839208	0.750761	0.838825	4.851196	0.105029	0.001438	0.001520
93	23.020543	0.069602	0.753517	0.859526	10	50	{u'classifier__n_estimators': 50, u'classifier...	34	0.752030	0.859535	0.756805	0.859416	0.751716	0.859629	2.821898	0.010969	0.002329	0.000087
94	48.698386	0.163558	0.752443	0.879634	10	100	{u'classifier__n_estimators': 100, u'classifie...	47	0.751433	0.878817	0.754776	0.878906	0.751119	0.881178	6.103183	0.058687	0.001655	0.001093
95	79.078454	0.223173	0.751826	0.914138	10	200	{u'classifier__n_estimators': 200, u'classifie...	51	0.750418	0.912515	0.754716	0.913709	0.750343	0.916189	9.703191	0.011646	0.002044	0.001530
96	103.566639	0.267204	0.749955	0.943866	10	300	{u'classifier__n_estimators': 300, u'classifie...	65	0.748746	0.942722	0.751970	0.944065	0.749149	0.944813	5.281808	0.031177	0.001434	0.000865
97	116.578203	0.311290	0.748682	0.964014	10	400	{u'classifier__n_estimators': 400, u'classifie...	68	0.748806	0.962660	0.751074	0.963287	0.746164	0.966094	8.222602	0.034864	0.002006	0.001493
98	127.079094	0.455388	0.747209	0.977614	10	500	{u'classifier__n_estimators': 500, u'classifie...	78	0.746299	0.977644	0.749403	0.976539	0.745926	0.978659	10.908134	0.048461	0.001559	0.000866
99	140.428828	0.537423	0.745936	0.986648	10	600	{u'classifier__n_estimators': 600, u'classifie...	81	0.746180	0.986180	0.749463	0.986240	0.742165	0.987524	7.595946	0.030691	0.002984	0.000620

param_classifier__n_estimators	5	10	20	50	100	200	300	400	500	600
param_classifier__max_depth
1	0.624893	0.624873	0.631420	0.659337	0.712924	0.736922	0.744165	0.746572	0.748403	0.748682
2	0.648314	0.661526	0.722376	0.745319	0.750811	0.752542	0.753119	0.753318	0.754512	0.754214
3	0.713422	0.721520	0.743608	0.750572	0.753696	0.754631	0.754631	0.754353	0.754651	0.754870
4	0.729917	0.741061	0.749637	0.752781	0.754870	0.755746	0.755487	0.755049	0.753915	0.753975
5	0.738673	0.746373	0.750154	0.754651	0.755865	0.755208	0.754810	0.754393	0.753577	0.753219
6	0.745458	0.750095	0.753437	0.755228	0.755666	0.755547	0.754074	0.753000	0.752144	0.750672
7	0.748363	0.752841	0.754114	0.754412	0.755348	0.754333	0.752900	0.751249	0.750731	0.749557
8	0.747468	0.751507	0.754054	0.755407	0.754910	0.753398	0.752821	0.752005	0.750413	0.747786
9	0.747388	0.751050	0.754054	0.753975	0.753477	0.752025	0.750890	0.750114	0.748602	0.747866
10	0.743409	0.747866	0.750791	0.753517	0.752443	0.751826	0.749955	0.748682	0.747209	0.745936

param_classifier__n_estimators	5	10	20	50	100	200	300	400	500	600
param_classifier__max_depth
1	0.157627	0.310482	0.444347	0.408931	0.438215	0.753362	0.608320	0.746288	0.668688	0.661795
2	0.589295	0.427135	0.510969	0.660776	0.710274	0.954548	1.096493	1.232237	1.232925	1.448434
3	0.684730	0.523991	0.664971	1.031284	1.123374	1.545195	2.021149	2.434715	2.735663	3.115774
4	0.999132	0.986790	0.982137	1.357353	1.665964	2.492101	3.306819	4.050600	4.882813	5.513182
5	1.239164	1.290356	1.476166	1.943308	2.573316	4.065555	5.317269	6.526957	7.786961	9.003252
6	1.622932	1.872879	2.262045	3.095854	4.186856	6.090335	8.048344	9.838276	11.759570	13.522864
7	2.439575	2.681450	3.357696	4.749029	6.015542	8.922683	11.667150	14.289877	16.650764	18.789986
8	4.183528	4.534383	5.228923	6.821117	8.930362	12.442556	15.799701	18.825415	21.796779	24.221662
9	6.263314	7.081922	8.005016	10.230665	12.657794	17.091787	20.884565	24.146483	26.659312	28.764899
10	9.615901	10.544382	11.893405	14.068607	16.903790	21.589072	25.856349	28.761459	30.835396	32.269852

param_classifier__n_estimators	5	10	20	50	100	200	300	400	500	600
param_classifier__max_depth
1	0.118298	0.209823	0.424922	1.067445	2.326791	3.973685	5.449798	6.460932	8.501271	11.071048
2	0.226728	0.394157	0.777675	1.811893	3.038147	6.127894	8.540042	11.151531	13.852913	16.218600
3	0.209464	0.420541	0.861163	2.001204	3.800102	8.846494	12.297432	14.067836	19.236447	24.484771
4	0.336374	0.604984	1.235185	3.104119	5.119169	9.678869	15.730774	18.360161	23.640411	27.256437
5	0.404802	0.706598	1.183219	3.298988	6.213872	11.399018	17.786696	24.940043	54.227860	59.289663
6	0.491018	1.063702	1.855955	4.426094	8.132892	16.700048	20.557351	67.781456	83.113592	91.269366
7	0.698670	1.219647	2.862616	11.854092	34.097424	12.419558	19.483242	24.667472	71.512716	36.189908
8	4.632594	1.209142	2.245910	4.815686	32.550906	51.969422	22.415552	31.409309	46.906632	71.876275
9	0.840302	1.667498	3.427363	8.029299	15.329603	32.917098	50.195497	85.439795	96.674725	113.502156
10	2.485992	5.046360	11.946008	19.333625	35.845129	98.449756	104.557402	136.698006	160.922844	191.006499
11	4.815252	22.271014	20.515157	47.504083	85.621035	72.296619	136.622310	139.268169	193.004344	228.055230
12	12.154978	31.955266	31.745574	84.945010	59.340447	115.329009	117.266832	169.903285	179.132893	198.152193