defs_gb.py - functions and search space definitions for gradient boosting
defs_rf.py - definitions for random forest
defs_rf_xt.py - definitions for random forest and extra trees together
defs_xt.py - definitions for extremely randomized trees

hyperband.py - from hyperband import Hyperband
load_data.py - definitions import from this
main.py - a complete example
main_simple.py - a simple example	

from hyperband import Hyperband
from defs_gb import get_params, try_params

hb = Hyperband( get_params, try_params )
results = hb.run()

3 | Tue Feb 28 15:39:54 2017 | best so far: 0.5777 (run 2)

n_estimators: 5
{'bootstrap': False,
'class_weight': 'balanced',
'criterion': 'entropy',
'max_depth': 5,
'max_features': 'sqrt',
'min_samples_leaf': 5,
'min_samples_split': 6}

# training | log loss: 62.21%, AUC: 75.25%, accuracy: 67.20%
# testing  | log loss: 62.64%, AUC: 74.81%, accuracy: 66.78%

7 seconds.

4 | Tue Feb 28 15:40:01 2017 | best so far: 0.5777 (run 2)

n_estimators: 5
{'bootstrap': False,
'class_weight': None,
'criterion': 'gini',
'max_depth': 5,
'max_features': 'sqrt',
'min_samples_leaf': 1,
'min_samples_split': 2}

# training | log loss: 53.39%, AUC: 75.69%, accuracy: 72.37%
# testing  | log loss: 53.96%, AUC: 75.29%, accuracy: 71.89%

7 seconds.

5 | Tue Feb 28 15:40:07 2017 | best so far: 0.5396 (run 4)

n_estimators: 5
{'bootstrap': True,
'class_weight': None,
'criterion': 'gini',
'max_depth': 3,
'max_features': None,
'min_samples_leaf': 7,
'min_samples_split': 8}

# training | log loss: 50.20%, AUC: 77.04%, accuracy: 75.39%
# testing  | log loss: 50.67%, AUC: 76.77%, accuracy: 75.12%

8 seconds.