Code for tuning hyperparams with Hyperband, adapted from Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. Includes definitions for scikit-learn gradient boosting.
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
Edit load_data.py or defs_gb.py directly to provide your data. Then run main.py. The essence of it is
from hyperband import Hyperband
from defs_gb import get_params, try_params
hb = Hyperband( get_params, try_params )
results = hb.run()
Sample output from a run (three configurations tested) using defs_xt.py:
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.