Code for tuning hyperparams with Hyperband, adapted from Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.
defs/ - functions and search space definitions for various classifiers
defs_regression/ - the same for regression models
common_defs.py - imports and definitions shared by defs files
hyperband.py - from hyperband import Hyperband
load_data.py - classification defs import data from this file
load_data_regression.py - regression defs import data from this file
main.py - a complete example for classification
main_regression.py - the same, for regression
main_simple.py - a simple, bare-bones, example The goal is to provide a fully functional implementation of Hyperband, as well as a number of ready to use functions for a number of models (classifiers and regressors). Currently these include four from scikit-learn and four others:
Use defs.meta/defs_regression.meta to try many models in one Hyperband run. This is an automatic alternative to constructing search spaces with multiple models (like defs.rf_xt, or defs.polylearn_fm_pn) by hand.
Definitions files in defs/defs_regression import data from load_data.py and load_data_regression.py, respectively.
Edit these files, or a definitions file directly, to make your data available for tuning.
Regression defs use the kin8nm dataset in data/kin8nm. There is no attached data for classification.
For the provided models data format follows scikit-learn conventions, that is, there are _xtrain, _ytrain, _xtest and _ytest Numpy arrays.
Run main.py (with your own data), or main_regression.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()Here's a sample output from a run (three configurations tested) using defs.xt:
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.Some models may use early stopping (as the Keras MLP example does). If a configuration stopped early, it doesn't make sense to run it with more iterations (duh). To indicate this, make try_params()
return { 'loss': loss, 'early_stop': True }This way, Hyperband will know not to select that configuration for any further runs.
See http://fastml.com/tuning-hyperparams-fast-with-hyperband/ for a detailed description.