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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 - imports and definitions shared by defs files - from hyperband import Hyperband - classification defs import data from this file - regression defs import data from this file - a complete example for classification - the same, for regression - 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:

  • gradient boosting (GB)
  • random forest (RF)
  • extremely randomized trees (XT)
  • linear SGD
  • factorization machines from polylearn
  • polynomial networks from polylearn
  • a multilayer perceptron from Keras
  • gradient boosting from XGBoost (classification only)


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.

Loading data

Definitions files in defs/defs_regression import data from and, 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 x_train, y_train, x_test and y_test Numpy arrays.


Run (with your own data), or The essence of it is

from hyperband import Hyperband
from import get_params, try_params

hb = Hyperband( get_params, try_params )
results =

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.

Early stopping

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 for a detailed description.