A scikit-learn compatible implementation of hyperband.

Clone the git repository

git clone https://github.com/thuijskens/scikit-hyperband.git

and cd into the project directory and and install the package using setuptools as follows

python setup.py install

scikit-hyperband implements a class HyperbandSearchCV that works exactly as GridSearchCV and RandomizedSearchCV from scikit-learn do, except that it runs the hyperband algorithm under the hood.

Similarly to the existing model selection routines, HyperbandSearchCV works for (multi-label) classification and regression, and supports multi-metric scoring in the same way as scikit-learn supports it.

HyperbandSearchCV implements the following extra parameters, specific to the hyperband algorithm:

• resource_param: The name of the cost parameter of the estimator that you're tuning.
• eta: The inverse of the proportion of configurations that are discarded in each round of hyperband.
• min_iter: The minimum amount of resource that should be allocated to the cost parameter resource_param for a single configuration of the hyperparameters.
• max_iter: The maximum amount of resource that can be allocated to the cost parameter resource_param for a single configuration of the hyperparameters.
• skip_last: The number of last rounds to skip. For example, this can be used to skip the last round of hyperband, which is standard randomized search. It can also be used to inspect intermediate results, although warm-starting HyperbandSearchCV is not supported.

See the documentation for the full parameter list.

from hyperband import HyperbandSearchCV

from scipy.stats import randint as sp_randint
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelBinarizer

model = RandomForestClassifier()
param_dist = {
    'max_depth': [3, None],
    'max_features': sp_randint(1, 11),
    'min_samples_split': sp_randint(2, 11),
    'min_samples_leaf': sp_randint(1, 11),
    'bootstrap': [True, False],
    'criterion': ['gini', 'entropy']
}

digits = load_digits()
X, y = digits.data, digits.target
y = LabelBinarizer().fit_transform(y)

search = HyperbandSearchCV(model, param_dist, 
                           resource_param='n_estimators',
                           scoring='roc_auc')
search.fit(X, y)
print(search.best_params_)

• Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A., 2017. Hyperband: A novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, 18(1), pp.6765-6816. http://www.jmlr.org/papers/volume18/16-558/16-558.pdf
• Whilst developing scikit-hyperband, I stumbled on an implementation of hyperband in civisml-extensions. Their implementation exposes more parallelism than this one does, although this implementation has a couple of extra options you can tweak (skip_last, multi-metric scoring)
• FastML has a nice blog post explaining hyperband here.