Hyperopt: Distributed Asynchronous Hyper-parameter Optimization

Getting started

Install hyperopt from PyPI

pip install hyperopt

to run your first example

# define an objective function
def objective(args):
    case, val = args
    if case == 'case 1':
        return val
        return val ** 2

# define a search space
from hyperopt import hp
space = hp.choice('a',
        ('case 1', 1 + hp.lognormal('c1', 0, 1)),
        ('case 2', hp.uniform('c2', -10, 10))

# minimize the objective over the space
from hyperopt import fmin, tpe
best = fmin(objective, space, algo=tpe.suggest, max_evals=100)

print best
# -> {'a': 1, 'c2': 0.01420615366247227}
print hyperopt.space_eval(space, best)
# -> ('case 2', 0.01420615366247227}

If you're a developer, clone this repository and install from source:

git clone https://github.com/jaberg/hyperopt.git
cd hyperopt && python setup.py develop &&  pip install -e '.[MongoTrials, SparkTrials, ATPE]'


Currently three algorithms are implemented in hyperopt:

Hyperopt has been designed to accommodate Bayesian optimization algorithms based on Gaussian processes and regression trees, but these are not currently implemented.

All algorithms can be parallelized in two ways, using:


Hyperopt documentation can be found here, but is partly still hosted on the wiki. Here are some quick links to the most relevant pages:


See projects using hyperopt on the wiki.

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If you use this software for research, plase cite the following paper:

Bergstra, J., Yamins, D., Cox, D. D. (2013) Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. To appear in Proc. of the 30th International Conference on Machine Learning (ICML 2013).


This project has received support from