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Bayesian Optimization Hyperband Hyperparameter Optimization

# Bayesian Optimization Hyperband Hyperparameter Optimization

Implementation for BOHB

## Requirements

- numpy
- scipy
- statsmodels
- dask
- torch (example)


## Installation

pip3 install bohb-hpo


## Usage

from bohb import BOHB
import bohb.configspace as cs

def objective(step, alpha, beta):
return 1 / (alpha * step + 0.1) + beta

def evaluate(params, n_iterations):
loss = 0.0
for i in range(int(n_iterations)):
loss += objective(**params, step=i)
return loss/n_iterations

if __name__ == '__main__':
alpha = cs.CategoricalHyperparameter('alpha', [0.001, 0.01, 0.1])
beta = cs.CategoricalHyperparameter('beta', [1, 2, 3])
configspace = cs.ConfigurationSpace([alpha, beta])

opt = BOHB(configspace, evaluate, max_budget=10, min_budget=1)

# Parallel
# opt = BOHB(configspace, evaluate, max_budget=10, min_budget=1, n_proc=4)

logs = opt.optimize()


See examples

### Configspace Examples

• Basic
import dehb.configspace as cs
lr = cs.UniformHyperparameter('lr', 1e-4, 1e-1, log=True)
batch_size = cs.CategoricalHyperparameter('batch_size', [8, 16, 32])
configspace = cs.ConfigurationSpace([lr, batch_size], seed=123)

• Conditional Parameters
import bohb.configspace as cs
a = cs.IntegerUniformHyperparameter('a', 0, 4)
b = cs.CategoricalHyperparameter('b', ['a', 'b', 'c'], a == 0)
b_default = cs.CategoricalHyperparameter('b', ['d'], ~b.cond)
configspace = cs.ConfigurationSpace([a, b, b_default], seed=123)

• Complex Conditional Parameters
import bohb.configspace as cs
a = cs.IntegerUniformHyperparameter('a', 0, 4)
b1 = cs.UniformHyperparameter('b', 0, 0.5, a <= 1)
b2 = cs.UniformHyperparameter('b', 0.5, 1, ~b1.cond)
c1 = cs.CategoricalHyperparameter('c', ['a', 'b', 'c'], b1 < 0.25)
c2 = cs.CategoricalHyperparameter('c', ['c', 'd', 'e'], ~c1.cond)
d1 = cs.UniformHyperparameter('d', 0, 1, (b1 < 0.125) & (c1 == 'b'))
d2 = cs.NormalHyperparameter('d', 0, 0.1, (b1 > 0.125) & (c1 == 'c'))
d3 = cs.IntegerNormalHyperparameter('d', 5, 10, (b2 > 0.750) & (c2 == 'd'))
d4 = cs.UniformHyperparameter('d', 0, 0, ~(d1.cond | d2.cond | d3.cond))
configspace = cs.ConfigurationSpace([a, b1, b2, c1, c2, d1, d2, d3, d4], seed=123)


## License

bohb-hpo is licensed under the MIT License.

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### Source Distribution

BOHB_HPO-0.5.2.tar.gz (6.0 kB view hashes)

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