Manipulate Hyperparameter Spaces for Hyperparameter Tuning

This demonstrates how to manipulate hyperparameters and hyperparameter spaces.

from sklearn.decomposition import PCA

from neuraxle.base import Identity
from neuraxle.hyperparams.distributions import RandInt
from neuraxle.hyperparams.space import HyperparameterSpace
from neuraxle.pipeline import Pipeline
from neuraxle.steps.numpy import MultiplyByN


def main():
    p = Pipeline([
        ('step1', MultiplyByN()),
        ('step2', MultiplyByN()),
        Pipeline([
            Identity(),
            Identity(),
            PCA(n_components=4)
        ])
    ])

    p.set_hyperparams_space({
        'step1__multiply_by': RandInt(42, 50),
        'step2__multiply_by': RandInt(-10, 0),
        'Pipeline__PCA__n_components': RandInt(2, 3)
    })

    samples = p.get_hyperparams_space().rvs()
    p.set_hyperparams(samples)

    samples = p.get_hyperparams()
    assert 42 <= samples['step1__multiply_by'] <= 50
    assert -10 <= samples['step2__multiply_by'] <= 0
    assert samples['Pipeline__PCA__n_components'] in [2, 3]
    assert p['Pipeline']['PCA'].get_wrapped_sklearn_predictor().n_components in [2, 3]


if __name__ == "__main__":
    main()