btb.tuning.tuners package

Submodules

• btb.tuning.tuners.base module
• btb.tuning.tuners.gaussian_process module
• btb.tuning.tuners.uniform module

Module contents

Package where all the available tuners are imported.

class btb.tuning.tuners.GCPEiTuner(tunable, maximize=True, num_candidates=1000, min_trials=5, length_scale=0.1)

Bases: btb.tuning.metamodels.gaussian_process.GaussianCopulaProcessMetaModel, btb.tuning.tuners.gaussian_process.GPEiTuner

Gaussian Copula Process Expected Improvement Tuner.

This class uses a GaussianProcessRegressor model from the sklearn.gaussian_process package, using an ExpectedImprovement function to return the better configurations predicted from the meta model that converts the input data using a Univariate copula.

class btb.tuning.tuners.GCPTuner(tunable, maximize=True, num_candidates=1000, min_trials=5, length_scale=0.1)

Bases: btb.tuning.metamodels.gaussian_process.GaussianCopulaProcessMetaModel, btb.tuning.tuners.gaussian_process.GPTuner

Gaussian Copula Process Tuner.

This class uses a GaussianProcessRegressor model from the sklearn.gaussian_process package, using a numpy.argmax function to return the better configurations predicted from the meta model that converts the input data using a Univariate copula.

class btb.tuning.tuners.GPEiTuner(tunable, maximize=True, num_candidates=1000, min_trials=5, length_scale=0.1)

Bases: btb.tuning.metamodels.gaussian_process.GaussianProcessMetaModel, btb.tuning.acquisition.expected_improvement.ExpectedImprovementAcquisition, btb.tuning.tuners.base.BaseMetaModelTuner

Gaussian Process Expected Improvement Tuner.

This class uses a GaussianProcessRegressor model from the sklearn.gaussian_process package, using an ExpectedImprovement function to return the better configurations predicted from the model.

class btb.tuning.tuners.GPTuner(tunable, maximize=True, num_candidates=1000, min_trials=5, length_scale=0.1)

Bases: btb.tuning.metamodels.gaussian_process.GaussianProcessMetaModel, btb.tuning.acquisition.predicted_score.PredictedScoreAcquisition, btb.tuning.tuners.base.BaseMetaModelTuner

Gaussian Process Tuner.

This class uses a GaussianProcessRegressor model from the sklearn.gaussian_process package, using a numpy.argmax function to return the better configurations predicted from the model.

class btb.tuning.tuners.UniformTuner(tunable, maximize=True)

Bases: btb.tuning.tuners.base.BaseTuner