Pipeline Steps Modules for PySkOptimize

class pyskoptimize.steps.FeatureProcessingModel(*, pipeline: List[pyskoptimize.steps.SklearnTransformerModel])
_get_parameter_space(prefix: str) → Dict

The private method for creating the parameter search space :param prefix: The name to prefix the parameters :return: A dictionray of the parameter search space

to_sklearn_pipeline() → sklearn.pipeline.Pipeline

This creates the sklearn pipeline for the features in the pod

Returns

A sklearn Pipeline that represents the feature pod

class pyskoptimize.steps.PostProcessingFeaturePodModel(*, pipeline: List[pyskoptimize.steps.SklearnTransformerModel])

This is represents the pod of features and the transformations that need to be applied.

get_parameter_space(name: str) → Dict
Parameters

name –

Returns

class pyskoptimize.steps.PreprocessingFeaturePodModel(*, pipeline: List[pyskoptimize.steps.SklearnTransformerModel], name: str, features: List[str])

This is represents the pod of features and the transformations that need to be applied.

Variables

  • name – The name of the feature pod

  • pipeline – The list of transformations to apply onto features

  • features – The optional list of features

get_parameter_space(prefix: str) → Dict
Parameters

prefix –

Returns

class pyskoptimize.steps.SklearnTransformerModel(*, name: pydantic.types.PyObject, params: List[Union[pyskoptimize.params.NormallyDistributedParamModel, pyskoptimize.params.UniformlyDistributedParamModel, pyskoptimize.params.CategoricalParamModel, pyskoptimize.params.UniformlyDistributedIntegerParamModel]] = None, default_params: List[Union[pyskoptimize.params.DefaultFloatParamModel, pyskoptimize.params.DefaultCollectionParamModel, pyskoptimize.params.DefaultBooleanParamModel, pyskoptimize.params.DefaultStringParamModel, pyskoptimize.params.DefaultIntegerParamModel]] = None)

This represents the meta information needed for a scikit-learn transformer

Variables

  • name – The name of the transformer

  • params – The parameters to perform the bayesian optimization on

  • default_params – The default parameters

static _get_space(prefix: Optional[str], params: List[pyskoptimize.params.BaseParamModel])
Parameters

prefix –

Returns

get_default_parameter_space() → Dict

This gets the parameter space of the transformer

Returns

The parameter search

get_parameter_space(prefix: Optional[str] = None)

This gets the parameter space of the transformer

Returns

The parameter search

to_model()

This performs the import of the scikit-learn transformer

Returns

The sklearn object