simpleml.models.base_sklearn_model

Base module for Sklearn models.

Module Contents

Classes

SklearnModel

No different than base model. Here just to maintain the pattern

Attributes

LOGGER

__author__

simpleml.models.base_sklearn_model.LOGGER[source]
simpleml.models.base_sklearn_model.__author__ = Elisha Yadgaran[source]
class simpleml.models.base_sklearn_model.SklearnModel(has_external_files=True, external_model_kwargs=None, params=None, fitted=False, pipeline_id=None, **kwargs)[source]

Bases: simpleml.models.base_model.LibraryModel

No different than base model. Here just to maintain the pattern Generic Base -> Library Base -> Domain Base -> Individual Models (ex: [Library]Model -> SklearnModel -> SklearnClassifier -> SklearnLogisticRegression)

Need to explicitly separate passthrough kwargs to external models since most do not support arbitrary **kwargs in the constructors

Two supported patterns - full initialization in constructor or stepwise configured before fit and save

Parameters

  • has_external_files (bool) –

  • external_model_kwargs (Optional[Dict[str, Any]]) –

  • params (Optional[Dict[str, Any]]) –

  • fitted (bool) –

  • pipeline_id (Optional[Union[str, uuid.uuid4]]) –

_fit(self)[source]

Separate out actual fit call for optional overwrite in subclasses

Sklearn estimators don’t support data generators, so do not expose fit_generator method