simpleml.pipelines.sklearn
Pipeline Library support for Scikit-Learn
Submodules
Package Contents
Classes
Scikit-Learn Pipeline implementation |
|
Pipeline Wrapper with support for projected random splits on dataset |
|
Scikit-Learn Pipeline implementation |
Attributes
- class simpleml.pipelines.sklearn.ExplicitSplitSklearnPipeline(has_external_files=True, transformers=None, fitted=False, dataset_id=None, **kwargs)[source]
Bases:
simpleml.pipelines.validation_split_mixins.ExplicitSplitMixin
,simpleml.pipelines.sklearn.base.SklearnPipeline
Scikit-Learn Pipeline implementation
- class simpleml.pipelines.sklearn.RandomSplitSklearnPipeline(train_size, test_size=None, validation_size=0.0, random_state=123, shuffle=True, **kwargs)[source]
Bases:
simpleml.pipelines.validation_split_mixins.RandomSplitMixin
,simpleml.pipelines.sklearn.base.SklearnPipeline
Pipeline Wrapper with support for projected random splits on dataset Useful to create a train/test/validation split on any dataset
Set splitting params: By default validation is 0.0 because it is only used for hyperparameter tuning
- class simpleml.pipelines.sklearn.SklearnPipeline(has_external_files=True, transformers=None, fitted=False, dataset_id=None, **kwargs)[source]
Bases:
simpleml.pipelines.base_pipeline.Pipeline
Scikit-Learn Pipeline implementation
- Parameters
- _create_external_pipeline(self, transformers, **kwargs)
Initialize a scikit-learn pipeline object
- Parameters
transformers (List[Any]) –
- Return type
simpleml.pipelines.sklearn.external_pipeline.SklearnExternalPipeline
- _filter_fit_params(self, split)
Sklearn Pipelines register arbitrary input kwargs but validate non X,y as stepname__parameter format
- Parameters
split (simpleml.pipelines.projected_splits.ProjectedDatasetSplit) –
- Return type
Dict[str, Any]