simpleml.pipelines.ordered_dict

Pipeline Library support for native python dictionaries

Submodules

Package Contents

Classes

ExplicitSplitOrderedDictPipeline

Native python dict pipeline implementation

OrderedDictPipeline

Native python dict pipeline implementation

RandomSplitOrderedDictPipeline

Class to randomly split dataset into different sets

Attributes

__author__

simpleml.pipelines.ordered_dict.__author__ = Elisha Yadgaran[source]
class simpleml.pipelines.ordered_dict.ExplicitSplitOrderedDictPipeline(has_external_files=True, transformers=None, fitted=False, dataset_id=None, **kwargs)[source]

Bases: simpleml.pipelines.validation_split_mixins.ExplicitSplitMixin, simpleml.pipelines.ordered_dict.base.OrderedDictPipeline

Native python dict pipeline implementation

Parameters

  • has_external_files (bool) –

  • transformers (Optional[List[Any]]) –

  • fitted (bool) –

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

class simpleml.pipelines.ordered_dict.OrderedDictPipeline(has_external_files=True, transformers=None, fitted=False, dataset_id=None, **kwargs)[source]

Bases: simpleml.pipelines.base_pipeline.Pipeline

Native python dict pipeline implementation

Parameters

  • has_external_files (bool) –

  • transformers (Optional[List[Any]]) –

  • fitted (bool) –

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

_create_external_pipeline(self, transformers, **kwargs)

each subclass should instantiate the respective pipeline library

Parameters

transformers (List[Any]) –

Return type

simpleml.pipelines.ordered_dict.external_pipeline.OrderedDictExternalPipeline

class simpleml.pipelines.ordered_dict.RandomSplitOrderedDictPipeline(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.ordered_dict.base.OrderedDictPipeline

Class to randomly split dataset into different sets

Redefines splits so custom named splits in dataset cannot be referenced by the same names. Only TRAIN/TEST/VALIDATION

Set splitting params: By default validation is 0.0 because it is only used for hyperparameter tuning

Parameters

  • train_size (Union[float, int]) –

  • test_size (Optional[Union[float, int]]) –

  • validation_size (Union[float, int]) –

  • random_state (int) –

  • shuffle (bool) –