neuraxle.steps.output_handlers

Module-level documentation for neuraxle.steps.output_handlers. Here is an inheritance diagram, including dependencies to other base modules of Neuraxle:

Output Handlers Steps

You can find here output handlers steps that changes especially the data outputs.

Classes

InputAndOutputTransformerMixin()	Base output transformer step that can modify data inputs, and expected_outputs at the same time.
InputAndOutputTransformerWrapper(wrapped[, …])	Wrapper step to transform both data inputs, and expected output at the same.
OutputTransformerWrapper(wrapped[, …])	A step that can sends the expected_outputs to the wrapped step so that it can transform the expected outputs.

Examples using neuraxle.steps.output_handlers.OutputTransformerWrapper

Usage of AutoML loop, and hyperparams with sklearn models.

class neuraxle.steps.output_handlers.OutputTransformerWrapper(wrapped, cache_folder_when_no_handle=None)

Bases: neuraxle.base.ForceHandleOnlyMixin, neuraxle.base.MetaStep

A step that can sends the expected_outputs to the wrapped step so that it can transform the expected outputs.

__init__(wrapped, cache_folder_when_no_handle=None)

Initialize self. See help(type(self)) for accurate signature.

_transform_data_container(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → neuraxle.data_container.DataContainer

Handle transform by passing expected outputs to the wrapped step transform method. Update the expected outputs with the outputs.

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) –

Returns

data container

Return type

DataContainer

_fit_data_container(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → Tuple[neuraxle.base.BaseStep, neuraxle.data_container.DataContainer]

Handle fit by passing expected outputs to the wrapped step fit method.

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) – data container to fit on

Returns

self, data container

_fit_transform_data_container(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → Tuple[neuraxle.base.BaseStep, neuraxle.data_container.DataContainer]

Handle fit transform by passing expected outputs to the wrapped step fit method. Update the expected outputs with the outputs.

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) – data container to fit on

Returns

self, data container

handle_inverse_transform(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → neuraxle.data_container.DataContainer

Handle inverse transform by passing expected outputs to the wrapped step inverse transform method. Update the expected outputs with the outputs.

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) –

Returns

data container

Return type

DataContainer

_set_expected_outputs(data_container: neuraxle.data_container.DataContainer, new_expected_outputs_data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → neuraxle.data_container.DataContainer

_abc_impl = <_abc_data object>

class neuraxle.steps.output_handlers._DidProcessInputOutputHandlerMixin

Bases: neuraxle.base.MixinForBaseTransformer

_did_process(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → neuraxle.data_container.DataContainer

class neuraxle.steps.output_handlers.InputAndOutputTransformerWrapper(wrapped, cache_folder_when_no_handle=None)

Bases: neuraxle.steps.output_handlers._DidProcessInputOutputHandlerMixin, neuraxle.base.ForceHandleOnlyMixin, neuraxle.base.MetaStep

Wrapper step to transform both data inputs, and expected output at the same. It sends the data_inputs, and the expected_outputs to the wrapped step so that it can transform them.

See also

BaseStep, ForceHandleOnlyMixin

__init__(wrapped, cache_folder_when_no_handle=None)

Initialize self. See help(type(self)) for accurate signature.

_transform_data_container(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → neuraxle.data_container.DataContainer

Handle transform by passing data_inputs, and expected outputs to the wrapped step transform method. Update the expected outputs with the outputs.

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) –

Returns

data container

Return type

DataContainer

_fit_data_container(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → Tuple[neuraxle.base.BaseStep, neuraxle.data_container.DataContainer]

Handle fit by passing the data inputs, and the expected outputs to the wrapped step fit method.

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) – data container to fit on

Returns

self, data container

_fit_transform_data_container(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → Tuple[neuraxle.base.BaseStep, neuraxle.data_container.DataContainer]

Handle fit transform by passing the data inputs, and the expected outputs to the wrapped step fit method. Update the expected outputs with the outputs.

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) – data container to fit on

Returns

self, data container

handle_inverse_transform(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → neuraxle.data_container.DataContainer

Handle inverse transform by passing the data inputs, and the expected outputs to the wrapped step inverse transform method. Update the expected outputs with the outputs.

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) –

Returns

data container

Return type

DataContainer

_abc_impl = <_abc_data object>

class neuraxle.steps.output_handlers.InputAndOutputTransformerMixin

Bases: neuraxle.steps.output_handlers._DidProcessInputOutputHandlerMixin

Base output transformer step that can modify data inputs, and expected_outputs at the same time.

_transform_data_container(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → neuraxle.data_container.DataContainer

Handle inverse transform by updating the data inputs, and expected outputs inside the data container.

Return type

DataContainer

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) –

Returns

_fit_data_container(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → neuraxle.base.BaseStep

Handle transform by fitting the step, and updating the data inputs, and expected outputs inside the data container.

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) –

Returns

_fit_transform_data_container(data_container: neuraxle.data_container.DataContainer, context: neuraxle.base.ExecutionContext) → Tuple[neuraxle.base.BaseStep, neuraxle.data_container.DataContainer]

Handle transform by fitting the step, and updating the data inputs, and expected outputs inside the data container.

Parameters

• context (ExecutionContext) – execution context

• data_container (DataContainer) –

Returns