neuraxle.steps.sklearn

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

Pipeline Steps Based on Scikit-Learn

Those steps works with scikit-learn (sklearn) transformers and estimators.

Classes

RidgeModelStacking(brothers)
SKLearnWrapper(wrapped_sklearn_predictor, …)

Examples using neuraxle.steps.sklearn.RidgeModelStacking

Easy REST API Model Serving with Neuraxle

Boston Housing Regression

Examples using neuraxle.steps.sklearn.SKLearnWrapper

Usage of AutoML loop, and hyperparams with sklearn models.

Boston Housing Regression with Meta Optimization

class neuraxle.steps.sklearn.SKLearnWrapper(wrapped_sklearn_predictor, hyperparams_space: neuraxle.hyperparams.space.HyperparameterSpace = None, return_all_sklearn_default_params_on_get: bool = False, use_partial_fit: bool = False, use_predict_proba: bool = False, partial_fit_kwargs: dict = None)

Bases: neuraxle.base.BaseStep

__init__(wrapped_sklearn_predictor, hyperparams_space: neuraxle.hyperparams.space.HyperparameterSpace = None, return_all_sklearn_default_params_on_get: bool = False, use_partial_fit: bool = False, use_predict_proba: bool = False, partial_fit_kwargs: dict = None)

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

_setup(context: neuraxle.base.ExecutionContext = None) → neuraxle.steps.sklearn.SKLearnWrapper

Internal method to setup the step. May be used by Pipeline to setup the pipeline progressively instead of all at once.

_delete_base_estimator_from_dict(params)

Sklearn BaseEnsemble models contain other models as parameter; those can’t be json encoded. We retrieve the parameters of theses sub-models on a .get_params(deep=True) call, we simply need to delete them from the parameter dictionary to avoid errors when saving/loading hyperparameters.

fit_transform(data_inputs, expected_outputs=None) → Tuple[neuraxle.base.BaseStep, Any]

Fit, and transform step with the given data inputs, and expected outputs.

Parameters

• data_inputs – data inputs

• expected_outputs – expected outputs to fit on

Returns

(fitted self, tranformed data inputs)

_sklearn_fit_transform_with_expected_outputs(data_inputs, expected_outputs)

_sklearn_fit_transform_without_expected_outputs(data_inputs)

fit(data_inputs, expected_outputs=None) → neuraxle.steps.sklearn.SKLearnWrapper

Fit data inputs on the given expected outputs.

Parameters

• data_inputs – data inputs

• expected_outputs – expected outputs to fit on.

Returns

self

_sklearn_fit_with_expected_outputs(data_inputs, expected_outputs)

_sklearn_fit_without_expected_outputs(data_inputs)

transform(data_inputs)

Transform given data inputs.

Parameters

data_inputs – data inputs

Returns

transformed data inputs

_set_hyperparams(hyperparams: neuraxle.hyperparams.space.HyperparameterSamples) → neuraxle.base.BaseStep

Set hyperparams for base step, and the wrapped sklearn_predictor.

Return type

BaseStep

Parameters

hyperparams (HyperparameterSamples) –

Returns

self

_update_hyperparams(hyperparams: neuraxle.hyperparams.space.HyperparameterSamples) → neuraxle.base.BaseStep

Update hyperparams for base step, and the wrapped sklearn_predictor.

Return type

BaseStep

Parameters

hyperparams (HyperparameterSamples) –

Returns

self

_get_hyperparams()

get_wrapped_sklearn_predictor()

_repr(level=0, verbose=False) → str

_abc_impl = <_abc_data object>

class neuraxle.steps.sklearn.RidgeModelStacking(brothers)

Bases: neuraxle.union.ModelStacking

__init__(brothers)

Perform model stacking. The steps will be merged with a FeatureUnion, and the judge will recombine the predictions. :param steps_as_tuple: the NamedStepsList of steps to process in parallel and to join. :param judge: a BaseStep that will learn to judge the best answer and who to trust out of every parallel steps. :param kwargs: Other arguments to send to FeatureUnion.

_abc_impl = <_abc_data object>