neuraxle.steps.sklearn

Pipeline Steps Based on Scikit-Learn

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

Classes

RidgeModelStacking(brothers)

SKLearnWrapper(wrapped_sklearn_predictor, …)

class neuraxle.steps.sklearn.RidgeModelStacking(brothers)[source]
class neuraxle.steps.sklearn.SKLearnWrapper(wrapped_sklearn_predictor, hyperparams_space: neuraxle.hyperparams.space.HyperparameterSpace = None, return_all_sklearn_default_params_on_get=False)[source]
fit(data_inputs, expected_outputs=None) → neuraxle.steps.sklearn.SKLearnWrapper[source]

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

Parameters
  • data_inputs – data inputs

  • expected_outputs – expected outputs to fit on

Returns

fitted self

fit_transform(data_inputs, expected_outputs=None) -> ('BaseStep', typing.Any)[source]

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)

get_hyperparams()[source]

Get step hyperparameters as HyperparameterSamples.

Returns

step hyperparameters

get_wrapped_sklearn_predictor()[source]
set_hyperparams(flat_hyperparams: neuraxle.hyperparams.space.HyperparameterSamples) → neuraxle.base.BaseStep[source]

Set the step hyperparameters.

Example :

step.set_hyperparams(HyperparameterSamples({
    'learning_rate': 0.10
}))
Parameters

hyperparams – hyperparameters

Returns

self

transform(data_inputs)[source]

Transform given data inputs.

Parameters

data_inputs – data inputs

Returns

transformed data inputs