pyreal.transformers.StandardScaler#

class pyreal.transformers.StandardScaler(*, with_mean=True, with_std=True, **kwargs)[source]#

Standardizes numeric features to mean=0 and variance=1

__init__(*, with_mean=True, with_std=True, **kwargs)[source]#

Creates a pyreal StandardScaler, and wraps it a DataFrameWrapper, then wraps the DataFrameWrapper

Parameters:

  • with_mean (bool, optional) – If True, center the data before scaling.

  • with_std (bool, optional) – If True, scale the data to unit variance

  • equivalently ((or) –

  • deviation). (unit standard) –

Methods

__init__(*[, with_mean, with_std])

Creates a pyreal StandardScaler, and wraps it a DataFrameWrapper, then wraps the DataFrameWrapper

data_transform(X)

Transform a dataset

fit(X[, y, sample_weight])

Fits a dataset to the transformer

fit_transform(x, **fit_params)

Fits this transformer to data and then transforms the same data

inverse_data_transform(x_new)

Wrapper for inverse_data_transform.

inverse_transform(X)

Inverse transform X

inverse_transform_explanation(explanation)

Transforms the explanation from the second feature space handled by this transformer to the first.

inverse_transform_explanation_additive_feature_contribution(...)

Inverse transforms additive feature contribution explanations

inverse_transform_explanation_additive_feature_importance(...)

Inverse transforms additive feature importance explanations

inverse_transform_explanation_decision_tree(...)

Inverse transforms decision-tree explanations

inverse_transform_explanation_example(...)

Inverse transforms example-based explanations

inverse_transform_explanation_feature_based(...)

Inverse transforms feature-based explanations

inverse_transform_explanation_feature_contribution(...)

Inverse transforms feature contribution explanations

inverse_transform_explanation_feature_importance(...)

Inverse transforms feature importance explanations

inverse_transform_explanation_similar_example(...)

Inverse transforms similar-example-based explanations

set_flags([model, interpret, algorithm])

transform(x)

Wrapper for data_transform.

transform_explanation(explanation)

Transforms the explanation from the first feature space handled by this transformer to the second.

transform_explanation_additive_feature_contribution(...)

Transforms additive feature contribution explanations

transform_explanation_additive_feature_importance(...)

Transforms additive feature importance explanations

transform_explanation_decision_tree(explanation)

Inverse transforms feature-based explanations

transform_explanation_example(explanation)

Transforms example-based explanations

transform_explanation_feature_based(explanation)

Transforms feature-based explanations

transform_explanation_feature_contribution(...)

Transforms feature contribution explanations

transform_explanation_feature_importance(...)

Transforms feature importance explanations

transform_explanation_similar_example(...)

Transforms example-based explanations