pyreal.transformers.MinMaxScaler#

class pyreal.transformers.MinMaxScaler(feature_range=(0, 1), clip=False, **kwargs)[source]#

Scales numeric features within a given range

__init__(feature_range=(0, 1), clip=False, **kwargs)[source]#

Initialize a wrapped transformer and DataFrameWrapper, then wrap the DataFrameWrapper

Parameters:

  • feature_range (tuple (min, max), default=(0, 1)) – Desired range of transformed data.

  • clip (bool, default=False) – Set to True to clip transformed values of held-out data to provided feature range.

Methods

__init__([feature_range, clip])

Initialize a wrapped transformer and DataFrameWrapper, then wrap the DataFrameWrapper

data_transform(X)

Transform a dataset

fit(X[, y])

computes per-feature min & max (self.data_min_, self.data_max_)

fit_transform(X[, y])

Fits and transforms

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