pyreal.transformers.OneHotEncoder

class pyreal.transformers.OneHotEncoder(columns=None, handle_unknown='error', **kwargs)

One-hot encodes categorical feature values

__init__(columns=None, handle_unknown='error', **kwargs)

Initializes the base one-hot encoder

Parameters:

• columns (list, None, or "all_categorical") –

  List of columns to apply one-hot encoding to. If None, all columns will be encoded. If “all_categorical”, all columns with an object dtype will be

  automatically encoded.

• handle_unknown (one of "error", "ignore", "infrequent_if_exist") – How to handle unknown categories encountered during transform. “error” will raise an error, “ignore” will ignore the unknown category, and “infrequent_if_exist” will treat the unknown category as if it were an infrequent category.

Methods

__init__([columns, handle_unknown])	Initializes the base one-hot encoder
data_transform(x)	One-hot encode x.
fit(x, **params)	Fit this transformer to data
fit_transform(x, **fit_params)	Fits this transformer to data and then transforms the same data
inverse_data_transform(x_new)	Transforms one-hot encoded data x_new back into the original feature space.
inverse_transform(x_new)	Transforms data x_new from new feature space back into the original feature space.
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(...)	Combine the contributions of one-hot-encoded features through adding to get the contributions of the original categorical feature.
inverse_transform_explanation_additive_feature_importance(...)	Combine the importances of one-hot-encoded features through adding to get the contributions of the original categorical feature.
inverse_transform_explanation_decision_tree(...)	Features cannot be decoded in existing decision trees, so raise a BreakingTransformError
inverse_transform_explanation_example(...)	Inverse transforms example-based explanations
inverse_transform_explanation_feature_based(...)	For non-additive feature-based explanations, the contributions or importances of the one-hot encoded features cannot be combined.
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)	Features cannot be added to encoded in existing decision trees, so raise a BreakingTransformError
transform_explanation_example(explanation)	Transforms example-based explanations
transform_explanation_feature_based(explanation)	For feature-based explanations, the contributions or importances of categorical features cannot be split into per-category features.
transform_explanation_feature_contribution(...)	Transforms feature contribution explanations
transform_explanation_feature_importance(...)	Transforms feature importance explanations
transform_explanation_similar_example(...)	Transforms example-based explanations