mealy.preprocessing¶

Classes¶

class mealy.preprocessing.DummyPipelinePreprocessor(model_performance_predictor_features)[source]¶

Parameters: x (array-like or dataframe of shape (number of samples, number of features)) – input feature values.
Returns: ndarray

class mealy.preprocessing.FeatureNameTransformer(original_features, preprocessed_features)[source]¶

Transformer of feature names and indices.

A FeatureNameTransformer parses an input Pipeline preprocessor and generate a mapping between the input unprocessed feature names/indices and the output preprocessed feature names/indices.

Parameters

ct_preprocessor (sklearn.compose.ColumnTransformer) – preprocessor.
orig_feats (list) – list of original unpreprocessed feature names, default=None.

original_feature_names¶

list of original unpreprocessed feature names.

Type: list

preprocessed_feature_names¶

list of preprocessed feature names.

Type: list

class mealy.preprocessing.PipelinePreprocessor(ct_preprocessor, original_features=None)[source]¶

Transformer of feature values from the original values to preprocessed ones.

A PipelinePreprocessor parses an input Pipeline preprocessor and generate a mapping between the input unprocessed feature values and the output preprocessed feature values.

Parameters

ct_preprocessor (sklearn.compose.ColumnTransformer) – preprocessing steps.
original_features (list) – list of original unpreprocessed feature names, default=None.

inverse_transform(preprocessed_x)[source]¶

Invert the preprocessing pipeline and inverse transform feature values.

Parameters: preprocessed_x (numpy.ndarray or scipy sparse matrix) – preprocessed feature values.
Returns: feature values without preprocessing.
Return type: numpy.ndarray

inverse_transform_feature_id(index)[source]¶

Undo preprocessing of feature name.

Transform the preprocessed feature name at given index back into the original unprocessed feature index.

Parameters: index (int) – feature index.
Returns: index of the unprocessed feature corresponding to the input preprocessed feature index.
Return type: int

is_categorical(index=None, name=None)[source]¶

Check whether an unprocessed feature at a given index or with a given name is categorical.

Parameters

index (int) – feature index.
name (str) – feature name.

Returns

True if the input feature is categorical, else False. If both index and name are provided, the index: is retained.

Return type

bool

transform(x)[source]¶

Transform the input feature values according to the preprocessing pipeline.

Parameters: x (array-like or dataframe of shape (number of samples, number of features)) – input feature values.
Returns: transformed feature values.
Return type: numpy.ndarray