module feature_selection.river.river_feature_selector

River Feature Selection Model Wrapper.

This module contains a wrapper class for river feature selection models.

Copyright (C) 2022 Johannes Haug.

class RiverFeatureSelector

Wrapper for river feature selection models.

method RiverFeatureSelector.__init__

__init__(
    model: river.base.transformer.Transformer,
    feature_names: List[str],
    n_total_features: int,
    reset_after_drift: bool = False,
    baseline: str = 'constant',
    ref_sample: Union[float, numpy._array_like._SupportsArray[numpy.dtype], numpy._nested_sequence._NestedSequence[numpy._array_like._SupportsArray[numpy.dtype]], bool, int, complex, str, bytes, numpy._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]] = 0
)

Inits the wrapper.

Args:

  • model: The river feature selector object (one of SelectKBest, PoissonInclusion or VarianceThreshold).
  • feature_names: A list of all feature names.
  • n_total_features: The total number of features.
  • reset_after_drift: A boolean indicating if the change detector will be reset after a drift was detected.
  • baseline: A string identifier of the baseline method. The baseline is the value that we substitute non-selected features with. This is necessary, because most online learning models are not able to handle arbitrary patterns of missing data.
  • ref_sample: A sample used to compute the baseline. If the constant baseline is used, one needs to provide a single float value.

method RiverFeatureSelector.reset

reset()

Resets the feature selector.

method RiverFeatureSelector.select_features

select_features(
    X: Union[numpy._array_like._SupportsArray[numpy.dtype], numpy._nested_sequence._NestedSequence[numpy._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]],
    rng: numpy.random._generator.Generator
) → Union[numpy._array_like._SupportsArray[numpy.dtype], numpy._nested_sequence._NestedSequence[numpy._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]

Selects features with highest absolute weights.

This overrides the corresponding parent class function.

Args:

  • X: Array/matrix of observations.
  • rng: A numpy random number generator object.

Returns:

  • ArrayLike: The observation array/matrix where all non-selected features have been replaced by the baseline value.

method RiverFeatureSelector.weight_features

weight_features(
    X: Union[numpy._array_like._SupportsArray[numpy.dtype], numpy._nested_sequence._NestedSequence[numpy._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]],
    y: Union[numpy._array_like._SupportsArray[numpy.dtype], numpy._nested_sequence._NestedSequence[numpy._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]
)

Updates feature weights.

Args:

  • X: Array/matrix of observations.
  • y: Array of corresponding labels.

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