module data.preprocessing.sklearn_scaler

Sklearn Scaling Function Wrapper.

This module contains a wrapper for the scikit-learn scaling functions.

Copyright (C) 2022 Johannes Haug.

class SklearnScaler

Wrapper for sklearn scaler functions.

Attributes:

  • scaler_obj (Any): A scikit-learn scaler object (e.g. MinMaxScaler)

method SklearnScaler.__init__

__init__(scaler_obj: Any, reset_after_drift: bool = False)

Inits the sklearn scaler.

Args:

  • scaler_obj: A scikit-learn scaler object (e.g. MinMaxScaler)
  • reset_after_drift: A boolean indicating if the scaler will be reset after a drift was detected.

method SklearnScaler.partial_fit

partial_fit(
    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]]]
)

Updates the scaler.

Args:

  • X: Array/matrix of observations.

method SklearnScaler.reset

reset()

Resets the scaler.

We automatically re-fit the scaler upon the next call to partial_fit.

method SklearnScaler.transform

transform(
    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]]]
) → 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]]]

Scales the given observations.

Args:

  • X: Array/matrix of observations.

Returns:

  • ArrayLike: The scaled observations.

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