BoxCoxTransform

class BoxCoxTransform(in_column: Optional[Union[str, List[str]]] = None, inplace: bool = True, out_column: Optional[str] = None, standardize: bool = True, mode: Union[etna.transforms.math.sklearn.TransformMode, str] = 'per-segment')[source]

Bases: etna.transforms.math.sklearn.SklearnTransform

BoxCoxTransform applies Box-Cox transformation to DataFrame.

Warning

This transform can suffer from look-ahead bias. For transforming data at some timestamp it uses information from the whole train part.

Create instance of BoxCoxTransform.

Parameters

  • in_column (Optional[Union[str, List[str]]]) – columns to be transformed, if None - all columns will be transformed.

  • inplace (bool) –

    • if True, apply transformation inplace to in_column,

    • if False, add column to dataset.

  • out_column (Optional[str]) – base for the names of generated columns, uses self.__repr__() if not given.

  • standardize (bool) – Set to True to apply zero-mean, unit-variance normalization to the transformed output.

  • mode (Union[etna.transforms.math.sklearn.TransformMode, str]) –

Raises

ValueError: – if incorrect mode given

Inherited-members

Methods

fit(df)

Fit transformer with data from df.

fit_transform(df)

May be reimplemented.

inverse_transform(df)

Apply inverse transformation to DataFrame.

transform(df)

Transform given data with fitted transformer.