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')

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(ts)	Fit the transform.
fit_transform(ts)	Fit and transform TSDataset.
get_regressors_info()	Return the list with regressors created by the transform.
inverse_transform(ts)	Inverse transform TSDataset.
load(path)	Load an object.
params_to_tune()	Get default grid for tuning hyperparameters.
save(path)	Save the object.
set_params(**params)	Return new object instance with modified parameters.
to_dict()	Collect all information about etna object in dict.
transform(ts)	Transform TSDataset inplace.

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

Get default grid for tuning hyperparameters.

This grid tunes parameters: mode, standardize. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]