StandardScalerTransform

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

Bases: etna.transforms.math.sklearn.SklearnTransform

Standardize features by removing the mean and scaling to unit variance.

Uses sklearn.preprocessing.StandardScaler inside.

Warning

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

Init StandardScalerPreprocess.

Parameters

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

• inplace (bool) – features are changed by scaled.

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

• with_mean (bool) – if True, center the data before scaling.

• with_std (bool) – if True, scale the data to unit standard deviation.

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

  “macro” or “per-segment”, way to transform features over segments.

  • If “macro”, transforms features globally, gluing the corresponding ones for all segments.

  • If “per-segment”, transforms features for each segment separately.

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, with_mean, with_std. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

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