RobustScalerTransform

class RobustScalerTransform(in_column: Optional[Union[str, List[str]]] = None, inplace: bool = True, out_column: Optional[str] = None, with_centering: bool = True, with_scaling: bool = True, quantile_range: Tuple[float, float] = (25, 75), unit_variance: bool = False, mode: Union[etna.transforms.math.sklearn.TransformMode, str] = 'per-segment')[source]

Bases: etna.transforms.math.sklearn.SklearnTransform

Scale features using statistics that are robust to outliers.

Uses sklearn.preprocessing.RobustScaler 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 RobustScalerPreprocess.

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

  • with_scaling (bool) – if True, scale the data to interquartile range.

  • quantile_range (Tuple[float, float]) – quantile range.

  • unit_variance (bool) –

    If True, scale data so that normally distributed features have a variance of 1.

    In general, if the difference between the x-values of q_max and q_min for a standard normal distribution is greater than 1, the dataset will be scaled down. If less than 1, the dataset will be scaled up.

  • 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][source]

Get default grid for tuning hyperparameters.

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

Returns

Grid to tune.

Return type

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