etna.transforms.RobustScalerTransform#

class RobustScalerTransform(in_column: str | List[str] | None = None, inplace: bool = True, out_column: str | None = None, with_centering: bool = True, with_scaling: bool = True, quantile_range: Tuple[float, float] = (25, 75), unit_variance: bool = False, mode: TransformMode | str = 'per-segment')[source]#

Bases: 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 (str | List[str] | None) – columns to be scaled, if None - all columns will be scaled.

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

  • out_column (str | None) – 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 (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

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.

Attributes

This class stores its __init__ parameters as attributes.

fit(ts: TSDataset) → SklearnTransform[source]#

Fit the transform.

Parameters:

ts (TSDataset) –

Return type:

SklearnTransform

fit_transform(ts: TSDataset) → TSDataset[source]#

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters:

ts (TSDataset) – TSDataset to transform.

Returns:

Transformed TSDataset.

Return type:

TSDataset

get_regressors_info() → List[str][source]#

Return the list with regressors created by the transform.

Return type:

List[str]

inverse_transform(ts: TSDataset) → TSDataset[source]#

Inverse transform TSDataset.

Apply the _inverse_transform method.

Parameters:

ts (TSDataset) – TSDataset to be inverse transformed.

Returns:

TSDataset after applying inverse transformation.

Return type:

TSDataset

classmethod load(path: Path) → Self[source]#

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters:

path (Path) – Path to load object from.

Returns:

Loaded object.

Return type:

Self

params_to_tune() → Dict[str, 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, BaseDistribution]

save(path: Path)[source]#

Save the object.

Parameters:

path (Path) – Path to save object to.

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:

**params (dict) – Estimator parameters

Returns:

New instance with changed parameters

Return type:

Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )
to_dict()[source]#

Collect all information about etna object in dict.

transform(ts: TSDataset) → TSDataset[source]#

Transform TSDataset inplace.

Parameters:

ts (TSDataset) – Dataset to transform.

Returns:

Transformed TSDataset.

Return type:

TSDataset