MinMaxScalerTransform

class MinMaxScalerTransform(in_column: Optional[Union[str, List[str]]] = None, inplace: bool = True, out_column: Optional[str] = None, feature_range: Tuple[float, float] = (0, 1), clip: bool = True, mode: Union[etna.transforms.math.sklearn.TransformMode, str] = 'per-segment')[source]

Bases: etna.transforms.math.sklearn.SklearnTransform

Transform features by scaling each feature to a given range.

Uses sklearn.preprocessing.MinMaxScaler 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 MinMaxScalerPreprocess.

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.

  • feature_range (Tuple[float, float]) – desired range of transformed data.

  • clip (bool) – set to True to clip transformed values of held-out data to provided feature range.

  • 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, clip. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

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