MultiNormalizer#

class pytorch_forecasting.data.encoders.MultiNormalizer(normalizers: list[TorchNormalizer])[source]#

Bases: TorchNormalizer

Normalizer for multiple targets.

This normalizers wraps multiple other normalizers.

Parameters:

normalizers (List[TorchNormalizer]) – list of normalizers to apply to targets

Inherited-members:

Methods

extra_repr()

Return extra information about parameters for representation/logging.

fit(y[, X])

Fit transformer, i.e. determine center and scale of data.

fit_transform(X[, y])

Fit to data, then transform it.

get_metadata_routing()

Get metadata routing of this object.

get_parameters(*args, **kwargs)

Returns parameters that were used for encoding.

get_params([deep])

Get parameters for this estimator.

get_transform(transformation)

Return transformation functions.

inverse_preprocess(y)

Inverse preprocess re-scaled data (e.g. take exp).

inverse_transform(y)

Inverse scale.

preprocess(y)

Preprocess input data (e.g. take log).

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

set_transform_request(*[, return_norm, ...])

Configure whether metadata should be requested to be passed to the transform method.

transform(y[, X, return_norm, target_scale])

Scale input data.

Attributes

TRANSFORMATIONS

fit(y: DataFrame | ndarray | Tensor, X: DataFrame = None)[source]#

Fit transformer, i.e. determine center and scale of data

Parameters:

y (Union[pd.Series, np.ndarray, torch.Tensor]) – input data

Returns:

MultiNormalizer

Return type:

self

get_parameters(*args, **kwargs) → list[Tensor][source]#

Returns parameters that were used for encoding.

Returns:

First element is center of data and second is scale

Return type:

List[torch.Tensor]

set_transform_request(*, return_norm: bool | None | str = '$UNCHANGED$', target_scale: bool | None | str = '$UNCHANGED$') → MultiNormalizer#

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

  • return_norm (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for return_norm parameter in transform.

  • target_scale (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for target_scale parameter in transform.

Returns:

self – The updated object.

Return type:

object

transform(y: DataFrame | ndarray | Tensor, X: DataFrame = None, return_norm: bool = False, target_scale: list[Tensor] = None) → list[tuple[ndarray | Tensor, ndarray]] | list[ndarray | Tensor][source]#

Scale input data.

Parameters:

  • y (Union[pd.DataFrame, np.ndarray, torch.Tensor]) – data to scale

  • X (pd.DataFrame) – dataframe with groups columns. Only necessary if GroupNormalizer is among normalizers

  • return_norm (bool, optional) – If to return . Defaults to False.

  • target_scale (List[torch.Tensor]) – target scale to use instead of fitted center and scale

Returns:

List of scaled data, if return_norm=True, returns also scales as second element

Return type:

Union[List[Tuple[Union[np.ndarray, torch.Tensor], np.ndarray]], List[Union[np.ndarray, torch.Tensor]]]