MultiNormalizer

class pytorch_forecasting.data.encoders.MultiNormalizer(normalizers: List[pytorch_forecasting.data.encoders.TorchNormalizer])

Bases: pytorch_forecasting.data.encoders.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

fit(y[, X])	Fit transformer, i.e. determine center and scale of data.
fit_transform(X[, y])	Fit to data, then transform it.
get_parameters(*args, **kwargs)	Returns parameters that were used for encoding.
get_params([deep])	Get parameters for this estimator.
inverse_preprocess(y)	Inverse preprocess re-scaled data (e.g.
inverse_transform(y)	Inverse scale.
preprocess(y)	Preprocess input data (e.g.
set_params(**params)	Set the parameters of this estimator.
transform(y[, X, return_norm, target_scale])	Scale input data.

Attributes

TRANSFORMATIONS

fit(y: Union[pandas.core.frame.DataFrame, numpy.ndarray, torch.Tensor], X: Optional[pandas.core.frame.DataFrame] = None)

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

Parameters

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

Returns

self

Return type

MultiNormalizer

get_parameters(*args, **kwargs) → List[torch.Tensor]

Returns parameters that were used for encoding.

Returns

First element is center of data and second is scale

Return type

List[torch.Tensor]

transform(y: Union[pandas.core.frame.DataFrame, numpy.ndarray, torch.Tensor], X: Optional[pandas.core.frame.DataFrame] = None, return_norm: bool = False, target_scale: Optional[List[torch.Tensor]] = None) → Union[List[Tuple[Union[numpy.ndarray, torch.Tensor], numpy.ndarray]], List[Union[numpy.ndarray, torch.Tensor]]]

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]]]

GroupNormalizer NaNLabelEncoder