MultiNormalizer¶

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

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)[source]¶

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][source]¶

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]]][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]]]

GroupNormalizer

NaNLabelEncoder