EncoderNormalizer#

class pytorch_forecasting.data.encoders.EncoderNormalizer(method: str = 'standard', center: bool = True, max_length: Optional[Union[int, List[int]]] = None, transformation: Optional[Union[str, Tuple[Callable, Callable]]] = None, method_kwargs: Dict[str, Any] = {})[source]#

Bases: TorchNormalizer

Special Normalizer that is fit on each encoding sequence.

If used, this transformer will be fitted on each encoder sequence separately. This normalizer can be particularly useful as target normalizer.

Initialize

Parameters
  • method (str, optional) – method to rescale series. Either “identity”, “standard” (standard scaling) or “robust” (scale using quantiles 0.25-0.75). Defaults to “standard”.

  • method_kwargs (Dict[str, Any], optional) – Dictionary of method specific arguments as listed below * “robust” method: “upper”, “lower”, “center” quantiles defaulting to 0.75, 0.25 and 0.5

  • center (bool, optional) – If to center the output to zero. Defaults to True.

  • max_length (Union[int, List[int]], optional) – Maximum length to take into account for calculating parameters. If tuple, first length is maximum length for calculating center and second is maximum length for calculating scale. Defaults to entire length of time series.

  • transformation (Union[str, Tuple[Callable, Callable]] optional) –

    Transform values before applying normalizer. Available options are

    • None (default): No transformation of values

    • log: Estimate in log-space leading to a multiplicative model

    • logp1: Estimate in log-space but add 1 to values before transforming for stability

      (e.g. if many small values <<1 are present). Note, that inverse transform is still only torch.exp() and not torch.expm1().

    • logit: Apply logit transformation on values that are between 0 and 1

    • count: Apply softplus to output (inverse transformation) and x + 1 to input (transformation)

    • softplus: Apply softplus to output (inverse transformation) and inverse softplus to input

      (transformation)

    • relu: Apply max(0, x) to output

    • Dict[str, Callable] of PyTorch functions that transforms and inversely transforms values. forward and reverse entries are required. inverse transformation is optional and should be defined if reverse is not the inverse of the forward transformation. inverse_torch can be defined to provide a torch distribution transform for inverse transformations.

Inherited-members

Methods

extra_repr()

fit(y)

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.

get_transform(transformation)

Return transformation functions.

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[, return_norm, target_scale])

Rescale data.

Attributes

TRANSFORMATIONS

min_length

fit(y: Union[Series, ndarray, Tensor])[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

TorchNormalizer