EncoderNormalizer¶
- class pytorch_forecasting.data.encoders.EncoderNormalizer(method: str = 'standard', center: bool = True, transformation: Optional[Union[str, Tuple[Callable, Callable]]] = None, eps: float = 1e-08)[source]¶
Bases:
pytorch_forecasting.data.encoders.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”.
center (bool, optional) – If to center the output to zero. Defaults to True.
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
softplus: Apply softplus to output (inverse transformation) and x + 1 to input (transformation)
relu: Apply max(0, x) to output
Tuple[Callable, Callable] of PyTorch functions that transforms and inversely transforms values.
eps (float, optional) – Number for numerical stability of calculations. Defaults to 1e-8.
- Inherited-members
Methods
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.
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