GroupNormalizer#

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

Bases: TorchNormalizer

Normalizer that scales by groups.

For each group a scaler is fitted and applied. This scaler can be used as target normalizer or also to normalize any other variable.

Group normalizer to normalize a given entry by groups. Can be used as target normalizer.

Parameters
  • method (str, optional) – method to rescale series. Either “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

  • groups (List[str], optional) – Group names to normalize by. Defaults to [].

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

  • scale_by_group (bool, optional) – If to scale the output by group, i.e. norm is calculated as (group1_norm * group2_norm * ...) ^ (1 / n_groups). Defaults to False.

  • 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, X)

Determine scales for each group

fit_transform(y, X[, return_norm])

Fit normalizer and scale input data.

get_norm(X)

Get scaling parameters for multiple groups.

get_parameters(groups[, group_names])

Get fitted scaling parameters for a given group.

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, X)

Rescaling data to original scale - not implemented - call class with target scale instead.

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

names

Names of determined scales.

fit(y: Series, X: DataFrame)[source]#

Determine scales for each group

Parameters
  • y (pd.Series) – input data

  • X (pd.DataFrame) – dataframe with columns for each group defined in groups parameter.

Returns

self

fit_transform(y: Series, X: DataFrame, return_norm: bool = False) Union[ndarray, Tuple[ndarray, ndarray]][source]#

Fit normalizer and scale input data.

Parameters
  • y (pd.Series) – data to scale

  • X (pd.DataFrame) – dataframe with groups columns

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

Returns

Scaled data, if return_norm=True, returns also scales

as second element

Return type

Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]

get_norm(X: DataFrame) DataFrame[source]#

Get scaling parameters for multiple groups.

Parameters

X (pd.DataFrame) – dataframe with groups columns

Returns

dataframe with scaling parameterswhere each row corresponds to the input dataframe

Return type

pd.DataFrame

get_parameters(groups: Union[Tensor, list, tuple], group_names: Optional[List[str]] = None) ndarray[source]#

Get fitted scaling parameters for a given group.

Parameters
  • groups (Union[torch.Tensor, list, tuple]) – group ids for which to get parameters

  • group_names (List[str], optional) – Names of groups corresponding to positions in groups. Defaults to None, i.e. the instance attribute groups.

Returns

parameters used for scaling

Return type

np.ndarray

inverse_transform(y: Series, X: DataFrame)[source]#

Rescaling data to original scale - not implemented - call class with target scale instead.

transform(y: Series, X: Optional[DataFrame] = None, return_norm: bool = False, target_scale: Optional[Tensor] = None) Union[ndarray, Tuple[ndarray, ndarray]][source]#

Scale input data.

Parameters
  • y (pd.Series) – data to scale

  • X (pd.DataFrame) – dataframe with groups columns

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

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

Returns

Scaled data, if return_norm=True, returns also scales

as second element

Return type

Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]

property names: List[str]#

Names of determined scales.

Returns

list of names

Return type

List[str]