pytorch_forecasting.data.encoders.MultiNormalizer

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

Normalizer for multiple targets.

This normalizers wraps multiple other normalizers.

Parameters:

normalizers (List[TorchNormalizer]) – list of normalizers to apply to targets

__init__(normalizers: list[TorchNormalizer])

Parameters:

normalizers (List[TorchNormalizer]) – list of normalizers to apply to targets

Methods

__call__(data)	Inverse transformation but with network output as input.
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattr__(name)	Return dynamically attributes.
__getattribute__(name, /)	Return getattr(self, name).
__getitem__(idx)	Return normalizer.
__getstate__()	Helper for pickle.
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init_subclass__(**kwargs)	Set the set_{method}_request methods.
__iter__()	Iter over normalizers.
__le__(value, /)	Return self<=value.
__len__()	Number of normalizers.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(*args, **kwargs)
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__setstate__(state)
__sizeof__()	Size of object in memory, in bytes.
__sklearn_clone__()
__sklearn_tags__()
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_get_class_level_metadata_request_values(...)	Get class level metadata request values.
_get_doc_link()	Generates a link to the API documentation for a given estimator.
_get_fitted_attr_html([doc_link])	Get fitted attributes of the estimator.
_get_metadata_request()	Get requested metadata for the instance.
_get_param_names()	Get parameter names for the estimator
_get_params_html([deep, doc_link])	Get parameters for this estimator with a specific HTML representation.
_html_repr()	Build an HTML representation of an estimator.
_repr_html_inner()	This function is returned by the @property _repr_html_ to make hasattr(estimator, "_repr_html_") return `True or False depending on get_config()["display"].
_repr_mimebundle_(**kwargs)	Mime bundle used by jupyter kernels to display estimator
_set_parameters(y_center, y_scale)	Calculate parameters for scale and center based on input timeseries
_validate_params()	Validate types and values of constructor parameters
extra_repr()	Return extra information about parameters for representation/logging.
fit(y[, X])	Fit transformer, i.e. determine center and scale of data.
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
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. take exp).
inverse_transform(y)	Inverse scale.
preprocess(y)	Preprocess input data (e.g. take log).
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
set_transform_request(*[, return_norm, ...])	Configure whether metadata should be requested to be passed to the transform method.
transform(y[, X, return_norm, target_scale])	Scale input data.

Attributes

TRANSFORMATIONS
__annotations__
__dict__
__doc__
__module__
__weakref__	list of weak references to the object
_doc_link_module
_doc_link_template
_doc_link_url_param_generator
_repr_html_	HTML representation of estimator.
_sklearn_auto_wrap_output_keys