pytorch_forecasting.data.encoders.NaNLabelEncoder

class pytorch_forecasting.data.encoders.NaNLabelEncoder(add_nan: bool = False, warn: bool = True)

Labelencoder that can optionally always encode nan and unknown classes (in transform) as class 0

init NaNLabelEncoder

Returns:

• add_nan – if to force encoding of nan at 0

• warn – if to warn if additional nans are added because items are unknown

__init__(add_nan: bool = False, warn: bool = True)

init NaNLabelEncoder

Returns:

• add_nan – if to force encoding of nan at 0

• warn – if to warn if additional nans are added because items are unknown

Methods

__call__(data)	Extract prediction from network output.
__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.
__getattribute__(name, /)	Return getattr(self, name).
__getstate__()	Helper for pickle.
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init_subclass__(**kwargs)	Set the set_{method}_request methods.
__le__(value, /)	Return self<=value.
__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
_validate_params()	Validate types and values of constructor parameters
extra_repr()	Return extra information about parameters for representation/logging.
fit(y[, overwrite])	Fit transformer
fit_transform(y[, overwrite])	Fit and transform data.
get_metadata_routing()	Get metadata routing of this object.
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. take exp).
inverse_transform(y)	Decode data, i.e. transform from integers to labels.
is_numeric(y)	Determine if series is numeric or not.
preprocess(y)	Preprocess input data (e.g. take log).
set_fit_request(*[, overwrite])	Configure whether metadata should be requested to be passed to the fit method.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
set_transform_request(*[, ignore_na, ...])	Configure whether metadata should be requested to be passed to the transform method.
transform(y[, return_norm, target_scale, ...])	Encode iterable with integers.

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