QuantileLoss_pkg#
- class pytorch_forecasting.metrics._quantile_pkg._quantile_loss_pkg.QuantileLoss_pkg[source]#
Bases:
_BasePtMetricQuantile loss metric for quantile forecasts.
Defined as
(y_pred - target).abs().Construct BaseObject.
- Inherited-members:
Methods
clone()Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])Clone tags from another object as dynamic override.
create_test_instance([parameter_set])Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])Create list of all test instances and a list of names for them.
get_class_tag(tag_name[, tag_value_default])Get class tag value from class, with tag level inheritance from parents.
get_class_tags()Get class tags from class, with tag level inheritance from parent classes.
get_cls()Get the metric class.
get_config()Get config flags for self.
get_encoder()Get the encoder for the metric.
Returns parameters for initializing the metric for testing.
get_param_defaults()Get object's parameter defaults.
get_param_names([sort])Get object's parameter names.
get_params([deep])Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])Get tag value from instance, with tag level inheritance and overrides.
get_tags()Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])Return testing parameter settings for the skbase object.
is_composite()Check if the object is composed of other BaseObjects.
name()Get the name of the metric.
prepare_test_inputs(test_case)Prepare test inputs for the metric.
reset()Reset the object to a clean post-init state.
set_config(**config_dict)Set config flags to given values.
set_params(**params)Set the parameters of this object.
set_random_state([random_state, deep, ...])Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)Set instance level tag overrides to given values.