_BasePtMetric

class pytorch_forecasting.metrics.base_metrics._base_object._BasePtMetric

Bases: _BaseObject

Base class for metric object that can be discovered for testing.

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.
get_metric_test_params()	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.

classmethod get_cls()

Get the metric class.

Returns:

The metric class.

Return type:

type

classmethod get_encoder()

Get the encoder for the metric.

This can be overridden by subclasses to provide a specific encoder.

Returns:

An instance of TorchNormalizer or similar encoder.

Return type:

TorchNormalizer

classmethod get_metric_test_params()

Returns parameters for initializing the metric for testing.

Returns:

Dictionary containing parameters for initializing the metric.d

Return type:

dict

classmethod name()

Get the name of the metric.

Returns:

The name of the metric.

Return type:

str

classmethod prepare_test_inputs(test_case)

Prepare test inputs for the metric.

This can be overridden by subclasses to provide special handling of test inputs.

Parameters:

test_case (dict) – Dictionary containing test case parameters.

Returns:

(y_pred, y_actual, kwargs) – Tuple containing the predicted values, actual values, and any additional keyword arguments.

Return type:

tuple