TIDE_pkg_v2

class pytorch_forecasting.models.tide._tide_dsipts._tide_v2_pkg.TIDE_pkg_v2(model_cfg: dict[str, Any] | str | Path | None = None, trainer_cfg: dict[str, Any] | str | Path | None = None, datamodule_cfg: dict[str, Any] | str | Path | None = None, ckpt_path: str | Path | None = None)

Bases: Base_pkg

TIDE package container.

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.
fit(data[, save_ckpt, ckpt_dir, ckpt_kwargs])	Fit the model to the training data.
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 model class.
get_config()	Get config flags for self.
get_datamodule_cls()	Get the underlying DataModule class.
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_dataset_from(**kwargs)	Creates and returns D1 TimeSeries dataSet objects for testing.
get_test_params([parameter_set])	Return testing parameter settings for the skbase object.
get_test_train_params()	Return testing parameter settings for the trainer.
is_composite()	Check if the object is composed of other BaseObjects.
name()	Get model name.
predict(data[, output_dir])	Generate predictions by wrapping the model's predict method.
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 model class.

classmethod get_datamodule_cls()

Get the underlying DataModule class.

classmethod get_test_train_params()

Return testing parameter settings for the trainer.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}