test integration

pytorch_forecasting/models/deepar/_deepar_metadata.py

@@ -23,29 +23,8 @@ def get_model_cls(cls):
         return DeepAR
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
-        """Return testing parameter settings for the skbase object.
-
-        ``get_test_params`` is a unified interface point to store
-        parameter settings for testing purposes. This function is also
-        used in ``create_test_instance`` and ``create_test_instances_and_names``
-        to construct test instances.
-
-        ``get_test_params`` should return a single ``dict``, or a ``list`` of ``dict``.
-
-        Each ``dict`` is a parameter configuration for testing,
-        and can be used to construct an "interesting" test instance.
-        A call to ``cls(**params)`` should
-        be valid for all dictionaries ``params`` in the return of ``get_test_params``.
-
-        The ``get_test_params`` need not return fixed lists of dictionaries,
-        it can also return dynamic or stochastic parameter settings.
-
-        Parameters
-        ----------
-        parameter_set : str, default="default"
-            Name of the set of test parameters to return, for use in tests. If no
-            special parameters are defined for a value, will return `"default"` set.
+    def get_test_train_params(cls):
+        """Return testing parameter settings for the trainer.
 
         Returns
         -------

pytorch_forecasting/tests/test_all_estimators.py

@@ -71,6 +71,8 @@ class BaseFixtureGenerator(_BaseFixtureGenerator):
     object_instance: instance of estimator inheriting from BaseObject
         ranges over estimator classes not excluded by EXCLUDE_ESTIMATORS, EXCLUDED_TESTS
         instances are generated by create_test_instance class method of object_class
+    trainer_kwargs: list of dict
+        ranges over dictionaries of kwargs for the trainer
     """
 
     # overrides object retrieval in scikit-base
@@ -111,6 +113,7 @@ def _all_objects(self):
         "object_metadata",
         "object_class",
         "object_instance",
+        "trainer_kwargs",
     ]
 
     def _generate_object_metadata(self, test_name, **kwargs):
@@ -145,6 +148,30 @@ def _generate_object_class(self, test_name, **kwargs):
 
         return object_classes_to_test, object_names
 
+    def _generate_trainer_kwargs(self, test_name, **kwargs):
+        """Return kwargs for the trainer.
+
+        Fixtures parametrized
+        ---------------------
+        trainer_kwargs: dict
+            ranges over all kwargs for the trainer
+        """
+        # call _generate_object_class to get all the classes
+        object_meta_to_test, _ = self._generate_object_metadata(test_name=test_name)
+
+        # create instances from the classes
+        train_kwargs_to_test = []
+        train_kwargs_names = []
+        # retrieve all object parameters if multiple, construct instances
+        for est in object_meta_to_test:
+            est_name = est.__name__
+            all_train_kwargs = est.get_test_train_params()
+            train_kwargs_to_test += all_train_kwargs
+            rg = range(len(all_train_kwargs))
+            train_kwargs_names += [f"{est_name}_{i}" for i in rg]
+
+        return train_kwargs_to_test, train_kwargs_names
+
 
 def _integration(
     estimator_cls,
@@ -250,6 +277,16 @@ def test_doctest_examples(self, object_class):
 
         doctest.run_docstring_examples(object_class, globals())
 
-    def test_certain_failure(self, object_class):
+    def test_integration(
+        self, object_class, trainer_kwargs, data_with_covariates, tmp_path
+    ):
         """Fails for certain, for testing."""
-        assert False
+        from pytorch_forecasting.metrics import NegativeBinomialDistributionLoss
+
+        if "loss" in trainer_kwargs and isinstance(
+            trainer_kwargs["loss"], NegativeBinomialDistributionLoss
+        ):
+            data_with_covariates = data_with_covariates.assign(
+                volume=lambda x: x.volume.round()
+            )
+        _integration(object_class, data_with_covariates, tmp_path, **trainer_kwargs)