uddate fit/predict wrappers

darts/models/forecasting/forecasting_model.py

@@ -321,18 +321,17 @@ def _fit_wrapper(
     ):
         supported_params = inspect.signature(self.fit).parameters
         kwargs_ = {k: v for k, v in kwargs.items() if k in supported_params}
-        if self.supports_past_covariates:
-            kwargs_["past_covariates"] = past_covariates
-        elif past_covariates is not None:
-            raise_log(
-                ValueError("Model cannot be fitted with `past_covariates`."), logger
-            )
-        if self.supports_future_covariates:
-            kwargs_["future_covariates"] = future_covariates
-        elif future_covariates is not None:
-            raise_log(
-                ValueError("Model cannot be fitted with `future_covariates`."), logger
-            )
+
+        # handle past and future covariates based on model support
+        for covs, name in zip([past_covariates, future_covariates], ["past", "future"]):
+            covs_name = f"{name}_covariates"
+            if getattr(self, f"supports_{covs_name}"):
+                kwargs_[covs_name] = covs
+            elif covs is not None:
+                raise_log(
+                    ValueError(f"Model cannot be fit/trained with `{covs_name}`."),
+                    logger,
+                )
         self.fit(series, **kwargs_)
 
     def _predict_wrapper(
@@ -341,29 +340,21 @@ def _predict_wrapper(
         **kwargs,
     ) -> Union[TimeSeries, Sequence[TimeSeries]]:
         supported_params = set(inspect.signature(self.predict).parameters)
+
         # if predict() accepts covariates, the model might not support them at inference
-        if "past_covariates" in kwargs and not self.supports_past_covariates:
-            if kwargs["past_covariates"] is None:
-                supported_params = supported_params - {"past_covariates"}
-            else:
-                raise_log(
-                    ValueError(
-                        "Model does not support `past_covariates` at inference, either remove this covariate or "
-                        "fit the model with this covariate."
-                    ),
-                    logger,
-                )
-        if "future_covariates" in kwargs and not self.supports_future_covariates:
-            if kwargs["future_covariates"] is None:
-                supported_params = supported_params - {"future_covariates"}
-            else:
-                raise_log(
-                    ValueError(
-                        "Model does not support `future_covariates` at inference, either remove this covariate or "
-                        "fit the model with this covariate."
-                    ),
-                    logger,
-                )
+        for covs_name in ["past_covariates", "future_covariates"]:
+            if covs_name in kwargs and not getattr(self, f"supports_{covs_name}"):
+                if kwargs[covs_name] is None:
+                    supported_params = supported_params - {covs_name}
+                else:
+                    raise_log(
+                        ValueError(
+                            f"Model prediction does not support `{covs_name}`, either because it "
+                            f"does not support `{covs_name}` in general, or because it was fit/trained "
+                            f"without using `{covs_name}`."
+                        ),
+                        logger,
+                    )
 
         kwargs_ = {k: v for k, v in kwargs.items() if k in supported_params}
         return self.predict(n, **kwargs_)

darts/tests/models/forecasting/test_backtesting.py

@@ -476,13 +476,13 @@ def test_backtest_bad_covariates(self, model_cls):
         with pytest.raises(ValueError) as msg:
             model.backtest(series=series, past_covariates=series, **bt_kwargs)
         assert str(msg.value).startswith(
-            "Model cannot be fitted with `past_covariates`."
+            "Model cannot be fit/trained with `past_covariates`."
         )
         if not model.supports_future_covariates:
             with pytest.raises(ValueError) as msg:
                 model.backtest(series=series, future_covariates=series, **bt_kwargs)
             assert str(msg.value).startswith(
-                "Model cannot be fitted with `future_covariates`."
+                "Model cannot be fit/trained with `future_covariates`."
             )
 
     def test_gridsearch(self):
@@ -679,7 +679,7 @@ def test_gridsearch_bad_covariates(self, model_cls, parameters):
                 **bt_kwargs
             )
         assert str(msg.value).startswith(
-            "Model cannot be fitted with `past_covariates`."
+            "Model cannot be fit/trained with `past_covariates`."
         )
         if not model.supports_future_covariates:
             with pytest.raises(ValueError) as msg:
@@ -691,5 +691,5 @@ def test_gridsearch_bad_covariates(self, model_cls, parameters):
                     **bt_kwargs
                 )
             assert str(msg.value).startswith(
-                "Model cannot be fitted with `future_covariates`."
+                "Model cannot be fit/trained with `future_covariates`."
             )

darts/tests/models/forecasting/test_historical_forecasts.py

@@ -388,7 +388,7 @@ def test_historical_forecasts_transferrable_future_cov_local_models(self):
                 retrain=False,
             )
         assert str(msg.value).startswith(
-            "Model does not support `past_covariates` at inference"
+            "Model prediction does not support `past_covariates`"
         )
 
     def test_historical_forecasts_future_cov_local_models(self):
@@ -421,7 +421,7 @@ def test_historical_forecasts_future_cov_local_models(self):
                 retrain=True,
             )
         assert str(msg.value).startswith(
-            "Model cannot be fitted with `past_covariates`."
+            "Model cannot be fit/trained with `past_covariates`."
         )
 
     def test_historical_forecasts_local_models(self):
@@ -655,7 +655,7 @@ def test_historical_forecasts(self, config):
                     retrain=True,
                 )
             assert str(msg.value).startswith(
-                "Model cannot be fitted with `past_covariates`."
+                "Model cannot be fit/trained with `past_covariates`."
             )
 
         if not model.supports_future_covariates:
@@ -666,7 +666,7 @@ def test_historical_forecasts(self, config):
                     last_points_only=False,
                 )
             assert str(msg.value).startswith(
-                "Model cannot be fitted with `future_covariates`."
+                "Model cannot be fit/trained with `future_covariates`."
             )
 
     def test_sanity_check_invalid_start(self):