[BUG] EXPERIMENTAL PR: Solve the bug in data_module (sktime#1834)

This PR solves the bug in `data_module` where the
`static_categorical_features` and `static_continuous_features` were not
correctly calculated in `__getitem__` of nested class

Author: phoeenniixx

pytorch_forecasting/data/data_module.py

@@ -519,8 +519,40 @@ def __getitem__(self, idx):
                 "decoder_mask": decoder_mask,
             }
             if data["static"] is not None:
-                x["static_categorical_features"] = data["static"].unsqueeze(0)
-                x["static_continuous_features"] = torch.zeros((1, 0))
+                raw_st_tensor = data.get("static")
+                static_col_names = self.data_module.time_series_metadata["cols"]["st"]
+
+                is_categorical_mask = torch.tensor(
+                    [
+                        self.data_module.time_series_metadata["col_type"].get(col_name)
+                        == "C"
+                        for col_name in static_col_names
+                    ],
+                    dtype=torch.bool,
+                )
+
+                is_continuous_mask = ~is_categorical_mask
+
+                st_cat_values_for_item = raw_st_tensor[is_categorical_mask]
+                st_cont_values_for_item = raw_st_tensor[is_continuous_mask]
+
+                if st_cat_values_for_item.shape[0] > 0:
+                    x["static_categorical_features"] = st_cat_values_for_item.unsqueeze(
+                        0
+                    )
+                else:
+                    x["static_categorical_features"] = torch.zeros(
+                        (1, 0), dtype=torch.float32
+                    )
+
+                if st_cont_values_for_item.shape[0] > 0:
+                    x["static_continuous_features"] = st_cont_values_for_item.unsqueeze(
+                        0
+                    )
+                else:
+                    x["static_continuous_features"] = torch.zeros(
+                        (1, 0), dtype=torch.float32
+                    )
 
             y = data["target"][decoder_indices]
             if y.ndim == 1:

tests/test_data/test_data_module.py

@@ -405,6 +405,14 @@ def test_with_static_features():
     x, y = dm.train_dataset[0]
     assert "static_categorical_features" in x
     assert "static_continuous_features" in x
+    assert (
+        x["static_categorical_features"].shape[1]
+        == metadata["static_categorical_features"]
+    )
+    assert (
+        x["static_continuous_features"].shape[1]
+        == metadata["static_continuous_features"]
+    )
 
 
 def test_different_train_val_test_split(sample_timeseries_data):