check fill value

tests/toolkit/test_dataset.py

@@ -196,6 +196,7 @@ def test_forecasting_df_dataset_stride(ts_data_with_categorical):
 def test_forecasting_observed_mask(ts_data_with_categorical):
     prediction_length = 2
     context_length = 5
+    fill_value = 0.0
     target_columns = ["value2", "value3"]
 
     df = ts_data_with_categorical.copy()
@@ -208,6 +209,7 @@ def test_forecasting_observed_mask(ts_data_with_categorical):
         target_columns=target_columns,
         context_length=context_length,
         prediction_length=prediction_length,
+        fill_value=fill_value,
     )
 
     # check matching size
@@ -218,6 +220,23 @@ def test_forecasting_observed_mask(ts_data_with_categorical):
     np.testing.assert_allclose(ds[4]["future_observed_mask"], np.array([[True, True], [True, False]]))
     np.testing.assert_allclose(ds[6]["past_observed_mask"][-1, :], np.array([True, False]))
 
+    # Check mask value is correct
+    ds[4]["future_values"][1, 1] == fill_value
+
+    # Check mask value is correct again
+    fill_value = -100.0
+    ds = ForecastDFDataset(
+        df,
+        timestamp_column="timestamp",
+        id_columns=["id"],
+        target_columns=target_columns,
+        context_length=context_length,
+        prediction_length=prediction_length,
+        fill_value=fill_value,
+    )
+
+    ds[4]["future_values"][1, 1] == fill_value
+
 
 def test_forecasting_df_dataset_non_autoregressive(ts_data_with_categorical):
     prediction_length = 2