[xarray] fix tests

bob/pipelines/tests/test_xarray.py

@@ -16,6 +16,7 @@ from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
 from sklearn.linear_model import SGDClassifier
 from sklearn.preprocessing import StandardScaler
 
+import bob.io.base
 import bob.pipelines as mario
 
 
@@ -178,8 +179,8 @@ def test_dataset_pipeline_with_checkpoints():
             )
         ):
             path = os.path.join(pca_features, path)
-            assert path.endswith(f"{i}.npy"), path
-            np.testing.assert_array_equal(np.load(path).shape, (3,))
+            assert path.endswith(f"{i}.hdf5"), path
+            np.testing.assert_array_equal(bob.io.base.load(path).shape, (3,))
 
         # now this time it should load features
         # delete one of the features

bob/pipelines/xarray.py

@@ -154,7 +154,7 @@ class Block(_ReprMixin):
         estimator_name=None,
         model_path=None,
         features_dir=None,
-        extension=".npy",
+        extension=".hdf5",
         save_func=None,
         load_func=None,
         dataset_map=None,
@@ -179,12 +179,18 @@ class Block(_ReprMixin):
         self.model_path = model_path
         self.features_dir = features_dir
         self.extension = extension
-        self.save_func = (
-            save_func or estimator._get_tags().get("bob_features_save_fn") or save
+        estimator_save_fn = (
+            None
+            if estimator is None
+            else estimator._get_tags().get("bob_features_save_fn")
         )
-        self.load_func = (
-            load_func or estimator._get_tags().get("bob_features_load_fn") or load
+        estimator_load_fn = (
+            None
+            if estimator is None
+            else estimator._get_tags().get("bob_features_load_fn")
         )
+        self.save_func = save_func or estimator_save_fn or save
+        self.load_func = load_func or estimator_load_fn or load
         self.dataset_map = dataset_map
         self.input_dask_array = input_dask_array
         self.fit_kwargs = fit_kwargs or {}