Add test for tags

autointent/modules/prediction/_adaptive.py

@@ -129,7 +129,9 @@ def dump(self, path: str) -> None:
         dump_dir = Path(path)
 
         metadata = AdaptivePredictorDumpMetadata(
-            r=self._r, tags=[t.model_dump() for t in self.tags] if self.tags else None, n_classes=self.n_classes
+            r=self._r,
+            tags=[t.model_dump() for t in self.tags] if self.tags else None,  # type: ignore[misc]
+            n_classes=self.n_classes,
         )
 
         with (dump_dir / self.metadata_dict_name).open("w") as file:

autointent/modules/prediction/_threshold.py

@@ -117,7 +117,7 @@ def dump(self, path: str) -> None:
 
         dump_dir = Path(path)
         metadata_json = self.metadata
-        metadata_json["tags"] = [tag.model_dump() for tag in metadata_json["tags"]] if metadata_json["tags"] else None
+        metadata_json["tags"] = [tag.model_dump() for tag in metadata_json["tags"]] if metadata_json["tags"] else None  # type: ignore[misc]
 
         with (dump_dir / self.metadata_dict_name).open("w") as file:
             json.dump(metadata_json, file, indent=4)

autointent/modules/prediction/_tunable.py

@@ -124,7 +124,7 @@ def dump(self, path: str) -> None:
 
         dump_dir = Path(path)
         metadata_json = self.metadata
-        metadata_json["tags"] = [tag.model_dump() for tag in metadata_json["tags"]] if metadata_json["tags"] else None
+        metadata_json["tags"] = [tag.model_dump() for tag in metadata_json["tags"]] if metadata_json["tags"] else None  # type: ignore[misc]
 
         with (dump_dir / self.metadata_dict_name).open("w") as file:
             json.dump(metadata_json, file, indent=4)
@@ -138,12 +138,14 @@ def load(self, path: str) -> None:
         dump_dir = Path(path)
 
         with (dump_dir / self.metadata_dict_name).open() as file:
-            metadata: TunablePredictorDumpMetadata = json.load(file)
+            metadata = json.load(file)
 
-        self.metadata = metadata
+        metadata["tags"] = [Tag(**tag) for tag in metadata["tags"]] if metadata["tags"] else None
+
+        self.metadata: TunablePredictorDumpMetadata = metadata
         self.thresh = np.array(metadata["thresh"])
         self.multilabel = metadata["multilabel"]
-        self.tags = [Tag(**t) for t in metadata["tags"]] if metadata["tags"] else None
+        self.tags = metadata["tags"]
         self.n_classes = metadata["n_classes"]
 
 

tests/modules/prediction/test_argmax.py

@@ -8,7 +8,7 @@
 def test_multiclass(multiclass_fit_data):
     predictor = ArgmaxPredictor()
     predictor.fit(*multiclass_fit_data)
-    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.2, 0.5], [0, 0.3, 0.7, 0.1]])
+    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.1, 0.1], [0, 0.2, 0.7, 0.1]])
     predictions = predictor.predict(scores)
     np.testing.assert_array_equal(predictions, np.array([1, 0, 2]))
 

tests/modules/prediction/test_jinoos.py

@@ -21,7 +21,7 @@ def detect_oos(scores: npt.NDArray[Any], labels: npt.NDArray[Any], thresh: float
 def test_predict_returns_correct_indices(multiclass_fit_data):
     predictor = JinoosPredictor()
     predictor.fit(*multiclass_fit_data)
-    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.2, 0.5], [0, 0.3, 0.7, 0.1]])
+    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.1, 0.1], [0, 0.2, 0.7, 0.1]])
 
     # inference
     predictions = predictor.predict(scores)

tests/modules/prediction/test_threshold.py

@@ -16,25 +16,25 @@ def test_multiclass(multiclass_fit_data):
 def test_multilabel(multilabel_fit_data):
     predictor = ThresholdPredictor(thresh=0.5)
     predictor.fit(*multilabel_fit_data)
-    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.2, 0.5], [0, 0.3, 0.7, 0.1]])
+    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.1, 0.1], [0, 0.2, 0.7, 0.1]])
     predictions = predictor.predict(scores)
-    np.testing.assert_array_equal(predictions, np.array([[0, 1, 0, 0], [1, 0, 0, 1], [0, 0, 1, 0]]))
+    np.testing.assert_array_equal(predictions, np.array([[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0]]))
 
 
 def test_multiclass_list(multiclass_fit_data):
     predictor = ThresholdPredictor(np.array([0.5, 0.5, 0.8, 0.5]))
     predictor.fit(*multiclass_fit_data)
-    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.2, 0.5], [0, 0.3, 0.7, 0.1]])
+    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.1, 0.1], [0, 0.2, 0.7, 0.1]])
     predictions = predictor.predict(scores)
     np.testing.assert_array_equal(predictions, np.array([1, 0, -1]))
 
 
 def test_multilabel_list(multilabel_fit_data):
     predictor = ThresholdPredictor(np.array([0.5, 0.5, 0.8, 0.5]))
     predictor.fit(*multilabel_fit_data)
-    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.2, 0.5], [0, 0.3, 0.7, 0.1]])
+    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.1, 0.1], [0, 0.2, 0.7, 0.1]])
     predictions = predictor.predict(scores)
-    np.testing.assert_array_equal(predictions, np.array([[0, 1, 0, 0], [1, 0, 0, 1], [0, 0, 0, 0]]))
+    np.testing.assert_array_equal(predictions, np.array([[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0]]))
 
 
 def test_fails_on_wrong_n_classes_predict(multiclass_fit_data):

tests/modules/prediction/test_tunable.py

@@ -19,7 +19,7 @@ def test_multiclass(multiclass_fit_data):
 def test_multilabel(multilabel_fit_data):
     predictor = TunablePredictor()
     predictor.fit(*multilabel_fit_data)
-    scores = np.array([[0.2, 0.9, 0, 0.5], [0.8, 0, 0.6, 0.5], [0, 0.4, 0.7, 0.5]])
+    scores = np.array([[0.1, 0.9, 0, 0.1], [0.8, 0, 0.1, 0.1], [0, 0.2, 0.7, 0.1]])
     predictions = predictor.predict(scores)
     desired = np.array([[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0]])