[FIX | MNT] fix bugs for hetero organizer, and modify tests

learnware/market/heterogeneous/organizer/__init__.py

@@ -206,6 +206,9 @@ class HeteroMapTableOrganizer(EasyOrganizer):
             str: id of target learware
             List[str]: A list of ids of target learnwares
         """
+        if isinstance(ids, str):
+            ids = [ids]
+
         for idx in ids:
             try:
                 spec = self.learnware_list[idx].get_specification()
@@ -218,7 +221,8 @@ class HeteroMapTableOrganizer(EasyOrganizer):
                 hetero_spec.save(save_path)
 
             except Exception as err:
-                logger.warning(f"Learnware {idx} generate HeteroMapTableSpecification failed! Due to {err}")
+                traceback.print_exc()
+                logger.warning(f"Learnware {idx} generate HeteroMapTableSpecification failed!")
 
     def _get_hetero_learnware_ids(self, ids: Union[str, List[str]]) -> List[str]:
         """Get learnware ids that supports heterogeneous market training and search.

tests/test_hetero_market/example_learnwares/example_learnware_1/learnware.yaml

@@ -1,8 +0,0 @@
-model:
-  class_name: MyModel
-  kwargs: {}
-stat_specifications:
-  - module_path: learnware.specification
-    class_name: RKMETableSpecification
-    file_name: stat.json
-    kwargs: {}

tests/test_hetero_market/example_learnwares/example_learnware_1/requirements.txt

@@ -1 +0,0 @@
-learnware == 0.1.0.999

tests/test_hetero_market/example_learnwares/example_learnware_0/__init__.py → tests/test_hetero_market/example_learnwares/model0.py

@@ -12,11 +12,5 @@ class MyModel(BaseModel):
         model = joblib.load(model_path)
         self.model = model
 
-    def fit(self, X: np.ndarray, y: np.ndarray):
-        pass
-
     def predict(self, X: np.ndarray) -> np.ndarray:
         return self.model.predict(X)
-
-    def finetune(self, X: np.ndarray, y: np.ndarray):
-        pass

tests/test_hetero_market/example_learnwares/example_learnware_1/__init__.py → tests/test_hetero_market/example_learnwares/model1.py

@@ -12,11 +12,5 @@ class MyModel(BaseModel):
         model = joblib.load(model_path)
         self.model = model
 
-    def fit(self, X: np.ndarray, y: np.ndarray):
-        pass
-
     def predict(self, X: np.ndarray) -> np.ndarray:
         return self.model.predict(X)
-
-    def finetune(self, X: np.ndarray, y: np.ndarray):
-        pass

tests/test_hetero_market/test_hetero.py

@@ -75,7 +75,7 @@ class TestMarket(unittest.TestCase):
 
             example_learnware_idx = i % 2
             input_dim = input_shape_list[example_learnware_idx]
-            example_learnware_name = "example_learnwares/example_learnware_%d" % (example_learnware_idx)
+            learnware_example_dir = "example_learnwares"
 
             X, y = make_regression(n_samples=5000, n_informative=15, n_features=input_dim, noise=0.1, random_state=42)
 
@@ -89,16 +89,16 @@ class TestMarket(unittest.TestCase):
 
             init_file = os.path.join(dir_path, "__init__.py")
             copyfile(
-                os.path.join(curr_root, example_learnware_name, "__init__.py"), init_file
+                os.path.join(curr_root, learnware_example_dir, f"model{example_learnware_idx}.py"), init_file
             )  # cp example_init.py init_file
 
             yaml_file = os.path.join(dir_path, "learnware.yaml")
             copyfile(
-                os.path.join(curr_root, example_learnware_name, "learnware.yaml"), yaml_file
+                os.path.join(curr_root, learnware_example_dir, "learnware.yaml"), yaml_file
             )  # cp example.yaml yaml_file
 
             env_file = os.path.join(dir_path, "requirements.txt")
-            copyfile(os.path.join(curr_root, example_learnware_name, "requirements.txt"), env_file)
+            copyfile(os.path.join(curr_root, learnware_example_dir, "requirements.txt"), env_file)
 
             zip_file = dir_path + ".zip"
             # zip -q -r -j zip_file dir_path