[MNT] black format

learnware/market/easy2/checker.py

@@ -62,7 +62,6 @@ class EasySemanticChecker(BaseChecker):
 
 
 class EasyStatisticalChecker(BaseChecker):
-
     @staticmethod
     def _generate_random_text_list(num, text_type="en", min_len=10, max_len=1000):
         text_list = []
@@ -78,7 +77,7 @@ class EasyStatisticalChecker(BaseChecker):
             else:
                 raise ValueError("Type should be en or zh")
         return text_list
-        
+
     def __call__(self, learnware):
         semantic_spec = learnware.get_specification().get_semantic_spec()
 
@@ -97,15 +96,17 @@ class EasyStatisticalChecker(BaseChecker):
             input_shape = learnware_model.input_shape
 
             ## WHY: why write this?
-            if semantic_spec["Data"]["Values"][0] == "Table" and input_shape != (int(semantic_spec["Input"]["Dimension"]),):
-               logger.warning("input shapes of model and semantic specifications are different")
-               return self.INVALID_LEARNWARE
-                
+            if semantic_spec["Data"]["Values"][0] == "Table" and input_shape != (
+                int(semantic_spec["Input"]["Dimension"]),
+            ):
+                logger.warning("input shapes of model and semantic specifications are different")
+                return self.INVALID_LEARNWARE
+
             spec_type = parse_specification_type(learnware.get_specification())
             if spec_type is None:
                 logger.warning(f"No valid specification is found in stat spec {stat_spec}")
                 return self.INVALID_LEARNWARE
-                
+
             if spec_type == "RKMETableSpecification":
                 stat_spec = learnware.get_specification().get_stat_spec_by_name(spec_type)
                 if stat_spec.get_z().shape[1:] != input_shape:
@@ -136,7 +137,9 @@ class EasyStatisticalChecker(BaseChecker):
                     return self.INVALID_LEARNWARE
 
                 # Check output shape
-                if outputs[0].shape != learnware_model.output_shape or learnware_model.output_shape != int(semantic_spec["Output"]["Dimension"]):
+                if outputs[0].shape != learnware_model.output_shape or learnware_model.output_shape != int(
+                    semantic_spec["Output"]["Dimension"]
+                ):
                     logger.warning(f"The learnware [{learnware.id}] output dimention mismatch!")
                     return self.INVALID_LEARNWARE
 

learnware/market/easy2/searcher.py

@@ -415,7 +415,9 @@ class EasyStatSearcher(BaseSearcher):
         return sorted_score_list[:idx], learnware_list[:idx]
 
     def _filter_by_rkme_spec_dimension(
-        self, learnware_list: List[Learnware], user_rkme: Union[RKMETableSpecification, RKMEImageSpecification, RKMETextSpecification]
+        self,
+        learnware_list: List[Learnware],
+        user_rkme: Union[RKMETableSpecification, RKMEImageSpecification, RKMETextSpecification],
     ) -> List[Learnware]:
         """Filter learnwares whose rkme dimension different from user_rkme
 
@@ -520,7 +522,9 @@ class EasyStatSearcher(BaseSearcher):
         return mmd_dist, weight_min, mixture_list
 
     def _search_by_rkme_spec_single(
-        self, learnware_list: List[Learnware], user_rkme: Union[RKMETableSpecification, RKMEImageSpecification, RKMETextSpecification]
+        self,
+        learnware_list: List[Learnware],
+        user_rkme: Union[RKMETableSpecification, RKMEImageSpecification, RKMETextSpecification],
     ) -> Tuple[List[float], List[Learnware]]:
         """Calculate the distances between learnwares in the given learnware_list and user_rkme
 

learnware/market/utils.py

@@ -1,12 +1,13 @@
 from ..specification import Specification
 from ..logger import get_module_logger
 
-logger = get_module_logger('market_utils')
+logger = get_module_logger("market_utils")
+
 
 def parse_specification_type(stat_spec: Specification):
     stat_specs = stat_spec.stat_spec
-    spec_list =['RKMETableSpecification', 'RKMETextSpecification', 'RKMEImageSpecification']
+    spec_list = ["RKMETableSpecification", "RKMETextSpecification", "RKMEImageSpecification"]
     for spec in spec_list:
         if spec in stat_specs:
             return spec
-    return None
+    return None

tests/test_workflow/test_workflow.py

@@ -97,7 +97,9 @@ class TestAllWorkflow(unittest.TestCase):
             semantic_spec["Name"]["Values"] = "learnware_%d" % (idx)
             semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (idx)
             semantic_spec["Input"] = {"Dimension": 64}
-            semantic_spec["Input"].update({f"{i}": f"The value in the digit image with row is {i // 8} and col is {i % 8}." for i in range(64)})
+            semantic_spec["Input"].update(
+                {f"{i}": f"The value in the digit image with row is {i // 8} and col is {i % 8}." for i in range(64)}
+            )
             semantic_spec["Output"] = {"Dimension": 1, "Description": {"0": "The label of the hand-written digit."}}
             easy_market.add_learnware(zip_path, semantic_spec)