[MNT] change semantic_spec in examples

examples/example_image/main.py

@@ -38,45 +38,17 @@ os.makedirs(model_save_root, exist_ok=True)
 semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
-        "Scenario": {"Values": ["Nature"], "Type": "Tag"},
-        "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_1", "Type": "String"},
-    },
-    {
-        "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
-        "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
-        "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_2", "Type": "String"},
-    },
-    {
-        "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
+        "Task": {"Values": ["Classification"], "Type": "Class"},
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_3", "Type": "String"},
-    },
+        "Name": {"Values": "learnware_1", "Type": "String"},
+    }
 ]
 
 user_senmantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {
-        "Values": ["Classification"],
-        "Type": "Class",
-    },
+    "Task": {"Values": ["Classification"], "Type": "Class"},
     "Device": {"Values": ["GPU"], "Type": "Tag"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
@@ -144,7 +116,7 @@ def prepare_market():
         new_learnware_path = prepare_learnware(
             data_path, model_path, init_file_path, yaml_file_path, tmp_dir, "%s_%d" % (dataset, i)
         )
-        semantic_spec = semantic_specs[i % 3]
+        semantic_spec = semantic_specs[0]
         semantic_spec["Name"]["Values"] = "learnware_%d" % (i)
         semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (i)
         image_market.add_learnware(new_learnware_path, semantic_spec)

examples/example_m5/main.py

@@ -15,45 +15,17 @@ from m5 import DataLoader
 semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
-        "Scenario": {"Values": ["Nature"], "Type": "Tag"},
-        "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_1", "Type": "String"},
-    },
-    {
-        "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
-        "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
-        "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_2", "Type": "String"},
-    },
-    {
-        "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
+        "Task": {"Values": ["Classification"], "Type": "Class"},
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_3", "Type": "String"},
-    },
+        "Name": {"Values": "learnware_1", "Type": "String"},
+    }
 ]
 
 user_senmantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {
-        "Values": ["Classification"],
-        "Type": "Class",
-    },
+    "Task": {"Values": ["Classification"], "Type": "Class"},
     "Device": {"Values": ["GPU"], "Type": "Tag"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
@@ -86,7 +58,7 @@ class M5DatasetWorkflow:
             zip_path_list.append(os.path.join(curr_root, zip_path))
 
         for idx, zip_path in enumerate(zip_path_list):
-            semantic_spec = semantic_specs[idx % 3]
+            semantic_spec = semantic_specs[0]
             semantic_spec["Name"]["Values"] = "learnware_%d" % (idx)
             semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (idx)
             easy_market.add_learnware(zip_path, semantic_spec)
@@ -101,7 +73,7 @@ class M5DatasetWorkflow:
 
         m5 = DataLoader()
         idx_list = m5.get_idx_list()
-        algo_list = ['lgb']  # algo_list = ["ridge", "lgb"]
+        algo_list = ["lgb"]  # algo_list = ["ridge", "lgb"]
 
         curr_root = os.path.dirname(os.path.abspath(__file__))
         curr_root = os.path.join(curr_root, "learnware_pool")

examples/example_pfs/main.py

@@ -15,45 +15,17 @@ from pfs import Dataloader
 semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
-        "Scenario": {"Values": ["Nature"], "Type": "Tag"},
-        "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_1", "Type": "String"},
-    },
-    {
-        "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
-        "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
-        "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_2", "Type": "String"},
-    },
-    {
-        "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
+        "Task": {"Values": ["Classification"], "Type": "Class"},
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_3", "Type": "String"},
-    },
+        "Name": {"Values": "learnware_1", "Type": "String"},
+    }
 ]
 
 user_senmantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {
-        "Values": ["Classification"],
-        "Type": "Class",
-    },
+    "Task": {"Values": ["Classification"], "Type": "Class"},
     "Device": {"Values": ["GPU"], "Type": "Tag"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
@@ -86,7 +58,7 @@ class PFSDatasetWorkflow:
             zip_path_list.append(os.path.join(curr_root, zip_path))
 
         for idx, zip_path in enumerate(zip_path_list):
-            semantic_spec = semantic_specs[idx % 3]
+            semantic_spec = semantic_specs[0]
             semantic_spec["Name"]["Values"] = "learnware_%d" % (idx)
             semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (idx)
             easy_market.add_learnware(zip_path, semantic_spec)

examples/workflow_by_code/main.py

@@ -18,37 +18,12 @@ curr_root = os.path.dirname(os.path.abspath(__file__))
 semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
-        "Scenario": {"Values": ["Nature"], "Type": "Tag"},
-        "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_1", "Type": "String"},
-    },
-    {
-        "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
-        "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
-        "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_2", "Type": "String"},
-    },
-    {
-        "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {
-            "Values": ["Classification"],
-            "Type": "Class",
-        },
+        "Task": {"Values": ["Classification"], "Type": "Class"},
         "Device": {"Values": ["GPU"], "Type": "Tag"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "learnware_3", "Type": "String"},
-    },
+        "Name": {"Values": "learnware_1", "Type": "String"},
+    }
 ]
 
 user_senmantic = {
@@ -118,7 +93,7 @@ class LearnwareMarketWorkflow:
         print("Total Item:", len(easy_market))
 
         for idx, zip_path in enumerate(self.zip_path_list):
-            semantic_spec = semantic_specs[idx % 3]
+            semantic_spec = semantic_specs[0]
             semantic_spec["Name"]["Values"] = "learnware_%d" % (idx)
             semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (idx)
             easy_market.add_learnware(zip_path, semantic_spec)

learnware/market/easy.py

@@ -333,7 +333,7 @@ class EasyMarket(BaseMarket):
         learnware_list: List[Learnware],
         user_rkme: RKMEStatSpecification,
         max_search_num: int,
-        weight_cutoff: float = 0.95
+        weight_cutoff: float = 0.95,
     ) -> Tuple[List[float], List[Learnware]]:
         """Select learnwares based on a total mixture ratio, then recalculate their mixture weights
 
@@ -372,15 +372,15 @@ class EasyMarket(BaseMarket):
                 mixture_list.append(learnware_list[idx])
             else:
                 break
-        
+
         if len(mixture_list) <= 1:
             mixture_list = [learnware_list[sort_by_weight_idx_list[0]]]
             mixture_weight = [1]
         else:
             if len(mixture_list) > max_search_num:
-                mixture_list = mixture_list[:max_search_num] 
+                mixture_list = mixture_list[:max_search_num]
             mixture_weight, _ = self._calculate_rkme_spec_mixture_weight(mixture_list, user_rkme)
-            
+
         return mixture_weight, mixture_list
 
     def _filter_by_rkme_spec_single(
@@ -618,11 +618,11 @@ class EasyMarket(BaseMarket):
             sorted_score_list, single_learnware_list = self._filter_by_rkme_spec_single(
                 sorted_score_list, single_learnware_list
             )
-            if search_method == 'auto':
+            if search_method == "auto":
                 weight_list, mixture_learnware_list = self._search_by_rkme_spec_mixture_auto(
                     learnware_list, user_rkme, max_search_num
                 )
-            elif search_method == 'greedy':
+            elif search_method == "greedy":
                 weight_list, mixture_learnware_list = self._search_by_rkme_spec_mixture_greedy(
                     learnware_list, user_rkme, max_search_num
                 )