[FIX] merge failures

examples/dataset_table_workflow/homo.py

@@ -105,22 +105,13 @@ class HomogeneousDatasetWorkflow(TableWorkflow):
         )
         
         
-    def labeled_homo_table_example(self, skip_test=False):
+    def labeled_homo_table_example(self, skip_test=True):
         logger.info("Total Item: %d" % (len(self.market)))
         methods = ["user_model", "homo_single_aug", "homo_ensemble_pruning"]
         methods_to_retest = []
         recorders = {method: Recorder() for method in methods}
         user = self.benchmark.name
         
-        if not skip_test:
-            for idx in range(self.benchmark.user_num):
-                test_x, test_y = self.benchmark.get_test_data(user_ids=idx)
-                test_x, test_y = test_x.values, test_y.values
-                
-                train_x, train_y = self.benchmark.get_train_data(user_ids=idx)
-                train_x, train_y = train_x.values, train_y.values
-                train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y)
-        
         if not skip_test:
             for idx in range(self.benchmark.user_num):
                 test_x, test_y = self.benchmark.get_test_data(user_ids=idx)
@@ -130,10 +121,6 @@ class HomogeneousDatasetWorkflow(TableWorkflow):
                 train_x, train_y = train_x.values, train_y.values
                 train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y)
 
-                user_stat_spec = generate_stat_spec(type="table", X=test_x)
-                user_info = BaseUserInfo(
-                    semantic_spec=self.user_semantic, stat_info={"RKMETableSpecification": user_stat_spec}
-                )
                 logger.info(f"Searching Market for user: {user}_{idx}")
                 user_stat_spec = generate_stat_spec(type="table", X=test_x)
                 user_info = BaseUserInfo(
@@ -141,28 +128,15 @@ class HomogeneousDatasetWorkflow(TableWorkflow):
                 )
                 logger.info(f"Searching Market for user: {user}_{idx}")
 
-                search_result = self.market.search_learnware(user_info)
-                single_result = search_result.get_single_results()
-                multiple_result = search_result.get_multiple_results()
                 search_result = self.market.search_learnware(user_info)
                 single_result = search_result.get_single_results()
                 multiple_result = search_result.get_multiple_results()
 
-                logger.info(f"search result of user {user}_{idx}:")
-                logger.info(
-                    f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}"
-                )
                 logger.info(f"search result of user {user}_{idx}:")
                 logger.info(
                     f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}"
                 )
 
-                if len(multiple_result) > 0:
-                    mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares])
-                    logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}")
-                    mixture_learnware_list = multiple_result[0].learnwares
-                else:
-                    mixture_learnware_list = [single_result[0].learnware]
                 if len(multiple_result) > 0:
                     mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares])
                     logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}")
@@ -177,13 +151,6 @@ class HomogeneousDatasetWorkflow(TableWorkflow):
                     "homo_single_aug": {"single_learnware": [single_result[0].learnware]},
                     "homo_ensemble_pruning": common_config
                 }
-                test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y}
-                common_config = {"learnwares": mixture_learnware_list}
-                method_configs = {
-                    "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"},
-                    "homo_single_aug": {"single_learnware": [single_result[0].learnware]},
-                    "homo_ensemble_pruning": common_config
-                }
 
                 for method_name in methods:
                     logger.info(f"Testing method {method_name}")
@@ -194,15 +161,5 @@ class HomogeneousDatasetWorkflow(TableWorkflow):
             
             for method, recorder in recorders.items():
                 recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json"))
-                for method_name in methods:
-                    logger.info(f"Testing method {method_name}")
-                    test_info["method_name"] = method_name
-                    test_info["force"] = method_name in methods_to_retest
-                    test_info.update(method_configs[method_name])
-                    self.test_method(test_info, recorders, loss_func=loss_func_rmse)
-            
-            for method, recorder in recorders.items():
-                recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json"))
-        
-        plot_performance_curves(self.curves_result_path, user, recorders, task="Homo", n_labeled_list=homo_n_labeled_list)
+
         plot_performance_curves(self.curves_result_path, user, recorders, task="Homo", n_labeled_list=homo_n_labeled_list)