Merge branch 'dev' of git.nju.edu.cn:learnware/learnware-market into dev

examples/example_pfs/main.py

@@ -8,7 +8,7 @@ from shutil import copyfile, rmtree
 import learnware
 from learnware.market import EasyMarket, BaseUserInfo
 from learnware.market import database_ops
-from learnware.learnware import Learnware, JobSelectorReuser, EnsembleReuser
+from learnware.learnware import Learnware, JobSelectorReuser, AveragingReuser
 import learnware.specification as specification
 from pfs import Dataloader
 
@@ -163,7 +163,7 @@ class PFSDatasetWorkflow:
             job_selector_score = pfs.score(test_y, job_selector_predict_y)
             print(f"mixture reuse loss (job selector): {job_selector_score}")
 
-            reuse_ensemble = EnsembleReuser(learnware_list=mixture_learnware_list)
+            reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list)
             ensemble_predict_y = reuse_ensemble.predict(user_data=test_x)
             ensemble_score = pfs.score(test_y, ensemble_predict_y)
             print(f"mixture reuse loss (ensemble): {ensemble_score}\n")

examples/workflow_by_code/learnware_example/example_init.py

@@ -6,6 +6,7 @@ from learnware.model import BaseModel
 
 class SVM(BaseModel):
     def __init__(self):
+        super(SVM, self).__init__(input_shape=(20,), output_shape=())
         dir_path = os.path.dirname(os.path.abspath(__file__))
         self.model = joblib.load(os.path.join(dir_path, "svm.pkl"))
 

examples/workflow_by_code/main.py

@@ -161,11 +161,17 @@ class LearnwareMarketWorkflow:
             user_info = BaseUserInfo(
                 id="user_0", semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_spec}
             )
-            sorted_score_list, single_learnware_list, mixture_learnware_list = easy_market.search_learnware(user_info)
+            (
+                sorted_score_list,
+                single_learnware_list,
+                mixture_score,
+                mixture_learnware_list,
+            ) = easy_market.search_learnware(user_info)
 
             print(f"search result of user{idx}:")
             for score, learnware in zip(sorted_score_list, single_learnware_list):
                 print(f"score: {score}, learnware_id: {learnware.id}")
+            print(f"mixture_score: {mixture_score}\n")
             mixture_id = " ".join([learnware.id for learnware in mixture_learnware_list])
             print(f"mixture_learnware: {mixture_id}\n")
 

learnware/learnware/__init__.py

@@ -2,7 +2,7 @@ import os
 import copy
 
 from .base import Learnware, BaseReuser
-from .reuse import JobSelectorReuser, EnsembleReuser
+from .reuse import JobSelectorReuser, AveragingReuser
 
 from .utils import get_stat_spec_from_config, get_model_from_config
 from ..specification import Specification

learnware/learnware/reuse.py

@@ -226,7 +226,7 @@ class JobSelectorReuser(BaseReuser):
         return model
 
 
-class EnsembleReuser(BaseReuser):
+class AveragingReuser(BaseReuser):
     """Baseline Multiple Learnware Reuser uing Ensemble Method"""
 
     def __init__(self, learnware_list: List[Learnware], mode="mean"):
@@ -237,7 +237,7 @@ class EnsembleReuser(BaseReuser):
         learnware_list : List[Learnware]
             The learnware list, which should have RKME Specification for each learnweare
         """
-        super(EnsembleReuser, self).__init__(learnware_list)
+        super(AveragingReuser, self).__init__(learnware_list)
         self.mode = mode
 
     def predict(self, user_data: np.ndarray) -> np.ndarray:

learnware/market/easy.py

@@ -78,12 +78,12 @@ class EasyMarket(BaseMarket):
         try:
             learnware.instantiate_model()
         except Exception as e:
-            logger.warning(f"The learnware [{learnware.id}] is instantiated failed! Due to {repr(e)}")
+            logger.warning(f"The learnware [{learnware.id}] is instantiated failed! Due to {e}")
             return cls.NONUSABLE_LEARNWARE
 
         try:
             learnware_model = learnware.get_model()
-            inputs = np.random.randn((10, *learnware_model.input_shape))
+            inputs = np.random.randn(10, *learnware_model.input_shape)
             outputs = learnware.predict(inputs)
             if outputs.shape[1:] != learnware_model.output_shape:
                 logger.warning(f"The learnware [{learnware.id}] input and output dimention is error")