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

examples/example_market_db/example_db.py

@@ -167,13 +167,13 @@ def test_stat_search():
         user_spec = specification.rkme.RKMEStatSpecification()
         user_spec.load(os.path.join(unzip_dir, "svm.json"))
         user_info = BaseUserInfo(
-            id="user_0", semantic_spec={"desc": "test_user_number_0"}, stat_info={"RKME": user_spec}
+            id="user_0", semantic_spec={"desc": "test_user_number_0"}, stat_info={"RKMEStatSpecification": user_spec}
         )
         sorted_dist_list, single_learnware_list, mixture_learnware_list = easy_market.search_learnware(user_info)
 
         print(f"search result of user{idx}:")
         for dist, learnware in zip(sorted_dist_list, single_learnware_list):
-            print(f"dist: {dist}, learnware_id: {learnware.id}, learnware_name: {learnware.name}")
+            print(f"dist: {dist}, learnware_id: {learnware.id}")
         mixture_id = " ".join([learnware.id for learnware in mixture_learnware_list])
         print(f"mixture_learnware: {mixture_id}\n")
 
@@ -182,7 +182,7 @@ def test_stat_search():
 
 if __name__ == "__main__":
     learnware_num = 5
-    prepare_learnware(learnware_num)
+    # prepare_learnware(learnware_num)
 
     # test_market()
     # test_stat_search()

examples/example_market_db/test_stat/0/__init__.py

@@ -1,19 +0,0 @@
-import os
-import joblib
-import numpy as np
-from learnware.model import BaseModel
-
-
-class SVM(BaseModel):
-    def __init__(self):
-        dir_path = os.path.dirname(os.path.abspath(__file__))
-        self.model = joblib.load(os.path.join(dir_path, "svm.pkl"))
-
-    def fit(self, X: np.ndarray, y: np.ndarray):
-        pass
-
-    def predict(self, X: np.ndarray) -> np.ndarray:
-        return self.model.predict(X)
-
-    def fintune(self, X: np.ndarray, y: np.ndarray):
-        pass

examples/example_market_db/test_stat/0/learnware.yaml

@@ -1,8 +0,0 @@
-model:
-  class_name: SVM
-  kwargs: {}
-stat_specifications:
-  - module_path: learnware.specification
-    class_name: RKMEStatSpecification
-    file_name: svm.json
-    kwargs: {}  

learnware/learnware/__init__.py

@@ -51,7 +51,7 @@ def get_learnware_from_dirpath(id: str, semantic_spec: dict, learnware_dirpath:
         learnware_config["name"] = yaml_config["name"]
     if "model" in yaml_config:
         learnware_config["model"].update(yaml_config["model"])
-    if "stats_specifications" in yaml_config:
+    if "stat_specifications" in yaml_config:
         learnware_config["stat_specifications"] = yaml_config["stat_specifications"].copy()
 
     if "module_path" not in learnware_config["model"]:
@@ -60,6 +60,7 @@ def get_learnware_from_dirpath(id: str, semantic_spec: dict, learnware_dirpath:
     try:
         learnware_spec = Specification()
         for _stat_spec in learnware_config["stat_specifications"]:
+            _stat_spec["file_name"] = os.path.join(learnware_dirpath, _stat_spec["file_name"])
             stat_spac_name, stat_spec_inst = get_stat_spec_from_config(_stat_spec)
             learnware_spec.update_stat_spec(**{stat_spac_name: stat_spec_inst})
 

learnware/learnware/utils.py

@@ -44,7 +44,7 @@ def get_model_from_config(model: Union[BaseModel, dict]) -> BaseModel:
 def get_stat_spec_from_config(stat_spec: dict) -> BaseStatSpecification:
     stat_spec_module = get_module_by_module_path(stat_spec["module_path"])
     stat_spec_inst = getattr(stat_spec_module, stat_spec["class_name"])(**stat_spec["kwargs"])
-
+    
     if not isinstance(stat_spec_inst, BaseStatSpecification):
         raise TypeError(
             f"Statistic specification must be type of BaseStatSpecification, not {BaseStatSpecification.__class__.__name__}"

learnware/market/easy.py

@@ -44,7 +44,7 @@ class EasyMarket(BaseMarket):
             A flag indicating whether the learnware can be accepted.
         """
         try:
-            spec_data = learnware.specification.stat_spec["RKME"].get_z()
+            spec_data = learnware.specification.stat_spec["RKMEStatSpecification"].get_z()
             pred_spec = learnware.predict(spec_data)
         except Exception:
             logger.warning(f"The learnware [{learnware.id}-{learnware.name}] is not avaliable!")
@@ -88,7 +88,7 @@ class EasyMarket(BaseMarket):
         """
         rkme_stat_spec = RKMEStatSpecification()
         rkme_stat_spec.load(stat_spec_path)
-        stat_spec = {"RKME": rkme_stat_spec}
+        stat_spec = {"RKMEStatSpecification": rkme_stat_spec}
         specification = Specification(semantic_spec=semantic_spec, stat_spec=stat_spec)
         """
 
@@ -154,7 +154,7 @@ class EasyMarket(BaseMarket):
             The second is the mmd dist between the mixture of learnware rkmes and the user's rkme
         """
         learnware_num = len(learnware_list)
-        RKME_list = [learnware.specification.get_stat_spec_by_name("RKME") for learnware in learnware_list]
+        RKME_list = [learnware.specification.get_stat_spec_by_name("RKMEStatSpecification") for learnware in learnware_list]
 
         if type(intermediate_K) == np.ndarray:
             K = intermediate_K
@@ -213,7 +213,7 @@ class EasyMarket(BaseMarket):
             The second is the intermediate value of C
         """
         num = intermediate_K.shape[0] - 1
-        RKME_list = [learnware.specification.get_stat_spec_by_name("RKME") for learnware in learnware_list]
+        RKME_list = [learnware.specification.get_stat_spec_by_name("RKMEStatSpecification") for learnware in learnware_list]
         for i in range(intermediate_K.shape[0]):
             intermediate_K[num, i] = RKME_list[-1].inner_prod(RKME_list[i])
         intermediate_C[num, 0] = user_rkme.inner_prod(RKME_list[-1])
@@ -297,7 +297,7 @@ class EasyMarket(BaseMarket):
             the second is the list of Learnware
             both lists are sorted by mmd dist
         """
-        RKME_list = [learnware.specification.get_stat_spec_by_name("RKME") for learnware in learnware_list]
+        RKME_list = [learnware.specification.get_stat_spec_by_name("RKMEStatSpecification") for learnware in learnware_list]
         mmd_dist_list = []
         for RKME in RKME_list:
             mmd_dist = RKME.dist(user_rkme)
@@ -369,10 +369,10 @@ class EasyMarket(BaseMarket):
         learnware_list_description = self._search_by_semantic_description(learnware_list, user_info)
         learnware_list = list(set(learnware_list_tags + learnware_list_description))
 
-        if "RKME" not in user_info.stat_info:
+        if "RKMEStatSpecification" not in user_info.stat_info:
             return None, learnware_list, None
         else:
-            user_rkme = user_info.stat_info["RKME"]
+            user_rkme = user_info.stat_info["RKMEStatSpecification"]
             sorted_dist_list, single_learnware_list = self._search_by_rkme_spec_single(learnware_list, user_rkme)
             weight_list, mixture_learnware_list = self._search_by_rkme_spec_mixture(
                 learnware_list, user_rkme, search_num