[MNT] rename table and image rkme

README.md

@@ -80,7 +80,7 @@ is composed of the following four parts.
 
 - ``learnware.yaml``
     
-    A config file describing your model class name, type of statistical specification(e.g. Reduced Kernel Mean Embedding, ``RKMEStatSpecification``), and 
+    A config file describing your model class name, type of statistical specification(e.g. Reduced Kernel Mean Embedding, ``RKMETableSpecification``), and 
     the file name of your statistical specification file.
 
 - ``environment.yaml``
@@ -178,10 +178,10 @@ For example, the following code is designed to work with Reduced Set Kernel Embe
 ```python
 import learnware.specification as specification
 
-user_spec = specification.RKMEStatSpecification()
+user_spec = specification.RKMETableSpecification()
 user_spec.load(os.path.join(unzip_path, "rkme.json"))
 user_info = BaseUserInfo(
-    semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
+    semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_spec}
 )
 (sorted_score_list, single_learnware_list,
     mixture_score, mixture_learnware_list) = easy_market.search_learnware(user_info)

docs/references/api.rst

@@ -50,7 +50,7 @@ Specification
 .. autoclass:: learnware.specification.BaseStatSpecification
     :members:
 
-.. autoclass:: learnware.specification.RKMEStatSpecification
+.. autoclass:: learnware.specification.RKMETableSpecification
     :members:
 
 Model

docs/start/client.rst

@@ -117,13 +117,13 @@ You can search learnwares in official market using semantic specification. All t
 Statistical Specification Search
 ---------------------------------
 
-You can search learnware by providing a statistical specification. The statistical specification is a json file that contains the statistical information of your training data. For example, the code below searches learnwares with `RKMEStatSpecification`:
+You can search learnware by providing a statistical specification. The statistical specification is a json file that contains the statistical information of your training data. For example, the code below searches learnwares with `RKMETableSpecification`:
 
 .. code-block:: python
 
     import learnware.specification as specification
 
-    user_spec = specification.RKMEStatSpecification()
+    user_spec = specification.RKMETableSpecification()
     user_spec.load(os.path.join(unzip_path, "rkme.json"))
     
     specification = learnware.specification.Specification()
@@ -138,7 +138,7 @@ You can search learnware by providing a statistical specification. The statistic
 
 Combine Semantic and Statistical Search
 ----------------------------------------
-You can provide both semantic and statistical specification to search learnwares. The engine will first filter learnwares by semantic specification and then search by statistical specification. For example, the code below searches learnwares with `Table` data type and `RKMEStatSpecification`:
+You can provide both semantic and statistical specification to search learnwares. The engine will first filter learnwares by semantic specification and then search by statistical specification. For example, the code below searches learnwares with `Table` data type and `RKMETableSpecification`:
 
 .. code-block:: python
 
@@ -151,7 +151,7 @@ You can provide both semantic and statistical specification to search learnwares
         senarioes=[],
         input_description={}, output_description={})
 
-    stat_spec = specification.RKMEStatSpecification()
+    stat_spec = specification.RKMETableSpecification()
     stat_spec.load(os.path.join(unzip_path, "rkme.json"))
     specification = learnware.specification.Specification()
     specification.update_semantic_spec(semantic_spec)

docs/start/quick.rst

@@ -47,7 +47,7 @@ includes the following four components:
 
 - ``learnware.yaml``
     
-    A configuration file that details your model's class name, the type of statistical specification(e.g. ``RKMEStatSpecification`` for Reduced Kernel Mean Embedding), and 
+    A configuration file that details your model's class name, the type of statistical specification(e.g. ``RKMETableSpecification`` for Reduced Kernel Mean Embedding), and 
     the file name of your statistical specification file.
 
 - ``environment.yaml`` or ``requirements.txt``
@@ -170,12 +170,12 @@ For example, the code below executes learnware search when using Reduced Set Ker
 
     import learnware.specification as specification
 
-    user_spec = specification.RKMEStatSpecification()
+    user_spec = specification.RKMETableSpecification()
 
     # unzip_path: directory for unzipped learnware zipfile
     user_spec.load(os.path.join(unzip_path, "rkme.json"))
     user_info = BaseUserInfo(
-        semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
+        semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_spec}
     )
     (sorted_score_list, single_learnware_list,
         mixture_score, mixture_learnware_list) = easy_market.search_learnware(user_info)

docs/workflow/identify.rst

@@ -73,10 +73,10 @@ For example, the following code is designed to work with Reduced Kernel Mean Emb
 
     import learnware.specification as specification
 
-    user_spec = specification.RKMEStatSpecification()
+    user_spec = specification.RKMETableSpecification()
     user_spec.load(os.path.join("rkme.json"))
     user_info = BaseUserInfo(
-        semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
+        semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_spec}
     )
     (sorted_score_list, single_learnware_list,
         mixture_score, mixture_learnware_list) = easy_market.search_learnware(user_info)

docs/workflow/submit.rst

@@ -94,7 +94,7 @@ guaranteeing the security and privacy of your local original data.
 ------------------
 
 Additionally, you are asked to prepare a configuration file in YAML format.
-The file should detail your model's class name, the type of statistical specification(e.g. Reduced Kernel Mean Embedding, ``RKMEStatSpecification``), and 
+The file should detail your model's class name, the type of statistical specification(e.g. Reduced Kernel Mean Embedding, ``RKMETableSpecification``), and 
 the file name of your statistical specification file. The following ``learnware.yaml`` provides an example of
 how your learnware configuration file should be structured, based on our previous discussion:
 
@@ -105,7 +105,7 @@ how your learnware configuration file should be structured, based on our previou
       kwargs: {}
     stat_specifications:
       - module_path: learnware.specification
-        class_name: RKMEStatSpecification
+        class_name: RKMETableSpecification
         file_name: stat.json
         kwargs: {}  
 

examples/dataset_image_workflow/example_files/example_yaml.yaml

@@ -3,6 +3,6 @@ model:
   kwargs: {}
 stat_specifications:
   - module_path: learnware.specification
-    class_name: RKMEImageStatSpecification
+    class_name: RKMEImageSpecification
     file_name: rkme.json
     kwargs: {}  

examples/dataset_image_workflow/main.py

@@ -6,7 +6,7 @@ from get_data import *
 import os
 import random
 
-from learnware.specification.image import RKMEImageStatSpecification
+from learnware.specification.image import RKMEImageSpecification
 from learnware.reuse.averaging import AveragingReuser
 from utils import generate_uploader, generate_user, ImageDataLoader, train, eval_prediction
 from learnware.learnware import Learnware
@@ -100,7 +100,7 @@ def prepare_learnware(data_path, model_path, init_file_path, yaml_path, save_roo
     X_sampled = X[indices]
 
     st = time.time()
-    user_spec = RKMEImageStatSpecification(cuda_idx=0)
+    user_spec = RKMEImageSpecification(cuda_idx=0)
     user_spec.generate_stat_spec_from_data(X=X_sampled)
     ed = time.time()
     logger.info("Stat spec generated in %.3f s" % (ed - st))
@@ -164,9 +164,9 @@ def test_search(gamma=0.1, load_market=True):
         user_label_path = os.path.join(user_save_root, "user_%d_y.npy" % (i))
         user_data = np.load(user_data_path)
         user_label = np.load(user_label_path)
-        user_stat_spec = RKMEImageStatSpecification(cuda_idx=0)
+        user_stat_spec = RKMEImageSpecification(cuda_idx=0)
         user_stat_spec.generate_stat_spec_from_data(X=user_data, resize=False)
-        user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_stat_spec})
+        user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_stat_spec})
         logger.info("Searching Market for user: %d" % i)
         sorted_score_list, single_learnware_list, mixture_score, mixture_learnware_list = image_market.search_learnware(
             user_info

examples/dataset_m5_workflow/example.yaml

@@ -3,6 +3,6 @@ model:
   kwargs: {}
 stat_specifications:
   - module_path: learnware.specification
-    class_name: RKMEStatSpecification
+    class_name: RKMETableSpecification
     file_name: rkme.json
     kwargs: {}  

examples/dataset_m5_workflow/main.py

@@ -144,7 +144,7 @@ class M5DatasetWorkflow:
             user_spec_path = f"./user_spec/user_{idx}.json"
             user_spec.save(user_spec_path)
 
-            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec})
+            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_spec})
             (
                 sorted_score_list,
                 single_learnware_list,

examples/dataset_pfs_workflow/example.yaml

@@ -3,6 +3,6 @@ model:
   kwargs: {}
 stat_specifications:
   - module_path: learnware.specification
-    class_name: RKMEStatSpecification
+    class_name: RKMETableSpecification
     file_name: rkme.json
     kwargs: {}  

examples/dataset_pfs_workflow/main.py

@@ -142,7 +142,7 @@ class PFSDatasetWorkflow:
             user_spec_path = f"./user_spec/user_{idx}.json"
             user_spec.save(user_spec_path)
 
-            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec})
+            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_spec})
             (
                 sorted_score_list,
                 single_learnware_list,

examples/workflow_by_code/learnware_example/example.yaml

@@ -3,6 +3,6 @@ model:
   kwargs: {}
 stat_specifications:
   - module_path: learnware.specification
-    class_name: RKMEStatSpecification
+    class_name: RKMETableSpecification
     file_name: svm.json
     kwargs: {}  

examples/workflow_by_code/main.py

@@ -148,9 +148,9 @@ class LearnwareMarketWorkflow:
             with zipfile.ZipFile(zip_path, "r") as zip_obj:
                 zip_obj.extractall(path=unzip_dir)
 
-            user_spec = specification.RKMEStatSpecification()
+            user_spec = specification.RKMETableSpecification()
             user_spec.load(os.path.join(unzip_dir, "svm.json"))
-            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec})
+            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_spec})
             (
                 sorted_score_list,
                 single_learnware_list,
@@ -175,7 +175,7 @@ class LearnwareMarketWorkflow:
         _, data_X, _, data_y = train_test_split(X, y, test_size=0.3, shuffle=True)
 
         stat_spec = specification.utils.generate_rkme_spec(X=data_X, gamma=0.1, cuda_idx=0)
-        user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": stat_spec})
+        user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": stat_spec})
 
         _, _, _, mixture_learnware_list = easy_market.search_learnware(user_info)
 

learnware/learnware/__init__.py

@@ -37,7 +37,7 @@ def get_learnware_from_dirpath(id: str, semantic_spec: dict, learnware_dirpath:
         "stat_specifications": [
             {
                 "module_path": "learnware.specification",
-                "class_name": "RKMEStatSpecification",
+                "class_name": "RKMETableSpecification",
                 "file_name": "stat_spec.json",
                 "kwargs": {},
             },

learnware/market/easy.py

@@ -18,7 +18,7 @@ from .. import utils
 from ..config import C as conf
 from ..logger import get_module_logger
 from ..learnware import Learnware, get_learnware_from_dirpath
-from ..specification import RKMEStatSpecification, Specification
+from ..specification import RKMETableSpecification, Specification
 
 
 logger = get_module_logger("market", "INFO")
@@ -116,7 +116,7 @@ class EasyMarket(LearnwareMarket):
                 pass
 
             # check rkme dimension
-            stat_spec = learnware.get_specification().get_stat_spec_by_name("RKMEStatSpecification")
+            stat_spec = learnware.get_specification().get_stat_spec_by_name("RKMETableSpecification")
             if stat_spec is not None:
                 if stat_spec.get_z().shape[1:] != input_shape:
                     logger.warning(f"The learnware [{learnware.id}] input dimension mismatch with stat specification")
@@ -296,7 +296,7 @@ class EasyMarket(LearnwareMarket):
     def _calculate_rkme_spec_mixture_weight(
         self,
         learnware_list: List[Learnware],
-        user_rkme: RKMEStatSpecification,
+        user_rkme: RKMETableSpecification,
         intermediate_K: np.ndarray = None,
         intermediate_C: np.ndarray = None,
     ) -> Tuple[List[float], float]:
@@ -306,7 +306,7 @@ class EasyMarket(LearnwareMarket):
         ----------
         learnware_list : List[Learnware]
             A list of existing learnwares
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             User RKME statistical specification
         intermediate_K : np.ndarray, optional
             Intermediate kernel matrix K, by default None
@@ -321,7 +321,7 @@ class EasyMarket(LearnwareMarket):
         """
         learnware_num = len(learnware_list)
         RKME_list = [
-            learnware.specification.get_stat_spec_by_name("RKMEStatSpecification") for learnware in learnware_list
+            learnware.specification.get_stat_spec_by_name("RKMETableSpecification") for learnware in learnware_list
         ]
 
         if type(intermediate_K) == np.ndarray:
@@ -365,7 +365,7 @@ class EasyMarket(LearnwareMarket):
     def _calculate_intermediate_K_and_C(
         self,
         learnware_list: List[Learnware],
-        user_rkme: RKMEStatSpecification,
+        user_rkme: RKMETableSpecification,
         intermediate_K: np.ndarray = None,
         intermediate_C: np.ndarray = None,
     ) -> Tuple[np.ndarray, np.ndarray]:
@@ -375,7 +375,7 @@ class EasyMarket(LearnwareMarket):
         ----------
         learnware_list : List[Learnware]
             The list of learnwares up till now
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             User RKME statistical specification
         intermediate_K : np.ndarray, optional
             Intermediate kernel matrix K, by default None
@@ -390,7 +390,7 @@ class EasyMarket(LearnwareMarket):
         """
         num = intermediate_K.shape[0] - 1
         RKME_list = [
-            learnware.specification.get_stat_spec_by_name("RKMEStatSpecification") for learnware in learnware_list
+            learnware.specification.get_stat_spec_by_name("RKMETableSpecification") 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])
@@ -400,7 +400,7 @@ class EasyMarket(LearnwareMarket):
     def _search_by_rkme_spec_mixture_auto(
         self,
         learnware_list: List[Learnware],
-        user_rkme: RKMEStatSpecification,
+        user_rkme: RKMETableSpecification,
         max_search_num: int,
         weight_cutoff: float = 0.98,
     ) -> Tuple[float, List[float], List[Learnware]]:
@@ -410,7 +410,7 @@ class EasyMarket(LearnwareMarket):
         ----------
         learnware_list : List[Learnware]
             The list of learnwares whose mixture approximates the user's rkme
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             User RKME statistical specification
         max_search_num : int
             The maximum number of the returned learnwares
@@ -446,7 +446,7 @@ class EasyMarket(LearnwareMarket):
         if len(mixture_list) <= 1:
             mixture_list = [learnware_list[sort_by_weight_idx_list[0]]]
             mixture_weight = [1]
-            mmd_dist = user_rkme.dist(mixture_list[0].specification.get_stat_spec_by_name("RKMEStatSpecification"))
+            mmd_dist = user_rkme.dist(mixture_list[0].specification.get_stat_spec_by_name("RKMETableSpecification"))
         else:
             if len(mixture_list) > max_search_num:
                 mixture_list = mixture_list[:max_search_num]
@@ -488,7 +488,7 @@ class EasyMarket(LearnwareMarket):
         return sorted_score_list[:idx], learnware_list[:idx]
 
     def _filter_by_rkme_spec_dimension(
-        self, learnware_list: List[Learnware], user_rkme: RKMEStatSpecification
+        self, learnware_list: List[Learnware], user_rkme: RKMETableSpecification
     ) -> List[Learnware]:
         """Filter learnwares whose rkme dimension different from user_rkme
 
@@ -496,7 +496,7 @@ class EasyMarket(LearnwareMarket):
         ----------
         learnware_list : List[Learnware]
             The list of learnwares whose mixture approximates the user's rkme
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             User RKME statistical specification
 
         Returns
@@ -508,7 +508,7 @@ class EasyMarket(LearnwareMarket):
         user_rkme_dim = str(list(user_rkme.get_z().shape)[1:])
 
         for learnware in learnware_list:
-            rkme = learnware.specification.get_stat_spec_by_name("RKMEStatSpecification")
+            rkme = learnware.specification.get_stat_spec_by_name("RKMETableSpecification")
             rkme_dim = str(list(rkme.get_z().shape)[1:])
             if rkme_dim == user_rkme_dim:
                 filtered_learnware_list.append(learnware)
@@ -518,7 +518,7 @@ class EasyMarket(LearnwareMarket):
     def _search_by_rkme_spec_mixture_greedy(
         self,
         learnware_list: List[Learnware],
-        user_rkme: RKMEStatSpecification,
+        user_rkme: RKMETableSpecification,
         max_search_num: int,
         score_cutoff: float = 0.001,
     ) -> Tuple[float, List[float], List[Learnware]]:
@@ -528,7 +528,7 @@ class EasyMarket(LearnwareMarket):
         ----------
         learnware_list : List[Learnware]
             The list of learnwares whose mixture approximates the user's rkme
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             User RKME statistical specification
         max_search_num : int
             The maximum number of the returned learnwares
@@ -588,7 +588,7 @@ class EasyMarket(LearnwareMarket):
         return mmd_dist, weight_min, mixture_list
 
     def _search_by_rkme_spec_single(
-        self, learnware_list: List[Learnware], user_rkme: RKMEStatSpecification
+        self, learnware_list: List[Learnware], user_rkme: RKMETableSpecification
     ) -> Tuple[List[float], List[Learnware]]:
         """Calculate the distances between learnwares in the given learnware_list and user_rkme
 
@@ -596,7 +596,7 @@ class EasyMarket(LearnwareMarket):
         ----------
         learnware_list : List[Learnware]
             The list of learnwares whose mixture approximates the user's rkme
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             user RKME statistical specification
 
         Returns
@@ -607,7 +607,7 @@ class EasyMarket(LearnwareMarket):
             both lists are sorted by mmd dist
         """
         RKME_list = [
-            learnware.specification.get_stat_spec_by_name("RKMEStatSpecification") for learnware in learnware_list
+            learnware.specification.get_stat_spec_by_name("RKMETableSpecification") for learnware in learnware_list
         ]
         mmd_dist_list = []
         for RKME in RKME_list:
@@ -819,12 +819,12 @@ class EasyMarket(LearnwareMarket):
         # if len(learnware_list) == 0:
         learnware_list = self._search_by_semantic_spec_fuzz(learnware_list, user_info)
 
-        if "RKMEStatSpecification" not in user_info.stat_info:
+        if "RKMETableSpecification" not in user_info.stat_info:
             return None, learnware_list, 0.0, None
         elif len(learnware_list) == 0:
             return [], [], 0.0, []
         else:
-            user_rkme = user_info.stat_info["RKMEStatSpecification"]
+            user_rkme = user_info.stat_info["RKMETableSpecification"]
             learnware_list = self._filter_by_rkme_spec_dimension(learnware_list, user_rkme)
             logger.info(f"After filter by rkme dimension, learnware_list length is {len(learnware_list)}")
 

learnware/market/easy2/checker.py

@@ -77,7 +77,7 @@ class EasyStatisticalChecker(BaseChecker):
                 input_shape = learnware_model.input_shape
 
             # Check rkme dimension
-            stat_spec = learnware.get_specification().get_stat_spec_by_name("RKMEStatSpecification")
+            stat_spec = learnware.get_specification().get_stat_spec_by_name("RKMETableSpecification")
             if stat_spec is not None:
                 if stat_spec.get_z().shape[1:] != input_shape:
                     logger.warning(f"The learnware [{learnware.id}] input dimension mismatch with stat specification.")

learnware/market/easy2/searcher.py

@@ -7,7 +7,7 @@ from typing import Tuple, List
 from .organizer import EasyOrganizer
 from ..base import BaseUserInfo, BaseSearcher
 from ...learnware import Learnware
-from ...specification import RKMEStatSpecification
+from ...specification import RKMETableSpecification
 from ...logger import get_module_logger
 
 logger = get_module_logger("easy_seacher")
@@ -227,7 +227,7 @@ class EasyTableSearcher(BaseSearcher):
     def _calculate_rkme_spec_mixture_weight(
         self,
         learnware_list: List[Learnware],
-        user_rkme: RKMEStatSpecification,
+        user_rkme: RKMETableSpecification,
         intermediate_K: np.ndarray = None,
         intermediate_C: np.ndarray = None,
     ) -> Tuple[List[float], float]:
@@ -237,7 +237,7 @@ class EasyTableSearcher(BaseSearcher):
         ----------
         learnware_list : List[Learnware]
             A list of existing learnwares
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             User RKME statistical specification
         intermediate_K : np.ndarray, optional
             Intermediate kernel matrix K, by default None
@@ -252,7 +252,7 @@ class EasyTableSearcher(BaseSearcher):
         """
         learnware_num = len(learnware_list)
         RKME_list = [
-            learnware.specification.get_stat_spec_by_name("RKMEStatSpecification") for learnware in learnware_list
+            learnware.specification.get_stat_spec_by_name("RKMETableSpecification") for learnware in learnware_list
         ]
 
         if type(intermediate_K) == np.ndarray:
@@ -296,7 +296,7 @@ class EasyTableSearcher(BaseSearcher):
     def _calculate_intermediate_K_and_C(
         self,
         learnware_list: List[Learnware],
-        user_rkme: RKMEStatSpecification,
+        user_rkme: RKMETableSpecification,
         intermediate_K: np.ndarray = None,
         intermediate_C: np.ndarray = None,
     ) -> Tuple[np.ndarray, np.ndarray]:
@@ -306,7 +306,7 @@ class EasyTableSearcher(BaseSearcher):
         ----------
         learnware_list : List[Learnware]
             The list of learnwares up till now
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             User RKME statistical specification
         intermediate_K : np.ndarray, optional
             Intermediate kernel matrix K, by default None
@@ -321,7 +321,7 @@ class EasyTableSearcher(BaseSearcher):
         """
         num = intermediate_K.shape[0] - 1
         RKME_list = [
-            learnware.specification.get_stat_spec_by_name("RKMEStatSpecification") for learnware in learnware_list
+            learnware.specification.get_stat_spec_by_name("RKMETableSpecification") 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])
@@ -331,7 +331,7 @@ class EasyTableSearcher(BaseSearcher):
     def _search_by_rkme_spec_mixture_auto(
         self,
         learnware_list: List[Learnware],
-        user_rkme: RKMEStatSpecification,
+        user_rkme: RKMETableSpecification,
         max_search_num: int,
         weight_cutoff: float = 0.98,
     ) -> Tuple[float, List[float], List[Learnware]]:
@@ -341,7 +341,7 @@ class EasyTableSearcher(BaseSearcher):
         ----------
         learnware_list : List[Learnware]
             The list of learnwares whose mixture approximates the user's rkme
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             User RKME statistical specification
         max_search_num : int
             The maximum number of the returned learnwares
@@ -377,7 +377,7 @@ class EasyTableSearcher(BaseSearcher):
         if len(mixture_list) <= 1:
             mixture_list = [learnware_list[sort_by_weight_idx_list[0]]]
             mixture_weight = [1]
-            mmd_dist = user_rkme.dist(mixture_list[0].specification.get_stat_spec_by_name("RKMEStatSpecification"))
+            mmd_dist = user_rkme.dist(mixture_list[0].specification.get_stat_spec_by_name("RKMETableSpecification"))
         else:
             if len(mixture_list) > max_search_num:
                 mixture_list = mixture_list[:max_search_num]
@@ -419,7 +419,7 @@ class EasyTableSearcher(BaseSearcher):
         return sorted_score_list[:idx], learnware_list[:idx]
 
     def _filter_by_rkme_spec_dimension(
-        self, learnware_list: List[Learnware], user_rkme: RKMEStatSpecification
+        self, learnware_list: List[Learnware], user_rkme: RKMETableSpecification
     ) -> List[Learnware]:
         """Filter learnwares whose rkme dimension different from user_rkme
 
@@ -427,7 +427,7 @@ class EasyTableSearcher(BaseSearcher):
         ----------
         learnware_list : List[Learnware]
             The list of learnwares whose mixture approximates the user's rkme
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             User RKME statistical specification
 
         Returns
@@ -439,7 +439,7 @@ class EasyTableSearcher(BaseSearcher):
         user_rkme_dim = str(list(user_rkme.get_z().shape)[1:])
 
         for learnware in learnware_list:
-            rkme = learnware.specification.get_stat_spec_by_name("RKMEStatSpecification")
+            rkme = learnware.specification.get_stat_spec_by_name("RKMETableSpecification")
             rkme_dim = str(list(rkme.get_z().shape)[1:])
             if rkme_dim == user_rkme_dim:
                 filtered_learnware_list.append(learnware)
@@ -449,7 +449,7 @@ class EasyTableSearcher(BaseSearcher):
     def _search_by_rkme_spec_mixture_greedy(
         self,
         learnware_list: List[Learnware],
-        user_rkme: RKMEStatSpecification,
+        user_rkme: RKMETableSpecification,
         max_search_num: int,
         score_cutoff: float = 0.001,
     ) -> Tuple[float, List[float], List[Learnware]]:
@@ -459,7 +459,7 @@ class EasyTableSearcher(BaseSearcher):
         ----------
         learnware_list : List[Learnware]
             The list of learnwares whose mixture approximates the user's rkme
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             User RKME statistical specification
         max_search_num : int
             The maximum number of the returned learnwares
@@ -519,7 +519,7 @@ class EasyTableSearcher(BaseSearcher):
         return mmd_dist, weight_min, mixture_list
 
     def _search_by_rkme_spec_single(
-        self, learnware_list: List[Learnware], user_rkme: RKMEStatSpecification
+        self, learnware_list: List[Learnware], user_rkme: RKMETableSpecification
     ) -> Tuple[List[float], List[Learnware]]:
         """Calculate the distances between learnwares in the given learnware_list and user_rkme
 
@@ -527,7 +527,7 @@ class EasyTableSearcher(BaseSearcher):
         ----------
         learnware_list : List[Learnware]
             The list of learnwares whose mixture approximates the user's rkme
-        user_rkme : RKMEStatSpecification
+        user_rkme : RKMETableSpecification
             user RKME statistical specification
 
         Returns
@@ -538,7 +538,7 @@ class EasyTableSearcher(BaseSearcher):
             both lists are sorted by mmd dist
         """
         RKME_list = [
-            learnware.specification.get_stat_spec_by_name("RKMEStatSpecification") for learnware in learnware_list
+            learnware.specification.get_stat_spec_by_name("RKMETableSpecification") for learnware in learnware_list
         ]
         mmd_dist_list = []
         for RKME in RKME_list:
@@ -558,7 +558,7 @@ class EasyTableSearcher(BaseSearcher):
         max_search_num: int = 5,
         search_method: str = "greedy",
     ) -> Tuple[List[float], List[Learnware], float, List[Learnware]]:
-        user_rkme = user_info.stat_info["RKMEStatSpecification"]
+        user_rkme = user_info.stat_info["RKMETableSpecification"]
         learnware_list = self._filter_by_rkme_spec_dimension(learnware_list, user_rkme)
         logger.info(f"After filter by rkme dimension, learnware_list length is {len(learnware_list)}")
 
@@ -631,7 +631,7 @@ class EasySearcher(BaseSearcher):
 
         if len(learnware_list) == 0:
             return [], [], 0.0, []
-        elif "RKMEStatSpecification" in user_info.stat_info:
+        elif "RKMETableSpecification" in user_info.stat_info:
             return self.table_searcher(learnware_list, user_info, max_search_num, search_method)
         else:
             return None, learnware_list, 0.0, None

learnware/reuse/job_selector.py

@@ -9,7 +9,7 @@ from sklearn.metrics import accuracy_score
 from learnware.learnware import Learnware
 import learnware.specification as specification
 from .base import BaseReuser
-from ..specification import RKMEStatSpecification
+from ..specification import RKMETableSpecification
 from ..logger import get_module_logger
 
 logger = get_module_logger("job_selector_reuse")
@@ -86,7 +86,7 @@ class JobSelectorReuser(BaseReuser):
             return np.array([0] * user_data_num)
         else:
             learnware_rkme_spec_list = [
-                learnware.specification.get_stat_spec_by_name("RKMEStatSpecification")
+                learnware.specification.get_stat_spec_by_name("RKMETableSpecification")
                 for learnware in self.learnware_list
             ]
 
@@ -154,7 +154,7 @@ class JobSelectorReuser(BaseReuser):
             return job_select_result
 
     def _calculate_rkme_spec_mixture_weight(
-        self, user_data: np.ndarray, task_rkme_list: List[RKMEStatSpecification], task_rkme_matrix: np.ndarray
+        self, user_data: np.ndarray, task_rkme_list: List[RKMETableSpecification], task_rkme_matrix: np.ndarray
     ) -> List[float]:
         """_summary_
 
@@ -162,7 +162,7 @@ class JobSelectorReuser(BaseReuser):
         ----------
         user_data : np.ndarray
             Raw user data.
-        task_rkme_list : List[RKMEStatSpecification]
+        task_rkme_list : List[RKMETableSpecification]
             The list of learwares' rkmes whose mixture approximates the user's rkme
         task_rkme_matrix : np.ndarray
             Inner product matrix calculated from task_rkme_list.

learnware/specification/__init__.py

@@ -1,3 +1,3 @@
 from .utils import generate_stat_spec, generate_rkme_spec, generate_rkme_image_spec
 from .base import Specification, BaseStatSpecification
-from .regular import RegularStatsSpecification, RKMEStatSpecification, RKMEImageStatSpecification
+from .regular import RegularStatsSpecification, RKMETableSpecification, RKMEImageSpecification

learnware/specification/regular/__init__.py

@@ -1,3 +1,3 @@
-from .table import RKMEStatSpecification
-from .image import RKMEImageStatSpecification
+from .table import RKMETableSpecification, RKMEStatSpecification
+from .image import RKMEImageSpecification
 from .base import RegularStatsSpecification

learnware/specification/regular/image/__init__.py

@@ -1 +1 @@
-from .rkme import RKMEImageStatSpecification
+from .rkme import RKMEImageSpecification

learnware/specification/regular/image/rkme.py

@@ -21,7 +21,7 @@ from ..base import BaseStatSpecification
 from ..table.rkme import solve_qp, choose_device, setup_seed
 
 
-class RKMEImageStatSpecification(BaseStatSpecification):
+class RKMEImageSpecification(BaseStatSpecification):
     # INNER_PRODUCT_COUNT = 0
     IMAGE_WIDTH = 32
 
@@ -49,7 +49,7 @@ class RKMEImageStatSpecification(BaseStatSpecification):
         )
 
         setup_seed(0)
-        super(RKMEImageStatSpecification, self).__init__(type=self.__class__.__name__)
+        super(RKMEImageSpecification, self).__init__(type=self.__class__.__name__)
 
     def _generate_models(self, n_models: int, channel: int = 3, fixed_seed=None):
         model_class = functools.partial(_ConvNet_wide, channel=channel, **self.model_config)
@@ -98,12 +98,12 @@ class RKMEImageStatSpecification(BaseStatSpecification):
 
         """
         if (
-            X.shape[2] != RKMEImageStatSpecification.IMAGE_WIDTH or X.shape[3] != RKMEImageStatSpecification.IMAGE_WIDTH
+            X.shape[2] != RKMEImageSpecification.IMAGE_WIDTH or X.shape[3] != RKMEImageSpecification.IMAGE_WIDTH
         ) and not resize:
             raise ValueError(
                 "X should be in shape of [N, C, {0:d}, {0:d}]. "
                 "Or set resize=True and the image will be automatically resized to {0:d} x {0:d}.".format(
-                    RKMEImageStatSpecification.IMAGE_WIDTH
+                    RKMEImageSpecification.IMAGE_WIDTH
                 )
             )
 
@@ -121,9 +121,9 @@ class RKMEImageStatSpecification(BaseStatSpecification):
                     img_mean = torch.nanmean(img)
                     X[i] = torch.where(is_nan, img_mean, img)
 
-        if X.shape[2] != RKMEImageStatSpecification.IMAGE_WIDTH or X.shape[3] != RKMEImageStatSpecification.IMAGE_WIDTH:
+        if X.shape[2] != RKMEImageSpecification.IMAGE_WIDTH or X.shape[3] != RKMEImageSpecification.IMAGE_WIDTH:
             X = Resize(
-                (RKMEImageStatSpecification.IMAGE_WIDTH, RKMEImageStatSpecification.IMAGE_WIDTH), antialias=None
+                (RKMEImageSpecification.IMAGE_WIDTH, RKMEImageSpecification.IMAGE_WIDTH), antialias=None
             )(X)
 
         num_points = X.shape[0]
@@ -253,12 +253,12 @@ class RKMEImageStatSpecification(BaseStatSpecification):
             Y_features = Y_features / torch.sqrt(torch.asarray(Y_features.shape[1], device=self.device))
             return X_features, Y_features
 
-    def inner_prod(self, Phi2: RKMEImageStatSpecification) -> float:
+    def inner_prod(self, Phi2: RKMEImageSpecification) -> float:
         """Compute the inner product between two RKME Image specifications
 
         Parameters
         ----------
-        Phi2 : RKMEImageStatSpecification
+        Phi2 : RKMEImageSpecification
             The other RKME Image specification.
 
         Returns
@@ -269,7 +269,7 @@ class RKMEImageStatSpecification(BaseStatSpecification):
         v = self._inner_prod_nngp(Phi2)
         return v
 
-    def _inner_prod_nngp(self, Phi2: RKMEImageStatSpecification) -> float:
+    def _inner_prod_nngp(self, Phi2: RKMEImageSpecification) -> float:
         beta_1 = self.beta.reshape(1, -1).detach().to(self.device)
         beta_2 = Phi2.beta.reshape(1, -1).detach().to(self.device)
 
@@ -283,15 +283,15 @@ class RKMEImageStatSpecification(BaseStatSpecification):
             K_zz = kernel_fn(Z1, Z2)
         v = torch.sum(K_zz * (beta_1.T @ beta_2)).item()
 
-        # RKMEImageStatSpecification.INNER_PRODUCT_COUNT += 1
+        # RKMEImageSpecification.INNER_PRODUCT_COUNT += 1
         return v
 
-    def dist(self, Phi2: RKMEImageStatSpecification, omit_term1: bool = False) -> float:
+    def dist(self, Phi2: RKMEImageSpecification, omit_term1: bool = False) -> float:
         """Compute the Maximum-Mean-Discrepancy(MMD) between two RKME Image specifications
 
         Parameters
         ----------
-        Phi2 : RKMEImageStatSpecification
+        Phi2 : RKMEImageSpecification
             The other RKME specification.
         omit_term1 : bool, optional
             True if the inner product of self with itself can be omitted, by default False.

learnware/specification/regular/table/__init__.py

@@ -1 +1 @@
-from .rkme import RKMEStatSpecification
+from .rkme import RKMETableSpecification

learnware/specification/regular/table/rkme.py

@@ -29,7 +29,7 @@ if not _FAISS_INSTALLED:
     logger.warning("Required faiss version >= 1.7.1 is not detected! Please run 'conda install -c pytorch faiss-cpu' first")
 
 
-class RKMEStatSpecification(RegularStatsSpecification):
+class RKMETableSpecification(RegularStatsSpecification):
     """Reduced Kernel Mean Embedding (RKME) Specification"""
 
     def __init__(self, gamma: float = 0.1, cuda_idx: int = -1):
@@ -50,7 +50,7 @@ class RKMEStatSpecification(RegularStatsSpecification):
         torch.cuda.empty_cache()
         self.device = choose_device(cuda_idx=cuda_idx)
         setup_seed(0)
-        super(RKMEStatSpecification, self).__init__(type=self.__class__.__name__)
+        super(RKMETableSpecification, self).__init__(type=self.__class__.__name__)
 
     def get_beta(self) -> np.ndarray:
         """Move beta(RKME weights) back to memory accessible to the CPU.
@@ -333,12 +333,12 @@ class RKMEStatSpecification(RegularStatsSpecification):
         else:
             logger.warning("Not enough candidates for herding!")
 
-    def inner_prod(self, Phi2: RKMEStatSpecification) -> float:
+    def inner_prod(self, Phi2: RKMETableSpecification) -> float:
         """Compute the inner product between two RKME specifications
 
         Parameters
         ----------
-        Phi2 : RKMEStatSpecification
+        Phi2 : RKMETableSpecification
             The other RKME specification.
 
         Returns
@@ -354,12 +354,12 @@ class RKMEStatSpecification(RegularStatsSpecification):
 
         return float(v)
 
-    def dist(self, Phi2: RKMEStatSpecification, omit_term1: bool = False) -> float:
+    def dist(self, Phi2: RKMETableSpecification, omit_term1: bool = False) -> float:
         """Compute the Maximum-Mean-Discrepancy(MMD) between two RKME specifications
 
         Parameters
         ----------
-        Phi2 : RKMEStatSpecification
+        Phi2 : RKMETableSpecification
             The other RKME specification.
         omit_term1 : bool, optional
             True if the inner product of self with itself can be omitted, by default False.
@@ -463,6 +463,11 @@ class RKMEStatSpecification(RegularStatsSpecification):
         else:
             return False
 
+class RKMEStatSpecification(RKMETableSpecification):
+    """nickname for RKMETableSpecification, for compatibility currently.
+    TODO: modify all learnware in database and remove this nickname
+    """
+    pass
 
 def setup_seed(seed):
     """Fix a random seed for addressing reproducibility issues.

learnware/specification/utils.py

@@ -4,7 +4,7 @@ import pandas as pd
 from typing import Union
 
 from .base import BaseStatSpecification
-from .regular import RKMEStatSpecification, RKMEImageStatSpecification
+from .regular import RKMETableSpecification, RKMEImageSpecification
 from ..config import C
 
 
@@ -42,10 +42,10 @@ def generate_rkme_spec(
     nonnegative_beta: bool = True,
     reduce: bool = True,
     cuda_idx: int = None,
-) -> RKMEStatSpecification:
+) -> RKMETableSpecification:
     """
         Interface for users to generate Reduced Kernel Mean Embedding (RKME) specification.
-        Return a RKMEStatSpecification object, use .save() method to save as json file.
+        Return a RKMETableSpecification object, use .save() method to save as json file.
 
     Parameters
     ----------
@@ -73,8 +73,8 @@ def generate_rkme_spec(
 
     Returns
     -------
-    RKMEStatSpecification
-        A RKMEStatSpecification object
+    RKMETableSpecification
+        A RKMETableSpecification object
     """
     # Convert data type
     X = convert_to_numpy(X)
@@ -94,7 +94,7 @@ def generate_rkme_spec(
             cuda_idx = 0
 
     # Generate rkme spec
-    rkme_spec = RKMEStatSpecification(gamma=gamma, cuda_idx=cuda_idx)
+    rkme_spec = RKMETableSpecification(gamma=gamma, cuda_idx=cuda_idx)
     rkme_spec.generate_stat_spec_from_data(X, reduced_set_size, step_size, steps, nonnegative_beta, reduce)
     return rkme_spec
 
@@ -109,10 +109,10 @@ def generate_rkme_image_spec(
     reduce: bool = True,
     verbose: bool = True,
     cuda_idx: int = None,
-) -> RKMEImageStatSpecification:
+) -> RKMEImageSpecification:
     """
         Interface for users to generate Reduced Kernel Mean Embedding (RKME) specification for Image.
-        Return a RKMEImageStatSpecification object, use .save() method to save as json file.
+        Return a RKMEImageSpecification object, use .save() method to save as json file.
 
     Parameters
     ----------
@@ -144,8 +144,8 @@ def generate_rkme_image_spec(
 
     Returns
     -------
-    RKMEImageStatSpecification
-        A RKMEImageStatSpecification object
+    RKMEImageSpecification
+        A RKMEImageSpecification object
     """
 
     # Check cuda_idx
@@ -157,7 +157,7 @@ def generate_rkme_image_spec(
             cuda_idx = 0
 
     # Generate rkme spec
-    rkme_image_spec = RKMEImageStatSpecification(cuda_idx=cuda_idx)
+    rkme_image_spec = RKMEImageSpecification(cuda_idx=cuda_idx)
     rkme_image_spec.generate_stat_spec_from_data(
         X, reduced_set_size, step_size, steps, resize, nonnegative_beta, reduce, verbose
     )

tests/test_market/learnware_example/example.yaml

@@ -3,6 +3,6 @@ model:
   kwargs: {}
 stat_specifications:
   - module_path: learnware.specification
-    class_name: RKMEStatSpecification
+    class_name: RKMETableSpecification
     file_name: svm.json
     kwargs: {}  

tests/test_market/test_easy.py

@@ -170,9 +170,9 @@ class TestMarket(unittest.TestCase):
             with zipfile.ZipFile(zip_path, "r") as zip_obj:
                 zip_obj.extractall(path=unzip_dir)
 
-            user_spec = specification.rkme.RKMEStatSpecification()
+            user_spec = specification.rkme.RKMETableSpecification()
             user_spec.load(os.path.join(unzip_dir, "svm.json"))
-            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec})
+            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_spec})
             (
                 sorted_score_list,
                 single_learnware_list,

tests/test_specification/test_rkme.py

@@ -5,7 +5,7 @@ import unittest
 import tempfile
 import numpy as np
 
-from learnware.specification import RKMEStatSpecification, RKMEImageStatSpecification
+from learnware.specification import RKMETableSpecification, RKMEImageSpecification
 from learnware.specification import generate_rkme_image_spec, generate_rkme_spec
 
 
@@ -22,11 +22,11 @@ class TestRKME(unittest.TestCase):
 
             with open(rkme_path, "r") as f:
                 data = json.load(f)
-                assert data["type"] == "RKMEStatSpecification"
+                assert data["type"] == "RKMETableSpecification"
 
-            rkme2 = RKMEStatSpecification()
+            rkme2 = RKMETableSpecification()
             rkme2.load(rkme_path)
-            assert rkme2.type == "RKMEStatSpecification"
+            assert rkme2.type == "RKMETableSpecification"
 
     def test_image_rkme(self):
         def _test_image_rkme(X):
@@ -38,11 +38,11 @@ class TestRKME(unittest.TestCase):
 
                 with open(rkme_path, "r") as f:
                     data = json.load(f)
-                    assert data["type"] == "RKMEImageStatSpecification"
+                    assert data["type"] == "RKMEImageSpecification"
 
-                rkme2 = RKMEImageStatSpecification()
+                rkme2 = RKMEImageSpecification()
                 rkme2.load(rkme_path)
-                assert rkme2.type == "RKMEImageStatSpecification"
+                assert rkme2.type == "RKMEImageSpecification"
 
         _test_image_rkme(np.random.randint(0, 255, size=(2000, 3, 32, 32)))
         _test_image_rkme(np.random.randint(0, 255, size=(2000, 3, 128, 128)))

tests/test_workflow/learnware_example/example.yaml

@@ -3,6 +3,6 @@ model:
   kwargs: {}
 stat_specifications:
   - module_path: learnware.specification
-    class_name: RKMEStatSpecification
+    class_name: RKMETableSpecification
     file_name: svm.json
     kwargs: {}  

tests/test_workflow/test_workflow.py

@@ -155,9 +155,9 @@ class TestAllWorkflow(unittest.TestCase):
             with zipfile.ZipFile(zip_path, "r") as zip_obj:
                 zip_obj.extractall(path=unzip_dir)
 
-            user_spec = specification.RKMEStatSpecification()
+            user_spec = specification.RKMETableSpecification()
             user_spec.load(os.path.join(unzip_dir, "svm.json"))
-            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec})
+            user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_spec})
             (
                 sorted_score_list,
                 single_learnware_list,
@@ -182,7 +182,7 @@ class TestAllWorkflow(unittest.TestCase):
         train_X, data_X, train_y, data_y = train_test_split(X, y, test_size=0.3, shuffle=True)
 
         stat_spec = specification.utils.generate_rkme_spec(X=data_X, gamma=0.1, cuda_idx=0)
-        user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": stat_spec})
+        user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": stat_spec})
 
         _, _, _, mixture_learnware_list = easy_market.search_learnware(user_info)