[FIX] fix dataset_m5_workflow

examples/dataset_m5_workflow/example_init.py

@@ -7,7 +7,7 @@ from learnware.model import BaseModel
 
 class Model(BaseModel):
     def __init__(self):
-        super(Model, self).__init__(input_shape=(82,), output_shape=())
+        super(Model, self).__init__(input_shape=(82,), output_shape=(1,))
         dir_path = os.path.dirname(os.path.abspath(__file__))
         self.model = lgb.Booster(model_file=os.path.join(dir_path, "model.out"))
 

examples/dataset_m5_workflow/main.py

@@ -8,7 +8,7 @@ from shutil import copyfile, rmtree
 
 import learnware
 from learnware.market import instantiate_learnware_market, BaseUserInfo
-from learnware.market import database_ops
+# from learnware.market import database_ops
 from learnware.reuse import JobSelectorReuser, AveragingReuser
 from learnware.specification import generate_rkme_spec
 from m5 import DataLoader
@@ -17,27 +17,40 @@ from learnware.logger import get_module_logger
 logger = get_module_logger("m5_test", level="INFO")
 
 
+output_description = {
+    "Dimension": 1,
+    "Description": {},
+}
+
+input_description = {
+    "Dimension": 82,
+    "Description": {},
+}
+
 semantic_specs = [
     {
-        "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class"},
+        "Data": {"Values": ["Table"], "Type": "Class"},
+        "Task": {"Values": ["Regression"], "Type": "Class"},
         "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_1", "Type": "String"},
+        "Input": input_description,
+        "Output": output_description,
     }
 ]
 
 user_semantic = {
-    "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {"Values": ["Classification"], "Type": "Class"},
+    "Data": {"Values": ["Table"], "Type": "Class"},
+    "Task": {"Values": ["Regression"], "Type": "Class"},
     "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
     "Name": {"Values": "", "Type": "String"},
+    "Input": input_description,
+    "Output": output_description,
 }
 
-
 class M5DatasetWorkflow:
     def _init_m5_dataset(self):
         m5 = DataLoader()
@@ -69,8 +82,8 @@ class M5DatasetWorkflow:
             easy_market.add_learnware(zip_path, semantic_spec)
 
         print("Total Item:", len(easy_market))
-        curr_inds = easy_market._get_ids()
-        print("Available ids:", curr_inds)
+        # curr_inds = easy_market._get_ids()
+        # print("Available ids:", curr_inds)
 
     def prepare_learnware(self, regenerate_flag=False):
         if regenerate_flag:
@@ -171,7 +184,7 @@ class M5DatasetWorkflow:
             job_selector_score = m5.score(test_y, job_selector_predict_y)
             print(f"mixture reuse loss (job selector): {job_selector_score}")
 
-            reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="vote")
+            reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="vote_by_prob")
             ensemble_predict_y = reuse_ensemble.predict(user_data=test_x)
             ensemble_score = m5.score(test_y, ensemble_predict_y)
             print(f"mixture reuse loss (ensemble): {ensemble_score}\n")

examples/dataset_pfs_workflow/main.py

@@ -15,25 +15,38 @@ from learnware.logger import get_module_logger
 
 logger = get_module_logger("pfs_test", level="INFO")
 
+output_description = {
+    "Dimension": 1,
+    "Description": {},
+}
+
+input_description = {
+    "Dimension": 31,
+    "Description": {},
+}
 
 semantic_specs = [
     {
-        "Data": {"Values": ["Tabular"], "Type": "Class"},
-        "Task": {"Values": ["Classification"], "Type": "Class"},
+        "Data": {"Values": ["Table"], "Type": "Class"},
+        "Task": {"Values": ["Regression"], "Type": "Class"},
         "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_1", "Type": "String"},
+        "Input": input_description,
+        "Output": output_description,
     }
 ]
 
 user_semantic = {
-    "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {"Values": ["Classification"], "Type": "Class"},
+    "Data": {"Values": ["Table"], "Type": "Class"},
+    "Task": {"Values": ["Regression"], "Type": "Class"},
     "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
-    "Name": {"Values": "", "Type": "String"},
+    "Name": {"Values": "learnware_1", "Type": "String"},
+    "Input": input_description,
+    "Output": output_description,
 }
 
 
@@ -66,8 +79,8 @@ class PFSDatasetWorkflow:
             easy_market.add_learnware(zip_path, semantic_spec)
 
         print("Total Item:", len(easy_market))
-        curr_inds = easy_market._get_ids()
-        print("Available ids:", curr_inds)
+        # curr_inds = easy_market._get_ids()
+        # print("Available ids:", curr_inds)
 
     def prepare_learnware(self, regenerate_flag=False):
         if regenerate_flag:

examples/dataset_text_workflow/main.py

@@ -196,10 +196,6 @@ def test_search(gamma=0.1, load_market=True):
         ensemble_score_list.append(ensemble_score)
         print(f"mixture reuse accuracy (ensemble): {ensemble_score}")
 
-        select_list.append(acc_list[0])
-        avg_list.append(np.mean(acc_list))
-        improve_list.append((acc_list[0] - np.mean(acc_list)) / np.mean(acc_list))
-
         # test reuse (ensemblePruning)
         reuse_pruning = EnsemblePruningReuser(learnware_list=mixture_learnware_list)
         pruning_predict_y = reuse_pruning.predict(user_data=user_data)

learnware/market/easy/checker.py

@@ -120,6 +120,7 @@ class EasyStatChecker(BaseChecker):
                 raise ValueError(f"not supported spec type for spec_type = {spec_type}")
 
             # Check output
+            outputs = learnware.predict(inputs)
             try:
                 outputs = learnware.predict(inputs)
             except Exception:

learnware/market/easy/searcher.py

@@ -277,7 +277,7 @@ class EasyStatSearcher(BaseSearcher):
 
         # beta must be nonnegative
         weight, obj = rkme_solve_qp(K, C)
-        weight = torch.from_numpy(weight).reshape(-1).double().to(user_rkme.device)
+        weight = weight.double().to(user_rkme.device)
         score = user_rkme.inner_prod(user_rkme) + 2 * obj
 
         return weight.detach().cpu().numpy().reshape(-1), score

learnware/specification/regular/table/rkme.py

@@ -13,6 +13,7 @@ from qpsolvers import solve_qp, Problem, solve_problem
 from collections import Counter
 from typing import Tuple, Any, List, Union, Dict
 import scipy
+from sklearn.cluster import MiniBatchKMeans
 
 try:
     import faiss
@@ -27,10 +28,10 @@ from ....logger import get_module_logger
 
 logger = get_module_logger("rkme")
 
-if not _FAISS_INSTALLED:
-    logger.warning(
-        "Required faiss version >= 1.7.1 is not detected! Please run 'conda install -c pytorch faiss-cpu' first"
-    )
+# 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 RKMETableSpecification(RegularStatsSpecification):
@@ -127,8 +128,8 @@ class RKMETableSpecification(RegularStatsSpecification):
             self.beta = torch.from_numpy(self.beta).double().to(self.device)
             return
 
-        # Initialize Z by clustering, utiliing faiss to speed up the process.
-        self._init_z_by_faiss(X, K)
+        # Initialize Z by clustering, utiliing kmeans or faiss to speed up the process.
+        self._init_z_by_kmeans(X, K)
         self._update_beta(X, nonnegative_beta)
 
         # Alternating optimize Z and beta
@@ -156,6 +157,22 @@ class RKMETableSpecification(RegularStatsSpecification):
         center = torch.from_numpy(kmeans.centroids).double()
         self.z = center
 
+    def _init_z_by_kmeans(self, X: Union[np.ndarray, torch.tensor], K: int):
+        """Intialize Z by kmeans clustering.
+
+        Parameters
+        ----------
+        X : np.ndarray or torch.tensor
+            Raw data in np.ndarray format or torch.tensor format.
+        K : int
+            Size of the construced reduced set.
+        """
+        X = X.astype("float32")
+        kmeans = MiniBatchKMeans(n_clusters=K, max_iter=100, verbose=False, n_init="auto")
+        kmeans.fit(X)
+        center = torch.from_numpy(kmeans.cluster_centers_).double()
+        self.z = center
+
     def _update_beta(self, X: Any, nonnegative_beta: bool = True):
         """Fix Z and update beta using its closed-form solution.
 

setup.py

@@ -75,11 +75,11 @@ REQUIRED = [
     "langdetect>=1.0.9",
     "huggingface-hub<0.18",
     "portalocker>=2.0.0",
-    "qpsolvers[clarabel]>=4.0.1"
+    "qpsolvers[clarabel]>=4.0.1",
 ]
 
-if get_platform() != MACOS:
-    REQUIRED.append("faiss-cpu>=1.7.1")
+# if get_platform() != MACOS:
+#     REQUIRED.append("faiss-cpu>=1.7.1")
 
 here = os.path.abspath(os.path.dirname(__file__))
 with open(os.path.join(here, "README.md"), encoding="utf-8") as f: