[FIX] try to fix pfs

examples/dataset_pfs_workflow/example_init.py

@@ -6,7 +6,7 @@ from learnware.model import BaseModel
 
 class Model(BaseModel):
     def __init__(self):
-        super(Model, self).__init__(input_shape=(31,), output_shape=())
+        super(Model, self).__init__(input_shape=(31,), output_shape=(1,))
         dir_path = os.path.dirname(os.path.abspath(__file__))
         self.model = joblib.load(os.path.join(dir_path, "model.out"))
 

examples/dataset_pfs_workflow/main.py

@@ -44,7 +44,7 @@ user_semantic = {
     "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
-    "Name": {"Values": "learnware_1", "Type": "String"},
+    "Name": {"Values": "", "Type": "String"},
     "Input": input_description,
     "Output": output_description,
 }
@@ -55,7 +55,7 @@ class PFSDatasetWorkflow:
         pfs = Dataloader()
         pfs.regenerate_data()
 
-        algo_list = ["ridge", "lgb"]
+        algo_list = ["ridge"] # , "lgb"
         for algo in algo_list:
             pfs.set_algo(algo)
             pfs.retrain_models()
@@ -88,7 +88,7 @@ class PFSDatasetWorkflow:
 
         pfs = Dataloader()
         idx_list = pfs.get_idx_list()
-        algo_list = ["lgb"]  # ["ridge", "lgb"]
+        algo_list = ["ridge"]  # ["ridge", "lgb"]
 
         curr_root = os.path.dirname(os.path.abspath(__file__))
         curr_root = os.path.join(curr_root, "learnware_pool")

learnware/market/easy/checker.py

@@ -89,7 +89,6 @@ class EasyStatChecker(BaseChecker):
             traceback.print_exc()
             logger.warning(f"The learnware [{learnware.id}] is instantiated failed! Due to {e}.")
             return self.INVALID_LEARNWARE
-
         try:
             learnware_model = learnware.get_model()
             # Check input shape
@@ -117,10 +116,9 @@ class EasyStatChecker(BaseChecker):
             elif spec_type == "RKMEImageSpecification":
                 inputs = np.random.randint(0, 255, size=(10, *input_shape))
             else:
-                raise ValueError(f"not supported spec type for spec_type = {spec_type}")
+                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/specification/regular/table/rkme.py

@@ -129,7 +129,7 @@ class RKMETableSpecification(RegularStatsSpecification):
             return
 
         # Initialize Z by clustering, utiliing kmeans or faiss to speed up the process.
-        self._init_z_by_kmeans(X, K)
+        self._init_z_by_faiss(X, K)
         self._update_beta(X, nonnegative_beta)
 
         # Alternating optimize Z and beta