[FIX] Fix bugs in M5

examples/example_image/main.py

@@ -4,7 +4,7 @@ from get_data import *
 import os
 import random
 from utils import generate_uploader, generate_user, ImageDataLoader, train, eval_prediction
-from learnware.learnware import Learnware, JobSelectorReuser, EnsembleReuser
+from learnware.learnware import Learnware, JobSelectorReuser, AveragingReuser
 import time
 
 from learnware.market import EasyMarket, BaseUserInfo
@@ -173,7 +173,7 @@ def test_search(gamma=0.1, load_market=True):
         job_selector_score_list.append(reuse_score)
         print(f"mixture reuse loss: {reuse_score}\n")
 
-        reuse_ensemble = EnsembleReuser(learnware_list=mixture_learnware_list, mode="vote")
+        reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="vote")
         ensemble_predict_y = reuse_ensemble.predict(user_data=user_data)
         ensemble_score = eval_prediction(ensemble_predict_y, user_label)
         ensemble_score_list.append(ensemble_score)
@@ -198,4 +198,4 @@ def test_search(gamma=0.1, load_market=True):
 if __name__ == "__main__":
     # prepare_data()
     # prepare_model()
-    test_search(load_market=True)
+    test_search(load_market=False)

examples/example_m5/example_init.py

@@ -1,13 +1,14 @@
 import os
 import joblib
 import numpy as np
+import lightgbm as lgb
 from learnware.model import BaseModel
 
 
 class Model(BaseModel):
     def __init__(self):
         dir_path = os.path.dirname(os.path.abspath(__file__))
-        self.model = joblib.load(os.path.join(dir_path, "model.out"))
+        self.model = lgb.Booster(model_file=os.path.join(dir_path, "model.out"))
 
     def fit(self, X: np.ndarray, y: np.ndarray):
         pass

examples/example_pfs/example_init.py

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