diff --git a/examples/example_image/main.py b/examples/example_image/main.py
index dbe13e1..097da01 100644
--- a/examples/example_image/main.py
+++ b/examples/example_image/main.py
@@ -1,9 +1,10 @@
 import numpy as np
 import torch
-import get_data
+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
 import time
 
 from learnware.market import EasyMarket, BaseUserInfo
@@ -58,9 +59,9 @@ user_senmantic = {
 
 def prepare_data():
     if dataset == "cifar10":
-        X_train, y_train, X_test, y_test = get_data.get_cifar10(data_root)
+        X_train, y_train, X_test, y_test = get_cifar10(data_root)
     elif dataset == "mnist":
-        X_train, y_train, X_test, y_test = get_data.get_mnist(data_root)
+        X_train, y_train, X_test, y_test = get_mnist(data_root)
     else:
         return
     generate_uploader(X_train, y_train, n_uploaders=n_uploaders, data_save_root=uploader_save_root)
@@ -130,7 +131,7 @@ def prepare_market():
     logger.info("Available ids: " + str(curr_inds))
 
 
-def test_search(load_market=True):
+def test_search(gamma=0.1, load_market=True):
     if load_market:
         image_market = EasyMarket()
     else:
@@ -141,12 +142,13 @@ def test_search(load_market=True):
     select_list = []
     avg_list = []
     improve_list = []
+    job_selector_score_list = []
     for i in range(n_users):
         user_data_path = os.path.join(user_save_root, "user_%d_X.npy" % (i))
         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 = specification.utils.generate_rkme_spec(X=user_data, gamma=0.1, cuda_idx=0)
+        user_stat_spec = specification.utils.generate_rkme_spec(X=user_data, gamma=gamma, cuda_idx=0)
         user_info = BaseUserInfo(
             id=f"user_{i}", semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_stat_spec}
         )
@@ -163,17 +165,26 @@ def test_search(load_market=True):
             acc = eval_prediction(pred_y, user_label)
             acc_list.append(acc)
             logger.info("search rank: %d, score: %.3f, learnware_id: %s, acc: %.3f" % (idx, score, learnware.id, acc))
-
+        # test reuse
+        """
+        reuse_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list)
+        reuse_predict = reuse_baseline.predict(user_data=user_data)
+        reuse_score = eval_prediction(reuse_predict, user_label)
+        job_selector_score_list.append(reuse_score)
+        """
+        print(f"mixture reuse loss: {reuse_score}\n")
         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))
     logger.info(
-        "Accuracy of selected learnware: %.3f, Average performance: %.3f" % (np.mean(select_list), np.mean(avg_list))
+        "Accuracy of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f"
+        % (np.mean(select_list), np.std(select_list), np.mean(avg_list), np.std(avg_list))
     )
     logger.info("Average performance improvement: %.3f" % (np.mean(improve_list)))
+    # logger.info("Average Job Selector Reuse Performance: %.3f +/- %.3f"%(np.mean(job_selector_score_list), np.std(job_selector_score_list)))
 
 
 if __name__ == "__main__":
     # prepare_data()
     # prepare_model()
-    test_search()
+    test_search(load_market=True)
diff --git a/learnware/learnware/reuse.py b/learnware/learnware/reuse.py
index c834f8f..58c3914 100644
--- a/learnware/learnware/reuse.py
+++ b/learnware/learnware/reuse.py
@@ -200,7 +200,7 @@ class JobSelectorReuser(BaseReuser):
                     boosting_type="gbdt",
                     seed=0,
                 )
-                train_y = train_y.astype(np.int)
+                train_y = train_y.astype(int)
                 model.fit(train_x, train_y, eval_set=[(val_x, val_y)], verbose=-1, early_stopping_rounds=300)
                 pred_y = model.predict(org_train_x)
                 score = accuracy_score(pred_y, org_train_y)