[MNT] Update image example

examples/example_image/example_yaml.yaml

@@ -0,0 +1,8 @@
+model:
+  class_name: Model
+  kwargs: {}
+stat_specifications:
+  - module_path: learnware.specification
+    class_name: RKMEStatSpecification
+    file_name: rkme.json
+    kwargs: {}  

examples/example_image/main.py

@@ -10,8 +10,13 @@ from learnware.market import database_ops
 from learnware.learnware import Learnware
 import learnware.specification as specification
 
+from shutil import copyfile, rmtree
+import zipfile
+
 origin_data_root = "./data/origin_data"
 processed_data_root = "./data/processed_data"
+tmp_dir = "./data/tmp"
+learnware_pool_dir = "./data/learnware_pool"
 dataset = "cifar10"
 n_uploaders = 50
 n_users = 10
@@ -98,17 +103,24 @@ def prepare_model():
         print("Model saved to '%s'" % (model_save_path))
 
 
-def prepare_learnware():
-    pass
+def prepare_learnware(data_path, model_path, init_file_path, yaml_path):
+    X = np.load(data_path)
+    user_spec = specification.utils.generate_rkme_spec(X=X, gamma=0.1, cuda_idx=0)
+    print(user_spec.shape)
 
 
 def prepare_market():
+    image_market = EasyMarket(rebuild=True)
+    os.makedirs(learnware_pool_dir)
     for i in range(n_uploaders):
         data_path = os.path.join(uploader_save_root, "uploader_%d_X.npy" % (i))
         model_path = os.path.join(model_save_root, "uploader_%d.pth" % (i))
+        init_file_path = "./example_init.py"
+        yaml_file_path = "./example_yaml.yaml"
+        prepare_learnware(data_path, model_path, init_file_path, yaml_file_path)
 
 
 if __name__ == "__main__":
-    prepare_data()
-    prepare_model()
+    # prepare_data()
+    # prepare_model()
     prepare_market()

examples/example_pfs/pfs/pfs_cross_transfer.py

@@ -67,7 +67,7 @@ def get_split_errs(algo):
             for tmp in range(len(proportion_list)):
                 model = lgb.LGBMModel(
                     boosting_type="gbdt",
-                    num_leaves=2 ** 7 - 1,
+                    num_leaves=2**7 - 1,
                     learning_rate=0.01,
                     objective="rmse",
                     metric="rmse",
@@ -119,7 +119,7 @@ def get_errors(algo):
         if algo == "lgb":
             model = lgb.LGBMModel(
                 boosting_type="gbdt",
-                num_leaves=2 ** 7 - 1,
+                num_leaves=2**7 - 1,
                 learning_rate=0.01,
                 objective="rmse",
                 metric="rmse",

learnware/learnware/reuse.py

@@ -208,6 +208,8 @@ class ReuseBaseline:
             booster="gbtree",
             seed=0,
         )
-        model.fit(org_train_x, org_train_y, eval_set=[(org_train_x, org_train_y)], verbose=-1, early_stopping_rounds=300)
+        model.fit(
+            org_train_x, org_train_y, eval_set=[(org_train_x, org_train_y)], verbose=-1, early_stopping_rounds=300
+        )
 
         return model