[MNT] add requirements for text_workflow2

examples/dataset_pfs_workflow/pfs/pfs_cross_transfer.py

@@ -85,7 +85,9 @@ def get_split_errs(algo):
                 split = train_xs.shape[0] - proportion_list[tmp]
 
                 model.fit(
-                    train_xs[split:,],
+                    train_xs[
+                        split:,
+                    ],
                     train_ys[split:],
                     eval_set=[(val_xs, val_ys)],
                     early_stopping_rounds=50,

examples/dataset_text_workflow/example_files/example_init.py

@@ -19,7 +19,7 @@ class Model(BaseModel):
         input_dim = 768
         classifier_head = RobertaClassificationHead(num_classes=num_classes, input_dim=input_dim)
         self.model = XLMR_BASE_ENCODER.get_model(head=classifier_head).to(self.device)
-        self.model.load_state_dict(torch.load(os.path.join(dir_path, "model.pth"), map_location=torch.device('cpu')))
+        self.model.load_state_dict(torch.load(os.path.join(dir_path, "model.pth"), map_location=torch.device("cpu")))
 
     def fit(self, X: np.ndarray, y: np.ndarray):
         pass

examples/dataset_text_workflow2/example_files/requirements.txt

@@ -0,0 +1,4 @@
+numpy
+pickle
+lightgbm
+scikit-learn

examples/dataset_text_workflow2/main.py

@@ -87,7 +87,9 @@ def prepare_model():
         logger.info("Model saved to '%s' and '%s'" % (modelv_save_path, modell_save_path))
 
 
-def prepare_learnware(data_path, modelv_path, modell_path, init_file_path, yaml_path, save_root, zip_name):
+def prepare_learnware(
+    data_path, modelv_path, modell_path, init_file_path, yaml_path, env_file_path, save_root, zip_name
+):
     os.makedirs(save_root, exist_ok=True)
     tmp_spec_path = os.path.join(save_root, "rkme.json")
 
@@ -96,6 +98,7 @@ def prepare_learnware(data_path, modelv_path, modell_path, init_file_path, yaml_
 
     tmp_yaml_path = os.path.join(save_root, "learnware.yaml")
     tmp_init_path = os.path.join(save_root, "__init__.py")
+    tmp_env_path = os.path.join(save_root, "requirements.txt")
 
     with open(data_path, "rb") as f:
         X = pickle.load(f)
@@ -115,6 +118,7 @@ def prepare_learnware(data_path, modelv_path, modell_path, init_file_path, yaml_
 
     copyfile(yaml_path, tmp_yaml_path)
     copyfile(init_file_path, tmp_init_path)
+    copyfile(env_file_path, tmp_env_path)
     zip_file_name = os.path.join(learnware_pool_dir, "%s.zip" % (zip_name))
     with zipfile.ZipFile(zip_file_name, "w", compression=zipfile.ZIP_DEFLATED) as zip_obj:
         zip_obj.write(tmp_spec_path, "rkme.json")
@@ -124,6 +128,7 @@ def prepare_learnware(data_path, modelv_path, modell_path, init_file_path, yaml_
 
         zip_obj.write(tmp_yaml_path, "learnware.yaml")
         zip_obj.write(tmp_init_path, "__init__.py")
+        zip_obj.write(tmp_env_path, "requirements.txt")
     rmtree(save_root)
     logger.info("New Learnware Saved to %s" % (zip_file_name))
     return zip_file_name
@@ -144,8 +149,16 @@ def prepare_market():
 
         init_file_path = "./example_files/example_init.py"
         yaml_file_path = "./example_files/example_yaml.yaml"
+        env_file_path = "./example_files/requirements.txt"
         new_learnware_path = prepare_learnware(
-            data_path, modelv_path, modell_path, init_file_path, yaml_file_path, tmp_dir, "%s_%d" % (dataset, i)
+            data_path,
+            modelv_path,
+            modell_path,
+            init_file_path,
+            yaml_file_path,
+            env_file_path,
+            tmp_dir,
+            "%s_%d" % (dataset, i),
         )
         semantic_spec = semantic_specs[0]
         semantic_spec["Name"]["Values"] = "learnware_%d" % (i)
@@ -239,6 +252,6 @@ def test_search(load_market=True):
 
 
 if __name__ == "__main__":
-    prepare_data()
-    prepare_model()
+    # prepare_data()
+    # prepare_model()
     test_search(load_market=False)

learnware/market/easy/checker.py

@@ -51,7 +51,7 @@ class EasySemanticChecker(BaseChecker):
                     assert int(k) >= 0 and int(k) < dim, f"Dimension number in [0, {dim})"
                     assert isinstance(v, str), "Description must be string"
 
-            return EasySemanticChecker.NONUSABLE_LEARNWARE, 'EasySemanticChecker Success'
+            return EasySemanticChecker.NONUSABLE_LEARNWARE, "EasySemanticChecker Success"
 
         except AssertionError as err:
             logger.warning(f"semantic_specification is not valid due to {err}!")
@@ -128,7 +128,7 @@ class EasyStatChecker(BaseChecker):
             except Exception:
                 message = f"The learnware {learnware.id} prediction is not avaliable!"
                 logger.warning(message)
-                message += '\r\n' + traceback.format_exc()
+                message += "\r\n" + traceback.format_exc()
                 return self.INVALID_LEARNWARE, message
 
             if semantic_spec["Task"]["Values"][0] in ("Classification", "Regression"):