Update to hats

examples/workflow_by_code_hats.py

@@ -3,24 +3,17 @@
 
 import sys
 from pathlib import Path
-
 import qlib
 import pandas as pd
 from qlib.config import REG_CN
-from qlib.contrib.model.pytorch_hats import HATS
-from qlib.contrib.data.handler import ALPHA360_Denoise
 from qlib.contrib.strategy.strategy import TopkDropoutStrategy
 from qlib.contrib.evaluate import (
     backtest as normal_backtest,
     risk_analysis,
 )
 from qlib.utils import exists_qlib_data
-
-# from qlib.model.learner import train_model
 from qlib.utils import init_instance_by_config
 
-import pickle
-
 if __name__ == "__main__":
 
     # use default data
@@ -30,7 +23,7 @@ if __name__ == "__main__":
         sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
         from get_data import GetData
 
-        GetData().qlib_data_cn(target_dir=provider_uri)
+        GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
 
     qlib.init(provider_uri=provider_uri, region=REG_CN)
 
@@ -74,7 +67,7 @@ if __name__ == "__main__":
                 "loss": "mse",
                 "base_model": "LSTM",
                 "seed": 0,
-                "GPU": 0,
+                "GPU": "1",
             },
         },
         "dataset": {
@@ -97,7 +90,7 @@ if __name__ == "__main__":
         # "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
     }
 
-    # model = train_model(task)
+
     model = init_instance_by_config(task["model"])
     dataset = init_instance_by_config(task["dataset"])
     model.fit(dataset, save_path="benchmarks/HATS/model_hat.pkl")