Update settings.

Author: lwwang1995

examples/benchmarks/SFM/workflow_config_sfm_Alpha158.yaml

@@ -57,7 +57,7 @@ task:
             num_layers: 2
             dropout: 0.0
             n_epochs: 200
-            lr: 1e-1
+            lr: 5e-2
             early_stop: 10
             batch_size: 800
             metric: loss

qlib/contrib/model/pytorch_gats_ts.py

@@ -32,15 +32,17 @@
 
 
 class DailyBatchSampler(Sampler):
+    
     def __init__(self, data_source):
+
         self.data_source = data_source
         self.data = self.data_source.data.loc[self.data_source.get_index()]
-        self.daily_count = self.data.groupby(level=0).size().values
-        self.daily_index = np.roll(np.cumsum(self.daily_count), 1)
+        self.daily_count = self.data.groupby(level=0).size().values[1:]
+        self.daily_index = np.roll(np.cumsum(self.daily_count), 1)[1:]
 
     def __iter__(self):
         for idx, count in zip(self.daily_index, self.daily_count):
-            yield slice(idx, idx + count)
+            yield np.arange(idx, idx + count)
 
     def __len__(self):
         return len(self.data_source)
@@ -202,6 +204,8 @@ def train_epoch(self, data_loader):
         self.GAT_model.train()
 
         for data in data_loader:
+
+            data = data.squeeze()
             feature = data[:, :, 0:-1].to(self.device)
             label = data[:, -1, -1].to(self.device)
 
@@ -222,6 +226,7 @@ def test_epoch(self, data_loader):
 
         for data in data_loader:
 
+            data = data.squeeze()
             feature = data[:, :, 0:-1].to(self.device)
             # feature[torch.isnan(feature)] = 0
             label = data[:, -1, -1].to(self.device)
@@ -335,6 +340,7 @@ def predict(self, dataset):
 
         for data in test_loader:
 
+            data = data.squeeze()
             feature = data[:, :, 0:-1].to(self.device)
 
             with torch.no_grad():