Update training setting.

qlib/contrib/model/pytorch_lstm.py

@@ -46,7 +46,7 @@ class LSTM(Model):
         dropout=0.0,
         n_epochs=200,
         lr=0.001,
-        metric="loss",
+        metric="",
         batch_size=2000,
         early_stop=20,
         loss="mse",
@@ -140,16 +140,12 @@ class LSTM(Model):
     def metric_fn(self, pred, label):
 
         mask = torch.isfinite(label)
-        if self.metric == "IC":
-            return self.cal_ic(pred[mask], label[mask])
 
         if self.metric == "" or self.metric == "loss":  # use loss
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)
 
-    def cal_ic(self, pred, label):
-        return torch.mean(pred * label)
 
     def train_epoch(self, x_train, y_train):
 
@@ -193,7 +189,6 @@ class LSTM(Model):
         losses = []
 
         indices = np.arange(len(x_values))
-        np.random.shuffle(indices)
 
         for i in range(len(indices))[:: self.batch_size]: