Temp / 0.5

lib/trade_models/quant_transformer.py

@@ -9,7 +9,6 @@ import numpy as np
 import pandas as pd
 import copy
 from functools import partial
-from sklearn.metrics import roc_auc_score, mean_squared_error
 from typing import Optional
 import logging
 
@@ -23,10 +22,11 @@ from qlib.log import get_module_logger, TimeInspector
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 import torch.optim as optim
 
 import layers as xlayers
-from utils import count_parameters_in_MB
+from utils import count_parameters
 
 from qlib.model.base import Model
 from qlib.data.dataset import DatasetH
@@ -137,9 +137,11 @@ class QuantTransformer(Model):
     mask = ~torch.isnan(label)
 
     if self.loss == "mse":
+      import pdb; pdb.set_trace()
+      print('--')
       return self.mse(pred[mask], label[mask])
-
-    raise ValueError("unknown loss `%s`" % self.loss)
+    else:
+      raise ValueError("unknown loss `{:}`".format(self.loss))
 
   def metric_fn(self, pred, label):
 
@@ -147,8 +149,8 @@ class QuantTransformer(Model):
 
     if self.metric == "" or self.metric == "loss":
       return -self.loss_fn(pred[mask], label[mask])
-
-    raise ValueError("unknown metric `%s`" % self.metric)
+    else:
+      raise ValueError("unknown metric `{:}`".format(self.metric))
 
   def train_epoch(self, x_train, y_train):