From e637cddc39223f7c96685baa9bce302214500044 Mon Sep 17 00:00:00 2001
From: D-X-Y <280835372@qq.com>
Date: Mon, 29 Mar 2021 04:23:33 +0000
Subject: [PATCH] Update organize

---
 .latent-data/qlib                     |  2 +-
 exps/trading/organize_results.py      |  4 +--
 lib/trade_models/quant_transformer.py | 36 +++++++++++++--------------
 3 files changed, 20 insertions(+), 22 deletions(-)

diff --git a/.latent-data/qlib b/.latent-data/qlib
index f809f0a..253378a 160000
--- a/.latent-data/qlib
+++ b/.latent-data/qlib
@@ -1 +1 @@
-Subproject commit f809f0a0636ca7baeb8e7e98c5a8b387096e7217
+Subproject commit 253378a44e88a9fcff17d23b589e2d4832f587aa
diff --git a/exps/trading/organize_results.py b/exps/trading/organize_results.py
index 50017e7..1a66b8d 100644
--- a/exps/trading/organize_results.py
+++ b/exps/trading/organize_results.py
@@ -131,9 +131,9 @@ def query_info(save_dir, verbose):
         "ICIR": "ICIR",
         "Rank IC": "Rank_IC",
         "Rank ICIR": "Rank_ICIR",
-        "excess_return_with_cost.annualized_return": "Annualized_Return",
+        # "excess_return_with_cost.annualized_return": "Annualized_Return",
         # "excess_return_with_cost.information_ratio": "Information_Ratio",
-        "excess_return_with_cost.max_drawdown": "Max_Drawdown",
+        # "excess_return_with_cost.max_drawdown": "Max_Drawdown",
     }
     all_keys = list(key_map.values())
 
diff --git a/lib/trade_models/quant_transformer.py b/lib/trade_models/quant_transformer.py
index 69cc8c5..993453b 100644
--- a/lib/trade_models/quant_transformer.py
+++ b/lib/trade_models/quant_transformer.py
@@ -307,25 +307,23 @@ class QuantTransformer(Model):
     def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
         if not self.fitted:
             raise ValueError("The model is not fitted yet!")
-        x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
+        x_test = dataset.prepare(
+            segment, col_set="feature", data_key=DataHandlerLP.DK_I
+        )
         index = x_test.index
 
-        self.model.eval()
-        x_values = x_test.values
-        sample_num, batch_size = x_values.shape[0], self.opt_config["batch_size"]
-        preds = []
-
-        for begin in range(sample_num)[::batch_size]:
-
-            if sample_num - begin < batch_size:
-                end = sample_num
-            else:
-                end = begin + batch_size
-
-            x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
-
-            with torch.no_grad():
-                pred = self.model(x_batch).detach().cpu().numpy()
-            preds.append(pred)
-
+        with torch.no_grad():
+            self.model.eval()
+            x_values = x_test.values
+            sample_num, batch_size = x_values.shape[0], self.opt_config["batch_size"]
+            preds = []
+            for begin in range(sample_num)[::batch_size]:
+                if sample_num - begin < batch_size:
+                    end = sample_num
+                else:
+                    end = begin + batch_size
+                x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
+                with torch.no_grad():
+                    pred = self.model(x_batch).detach().cpu().numpy()
+                preds.append(pred)
         return pd.Series(np.concatenate(preds), index=index)