Merge pull request #329 from D-X-Y/main

Fix Various Bugs for contrib.pytorch_ models
microsoft · Mar 12, 2021 · 0cffb87 · 0cffb87
2 parents df56e3b + 1d43524
commit 0cffb87
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Showing 15 changed files with 119 additions and 99 deletions.
diff --git a/.gitignore b/.gitignore
@@ -36,3 +36,5 @@ tags
 .vscode/
 
 *.swp
+
+./pretrain
diff --git a/examples/benchmarks/README.md b/examples/benchmarks/README.md
@@ -17,6 +17,7 @@ The numbers shown below demonstrate the performance of the entire `workflow` of
 | ALSTM (Yao Qin, et al.) | Alpha360 | 0.0493±0.01 | 0.3778±0.06| 0.0585±0.00 | 0.4606±0.04 | 0.0513±0.03 | 0.6727±0.38| -0.1085±0.02 |
 | GATs (Petar Velickovic, et al.) | Alpha360 | 0.0475±0.00 | 0.3515±0.02| 0.0592±0.00 | 0.4585±0.01 | 0.0876±0.02 | 1.1513±0.27| -0.0795±0.02 |
 | DoubleEnsemble (Chuheng Zhang, et al.) | Alpha360 | 0.0407±0.00| 0.3053±0.00 | 0.0490±0.00 | 0.3840±0.00 | 0.0380±0.02 | 0.5000±0.21 | -0.0984±0.02 |
+
 ## Alpha158 dataset
 | Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
 |---|---|---|---|---|---|---|---|---|
@@ -25,7 +26,6 @@ The numbers shown below demonstrate the performance of the entire `workflow` of
 | XGBoost (Tianqi Chen, et al.) | Alpha158 | 0.0481±0.00 | 0.3659±0.00| 0.0495±0.00 | 0.4033±0.00 | 0.1111±0.00 | 1.2915±0.00| -0.0893±0.00 |
 | LightGBM (Guolin Ke, et al.) | Alpha158 | 0.0475±0.00 | 0.3979±0.00| 0.0485±0.00 | 0.4123±0.00 | 0.1143±0.00 | 1.2744±0.00| -0.0800±0.00 |
 | MLP | Alpha158 | 0.0358±0.00 | 0.2738±0.03| 0.0425±0.00 | 0.3221±0.01 | 0.0836±0.02 | 1.0323±0.25| -0.1127±0.02 |
-| TabNet with pretrain (Sercan O. Arikm et al) | Alpha158 | 0.0344±0.00|0.205±0.11|0.0398±0.00 |0.3479±0.01|0.0827±0.02|1.1141±0.32 |-0.0925±0.02  |
 | TFT (Bryan Lim, et al.) | Alpha158 (with selected 20 features) | 0.0343±0.00 | 0.2071±0.02| 0.0107±0.00 | 0.0660±0.02 | 0.0623±0.02 | 0.5818±0.20| -0.1762±0.01 |
 | GRU (Kyunghyun Cho, et al.) | Alpha158 (with selected 20 features) | 0.0311±0.00 | 0.2418±0.04| 0.0425±0.00 | 0.3434±0.02 | 0.0330±0.02 | 0.4805±0.30| -0.1021±0.02 |
 | LSTM (Sepp Hochreiter, et al.) | Alpha158 (with selected 20 features) | 0.0312±0.00 | 0.2394±0.04| 0.0418±0.00 | 0.3324±0.03 | 0.0298±0.02 | 0.4198±0.33| -0.1348±0.03 |

diff --git a/examples/benchmarks/TabNet/pretrain/best.model b/examples/benchmarks/TabNet/pretrain/best.model
diff --git a/examples/benchmarks/TabNet/workflow_config_TabNet_Alpha158.yaml b/examples/benchmarks/TabNet/workflow_config_TabNet_Alpha158.yaml
@@ -55,7 +55,7 @@ task:
                 kwargs: *data_handler_config
             segments:
                 pretrain: [2008-01-01, 2014-12-31]
-                pretrain_validation: [2015-01-01, 2020-08-01]
+                pretrain_validation: [2015-01-01, 2016-12-31]
                 train: [2008-01-01, 2014-12-31]
                 valid: [2015-01-01, 2016-12-31]
                 test: [2017-01-01, 2020-08-01]

diff --git a/qlib/contrib/model/pytorch_alstm.py b/qlib/contrib/model/pytorch_alstm.py
@@ -78,7 +78,6 @@ def __init__(
         self.optimizer = optimizer.lower()
         self.loss = loss
         self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
-        self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
         self.logger.info(
@@ -94,7 +93,7 @@ def __init__(
             "\nearly_stop : {}"
             "\noptimizer : {}"
             "\nloss_type : {}"
-            "\nvisible_GPU : {}"
+            "\ndevice : {}"
             "\nuse_GPU : {}"
             "\nseed : {}".format(
                 d_feat,
@@ -108,7 +107,7 @@ def __init__(
                 early_stop,
                 optimizer.lower(),
                 loss,
-                GPU,
+                self.device,
                 self.use_gpu,
                 seed,
             )
@@ -137,6 +136,10 @@ def __init__(
         self.fitted = False
         self.ALSTM_model.to(self.device)
 
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
     def mse(self, pred, label):
         loss = (pred - label) ** 2
         return torch.mean(loss)
@@ -205,12 +208,13 @@ def test_epoch(self, data_x, data_y):
             feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
             label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
 
-            pred = self.ALSTM_model(feature)
-            loss = self.loss_fn(pred, label)
-            losses.append(loss.item())
+            with torch.no_grad():
+                pred = self.ALSTM_model(feature)
+                loss = self.loss_fn(pred, label)
+                losses.append(loss.item())
 
-            score = self.metric_fn(pred, label)
-            scores.append(score.item())
+                score = self.metric_fn(pred, label)
+                scores.append(score.item())
 
         return np.mean(losses), np.mean(scores)
 
@@ -292,10 +296,7 @@ def predict(self, dataset):
             x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
 
             with torch.no_grad():
-                if self.use_gpu:
-                    pred = self.ALSTM_model(x_batch).detach().cpu().numpy()
-                else:
-                    pred = self.ALSTM_model(x_batch).detach().numpy()
+                pred = self.ALSTM_model(x_batch).detach().cpu().numpy()
 
             preds.append(pred)
 

diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py
@@ -81,7 +81,6 @@ def __init__(
         self.loss = loss
         self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
         self.n_jobs = n_jobs
-        self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
         self.logger.info(
@@ -97,7 +96,7 @@ def __init__(
             "\nearly_stop : {}"
             "\noptimizer : {}"
             "\nloss_type : {}"
-            "\nvisible_GPU : {}"
+            "\ndevice : {}"
             "\nn_jobs : {}"
             "\nuse_GPU : {}"
             "\nseed : {}".format(
@@ -112,7 +111,7 @@ def __init__(
                 early_stop,
                 optimizer.lower(),
                 loss,
-                GPU,
+                self.device,
                 n_jobs,
                 self.use_gpu,
                 seed,
@@ -142,6 +141,10 @@ def __init__(
         self.fitted = False
         self.ALSTM_model.to(self.device)
 
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
     def mse(self, pred, label):
         loss = (pred - label) ** 2
         return torch.mean(loss)
@@ -192,12 +195,13 @@ def test_epoch(self, data_loader):
             # feature[torch.isnan(feature)] = 0
             label = data[:, -1, -1].to(self.device)
 
-            pred = self.ALSTM_model(feature.float())
-            loss = self.loss_fn(pred, label)
-            losses.append(loss.item())
+            with torch.no_grad():
+                pred = self.ALSTM_model(feature.float())
+                loss = self.loss_fn(pred, label)
+                losses.append(loss.item())
 
-            score = self.metric_fn(pred, label)
-            scores.append(score.item())
+                score = self.metric_fn(pred, label)
+                scores.append(score.item())
 
         return np.mean(losses), np.mean(scores)
 
@@ -277,10 +281,7 @@ def predict(self, dataset):
             feature = data[:, :, 0:-1].to(self.device)
 
             with torch.no_grad():
-                if self.use_gpu:
-                    pred = self.ALSTM_model(feature.float()).detach().cpu().numpy()
-                else:
-                    pred = self.ALSTM_model(feature.float()).detach().numpy()
+                pred = self.ALSTM_model(feature.float()).detach().cpu().numpy()
 
             preds.append(pred)
 

diff --git a/qlib/contrib/model/pytorch_gats.py b/qlib/contrib/model/pytorch_gats.py
@@ -103,7 +103,7 @@ def __init__(
             "\nbase_model : {}"
             "\nwith_pretrain : {}"
             "\nmodel_path : {}"
-            "\nvisible_GPU : {}"
+            "\ndevice : {}"
             "\nuse_GPU : {}"
             "\nseed : {}".format(
                 d_feat,
@@ -119,7 +119,7 @@ def __init__(
                 base_model,
                 with_pretrain,
                 model_path,
-                GPU,
+                self.device,
                 self.use_gpu,
                 seed,
             )
@@ -149,6 +149,10 @@ def __init__(
         self.fitted = False
         self.GAT_model.to(self.device)
 
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
     def mse(self, pred, label):
         loss = (pred - label) ** 2
         return torch.mean(loss)
@@ -326,10 +330,7 @@ def predict(self, dataset):
             x_batch = torch.from_numpy(x_values[batch]).float().to(self.device)
 
             with torch.no_grad():
-                if self.use_gpu:
-                    pred = self.GAT_model(x_batch).detach().cpu().numpy()
-                else:
-                    pred = self.GAT_model(x_batch).detach().numpy()
+                pred = self.GAT_model(x_batch).detach().cpu().numpy()
 
             preds.append(pred)
 

diff --git a/qlib/contrib/model/pytorch_gats_ts.py b/qlib/contrib/model/pytorch_gats_ts.py
@@ -107,7 +107,6 @@ def __init__(
         self.model_path = model_path
         self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
         self.n_jobs = n_jobs
-        self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
         self.logger.info(
@@ -171,6 +170,10 @@ def __init__(
         self.fitted = False
         self.GAT_model.to(self.device)
 
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
     def mse(self, pred, label):
         loss = (pred - label) ** 2
         return torch.mean(loss)
@@ -347,10 +350,7 @@ def predict(self, dataset):
             feature = data[:, :, 0:-1].to(self.device)
 
             with torch.no_grad():
-                if self.use_gpu:
-                    pred = self.GAT_model(feature.float()).detach().cpu().numpy()
-                else:
-                    pred = self.GAT_model(feature.float()).detach().numpy()
+                pred = self.GAT_model(feature.float()).detach().cpu().numpy()
 
             preds.append(pred)
 

diff --git a/qlib/contrib/model/pytorch_gru.py b/qlib/contrib/model/pytorch_gru.py
@@ -78,7 +78,6 @@ def __init__(
         self.optimizer = optimizer.lower()
         self.loss = loss
         self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
-        self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
         self.logger.info(
@@ -137,6 +136,10 @@ def __init__(
         self.fitted = False
         self.gru_model.to(self.device)
 
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
     def mse(self, pred, label):
         loss = (pred - label) ** 2
         return torch.mean(loss)
@@ -205,12 +208,13 @@ def test_epoch(self, data_x, data_y):
             feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
             label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
 
-            pred = self.gru_model(feature)
-            loss = self.loss_fn(pred, label)
-            losses.append(loss.item())
+            with torch.no_grad():
+                pred = self.gru_model(feature)
+                loss = self.loss_fn(pred, label)
+                losses.append(loss.item())
 
-            score = self.metric_fn(pred, label)
-            scores.append(score.item())
+                score = self.metric_fn(pred, label)
+                scores.append(score.item())
 
         return np.mean(losses), np.mean(scores)
 
@@ -292,10 +296,7 @@ def predict(self, dataset):
             x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
 
             with torch.no_grad():
-                if self.use_gpu:
-                    pred = self.gru_model(x_batch).detach().cpu().numpy()
-                else:
-                    pred = self.gru_model(x_batch).detach().numpy()
+                pred = self.gru_model(x_batch).detach().cpu().numpy()
 
             preds.append(pred)
 

diff --git a/qlib/contrib/model/pytorch_gru_ts.py b/qlib/contrib/model/pytorch_gru_ts.py
@@ -81,7 +81,6 @@ def __init__(
         self.loss = loss
         self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
         self.n_jobs = n_jobs
-        self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
         self.logger.info(
@@ -97,7 +96,7 @@ def __init__(
             "\nearly_stop : {}"
             "\noptimizer : {}"
             "\nloss_type : {}"
-            "\nvisible_GPU : {}"
+            "\ndevice : {}"
             "\nn_jobs : {}"
             "\nuse_GPU : {}"
             "\nseed : {}".format(
@@ -112,7 +111,7 @@ def __init__(
                 early_stop,
                 optimizer.lower(),
                 loss,
-                GPU,
+                self.device,
                 n_jobs,
                 self.use_gpu,
                 seed,
@@ -142,6 +141,10 @@ def __init__(
         self.fitted = False
         self.GRU_model.to(self.device)
 
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
     def mse(self, pred, label):
         loss = (pred - label) ** 2
         return torch.mean(loss)
@@ -192,12 +195,13 @@ def test_epoch(self, data_loader):
             # feature[torch.isnan(feature)] = 0
             label = data[:, -1, -1].to(self.device)
 
-            pred = self.GRU_model(feature.float())
-            loss = self.loss_fn(pred, label)
-            losses.append(loss.item())
+            with torch.no_grad():
+                pred = self.GRU_model(feature.float())
+                loss = self.loss_fn(pred, label)
+                losses.append(loss.item())
 
-            score = self.metric_fn(pred, label)
-            scores.append(score.item())
+                score = self.metric_fn(pred, label)
+                scores.append(score.item())
 
         return np.mean(losses), np.mean(scores)
 
@@ -277,10 +281,7 @@ def predict(self, dataset):
             feature = data[:, :, 0:-1].to(self.device)
 
             with torch.no_grad():
-                if self.use_gpu:
-                    pred = self.GRU_model(feature.float()).detach().cpu().numpy()
-                else:
-                    pred = self.GRU_model(feature.float()).detach().numpy()
+                pred = self.GRU_model(feature.float()).detach().cpu().numpy()
 
             preds.append(pred)
 

diff --git a/qlib/contrib/model/pytorch_lstm.py b/qlib/contrib/model/pytorch_lstm.py
@@ -77,7 +77,6 @@ def __init__(
         self.optimizer = optimizer.lower()
         self.loss = loss
         self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
-        self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
         self.logger.info(
@@ -133,6 +132,10 @@ def __init__(
         self.fitted = False
         self.lstm_model.to(self.device)
 
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
     def mse(self, pred, label):
         loss = (pred - label) ** 2
         return torch.mean(loss)
@@ -288,10 +291,7 @@ def predict(self, dataset):
             x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
 
             with torch.no_grad():
-                if self.use_gpu:
-                    pred = self.lstm_model(x_batch).detach().cpu().numpy()
-                else:
-                    pred = self.lstm_model(x_batch).detach().numpy()
+                pred = self.lstm_model(x_batch).detach().cpu().numpy()
 
             preds.append(pred)