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update dnn

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This commit is contained in:
bxdd
2020-11-27 13:00:14 +08:00
parent 52c7076917
commit a144a9c3c6
4 changed files with 36 additions and 19 deletions
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16
qlib/contrib/model/pytorch_nn.py
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@@ -20,7 +20,7 @@ from ...data.dataset import DatasetH
from ...data.dataset.handler import DataHandlerLP
from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, create_save_path, drop_nan_by_y_index
from ...log import get_module_logger, TimeInspector
from ...workflow import R
class DNNModelPytorch(Model):
"""DNN Model
@@ -151,7 +151,6 @@ class DNNModelPytorch(Model):
verbose=True,
save_path=None,
):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
)
@@ -170,7 +169,6 @@ class DNNModelPytorch(Model):
best_loss = np.inf
evals_result["train"] = []
evals_result["valid"] = []
# train
self.logger.info("training...")
self._fitted = True
@@ -184,9 +182,6 @@ class DNNModelPytorch(Model):
x_val_auto = torch.from_numpy(x_valid.values).float()
y_val_auto = torch.from_numpy(y_valid.values).float()
w_val_auto = torch.from_numpy(w_valid.values).float()
#print('valiadationx:', x_val_auto)
#print('valiadationy:', y_val_auto)
#print('valiadationw:', w_val_auto)
if self.use_GPU:
x_val_auto = x_val_auto.cuda()
y_val_auto = y_val_auto.cuda()
@@ -200,7 +195,6 @@ class DNNModelPytorch(Model):
loss = AverageMeter()
self.dnn_model.train()
self.train_optimizer.zero_grad()
choice = np.random.choice(train_num, self.batch_size)
x_batch_auto = x_train_values[choice]
y_batch_auto = y_train_values[choice]
@@ -213,16 +207,14 @@ class DNNModelPytorch(Model):
# forward
preds = self.dnn_model(x_batch_auto)
#print('pred_train:', preds.detach().cpu().numpy())
#print('label_train:', y_batch_auto.cpu().numpy())
cur_loss = self.get_loss(preds, w_batch_auto, y_batch_auto, self.loss_type)
cur_loss.backward()
self.train_optimizer.step()
loss.update(cur_loss.item())
R.log_metrics(train_loss=loss.avg, step=step)
# validation
train_loss += loss.val
# print(loss.val)
if step and step % self.eval_steps == 0:
stop_steps += 1
train_loss /= self.eval_steps
@@ -232,10 +224,10 @@ class DNNModelPytorch(Model):
loss_val = AverageMeter()
# forward
preds = self.dnn_model(x_val_auto)
cur_loss_val = self.get_loss(preds, w_val_auto, y_val_auto, self.loss_type)
loss_val.update(cur_loss_val.item())
R.log_metrics(val_loss=loss_val.val, step=step)
if verbose:
self.logger.info(
"[Epoch {}]: train_loss {:.6f}, valid_loss {:.6f}".format(step, train_loss, loss_val.val)
@@ -276,7 +268,6 @@ class DNNModelPytorch(Model):
if not self._fitted:
raise ValueError("model is not fitted yet!")
x_test_pd = dataset.prepare("test", col_set="feature")
print(x_test_pd)
x_test = torch.from_numpy(x_test_pd.values).float()
if self.use_GPU:
x_test = x_test.cuda()
@@ -287,7 +278,6 @@ class DNNModelPytorch(Model):
preds = self.dnn_model(x_test).detach().cpu().numpy()
else:
preds = self.dnn_model(x_test).detach().numpy()
print(preds)
return pd.Series(np.squeeze(preds), index=x_test_pd.index)
def save(self, filename, **kwargs):