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update run_all_model and black format

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Young
2021-07-18 12:09:57 +08:00
committed by you-n-g
parent b07e0bffb1
commit 59d4bc9394
4 changed files with 41 additions and 14 deletions
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5
qlib/contrib/model/pytorch_localformer.py
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@@ -24,6 +24,7 @@ from ...model.base import Model
from ...data.dataset import DatasetH, TSDatasetH
from ...data.dataset.handler import DataHandlerLP
from torch.nn.modules.container import ModuleList
# qrun examples/benchmarks/Localformer/workflow_config_localformer_Alpha360.yaml ”
@@ -150,8 +151,8 @@ class LocalformerModel(Model):
if len(indices) - i < self.batch_size:
break
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)
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)
with torch.no_grad():
pred = self.model(feature)

1
qlib/contrib/model/pytorch_localformer_ts.py
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@@ -154,6 +154,7 @@ class LocalformerModel(Model):
dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
import pdb
pdb.set_trace()
dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader

5
qlib/contrib/model/pytorch_transformer.py
View File

@@ -23,6 +23,7 @@ from .pytorch_utils import count_parameters
from ...model.base import Model
from ...data.dataset import DatasetH, TSDatasetH
from ...data.dataset.handler import DataHandlerLP
# qrun examples/benchmarks/Transformer/workflow_config_transformer_Alpha360.yaml ”
@@ -149,8 +150,8 @@ class TransformerModel(Model):
if len(indices) - i < self.batch_size:
break
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)
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)
with torch.no_grad():
pred = self.model(feature)