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Update pytorch_transformer.py

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Ying-Tao Luo
2021-07-14 15:50:33 +08:00
committed by you-n-g
parent dd0eebed53
commit 096ef5a62b
1 changed files with 3 additions and 46 deletions
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49
qlib/contrib/model/pytorch_transformer.py
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@@ -23,32 +23,6 @@ from ...model.base import Model
from ...data.dataset import DatasetH, TSDatasetH from ...data.dataset import DatasetH, TSDatasetH
from ...data.dataset.handler import DataHandlerLP from ...data.dataset.handler import DataHandlerLP
import pdb
# qrun benchmarks/Transformer/workflow_config_transformer_Alpha158.yaml
# 0.993681, @11,
'''
'IC': 0.03186587768611013,
'ICIR': 0.2556910881045764,
'Rank IC': 0.04735251936658551,
'Rank ICIR': 0.388378955424602
'The following are analysis results of the excess return without cost.'
risk
mean 0.000309
std 0.004209
annualized_return 0.077839
information_ratio 1.164993
max_drawdown -0.106215
'The following are analysis results of the excess return with cost.'
risk
mean 0.000126
std 0.004209
annualized_return 0.031707
information_ratio 0.474567
max_drawdown -0.131948
'''
class TransformerModel(Model): class TransformerModel(Model):
def __init__( def __init__(
@@ -87,12 +61,7 @@ class TransformerModel(Model):
self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu") self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu")
self.seed = seed self.seed = seed
self.logger = get_module_logger("TransformerModel") self.logger = get_module_logger("TransformerModel")
print('do we have gpu?{}'.format(torch.cuda.is_available())) self.logger.info("Naive Transformer:" "\nbatch_size : {}" "\ndevice : {}".format(self.batch_size, self.device))
self.logger.info(
"Naive Transformer:"
"\nbatch_size : {}"
"\ndevice : {}".format(self.batch_size, self.device)
)
if self.seed is not None: if self.seed is not None:
np.random.seed(self.seed) np.random.seed(self.seed)
@@ -160,7 +129,6 @@ class TransformerModel(Model):
for data in data_loader: for data in data_loader:
feature = data[:, :, 0:-1].to(self.device) feature = data[:, :, 0:-1].to(self.device)
# feature[torch.isnan(feature)] = 0
label = data[:, -1, -1].to(self.device) label = data[:, -1, -1].to(self.device)
with torch.no_grad(): with torch.no_grad():
@@ -265,23 +233,16 @@ class PositionalEncoding(nn.Module):
pe[:, 0::2] = torch.sin(position * div_term) pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term) pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1) pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe) self.register_buffer("pe", pe)
def forward(self, x): def forward(self, x):
# [T, N, F] # [T, N, F]
return x + self.pe[:x.size(0), :] return x + self.pe[: x.size(0), :]
class Transformer(nn.Module): class Transformer(nn.Module):
def __init__(self, d_feat=6, d_model=8, nhead=4, num_layers=2, dropout=0.5, device=None): def __init__(self, d_feat=6, d_model=8, nhead=4, num_layers=2, dropout=0.5, device=None):
super(Transformer, self).__init__() super(Transformer, self).__init__()
self.rnn = nn.GRU(
input_size=d_feat,
hidden_size=d_model,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
self.feature_layer = nn.Linear(d_feat, d_model) self.feature_layer = nn.Linear(d_feat, d_model)
self.pos_encoder = PositionalEncoding(d_model) self.pos_encoder = PositionalEncoding(d_model)
self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout) self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
@@ -291,10 +252,7 @@ class Transformer(nn.Module):
self.d_feat = d_feat self.d_feat = d_feat
def forward(self, src): def forward(self, src):
# pdb.set_trace()
# src [N, T, F], [512, 60, 6] # src [N, T, F], [512, 60, 6]
# out, _ = self.rnn(src)
src = self.feature_layer(src) # [512, 60, 8] src = self.feature_layer(src) # [512, 60, 8]
# src [N, T, F] --> [T, N, F], [60, 512, 8] # src [N, T, F] --> [T, N, F], [60, 512, 8]
@@ -309,4 +267,3 @@ class Transformer(nn.Module):
output = self.decoder_layer(output.transpose(1, 0)[:, -1, :]) # [512, 1] output = self.decoder_layer(output.transpose(1, 0)[:, -1, :]) # [512, 1]
return output.squeeze() return output.squeeze()