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Fix bugs of model.

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This commit is contained in:
lwwang1995
2020-12-07 19:49:03 +08:00
committed by you-n-g
parent fb4a2e65cc
commit 4a748525bc
3 changed files with 20 additions and 21 deletions
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4
examples/benchmarks/GATs/workflow_config_gats_Alpha158.yaml
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@@ -51,7 +51,7 @@ task:
class: GATs class: GATs
module_path: qlib.contrib.model.pytorch_gats_ts module_path: qlib.contrib.model.pytorch_gats_ts
kwargs: kwargs:
d_feat: 6 d_feat: 20
hidden_size: 64 hidden_size: 64
num_layers: 2 num_layers: 2
dropout: 0.7 dropout: 0.7
@@ -62,7 +62,7 @@ task:
loss: mse loss: mse
base_model: LSTM base_model: LSTM
with_pretrain: True with_pretrain: True
model_path: "benchmarks/LSTM/model_lstm_ts.pkl" model_path: "benchmarks/LSTM/csi300_lstm_ts.pkl"
GPU: 0 GPU: 0
dataset: dataset:
class: TSDatasetH class: TSDatasetH

2
examples/benchmarks/SFM/workflow_config_sfm_Alpha158.yaml
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@@ -57,7 +57,7 @@ task:
num_layers: 2 num_layers: 2
dropout: 0.0 dropout: 0.0
n_epochs: 200 n_epochs: 200
lr: 1e-2 lr: 1e-1
early_stop: 10 early_stop: 10
batch_size: 800 batch_size: 800
metric: loss metric: loss

35
qlib/contrib/model/pytorch_gats_ts.py
View File

@@ -32,15 +32,16 @@ from ...contrib.model.pytorch_gru import GRUModel
class DailyBatchSampler(Sampler): class DailyBatchSampler(Sampler):
def __init__(self, data_souce):
def __init__(self, data_source):
self.data_source = data_source self.data_source = data_source
self.data = self.data_source.loc[self.data_source.get_index()] self.data = self.data_source.data.loc[self.data_source.get_index()]
self.daily_count = self.data.groupby(level=0).size().values self.daily_count = self.data.groupby(level=0).size().values
self.daily_index = np.roll(np.cumsum(self.daily_count), 1) self.daily_index = np.roll(np.cumsum(self.daily_count), 1)
def __iter__(self): def __iter__(self):
for idx, count in zip(self.daily_index, self.daily_count): for idx, count in zip(self.daily_index, self.daily_count):
yield slice(idx, idx + count) yield slice(idx, idx+count)
def __len__(self): def __len__(self):
return len(self.data_source) return len(self.data_source)
@@ -65,7 +66,7 @@ class GATs(Model):
def __init__( def __init__(
self, self,
d_feat=6, d_feat=20,
hidden_size=64, hidden_size=64,
num_layers=2, num_layers=2,
dropout=0.0, dropout=0.0,
@@ -81,7 +82,6 @@ class GATs(Model):
GPU="0", GPU="0",
n_jobs=10, n_jobs=10,
seed=None, seed=None,
batch_size=800,
**kwargs **kwargs
): ):
# Set logger. # Set logger.
@@ -106,7 +106,6 @@ class GATs(Model):
self.n_jobs = n_jobs self.n_jobs = n_jobs
self.use_gpu = torch.cuda.is_available() self.use_gpu = torch.cuda.is_available()
self.seed = seed self.seed = seed
self.batch_size = batch_size
self.logger.info( self.logger.info(
"GATs parameters setting:" "GATs parameters setting:"
@@ -201,23 +200,23 @@ class GATs(Model):
def train_epoch(self, data_loader): def train_epoch(self, data_loader):
self.ALSTM_model.train() self.GAT_model.train()
for data in data_loader: for data in data_loader:
feature = data[:, :, 0:-1].to(self.device) feature = data[:, :, 0:-1].to(self.device)
label = data[:, -1, -1].to(self.device) label = data[:, -1, -1].to(self.device)
pred = self.ALSTM_model(feature.float()) pred = self.GAT_model(feature.float())
loss = self.loss_fn(pred, label) loss = self.loss_fn(pred, label)
self.train_optimizer.zero_grad() self.train_optimizer.zero_grad()
loss.backward() loss.backward()
torch.nn.utils.clip_grad_value_(self.ALSTM_model.parameters(), 3.0) torch.nn.utils.clip_grad_value_(self.GAT_model.parameters(), 3.0)
self.train_optimizer.step() self.train_optimizer.step()
def test_epoch(self, data_loader): def test_epoch(self, data_loader):
self.ALSTM_model.eval() self.GAT_model.eval()
scores = [] scores = []
losses = [] losses = []
@@ -228,7 +227,7 @@ class GATs(Model):
# feature[torch.isnan(feature)] = 0 # feature[torch.isnan(feature)] = 0
label = data[:, -1, -1].to(self.device) label = data[:, -1, -1].to(self.device)
pred = self.ALSTM_model(feature.float()) pred = self.GAT_model(feature.float())
loss = self.loss_fn(pred, label) loss = self.loss_fn(pred, label)
losses.append(loss.item()) losses.append(loss.item())
@@ -273,10 +272,10 @@ class GATs(Model):
raise ValueError("the path of the pretrained model should be given first!") raise ValueError("the path of the pretrained model should be given first!")
self.logger.info("Loading pretrained model...") self.logger.info("Loading pretrained model...")
if self.base_model == "LSTM": if self.base_model == "LSTM":
pretrained_model = LSTMModel() pretrained_model = LSTMModel(d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers)
pretrained_model.load_state_dict(torch.load(self.model_path)) pretrained_model.load_state_dict(torch.load(self.model_path))
elif self.base_model == "GRU": elif self.base_model == "GRU":
pretrained_model = GRUModel() pretrained_model = GRUModel(d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers)
pretrained_model.load_state_dict(torch.load(self.model_path)) pretrained_model.load_state_dict(torch.load(self.model_path))
else: else:
raise ValueError("unknown base model name `%s`" % self.base_model) raise ValueError("unknown base model name `%s`" % self.base_model)
@@ -306,7 +305,7 @@ class GATs(Model):
best_score = val_score best_score = val_score
stop_steps = 0 stop_steps = 0
best_epoch = step best_epoch = step
best_param = copy.deepcopy(self.ALSTM_model.state_dict()) best_param = copy.deepcopy(self.GAT_model.state_dict())
else: else:
stop_steps += 1 stop_steps += 1
if stop_steps >= self.early_stop: if stop_steps >= self.early_stop:
@@ -314,7 +313,7 @@ class GATs(Model):
break break
self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch)) self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
self.ALSTM_model.load_state_dict(best_param) self.GAT_model.load_state_dict(best_param)
torch.save(best_param, save_path) torch.save(best_param, save_path)
if self.use_gpu: if self.use_gpu:
@@ -328,7 +327,7 @@ class GATs(Model):
dl_test.config(fillna_type="ffill+bfill") dl_test.config(fillna_type="ffill+bfill")
sampler_test = DailyBatchSampler(dl_test) sampler_test = DailyBatchSampler(dl_test)
test_loader = DataLoader(dl_test, sampler=sampler_test, num_workers=self.n_jobs) test_loader = DataLoader(dl_test, sampler=sampler_test, num_workers=self.n_jobs)
self.ALSTM_model.eval() self.GAT_model.eval()
preds = [] preds = []
for data in test_loader: for data in test_loader:
@@ -337,9 +336,9 @@ class GATs(Model):
with torch.no_grad(): with torch.no_grad():
if self.use_gpu: if self.use_gpu:
pred = self.ALSTM_model(feature.float()).detach().cpu().numpy() pred = self.GAT_model(feature.float()).detach().cpu().numpy()
else: else:
pred = self.ALSTM_model(feature.float()).detach().numpy() pred = self.GAT_model(feature.float()).detach().numpy()
preds.append(pred) preds.append(pred)