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Sync qlib

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D-X-Y
2021-03-17 09:10:45 +00:00
parent 1ba1585f20
commit a9093e41e1
4 changed files with 20 additions and 12 deletions
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2
.latent-data/qlib

Submodule .latent-data/qlib updated: aa552fdb20...6ef204f190

1
exps/trading/organize_results.py
View File

@@ -111,6 +111,7 @@ def query_info(save_dir, verbose):
experiments = R.list_experiments() experiments = R.list_experiments()
key_map = { key_map = {
"RMSE": "RMSE",
"IC": "IC", "IC": "IC",
"ICIR": "ICIR", "ICIR": "ICIR",
"Rank IC": "Rank_IC", "Rank IC": "Rank_IC",

6
lib/procedures/q_exps.py
View File

@@ -68,7 +68,7 @@ def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
model_fit_kwargs = dict(dataset=dataset) model_fit_kwargs = dict(dataset=dataset)
# Let's start the experiment. # Let's start the experiment.
with R.start(experiment_name=experiment_name, recorder_name=recorder_name, uri=uri): with R.start(experiment_name=experiment_name, recorder_name=recorder_name, uri=uri, resume=True):
# Setup log # Setup log
recorder_root_dir = R.get_recorder().get_local_dir() recorder_root_dir = R.get_recorder().get_local_dir()
log_file = os.path.join(recorder_root_dir, "{:}.log".format(experiment_name)) log_file = os.path.join(recorder_root_dir, "{:}.log".format(experiment_name))
@@ -81,7 +81,9 @@ def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
# Train model # Train model
R.log_params(**flatten_dict(task_config)) R.log_params(**flatten_dict(task_config))
if "save_path" in inspect.getfullargspec(model.fit).args: if "save_path" in inspect.getfullargspec(model.fit).args:
model_fit_kwargs["save_path"] = os.path.join(recorder_root_dir, "model.ckps") model_fit_kwargs["save_path"] = os.path.join(recorder_root_dir, "model.ckp")
elif "save_dir" in inspect.getfullargspec(model.fit).args:
model_fit_kwargs["save_dir"] = os.path.join(recorder_root_dir, "model-ckps")
model.fit(**model_fit_kwargs) model.fit(**model_fit_kwargs)
# Get the recorder # Get the recorder
recorder = R.get_recorder() recorder = R.get_recorder()

23
lib/trade_models/quant_transformer.py
View File

@@ -138,7 +138,7 @@ class QuantTransformer(Model):
def fit( def fit(
self, self,
dataset: DatasetH, dataset: DatasetH,
save_path: Optional[Text] = None, save_dir: Optional[Text] = None,
): ):
def _prepare_dataset(df_data): def _prepare_dataset(df_data):
return th_data.TensorDataset( return th_data.TensorDataset(
@@ -172,8 +172,8 @@ class QuantTransformer(Model):
_prepare_loader(test_dataset, False), _prepare_loader(test_dataset, False),
) )
save_path = get_or_create_path(save_path, return_dir=True) save_dir = get_or_create_path(save_dir, return_dir=True)
self.logger.info("Fit procedure for [{:}] with save path={:}".format(self.__class__.__name__, save_path)) self.logger.info("Fit procedure for [{:}] with save path={:}".format(self.__class__.__name__, save_dir))
def _internal_test(ckp_epoch=None, results_dict=None): def _internal_test(ckp_epoch=None, results_dict=None):
with torch.no_grad(): with torch.no_grad():
@@ -196,15 +196,18 @@ class QuantTransformer(Model):
return dict(train=train_score, valid=valid_score, test=test_score), xstr return dict(train=train_score, valid=valid_score, test=test_score), xstr
# Pre-fetch the potential checkpoints # Pre-fetch the potential checkpoints
ckp_path = os.path.join(save_path, "{:}.pth".format(self.__class__.__name__)) ckp_path = os.path.join(save_dir, "{:}.pth".format(self.__class__.__name__))
if os.path.exists(ckp_path): if os.path.exists(ckp_path):
ckp_data = torch.load(ckp_path) ckp_data = torch.load(ckp_path)
import pdb stop_steps, best_score, best_epoch = ckp_data['stop_steps'], ckp_data['best_score'], ckp_data['best_epoch']
start_epoch, best_param = ckp_data['start_epoch'], ckp_data['best_param']
pdb.set_trace() results_dict = ckp_data['results_dict']
self.model.load_state_dict(ckp_data['net_state_dict'])
self.train_optimizer.load_state_dict(ckp_data['opt_state_dict'])
self.logger.info("Resume from existing checkpoint: {:}".format(ckp_path))
else: else:
stop_steps, best_score, best_epoch = 0, -np.inf, -1 stop_steps, best_score, best_epoch = 0, -np.inf, -1
start_epoch = 0 start_epoch, best_param = 0, None
results_dict = dict(train=OrderedDict(), valid=OrderedDict(), test=OrderedDict()) results_dict = dict(train=OrderedDict(), valid=OrderedDict(), test=OrderedDict())
_, eval_str = _internal_test(-1, results_dict) _, eval_str = _internal_test(-1, results_dict)
self.logger.info("Training from scratch, metrics@start: {:}".format(eval_str)) self.logger.info("Training from scratch, metrics@start: {:}".format(eval_str))
@@ -215,7 +218,6 @@ class QuantTransformer(Model):
iepoch, self.opt_config["epochs"], best_epoch, best_score iepoch, self.opt_config["epochs"], best_epoch, best_score
) )
) )
train_loss, train_score = self.train_or_test_epoch( train_loss, train_score = self.train_or_test_epoch(
train_loader, self.model, self.loss_fn, self.metric_fn, True, self.train_optimizer train_loader, self.model, self.loss_fn, self.metric_fn, True, self.train_optimizer
) )
@@ -241,11 +243,14 @@ class QuantTransformer(Model):
stop_steps=stop_steps, stop_steps=stop_steps,
best_score=best_score, best_score=best_score,
best_epoch=best_epoch, best_epoch=best_epoch,
results_dict=results_dict,
start_epoch=iepoch + 1, start_epoch=iepoch + 1,
) )
torch.save(save_info, ckp_path) torch.save(save_info, ckp_path)
self.logger.info("The best score: {:.6f} @ {:02d}-th epoch".format(best_score, best_epoch)) self.logger.info("The best score: {:.6f} @ {:02d}-th epoch".format(best_score, best_epoch))
self.model.load_state_dict(best_param) self.model.load_state_dict(best_param)
_, eval_str = _internal_test('final', results_dict)
self.logger.info("Reload the best parameter :: {:}".format(eval_str))
if self.use_gpu: if self.use_gpu:
torch.cuda.empty_cache() torch.cuda.empty_cache()