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Update organize

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D-X-Y
2021-03-29 04:23:33 +00:00
parent 53cb5f1fdd
commit e637cddc39
3 changed files with 20 additions and 22 deletions
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2
.latent-data/qlib

Submodule .latent-data/qlib updated: f809f0a063...253378a44e

4
exps/trading/organize_results.py
View File

@@ -131,9 +131,9 @@ def query_info(save_dir, verbose):
"ICIR": "ICIR", "ICIR": "ICIR",
"Rank IC": "Rank_IC", "Rank IC": "Rank_IC",
"Rank ICIR": "Rank_ICIR", "Rank ICIR": "Rank_ICIR",
"excess_return_with_cost.annualized_return": "Annualized_Return", # "excess_return_with_cost.annualized_return": "Annualized_Return",
# "excess_return_with_cost.information_ratio": "Information_Ratio", # "excess_return_with_cost.information_ratio": "Information_Ratio",
"excess_return_with_cost.max_drawdown": "Max_Drawdown", # "excess_return_with_cost.max_drawdown": "Max_Drawdown",
} }
all_keys = list(key_map.values()) all_keys = list(key_map.values())

10
lib/trade_models/quant_transformer.py
View File

@@ -307,25 +307,23 @@ class QuantTransformer(Model):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"): def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if not self.fitted: if not self.fitted:
raise ValueError("The model is not fitted yet!") raise ValueError("The model is not fitted yet!")
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I) x_test = dataset.prepare(
segment, col_set="feature", data_key=DataHandlerLP.DK_I
)
index = x_test.index index = x_test.index
with torch.no_grad():
self.model.eval() self.model.eval()
x_values = x_test.values x_values = x_test.values
sample_num, batch_size = x_values.shape[0], self.opt_config["batch_size"] sample_num, batch_size = x_values.shape[0], self.opt_config["batch_size"]
preds = [] preds = []
for begin in range(sample_num)[::batch_size]: for begin in range(sample_num)[::batch_size]:
if sample_num - begin < batch_size: if sample_num - begin < batch_size:
end = sample_num end = sample_num
else: else:
end = begin + batch_size end = begin + batch_size
x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device) x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
with torch.no_grad(): with torch.no_grad():
pred = self.model(x_batch).detach().cpu().numpy() pred = self.model(x_batch).detach().cpu().numpy()
preds.append(pred) preds.append(pred)
return pd.Series(np.concatenate(preds), index=index) return pd.Series(np.concatenate(preds), index=index)