Update organize
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@ -1 +1 @@
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Subproject commit f809f0a0636ca7baeb8e7e98c5a8b387096e7217
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Subproject commit 253378a44e88a9fcff17d23b589e2d4832f587aa
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@ -131,9 +131,9 @@ def query_info(save_dir, verbose):
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"ICIR": "ICIR",
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"Rank IC": "Rank_IC",
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"Rank ICIR": "Rank_ICIR",
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"excess_return_with_cost.annualized_return": "Annualized_Return",
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# "excess_return_with_cost.annualized_return": "Annualized_Return",
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# "excess_return_with_cost.information_ratio": "Information_Ratio",
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"excess_return_with_cost.max_drawdown": "Max_Drawdown",
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# "excess_return_with_cost.max_drawdown": "Max_Drawdown",
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}
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all_keys = list(key_map.values())
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@ -307,25 +307,23 @@ class QuantTransformer(Model):
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def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
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if not self.fitted:
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raise ValueError("The model is not fitted yet!")
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x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
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x_test = dataset.prepare(
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segment, col_set="feature", data_key=DataHandlerLP.DK_I
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)
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index = x_test.index
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self.model.eval()
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x_values = x_test.values
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sample_num, batch_size = x_values.shape[0], self.opt_config["batch_size"]
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preds = []
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for begin in range(sample_num)[::batch_size]:
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if sample_num - begin < batch_size:
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end = sample_num
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else:
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end = begin + batch_size
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x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
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with torch.no_grad():
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pred = self.model(x_batch).detach().cpu().numpy()
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preds.append(pred)
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with torch.no_grad():
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self.model.eval()
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x_values = x_test.values
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sample_num, batch_size = x_values.shape[0], self.opt_config["batch_size"]
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preds = []
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for begin in range(sample_num)[::batch_size]:
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if sample_num - begin < batch_size:
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end = sample_num
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else:
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end = begin + batch_size
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x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
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with torch.no_grad():
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pred = self.model(x_batch).detach().cpu().numpy()
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preds.append(pred)
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return pd.Series(np.concatenate(preds), index=index)
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