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| class StockDataset(Dataset): | |
| def __init__(self, df_seq, feat_num, seq_len, target_len, df_cat): | |
| # SEQUENTIAL PART | |
| self.df_seq = df_seq.iloc[:,:-target_len] | |
| self.df_cat = df_cat | |
| self.target = df_seq.iloc[:,-target_len:] | |
| def __getitem__(self, index): | |
| return(torch.tensor(self.df_seq.iloc[index].values.reshape(seq_len,feat_num), dtype=torch.float, device=device), | |
| torch.tensor(self.df_cat.iloc[index], dtype=torch.long, device=device), | |
| torch.tensor(self.target.iloc[index], dtype=torch.float, device=device)) | |
| def __len__(self): | |
| return(self.df_seq.shape[0]) | |
| feat_num = 6 | |
| seq_len = 30 | |
| target_len = 5 | |
| bs = 32 | |
| train_ds = StockDataset(df_seq_train, feat_num, seq_len, target_len, df_dt_feat_train) | |
| train_dl = DataLoader(train_ds, batch_size=bs, shuffle=False) | |
| test_ds = StockDataset(df_seq_test, feat_num, seq_len, target_len, df_dt_feat_test) | |
| test_dl = DataLoader(test_ds, batch_size=len(test_ds), shuffle=False) |
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