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import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn.functional as F
from sklearn.preprocessing import MinMaxScaler, StandardScaler
import numpy as np
class TimeDataset(Dataset):
def __init__(self, raw_data, edge_index, mode='train', config = None):
self.raw_data = raw_data
self.config = config
self.edge_index = edge_index
self.mode = mode
x_data = raw_data[:-1]
labels = raw_data[-1]
data = x_data
# to tensor
data = torch.tensor(data).double()
labels = torch.tensor(labels).double()
self.x, self.y, self.labels = self.process(data, labels)
def __len__(self):
return len(self.x)
def process(self, data, labels):
x_arr, y_arr = [], []
labels_arr = []
slide_win, slide_stride = [self.config[k] for k
in ['slide_win', 'slide_stride']
]
is_train = self.mode == 'train'
node_num, total_time_len = data.shape
rang = range(slide_win, total_time_len, slide_stride) if is_train else range(slide_win, total_time_len)
for i in rang:
ft = data[:, i-slide_win:i]
tar = data[:, i]
x_arr.append(ft)
y_arr.append(tar)
labels_arr.append(labels[i])
x = torch.stack(x_arr).contiguous()
y = torch.stack(y_arr).contiguous()
labels = torch.Tensor(labels_arr).contiguous()
return x, y, labels
def __getitem__(self, idx):
feature = self.x[idx].double()
y = self.y[idx].double()
edge_index = self.edge_index.long()
label = self.labels[idx].double()
return feature, y, label, edge_index