| import torch |
| from torch.utils.data import Dataset |
| import numpy as np |
| class STDataset(Dataset): |
| def __init__(self, data, config,if_train=True,index=100): |
| ''' |
| data:nparray (num_nodes,day_num,24,2) |
| ''' |
|
|
| |
| |
| self.data = data |
| self.data = self.data.reshape(data.shape[0],-1,2) |
| self.input_window = 6 |
| self.output_window = 1 |
| self.if_train = if_train |
| self.index=index |
|
|
| self.max_index=(self.data.shape[1] - (self.input_window + self.output_window) + 1)//(30*24) |
| def __len__(self): |
| return self.data.shape[1] - (self.input_window + self.output_window) + 1 |
|
|
| def __getitem__(self, index): |
| |
| x = self.data[:, index:index + self.input_window, :].transpose(1,0,2) |
| y = self.data[:, index + self.input_window:index + self.input_window + self.output_window, :].reshape(-1,self.output_window,2).transpose(1,0,2) |
| |
| return torch.tensor(x,dtype=torch.float32), torch.tensor(y,dtype=torch.float32) |
| class Auxility_dataset(Dataset): |
| def __init__(self, dataset, classindex,config): |
| super(Auxility_dataset, self).__init__() |
| self.input_window = config['input_window'] |
| self.output_window = config['output_window'] |
| self.data = dataset |
| self.classindex = torch.zeros(self.data.shape[1] - (self.input_window + self.output_window) + 1,dtype=torch.float32) |
| self.classindex[-classindex:] = 1 |
| |
| def __getitem__(self, index): |
| x = self.data[:, index:index + self.input_window, :].transpose(1,0,2) |
| return torch.tensor(x,dtype=torch.float32),self.classindex[index] |
| |
| def __len__(self): |
| return self.data.shape[1] - (self.input_window + self.output_window) + 1 |
| import random |
| class TrafficDataset(Dataset): |
| def __init__(self, data, batch_size, input_window=288, output_window=1, eval_only=1): |
| self.data = data |
| self.input_window = input_window |
| self.output_window = output_window |
|
|
| |
| self.windows = [ |
| (data[i:i + input_window], data[i + input_window:i + input_window + output_window]) |
| for i in range(len(data) - input_window - output_window + 1) |
| ] |
|
|
| |
| if eval_only==False: |
| random.shuffle(self.windows) |
| if len(self.windows) % batch_size != 0: |
| self.windows = self.windows[:-(len(self.windows) % batch_size)] |
|
|
|
|
| |
| self.batches = [ |
| self.windows[i:i + batch_size] |
| for i in range(0, len(self.windows), batch_size) |
| ] |
|
|
| def __len__(self): |
| return len(self.batches) |
|
|
| def __getitem__(self, idx): |
| batch_x, batch_y = zip(*self.batches[idx]) |
| return torch.from_numpy(np.stack(batch_x)).float(), torch.from_numpy(np.stack(batch_y)).float() |
| |
|
|
