Datasets:
File size: 2,901 Bytes
aa0c0b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | 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)
'''
# 根据索引创建新的data数组
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
# preprocess
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)
]
# drop_last & shuffle
if eval_only==False:
random.shuffle(self.windows)
if len(self.windows) % batch_size != 0:
self.windows = self.windows[:-(len(self.windows) % batch_size)]
# batch
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()
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