以WTH为例

import os
import numpy as np
import pandas as pdimport torch
from torch.utils.data import Dataset, DataLoader
# from sklearn.preprocessing import StandardScalerfrom utils.tools import StandardScaler
from utils.timefeatures import time_featuresimport warnings
warnings.filterwarnings('ignore')

1 WTH

1.1 数据

1.2 Dataset_Custom

1.2.1 __init__

class Dataset_Custom(Dataset):def __init__(self, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv', target='OT', scale=True, inverse=False, timeenc=0, freq='h', cols=None):# size [seq_len, label_len, pred_len]# infoif size == None:self.seq_len = 24*4*4self.label_len = 24*4self.pred_len = 24*4else:self.seq_len = size[0]self.label_len = size[1]self.pred_len = size[2]#设置seq_len,label_len和pred_len# initassert flag in ['train', 'test', 'val']type_map = {'train':0, 'val':1, 'test':2}self.set_type = type_map[flag]self.features = featuresself.target = targetself.scale = scaleself.inverse = inverseself.timeenc = timeencself.freq = freqself.cols=colsself.root_path = root_pathself.data_path = data_pathself.__read_data__()

1.2.2 __read_data__

def __read_data__(self):self.scaler = StandardScaler()df_raw = pd.read_csv(os.path.join(self.root_path,self.data_path))if self.cols:cols=self.cols.copy()cols.remove(self.target)else:cols = list(df_raw.columns); cols.remove(self.target); cols.remove('date')df_raw = df_raw[['date']+cols+[self.target]]#数据被重新组织，使得日期列在前，然后是其他特征列，最后是目标特征列。#对于Weather，只有target=WetBulbCelsius和date，没有colsnum_train = int(len(df_raw)*0.7)num_test = int(len(df_raw)*0.2)num_vali = len(df_raw) - num_train - num_test#数据集被划分为70%训练集、20%测试集和剩余的作为验证集。border1s = [0, num_train-self.seq_len, len(df_raw)-num_test-self.seq_len]border2s = [num_train, num_train+num_vali, len(df_raw)]border1 = border1s[self.set_type]border2 = border2s[self.set_type]#根据是train还是test还是vali，计算这些集合的边界，即数据中的索引位置。if self.features=='M' or self.features=='MS':cols_data = df_raw.columns[1:]df_data = df_raw[cols_data]elif self.features=='S':df_data = df_raw[[self.target]]'''根据self.features的值选择特征。如果是'M'或'MS'，选择除日期外的所有特征； [多变量预测多变量,多变量预测单变量]如果是'S'，只选择目标特征。 [S：单变量预测单变量]'''if self.scale:train_data = df_data[border1s[0]:border2s[0]]self.scaler.fit(train_data.values)data = self.scaler.transform(df_data.values)else:data = df_data.valuesdf_stamp = df_raw[['date']][border1:border2]df_stamp['date'] = pd.to_datetime(df_stamp.date)data_stamp = time_features(df_stamp, timeenc=self.timeenc, freq=self.freq)'''从数据中提取时间戳，并转换成pandas的datetime对象。使用time_features函数来提取时间相关的特征。'''self.data_x = data[border1:border2]if self.inverse:self.data_y = df_data.values[border1:border2]else:self.data_y = data[border1:border2]self.data_stamp = data_stamp'''self.data_x: 用于模型训练的特征数据。self.data_y: 目标数据，如果self.inverse为真，则使用原始数据，否则使用标准化后的数据。(和data_x一样）self.data_stamp: 时间特征数据。'''

1.2.3 其他

'''
获取数据集的一个样本，其中index是样本的索引seq_x,seq_y:特征序列，目标序列
seq_x_mark,seq_y_mark:相对应的时间特征序列
'''def __getitem__(self, index):s_begin = indexs_end = s_begin + self.seq_lenr_begin = s_end - self.label_len r_end = r_begin + self.label_len + self.pred_lenseq_x = self.data_x[s_begin:s_end]if self.inverse:seq_y = np.concatenate([self.data_x[r_begin:r_begin+self.label_len], self.data_y[r_begin+self.label_len:r_end]], 0)else:seq_y = self.data_y[r_begin:r_end]seq_x_mark = self.data_stamp[s_begin:s_end]seq_y_mark = self.data_stamp[r_begin:r_end]return seq_x, seq_y, seq_x_mark, seq_y_mark'''
返回数据集中样本的总数
'''    def __len__(self):return len(self.data_x) - self.seq_len- self.pred_len + 1def inverse_transform(self, data):return self.scaler.inverse_transform(data)