import os import pandas as pd from torch.utils.data import Dataset from sklearn.preprocessing import StandardScaler from utils.timefeatures import time_features import warnings warnings.filterwarnings('ignore') class Dataset_ETT_hour(Dataset): def __init__(self, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv', target='OT', scale=True, timeenc=0, freq='h', percent=100, seasonal_patterns=None, pretrain=True): if size == None: self.seq_len = 24 * 4 * 4 self.label_len = 24 * 4 self.pred_len = 24 * 4 else: self.seq_len = size[0] self.label_len = size[1] self.pred_len = size[2] # init assert flag in ['train', 'test', 'val'] type_map = {'train': 0, 'val': 1, 'test': 2} self.set_type = type_map[flag] self.percent = percent self.pretrain = pretrain self.features = features self.target = target self.scale = scale self.timeenc = timeenc self.freq = freq # self.percent = percent self.root_path = root_path self.data_path = data_path self.__read_data__() self.enc_in = self.data_x.shape[-1] self.tot_len = len(self.data_x) - self.seq_len - self.pred_len + 1 def __read_data__(self): self.scaler = StandardScaler() df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path)) if self.pretrain: # border1s = [0, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len] # border2s = [12 * 30 * 24 + 8 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24] border1s = [0, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len] border2s = [12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24] else: border1s = [0, 12 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len] border2s = [12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24] border1 = border1s[self.set_type] border2 = border2s[self.set_type] if self.set_type == 0: border2 = (border2 - self.seq_len) * self.percent // 100 + self.seq_len 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]] 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.values df_stamp = df_raw[['date']][border1:border2] df_stamp['date'] = pd.to_datetime(df_stamp.date) if self.timeenc == 0: df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1) df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1) df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1) df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1) data_stamp = df_stamp.drop(['date'], 1).values elif self.timeenc == 1: data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq) data_stamp = data_stamp.transpose(1, 0) self.data_x = data[border1:border2] self.data_y = data[border1:border2] self.data_stamp = data_stamp def __getitem__(self, index): feat_id = index // self.tot_len s_begin = index % self.tot_len s_end = s_begin + self.seq_len r_begin = s_end - self.label_len r_end = r_begin + self.label_len + self.pred_len seq_x = self.data_x[s_begin:s_end, feat_id:feat_id + 1] seq_y = self.data_y[r_begin:r_end, feat_id:feat_id + 1] 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 def __len__(self): return (len(self.data_x) - self.seq_len - self.pred_len + 1) * self.enc_in def inverse_transform(self, data): return self.scaler.inverse_transform(data) class Dataset_ETT_minute(Dataset): def __init__(self, root_path, flag='train', size=None, features='S', data_path='ETTm1.csv', target='OT', scale=True, timeenc=0, freq='t', percent=100, seasonal_patterns=None, pretrain=True): if size == None: self.seq_len = 24 * 4 * 4 self.label_len = 24 * 4 self.pred_len = 24 * 4 else: self.seq_len = size[0] self.label_len = size[1] self.pred_len = size[2] # init assert flag in ['train', 'test', 'val'] type_map = {'train': 0, 'val': 1, 'test': 2} self.set_type = type_map[flag] self.percent = percent self.pretrain = pretrain self.features = features self.target = target self.scale = scale self.timeenc = timeenc self.freq = freq self.root_path = root_path self.data_path = data_path self.__read_data__() self.enc_in = self.data_x.shape[-1] self.tot_len = len(self.data_x) - self.seq_len - self.pred_len + 1 def __read_data__(self): self.scaler = StandardScaler() df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path)) if self.pretrain: # border1s = [0, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len, # 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len] # border2s = [12 * 30 * 24 * 4 + 8 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4, # 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4] border1s = [0, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len] border2s = [12 * 30 * 24 * 4 + 4 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4] else: border1s = [0, 12 * 30 * 24 * 4 - self.seq_len, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len] border2s = [12 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4] border1 = border1s[self.set_type] border2 = border2s[self.set_type] if self.set_type == 0: border2 = (border2 - self.seq_len) * self.percent // 100 + self.seq_len 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]] 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.values df_stamp = df_raw[['date']][border1:border2] df_stamp['date'] = pd.to_datetime(df_stamp.date) if self.timeenc == 0: df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1) df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1) df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1) df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1) df_stamp['minute'] = df_stamp.date.apply(lambda row: row.minute, 1) df_stamp['minute'] = df_stamp.minute.map(lambda x: x // 15) data_stamp = df_stamp.drop(['date'], 1).values elif self.timeenc == 1: data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq) data_stamp = data_stamp.transpose(1, 0) self.data_x = data[border1:border2] self.data_y = data[border1:border2] self.data_stamp = data_stamp def __getitem__(self, index): feat_id = index // self.tot_len s_begin = index % self.tot_len s_end = s_begin + self.seq_len r_begin = s_end - self.label_len r_end = r_begin + self.label_len + self.pred_len seq_x = self.data_x[s_begin:s_end, feat_id:feat_id + 1] seq_y = self.data_y[r_begin:r_end, feat_id:feat_id + 1] 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 def __len__(self): return (len(self.data_x) - self.seq_len - self.pred_len + 1) * self.enc_in def inverse_transform(self, data): return self.scaler.inverse_transform(data)