mirror of
https://github.com/KimMeen/Time-LLM.git
synced 2024-11-21 03:13:47 +08:00
222 lines
8.8 KiB
Python
222 lines
8.8 KiB
Python
import os
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import pandas as pd
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from torch.utils.data import Dataset
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from sklearn.preprocessing import StandardScaler
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from utils.timefeatures import time_features
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import warnings
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warnings.filterwarnings('ignore')
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class Dataset_ETT_hour(Dataset):
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def __init__(self, root_path, flag='train', size=None,
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features='S', data_path='ETTh1.csv',
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target='OT', scale=True, timeenc=0, freq='h', percent=100,
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seasonal_patterns=None, pretrain=True):
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if size == None:
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self.seq_len = 24 * 4 * 4
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self.label_len = 24 * 4
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self.pred_len = 24 * 4
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else:
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self.seq_len = size[0]
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self.label_len = size[1]
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self.pred_len = size[2]
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# init
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assert flag in ['train', 'test', 'val']
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type_map = {'train': 0, 'val': 1, 'test': 2}
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self.set_type = type_map[flag]
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self.percent = percent
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self.pretrain = pretrain
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self.features = features
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self.target = target
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self.scale = scale
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self.timeenc = timeenc
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self.freq = freq
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# self.percent = percent
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self.root_path = root_path
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self.data_path = data_path
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self.__read_data__()
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self.enc_in = self.data_x.shape[-1]
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self.tot_len = len(self.data_x) - self.seq_len - self.pred_len + 1
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def __read_data__(self):
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self.scaler = StandardScaler()
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df_raw = pd.read_csv(os.path.join(self.root_path,
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self.data_path))
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if self.pretrain:
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# border1s = [0, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len]
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# border2s = [12 * 30 * 24 + 8 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24]
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border1s = [0, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len]
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border2s = [12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24]
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else:
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border1s = [0, 12 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len]
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border2s = [12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24]
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border1 = border1s[self.set_type]
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border2 = border2s[self.set_type]
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if self.set_type == 0:
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border2 = (border2 - self.seq_len) * self.percent // 100 + self.seq_len
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if self.features == 'M' or self.features == 'MS':
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cols_data = df_raw.columns[1:]
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df_data = df_raw[cols_data]
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elif self.features == 'S':
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df_data = df_raw[[self.target]]
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if self.scale:
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train_data = df_data[border1s[0]:border2s[0]]
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self.scaler.fit(train_data.values)
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data = self.scaler.transform(df_data.values)
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else:
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data = df_data.values
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df_stamp = df_raw[['date']][border1:border2]
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df_stamp['date'] = pd.to_datetime(df_stamp.date)
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if self.timeenc == 0:
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df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
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df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
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df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
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df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
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data_stamp = df_stamp.drop(['date'], 1).values
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elif self.timeenc == 1:
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data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
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data_stamp = data_stamp.transpose(1, 0)
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self.data_x = data[border1:border2]
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self.data_y = data[border1:border2]
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self.data_stamp = data_stamp
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def __getitem__(self, index):
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feat_id = index // self.tot_len
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s_begin = index % self.tot_len
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s_end = s_begin + self.seq_len
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r_begin = s_end - self.label_len
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r_end = r_begin + self.label_len + self.pred_len
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seq_x = self.data_x[s_begin:s_end, feat_id:feat_id + 1]
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seq_y = self.data_y[r_begin:r_end, feat_id:feat_id + 1]
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seq_x_mark = self.data_stamp[s_begin:s_end]
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seq_y_mark = self.data_stamp[r_begin:r_end]
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return seq_x, seq_y, seq_x_mark, seq_y_mark
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def __len__(self):
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return (len(self.data_x) - self.seq_len - self.pred_len + 1) * self.enc_in
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def inverse_transform(self, data):
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return self.scaler.inverse_transform(data)
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class Dataset_ETT_minute(Dataset):
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def __init__(self, root_path, flag='train', size=None,
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features='S', data_path='ETTm1.csv',
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target='OT', scale=True, timeenc=0, freq='t', percent=100,
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seasonal_patterns=None, pretrain=True):
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if size == None:
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self.seq_len = 24 * 4 * 4
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self.label_len = 24 * 4
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self.pred_len = 24 * 4
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else:
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self.seq_len = size[0]
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self.label_len = size[1]
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self.pred_len = size[2]
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# init
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assert flag in ['train', 'test', 'val']
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type_map = {'train': 0, 'val': 1, 'test': 2}
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self.set_type = type_map[flag]
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self.percent = percent
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self.pretrain = pretrain
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self.features = features
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self.target = target
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self.scale = scale
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self.timeenc = timeenc
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self.freq = freq
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self.root_path = root_path
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self.data_path = data_path
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self.__read_data__()
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self.enc_in = self.data_x.shape[-1]
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self.tot_len = len(self.data_x) - self.seq_len - self.pred_len + 1
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def __read_data__(self):
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self.scaler = StandardScaler()
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df_raw = pd.read_csv(os.path.join(self.root_path,
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self.data_path))
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if self.pretrain:
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# border1s = [0, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len,
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# 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len]
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# border2s = [12 * 30 * 24 * 4 + 8 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4,
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# 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4]
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border1s = [0, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len,
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12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len]
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border2s = [12 * 30 * 24 * 4 + 4 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4,
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12 * 30 * 24 * 4 + 8 * 30 * 24 * 4]
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else:
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border1s = [0, 12 * 30 * 24 * 4 - self.seq_len, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len]
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border2s = [12 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4]
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border1 = border1s[self.set_type]
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border2 = border2s[self.set_type]
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if self.set_type == 0:
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border2 = (border2 - self.seq_len) * self.percent // 100 + self.seq_len
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if self.features == 'M' or self.features == 'MS':
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cols_data = df_raw.columns[1:]
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df_data = df_raw[cols_data]
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elif self.features == 'S':
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df_data = df_raw[[self.target]]
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if self.scale:
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train_data = df_data[border1s[0]:border2s[0]]
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self.scaler.fit(train_data.values)
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data = self.scaler.transform(df_data.values)
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else:
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data = df_data.values
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df_stamp = df_raw[['date']][border1:border2]
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df_stamp['date'] = pd.to_datetime(df_stamp.date)
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if self.timeenc == 0:
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df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
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df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
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df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
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df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
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df_stamp['minute'] = df_stamp.date.apply(lambda row: row.minute, 1)
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df_stamp['minute'] = df_stamp.minute.map(lambda x: x // 15)
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data_stamp = df_stamp.drop(['date'], 1).values
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elif self.timeenc == 1:
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data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
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data_stamp = data_stamp.transpose(1, 0)
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self.data_x = data[border1:border2]
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self.data_y = data[border1:border2]
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self.data_stamp = data_stamp
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def __getitem__(self, index):
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feat_id = index // self.tot_len
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s_begin = index % self.tot_len
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s_end = s_begin + self.seq_len
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r_begin = s_end - self.label_len
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r_end = r_begin + self.label_len + self.pred_len
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seq_x = self.data_x[s_begin:s_end, feat_id:feat_id + 1]
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seq_y = self.data_y[r_begin:r_end, feat_id:feat_id + 1]
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seq_x_mark = self.data_stamp[s_begin:s_end]
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seq_y_mark = self.data_stamp[r_begin:r_end]
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return seq_x, seq_y, seq_x_mark, seq_y_mark
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def __len__(self):
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return (len(self.data_x) - self.seq_len - self.pred_len + 1) * self.enc_in
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def inverse_transform(self, data):
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return self.scaler.inverse_transform(data)
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