331 lines
12 KiB
Python
331 lines
12 KiB
Python
import os
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import numpy as np
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import pandas as pd
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import os
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import torch
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from torch.utils.data import Dataset, DataLoader
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from sklearn.preprocessing import StandardScaler
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from utils.timefeatures import time_features
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from utils.tools import convert_tsf_to_dataframe
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import warnings
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from pathlib import Path
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warnings.filterwarnings('ignore')
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class Dataset_Custom(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',
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percent=10, max_len=-1, train_all=False):
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# size [seq_len, label_len, pred_len]
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# info
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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.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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'''
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df_raw.columns: ['date', ...(other features), target feature]
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'''
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cols = list(df_raw.columns)
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cols.remove(self.target)
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cols.remove('date')
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df_raw = df_raw[['date'] + cols + [self.target]]
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# print(cols)
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num_train = int(len(df_raw) * 0.7)
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num_test = int(len(df_raw) * 0.2)
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num_vali = len(df_raw) - num_train - num_test
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border1s = [0, num_train - self.seq_len, len(df_raw) - num_test - self.seq_len]
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border2s = [num_train, num_train + num_vali, len(df_raw)]
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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_Pred(Dataset):
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def __init__(self, root_path, flag='pred', size=None,
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features='S', data_path='ETTh1.csv',
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target='OT', scale=True, inverse=False, timeenc=0, freq='15min', cols=None,
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percent=None, train_all=False):
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# size [seq_len, label_len, pred_len]
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# info
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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 ['pred']
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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.inverse = inverse
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self.timeenc = timeenc
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self.freq = freq
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self.cols = cols
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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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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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'''
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df_raw.columns: ['date', ...(other features), target feature]
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'''
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if self.cols:
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cols = self.cols.copy()
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cols.remove(self.target)
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else:
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cols = list(df_raw.columns)
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cols.remove(self.target)
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cols.remove('date')
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df_raw = df_raw[['date'] + cols + [self.target]]
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border1 = len(df_raw) - self.seq_len
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border2 = len(df_raw)
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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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self.scaler.fit(df_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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tmp_stamp = df_raw[['date']][border1:border2]
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tmp_stamp['date'] = pd.to_datetime(tmp_stamp.date)
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pred_dates = pd.date_range(tmp_stamp.date.values[-1], periods=self.pred_len + 1, freq=self.freq)
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df_stamp = pd.DataFrame(columns=['date'])
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df_stamp.date = list(tmp_stamp.date.values) + list(pred_dates[1:])
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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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if self.inverse:
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self.data_y = df_data.values[border1:border2]
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else:
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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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s_begin = index
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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]
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if self.inverse:
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seq_y = self.data_x[r_begin:r_begin + self.label_len]
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else:
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seq_y = self.data_y[r_begin:r_begin + self.label_len]
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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 + 1
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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_TSF(Dataset):
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def __init__(self, root_path, flag='train', size=None,
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features='S', data_path=None,
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target='OT', scale=True, timeenc=0, freq='Daily',
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percent=10, max_len=-1, train_all=False):
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self.train_all = train_all
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self.seq_len = size[0]
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self.pred_len = size[2]
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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.max_len = max_len
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if self.max_len == -1:
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self.max_len = 1e8
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self.root_path = root_path
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self.data_path = data_path
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self.timeseries = self.__read_data__()
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def __read_data__(self):
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df, frequency, forecast_horizon, contain_missing_values, contain_equal_length = convert_tsf_to_dataframe(os.path.join(self.root_path,
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self.data_path))
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self.freq = frequency
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def dropna(x):
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return x[~np.isnan(x)]
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timeseries = [dropna(ts).astype(np.float32) for ts in df.series_value]
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self.tot_len = 0
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self.len_seq = []
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self.seq_id = []
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for i in range(len(timeseries)):
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res_len = max(self.pred_len + self.seq_len - timeseries[i].shape[0], 0)
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pad_zeros = np.zeros(res_len)
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timeseries[i] = np.hstack([pad_zeros, timeseries[i]])
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_len = timeseries[i].shape[0]
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train_len = _len-self.pred_len
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if self.train_all:
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border1s = [0, 0, train_len-self.seq_len]
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border2s = [train_len, train_len, _len]
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else:
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border1s = [0, train_len - self.seq_len - self.pred_len, train_len-self.seq_len]
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border2s = [train_len - self.pred_len, train_len, _len]
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border2s[0] = (border2s[0] - self.seq_len) * self.percent // 100 + self.seq_len
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# print("_len = {}".format(_len))
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curr_len = border2s[self.set_type] - max(border1s[self.set_type], 0) - self.pred_len - self.seq_len + 1
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curr_len = max(0, curr_len)
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self.len_seq.append(np.zeros(curr_len) + self.tot_len)
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self.seq_id.append(np.zeros(curr_len) + i)
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self.tot_len += curr_len
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self.len_seq = np.hstack(self.len_seq)
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self.seq_id = np.hstack(self.seq_id)
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return timeseries
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def __getitem__(self, index):
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len_seq = self.len_seq[index]
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seq_id = int(self.seq_id[index])
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index = index - int(len_seq)
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_len = self.timeseries[seq_id].shape[0]
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train_len = _len - self.pred_len
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if self.train_all:
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border1s = [0, 0, train_len-self.seq_len]
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border2s = [train_len, train_len, _len]
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else:
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border1s = [0, train_len - self.seq_len - self.pred_len, train_len-self.seq_len]
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border2s = [train_len - self.pred_len, train_len, _len]
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border2s[0] = (border2s[0] - self.seq_len) * self.percent // 100 + self.seq_len
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s_begin = index + border1s[self.set_type]
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s_end = s_begin + self.seq_len
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r_begin = s_end
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r_end = r_begin + self.pred_len
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if self.set_type == 2:
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s_end = -self.pred_len
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data_x = self.timeseries[seq_id][s_begin:s_end]
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data_y = self.timeseries[seq_id][r_begin:r_end]
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data_x = np.expand_dims(data_x, axis=-1)
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data_y = np.expand_dims(data_y, axis=-1)
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# if self.set_type == 2:
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# print("data_x.shape = {}, data_y.shape = {}".format(data_x.shape, data_y.shape))
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return data_x, data_y, data_x, data_y
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def __len__(self):
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if self.set_type == 0:
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# return self.tot_len
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return min(self.max_len, self.tot_len)
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else:
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return self.tot_len
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