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This commit is contained in:
gameloader
748
data_provider/data_loader.py
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748
data_provider/data_loader.py
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import os
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import numpy as np
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import pandas as pd
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import glob
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import re
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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 data_provider.m4 import M4Dataset, M4Meta
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from data_provider.uea import subsample, interpolate_missing, Normalizer
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from sktime.datasets import load_from_tsfile_to_dataframe
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import warnings
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from utils.augmentation import run_augmentation_single
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warnings.filterwarnings('ignore')
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class Dataset_ETT_hour(Dataset):
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def __init__(self, args, 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', seasonal_patterns=None):
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# size [seq_len, label_len, pred_len]
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self.args = args
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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.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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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.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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if self.set_type == 0 and self.args.augmentation_ratio > 0:
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self.data_x, self.data_y, augmentation_tags = run_augmentation_single(self.data_x, self.data_y, self.args)
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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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seq_y = self.data_y[r_begin:r_end]
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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
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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, args, 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', seasonal_patterns=None):
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# size [seq_len, label_len, pred_len]
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self.args = args
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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.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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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.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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if self.set_type == 0 and self.args.augmentation_ratio > 0:
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self.data_x, self.data_y, augmentation_tags = run_augmentation_single(self.data_x, self.data_y, self.args)
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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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seq_y = self.data_y[r_begin:r_end]
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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
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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_Custom(Dataset):
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def __init__(self, args, 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', seasonal_patterns=None):
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# size [seq_len, label_len, pred_len]
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self.args = args
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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.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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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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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.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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if self.set_type == 0 and self.args.augmentation_ratio > 0:
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self.data_x, self.data_y, augmentation_tags = run_augmentation_single(self.data_x, self.data_y, self.args)
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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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seq_y = self.data_y[r_begin:r_end]
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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
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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_M4(Dataset):
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def __init__(self, args, root_path, flag='pred', size=None,
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features='S', data_path='ETTh1.csv',
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target='OT', scale=False, inverse=False, timeenc=0, freq='15min',
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seasonal_patterns='Yearly'):
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# size [seq_len, label_len, pred_len]
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# init
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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.root_path = root_path
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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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self.seasonal_patterns = seasonal_patterns
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self.history_size = M4Meta.history_size[seasonal_patterns]
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self.window_sampling_limit = int(self.history_size * self.pred_len)
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self.flag = flag
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self.__read_data__()
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def __read_data__(self):
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# M4Dataset.initialize()
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if self.flag == 'train':
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dataset = M4Dataset.load(training=True, dataset_file=self.root_path)
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else:
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dataset = M4Dataset.load(training=False, dataset_file=self.root_path)
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training_values = np.array(
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[v[~np.isnan(v)] for v in
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dataset.values[dataset.groups == self.seasonal_patterns]]) # split different frequencies
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self.ids = np.array([i for i in dataset.ids[dataset.groups == self.seasonal_patterns]])
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self.timeseries = [ts for ts in training_values]
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def __getitem__(self, index):
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insample = np.zeros((self.seq_len, 1))
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insample_mask = np.zeros((self.seq_len, 1))
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outsample = np.zeros((self.pred_len + self.label_len, 1))
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outsample_mask = np.zeros((self.pred_len + self.label_len, 1)) # m4 dataset
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sampled_timeseries = self.timeseries[index]
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cut_point = np.random.randint(low=max(1, len(sampled_timeseries) - self.window_sampling_limit),
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high=len(sampled_timeseries),
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size=1)[0]
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insample_window = sampled_timeseries[max(0, cut_point - self.seq_len):cut_point]
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insample[-len(insample_window):, 0] = insample_window
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insample_mask[-len(insample_window):, 0] = 1.0
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outsample_window = sampled_timeseries[
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max(0, cut_point - self.label_len):min(len(sampled_timeseries), cut_point + self.pred_len)]
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outsample[:len(outsample_window), 0] = outsample_window
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outsample_mask[:len(outsample_window), 0] = 1.0
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return insample, outsample, insample_mask, outsample_mask
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def __len__(self):
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return len(self.timeseries)
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def inverse_transform(self, data):
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return self.scaler.inverse_transform(data)
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def last_insample_window(self):
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"""
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The last window of insample size of all timeseries.
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This function does not support batching and does not reshuffle timeseries.
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:return: Last insample window of all timeseries. Shape "timeseries, insample size"
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"""
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insample = np.zeros((len(self.timeseries), self.seq_len))
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insample_mask = np.zeros((len(self.timeseries), self.seq_len))
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for i, ts in enumerate(self.timeseries):
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ts_last_window = ts[-self.seq_len:]
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insample[i, -len(ts):] = ts_last_window
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insample_mask[i, -len(ts):] = 1.0
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return insample, insample_mask
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class PSMSegLoader(Dataset):
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def __init__(self, args, root_path, win_size, step=1, flag="train"):
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self.flag = flag
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self.step = step
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self.win_size = win_size
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self.scaler = StandardScaler()
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data = pd.read_csv(os.path.join(root_path, 'train.csv'))
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data = data.values[:, 1:]
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data = np.nan_to_num(data)
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self.scaler.fit(data)
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data = self.scaler.transform(data)
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test_data = pd.read_csv(os.path.join(root_path, 'test.csv'))
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test_data = test_data.values[:, 1:]
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test_data = np.nan_to_num(test_data)
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self.test = self.scaler.transform(test_data)
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self.train = data
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data_len = len(self.train)
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self.val = self.train[(int)(data_len * 0.8):]
|
||||
self.test_labels = pd.read_csv(os.path.join(root_path, 'test_label.csv')).values[:, 1:]
|
||||
print("test:", self.test.shape)
|
||||
print("train:", self.train.shape)
|
||||
|
||||
def __len__(self):
|
||||
if self.flag == "train":
|
||||
return (self.train.shape[0] - self.win_size) // self.step + 1
|
||||
elif (self.flag == 'val'):
|
||||
return (self.val.shape[0] - self.win_size) // self.step + 1
|
||||
elif (self.flag == 'test'):
|
||||
return (self.test.shape[0] - self.win_size) // self.step + 1
|
||||
else:
|
||||
return (self.test.shape[0] - self.win_size) // self.win_size + 1
|
||||
|
||||
def __getitem__(self, index):
|
||||
index = index * self.step
|
||||
if self.flag == "train":
|
||||
return np.float32(self.train[index:index + self.win_size]), np.float32(self.test_labels[0:self.win_size])
|
||||
elif (self.flag == 'val'):
|
||||
return np.float32(self.val[index:index + self.win_size]), np.float32(self.test_labels[0:self.win_size])
|
||||
elif (self.flag == 'test'):
|
||||
return np.float32(self.test[index:index + self.win_size]), np.float32(
|
||||
self.test_labels[index:index + self.win_size])
|
||||
else:
|
||||
return np.float32(self.test[
|
||||
index // self.step * self.win_size:index // self.step * self.win_size + self.win_size]), np.float32(
|
||||
self.test_labels[index // self.step * self.win_size:index // self.step * self.win_size + self.win_size])
|
||||
|
||||
|
||||
class MSLSegLoader(Dataset):
|
||||
def __init__(self, args, root_path, win_size, step=1, flag="train"):
|
||||
self.flag = flag
|
||||
self.step = step
|
||||
self.win_size = win_size
|
||||
self.scaler = StandardScaler()
|
||||
data = np.load(os.path.join(root_path, "MSL_train.npy"))
|
||||
self.scaler.fit(data)
|
||||
data = self.scaler.transform(data)
|
||||
test_data = np.load(os.path.join(root_path, "MSL_test.npy"))
|
||||
self.test = self.scaler.transform(test_data)
|
||||
self.train = data
|
||||
data_len = len(self.train)
|
||||
self.val = self.train[(int)(data_len * 0.8):]
|
||||
self.test_labels = np.load(os.path.join(root_path, "MSL_test_label.npy"))
|
||||
print("test:", self.test.shape)
|
||||
print("train:", self.train.shape)
|
||||
|
||||
def __len__(self):
|
||||
if self.flag == "train":
|
||||
return (self.train.shape[0] - self.win_size) // self.step + 1
|
||||
elif (self.flag == 'val'):
|
||||
return (self.val.shape[0] - self.win_size) // self.step + 1
|
||||
elif (self.flag == 'test'):
|
||||
return (self.test.shape[0] - self.win_size) // self.step + 1
|
||||
else:
|
||||
return (self.test.shape[0] - self.win_size) // self.win_size + 1
|
||||
|
||||
def __getitem__(self, index):
|
||||
index = index * self.step
|
||||
if self.flag == "train":
|
||||
return np.float32(self.train[index:index + self.win_size]), np.float32(self.test_labels[0:self.win_size])
|
||||
elif (self.flag == 'val'):
|
||||
return np.float32(self.val[index:index + self.win_size]), np.float32(self.test_labels[0:self.win_size])
|
||||
elif (self.flag == 'test'):
|
||||
return np.float32(self.test[index:index + self.win_size]), np.float32(
|
||||
self.test_labels[index:index + self.win_size])
|
||||
else:
|
||||
return np.float32(self.test[
|
||||
index // self.step * self.win_size:index // self.step * self.win_size + self.win_size]), np.float32(
|
||||
self.test_labels[index // self.step * self.win_size:index // self.step * self.win_size + self.win_size])
|
||||
|
||||
|
||||
class SMAPSegLoader(Dataset):
|
||||
def __init__(self, args, root_path, win_size, step=1, flag="train"):
|
||||
self.flag = flag
|
||||
self.step = step
|
||||
self.win_size = win_size
|
||||
self.scaler = StandardScaler()
|
||||
data = np.load(os.path.join(root_path, "SMAP_train.npy"))
|
||||
self.scaler.fit(data)
|
||||
data = self.scaler.transform(data)
|
||||
test_data = np.load(os.path.join(root_path, "SMAP_test.npy"))
|
||||
self.test = self.scaler.transform(test_data)
|
||||
self.train = data
|
||||
data_len = len(self.train)
|
||||
self.val = self.train[(int)(data_len * 0.8):]
|
||||
self.test_labels = np.load(os.path.join(root_path, "SMAP_test_label.npy"))
|
||||
print("test:", self.test.shape)
|
||||
print("train:", self.train.shape)
|
||||
|
||||
def __len__(self):
|
||||
|
||||
if self.flag == "train":
|
||||
return (self.train.shape[0] - self.win_size) // self.step + 1
|
||||
elif (self.flag == 'val'):
|
||||
return (self.val.shape[0] - self.win_size) // self.step + 1
|
||||
elif (self.flag == 'test'):
|
||||
return (self.test.shape[0] - self.win_size) // self.step + 1
|
||||
else:
|
||||
return (self.test.shape[0] - self.win_size) // self.win_size + 1
|
||||
|
||||
def __getitem__(self, index):
|
||||
index = index * self.step
|
||||
if self.flag == "train":
|
||||
return np.float32(self.train[index:index + self.win_size]), np.float32(self.test_labels[0:self.win_size])
|
||||
elif (self.flag == 'val'):
|
||||
return np.float32(self.val[index:index + self.win_size]), np.float32(self.test_labels[0:self.win_size])
|
||||
elif (self.flag == 'test'):
|
||||
return np.float32(self.test[index:index + self.win_size]), np.float32(
|
||||
self.test_labels[index:index + self.win_size])
|
||||
else:
|
||||
return np.float32(self.test[
|
||||
index // self.step * self.win_size:index // self.step * self.win_size + self.win_size]), np.float32(
|
||||
self.test_labels[index // self.step * self.win_size:index // self.step * self.win_size + self.win_size])
|
||||
|
||||
|
||||
class SMDSegLoader(Dataset):
|
||||
def __init__(self, args, root_path, win_size, step=100, flag="train"):
|
||||
self.flag = flag
|
||||
self.step = step
|
||||
self.win_size = win_size
|
||||
self.scaler = StandardScaler()
|
||||
data = np.load(os.path.join(root_path, "SMD_train.npy"))
|
||||
self.scaler.fit(data)
|
||||
data = self.scaler.transform(data)
|
||||
test_data = np.load(os.path.join(root_path, "SMD_test.npy"))
|
||||
self.test = self.scaler.transform(test_data)
|
||||
self.train = data
|
||||
data_len = len(self.train)
|
||||
self.val = self.train[(int)(data_len * 0.8):]
|
||||
self.test_labels = np.load(os.path.join(root_path, "SMD_test_label.npy"))
|
||||
|
||||
def __len__(self):
|
||||
if self.flag == "train":
|
||||
return (self.train.shape[0] - self.win_size) // self.step + 1
|
||||
elif (self.flag == 'val'):
|
||||
return (self.val.shape[0] - self.win_size) // self.step + 1
|
||||
elif (self.flag == 'test'):
|
||||
return (self.test.shape[0] - self.win_size) // self.step + 1
|
||||
else:
|
||||
return (self.test.shape[0] - self.win_size) // self.win_size + 1
|
||||
|
||||
def __getitem__(self, index):
|
||||
index = index * self.step
|
||||
if self.flag == "train":
|
||||
return np.float32(self.train[index:index + self.win_size]), np.float32(self.test_labels[0:self.win_size])
|
||||
elif (self.flag == 'val'):
|
||||
return np.float32(self.val[index:index + self.win_size]), np.float32(self.test_labels[0:self.win_size])
|
||||
elif (self.flag == 'test'):
|
||||
return np.float32(self.test[index:index + self.win_size]), np.float32(
|
||||
self.test_labels[index:index + self.win_size])
|
||||
else:
|
||||
return np.float32(self.test[
|
||||
index // self.step * self.win_size:index // self.step * self.win_size + self.win_size]), np.float32(
|
||||
self.test_labels[index // self.step * self.win_size:index // self.step * self.win_size + self.win_size])
|
||||
|
||||
|
||||
class SWATSegLoader(Dataset):
|
||||
def __init__(self, args, root_path, win_size, step=1, flag="train"):
|
||||
self.flag = flag
|
||||
self.step = step
|
||||
self.win_size = win_size
|
||||
self.scaler = StandardScaler()
|
||||
|
||||
train_data = pd.read_csv(os.path.join(root_path, 'swat_train2.csv'))
|
||||
test_data = pd.read_csv(os.path.join(root_path, 'swat2.csv'))
|
||||
labels = test_data.values[:, -1:]
|
||||
train_data = train_data.values[:, :-1]
|
||||
test_data = test_data.values[:, :-1]
|
||||
|
||||
self.scaler.fit(train_data)
|
||||
train_data = self.scaler.transform(train_data)
|
||||
test_data = self.scaler.transform(test_data)
|
||||
self.train = train_data
|
||||
self.test = test_data
|
||||
data_len = len(self.train)
|
||||
self.val = self.train[(int)(data_len * 0.8):]
|
||||
self.test_labels = labels
|
||||
print("test:", self.test.shape)
|
||||
print("train:", self.train.shape)
|
||||
|
||||
def __len__(self):
|
||||
"""
|
||||
Number of images in the object dataset.
|
||||
"""
|
||||
if self.flag == "train":
|
||||
return (self.train.shape[0] - self.win_size) // self.step + 1
|
||||
elif (self.flag == 'val'):
|
||||
return (self.val.shape[0] - self.win_size) // self.step + 1
|
||||
elif (self.flag == 'test'):
|
||||
return (self.test.shape[0] - self.win_size) // self.step + 1
|
||||
else:
|
||||
return (self.test.shape[0] - self.win_size) // self.win_size + 1
|
||||
|
||||
def __getitem__(self, index):
|
||||
index = index * self.step
|
||||
if self.flag == "train":
|
||||
return np.float32(self.train[index:index + self.win_size]), np.float32(self.test_labels[0:self.win_size])
|
||||
elif (self.flag == 'val'):
|
||||
return np.float32(self.val[index:index + self.win_size]), np.float32(self.test_labels[0:self.win_size])
|
||||
elif (self.flag == 'test'):
|
||||
return np.float32(self.test[index:index + self.win_size]), np.float32(
|
||||
self.test_labels[index:index + self.win_size])
|
||||
else:
|
||||
return np.float32(self.test[
|
||||
index // self.step * self.win_size:index // self.step * self.win_size + self.win_size]), np.float32(
|
||||
self.test_labels[index // self.step * self.win_size:index // self.step * self.win_size + self.win_size])
|
||||
|
||||
|
||||
class UEAloader(Dataset):
|
||||
"""
|
||||
Dataset class for datasets included in:
|
||||
Time Series Classification Archive (www.timeseriesclassification.com)
|
||||
Argument:
|
||||
limit_size: float in (0, 1) for debug
|
||||
Attributes:
|
||||
all_df: (num_samples * seq_len, num_columns) dataframe indexed by integer indices, with multiple rows corresponding to the same index (sample).
|
||||
Each row is a time step; Each column contains either metadata (e.g. timestamp) or a feature.
|
||||
feature_df: (num_samples * seq_len, feat_dim) dataframe; contains the subset of columns of `all_df` which correspond to selected features
|
||||
feature_names: names of columns contained in `feature_df` (same as feature_df.columns)
|
||||
all_IDs: (num_samples,) series of IDs contained in `all_df`/`feature_df` (same as all_df.index.unique() )
|
||||
labels_df: (num_samples, num_labels) pd.DataFrame of label(s) for each sample
|
||||
max_seq_len: maximum sequence (time series) length. If None, script argument `max_seq_len` will be used.
|
||||
(Moreover, script argument overrides this attribute)
|
||||
"""
|
||||
|
||||
def __init__(self, args, root_path, file_list=None, limit_size=None, flag=None):
|
||||
self.args = args
|
||||
self.root_path = root_path
|
||||
self.flag = flag
|
||||
self.all_df, self.labels_df = self.load_all(root_path, file_list=file_list, flag=flag)
|
||||
self.all_IDs = self.all_df.index.unique() # all sample IDs (integer indices 0 ... num_samples-1)
|
||||
|
||||
if limit_size is not None:
|
||||
if limit_size > 1:
|
||||
limit_size = int(limit_size)
|
||||
else: # interpret as proportion if in (0, 1]
|
||||
limit_size = int(limit_size * len(self.all_IDs))
|
||||
self.all_IDs = self.all_IDs[:limit_size]
|
||||
self.all_df = self.all_df.loc[self.all_IDs]
|
||||
|
||||
# use all features
|
||||
self.feature_names = self.all_df.columns
|
||||
self.feature_df = self.all_df
|
||||
|
||||
# pre_process
|
||||
normalizer = Normalizer()
|
||||
self.feature_df = normalizer.normalize(self.feature_df)
|
||||
print(len(self.all_IDs))
|
||||
|
||||
def load_all(self, root_path, file_list=None, flag=None):
|
||||
"""
|
||||
Loads datasets from ts files contained in `root_path` into a dataframe, optionally choosing from `pattern`
|
||||
Args:
|
||||
root_path: directory containing all individual .ts files
|
||||
file_list: optionally, provide a list of file paths within `root_path` to consider.
|
||||
Otherwise, entire `root_path` contents will be used.
|
||||
Returns:
|
||||
all_df: a single (possibly concatenated) dataframe with all data corresponding to specified files
|
||||
labels_df: dataframe containing label(s) for each sample
|
||||
"""
|
||||
# Select paths for training and evaluation
|
||||
if file_list is None:
|
||||
data_paths = glob.glob(os.path.join(root_path, '*')) # list of all paths
|
||||
else:
|
||||
data_paths = [os.path.join(root_path, p) for p in file_list]
|
||||
if len(data_paths) == 0:
|
||||
raise Exception('No files found using: {}'.format(os.path.join(root_path, '*')))
|
||||
if flag is not None:
|
||||
data_paths = list(filter(lambda x: re.search(flag, x), data_paths))
|
||||
input_paths = [p for p in data_paths if os.path.isfile(p) and p.endswith('.ts')]
|
||||
if len(input_paths) == 0:
|
||||
pattern='*.ts'
|
||||
raise Exception("No .ts files found using pattern: '{}'".format(pattern))
|
||||
|
||||
all_df, labels_df = self.load_single(input_paths[0]) # a single file contains dataset
|
||||
|
||||
return all_df, labels_df
|
||||
|
||||
def load_single(self, filepath):
|
||||
df, labels = load_from_tsfile_to_dataframe(filepath, return_separate_X_and_y=True,
|
||||
replace_missing_vals_with='NaN')
|
||||
labels = pd.Series(labels, dtype="category")
|
||||
self.class_names = labels.cat.categories
|
||||
labels_df = pd.DataFrame(labels.cat.codes,
|
||||
dtype=np.int8) # int8-32 gives an error when using nn.CrossEntropyLoss
|
||||
|
||||
lengths = df.applymap(
|
||||
lambda x: len(x)).values # (num_samples, num_dimensions) array containing the length of each series
|
||||
|
||||
horiz_diffs = np.abs(lengths - np.expand_dims(lengths[:, 0], -1))
|
||||
|
||||
if np.sum(horiz_diffs) > 0: # if any row (sample) has varying length across dimensions
|
||||
df = df.applymap(subsample)
|
||||
|
||||
lengths = df.applymap(lambda x: len(x)).values
|
||||
vert_diffs = np.abs(lengths - np.expand_dims(lengths[0, :], 0))
|
||||
if np.sum(vert_diffs) > 0: # if any column (dimension) has varying length across samples
|
||||
self.max_seq_len = int(np.max(lengths[:, 0]))
|
||||
else:
|
||||
self.max_seq_len = lengths[0, 0]
|
||||
|
||||
# First create a (seq_len, feat_dim) dataframe for each sample, indexed by a single integer ("ID" of the sample)
|
||||
# Then concatenate into a (num_samples * seq_len, feat_dim) dataframe, with multiple rows corresponding to the
|
||||
# sample index (i.e. the same scheme as all datasets in this project)
|
||||
|
||||
df = pd.concat((pd.DataFrame({col: df.loc[row, col] for col in df.columns}).reset_index(drop=True).set_index(
|
||||
pd.Series(lengths[row, 0] * [row])) for row in range(df.shape[0])), axis=0)
|
||||
|
||||
# Replace NaN values
|
||||
grp = df.groupby(by=df.index)
|
||||
df = grp.transform(interpolate_missing)
|
||||
|
||||
return df, labels_df
|
||||
|
||||
def instance_norm(self, case):
|
||||
if self.root_path.count('EthanolConcentration') > 0: # special process for numerical stability
|
||||
mean = case.mean(0, keepdim=True)
|
||||
case = case - mean
|
||||
stdev = torch.sqrt(torch.var(case, dim=1, keepdim=True, unbiased=False) + 1e-5)
|
||||
case /= stdev
|
||||
return case
|
||||
else:
|
||||
return case
|
||||
|
||||
def __getitem__(self, ind):
|
||||
batch_x = self.feature_df.loc[self.all_IDs[ind]].values
|
||||
labels = self.labels_df.loc[self.all_IDs[ind]].values
|
||||
if self.flag == "TRAIN" and self.args.augmentation_ratio > 0:
|
||||
num_samples = len(self.all_IDs)
|
||||
num_columns = self.feature_df.shape[1]
|
||||
seq_len = int(self.feature_df.shape[0] / num_samples)
|
||||
batch_x = batch_x.reshape((1, seq_len, num_columns))
|
||||
batch_x, labels, augmentation_tags = run_augmentation_single(batch_x, labels, self.args)
|
||||
|
||||
batch_x = batch_x.reshape((1 * seq_len, num_columns))
|
||||
|
||||
return self.instance_norm(torch.from_numpy(batch_x)), \
|
||||
torch.from_numpy(labels)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.all_IDs)
|
||||
Reference in New Issue
Block a user