import os import numpy as np import pandas as pd import glob import re import torch from torch.utils.data import Dataset, DataLoader from sklearn.preprocessing import StandardScaler from utils.timefeatures import time_features from data_provider.m4 import M4Dataset, M4Meta from data_provider.uea import subsample, interpolate_missing, Normalizer from sktime.datasets import load_from_tsfile_to_dataframe import warnings from utils.augmentation import run_augmentation_single warnings.filterwarnings('ignore') class TIDE_LEVEL_15MIN_MULTI(Dataset): def __init__(self, args, root_path, flag='train', size=None, features='MS', data_path='DT_0020.csv', target='tide_level', scale=True, timeenc=1, freq='15min', seasonal_patterns=None): # size [seq_len, label_len, pred_len] self.args = args # info 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.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__() def __read_data__(self): self.scaler = StandardScaler() df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path)) # Dynamically calculate data split points data_len = len(df_raw) train_ratio = 0.7 val_ratio = 0.1 # test_ratio is implicitly 1 - train_ratio - val_ratio train_len = int(data_len * train_ratio) val_len = int(data_len * val_ratio) test_len = data_len - train_len - val_len border1s = [ 0, train_len - self.seq_len, train_len + val_len - self.seq_len ] border2s = [ train_len, train_len + val_len, data_len ] border1 = border1s[self.set_type] border2 = border2s[self.set_type] 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: # Scaler is fit only on the training data train_data = df_data.iloc[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) df_stamp['day'] = df_stamp['date'].apply(lambda row: row.day) df_stamp['weekday'] = df_stamp['date'].apply(lambda row: row.weekday()) df_stamp['hour'] = df_stamp['date'].apply(lambda row: row.hour) df_stamp['minute'] = df_stamp['date'].apply(lambda row: row.minute // 15) data_stamp = df_stamp.drop(columns=['date']).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] #if self.set_type == 0 and self.args.augmentation_ratio > 0: # self.data_x, self.data_y, augmentation_tags = run_augmentation_single(self.data_x, self.data_y, self.args) self.data_stamp = data_stamp def __getitem__(self, index): s_begin = index 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] seq_y = self.data_y[r_begin:r_end] seq_x_mark = self.data_stamp[s_begin:s_end] seq_y_mark = self.data_stamp[r_begin:r_end] return seq_x, seq_y, seq_x_mark, seq_y_mark def __len__(self): return len(self.data_x) - self.seq_len - self.pred_len + 1 def inverse_transform(self, data): return self.scaler.inverse_transform(data) class Dataset_Pred(Dataset): def __init__(self, root_path, flag='pred', size=None, features='S', data_path='tide_data_DT_0001.csv', target='tide_level', scale=True, inverse=False, timeenc=0, freq='t', cols=None): # size [seq_len, label_len, pred_len] # info if size == None: self.seq_len = 3 * 24 * 60 # 3일치 데이터 (4320분) self.label_len = 1 * 24 * 60 # 1일치 데이터 (1440분) self.pred_len = 1 * 24 * 60 # 1일치 데이터 (1440분) else: self.seq_len = size[0] self.label_len = size[1] self.pred_len = size[2] # init assert flag in ['pred'] self.features = features self.target = target self.scale = scale self.inverse = inverse self.timeenc = timeenc self.freq = freq self.cols = cols self.root_path = root_path self.data_path = data_path self.__read_data__() def __read_data__(self): self.scaler = StandardScaler() df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path)) # Dynamically calculate data split points data_len = len(df_raw) train_ratio = 0.7 val_ratio = 0.1 # test_ratio is implicitly 1 - train_ratio - val_ratio train_len = int(data_len * train_ratio) val_len = int(data_len * val_ratio) test_len = data_len - train_len - val_len border1s = [ 0, train_len - self.seq_len, train_len + val_len - self.seq_len ] border2s = [ train_len, train_len + val_len, data_len ] border1 = border1s[self.set_type] border2 = border2s[self.set_type] 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: # Scaler is fit only on the training data train_data = df_data.iloc[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) df_stamp['day'] = df_stamp['date'].apply(lambda row: row.day) df_stamp['weekday'] = df_stamp['date'].apply(lambda row: row.weekday()) df_stamp['hour'] = df_stamp['date'].apply(lambda row: row.hour) df_stamp['minute'] = df_stamp['date'].apply(lambda row: row.minute // 15) data_stamp = df_stamp.drop(columns=['date']).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] if self.set_type == 0 and self.args.augmentation_ratio > 0: self.data_x, self.data_y, augmentation_tags = run_augmentation_single(self.data_x, self.data_y, self.args) self.data_stamp = data_stamp def __getitem__(self, index): s_begin = index 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] if self.inverse: seq_y = self.data_x[r_begin:r_begin + self.label_len] else: seq_y = self.data_y[r_begin:r_begin + self.label_len] 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 + 1 def inverse_transform(self, data): return self.scaler.inverse_transform(data) class Dataset_ETT_hour(Dataset): def __init__(self, args, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv', target='OT', scale=True, timeenc=0, freq='h', seasonal_patterns=None): # size [seq_len, label_len, pred_len] self.args = args # info 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.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__() def __read_data__(self): self.scaler = StandardScaler() df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path)) 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.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] if self.set_type == 0 and self.args.augmentation_ratio > 0: self.data_x, self.data_y, augmentation_tags = run_augmentation_single(self.data_x, self.data_y, self.args) self.data_stamp = data_stamp def __getitem__(self, index): s_begin = index 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] seq_y = self.data_y[r_begin:r_end] seq_x_mark = self.data_stamp[s_begin:s_end] seq_y_mark = self.data_stamp[r_begin:r_end] return seq_x, seq_y, seq_x_mark, seq_y_mark def __len__(self): return len(self.data_x) - self.seq_len - self.pred_len + 1 def inverse_transform(self, data): return self.scaler.inverse_transform(data) class Dataset_ETT_minute(Dataset): def __init__(self, args, root_path, flag='train', size=None, features='S', data_path='ETTm1.csv', target='OT', scale=True, timeenc=0, freq='t', seasonal_patterns=None): # size [seq_len, label_len, pred_len] self.args = args # info 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.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__() def __read_data__(self): self.scaler = StandardScaler() df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path)) 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.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] if self.set_type == 0 and self.args.augmentation_ratio > 0: self.data_x, self.data_y, augmentation_tags = run_augmentation_single(self.data_x, self.data_y, self.args) self.data_stamp = data_stamp def __getitem__(self, index): s_begin = index 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] seq_y = self.data_y[r_begin:r_end] seq_x_mark = self.data_stamp[s_begin:s_end] seq_y_mark = self.data_stamp[r_begin:r_end] return seq_x, seq_y, seq_x_mark, seq_y_mark def __len__(self): return len(self.data_x) - self.seq_len - self.pred_len + 1 def inverse_transform(self, data): return self.scaler.inverse_transform(data) class Dataset_Custom(Dataset): def __init__(self, args, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv', target='OT', scale=True, timeenc=0, freq='h', seasonal_patterns=None): # size [seq_len, label_len, pred_len] self.args = args # info 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.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__() def __read_data__(self): self.scaler = StandardScaler() df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path)) ''' df_raw.columns: ['date', ...(other features), target feature] ''' cols = list(df_raw.columns) cols.remove(self.target) cols.remove('date') df_raw = df_raw[['date'] + cols + [self.target]] num_train = int(len(df_raw) * 0.7) num_test = int(len(df_raw) * 0.2) num_vali = len(df_raw) - num_train - num_test border1s = [0, num_train - self.seq_len, len(df_raw) - num_test - self.seq_len] border2s = [num_train, num_train + num_vali, len(df_raw)] border1 = border1s[self.set_type] border2 = border2s[self.set_type] 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] if self.set_type == 0 and self.args.augmentation_ratio > 0: self.data_x, self.data_y, augmentation_tags = run_augmentation_single(self.data_x, self.data_y, self.args) self.data_stamp = data_stamp def __getitem__(self, index): s_begin = index 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] seq_y = self.data_y[r_begin:r_end] seq_x_mark = self.data_stamp[s_begin:s_end] seq_y_mark = self.data_stamp[r_begin:r_end] return seq_x, seq_y, seq_x_mark, seq_y_mark def __len__(self): return len(self.data_x) - self.seq_len - self.pred_len + 1 def inverse_transform(self, data): return self.scaler.inverse_transform(data) class Dataset_M4(Dataset): def __init__(self, args, root_path, flag='pred', size=None, features='S', data_path='ETTh1.csv', target='OT', scale=False, inverse=False, timeenc=0, freq='15min', seasonal_patterns='Yearly'): # size [seq_len, label_len, pred_len] # init self.features = features self.target = target self.scale = scale self.inverse = inverse self.timeenc = timeenc self.root_path = root_path self.seq_len = size[0] self.label_len = size[1] self.pred_len = size[2] self.seasonal_patterns = seasonal_patterns self.history_size = M4Meta.history_size[seasonal_patterns] self.window_sampling_limit = int(self.history_size * self.pred_len) self.flag = flag self.__read_data__() def __read_data__(self): # M4Dataset.initialize() if self.flag == 'train': dataset = M4Dataset.load(training=True, dataset_file=self.root_path) else: dataset = M4Dataset.load(training=False, dataset_file=self.root_path) training_values = np.array( [v[~np.isnan(v)] for v in dataset.values[dataset.groups == self.seasonal_patterns]]) # split different frequencies self.ids = np.array([i for i in dataset.ids[dataset.groups == self.seasonal_patterns]]) self.timeseries = [ts for ts in training_values] def __getitem__(self, index): insample = np.zeros((self.seq_len, 1)) insample_mask = np.zeros((self.seq_len, 1)) outsample = np.zeros((self.pred_len + self.label_len, 1)) outsample_mask = np.zeros((self.pred_len + self.label_len, 1)) # m4 dataset sampled_timeseries = self.timeseries[index] cut_point = np.random.randint(low=max(1, len(sampled_timeseries) - self.window_sampling_limit), high=len(sampled_timeseries), size=1)[0] insample_window = sampled_timeseries[max(0, cut_point - self.seq_len):cut_point] insample[-len(insample_window):, 0] = insample_window insample_mask[-len(insample_window):, 0] = 1.0 outsample_window = sampled_timeseries[ cut_point - self.label_len:min(len(sampled_timeseries), cut_point + self.pred_len)] outsample[:len(outsample_window), 0] = outsample_window outsample_mask[:len(outsample_window), 0] = 1.0 return insample, outsample, insample_mask, outsample_mask def __len__(self): return len(self.timeseries) def inverse_transform(self, data): return self.scaler.inverse_transform(data) def last_insample_window(self): """ The last window of insample size of all timeseries. This function does not support batching and does not reshuffle timeseries. :return: Last insample window of all timeseries. Shape "timeseries, insample size" """ insample = np.zeros((len(self.timeseries), self.seq_len)) insample_mask = np.zeros((len(self.timeseries), self.seq_len)) for i, ts in enumerate(self.timeseries): ts_last_window = ts[-self.seq_len:] insample[i, -len(ts):] = ts_last_window insample_mask[i, -len(ts):] = 1.0 return insample, insample_mask class PSMSegLoader(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 = pd.read_csv(os.path.join(root_path, 'train.csv')) data = data.values[:, 1:] data = np.nan_to_num(data) self.scaler.fit(data) data = self.scaler.transform(data) test_data = pd.read_csv(os.path.join(root_path, 'test.csv')) test_data = test_data.values[:, 1:] test_data = np.nan_to_num(test_data) 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 = 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 csv files contained in `root_path` into a dataframe, optionally choosing from `pattern` Args: root_path: directory containing all individual .csv 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) class Dataset_Meteorology(Dataset): def __init__(self, args, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv', target='OT', scale=True, timeenc=0, freq='h', seasonal_patterns=None): # size [seq_len, label_len, pred_len] # info 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.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.stations_num = 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() data = np.load(os.path.join(self.root_path, self.data_path)) # (L, S, 1) data = np.squeeze(data) # (L S) era5 = np.load(os.path.join(self.root_path, 'era5_norm.npy')) # new add era5 = era5.reshape((era5.shape[0], 4, 9, era5.shape[-1])) repeat_era5 = np.repeat(era5, 3, axis=0)[:len(data), :, :, :] # (L, 4, 9, S) repeat_era5 = repeat_era5.reshape(repeat_era5.shape[0], -1, repeat_era5.shape[3]) # (L, 36, S) num_train = int(len(data) * 0.7) num_test = int(len(data) * 0.2) num_vali = len(data) - num_train - num_test border1s = [0, num_train - self.seq_len, len(data) - num_test - self.seq_len] border2s = [num_train, num_train + num_vali, len(data)] border1 = border1s[self.set_type] border2 = border2s[self.set_type] if self.scale: train_data = data[border1s[0]:border2s[0]] self.scaler.fit(train_data) data = self.scaler.transform(data) else: pass self.data_x = data[border1:border2] self.data_y = data[border1:border2] self.covariate = repeat_era5[border1:border2] def __getitem__(self, index): station_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, station_id:station_id + 1] seq_y = self.data_y[r_begin:r_end, station_id:station_id + 1] # (L 1) t1 = self.covariate[s_begin:s_end, :, station_id:station_id + 1].squeeze() t2 = self.covariate[r_begin:r_end, :, station_id:station_id + 1].squeeze() seq_x = np.concatenate([t1, seq_x], axis=1) seq_y = np.concatenate([t2, seq_y], axis=1) seq_x_mark = torch.zeros((seq_x.shape[0], 1)) seq_y_mark = torch.zeros((seq_y.shape[0], 1)) return seq_x, seq_y, seq_x_mark, seq_y_mark def __len__(self): l = (len(self.data_x) - self.seq_len - self.pred_len + 1) * self.stations_num return l def inverse_transform(self, data): return self.scaler.inverse_transform(data)