213 lines
8.7 KiB
Python
Executable File
213 lines
8.7 KiB
Python
Executable File
import numpy as np
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import gc
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import os
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import torch
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import h5py
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from lib.normalization import normalize_dataset
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def get_dataloader(args, normalizer='std', single=True):
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# args should now include 'cycle'
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data = load_st_dataset(args['type'], args['sample']) # [T, N, F]
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L, N, F = data.shape
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# compute cycle index
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cycle_arr = np.arange(L) % args['cycle'] # length-L array
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# Step 1: sliding windows for X and Y
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x = add_window_x(data, args['lag'], args['horizon'], single)
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y = add_window_y(data, args['lag'], args['horizon'], single)
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# window count = M = L - lag - horizon + 1
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M = x.shape[0]
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# Step 2: time features
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time_in_day = np.tile(
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np.array([i % args['steps_per_day'] / args['steps_per_day'] for i in range(L)]),
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(N, 1)
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).T.reshape(L, N, 1)
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day_in_week = np.tile(
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np.array([(i // args['steps_per_day']) % args['days_per_week'] for i in range(L)]),
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(N, 1)
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).T.reshape(L, N, 1)
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x_day = add_window_x(time_in_day, args['lag'], args['horizon'], single)
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x_week = add_window_x(day_in_week, args['lag'], args['horizon'], single)
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x = np.concatenate([x, x_day, x_week], axis=-1)
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# del x_day, x_week
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# gc.collect()
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# Step 3: extract cycle index per window: take value at end of sequence
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cycle_win = np.array([cycle_arr[i + args['lag']] for i in range(M)]) # shape [M]
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# Step 4: split into train/val/test
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if args['test_ratio'] > 1:
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x_train, x_val, x_test = split_data_by_days(x, args['val_ratio'], args['test_ratio'])
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y_train, y_val, y_test = split_data_by_days(y, args['val_ratio'], args['test_ratio'])
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c_train, c_val, c_test = split_data_by_days(cycle_win, args['val_ratio'], args['test_ratio'])
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else:
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x_train, x_val, x_test = split_data_by_ratio(x, args['val_ratio'], args['test_ratio'])
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y_train, y_val, y_test = split_data_by_ratio(y, args['val_ratio'], args['test_ratio'])
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c_train, c_val, c_test = split_data_by_ratio(cycle_win, args['val_ratio'], args['test_ratio'])
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# del x, y, cycle_win
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# gc.collect()
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# Step 5: normalization on X only
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scaler = normalize_dataset(x_train[..., :args['input_dim']], normalizer, args['column_wise'])
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x_train[..., :args['input_dim']] = scaler.transform(x_train[..., :args['input_dim']])
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x_val[..., :args['input_dim']] = scaler.transform(x_val[..., :args['input_dim']])
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x_test[..., :args['input_dim']] = scaler.transform(x_test[..., :args['input_dim']])
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# add time features to Y
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y_day = add_window_y(time_in_day, args['lag'], args['horizon'], single)
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y_week = add_window_y(day_in_week, args['lag'], args['horizon'], single)
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y = np.concatenate([y, y_day, y_week], axis=-1)
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# del y_day, y_week, time_in_day, day_in_week
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# gc.collect()
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# split Y time-augmented
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if args['test_ratio'] > 1:
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y_train, y_val, y_test = split_data_by_days(y, args['val_ratio'], args['test_ratio'])
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else:
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y_train, y_val, y_test = split_data_by_ratio(y, args['val_ratio'], args['test_ratio'])
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# del y
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# Step 6: create dataloaders including cycle index
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train_loader = data_loader_with_cycle(x_train, y_train, c_train, args['batch_size'], shuffle=True, drop_last=True)
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val_loader = data_loader_with_cycle(x_val, y_val, c_val, args['batch_size'], shuffle=False, drop_last=True)
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test_loader = data_loader_with_cycle(x_test, y_test, c_test, args['batch_size'], shuffle=False, drop_last=False)
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return train_loader, val_loader, test_loader, scaler
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def data_loader_with_cycle(X, Y, C, batch_size, shuffle=True, drop_last=True):
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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X_t = torch.tensor(X, dtype=torch.float32, device=device)
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Y_t = torch.tensor(Y, dtype=torch.float32, device=device)
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C_t = torch.tensor(C, dtype=torch.long, device=device).unsqueeze(-1) # [B,1]
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dataset = torch.utils.data.TensorDataset(X_t, Y_t, C_t)
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loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)
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return loader
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# Rest of the helper functions (load_st_dataset, split_data..., add_window_x/y) unchanged
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def load_st_dataset(dataset, sample):
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# output B, N, D
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match dataset:
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case 'PEMSD3':
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data_path = os.path.join('./data/PEMS03/PEMS03.npz')
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data = np.load(data_path)['data'][:, :, 0] # only the first dimension, traffic flow data
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case 'PEMSD4':
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data_path = os.path.join('./data/PEMS04/PEMS04.npz')
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data = np.load(data_path)['data'][:, :, 0] # only the first dimension, traffic flow data
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case 'PEMSD7':
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data_path = os.path.join('./data/PEMS07/PEMS07.npz')
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data = np.load(data_path)['data'][:, :, 0] # only the first dimension, traffic flow data
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case 'PEMSD8':
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data_path = os.path.join('./data/PEMS08/PEMS08.npz')
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data = np.load(data_path)['data'][:, :, 0] # only the first dimension, traffic flow data
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case 'PEMSD7(L)':
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data_path = os.path.join('./data/PEMS07(L)/PEMS07L.npz')
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data = np.load(data_path)['data'][:, :, 0] # only the first dimension, traffic flow data
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case 'PEMSD7(M)':
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data_path = os.path.join('./data/PEMS07(M)/V_228.csv')
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data = np.genfromtxt(data_path, delimiter=',') # Read CSV directly with numpy
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case 'METR-LA':
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data_path = os.path.join('./data/METR-LA/METR.h5')
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with h5py.File(data_path, 'r') as f: # Use h5py to handle HDF5 files without pandas
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data = np.array(f['data'])
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case 'BJ':
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data_path = os.path.join('./data/BJ/BJ500.csv')
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data = np.genfromtxt(data_path, delimiter=',', skip_header=1) # Skip header if present
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case 'Hainan':
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data_path = os.path.join('./data/Hainan/Hainan.npz')
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data = np.load(data_path)['data'][:, :, 0]
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case 'SD':
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data_path = os.path.join('./data/SD/data.npz')
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data = np.load(data_path)["data"][:, :, 0].astype(np.float32)
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case _:
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raise ValueError(f"Unsupported dataset: {dataset}")
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# Ensure data shape compatibility
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if len(data.shape) == 2:
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data = np.expand_dims(data, axis=-1)
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print('加载 %s 数据集中... ' % dataset)
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return data[::sample]
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def split_data_by_days(data, val_days, test_days, interval=30):
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t = int((24 * 60) / interval)
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test_data = data[-t * int(test_days):]
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val_data = data[-t * int(test_days + val_days):-t * int(test_days)]
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train_data = data[:-t * int(test_days + val_days)]
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return train_data, val_data, test_data
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def split_data_by_ratio(data, val_ratio, test_ratio):
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data_len = data.shape[0]
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test_data = data[-int(data_len * test_ratio):]
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val_data = data[-int(data_len * (test_ratio + val_ratio)):-int(data_len * test_ratio)]
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train_data = data[:-int(data_len * (test_ratio + val_ratio))]
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return train_data, val_data, test_data
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def data_loader(X, Y, batch_size, shuffle=True, drop_last=True):
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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X = torch.tensor(X, dtype=torch.float32, device=device)
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Y = torch.tensor(Y, dtype=torch.float32, device=device)
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data = torch.utils.data.TensorDataset(X, Y)
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dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size,
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shuffle=shuffle, drop_last=drop_last)
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return dataloader
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def add_window_x(data, window=3, horizon=1, single=False):
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"""
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Generate windowed X values from the input data.
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:param data: Input data, shape [B, ...]
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:param window: Size of the sliding window
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:param horizon: Horizon size
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:param single: If True, generate single-step windows, else multi-step
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:return: X with shape [B, W, ...]
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"""
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length = len(data)
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end_index = length - horizon - window + 1
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x = [] # Sliding windows
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index = 0
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while index < end_index:
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x.append(data[index:index + window])
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index += 1
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return np.array(x)
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def add_window_y(data, window=3, horizon=1, single=False):
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"""
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Generate windowed Y values from the input data.
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:param data: Input data, shape [B, ...]
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:param window: Size of the sliding window
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:param horizon: Horizon size
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:param single: If True, generate single-step windows, else multi-step
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:return: Y with shape [B, H, ...]
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"""
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length = len(data)
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end_index = length - horizon - window + 1
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y = [] # Horizon values
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index = 0
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while index < end_index:
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if single:
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y.append(data[index + window + horizon - 1:index + window + horizon])
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else:
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y.append(data[index + window:index + window + horizon])
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index += 1
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return np.array(y)
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if __name__ == '__main__':
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res = load_st_dataset('SD', 1)
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k = 1 |