diff --git a/.idea/workspace.xml b/.idea/workspace.xml index d789a68..d1a09b9 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -5,9 +5,14 @@ + + + + + - @@ -57,10 +62,11 @@ - + - + - + @@ -114,7 +120,7 @@ - + + + + + + + + @@ -157,6 +191,7 @@ 1756727620810 + @@ -176,5 +211,6 @@ + \ No newline at end of file diff --git a/configs/STGODE/PEMS08.yaml b/configs/STGODE/PEMS08.yaml new file mode 100644 index 0000000..d9b7f29 --- /dev/null +++ b/configs/STGODE/PEMS08.yaml @@ -0,0 +1,60 @@ +basic: + device: cuda:0 + dataset: PEMS08 + model: STGODE + mode: train + seed: 2025 + +data: + dataset_dir: data/PEMS08 + val_batch_size: 32 + graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy + num_nodes: 170 + batch_size: 64 + input_dim: 1 + lag: 12 + horizon: 12 + val_ratio: 0.2 + test_ratio: 0.2 + tod: False + normalizer: std + column_wise: False + default_graph: True + add_time_in_day: True + add_day_in_week: True + steps_per_day: 24 + days_per_week: 7 + +model: + input_dim: 1 + output_dim: 1 + history: 12 + horizon: 12 + num_features: 1 + rnn_units: 64 + sigma1: 0.1 + sigma2: 10 + thres1: 0.6 + thres2: 0.5 + + +train: + loss: mae + batch_size: 64 + epochs: 100 + lr_init: 0.003 + mape_thresh: 0.001 + mae_thresh: None + debug: False + output_dim: 1 + weight_decay: 0 + lr_decay: False + lr_decay_rate: 0.3 + lr_decay_step: "5,20,40,70" + early_stop: True + early_stop_patience: 15 + grad_norm: False + max_grad_norm: 5 + real_value: True + log_step: 3000 + \ No newline at end of file diff --git a/models/STGODE/STGODE.py b/models/STGODE/STGODE.py index cf60e77..f978623 100755 --- a/models/STGODE/STGODE.py +++ b/models/STGODE/STGODE.py @@ -117,7 +117,7 @@ class STGCNBlock(nn.Module): class ODEGCN(nn.Module): """ the overall network framework """ - def __init__(self, args): + def __init__(self, config): """ Args: num_nodes : number of nodes in the graph @@ -129,11 +129,12 @@ class ODEGCN(nn.Module): """ super(ODEGCN, self).__init__() - num_nodes = args['num_nodes'] + args = config['model'] + num_nodes = config['data']['num_nodes'] num_features = args['num_features'] num_timesteps_input = args['history'] num_timesteps_output = args['horizon'] - A_sp_hat, A_se_hat = get_A_hat(args) + A_sp_hat, A_se_hat = get_A_hat(config) # spatial graph self.sp_blocks = nn.ModuleList( diff --git a/models/STGODE/adj.py b/models/STGODE/adj.py index e5da55d..5dca96b 100755 --- a/models/STGODE/adj.py +++ b/models/STGODE/adj.py @@ -17,7 +17,7 @@ files = { } -def get_A_hat(args): +def get_A_hat(config): """read data, generate spatial adjacency matrix and semantic adjacency matrix by dtw Args: @@ -31,12 +31,13 @@ def get_A_hat(args): dtw_matrix: array, semantic adjacency matrix sp_matrix: array, spatial adjacency matrix """ - filepath = './data/' - num_node = args['num_nodes'] - file = files[num_node] - filename = file[0][:6] + file_path = config['data']['graph_pkl_filename'] + filename = config['basic']['dataset'] + dataset_path = config['data']['dataset_dir'] + args = config['model'] - data = np.load(filepath + file[0])['data'] + data = np.load(file_path) + data = np.nan_to_num(data, nan=0.0, posinf=0.0, neginf=0.0) num_node = data.shape[1] mean_value = np.mean(data, axis=(0, 1)).reshape(1, 1, -1) std_value = np.std(data, axis=(0, 1)).reshape(1, 1, -1) @@ -72,7 +73,7 @@ def get_A_hat(args): with open(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}.txt', 'r') as f: id_dict = {int(i): idx for idx, i in enumerate(f.read().strip().split('\n'))} # 建立映射列表 # 使用 pandas 读取 CSV 文件，跳过标题行 - df = pd.read_csv(filepath + file[1], skiprows=1, header=None) + df = pd.read_csv(f'{dataset_path}/{filename}.csv', skiprows=1, header=None) dist_matrix = np.zeros((num_node, num_node)) + float('inf') for _, row in df.iterrows(): start = int(id_dict[row[0]]) @@ -82,7 +83,7 @@ def get_A_hat(args): np.save(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy', dist_matrix) else: # 使用 pandas 读取 CSV 文件，跳过标题行 - df = pd.read_csv(filepath + file[1], skiprows=1, header=None) + df = pd.read_csv(f'{dataset_path}/{filename}.csv', skiprows=1, header=None) dist_matrix = np.zeros((num_node, num_node)) + float('inf') for _, row in df.iterrows(): start = int(row[0]) @@ -98,7 +99,8 @@ def get_A_hat(args): sp_matrix = np.exp(- dist_matrix ** 2 / sigma ** 2) sp_matrix[sp_matrix < args['thres2']] = 0 - return get_normalized_adj(dtw_matrix).to(args['device']), get_normalized_adj(sp_matrix).to(args['device']) + return (get_normalized_adj(dtw_matrix).to(config['basic']['device']), + get_normalized_adj(sp_matrix).to(config['basic']['device'])) def get_normalized_adj(A): @@ -115,16 +117,16 @@ def get_normalized_adj(A): return torch.from_numpy(A_reg.astype(np.float32)) -if __name__ == '__main__': - if __name__ == '__main__': - config = { - 'sigma1': 0.1, - 'sigma2': 10, - 'thres1': 0.6, - 'thres2': 0.5, - 'device': 'cuda:0' if torch.cuda.is_available() else 'cpu' - } - for nodes in [358, 170, 883]: - args = {'num_nodes': nodes, **config} - get_A_hat(args) +if __name__ == '__main__': + config = { + 'sigma1': 0.1, + 'sigma2': 10, + 'thres1': 0.6, + 'thres2': 0.5, + 'device': 'cuda:0' if torch.cuda.is_available() else 'cpu' + } + + for nodes in [358, 170, 883]: + args = {'num_nodes': nodes, **config} + get_A_hat(args) diff --git a/models/model_selector.py b/models/model_selector.py index ab80d63..cb07152 100644 --- a/models/model_selector.py +++ b/models/model_selector.py @@ -1,8 +1,12 @@ from models.STDEN.stden_model import STDENModel +from models.STGODE.STGODE import ODEGCN def model_selector(config): model_name = config['basic']['model'] model = None match model_name: - case 'STDEN': model = STDENModel(config) + case 'STDEN': + model = STDENModel(config) + case 'STGODE': + model = ODEGCN(config) return model \ No newline at end of file diff --git a/test_semantic.npy b/test_semantic.npy new file mode 100644 index 0000000..e0d1b75 Binary files /dev/null and b/test_semantic.npy differ diff --git a/test_spatial.npy b/test_spatial.npy new file mode 100644 index 0000000..747906d Binary files /dev/null and b/test_spatial.npy differ