mv dir name

.gitignore

@@ -2,7 +2,9 @@
 review_lab/
 scripts/
 experiments/
-
+*.csv
+*.npz
+*.pkl
 
 # ---> Python
 # Byte-compiled / optimized / DLL files

dataloader/DCRNNdataloader.py

@@ -94,22 +94,22 @@ def load_st_dataset(dataset, sample):
     # output B, N, D
     match dataset:
         case 'PEMSD3':
-            data_path = os.path.join('./data/PeMS03/PEMS03.npz')
+            data_path = os.path.join('./data/PEMS03/PEMS03.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD4':
-            data_path = os.path.join('./data/PeMS04/PEMS04.npz')
+            data_path = os.path.join('./data/PEMS04/PEMS04.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD7':
-            data_path = os.path.join('./data/PeMS07/PEMS07.npz')
+            data_path = os.path.join('./data/PEMS07/PEMS07.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD8':
-            data_path = os.path.join('./data/PeMS08/PeMS08.npz')
+            data_path = os.path.join('./data/PEMS08/PEMS08.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD7(L)':
-            data_path = os.path.join('./data/PeMS07(L)/PEMS07L.npz')
+            data_path = os.path.join('./data/PEMS07(L)/PEMS07L.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD7(M)':
-            data_path = os.path.join('./data/PeMS07(M)/V_228.csv')
+            data_path = os.path.join('./data/PEMS07(M)/V_228.csv')
             data = np.genfromtxt(data_path, delimiter=',')  # Read CSV directly with numpy
         case 'METR-LA':
             data_path = os.path.join('./data/METR-LA/METR.h5')

dataloader/PeMSDdataloader.py

@@ -94,22 +94,22 @@ def load_st_dataset(dataset, sample):
     # output B, N, D
     match dataset:
         case 'PEMSD3':
-            data_path = os.path.join('./data/PeMS03/PEMS03.npz')
+            data_path = os.path.join('./data/PEMS03/PEMS03.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD4':
-            data_path = os.path.join('./data/PeMS04/PEMS04.npz')
+            data_path = os.path.join('./data/PEMS04/PEMS04.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD7':
-            data_path = os.path.join('./data/PeMS07/PEMS07.npz')
+            data_path = os.path.join('./data/PEMS07/PEMS07.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD8':
-            data_path = os.path.join('./data/PeMS08/PeMS08.npz')
+            data_path = os.path.join('./data/PEMS08/PEMS08.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD7(L)':
-            data_path = os.path.join('./data/PeMS07(L)/PEMS07L.npz')
+            data_path = os.path.join('./data/PEMS07(L)/PEMS07L.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD7(M)':
-            data_path = os.path.join('./data/PeMS07(M)/V_228.csv')
+            data_path = os.path.join('./data/PEMS07(M)/V_228.csv')
             data = np.genfromtxt(data_path, delimiter=',')  # Read CSV directly with numpy
         case 'METR-LA':
             data_path = os.path.join('./data/METR-LA/METR.h5')

dataloader/PeMSDdataloader_old.py

@@ -94,22 +94,22 @@ def load_st_dataset(dataset):
     # output B, N, D
     match dataset:
         case 'PEMSD3':
-            data_path = os.path.join('./data/PeMS03/PEMS03.npz')
+            data_path = os.path.join('./data/PEMS03/PEMS03.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD4':
-            data_path = os.path.join('./data/PeMS04/PEMS04.npz')
+            data_path = os.path.join('./data/PEMS04/PEMS04.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD7':
-            data_path = os.path.join('./data/PeMS07/PEMS07.npz')
+            data_path = os.path.join('./data/PEMS07/PEMS07.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD8':
-            data_path = os.path.join('./data/PeMS08/PeMS08.npz')
+            data_path = os.path.join('./data/PEMS08/PEMS08.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD7(L)':
-            data_path = os.path.join('./data/PeMS07(L)/PEMS07L.npz')
+            data_path = os.path.join('./data/PEMS07(L)/PEMS07L.npz')
             data = np.load(data_path)['data'][:, :, 0]  # only the first dimension, traffic flow data
         case 'PEMSD7(M)':
-            data_path = os.path.join('./data/PeMS07(M)/V_228.csv')
+            data_path = os.path.join('./data/PEMS07(M)/V_228.csv')
             data = np.genfromtxt(data_path, delimiter=',')  # Read CSV directly with numpy
         case 'METR-LA':
             data_path = os.path.join('./data/METR-LA/METR.h5')

model/STGODE/adj.py

@@ -7,13 +7,13 @@ from tqdm import tqdm
 import torch
 
 files = {
-    358: ['PeMS03/PEMS03.npz', 'PeMS03/PEMS03.csv'],
+    358: ['PEMS03/PEMS03.npz', 'PEMS03/PEMS03.csv'],
-    307: ['PeMS04/PEMS04.npz', 'PeMS04/PEMS04.csv'],
+    307: ['PEMS04/PEMS04.npz', 'PEMS04/PEMS04.csv'],
-    883: ['PeMS07/PEMS07.npz', 'PeMS07/PEMS07.csv'],
+    883: ['PEMS07/PEMS07.npz', 'PEMS07/PEMS07.csv'],
-    170: ['PeMS08/PEMS08.npz', 'PeMS08/PEMS08.csv'],
+    170: ['PEMS08/PEMS08.npz', 'PEMS08/PEMS08.csv'],
     # 'pemsbay': ['PEMSBAY/pems_bay.npz', 'PEMSBAY/distance.csv'],
-    # 'pemsD7M': ['PeMSD7M/PeMSD7M.npz', 'PeMSD7M/distance.csv'],
+    # 'pemsD7M': ['PEMSD7M/PEMSD7M.npz', 'PEMSD7M/distance.csv'],
-    # 'pemsD7L': ['PeMSD7L/PeMSD7L.npz', 'PeMSD7L/distance.csv']
+    # 'pemsD7L': ['PEMSD7L/PEMSD7L.npz', 'PEMSD7L/distance.csv']
 }
 
 
@@ -43,7 +43,7 @@ def get_A_hat(args):
     data = (data - mean_value) / std_value
 
     # 计算dtw_distance, 如果存在缓存则直接读取缓存
-    if not os.path.exists(f'data/PeMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy'):
+    if not os.path.exists(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy'):
         data_mean = np.mean([data[:, :, 0][24 * 12 * i: 24 * 12 * (i + 1)] for i in range(data.shape[0] // (24 * 12))],
                             axis=0)
         data_mean = data_mean.squeeze().T
@@ -54,9 +54,9 @@ def get_A_hat(args):
         for i in range(num_node):
             for j in range(i):
                 dtw_distance[i][j] = dtw_distance[j][i]
-        np.save(f'data/PeMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy', dtw_distance)
+        np.save(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy', dtw_distance)
 
-    dist_matrix = np.load(f'data/PeMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy')
+    dist_matrix = np.load(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy')
 
     mean = np.mean(dist_matrix)
     std = np.std(dist_matrix)
@@ -67,9 +67,9 @@ def get_A_hat(args):
     dtw_matrix[dist_matrix > args['thres1']] = 1
 
     # 计算spatial_distance, 如果存在缓存则直接读取缓存
-    if not os.path.exists(f'data/PeMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy'):
+    if not os.path.exists(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy'):
         if num_node == 358:
-            with open(f'data/PeMS0{filename[-1]}/PEMS0{filename[-1]}.txt', 'r') as f:
+            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)
@@ -79,7 +79,7 @@ def get_A_hat(args):
                     end = int(id_dict[row[1]])
                     dist_matrix[start][end] = float(row[2])
                     dist_matrix[end][start] = float(row[2])
-                np.save(f'data/PeMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy', dist_matrix)
+                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)
@@ -89,7 +89,7 @@ def get_A_hat(args):
                 end = int(row[1])
                 dist_matrix[start][end] = float(row[2])
                 dist_matrix[end][start] = float(row[2])
-            np.save(f'data/PeMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy', dist_matrix)
+            np.save(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy', dist_matrix)
     # normalization
     std = np.std(dist_matrix[dist_matrix != float('inf')])
     mean = np.mean(dist_matrix[dist_matrix != float('inf')])

model/STGODE/new_adj.py

@@ -1,116 +0,0 @@
-import os
-import pandas as pd
-import numpy as np
-from fastdtw import fastdtw
-from tqdm import tqdm
-import torch
-from joblib import Parallel, delayed
-
-files = {
-    358: ('PeMS03/PEMS03.npz', 'PeMS03/PEMS03.csv'),
-    307: ('PeMS04/PEMS04.npz', 'PEMS04.csv'),
-    883: ('PeMS07/PEMS07.npz', 'PEMS07.csv'),
-    170: ('PeMS08/PEMS08.npz', 'PEMS08.csv')
-}
-
-
-def compute_dtw_pair(i, j, data_mean):
-    return i, j, fastdtw(data_mean[i], data_mean[j], radius=6)[0]
-
-
-def get_A_hat(args):
-    """Optimized version with GPU support and parallel computing"""
-    # 基础配置
-    device = torch.device(args['device'])
-    data_dir = './data/'
-    num_node = args['num_nodes']
-    file_npz, file_csv = files[num_node]
-    dataset_name = file_npz.split('/')[0]
-    os.makedirs(f'{data_dir}{dataset_name}', exist_ok=True)
-
-    # 数据加载与标准化
-    with np.load(f'{data_dir}{file_npz}') as data:
-        arr_data = data['data']
-    arr_data = (arr_data - arr_data.mean((0, 1))) / arr_data.std((0, 1))
-    arr_data = torch.from_numpy(arr_data).float().to(device)
-
-    # DTW矩阵计算（带缓存）
-    dtw_path = f'{data_dir}{dataset_name}/{dataset_name}_dtw_distance.npy'
-    if not os.path.exists(dtw_path):
-        # 使用GPU加速的均值计算
-        daily_data = arr_data[..., 0].unfold(0, 288, 288).mean(dim=0).T.cpu().numpy()
-
-        # 并行计算DTW
-        print("Computing DTW matrix with parallel optimization...")
-        results = Parallel(n_jobs=-1)(
-            delayed(compute_dtw_pair)(i, j, daily_data)
-            for i in tqdm(range(num_node)) for j in range(i, num_node)
-        )
-
-        dtw_matrix = np.full((num_node, num_node), np.inf)
-        for i, j, d in results:
-            dtw_matrix[i, j] = d
-            dtw_matrix[j, i] = d
-        np.save(dtw_path, dtw_matrix)
-    else:
-        dtw_matrix = np.load(dtw_path)
-
-    # DTW矩阵标准化（GPU加速）
-    dtw_tensor = torch.from_numpy(dtw_matrix).to(device)
-    dtw_normalized = (dtw_tensor - dtw_tensor.mean()) / dtw_tensor.std()
-    semantic_adj = torch.exp(-dtw_normalized ** 2 / args['sigma1'] ** 2)
-    semantic_adj = (semantic_adj > args['thres1']).float()
-
-    # 空间矩阵计算（带缓存）
-    spatial_path = f'{data_dir}{dataset_name}/{dataset_name}_spatial_distance.npy'
-    if not os.path.exists(spatial_path):
-        # 使用Pandas高效读取
-        df = pd.read_csv(f'{data_dir}{file_csv}', header=None)
-        if num_node == 358:  # 特殊处理节点ID映射
-            with open(f'{data_dir}{dataset_name}/{dataset_name}.txt') as f:
-                node_ids = [int(line.strip()) for line in f]
-            id_map = {nid: idx for idx, nid in enumerate(node_ids)}
-            df[0] = df[0].map(id_map)
-            df[1] = df[1].map(id_map)
-
-        # 构建稀疏矩阵
-        spatial_adj = torch.full((num_node, num_node), float('inf'), device=device)
-        for row in df.itertuples():
-            i, j, d = int(row[1]), int(row[2]), float(row[3])
-            spatial_adj[i, j] = spatial_adj[j, i] = d
-        spatial_adj = spatial_adj.cpu().numpy()
-        np.save(spatial_path, spatial_adj)
-    else:
-        spatial_adj = np.load(spatial_path)
-
-    # 空间矩阵标准化（GPU加速）
-    mask = spatial_adj != float('inf')
-    valid_values = torch.from_numpy(spatial_adj[mask]).to(device)
-    spatial_normalized = (spatial_adj - valid_values.mean().item()) / valid_values.std().item()
-    spatial_adj = torch.exp(-torch.tensor(spatial_normalized) ** 2 / args['sigma2'] ** 2)
-    spatial_adj = (spatial_adj > args['thres2']).float()
-
-    # 归一化处理
-    def normalize_adj(adj):
-        D = adj.sum(1)
-        D = torch.clamp(D, min=1e-5)
-        D_inv_sqrt = 1.0 / torch.sqrt(D)
-        return 0.8 * (torch.eye(adj.size(0), device=device) +
-                      0.8 * D_inv_sqrt.view(-1, 1) * adj * D_inv_sqrt.view(1, -1))
-
-    return (normalize_adj(semantic_adj.to(args['device'])), normalize_adj(spatial_adj.to(args['device'])))
-
-
-# 测试代码
-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, 883, 170]:
-        args = {'num_nodes': nodes, **config}
-        get_A_hat(args)