import csv import os import numpy as np from scipy.sparse.linalg import norm import scipy.sparse as sp import torch def get_adj(args): dataset_path = './data' match args['num_nodes']: case 358: dataset_name = 'PEMS03' adj_path = os.path.join(dataset_path, dataset_name, 'PEMS03.csv') id = os.path.join(dataset_path, dataset_name, 'PEMS03.txt') A, adj = load_adj(args['num_nodes'], adj_path, id_filename=id) case 307: dataset_name = 'PEMS04' adj_path = os.path.join(dataset_path, dataset_name, 'PEMS04.csv') A, adj = load_adj(args['num_nodes'], adj_path, std=True) case 883: dataset_name = 'PEMS07' adj_path = os.path.join(dataset_path, dataset_name, 'PEMS07.csv') A, adj = load_adj(args['num_nodes'], adj_path) case 170: dataset_name = 'PEMS08' adj_path = os.path.join(dataset_path, dataset_name, 'PEMS08.csv') A, adj = load_adj(args['num_nodes'], adj_path, std=True) return adj def get_gso(args): dataset_path = './data' match args['num_nodes']: case 358: dataset_name = 'PEMS03' adj_path = os.path.join(dataset_path, dataset_name, 'PEMS03.csv') id = os.path.join(dataset_path, dataset_name, 'PEMS03.txt') A, adj = load_adj(args['num_nodes'], adj_path, id_filename=id) case 307: dataset_name = 'PEMS04' adj_path = os.path.join(dataset_path, dataset_name, 'PEMS04.csv') A, adj = load_adj(args['num_nodes'], adj_path, std=True) case 883: dataset_name = 'PEMS07' adj_path = os.path.join(dataset_path, dataset_name, 'PEMS07.csv') A, adj = load_adj(args['num_nodes'], adj_path) case 170: dataset_name = 'PEMS08' adj_path = os.path.join(dataset_path, dataset_name, 'PEMS08.csv') A, adj = load_adj(args['num_nodes'], adj_path, std=True) gso = calc_gso(adj, args['gso_type']) if args['graph_conv_type'] == 'cheb_graph_conv': gso = calc_chebynet_gso(gso) gso = gso.toarray() gso = gso.astype(dtype=np.float32) gso = torch.from_numpy(gso).to(args['device']) return gso def load_adj(num_nodes, adj_path, id_filename=None, std=False): ''' Parameters ---------- adj_path: str, path of the csv file contains edges information num_nodes: int, the number of vertices id_filename: str, optional, path of the file containing node IDs (if not starting from 0) std: bool, if True, normalize the cost values in the CSV file using Gaussian normalization Returns ---------- A: np.ndarray, adjacency matrix distanceA: np.ndarray, distance matrix (normalized if std=True) ''' if 'npy' in adj_path: adj_mx = np.load(adj_path) return adj_mx, None else: A = np.zeros((int(num_nodes), int(num_nodes)), dtype=np.float32) distanceA = np.zeros((int(num_nodes), int(num_nodes)), dtype=np.float32) # 如果提供了id_filename，说明节点ID不是从0开始的，需要重新映射 if id_filename: with open(id_filename, 'r') as f: id_dict = {int(i): idx for idx, i in enumerate(f.read().strip().split('\n'))} with open(adj_path, 'r') as f: f.readline() # 略过表头那一行 reader = csv.reader(f) costs = [] # 用于收集所有cost值 for row in reader: if len(row) != 3: continue i, j, distance = int(row[0]), int(row[1]), float(row[2]) A[id_dict[i], id_dict[j]] = 1 # 确保距离值为正 distance = max(distance, 1e-6) costs.append(distance) # 收集cost值 distanceA[id_dict[i], id_dict[j]] = distance else: # 如果没有提供id_filename，说明节点ID是从0开始的 with open(adj_path, 'r') as f: f.readline() # 略过表头那一行 reader = csv.reader(f) costs = [] # 用于收集所有cost值 for row in reader: if len(row) != 3: continue i, j, distance = int(row[0]), int(row[1]), float(row[2]) A[i, j] = 1 # 确保距离值为正 distance = max(distance, 1e-6) costs.append(distance) # 收集cost值 distanceA[i, j] = distance # 如果std=True，对CSV中的所有cost值进行高斯正态分布标准化 if std: mean_cost = np.mean(costs) # 计算cost值的均值 std_cost = np.std(costs) # 计算cost值的标准差 for idx in np.ndindex(distanceA.shape): # 遍历矩阵 if distanceA[idx] > 0: # 只对非零元素进行标准化 normalized_value = (distanceA[idx] - mean_cost) / std_cost # 确保标准化后的值为正 normalized_value = max(normalized_value, 1e-6) distanceA[idx] = normalized_value # 确保矩阵中没有零行 row_sums = distanceA.sum(axis=1) zero_rows = np.where(row_sums == 0)[0] for row in zero_rows: distanceA[row, :] = 1e-6 # 将零行替换为一个非零的默认值 return A, distanceA def calc_gso(dir_adj, gso_type): n_vertex = dir_adj.shape[0] if not sp.issparse(dir_adj): dir_adj = sp.csc_matrix(dir_adj) elif dir_adj.format != 'csc': dir_adj = dir_adj.tocsc() id = sp.identity(n_vertex, format='csc') # Symmetrizing an adjacency matrix adj = dir_adj + dir_adj.T.multiply(dir_adj.T > dir_adj) - dir_adj.multiply(dir_adj.T > dir_adj) # adj = 0.5 * (dir_adj + dir_adj.transpose()) if gso_type in ['sym_renorm_adj', 'rw_renorm_adj', 'sym_renorm_lap', 'rw_renorm_lap']: adj = adj + id if gso_type in ['sym_norm_adj', 'sym_renorm_adj', 'sym_norm_lap', 'sym_renorm_lap']: row_sum = adj.sum(axis=1).A1 # Check for zero or negative values in row_sum if np.any(row_sum <= 0): raise ValueError( "Row sum contains zero or negative values, which is not allowed for symmetric normalization.") row_sum_inv_sqrt = np.power(row_sum, -0.5) row_sum_inv_sqrt[np.isinf(row_sum_inv_sqrt)] = 0. # Handle inf values deg_inv_sqrt = sp.diags(row_sum_inv_sqrt, format='csc') # A_{sym} = D^{-0.5} * A * D^{-0.5} sym_norm_adj = deg_inv_sqrt.dot(adj).dot(deg_inv_sqrt) if gso_type in ['sym_norm_lap', 'sym_renorm_lap']: sym_norm_lap = id - sym_norm_adj gso = sym_norm_lap else: gso = sym_norm_adj elif gso_type in ['rw_norm_adj', 'rw_renorm_adj', 'rw_norm_lap', 'rw_renorm_lap']: row_sum = np.sum(adj, axis=1).A1 # Check for zero or negative values in row_sum if np.any(row_sum <= 0): raise ValueError( "Row sum contains zero or negative values, which is not allowed for random walk normalization.") row_sum_inv = np.power(row_sum, -1) row_sum_inv[np.isinf(row_sum_inv)] = 0. # Handle inf values deg_inv = sp.diags(row_sum_inv, format='csc') # A_{rw} = D^{-1} * A rw_norm_adj = deg_inv.dot(adj) if gso_type in ['rw_norm_lap', 'rw_renorm_lap']: rw_norm_lap = id - rw_norm_adj gso = rw_norm_lap else: gso = rw_norm_adj else: raise ValueError(f'{gso_type} is not defined.') # Check for nan or inf in the final result if np.isnan(gso.data).any() or np.isinf(gso.data).any(): raise ValueError("NaN or Inf detected in the final GSO matrix. Please check the input adjacency matrix.") return gso def calc_chebynet_gso(gso): if sp.issparse(gso) == False: gso = sp.csc_matrix(gso) elif gso.format != 'csc': gso = gso.tocsc() id = sp.identity(gso.shape[0], format='csc') # If you encounter a NotImplementedError, please update your scipy version to 1.10.1 or later. eigval_max = norm(gso, 2) # If the gso is symmetric or random walk normalized Laplacian, # then the maximum eigenvalue is smaller than or equals to 2. if eigval_max >= 2: gso = gso - id else: gso = 2 * gso / eigval_max - id return gso