diff --git a/README.md b/README.md index 8302f7d..c843f1b 100644 --- a/README.md +++ b/README.md @@ -13,8 +13,12 @@ mkdir -p models/gpt2 Download config.json & pytorch_model.bin from https://huggingface.co/openai-community/gpt2/tree/main +wget https://huggingface.co/openai-community/gpt2/resolve/main/config.json?download=true -O ./models/gpt2/config.json + +wget https://huggingface.co/openai-community/gpt2/resolve/main/pytorch_model.bin?download=true -O ./models/gpt2/config.json + Use pytorch >= 2.6 to load model. ## Run -Run: `python.py --config configs/STGODE_LLM_GPT2/PEMS08.yaml \ No newline at end of file +Run: `python.py --config configs/STGODE_LLM_GPT2/PEMS08.yaml diff --git a/data/PEMS03/PEMS03.csv b/data/PEMS03/PEMS03.csv new file mode 100644 index 0000000..00dec00 --- /dev/null +++ b/data/PEMS03/PEMS03.csv @@ -0,0 +1,548 @@ +from,to,distance +317842,318711,0.872 +318721,315955,1.322 +315927,318236,1.222 +318711,318721,0.233 +318236,317843,1.22 +315955,315938,0.239 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100644 diff --git a/data/get_adj.py b/data/get_adj.py new file mode 100644 index 0000000..87f6de3 --- /dev/null +++ b/data/get_adj.py @@ -0,0 +1,220 @@ +import csv +import os +import numpy as np +import pandas as pd +from scipy.sparse import coo_matrix +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 diff --git a/requirements.txt b/requirements.txt index 0c0c649..b9edec9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -33,7 +33,6 @@ matplotlib requests # 文件压缩处理 -zipfile # Kaggle数据下载 kagglehub diff --git a/utils/download.py b/utils/download.py index ed7c929..9b7ec19 100644 --- a/utils/download.py +++ b/utils/download.py @@ -74,7 +74,7 @@ def download_adj_data(current_dir, max_retries=3): 下载并解压 adj.zip 文件，并显示下载进度条。 如果下载失败，最多重试 max_retries 次。 """ - url = "https://code.zhang-heng.com/static/adj.zip" + url = "http://code.zhang-heng.com/static/adj.zip" retries = 0 while retries <= max_retries: @@ -143,4 +143,4 @@ def download_kaggle_data(current_dir): # 主程序 if __name__ == "__main__": - check_and_download_data() \ No newline at end of file + check_and_download_data()