优化READMER，注册STGODE-LLM

.gitignore

@@ -165,4 +165,6 @@ cython_debug/
 exp/
 STDEN/
 models/gpt2/
-pre-trained/
+pre-trained/
+data/*
+

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README.md

@@ -2,19 +2,34 @@
 
 Secret Projct
 
+```
 mkdir -p models/gpt2
+```
+
+## Prepare Env.
+```
+pip install -r requirement.txt
+```
 
 ## Download dataset
+```
 python utils/download.py
+```
 
 ## Download gpt weight
 
-mkdir -p models/gpt2
+`mkdir -p models/gpt2`
 
 Download config.json & pytorch_model.bin from https://huggingface.co/openai-community/gpt2/tree/main
 
+```bash
+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
+```
+`python main.py --config configs/STGODE_LLM_GPT2/PEMS08.yaml
+```

data/PEMS08/PEMS08.csv

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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

models/STGODE_LLM_GPT2/STGODE_LLM_GPT2.py

@@ -66,11 +66,11 @@ class GPT2BackboneHF(nn.Module):
         super().__init__()
         from transformers import GPT2Model
         if local_dir is not None and len(local_dir) > 0:
-            self.model = GPT2Model.from_pretrained(local_dir, local_files_only=True)
+            self.model = GPT2Model.from_pretrained(local_dir, local_files_only=True, use_cache=False)
         else:
             if model_name is None:
                 model_name = 'gpt2'
-            self.model = GPT2Model.from_pretrained(model_name)
+            self.model = GPT2Model.from_pretrained(model_name, use_cache=False)
         if gradient_checkpointing:
             self.model.gradient_checkpointing_enable()
         self.hidden_size = self.model.config.hidden_size

models/model_selector.py

@@ -1,12 +1,16 @@
 from models.STDEN.stden_model import STDENModel
 from models.STGODE.STGODE import ODEGCN
+from models.STGODE_LLM_GPT2.STGODE_LLM_GPT2 import ODEGCN_LLM_GPT2
+
 
 def model_selector(config):
     model_name = config['basic']['model']
     model = None
     match model_name:
-        case 'STDEN': 
+        case 'STDEN':
             model = STDENModel(config)
         case 'STGODE':
             model = ODEGCN(config)
-    return model
+        case 'STGODE-LLM-GPT2':
+            model = ODEGCN_LLM_GPT2(config)
+    return model

requirements.txt

@@ -33,7 +33,6 @@ matplotlib
 requests
 
 # 文件压缩处理
-zipfile
 
 # Kaggle数据下载
 kagglehub

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()
+    check_and_download_data()