English

exp/global.py

@@ -1,12 +1,12 @@
 import re
 import matplotlib.pyplot as plt
 import numpy as np
-import os
+# import os
 from matplotlib.lines import Line2D
 from matplotlib.gridspec import GridSpec
-from matplotlib.font_manager import FontProperties
+# from matplotlib.font_manager import FontProperties
 
-# 全局字典，包含每个指标的基准线数据和颜色
+# Global dictionary containing baseline data and colors for each metric
 MAE_baselines = {
     'STG-NCDE': ([15.57, 19.21, 20.53, 15.45], 'darkblue'),
     'DCRNN': ([17.99, 21.22, 25.22, 16.82], 'darkgreen'),
@@ -29,6 +29,7 @@ baseline_dict = {'MAE': MAE_baselines, 'RMSE': RMSE_baselines, 'MAPE': MAPE_base
 
 
 def extract_avg_loss(log_file_path):
+    # Regex to extract test loss from log files
     avg_loss_pattern = re.compile(r"Client #(\d+).*?Round.*?(\d+).*?test_loss': (\d+\.\d+)")
     client_loss_data = {}
 
@@ -60,7 +61,7 @@ def plot_avg_loss(client_loss_data, dataset, metric, ax, baselines):
         rounds = [r[0] for r in losses]
         avg_losses = [r[1] for r in losses]
 
-        # 当数据集为 D7 且指标为 MAPE 时，限制最多绘制前 20 轮
+        # Limit to the first 20 rounds for dataset D7 and metric MAPE
         if dataset == '7' and metric == 'MAPE':
             rounds = [r for r in rounds if r <= 60]
             avg_losses = avg_losses[:len(rounds)]
@@ -89,9 +90,9 @@ def main(legend_ncol=8):
 
     subfig_labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)', '(k)', '(l)']
 
-    # 设置字体为 Times New Roman
+    # Set font to Times New Roman
     # times_new_roman_font = FontProperties(fname='./times.ttf')
-    # 遍历指标和数据集，并绘制子图
+    # Iterate through metrics and datasets, and plot subfigures
     for i, metric in enumerate(metrics):
         for j, dataset in enumerate(datasets):
             ax_idx = i * 4 + j
@@ -103,14 +104,14 @@ def main(legend_ncol=8):
             ax.set_ylabel(metric)
             ax.set_xlabel('Round')
 
-            # 在底部居中添加标题
+            # Add title at the bottom center
             title = f'{subfig_labels[ax_idx]} {metric} on PEMSD{dataset}'
             # ax.text(0.5, -0.4, title, transform=ax.transAxes,
             #         fontsize=10, fontweight='bold', va='center', ha='center', fontproperties=times_new_roman_font)
             ax.text(0.5, -0.4, title, transform=ax.transAxes,
                     fontsize=10, fontweight='bold', va='center', ha='center')
 
-    # 设置统一的图例在顶部
+    # Set unified legend at the top
     handles = [
         Line2D([0], [0], color='red', linewidth=3, label='FedDGCN-Avg')
     ]
@@ -119,12 +120,12 @@ def main(legend_ncol=8):
     client_handles = [Line2D([0], [0], color=plt.get_cmap('tab20')(i), lw=2) for i in range(10)]
     client_labels = [f'Client #{i + 1}' for i in range(10)]
 
-    # 创建图例并加黑框背景
+    # Create legend with black border
     legend = fig.legend(handles=handles + client_handles,
                         labels=[h.get_label() for h in handles] + client_labels,
                         loc='upper center', bbox_to_anchor=(0.5, 0.98),
                         ncol=legend_ncol, fontsize='small', frameon=True, edgecolor='black')
-    legend.get_frame().set_linewidth(1.5)  # 黑框宽度
+    legend.get_frame().set_linewidth(1.5)  # Black border width
 
     plt.savefig('baseline.jpg', bbox_inches='tight')
     plt.show()

federatedscope/core/auxiliaries/dataloader_builder.py

@@ -63,7 +63,7 @@ def get_dataloader(dataset, config, split='train'):
         from federatedscope.mf.dataloader import MFDataLoader
         loader_cls = MFDataLoader
     elif config.dataloader.type == 'trafficflow':
-    # 待定
+    # This if is not strictly necessary but helps avoid potential bugs
         from torch.utils.data import DataLoader
         loader_cls = DataLoader
     else:

federatedscope/core/trainers/trainer_multi_model.py

@@ -80,12 +80,12 @@ class GeneralMultiModelTrainer(GeneralTorchTrainer):
                    "and its subclasses, or " \
                    "`GeneralTorchTrainer` and its subclasses"
             # self.__dict__ = copy.deepcopy(base_trainer.__dict__)
-            # 逐个复制 base_trainer 的属性，跳过不可拷贝的对象
+            # Copy attributes from base_trainer one by one, skipping non-copyable objects
             for key, value in base_trainer.__dict__.items():
                 try:
                     self.__dict__[key] = copy.deepcopy(value)
                 except TypeError:
-                    self.__dict__[key] = value  # 如果不能 deepcopy，使用浅拷贝
+                    self.__dict__[key] = value  # If unable to deepcopy, use shallow copy
 
         assert models_interact_mode in ["sequential", "parallel"], \
             f"Invalid models_interact_mode, should be `sequential` or " \

federatedscope/trafficflow/dataloader/traffic_dataloader.py

@@ -135,7 +135,7 @@ def load_traffic_data(config, client_cfgs):
     # y_val[..., :config.model.output_dim] = scaler.transform(y_val[..., :config.model.output_dim])
     # y_test[..., :config.model.output_dim] = scaler.transform(y_test[..., :config.model.output_dim])
 
-    # 客户端分割数据集
+    # Client-side dataset splitting
     node_num = config.data.num_nodes
     client_num = config.federate.client_num
     per_samples = node_num // client_num
@@ -143,7 +143,7 @@ def load_traffic_data(config, client_cfgs):
     input_dim, output_dim = config.model.input_dim, config.model.output_dim
     for i in range(client_num):
         if cur_index + per_samples <= node_num:
-            # 正常截取
+            # Normal slicing
             sub_array_train = x_train[:, :, cur_index:cur_index + per_samples, :]
             sub_array_val = x_val[:, :, cur_index:cur_index + per_samples, :]
             sub_array_test = x_test[:, :, cur_index:cur_index + per_samples, :]
@@ -152,7 +152,7 @@ def load_traffic_data(config, client_cfgs):
             sub_y_val = y_val[:, :, cur_index:cur_index + per_samples, :output_dim]
             sub_y_test = y_test[:, :, cur_index:cur_index + per_samples, :output_dim]
         else:
-            # 不足一个per_samples，补0列
+            # If there are not enough nodes to fill per_samples, pad with zero columns
             sub_array_train = x_train[:, :, cur_index:cur_index + per_samples, :]
             sub_array_val = x_val[:, :, cur_index:cur_index + per_samples, :]
             sub_array_test = x_test[:, :, cur_index:cur_index + per_samples, :]

federatedscope/trafficflow/model/DGCN.py

@@ -13,7 +13,7 @@ class DGCN(nn.Module):
         self.weights = nn.Parameter(torch.FloatTensor(cheb_k, dim_in, dim_out))
         self.bias_pool = nn.Parameter(torch.FloatTensor(embed_dim, dim_out))
         self.bias = nn.Parameter(torch.FloatTensor(dim_out))
-        # 初始化参数
+        # Initialize parameters
         nn.init.xavier_uniform_(self.weights_pool)
         nn.init.xavier_uniform_(self.weights)
         nn.init.zeros_(self.bias_pool)

federatedscope/trafficflow/model/FedDGCN.py

@@ -54,7 +54,7 @@ class FedDGCN(nn.Module):
         self.node_embeddings2 = nn.Parameter(torch.randn(self.num_node, args.embed_dim), requires_grad=True)
         self.T_i_D_emb = nn.Parameter(torch.empty(288, args.embed_dim))
         self.D_i_W_emb = nn.Parameter(torch.empty(7, args.embed_dim))
-        # 初始化参数
+        # Initialize parameters
         nn.init.xavier_uniform_(self.node_embeddings1)
         nn.init.xavier_uniform_(self.T_i_D_emb)
         nn.init.xavier_uniform_(self.D_i_W_emb)

federatedscope/trafficflow/splitters/trafficSplitter.py

@@ -9,7 +9,7 @@ class TrafficSplitter(BaseSplitter):
 
     def __call__(self, dataset, *args, **kwargs):
         """
-        后面考虑子图标记划分
+        TODO:subgraph partition
 
         Args:
             dataset: ndarray(timestep, num_node, channel)