our experiment log

exp/MAPE/drawResult.py

@@ -1,85 +0,0 @@
-import re
-import matplotlib.pyplot as plt
-import numpy as np
-from matplotlib import cm
-
-# 全局字典，包含模型在不同数据集上的效果值和颜色
-MAPE_baselines = {
-    'STG-NCDE': ([15.57, 19.21, 20.53, 15.45], 'darkblue'),
-    'DCRNN': ([17.99, 21.22, 25.22, 16.82], 'darkgreen'),
-    'AGCRN': ([15.98, 19.83, 22.37, 15.95], 'darkorange'),
-    'STGODE': ([16.50, 20.84, 22.59, 16.81], 'purple')
-}
-
-def extract_avg_loss(log_file_path):
-    avg_loss_pattern = re.compile(r"Client #(\d+).*?Round.*?(\d+).*?test_loss': (\d+\.\d+)")
-    client_loss_data = {}
-
-    with open(log_file_path, 'r') as f:
-        log_content = f.read()
-    matches = avg_loss_pattern.findall(log_content)
-
-    for match in matches:
-        if int(match[1]) <= 20:
-            client_id = int(match[0])
-            round_id = int(match[1])
-            avg_loss = float(match[2])
-        else:
-            continue
-
-        if client_id not in client_loss_data:
-            client_loss_data[client_id] = []
-        client_loss_data[client_id].append((round_id, avg_loss))
-
-    return client_loss_data
-
-
-def plot_avg_loss(client_loss_data, dataset):
-    plt.figure(figsize=(6, 4))
-    all_clients_avg_losses = []
-    handles = []
-    labels = []
-
-    sorted_clients = sorted(client_loss_data.keys())
-    colors = plt.get_cmap('tab20').colors  # 使用 tab20 调色板
-
-    for idx, client_id in enumerate(sorted_clients):
-        losses = client_loss_data[client_id]
-        rounds = [r[0] for r in losses]
-        avg_losses = [r[1] for r in losses]
-        color = colors[idx % len(colors)]  # 为每个 client 分配不同颜色
-        line, = plt.plot(rounds, avg_losses, label=f'Client #{client_id}', color=color)
-        handles.append(line)
-        labels.append(f'Client #{client_id}')
-        all_clients_avg_losses.append(avg_losses)
-
-    mean_avg_loss_per_round = np.mean(np.array(all_clients_avg_losses), axis=0)
-    mean_line, = plt.plot(rounds, mean_avg_loss_per_round, label='Mean Test Loss', color='red', linewidth=3)
-    handles.append(mean_line)
-    labels.append('Mean Avg Loss')
-
-    # 添加模型基准线
-    dataset_index = ['3', '4', '7', '8'].index(dataset)
-    for model_name, (baseline_values, color) in model_baselines.items():
-        baseline_value = baseline_values[dataset_index]
-        line = plt.axhline(y=baseline_value, color=color, linestyle='--', label=model_name)
-        handles.append(line)
-        labels.append(f'{model_name}')
-
-    plt.xlabel('Round')
-    plt.ylabel('Test Loss')
-    plt.title(f'Client Test Loss over Rounds in PeMSD{dataset}')
-    plt.grid(True)
-
-    plt.legend(handles=handles, labels=labels, loc='center left', bbox_to_anchor=(1, 0.5))
-    plt.savefig(f'D{dataset}_MAPE.png', bbox_inches='tight')
-    plt.show()
-
-
-if __name__ == '__main__':
-    datasets = ['4', '7']
-
-    for dataset in datasets:
-        log_file_path = f'./D{dataset}_MAPE/exp_print.log'
-        client_loss_data = extract_avg_loss(log_file_path)
-        plot_avg_loss(client_loss_data, dataset)

exp/RMSE/drawResult.py

@@ -1,85 +0,0 @@
-import re
-import matplotlib.pyplot as plt
-import numpy as np
-from matplotlib import cm
-
-# 全局字典，包含模型在不同数据集上的效果值和颜色
-RMSE_baselines = {
-    'STG-NCDE': ([27.09, 31.09, 33.84, 24.81], 'darkblue'),
-    'DCRNN': ([30.31, 33.44, 38.61, 26.36], 'darkgreen'),
-    'AGCRN': ([28.25, 32.26, 36.55, 25.22], 'darkorange'),
-    'STGODE': ([27.84, 32.82, 37.54, 25.97], 'purple')
-}
-
-def extract_avg_loss(log_file_path):
-    avg_loss_pattern = re.compile(r"Client #(\d+).*?Round.*?(\d+).*?test_loss': (\d+\.\d+)")
-    client_loss_data = {}
-
-    with open(log_file_path, 'r') as f:
-        log_content = f.read()
-    matches = avg_loss_pattern.findall(log_content)
-
-    for match in matches:
-        if int(match[1]) <= 59:
-            client_id = int(match[0])
-            round_id = int(match[1])
-            avg_loss = float(match[2])
-        else:
-            continue
-
-        if client_id not in client_loss_data:
-            client_loss_data[client_id] = []
-        client_loss_data[client_id].append((round_id, avg_loss))
-
-    return client_loss_data
-
-
-def plot_avg_loss(client_loss_data, dataset):
-    plt.figure(figsize=(6, 4))
-    all_clients_avg_losses = []
-    handles = []
-    labels = []
-
-    sorted_clients = sorted(client_loss_data.keys())
-    colors = plt.get_cmap('tab20').colors  # 使用 tab20 调色板
-
-    for idx, client_id in enumerate(sorted_clients):
-        losses = client_loss_data[client_id]
-        rounds = [r[0] for r in losses]
-        avg_losses = [r[1] for r in losses]
-        color = colors[idx % len(colors)]  # 为每个 client 分配不同颜色
-        line, = plt.plot(rounds, avg_losses, label=f'Client #{client_id}', color=color)
-        handles.append(line)
-        labels.append(f'Client #{client_id}')
-        all_clients_avg_losses.append(avg_losses)
-
-    mean_avg_loss_per_round = np.mean(np.array(all_clients_avg_losses), axis=0)
-    mean_line, = plt.plot(rounds, mean_avg_loss_per_round, label='Mean Test Loss', color='red', linewidth=3)
-    handles.append(mean_line)
-    labels.append('Mean Avg Loss')
-
-    # 添加模型基准线
-    dataset_index = ['3', '4', '7', '8'].index(dataset)
-    for model_name, (baseline_values, color) in model_baselines.items():
-        baseline_value = baseline_values[dataset_index]
-        line = plt.axhline(y=baseline_value, color=color, linestyle='--', label=model_name)
-        handles.append(line)
-        labels.append(f'{model_name}')
-
-    plt.xlabel('Round')
-    plt.ylabel('Test Loss')
-    plt.title(f'Client Test Loss over Rounds in PeMSD{dataset}')
-    plt.grid(True)
-
-    plt.legend(handles=handles, labels=labels, loc='center left', bbox_to_anchor=(1, 0.5))
-    plt.savefig(f'D{dataset}_RMSE.png', bbox_inches='tight')
-    plt.show()
-
-
-if __name__ == '__main__':
-    datasets = ['4', '3', '8', '7']
-
-    for dataset in datasets:
-        log_file_path = f'./D{dataset}/exp_print.log'
-        client_loss_data = extract_avg_loss(log_file_path)
-        plot_avg_loss(client_loss_data, dataset)