""" Co-MADDPG Visualization Module | Co-MADDPG 可视化模块 This module handles the generation of IEEE-standard figures for the resource allocation performance evaluation. It maps specifically to Section VII (Experimental Results) of the associated research paper. 本模块负责生成符合 IEEE 标准的资源分配性能评估图表。它专门对应于相关研究论文的 第七节（实验结果）。 Reference Figures (from Section VII): - Fig 2: Training convergence curves | 训练收敛曲线 - Fig 3: System QoE vs. SNR | 系统 QoE 随 SNR 的变化 - Fig 4: Jain's Fairness Index vs. SNR | Jain 公平性指数随 SNR 的变化 - Fig 5: System QoE vs. Total Users (K) | 系统 QoE 随总用户数 (K) 的变化 - Fig 6: Rate Satisfaction Ratio vs. Total Users (K) | 速率满足率随总用户数 (K) 的变化 - Fig 7: Trajectory of λ(t) over time | λ(t) 随时间的变化轨迹 - Fig 8: Correlation scatter of λ and System QoE | λ 与系统 QoE 的相关性散点图 - Fig 9: System QoE vs. Semantic User Ratio | 系统 QoE 随语义用户比例的变化 - Fig 10: Ablation study results | 消融实验结果 - Fig 11: Sensitivity analysis of β | β 参数的敏感性分析 - Fig 12: Sensitivity analysis of Q_th | Q_th 阈值的敏感性分析 """ import os import numpy as np import matplotlib # Use non-interactive backend to avoid requiring an X server or display # 使用非交互式后端以避免需要 X 服务器或显示器 matplotlib.use('Agg') import matplotlib.pyplot as plt from utils.metrics import moving_average # IEEE-quality plotting defaults | IEEE 质量绘图默认设置 plt.rcParams.update({ 'font.family': 'serif', 'font.serif': ['Times New Roman', 'DejaVu Serif'], 'font.size': 12, 'axes.grid': True, 'figure.figsize': (8, 6), 'figure.autolayout': True, # Equivalent to tight_layout | 等同于 tight_layout 'savefig.dpi': 300, 'savefig.bbox': 'tight' }) # Consistent algorithm styles | 一致的算法绘图风格 # Color and marker choices distinguish between proposed, baselines, and ablation variants # 颜色和标记的选择用于区分建议算法、基准算法和消融变体 ALGO_STYLES = { 'Co-MADDPG': {'color': '#E24A33', 'marker': 'o', 'linestyle': '-'}, # Proposed (Red) | 建议算法（红色） 'Pure Cooperative': {'color': '#348ABD', 'marker': 's', 'linestyle': '--'}, # Baseline (Blue) | 基准（蓝色） 'Pure Competitive': {'color': '#988ED5', 'marker': '^', 'linestyle': '--'}, # Baseline (Purple) | 基准（紫色） 'Single-Agent DQN': {'color': '#777777', 'marker': 'D', 'linestyle': '-.'}, # Baseline (Gray) | 基准（灰色） 'IDDPG': {'color': '#FBC15E', 'marker': 'v', 'linestyle': '-.'}, # Baseline (Yellow) | 基准（黄色） 'Fixed λ=0.5': {'color': '#8EBA42', 'marker': 'p', 'linestyle': ':'}, # Ablation (Green) | 消融（绿色） 'Equal Allocation': {'color': '#FFB5B8', 'marker': '*', 'linestyle': ':'}, # Baseline (Pink) | 基准（粉色） 'Semantic-Only': {'color': '#6d904f', 'marker': 'h', 'linestyle': ':'}, # Baseline (Olive) | 基准（橄榄色） } class Plotter: """ IEEE-quality plotting module for all paper figures. | 用于所有论文图表的 IEEE 质量绘图模块。 """ def __init__(self): pass def _get_style(self, algo_name): """ Helper to get plotting style for an algorithm or a sensible default. 获取算法的绘图风格或合理的默认值。 """ return ALGO_STYLES.get(algo_name, {'color': 'k', 'marker': '', 'linestyle': '-'}) def _save_plot(self, save_path): """ Helper to save plot in both PDF and PNG formats at 300 DPI. 以 300 DPI 的分辨率将图表保存为 PDF 和 PNG 格式。 """ os.makedirs(os.path.dirname(os.path.abspath(save_path)), exist_ok=True) # Strip extension if the user provided one, to consistently save both .pdf and .png # 如果用户提供了扩展名，则将其去除，以便一致地保存 .pdf 和 .png base_path = os.path.splitext(save_path)[0] plt.savefig(f"{base_path}.pdf", format='pdf') plt.savefig(f"{base_path}.png", format='png', dpi=300) plt.close() def plot_convergence(self, data_dict, save_path): """ Fig 2: Episode QoE_sys curves. | 图 2：每回合系统 QoE 曲线。 Shows how the algorithm improves over training episodes. 展示算法在训练回合中如何改进。 data_dict: {algo_name: [episode_qoe_values]} """ plt.figure() for algo, qoe_vals in data_dict.items(): style = self._get_style(algo) # Remove markers for dense convergence plots to maintain clean look # 为密集的收敛图移除标记以保持画面整洁 plot_style = style.copy() if 'marker' in plot_style: plot_style.pop('marker') smoothed_qoe = moving_average(qoe_vals, window=50) x_vals = np.arange(len(smoothed_qoe)) plt.plot(x_vals, smoothed_qoe, label=algo, **plot_style) plt.xlabel('Episode') plt.ylabel('System QoE') plt.title('Training Convergence') plt.legend() self._save_plot(save_path) def plot_qoe_vs_snr(self, data_dict, save_path): """ Fig 3: QoE vs SNR. | 图 3：QoE 随 SNR 的变化。 Evaluates system robustness under different noise levels. 评估不同噪声水平下的系统鲁棒性。 data_dict: {algo_name: [qoe_per_snr_point]} """ plt.figure() snr_vals = [0, 5, 10, 15, 20, 25, 30] for algo, qoe_vals in data_dict.items(): style = self._get_style(algo) plt.plot(snr_vals, qoe_vals, label=algo, **style) plt.xlabel('SNR (dB)') plt.ylabel('System QoE') plt.title('System QoE vs. SNR') plt.legend() self._save_plot(save_path) def plot_fairness_vs_snr(self, data_dict, save_path): """ Fig 4: Jain Fairness Index vs SNR. | 图 4：Jain 公平性指数随 SNR 的变化。 Measures the balance of resource allocation across users. 衡量不同用户之间资源分配的平衡性。 data_dict: {algo_name: [fairness_per_snr_point]} """ plt.figure() snr_vals = [0, 5, 10, 15, 20, 25, 30] for algo, fairness_vals in data_dict.items(): style = self._get_style(algo) plt.plot(snr_vals, fairness_vals, label=algo, **style) plt.xlabel('SNR (dB)') plt.ylabel('Jain Fairness Index') plt.title('Fairness vs. SNR') plt.legend() self._save_plot(save_path) def plot_qoe_vs_users(self, data_dict, save_path): """ Fig 5: QoE vs Total Users K. | 图 5：QoE 随总用户数 K 的变化。 Tests system scalability as user density increases. 测试随着用户密度增加系统的可扩展性。 data_dict: {algo_name: [qoe_per_k_point]} """ plt.figure() users_vals = [4, 6, 8, 10, 12] for algo, qoe_vals in data_dict.items(): style = self._get_style(algo) plt.plot(users_vals, qoe_vals, label=algo, **style) plt.xlabel('Total Users (K)') plt.ylabel('System QoE') plt.title('System QoE vs. Total Users') plt.legend() self._save_plot(save_path) def plot_rate_satisfaction_vs_users(self, data_dict, save_path): """ Fig 6: Rate Satisfaction Ratio vs Total Users K. | 图 6：速率满足率随总用户数 K 的变化。 Evaluates the ability to meet minimum QoS requirements. 评估满足最小 QoS 要求的能力。 data_dict: {algo_name: [rate_satisfaction_per_k_point]} """ plt.figure() users_vals = [4, 6, 8, 10, 12] for algo, sat_vals in data_dict.items(): style = self._get_style(algo) plt.plot(users_vals, sat_vals, label=algo, **style) plt.xlabel('Total Users (K)') plt.ylabel('Rate Satisfaction Ratio') plt.title('Rate Satisfaction vs. Total Users') plt.legend() self._save_plot(save_path) def plot_lambda_trajectory(self, lambda_values, save_path): """ Fig 7: Lambda Trajectory. | 图 7：Lambda 轨迹。 Visualizes the dynamic switching between cooperation and competition. 可视化协作与竞争之间的动态切换。 lambda_values: list of lambda(t) values. """ plt.figure() time_steps = np.arange(len(lambda_values)) plt.plot(time_steps, lambda_values, label=r'$\lambda(t)$', color='#348ABD', linestyle='-') # Reference line for fixed weighting | 固定权重的参考线 plt.axhline(y=0.5, color='#E24A33', linestyle='--', label=r'Reference ($\lambda=0.5$)') plt.xlabel('Time Step') plt.ylabel(r'$\lambda(t)$') plt.title(r'Trajectory of Allocation Parameter $\lambda$') plt.legend() self._save_plot(save_path) def plot_lambda_qoe_scatter(self, lambdas, qoes, save_path): """ Fig 8: Scatter of (lambda, QoE_sys). | 图 8：(lambda, QoE_sys) 散点图。 Shows the correlation between the dynamic parameter and system performance. 展示动态参数与系统性能之间的相关性。 """ plt.figure() time_steps = np.arange(len(lambdas)) # Color points by time to show evolution | 按时间为点着色以显示演化过程 sc = plt.scatter(lambdas, qoes, c=time_steps, cmap='viridis', alpha=0.7) cbar = plt.colorbar(sc) cbar.set_label('Time Step') plt.xlabel(r'$\lambda$') plt.ylabel('System QoE') plt.title(r'Correlation between $\lambda$ and System QoE') self._save_plot(save_path) def plot_qoe_vs_ratio(self, data_dict, ratios, save_path): """ Fig 9: QoE vs Semantic User Ratio. | 图 9：QoE 随语义用户比例的变化。 Studies the impact of increasing semantic communication prevalence. 研究语义通信普及率增加的影响。 data_dict: {algo_name: [qoe_values]} """ plt.figure() for algo, qoe_vals in data_dict.items(): style = self._get_style(algo) plt.plot(ratios, qoe_vals, label=algo, **style) plt.xlabel('Semantic User Ratio') plt.ylabel('System QoE') plt.title('System QoE vs. Semantic User Ratio') plt.legend() self._save_plot(save_path) def plot_ablation(self, data, save_path): """ Fig 10: Horizontal bar chart for ablation study. | 图 10：消融研究的水平条形图。 Compares the full Co-MADDPG against its stripped-down variants. 将完整的 Co-MADDPG 与其简化变体进行比较。 data: {variant_label: qoe_value} """ plt.figure() labels = list(data.keys()) values = list(data.values()) y_pos = np.arange(len(labels)) # Highlight Co-MADDPG (Full) in red if present | 如果存在，用红色高亮 Co-MADDPG (Full) colors = ['#E24A33' if 'Co-MADDPG' in label and 'Full' in label else '#348ABD' for label in labels] plt.barh(y_pos, values, align='center', color=colors) plt.yticks(y_pos, labels) plt.xlabel('System QoE') plt.title('Ablation Study') self._save_plot(save_path) def plot_beta_sensitivity(self, data_dict, betas, save_path): """ Fig 11: QoE vs Beta values. | 图 11：QoE 随 Beta 值的变化。 Analyzes sensitivity to the sigmoid steepness parameter. 分析对 Sigmoid 陡峭度参数的敏感性。 data_dict: {label: qoe_value_list} """ plt.figure() for algo, qoe_vals in data_dict.items(): style = self._get_style(algo) plt.plot(betas, qoe_vals, label=algo, **style) plt.xlabel(r'$\beta$ Parameter') plt.ylabel('System QoE') plt.title(r'Sensitivity Analysis of $\beta$') plt.legend() self._save_plot(save_path) def plot_qth_sensitivity(self, data_dict, qths, save_path): """ Fig 12: QoE vs Q_th values. | 图 12：QoE 随 Q_th 值的变化。 Analyzes sensitivity to the cooperation threshold. 分析对协作阈值的敏感性。 data_dict: {label: qoe_value_list} """ plt.figure() for algo, qoe_vals in data_dict.items(): style = self._get_style(algo) plt.plot(qths, qoe_vals, label=algo, **style) plt.xlabel(r'$Q_{th}$ Threshold') plt.ylabel('System QoE') plt.title(r'Sensitivity Analysis of $Q_{th}$') plt.legend() self._save_plot(save_path)