From f64d74b986fbec399a1ae9ad027820fb276ad6da Mon Sep 17 00:00:00 2001 From: Maple-YZ Date: Fri, 27 Mar 2026 16:46:01 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=A8=A1=E5=9E=8B=E8=AF=84?= =?UTF-8?q?=E4=BC=B0=E5=8A=9F=E8=83=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- evaluate_models.py | 336 +++++++++++++++++++++++++++++++++++++++ plot_density_from_csv.py | 43 +++++ plot_global_variance.py | 33 ++++ 3 files changed, 412 insertions(+) create mode 100644 evaluate_models.py create mode 100644 plot_density_from_csv.py create mode 100644 plot_global_variance.py diff --git a/evaluate_models.py b/evaluate_models.py new file mode 100644 index 0000000..baddc84 --- /dev/null +++ b/evaluate_models.py @@ -0,0 +1,336 @@ +"""评估训练好的模型并绘制速度时空图""" +import os +import yaml +import numpy as np +import matplotlib.pyplot as plt +import matplotlib +matplotlib.use("Agg") +from sumo_edge_vsl_environment import SUMOEdgeVSLEnvironment +from ppo_agent import PPOAgent +from appo_sumo_agent import APPOSUMOAgent +from dqn_agent import DQNAgent +import traci + +def evaluate_model(model_type, checkpoint_dir, config): + """评估单个模型""" + env = SUMOEdgeVSLEnvironment(config) + + if model_type == "PPO": + agent = PPOAgent( + state_dim=env.state_dim, + action_dims=[env.action_dim] * env.num_edges, + hidden_layers=[256, 256, 128], + device="cuda" + ) + agent.load(os.path.join(checkpoint_dir, "model_best.pt")) + elif model_type == "APPO": + agent = APPOSUMOAgent( + state_dim=env.state_dim, + action_dims=[env.action_dim] * env.num_edges, + hidden_dim=128, + device="cuda", + total_episodes=500 + ) + agent.load(os.path.join(checkpoint_dir, "model_best.pt")) + elif model_type == "DQN": + agents = [] + for i in range(env.num_edges): + agent_i = DQNAgent( + state_dim=env.state_dim, + num_actions=5, + hidden_dim=256, + device="cuda" + ) + agent_i.load(os.path.join(checkpoint_dir, f"model_best_edge{i}.pt")) + agents.append(agent_i) + agent = agents + + # 收集所有车检器组位置（只包含有检测器的位置） + detector_groups = [] + for edge_id in env.control_edges: + edge_info = env.parser.edge_info[edge_id] + for pos in edge_info.detector_positions: + pos_idx = edge_info.detector_positions.index(pos) + # 检查该位置是否有检测器 + has_detector = False + for lane_idx in edge_info.traffic_lane_indices: + if edge_info.detectors.get((lane_idx, pos_idx)): + has_detector = True + break + if has_detector: + detector_groups.append((edge_id, pos)) + + # 运行仿真 + state = env.reset(seed=42) + speed_data = [] + density_data = [] + brake_counts = [] + speed_variances = [] + throughputs = [] + + done = False + step = 0 + + while not done: + if model_type == "DQN": + action = np.array([ag.select_action(state) for ag in agent]) + else: + action, _, _ = agent.select_action(state, deterministic=True) + + state, reward, done, info = env.step(action) + + if not done: + step_speeds = [] + step_densities = [] + for edge_id, pos in detector_groups: + edge_info = env.parser.edge_info[edge_id] + pos_idx = edge_info.detector_positions.index(pos) + + # 获取该位置所有车道的速度和占有率并取平均 + speeds = [] + occupancies = [] + for lane_idx in edge_info.traffic_lane_indices: + det_id = edge_info.detectors.get((lane_idx, pos_idx)) + if det_id: + spd = traci.inductionloop.getLastIntervalMeanSpeed(det_id) + occ = traci.inductionloop.getLastIntervalOccupancy(det_id) + if spd >= 0: + speeds.append(spd * 3.6) + if occ >= 0: + occupancies.append(occ) + + # 如果没有检测器数据，使用edge的实时速度作为备用 + if speeds: + avg_speed = np.mean(speeds) + else: + edge_speed = traci.edge.getLastStepMeanSpeed(edge_id) + avg_speed = edge_speed * 3.6 if edge_speed >= 0 else 0 + step_speeds.append(avg_speed) + + avg_occupancy = np.mean(occupancies) if occupancies else 0 + step_densities.append(avg_occupancy) + + speed_data.append(step_speeds) + density_data.append(step_densities) + + total_brakes = 0 + all_speeds = [] + for edge_id, edge_info in env.parser.edge_info.items(): + for det_id in edge_info.detectors.values(): + total_brakes += traci.inductionloop.getLastIntervalVehicleNumber(det_id) + spd = traci.inductionloop.getLastIntervalMeanSpeed(det_id) + if spd >= 0: + all_speeds.append(spd * 3.6) + + brake_counts.append(total_brakes) + speed_variances.append(np.var(all_speeds) if all_speeds else 0) + throughputs.append(info["throughput"]) + step += 1 + + env.close() + + return { + "speed_data": np.array(speed_data), + "density_data": np.array(density_data), + "brake_counts": brake_counts, + "speed_variances": speed_variances, + "throughputs": throughputs + } + +def plot_results(results_dict, output_dir): + """绘制对比图并保存CSV""" + os.makedirs(output_dir, exist_ok=True) + + # 保存CSV数据 + import pandas as pd + for name, results in results_dict.items(): + speed_df = pd.DataFrame(results["speed_data"]) + speed_df.to_csv(os.path.join(output_dir, f"{name}_speed.csv"), index=False) + + density_df = pd.DataFrame(results["density_data"]) + density_df.to_csv(os.path.join(output_dir, f"{name}_density.csv"), index=False) + + metrics_df = pd.DataFrame({ + "brake_counts": results["brake_counts"], + "speed_variances": results["speed_variances"], + "throughputs": results["throughputs"] + }) + metrics_df.to_csv(os.path.join(output_dir, f"{name}_metrics.csv"), index=False) + + # 速度时空图 - 2x2布局 + fig, axes = plt.subplots(2, 2, figsize=(16, 12)) + axes = axes.flatten() + + for idx, (name, results) in enumerate(results_dict.items()): + im = axes[idx].imshow(results["speed_data"].T, aspect="auto", cmap="RdYlGn", + vmin=0, vmax=120, origin="lower") + axes[idx].set_title(f"{name} Speed Heatmap") + axes[idx].set_xlabel("Time Step") + axes[idx].set_ylabel("Detector Group Index") + plt.colorbar(im, ax=axes[idx], label="Speed (km/h)") + + plt.tight_layout() + plt.savefig(os.path.join(output_dir, "speed_heatmaps.png"), dpi=150) + plt.close() + + # 密度时空图 - 2x2布局 + fig, axes = plt.subplots(2, 2, figsize=(16, 12)) + axes = axes.flatten() + + for idx, (name, results) in enumerate(results_dict.items()): + im = axes[idx].imshow(results["density_data"].T, aspect="auto", cmap="YlOrRd", + vmin=0, vmax=100, origin="lower") + axes[idx].set_title(f"{name} Density Heatmap") + axes[idx].set_xlabel("Time Step") + axes[idx].set_ylabel("Detector Group Index") + plt.colorbar(im, ax=axes[idx], label="Occupancy (%)") + + plt.tight_layout() + plt.savefig(os.path.join(output_dir, "density_heatmaps.png"), dpi=150) + plt.close() + + # 关键指标曲线 + fig, axes = plt.subplots(2, 2, figsize=(15, 10)) + + for name, results in results_dict.items(): + axes[0, 0].plot(results["brake_counts"], label=name, alpha=0.7) + axes[0, 1].plot(results["speed_variances"], label=name, alpha=0.7) + axes[1, 0].plot(results["throughputs"], label=name, alpha=0.7) + + axes[0, 0].set_title("Brake Counts Over Time") + axes[0, 0].set_xlabel("Time Step") + axes[0, 0].set_ylabel("Brake Count") + axes[0, 0].legend() + axes[0, 0].grid(True, alpha=0.3) + + axes[0, 1].set_title("Speed Variance Over Time") + axes[0, 1].set_xlabel("Time Step") + axes[0, 1].set_ylabel("Variance (km/h)²") + axes[0, 1].legend() + axes[0, 1].grid(True, alpha=0.3) + + axes[1, 0].set_title("Throughput Over Time") + axes[1, 0].set_xlabel("Time Step") + axes[1, 0].set_ylabel("Throughput (veh/h)") + axes[1, 0].legend() + axes[1, 0].grid(True, alpha=0.3) + + # 统计摘要 + summary_text = "" + for name, results in results_dict.items(): + summary_text += f"{name}:\n" + summary_text += f" Avg Brake: {np.mean(results['brake_counts']):.1f}\n" + summary_text += f" Avg Variance: {np.mean(results['speed_variances']):.2f}\n" + summary_text += f" Avg Throughput: {np.mean(results['throughputs']):.1f}\n\n" + + axes[1, 1].text(0.1, 0.5, summary_text, fontsize=10, family="monospace", + verticalalignment="center", transform=axes[1, 1].transAxes) + axes[1, 1].axis("off") + + plt.tight_layout() + plt.savefig(os.path.join(output_dir, "metrics_comparison.png"), dpi=150) + plt.close() + +def evaluate_no_control(config): + """评估无管控基准""" + env = SUMOEdgeVSLEnvironment(config) + + # 收集所有车检器组位置（只包含有检测器的位置） + detector_groups = [] + for edge_id in env.control_edges: + edge_info = env.parser.edge_info[edge_id] + for pos in edge_info.detector_positions: + pos_idx = edge_info.detector_positions.index(pos) + has_detector = False + for lane_idx in edge_info.traffic_lane_indices: + if edge_info.detectors.get((lane_idx, pos_idx)): + has_detector = True + break + if has_detector: + detector_groups.append((edge_id, pos)) + + state = env.reset(seed=42) + speed_data = [] + density_data = [] + brake_counts = [] + speed_variances = [] + throughputs = [] + done = False + + while not done: + action = np.array([4] * env.num_edges) + state, reward, done, info = env.step(action) + + if not done: + step_speeds = [] + step_densities = [] + for edge_id, pos in detector_groups: + edge_info = env.parser.edge_info[edge_id] + pos_idx = edge_info.detector_positions.index(pos) + speeds = [] + occupancies = [] + for lane_idx in edge_info.traffic_lane_indices: + det_id = edge_info.detectors.get((lane_idx, pos_idx)) + if det_id: + spd = traci.inductionloop.getLastIntervalMeanSpeed(det_id) + occ = traci.inductionloop.getLastIntervalOccupancy(det_id) + if spd >= 0: + speeds.append(spd * 3.6) + if occ >= 0: + occupancies.append(occ) + if speeds: + avg_speed = np.mean(speeds) + else: + edge_speed = traci.edge.getLastStepMeanSpeed(edge_id) + avg_speed = edge_speed * 3.6 if edge_speed >= 0 else 0 + step_speeds.append(avg_speed) + + avg_occupancy = np.mean(occupancies) if occupancies else 0 + step_densities.append(avg_occupancy) + + speed_data.append(step_speeds) + density_data.append(step_densities) + total_brakes = 0 + all_speeds = [] + for edge_id, edge_info in env.parser.edge_info.items(): + for det_id in edge_info.detectors.values(): + total_brakes += traci.inductionloop.getLastIntervalVehicleNumber(det_id) + spd = traci.inductionloop.getLastIntervalMeanSpeed(det_id) + if spd >= 0: + all_speeds.append(spd * 3.6) + brake_counts.append(total_brakes) + speed_variances.append(np.var(all_speeds) if all_speeds else 0) + throughputs.append(info["throughput"]) + + env.close() + return { + "speed_data": np.array(speed_data), + "density_data": np.array(density_data), + "brake_counts": brake_counts, + "speed_variances": speed_variances, + "throughputs": throughputs + } + +if __name__ == "__main__": + with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f: + config = yaml.safe_load(f) + + models = { + "PPO": "checkpoints_sumo_vsl/final", + "APPO": "checkpoints_sumo_appo/final", + "DQN": "checkpoints_sumo_dqn/final" + # "PPO": "checkpoints_sumo_vsl/20260324_202539", + # "APPO": "checkpoints_sumo_appo/20260324_202514", + # "DQN": "checkpoints_sumo_dqn/20260324_202548" + } + + results = {} + for name, ckpt in models.items(): + print(f"Evaluating {name}...") + results[name] = evaluate_model(name, ckpt, config) + + print("Evaluating No Control...") + results["No Control"] = evaluate_no_control(config) + + plot_results(results, "evaluation_results") + print("Evaluation complete! Results saved to evaluation_results/") diff --git a/plot_density_from_csv.py b/plot_density_from_csv.py new file mode 100644 index 0000000..00f7623 --- /dev/null +++ b/plot_density_from_csv.py @@ -0,0 +1,43 @@ +"""从CSV重新绘制密度时空图""" +import os +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import matplotlib +matplotlib.use("Agg") + +output_dir = "evaluation_results" + +# 读取所有模型的密度数据 +models = ["PPO", "APPO", "DQN", "No Control"] +density_data = {} + +for model in models: + csv_path = os.path.join(output_dir, f"{model}_density.csv") + df = pd.read_csv(csv_path) + density_data[model] = df.values + +# 计算全局密度范围以统一颜色映射 +all_densities = np.concatenate([data.flatten() for data in density_data.values()]) +vmin = 10 +vmax = 50 + +print(f"密度范围: {vmin:.2f}% - {vmax:.2f}%") + +# 绘制2x2密度热图 +fig, axes = plt.subplots(2, 2, figsize=(16, 12)) +axes = axes.flatten() + +for idx, model in enumerate(models): + im = axes[idx].imshow(density_data[model].T, aspect="auto", cmap="YlOrRd", + vmin=vmin, vmax=vmax, origin="lower") + axes[idx].set_title(f"{model} Density Heatmap") + axes[idx].set_xlabel("Time Step") + axes[idx].set_ylabel("Detector Group Index") + plt.colorbar(im, ax=axes[idx], label="Occupancy (%)") + +plt.tight_layout() +plt.savefig(os.path.join(output_dir, "density_heatmaps_adjusted.png"), dpi=150) +plt.close() + +print(f"密度热图已保存到 {output_dir}/density_heatmaps_adjusted.png") diff --git a/plot_global_variance.py b/plot_global_variance.py new file mode 100644 index 0000000..7ccd52f --- /dev/null +++ b/plot_global_variance.py @@ -0,0 +1,33 @@ +"""绘制全局区段速度方差曲线""" +import os +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import matplotlib +matplotlib.use("Agg") + +output_dir = "evaluation_results" +models = ["PPO", "APPO", "DQN", "No Control"] + +fig, ax = plt.subplots(figsize=(12, 6)) + +for model in models: + csv_path = os.path.join(output_dir, f"{model}_speed.csv") + df = pd.read_csv(csv_path) + + # 计算每个时刻所有区段的速度方差 + variances = df.var(axis=1) + + ax.plot(variances, label=model, alpha=0.8, linewidth=1.5) + +ax.set_xlabel("Time Step", fontsize=12) +ax.set_ylabel("Speed Variance (km/h)²", fontsize=12) +ax.set_title("Global Speed Variance Over Time", fontsize=14) +ax.legend(fontsize=11) +ax.grid(True, alpha=0.3) + +plt.tight_layout() +plt.savefig(os.path.join(output_dir, "global_speed_variance.png"), dpi=150) +plt.close() + +print(f"全局速度方差曲线已保存到 {output_dir}/global_speed_variance.png")