添加模型评估功能

evaluate_models.py

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+"""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/")

plot_density_from_csv.py

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+"""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")

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")