198 lines
6.4 KiB
Python
198 lines
6.4 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.manifold import TSNE
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import os
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import glob
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import pandas as pd
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from sklearn.metrics import silhouette_score, silhouette_samples, calinski_harabasz_score
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# ================= 配置区域 =================
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data_root = "/root/Coding/RoboGauge/logs_latent/moe"
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terrains = ['flat', 'wave', 'slope_fd', 'slope_bd', 'stairs_fd', 'stairs_bd', 'obstacle']
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command_map = {
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0: "Pos X (Forward)",
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1: "Neg X (Backward)",
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2: "Pos Y (Left)",
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3: "Neg Y (Right)",
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4: "Pos Yaw (Turn Left)",
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5: "Neg Yaw (Turn Right)"
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}
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cmd_colors = {
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0: '#d62728', 1: '#1f77b4', 2: '#2ca02c',
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3: '#ff7f0e', 4: '#9467bd', 5: '#8c564b'
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}
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# t-SNE 计算较慢，且点太多会严重重叠
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# 建议每种指令采样 800 - 1000 个点即可，足以看清分布
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MAX_SAMPLES_PER_CMD = 2000
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# t-SNE 参数
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# perplexity: 困惑度，通常在 5-50 之间。越大越关注全局结构，越小越关注局部邻居。
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TSNE_PERPLEXITY = 30
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TSNE_ITER = 1000
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# ================= 功能函数 =================
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# key = 'weights' # 'latent'
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key = 'latent'
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def load_combined_command_data(root_path, terrain_list, cmd_id):
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"""读取指定指令在所有地形下的数据并合并"""
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cmd_latents = []
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filename = f"moe_info_{cmd_id}.npz"
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for t_name in terrain_list:
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search_pattern = os.path.join(root_path, f"go2_moe_{t_name}_latent", "*", filename)
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files = glob.glob(search_pattern)
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for f in files:
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try:
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data = np.load(f)
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if key in data:
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cmd_latents.append(data[key])
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except: pass
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if not cmd_latents: return None
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return np.concatenate(cmd_latents, axis=0)
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# ================= 主程序 =================
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print(f"Start processing t-SNE visualization...")
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all_data = []
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all_labels = []
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# 1. 数据收集
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for cmd_id, cmd_name in command_map.items():
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raw_data = load_combined_command_data(data_root, terrains, cmd_id)
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if raw_data is not None:
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# 随机下采样
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n_total = len(raw_data)
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if n_total > MAX_SAMPLES_PER_CMD:
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indices = np.random.choice(n_total, MAX_SAMPLES_PER_CMD, replace=False)
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data_sample = raw_data[indices]
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else:
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data_sample = raw_data
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all_data.append(data_sample)
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all_labels.extend([cmd_id] * len(data_sample))
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print(f" - Cmd {cmd_id}: {len(data_sample)} samples")
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if not all_data:
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print("Error: No data loaded.")
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exit()
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X = np.concatenate(all_data, axis=0)
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y = np.array(all_labels)
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print(f"Running t-SNE on {X.shape} matrix...")
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print(f" (Perplexity={TSNE_PERPLEXITY}, Iterations={TSNE_ITER})")
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print(" This may take a moment...")
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# 2. t-SNE 降维
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# init='pca' 通常比 'random' 更稳定，能更好地保留全局结构
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tsne = TSNE(
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n_components=2,
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perplexity=TSNE_PERPLEXITY,
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n_iter=TSNE_ITER,
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init='pca',
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learning_rate='auto',
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random_state=42,
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verbose=1,
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)
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X_embedded = tsne.fit_transform(X)
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# 3. 可视化
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plt.figure(figsize=(12, 10), dpi=120)
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for cmd_id in command_map.keys():
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indices = (y == cmd_id)
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points = X_embedded[indices]
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if len(points) > 0:
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plt.scatter(
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points[:, 0],
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points[:, 1],
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c=cmd_colors[cmd_id],
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label=command_map[cmd_id],
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alpha=0.6,
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s=20 #稍微大一点的点
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)
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plt.title(f"t-SNE Visualization of {key.capitalize()} Space by Command\n(Perplexity={TSNE_PERPLEXITY}, Mixed Terrains)")
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# t-SNE 的坐标轴没有物理意义，所以隐藏刻度通常更好看
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plt.xticks([])
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plt.yticks([])
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plt.legend(title="Control Commands", markerscale=2, bbox_to_anchor=(1.05, 1), loc='upper left')
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plt.tight_layout()
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save_path = os.path.join(data_root, f'{key}_tsne_by_command.png')
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plt.savefig(save_path)
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print(f"Done! Visualization saved to {save_path}")
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plt.show()
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def evaluate_latent_space(X, y, command_map):
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"""
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X: (N, D) 原始高维 Latent 向量 (例如 32维)
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y: (N,) 对应的指令标签 (0-5)
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command_map: label到名称的映射字典
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"""
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print(f"--- Evaluating Latent Space (Samples: {X.shape[0]}, Dim: {X.shape[1]}) ---")
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# 1. 全局指标 (Global Metrics)
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# 轮廓系数 (Silhouette): 越接近 1 越好
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global_sil = silhouette_score(X, y)
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# Calinski-Harabasz Index (CHI): 数值越大越好 (表示类间离散度高，类内离散度低)
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global_chi = calinski_harabasz_score(X, y)
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print(f"Global Silhouette Score: {global_sil:.4f} (范围 -1 到 1, 越大越好)")
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print(f"Global Calinski-Harabasz: {global_chi:.1f} (数值越大越好)")
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print("-" * 30)
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# 2. 逐类指标 (Per-Class Metrics)
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# 计算每个样本的轮廓系数
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sample_silhouette_values = silhouette_samples(X, y)
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class_scores = []
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for label in sorted(command_map.keys()):
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# 提取当前类的所有样本的轮廓系数
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ith_class_silhouette_values = sample_silhouette_values[y == label]
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# 计算该类的平均分
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avg_score = np.mean(ith_class_silhouette_values)
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# 计算该类的紧密度 (Intra-class distance) - 可选
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# 这里直接用轮廓系数代表聚集程度
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class_scores.append({
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"Command ID": label,
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"Command Name": command_map[label],
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"Silhouette Score": avg_score,
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"Sample Count": len(ith_class_silhouette_values)
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})
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# 转为 DataFrame 展示
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df = pd.DataFrame(class_scores)
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print(df.to_string(index=False))
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print("-" * 30 + "\n")
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return df, global_sil
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# 1. 评估原始高维空间 (Intrinsic Quality)
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print(">>> Evaluating Original High-Dim Latent Space Quality...")
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df_original, score_original = evaluate_latent_space(X, y, command_map)
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print(f"Original Space Score: {score_original:.4f}")
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# 2. 评估 t-SNE 降维后的 2D 空间 (Visual Cluster Quality)
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print("\n>>> Evaluating t-SNE Embedded Space Quality (2D)...")
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# 直接传入 X_embedded 即可，evaluate_latent_space 函数对维度不敏感
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df_tsne, score_tsne = evaluate_latent_space(X_embedded, y, command_map)
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# 保存 t-SNE 的评估结果
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save_path_tsne = os.path.join(data_root, f'{key}_tsne_2d_evaluation.csv')
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df_tsne.to_csv(save_path_tsne, index=False)
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print(f"t-SNE 2D Evaluation results saved to {save_path_tsne}")
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print(f"t-SNE Space Score: {score_tsne:.4f}") |