76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
from pathlib import Path
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import numpy as np
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import matplotlib
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matplotlib.use("Agg") # 仅保存图片，不弹窗
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import matplotlib.pyplot as plt
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from umap import UMAP
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def umap_dir_to_pngs(dir_path: str) -> None:
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"""
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对目录内每个 .npy 文件执行 UMAP(30D->2D) 并保存散点图。
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- 输入 .npy 期望形状为 (n_samples, 30) 或 (30, n_samples)
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- 输出图片保存在同目录，命名为 <原文件名>_umap.png
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"""
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p = Path(dir_path)
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if not p.is_dir():
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raise ValueError(f"{dir_path!r} 不是有效文件夹")
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files = sorted(p.glob("*.npy"))
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if not files:
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print(f"目录 {p} 中未找到 .npy 文件")
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return
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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 data.ndim == 2:
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if data.shape[1] == 30:
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X = data
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elif data.shape[0] == 30:
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X = data.T
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else:
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print(f"[跳过] {f.name}: shape={data.shape}, 未检测到 30 维特征")
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continue
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else:
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print(f"[跳过] {f.name}: 期望二维数组，实际 shape={data.shape}")
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continue
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# 清理非数值行
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mask = np.isfinite(X).all(axis=1)
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if not np.all(mask):
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X = X[mask]
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print(f"[提示] {f.name}: 移除了含 NaN/Inf 的样本行")
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n_samples = X.shape[0]
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if n_samples < 3:
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print(f"[跳过] {f.name}: 样本数过少(n={n_samples})，无法稳定降维")
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continue
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# 确保 n_neighbors 合法
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n_neighbors = min(15, max(2, n_samples - 1))
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reducer = UMAP(
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n_components=2,
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n_neighbors=n_neighbors,
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min_dist=0.1,
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metric="euclidean",
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random_state=42,
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)
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emb = reducer.fit_transform(X)
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fig, ax = plt.subplots(figsize=(6, 5), dpi=150)
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ax.scatter(emb[:, 0], emb[:, 1], s=6, c="#1f77b4", alpha=0.8, edgecolors="none")
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ax.set_title(f"{f.name} • UMAP (n={len(X)}, nn={n_neighbors})", fontsize=10)
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ax.set_xlabel("UMAP-1")
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ax.set_ylabel("UMAP-2")
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ax.grid(True, linestyle="--", linewidth=0.3, alpha=0.5)
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fig.tight_layout()
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out_png = f.with_suffix("").as_posix() + "_umap.png"
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fig.savefig(out_png)
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plt.close(fig)
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print(f"[完成] {f.name} -> {out_png}")
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except Exception as e:
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print(f"[错误] 处理 {f.name} 失败: {e}")
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if __name__=="__main__":
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umap_dir_to_pngs("data") |