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216004d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | #!/usr/bin/env python3
"""检验 external_test 的构造: 未训练模型在 6 个域上应各得 ~1/6, 不应全 1.0。"""
import sys
import numpy as np
import torch
sys.path.insert(0, "/work/liuyuanjie/headless")
sys.path.insert(0, "/work/liuyuanjie/headless/tests")
from run_lm4_wave import (build_feature_matrix, build_windows, assign_domains,
evaluate, StatMLP)
t1, d1, F1, ok1 = build_feature_matrix("/work/liuyuanjie/headless/data/wave",
bands=13, use_wfr=True, use_lshell=True)
X1, yl1 = build_windows(t1, d1, F1, ok1)
y1, qs = assign_domains(yl1, 6)
t2, d2, F2, ok2 = build_feature_matrix("/work/liuyuanjie/headless/data/wave2016",
bands=13, use_wfr=True, use_lshell=True)
X2, yl2 = build_windows(t2, d2, F2, ok2)
y2, _ = assign_domains(yl2, 6, qs=qs)
print("2016 每域样本:", np.bincount(y2, minlength=6).tolist())
dev = torch.device("cpu")
ext = {}
for dd in range(6):
idx = np.where(y2 == dd)[0]
_X = torch.from_numpy(X2[idx].astype(np.float32))
_y = torch.from_numpy(y2[idx])
ext[dd] = torch.utils.data.DataLoader(
torch.utils.data.TensorDataset(_X, _y), batch_size=512)
torch.manual_seed(0)
m = StatMLP(n_feat=X1.shape[-1], n_classes=6, hidden=64, n_layers=2,
head_type="linear", norm="fixed")
m.eval()
print("\n未训练模型在各域外部测试集上的准确率 (应各 ~0.167):")
for dd in range(6):
a = evaluate(m, ext[dd], dev)
print(f" D{dd}: {a:.4f} (该域样本 {len(ext[dd].dataset)})")
# 再看一个常数预测器 (全判 5) 的表现
class Const(torch.nn.Module):
def __init__(self, c): super().__init__(); self.c = c
def forward(self, x, agg=None):
return torch.zeros(x.shape[0], 6).scatter_(1, torch.full((x.shape[0],1), self.c), 10.0)
cm = Const(5)
print("\n常数预测器 (全判 D5) 的表现 (D5 应 1.0, 其余 0.0):")
for dd in range(6):
print(f" D{dd}: {evaluate(cm, ext[dd], dev):.4f}")
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