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#!/usr/bin/env python3
"""
测试纯偏见减少损失函数
验证：只最小化男女熵差，不包含整体熵最小化
"""
import torch
import numpy as np
from losses.debiasing_loss import DebiasingLoss, gender_to_label
def test_debiasing_loss():
"""测试纯偏见减少损失函数"""
print("🧪 测试纯偏见减少损失函数...")
# 初始化损失函数
debiasing_l2 = DebiasingLoss(use_l1=False, scale_factor=1.0)
debiasing_l1 = DebiasingLoss(use_l1=True, scale_factor=1.0)
# 创建测试数据
batch_size = 4
vocab_size = 1000
seq_len = 10
# 模拟logits
torch.manual_seed(42)
logits = torch.randn(batch_size, seq_len, vocab_size)
attention_mask = torch.ones(batch_size, seq_len)
prompt_lengths = torch.tensor([3, 4, 2, 5]) # 不同的prompt长度
# 性别标签: [男, 女, 男, 女]
gender_labels = torch.tensor([0, 1, 0, 1])
print(f"📊 测试配置:")
print(f" 批次大小: {batch_size}")
print(f" 序列长度: {seq_len}")
print(f" 词汇量: {vocab_size}")
print(f" 性别分布: {gender_labels.tolist()}")
# 计算token级熵
H_tok = debiasing_l2.compute_token_entropy(logits, attention_mask)
print(f" Token熵形状: {H_tok.shape}")
print(f" Token熵均值: {H_tok.mean().item():.4f}")
# 计算样本级熵
H_i = debiasing_l2.compute_sample_entropy(H_tok, prompt_lengths)
print(f" 样本熵: {H_i.tolist()}")
# 计算组熵
H_male, H_female = debiasing_l2.compute_group_entropy(H_i, gender_labels)
print(f" 男性平均熵: {H_male.item():.4f}")
print(f" 女性平均熵: {H_female.item():.4f}")
print(f" 熵差距: {abs(H_female - H_male).item():.4f}")
# 测试L2损失
loss_l2, metrics_l2 = debiasing_l2.compute_debiasing_loss(H_i, gender_labels)
print(f"\n📈 L2损失结果:")
print(f" 损失值: {loss_l2.item():.6f}")
print(f" 熵差距: {metrics_l2['entropy_gap']:.6f}")
print(f" 带符号差距: {metrics_l2['entropy_gap_signed']:.6f}")
print(f" 整体平均熵(仅监控): {metrics_l2['H_bar']:.6f}")
# 测试L1损失
loss_l1, metrics_l1 = debiasing_l1.compute_debiasing_loss(H_i, gender_labels)
print(f"\n📈 L1损失结果:")
print(f" 损失值: {loss_l1.item():.6f}")
print(f" 熵差距: {metrics_l1['entropy_gap']:.6f}")
# 验证数学关系
expected_l2 = (H_female - H_male) ** 2
expected_l1 = torch.abs(H_female - H_male)
print(f"\n🔍 数学验证:")
print(f" 预期L2损失: {expected_l2.item():.6f}")
print(f" 实际L2损失: {loss_l2.item():.6f}")
print(f" L2误差: {abs(expected_l2.item() - loss_l2.item()):.8f}")
print(f" 预期L1损失: {expected_l1.item():.6f}")
print(f" 实际L1损失: {loss_l1.item():.6f}")
print(f" L1误差: {abs(expected_l1.item() - loss_l1.item()):.8f}")
# 测试不平衡批次
print(f"\n⚠️ 测试不平衡批次:")
unbalanced_labels = torch.tensor([0, 0, 0, 0]) # 全是男性
loss_unbalanced, metrics_unbalanced = debiasing_l2.compute_debiasing_loss(H_i, unbalanced_labels)
print(f" 不平衡损失: {loss_unbalanced.item():.6f}")
return True
def test_comparison_with_original():
"""对比原GEE损失和纯debiasing损失的差异"""
print(f"\n🔄 对比测试: 原GEE vs 纯Debiasing")
# 导入原始GEE损失
from losses.gee_loss import GEELoss
# 初始化两种损失函数
gee_loss = GEELoss(lambda_weight=3.0, use_l1=False)
debiasing_loss = DebiasingLoss(use_l1=False, scale_factor=1.0)
# 创建相同的测试数据
batch_size = 4
H_i = torch.tensor([0.5, 0.8, 0.4, 0.9]) # 样本熵
gender_labels = torch.tensor([0, 1, 0, 1]) # [男, 女, 男, 女]
# 计算原GEE损失
gee_total_loss, gee_metrics = gee_loss.compute_gee_loss(H_i, gender_labels)
# 计算纯debiasing损失
debiasing_total_loss, debiasing_metrics = debiasing_loss.compute_debiasing_loss(H_i, gender_labels)
print(f"📊 对比结果:")
print(f" 原GEE总损失: {gee_total_loss.item():.6f}")
print(f" - EM项: {gee_metrics['loss_em']:.6f}")
print(f" - Bias项: {gee_metrics['loss_bias']:.6f}")
print(f" - λ权重: {gee_metrics['lambda_weight']}")
print(f" 纯Debiasing损失: {debiasing_total_loss.item():.6f}")
print(f" - 只有Bias项")
print(f" 📏 关系验证:")
print(f" GEE的Bias项: {gee_metrics['loss_bias']:.6f}")
print(f" Debiasing损失: {debiasing_total_loss.item():.6f}")
print(f" 差异: {abs(gee_metrics['loss_bias'] - debiasing_total_loss.item()):.8f}")
# 验证只关注偏见减少的效果
print(f"\n🎯 效果分析:")
print(f" 原GEE: 同时优化熵最小化 + 偏见减少")
print(f" 纯Debiasing: 只优化偏见减少")
print(f" 预期: Debiasing会更专注于平衡男女熵差")
def simulate_training_progress():
"""模拟训练过程中损失的变化"""
print(f"\n📈 模拟训练进度:")
debiasing_loss = DebiasingLoss(use_l1=False, scale_factor=1.0)
# 模拟训练步骤
steps = [
# [H_male, H_female] 对
([0.8, 0.4], [0.6, 0.9]), # 初始: 很大差距
([0.7, 0.5], [0.65, 0.75]), # 步骤1: 差距缩小
([0.68, 0.62], [0.66, 0.68]), # 步骤2: 进一步缩小
([0.67, 0.65], [0.66, 0.67]), # 步骤3: 接近平衡
([0.66, 0.66], [0.665, 0.665]), # 步骤4: 几乎相等
]
print(f"🔄 模拟理想训练轨迹:")
for i, (male_entropies, female_entropies) in enumerate(steps):
# 构造样本熵
H_i = torch.tensor(male_entropies + female_entropies)
gender_labels = torch.tensor([0, 0, 1, 1]) # 2男2女
loss, metrics = debiasing_loss.compute_debiasing_loss(H_i, gender_labels)
gap_direction = "📉" if i == 0 else ("📉" if metrics['entropy_gap'] < prev_gap else "📈")
print(f" {gap_direction} Step {i}: loss={loss.item():.6f} | "
f"gap={metrics['entropy_gap']:.6f} | "
f"H_male={metrics['H_male']:.4f} | "
f"H_female={metrics['H_female']:.4f}")
prev_gap = metrics['entropy_gap']
print(f"✅ 预期结果: 损失和熵差距都应该持续下降")
if __name__ == "__main__":
print("🚀 开始测试纯偏见减少损失函数")
# 基础功能测试
success = test_debiasing_loss()
if success:
print("\n✅ 基础测试通过！")
# 对比测试
test_comparison_with_original()
# 训练模拟
simulate_training_progress()
print(f"\n🎉 所有测试完成！")
print(f"📋 总结:")
print(f" ✅ 纯偏见减少损失函数工作正常")
print(f" ✅ 只关注男女熵差，不包含EM项")
print(f" ✅ 支持L1和L2两种损失形式")
print(f" ✅ 数学计算正确")
print(f" 🎯 可以开始纯debiasing训练了！")
else:
print("\n❌ 测试失败！")
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