From 24e163f9211fb9a9af561de47898ea64f5f26df4 Mon Sep 17 00:00:00 2001
From: haoyuren <13851610112@163.com>
Date: Fri, 27 Jun 2025 11:46:59 -0700
Subject: fix loss

---
 test_gee_fix.py | 82 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 82 insertions(+)
 create mode 100644 test_gee_fix.py

(limited to 'test_gee_fix.py')

diff --git a/test_gee_fix.py b/test_gee_fix.py
new file mode 100644
index 0000000..3e773df
--- /dev/null
+++ b/test_gee_fix.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+"""
+测试修复后的GEE损失函数
+"""
+import torch
+import sys
+sys.path.append('.')
+
+from losses.gee_loss import GEELoss, gender_to_label
+from dataset.gee_processor import GEEProcessor
+
+print("🧪 测试修复后的GEE损失函数")
+print("="*50)
+
+# 创建模拟tokenizer
+class MockTokenizer:
+    def apply_chat_template(self, messages, tokenize=False, add_generation_prompt=True):
+        return messages[0]["content"]
+
+# 1. 测试数据生成
+processor = GEEProcessor(MockTokenizer())
+test_data = processor.create_test_data(num_samples=6)
+
+print(f"📊 生成 {len(test_data)} 条测试数据")
+for i, item in enumerate(test_data):
+    print(f"  {i+1}. {item['gender']}: {item['input'][:50]}...")
+
+# 2. 创建批次
+batch = {
+    "input": [item["input"] for item in test_data[:4]],
+    "gender": [item["gender"] for item in test_data[:4]]
+}
+
+print(f"\n📦 批次信息:")
+print(f"性别: {batch['gender']}")
+
+gender_labels = torch.tensor([gender_to_label(g) for g in batch["gender"]])
+print(f"标签: {gender_labels.tolist()}")
+
+# 3. 测试修复后的损失函数
+gee_loss = GEELoss(lambda_weight=1.0)  # 降低lambda权重
+
+# 模拟合理的熵值（包含一些接近0的值）
+H_i_test = torch.tensor([0.8, 0.1, 0.6, 0.2])  # male, female, male, female
+
+print(f"\n🧮 测试修复后的GEE损失:")
+print(f"输入熵值: {H_i_test.tolist()}")
+print(f"性别标签: {batch['gender']}")
+
+loss, metrics = gee_loss.compute_gee_loss(H_i_test, gender_labels)
+
+print(f"\n📈 结果:")
+print(f"总损失: {loss:.6f}")
+print(f"熵最小化损失: {metrics['loss_em']:.6f}")
+print(f"偏见损失: {metrics['loss_bias']:.6f}")
+print(f"男性平均熵: {metrics['H_male']:.6f}")
+print(f"女性平均熵: {metrics['H_female']:.6f}")
+print(f"熵差距: {metrics['entropy_gap']:.6f}")
+print(f"Lambda权重: {metrics['lambda_weight']}")
+
+# 4. 验证修复效果
+print(f"\n✅ 修复验证:")
+if metrics['H_female'] > 0:
+    print("✅ H_female不再为0")
+else:
+    print("❌ H_female仍为0，可能还有问题")
+
+if metrics['entropy_gap'] < 1.0:
+    print("✅ 熵差距在合理范围内")
+else:
+    print("⚠️ 熵差距较大")
+
+if loss < 10.0:
+    print("✅ 总损失在合理范围内")
+else:
+    print("⚠️ 总损失可能过大")
+
+print(f"\n💡 修复要点:")
+print("1. 移除了错误的零熵值过滤")
+print("2. 简化了GEE损失计算")
+print("3. 添加了调试信息")
+print("4. 建议降低lambda权重到0.5-1.0") 
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