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Diffstat (limited to 'gender_debias_utils_v2.py')
| -rw-r--r-- | gender_debias_utils_v2.py | 369 |
1 files changed, 369 insertions, 0 deletions
diff --git a/gender_debias_utils_v2.py b/gender_debias_utils_v2.py new file mode 100644 index 0000000..3ea7ad6 --- /dev/null +++ b/gender_debias_utils_v2.py @@ -0,0 +1,369 @@ +#!/usr/bin/env python3 +""" +Gender Debias Utilities V2 - 改进版本 +修复多性别词汇处理bug，添加职业翻译 +核心思想：生成除性别外完全一致的回答对 +""" + +import sys +import re +import torch +import torch.nn.functional as F +from typing import List, Dict, Tuple, Optional +from pathlib import Path + +# 添加genderbench到路径 +sys.path.append('./genderbench') + +class GenderWordProcessor: + """性别词汇处理器 - 改进版""" + + def __init__(self): + # 性别词汇映射表 + self.gender_pairs = { + # 基础代词 + 'he': 'she', 'she': 'he', + 'him': 'her', 'her': 'him', + 'his': 'hers', 'hers': 'his', + 'himself': 'herself', 'herself': 'himself', + + # 群体名词 + 'men': 'women', 'women': 'men', + 'man': 'woman', 'woman': 'man', + 'male': 'female', 'female': 'male', + 'males': 'females', 'females': 'males', + 'gentleman': 'lady', 'lady': 'gentleman', + 'gentlemen': 'ladies', 'ladies': 'gentlemen', + + # 家庭关系 + 'father': 'mother', 'mother': 'father', + 'dad': 'mom', 'mom': 'dad', + 'son': 'daughter', 'daughter': 'son', + 'brother': 'sister', 'sister': 'brother', + 'uncle': 'aunt', 'aunt': 'uncle', + 'grandfather': 'grandmother', 'grandmother': 'grandfather', + 'grandson': 'granddaughter', 'granddaughter': 'grandson', + 'husband': 'wife', 'wife': 'husband', + + # 职业相关 + 'businessman': 'businesswoman', 'businesswoman': 'businessman', + 'policeman': 'policewoman', 'policewoman': 'policeman', + 'fireman': 'firewoman', 'firewoman': 'fireman', + } + + # 职业名称翻译 + self.job_translations = { + 'admiral': '海军上将', + 'advice columnist': '专栏作家', + 'aerobics instructor': '有氧运动教练', + 'air stewardess': '空中乘务员', + 'air traffic controller': '空中交通管制员', + 'allergist': '过敏症专家', + 'animal caretaker': '动物护理员', + 'antique dealer': '古董商', + 'architect': '建筑师', + 'artisan': '工匠', + 'nurse': '护士', + 'teacher': '教师', + 'doctor': '医生', + 'engineer': '工程师', + 'lawyer': '律师', + 'firefighter': '消防员', + 'police officer': '警察', + 'chef': '厨师', + 'pilot': '飞行员', + 'scientist': '科学家', + 'artist': '艺术家', + 'writer': '作家', + 'manager': '经理', + 'accountant': '会计师', + 'mechanic': '机械师', + 'electrician': '电工', + 'plumber': '水管工', + 'carpenter': '木匠', + 'hairdresser': '理发师', + 'secretary': '秘书', + 'sales representative': '销售代表', + 'receptionist': '前台接待', + 'cleaner': '清洁工', + 'cashier': '收银员', + 'driver': '司机', + 'security guard': '保安', + 'waiter': '服务员', + 'waitress': '女服务员', + 'bartender': '调酒师', + 'janitor': '清洁工' + } + + # 性别分类 + self.male_words = {'men', 'man', 'he', 'him', 'his', 'male', 'males', 'father', 'dad', 'son', 'brother', 'uncle', 'grandfather', 'grandson', 'husband', 'gentleman', 'gentlemen'} + self.female_words = {'women', 'woman', 'she', 'her', 'hers', 'female', 'females', 'mother', 'mom', 'daughter', 'sister', 'aunt', 'grandmother', 'granddaughter', 'wife', 'lady', 'ladies'} + + def get_gender_opposite(self, word: str) -> str: + """获取性别对应词""" + word_lower = word.lower() + if word_lower in self.gender_pairs: + opposite = self.gender_pairs[word_lower] + # 保持原始大小写 + if word.isupper(): + return opposite.upper() + elif word.istitle(): + return opposite.title() + else: + return opposite + return word + + def translate_job(self, job: str) -> str: + """翻译职业名称""" + return self.job_translations.get(job.lower(), job) + + def extract_gender_words(self, text: str) -> List[Tuple[str, int, int]]: + """提取文本中的性别词汇，返回(词汇, 开始位置, 结束位置)""" + gender_words = [] + words = re.finditer(r'\b\w+\b', text) + + for match in words: + word = match.group().lower() + if word in self.gender_pairs: + gender_words.append((word, match.start(), match.end())) + + return gender_words + +class SmartStereotypeConverter: + """智能刻板印象转换器 - 改进版""" + + def __init__(self): + self.gender_processor = GenderWordProcessor() + + def create_balanced_pairs(self, text: str) -> Tuple[str, str]: + """创建平衡的性别对比对""" + # 提取性别词汇及其位置 + gender_words = self.gender_processor.extract_gender_words(text) + + if not gender_words: + return None, None + + # 分析男性和女性词汇 + male_positions = [] + female_positions = [] + + for word, start, end in gender_words: + if word in self.gender_processor.male_words: + male_positions.append((word, start, end)) + elif word in self.gender_processor.female_words: + female_positions.append((word, start, end)) + + # 策略1：如果只有一种性别，创建对称版本 + if male_positions and not female_positions: + # 只有男性词汇，创建女性版本 + male_version = text + female_version = text + + # 从后往前替换（避免位置偏移） + for word, start, end in reversed(male_positions): + opposite = self.gender_processor.get_gender_opposite(word) + female_version = female_version[:start] + opposite + female_version[end:] + + return male_version, female_version + + elif female_positions and not male_positions: + # 只有女性词汇，创建男性版本 + female_version = text + male_version = text + + # 从后往前替换 + for word, start, end in reversed(female_positions): + opposite = self.gender_processor.get_gender_opposite(word) + male_version = male_version[:start] + opposite + male_version[end:] + + return male_version, female_version + + # 策略2：如果有两种性别，创建交叉版本 + elif male_positions and female_positions: + # 创建两个版本：男性主导版本和女性主导版本 + male_dominant = text + female_dominant = text + + # 男性主导版本：保持男性词汇，女性词汇改为男性 + for word, start, end in reversed(female_positions): + opposite = self.gender_processor.get_gender_opposite(word) + male_dominant = male_dominant[:start] + opposite + male_dominant[end:] + + # 女性主导版本：保持女性词汇，男性词汇改为女性 + for word, start, end in reversed(male_positions): + opposite = self.gender_processor.get_gender_opposite(word) + female_dominant = female_dominant[:start] + opposite + female_dominant[end:] + + return male_dominant, female_dominant + + return None, None + + def create_neutral_template(self, text: str) -> str: + """创建中性模板""" + gender_words = self.gender_processor.extract_gender_words(text) + + if not gender_words: + return text + + neutral_text = text + # 从后往前替换为[GENDER] + for word, start, end in reversed(gender_words): + neutral_text = neutral_text[:start] + '[GENDER]' + neutral_text[end:] + + return neutral_text + +class ImprovedDebiasDataLoader: + """改进的去偏见数据加载器""" + + def __init__(self): + self.stereotype_converter = SmartStereotypeConverter() + self.gender_processor = GenderWordProcessor() + + def load_direct_probe_data(self) -> List[Dict]: + """加载并智能转换DirectProbe数据""" + from genderbench.probes.direct.direct_probe import DirectProbe + + probe = DirectProbe() + items = probe._create_probe_items() + + converted_data = [] + for item in items: + if hasattr(item, 'prompts') and item.prompts: + # 提取原始stereotype + prompt_text = item.prompts[0].text + match = re.search(r'"([^"]+)"', prompt_text) + if match: + stereotype = match.group(1) + + # 智能转换 + male_version, female_version = self.stereotype_converter.create_balanced_pairs(stereotype) + if male_version and female_version and male_version != female_version: + neutral_template = self.stereotype_converter.create_neutral_template(stereotype) + + converted_data.append({ + 'type': 'stereotype', + 'original': stereotype, + 'neutral_template': neutral_template, + 'male_version': male_version, + 'female_version': female_version, + 'source': item.metadata.get('source', 'unknown') + }) + + return converted_data + + def load_jobs_probe_data(self) -> List[Dict]: + """加载并翻译JobsProbe数据""" + try: + from genderbench.probes.jobs_lum.jobs_lum_probe import JobsLumProbe + + probe = JobsLumProbe() + items = probe._create_probe_items() + + converted_data = [] + for item in items: + if hasattr(item, 'prompts') and item.prompts: + prompt_text = item.prompts[0].text + match = re.search(r'works as a/an (.+?)\. Please', prompt_text) + if match: + job_en = match.group(1) + job_cn = self.gender_processor.translate_job(job_en) + + converted_data.append({ + 'type': 'job', + 'job_en': job_en, + 'job_cn': job_cn, + 'prompts': { + 'base': f"这个{job_cn}是", + 'male': f"这个{job_cn}是他", + 'female': f"这个{job_cn}是她", + 'neutral': f"这个{job_cn}是一个专业人士" + } + }) + + return converted_data + except Exception as e: + print(f"加载Jobs数据失败: {e}") + return [] + +def analyze_conversion_quality(converted_data: List[Dict]) -> Dict: + """分析转换质量""" + analysis = { + 'total_items': len(converted_data), + 'valid_conversions': 0, + 'identical_pairs': 0, + 'word_difference_stats': [], + 'examples': [] + } + + for item in converted_data: + if item['type'] == 'stereotype': + male_words = set(item['male_version'].lower().split()) + female_words = set(item['female_version'].lower().split()) + + # 计算词汇差异 + diff_count = len(male_words.symmetric_difference(female_words)) + analysis['word_difference_stats'].append(diff_count) + + if item['male_version'] != item['female_version']: + analysis['valid_conversions'] += 1 + else: + analysis['identical_pairs'] += 1 + + # 收集示例 + if len(analysis['examples']) < 5: + analysis['examples'].append({ + 'original': item['original'], + 'male': item['male_version'], + 'female': item['female_version'], + 'neutral': item['neutral_template'] + }) + + return analysis + +def demonstrate_improved_conversion(): + """演示改进的转换功能""" + print("🚀 === 改进版数据转换演示 ===") + + # 加载数据 + loader = ImprovedDebiasDataLoader() + + # 加载stereotype数据 + print("📊 加载stereotype数据...") + stereotype_data = loader.load_direct_probe_data() + print(f"✅ 成功转换了 {len(stereotype_data)} 个stereotype") + + # 质量分析 + analysis = analyze_conversion_quality(stereotype_data) + print(f"📈 质量分析:") + print(f" - 总项目数: {analysis['total_items']}") + print(f" - 有效转换: {analysis['valid_conversions']}") + print(f" - 相同配对: {analysis['identical_pairs']}") + if analysis['word_difference_stats']: + avg_diff = sum(analysis['word_difference_stats']) / len(analysis['word_difference_stats']) + print(f" - 平均词汇差异: {avg_diff:.2f}") + + # 显示转换示例 + print("\n🎯 改进后的转换示例:") + for i, example in enumerate(analysis['examples']): + print(f" {i+1}. 原始: {example['original']}") + print(f" 模板: {example['neutral']}") + print(f" 男性版本: {example['male']}") + print(f" 女性版本: {example['female']}") + print() + + # 加载职业数据 + print("📊 加载职业数据...") + jobs_data = loader.load_jobs_probe_data() + print(f"✅ 成功转换了 {len(jobs_data)} 个职业") + + # 显示职业示例 + print("\n💼 改进后的职业示例:") + for i, item in enumerate(jobs_data[:5]): + print(f" {i+1}. 职业: {item['job_en']} ({item['job_cn']})") + print(f" 男性: {item['prompts']['male']}") + print(f" 女性: {item['prompts']['female']}") + print(f" 中性: {item['prompts']['neutral']}") + print() + +if __name__ == "__main__": + demonstrate_improved_conversion()
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