116_大规模预训练数据管理与质量控制机制

数据管理层
├── 原始数据层（Raw Data）：原始采集数据，保持原貌
├── 清洗数据层（Processed Data）：经过基础清洗和标准化的数据
├── 质量控制层（QC Data）：通过质量评估的高质量数据
├── 训练数据集层（Training Sets）：最终用于训练的数据集版本
└── 评估数据集层（Evaluation Sets）：用于验证数据质量的数据集

采集 → 解析 → 清洗 → 去重 → 质量评估 → 采样 → 格式化 → 训练

class DistributedCrawler:
    def __init__(self, seed_urls, max_depth=3, concurrency=100):
        self.seed_urls = seed_urls
        self.max_depth = max_depth
        self.concurrency = concurrency
        self.url_queue = Queue()
        self.visited_urls = set()
        
    def crawl(self):
        # 初始化URL队列
        for url in self.seed_urls:
            self.url_queue.put((url, 0))
        
        # 创建工作进程池
        with concurrent.futures.ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:
            while not self.url_queue.empty():
                url, depth = self.url_queue.get()
                if url not in self.visited_urls and depth <= self.max_depth:
                    self.visited_urls.add(url)
                    executor.submit(self.fetch_and_process, url, depth)
    
    def fetch_and_process(self, url, depth):
        # 获取页面内容
        # 提取文本
        # 提取新URL
        # 保存内容
        pass

def text_preprocessing(text):
    # 1. 统一编码
    text = ensure_utf8_encoding(text)
    
    # 2. 去除控制字符
    text = remove_control_characters(text)
    
    # 3. 标准化空白字符
    text = normalize_whitespace(text)
    
    # 4. 处理HTML标签
    text = remove_html_tags(text)
    
    # 5. 修复编码问题
    text = fix_encoding_errors(text)
    
    return text

def multilingual_processing(text):
    # 检测文本语言
    lang = detect_language(text)
    
    # 根据语言应用不同的清洗规则
    if lang == 'zh':
        return chinese_text_processing(text)
    elif lang == 'en':
        return english_text_processing(text)
    else:
        return general_text_processing(text)

class MinHashLSH:
    def __init__(self, num_perm=128, threshold=0.8):
        self.num_perm = num_perm
        self.threshold = threshold
        self.permutations = self._generate_permutations()
        self.hash_tables = {}
        
    def _generate_permutations(self):
        # 生成随机排列函数
        return [random_permutation() for _ in range(self.num_perm)]
    
    def compute_minhash(self, text):
        # 将文本转换为特征集合
        shingles = extract_shingles(text)
        
        # 计算MinHash签名
        minhash = []
        for perm in self.permutations:
            min_val = float('inf')
            for shingle in shingles:
                hash_val = perm(shingle)
                if hash_val < min_val:
                    min_val = hash_val
            minhash.append(min_val)
        
        return minhash
    
    def add(self, doc_id, text):
        minhash = self.compute_minhash(text)
        # 添加到哈希表
        self._insert_into_hash_tables(doc_id, minhash)
    
    def query(self, text):
        minhash = self.compute_minhash(text)
        # 查询相似文档
        candidates = self._get_candidates(minhash)
        # 过滤低于阈值的结果
        return [doc_id for doc_id in candidates if self._jaccard_similarity(minhash, self.hash_tables[doc_id]) >= self.threshold]

def compute_quality_metrics(text):
    metrics = {}
    
    # 基础统计指标
    metrics['length'] = len(text)
    metrics['word_count'] = len(text.split())
    metrics['unique_word_ratio'] = len(set(text.split())) / len(text.split())
    
    # 可读性指标
    metrics['flesch_kincaid_grade'] = compute_flesch_kincaid(text)
    metrics['gunning_fog_index'] = compute_gunning_fog(text)
    
    # 质量指标
    metrics['sentiment_score'] = compute_sentiment(text)
    metrics['complexity_score'] = compute_complexity(text)
    metrics['coherence_score'] = compute_coherence(text)
    
    return metrics

def llm_quality_evaluation(text, model='gpt-4-turbo'):
    # 构建评估提示
    prompt = f"""请评估以下文本的质量，从准确性、完整性、一致性、清晰度四个维度进行评分（1-10分），并给出总体质量得分和改进建议。

文本：{text}

请以JSON格式输出评估结果。"""
    
    # 调用LLM进行评估
    response = call_llm_api(prompt, model=model)
    
    # 解析评估结果
    evaluation = json.loads(response)
    
    return evaluation

MAJOR.MINOR.PATCH
- MAJOR：大规模数据变更，可能影响模型性能
- MINOR：添加新数据源或特征，不破坏现有兼容性
- PATCH：小修复，如错误修正或小范围优化

class DatasetMetadata:
    def __init__(self, metadata_store):
        self.metadata_store = metadata_store
    
    def add_dataset_version(self, dataset_id, version, metadata):
        # 添加数据集版本元数据
        self.metadata_store.put(f"{dataset_id}:{version}", metadata)
    
    def get_dataset_history(self, dataset_id):
        # 获取数据集版本历史
        return self.metadata_store.query(f"{dataset_id}:*")
    
    def compare_versions(self, dataset_id, version1, version2):
        # 比较两个版本的元数据差异
        metadata1 = self.metadata_store.get(f"{dataset_id}:{version1}")
        metadata2 = self.metadata_store.get(f"{dataset_id}:{version2}")
        return self._deep_compare(metadata1, metadata2)

def data_anonymization(text):
    # 1. 实体识别
    entities = identify_entities(text)
    
    # 2. 敏感信息替换
    anonymized_text = text
    for entity in entities:
        if entity.type in ['PERSON', 'PHONE', 'EMAIL', 'ADDRESS']:
            anonymized_text = anonymized_text.replace(
                entity.text, f"[{entity.type}]")
    
    # 3. 差分隐私处理
    anonymized_text = apply_differential_privacy(anonymized_text)
    
    return anonymized_text

class DataPipeline:
    def __init__(self):
        self.stages = []
    
    def add_stage(self, stage, dependencies=None):
        # 添加处理阶段
        self.stages.append((stage, dependencies or []))
    
    def execute(self):
        # 拓扑排序确定执行顺序
        execution_order = self._topological_sort()
        
        # 并行执行无依赖的阶段
        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = {}
            results = {}
            
            for stage in execution_order:
                # 检查依赖是否已完成
                if all(dep in results for dep in self._get_dependencies(stage)):
                    # 提交执行
                    futures[stage] = executor.submit(stage.execute)
            
            # 收集结果
            for stage, future in futures.items():
                results[stage] = future.result()
        
        return results

def active_learning_selection(candidate_pool, model, budget=1000):
    # 1. 使用当前模型评估候选数据
    scores = []
    for data in candidate_pool:
        # 计算不确定性分数
        uncertainty = model.compute_uncertainty(data)
        # 计算信息增益
        info_gain = model.estimate_information_gain(data)
        # 综合评分
        score = 0.7 * uncertainty + 0.3 * info_gain
        scores.append((data, score))
    
    # 2. 选择评分最高的数据
    selected_data = sorted(scores, key=lambda x: x[1], reverse=True)[:budget]
    
    return [data for data, score in selected_data]

def data_reweighting(dataset):
    # 1. 计算样本重要性权重
    weights = {}
    
    # 基于稀有度的权重
    rarity_scores = compute_rarity_scores(dataset)
    
    # 基于质量的权重
    quality_scores = compute_quality_scores(dataset)
    
    # 基于多样性的权重
    diversity_scores = compute_diversity_scores(dataset)
    
    # 综合权重
    for doc_id in dataset:
        weights[doc_id] = (
            0.4 * rarity_scores[doc_id] +
            0.4 * quality_scores[doc_id] +
            0.2 * diversity_scores[doc_id]
        )
    
    # 2. 归一化权重
    total_weight = sum(weights.values())
    for doc_id in weights:
        weights[doc_id] /= total_weight
    
    return weights

def bias_detection(dataset):
    biases = {}
    
    # 1. 人口统计偏差分析
    demographic_stats = analyze_demographic_representation(dataset)
    biases['demographic'] = detect_demographic_bias(demographic_stats)
    
    # 2. 文化偏差分析
    cultural_indicators = extract_cultural_indicators(dataset)
    biases['cultural'] = detect_cultural_bias(cultural_indicators)
    
    # 3. 时间偏差分析
    temporal_distribution = analyze_temporal_distribution(dataset)
    biases['temporal'] = detect_temporal_bias(temporal_distribution)
    
    # 4. 来源偏差分析
    source_distribution = analyze_source_distribution(dataset)
    biases['source'] = detect_source_bias(source_distribution)
    
    return biases

大规模数据管理系统
├── 数据接入层
│   ├── 爬虫服务
│   ├── API集成
│   ├── 文件上传服务
│   └── 实时数据流
├── 数据处理层
│   ├── 清洗服务
│   ├── 去重服务
│   ├── 质量评估服务
│   └── 采样服务
├── 存储层
│   ├── 分布式文件系统
│   ├── 对象存储
│   ├── 元数据存储
│   └── 索引服务
├── 管理层
│   ├── 工作流编排
│   ├── 监控告警
│   ├── 权限管理
│   └── 版本控制
└── 服务层
    ├── 查询API
    ├── 数据可视化
    ├── 报告生成
    └── 模型集成

# docker-compose example for data processing service
version: '3'
services:
  data-processor:
    image: data-processing-service:latest
    deploy:
      replicas: 10
      resources:
        limits:
          cpus: '4'
          memory: 16G
    volumes:
      - data-storage:/data
    environment:
      - STORAGE_PATH=/data
      - WORKERS=4
      - BATCH_SIZE=1000
    networks:
      - data-network

volumes:
  data-storage:
    driver: local

networks:
  data-network:
    driver: bridge

数据质量控制流程
├── 数据采集前
│   ├── 数据源评估
│   ├── 采集策略制定
│   └── 质量目标定义
├── 数据采集阶段
│   ├── 实时质量监控
│   ├── 异常检测
│   └── 早期过滤
├── 数据处理阶段
│   ├── 清洗规则应用
│   ├── 质量评估
│   └── 问题修复
└── 数据使用阶段
    ├── 抽样验证
    ├── 效果评估
    └── 反馈优化

def quality_feedback_loop(dataset, model_performance):
    # 1. 分析模型性能问题
    performance_issues = analyze_performance_issues(model_performance)
    
    # 2. 将性能问题映射到数据质量问题
    data_quality_issues = map_to_data_issues(performance_issues)
    
    # 3. 识别问题数据区域
    problematic_data_regions = identify_problematic_regions(dataset, data_quality_issues)
    
    # 4. 生成改进建议
    improvement_suggestions = generate_improvement_suggestions(problematic_data_regions)
    
    # 5. 实施改进措施
    implement_improvements(dataset, improvement_suggestions)
    
    return improved_dataset