开源数据标注pipeline深度解析

# 示例架构
class DataIngestionService:
    def fetch_data(self, source_type, config):
        # 数据获取逻辑

class PreprocessingService:
    def clean_data(self, raw_data):
        # 数据清洗

class AnnotationService:
    def annotate(self, data, task_type):
        # 核心标注逻辑

class QualityControlService:
    def validate(self, annotations):
        # 质量检查

# 自定义标注工具示例
class CustomAnnotationTool:
    def __init__(self, task_type):
        self.task_type = task_type
        self.annotations = []
    
    def create_annotation(self, data, label):
        # 创建标注
        annotation = {
            'data_id': uuid.uuid4(),
            'label': label,
            'metadata': self.extract_metadata(data)
        }
        self.annotations.append(annotation)
        return annotation

class SemiAutoAnnotation:
    def __init__(self, model):
        self.model = model  # 预训练模型
    
    def predict_annotations(self, data_batch):
        # 模型预测初步标注
        predictions = self.model.predict(data_batch)
        return self.refine_predictions(predictions)
    
    def human_verification(self, predictions):
        # 人工验证和修正
        verified_annotations = []
        for pred in predictions:
            if pred.confidence < 0.8:  # 低置信度需要人工检查
                verified = human_verify(pred)
                verified_annotations.append(verified)
            else:
                verified_annotations.append(pred)
        return verified_annotations

class ActiveLearningPipeline:
    def select_samples(self, unlabeled_data, model):
        # 选择最有价值的样本进行标注
        uncertainties = model.calculate_uncertainty(unlabeled_data)
        selected_samples = self.select_by_uncertainty(uncertainties)
        return selected_samples
    
    def update_model(self, new_annotations):
        # 用新标注数据更新模型
        self.model.train(new_annotations)

class QualityControlSystem:
    def __init__(self, annotation_rules):
        self.rules = annotation_rules
    
    def validate_annotation(self, annotation):
        # 规则检查
        violations = []
        for rule in self.rules:
            if not rule.check(annotation):
                violations.append(rule.name)
        return violations
    
    def multi_level_review(self, annotations):
        # 多级审核流程
        level1_results = self.primary_review(annotations)
        level2_results = self.secondary_review(level1_results)
        final_results = self.tertiary_review(level2_results)
        return final_results

class CollaborationManager:
    def assign_tasks(self, annotators, tasks):
        # 智能任务分配
        assignments = self.optimize_assignment(annotators, tasks)
        self.notify_assignments(assignments)
        return assignments
    
    def track_progress(self):
        # 实时进度监控
        progress = self.collect_progress_data()
        self.generate_reports(progress)
        return progress

class DataVersionControl:
    def create_version(self, data, changes):
        # 创建新版本
        version = {
            'version_id': self.generate_version_id(),
            'data': data,
            'changes': changes,
            'timestamp': datetime.now()
        }
        self.save_version(version)
        return version
    
    def compare_versions(self, version1, version2):
        # 版本差异分析
        differences = self.calculate_differences(version1, version2)
        return differences

class BatchProcessing:
    def optimize_batch_size(self, task_type, data_size):
        # 动态调整批大小
        optimal_batch = self.calculate_optimal_batch(task_type, data_size)
        return optimal_batch
    
    def parallel_processing(self, tasks):
        # 并行处理提升效率
        with ThreadPoolExecutor() as executor:
            results = list(executor.map(self.process_task, tasks))
        return results

class CostAnalyzer:
    def calculate_cost(self, annotations):
        # 成本计算
        labor_cost = self.calculate_labor(annotations)
        tool_cost = self.calculate_tool_usage(annotations)
        time_cost = self.calculate_time(annotations)
        return {
            'total_cost': labor_cost + tool_cost + time_cost,
            'cost_per_annotation': (labor_cost + tool_cost + time_cost) / len(annotations)
        }

class MetaLearningAnnotation:
    def adapt_to_task(self, new_task):
        # 快速适应新标注任务
        self.model.fine_tune(new_task.samples, new_task.labels)
        return self.model
    
    def transfer_knowledge(self, source_tasks, target_task):
        # 知识迁移
        transferred_model = self.transfer_learning(source_tasks, target_task)
        return transferred_model