自我进化能力测试框架

框架概述

完整的自我进化能力测试框架，支持多种评估模式、自动化测试流程和实时性能监控。该框架可以用于评估任何AI系统在持续迭代过程中的自我改进能力。

核心实现代码

class SelfIterationFramework:
    def __init__(self, config_path: str):
        self.config = self._load_config(config_path)
        self.evaluation_modes = self.config.get('modes', ['qa', 'generation', 'reasoning'])
        self.history = {}
        self.current_iteration = 0
    
    def _load_config(self, path: str) -> dict:
        """加载配置文件"""
        import json
        with open(path, 'r') as f:
            return json.load(f)
    
    def setup_evaluation(self, mode: str):
        """设置评估模式"""
        evaluator = self._get_evaluator(mode)
        self.evaluators[mode] = evaluator
    
    def _get_evaluator(self, mode: str):
        """根据模式获取评估器"""
        if mode == 'qa':
            return QAEvaluator()
        elif mode == 'generation':
            return GenerationEvaluator()
        elif mode == 'reasoning':
            return ReasoningEvaluator()
        else:
            return BaselineEvaluator()
    
    def run_evaluation(self, model, iteration: int) -> dict:
        """运行评估"""
        results = {}
        for mode in self.evaluation_modes:
            evaluator = self._get_evaluator(mode)
            mode_results = evaluator.evaluate(model, iteration)
            results[mode] = mode_results
        return results
    
    def compare_iterations(self, iterations: list) -> dict:
        """比较多次迭代结果"""
        comparison = {}
        for i in range(1, len(iterations)):
            prev = iterations[i-1]
            curr = iterations[i]
            comparison[f"{i-1}_to_{i}"] = self._compute_improvement(prev, curr)
        return comparison
    
    def _compute_improvement(self, prev_results: dict, curr_results: dict) -> dict:
        """计算改进幅度"""
        improvement = {}
        for mode, results in curr_results.items():
            if mode in prev_results:
                prev_score = prev_results[mode]['avg_score']
                curr_score = results['avg_score']
                improvement[mode] = (curr_score - prev_score) / prev_score * 100
        return improvement
    
    def generate_report(self, results: dict) -> str:
        """生成评估报告"""
        report = f"""
=== 自我进化能力评估报告 ===
迭代次数: {self.current_iteration}

总体评分:
"""
        for mode, data in results.items():
            report += f"\n{mode.upper()}模式:\n"
            report += f"  - 平均分: {data['avg_score']:.4f}\n"
            report += f"  - 趋势分: {data['trend']}\n"
            report += f"  - 测试用例: {data['cases']}个\n"
        return report