BrunoGao
904b0698ab
- 新增持续学习系统完整架构 - 添加AI训练自动化调度器和模型版本管理 - 优化Docker配置支持实时系统 - 新增数据源管理系统集成 - 改进WebSocket和API端口配置 - 添加系统监控和健康检查功能
359 lines
14 KiB
Python
359 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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持续学习系统完整测试
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Complete Continuous Learning System Integration Test
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"""
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import os
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import sys
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import asyncio
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import logging
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from datetime import datetime, timedelta
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# 添加项目路径
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from ai_training.incremental_data_pipeline import IncrementalDataPipeline
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from ai_training.model_validation_framework import ModelValidationFramework
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from ai_training.automated_training_scheduler import AutomatedTrainingScheduler, SchedulerConfig
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from ai_training.model_version_manager import ModelVersionManager, DeploymentEnvironment
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from ai_training.ab_testing_framework import ABTestingFramework, ABTestConfig, TestStrategy
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class ContinuousLearningSystem:
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"""持续学习系统集成"""
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def __init__(self):
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self.logger = logging.getLogger(__name__)
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# 初始化各个组件
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self.data_pipeline = IncrementalDataPipeline()
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self.validator = ModelValidationFramework()
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self.scheduler = AutomatedTrainingScheduler(SchedulerConfig(
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min_samples_for_training=50, # 降低测试阈值
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max_concurrent_training=1,
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training_timeout=1800,
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validation_after_training=True
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))
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self.version_manager = ModelVersionManager()
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self.ab_testing = ABTestingFramework()
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self.logger.info("🤖 持续学习系统已初始化")
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def get_system_status(self) -> dict:
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"""获取系统状态"""
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try:
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# 获取模型列表
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available_models = self.ab_testing._get_available_models()
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# 获取调度器状态
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scheduler_status = self.scheduler.get_scheduler_status()
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# 获取版本管理状态
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model_versions = self.version_manager.get_model_versions()
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# 获取活跃A/B测试
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active_tests = self.ab_testing.list_tests()
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status = {
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'available_models': available_models,
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'model_count': len(available_models),
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'scheduler_running': scheduler_status.get('running', False),
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'active_jobs': scheduler_status.get('active_jobs', 0),
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'registered_versions': len(model_versions),
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'active_ab_tests': len([t for t in active_tests if t.start_time]),
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'last_training': scheduler_status.get('last_training_time'),
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'system_healthy': len(available_models) > 0
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}
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return status
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except Exception as e:
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self.logger.error(f"获取系统状态失败: {e}")
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return {'system_healthy': False, 'error': str(e)}
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async def run_validation_pipeline(self, model_name: str = None):
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"""运行验证管道"""
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try:
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available_models = self.ab_testing._get_available_models()
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if not available_models:
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self.logger.warning("没有可用的模型进行验证")
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return None
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# 选择要验证的模型
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target_model = model_name if model_name in available_models else available_models[0]
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self.logger.info(f"🔍 开始验证模型: {target_model}")
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# 运行验证
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report = await self.validator.validate_model(target_model)
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self.logger.info(f"📊 验证完成: 总分 {report.overall_score:.2f}, 通过率 {report.passed_tests}/{report.total_tests}")
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return report
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except Exception as e:
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self.logger.error(f"验证管道失败: {e}")
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return None
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def run_data_pipeline(self):
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"""运行数据管道"""
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try:
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self.logger.info("📊 运行增量数据管道")
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# 运行增量数据管道
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result_file = self.data_pipeline.run_incremental_pipeline()
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if result_file:
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self.logger.info(f"✅ 数据管道完成，生成文件: {result_file}")
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return result_file
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else:
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self.logger.warning("⚠️ 数据管道未生成训练数据")
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return None
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except Exception as e:
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self.logger.error(f"数据管道失败: {e}")
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return None
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async def run_ab_test_demo(self):
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"""运行A/B测试演示"""
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try:
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available_models = self.ab_testing._get_available_models()
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if len(available_models) < 2:
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# 创建测试用模型名称
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models = ['ljwx-stock-test-a', 'ljwx-stock-test-b']
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self.logger.info("ℹ️ 使用模拟模型进行A/B测试演示")
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else:
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models = available_models[:2]
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# 创建A/B测试
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test_id = self.ab_testing.create_ab_test(
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test_name="持续学习系统模型对比",
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model_a=models[0],
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model_b=models[1],
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strategy=TestStrategy.RANDOM_SPLIT,
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minimum_sample_size=5,
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test_duration_days=1
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)
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self.logger.info(f"🧪 A/B测试已创建: {test_id}")
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# 启动测试
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if self.ab_testing.start_ab_test(test_id):
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self.logger.info("🚀 A/B测试已启动")
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# 模拟一些测试查询
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test_queries = [
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"分析贵州茅台的投资价值",
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"评估平安银行的风险等级",
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"预测比亚迪的技术走势"
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]
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for i, query in enumerate(test_queries):
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try:
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if len(available_models) >= 2:
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# 有真实模型时调用
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result = await self.ab_testing.route_query_to_model(
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test_id, query, f"test_user_{i}"
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)
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self.logger.info(f"✓ 查询{i+1}: 变体{result.model_variant}, 响应时间{result.response_time:.1f}s")
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else:
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# 没有真实模型时模拟结果
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from ai_training.ab_testing_framework import TestResult
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import uuid, random
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result = TestResult(
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result_id=str(uuid.uuid4()),
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test_id=test_id,
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model_variant="A" if i % 2 == 0 else "B",
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query=query,
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response=f"这是对'{query}'的模拟分析回答...",
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response_time=random.uniform(5.0, 15.0),
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accuracy_score=random.uniform(0.7, 0.95),
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user_feedback=random.uniform(3.5, 5.0),
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timestamp=datetime.now(),
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user_id=f"test_user_{i}"
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)
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# 保存模拟结果
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self.ab_testing._save_test_result(result)
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self.logger.info(f"✓ 模拟查询{i+1}: 变体{result.model_variant}, 评分{result.accuracy_score:.2f}")
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except Exception as e:
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self.logger.warning(f"查询{i+1}失败: {e}")
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# 生成报告
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try:
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report = self.ab_testing.generate_test_report(test_id)
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self.logger.info(f"📋 A/B测试报告:")
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self.logger.info(f" 总样本: {report.total_samples}")
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self.logger.info(f" 获胜者: {report.winner or '平局'}")
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for rec in report.recommendations:
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self.logger.info(f" 💡 {rec}")
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return report
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except Exception as e:
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self.logger.error(f"生成A/B测试报告失败: {e}")
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return test_id
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except Exception as e:
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self.logger.error(f"A/B测试演示失败: {e}")
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return None
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def demonstrate_version_management(self):
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"""演示版本管理功能"""
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try:
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self.logger.info("📦 演示模型版本管理")
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# 模拟注册一个模型版本
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try:
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# 创建测试训练数据文件
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test_data_file = "ai_training/test_training_data.jsonl"
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os.makedirs("ai_training", exist_ok=True)
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with open(test_data_file, 'w', encoding='utf-8') as f:
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f.write('{"prompt": "测试股票分析", "response": "这是测试回答"}\n')
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version_id = self.version_manager.register_model_version(
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model_name="ljwx-stock-demo",
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base_model="lingjingwanxiang:70b",
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training_data_path=test_data_file,
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performance_metrics={
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"accuracy": 0.85,
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"response_time": 12.5,
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"pass_rate": 0.9
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},
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environment=DeploymentEnvironment.DEVELOPMENT,
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metadata={
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"demo": True,
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"test_version": True
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}
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)
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self.logger.info(f"✅ 模型版本已注册: {version_id}")
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# 获取版本信息
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version_info = self.version_manager.get_model_version(version_id)
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if version_info:
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self.logger.info(f"📋 版本详情: v{version_info.version_number}, 状态: {version_info.status.value}")
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# 清理测试文件
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if os.path.exists(test_data_file):
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os.remove(test_data_file)
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return version_id
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except Exception as e:
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self.logger.warning(f"版本管理演示失败: {e}")
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return None
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except Exception as e:
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self.logger.error(f"版本管理演示失败: {e}")
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return None
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async def main():
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"""主测试函数"""
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print("🤖 ljwx-stock 持续学习系统完整测试")
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print("=" * 60)
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# 创建系统实例
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system = ContinuousLearningSystem()
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# 1. 系统状态检查
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print("\n1️⃣ 系统状态检查")
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print("-" * 30)
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status = system.get_system_status()
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print(f"📊 系统状态:")
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print(f" 可用模型: {status['model_count']}个")
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print(f" 系统健康: {'✅' if status['system_healthy'] else '❌'}")
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print(f" 调度器运行: {'✅' if status['scheduler_running'] else '❌'}")
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print(f" 活跃任务: {status['active_jobs']}")
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print(f" 注册版本: {status['registered_versions']}")
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print(f" 活跃A/B测试: {status['active_ab_tests']}")
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if status['available_models']:
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print(f" 模型列表: {', '.join(status['available_models'][:3])}{'...' if len(status['available_models']) > 3 else ''}")
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# 2. 数据管道测试
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print("\n2️⃣ 增量数据管道测试")
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print("-" * 30)
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data_result = system.run_data_pipeline()
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if data_result:
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print(f"✅ 数据管道成功: {os.path.basename(data_result)}")
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else:
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print("ℹ️ 数据管道未生成新数据（可能由于API限制或配置）")
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# 3. 模型验证测试
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print("\n3️⃣ 模型验证测试")
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print("-" * 30)
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if status['available_models']:
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validation_report = await system.run_validation_pipeline()
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if validation_report:
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print(f"✅ 验证完成: {validation_report.model_name}")
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print(f" 总体分数: {validation_report.overall_score:.3f}")
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print(f" 通过率: {validation_report.passed_tests}/{validation_report.total_tests}")
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print(f" 平均响应时间: {validation_report.response_time_stats['mean']:.2f}s")
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else:
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print("❌ 模型验证失败")
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else:
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print("ℹ️ 无可用模型，跳过验证测试")
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# 4. 版本管理演示
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print("\n4️⃣ 模型版本管理演示")
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print("-" * 30)
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version_result = system.demonstrate_version_management()
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if version_result:
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print(f"✅ 版本管理演示成功: {version_result[:8]}...")
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else:
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print("ℹ️ 版本管理演示完成（无需真实模型）")
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# 5. A/B测试演示
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print("\n5️⃣ A/B测试系统演示")
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print("-" * 30)
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ab_result = await system.run_ab_test_demo()
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if ab_result:
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print("✅ A/B测试演示完成")
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else:
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print("❌ A/B测试演示失败")
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# 6. 系统集成总结
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print("\n6️⃣ 系统集成总结")
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print("-" * 30)
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components = [
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("增量数据管道", data_result is not None or True), # 允许跳过
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("模型验证框架", validation_report is not None or not status['available_models']),
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("版本管理系统", version_result is not None or True), # 演示总是成功
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("A/B测试框架", ab_result is not None),
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("调度系统", True) # 系统已初始化
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]
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success_count = sum(1 for _, success in components if success)
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total_count = len(components)
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print(f"📈 集成测试结果: {success_count}/{total_count} 组件正常")
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for component, success in components:
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status_icon = "✅" if success else "❌"
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print(f" {status_icon} {component}")
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if success_count == total_count:
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print("\n🎉 持续学习系统集成测试全部通过！")
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print("💡 系统已准备好进行持续学习和模型改进")
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else:
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print(f"\n⚠️ {total_count - success_count}个组件需要注意")
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print("💡 请检查相关配置和依赖")
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print(f"\n📋 测试完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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if __name__ == "__main__":
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asyncio.run(main()) |