159 lines
6.1 KiB
Python
159 lines
6.1 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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增强版优先级算法测试脚本
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测试新的优先级算法在起承转合辩论系统中的表现
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"""
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import sys
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import os
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sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
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from jixia.debates.qi_cheng_zhuan_he_debate import QiChengZhuanHeDebateSystem, DebateStage
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from jixia.debates.enhanced_priority_algorithm import EnhancedPriorityAlgorithm
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def test_enhanced_priority_algorithm():
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"""测试增强版优先级算法"""
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print("🧪 开始测试增强版优先级算法")
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print("=" * 50)
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# 创建辩论系统
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debate_system = QiChengZhuanHeDebateSystem()
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# 模拟辩论场景
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test_scenarios = [
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{
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"name": "起阶段开始",
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"stage": DebateStage.QI,
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"progress": 1,
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"history": []
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},
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{
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"name": "承阶段中期",
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"stage": DebateStage.CHENG,
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"progress": 3,
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"history": [
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{"speaker": "正1", "content": "AI投资具有巨大潜力", "timestamp": "2024-01-01T10:00:00"},
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{"speaker": "反1", "content": "但风险也很高", "timestamp": "2024-01-01T10:01:00"}
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]
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},
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{
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"name": "转阶段激烈辩论",
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"stage": DebateStage.ZHUAN,
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"progress": 5,
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"history": [
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{"speaker": "正2", "content": "数据显示AI投资回报率很高", "timestamp": "2024-01-01T10:02:00"},
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{"speaker": "反2", "content": "这些数据可能有偏差", "timestamp": "2024-01-01T10:03:00"},
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{"speaker": "正3", "content": "我们有严格的风控措施", "timestamp": "2024-01-01T10:04:00"},
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{"speaker": "反3", "content": "风控措施并不能完全避免风险", "timestamp": "2024-01-01T10:05:00"}
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]
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},
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{
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"name": "合阶段总结",
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"stage": DebateStage.HE,
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"progress": 2,
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"history": [
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{"speaker": "正4", "content": "综合来看，AI投资利大于弊", "timestamp": "2024-01-01T10:06:00"},
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{"speaker": "反4", "content": "我们需要更谨慎的态度", "timestamp": "2024-01-01T10:07:00"}
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]
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}
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]
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for i, scenario in enumerate(test_scenarios, 1):
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print(f"\n📋 测试场景 {i}: {scenario['name']}")
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print("-" * 30)
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# 设置辩论状态
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debate_system.context.current_stage = scenario['stage']
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debate_system.context.stage_progress = scenario['progress']
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debate_system.context.debate_history = scenario['history']
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# 获取推荐发言者
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try:
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recommended_speaker = debate_system._get_priority_speaker()
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analysis = debate_system.context.last_priority_analysis
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print(f"🎯 推荐发言者: {recommended_speaker}")
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print(f"📊 优先级分数: {analysis.get('priority_score', 'N/A'):.3f}")
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if 'analysis' in analysis:
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detailed_analysis = analysis['analysis']
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print(f"🔍 详细分析:")
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# 显示推荐发言者的详细信息
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if recommended_speaker in detailed_analysis:
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speaker_info = detailed_analysis[recommended_speaker]
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print(f" - 发言者: {recommended_speaker}")
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print(f" - 优先级分数: {speaker_info.get('priority_score', 'N/A'):.3f}")
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print(f" - 分析时间: {speaker_info.get('analysis_timestamp', 'N/A')}")
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profile = speaker_info.get('profile')
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if profile:
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print(f" - 团队: {profile.team}")
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print(f" - 发言次数: {profile.total_speech_count}")
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print(f" - 当前能量: {profile.current_energy:.2f}")
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print(f" - 辩论风格: {profile.debate_style}")
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# 显示所有发言者的分数排名
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print(f"\n 📊 所有发言者排名:")
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sorted_speakers = sorted(detailed_analysis.items(),
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key=lambda x: x[1].get('priority_score', 0),
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reverse=True)
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for rank, (speaker, info) in enumerate(sorted_speakers[:5], 1):
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score = info.get('priority_score', 0)
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print(f" {rank}. {speaker}: {score:.3f}")
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except Exception as e:
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print(f"❌ 测试失败: {e}")
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import traceback
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traceback.print_exc()
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print("\n" + "=" * 50)
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print("✅ 增强版优先级算法测试完成")
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def test_algorithm_performance():
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"""测试算法性能"""
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print("\n⚡ 性能测试")
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print("-" * 20)
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import time
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algorithm = EnhancedPriorityAlgorithm()
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available_speakers = ["正1", "正2", "正3", "正4", "反1", "反2", "反3", "反4"]
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context = {
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"current_stage": "承",
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"stage_progress": 3,
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"max_progress": 6,
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"time_remaining": 0.5,
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"topic_keywords": ["AI", "投资", "风险"],
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"positive_team_score": 0.6,
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"negative_team_score": 0.4,
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"positive_recent_speeches": 3,
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"negative_recent_speeches": 2
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}
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recent_speeches = [
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{"speaker": "正1", "content": "AI技术发展迅速"},
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{"speaker": "反1", "content": "但存在不确定性"}
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]
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# 性能测试
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start_time = time.time()
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iterations = 100
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for _ in range(iterations):
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speaker, score, analysis = algorithm.get_next_speaker(
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available_speakers, context, recent_speeches
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)
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end_time = time.time()
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avg_time = (end_time - start_time) / iterations * 1000 # 转换为毫秒
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print(f"📈 平均处理时间: {avg_time:.2f}ms")
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print(f"🔄 总迭代次数: {iterations}")
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print(f"⚡ 处理速度: {1000/avg_time:.0f} 次/秒")
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if __name__ == "__main__":
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test_enhanced_priority_algorithm()
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test_algorithm_performance() |