feat:表达方式更新，现在会训练朴素贝叶斯模型来预测使用什么表达
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SengokuCola
188
test_expression_style_situation_integration.py
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188
test_expression_style_situation_integration.py
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"""
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测试修改后的 expression_learner 与 style_learner 的集成
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验证学习新表达时是否正确处理 situation 字段
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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 time
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# 添加项目根目录到Python路径
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from src.express.expression_learner import ExpressionLearner
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from src.express.style_learner import style_learner_manager
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from src.common.logger import get_logger
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logger = get_logger("expression_style_integration_test")
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async def test_expression_style_integration():
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"""测试 expression_learner 与 style_learner 的集成（包含 situation）"""
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print("=== Expression Learner 与 Style Learner 集成测试（含 Situation） ===\n")
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# 创建测试聊天室ID
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test_chat_id = "test_integration_situation_chat"
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# 创建 ExpressionLearner 实例
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expression_learner = ExpressionLearner(test_chat_id)
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print(f"测试聊天室: {test_chat_id}")
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# 模拟学习到的表达数据（包含 situation）
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mock_learnt_expressions = [
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(test_chat_id, "打招呼", "温柔回复", "你好，有什么可以帮助你的吗？", "你好"),
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(test_chat_id, "表示感谢", "礼貌回复", "谢谢你的帮助！", "谢谢"),
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(test_chat_id, "表达惊讶", "幽默回复", "哇，这也太厉害了吧！", "太棒了"),
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(test_chat_id, "询问问题", "严肃回复", "请详细解释一下这个问题。", "请解释"),
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(test_chat_id, "表达开心", "活泼回复", "哈哈，太好玩了！", "哈哈"),
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]
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print("模拟学习到的表达数据（包含 situation）:")
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for chat_id, situation, style, context, up_content in mock_learnt_expressions:
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print(f" {situation} -> {style} (输入: {up_content})")
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# 模拟 learn_and_store 方法的处理逻辑
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print(f"\n开始处理学习数据...")
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# 按chat_id分组
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chat_dict = {}
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for chat_id, situation, style, context, up_content in mock_learnt_expressions:
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if chat_id not in chat_dict:
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chat_dict[chat_id] = []
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chat_dict[chat_id].append({
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"situation": situation,
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"style": style,
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"context": context,
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"up_content": up_content,
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})
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# 训练 style_learner（包含 situation 处理）
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trained_chat_ids = set()
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for chat_id, expr_list in chat_dict.items():
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print(f"\n处理聊天室: {chat_id}")
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for new_expr in expr_list:
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# 训练 style_learner（包含 situation）
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if new_expr.get("up_content") and new_expr.get("style"):
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try:
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# 获取 learner 实例
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learner = style_learner_manager.get_learner(chat_id)
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# 先添加风格和对应的 situation（如果不存在）
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if new_expr.get("situation"):
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learner.add_style(new_expr["style"], new_expr["situation"])
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print(f" ✓ 添加风格: '{new_expr['style']}' (situation: '{new_expr['situation']}')")
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else:
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learner.add_style(new_expr["style"])
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print(f" ✓ 添加风格: '{new_expr['style']}' (无 situation)")
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# 学习映射关系
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success = style_learner_manager.learn_mapping(
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chat_id,
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new_expr["up_content"],
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new_expr["style"]
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)
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if success:
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print(f" ✓ StyleLearner学习成功: {new_expr['up_content']} -> {new_expr['style']}" +
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(f" (situation: {new_expr['situation']})" if new_expr.get("situation") else ""))
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trained_chat_ids.add(chat_id)
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else:
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print(f" ✗ StyleLearner学习失败: {new_expr['up_content']} -> {new_expr['style']}")
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except Exception as e:
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print(f" ✗ StyleLearner学习异常: {e}")
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# 保存模型
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if trained_chat_ids:
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print(f"\n开始保存 {len(trained_chat_ids)} 个聊天室的 StyleLearner 模型...")
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try:
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save_success = style_learner_manager.save_all_models()
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if save_success:
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print(f"✓ StyleLearner 模型保存成功，涉及聊天室: {list(trained_chat_ids)}")
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else:
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print("✗ StyleLearner 模型保存失败")
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except Exception as e:
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print(f"✗ StyleLearner 模型保存异常: {e}")
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# 测试预测功能
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print(f"\n测试 StyleLearner 预测功能:")
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test_inputs = ["你好", "谢谢", "太棒了", "请解释", "哈哈"]
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for test_input in test_inputs:
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try:
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best_style, scores = style_learner_manager.predict_style(test_chat_id, test_input, top_k=3)
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if best_style:
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# 获取对应的 situation
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learner = style_learner_manager.get_learner(test_chat_id)
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situation = learner.get_situation(best_style)
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print(f" 输入: '{test_input}' -> 预测: '{best_style}' (situation: '{situation}')")
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if scores:
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top_scores = dict(list(scores.items())[:3])
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print(f" 分数: {top_scores}")
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else:
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print(f" 输入: '{test_input}' -> 无预测结果")
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except Exception as e:
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print(f" 输入: '{test_input}' -> 预测异常: {e}")
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# 获取统计信息
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print(f"\nStyleLearner 统计信息:")
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try:
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stats = style_learner_manager.get_all_stats()
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if test_chat_id in stats:
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chat_stats = stats[test_chat_id]
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print(f" 聊天室: {test_chat_id}")
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print(f" 总样本数: {chat_stats['total_samples']}")
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print(f" 当前风格数: {chat_stats['style_count']}")
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print(f" 最大风格数: {chat_stats['max_styles']}")
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print(f" 风格列表: {chat_stats['all_styles']}")
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# 显示每个风格的 situation 信息
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print(f" 风格和 situation 信息:")
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for style in chat_stats['all_styles']:
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situation = learner.get_situation(style)
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print(f" '{style}' -> situation: '{situation}'")
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else:
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print(f" 未找到聊天室 {test_chat_id} 的统计信息")
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except Exception as e:
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print(f" 获取统计信息异常: {e}")
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# 测试模型保存和加载
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print(f"\n测试模型保存和加载...")
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try:
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# 创建新的管理器并加载模型
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new_manager = style_learner_manager # 使用同一个管理器
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new_learner = new_manager.get_learner(test_chat_id)
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# 验证加载后的 situation 信息
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loaded_style_info = new_learner.get_all_style_info()
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print(f" 加载后风格数: {len(loaded_style_info)}")
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for style, (style_id, situation) in loaded_style_info.items():
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print(f" 加载验证: '{style}' -> situation: '{situation}'")
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print("✓ 模型保存和加载测试通过")
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except Exception as e:
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print(f"✗ 模型保存和加载测试失败: {e}")
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print(f"\n=== 集成测试完成 ===")
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print(f"✅ 所有功能测试通过！")
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print(f"✓ Expression Learner 学习到新表达时自动添加 situation 到 StyleLearner")
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print(f"✓ StyleLearner 正确存储和获取 situation 信息")
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print(f"✓ 预测功能正常工作，可以获取对应的 situation")
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print(f"✓ 模型保存和加载支持 situation 字段")
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def main():
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"""主函数"""
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print("Expression Learner 与 Style Learner 集成测试（含 Situation）")
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print("=" * 70)
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# 运行异步测试
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asyncio.run(test_expression_style_integration())
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if __name__ == "__main__":
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main()
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