155 lines
6.3 KiB
Python
155 lines
6.3 KiB
Python
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from kafka import KafkaConsumer
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import json
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import logging
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from ..models.user_profile import UserProfile
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from ..analyzer.preference_analyzer import PreferenceAnalyzer
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# 配置日志
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logger = logging.getLogger(__name__)
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class BookEventConsumer:
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def __init__(self, bootstrap_servers=['localhost:9092']):
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"""初始化Kafka消费者"""
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self.consumer = KafkaConsumer(
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'book-events', # topic名称
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bootstrap_servers=bootstrap_servers,
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group_id='python-recommendation-group',
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auto_offset_reset='earliest',
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enable_auto_commit=True,
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value_deserializer=lambda x: json.loads(x.decode('utf-8'))
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)
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self.user_profiles = {}
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self.analyzer = PreferenceAnalyzer()
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def _handle_read_event(self, user_id, event):
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"""处理阅读事件"""
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reading_record = {
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'book_id': event['bookId'],
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'duration': event['readDuration'],
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'progress': event['progress'],
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'category': event['bookInfo']['category'],
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'author': event['bookInfo']['author'],
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'timestamp': event['timestamp']
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}
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self.user_profiles[user_id].update_reading_history(reading_record)
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self.user_profiles[user_id].update_preferences(reading_record)
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logger.info(f"用户 {user_id} 的阅读记录已更新")
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# 分析用户阅读模式
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self._analyze_user_patterns(user_id)
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def _analyze_user_patterns(self, user_id):
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"""分析用户模式并输出结果"""
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try:
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# 获取用户的阅读模式分析
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patterns = self.analyzer.analyze_reading_pattern(
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self.user_profiles[user_id].reading_history
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)
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logger.info(f"\n用户 {user_id} 的阅读分析报告:")
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logger.info("=" * 50)
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# 输出时间偏好
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if '偏好时段' in patterns:
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logger.info(f"时间偏好: {patterns['偏好时段']['最佳阅读时段']}")
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logger.info(f"时段分布: {patterns['偏好时段']['时段分布']}")
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# 输出分类偏好
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if '分类权重' in patterns:
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logger.info("\n分类偏好:")
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for category, weight in patterns['分类权重'].items():
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logger.info(f"- {category}: {weight:.2%}")
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# 输出阅读完成度
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if '阅读完成度' in patterns:
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logger.info("\n阅读完成情况:")
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for key, value in patterns['阅读完成度'].items():
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logger.info(f"- {key}: {value}")
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# 输出阅读趋势
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if '阅读趋势' in patterns:
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logger.info("\n阅读趋势分析:")
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for key, value in patterns['阅读趋势'].items():
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logger.info(f"- {key}: {value}")
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# 输出推荐类别
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if '推荐类别' in patterns:
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logger.info("\n推荐类别:")
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logger.info(f"推荐类别: {patterns['推荐类别']['推荐类别']}")
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logger.info("推荐理由:")
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for category, reason in patterns['推荐类别']['推荐理由'].items():
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logger.info(f"- {category}: {reason}")
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# 分析用户相似度
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self._analyze_user_similarities(user_id)
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logger.info("=" * 50)
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except Exception as e:
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logger.error(f"分析用户模式时出错: {str(e)}")
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def _analyze_user_similarities(self, current_user_id):
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"""分析用户间的相似度"""
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try:
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if len(self.user_profiles) > 1:
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logger.info("\n用户相似度分析:")
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for other_user_id, other_profile in self.user_profiles.items():
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if other_user_id != current_user_id:
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similarity = self.analyzer.calculate_user_similarity(
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self.user_profiles[current_user_id].reading_history,
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other_profile.reading_history
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)
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logger.info(f"\n与用户 {other_user_id} 的相似度:")
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logger.info(f"- 相似度指数: {similarity['相似度']:.2%}")
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logger.info(f"- 共同兴趣: {', '.join(similarity['共同兴趣']) if similarity['共同兴趣'] else '暂无'}")
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except Exception as e:
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logger.error(f"分析用户相似度时出错: {str(e)}")
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def _handle_rate_event(self, user_id, event):
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"""处理评分事件"""
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self.user_profiles[user_id].update_book_rating(
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event['bookId'],
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event['rating']
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)
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logger.info(f"用户 {user_id} 的评分记录已更新")
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def _handle_bookmark_event(self, user_id, event):
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"""处理收藏事件"""
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self.user_profiles[user_id].add_bookmark(event['bookId'])
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logger.info(f"用户 {user_id} 的收藏记录已更新")
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def process_message(self, message):
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"""处理接收到的消息"""
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try:
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event = message.value
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logger.info(f"收到事件: {event}")
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# 获取用户画像，如果不存在则创建
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user_id = event['userId']
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if user_id not in self.user_profiles:
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self.user_profiles[user_id] = UserProfile()
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# 根据事件类型处理
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event_type = event['eventType']
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if event_type == 'READ':
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self._handle_read_event(user_id, event)
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elif event_type == 'RATE':
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self._handle_rate_event(user_id, event)
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elif event_type == 'BOOKMARK':
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self._handle_bookmark_event(user_id, event)
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except Exception as e:
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logger.error(f"处理消息时发生错误: {str(e)}")
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def start_consuming(self):
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"""开始消费消息"""
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logger.info("开始消费Kafka消息...")
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try:
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for message in self.consumer:
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self.process_message(message)
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except Exception as e:
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logger.error(f"消费消息时发生错误: {str(e)}")
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def close(self):
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"""关闭消费者"""
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self.consumer.close()
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logger.info("消费者已关闭")
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