368 lines
15 KiB
Python
368 lines
15 KiB
Python
from django.core.management.base import BaseCommand
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from rlhf.models import Conversation, Message, Feedback, DetailedFeedback, FeedbackTag
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from django.db.models import Count, Avg, Sum, Q, F
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from django.utils import timezone
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from django.contrib.auth import get_user_model
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import json
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from datetime import datetime, timedelta
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User = get_user_model()
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class Command(BaseCommand):
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help = '分析RLHF反馈数据，生成统计报告'
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def add_arguments(self, parser):
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parser.add_argument(
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'--export',
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action='store_true',
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help='导出数据到JSON文件',
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)
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parser.add_argument(
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'--days',
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type=int,
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default=30,
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help='分析最近的天数',
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)
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def handle(self, *args, **options):
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self.stdout.write(self.style.SUCCESS("=" * 60))
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self.stdout.write(self.style.SUCCESS("🤖 在线人类反馈强化学习系统 - 数据分析报告"))
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self.stdout.write(self.style.SUCCESS("=" * 60))
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# 基本统计
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feedback_stats = self.get_feedback_stats()
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self.stdout.write(self.style.SUCCESS(f"\n📊 反馈统计:"))
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self.stdout.write(f" 总反馈数量: {feedback_stats['total_feedback']}")
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self.stdout.write(f" 正面反馈: {feedback_stats['positive_feedback']} ({feedback_stats['positive_rate']:.1f}%)")
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self.stdout.write(f" 负面反馈: {feedback_stats['negative_feedback']}")
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self.stdout.write(f" 平均反馈分数: {feedback_stats['avg_feedback']:.2f}")
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# 对话统计
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conv_stats = self.get_conversation_stats()
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self.stdout.write(self.style.SUCCESS(f"\n💬 对话统计:"))
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self.stdout.write(f" 总对话数量: {conv_stats['total_conversations']}")
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self.stdout.write(f" 总消息数量: {conv_stats['total_messages']}")
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self.stdout.write(f" 平均每对话消息数: {conv_stats['avg_messages_per_conversation']:.1f}")
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# 标签统计
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tag_stats = self.get_tag_stats()
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self.stdout.write(self.style.SUCCESS(f"\n🏷️ 标签统计:"))
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self.stdout.write(f" 最常用的正面标签:")
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for tag in tag_stats['top_positive']:
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self.stdout.write(f" - {tag['tag_name']}: {tag['count']}次")
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self.stdout.write(f" 最常用的负面标签:")
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for tag in tag_stats['top_negative']:
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self.stdout.write(f" - {tag['tag_name']}: {tag['count']}次")
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# 每日趋势
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days = options['days']
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daily_trend = self.get_daily_feedback_trend(days)
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self.stdout.write(self.style.SUCCESS(f"\n📈 最近{days}天反馈趋势:"))
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for day in daily_trend:
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self.stdout.write(f" {day['date']}: {day['total']}条反馈 (正面率: {day['positive_rate']:.1f}%)")
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# 用户统计
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user_stats = self.get_user_stats()
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self.stdout.write(self.style.SUCCESS(f"\n👥 用户统计:"))
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self.stdout.write(f" 总用户数量: {user_stats['total_users']}")
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self.stdout.write(f" 活跃标注用户: {user_stats['active_users']}")
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self.stdout.write(f" 平均每用户标注量: {user_stats['avg_annotations_per_user']:.1f}")
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# 导出数据
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if options['export']:
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filename = self.export_data_to_json()
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self.stdout.write(self.style.SUCCESS(f"\n✅ 数据已导出到: {filename}"))
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def get_feedback_stats(self):
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"""获取反馈统计信息"""
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# 基本反馈统计
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basic_feedback = Feedback.objects.aggregate(
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total=Count('id'),
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positive=Sum(Case(When(feedback_value__gt=0, then=1), default=0)),
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negative=Sum(Case(When(feedback_value__lt=0, then=1), default=0)),
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avg=Avg('feedback_value')
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)
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# 详细反馈统计
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detailed_feedback = DetailedFeedback.objects.aggregate(
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total=Count('id'),
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positive=Count('id', filter=Q(feedback_type='positive')),
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negative=Count('id', filter=Q(feedback_type='negative'))
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)
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# 合并统计
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total = (basic_feedback['total'] or 0) + (detailed_feedback['total'] or 0)
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positive = (basic_feedback['positive'] or 0) + (detailed_feedback['positive'] or 0)
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negative = (basic_feedback['negative'] or 0) + (detailed_feedback['negative'] or 0)
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# 计算平均分和正面比例
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avg_feedback = basic_feedback['avg'] or 0
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positive_rate = (positive / total * 100) if total > 0 else 0
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return {
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'total_feedback': total,
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'positive_feedback': positive,
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'negative_feedback': negative,
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'avg_feedback': avg_feedback,
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'positive_rate': positive_rate
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}
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def get_conversation_stats(self):
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"""获取对话统计信息"""
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total_conversations = Conversation.objects.count()
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total_messages = Message.objects.count()
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# 计算每个对话的消息数量分布
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conversation_messages = Message.objects.values('conversation').annotate(count=Count('id'))
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avg_messages = conversation_messages.aggregate(Avg('count'))['count__avg'] or 0
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return {
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'total_conversations': total_conversations,
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'total_messages': total_messages,
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'avg_messages_per_conversation': avg_messages
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}
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def get_tag_stats(self):
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"""获取标签使用统计"""
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# 分析DetailedFeedback中的标签使用情况
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# 注意：由于标签可能存储为JSON字符串，这里需要解析
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# 首先获取所有的标签
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all_tags = FeedbackTag.objects.all()
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tag_id_to_name = {str(tag.id): tag.tag_name for tag in all_tags}
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# 计算每个标签的使用次数
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tag_counts = {}
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for feedback in DetailedFeedback.objects.all():
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if feedback.feedback_tags:
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try:
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# 尝试解析JSON标签列表
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tag_ids = json.loads(feedback.feedback_tags)
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if isinstance(tag_ids, list):
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for tag_id in tag_ids:
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tag_id = str(tag_id)
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if tag_id in tag_counts:
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tag_counts[tag_id] += 1
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else:
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tag_counts[tag_id] = 1
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except (json.JSONDecodeError, TypeError):
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# 如果不是有效的JSON，可能是单个标签ID
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tag_id = str(feedback.feedback_tags)
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if tag_id in tag_counts:
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tag_counts[tag_id] += 1
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else:
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tag_counts[tag_id] = 1
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# 获取排名前5的正面和负面标签
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positive_tags = FeedbackTag.objects.filter(tag_type='positive')
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negative_tags = FeedbackTag.objects.filter(tag_type='negative')
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top_positive = []
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for tag in positive_tags:
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tag_id = str(tag.id)
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if tag_id in tag_counts:
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top_positive.append({
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'tag_name': tag.tag_name,
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'count': tag_counts[tag_id]
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})
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top_negative = []
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for tag in negative_tags:
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tag_id = str(tag.id)
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if tag_id in tag_counts:
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top_negative.append({
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'tag_name': tag.tag_name,
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'count': tag_counts[tag_id]
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})
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# 按使用次数排序
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top_positive.sort(key=lambda x: x['count'], reverse=True)
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top_negative.sort(key=lambda x: x['count'], reverse=True)
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# 取前5
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return {
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'top_positive': top_positive[:5],
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'top_negative': top_negative[:5]
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}
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def get_daily_feedback_trend(self, days=30):
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"""获取每日反馈趋势"""
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# 计算开始日期
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start_date = timezone.now().date() - timedelta(days=days)
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# 基本反馈按日期分组
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basic_daily = Feedback.objects.filter(timestamp__date__gte=start_date) \
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.values('timestamp__date') \
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.annotate(
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date=F('timestamp__date'),
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total=Count('id'),
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positive=Sum(Case(When(feedback_value__gt=0, then=1), default=0)),
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negative=Sum(Case(When(feedback_value__lt=0, then=1), default=0))
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) \
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.order_by('date')
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# 详细反馈按日期分组
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detailed_daily = DetailedFeedback.objects.filter(created_at__date__gte=start_date) \
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.values('created_at__date') \
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.annotate(
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date=F('created_at__date'),
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total=Count('id'),
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positive=Count('id', filter=Q(feedback_type='positive')),
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negative=Count('id', filter=Q(feedback_type='negative'))
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) \
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.order_by('date')
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# 合并两种反馈数据
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daily_data = {}
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for item in basic_daily:
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date_str = item['date'].strftime('%Y-%m-%d')
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daily_data[date_str] = {
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'date': date_str,
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'total': item['total'],
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'positive': item['positive'],
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'negative': item['negative']
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}
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for item in detailed_daily:
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date_str = item['date'].strftime('%Y-%m-%d')
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if date_str in daily_data:
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daily_data[date_str]['total'] += item['total']
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daily_data[date_str]['positive'] += item['positive']
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daily_data[date_str]['negative'] += item['negative']
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else:
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daily_data[date_str] = {
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'date': date_str,
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'total': item['total'],
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'positive': item['positive'],
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'negative': item['negative']
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}
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# 计算正面反馈比例
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for date_str, data in daily_data.items():
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data['positive_rate'] = (data['positive'] / data['total'] * 100) if data['total'] > 0 else 0
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# 转换为列表并按日期排序
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result = list(daily_data.values())
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result.sort(key=lambda x: x['date'])
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return result
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def get_user_stats(self):
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"""获取用户统计信息"""
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# 总用户数
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total_users = User.objects.count()
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# 有反馈记录的用户数
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users_with_feedback = User.objects.filter(
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Q(feedback__isnull=False) | Q(detailed_feedback__isnull=False)
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).distinct().count()
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# 最近30天活跃的标注用户
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thirty_days_ago = timezone.now() - timedelta(days=30)
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active_users = User.objects.filter(
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Q(feedback__timestamp__gte=thirty_days_ago) |
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Q(detailed_feedback__created_at__gte=thirty_days_ago)
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).distinct().count()
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# 计算每个用户的标注量
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user_annotations = {}
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for feedback in Feedback.objects.all():
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user_id = str(feedback.user_id)
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if user_id in user_annotations:
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user_annotations[user_id] += 1
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else:
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user_annotations[user_id] = 1
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for feedback in DetailedFeedback.objects.all():
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user_id = str(feedback.user_id)
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if user_id in user_annotations:
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user_annotations[user_id] += 1
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else:
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user_annotations[user_id] = 1
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# 计算平均每用户标注量
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if user_annotations:
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avg_annotations = sum(user_annotations.values()) / len(user_annotations)
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else:
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avg_annotations = 0
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return {
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'total_users': total_users,
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'users_with_feedback': users_with_feedback,
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'active_users': active_users,
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'avg_annotations_per_user': avg_annotations
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}
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def export_data_to_json(self):
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"""导出数据到JSON文件"""
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data = {
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'conversations': [],
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'feedback_summary': self.get_feedback_stats(),
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'tag_stats': self.get_tag_stats(),
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'daily_trend': self.get_daily_feedback_trend(30),
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'export_time': timezone.now().isoformat()
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}
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# 导出对话和消息数据
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for conv in Conversation.objects.all().prefetch_related('messages'):
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conv_data = {
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'id': str(conv.id),
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'created_at': conv.created_at.isoformat(),
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'user_id': str(conv.user_id),
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'is_submitted': conv.is_submitted,
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'messages': []
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}
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for msg in conv.messages.all().order_by('timestamp'):
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msg_data = {
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'id': str(msg.id),
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'role': msg.role,
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'content': msg.content,
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'timestamp': msg.timestamp.isoformat(),
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'feedback': []
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}
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# 获取消息的反馈
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for fb in Feedback.objects.filter(message_id=msg.id):
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msg_data['feedback'].append({
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'id': str(fb.id),
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'type': 'basic',
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'value': fb.feedback_value,
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'user_id': str(fb.user_id),
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'timestamp': fb.timestamp.isoformat()
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})
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# 获取详细反馈
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for dfb in DetailedFeedback.objects.filter(message_id=msg.id):
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try:
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tags = json.loads(dfb.feedback_tags) if dfb.feedback_tags else []
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except (json.JSONDecodeError, TypeError):
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tags = [dfb.feedback_tags] if dfb.feedback_tags else []
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msg_data['feedback'].append({
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'id': str(dfb.id),
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'type': 'detailed',
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'feedback_type': dfb.feedback_type,
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'tags': tags,
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'custom_tags': dfb.custom_tags,
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'custom_content': dfb.custom_content,
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'is_inline': dfb.is_inline,
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'user_id': str(dfb.user_id),
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'timestamp': dfb.created_at.isoformat()
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})
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conv_data['messages'].append(msg_data)
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data['conversations'].append(conv_data)
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# 保存到文件
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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filename = f'rlhf_data_export_{timestamp}.json'
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with open(filename, 'w', encoding='utf-8') as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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return filename |