425 lines
16 KiB
Python
425 lines
16 KiB
Python
"""
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Simulation Analytics Service
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直接读取 actions.jsonl 提供模拟行为统计数据和真实 Agent 发言内容。
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用于报告生成时获取具体数字和可引用的原文。
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数据来源：SimulationRunner.get_all_actions() → actions.jsonl
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"""
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import json
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import random
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from typing import Dict, Any, List, Optional
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from collections import Counter, defaultdict
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from ..utils.logger import get_logger
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from .simulation_runner import SimulationRunner
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logger = get_logger('foresight.simulation_analytics')
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# 简单的情感关键词列表（避免 LLM 依赖）
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POSITIVE_KEYWORDS = {
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'好', '棒', '赞', '喜欢', '支持', '期待', '感谢', '满意', '优秀', '不错',
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'开心', '高兴', '推荐', '值得', '信任', '认同', '赞同', ' helpful', 'agree',
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'great', 'good', 'love', 'excellent', 'amazing', 'awesome', 'nice', 'best',
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'like', 'support', 'happy', 'thank', 'recommend', 'worth',
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}
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NEGATIVE_KEYWORDS = {
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'差', '烂', '坑', '骗', '垃圾', '失望', '不满', '退款', '投诉', '问题',
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'糟糕', '难过', '愤怒', '不推荐', '浪费', '后悔', '差评', '反感', 'bad',
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'terrible', 'worst', 'hate', 'awful', 'disappoint', 'waste', 'refund',
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'complaint', 'angry', 'frustrat', 'annoy', 'poor', 'fail', 'scam',
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}
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# 有文本内容的 action types（可提取引用）
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CONTENT_ACTION_TYPES = {'CREATE_POST', 'CREATE_COMMENT', 'QUOTE_POST'}
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# 互动类型的 action types
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ENGAGEMENT_ACTION_TYPES = {'LIKE_POST', 'DISLIKE_POST', 'REPOST', 'LIKE_COMMENT', 'DISLIKE_COMMENT'}
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class SimulationAnalyticsService:
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"""
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模拟数据分析服务
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直接读取 actions.jsonl 提供统计数据，不经过 Graphiti 知识图谱。
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"""
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def get_overview_stats(self, simulation_id: str) -> Dict[str, Any]:
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"""
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模拟全局统计概览
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Returns:
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{
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total_agents, total_actions, total_rounds,
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twitter_posts, reddit_posts, total_posts,
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total_engagement, avg_activity_per_agent,
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action_type_counts: {action_type: count},
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platform_breakdown: {platform: count},
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}
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"""
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actions = SimulationRunner.get_all_actions(simulation_id)
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if not actions:
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return self._empty_overview()
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agent_ids = set()
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rounds = set()
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action_type_counts = Counter()
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platform_counts = Counter()
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posts = 0
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engagement = 0
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for action in actions:
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agent_ids.add(action.agent_id)
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rounds.add(action.round_num)
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action_type_counts[action.action_type] += 1
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platform_counts[action.platform] += 1
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if action.action_type == 'CREATE_POST':
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posts += 1
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elif action.action_type in ENGAGEMENT_ACTION_TYPES:
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engagement += 1
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total_agents = len(agent_ids)
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total_actions = len(actions)
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return {
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'total_agents': total_agents,
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'total_actions': total_actions,
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'total_rounds': max(rounds) + 1 if rounds else 0,
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'total_posts': posts,
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'twitter_posts': sum(1 for a in actions if a.action_type == 'CREATE_POST' and a.platform == 'twitter'),
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'reddit_posts': sum(1 for a in actions if a.action_type == 'CREATE_POST' and a.platform == 'reddit'),
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'total_engagement': engagement,
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'avg_activity_per_agent': round(total_actions / max(total_agents, 1), 1),
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'action_type_counts': dict(action_type_counts),
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'platform_breakdown': dict(platform_counts),
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}
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def get_top_posts(
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self,
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simulation_id: str,
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n: int = 10,
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) -> List[Dict[str, Any]]:
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"""
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获取最活跃的帖子（按该帖获得的互动数排序）
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Returns:
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[{agent_name, content, platform, round_num, engagement_count}]
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"""
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actions = SimulationRunner.get_all_actions(simulation_id)
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if not actions:
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return []
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# 1. 收集所有原创帖
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posts = {}
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for action in actions:
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if action.action_type == 'CREATE_POST':
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content = action.action_args.get('content', '')
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key = (action.agent_name, content[:100], action.platform)
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posts[key] = {
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'agent_name': action.agent_name,
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'content': content,
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'platform': action.platform,
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'round_num': action.round_num,
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'engagement_count': 0,
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}
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# 2. 统计每个帖的互动（通过被引用的内容匹配）
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post_contents = {k: v['content'][:80] for k, v in posts.items()}
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for action in actions:
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if action.action_type in ('LIKE_POST', 'REPOST', 'CREATE_COMMENT', 'QUOTE_POST'):
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referenced = (
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action.action_args.get('post_content', '')
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or action.action_args.get('quoted_content', '')
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)
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if not referenced:
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continue
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ref_prefix = referenced[:80]
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for key, pc in post_contents.items():
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if ref_prefix and pc and (ref_prefix in pc or pc in ref_prefix):
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posts[key]['engagement_count'] += 1
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break
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# 3. 按互动量排序
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sorted_posts = sorted(posts.values(), key=lambda x: x['engagement_count'], reverse=True)
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return sorted_posts[:n]
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def get_agent_quotes(
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self,
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simulation_id: str,
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n_positive: int = 5,
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n_negative: int = 5,
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n_random: int = 5,
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) -> Dict[str, List[Dict[str, Any]]]:
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"""
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提取真实 Agent 发言摘录，按情感分类
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Returns:
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{
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positive: [{agent_name, quote, platform, action_type, round_num}],
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negative: [...],
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random: [...],
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}
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"""
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actions = SimulationRunner.get_all_actions(simulation_id)
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if not actions:
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return {'positive': [], 'negative': [], 'random': []}
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# 提取有文本内容的 action
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content_actions = []
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for action in actions:
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if action.action_type not in CONTENT_ACTION_TYPES:
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continue
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content = action.action_args.get('content', '')
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if not content or len(content.strip()) < 10:
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continue
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content_actions.append({
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'agent_name': action.agent_name,
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'quote': content.strip(),
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'platform': action.platform,
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'action_type': action.action_type,
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'round_num': action.round_num,
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})
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if not content_actions:
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return {'positive': [], 'negative': [], 'random': []}
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# 按情感分类
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positive = []
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negative = []
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neutral = []
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for item in content_actions:
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text_lower = item['quote'].lower()
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pos_score = sum(1 for kw in POSITIVE_KEYWORDS if kw in text_lower)
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neg_score = sum(1 for kw in NEGATIVE_KEYWORDS if kw in text_lower)
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if pos_score > neg_score:
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positive.append(item)
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elif neg_score > pos_score:
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negative.append(item)
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else:
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neutral.append(item)
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# 采样
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result = {
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'positive': self._deduplicate_and_sample(positive, n_positive),
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'negative': self._deduplicate_and_sample(negative, n_negative),
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'random': self._deduplicate_and_sample(
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neutral if neutral else content_actions, n_random
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),
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}
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return result
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def get_action_distribution(self, simulation_id: str) -> Dict[str, Any]:
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"""
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按类型/平台/轮次的动作分布
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Returns:
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{
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by_type: {action_type: count},
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by_platform: {platform: {action_type: count}},
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by_round: [{round_num, twitter, reddit, total}],
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}
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"""
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actions = SimulationRunner.get_all_actions(simulation_id)
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if not actions:
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return {'by_type': {}, 'by_platform': {}, 'by_round': []}
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by_type = Counter()
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by_platform: Dict[str, Counter] = defaultdict(Counter)
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round_data: Dict[int, Dict] = {}
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for action in actions:
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by_type[action.action_type] += 1
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by_platform[action.platform][action.action_type] += 1
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r = action.round_num
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if r not in round_data:
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round_data[r] = {'round_num': r, 'twitter': 0, 'reddit': 0, 'total': 0}
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round_data[r]['total'] += 1
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if action.platform == 'twitter':
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round_data[r]['twitter'] += 1
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else:
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round_data[r]['reddit'] += 1
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by_round = [round_data[k] for k in sorted(round_data.keys())]
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return {
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'by_type': dict(by_type),
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'by_platform': {p: dict(c) for p, c in by_platform.items()},
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'by_round': by_round,
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}
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def get_engagement_metrics(self, simulation_id: str) -> Dict[str, Any]:
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"""
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参与度指标
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Returns:
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{
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total_engagement, avg_engagement_per_post,
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top_agents: [{agent_name, total_actions, posts, engagement}],
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engagement_by_type: {type: count},
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}
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"""
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actions = SimulationRunner.get_all_actions(simulation_id)
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if not actions:
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return {'total_engagement': 0, 'avg_engagement_per_post': 0, 'top_agents': [], 'engagement_by_type': {}}
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total_posts = sum(1 for a in actions if a.action_type == 'CREATE_POST')
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engagement_by_type = Counter(a.action_type for a in actions if a.action_type in ENGAGEMENT_ACTION_TYPES)
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total_engagement = sum(engagement_by_type.values())
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# Top agents
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agent_data: Dict[str, Dict] = {}
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for action in actions:
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name = action.agent_name
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if name not in agent_data:
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agent_data[name] = {'agent_name': name, 'total_actions': 0, 'posts': 0, 'engagement': 0}
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agent_data[name]['total_actions'] += 1
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if action.action_type == 'CREATE_POST':
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agent_data[name]['posts'] += 1
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elif action.action_type in ENGAGEMENT_ACTION_TYPES:
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agent_data[name]['engagement'] += 1
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top_agents = sorted(agent_data.values(), key=lambda x: x['total_actions'], reverse=True)[:10]
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return {
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'total_engagement': total_engagement,
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'avg_engagement_per_post': round(total_engagement / max(total_posts, 1), 1),
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'top_agents': top_agents,
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'engagement_by_type': dict(engagement_by_type),
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}
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def get_sentiment_breakdown(self, simulation_id: str) -> Dict[str, Any]:
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"""
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情感分布（基于关键词的分类）
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Returns:
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{positive_count, negative_count, neutral_count, positive_ratio, negative_ratio}
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"""
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actions = SimulationRunner.get_all_actions(simulation_id)
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if not actions:
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return {'positive_count': 0, 'negative_count': 0, 'neutral_count': 0, 'positive_ratio': 0, 'negative_ratio': 0}
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positive_count = 0
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negative_count = 0
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neutral_count = 0
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for action in actions:
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if action.action_type not in CONTENT_ACTION_TYPES:
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continue
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content = action.action_args.get('content', '')
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if not content:
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continue
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text_lower = content.lower()
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pos_score = sum(1 for kw in POSITIVE_KEYWORDS if kw in text_lower)
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neg_score = sum(1 for kw in NEGATIVE_KEYWORDS if kw in text_lower)
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if pos_score > neg_score:
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positive_count += 1
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elif neg_score > pos_score:
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negative_count += 1
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else:
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neutral_count += 1
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total = positive_count + negative_count + neutral_count
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return {
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'positive_count': positive_count,
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'negative_count': negative_count,
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'neutral_count': neutral_count,
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'positive_ratio': round(positive_count / max(total, 1) * 100, 1),
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'negative_ratio': round(negative_count / max(total, 1) * 100, 1),
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'neutral_ratio': round(neutral_count / max(total, 1) * 100, 1),
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}
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def get_infographic_data(self, simulation_id: str) -> Dict[str, Any]:
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"""
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聚合所有数据，生成前端信息图所需的完整 JSON
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Returns:
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{key_metrics, action_distribution, sentiment_breakdown, top_posts, top_agents, timeline}
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"""
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overview = self.get_overview_stats(simulation_id)
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distribution = self.get_action_distribution(simulation_id)
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sentiment = self.get_sentiment_breakdown(simulation_id)
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top_posts = self.get_top_posts(simulation_id, n=5)
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engagement = self.get_engagement_metrics(simulation_id)
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return {
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'key_metrics': {
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'total_agents': overview['total_agents'],
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'total_posts': overview['total_posts'],
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'total_engagement': overview['total_engagement'],
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'avg_activity': overview['avg_activity_per_agent'],
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'total_rounds': overview['total_rounds'],
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'total_actions': overview['total_actions'],
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},
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'action_distribution': distribution,
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'sentiment_breakdown': sentiment,
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'top_posts': top_posts,
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'top_agents': engagement.get('top_agents', [])[:5],
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'timeline': distribution.get('by_round', []),
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}
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def get_analytics(self, simulation_id: str, query_type: str, n: int = 10) -> Dict[str, Any]:
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"""
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统一入口，供 ReportAgent 工具调用
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Args:
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simulation_id: 模拟ID
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query_type: overview_stats | top_posts | agent_quotes | action_distribution | engagement_metrics | sentiment_breakdown | infographic_data
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n: 返回数量（仅对 top_posts 和 agent_quotes 有效）
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"""
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try:
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if query_type == 'overview_stats':
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return {'success': True, 'data': self.get_overview_stats(simulation_id)}
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elif query_type == 'top_posts':
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return {'success': True, 'data': self.get_top_posts(simulation_id, n=n)}
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elif query_type == 'agent_quotes':
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result = self.get_agent_quotes(simulation_id, n_positive=n, n_negative=n, n_random=n)
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return {'success': True, 'data': result}
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elif query_type == 'action_distribution':
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return {'success': True, 'data': self.get_action_distribution(simulation_id)}
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elif query_type == 'engagement_metrics':
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return {'success': True, 'data': self.get_engagement_metrics(simulation_id)}
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elif query_type == 'sentiment_breakdown':
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return {'success': True, 'data': self.get_sentiment_breakdown(simulation_id)}
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elif query_type == 'infographic_data':
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return {'success': True, 'data': self.get_infographic_data(simulation_id)}
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else:
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return {'success': False, 'error': f'Unknown query_type: {query_type}'}
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except Exception as e:
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logger.error(f'Analytics query failed: {e}')
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return {'success': False, 'error': str(e)}
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# ── helpers ──
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def _empty_overview(self) -> Dict[str, Any]:
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return {
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'total_agents': 0, 'total_actions': 0, 'total_rounds': 0,
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'total_posts': 0, 'twitter_posts': 0, 'reddit_posts': 0,
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'total_engagement': 0, 'avg_activity_per_agent': 0,
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'action_type_counts': {}, 'platform_breakdown': {},
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}
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def _deduplicate_and_sample(
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self,
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items: List[Dict[str, Any]],
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n: int,
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) -> List[Dict[str, Any]]:
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"""去重（按 quote 前80字符）并采样"""
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seen = set()
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unique = []
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for item in items:
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key = item['quote'][:80]
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if key not in seen:
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seen.add(key)
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unique.append(item)
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if len(unique) <= n:
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return unique
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return random.sample(unique, n)
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