425 lines
16 KiB
Python
425 lines
16 KiB
Python
"""
|
||
Simulation Analytics Service
|
||
直接读取 actions.jsonl 提供模拟行为统计数据和真实 Agent 发言内容。
|
||
用于报告生成时获取具体数字和可引用的原文。
|
||
|
||
数据来源：SimulationRunner.get_all_actions() → actions.jsonl
|
||
"""
|
||
|
||
import json
|
||
import random
|
||
from typing import Dict, Any, List, Optional
|
||
from collections import Counter, defaultdict
|
||
|
||
from ..utils.logger import get_logger
|
||
from .simulation_runner import SimulationRunner
|
||
|
||
logger = get_logger('foresight.simulation_analytics')
|
||
|
||
# 简单的情感关键词列表（避免 LLM 依赖）
|
||
POSITIVE_KEYWORDS = {
|
||
'好', '棒', '赞', '喜欢', '支持', '期待', '感谢', '满意', '优秀', '不错',
|
||
'开心', '高兴', '推荐', '值得', '信任', '认同', '赞同', ' helpful', 'agree',
|
||
'great', 'good', 'love', 'excellent', 'amazing', 'awesome', 'nice', 'best',
|
||
'like', 'support', 'happy', 'thank', 'recommend', 'worth',
|
||
}
|
||
|
||
NEGATIVE_KEYWORDS = {
|
||
'差', '烂', '坑', '骗', '垃圾', '失望', '不满', '退款', '投诉', '问题',
|
||
'糟糕', '难过', '愤怒', '不推荐', '浪费', '后悔', '差评', '反感', 'bad',
|
||
'terrible', 'worst', 'hate', 'awful', 'disappoint', 'waste', 'refund',
|
||
'complaint', 'angry', 'frustrat', 'annoy', 'poor', 'fail', 'scam',
|
||
}
|
||
|
||
# 有文本内容的 action types（可提取引用）
|
||
CONTENT_ACTION_TYPES = {'CREATE_POST', 'CREATE_COMMENT', 'QUOTE_POST'}
|
||
|
||
# 互动类型的 action types
|
||
ENGAGEMENT_ACTION_TYPES = {'LIKE_POST', 'DISLIKE_POST', 'REPOST', 'LIKE_COMMENT', 'DISLIKE_COMMENT'}
|
||
|
||
|
||
class SimulationAnalyticsService:
|
||
"""
|
||
模拟数据分析服务
|
||
|
||
直接读取 actions.jsonl 提供统计数据，不经过 Graphiti 知识图谱。
|
||
"""
|
||
|
||
def get_overview_stats(self, simulation_id: str) -> Dict[str, Any]:
|
||
"""
|
||
模拟全局统计概览
|
||
|
||
Returns:
|
||
{
|
||
total_agents, total_actions, total_rounds,
|
||
twitter_posts, reddit_posts, total_posts,
|
||
total_engagement, avg_activity_per_agent,
|
||
action_type_counts: {action_type: count},
|
||
platform_breakdown: {platform: count},
|
||
}
|
||
"""
|
||
actions = SimulationRunner.get_all_actions(simulation_id, limit=50000)
|
||
if not actions:
|
||
return self._empty_overview()
|
||
|
||
agent_ids = set()
|
||
rounds = set()
|
||
action_type_counts = Counter()
|
||
platform_counts = Counter()
|
||
posts = 0
|
||
engagement = 0
|
||
|
||
for action in actions:
|
||
agent_ids.add(action.agent_id)
|
||
rounds.add(action.round_num)
|
||
action_type_counts[action.action_type] += 1
|
||
platform_counts[action.platform] += 1
|
||
|
||
if action.action_type == 'CREATE_POST':
|
||
posts += 1
|
||
elif action.action_type in ENGAGEMENT_ACTION_TYPES:
|
||
engagement += 1
|
||
|
||
total_agents = len(agent_ids)
|
||
total_actions = len(actions)
|
||
|
||
return {
|
||
'total_agents': total_agents,
|
||
'total_actions': total_actions,
|
||
'total_rounds': max(rounds) + 1 if rounds else 0,
|
||
'total_posts': posts,
|
||
'twitter_posts': sum(1 for a in actions if a.action_type == 'CREATE_POST' and a.platform == 'twitter'),
|
||
'reddit_posts': sum(1 for a in actions if a.action_type == 'CREATE_POST' and a.platform == 'reddit'),
|
||
'total_engagement': engagement,
|
||
'avg_activity_per_agent': round(total_actions / max(total_agents, 1), 1),
|
||
'action_type_counts': dict(action_type_counts),
|
||
'platform_breakdown': dict(platform_counts),
|
||
}
|
||
|
||
def get_top_posts(
|
||
self,
|
||
simulation_id: str,
|
||
n: int = 10,
|
||
) -> List[Dict[str, Any]]:
|
||
"""
|
||
获取最活跃的帖子（按该帖获得的互动数排序）
|
||
|
||
Returns:
|
||
[{agent_name, content, platform, round_num, engagement_count}]
|
||
"""
|
||
actions = SimulationRunner.get_all_actions(simulation_id, limit=50000)
|
||
if not actions:
|
||
return []
|
||
|
||
# 1. 收集所有原创帖
|
||
posts = {}
|
||
for action in actions:
|
||
if action.action_type == 'CREATE_POST':
|
||
content = action.action_args.get('content', '')
|
||
key = (action.agent_name, content[:100], action.platform)
|
||
posts[key] = {
|
||
'agent_name': action.agent_name,
|
||
'content': content,
|
||
'platform': action.platform,
|
||
'round_num': action.round_num,
|
||
'engagement_count': 0,
|
||
}
|
||
|
||
# 2. 统计每个帖的互动（通过被引用的内容匹配）
|
||
post_contents = {k: v['content'][:80] for k, v in posts.items()}
|
||
for action in actions:
|
||
if action.action_type in ('LIKE_POST', 'REPOST', 'CREATE_COMMENT', 'QUOTE_POST'):
|
||
referenced = (
|
||
action.action_args.get('post_content', '')
|
||
or action.action_args.get('quoted_content', '')
|
||
)
|
||
if not referenced:
|
||
continue
|
||
ref_prefix = referenced[:80]
|
||
for key, pc in post_contents.items():
|
||
if ref_prefix and pc and (ref_prefix in pc or pc in ref_prefix):
|
||
posts[key]['engagement_count'] += 1
|
||
break
|
||
|
||
# 3. 按互动量排序
|
||
sorted_posts = sorted(posts.values(), key=lambda x: x['engagement_count'], reverse=True)
|
||
return sorted_posts[:n]
|
||
|
||
def get_agent_quotes(
|
||
self,
|
||
simulation_id: str,
|
||
n_positive: int = 5,
|
||
n_negative: int = 5,
|
||
n_random: int = 5,
|
||
) -> Dict[str, List[Dict[str, Any]]]:
|
||
"""
|
||
提取真实 Agent 发言摘录，按情感分类
|
||
|
||
Returns:
|
||
{
|
||
positive: [{agent_name, quote, platform, action_type, round_num}],
|
||
negative: [...],
|
||
random: [...],
|
||
}
|
||
"""
|
||
actions = SimulationRunner.get_all_actions(simulation_id, limit=50000)
|
||
if not actions:
|
||
return {'positive': [], 'negative': [], 'random': []}
|
||
|
||
# 提取有文本内容的 action
|
||
content_actions = []
|
||
for action in actions:
|
||
if action.action_type not in CONTENT_ACTION_TYPES:
|
||
continue
|
||
content = action.action_args.get('content', '')
|
||
if not content or len(content.strip()) < 10:
|
||
continue
|
||
content_actions.append({
|
||
'agent_name': action.agent_name,
|
||
'quote': content.strip(),
|
||
'platform': action.platform,
|
||
'action_type': action.action_type,
|
||
'round_num': action.round_num,
|
||
})
|
||
|
||
if not content_actions:
|
||
return {'positive': [], 'negative': [], 'random': []}
|
||
|
||
# 按情感分类
|
||
positive = []
|
||
negative = []
|
||
neutral = []
|
||
|
||
for item in content_actions:
|
||
text_lower = item['quote'].lower()
|
||
pos_score = sum(1 for kw in POSITIVE_KEYWORDS if kw in text_lower)
|
||
neg_score = sum(1 for kw in NEGATIVE_KEYWORDS if kw in text_lower)
|
||
|
||
if pos_score > neg_score:
|
||
positive.append(item)
|
||
elif neg_score > pos_score:
|
||
negative.append(item)
|
||
else:
|
||
neutral.append(item)
|
||
|
||
# 采样
|
||
result = {
|
||
'positive': self._deduplicate_and_sample(positive, n_positive),
|
||
'negative': self._deduplicate_and_sample(negative, n_negative),
|
||
'random': self._deduplicate_and_sample(
|
||
neutral if neutral else content_actions, n_random
|
||
),
|
||
}
|
||
return result
|
||
|
||
def get_action_distribution(self, simulation_id: str) -> Dict[str, Any]:
|
||
"""
|
||
按类型/平台/轮次的动作分布
|
||
|
||
Returns:
|
||
{
|
||
by_type: {action_type: count},
|
||
by_platform: {platform: {action_type: count}},
|
||
by_round: [{round_num, twitter, reddit, total}],
|
||
}
|
||
"""
|
||
actions = SimulationRunner.get_all_actions(simulation_id, limit=50000)
|
||
if not actions:
|
||
return {'by_type': {}, 'by_platform': {}, 'by_round': []}
|
||
|
||
by_type = Counter()
|
||
by_platform: Dict[str, Counter] = defaultdict(Counter)
|
||
round_data: Dict[int, Dict] = {}
|
||
|
||
for action in actions:
|
||
by_type[action.action_type] += 1
|
||
by_platform[action.platform][action.action_type] += 1
|
||
|
||
r = action.round_num
|
||
if r not in round_data:
|
||
round_data[r] = {'round_num': r, 'twitter': 0, 'reddit': 0, 'total': 0}
|
||
round_data[r]['total'] += 1
|
||
if action.platform == 'twitter':
|
||
round_data[r]['twitter'] += 1
|
||
else:
|
||
round_data[r]['reddit'] += 1
|
||
|
||
by_round = [round_data[k] for k in sorted(round_data.keys())]
|
||
|
||
return {
|
||
'by_type': dict(by_type),
|
||
'by_platform': {p: dict(c) for p, c in by_platform.items()},
|
||
'by_round': by_round,
|
||
}
|
||
|
||
def get_engagement_metrics(self, simulation_id: str) -> Dict[str, Any]:
|
||
"""
|
||
参与度指标
|
||
|
||
Returns:
|
||
{
|
||
total_engagement, avg_engagement_per_post,
|
||
top_agents: [{agent_name, total_actions, posts, engagement}],
|
||
engagement_by_type: {type: count},
|
||
}
|
||
"""
|
||
actions = SimulationRunner.get_all_actions(simulation_id, limit=50000)
|
||
if not actions:
|
||
return {'total_engagement': 0, 'avg_engagement_per_post': 0, 'top_agents': [], 'engagement_by_type': {}}
|
||
|
||
total_posts = sum(1 for a in actions if a.action_type == 'CREATE_POST')
|
||
engagement_by_type = Counter(a.action_type for a in actions if a.action_type in ENGAGEMENT_ACTION_TYPES)
|
||
total_engagement = sum(engagement_by_type.values())
|
||
|
||
# Top agents
|
||
agent_data: Dict[str, Dict] = {}
|
||
for action in actions:
|
||
name = action.agent_name
|
||
if name not in agent_data:
|
||
agent_data[name] = {'agent_name': name, 'total_actions': 0, 'posts': 0, 'engagement': 0}
|
||
agent_data[name]['total_actions'] += 1
|
||
if action.action_type == 'CREATE_POST':
|
||
agent_data[name]['posts'] += 1
|
||
elif action.action_type in ENGAGEMENT_ACTION_TYPES:
|
||
agent_data[name]['engagement'] += 1
|
||
|
||
top_agents = sorted(agent_data.values(), key=lambda x: x['total_actions'], reverse=True)[:10]
|
||
|
||
return {
|
||
'total_engagement': total_engagement,
|
||
'avg_engagement_per_post': round(total_engagement / max(total_posts, 1), 1),
|
||
'top_agents': top_agents,
|
||
'engagement_by_type': dict(engagement_by_type),
|
||
}
|
||
|
||
def get_sentiment_breakdown(self, simulation_id: str) -> Dict[str, Any]:
|
||
"""
|
||
情感分布（基于关键词的分类）
|
||
|
||
Returns:
|
||
{positive_count, negative_count, neutral_count, positive_ratio, negative_ratio}
|
||
"""
|
||
actions = SimulationRunner.get_all_actions(simulation_id, limit=50000)
|
||
if not actions:
|
||
return {'positive_count': 0, 'negative_count': 0, 'neutral_count': 0, 'positive_ratio': 0, 'negative_ratio': 0}
|
||
|
||
positive_count = 0
|
||
negative_count = 0
|
||
neutral_count = 0
|
||
|
||
for action in actions:
|
||
if action.action_type not in CONTENT_ACTION_TYPES:
|
||
continue
|
||
content = action.action_args.get('content', '')
|
||
if not content:
|
||
continue
|
||
|
||
text_lower = content.lower()
|
||
pos_score = sum(1 for kw in POSITIVE_KEYWORDS if kw in text_lower)
|
||
neg_score = sum(1 for kw in NEGATIVE_KEYWORDS if kw in text_lower)
|
||
|
||
if pos_score > neg_score:
|
||
positive_count += 1
|
||
elif neg_score > pos_score:
|
||
negative_count += 1
|
||
else:
|
||
neutral_count += 1
|
||
|
||
total = positive_count + negative_count + neutral_count
|
||
return {
|
||
'positive_count': positive_count,
|
||
'negative_count': negative_count,
|
||
'neutral_count': neutral_count,
|
||
'positive_ratio': round(positive_count / max(total, 1) * 100, 1),
|
||
'negative_ratio': round(negative_count / max(total, 1) * 100, 1),
|
||
'neutral_ratio': round(neutral_count / max(total, 1) * 100, 1),
|
||
}
|
||
|
||
def get_infographic_data(self, simulation_id: str) -> Dict[str, Any]:
|
||
"""
|
||
聚合所有数据，生成前端信息图所需的完整 JSON
|
||
|
||
Returns:
|
||
{key_metrics, action_distribution, sentiment_breakdown, top_posts, top_agents, timeline}
|
||
"""
|
||
overview = self.get_overview_stats(simulation_id)
|
||
distribution = self.get_action_distribution(simulation_id)
|
||
sentiment = self.get_sentiment_breakdown(simulation_id)
|
||
top_posts = self.get_top_posts(simulation_id, n=5)
|
||
engagement = self.get_engagement_metrics(simulation_id)
|
||
|
||
return {
|
||
'key_metrics': {
|
||
'total_agents': overview['total_agents'],
|
||
'total_posts': overview['total_posts'],
|
||
'total_engagement': overview['total_engagement'],
|
||
'avg_activity': overview['avg_activity_per_agent'],
|
||
'total_rounds': overview['total_rounds'],
|
||
'total_actions': overview['total_actions'],
|
||
},
|
||
'action_distribution': distribution,
|
||
'sentiment_breakdown': sentiment,
|
||
'top_posts': top_posts,
|
||
'top_agents': engagement.get('top_agents', [])[:5],
|
||
'timeline': distribution.get('by_round', []),
|
||
}
|
||
|
||
def get_analytics(self, simulation_id: str, query_type: str, n: int = 10) -> Dict[str, Any]:
|
||
"""
|
||
统一入口，供 ReportAgent 工具调用
|
||
|
||
Args:
|
||
simulation_id: 模拟ID
|
||
query_type: overview_stats | top_posts | agent_quotes | action_distribution | engagement_metrics | sentiment_breakdown | infographic_data
|
||
n: 返回数量（仅对 top_posts 和 agent_quotes 有效）
|
||
"""
|
||
try:
|
||
if query_type == 'overview_stats':
|
||
return {'success': True, 'data': self.get_overview_stats(simulation_id)}
|
||
elif query_type == 'top_posts':
|
||
return {'success': True, 'data': self.get_top_posts(simulation_id, n=n)}
|
||
elif query_type == 'agent_quotes':
|
||
result = self.get_agent_quotes(simulation_id, n_positive=n, n_negative=n, n_random=n)
|
||
return {'success': True, 'data': result}
|
||
elif query_type == 'action_distribution':
|
||
return {'success': True, 'data': self.get_action_distribution(simulation_id)}
|
||
elif query_type == 'engagement_metrics':
|
||
return {'success': True, 'data': self.get_engagement_metrics(simulation_id)}
|
||
elif query_type == 'sentiment_breakdown':
|
||
return {'success': True, 'data': self.get_sentiment_breakdown(simulation_id)}
|
||
elif query_type == 'infographic_data':
|
||
return {'success': True, 'data': self.get_infographic_data(simulation_id)}
|
||
else:
|
||
return {'success': False, 'error': f'Unknown query_type: {query_type}'}
|
||
except Exception as e:
|
||
logger.error(f'Analytics query failed: {e}')
|
||
return {'success': False, 'error': str(e)}
|
||
|
||
# ── helpers ──
|
||
|
||
def _empty_overview(self) -> Dict[str, Any]:
|
||
return {
|
||
'total_agents': 0, 'total_actions': 0, 'total_rounds': 0,
|
||
'total_posts': 0, 'twitter_posts': 0, 'reddit_posts': 0,
|
||
'total_engagement': 0, 'avg_activity_per_agent': 0,
|
||
'action_type_counts': {}, 'platform_breakdown': {},
|
||
}
|
||
|
||
def _deduplicate_and_sample(
|
||
self,
|
||
items: List[Dict[str, Any]],
|
||
n: int,
|
||
) -> List[Dict[str, Any]]:
|
||
"""去重（按 quote 前80字符）并采样"""
|
||
seen = set()
|
||
unique = []
|
||
for item in items:
|
||
key = item['quote'][:80]
|
||
if key not in seen:
|
||
seen.add(key)
|
||
unique.append(item)
|
||
|
||
if len(unique) <= n:
|
||
return unique
|
||
return random.sample(unique, n)
|