knowledge/community/community_report.py

"""
社区报告生成模块

本模块用于从dump的图谱数据生成社区算法报告

主要功能:
1. 生成社区分析报告
2. 计算社区内部连接密度
3. 生成可视化分析报告
"""
import sys,os
current_path = os.getcwd()
sys.path.append(current_path)

import networkx as nx
import leidenalg
import igraph as ig
#import matplotlib.pyplot as plt
import json
from datetime import datetime
from collections import Counter

#社区报告的分辨率，数字越大，社区数量越少，数字越小，社区数量越多
#RESOLUTION = 0.07
#社区报告中是否包括节点的属性列表
REPORT_INCLUDE_DETAILS = False
# #图谱数据的缓存路径，数据从dump_graph_data.py生成
# CACHED_DATA_PATH = f"{current_path}\\web\\cached_data"
# #最终社区报告的输出路径
REPORT_PATH = f"{current_path}\\web\\cached_data\\report"
DENSITY = 0.52
# def load_entity_data():
#     print("load entity data")
#     with open(f"{CACHED_DATA_PATH}\\entities_med.json", "r", encoding="utf-8") as f:
#         entities = json.load(f)
#         return entities

# def load_relation_data(g):
#     for i in range(30):
#         if os.path.exists(f"{CACHED_DATA_PATH}\\relationship_med_{i}.json"):            
#             print("load entity data", f"{CACHED_DATA_PATH}\\relationship_med_{i}.json")
#             with open(f"{CACHED_DATA_PATH}\\relationship_med_{i}.json", "r", encoding="utf-8") as f:
#                 relations = json.load(f)
#                 for item in relations:                    
#                     g.add_edge(item[0], item[1], weight=1, **item[2])
        
            
        

# def generate_enterprise_network():

#     G = nx.Graph()
#     ent_data = load_entity_data()
#     print("load entities completed")
#     for data in ent_data:          
#         G.add_node(data[0], **data[1])
#     print("load entities into graph completed")
#     rel_data = load_relation_data(G)    
#     print("load relation completed")

#     return G

# def detect_communities(G):
#     """使用Leiden算法进行社区检测"""
#     # 转换networkx图到igraph格式
    
#     print("convert to igraph")
#     ig_graph = ig.Graph.from_networkx(G)
    
#     # 执行Leiden算法
#     partition = leidenalg.find_partition(
#         ig_graph, 
#         leidenalg.CPMVertexPartition,
#         resolution_parameter=RESOLUTION,
#         n_iterations=2
#     )
    
#     # 将社区标签添加到原始图
#     for i, node in enumerate(G.nodes()):
#         G.nodes[node]['community'] = partition.membership[i]
    
#     print("convert to igraph finished")
#     return G, partition

def generate_report(G, partition):
    """
    生成结构化分析报告
    
    参数:
        G: NetworkX图对象，包含节点和边的信息
        partition: Leiden算法返回的社区划分结果
    
    返回:
        str: 生成的分析报告内容
    """
    report = []
    # 报告头信息
    report.append(f"# 疾病图谱关系社区分析报告\n")
    report.append(f"**生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
    report.append(f"**检测算法**: Leiden Algorithm\n")
    report.append(f"**算法参数**:\n")
    report.append(f"- 分辨率参数: {partition.resolution_parameter:.3f}\n")
    # report.append(f"- 迭代次数: {partition.n_iterations}\n")
    report.append(f"**社区数量**: {len(set(partition.membership))}\n")
    report.append(f"**模块度(Q)**: {partition.quality():.4f}\n")
    print("generate_report header finished")

    report.append("\n## 社区结构分析\n")
    print("generate_report community structure started")
    communities = {}
    for node in G.nodes(data=True):
        comm = node[1]['community']
        if comm not in communities:
            communities[comm] = []
        if 'type' not in node[1]:
            node[1]['type'] = '未知'
        if 'description' not in node[1]:
            node[1]['description'] = '未见描述'
        
        communities[comm].append({
            'name': node[0],
            **node[1]
        })
       


    print("generate_report community structure finished")
    for comm_id, members in communities.items():
        print("community ", comm_id, "size: ", len(members))
        com_report = []
        com_report.append(f"### 第{comm_id+1}号社区报告 ")
        #com_report.append(f"**社区规模**: {len(members)} 个节点\n")

        # 行业类型分布
        type_dist = Counter([m['type'] for m in members])
        com_report.append(f"**类型分布**:")
        for industry, count in type_dist.most_common():
            com_report.append(f"- {industry}: {count} 个 ({count/len(members):.0%})")


        com_report.append("\n**成员节点**:")
        member_names = ''
        member_count = 0
        for member in members:
            if member_count < 8:
                member_names += member['name'] + '_'
                member_count += 1
            com_report.append(f"- {member['name']} ({member['type']})")
            if REPORT_INCLUDE_DETAILS == False:
                continue
            for k in member.keys():
                if k not in ['name', 'type', 'description', 'community']:
                    value = member[k]
                    com_report.append(f"\t- {value}")

        com_report.append("\n**成员节点关系**:\n")
        for member in members:
            entities, relations = graph_helper.neighbor_search(member['name'], 1)
            com_report.append(f"- {member['name']} ({member['type']})")
            com_report.append(f"\t- 相关节点")
            for entity in entities:
                com_report.append(f"\t\t- {entity['id']} ({entity['type']})")
            com_report.append(f"\t- 相关关系")
            for relation in relations:
                com_report.append(f"\t\t- {relation['src_name']}-({relation['type']})->{relation['dest_name']}")
            
                    
        # 计算社区内部连接密度
        subgraph = G.subgraph([m['name'] for m in members])
        density = nx.density(subgraph)
        com_report.append(f"\n**内部连接密度**: {density:.2f}\n")
        if density < DENSITY:
            com_report.append("**社区内部连接相对稀疏**\n")
        else:
            with open(f"{REPORT_PATH}\社区_{member_names}{comm_id}.md", "w", encoding="utf-8") as f:
                f.write("\n".join(com_report))
        print(f"社区 {comm_id+1} 报告文件大小：{len(''.join(com_report).encode('utf-8'))} 字节")  # 添加文件生成验证
    
    # 可视化图表
    report.append("\n## 可视化分析\n")
    
    return "\n".join(report)


if __name__ == "__main__":
    try:
        from graph_helper import GraphHelper
        graph_helper = GraphHelper()
        G = graph_helper.graph
        print("graph loaded")
        # 生成企业关系网络
        
        
        # 执行社区检测
        G, partition = graph_helper.detect_communities()
        
        # 生成分析报告
        report = generate_report(G, partition)
        with open('community_report.md', 'w', encoding='utf-8') as f:
            f.write(report)
            print(f"报告文件大小：{len(report.encode('utf-8'))} 字节")  # 添加文件生成验证
                        
            print("社区分析报告已生成：community_report.md")
            
       
    except Exception as e:
        
        print(f"运行时错误：{str(e)}")
        raise e