#coding:utf8 import re import pymysql import pandas as pd import numpy as np import networkx as nx import traceback import json from jsonpath_ng import jsonpath, parse def get_id(raw_data,taskid): # taskid = raw_data["metadata"]["admin"]["taskId"] all_result = raw_data['data'] param_split = taskid.split(":") datasourcestr = all_result[param_split[0]] datasource = json.loads(datasourcestr) # 创建 JsonPath 表达式对象 expr = parse(param_split[1]) # 使用表达式来选择 JSON 元素 match = [match.value for match in expr.find(datasource)] val = match[0] return val def mysqlData(raw_data,logging,dataTag,dbConfig): result='' taskid=raw_data["input"]["taskId"] postid=raw_data["input"]["postId"] taskId = get_id(raw_data,taskid) postId = get_id(raw_data,postid) if dataTag=='1': table="tw_account" else: table="tw_deep" try: db = pymysql.connect(host=dbConfig['host'], user=dbConfig['username'], passwd=dbConfig['password'], db=dbConfig['db'], port=int(dbConfig['port']), charset='utf8',cursorclass=pymysql.cursors.DictCursor, connect_timeout=30) db.ping(reconnect=True) cursor = db.cursor() sql="SELECT * FROM {} WHERE taskId={} and ssId={}".format(table,taskId,postId) cursor.execute(sql) result = cursor.fetchall() db.commit() cursor.close() db.close() except: logging.info("专题关系数据查询失败！") logging.info(traceback.format_exc()) return result def get_replyData(data): reply=pd.DataFrame(data) reply = reply.drop_duplicates().reset_index(drop=True) # 去重 reply=reply[['ReviewerAccountId', 'PostAccountId']] # reply.columns = ['ReviewerAccountId', 'ReviewerAccountName', 'PostAccountId', 'PostAccountName', # 'ShareCount', 'LikeCount', 'CommentCount', 'CommentTime'] reply = reply[['ReviewerAccountId', 'PostAccountId']] reply['ReviewerAccountId'] = reply['ReviewerAccountId'].astype(str) reply['PostAccountId'] = reply['PostAccountId'].astype(str) reply = reply.groupby(['ReviewerAccountId', 'PostAccountId']).size().reset_index() # user_net_df = user_net(reply) ##SNA数据清洗 edgeweightset = reply.fillna(0) edgeweightset.columns = ['source', 'target', 'count'] edgeweightset_l = [[] for _ in range(edgeweightset.shape[0])] for i in range(len(edgeweightset_l)): for j in range(edgeweightset.shape[1]): edgeweightset_l[i].append(edgeweightset.iloc[i, j]) g = nx.DiGraph() g.add_weighted_edges_from(edgeweightset_l) degree = [g.degree(), g.in_degree(), g.out_degree()] centrality = [nx.degree_centrality(g), # 计算图 g 中每个节点的度中心性。度中心性是指节点的度（与其他节点相连的边的数量）与图中节点总数的比值。 nx.closeness_centrality(g), # 计算图 g 中每个节点的接近中心性。接近中心性是指节点到其他节点的平均最短路径长度的倒数。 nx.pagerank(g), # 计算图 g 中每个节点的 PageRank 值。PageRank 是一种用于评估网页重要性的算法，也可以应用于其他网络中的节点重要性评估。 nx.clustering(g)] # 计算图 g 中每个节点的聚集系数。聚集系数是指节点的邻居之间存在连接的概率。 #把主贴相关信息拿出来 tmp=edgeweightset["target"].values node_list = [] nodes = g.nodes() # 提取网络中节点列表 for node in nodes: if node not in tmp: continue node_list.append([node, degree[0][node], degree[1][node], degree[2][node], centrality[0][node], centrality[1][node], centrality[2][node], centrality[3][node]]) node_list = pd.DataFrame(node_list) node_list.columns = ['Id', 'degree', 'in_degree', 'out_degree', 'degree_centrality', 'closeness_centrality', 'pagerank', 'clustering'] node_list['user_flag_infl'] = 0 node_list['user_flag_act'] = 0 node_list.user_flag_infl[node_list['out_degree'] > np.percentile(node_list['out_degree'], 95)] = 1 node_list.user_flag_act[(node_list['in_degree'] > np.percentile(node_list['in_degree'], 90)) & (node_list['closeness_centrality'] > np.percentile(node_list['closeness_centrality'], 50))] = 1 node_dic=node_list.set_index('Id')[['degree', 'in_degree','out_degree','degree_centrality','closeness_centrality','pagerank','clustering']].T.to_dict() return node_dic def get_content(inputdata,logging): """ 重新组装参数 :param inputdata:原json数据 :return: 组装的prompt及其他参数 """ res={} admin=inputdata["metadata"]["admin"] data=inputdata["data"] prompt=admin["prompt"] if_user=re.findall("{{(.*)}}",prompt) if_data=re.findall("@@(.*)@@",prompt) if if_user != []: user_data=inputdata["metadata"]["user"] if if_user[0] in user_data.keys(): tmp=user_data[if_user[0]] prompt=re.sub("{{(.*)}}",tmp,prompt) if if_data!=[] and if_data[0] in data.keys(): tmp1=data[if_data[0]] prompt=re.sub("@@(.*)@@",tmp1,prompt) res["prompt"]=prompt res["authorization"]=admin["authorization"] res["model"]=admin["model"] res["temperature"]=admin["temperature"] res["authorization"]=admin["authorization"] res["top_p"]=admin["top_p"] res["n"]=admin["n"] return res if __name__=="__main__": from log_util.set_logger import set_logger logging = set_logger('test.log') inputdata={ "metadata":{ "output":{ "output_type":"table", "label_col":[ "假新闻识别" ] }, "input":{ "input_type":"text", "label":[ ] }, "address":"http://172.24.12.127:9030/fakeNewIdentification/", "admin":{ "taskId":"1_twitter采集:$.taskId", "Host":"172.24.12.126", "User":"root", "Password":"baifendian123", "Database":"analyze", "Port":3306, "accountId":"1_twitter采集:$.authorId", "postId":"1_twitter采集:$.postId" }, 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"last_edit":1696757732000, "next_app_id":[ { "start_id":284, "edge_id":171, "end_id":285 } ], "transfer_id":10, "version":1, "blueprint_id":9, "scenes_id":9, "scenario":{ "dataloss":1, "autoCommitTriggerLast":1, "maxErrors":3, "autoCommit":1, "freshVariables":1 }, "wait_condition":[ ], "scheduling":{ "interval":-1, "type":"single" }, "name":"假新闻识别", "businessKey":"5f0297539eec6000", "id":284, "position":[ 100, 200 ], "describe":"假新闻识别" } dataTag="1" a=mysqlData(inputdata,logging,dataTag) print(a)