上海企业网站制作电话,天津市建设工程评标专家网站,三维家官网在线设计,网站建设的 文献综述文章目录 创建旅游景点图数据库Neo4J技术验证写在前面基础数据建库python3源代码KG效果KG入库效率优化方案PostGreSQL建库 创建旅游景点图数据库Neo4J技术验证
写在前面
本章主要实践内容#xff1a; #xff08;1#xff09;neo4j知识图谱库建库。使用导航poi中的公园、景… 文章目录 创建旅游景点图数据库Neo4J技术验证写在前面基础数据建库python3源代码KG效果KG入库效率优化方案PostGreSQL建库 创建旅游景点图数据库Neo4J技术验证
写在前面
本章主要实践内容 1neo4j知识图谱库建库。使用导航poi中的公园、景点两类csv直接建库。 2pg建库。携程poi入库tripdata的poibaseinfo表之后导航poi中的公园、景点也导入该表。
基础数据建库
python3源代码
以下实现了csv数据初始导入KG。如果是增量更新代码需要调整。 另外星级、旅游时间 是随机生成不具备任何真实性。
import csv
from py2neo import *
import random
import geohashdef importCSV2NeoKG( graph,csvPath,csvType ):#单纯的查询方法node_Match NodeMatcher(graph)seasons [春季,夏季,秋季,冬季]stars [A,AA,AAA,AAAA,AAAAA]with open(csvPath,r,encodingutf-8) as f:reader csv.reader(f)datas list(reader)print(csv连接成功,len(datas))newDatas []#for data in datas:for k in range(0,len(datas)):data datas[k]if k0:newDatas.append(data)else:if datas[k][0]datas[k-1][0] and datas[k][1]datas[k-1][1]:#通过 名称区县 组合判断是否唯一continueelse:newDatas.append(data)print(去除csv中重复记录)nodeCity_new Node(chengshi,name北京)cityMatch node_Match.match(chengshi,name北京)if cityMatchNone :graph.merge(nodeCity_new,chengshi,name)for i in range(0,len(newDatas)):nodeQu_new Node(quxian,namenewDatas[i][1]) rel1 Relationship(nodeQu_new,属于,nodeCity_new)graph.merge(rel1,quxian,name)geoxy_encode geohash.encode( newDatas[i][4],newDatas[i][3],6 )nodeJingdian Node(csvType,namenewDatas[i][0],quyunewDatas[i][1],jianjienewDatas[i][0],dizhinewDatas[i][2],zuobiaogeoxy_encode)jingdianMatch node_Match.match(csvType,namenewDatas[i][0]).where(quyunewDatas[i][1]).first()if jingdianMatchNone :graph.create(nodeJingdian)rel2 Relationship(nodeJingdian,位于,nodeQu_new)graph.create(rel2)nodeTime Node(traveltime,timerandom.choice(seasons))#graph.create(nodeTime)rel3 Relationship(nodeJingdian,旅游时间,nodeTime)graph.merge(rel3,traveltime,time)nodeAAA Node(Stars,starrandom.choice(stars))#graph.create(nodeAAA)rel4 Relationship(nodeJingdian,星级,nodeAAA)graph.merge(rel4,Stars,star)if __name__ __main__:graph Graph(bolt://localhost:7687,auth(neo4j,neo4j?))print(neo4j连接成功)importCSV2NeoKG(graph,公园2050101Attr.csv,gongyuan)print(gongyuan ok)importCSV2NeoKG(graph,景点2050201and20600102Attr.csv,jingdian)print(jingdian ok)坐标用到了geohash尝试安装过几种geohash库均有错误。最后直接复制源代码生成.py文件。 geohash.py代码如下
from __future__ import division
from collections import namedtuple
from builtins import range
import decimal
import mathbase32 0123456789bcdefghjkmnpqrstuvwxyzdef _indexes(geohash):if not geohash:raise ValueError(Invalid geohash)for char in geohash:try:yield base32.index(char)except ValueError:raise ValueError(Invalid geohash)def _fixedpoint(num, bound_max, bound_min):Return given num with precision of 2 - log10(range)Params------num: A numberbound_max: max bound, e.g max latitude of a geohash cell(NE)bound_min: min bound, e.g min latitude of a geohash cell(SW)Returns-------A decimaltry:decimal.getcontext().prec math.floor(2-math.log10(bound_max- bound_min))except ValueError:decimal.getcontext().prec 12return decimal.Decimal(num)def bounds(geohash):Returns SW/NE latitude/longitude bounds of a specified geohash::| .| NE| . || . |SW |. |:param geohash: string, cell that bounds are required of:returns: a named tuple of namedtuples Bounds(sw(lat, lon), ne(lat, lon)). bounds geohash.bounds(ezs42) bounds ((42.583, -5.625), (42.627, -5.58))) bounds.sw.lat 42.583geohash geohash.lower()even_bit Truelat_min -90lat_max 90lon_min -180lon_max 180# 5 bits for a char. So divide the decimal by power of 2, then AND 1# to get the binary bit - fast modulo operation.for index in _indexes(geohash):for n in range(4, -1, -1):bit (index n) 1if even_bit:# longitudelon_mid (lon_min lon_max) / 2if bit 1:lon_min lon_midelse:lon_max lon_midelse:# latitudelat_mid (lat_min lat_max) / 2if bit 1:lat_min lat_midelse:lat_max lat_mideven_bit not even_bitSouthWest namedtuple(SouthWest, [lat, lon])NorthEast namedtuple(NorthEast, [lat, lon])sw SouthWest(lat_min, lon_min)ne NorthEast(lat_max, lon_max)Bounds namedtuple(Bounds, [sw, ne])return Bounds(sw, ne)def decode(geohash):Decode geohash to latitude/longitude. Location is approximate centre of thecell to reasonable precision.:param geohash: string, cell that bounds are required of:returns: Namedtuple with decimal lat and lon as properties. geohash.decode(gkkpfve) (70.2995, -27.9993)(lat_min, lon_min), (lat_max, lon_max) bounds(geohash)lat (lat_min lat_max) / 2lon (lon_min lon_max) / 2lat _fixedpoint(lat, lat_max, lat_min)lon _fixedpoint(lon, lon_max, lon_min)Point namedtuple(Point, [lat, lon])return Point(lat, lon)def encode(lat, lon, precision):Encode latitude, longitude to a geohash.:param lat: latitude, a number or string that can be converted to decimal.Ideally pass a string to avoid floating point uncertainties.It will be converted to decimal.:param lon: longitude, a number or string that can be converted to decimal.Ideally pass a string to avoid floating point uncertainties.It will be converted to decimal.:param precision: integer, 1 to 12 represeting geohash levels upto 12.:returns: geohash as string. geohash.encode(70.2995, -27.9993, 7) gkkpfvelat decimal.Decimal(lat)lon decimal.Decimal(lon)index 0 # index into base32 mapbit 0 # each char holds 5 bitseven_bit Truelat_min -90lat_max 90lon_min -180lon_max 180ghash []while(len(ghash) precision):if even_bit:# bisect E-W longitudelon_mid (lon_min lon_max) / 2if lon lon_mid:index index * 2 1lon_min lon_midelse:index index * 2lon_max lon_midelse:# bisect N-S latitudelat_mid (lat_min lat_max) / 2if lat lat_mid:index index * 2 1lat_min lat_midelse:index index * 2lat_max lat_mideven_bit not even_bitbit 1if bit 5:# 5 bits gives a char in geohash. Start overghash.append(base32[index])bit 0index 0return .join(ghash)def adjacent(geohash, direction):Determines adjacent cell in given direction.:param geohash: cell to which adjacent cell is required:param direction: direction from geohash, string, one of n, s, e, w:returns: geohash of adjacent cell geohash.adjacent(gcpuyph, n) gcpuypkif not geohash:raise ValueError(Invalid geohash)if direction not in (nsew):raise ValueError(Invalid direction)neighbour {n: [p0r21436x8zb9dcf5h7kjnmqesgutwvy,bc01fg45238967deuvhjyznpkmstqrwx],s: [14365h7k9dcfesgujnmqp0r2twvyx8zb,238967debc01fg45kmstqrwxuvhjyznp],e: [bc01fg45238967deuvhjyznpkmstqrwx,p0r21436x8zb9dcf5h7kjnmqesgutwvy],w: [238967debc01fg45kmstqrwxuvhjyznp,14365h7k9dcfesgujnmqp0r2twvyx8zb],}border {n: [prxz, bcfguvyz],s: [028b, 0145hjnp],e: [bcfguvyz, prxz],w: [0145hjnp, 028b],}last_char geohash[-1]parent geohash[:-1] # parent is hash without last chartyp len(geohash) % 2# check for edge-cases which dont share common prefixif last_char in border[direction][typ] and parent:parent adjacent(parent, direction)index neighbour[direction][typ].index(last_char)return parent base32[index]def neighbours(geohash):Returns all 8 adjacent cells to specified geohash::| nw | n | ne || w | * | e || sw | s | se |:param geohash: string, geohash neighbours are required of:returns: neighbours as namedtuple of geohashes with properties n,ne,e,se,s,sw,w,nw neighbours geohash.neighbours(gcpuyph) neighbours (gcpuypk, gcpuypm, gcpuypj, gcpuynv, gcpuynu, gcpuyng, gcpuyp5, gcpuyp7) neighbours.ne gcpuypmn adjacent(geohash, n)ne adjacent(n, e)e adjacent(geohash, e)s adjacent(geohash, s)se adjacent(s, e)w adjacent(geohash, w)sw adjacent(s, w)nw adjacent(n, w)Neighbours namedtuple(Neighbours,[n, ne, e, se, s, sw, w, nw])return Neighbours(n, ne, e, se, s, sw, w, nw)
KG效果
命令行里启动neo4j neo4j.bat console
KG入库效率优化方案
上文的python方法是py2neo的基本方法经过本人亲测当节点量到3~5w的时候入库开始变慢以小时计。
百度后有大神提供了另外一种方法 采用这种方法建立50w个节点和50w个关系流程包括node、rel的建立、append到list、入库全过程4分钟以内搞定。测试环境在VM虚拟机实现。 代码如下
from py2neo import Graph, Subgraph, Node, Relationship
from progressbar import *
import datetimedef batch_create(graph, nodes_list, relations_list):subgraph Subgraph(nodes_list, relations_list)tx_ graph.begin()tx_.create(subgraph)graph.commit(tx_)if __name__ __main__:# 连接neo4jgraph Graph(bolt://localhost:7687,auth(neo4j,neo4j?))# 批量创建节点nodes_list [] # 一批节点数据relations_list [] # 一批关系数据nodeCity_new Node(chengshi,name北京)nodes_list.append(nodeCity_new)widgets [CSV导入KG进度: , Percentage(), , Bar(#), , Timer(), , ETA(), ]bar ProgressBar(widgetswidgets, maxval500000)bar.start()#for i in range(0,500000):bar.update(i1)nodeQu_new Node(quxian,nameTest{0}.format(i))nodes_list.append(nodeQu_new)rel1 Relationship(nodeQu_new,属于,nodeCity_new)relations_list.append(rel1)bar.finish()current_time datetime.datetime.now()print(current_time: str(current_time))# 批量创建节点/关系batch_create(graph, nodes_list, relations_list)current_time datetime.datetime.now()print(current_time: str(current_time))print(batch ok)PostGreSQL建库
pg建库。携程poi入库tripdata的poibaseinfo表之后导航poi中的公园、景点也导入该表。
携程poi导入代码psycopg2_004.py
import psycopg2
import csv
import random
import geohash
from progressbar import *#
#携程爬虫csv数据入库
#
def importCtripCSV2PG(cur,csvpath,csvcity,csvprovice):# csvPath pois_bj_ctrip.csvwith open(csvpath,r,encodingutf-8) as f:reader csv.reader(f)datas list(reader)print(csv datas number {}.format(len(datas)))print()widgets [爬虫数据导入PG进度: , Percentage(), , Bar(#), , Timer(), , ETA(), ]bar ProgressBar(widgetswidgets, maxvallen(datas))bar.start()##sCol namec,namec2,namee,tags,brief,ticket,ticketmin,ticketadult,ticketchild,ticketold,ticketstudent,scores,scorenumber,opentime,spendtime,introduceinfo,salesinfo,guid,quyu,city,province,contry#sCol namec,namee,tags,brief,ticket,ticketmin,ticketadult,ticketchild,ticketold,ticketstudent,scores,scorenumber,opentime,spendtime,introduceinfo,salesinfo,city,province,contrysCol namec,namee,tags,brief,ticket,ticketmin,ticketadult,ticketchild,ticketold,ticketstudent,scores,scorenumber,opentime,spendtime,introduceinfo,salesinfo,x,y,geos,photourl,city,province,contry# print(sCol number {}.format(len(sCol)))for i in range(0,len(datas)):bar.update(i1)data datas[i]if dataNone or len(data)0:#print({}行None值.format(i))continue if data[0]名称 or data[0]None :continue geoxy_encode geohash.encode( data[5],data[4],7 )values ,.join(\{0}\.format(w) for w in [data[0].replace(\,),data[1].replace(\,),data[6],data[7],data[8],data[9],data[13],data[16],data[14],data[15],data[10],data[11],data[18].replace(\,),data[17].replace(\,),data[19].replace(\,),data[20].replace(\,),data[5],data[4],geoxy_encode,data[12],csvcity,csvprovice,中国])# print(values)sqlpre insert into poibaseinfo({}).format(sCol)sql sqlpre values ({}).format(values)# print(sql)try:cur.execute(sql)except psycopg2.Error as e:print(e)bar.finish()if __name__ __main__:user postgrespwd 你的密码port 5432hostname 127.0.0.1conn psycopg2.connect(database tripdata, user user, password pwd, host 127.0.0.1, port port)print(conn)sql select * from poibaseinfocur conn.cursor()cur.execute(sql)cols cur.descriptionprint(PG cols number {}.format(len(cols)))#CSV文件导入PGcsvPath pois_bj_ctrip.csv importCtripCSV2PG(cur,csvPath,北京,北京)#其他CSV文件导入PG#TODO...conn.commit()cur.close()conn.close()print(ok)
