点击有惊喜
摩拜单车在北京的单车投放量已经超过40万。用户可以直接在人行道上找到停放的单车，用手机解锁，然后骑到目的地后再把单车停好并锁上。因此，为了更好地调配和管理这40万辆单车，需要准确地预测每个用户的骑行目的地。
标注数据中包含300万条出行记录数据，覆盖超过30万用户和40万摩拜单车。数据包括骑行起始时间和地点、车辆ID、车辆类型和用户ID等信息。参赛选手需要预测骑行目的地的区块位置。
以下代码是knn算法，结合了leak。这里主要有两点创新：
给算出来的距离值除以频度的1.1次方，这个加了很多分对于新用户又使用了一个新的knn，其他算法在处理新用户的时候也可以参考下，knn算法产生的特征可以融合进xgb再训练。
源码地址： 后台 回复 摩拜 即可获取
import csvimport mathimport datetime#user_habit_dict:每个用户的乘车记录:起点,终点,距离user_habit_dict={}#start_end_dict:每条记录的起点,终点对start_end_dict={}#end_start_dict:每条记录的起点,终点对end_start_dict={}#user_habit_dict_test:test中每个用户的记录user_habit_dict_test={}#bike_dict:bike中的记录bike_dict={}
弧度转换
def rad(tude):return (math.pi/180.0)*tude
geohash模块提取的
__base32 = '0123456789bcdefghjkmnpqrstuvwxyz'__decodemap = { }for i in range(len(__base32)):__decodemap[__base32[i]] = idel i
返回 精确的经纬度和误差
def decode_exactly(geohash):lat_interval, lon_interval = (-90.0, 90.0), (-180.0, 180.0)lat_err, lon_err = 90.0, 180.0is_even = Truefor c in geohash:cd = __decodemap[c]for mask in [16, 8, 4, 2, 1]:if is_even: # adds longitude infolon_err /= 2if cd & mask:lon_interval = ((lon_interval[0]+lon_interval[1])/2, lon_interval[1])else:lon_interval = (lon_interval[0], (lon_interval[0]+lon_interval[1])/2)else:# adds latitude infolat_err /= 2if cd & mask:lat_interval = ((lat_interval[0]+lat_interval[1])/2, lat_interval[1])else:lat_interval = (lat_interval[0], (lat_interval[0]+lat_interval[1])/2)is_even = not is_evenlat = (lat_interval[0] + lat_interval[1]) / 2lon = (lon_interval[0] + lon_interval[1]) / 2return lat, lon, lat_err, lon_err
返回 欧式距离 （其实还可以返回南北方向距离,东西方向距离,曼哈顿距离,方向(-0.5:0.5)，但是删了，没啥吊用）
def produceLocationInfo(latitude1, longitude1,latitude2, longitude2):radLat1 = rad(latitude1)radLat2 = rad(latitude2)a = radLat1-radLat2b = rad(longitude1)-rad(longitude2)R = 6378137d = R2math.asin(math.sqrt(math.pow(math.sin(a/2),2)+math.cos(radLat1)math.cos(radLat2)math.pow(math.sin(b/2),2)))detallat = abs(a)*RdetalLon = math.sqrt(d2-detallat2)if b==0:direction = 1/2 if a*b>0 else -1/2else:direction = math.atan(detallat/detalLon(1 if ab>0 else -1))/math.pireturn round(d)
返回 欧式距离
def loc_2_dis(hotStartLocation,hotEndLocation):StartLocation = decode_exactly(hotStartLocation[:7])EndLocation = decode_exactly(hotEndLocation[:7])latitude1 = StartLocation[0]longitude1 = StartLocation[1]latitude2 = EndLocation[0]longitude2 = EndLocation[1]return produceLocationInfo(latitude1, longitude1, latitude2, longitude2)
返回 是否放假,距0点的分钟数,距5月1的天数
def produceTimeInfo(TimeData):TimeData = TimeData.split(' ')baseData = datetime.datetime(, 5, 1, 0, 0, 1)mydata = TimeData[0].split('-')mytime = TimeData[1].split(':')mydata[0] = int(mydata[0])mydata[1] = int(mydata[1])mydata[2] = int(mydata[2])mytime[0] = int(mytime[0])mytime[1] = int(mytime[1])mytime[2] = int(mytime[2].split('.')[0])dt = datetime.datetime(mydata[0], mydata[1], mydata[2], mytime[0], mytime[1], mytime[2])minute = mytime[1]+mytime[0]*60# return int((dt-baseData).__str__().split(' ')[0]),miao,dt.weekday(),round(miao/900)isHoliday = 0if dt.weekday()in [5,6] or int((dt-baseData).__str__().split(' ')[0]) in [29,28]:isHoliday=1return isHoliday,minute,int((dt-baseData).__str__().split(' ')[0])
模型之间的融合，粗暴的取了最值，这个可以再提升
def add2result(result1,result2):for each in result2:if each in result1:result1[each] = min(result1[each] ,result2[each] )else:result1[each] = result2[each]return result1
其实就是knn算法，结合了leak。一般的knn+leak应该是0.26分。这里主要有两点创新。一是给算出来的距离值除以频度的1.1次方，这个加了很多分，二是对于新用户又使用了一个新的knn，其他算法在处理新用户的时候也可以参考下。knn算法产生的特征可以融合进xgb再训练，已实现，但内存不够弃赛
def training(trainfile = 'train.csv',testfile = 'test.csv',subfile = 'submission.csv' ,leak1 = 0.01 ,leak2 = 4 ,leak3 = 20, #leakqidianquan = 10,shijianquan = 10,jiejiaquan = 2,bikequan = 0.5,
都是拼音，字面意思，越大则这个特征比重越大，zhishu = 1.1 对结果影响很大
tr = csv.DictReader(open(trainfile))
利用train.csv建立user_habit_dict和start_end_dict
for rec in tr:user = rec['userid']start = rec['geohashed_start_loc']end = rec['geohashed_end_loc']rec['isHoliday'] , rec['minute'] , rec['data'] = produceTimeInfo(rec['starttime'])if user in user_habit_dict:user_habit_dict[user].append(rec)else:user_habit_dict[user] = [rec]if start in start_end_dict:start_end_dict[start].append(rec)else:start_end_dict[start] = [rec]if end in end_start_dict:end_start_dict[end].append(rec)else:end_start_dict[end] = [rec]print('train done!')
te是测试文件
te = csv.DictReader(open(testfile))for rec in te:user = rec['userid']bike = rec['bikeid']rec['isHoliday'], rec['minute'], rec['data'] = produceTimeInfo(rec['starttime'])if user in user_habit_dict_test:user_habit_dict_test[user].append(rec)else:user_habit_dict_test[user] = [rec]if bike in bike_dict:bike_dict[bike].append(rec)else:bike_dict[bike] = [rec]print("test done!")
sub是提交文件
sub = open(subfile, 'w')iter1 = 0# AllhotLocSort = sorted(end_start_dict.items(), key=lambda d: len(d[1]), reverse=True)te1 = csv.DictReader(open(testfile))for rec in te1:iter1 += 1if iter1 % 10000== 0:print(iter1/20000,'%',sep='')# testTime = timeSlipt(rec['minute'])rec['isHoliday'], rec['minute'], rec['data'] = produceTimeInfo(rec['starttime'])user1 = rec['userid']bikeid1 = rec['bikeid']order1 = rec['orderid']start1 = rec['geohashed_start_loc']hour1 = rec['minute']/60minute1 = rec['minute']isHoliday1 = rec['isHoliday']biketype1 = rec['biketype']data1 = rec['data']result = {}hotLoc = {}knnif user1 in user_habit_dict:for eachAct in user_habit_dict[user1]:start2 = eachAct['geohashed_start_loc']end2 = eachAct['geohashed_end_loc']hour2 = eachAct['minute']/60isHoliday2 = eachAct['isHoliday']biketype2 = eachAct['biketype']data2 = rec['data']dis = loc_2_dis(start1, start2)dis = min(dis, 1000) #1000qidian= qidianquan (dis / 100) * 2detalaTime = abs(hour2 - hour1) if abs(hour2 - hour1) < 12 else 24 - abs(hour2 - hour1)shijian= shijianquan (detalaTime / 12 10) ** 2dayType = isHoliday2 - isHoliday1jiejia= jiejiaquan (dayType 10) ** 2 #?biType = int(biketype2) - int(biketype1)bike= bikequan (biType 10) ** 2 #0.5
利用终点预测
# return 欧式距离,南北方向距离,东西方向距离,曼哈顿距离,方向(-0.5:0.5)# test2train_dis = loc_2_dis(start1,end2)# train2train_dis = loc_2_dis(start2,end2)# dis_detal = min(abs(test2train_dis[3]-train2train_dis[3]),1000) #1000# direction_detal = abs(test2train_dis[4]-train2train_dis[4])# direction_detal = direction_detal if direction_detal<0.5 else 1-direction_detal# jvli = 4 (dis_detal/100)*2# fangxiang = 1 (direction_detal/0.510)**2score = qidian+shijian+jiejia+bike #jvli+fangxiang# print(qidian,shijian,jiejia,bike,jvli,fangxiang)if end2 in hotLoc:hotLoc[end2] += 1else:hotLoc[end2] = 1if end2 in result:if result[end2] > score:result[end2] = scoreelse:result[end2] = scorefor each in hotLoc:result[each] = result[each] / (hotLoc[each]**zhishu) #0for each in result:result[each] = math.sqrt(result[each])
利用test中的用户历史记录
if user1 in user_habit_dict_test:resulttest = {}user_habit_dict_test[user1].sort(key = lambda x:x['data']6024+x['minute'])xuhao = 0for i in range(len(user_habit_dict_test[user1])-1):if user_habit_dict_test[user1][i]['orderid'] == order1:xuhao = iresulttest[user_habit_dict_test[user1][i+1]['geohashed_start_loc']] = 21for i in range(len(user_habit_dict_test[user1])):if i not in [xuhao,xuhao+1]:resulttest[user_habit_dict_test[user1][i]['geohashed_start_loc']] = 21+abs(i-xuhao)result = add2result(result, resulttest)leakif bikeid1 in bike_dict:resultleak = {}bike_dict[bikeid1].sort(key = lambda x:x['data']6024+x['minute'])for i in range(len(bike_dict[bikeid1])-1):if bike_dict[bikeid1][i]['orderid'] == order1:zhong = bike_dict[bikeid1][i+1]['data']6024+bike_dict[bikeid1][i+1]['minute']qi = bike_dict[bikeid1][i]['data']6024+bike_dict[bikeid1][i]['minute']detal = zhong-qiif detal<30:resultleak[bike_dict[bikeid1][i + 1]['geohashed_start_loc']] = leak1elif detal<2*60:resultleak[bike_dict[bikeid1][i + 1]['geohashed_start_loc']] = leak2 #4else:resultleak[bike_dict[bikeid1][i + 1]['geohashed_start_loc']] = leak3 #20result = add2result(result,resultleak)
起点终点对的knn
if start1 in start_end_dict:endDict = {}resultqizhong={}for eachAct in start_end_dict[start1]:score = 0score += (24-abs(hour1-eachAct['minute']/60))/24score += (1-abs(isHoliday1-eachAct['isHoliday']))*0.4if eachAct['geohashed_end_loc'] in endDict:endDict[eachAct['geohashed_end_loc']] += scoreelse:endDict[eachAct['geohashed_end_loc']] = scorehotLoc = sorted(endDict.items(),key = lambda x:x[1],reverse=True)if len(hotLoc)>=1:resultqizhong[hotLoc[0][0]] = 1000if len(hotLoc) >= 2:resultqizhong[hotLoc[1][0]] = 1001if len(hotLoc) >= 3:resultqizhong[hotLoc[2][0]] = 1002result = add2result(result, resultqizhong)
剔除不合理结果
for each in result:distance = loc_2_dis(each,start1)if distance > 2500:result[each] = 1999if start1 in result:result[start1] = min(2000, result[start1])else:result[start1]=2000result['fuck2'] = 2001result['fuck3'] = 2002bestResult = sorted(result.items(), key=lambda d: d[1])string = rec['orderid']num = 0for item in bestResult:string += ',' + item[0]# string += ':' + str(item[1]) + '\t'num += 1if num == 3:breaksub.write(string + '\n')sub.close()print('ok')if name =="__main__":training('train.csv', 'test.csv', 'submission.csv' )
点击有惊喜
