from openpyxl import Workbook
import xlrd
import time
import Levenshtein as Le target_city_list = ['北京市', '上海市', '深圳市', '广州市']
source_name = 'JMTool任务_csv_py_wholeCSV-加百度170826165729'
BDpoi_list_tag, BDpoi_list_tagb = '|-|', '|--|'
FEXCEL = '%s%s' % (source_name, '.xlsx') weight_ratio, weight_seqratio = 0.7, 0.3 def main_():
global source_name
data = xlrd.open_workbook(FEXCEL)
table = data.sheets()[0]
nrows, ncols = table.nrows, table.ncols
res_dic = {}
for i in range(0, nrows):
l = table.row_values(i)
dbid, area_code, ref_area_type_code, city, district, address, city_street, name_, BDpoi_list = l
if dbid == 'dbid':
continue
if city not in target_city_list:
continue
if city not in res_dic:
res_dic[city] = {}
if district not in res_dic[city]:
res_dic[city][district] = {}
if name_ not in res_dic[city][district]:
res_dic[city][district][name_] = [] if BDpoi_list.find(BDpoi_list_tag) == -1:
ll = dbid, area_code, ref_area_type_code, city, district, address, city_street, name_, BDpoi_list, '', '', ''
res_dic[city][district][name_].append(ll)
else:
addr_ = '%s%s%s%s' % (city, district, address, city_street)
chk_name_lsit, cmp_list, sorted_ratio_seqratio_res_dic = [name_, addr_], BDpoi_list.split(
BDpoi_list_tag), {}
for ii in cmp_list:
if len(ii) == 0:
continue
cmp_, BD_name, BD_addr = ['', ''], '', ''
cmp_one = ii.split(BDpoi_list_tagb)
if len(cmp_one) == 2:
# format data -fair
BD_name, BD_addr = cmp_[0], cmp_[1] = cmp_one[0], cmp_one[1].replace(city, '').replace(district, '')
else:
BD_name = cmp_[0] = cmp_one[0]
ratio_res, seqratio_res = Le.ratio(name_, BD_name), Le.seqratio(chk_name_lsit, cmp_)
ratio_seqratio_res = weight_ratio * ratio_res + weight_seqratio * seqratio_res
ll = dbid, area_code, ref_area_type_code, city, district, address, city_street, name_, BDpoi_list, BD_name, BD_addr, ratio_seqratio_res, ratio_res, seqratio_res
if ratio_seqratio_res not in sorted_ratio_seqratio_res_dic:
sorted_ratio_seqratio_res_dic[ratio_seqratio_res] = []
sorted_ratio_seqratio_res_dic[ratio_seqratio_res].append(ll)
sorted_seqratio_res_list = sorted(sorted_ratio_seqratio_res_dic)
for ratio_seqratio_res in sorted_seqratio_res_list:
lll = sorted_ratio_seqratio_res_dic[ratio_seqratio_res]
for vl in lll:
res_dic[city][district][name_].append(vl) wb = Workbook()
worksheet = wb.active
file_title_str = ' dbid, area_code, ref_area_type_code, city, district, address, city_street, name_, BDpoi_list, BD_name, BD_addr, ratio_seqratio_res, ratio_res, seqratio_res'
file_title_l = file_title_str.replace(' ', '').split(',')
worksheet.append(file_title_l)
for city in res_dic:
for district in res_dic[city]:
for name_ in res_dic[city][district]:
l = res_dic[city][district][name_]
for ll in l:
worksheet.append(ll)
localtime_ = time.strftime("%y%m%d%H%M%S", time.localtime())
file_name = '%s%s%s' % (source_name, '-Levenshtein', localtime_)
file_name_save = '%s%s' % (file_name, '.xlsx')
wb.save(file_name_save) wb = Workbook()
worksheet = wb.active
file_title_str = ' dbid, area_code, ref_area_type_code, city, district, address, city_street, name_,BDpoi_list,max_BD_name, max_BD_addr, max_ratio_seqratio_res, ratio_res, seqratio_res'
file_title_l = file_title_str.replace(' ', '').split(',')
worksheet.append(file_title_l)
for city in res_dic:
for district in res_dic[city]:
for name_ in res_dic[city][district]:
l = res_dic[city][district][name_]
lll = l[-1] worksheet.append(lll)
localtime_ = time.strftime("%y%m%d%H%M%S", time.localtime())
file_name = '%s%s%s' % (file_name, '-Levenshtein-ordered', localtime_)
file_name_save = '%s%s' % (file_name, '.xlsx')
wb.save(file_name_save) main_()

  

from openpyxl import Workbook
import xlrd
import time
import Levenshtein as Le target_city_list = ['深圳市']
BDpoi_list_tag, BDpoi_list_tagb = '|-|', '|--|' source_name = 'JMTool任务_csv_py_wholeCSV_住宅小区-加百度170826152533'
FEXCEL = '%s%s' % (source_name, '.xlsx')
weight_ratio, weight_seqratio = 0.7, 0.3 def main_():
global source_name
data = xlrd.open_workbook(FEXCEL)
table = data.sheets()[0]
nrows, ncols = table.nrows, table.ncols
res_dic = {}
for i in range(0, nrows):
l = table.row_values(i)
dbid, area_code, ref_area_type_code, city, district, address, city_street, name_, name_reduction, BDpoi_list = l
if dbid == 'dbid':
continue
if city not in target_city_list:
continue
if city not in res_dic:
res_dic[city] = {}
if district not in res_dic[city]:
res_dic[city][district] = {}
if name_ not in res_dic[city][district]:
res_dic[city][district][name_] = [] if BDpoi_list.find(BDpoi_list_tag) == -1:
ll = dbid, area_code, ref_area_type_code, city, district, address, city_street, name_, name_reduction, BDpoi_list, '', '', ''
res_dic[city][district][name_].append(ll)
else:
addr_ = '%s%s%s%s' % (city, district, address, city_street)
chk_name_lsit, cmp_list, sorted_ratio_seqratio_res_dic = [name_reduction, addr_], BDpoi_list.split(
BDpoi_list_tag), {}
for ii in cmp_list:
if len(ii) == 0:
continue
cmp_, BD_name, BD_addr = ['', ''], '', ''
cmp_one = ii.split(BDpoi_list_tagb)
if len(cmp_one) == 2:
# format data -fair
BD_name, BD_addr = cmp_[0], cmp_[1] = cmp_one[0], cmp_one[1].replace(city, '').replace(district, '')
else:
BD_name = cmp_[0] = cmp_one[0]
ratio_res, seqratio_res = Le.ratio(name_reduction, BD_name), Le.seqratio(chk_name_lsit, cmp_)
ratio_seqratio_res = weight_ratio * ratio_res + weight_seqratio * seqratio_res
ll = dbid, area_code, ref_area_type_code, city, district, address, city_street, name_, name_reduction, BDpoi_list, BD_name, BD_addr, ratio_seqratio_res, ratio_res, seqratio_res
if ratio_seqratio_res not in sorted_ratio_seqratio_res_dic:
sorted_ratio_seqratio_res_dic[ratio_seqratio_res] = []
sorted_ratio_seqratio_res_dic[ratio_seqratio_res].append(ll)
sorted_seqratio_res_list = sorted(sorted_ratio_seqratio_res_dic)
for ratio_seqratio_res in sorted_seqratio_res_list:
lll = sorted_ratio_seqratio_res_dic[ratio_seqratio_res]
for vl in lll:
res_dic[city][district][name_].append(vl) wb = Workbook()
worksheet = wb.active
file_title_str = 'dbid, area_code, ref_area_type_code, city, district, address, city_street, name_, name_reduction, BDpoi_list, BD_name, BD_addr, ratio_seqratio_res, ratio_res, seqratio_res'
file_title_l = file_title_str.replace(' ', '').split(',')
worksheet.append(file_title_l)
for city in res_dic:
for district in res_dic[city]:
for name_ in res_dic[city][district]:
l = res_dic[city][district][name_]
for ll in l:
worksheet.append(ll)
localtime_ = time.strftime("%y%m%d%H%M%S", time.localtime())
file_name = '%s%s%s' % (source_name, '-Levenshtein', localtime_)
file_name_save = '%s%s' % (file_name, '.xlsx')
wb.save(file_name_save) wb = Workbook()
worksheet = wb.active
file_title_str = 'dbid, area_code, ref_area_type_code, city, district, address, city_street, name_, name_reduction,BDpoi_list,max_BD_name, max_BD_addr, max_ratio_seqratio_res, ratio_res, seqratio_res'
file_title_l = file_title_str.replace(' ', '').split(',')
worksheet.append(file_title_l)
for city in res_dic:
for district in res_dic[city]:
for name_ in res_dic[city][district]:
l = res_dic[city][district][name_]
lll = l[-1] worksheet.append(lll)
localtime_ = time.strftime("%y%m%d%H%M%S", time.localtime())
file_name = '%s%s%s' % (file_name, '-Levenshtein-ordered', localtime_)
file_name_save = '%s%s' % (file_name, '.xlsx')
wb.save(file_name_save) main_()

  

算法调参 weight_ratio, weight_seqratio的更多相关文章

  1. annoy ANN算法 调参

    search_k serach_k越大,越准确,但是要在时间和准确率之间取个trade off During the query it will inspect up to search_k node ...

  2. CatBoost算法和调参

    欢迎关注博主主页,学习python视频资源 sklearn实战-乳腺癌细胞数据挖掘(博主亲自录制视频) https://study.163.com/course/introduction.htm?co ...

  3. k-近邻算法采用for循环调参方法

    //2019.08.02下午#机器学习算法中的超参数与模型参数1.超参数:是指机器学习算法运行之前需要指定的参数,是指对于不同机器学习算法属性的决定参数.通常来说,人们所说的调参就是指调节超参数.2. ...

  4. 调参、最优化、ml算法(未完成)

    最优化方法 调参方法 ml算法 梯度下降gd grid search lr 梯度上升 随机梯度下降 pca 随机梯度下降sgd  贝叶斯调参 lda 牛顿算法   knn 拟牛顿算法   kmeans ...

  5. scikit-learn随机森林调参小结

    在Bagging与随机森林算法原理小结中,我们对随机森林(Random Forest, 以下简称RF)的原理做了总结.本文就从实践的角度对RF做一个总结.重点讲述scikit-learn中RF的调参注 ...

  6. scikit-learn 梯度提升树(GBDT)调参小结

    在梯度提升树(GBDT)原理小结中,我们对GBDT的原理做了总结,本文我们就从scikit-learn里GBDT的类库使用方法作一个总结,主要会关注调参中的一些要点. 1. scikit-learn ...

  7. word2vec参数调整 及lda调参

     一.word2vec调参   ./word2vec -train resultbig.txt -output vectors.bin -cbow 0 -size 200 -window 5 -neg ...

  8. 漫谈PID——实现与调参

    闲话: 作为一个控制专业的学生,说起PID,真是让我又爱又恨.甚至有时候会觉得我可能这辈子都学不会pid了,但是经过一段时间的反复琢磨,pid也不是很复杂.所以在看懂pid的基础上,写下这篇文章,方便 ...

  9. hyperopt自动调参

    hyperopt自动调参 在传统机器学习和深度学习领域经常需要调参,调参有些是通过通过对数据和算法的理解进行的,这当然是上上策,但还有相当一部分属于"黑盒" hyperopt可以帮 ...

随机推荐

  1. memcache运行机制(转)

    网上其实有很多文章说明了memcached是如何运作的,特别是底层的内存分配是如何运作的.我参考过很多资料,比较有启发意义的有几个: 首先是官方的英文资料,虽然文章太多.很难看懂,我个人觉得说得也不是 ...

  2. IQueryable接口与IEnumberable 区别

    总结一下: IEnumerable<T> 泛型类在调用自己的SKip 和 Take 等扩展方法之前数据就已经加载在本地内存里了,而IQueryable<T> 是将Skip ,t ...

  3. 2017.5.15 markdown简明教程

    0.说明 markdown是一种书写格式,html是一种发布格式.markdown的语法种类只对应html标记的一小部分(只涵盖纯文本). 不在markdown涵盖范围的标签,都可以直接在文档里用ht ...

  4. 固态硬盘(Solid State Drives)

    固态硬盘(Solid State Drives) 学习了:https://baike.baidu.com/item/%E5%9B%BA%E6%80%81%E7%A1%AC%E7%9B%98/45351 ...

  5. [Tips + Javascript] Make a unique array

    To make an array uniqued, we can use Set() from Javascript. const ary = ["a", "b" ...

  6. json-server模拟接口获取mock数据

    转载:http://blog.csdn.net/stevennest/article/details/76167343 安装json-server 运行以下命令 cnpm install json-s ...

  7. 使用SQLite存储数据

    一.SQLiteAndroid 为了让我们能够更加方便地管理数据库, 专门提供了一个SQLiteOpenHelper 帮助类,借助这个类就可以非常简单地对数据库进行创建和升级. 1.SQLiteOpe ...

  8. kettle入门(七) 之kettle增量方案(一)全量比对取增量-依据唯一标示

    引: ods有个project表来自于上游系统,数据量不大 十几万,下游系统须要此数据,而且须要每天提供截止当天的增量数据 要求每条数据给出数据变化时间及标示,即数据若是插入 有插入时间和插入标示 若 ...

  9. windows快捷键之打开网络连接

      在Win 7"開始"->"执行"对话框输入"cmd"例如以下图红框所看到的,再点击"确定"button. 步骤 ...

  10. Ubuntu14.04下MySQL的安装与卸载

    转载自:https://www.2cto.com/os/201408/329502.html 安装MysQL 执行以下命令:sudo apt-get install mysql-server 2. 继 ...