From df987e79554a1d3ca67fb413df372d9b0a9c3f3c Mon Sep 17 00:00:00 2001 From: ZhouHang Date: Thu, 26 May 2022 13:46:13 +0800 Subject: [PATCH] Readme --- README.md | 119 ++++++++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 103 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index c8a6009..e46e42b 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,54 @@ # Geocoding +![Mac](https://img.shields.io/badge/MacOS-pass-success) +![Linux](https://img.shields.io/badge/Linux-pass-success) +![Windows](https://img.shields.io/badge/Windows-bug-red) + +[![PypiVersion](https://img.shields.io/badge/pypi-1.4.1-blue)](https://github.com/casuallyName/Geocoding) +[![JarVersion](https://img.shields.io/badge/jar-1.3.0-blue)](https://github.com/IceMimosa/geocoding) +![Python wheels](https://img.shields.io/badge/wheels-%E2%9C%93-4c1.svg?longCache=true&style=flat-square&logo=python&logoColor=white) + * 该模块用于将不规范(或者连续)的文本地址进行尽可能的标准化, 以及对两个地址进行相似度的计算 * 该模块为 [IceMimosa/geocoding](https://github.com/IceMimosa/geocoding) 项目的Python封装，原项目为Kotlin开发 * 为方便使用Python方法调用，这里使用Python的`jpype`模块将 [IceMimosa/geocoding](https://github.com/IceMimosa/geocoding) 进行封装,因此该模块需要Java环境的支持(需要添加JAVA_HOME等环境变量) +* `GeocodingCHN`重新加载功能在Windows平台上可能会遇到错误，参考[Jpype Changelog](https://jpype.readthedocs.io/en/latest/CHANGELOG.html) 1.2.0 - 2020-11-29 更新信息。 * 安装命令 `pip install GeocodingCHN` -## 地址标准化 -`Geocoding.normalizing(address) ` + +## 更新信息： +随[原项目](https://github.com/IceMimosa/geocoding)更新jar包,并适配新增功能。 [新增功能](https://github.com/bitlap/geocoding/releases/tag/v1.3.0)： +- [x] `GeocodingCHN.Geocoding`新增参数设定（为适配`org.bitlap.geocoding.GeocodingX`类） + * 新增`data_class_path`参数，支持自定义地址文件路径，要求该路径为文件绝对路径，默认使用内置地址`core/region.dat` + * 新增`strict`参数，默认 `False`。当发现没有省和市，且匹配的父项数量等于1时，能成功匹配。 + * `True`: 严格模式，当发现没有省和市，且匹配的父项数量大于1时，返回 `None` + * `False`: 非严格模式，当发现没有省和市，且匹配的父项数量大于1时，匹配随机一项省和市 + * 新增`jvm_path`，允许设置JVM路径，但要求该路径为文件绝对路径 +- [x] `addRegionEntry` 方法新增 `replace` 参数，表示是否替换旧地址，默认为`True` + +其他更新： +-[x] 区分 `similarityWithResult` 与 `similarity` 方法，`similarityWithResult` 返回MatchedResult类型结果，`similarity` 返回float类型结果 +-[x] 封装分词方法 `segment` + +## GeocodingCHN.Geocoding +```python +from GeocodingCHN import Geocoding +geocoding = Geocoding(data_class_path="core/region.dat", + strict= False, + jvm_path= None) +``` +* data_class_path : 自定义地址文件路径 +* strict : 模式设置 +* jvm_path : JVM路径 + +### GeocodingCHN.Geocoding.normalizing +提供地址标准化 + +`normalizing(address) -> Address` * address: 文本地址 + ```python from GeocodingCHN import Geocoding +geocoding = Geocoding() text = '山东青岛李沧区延川路116号绿城城园东区7号楼2单元802户' -address_nor = Geocoding.normalizing(text) +address_nor = geocoding.normalizing(text) print(address_nor) ``` ``` @@ -26,34 +65,64 @@ Address( text=绿城城园东区 ) ``` -## 地址相似度计算 -`Geocoding.similarityWithResult(Address1, Address2)` -* Address1: 地址1, 由 Geocoding.normalizing 方法返回的 Address 类 -* Address2: 地址2, 由 Geocoding.normalizing 方法返回的 Address 类 +### GeocodingCHN.Geocoding.similarityWithResult +地址相似度计算，返回详细结果 + +`similarityWithResult(Address1:Address, Address2:Address) -> MatchedResult` +* Address1: 地址1, 由 normalizing 方法返回的 Address 类 +* Address2: 地址2, 由 normalizing 方法返回的 Address 类 +```python +from GeocodingCHN import Geocoding +geocoding = Geocoding() +text1 = '山东青岛李沧区延川路116号绿城城园东区7号楼2单元802户' +text2 = '山东青岛李沧区延川路绿城城园东区7-2-802' +Address_1 = geocoding.normalizing(text1) +Address_2 = geocoding.normalizing(text2) +print(geocoding.similarityWithResult(Address_1, Address_2)) +``` +``` +MatchedResult( + doc1=Document(terms=[Term(延川路), Term(116号), Term(7), Term(2), Term(802), Term(绿城), Term(城), Term(园), Term(东区)], town=None, village=None, road=Term(延川路), roadNum=Term(116号), roadNumValue=116), + doc2=Document(terms=[Term(延川路), Term(7), Term(2), Term(802), Term(绿城), Term(城), Term(园), Term(东区)], town=None, village=None, road=Term(延川路), roadNum=None, roadNumValue=0), + terms=['MatchedTerm(Term(延川路), coord=-1.0, density=-1.0, boost=2.0, tfidf=8.0)', 'MatchedTerm(Term(7), coord=-1.0, density=-1.0, boost=1.0, tfidf=2.0)', 'MatchedTerm(Term(2), coord=-1.0, density=-1.0, boost=1.0, tfidf=2.0)', 'MatchedTerm(Term(802), coord=-1.0, density=-1.0, boost=1.0, tfidf=2.0)', 'MatchedTerm(Term(绿城), coord=1.0, density=1.0, boost=1.0, tfidf=4.0)', 'MatchedTerm(Term(城), coord=1.0, density=1.0, boost=1.0, tfidf=4.0)', 'MatchedTerm(Term(园), coord=1.0, density=1.0, boost=1.0, tfidf=4.0)', 'MatchedTerm(Term(东区), coord=1.0, density=1.0, boost=1.0, tfidf=4.0)'], + similarity=0.9473309334313418 +) +``` +### GeocodingCHN.Geocoding.similarity +地址相似度计算 + +`similarityWithResult(Address1:Address, Address2:Address)` +* Address1: 地址1, 由 normalizing 方法返回的 Address 类 +* Address2: 地址2, 由 normalizing 方法返回的 Address 类 ```python from GeocodingCHN import Geocoding +geocoding = Geocoding() text1 = '山东青岛李沧区延川路116号绿城城园东区7号楼2单元802户' text2 = '山东青岛李沧区延川路绿城城园东区7-2-802' -Address_1 = Geocoding.normalizing(text1) -Address_2 = Geocoding.normalizing(text2) -similar = Geocoding.similarityWithResult(Address_1, Address_2) -print(similar) +Address_1 = geocoding.normalizing(text1) +Address_2 = geocoding.normalizing(text2) +print(geocoding.similarity(Address_1, Address_2)) ``` ``` 0.9473309334313418 ``` -## 添加自定义地址 -`Geocoding.addRegionEntry(Id, parentId, name, RegionType, alias='')` + +### GeocodingCHN.Geocoding.addRegionEntry +添加自定义地址 + +`addRegionEntry(Id, parentId, name, RegionType, alias='', replace=True) -> bool` * Id: 地址的ID * parentId: 地址的父ID, 必须存在 * name: 地址的名称 * RegionType: RegionType,地址类型 * alias: 地址的别名, default='' -* return: bool +* replace: 是否替换旧地址, default=True ```python from GeocodingCHN import Geocoding -Geocoding.addRegionEntry(1, 321200000000, "A街道", Geocoding.RegionType.Street) -test_address = Geocoding.normalizing("江苏泰州A街道") +geocoding = Geocoding() +geocoding.addRegionEntry(1, 321200000000, "A街道", geocoding.RegionType.Street) +address_nor = geocoding.normalizing("江苏泰州A街道") +print(address_nor) ``` ``` Address( @@ -69,4 +138,22 @@ Address( text= ) ``` +### GeocodingCHN.Geocoding.segment +分词 + +`segment(text: str, seg_type: str = 'ik') -> list` +* text: 输入 +* seg_type: 支持 ['ik', 'simple', 'smart', 'word']，default = 'ik' +```python +from GeocodingCHN import Geocoding +geocoding = Geocoding() +text = '山东青岛李沧区延川路绿城城园东区7-2-802' +print(geocoding.segment(text)) +``` +``` +['山东', '青岛', '李沧区', '延川路', '绿城', '城', '园', '东区', '7-2-802'] +``` +# 感谢 +* 感谢[原作者](https://github.com/IceMimosa/geocoding)的辛苦付出！ +* 感谢[原作者](https://github.com/IceMimosa/geocoding)的感谢！