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https://github.com/observerss/ngender
根据姓名来判断性别
https://github.com/observerss/ngender
Last synced: 4 days ago
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根据姓名来判断性别
- Host: GitHub
- URL: https://github.com/observerss/ngender
- Owner: observerss
- License: other
- Created: 2015-05-21T08:09:34.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2020-02-27T01:47:02.000Z (almost 5 years ago)
- Last Synced: 2024-12-01T01:03:37.114Z (11 days ago)
- Language: Python
- Size: 195 KB
- Stars: 612
- Watchers: 15
- Forks: 166
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-github-star - ngender
README
# NGender
根据中文姓名猜测其性别
- 不到20行纯Python代码(核心部分)
- 无任何依赖库
- 兼容python3, python2, pypy
- 82%的准确率
- 可用于猜测性别
- 也可用于判断名字的男性化/女性化程度
## 使用
> pip install ngender
或者(OSX)
> brew install https://raw.githubusercontent.com/observerss/homebrew/61b3623967dc9507958dfb517e7f746baa96dcf1/Library/Formula/ngender.rb
然后在命令行中
```bash
$ ng 赵本山 宋丹丹
name: 赵本山 => gender: male, probability: 0.9836229687547046
name: 宋丹丹 => gender: female, probability: 0.9759486128949907
```
当然也可以在Python程序中用
```py
>>> import ngender
>>> ngender.guess('赵本山')
('male', 0.9836229687547046)
>>> ngender.guess('宋丹丹')
('female', 0.9759486128949907)
>>> %timeit guess('宋丹丹')
100000 loops, best of 3: 4.01 µs per loop
```
## 原理
### 数学
贝叶斯公式: ```P(Y|X) = P(X|Y) * P(Y) / P(X)```
当X条件独立时, ```P(X|Y) = P(X1|Y) * P(X2|Y) * ...```
应用到猜名字上
```
P(gender=男|name=本山)
= P(name=本山|gender=男) * P(gender=男) / P(name=本山)
= P(name has 本|gender=男) * P(name has 山|gender=男) * P(gender=男) / P(name=本山)
```
### 计算
0. 文件`charfreq.csv`是怎么来的?
曾经有个东西叫开房记录.avi(雾)，里面有名字和性别, 2000w条, 统计一下得出
0. 怎么算 `P(name has 本|gender=男)`?
“本”在男性名字中出现的次数 / 男性字出现的总次数
0. 怎么算 `P(gender=男)`?
男性名出现的次数 / 总次数
0. 怎么算 `P(name=本山)`?
不用算, 在算概率的时候会互相约去
## 坑
```py
>>> ngender.guess('李胜男')
('male', 0.851334658742)
```
虽然两个字都很偏男性，但是结合起来就是女性名