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name_gender.py
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name_gender.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""通过姓名(汉字或拼音,猜测性别)
还能自动取名。这是你见过的最好玩的Bayes分类项目。*_*
"""
import pandas as pd
import numpy as np
import nltk
try:
import pypinyin
except:
print('If you want to use pinyin instead of characters, then you have to download pypinyin')
PATH = 'namelist.xls' # name list of students in zjc
classes = ['xinji%d' % n for n in range(13, 19)] + ['xinjiang1', 'xinjiang2']
for n, class_ in enumerate(classes):
df = pd.read_excel(PATH, sheet_name=class_)
if not (set(df.columns) & {'学号','no','No', 'no.', 'No.'}):
df = pd.read_excel(PATH, sheet_name=class_, skiprows=1)
if 'name' in df.columns:
name_key = 'name'
else:
name_key = '姓名'
if 'gender' in df.columns:
gender_key = 'gender'
else:
gender_key = '性别'
df = df[[name_key, gender_key]]
df.dropna()
if n == 0:
data = df.values
else:
data = np.vstack((data, df.values))
# get raw data from xls file
df = pd.DataFrame(data=data, columns=('name', 'gender'))
def get_feature(name):
"""name -> feature dict
feature:
X1: 最后第二个字或空,X2: 最后一个字
"""
if len(name)==2:
return {'first':'', 'second':name[-1]}
elif len(name)>=3:
return {'first':name[-2], 'second':name[-1]}
else:
print(name, 'is a invalid name!')
return {'first':'', 'second':''}
def get_feature_pinyin(name):
"""name -> feature dict
feature:
X1: 第二个字的拼音或空,X2: 最后一个字的拼音
"""
if len(name)==2:
return {'first':'', 'second':name[-1]}
else:
return {'first':pypinyin.lazy_pinyin(name[-2])[0], 'second':pypinyin.lazy_pinyin(name[-1])[0]}
# get all data
def get_data(df, get_feature=get_feature):
# dataframe -> List[(feature dict, label),...]
featrues = []
for k, row in df.iterrows():
name = row['name']; gender = row['gender']
if isinstance(name, str):
if ' ' in name:
name = name.replace(' ', '')
if '(' not in name:
featrues.append((get_feature(name), gender.strip('() ')))
else:
name = name.partition('(')[0]
featrues.append((get_feature(name), gender.strip('() ')))
return featrues
def get_train_test(featrues, ratio=0.9):
# 分割训练数据集、测试数据集
N = len(featrues)
T = int(N * ratio)
train = featrues[:T]
test = featrues[T:]
return train, test
def gender_classifier(df, f=get_feature):
data = get_data(df, f)
train, test = get_train_test(data)
classifier = nltk.NaiveBayesClassifier.train(train)
acc = nltk.classify.accuracy(classifier, test)
return classifier, acc
def show_gender(name, pinyin=False, show_acc=False):
# 姓名 -> 性别
f = get_feature_pinyin if pinyin else get_feature
classifier, acc = gender_classifier(df, f)
if show_acc:
print(f'精确度: {acc:.4}')
# predict
gender = classifier.classify(f(name))
print(f'{name}: {gender}')
classifier.show_most_informative_features(10)
def give_name(first='欣', gender='女'):
# 自动取名
def get_data_(df, get_feature=get_feature):
data = get_data(df, get_feature)
return [({'gender':g, 'first':n['first']}, n['second']) for n, g in data]
data = get_data_(df, get_feature)
classifier = nltk.NaiveBayesClassifier.train(data)
following = classifier.prob_classify({'gender':gender, 'first':first})
x = following.generate()
print(f'{gender}: {first}{x}')
if __name__ == '__main__':
print('With Chinese:')
show_gender("蔡徐坤")
print('With Pinyin:')
show_gender("曹楚奇", True)
print('取名字:(给出性别和第一个字)')
give_name(first='红')