1、one-hot
一般是针对于标签而言，比如现在有猫：0，狗：1，人：2，船：3，车：4这五类，那么就有：
猫：[1,0,0,0,0]
狗：[0,1,0,0,0]
人：[0,0,1,0,0]
船：[0,0,0,1,0]
车：[0,0,0,0,1]
代码语言：javascript复制from sklearn import preprocessing
import numpy as np
enc = OneHotEncoder(sparse = False)
labels=[0,1,2,3,4]
labels=np.array(labels).reshape(len(labels),-1)
ans = enc.fit_transform(labels)结果：array([[1., 0., 0., 0., 0.], [0., 1., 0., 0., 0.], [0., 0., 1., 0., 0.], [0., 0., 0., 1., 0.], [0., 0., 0., 0., 1.]])
2、Bags of Words
统计单词出现的次数并进行赋值。
代码语言：javascript复制import re
"""
corpus = [
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
'Is this the first document?',
]
"""
corpus = [
'Bob likes to play basketball, Jim likes too.',
'Bob also likes to play football games.'
]
#所有单词组成的列表
words=[]
for sentence in corpus:
#过滤掉标点符号
sentence=re.sub(r'[^ws]','',sentence.lower())
#拆分句子为单词
for word in sentence.split(" "):
if word not in words:
words.append(word)
else:
continue
word2idx={}
#idx2word={}
for i in range(len(words)):
word2idx[words[i]]=i
#idx2word[i]=words[i]
#按字典的值排序
word2idx=sorted(word2idx.items(),key=lambda x:x[1])import collections
BOW=[]
for sentence in corpus:
sentence=re.sub(r'[^ws]','',sentence.lower())
print(sentence)
tmp=[0 for _ in range(len(word2idx))]
for word in sentence.split(" "):
for k,v in word2idx:
if k==word:
tmp[v] =1
else:
continue
BOW.append(tmp)
print(word2idx)
print(BOW)输出：
代码语言：javascript复制bob likes to play basketball jim likes too
bob also likes to play football games
[('bob', 0), ('likes', 1), ('to', 2), ('play', 3), ('basketball', 4), ('jim', 5), ('too', 6), ('also', 7), ('football', 8), ('games', 9)]
[[1, 2, 1, 1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0, 1, 1, 1]]需要注意的是，我们是从单词表中进行读取判断其出现在句子中的次数。
在sklearn中的实现：
代码语言：javascript复制vectorizer = CountVectorizer()
vectorizer.fit_transform(corpus).toarray()结果：array([[0, 1, 1, 0, 0, 1, 2, 1, 1, 1], [1, 0, 1, 1, 1, 0, 1, 1, 1, 0]])
构建的单词的列表的单词的顺序不同，结果会稍有不同。
3、N-gram
核心思想：滑动窗口。来获取单词的上下文信息。
sklearn实现：
代码语言：javascript复制from sklearn.feature_extraction.text import CountVectorizer
corpus = [
'Bob likes to play basketball, Jim likes too.',
'Bob also likes to play football games.'
]
# ngram_range=(2, 2)表明适应2-gram,decode_error="ignore"忽略异常字符,token_pattern按照单词切割
ngram_vectorizer = CountVectorizer(ngram_range=(2, 2), decode_error="ignore",
token_pattern = r'bw b',min_df=1)
x1 = ngram_vectorizer.fit_transform(corpus) (0, 3) 1
(0, 6) 1
(0, 10) 1
(0, 8) 1
(0, 1) 1
(0, 5) 1
(0, 7) 1
(1, 6) 1
(1, 10) 1
(1, 2) 1
(1, 0) 1
(1, 9) 1
(1, 4) 1上面的第一列中第一个值标识句子顺序，第二个值标识滑动窗口单词顺序。与BOW相同，再计算每个窗口出现的次数。
[[0 1 0 1 0 1 1 1 1 0 1] [1 0 1 0 1 0 1 0 0 1 1]]
代码语言：javascript复制# 查看生成的词表
print(ngram_vectorizer.vocabulary_){
'bob likes': 3,
'likes to': 6,
'to play': 10,
'play basketball': 8,
'basketball jim': 1,
'jim likes': 5,
'likes too': 7,
'bob also': 2,
'also likes': 0,
'play football': 9,
'football games': 4
}
4、TF-IDF
TF-IDF分数由两部分组成：第一部分是词语频率(Term Frequency)，第二部分是逆文档频率(Inverse Document Frequency)
参考：
https://blog.csdn.net/u011311291/article/details/79164289
https://mp.weixin.qq.com/s/6vkz18Xw4USZ3fldd_wf5g
https://blog.csdn.net/jyz4mfc/article/details/81223572
