朴素贝叶斯python代码实现

朴素贝叶斯优点：在数据较少的状况下仍然有效，能够解决多类别问题缺点：对于输入数据的准备方式较为敏感合用数据类型：标称型数据贝叶斯准则：使用朴素贝叶斯进行文档分类朴素贝叶斯的普通过程（1）收集数据：能够使用任何办法。本文使用RSS源（2）准备数据：需要数值型或者布尔型数据（3）分析数据：有大量特性时，绘制特性作用不大，此时使用直方图效果更加好（4）训练算法：计算不同的独立特性的条件概率（5）测试算法：计算错误率（6）使用算法：一种常见的朴素贝叶斯应用是文档分类。能够在任意的分类场景中使用朴素贝叶斯分类器，不一定非要是文本。准备数据：从文本中构建词向量摘自机器学习实战。[['my','dog','has','flea','problems','help','please'],0['maybe','not','take','him','to','dog','park','stupid'],1['my','dalmation','is','so','cute','I','love','him'],0['stop','posting','stupid','worthless','garbage'],1['mr','licks','ate','my','steak','how','to','stop','him'],0['quit','buying','worthless','dog','food','stupid']]1以上是六句话，标记是0句子的表达正常句，标记是1句子的表达为粗口。我们通过分析每个句子中的每个词，在粗口句或是正常句出现的概率，能够找出那些词是粗口。在bayes.py文献中添加以下代码：[python]

\o"viewplain"viewplain\o"copy"copy#

coding=utf-8

def

loadDataSet():

postingList

=

[['my',

'dog',

'has',

'flea',

'problems',

'help',

'please'],

['maybe',

'not',

'take',

'him',

'to',

'dog',

'park',

'stupid'],

['my',

'dalmation',

'is',

'so',

'cute',

'I',

'love',

'him'],

['stop',

'posting',

'stupid',

'worthless',

'garbage'],

['mr',

'licks',

'ate',

'my',

'steak',

'how',

'to',

'stop',

'him'],

['quit',

'buying',

'worthless',

'dog',

'food',

'stupid']]

classVec

=

[0,

1,

0,

1,

0,

1]

#

1代表侮辱性文字，0代表正常言论

return

postingList,

classVec

def

createVocabList(dataSet):

vocabSet

=

set([])

for

document

in

dataSet:

vocabSet

=

vocabSet

|

set(document)

return

list(vocabSet)

def

setOfWords2Vec(vocabList,

inputSet):

returnVec

=

[0]

*

len(vocabList)

for

word

in

inputSet:

if

word

in

vocabList:

returnVec[vocabList.index(word)]

=

1

else:

print

"the

word:

%s

is

not

in

my

Vocabulary!"

%

word

return

returnVec

运行成果：训练算法：从词向量计算概率[python]

\o"viewplain"viewplain\o"copy"copy#

朴素贝叶斯分类器训练函数

#

trainMatrix:

文档矩阵，

trainCategory:

由每篇文档类别标签所构成的向量

def

trainNB0(trainMatrix,

trainCategory):

numTrainDocs

=

len(trainMatrix)

numWords

=

len(trainMatrix[0])

pAbusive

=

sum(trainCategory)

/

float(numTrainDocs)

p0Num

=

zeros(numWords);

p1Num

=

zeros(numWords);

p0Denom

=

0.0;

p1Denom

=

0.0;

for

i

in

range(numTrainDocs):

if

trainCategory[i]

==

1:

p1Num

+=

trainMatrix[i]

p1Denom

+=

sum(trainMatrix[i])

else:

p0Num

+=

trainMatrix[i]

p0Denom

+=

sum(trainMatrix[i])

p1Vect

=

p1Num

/

p1Denom

p0Vect

=

p0Num

/

p1Denom

return

p0Vect,

p1Vect,

pAbusive

运行成果：测试算法：根据现状修改分类器上一节中的trainNB0函数中修改几处：p0Num

=

ones(numWords);p1Num

=

ones(numWords);p0Denom

=

2.0;p1Denom

=

2.0;p1Vect

=

log(p1Num

/

p1Denom)p0Vect

=

log(p0Num

/

p1Denom)[python]

\o"viewplain"viewplain\o"copy"copy#

朴素贝叶斯分类器训练函数

#

trainMatrix:

文档矩阵，

trainCategory:

由每篇文档类别标签所构成的向量

def

trainNB0(trainMatrix,

trainCategory):

numTrainDocs

=

len(trainMatrix)

numWords

=

len(trainMatrix[0])

pAbusive

=

sum(trainCategory)

/

float(numTrainDocs)

p0Num

=

ones(numWords);

p1Num

=

ones(numWords);

p0Denom

=

2.0;

p1Denom

=

2.0;

for

i

in

range(numTrainDocs):

if

trainCategory[i]

==

1:

p1Num

+=

trainMatrix[i]

p1Denom

+=

sum(trainMatrix[i])

else:

p0Num

+=

trainMatrix[i]

p0Denom

+=

sum(trainMatrix[i])

p1Vect

=

log(p1Num

/

p1Denom)

p0Vect

=

log(p0Num

/

p1Denom)

return

p0Vect,

p1Vect,

pAbusive

#

朴素贝叶斯分类函数

def

classifyNB(vec2Classify,

p0Vec,

p1Vec,

pClass1):

p1

=

sum(vec2Classify

*

p1Vec)

+

log(pClass1)

p0

=

sum(vec2Classify

*

p0Vec)

+

log(1.0

-

pClass1)

if

p1

>

p0:

return

1

else:

return

0

def

testingNB():

listOPosts,

listClasses

=

loadDataSet()

myVocabList

=

createVocabList(listOPosts)

trainMat

=

[]

for

postinDoc

in

listOPosts:

trainMat.append(setOfWords2Vec(myVocabList,

postinDoc))

p0V,

p1V,

pAb

=

trainNB0(array(trainMat),

array(listClasses))

testEntry

=

['love',

'my',

'dalmation']

thisDoc

=

array(setOfWords2Vec(myVocabList,

testEntry))

print

testEntry,

'classified

as:

',

classifyNB(thisDoc,

p0V,

p1V,

pAb)

testEntry

=

['stupid',

'garbage']

thisDoc

=

array(setOfWords2Vec(myVocabList,

testEntry))

print

testEntry,

'classified

as:

',

classifyNB(thisDoc,

p0V,

p1V,

pAb)

运行成果：准备数据：文档词袋模型词集模型（set-of-words

model）：每个词与否出现，每个词只能出现一次词袋模型（bag-of-words

model）：一种词能够出现不止一次[python]

\o"viewplain"viewplain\o"copy"copy#

朴素贝叶斯词袋模型

def

bagOfWords2VecMN(vocabList,

inputSet):

returnVec

=

[0]

*

len(vocabList)

for

word

in

inputSet:

if

word

in

vocabList:

returnVec[vocabList.index(word)]

+=

1

return

returnVec

示例：使用朴素贝叶斯过滤垃圾邮件（1）收集数据：提供文本文献（2）准备数据：将文本文献解析成词条向量（3）分析数据：检查词条确保解析的对的性（4）训练算法：使用我们之前建立的trainNB0()函数（5）测试算法：使用classifyNB()，并且构建一种新的测试函数来计算文档集的错误率（6）使用算法：构建一种完整的程序对一组文档进行分类，将错分的文档输出到屏幕上准备数据：切分文本使用正则体现式切分句子测试算法：使用朴素贝叶斯进行交叉验证[python]

\o"viewplain"viewplain\o"copy"copy#

该函数接受一种大写字符的字串，将其解析为字符串列表

#

该函数去掉少于两个字符的字符串，并将全部字符串转换为小写

def

textParse(bigString):

import

re

listOfTokens

=

re.split(r'\W*',

bigString)

return

[tok.lower()

for

tok

in

listOfTokens

if

len(tok)

>

2]

#

完整的垃圾邮件测试函数

def

spamTest():

docList

=

[]

classList

=

[]

fullText

=

[]

#

导入并解析文本文献

for

i

in

range(1,

26):

wordList

=

textParse(open('email/spam/%d.txt'

%

i).read())

docList.append(wordList)

fullText.extend(wordList)

classList.append(1)

wordList

=

textParse(open('email/ham/%d.txt'

%

i).read())

docList.append(wordList)

fullText.extend(wordList)

classList.append(0)

vocabList

=

createVocabList(docList)

trainingSet

=

range(50)

testSet

=

[]

#

随机构建训练集

for

i

in

range(10):

randIndex

=

int(random.uniform(0,

len(trainingSet)))

testSet.append(trainingSet[randIndex])

del(trainingSet[randIndex