sklearn中的朴素贝叶斯模型及其应用

from sklearn import datasets
iris=datasets.load_iris()
from sklearn.naive_bayes import GaussianNB
gnb=GaussianNB()
pred=gnb.fit(iris.data,iris.target)
y_pred=pred.predict(iris.data)
print(iris.data.shape[0],(iris.target!=y_pred).sum())

150 6



iris.target


array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])



y_pred

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])



from sklearn import datasets
iris=datasets.load_iris()
from sklearn.naive_bayes import BernoulliNB
gnb=BernoulliNB()
pred=gnb.fit(iris.data,iris.target)
y_pred=pred.predict(iris.data)
print(iris.data.shape[0],(iris.target!=y_pred).sum())


150 100


iris.target

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])


y_pred

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])



from sklearn import datasets
iris=datasets.load_iris()
from sklearn.naive_bayes import  MultinomialNB
gnb= MultinomialNB()
pred=gnb.fit(iris.data,iris.target)
y_pred=pred.predict(iris.data)
print(iris.data.shape[0],(iris.target!=y_pred))

150 [False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False  True False  True False
  True False False False False False False False False False False  True
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False  True False  True
 False  True False False False False False False False False False False
 False False False False False False]


iris.target

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

y_pred

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1,
       2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])


from  sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score
gnb=GaussianNB()
scores=cross_val_score(gnb,iris.data,iris.target,cv=10)
print("Accuracy:%.15f"%scores.mean())


Accuracy:0.953333333333333

scores
array([0.93333333, 0.93333333, 1.        , 0.93333333, 0.93333333,
       0.93333333, 0.86666667, 1.        , 1.        , 1.        ])

from sklearn.naive_bayes import BernoulliNB
from sklearn.model_selection import cross_val_score
gnb=BernoulliNB()
scores=cross_val_score(gnb,iris.data,iris.target,cv=10)
print("Acdcuracy:%.3f"%scores.mean())

Acdcuracy:0.333

scores
array([0.33333333, 0.33333333, 0.33333333, 0.33333333, 0.33333333,
       0.33333333, 0.33333333, 0.33333333, 0.33333333, 0.33333333])

from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import cross_val_score
gnb=MultinomialNB()
scores=cross_val_score(gnb,iris.data,iris.target,cv=10)
print("Acdcuracy:%.15f"%scores.mean())

Acdcuracy:0.953333333333333


scores

array([1.        , 1.        , 1.        , 0.93333333, 0.86666667,
       0.93333333, 0.8       , 1.        , 1.        , 1.        ])


import csv
with open(r'd:/SMSSpamCollectionjsn.txt',encoding = "utf-8")as file_path:
# with open('C:UsersAdministratorDesktopSMSSpamCollection.csv','r',encoding='utf-8')as file_path:
    sms=file_path.read()
# print(sms)
sms_data=[]
sms_label=[]
reader=csv.reader(sms,delimiter='	')
for  line in reader:
    sms_label.append(line[0])
    sms_data.append(line[1])
sms.colse()
 sms_data



cc=sms.replace('.',' ')
cclist=sms.split()
print(len(cc),cclist)
ccset=set(cclist)
print(ccset)
strDict={}
for star in ccset:
    strDict[star]=sms.count(star)
for key in ccset:
    print(key,strDict[key])
wclist=list(ccsetr.items())
print(wclist)
def takeSecond(elem):
    return elem[1]
wclist.sort(key=takeSecond,reverse=True)
print(wclist)



',', 'I', 'need', 'you,', 'I', 'crave', 'you', '...', 'But', 'most', 'of', 'all', '...', 'I', 'love', 'you', 'my', 'sweet', 'Arabian', 'steed', '...', 'Mmmmmm', '...', 'Yummy"', 'spam', '07732584351', '-', 'Rodger', 'Burns', '-', 'MSG', '=', 'We', 'tried', 'to', 'call', 'you', 're', 'your', 'reply', 'to', 'our', 'sms', 'for', 'a', 'free', 'nokia', 'mobile', '+', 'free', 'camcorder.', 'Please', 'call', 'now', '08000930705', 'for', 'delivery', 'tomorrow', 'ham', 'WHO', 'ARE', 'YOU', 'SEEING?', 'ham', 'Great!', 'I', 'hope', 'you', 'like', 'your', 'man', 'well', 'endowed.', 'I', 'am', '<#>', 'inches...', 'ham', 'No', 'calls..messages..missed', 'calls', 'ham', "Didn't", 'you', 'get', 'hep', 'b', 'immunisation', 'in', 'nigeria.', 'ham', '"Fair', 'enough,', 'anything', 'going', 'on?"', 'ham', '"Yeah', 'hopefully,', 'if', 'tyler', "can't", 'do', 'it', 'I', 'could', 'maybe', 'ask', 'around', 'a', 'bit"', 'ham', 'U', "don't", 'know', 'how', 'stubborn', 'I', 'am.', 'I', "didn't", 'even', 'want', 'to', 'go', 'to', 'the', 'hospital.', 'I', 'kept', 'telling', 'Mark', "I'm", 'not', 'a', 'weak', 'sucker.', 'Hospitals', 'are', 'for', 'weak', 'suckers.', 'ham', 'What', 'you', 'thinked', 'about', 'me.', 'First', 'time', 'you', 'saw', 'me', 'in', 'class.', 'ham', '"A', 'gram', 'usually', 'runs', 'like', '<#>', ',', 'a', 'half', 'eighth', 'is', 'smarter', 'though'



from nltk.corpus import stopwords
stops=stopwords.words('english')
stops


['i',
 'me',
 'my',
 'myself',
 'we',
 'our',
 'ours',
 'ourselves',
 'you',
 "you're",
 "you've",
 "you'll",
 "you'd",
 'your',
 'yours',
 'yourself',
 'yourselves',
 'he',
 'him',
 'his',
 'himself',
 'she',