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:\Users\Administrator\Desktop\SMSSpamCollection.csv','r',encoding='utf-8')as file_path:
sms=file_path.read()
# print(sms)
sms_data=[]
sms_label=[]
reader=csv.reader(sms,delimiter='\t')
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',
