作业十一

1.使用朴素贝叶斯模型对iris数据集进行花分类，尝试使用3种不同类型的朴素贝叶斯：高斯分布型，多项式型，伯努利型

#高斯分布型
import numpy as np
from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
iris=load_iris()
gnb=GaussianNB()
pred=gnb.fit(iris.data,iris.target)
y_pre=pred.predict(iris.data)

print("总数:",iris.data.shape[0],"错误个数:",(iris.target!=y_pre).sum())

#伯努利型
import numpy as np
from sklearn.datasets import load_iris
from sklearn.naive_bayes import BernoulliNB

iris=load_iris()
gnb=BernoulliNB()
pred=gnb.fit(iris.data,iris.target)
y_pre=pred.predict(iris.data)

print("总数:",iris.data.shape[0],"错误个数:",(iris.target!=y_pre).sum())

#多项式型
import numpy as np
from sklearn.datasets import load_iris
from sklearn.naive_bayes import MultinomialNB

iris=load_iris()
gnb=MultinomialNB()
pred=gnb.fit(iris.data,iris.target)
y_pre=pred.predict(iris.data)

print("总数:",iris.data.shape[0],"错误个数:",(iris.target!=y_pre).sum())

iris.data[66]

gnb.predict([[5.6, 3. , 4.5, 1.5]])

 2.使用sklearn.model_selection.cross_val_score()，对模型进行验证

#高斯分布型
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:%.3f"%scores.mean())

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("Accuracy:%.3f"%scores.mean())

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("Accuracy:%.3f"%scores.mean())

import csv
file_path=r'F:\sms.txt'
sms=open(file_path,'r',encoding='utf-8')
sms_data=[]
sms_label=[]
csv_reader=csv.reader(sms,delimiter='\t')
for line in csv_reader:
    sms_label.append(line[0])
    sms_data.append(line[1])
sms.close();
print("邮件总数:",len(sms_label))
sms_label

sms_data