w12

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

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())

 

多项式型

 

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).sum())

 

 

伯努利型

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())

 

 

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())

 

多项式型

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())

 

伯努利型

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())

 

 

 

 

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

3. 垃圾邮件分类

数据准备：

• 用csv读取邮件数据，分解出邮件类别及邮件内容。
• 对邮件内容进行预处理：去掉长度小于3的词，去掉没有语义的词等

尝试使用nltk库：

pip install nltk

import nltk

nltk.download

不成功：就使用词频统计的处理方法

 

import nltk
nltk.download()

text = '''ham    "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat..."'''
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
def preprocessing(text):
    tokens=[woed for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]
    stops=stopwords.words('english')
    tokens=[token for token in tokens if token not in stops]
    tokens=[token.lower() for token in tokens if len(token)>=3]
    lmtzr= WordNetLemmatizer()
    tokens=[lmtzr.lemmatizer(token) for token in tokens]
    preprocessed_text=' '.join(tokens)
    return preprocessed_text
preprocessing(text)

import csv
file_path=r'C:\Users\Administrator\Desktop\SMSSpamCollectionjsn.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(len(sms_label))
sms_label

def preprocessing(text):
    preprocessing_text = text
    return preprocessed_text

import csv
file_path=r'H:\杜云梅\SMSSpamCollection'
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(preprocessing(line[1]))
sms.close()
sms_data

 

 

训练集和测试集数据划分

• from sklearn.model_selection import train_test_split

from sklearn.model_selection import train_test_split
x_train, x_text, y_train, y_test = train_test_split(sms_data, sms_label, test_size=0.3, random_state=0, stratify=sms_label)

x_train
x_test

from sklearn.naive_bayes import MultinomialNB
clf=MultinomialNB().fit(x_train,y_train)