13.垃圾邮件分类2

时间：2020-05-23 00:17:30 阅读：55 评论：0 收藏：0 [点我收藏+]

标签：lower int img idt positive https modules word code

1.读取

file_path = r‘D:\DingDing\main\current\download\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]))  # 对每封邮件做预处理
sms.close()

2.数据预处理

# 根据词性，生成还原参数 pos
def get_wordnet_pos(treebank_tag):
    if treebank_tag.startswith("J"):
        return nltk.corpus.wordnet.ADJ
    elif treebank_tag.startswith("V"):
        return nltk.corpus.wordnet.VERB
    elif treebank_tag.startswith("N"):
        return nltk.corpus.wordnet.NOUN
    elif treebank_tag.startswith("R"):
        return nltk.corpus.wordnet.ADV
    else:
        return nltk.corpus.wordnet.NOUN


# 预处理
def preprocessing(text):
    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]  # 分词
    stops = stopwords.words(‘english‘)  # 停用词
    tokens = [token for token in tokens if token not in stops]  # 去掉停用词
    tokens = [token.lower() for token in tokens if len(token) >= 3]

    tag = nltk.pos_tag(tokens)  # 词性标注
    imtzr = WordNetLemmatizer()
    tokens = [imtzr.lemmatize(token, pos=get_wordnet_pos(tag[i][1])) for i, token in enumerate(tokens)]  # 词性还原
    preprocessed_text = ‘‘.join(tokens)
    return preprocessed_text

3.数据划分—训练集和测试集数据划分

from sklearn.model_selection import train_test_split

x_train,x_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=0, stratify=y_train)

# 数据划分
from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(sms_data, sms_label, test_size=0.2, random_state=0,
                                                    stratify=sms_label)

print(‘原数据长度:‘, len(sms_data), ‘\n训练数据长度:‘, len(x_train), ‘\n测试数据长度:‘, len(x_test))

技术图片

4.文本特征提取

sklearn.feature_extraction.text.CountVectorizer

https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html?highlight=sklearn%20feature_extraction%20text%20tfidfvectorizer

sklearn.feature_extraction.text.TfidfVectorizer

https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html?highlight=sklearn%20feature_extraction%20text%20tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer

from sklearn.feature_extraction.text import TfidfVectorizer

tfidf2 = TfidfVectorizer()

# 向量化
from sklearn.feature_extraction.text import TfidfVectorizer

tfidf2 = TfidfVectorizer()
X_train = tfidf2.fit_transform(x_train)
X_test = tfidf2.transform(x_test)
print(‘X_train矩阵长度：‘, X_train.toarray().shape, ‘\nX_test矩阵长度：‘, X_test.toarray().shape)
print(‘邮件以及向量关系矩阵：\n‘, X_train.toarray())
print(‘词汇表：\n‘, tfidf2.vocabulary_)

观察邮件与向量的关系

技术图片

向量还原为邮件

# 向量还原成邮件
import numpy as np
s = X_train.toarray()[0]    # 邮件0的向量
a = np.flatnonzero(s)       # 非零元素的位置（index）
s[a]                        # 向量的非0元素
b = tfidf2.vocabulary_      # 词汇表
key_list = []
for key,value in b.items():
    if value in a:
        key_list.append(key)    # key非0元素对应的单词
key_list      # 向量非零元素对应的单词
x_train[0]    # 向量化之前的邮件

技术图片

4.模型选择

from sklearn.naive_bayes import GaussianNB

from sklearn.naive_bayes import MultinomialNB

说明为什么选择这个模型？

# 模型构建
from sklearn.naive_bayes import MultinomialNB

mnb = MultinomialNB()
mnb.fit(X_train, y_train)      # 训练
y_mnb = mnb.predict(X_test)   # 预测结果

因为高斯分布模型适合于正态分布将特征连续型变量的值转换成离散型变量的值；而多项式分布模型则适合于文本分类问题，其中单词为特征，单词出现的次数为值，所以选择该模型比较合适。

5.模型评价：混淆矩阵，分类报告

from sklearn.metrics import confusion_matrix

confusion_matrix = confusion_matrix(y_test, y_predict)

说明混淆矩阵的含义

from sklearn.metrics import classification_report

说明准确率、精确率、召回率、F值分别代表的意义

# 模型评价：混淆矩阵，分类报告
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

cm = confusion_matrix(y_test, y_mnb)     # 真实值和预测值传入比对
print(cm)
cr = classification_report(y_test, y_mnb)
print(cr)
print(‘准确度:‘, (cm[0][0]+cm[1][1])/np.sum(cm))