13-垃圾邮件分类2
1.读取
file_path = r'D:\mry197\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
#预处理 def preprocessing(text): tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] #用nltk做分词 stops = stopwords.words('english') #停用词 tokens = [token for token in tokens if token not in stops] #去掉停用词 lemmatizer = WordNetLemmatizer() tag = nltk.pos_tag(tokens) #词性标注 newtokens = [] for i, token in enumerate(tokens): if token: pos = get_wordnet_pos(tag[i][1]) if pos: word = lemmatizer.lemmatize(token, pos) newtokens.append(word) return newtokens
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)
4.文本特征提取
sklearn.feature_extraction.text.CountVectorizer
sklearn.feature_extraction.text.TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf2 = TfidfVectorizer()
观察邮件与向量的关系
向量还原为邮件
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?
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
6.比较与总结
如果用CountVectorizer进行文本特征生成,与TfidfVectorizer相比,效果如何?