朴素贝叶斯应用:垃圾邮件分类
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import nltk nltk.download() from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer #预处理 def preprocessing(text): tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokrnize(sent)] stops = stopwords.words(‘english‘) 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)>=2] #去掉长度小于2的词 lmtzr = WordNetLemmatizer() tokens = (lmtzr.lemmatize(token) for token in tokens) #词性还原 preprocessed_text = ‘ ‘.join(tokens) return preprocessed_text #读取数据集 import csv file_path = r‘C:UsersAdministratorDesktopSMSSpamCollectionjsn.txt‘ sms = open(file_path,‘r‘,encoding=‘utf-8‘) sms_data = [] sms_label = [] csv_reader = csv.reader(sms,delimiter = ‘ ‘) for line in csv_reader: sms_label.append(line[0]) sms_data.append(preprocessing(line[1])) sms.close() #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(sms_data,sms_label,test_size = 0.3,random_state=0,stratify=sms_label) #将其向量化 from sklearn.feature_extraction.text import TfidfVectorizer vectorizer = TfidfVectorizer(min_df=2,ngram_range=(1,2),stop_words=‘english‘,strip_accents=‘unicode‘,norm=‘12‘) X_train = vectorizer.fit_transform(x_train) X_test = vectorizer.transform(x_test) #朴素贝叶斯分类器 from sklearn.navie_bayes import MultinomiaNB clf = MultinomiaNB().fit(X_train,y_train) #测试模型 y_nb_pred = clf.predict(X_test) #测试模型:结果显示 from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report print(y_nb_pred.shape,y_nb_pred) #x_test预测结果 print(‘nb_confusion_matrix:‘) cm = confusion_matrix(y_test,y_nb_pred)#混淆矩阵 print(cm) print(‘nb_classification_report:‘) cr = classification_report(y_test,y_nb_pred) #主要分类指标的文本报告 print(cr)
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