垃圾

#导入邮件数据

1 2 3 4 5 6 7 8 9 10 11

import csv file_path=r'F:\Pycharm\11.22\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

#将数据分类并对模型进行类别预测

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

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)      from sklearn.feature_extraction.text import TfidfVectorizer  #向量化 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_text=vectorizer.transform(x_test)   X_train a=X_train.toarray() print(a)   for i in range(1000):     for j in range(5984):         if a[i,j]!=0:             print(i,j,a[i,j])       from sklearn.navie_bayes import MultinomialNB  #导入贝叶斯分类器 clf= MultinomialNB().fit(X_train,y_train) y_nb_pred=clf.predict(X_test)     from sklearn.metrics import confusion_matrix #导入混淆矩阵 from sklearn.metrics import classification_report  #分类报告   print(y_nb_pred.shape,y_nb_pred)#预测结果 print('nb_confusion_matrix:') cm=confusion_matrix(y_test,y_nb_pred) print(cm) print('nb_classification_report:') cr=classification_report(y_test,y_nb_pred)#分类指标文本报告 print(cr)   feature_name=vectorizer.get_feature_name() coefs=clf_coef_ intercept=clf.intercept_ coefs_with_fns=sorted(zip(coefs[0],feature_names))#对数概率p(x_i|y)与单词x_i映射   n=10 top=zip(coefs_with_fns[:n],coefs_with_fns[:-(n+1):-1])#最大的10个单词与最小的10个单词 for (coef_1,fn_1),(coef_2,fn_2) in top:     print('\t%.4f\t%-15s\t\t%.4f\t%-15s' % (coef_1,fn_1,coef_2,fn_2))

#运行结果