import csv # 读数据 file_path = r'D:\垃圾邮箱邮件数据\SMSSpamCollectionjsn.txt' smsData = open(file_path,'r',encoding='utf-8') E_data = [] E_target = [] csv_reader = csv.reader(smsData,delimiter='\t') # 将数据分别存入数据列表和目标分类列表 for line in csv_reader: E_data.append(line[1]) E_target.append(line[0]) smsData.close() # 把无意符号替换成空格 E_data_clear = [] for line in E_data: # 去掉无意义符号并按空格分词 for char in line: if char.isalpha() is False: # 不是字母,发生替换操作: newString = line.replace(char," ") tempList = newString.split(" ") # 将处理好后的一行数据追加到存放干净数据的列表 E_data_clear.append(tempList) # 去掉长度不大于3的词和没有语义的词 Email_data_clear2 = [] for line in E_data_clear: tempList = [] for word in line: if word != '' and len(word) > 3 and word.isalpha(): tempList.append(word) tempString = ' '.join(tempList) Email_data_clear2.append(tempString) Email_data_clear = Email_data_clear2 # 将数据分为训练集和测试集 from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test = train_test_split(Email_data_clear2,E_target,test_size=0.3,random_state=0,stratify=E_target) # 将其 转化为向量 from sklearn.feature_extraction.text import TfidfVectorizer vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(x_train) X_test = vectorizer.transform(x_test) # 观察向量 import numpy as np X_train = X_train.toarray() X_test = X_test.toarray() X_train.shape # 输出不为0的列 for i in range(X_train.shape[0]): for j in range(X_train.shape[1]): if X_train[i][j] != 0: print(i,j,X_train[i][j]) # 贝叶斯分类器 from sklearn.naive_bayes import GaussianNB clf = GaussianNB() module = clf.fit(X_train,y_train) y_predict = module.predict(X_test) # 输出模型分类 from sklearn.metrics import classification_report cr = classification_report(y_predict,y_test) print(cr)