import csv# 读数据file_path = r'data\EmailData.txt'EmailData = open(file_path,'r',encoding='utf-8')Email_data = []Email_target = []csv_reader = csv.reader(EmailData,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:    Email_data.append(line[1])    Email_target.append(line[0])EmailData.close()

# 把无意义的符号都替换成空格Email_data_clear = []for line in Email_data:    # line :'Go until jurong point, crazy.. Available only in bugis n great world la e buffet...'    # 每一行都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    Email_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 Email_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_splitx_train,x_test,y_train,y_test = train_test_split(Email_data_clear2,Email_target,test_size=0.3,random_state=0,stratify=Email_target)

# 建立数据的特征向量from sklearn.feature_extraction.text import TfidfVectorizertfidf = TfidfVectorizer()

X_train = tfidf.fit_transform(x_train)X_test = tfidf.transform(x_test)

print(type(X_train),type(X_test))

# 观察向量import numpy as npX_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])

# 提取特征值tfidf.get_feature_names()[630:650]

#建立模型from sklearn.naive_bayes import GaussianNBgnb = GaussianNB()

module = gnb.fit(X_train,y_train)y_predict = module.predict(X_test)

# 输出模型分类的各个指标 from sklearn.metrics import classification_report

cr = classification_report(y_predict,y_test)print(cr)