一、垃圾邮件过滤技术项目需求与设计方案
二、数据的内容分析
（1、是否为垃圾邮件的标签，spam——是垃圾邮件；ham——不是垃圾邮件）
（2、邮件的内容分析——主要包含：发件人、收件人、发件时间以及邮件的内容）
三、需求分析、模型选择与架构
四、数据清洗
（一）·代码中应用的知识点
（1）字典的get()函数
（2）os.listdir()
（二）数据清洗代码
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:ZhengzhengLiuimport os#1、索引文件(分类标签)读取，该文件中分为两列
#第一列：分类标签是否为垃圾邮件（是：spam、否：ham）；
# 第二列：存放邮件对应文件夹路径，两列之间通过空格分割
def read_index_file(file_path):type_dict = {"spam":"1","ham":"0"} #用字典存放垃圾邮件的分类标签index_file = open(file_path)index_dict = {}try:for line in index_file: # 按行循环读取文件arr = line.split(" ") # 用“空格”进行分割#pd.read_csv("full/index",sep=" ") #pandas来写与上面等价if len(arr) == 2: #分割完之后如果长度是2key,value = arr ##分别将spam ../data/178/129赋值给key与value#添加到字段中value = value.replace("../data","").replace("\n","") #替换# 字典赋值，字典名[键]=值，lower()将所有的字母转换成小写index_dict[value] = type_dict[key.lower()] #finally:index_file.close()return index_dict#2、邮件的文件内容数据读取
def read_file(file_path):# 读操作，邮件数据编码为"gb2312",数据读取有异常就ignore忽略file = open(file_path,"r",encoding="gb2312",errors="ignore")content_dict = {}try:is_content = Falsefor line in file: # 按行读取line = line.strip() # 每行的空格去掉用strip()if line.startswith("From:"):content_dict["from"] = line[5:]elif line.startswith("To:"):content_dict["to"] = line[3:]elif line.startswith("Date:"):content_dict["data"] = line[5:]elif not line:# 邮件内容与上面信息存在着第一个空行，遇到空行时，这里标记为True以便进行下面的邮件内容处理# line文件的行为空时是False，不为空时是Trueis_content = True# 处理邮件内容（处理到为空的行时接着处理邮件的内容）if is_content:if "content" in content_dict:content_dict["content"] += lineelse:content_dict["content"] = linefinally:file.close()return content_dict#3、邮件数据处理(内容的拼接,并用逗号进行分割)
def process_file(file_path):content_dict = read_file(file_path)#进行处理(拼接),get()函数返回指定键的值，指定键的值不存在用指定的默认值unkown代替result_str = content_dict.get("from","unkown").replace(",","").strip()+","result_str += content_dict.get("to","unkown").replace(",","").strip()+","result_str += content_dict.get("data","unkown").replace(",","").strip()+","result_str += content_dict.get("content","unkown").replace(",","").strip()return result_str#4、开始进行数据处理——函数调用
## os.listdir 返回指定的文件夹包含的文件或文件夹包含的名称列表
index_dict = read_index_file('../data/full/index')
list0 = os.listdir('../data/data') #list0是范围为[000-215]的列表
# print(list0)
for l1 in list0: # l1:循环000--215l1_path = '../data/data/' + l1 #l1_path ../data/data/215print('开始处理文件夹:' + l1_path)list1 = os.listdir(l1_path) #list1:['000', '001', '002', '003'....'299']# print(list1)write_file_path = '../data/process01_' + l1with open(write_file_path, "w", encoding='utf-8') as writer:for l2 in list1: # l2:循环000--299l2_path = l1_path + "/" + l2 # l2_path ../data/data/215/000# 得到具体的文件内容后，进行文件数据的读取index_key = "/" + l1 + "/" + l2 # index_key: /215/000if index_key in index_dict:# 读取数据content_str = process_file(l2_path)# 添加分类标签（0、1）也用逗号隔开content_str += "," + index_dict[index_key] + "\n"# 进行数据输出writer.writelines(content_str)# 再合并所有第一次构建好的内容
with open('../data/result_process01', 'w', encoding='utf-8') as writer:for l1 in list0:file_path = '../data/process01_' + l1print("开始合并文件:" + file_path)with open(file_path, encoding='utf-8') as file:for line in file:writer.writelines(line)（三）邮件存放路径框架与各步骤运行结果
最后运行的结果：
五、特征工程
（一）邮件服务器处理
知识点应用：
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:ZhengzhengLiuimport re
import time
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt## 设置字符集，防止中文乱码
mpl.rcParams['font.sans-serif']=[u'simHei']
mpl.rcParams['axes.unicode_minus']=False# 1、文件数据读取
df = pd.read_csv("../data/result_process01",sep=",",header=None,names=["from","to","date","content","label"])
# print(df.head())#2(1)、特征工程1 =>提取发件人和收件人的邮件服务器地址
def extract_email_server_address(str1):it = re.findall(r"@([A-Za-z0-9]*\.[A-Za-z0-9\.]+)",str(str1))result = ""if len(it)>0:result = it[0]if not result:result = "unknown"return resultdf["to_address"] = pd.Series(map(lambda str:extract_email_server_address(str),df["to"]))
df["from_address"] = pd.Series(map(lambda str:extract_email_server_address(str),df["from"]))
# print(df.head(2))#2(2)、特征工程1 =>查看邮件服务器的数量
print("=================to address================")
print(df.to_address.value_counts().head(5))
print("总邮件接收服务器类别数量为:"+str(df.to_address.unique().shape))print("=================from address================")
print(df.from_address.value_counts().head(5))
print("总邮件接收服务器类别数量为:"+str(df.from_address.unique().shape))from_address_df = df.from_address.value_counts().to_frame()
len_less_10_from_adderss_count = from_address_df[from_address_df.from_address<=10].shape
print("发送邮件数量小于10封的服务器数量为:"+str(len_less_10_from_adderss_count))
（二）时间属性处理
#3、特征工程2 =>邮件的时间提取
def extract_email_date(str1):if not isinstance(str1,str): #判断变量是否是str类型str1 = str(str1) #str类型的强转str_len = len(str1)week = ""hour = ""# 0表示：上午[8,12]；1表示：下午[13,18]；2表示：晚上[19,23]；3表示：凌晨[0,7]time_quantum = ""if str_len < 10:#unknownweek = "unknown"hour = "unknown"time_quantum ="unknown"passelif str_len == 16:# 2005-9-2 上午10:55rex = r"(\d{2}):\d{2}" # \d 匹配任意数字,这里匹配10:55it = re.findall(rex,str1)if len(it) == 1:hour = it[0]else:hour = "unknown"week = "Fri"time_quantum = "0"passelif str_len == 19:# Sep 23 2005 1:04 AMweek = "Sep"hour = "01"time_quantum = "3"passelif str_len == 21:# August 24 2005 5:00pmweek = "Wed"hour = "17"time_quantum = "1"passelse:#匹配一个字符开头，+表示至少一次 \d 表示数字 ？表示可有可无 *? 非贪婪模式rex = r"([A-Za-z]+\d?[A-Za-z]*) .*?(\d{2}):\d{2}:\d{2}.*"it = re.findall(rex,str1)if len(it) == 1 and len(it[0]) == 2:week = it[0][0][-3]hour = it[0][1]int_hour = int(hour)if int_hour < 8:time_quantum = "3"elif int_hour < 13:time_quantum = "0"elif int_hour < 19:time_quantum = "1"else:time_quantum = "2"passelse:week = "unknown"hour = "unknown"time_quantum = "unknown"week = week.lower()hour = hour.lower()time_quantum = time_quantum.lower()return (week,hour,time_quantum)#数据转换
data_time_extract_result = list(map(lambda st:extract_email_date(st),df["date"]))
df["date_week"] = pd.Series(map(lambda t:t[0],data_time_extract_result))
df["date_hour"] = pd.Series(map(lambda t:t[1],data_time_extract_result))
df["date_time_quantum"] = pd.Series(map(lambda t:t[2],data_time_extract_result))
print(df.head(2))print("=======星期属性字段描述======")
print(df.date_week.value_counts().head(3))
print(df[["date_week","label"]].groupby(["date_week","label"])["label"].count())print("=======小时属性字段描述======")
print(df.date_hour.value_counts().head(3))
print(df[['date_hour', 'label']].groupby(['date_hour', 'label'])['label'].count())print("=======时间段属性字段描述======")
print(df.date_hour.value_counts().head(3))
print(df[["date_time_quantum","label"]].groupby(["date_time_quantum","label"])["label"].count())#添加是否有时间
df["has_date"] = df.apply(lambda c: 0 if c["date_week"] == "unknown" else 1,axis=1)
print(df.head(2))
（三）邮件内容分词——jieba分词
#4、特征工程之三 => jieba分词操作#将文本类型全部转换为str类型，然后进行分词操作
df["content"] = df["content"].astype("str")'''
#1、jieba分词的重点在于：自定义词典
#2、jieba添加分词字典，jieba.load_userdict("userdict.txt"),字典格式为：单词 词频(可选的) 词性(可选的)
# 词典构建方式：一般都是基于jieba分词之后的效果进行人工干预
#3、添加新词、删除词 jieba.add_word("") jieba.del_word("")
#4、jieba.cut: def cut(self, sentence, cut_all=False, HMM=True)
# sentence:需要分割的文本，cut_all:分割模式，分为精准模式False、全分割True，HMM：新词可进行推测
#5、长文本采用精准分割，短文本采用全分割模式
# 一般在短文本处理过程中还需要考虑词性，并且还可能将分割好的单词进行组合
# 词性需要导入的包：import jieba.posseg
'''
df["jieba_cut_content"] = list(map(lambda st:" ".join(jieba.cut(st)),df["content"])) #分开的词用空格隔开
print(df.head(2))运行结果为：
注意内容：
（四）邮件信息量/长度对是否为垃圾邮件的影响
（1）应用知识点——groupby()技术
详细参照链接：http://www.jianshu.com/p/2d49cb87626b
在数据分析中，我们往往需要在将数据拆分，在每一个特定的组里进行运算。比如根据教育水平和年龄段计算某个城市的工作人口的平均收入。pandas中的groupby提供了一个高效的数据的分组运算。我们通过一个或者多个分类变量数据拆分，然后分别在拆分以后的数据上进行需要的计算。
#5、特征工程之四 =>邮件长度对是否是垃圾邮件的影响
def process_content_length(lg):if lg < 10:return 0elif lg <= 100:return 1elif lg <= 500:return 2elif lg <= 1000:return 3elif lg <= 1500:return 4elif lg <= 2000:return 5elif lg <= 2500:return 6elif lg <= 3000:return 7elif lg <= 4000:return 8elif lg <= 5000:return 9elif lg <= 10000:return 10elif lg <= 20000:return 11elif lg <= 30000:return 12elif lg <= 50000:return 13else:return 14df["content_length"] = pd.Series(map(lambda st:len(st),df["content"]))
df["content_length_type"] = pd.Series(map(lambda st:process_content_length(st),df["content_length"]))
#按照邮件长度类别和标签进行分组groupby，抽取这两列数据相同的放到一起，
# 用agg和内置函数count聚合不同长度邮件分贝是否为垃圾邮件的数量,
# reset_insex:将对象重新进行索引的构建
df2 = df.groupby(["content_length_type","label"])["label"].agg(["count"]).reset_index()
#label == 1：是垃圾邮件，对长度和数量进行重命名，count命名为c1
df3 = df2[df2.label == 1][["content_length_type","count"]].rename(columns={"count":"c1"})
df4 = df2[df2.label == 0][["content_length_type","count"]].rename(columns={"count":"c2"})
df5 = pd.merge(df3,df4) #数据集的合并，pandas.merge可依据一个或多个键将不同DataFrame中的行连接起来df5["c1_rage"] = df5.apply(lambda r:r["c1"]/(r["c1"]+r["c2"]),axis=1) #按行进行统计
df5["c2_rage"] = df5.apply(lambda r:r["c2"]/(r["c1"]+r["c2"]),axis=1)
print(df5.head())#画图
plt.plot(df5["content_length_type"],df5["c1_rage"],label=u"垃圾邮件比例")
plt.plot(df5["content_length_type"],df5["c2_rage"],label=u"正常邮件比例")
plt.xlabel(u"邮件长度标记")
plt.ylabel(u"邮件比例")
plt.grid(True)
plt.legend(loc=0)
plt.savefig("垃圾和正常邮件比例.png")
plt.show()运行结果：
（五）添加信号量
#6、特征工程之五 ==> 添加信号量
def precess_content_sema(x):if x>10000:return 0.5/np.exp(np.log10(x)-np.log10(500))+np.log(abs(x-500)+1)-np.log(abs(x-10000))+1else:return 0.5/np.exp(np.log10(x)-np.log10(500))+np.log(abs(x-500)+1)+1a = np.arange(1,20000)
plt.plot(a,list(map(lambda t:precess_content_sema(t),a)),label=u"信息量")
plt.grid(True)
plt.legend(loc=0)
plt.savefig("信息量.png")
plt.show()df["content_sema"] = list(map(lambda st:precess_content_sema(st),df["content_length"]))
print(df.head(2))#查看列名称
print(df.dtypes)#获取需要的列,drop删除不需要的列
df.drop(["from","to","date","content","to_address","from_address","date_week","date_hour","date_time_quantum","content_length","content_length_type"],1,inplace=True)
print(df.info())
print(df.head())#结果输出到CSV文件中
df.to_csv("../data/result_process02",encoding="utf-8",index=False)运行结果：
六、模型效果评估
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:ZhengzhengLiuimport time
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as pltfrom sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.decomposition import TruncatedSVD #降维
from sklearn.naive_bayes import BernoulliNB #伯努利分布的贝叶斯公式
from sklearn.metrics import f1_score,precision_score,recall_score## 设置字符集，防止中文乱码
mpl.rcParams['font.sans-serif']=[u'simHei']
mpl.rcParams['axes.unicode_minus']=False#1、文件数据读取
df = pd.read_csv("../data/result_process02",encoding="utf-8",sep=",")
#如果有nan值，进行上删除操作
df.dropna(axis=0,how="any",inplace=True) #删除表中含有任何NaN的行
print(df.head())
print(df.info())#2、数据分割
x_train,x_test,y_train,y_test = train_test_split(df[["has_date","jieba_cut_content","content_sema"]],df["label"],test_size=0.2,random_state=0)
print("训练数据集大小:%d" %x_train.shape[0])
print("测试数据集大小:%d" %x_test.shape[0])
print(x_train.head())#3、开始模型训练
#3.1、特征工程，将文本数据转换为数值型数据
transformer = TfidfVectorizer(norm="l2",use_idf=True)
svd = TruncatedSVD(n_components=20) #奇异值分解，降维
jieba_cut_content = list(x_train["jieba_cut_content"].astype("str"))
transformer_model = transformer.fit(jieba_cut_content)
df1 = transformer_model.transform(jieba_cut_content)
svd_model = svd.fit(df1)
df2 = svd_model.transform(df1)data = pd.DataFrame(df2)
print(data.head())
print(data.info())#3.2、数据合并
data["has_date"] = list(x_train["has_date"])
data["content_sema"] = list(x_train["content_sema"])
print("========数据合并后的data信息========")
print(data.head())
print(data.info())t1 = time.time()
nb = BernoulliNB(alpha=1.0,binarize=0.0005) #贝叶斯分类模型构建
model = nb.fit(data,y_train)
t = time.time()-t1
print("贝叶斯模型构建时间为:%.5f ms" %(t*1000))#4.1 对测试数据进行转换
jieba_cut_content_test = list(x_test["jieba_cut_content"].astype("str"))
data_test = pd.DataFrame(svd_model.transform(transformer_model.transform(jieba_cut_content_test)))
data_test["has_date"] = list(x_test["has_date"])
data_test["content_sema"] = list(x_test["content_sema"])
print(data_test.head())
print(data_test.info())#4.2 对测试数据进行测试
y_predict = model.predict(data_test)#5、效果评估
print("准确率为:%.5f" % precision_score(y_test,y_predict))
print("召回率为:%.5f" % recall_score(y_test,y_predict))
print("F1值为:%.5f" % f1_score(y_test,y_predict))
运行结果：
