75-R對郵件進(jìn)行排序?qū)崿F(xiàn)智能收件箱

《機(jī)器學(xué)習(xí)-實(shí)用案例解析》學(xué)習(xí)筆記

1、數(shù)據(jù)準(zhǔn)備

數(shù)據(jù)下載：https://spamassassin.apache.org/old/publiccorpus/

參考谷歌Gmail服務(wù)，他們將郵件特征分為社交特征、內(nèi)容特征、線程特征和標(biāo)簽特征。我們的數(shù)據(jù)中沒有詳細(xì)的時(shí)間戳及無法得知用戶何時(shí)做了何種響應(yīng)。但我們可以測量接收量，因此可以假設(shè)這種單向度量能夠較好地代表數(shù)據(jù)中的社交特征類型。
社交特征。用同一主題郵件的發(fā)送間隔時(shí)間來決定郵件的重要性，很自然的方法就是計(jì)算收件人在收到郵件后過了多久才處理這封郵件，在給定特征集下，這個(gè)平均時(shí)間越短，說明郵件在所屬類型中的重要性越高。
線程特征。匹配線程特征詞項(xiàng)，比如“RE:”，線程很活躍，那么就比不活躍的更重要。
內(nèi)容特征。抽取郵件正文中的詞項(xiàng)，新來一封郵件當(dāng)它們包含更多的特征詞項(xiàng)時(shí)，說明更重要。
標(biāo)簽特征。暫不考慮。

我們只需要正常的郵件數(shù)據(jù)，對所有郵件信息按時(shí)間排序，然后將數(shù)據(jù)拆分為訓(xùn)練集和測試集。第一部分用于訓(xùn)練排序算法，第二部分用來測試模型效果。

> library(pacman)
> p_load(chinese.misc,stringr,dplyr,ggplot2)

> easy.ham.files <- dir_or_file("./easy_ham")
> easy.ham2.files <- dir_or_file("./easy_ham_2")
> hard.ham.files <- dir_or_file("./hard_ham")
> hard.ham2.files <- dir_or_file("./hard_ham_2")
> 
> emails <- c(easy.ham.files,easy.ham2.files,
+             hard.ham.files,hard.ham2.files) %>% unique()

郵件頭信息：
From：這封郵件來自誰？使用來自該發(fā)件人的郵件量作為社交特征的表征量。
Date：何時(shí)收到這封郵件？作為時(shí)間度量。
Subj：這是一個(gè)活躍線程嗎？如果來自一個(gè)已知線程，那么可以確定其活躍程度以作為線程特征。
正文：郵件內(nèi)容是什么？找到最常出現(xiàn)的詞項(xiàng)作為內(nèi)容特征。
構(gòu)造函數(shù)，在讀取時(shí)從每一封郵件中抽取如上內(nèi)容，將半結(jié)構(gòu)化數(shù)據(jù)轉(zhuǎn)換為高度結(jié)構(gòu)化的訓(xùn)練數(shù)據(jù)集。

> pre_fun <- function(string) {
+   string <- str_replace_all(string,"\\s+"," ")
+   string <- tolower(string)
+   string <- str_replace_all(string,"[^a-z]"," ")
+   string <- str_replace_all(string,"\\s+"," ")
+   string <- str_trim(string,side = "both")
+   return(string)
+ }
> 
> # 數(shù)據(jù)讀取函數(shù)
> read_fun <- function(f) {
+   if (!str_detect(f,"cmds")) {
+     f.txt <- readr::read_file(f)
+     # 抽取From
+     from <- str_extract_all(f.txt,"From:(.*)") %>% unlist
+     from <- ifelse(length(from>1),from[str_detect(from,"@")],from)
+     # 如果檢測到郵箱地址在<>中，提取
+     if(str_detect(from,"<")) {
+       from <- str_extract(from,"<+(.*?)+>") %>% 
+         str_remove_all("<|>")
+     } else {
+       # 如果沒有檢測到尖括號(hào)，清除From和括號(hào)中的內(nèi)容
+       from <- str_remove_all(from,"From: |\\(.*?\\)")}
+     # 抽取Date
+     date <- str_extract(f.txt,"Date:(.*)") %>% 
+       str_remove("Date: ")
+     # 抽取Subject
+     subject <- str_extract(f.txt,"Subject:(.*)") %>% 
+       str_remove("Subject: ")
+     # 按第一個(gè)空行切割，抽取郵件正文
+     message <- str_split_fixed(f.txt,"\n\n",2)
+     message <- message[1,2] %>% pre_fun
+     df <- tibble(from=from,date=date,subject=subject,message=message,id=f)
+     return(df)
+   }
+ }

> dt <- sapply(emails,read_fun) %>% 
+   do.call(bind_rows,.) %>% distinct()
> head(dt)

## # A tibble: 6 x 5
##   from       date       subject         message             id               
##   <chr>      <chr>      <chr>           <chr>               <chr>            
## 1 kre@munna~ Thu, 22 A~ Re: New Sequen~ date wed aug from ~ D:/R/data_set/sp~
## 2 steve.bur~ Thu, 22 A~ [zzzzteana] RE~ martin a posted ta~ D:/R/data_set/sp~
## 3 timc@2ubh~ Thu, 22 A~ [zzzzteana] Mo~ man threatens expl~ D:/R/data_set/sp~
## 4 monty@ros~ Thu, 22 A~ [IRR] Klez: Th~ klez the virus tha~ D:/R/data_set/sp~
## 5 Stewart.S~ Thu, 22 A~ Re: [zzzzteana~ in adding cream to~ D:/R/data_set/sp~
## 6 martin@sr~ Thu, 22 A~ Re: [zzzzteana~ i just had to jump~ D:/R/data_set/sp~

> mice::md.pattern(dt)
檢查缺失值
##      from date message id subject  
## 6944    1    1       1  1       1 0
## 7       1    1       1  1       0 1
##         0    0       0  0       7 7

subject變量存在7個(gè)缺失值。
另外，我們還需要針對具體變量做更詳細(xì)的檢查。

> # 檢查from中是否都存在@符號(hào)
> table(str_detect(dt$from,"@"))

## 
## TRUE 
## 6951

說明郵箱中發(fā)件人信息從郵箱格式上看是沒有問題的。

隨機(jī)查看30個(gè)date列的值：

> dt$date[sample(nrow(dt),30)]

##  [1] "Tue, 27 Aug 2002 21:36:22 -0400"      
##  [2] "Fri, 9 Aug 2002 20:09:02 -0700"       
##  [3] "Thu, 25 Jul 2002 04:56:39 -0400 (EDT)"
##  [4] "Sat, 24 Aug 2002 10:57:13 -0400 (EDT)"
##  [5] "Fri, 04 Oct 2002 10:03:14 +0300"      
##  [6] "Mon, 2 Sep 2002 09:33:47 -0400"       
##  [7] "Mon, 09 Sep 2002 12:29:51 -0400"      
##  [8] "Thu, 22 Aug 2002 12:39:47 -0300"      
##  [9] "Wed, 28 Aug 2002 07:45:18 -0700"      
## [10] "Tue, 24 Sep 2002 08:00:11 -0000"      
## [11] "Sat, 03 Aug 2002 22:31:23 -0700"      
## [12] "Mon, 07 Oct 2002 08:00:59 -0000"      
## [13] "20 Jul 2002 10:50:58 +1200"           
## [14] "Wed, 10 Jul 2002 16:34:42 -0700 (PDT)"
## [15] "Tue, 08 Oct 2002 13:28:56 +0100"      
## [16] "Tue, 20 Aug 2002 16:30:38 -0300"      
## [17] "Thu, 26 Sep 2002 08:01:56 -0000"      
## [18] "Tue, 1 Oct 2002 14:16:16 +0300 (EEST)"
## [19] "Mon, 30 Sep 2002 15:55:47 -0400"      
## [20] "Thu, 18 Jul 2002 17:20:20 -0700 (PDT)"
## [21] "Tue, 20 Aug 2002 15:31:17 +0100"      
## [22] "03 Oct 2002 21:58:55 -0400"           
## [23] "Sun, 01 Dec 2002 18:03:10 -0700"      
## [24] "Thu, 18 Jul 2002 13:46:12 -0700 (PDT)"
## [25] "Sun, 29 Sep 2002 08:00:02 -0000"      
## [26] "Thu, 26 Sep 2002 15:32:19 -0000"      
## [27] "Wed, 31 Jul 2002 16:37:42 +0100"      
## [28] "Mon, 12 Aug 2002 09:29:38 +0100"      
## [29] "Sat Sep  7 04:38:51 2002"             
## [30] "Wed, 09 Oct 2002 08:00:35 -0000"

多抽樣幾次，可以發(fā)現(xiàn)date列的格式比較多，比如
"Sun, 15 Sep 2002 21:22:52 -0400"，
"01 Oct 2002 19:22:16 -0700"，
"Wed, 17 Jul 2002 20:58:30 -0700 (PDT)"，
"Tue, 24 Sep 2002 08:46:08 EDT"，
"Tue Sep 10 10:29:19 2002"，
需要重新整理成統(tǒng)一的格式。構(gòu)建日期轉(zhuǎn)換函數(shù)：

> trans_date <- function(string) {
+ 
+   string <- str_split(string," ") %>% unlist %>% 
+ 
+     str_remove_all("Sun|Mon|Tue|Wed|Thu|Fri|Sat|,") %>%
+ 
+     str_remove_all("[+|-](.*)") %>% str_remove_all("\\(.*\\)") %>%
+ 
+     str_remove_all("[A-Z]{2,}")
+   year <- string[nchar(string)==4]
+   month <- string[nchar(string)==3]
+   day <- string[nchar(string)==1|nchar(string)==2]
+   time <- string[nchar(string)==8]
+   string.new <- paste(day,month,year,time) %>% lubridate::dmy_hms()
+   return(string.new)
+ }
> 
> dt$date <- dt$date %>% trans_date
> dt$date[sample(nrow(dt),10)]

##  [1] "2002-07-15 03:00:01 UTC" "2002-10-08 08:01:21 UTC"
##  [3] "2002-08-29 08:32:08 UTC" "2002-09-25 08:00:22 UTC"
##  [5] "2002-10-08 08:01:05 UTC" "2002-09-12 09:05:50 UTC"
##  [7] "2002-09-30 22:00:02 UTC" "2002-08-06 16:50:07 UTC"
##  [9] "2002-07-10 16:05:42 UTC" "2002-10-08 08:00:31 UTC"

轉(zhuǎn)換后的結(jié)果很規(guī)整，完全符合我們的要求。

> head(dt)

## # A tibble: 6 x 5
##   from      date                subject       message          id            
##   <chr>     <dttm>              <chr>         <chr>            <chr>         
## 1 kre@munn~ 2002-08-22 18:26:25 Re: New Sequ~ date wed aug fr~ D:/R/data_set~
## 2 steve.bu~ 2002-08-22 12:46:18 [zzzzteana] ~ martin a posted~ D:/R/data_set~
## 3 timc@2ub~ 2002-08-22 13:52:38 [zzzzteana] ~ man threatens e~ D:/R/data_set~
## 4 monty@ro~ 2002-08-22 09:15:25 [IRR] Klez: ~ klez the virus ~ D:/R/data_set~
## 5 Stewart.~ 2002-08-22 14:38:22 Re: [zzzztea~ in adding cream~ D:/R/data_set~
## 6 martin@s~ 2002-08-22 14:50:31 Re: [zzzztea~ i just had to j~ D:/R/data_set~

現(xiàn)在數(shù)據(jù)基本轉(zhuǎn)換成了我們需要的樣子，下面繼續(xù)做一些必要的轉(zhuǎn)換。將from和subject全部轉(zhuǎn)換為小寫，并且將整個(gè)數(shù)據(jù)框按date列排序。最后將數(shù)據(jù)拆分為訓(xùn)練集和測試集。

> dt$from <- tolower(dt$from)
> dt$subject <- tolower(dt$subject)
> dt <- arrange(dt,date)
> 
> set.seed(123)
> ind <- sample(1:nrow(dt),nrow(dt)*0.8,replace = T)
> train <- dt[ind,]
> test <- dt[-ind,]

2、郵件發(fā)送量權(quán)重計(jì)算策略

來自同一地址（from）的郵件越頻繁，說明該郵件越重要。所以按郵件中的from計(jì)數(shù)來設(shè)計(jì)用于重要性排序的權(quán)重。

> p_load(ggplot2)
> from.weight <- train[,"from"] %>% group_by(from) %>% 
+   summarise(freq=n()) %>% arrange(-freq)
> head(from.weight)

## # A tibble: 6 x 2
##   from                             freq
##   <chr>                           <int>
## 1 rssfeeds@example.com              498
## 2 rssfeeds@spamassassin.taint.org   468
## 3 tomwhore@slack.net                112
## 4 garym@canada.com                  104
## 5 pudge@perl.org                    102
## 6 matthias@egwn.net                  86

> # 查看郵件數(shù)量最多的前30個(gè)賬號(hào)
> from.weight %>% top_n(30) %>% 
+   ggplot(aes(freq,reorder(from,freq))) +
+   geom_bar(stat = "identity") +
+   theme_bw() +
+   labs(x="接收郵件數(shù)量",y="")
最密切的發(fā)件人

對發(fā)送量做對數(shù)轉(zhuǎn)換。

> from.weight %>%
+   ggplot(aes(x=freq)) +
+   geom_line(aes(y=log10(freq)),col="green") +
+   geom_text(aes(500,2.5,label="對數(shù)變換")) +
+   geom_line(aes(y=log(freq)),col="red") +
+   geom_text(aes(500,6,label="自然對數(shù)變換")) +
+   theme_bw() +
+   labs(x="",y="接收的郵件量")
對數(shù)變換效果

做對數(shù)變換后曲線會(huì)更平緩，同時(shí)，自然對數(shù)變換相對對數(shù)變換程度更小，更能保留原始數(shù)據(jù)的一些差異，所以最終我們選擇自然對數(shù)變換后的值作為發(fā)送量特征的權(quán)重。
但是在做對數(shù)變換時(shí)需要注意的是，如果觀測值為1，轉(zhuǎn)換后就為0，計(jì)算權(quán)重時(shí)0乘以其他任何值都為0。為了避免這種情況，在轉(zhuǎn)換前一般對觀測值都加1。

> # log1p()函數(shù)計(jì)算log(p+1)
> from.weight$freq <- log1p(from.weight$freq)
> # 檢查下變換為的數(shù)據(jù)
> summary(from.weight$freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.6931  0.6931  1.3863  1.4869  1.7918  6.2126

3、郵件線程活躍度權(quán)重計(jì)算策略

從subject中查找“re:”，然后查找這個(gè)線程里面的其他郵件，并測量其活躍度。在短時(shí)間內(nèi)有更多郵件發(fā)送的線程就更活躍，因此也更重要。

> # 提取包含“re:”的subject，并提取“re:”后面的內(nèi)容作為主題
> threads.train <- train %>% filter(str_detect(subject,"re:"))
> 
> extract_subject <- function(string){
+   string <- str_split(string,"re:") %>% 
+               unlist %>% .[2] %>% str_trim()
+   return(string)
+ }
> threads.train$subject <- threads.train$subject %>% 
+   lapply(extract_subject) %>% unlist
> 
> # 分組統(tǒng)計(jì)數(shù)量
> threads.freq <- threads.train %>% group_by(subject) %>% 
+   summarise(freq=n()) %>% arrange(freq)

數(shù)據(jù)中存在freq<2的情況，是因?yàn)閿?shù)據(jù)集在采集的時(shí)候存在一部分主題郵件是在采集時(shí)間開始之前發(fā)起的，這時(shí)候主題中也存在“re:”標(biāo)記，但是該線程發(fā)起時(shí)間并不在數(shù)據(jù)集中，所以需要去掉這部分?jǐn)?shù)據(jù)。

> # 線程時(shí)間跨度，即第一封郵件和最后一封郵件之間的時(shí)間間隔
> time_span <- function(df){
+   max.time <- max(df$date)
+   min.time <- min(df$date)
+   threads.span <- difftime(max.time,min.time,units = "secs")
+   df.new <- tibble(subject=df$subject[1],threads.span=threads.span)
+   return(df.new)
+ }
> 
> # 將數(shù)據(jù)框按subject拆分
> threads.train.split <- split.data.frame(threads.train,
+                                         threads.train$subject)
> 
> threads.span <- lapply(threads.train.split,time_span) %>% 
+   do.call(rbind.data.frame,.)

按主題合并兩個(gè)數(shù)據(jù)框。

> subject.weight <- left_join(threads.freq,threads.span,by="subject") %>% 
+   # 轉(zhuǎn)換為數(shù)值型
+   transform(threads.span=as.numeric(threads.span)) %>% 
+   filter(freq>=2 & threads.span!=0) %>% 
+   mutate(weight=freq/threads.span) %>% 
+   # 仿射變換
+   transform(weight=log10(weight)+10) %>% 
+   arrange(weight)
> head(subject.weight)

##                  subject freq threads.span   weight
## 1            activebuddy   17    820721053 2.316253
## 2            [zzzzteana]    6      8275106 3.860378
## 3 no matter where you go    4      2672629 4.175121
## 4                [sadev]    7      3325905 4.323188
## 5          [ilug-social]    4      1649403 4.384733
## 6          [razor-users]   16      5174599 4.490243

> summary(subject.weight)

##    subject               freq         threads.span           weight     
##  Length:287         Min.   : 2.000   Min.   :       16   Min.   :2.316  
##  Class :character   1st Qu.: 3.000   1st Qu.:    15482   1st Qu.:5.675  
##  Mode  :character   Median : 5.000   Median :    49344   Median :6.126  
##                     Mean   : 7.784   Mean   :  3094884   Mean   :6.115  
##                     3rd Qu.: 9.000   3rd Qu.:   145816   3rd Qu.:6.512  
##                     Max.   :41.000   Max.   :820721053   Max.   :9.097

從摘要中可以看到freq平均為7.784，threads.span平均為3094884，這樣計(jì)算的weight將會(huì)很小，平均為2.515118e-06，在做對數(shù)轉(zhuǎn)換時(shí)，就會(huì)得到負(fù)值：log10(7.784/3094884)=-5.600825。計(jì)算時(shí)權(quán)重不能為負(fù)值，所以這里進(jìn)行仿射變換，簡單地給所有轉(zhuǎn)換值加10，以保證所有權(quán)重值為正數(shù)。

4、郵件內(nèi)容中高頻詞項(xiàng)的權(quán)重策略

假設(shè)出現(xiàn)在活躍線程郵件主題中的高頻詞比低頻詞和出現(xiàn)在不活躍線程中的詞項(xiàng)更重要。

> p_load(text2vec)
> 
> it <- itoken(threads.dt$message,ids = threads.dt$id,progressbar = F)
> 
> # 創(chuàng)建訓(xùn)練集詞匯表
> vocab <- create_vocabulary(it)
> 
> # 去除停用詞
> stopword <- readr::read_table("D:/R/dict/english_stopword.txt",
+                               col_names = F)
> 
> # 還是以對數(shù)轉(zhuǎn)換計(jì)算高頻詞的權(quán)重
> term.weight <- anti_join(vocab,stopword,by=c("term"="X1")) %>% 
+   mutate(term.weight=log10(term_count)) %>% 
+   filter(term.weight>0)

5、訓(xùn)練和測試排序算法

一封郵件的整體權(quán)重（優(yōu)先級(jí)）等于前面三種權(quán)重的乘積。當(dāng)收到一封郵件的時(shí)候，我們需要先對其進(jìn)行解析，計(jì)算其權(quán)重，然后對其進(jìn)行優(yōu)先級(jí)排序。
構(gòu)造排序函數(shù)：

> get_weight <- function(newemail){
+ 
+   from.new.n <- left_join(newemail[,1],from.weight,by = "from")
+   from.new <- ifelse(is.na(from.new.n$freq),1,from.new.n$freq)
+   
+ 
+   if (!is.na(newemail$subject) & str_detect(newemail$subject,"re:")) {
+     newemail$subject <- extract_subject(newemail$subject)
+     subject.new.n <- left_join(newemail,subject.weight,by="subject")
+ 
+     subject.new <- ifelse(is.na(subject.new.n$weight),1,
+                           subject.new.n$weight)
+   } else {
+ 
+     subject.new <- 1
+   }
+   
+ 
+   if (newemail$message!="") {
+ 
+     msg.weight <- str_split(newemail$message," ") %>% unlist %>% 
+       jiebaR::freq() %>% anti_join(stopword,by=c("char"="X1")) %>% 
+       filter(char!="")
+ 
+     if (nrow(msg.weight)!=0) {
+       msg.weight.n <- left_join(msg.weight,term.weight[,c(1,4)],
+                              by=c("char"="term")) %>% 
+         summarise(msg.new=sum(freq*term.weight,na.rm = T))
+       msg.new <- msg.weight.n$msg.new
+     }
+   } else {
+     msg.new <- 1
+   }
+   
+ 
+   return(prod(from.new,subject.new,msg.new))
+ }

5.1 對訓(xùn)練集進(jìn)行排序

> rank.train <- vector(length = nrow(train))
> for (i in 1:nrow(train)) {
+   rank.train[i] <- get_weight(train[i,])
+ }
> 
> train.rank <- tibble(id=train$id,rank=rank.train)
> 
> summary(train.rank$rank)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##      8.64    282.39    859.82   2300.37   2702.20 241506.57

> # 檢查排序值的分布
> p1 <- ggplot(train.rank,aes(rank)) +
+   geom_histogram(bins = 1000, fill = "dodgerblue") +
+   geom_vline(xintercept = median(rank.train),size=1) +
+   xlim(c(0,25000)) +
+   theme_bw() +
+   labs(y="")

6、對測試集進(jìn)行排序

> rank <- vector(length = nrow(test))
> for (i in 1:nrow(test)) {
+   rank[i] <- get_weight(test[i,])
+ }
> 
> test.rank <- tibble(id=test$id,rank=rank)
> 
> summary(test.rank$rank)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     8.64   237.50   716.02  1913.47  2301.08 36852.14

> # 檢查排序值的分布
> p2 <- ggplot(test.rank,aes(rank)) +
+   geom_histogram(bins = 1000, fill = "red") +
+   geom_vline(xintercept = median(rank),size=1) +
+   xlim(c(0,25000)) +
+   theme_bw() +
+   labs(y="")

對比訓(xùn)練集和測試的排序分布。

> p_load(patchwork)
> p1 + p2 + plot_layout(nrow = 2)
訓(xùn)練集和測試集排序值分布

可以看到訓(xùn)練集和測試集的排序分布幾乎一模一樣，都是長尾分布，意味著更多的郵件的優(yōu)先級(jí)排序不高，這也符合常理。
然后檢查一下測試集排序最靠前的20行。

> test[,3] %>% cbind(rank=rank) %>% arrange(-rank) %>% head(20)

##                                                         subject     rank
## 1                                      re: apple sauced...again 36852.14
## 2                                      re: apple sauced...again 36852.14
## 3                           sed /s/united states/roman empire/g 33739.40
## 4                    re: selling wedded bliss (was re: ouch...) 25141.77
## 5                    re: selling wedded bliss (was re: ouch...) 25141.77
## 6                                      re: new sequences window 22509.49
## 7                      [lockergnome windows daily]  fraud wipes 21055.61
## 8                      [lockergnome windows daily]  fraud wipes 21023.90
## 9               [lockergnome windows daily]  brilliant mistakes 21004.60
## 10              [lockergnome penguin shell]  recursive metaphor 20823.06
## 11                    [lockergnome windows daily]  cranky beats 20449.50
## 12 re: comrade communism (was re: crony capitalism (was re: sed 20073.10
## 13                   [lockergnome windows daily]  deeper uplink 20043.92
## 14                                   bush covers the waterfront 19548.67
## 15                   [lockergnome digital media]  clever ritual 19499.38
## 16                   [lockergnome digital media]  clever ritual 19467.67
## 17               [lockergnome windows daily]  dignity shakedown 19325.42
## 18                     [lockergnome penguin shell]  good hearts 19295.45
## 19               [lockergnome windows daily]  dignity shakedown 19293.71
## 20                [lockergnome windows daily]  sticker courtesy 19132.87

主題中幾乎有一大半是不活躍的郵件，因?yàn)閟ubject中不包含“re:”，也表明排序算法可以將主題之外的其他權(quán)重應(yīng)用到數(shù)據(jù)中。
盡管這種非監(jiān)督的排序算法無法測算其準(zhǔn)確度，但這結(jié)果仍然是很鼓舞人心的。

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