入門機械学習４章　日本語ローカライズ版です。

ML_for_Hackers_jp

getwd()
setwd("ML_for_Hackers-master/04-Ranking")

# Load libraries
library('tm')
library('ggplot2')
library('plyr')

# Set the global paths
data.path <- file.path("..", "03-Classification", "data")
easyham.path <- file.path(data.path, "easy_ham")

##encoding="latin1"→endoding="native.nec"→encodingオプション無しと諸説有り
msg.full <- function(path)
{
  con <- file(path, open = "rt")
  msg <- readLines(con)
  close(con)
  return(msg)
}


get.from <- function(msg.vec)
{
  from <- msg.vec[grepl("From: ", msg.vec)]
  from <- strsplit(from, '[":<> ]')[[1]]
  from <- from[which(from  != "" & from != " ")]
  return(from[grepl("@", from)][1])
}

get.subject <- function(msg.vec)
{
  subj <- msg.vec[grepl("Subject: ", msg.vec)]
  if(length(subj) > 0)
  {
    return(strsplit(subj, "Subject: ")[[1]][2])
  }
  else
  {
    return("")
  }
}

get.msg <- function(msg.vec)
{
  msg <- msg.vec[seq(which(msg.vec == "")[1] + 1, length(msg.vec), 1)]
  return(paste(msg, collapse = "\n"))
}

get.date <- function(msg.vec)
{
  date.grep <- grepl("^Date: ", msg.vec)
  date.grep <- which(date.grep == TRUE)
  date <- msg.vec[date.grep[1]]
  date <- strsplit(date, "\\+|\\-|: ")[[1]][2]
  date <- gsub("^\\s+|\\s+$", "", date)
  return(strtrim(date, 25))
}

parse.email <- function(path)
{
  full.msg <- msg.full(path)
  date <- get.date(full.msg)
  from <- get.from(full.msg)
  subj <- get.subject(full.msg)
  msg <- get.msg(full.msg)
  return(c(date, from, subj, msg, path))
}

easyham.docs <- dir(easyham.path)
easyham.docs <- easyham.docs[which(easyham.docs != "cmds")]
easyham.parse <- lapply(easyham.docs,
                        function(p) parse.email(file.path(easyham.path, p)))

ehparse.matrix <- do.call(rbind, easyham.parse)
allparse.df <- data.frame(ehparse.matrix, stringsAsFactors = FALSE)
names(allparse.df) <- c("Date", "From.EMail", "Subject", "Message", "Path")

date.converter <- function(dates, pattern1, pattern2)
{
  pattern1.convert <- strptime(dates, pattern1)
  pattern2.convert <- strptime(dates, pattern2)
  pattern1.convert[is.na(pattern1.convert)] <- pattern2.convert[is.na(pattern1.convert)]
  return(pattern1.convert)
}
##日本語環境対策
Sys.setlocale("LC_TIME","C") 

pattern1 <- "%a, %d %b %Y %H:%M:%S"
pattern2 <- "%d %b %Y %H:%M:%S"

allparse.df$Date <- date.converter(allparse.df$Date, pattern1, pattern2)

allparse.df$Subject <- tolower(allparse.df$Subject)
allparse.df$From.EMail <- tolower(allparse.df$From.EMail)

priority.df <- allparse.df[with(allparse.df, order(Date)), ]

priority.train <- priority.df[1:(round(nrow(priority.df) / 2)), ]

#重みつけの方式設計
priority.train$Date <- as.POSIXct(priority.train$Date)
from.weight <- ddply(priority.train, .(From.EMail),summarise, Freq=length(Subject))
from.weight <- from.weight[with(from.weight, order(Freq)), ]


#図4-2の描画
from.ex <- subset(from.weight, Freq > 6)

ggplot(from.ex) +
  geom_rect(aes(xmin = 1:nrow(from.ex) - 0.5,
                xmax = 1:nrow(from.ex) + 0.5,
                ymin = 0,
                ymax = Freq,
                fill = "lightgrey",
                color = "darkblue")) +
  scale_x_continuous(breaks = 1:nrow(from.ex), labels = from.ex$From.EMail) +
  coord_flip() +
  scale_fill_manual(values = c("lightgrey" = "lightgrey"), guide = "none") +
  scale_color_manual(values = c("darkblue" = "darkblue"), guide = "none") +
  ylab("Number of Emails Received (truncated at 6)") +
  xlab("Sender Address") +
  theme_bw() +
  theme(axis.text.y = element_text(size = 5, hjust = 1))

#対数による重みつけをし、平坦化した電子メール受信数
from.weight <- transform(from.weight,
                         Weight = log(Freq + 1),
                         log10Weight = log10(Freq + 1))

ggplot(from.weight, aes(x = 1:nrow(from.weight))) +
  geom_line(aes(y = Weight, linetype = "ln")) +
  geom_line(aes(y = log10Weight, linetype = "log10")) +
  geom_line(aes(y = Freq, linetype = "Absolute")) +
  scale_linetype_manual(values = c("ln" = 1,
                                   "log10" = 2,
                                   "Absolute" = 3),
                        name = "Scaling") +
  xlab("") +
  ylab("Number of emails Receieved") +
  theme_bw() +
  theme(axis.text.y = element_blank(), axis.text.x = element_blank())

#電子メールのスレッド活動量を重み付けする
find.threads <- function(email.df){
  response.threads <- strsplit(email.df$Subject, "re: ")
  is.thread <- sapply(response.threads, function(subj)
    ifelse(subj[1] == "",TRUE,FALSE))
  threads <- response.threads[is.thread]
  senders <- email.df$From.EMail[is.thread]
  threads <- sapply(threads, function(t) paste(t[2:length(t)],
                                               collapse="re: "))
  return(cbind(senders, threads))
}

threads.matrix <- find.threads(priority.train)

#最も活動的な送信者にあわせて重み付け
email.thread <- function(thread.matrix){
  senders <- threads.matrix[,1]
  senders.freq <- table(senders)
  senders.matrix <- cbind(names(senders.freq),senders.freq, log(senders.freq +1))
  senders.df <- data.frame(senders.matrix,stringsAsFactors=FALSE)
  row.names(senders.df) <- 1:nrow(senders.df)
  names(senders.df) <- c("From.EMail","Freq","Weight")
  senders.df$Freq <- as.numeric(senders.df$Freq)
  senders.df$Weight <- as.numeric(senders.df$Weight)
  return(senders.df)
}
senders.df <-email.thread(thread.matrix)

#活動的と認識されたスレッドに基づく重み付け
#trans.weights 一定の時間に送られたスレッドのメッセージ頻度による重み付け
get.threads <- function(thrads.matrix, email.df){
  threads <- unique(threads.matrix[,2])
  thread.counts <- lapply(threads, function(t) thread.counts(t, email.df))
  thread.matrix <- do.call(rbind, thread.counts)
  return(cbind(threads, thread.matrix))
}
thread.counts <- function(thread, email.df){
  thread.times <- email.df$Date[which(email.df$Subject== thread
  | email.df$Subject == paste("re:", thread))]
  freq <- length(thread.times)
  min.time <- min(thread.times)
  max.time <- max(thread.times)
  time.span <- as.numeric(difftime(max.time, min.time, units="secs"))
  if(freq < 2){
    return(c(NA,NA,NA))
  }
  else{
    trans.weight <- freq /time.span
    log.trans.weight <- 10 + log(trans.weight, base=10)
    return(c(freq,time.span, log.trans.weight))
  }
}
thread.weights <- get.threads(threads.matrix, priority.train)
thread.weights <- data.frame(thread.weights, stringsAsFactors=FALSE)
names(thread.weights) <- c("Thread", "Freq", "Response", "Weight")
thread.weights$Freq <- as.numeric(thread.weights$Freq)
thread.weights$Response <- as.numeric(thread.weights$Response)
thread.weights$Weight <- as.numeric(thread.weights$Weight)
thread.weights <- subset(thread.weights, is.na(thread.weights$Freq) == FALSE)

head(thread.weights)

# 活動的なスレッドに頻出する単語による重み付け
term.counts <- function(term.vec, control){
  vec.corpus <- Corpus(VectorSource(term.vec))
  vec.tdm <- TermDocumentMatrix(vec.corpus, control=control)
  return(rowSums(as.matrix(vec.tdm)))
}

thread.terms <- term.counts(thread.weights$Thread,
                            control= list(stopwords=TRUE))

thread.terms <- names(thread.terms)
                            
term.weights <- sapply(thread.terms,
                       function(t) mean(thread.weights$Weight[grepl(t, thread.weights$Thread, fixed = TRUE)]))
term.weights <- data.frame(list(Term = names(term.weights),
                                Weight = term.weights),
                           stringsAsFactors = FALSE,
                           row.names = 1:length(term.weights))

#全ての電子メールメッセージの単語頻度による重み付け
##Sys.setlocaleの設定がないと“tolower(txt):1は不正なマルチバイト文字です”のエラーが発生します
Sys.setlocale("LC_ALL", "C") 
msg.terms <- term.counts(priority.train$Message,
                         control = list(stopwords = TRUE,
                                        removePunctuation = TRUE,
                                        removeNumbers = TRUE))

msg.weights <- data.frame(list(Term=names(msg.terms),
                               Weight=log(msg.terms, base=10)),
                          stringAsFactors=FALSE,
                              row.names=1:length(msg.terms))

msg.weights <- subset(msg.weights, Weight > 0)

#順位付けの訓練と検証
get.weights <- function(search.term, weight.df, term=TRUE){
  if(length(search.term)>0){
    if(term){
    term.match <- match(names(search.term),weight.df$Term)
    }
    else{
      term.match <- match(search.term, weight.df$Thread)
   }
    match.weights <- weight.df$Weight[which(!is.na(term.match))]
    if(length(match.weights) > 1){
      return(1)
    }
    else{
      return(mean(match.weights))
    }
  }
  else{
    return(1)
  }
}

rank.message <- function(path)
{
  msg <- parse.email(path)
  from <- ifelse(length(which(from.weight$From.EMail == msg[2])) > 0,
                 from.weight$Weight[which(from.weight$From.EMail == msg[2])],
                 1)
  
  thread.from <- ifelse(length(which(senders.df$From.EMail == msg[2])) > 0,
                        senders.df$Weight[which(senders.df$From.EMail == msg[2])],
                        1)
  
  subj <- strsplit(tolower(msg[3]), "re: ")
  is.thread <- ifelse(subj[[1]][1] == "", TRUE, FALSE)
  if(is.thread)
  {
    activity <- get.weights(subj[[1]][2], thread.weights, term = FALSE)
  }
  else
  {
    activity <- 1
  }
  thread.terms <- term.counts(msg[3], control = list(stopwords = TRUE))
  thread.terms.weights <- get.weights(thread.terms, term.weights)
  
  msg.terms <- term.counts(msg[4],
                           control = list(stopwords = TRUE,
                                          removePunctuation = TRUE,
                                          removeNumbers = TRUE))
  msg.weights <- get.weights(msg.terms, msg.weights)

  rank <- prod(from,
               thread.from,
               activity, 
               thread.terms.weights,
               msg.weights)
  
  return(c(msg[1], msg[2], msg[3], rank))
}

#データを時系列順に２分する
train.paths <- priority.df$Path[1:(round(nrow(priority.df) / 2))]
test.paths <- priority.df$Path[((round(nrow(priority.df) / 2)) + 1):nrow(priority.df)]
#訓練データの生成
train.ranks <- suppressWarnings(lapply(train.paths, rank.message))
train.ranks.matrix <- do.call(rbind, train.ranks)
train.ranks.matrix <- cbind(train.paths, train.ranks.matrix, "TRAINING")
train.ranks.df <- data.frame(train.ranks.matrix, stringsAsFactors = FALSE)
names(train.ranks.df) <- c("Message", "Date", "From", "Subj", "Rank", "Type")
train.ranks.df$Rank <- as.numeric(train.ranks.df$Rank)

#優先メールのしきい値を計算
##na.rm = TRUEが無いとpriority.thresholdが欠損値になる
##ただし、このスクリプトで算出される値は本の中の値よりかない低くなります。
priority.threshold <- median(train.ranks.df$Rank, na.rm = TRUE)

train.ranks.df$Priority <- ifelse(train.ranks.df$Rank >= priority.threshold, 1, 0)

#しきい値を可視化します。
ggplot(train.ranks.df, aes(x = Rank)) +
  stat_density(aes(fill="darkred")) +
  geom_vline(xintercept = priority.threshold, linetype = 2) +
  scale_fill_manual(values = c("darkred" = "darkred"), guide = "none") +
  theme_bw()

train.ranks.df$Priority <- ifelse(train.ranks.df$Rank >= priority.threshold, 1, 0)

#全ての電子メールの優先度を計算
test.ranks <- suppressWarnings(lapply(test.paths,rank.message))
test.ranks.matrix <- do.call(rbind, test.ranks)
test.ranks.matrix <- cbind(test.paths, test.ranks.matrix, "TESTING")
test.ranks.df <- data.frame(test.ranks.matrix, stringsAsFactors = FALSE)
names(test.ranks.df) <- c("Message","Date","From","Subj","Rank","Type")
test.ranks.df$Rank <- as.numeric(test.ranks.df$Rank)
test.ranks.df$Priority <- ifelse(test.ranks.df$Rank >= priority.threshold, 1, 0)

final.df <- rbind(train.ranks.df, test.ranks.df)
final.df$Date <- date.converter(final.df$Date, pattern1, pattern2)
final.df <- final.df[rev(with(final.df, order(Date))), ]

write.csv(final.df, file.path("data", "final_df.csv"), row.names = FALSE)

ggplot(subset(final.df, Type == "TRAINING"), aes(x = Rank)) +
  stat_density(aes(fill = Type, alpha = 0.65)) +
  stat_density(data = subset(final.df, Type == "TESTING"),
               aes(fill = Type, alpha = 0.65)) +
  geom_vline(xintercept = priority.threshold, linetype = 2) +
  scale_alpha(guide = "none") +
  scale_fill_manual(values = c("TRAINING" = "darkred", "TESTING" = "darkblue")) +
  theme_bw()

##この設定だとtest.ranks.df$Rankに大量に欠損値が発生します。
##関係していそうなencodingとSys.setlocaleの値を最適化することで多少変わるかもしれません。