AS10-1：誰是來亂的？

The data for this problem is based on the revision history of the page Language. Wikipedia provides a history for each page that consists of the state of the page at each revision. Rather than manually considering each revision, a script was run that checked whether edits stayed or were reverted. If a change was eventually reverted then that revision is marked as vandalism. This may result in some misclassifications, but the script performs well enough for our needs.

As a result of this preprocessing, some common processing tasks have already been done, including lower-casing and punctuation removal. The columns in the dataset are:

• Vandal = 1 if this edit was vandalism, 0 if not.
• Minor = 1 if the user marked this edit as a “minor edit”, 0 if not.
• Loggedin = 1 if the user made this edit while using a Wikipedia account, 0 if they did not.
• Added = The unique words added.
• Removed = The unique words removed.

---

packages = c(
  "dplyr","ggplot2","caTools","tm","SnowballC","ROCR","rpart","rpart.plot","randomForest")
existing = as.character(installed.packages()[,1])
for(pkg in packages[!(packages %in% existing)]) install.packages(pkg)

rm(list=ls(all=TRUE))
Sys.setlocale("LC_ALL","C")

[1] "C"

options(digits=5, scipen=10)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tm)

Loading required package: NLP

library(SnowballC)
library(ROCR)

Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

library(caTools)
library(rpart)
library(rpart.plot)
library(randomForest)

randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.

Attaching package: 'randomForest'

The following object is masked from 'package:dplyr':

    combine

Problem 1 - Bags of Words

1.1 The data set

wiki = read.csv("data/wiki.csv", stringsAsFactors = F)
wiki$Vandal = factor(wiki$Vandal)
table(wiki$Vandal)

   0    1 
2061 1815 

#Vandal 將wiki的資料建成錯的資訊 1=true
#Minor  是否為第一個編輯資料的人
#Loggedin 是否為登入帳號修改的
#Added 加進的字有多少不同的字
#Removed 移除的字有多少不同的字

【P1.1】How many cases of vandalism were detected in the history of this page?

• 1815

1.2 DTM, The Added Words

library(tm)
library(SnowballC)
# Create corpus for Added Words
txt = iconv(wiki$Added, to = "utf-8", sub="")
corpus = Corpus(VectorSource(txt))
corpus = tm_map(corpus, removeWords, stopwords("english"))

transformation drops documents

corpus = tm_map(corpus, stemDocument)

transformation drops documents

dtm = DocumentTermMatrix(corpus)
dtm

<<DocumentTermMatrix (documents: 3876, terms: 6675)>>
Non-/sparse entries: 15368/25856932
Sparsity           : 100%
Maximal term length: 784
Weighting          : term frequency (tf)

【P1.2】How many terms appear in dtmAdded?

• 6675

1.3 Handle Sparsity

Filter out sparse terms by keeping only terms that appear in 0.3% or more of the revisions, and call the new matrix sparseAdded.

nwAdded = rowSums(as.matrix(dtm))     # no. word added in each edit
dtm = removeSparseTerms(dtm, 0.997)
dtm

<<DocumentTermMatrix (documents: 3876, terms: 166)>>
Non-/sparse entries: 2681/640735
Sparsity           : 100%
Maximal term length: 28
Weighting          : term frequency (tf)

【P1.3】How many terms appear in sparseAdded?

• 166

1.4 Create Data Frames, wordAdded & wordRemoved

Convert sparseAdded to a data frame called wordsAdded, and then prepend all the words with the letter A, by using the command:

wordsAdded = as.data.frame(as.matrix(dtm))
colnames(wordsAdded) = paste("A", colnames(wordsAdded))  # for proper column names

Now repeat all of the steps we’ve done so far to create a Removed bag-of-words dataframe, called wordsRemoved, except this time, prepend all of the words with the letter R:

# Create corpus
txt = iconv(wiki$Removed, to = "utf-8", sub="")
corpus = Corpus(VectorSource(txt))
corpus = tm_map(corpus, removeWords, stopwords("english"))

transformation drops documents

corpus = tm_map(corpus, stemDocument)

transformation drops documents

dtm = DocumentTermMatrix(corpus)
dtm

<<DocumentTermMatrix (documents: 3876, terms: 5404)>>
Non-/sparse entries: 13294/20932610
Sparsity           : 100%
Maximal term length: 784
Weighting          : term frequency (tf)

nwRemoved = rowSums(as.matrix(dtm))
dtm = removeSparseTerms(dtm, 0.997)
dtm

<<DocumentTermMatrix (documents: 3876, terms: 162)>>
Non-/sparse entries: 2552/625360
Sparsity           : 100%
Maximal term length: 28
Weighting          : term frequency (tf)

wordsRemoved = as.data.frame(as.matrix(dtm))
colnames(wordsRemoved) = paste("R", colnames(wordsRemoved))

【P1.4】How many words are in the wordsRemoved data frame?

• 162

1.5 Prepare the Data Frame

Combine the Data Frames wordsAdded & wordsRemoved with the Target Variable wiki$Vandal

wikiWords = cbind(wordsAdded, wordsRemoved)
wikiWords$Vandal = wiki$Vandal

Split the data frame for train and test data

library(caTools)
set.seed(123)
spl = sample.split(wikiWords$Vandal, 0.7)
train = subset(wikiWords, spl == TRUE)
test = subset(wikiWords, spl == FALSE)
table(test$Vandal) %>% prop.table

      0       1 
0.53138 0.46862 

【P1.5】What is the accuracy on the test set of a baseline method that always predicts “not vandalism”?

• 0.53138

1.6 CART Model

library(rpart)
library(rpart.plot)
cart = rpart(Vandal~., train, method="class")
pred = predict(cart,test,type='class')
table(test$Vandal, pred) %>% {sum(diag(.)) / sum(.)} # 0.54428

[1] 0.54428

【P1.6】What is the accuracy of the model on the test set, using a threshold of 0.5?

• 0.54428

1.7 Plot the Decision Tree

prp(cart)

【P1.7】How many word stems does the CART model use?

• 3

1.8 Predictability of the CART model

【P1.8】Given the performance of the CART model relative to the baseline, what is the best explanation of these results?

• Although it beats the baseline, bag of words is not very predictive for this problem.

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Problem 2 - Add Features with Problem-specific Knowledge

2.1 Add HTTP column

Add a new column based on whether "http" is added

wiki2 = wikiWords
wiki2$HTTP = ifelse( grepl("http",wiki$Added,fixed=TRUE) , 1, 0)
table(wiki2$HTTP) # 217

   0    1 
3659  217 

【P2.1】Based on this new column, how many revisions added a link?

• 217

2.2 Check accuracy again

train2 = subset(wiki2, spl==T)
test2 = subset(wiki2, spl==F)
cart2 = rpart(Vandal~., train2, method="class")
pred2 = predict(cart2,test2,type='class')
table(test2$Vandal, pred2) %>% {sum(diag(.)) / sum(.)} # 0.57524

[1] 0.57524

【P2.2】What is the new accuracy of the CART model on the test set, using a threshold of 0.5?

• 0.57524

2.3 Total numbers of words added and removed

wiki2$nwAdded = nwAdded
wiki2$nwRemoved = nwRemoved
mean(nwAdded) # 4.0501

[1] 4.0501

【P2.3】What is the average number of words added?

• 4.0501

2.4 Check accuracy again

train = subset(wiki2, spl)
test = subset(wiki2, !spl)
cart = rpart(Vandal~., train, method="class")
pred = predict(cart,test,type='class')
table(test$Vandal, pred) %>% {sum(diag(.)) / sum(.)} # 0.6552

[1] 0.6552

【P2.4】What is the new accuracy of the CART model on the test set?

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Problem 3 - Using Non-Textual Data

原始資料之中還有一些之前沒有用到的欄位，我們把它們也加進來

wiki3 = wiki2
wiki3$Minor = wiki$Minor
wiki3$Loggedin = wiki$Loggedin

3.1 Check accuracy again

train = subset(wiki3, spl=T)
test = subset(wiki3, spl=F)
cart = rpart(Vandal~., train, method="class")
pred = predict(cart,test,type='class')
table(test$Vandal, pred) %>% {sum(diag(.)) / sum(.)} # .72472

[1] 0.72472

【P3.1】What is the accuracy of the model on the test set?

• 0.72472

3.2 The Decision Tree

prp(cart)

【P3.2】How many splits are there in the tree?

• 3

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討論議題：
  ■ 請舉出一些可以繼續提高模型準確率的方法，方法越多越好：
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