CITS1401 Computational Thinking with Python
Project 2 Semester 2 2019
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Project 2: Using Stylometry to Verify Authorship
Submission deadlines:
Stage 1: 5:00pm, Friday 18 October 2019 for the pseudocode
Stage 2: 5:00pm, Friday 25 October 2019 for the code.
Value: 20% of CITS1401.

To be done individually.
You should construct a Python 3 program containing your solution to the following
problem and submit your program electronically on LMS. No other method of
submission is allowed.
You are expected to have read and understood the University's guidelines on
academic conduct. In accordance with this policy, you may discuss with other
students the general principles required to understand this project, but the work
you submit must be the result of your own effort. Plagiarism detection, and other
systems for detecting potential malpractice, will therefore be used. Besides, if
what you submit is not your own work then you will have learnt little and will
therefore, likely, fail the final exam.
You must submit your project before the submission deadline listed above.
Following UWA policy, a late penalty of 10% will be deducted for each day (or part
day), after the deadline, that the assignment is submitted. However, in order to
facilitate marking of the assignments in a timely manner, no submissions will be
allowed after 7 days following the deadline.

Overview
UWA, like every university around the country (probably around the galaxy) is
very worried about ghost-written submissions for assignments. This is also known
as contract cheating. Whatever you call it, ghost-writing is about getting someone
else to do your work, but submitting it as if it was only your work. In this case we
are concerned with essays. The incidence is believed to be low, but it's clearly not
a good thing.
Coming from a different angle, debates have raged at various times about whether
different authors' works were actually by those authors. For example, were all the
works attributed to William Shakespeare actually by him? One approach to
examining both of these issues is to use stylometry. That is, rather than looking
directly at the content of texts, as one does when looking for suspected plagiarism,
stylometic looks for stylistic similarities. In other words, similarities in the ways a
particular author uses language, rather than similarities in the actual words on the
page, on the assumption that an author will use a similar style for similar sorts of
content, fiction, non-fiction, etc.
CITS1401 Computational Thinking with Python
Project 2 Semester 2 2019
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What you will do for this Project is write a program that reads in two text files
containing the works to be analysed and builds a profile for each. The two profiles
are compared and returned besides a score which reflects the distance between
the two works in terms of their style; low scores, down to 0, imply that the same
author is likely responsible for both works, while large scores imply different
authors.
Specification: What your program will need to do
Input:
Your program must define the function main with the following signature:
def main(textfile1, textfile2, feature)
The first and second arguments are the names of the text files with a work to be
analysed. The third argument is the type of feature that will be used to compare
the document profiles. The allowed feature names are: "punctuation", "unigrams",
"conjunctions" and "composite".
Output:
The function is required to return the following outputs in the order provided
below:
• the score from a pairwise comparison rounded to four decimal places,
• the dictionary containing the profile of first file (textfile1), and
• the dictionary containing the profile of second file (textfile2)
A more detailed specification
• For the purposes of this project, a sentence is a sequence of words followed by
either a full-stop, question mark or exclamation mark, which in turn must be
followed either by a quotation mark (so the sentence is the end of a quote or
spoken utterance), or white space (space, tab or new-line character). Thus:
This is some text. This is yet more text
contains one sentence followed by the start of another sentence.
• You are required to create the profile of input files using dictionaries. The profile
for each document will contain the number of occurrences of certain words
(case insensitive) and pieces of punctuation.
• The counted words or punctuations are dependent on the input feature which
can be: "punctuation", "unigrams", "conjunctions" and "composite".
• For conjunctions: your program is required to count the number of
occurrences of the following words:
"also", "although", "and", "as", "because", "before", "but", "for", "if", "nor", "of",
"or", "since", "that", "though", "until", "when", "whenever", "whereas",
"which", "while", "yet"
CITS1401 Computational Thinking with Python
Project 2 Semester 2 2019
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• For unigrams: your program is required to count the number of occurrences
of each word in the files. Consider the following three lines of text contained in
a file:
This is a Document.
This is only a document
A test should not cause problem

The word count will be: "a":3, "document":2, "this":2, "is":2, "only":1,
"should":1, "not""1, "cause":1, "problem":1
• For punctuation: your program should count certain pieces of punctuation:
comma and semicolon. In addition, your program should also count single-
quote and hyphen, but only under certain circumstances. Specifically, your
program should count single-quote marks, but only when they appear as
apostrophes surrounded by letters, i.e. indicating a contraction such as
"shouldn't" or "won't". (Apostrophe is being included as an indication of more
informal writing, perhaps direct speech.). Your program should count dash
(minus) signs, but only when they are surrounded by letters, indicating a
compound-word, such as "compound-word". Any other punctuation or letters,
e.g '.' when not at the end of a sentence, should be regarded as white space,
so serve to end words. For these purposes, strings of digits are also words as
they convey information. Therefore, in the unlikely event that a floating point
number, such as 3.142, appears, that is regarded as two words.

Note: Some of the texts we will use include double hyphen, i.e. "--". This is to
be regarded as a space character.
• For composite: your program should contain number of occurrences of
punctuations (as explained above) and conjunctions. In addition, your program
should also add to the profile two further parameters relating to the text: the
average number of words per sentence and the average number of sentences
per paragraph, where a paragraph is any number of sentences followed by a
blank line or by the end of the text.
• Each of the words and punctuation symbols should be placed, together with
their respective counts, in a dictionary, which is called a profile.
• The first output by the main function is the distance between the corresponding
profiles which should be computed using the standard distance formula:
= ��(1 − 2)2


• The second and third outputs returned by the main function are
the profiles corresponding to the first and second text files respectively. The
returned profiles as dictionaries in which each word is the key and value is the
number of occurrences of the key, such as {“also”:10, ”got”: 6} where
“also” and “got” are the keys and have occurred 10 and 6 times respectively.
CITS1401 Computational Thinking with Python
Project 2 Semester 2 2019
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Example:
Download the project2data.zip file from the folder of Project 2 on LMS. An example
interaction is provided as a sampleanswers.txt which you can find in
sampleresult.txt. The results are based on three files: sample1.txt and
sample2.txt, both excerpts taken from "Life on the Mississippi", by Mark Twain.
Some Text Files to Examine
Some text files are also included in the zip file for you to try out. All of the texts,
apart from "Kangaroo", were obtained from Project Gutenberg
(www.gutenberg.org). All the files have a long text at the end which contains
Project Gutenberg license and terms of use. I have removed the Gutenberg terms
and license in the files rather than left them in the texts because that may affect
the profiles.
Author Title Fiction/Non-fiction
Henry Lawson Children of the Bush Fiction
D. H. Lawrence Fantasia of the Unconscious Non Fiction
Mark Twain Life on the Mississippi Non Fiction
D. H. Lawrence Sea and Sardinia Non Fiction
D. H. Lawrence Kangaroo Fiction
Mark Twain Adventures of Hucklebery Finn Fiction
Andrew Barton
'Banjo' Paterson
Three Elephant Power Fiction
A small note of warning. If you decide to download your own texts from Project
Gutenberg, please be aware that many of the texts include spurious Unicode
characters. Unfortunately, the file input-output functions we use in CITS1401 (and
I use on a daily basis) only work with the standard ASCII character set, so will
cause an exception if Unicode characters are in the text. While Python is well able
to deal with Unicode, special input-output functions are needed, which are beyond
the scope of this unit. What I have done is use the Unix command: cat –vet
filename to make the Unicode characters visible in the ASCII character set, and
then use a text editor to remove them. (Tedious.)
Important:
You will have noticed that you have not been asked to write specific functions.
That has been left to you. However, as in Project 1, it is important that your
program defines the top-level function main() as described above. main()
should then call the other functions. (Of course, these may call further functions.)
CITS1401 Computational Thinking with Python
Project 2 Semester 2 2019
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The reason this is important is that when I test your program, my testing program
will call your main() function. So, if you fail to define main(), or define it with a
different signature, my program will not be able to test your program.
Things to avoid:
There are a few things for your program to avoid.
• You are not allowed to import any Python module except math or os. While
use of other modules are perfectly sensible thing to do (and the way I often
may do it), it takes away much of the point of different aspects of the project,
which is about getting practice creating code to accurately extract the parts of
strings that that you need, and use of basic Python structures, in this case
dictionaries.
• Please do not assume that the input file names will end in .txt. File name
suffixes such as .csv and .txt are not mandatory in systems other than Microsoft
Windows.
• Please make sure your program does NOT call the input() or print()
functions. That will cause your program to hang, waiting for input that my
automated testing system will not provide. In fact, what will happen is that the
marking program detects the call(s), and will not test your code at all.
Submission:
Stage 1:
Submit a single PDF file containing your approach and/or pseudocode for the
solution of the problem as per guidelines discussed in Lecture L2 Software
Development Process and Project 1 Stage 1 submission. You need to discuss the
document with lab demonstrator before submission. It is mandatory to submit this
file before 5:00pm 18 October 2019 on LMS to avoid 10% deduction in Project
2 grading. This will be a formative feedback of your problem solving skills
developed in the course. In case you do not submit the file, 10% of the total marks
of the project will be deducted from your obtained grade of the Stage 2
submission.
Stage 2:
Submit a single Python (.py) file containing all of your functions via LMS before
5:00pm 25 October 2019 on LMS
You need to contact unit coordinator if you have special considerations or making
late submission.
Marking Rubric:
Your program will be marked out of 30 (later scaled to be out of 20% of the final
mark).
22 out of 30 marks will be awarded based on how well your program completes a
number of tests, reflecting normal use of the program, and also how the program
handles various error states, such as the input file not being present. Other than
CITS1401 Computational Thinking with Python
Project 2 Semester 2 2019
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things that you were asked to assume, you need to think creatively about the
inputs your program may face.
8 out of 30 marks will be style (4/8) “the code is clear to read” and efficiency (4/8)
“your program is well constructed and runs efficiently”. For style, think about use
of comments, sensible variable names, your name at the top of the program, etc.
(Please look at your lecture notes, where this is discussed.)
Style Rubric:
0 Gibberish, impossible to understand
1-2 Style is really poor
3 Style is good or very good, with small lapses
4 Excellent style, really easy to read and follow
Your program will be traversing text files of various sizes (possibly including large
corpora) so try to minimise the number of times your program looks at the same
data items. You may wish to use dictionaries (or sets, if you are prepared to read
the documentation), rather than lists.
Efficiency Rubric:
0 Code too incomplete to judge efficiency, or wrong problem tackled
1 Very poor efficiency, additional loops, inappropriate use of readline()
2 Acceptable efficiency, one or more lapses
3 Good efficiency, small lapses
4 Excellent efficiency, should have no problem on large files
Automated testing is being used so that all submitted programs are being tested
the same way. Sometimes it happens that there is one mistake in the program
that means that no tests are passed. If the marker is able to spot the cause and
fix it readily, then they are allowed to do that and your - now fixed - program will
score whatever it scores from the tests, minus 2 marks, because other students
will not have had the benefit of marker intervention. Still, that's way better than
getting zero. On the other hand, if the bug is too hard to fix, the marker needs to
move on to other submissions.