Week 08 Laboratory Exercises
Objectives
- Proficiency at text processing in Python.
- Understanding multi-dimensional dicts.
- Explore a simple machine learning algorithm.
Preparation
Before the lab you should re-read the relevant lecture slides and their accompanying examples.
Getting Started
lab08 and changing to this directory.
mkdir lab08 cd lab08
There are some provided files for this lab which you can fetch with this command:
2041 fetch lab08
If you're not working at CSE, you can download the provided files as a zip file or a tar file.
Exercise:
How many words in standard input?
In these exercises you will work with a dataset containing sing lyrics.
This dataset contains the lyrics of the songs of 10 well-known artists.
unzip lyrics.zip Archive: lyrics.zip creating: lyrics/ inflating: lyrics/David_Bowie.txt inflating: lyrics/Adele.txt inflating: lyrics/Metallica.txt inflating: lyrics/Rage_Against_The_Machine.txt inflating: lyrics/Taylor_Swift.txt inflating: lyrics/Keith_Urban.txt inflating: lyrics/Ed_Sheeran.txt inflating: lyrics/Justin_Bieber.txt inflating: lyrics/Rihanna.txt inflating: lyrics/Leonard_Cohen.txt inflating: song0.txt inflating: song1.txt inflating: song2.txt inflating: song3.txt inflating: song4.txt
The lyrics for each song have been re-ordered to avoid copyright concerns.
The dataset also contains lyrics from 5 songs where we don't know the artists.
cat song0.txt I've made up my mind, Don't need to think it over, If I'm wrong I am right, Don't need to look no further, This ain't lust, I know this is love but, If I tell the world, I'll never say enough, Cause it was not said to you, And that's exactly what I need to do, If I'm in love with you, cat song1.txt Come Mr. DJ song pon de replay Come Mr. DJ won't you turn the music up All the gal pon the dance floor wantin' some more what Come Mr. DJ won't you turn the music up cat song2.txt And they say She's in the class A team Stuck in her daydream Been this way since eighteen But lately her face seems Slowly sinking, wasting Crumbling like pastries cat song3.txt Ooh whoa, ooh whoa, ooh whoa You know you love me, you know you care Just shout whenever and I'll be there You are my love, you are my heart And we will never, ever, ever be apart Are we an item? Girl quit playin' We're just friends, what are you sayin' Said there's another, look right in my eyes My first love, broke my heart for the first time And I was like baby, baby, baby oh Like baby, baby, baby no Like baby, baby, baby oh I thought you'd always be mine (Mine) Baby, baby, baby oh Like baby, baby, baby no Like baby, baby, baby ooh I thought you'd always be mine Oh for you, I would have done whatever And I just can't believe we ain't together And I wanna play it cool But I'm losin' you I'll buy you anything I'll buy you any ring And I'm in pieces, baby fix me And just shake me, til you wake me from this bad dream I'm goin' down, down, down, down And I can't believe my first love won't be around cat song4.txt The birds they sang At the break of day Start again I heard them say Don't dwell on what Has passed away Or what is yet to be. Ah the wars they will Be fought again The holy dove She will be caught again Bought and sold And bought again The dove is never free. Ring the bells that still can ring Forget your perfect offering There is a crack in everything That's how the light gets in.
Each is from one of the artists in the dataset but they are not from a song in the dataset.
As a first step in this analysis, write a Python script total_words.py
which counts the total number of words in its stdin.
For the purposes of this program (and the following programs)
we will define a word to be a maximal, non-empty, contiguous, sequence of alphabetic characters
([a-zA-Z]).
Any characters other than [a-zA-Z] separate words.
So for example the phrase "The soul's desire" contains 4 words: ("The", "soul", "s", "desire")
./total_words.py < lyrics/Justin_Bieber.txt 46589 words ./total_words.py < lyrics/Metallica.txt 38096 words ./total_words.py < lyrics/Rihanna.txt 53157 words
When you think your program is working,
you can use autotest
to run some simple automated tests:
2041 autotest total_words
When you are finished working on this exercise,
you must
submit your work by running give:
give cs2041 lab08_total_words total_words.py
before Tuesday 09 April 12:00 (midday) (2024-04-09 12:00:00) to obtain the marks for this lab exercise.
Exercise:
How many times does a word occur in standard input
Write a Python script count_word.py
that counts the number of times a specified word is found in its stdin
The word you should count will be specified as a command line argument.
Your program should ignore the case of words.
./count_word.py death < lyrics/Metallica.txt death occurred 69 times ./count_word.py death < lyrics/Justin_Bieber.txt death occurred 0 times ./count_word.py love < lyrics/Ed_Sheeran.txt love occurred 218 times ./count_word.py love < lyrics/Rage_Against_The_Machine.txt love occurred 4 times
When you think your program is working,
you can use autotest
to run some simple automated tests:
2041 autotest count_word
When you are finished working on this exercise,
you must
submit your work by running give:
give cs2041 lab08_count_word count_word.py
before Tuesday 09 April 12:00 (midday) (2024-04-09 12:00:00) to obtain the marks for this lab exercise.
Exercise:
Do you use that word often?
Write a Python script frequency.py
thar prints the frequency with which each artist uses a word specified as an argument.
So if Justin Bieber uses the word "love" 493 times in the 46583 words of his songs, then its frequency is 493/46583 = 0.0105832599875491.
./frequency.py love 165/ 16359 = 0.010086191 Adele 189/ 34080 = 0.005545775 David Bowie 218/ 18207 = 0.011973417 Ed Sheeran 493/ 46589 = 0.010581897 Justin Bieber 217/ 27016 = 0.008032277 Keith Urban 212/ 26192 = 0.008094075 Leonard Cohen 57/ 38096 = 0.001496220 Metallica 4/ 18985 = 0.000210693 Rage Against The Machine 494/ 53157 = 0.009293226 Rihanna 89/ 26188 = 0.003398503 Taylor Swift ./frequency.py death 1/ 16359 = 0.000061128 Adele 9/ 34080 = 0.000264085 David Bowie 3/ 18207 = 0.000164772 Ed Sheeran 0/ 46589 = 0.000000000 Justin Bieber 1/ 27016 = 0.000037015 Keith Urban 16/ 26192 = 0.000610874 Leonard Cohen 69/ 38096 = 0.001811214 Metallica 23/ 18985 = 0.001211483 Rage Against The Machine 0/ 53157 = 0.000000000 Rihanna 0/ 26188 = 0.000000000 Taylor Swift
Make sure your Python script produces exactly the output above.
When you think your program is working,
you can use autotest
to run some simple automated tests:
2041 autotest frequency
When you are finished working on this exercise,
you must
submit your work by running give:
give cs2041 lab08_frequency frequency.py
before Tuesday 09 April 12:00 (midday) (2024-04-09 12:00:00) to obtain the marks for this lab exercise.
Exercise:
When numbers get very small, logarithms are your friend
Now suppose we have the song line "truth is beauty".
Given that David Bowie uses:
the word "truth" with frequency 0.000146714
the word "is" with frequency 0.005897887
the word "beauty" with frequency 0.000264085
we can estimate the probability of Bowie writing the phrase "truth is beauty" as:
0.000146714 * 0.005897887 * 0.000264085 = 2.2851343535638401e-10
We could similarly estimate probabilities for each of the other 9 artists
and then determine which of the 10 artists is most likely to sing "truth is beauty"
(it's Leonard Cohen).
A sidenote: we are actually making a large simplifying assumption in calculating this probability.
It is often called the bag of words model.
Multiplying probabilities like this quickly leads to very small numbers
and may result in arithmetic underflow of our floating point representation.
A common solution to this underflow is instead to work with the log of the numbers.
So instead we will calculate the log of the probability of the phrase.
You do this by adding the log of the probabilities of each word.
For example, you calculate the log-probability of
Bowie singing the phrase "Truth is beauty." like this:
log(0.000146714) + log(0.005897887) + log(0.000264085) = -22.19942610926425
Log-probabilities can be used directly to determine the most likely artist, as the artist with the highest log-probability will also have the highest probability.
Another problem is that we might be given a word that an artist has not used in the dataset we have.
You should avoid this when estimating probabilities by adding 1 to the count of occurrences of each word.
So for example we'd estimate the probability of Ed Sheeran using the word fear as (0+1)/18205
and the probability of Metallica using the word fear as (39+1)/38096.
This is a simple version of Additive smoothing.
Write a Python script log_probability.py
which given a phrase (sequence of words) as arguments,
prints the estimated log of the probability that each artist would use this phrase.
./log_probability.py truth is beauty -23.11614 Adele -21.90679 David Bowie -23.10075 Ed Sheeran -21.70202 Justin Bieber -23.45248 Keith Urban -18.58417 Leonard Cohen -21.08903 Metallica -21.98171 Rage Against The Machine -22.51582 Rihanna -24.40992 Taylor Swift ./log_probability.py death and taxes -22.64301 Adele -22.42756 David Bowie -21.66227 Ed Sheeran -25.56650 Justin Bieber -23.20281 Keith Urban -20.97467 Leonard Cohen -20.90589 Metallica -20.26248 Rage Against The Machine -25.84396 Rihanna -23.90310 Taylor Swift
Make sure your output matches the above exactly
When you think your program is working,
you can use autotest
to run some simple automated tests:
2041 autotest log_probability
When you are finished working on this exercise,
you must
submit your work by running give:
give cs2041 lab08_log_probability log_probability.py
before Tuesday 09 April 12:00 (midday) (2024-04-09 12:00:00) to obtain the marks for this lab exercise.
Exercise:
Who sang those words?
Write a Python script identify_artist.py
that given 1 or more files (each containing part of a song),
prints the most likely artist to have sung those words.
For each file given as argument, you should go through all artists and for each calculate the log-probability that the artist sung those words.
You calculate the log-probability that the artist sung the words in the file, by for each word in the file calculating the log-probability of that artist using that word, and summing all the the log-probabilities.
You should print the artist with the highest log-probability.
Your program should produce exactly this output:
./identify_artist.py song?.txt song0.txt most resembles the work of Adele (log-probability=-352.4) song1.txt most resembles the work of Rihanna (log-probability=-254.9) song2.txt most resembles the work of Ed Sheeran (log-probability=-206.6) song3.txt most resembles the work of Justin Bieber (log-probability=-1089.8) song4.txt most resembles the work of Leonard Cohen (log-probability=-493.8)
When you think your program is working,
you can use autotest
to run some simple automated tests:
2041 autotest identify_artist
When you are finished working on this exercise,
you must
submit your work by running give:
give cs2041 lab08_identify_artist identify_artist.py
before Tuesday 09 April 12:00 (midday) (2024-04-09 12:00:00) to obtain the marks for this lab exercise.
Submission
give.
You can run give multiple times.
Only your last submission will be marked.
Don't submit any exercises you haven't attempted.
If you are working at home, you may find it more convenient to upload your work via give's web interface.
Remember you have until Week 9 Tuesday 12:00:00 (midday) to submit your work.
You cannot obtain marks by e-mailing your code to tutors or lecturers.
You check the files you have submitted here.
Automarking will be run by the lecturer several days after the submission deadline,
using test cases different to those autotest runs for you.
(Hint: do your own testing as well as running autotest.)
After automarking is run by the lecturer you can view your results here. The resulting mark will also be available via give's web interface.
Lab Marks
When all components of a lab are automarked you should be able to view the the marks via give's web interface or by running this command on a CSE machine:
2041 classrun -sturec