This week we will start writing some code! We will be using Python for most of this course, and this assignment examines your skills writing regular expressions, control flows, and string processing in Python. Being able to process files from the command line will also be incredibly useful for your life as a computational linguist, and we ask you to implement several operations in Bash.
You will submit your assignment via Gradescope. We’ll post instructions on Piazza.
For this class, we expect you to have access to a Unix command line. If you have a Mac, you can open up the
Terminal app. If you are
on Windows, please install PuTTY or its more modern, easier-to-use cousin, MobaXterm,
and follow the instructions here.
bash refers to both the program (or shell)–run by the terminal–that you type your commands into, and the programming
language you use to write those commands. There exist other shells, such as
fish, but we will stick to
bash. When you type
commands into the shell, we refer to these as bash commands. When you write a file with a long sequence of these command, we call that a bash program.
In order to learn bash, we’ve picked 3 commands for you to implement, each of which we’ve found useful in our research. These questions might be tricky; you should take advantage of Piazza and TA office hours for guidance. Our basic, and advanced bash tutorials may be of particular use.
When you’ve finished getting your solutions
working on the command line, use the template files
bash_q3.sh which can be downloaded here,
and write your solution in the file.
In each bash template, you’ll notice the variable
This refers to the index-1 argument in the command used to invoke the bash script.
For example, to test
bash_q1.sh, you may run
bash_q1.sh, the variable
$1 refers to
This question corresponds to
For this question, you are allowed to use
A vocabulary file contains a list of all of the words in a text document along with a count of the number of occurences of each word.
Given a text file, output a list of the words present, tab-separated by their frequency. The words should be ordered from least frquent to most frequent. You can assume all words are space-separated.
For example, the input file:
Seven lazy researchers like using bash. The researchers like, like Python too.
bash. 1 lazy 1 like, 1 Python 1 Seven 1 The 1 too. 1 using 1 like 2 researchers 2
This question corresponds to
For this question, you’re allowed to use
ls, if statements, for statements,
Check for a file named
results.txt in each directory within a specified directory.
For each existing
results.txt, print the directory name and then
only all lines from the corresponding
results.txt that contain
the following substring:
For example, the directory structure
root_directory/ 1/ results.txt 2/ 3/ results.txt
1 Accuracy: 54.44 Accuracy: 52.23 3 Accuracy: 44.34 Accuracy: 45.34
This question corresponds to
Frequently, when dealing with large sets of experiments, you want
to summarize a bunch of semi-structured results text files quickly.
In this exercise, you’ll use bash to take results of the form below
in the file at path
$1 and pull out the accuracies as well as the name
of the experiment.
For example, the lines
Base accuracy: 0.3267522959523133 time: .4555 Augment accuracy: 0.34124123125 time: .785 steps 5
should be transformed to the lines
Base 0.3267522959523133 Augment 0.34124123125
Note: The bash template you downloaded may ask you to average the lines. Don’t do that; follow the example above.
All your answers should be added to
python_questions.py, which can be downloaded here.
If you’d like to include any import statements other than the ones already provided, post on Piazza for permission first.
You can open, read, and write files using the aptly-named open(), read(), and write() commands. read() returns the entire contents of the file as a string. readlines() will split on the newline character and return the lines as a list, which is generally nicer for allowing you to iterate line-by-line. I won’t go through an example here, but I highly recommend playing with the csv module, which is incredibly useful and we will likely use regularly throughout the semester.
Writing a file:
Reading an entire file (if a file is too large to fit easily into memory, you should avoid this);
>>> with open('test.txt') as f: >>> contents = f.read() >>> contents 'line1\nline2\nline3\nline4\n'
Reading a file line-by-line without loading it entirely into memory:
>>> contents = '' >>> with open('test.txt') as f: >>> for line in f: >>> contents += line >>> contents 'line1\nline2\nline3\nline4\n'
No need to submit anything for this question, but you should make sure you are familiar with Python file I/O.
Regular expressions are a powerful way to process text by describing text patterns. If you are new to regular expressions, Chapter 2 in the course textbook has a good introduction.
python_questions.py, fill in the functions
according to their function docstrings. Use the builtin Python regular expressions library, whose documentation is found
You may want to write yourself test cases to make sure you’re covering all edgecases. See the unittest documentation.
I also highly recommend testing out your expressions using this fancy GUI tool.
To compute the similarity between two strings of text, linguists often use a metric called edit distance. Edit distance measures how similar two strings are based on the number of insertions, deletions, and substitutions necessary to turn one of the strings into the other.
Use dynamic programming to implement edit distance. Chapter 2 in the textbook provides pseudocode that you can follow.
Write your solution in the
edit_distance function in
For this part, you will need to submit your code to answer the following questions.
We will be playing with a small but oh so wonderful data set of wine reviews! You can download the data here. You can down it and unpack it as follows, and should see two files:
wine.txt is in the format of one review per line, followed but a star rating between 1 and 5 (except for 3 reviews, where the review
decided to go rogue and give 6 stars. Pft.) The text of the review and the star rating are separated by a single tab character. There is also a file called
stopwords.txt, which you will use for question 6.
wine_text_processing function in
python_questions.py, write code that answers each of the following questions and prints the
answer to standard output, followed by a newline. Since this question is meant as a tutorial, there are no secrets: your script should produce
this output when you are done. If you get this output it’s very likely your code works correctly, but we will ultimately be running your code on a different input file,
so start early and come ask for help if you get stuck!
For questions where there are ties, please break the tie alphabetically (e.g. apple would come before banana).
I highly recommend looking into the functions available in the
python string module.
Thats it! Again, you can compare your answers against our key to see if you have done things correctly.
Your code is due Tuesday, January 29, 2019. Please submit the 3 bash files and the 1 Python file using Gradescope.