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Homework 1 – regular expressions and text classification

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Homework 1 –
Objective: The purpose of this homework is for you to gauge how comfortable you are with
regular expressions and text classification.
How to do this assignment: Using Google Colab (https://colab.research.google.com/) please
answer the following questions in a new notebook. You are to write the question on a Text field,
and your programming answer on a Code field. Please write your name and student ID number
on a Text field at the very beginning of this notebook. Note that when you are testing your code,
the output needs to be shown
What to submit: Via iCollege you are to download a copy of the executed Colab notebook in
PDF format, and submit this file ALONGSIDE a link (in the comments/description section) to
your own GitHub repository where this Colab notebook is stored. In other words, you are
expected to keep your own personal GitHub repository for this class and place ALL your
assignments and project code in that location.
Questions (150 total points): NOTE: You can only use Python’s built in regular
expressions (re) and scikit-learn as your ML library for these exercises. Any code
using any other libraries will not be graded.
TEXT: Using Shakespeare’s “The Life and Death of Julius Caesar” found as
hw_1-text.txt under this iCollege assignment. Write the following pieces of code:
1. Write a piece of code using a regular expression in Python to count how many times
the following name appears: CICERO, this regex needs to be case-sensitive. (10 points)
2. Write a regular expression in Python that finds all 6 character words found in this play.
(10 points)
3. Write a piece of code that uses a regular expression in Python to count how many
words are capitalized in the first letter only. (20 points)
4. Write a regular expression using groups in Python that extracts all phrases that have
“son of ” and extract/output the answer in a case-insensitive way. For example, for the
following “I am the son of earth”, should find “son of earth” and extract/output “earth”.
(20 points)
5. Plays are divided in acts, write a regular expression using groups in Python that
extracts the number of each act found in this play in a case-sensitive way. (20 points)
6. Write a Python function that takes the file as an input and outputs how many lines end
with: ? , that is a question mark at the end of the line. This function needs to use a
regular expression. (20 points)
7. Using the code from Class 04, train a Random Forest classification model for the same
two newsgroups, generate the confusion matrix, and all the same performance metrics.
(50 points)
Bonus (15 points): Using the code from Class 04. Write a function to find the best
performing model for the TF-IDF vectorization. The requirements for this are: 1) Pass all
metrics for each model in a single dataframe (each row has a metric value, each column
is a model type). 2) Make the decision of the best model by counting which column has
the highest F1-score and accuracy.