Wright State University Department of Computer Science and Engineering
CS7800 Summer 2022 Python Assignment 2 (Solution)
In this project, you will implement several different classifiers in Python using scikit-learn APIs. The project has two phases. In Phase I, you will build classifiers and run them
on the same modified 20 Newsgroup dataset. In Phase II, you will evaluate these
classifiers to elucidate their quality of predictions (e.g., using accuracy and confusion
matrix).
The project may be done in a team of two or three members like before, to promote
discussions and insights. If a fourth member is included because someone cannot find a
teammate, the team must also implement and evaluate two of the classifiers (e.g., kNN
and Rocchio) manually from scratch as required below. All the team members are
expected to contribute to all aspects of the project: design, implementation,
documentation, and testing, for their own good.
Phase I
scikit-learn provides a mature set of APIs for building models using regression,
classification and clustering techniques, and has been used extensively for prediction
tasks.
Classification on Newsgroups
For this project, you will use a subset of the 20 Newsgroups dataset. The full data
set contains 20,000 newsgroup documents, partitioned (nearly) evenly across 20
different newsgroups and has been used for experiments in text applications of
machine learning techniques, such as text classification and text clustering. This
assignment dataset contains a pre-processed subset of 1000 documents and a
vocabulary (dictionary) of 5,500 terms. As you are already familiar with the text pre-processing pipeline for parsing and converting text into sequence of terms, we are
providing associated term-document matrix representation of the dataset as input.
Each document belongs to one of two classes Hockey (class label 1) and Microsoft
Windows (class label 0). The data has already been split (80%, 20%) into training
data and test data. The class labels for training data and test data are also provided in
separate files. The training data and test data, in term x document format, contains a
row for each term in the vocabulary and a column for each document. The values in
the table represent raw term occurrence counts. The data has already been
preprocessed to extract tokens, remove stop words and perform stemming (so, the
terms in the vocabulary are stems, not full terms). Please be sure to read the
readme.txt file in the distribution. Your task is to exercise several different classifiers
available in scikit-learn for classification on the given 2Newsgroups dataset.
Specifically, you must use K-Nearest-Neighbor (kNN), Centroid-basedRocchio Method,
Naive Bayes (NB) and Support Vector Machine (SVM) classifiers. You may additionally
use Pandas, NumPy, standard Python libraries, and Matplotlib in your programs.
Milestones for Phase I
• Read and understand the input dataset format.
• Implement a program to exercise the different classifiers given.
• For a four member team: implement K-Nearest-Neighbor (kNN) and Centroid-based Rocchio classifiers from scratch (that is, without using scikit-learn APIs)
in Python.
Phase I resources
• Dataset: 2Newsgroups.zip
• Relevant tutorials and descriptions
• See: https://scikit-learn.org/stable/supervised_learning.html
• See: https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html
• See: https://scikit-learn.org/stable/datasets/real_world.html
Phase I environment setup
• Python 3 or higher
• Do not use Jupiter notebook
• Install scikit-learn:
• See: https://www.activestate.com/resources/quick-reads/how-to-install-scikit-learn/
• Name your well-documented Python script as assignment2.py.
• Students are required to use a Python virtual environment. For those unfamiliar
with Python virtualenvironments here are two reference articles explaining how
to use and activate:
• See: https://uoa-eresearch.github.io/eresearch-cookbook/recipe/2014/11/26/python-virtual-env/
• See: https://realpython.com/lessons/creating-virtual-environment/
• Once you finish this assignment and are ready to submit it to pilot, you need to
create a requirements.txt file with all the libraries used in your development. Use
the following command onlyafter you’ve activated your Python environment.
• source venv/bin/activate
• pip list --format=freeze > requirements.txt
• deactivate
• See: https://openclassrooms.com/en/courses/6900846-set-up-a-python-environment/6990546-manage-virtual-environments-using-requirements-files
Phase II
Briefly discuss the evaluation metrics to be used to elucidate and quantify the
quality of classifier pre-dictions.
Milestones for Phase II
• For each classifier, at the minimum, provide confusion matrix and accuracy.
• For a four-member team: additionally, evaluate and compare K-Nearest-Neighbor
(kNN) and Centroid-based Rocchio classifiers implemented from scratch with
those implemented using scikit- learn APIs in Python.
Deliverables
TURN IN: Upload one tar archive file per team that contains the following files:
Code and accompanying documentation: Include well-documented
source code for the entireproject.
Evaluation information: Provide comparative analysis of the different classifier
performance usingthe chosen evaluation metrics.
README.txt: This document should briefly explain your application, how to
launch the application, any external libraries used, what version of Python 3
used, all team members names and UIDs, and any other relevant information.
The more information you provide in this document the easier it is for us to grade
andgive extra credit if something does not work on our system. Additionally, all
team member names, and email addresses must be included in this document.
Application execution: Make sure that your Python program runs from the command-line. We will use the following command to execute your code: python3 assignment2.py
Do not use any absolute path for input files or any data files. All paths should be
local to your working directory. We suggest testing your application on one of your
teammates computers to make sure everything works, and you did not hardcode
anything. Points will be deducted if you use absolute paths for your input and output files.
Do not use Jupyter notebook, as your application must launch from the command-line
with the above given syntax.
Input and output files: All file(s) must be placed in your working directory, all
generated output file(s) must also be placed in your working directory, and no
subdirectories.
requirements.txt: all the libraries used in your virtual Python environment.
TAR archive: Submit your application using the following command exactly as
written to tar up your working directory: “tar -zcvf assignment2.tgz
yourWorkDirectoryName/”
Upload the archive asg1.zip onto Pilot $>$ Dropbox $>$ Assignment 2 folder.
(Only one submission per team.) You are also expected to demo your program
to us (if necessary) and be prepared to answer questions about its design,
implementation, and comparative evaluation.
Grading Criteria
You must obtain a PASS on this assignment to PASS the course. At the minimum, your code
should compile, process some queries, and return reasonable results. A passing grade is 60%.
Assignments are designed to help you learn the course concepts and are the primary
course "homework". Corrupt files or other computer problems will not be considered a
sufficient excuse to extend a deadline. It is your responsibility to back-up your work. We
strongly suggest that you save your work to multiple locations/media to aid in the recovery of
corrupt files. If you have questions regarding the projects, you can contact the instructor or
the GTA.
Assignments that are submitted late will incur a penalty of 25% reduction on total grade
per day the assignment is late. The project must be turned in Pilot as described in the project
description to receive full credit. Assignments emailed to the GTA, or professor will receive an immediate 25% reduction in total grade.
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