CMPE 462 -  Assignment 2 Solved

CMPE 462 - 

Assignment 2

Introduction
This assignment consists of 2 parts.

The first one is about logistic regression and the second part is about naive bayes.

Part1
In Part 1 of this assignment, you will implement Logistic Regression (LR) from scratch. We will give you the dataset as a csv file. Each line is a sample and each column is a feature. The last column is the class value.

The dataset file is vehicle.csv. It is the Vehicle Silhouettes dataset from UCI Machine Learning Repository which includes attributes for 4 classes. You can find more information on the website https://archive.ics.uci.edu/ml/index.php. For this assignment we will be using classes "saab" and "van" and all features. You will implement LR with 5-fold cross-validation.

The steps of Part1 are below:

•   Step1: Implement LR with batch gradient descent (update weights after a full pass over data) and apply on the dataset.

•   Step2: Implement LR with stochastic gradient descent (update weights after mini batches) and apply on the dataset.

For each step, save the loss value at each iteration and after convergence plot the loss over iterations graph. Try 3 different (small, medium and big) step sizes in range 0 and 1. Provide plots for each step size.

You can use mathplot library. But special functions or libraries like scikit-learn is forbidden.

In your assignment report, include results of your runs for each step. Place the plots and discuss over them. Discuss the effect of gradient descent model on convergence. Compare your runs with different step sizes. Also discuss the number of iterations and time you need for each step size.

Part2
In Part 2 of this assignment, you will focus on Naive Bayes.

Consider the table below. Use Naive Bayes to classify the test sample in the last row. Include your full solution in the assignment report. (Name column is informative.)

Name
GiveBirth
CanFly
LiveInWater
HaveLegs
Class
human
yes
no
no
yes
mammals
python
no
no
no
no
non-mammals
salmon
no
no
yes
no
non-mammals
whale
yes
no
yes
no
mammals
frog
no
no
sometimes
yes
non-mammals
komodo
no
no
no
yes
non-mammals
bat
yes
yes
no
yes
mammals
pigeon
no
yes
no
yes
non-mammals
cat
yes
no
no
yes
mammals
leopard shark
yes
no
yes
no
non-mammals
turtle
no
no
sometimes
yes
non-mammals
penguin
no
no
sometimes
yes
non-mammals
porcupine
yes
no
no
yes
mammals
eel
no
no
yes
no
non-mammals
salamander
no
no
sometimes
yes
non-mammals
gila monster
no
no
no
yes
non-mammals
platypus
no
no
no
yes
mammals
owl
no
yes
no
yes
non-mammals
dolphin
yes
no
yes
no
mammals
eagle
no
yes
no
yes
non-mammals
test
yes
no
yes
no
???
Base Environment
You will be implementing your code with Python 3.6.

You need to create a python virtual environment with Anaconda for your project. After installing Anaconda, a base environment can be created with below commands:

conda create -n 462assignment python=3.6 conda activate 462assignment
While you keep working on your models, you will need to import additional libraries. List these libraries in a requirements.txt file. State any special versions if needed. A sample requirements file can be as below:

scikit-learn >= 0.22.2 scipy pandas sentencepiece==0.1.91
For grading, we will load your requirements with the command below:

python3 -m pip install -r requirements.txt
Before submission, test your code on a clear new conda environment by installing additional libraries from your requirements file. Because, there will be penalty if your code doesn’t run like this.

Grading Details
The assignment will be graded over 100 points. You will be graded for your code and report.

•   60 points for report

–    20 points for Part1

–    40 points for Part2

•   40 points for code (Part1)

–    20 points for step 1

–    20 points for step 2

We will run your code on a clear new conda environment. First we will load your requirements.txt file. Then we will test your code with below commands:

•   Part1

python3 assignment2.py part1 step1 python3 assignment2.py part1 step2
Consider second command, you will run LR with stochastic gradient descent.