Description
1 Regression using Enhanced Gradient Descent
1.1 Background
In class, we studied the basic (“vanilla”) form of gradient descent applied to a
linear regression problem. There have been various enhancements proposed to
the basic algorithm over the years to obtain better performance.
You can read about some of the enhancement at the following links:
• https://towardsdatascience.com/improving-vanilla-gradientdescent-f9d91031ab1d
• https://towardsdatascience.com/10-gradient-descentoptimisation-algorithms-86989510b5e9
• https://hackernoon.com/demystifying-different-variants-ofgradient-descent-optimization-algorithm-19ae9ba2e9bc
For this assignment, you will use gradient descent with at least one enhancement chosen from above links. Note that you cannot use stochastic or
mini-batch versions of gradient descent. These are very similar to the
basic algorithm.
Before starting to code, make sure you understand the enhanced algorithm. Remember to write the equations for error function, learning rate, weight update.
1.2 Coding in Python (75 points)
For this part, you will write your own code in Python for implementing the
enhanced gradient descent algorithm that you selected, and apply it to a linear
regression problem. You are free to use any data loading, pre-processing, and
graphing library, such as numpy, pandas, graphics. However, you cannot use
any library that implements gradient descent, its variations or linear
regression.
You will need to perform the following:
1. Decide on the enhancement you will use on the basic gradient descent.
2. Choose a dataset suitable for regression from UCI ML Repository: –
https://archive.ics.uci.edu/ml/datasets.php. If the above link doesn’t
work, you can go to the main page:
https://archive.ics.uci.edu/ml/index.php and choose “view all datasets
option”. Host the dataset on a public location e.g. UTD web account.
Please do not hard code paths to your local computer.
3. Pre-process your dataset. Pre-processing includes the following activities:
• Remove null or NA values
• Remove any redundant rows
• Convert categorical variables to numerical variables
• If you feel an attribute is not suitable or is not correlated with the
outcome, you might want to get rid of it.
• Any other pre-processing that you may need to perform.
4. After pre-processing split the dataset into training and test parts. It is
up to you to choose the train/test ratio, but commonly used values are
80/20, 90/10, etc.
5. Use the training dataset to construct a linear regression model using the
equations in the previous part and develop a model. Note again: you
cannot use a library that implements gradient descent or linear
regression.
There are various parameters such as learning rate, number of iterations
or other stopping condition, etc. You need to tune these parameters to
achieve the optimum error value. Tuning involves testing various combinations, and then using the best one. You need to create a log file that
indicates parameters used and error (MSE) value obtained for various trials.
6. Apply the model you created in the previous step to the test part of the
dataset. Report the test dataset error values for the best set of parameters
obtained from previous part. If you are not satisfied with your answer,
you can repeat the training step.
7. Answer this question: Are you satisfied that you have found the best
solution? Explain.
2 Linear Regression using ML libraries (25 points)
In the second part of this assignment, you will select a regression library from
Scikit Learn linear regression package: https://scikit-learn.org/stable/
modules/linear_model.html.
You need to find a library that is similar to the
algorithm you chose in part 1. If you cannot find any library that is exactly
similar, you can choose one that is similar. Also, you need to use the same
dataset that you used in part 1.
Use similar workflow like in part 1. The difference would be that the package
will build the model for you. Report output similar to earlier part.
3 Additional Requirements
• Submit a report indicating details of the enhanced gradient descent algorithm that you chose. Please provide equations with meaning of each
symbol used.
• You will be judged on the basis of your code. Write clean and elegant
code that should be in the form of Python classes, with appropriate constructors, and other methods.
• Parameters used such as number of iterations, learning rate, should be optimized. Keep a log file of your trials indicating parameters used, training
error, and test error.
• You need to provide as many plots as possible. They could be MSE vs
number of iterations, the output variable plotted against one or more of
important attributes. Use your judgement and be creative.
• Output as many evaluation statistics as possble. Some examples are
weight coefficients, MSE, R2 value,
https://en.wikipedia.org/wiki/Coefficient_of_determination,
Explained Variance
https://en.wikipedia.org/wiki/Explained_variation, and any other
• Be sure to answer the following question: Are you satisfied that the package has found the best solution. How can you check. Explain.
4 What to Submit
• Brief report containing details of your algorithm.
• For parts 1 and 2, completed Python code file(s). Remember to properly
name your files such as part1.py and part2.py.
• README file indicating how to build and run your code. For part 2,
indicate which libraries you have used. If the TA cannot run your code,
you don’t get any credit.
• Please do not submit any dataset files. Host your data on a public
web source, such as your UTD web account, AWS, etc.
• Do not hardcode paths on your local computer.
• A separate file containing log of your trials with different parameters, and
plots. It should also answer the question whether you have found the
global best solution in both parts.
