Mini-project 1 Python

Mini-project 1
• This mini-project is to be completed in groups of up to three.
You will submit your assignment on MyCourses as a group. You
must register your group on MyCourses.
• You are free to use libraries with general utilities, such as
matplotlib, numpy, pandas and scipy for Python. However, you
should implement everything (e.g., the models and evaluation
functions) yourself, which means you should not use preexisting implementations of the algorithms or functions as
found in SciKit learn, etc.
Introduction
The goal of this homework is to gain experience on
implementing the well-known linear classifier logistic
regression, from scratch, as are discussed in the class lectures.
You are required to perform two-class classification on two
datasets Hepatitis and Bankruptcy by using Logistic Regression
classifier. Datasets are attached.
Dataset Hepatitis consists of various features for hepatitis
patients. The main aim of the Hepatitis dataset is to discriminate
two classes: survivors and patients for whom the hepatitis
proved terminal. For the purpose of this project we will use
Hepatitis.csv to build observation matrix X and its associated
class label vector y.
Dataset Bankruptcy contains various econometric attributes for
bankruptcy status prediction. For the purpose of this project we
will use Bankrupcy.csv to build observation matrix X and its
associated class label vector y.
Your Tasks
• You need to download the corresponding data files discussed in
Section Introduction and load each dataset into numpy objects
(arrays or matrices) in Python.
• Perform some statistical analysis on the datasets e.g. what are
the distribution of the two classes? what are the distribution of
some of the features? etc. You may visualize your results using
histogram plots.
• Implement the linear classifiers logistic regression from scratch,
by following the equations discussed in class lectures and apply
your implemented algorithms to the datasets. To clarify, for
example, basic functions such as transpose, shuffle and
panda's .mean() and .sum() are okay to be used, but using the
train_test_split from sklearn is not ok.
o You are free to implement the method in anyway you want,
however we recommend to implement both models as python
classes (use of constructor is recommended). Each of your
model class should have at least two
functions: fit and predict. The function fit takes the training
data X and its corresponding labels vector y as well as other
hyperparameters (such as learning rate) as input, and execute
the model training through modifying the model parameters
(i.e. W). predict takes a set of test data as input and outputs
predicted labels for the input points. Note that you need to
convert probabilities to binary 0-1 predictions by thresholding
the output at 0.5. The ground-truth labels should also be
converted to binary 0-1.
o Define a function Accu-eval to evaluate the models'
accuracy. Accu-eval takes the predicted labels and the true
labels as input and outputs the accuracy score.
o Implement k-fold cross validation from scratch as a Python
class. Use 10-fold cross validation to estimate performance in
all of your experiments and you should evaluate performance
using accuracy. For model selection, if you have T different
models, you should run 10-fold cross validation T times and
compare the results.
o At least, complete the followings: test different learning rates
for your logistic regression, discuss the run time and accuracy of
your logistic regression on both datasets, explore if the accuracy
can be improved by a subset of features and/or by inserting new
features to the dataset.
Report (Maximum 5 pages of content and Maximum 2 pages of
references.):
We are flexible on how you report your results, but you must
adhere to the following structure:
• Abstract (100-250 words): provide a summary of the project
task and highlight your most important findings. For example,
include sentences like "In this project we investigated the
performance of linear classification models on two benchmark
datasets'', "We found that the logistic regression approach was
achieved worse/better accuracy when some features were
removed, added.''
• Introduction (at least one paragraph): Summarize the project
task, the two datasets and your important findings. This is
similar to the abstract but you should provide more details.
• Datasets (at least one paragraph): Briefly describe the dataset
and its characteristics such as number of samples, features, type
of features, etc. Describe the new features you come up with in
detail. Note: You do not need to explicitly verify that the data
satisfies the i.i.d. assumption (or any of the other formal
assumptions for logistic regression).
• Results: Describe the results of all your experiments; including
tables and/or figures will be a great help when discussing your
results and findings. At a minimum discuss how the logistic
regression performance (e.g. convergence speed) depends on
the learning rate and demonstrate if the new features and/or the
feature subsets you used will improve the performance. You
should properly cite and acknowledge previous
works/publications that you use or build on.
• Discussion and conclusion: Discuss and summarize the key
takeaways from the project and possible directions for future
investigation.
• Statement of Contributions (1-3 sentences) State the breakdown
of the workload across the team members.
• Appendix To facilitate the grading process, attach the codes for
your implementations to the end of your report. This
does not count towards the page limit of the report.
Please keep your analysis as short as possible while still
covering the requirements of the assignment. The analysis
report is limited to 5 pages (single-spaced, minimum font size
of 11 and 1 inch minimum margin each side). We highly
recommend to use LaTeX for preparing your report. We
recommend to use NeurIPS 2020 template. Your report should
look technical. Imagine you are writing a paper for a major
machine learning conference.

Deliverables
• report.pdf: Your report (including Appendix) as a single pdf file.}
• code.zip: Your codes (e.g. .py, .ipynb, etc.) must work with
Python 3.6 in Colab. Include a readme file and provide
instruction for TA on how to replicate your results on Colab. All
the results must be reproducible in Colab using the
submitted code.zip. Points will be deducted if we have a hard
time reading or understanding the structure of your code.
The report should be self contained. TA's will do the grading
mainly based on the report.pdf, and will not be obliged to
consult the supplementary codes.
Evaluation
This assignment is out of 100 points.
Your report should be both thorough and concise. It will be
judged based on its scientific quality including but not limited
to:
• Does the report include all the required experiments?
• Is the report technically sound? (i.e. do the steps taken make
sense? Are the results in an acceptable range?)
• How thorough/rigorous is the experimental validation?
• Is the report well-organized and coherent?
• Is the report clear and free of grammatical errors and typos?
• Does the report contain sufficient and appropriate references
and related work?
All members of a group will receive the same mark.
To get an A grade you need to go beyond the requested
steps/parts. For example, you might investigate different
stopping criteria for the gradient descent in logistic regression
or develop an automated approach to select a good subset of
features. Try to offer and implement solutions for solving the
classification problem better or increase the performance of
your model. Or maybe explore the data in such a thorough
manner that you can justify removing the features you might
have removed. You do not need to necessarily do all/any of
these, but you should demonstrate creativity, rigour, and an
understanding of the course material in how you run your
chosen experiments and how you report on them in your writeup.
About Colab
The easiest way to set up your environment is to use Colab.
Colaboratory (also known as Colab) is a free Jupyter notebook
environment from Google that runs in the cloud and stores its
notebooks on Google Drive. It is more suitable for interactive
jobs rather than long runs. It is free. Upload the data into your
GoogleDrive. Create a Jupyter notebook. Mount your
GoogleDrive using the following commands and follow the
instructions (authorization code, etc.).
from google.colab import drive
drive.mount('/content/gdrive')
You can read the data using the Pandas dataframes:
import pandas as pd, numpy as np
df_data = pd.read_csv('./dataset.csv')
You can see a glimpse of the training data using df_data.head().
It returns the first few rows of data.
Final remarks
You are expected to display initiative, creativity, scientific rigour,
critical thinking, and good communication skills. You don't need
to restrict yourself to the requirements listed above - feel free to
go beyond, and explore further.
You can discuss methods and technical issues with members of
other teams, but you cannot share any code or data with other
teams. Any team found to cheat (e.g. use external information,
use resources without proper references) on either the code,
predictions or written report will receive a score of 0 for all
components of the project.