This repository contains all resources for Homework 1 of TDT4173 fall 2021.
In this assignment you will be developing well-known and simple (but occasionally very useful) machine learning algorithms. The interface you are asked to implement strongly resembles the one used in Scikit Learn. However, you are not allowed to use this library (or any other third-party solutions). Instead, you will implement the algorithms from scratch, using only basic tools like numpy.
- You are presented with three common machine learning algorithms; K-Means, Decision Tree, and Logistic Regression.
- All material for each algorithm can be found in its own self-contained folder.
- The <algorithm_name>.py files (e.g. k_means.py) contains skeleton code for an sklearn-style implementation.
- For each algorithm, there are two datasets that you are asked to solve.
- The data_1.csv files contain very easy problems that can be used to debug your implementation of the algorithm.
- The data_2.csv files contain slightly harder problems that require you to tweak your machine learning pipeline for good results.
- The experiment.ipynb files are jupyter notebooks with additional instructions/tips, as well as code for loading the datasets, training, and evaluating the models. You should be able to run them once the algorithm files are implemented. They also contain reference values for decent performance on all the problems.
- Pick at least two out of the three algorithms presented.
- Implement the selected algorithms by filling out the methods marked with
TODOin the <algorithm_name>.py files. Add any extra helping functions and constructor arguments as you see fit.- Use only numpy, pandas, and the python standard libraries for this.
- Run the cells in the “experiment.ipynb” notebooks to verify that things works as it should. Feel free to make changes to the code in the notebook too.
- Make sure that your results match (or exceed) the reference values.
- Write up a short report (max 2 pages) with your results, plots, and a description of your work. For each algorithm, we look for answers to the following questions:
- How does the algorithm work on a technical level and what kind of machine learning problems is it suited for?
- What is its inductive bias, i.e., what assumptions does it make about the data in order to generalize?
- What happens in the second dataset that makes it harder than the first and how does this problem relate to the algorithm’s inductive bias?
- What modifications did you do to get around this problem?
- Submit your report and code to Blackboard.
- The code should be a zip file and only contain (your modified versions of) the files currently present in this repository (i.e. no pip packages etc.).
- The submissions are individual
- We encourage you to work with and discuss the project with your fellow students, but at the end of the day, you will write your own code and report.
- The deadline for submission is September 17th.
- Your submission is evaluated on a pass/fail basis, but passing it is a requirement for passing the course.
Below follows instructions for getting started. Last year we got a mix of students with all sorts of backgrounds, so we will try to give a detailed set of installation instructions in the sections below.
TL;DR for those who are familiar with python and git
- Python >= 3.6
- Dependences in requirements.txt
For those of you familiar with git, all you need to do is clone this repository. If you do not have git, it can be freely downloaded and installed on all major platforms. Once you have it, you can clone this repository by executing:
git clone git@github.com:ntnu-ai-lab/tdt4173-2021-task-1.git
This shell command will clone the repository to your local computer. You will be able to find it in the folder you executed the command from. If you don't want to bother with git, just click the green Code button in the top right corner of the repository and select the Download ZIP option from the dropdown.
The boilerplate code provided here was designed with Python 3.6 or higher in mind. In particular, it uses f-strings pretty liberally, which will crash for earlier versions of python. If you don't have Python 3.6+, it can be freely downloaded and installed on all major platforms too.
Once you have Python 3.6 or higher up and running, you need to install additional python packages. The main ones used are jupyter, numpy, pandas, matplotlib, and seaborn. However, an exahstive list of the exact packages and versions used by the authors of this assignment can be found in requirements.txt.
NOTE: For all the commands mentioned below, it is assumed that the current working directory of your shell is the root folder of this repository (wherever you cloned/downloaded it to). You can change the current working directory in your terminal with:
cd /path/to/project # Linux / MacOS
cd \path\to\project # Windows
One of the most common ways to install python packages is with the pip package manager. There are several ways to install it. If you're on Linux, then just use your normal (system) package manager. If you use Homebrew on MacOS, then it can be installed from there. Alternatively, you can install it using python on all platforms.
When using pip, it is also good practice to install package not on a global/system level, but in "virtual environments" organized for each of your projects. The virtualenv package helps you do this and can be installed through pip:
# Install only the virtualenv pacakge globally
python -m pip install --user virtualenv # ("py -m pip ..." on windows)
# Create and activate a virtual environment with an appropriate python version
virtualenv --python python3.6 venv # (or another python version >= 3.6 that you have installed)
source venv/bin/activate # (".\venv\Scripts\activate" on windows)
This will create a virtual environment in a folder called venv that is located in the folder you executed the commands from. All subsequent python packages installed with pip should end up within this folder structure (and not in the global package folder). If you want to scrap all the packages associated with your project later, you can just delete this folder while not having to worry about breaking other projects. It also solves the problem of having to deal with different projects requireing different versions of the same pacakge. After having created and activated your new virtual environment, all the packages required for this project can be installed with:
python -m pip install -r requirements.txt # ("py -m pip ..." on windows)
If you use Anaconda to manage your python versions and packages, then you can create a new python environment and install all the dependencies with the following commands.
conda create --name tdt4173 python=3.6 # (or another python version >= 3.6)
source activate tdt4173 # (just "activate tdt4173" on windows)
conda install --yes --file requirements.txt
After you have installed all the dependencies, you can run a notebook server with:
jupyter lab
This will start a slightly fancier version of jupyter notebook. It is a single-page application that allows you to navigate, edit, and run python and jupyter notebook files from your browser. By default the server should be exposed at localhost port 8888. If you're running this command from your laptop or desktop computer, it should automatically open in your default web browser. If for some reason not, try manually navigating to localhost:8888 in your web browser (or copy the full URL from the logging output in the shell you ran the command). If it asks for a password or token, this can also be found in the shell output.
From here, you can start running and editing the files in the project. If you need more help with the interface, there are several guides online.