A comedy handbook

Team

ADAptables :

Arthur Removille, Caroline Verchère, Quentin Gallien, Théo Le Blan, Victor Garvalov

Repository presentation

This repository contains the code used for our project titled "A comedy Handbook". You can read the data story here. All the code used to generate the various graphs and conclusions in it can be obtained by reading through the results notebook.

Quickstart

This project was run using Python 3.10.12. We cannot provide certainty that other versions will work (though anything newer should be fine). Some of our code elegantly uses Python's pattern matching, which was introduced in 3.10. Earlier Python versions will therefore not work out of the box (but can be adapted by making very small changes).

Important note!

One of the additional datasets we are using is too large to be hosted on Git, and we cannot use Git LFS as the repossitory is tied to one owner - the organization. As such, please download it manually from here, and place the rotten_tomatoes_movie_reviews.csv under data/raw/.

Furthermore, the aforementionned file and some of the files needed to recreate our graphs (data/processed/) are too big for GitHub. We therefore saved them in a zip which you can download from here. Simply extract it (unzip extra_files.zip) inside the root directory, and all the files will place themselves.

# clone project
git clone [email protected]:epfl-ada/ada-2024-project-adaptables.git
cd ada-2024-project-adaptables

# [OPTIONAL] create conda environment
# conda create -n <env_name> python=3.11 or ...
# conda activate <env_name>

# [OR, OPTIONAL] create venv
python3 -m venv venv
source venv/bin/activate        # POSIX (Linux, MAC ; bash)
# venv/Scripts/activate.bat     # Windows (cmd)
# venv/Scripts/Activate.ps1     # Windows (PS)

# install requirements
pip install -r pip_requirements.txt

Project Structure

The directory structure of new project looks like this:

├── data                        <- Datasets
│   ├── raw                             <- raw files (for subset of smaller datasets)
│   ├── processed                       <- pre-processed datasets (see below)
│
├── src                         <- Source code
│   ├── data                            <- Dataloaders/datamodules code 
│   ├── models                          <- Model directory (TODO: custom KeyBert)
│   ├── plots                           <- Utilitary functions used to generate all our plots
│   ├── scripts                         <- Python scripts used to recreate some of the pre-processed data (see below)
│   ├── utils                           <- Utility directory
│
├── personal_work               <- Temporary directory for M2 with individual work - can be safely ignored (TODO remove)
│
├── results.ipynb               <- The main notebook showing the results and conclusions used in our data story
│
├── .gitignore                  <- List of files ignored by git
├── pip_requirements.txt        <- File for installing python dependencies
└── README.md

The data/processed folder contains some intermediate pre-computed data which takes more time to get. See its README to understand what to do to recreate it.