Template Experiment Project

This is a template repository for creating an experiment environment in Python. It intends to speed up the research process - reducing the repository structure design - and to have it clean and concise through multiple experiments.

Inspired by the cookiecutter folder structure.

📁 Project structure

The project is structured as follows:

.
├── data/                    # Data used in the experiments
├── models/                  # Models container folder
├── notebooks/               # Experiment notebooks
├── pipelines/               # The experiment pipelines
├── results/                 # Experiment results
├── scripts/                 # The scripts
├── src/                     # Source code
├── .gitignore               # Git ignore config
├── .pre-commit-config.yaml  # Pre-commit config
├── LICENSE                  # License
├── README.md                # Project readme
├── pyproject.toml           # The project config
└── requirements.txt         # Project dependencies

☑️ Requirements

Before starting the project make sure these requirements are available:

• python. For setting up the environment and Python dependencies (version 3.8 or higher).
• git. For versioning your code.

🛠️ Setup

Create a python environment

First, create a virtual environment where all the modules will be stored.

Using venv

Using the venv command, run the following commands:

# create a new virtual environment
python -m venv venv

# activate the environment (UNIX)
source ./venv/bin/activate

# activate the environment (WINDOWS)
./venv/Scripts/activate

# deactivate the environment (UNIX & WINDOWS)
deactivate

Install

Check the requirements.txt file. If you have any additional requirements, add them here.

Using pip

To install the requirements run:

# install the dependencies
pip install -e .[dev,test]

Install pre-commit hooks

To install the pre-commit hooks, run the following command:

pre-commit install

🗃️ Data

TODO: Provide information about the data used in the experiments

• Where is the data found
• How is the data structured

⚗️ Experiments

To run the experiments, run the following commands:

TODO: Provide scripts for the experiments

Results

The results folder contains the experiment

TODO: Provide a list/table of experiment results

📦️ Available models

This project produced the following models:

• TODO: Name and the link to the model

🚀 Using the trained model

When the model is trained, the following script shows how one can use the model:

TODO: Provide an example of how to use the model

📣 Acknowledgments

TODO: Acknowledgements