Diffusion Models for Tabular and Time Series Bootcamp

This repository contains codes for diffusion models bootcamp.

About Diffusion Models for Tabular and Time Series Bootcamp

Diffusion models are a class of generative models that have shown promising results in various domains, including image generation, language modeling, and time series forecasting. In this bootcamp, we will explore the application of diffusion models to tabular and time series data.

Repository Structure

• docs/: Contains detailed documentation, additional resources, installation guides, and setup instructions that are not covered in this README.
• implementations/: Implementations are organized by topics. Each topic has its own directory containing codes, notebooks, and a README for guidance.
• pyproject.toml: The pyproject.toml file in this repository configures various build system requirements and dependencies using uv, centralizing project settings in a standardized format.

Implementations Directory

Each topic within the bootcamp has a dedicated directory in the implementations/ directory. In each directory, there is a README file that provides an overview of the topic, prerequisites, and notebook descriptions.

Here is the list of the covered topics:

• Tabular Data
  • TabDDPM for Tabular Data Generation
  • TabSyn for Tabular Data Generation and Imputation
  • ClavaDDPM for Multi-relational Tabular Data Generation
• Time series
  • CSDI for Time Series Forecasting and Imputation
  • TS-Diff for Time Series Forecasting, Synthesizing and Refining other Forecasting Models

Getting Started

To get started with this bootcamp:

1. Clone this repository to your machine.
2. Activate your python environment. If you are using Vector's cluster, you can activate it by refering to the docs/technical_onboarding, otherwise you can create a new environment using the following command:

# Install uv if you don't have it already
curl -sL https://github.com/astral-sh/uv/releases/download/0.1.25/uv-installer.sh | bash

# Create a virtual environment (Python >=3.11 required)
uv venv
source .venv/bin/activate

# Install dependencies with default groups
uv sync

# Install the kernel for jupyter (only need to do it once)
ipython kernel install --user --name=diffusion_models

3. Begin with each topic in the implementations/ directory, as guided by the README files.

License

This project is licensed under the terms of the LICENSE.md file located in the root directory of this repository.

Contribution

To get started with contributing to our project, please read our CONTRIBUTING.md guide.

Contact Information

For more information or help with navigating this repository, please contact Vector AI ENG Team at [email protected] .