Py-boost: a research tool for exploring GBDTs

Modern gradient boosting toolkits are very complex and are written in low-level programming languages. As a result,

• It is hard to customize them to suit one’s needs
• New ideas and methods are not easy to implement
• It is difficult to understand how they work

Py-boost is a Python-based gradient boosting library which aims at overcoming the aforementioned problems.

Authors: Anton Vakhrushev, Leonid Iosipoi.

Py-boost Key Features

Simple. Py-boost is a simplified gradient boosting library but it supports all main features and hyperparameters available in other implementations.

Fast with GPU. Despite the fact that Py-boost is written in Python, it works only on GPU and uses Python GPU libraries such as CuPy and Numba.

Easy to customize. Py-boost can be easily customized even if one is not familiar with GPU programming (just replace np with cp). What can be customized? Almost everuthing via custom callbacks. Examples: Row/Col sampling strategy, Training control, Losses/metrics, Multioutput handling strategy, Anything via custom callbacks

Installation

Before installing py-boost via pip you should have cupy installed. You can use:

pip install -U cupy-cuda110 py-boost

Note: replace with your cuda version! For the details see this guide

Quick tour

Py-boost is easy to use since it has similar to scikit-learn interface. For usage example please see:

• Tutorial_1_Basics for simple usage examples
• Tutorial_2_Advanced_multioutput for advanced multioutput features
• Tutorial_3_Custom_features for examples of customization

More examples are comming soon

Other Sber AI Lab Projects

LightAutoML: https://github.com/sberbank-ai-lab/LightAutoML
AutoWoE: https://github.com/sberbank-ai-lab/AutoMLWhitebox
RePlay: https://github.com/sberbank-ai-lab/RePlay