Py-Boost 0.1.0

Python based GBDT

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

pip install -U py_boost

Quick tour

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

• Tutorial_1_Basics.ipynb for simple usage examples
• Tutorial_2_Advanced_multioutput.ipynb for advanced multioutput features

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