qinghsui/fastbdt

Stochastic Gradient Boosted Decision Trees as Standalone, TMVAPlugin and Python-Interface

FastBDT

Stochastic Gradient Boosted Decision Trees, usable standalone, as TMVA Plugin and via Python Interface.

Paper on ArXiv: http://arxiv.org/abs/1609.06119

FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification

Stochastic gradient-boosted decision trees are widely employed for multivariate classification and regression tasks. This paper presents a speed-optimized and cache-friendly implementation for multivariate classification called FastBDT. FastBDT is one order of magnitude faster during the fitting-phase and application-phase, in comparison with popular implementations in software frameworks like TMVA, scikit-learn and XGBoost. The concepts used to optimize the execution time and performance studies are discussed in detail in this paper. The key ideas include: An equal-frequency binning on the input data, which allows replacing expensive floating-point with integer operations, while at the same time increasing the quality of the classification; a cache-friendly linear access pattern to the input data, in contrast to usual implementations, which exhibit a random access pattern. FastBDT provides interfaces to C/C++, Python and TMVA. It is extensively used in the field of high energy physics by the Belle II experiment.

Installation

• cmake .
• make
• make install

Usage

Before you do anything you want to execute the unittests:

• ./unittest

There are multiple ways to use FastBDT. I prepared some standalone executables

• ./FastBDTMain
• or python3 examples/FastBDTMain.py

for the python3 version you probably have to set the LD_LIBRARY_PATH

But usually it should be more convinient to use FastBDT as a library and integrate FastBDT directly into your application using

• the C++ shared/static library,
• the C shared library,
• the TMVA plugin if you're using ROOT ( root examples/TMVAExample.C("files/TMVA.root") ),
• or the Python3 library python/FastBDT.py.