Skip to main content

Transpile BDTs to C++ code.

Project description

# Generate C++ representations of boosted decision trees

This project tries to provide a generic functionality to transpile trained BDTs into minimal, efficient C++ functions to evaluate single vectors of features.

While many frameworks exist to train, evaluate and store BDTs, its often hard to use the results in a productive manner.

## Installation

So far there is only python3 support. Run ` pip install bdt2cpp ` to install the latest tagged version or ` pip install git+ ` for the current master version.

## Usage

To generate a minimal Makefile together with the C++ code inside a build/ directory from a given XGBoost dump or TMVA .xml file, simply run ` bdt2cpp my-bdt-dump.xgb ` You will find the corresponding files within the build/ directory and if you have installed clang, you can simply ` cd build make `

The generated executable is essentially a very minimal placeholder, if you had 3 input features you could quickly cross-check the predictions against the original training framework: ` cd build ./main 1 2 3 ` should give the same output as received within the training framework if a feature vector f = (1, 2, 3) is evaluated.

To see the complete list of features with some explanations, run ` bdt2cpp -h `

Project details

Release history Release notifications

This version
History Node


History Node


History Node


History Node


History Node


History Node


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
bdt2cpp-0.1.2-py3-none-any.whl (10.1 kB) Copy SHA256 hash SHA256 Wheel py3 Dec 30, 2017

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page