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Relational Functional Gradient Boosting in Python.

Project description

Relational Functional Gradient Boosting in Python.

Kaushik Roy (@kkroy36) and Alexander L. Hayes (@batflyer)


Stable builds on PyPi

pip install rfgb

Development builds on GitHub

pip install git+git://


  1. git clone

  2. cd

  3. Perform classification in a logistics domain:

    python -m rfgb -target unload -train testDomains/Logistics/train/ -test testDomains/Logistics/test/ -trees 10

Classification with Expert Advice (-expAdvice)

Preferred and non-preferred labels may be provided as advice during classification via logical rules. This advice may be specified in a file named advice.txt in the train directory for a dataset.

Four datasets (BlocksWorld, HeartAttack, Logistics, and MoodDisorder) have an advice file included for demonstration

  1. Logistics

    python -m rfgb -expAdvice -target unload -train testDomains/Logistics/train/ -test testDomains/Logistics/test/ -trees 10
  2. HeartAttack

    python -m rfgb -expAdvice -target ha -train testDomains/HeartAttack/train/ -test testDomains/HeartAttack/test/ -trees 10


“Targets” specify what is learned, examples of the target are provided in pos.txt, neg.txt, or examples.txt (for regression). These are specified here for convenience.

Dataset Target
BlocksWorld putdown
BostonHousing medv
HeartAttack ha
Insurance value
Logistics unload
MoodDisorder bipolar
TicTacToe put or dontput
ToyCancer cancer
XOR xor

In Development

  • [ ] Test cases (codecov >90%)
  • [ ] Learning Markov Logic Networks
  • [ ] Learning with Soft-Margin


This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

A full copy of the license is available in the base of this repository. For more information, see


The authors would like to thank Professor Sriraam Natarajan, Professor Gautam Kunapuli, and fellow members of the StARLinG Lab at the University of Texas at Dallas.

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