rfgb 0.2.2

Relational Functional Gradient Boosting in Python.

Relational Functional Gradient Boosting in Python.

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

Installation

Stable builds on PyPi

pip install rfgb

Development builds on GitHub

pip install git+git://github.com/starling-lab/rfgb.py.git

Quick-Start

1. git clone https://github.com/starling-lab/rfgb.py.git

2. cd rfgb.py

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

“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

License

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 https://www.gnu.org/licenses/

Acknowledgements

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.