RFGB in Python. Inspired by BoostSRL.
Prerequisites
- Python (2.7, 3.3, 3.4, 3.5, 3.6)
Installation
None currently. Clone the repository with git or download a zip archive, then run the scripts from the command line.
-
Logistics
python src/main.py -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.
-
Logistics
python src/main.py -expAdvice -target unload -train testDomains/Logistics/train/ -test testDomains/Logistics/test/ -trees 10 -
HeartAttack
python src/main.py -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 |
- 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 https://www.gnu.org/licenses/
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.