Skip to content

Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.

License

Notifications You must be signed in to change notification settings

crunchiness/lernd

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

61 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

LERND

DOI

Lernd stands for Learning Explanatory Rules from Noisy Data. It is my implementation of the algorithm in the linked paper.

Learning the concept of even numbers from scratch* lernd.gif

If you found this code useful for your research please cite in your work:

@software{ingvaras_merkys_2020_4294059,
  author       = {Ingvaras Merkys},
  title        = {crunchiness/lernd: LERND - implementation of $\partial$ILP},
  month        = nov,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v1.1-alpha},
  doi          = {10.5281/zenodo.4294059},
  url          = {https://github.com/crunchiness/lernd}
}

Demo Jupyter notebooks

Demo Jupyter notebooks are available online on Kaggle for a quick look into how it works:

  1. https://www.kaggle.com/ingvaras/lernd-intro-predecessor
  2. https://www.kaggle.com/ingvaras/lernd-even

Notebook files for local use can be found on https://github.com/crunchiness/lernd-notebooks

Set up and run

Step 1

Run Lernd on Python 3.8+

You may create a conda environment (here named "lernd"):

conda create -n lernd python=3.8

(Follow instructions at anaconda.com to get and install the conda package manager.)

Activate the environment:

conda activate lernd

Step 2

Install requirements:

pip install -r requirements.txt

Step 3

Run experiments.

Some benchmark problems are defined in file lernd/experiments.py.

You may run lernd on them:

conda activate lernd  # activate environment if using conda
python -m lernd.experiments <problem>  # problems: predecessor, even 

Do a 100 runs at once, saving the output:

python -m lernd.run_many <problem> 100

Tests

Unit tests are in lernd/test.py.

Run them:

conda activate lernd  # activate environment if using conda
python -m lernd.test