Neural Datalog Through Time

Source code for Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification (ICML 2020).

Reference

If you use this code as part of any published research, please acknowledge the following paper (it encourages researchers who publish their code!):

@inproceedings{mei-2020-smoothing,
  author =      {Hongyuan Mei and Guanghui Qin and Minjie Xu and Jason Eisner},
  title =       {Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification},
  booktitle =   {Proceedings of the International Conference on Machine Learning},
  year =        {2020}
}

Instructions

Here are the instructions to use the code base.

Dependencies and Installation

This code is written in Python 3, and I recommend you to install:

• Anaconda that provides almost all the Python-related dependencies;

Run the command line below to install the package (add -e option if you need an editable installation):

pip install .

It will automatically install the following important dependencies:

• PyTorch 1.1.0 that handles auto-differentiation.
• pyDatalog that handles back-end Datalog deductible database.

Write Datalog Programs

Write down the Datalog programs. Some examples used in our experiments can be downloaded from this Google Drive directory.

To replicate our experiments, download our datasets from the same Google drive directory.

Organize your Datalog programs and datasets like:

domains/YOUR_DOMAIN/YOUR_PROGRAMS_AND_DATA

Build Dynamic Databases

Go to the ndtt/run directory.

To build the dynamic databases for your data, try the command line below for detailed guide:

python build.py --help

The generated dynamic model architectures (represented by database facts) are stored in this directory:

domains/YOUR_DOMAIN/YOUR_PROGRAMS_AND_DATA/tdbcache

Train Models

To train the model specified by your Datalog probram, try the command line below for detailed guide:

python train.py --help

The training log and model parameters are stored in this directory:

domains/YOUR_DOMAIN/YOUR_PROGRAMS_AND_DATA/Logs

Test Models

To test the trained model, use the command line below for detailed guide:

python test.py --help

To evaluate the model predictions, use the command line below for detailed guide:

python eval.py --help

License

This project is licensed under the MIT License - see the LICENSE file for details.