# DeepProbLog
[](https://github.com/ML-KULeuven/deepproblog/actions/workflows/python-app.yml)
DeepProbLog is an extension of [ProbLog](https://dtai.cs.kuleuven.be/problog/)
that integrates Probabilistic Logic Programming with deep learning by introducing the neural predicate.
The neural predicate represents probabilistic facts whose probabilites are parameterized by neural networks.
For more information, consult the papers listed below.
## Installation
DeepProbLog can easily be installed using the following command:
Make sure the following packages are installed:
```
pip install deepproblog
```
## Test
To make sure your installation works, install pytest
```
pip install pytest
````
and run
```
python -m deepproblog test
```
## Requirements
DeepProbLog has the following requirements:
* Python > 3.9
* [ProbLog](https://dtai.cs.kuleuven.be/problog/)
* [PySDD](https://pysdd.readthedocs.io/en/latest/)
* [PyTorch](https://pytorch.org/)
* [TorchVision](https://pytorch.org/vision/stable/index.html)
## Approximate Inference
To use Approximate Inference, we have the followign additional requirements
* [PySwip](https://github.com/ML-KULeuven/pyswip)
- Use `pip install git+https://github.com/ML-KULeuven/pyswip`
* [SWI-Prolog < 9.0.0](https://www.swi-prolog.org/)
The latter can be installed on Ubuntu with the following commands:
```
sudo apt-add-repository ppa:swi-prolog/stable
sudo apt install swi-prolog=8.4* swi-prolog-nox=8.4* swi-prolog-x=8.4*
```
## Experiments
The experiments are presented in the papers are available in the [src/deepproblog/examples](src/deepproblog/examples) directory.
## Papers
1. Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
*DeepProbLog: Neural Probabilistic Logic Programming*. NeurIPS 2018: 3753-3763 ([paper](https://papers.nips.cc/paper/2018/hash/dc5d637ed5e62c36ecb73b654b05ba2a-Abstract.html))
2. Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
*Neural Probabilistic Logic Programming in DeepProbLog*. AIJ ([paper](https://www.sciencedirect.com/science/article/abs/pii/S0004370221000552))
3. Robin Manhaeve, Giuseppe Marra, Luc De Raedt:
*Approximate Inference for Neural Probabilistic Logic Programming*. KR 2021
## License
Copyright 2023 KU Leuven, DTAI Research Group
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
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"description": "# DeepProbLog\n[](https://github.com/ML-KULeuven/deepproblog/actions/workflows/python-app.yml)\n\nDeepProbLog is an extension of [ProbLog](https://dtai.cs.kuleuven.be/problog/)\nthat integrates Probabilistic Logic Programming with deep learning by introducing the neural predicate. \nThe neural predicate represents probabilistic facts whose probabilites are parameterized by neural networks.\nFor more information, consult the papers listed below.\n\n## Installation\nDeepProbLog can easily be installed using the following command:\nMake sure the following packages are installed:\n```\npip install deepproblog\n```\n\n## Test\nTo make sure your installation works, install pytest \n```\npip install pytest\n````\nand run \n```\npython -m deepproblog test\n```\n\n## Requirements\n\nDeepProbLog has the following requirements:\n* Python > 3.9\n* [ProbLog](https://dtai.cs.kuleuven.be/problog/)\n* [PySDD](https://pysdd.readthedocs.io/en/latest/)\n* [PyTorch](https://pytorch.org/)\n* [TorchVision](https://pytorch.org/vision/stable/index.html)\n\n## Approximate Inference\nTo use Approximate Inference, we have the followign additional requirements\n* [PySwip](https://github.com/ML-KULeuven/pyswip) \n - Use `pip install git+https://github.com/ML-KULeuven/pyswip`\n* [SWI-Prolog < 9.0.0](https://www.swi-prolog.org/)\nThe latter can be installed on Ubuntu with the following commands:\n```\nsudo apt-add-repository ppa:swi-prolog/stable\nsudo apt install swi-prolog=8.4* swi-prolog-nox=8.4* swi-prolog-x=8.4*\n```\n## Experiments\n\nThe experiments are presented in the papers are available in the [src/deepproblog/examples](src/deepproblog/examples) directory.\n\n## Papers\n1. Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:\n*DeepProbLog: Neural Probabilistic Logic Programming*. NeurIPS 2018: 3753-3763 ([paper](https://papers.nips.cc/paper/2018/hash/dc5d637ed5e62c36ecb73b654b05ba2a-Abstract.html))\n2. Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:\n*Neural Probabilistic Logic Programming in DeepProbLog*. AIJ ([paper](https://www.sciencedirect.com/science/article/abs/pii/S0004370221000552))\n3. Robin Manhaeve, Giuseppe Marra, Luc De Raedt:\n*Approximate Inference for Neural Probabilistic Logic Programming*. KR 2021\n## License\nCopyright 2023 KU Leuven, DTAI Research Group\n\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n\n\n",
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