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https://github.com/acerbilab/relational-neural-processes

Practical Equivariances via Relational Conditional Neural Processes (Huang et al., NeurIPS 2023)
https://github.com/acerbilab/relational-neural-processes

equivariance machine-learning meta-learning neural-processes

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Practical Equivariances via Relational Conditional Neural Processes (Huang et al., NeurIPS 2023)

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# Practical Equivariances via Relational Conditional Neural Processes



This repository provides the implementation and code used in the experiments for the article *Practical Equivariances via Relational Conditional Neural Processes* (Huang et al., NeurIPS 2023) [[1]](#citation).

The full paper can be found at [arXiv](https://arxiv.org/abs/2306.10915).



**Note:** Our implementations build on the [neuralprocesses](http://github.com/wesselb/neuralprocesses) library by Wessel Bruinsma. 

You can find further details on its structure and the dependencies at the github website.



_______



## Installation

```

git clone https://github.com/acerbilab/relational-neural-processes.git



cd relational-neural-processes



conda create --name np python=3.10.10



conda activate np



pip install -r requirements.txt

```



To use our RCNP models, you can specify `--model` as one of `[rcnp, rgnp, fullrcnp, fullrgnp]`, where the latter two represents the full version of our models. You can also choose comparison function for relational encoding by using `--comparison-function`, where `difference` stands for encoding translational equivariance, and `distance` stands for encoding equivariance for rigid transformations.



We also support partial encoding, where some dimensions of the data do not need relational encoding, you can specify the dimensions that do not require relational encoding through `--non-equivariant-dim`, e.g., `--non-equivariant-dim=“0,1”` means the first and the second dimension will not be encoded by relational encoder.



## Synthetic Regression

To run the synthetic regression experiment, you can use the following template command lines:

```angular2html

python train.py \

    --model=“rcnp” \

    --data=“eq” \

    --dim-x=2 \

    --dim-y=1 \

    --seed=1 \

    --comparison-function=“difference”

```

For the above template command, we use RCNP model with `difference` comparison function on x2_y1 eq data.



## Bayesian Optimization

To run the Bayesian optimization experiment first run 

```

python bayesopt/bo_train.py \

    --target=hartmann3d \

    --exp="bo_fixed" \

    --model="rcnp"

 ```

to pretrain a model with the desired set of kernels. Here, `--target` is one of `[hartmann3d, hartmann6d]`, `--exp` is one of `[bo_fixed, bo_matern, bo_single, bo_sumprod]` corresponding to scenario (i)-(iv) in the paper. `--model` is one of `[cnp, gnp, acnp, agnp, rcnp, rgnp]`.



Once trained, apply it to the BO task as

```

python bayesopt/bo_apply.py \

    --exp="bo_fixed" \

    --target_name="hartmann3d" \

    --n_rep=10 \

    --n_steps=50

 ```

where `--exp` and `--target_name` as above and `--n_rep`, `--n_steps` specify the number of replications and the number of query steps respectively.



## Lotka-Volterra Model

For the Lotka-Volterra model experiments, we used the "predprey" experiment available in the original package.

To rerun our CNP experiments, use

```

python train.py \

    --data="predprey" \ 

    --seed=x \ 

    --model=m \

    --enc-same

```



with x in `(1, 10)` and m in `(rcnp, cnp, acnp, convcnp)`.



To rerun our GNP experiments, use

```

python train.py \

    --data="predprey" \

    --seed=x \

    --model=m \

    --enc-same \

    --num-basis-functions=32

```

with x in `(1, 10)` and m in `(rgnp, gnp, agnp, convgnp)`



## Reaction-Diffusion Model

For the Reaction-Diffusion example, all the parameters selected for the experiments are listed in `experiments/data/cancer.py`. 



The dataset used from the Reaction-Diffusion simulator is stored in the `experiments/data/dataset_cancer` folder, along with the python file that produces simulations (`RD-simulator.py`).

To run experiments, use, with x the seed chosen and m the method chosen `(rcnp, rgnp, cnp, gnp, acnp, agnp)`:

```

python train.py \

    --data="cancer" \

    --seed=x \

    --model=m

```

For the additional study, run:

```

python train.py \

    --data="cancer_latent" \

    --seed=x \

    --model=m \ 

    --non-equivariant-dim="3" \

    --comparison-function="difference"

```

with x in `(1, 10)` and m in `(rgnp, gnp, agnp, convgnp)`



## Customize your own comparison function

Most examples in our paper use the `difference` and `distance` comparison functions that are available as built-in options. 

However, you can also write your own comparison function and pass it to the model constructor. 



See `neuralprocesses/comparison_functions.py` for example comparison functions and `experiments/data/image.py` for an example on how to pass a custom comparison function to the model constructor.



## Citation

Please cite our paper if you find this work useful for your research

```

@article{huang2023practical,

  title={Practical Equivariances via Relational Conditional Neural Processes},

  author={Huang, Daolang and Haussmann, Manuel and Remes, Ulpu and John, ST and Clart{\'e}, Gr{\'e}goire and Luck, Kevin Sebastian and Kaski, Samuel and Acerbi, Luigi},

  journal={Advances in Neural Information Processing Systems},

  year={2023}

}

```

For the full paper, please refer to our [arXiv page](https://arxiv.org/abs/2306.10915).



## License

This code is under the MIT License.