LADDER

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Python implementation for Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces.

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This repository contains source code for the paper **[Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces](https://arxiv.org/abs/2111.01186)** 
accepted at [NeurIPS-2021](https://nips.cc/Conferences/2021/Schedule?type=Poster).

- Running the code

- Setting up conda environment from https://github.com/cambridge-mlg/weighted-retraining because we use their pre-existing VAE models. See 'Install dependencies' section.

- Download datasets by following the 'Set up Data' section in the above repository.

- Relative imports are setup as if LADDER directory's code is in ``weighted-retraining/weighted_retraining/opt-scripts/``.

- To run LADDER for chemical design task: ``python ladder_chemical_design.py``
	
  - To run LADDER for arithmetic expression optimization, some things need to be set up in above weighted-retraining environment
    - Add a comma in line 34 of weighted-retraining/weighted_retraining/train_scripts/train_expr.py
    - unzip eq2_grammar_dataset.zip in weighted-retraining/assets/data/expr
    - run ``python ladder_expression.py``

This code builds upon the existing [code](https://github.com/cambridge-mlg/weighted-retraining) provided by authors of 
*Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining*. We thank the authors for their code.

Please consider citing both the papers if you use this work:

- Austin Tripp, Erik Daxberger, and Jos{\'e} Miguel Hern{\'a}ndez-Lobato. Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining, NeurIPS 2020.
- Aryan Deshwal and Janardhan Rao Doppa. Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces, NeurIPS 2021.

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