https://github.com/0h-n0/botorchex

botorch extention library (including custom acquistion functions and surrogate models)
https://github.com/0h-n0/botorchex

Last synced: about 2 months ago
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botorch extention library (including custom acquistion functions and surrogate models)

• Host: GitHub
• URL: https://github.com/0h-n0/botorchex
• Owner: 0h-n0
• Created: 2023-01-11T14:14:46.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-12T04:15:23.000Z (about 2 years ago)
• Last Synced: 2024-11-09T09:49:09.736Z (3 months ago)
• Language: Jupyter Notebook
• Size: 362 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# botorchex

Botorch extention library including custom acquistion functions and surrogate models.

## Installation 

```

$ pip install botorchex

```

## Ease to use

Botorch compatible interface.

## Implementation List

### Custom Acqusition function

* Multi Objective Monte-Carlo Probability Improvement

This acquistion function can deacrease more computational resource(wall-time) comparing to other multi objective acqusition function. This performance especially is shown in the more than 3 objctive cases. However, the convergence speed is longer than the others and there is no theoretical background. 

```python

from botorch.models.gp_regression import SingleTaskGP

from botorch.models.model_list_gp_regression import ModelListGP

from botorchex.acquisition.multi_objective.monte_carlo import qMultiProbabilityOfImprovement

model1 = SingleTaskGP(train_X, train_Y[0, :])

model2 = SingleTaskGP(train_X, train_Y[1, :])

# we assume the outputs are independent each other.

best_f = train_Y.max(dim=1)

modes = ModelListGP([model1, model2])

qPI = qMultiProbabilityOfImprovement(models, best_f)

qmpi = qMPI(test_X)

```

If you want to know more examples, you can check the example([multi_objective_bo.ipynb](https://github.com/0h-n0/botorchex/blob/main/tutorials/multi_objective_bo.ipynb))

### Custom Surrogates

* GNN based surrogates?

### Referances

* https://botorch.org/

* https://gpytorch.ai/