Getting Started

This section shows you how to get your feet wet with BoTorch.

Before jumping the gun, we recommend you start with the high-level Overview to learn about the basic concepts in BoTorch.

Installing BoTorch

Installation Requirements:

BoTorch is easily installed via pip (recommended). It is also possible to use the (unofficial) Anaconda package from the -c conda-forge channel.

pip

pip install botorch

Conda

conda install botorch -c gpytorch -c conda-forge

For more installation options and detailed instructions, please see the Project Readme on GitHub.

Basic Components

Here's a quick run down of the main components of a Bayesian Optimization loop.

1. Fit a Gaussian Process model to data

   import torch
   from botorch.models import SingleTaskGP
   from botorch.models.transforms import Normalize, Standardize
   from botorch.fit import fit_gpytorch_mll
   from gpytorch.mlls import ExactMarginalLogLikelihood
   
   train_X = torch.rand(10, 2, dtype=torch.double) * 2
   # explicit output dimension -- Y is 10 x 1
   train_Y = 1 - (train_X - 0.5).norm(dim=-1, keepdim=True)
   train_Y += 0.1 * torch.rand_like(train_Y)
   
   gp = SingleTaskGP(
       train_X=train_X,
       train_Y=train_Y,
       input_transform=Normalize(d=2),
       outcome_transform=Standardize(m=1),
   )
   mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
   fit_gpytorch_mll(mll)

2. Construct an acquisition function

   from botorch.acquisition import LogExpectedImprovement
   
   logNEI = LogExpectedImprovement(model=gp, best_f=train_Y.max())

3. Optimize the acquisition function

   from botorch.optim import optimize_acqf
   
   bounds = torch.stack([torch.zeros(2), torch.ones(2)]).to(torch.double)
   candidate, acq_value = optimize_acqf(
       logNEI, bounds=bounds, q=1, num_restarts=5, raw_samples=20,
   )

Tutorials

Our Jupyter notebook tutorials help you get off the ground with BoTorch. View and download them here.

API Reference

For an in-depth reference of the various BoTorch internals, see our API Reference.

Contributing

You'd like to contribute to BoTorch? Great! Please see here for how to help out.