Tutorial addition: Feasibility-driven trust Region Bayesian Optimization #3048
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Commit with the tutorial on Feasibility-Driven trust Region Bayesian Optimization.
Title update
SCBO url update
Title update
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Thanks @paoloascia for the contribution! Overall I think this looks good and about ready to be merged. I'll get back later today with a few suggestions for improvements. |
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Hi @hvarfner ! Thank you for the comment. I'm looking forward to your feedback :). |
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Looks good implementation-wise, but there are some changes that would be good to see.
- Please make the notebook more informative: The user should get a sense of what the method does from the markdown cells and plots. Now, they have to go through the code. Since the community is pretty well-informed on what TurBO does by now (and probably even SCBO to some extent), try to focus on what distinguishes FurBO from those two. This may entail some extra work, such as adding an informative plot, some math and maybe even a comparison to SCBO.
- Please use a linter to clean up the code. This includes unused imports, having comments in the right places and using docstrings instead of comments where appropriate.
- Get rid of arguments that are not actually used. Max_cholesky_size and "method": L-BFGS-B are covered by current defaults, so they shouldn't be needed.
- Warnings & try/catches: It's odd to me that GP fitting fails - I really don't think it should with this few observations. Please have a look at this. I'm happy to help here.
| "from gpytorch.likelihoods import GaussianLikelihood\n", | ||
| "from gpytorch.mlls import ExactMarginalLogLikelihood\n", | ||
| "\n", | ||
| "from scipy.stats import invgauss\n", |
| "from gpytorch.mlls import ExactMarginalLogLikelihood\n", | ||
| "\n", | ||
| "from scipy.stats import invgauss\n", | ||
| "from scipy.stats import ecdf\n", |
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "class norm_():\n", |
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It would be good to see the utilities we have in botorch used here. The class Normalize(d=dim, bounds=bounds) should do the trick to normalize the inputs to [0, 1] for the models. Similarly, creating a different class ack that wraps Ackley can be avoided by using the same transforms.
| "from botorch.models.model_list_gp_regression import ModelListGP\n", | ||
| "from torch.quasirandom import SobolEngine\n", | ||
| "\n", | ||
| "class Furbo_state():\n", |
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Please use pythonic class naming (i.e. FurboState). Ideally, use type hints too, so that users understan what is going on.
| "class Furbo_state():\n", | ||
| " '''Class to track optimization status with restart'''\n", | ||
| " # Initialization of the status\n", | ||
| " def __init__(self, \n", |
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Thank for commenting what each variable does. However, the conventional way to do this would be via a docstring. This is also cleaner, and should be a quick fix.
| "import numpy as np\n", | ||
| "from matplotlib import rc\n", | ||
| "\n", | ||
| "def ensure_tensor(x, tkwargs, dim=0):\n", |
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Isn't torch.cat(x).to(**tkwargs) enough here?
| " samples = multivariate_circular(x_candidate, radius, n_samples, lb=lb, ub=ub, **tkwargs)\n", | ||
| " \n", | ||
| " # Evaluate samples on the models of the objective -> yy Tensor\n", | ||
| " state.Y_model.eval()\n", |
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posterior() sets the model in eval mode, so these can be removed!
| " samples_yy = posterior.mean.squeeze()\n", | ||
| " \n", | ||
| " # Evaluate samples on the models of the constraints -> yy Tensor\n", | ||
| " state.C_model.eval()\n", |
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def multivariate_circular(centre, # Centre of the multivariate distribution\n", |
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This to me seems like the secret sauce for the method, right? If so, it could benefit from some additional explanation in the markdown cells!
| "outputs": [], | ||
| "source": [ | ||
| "def stopping_criterion(state, n_iteration):\n", | ||
| " '''Function to evaluate if the maximum number of allowed iterations is reached.'''\n", |
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return state.it_counter <= n_iteration
2 participants
Motivation
Hi! This tutorial explains how to implement the Feasibility-driven trust Region Bayesian Optimization (FuRBO) method presented last September at AutoML2025. We believe it is a relevant contribution, as it builds upon Scalable Constrained BO to enhance the treatment of constraints and accelerate convergence to a feasible region.
Have you read the Contributing Guidelines on pull requests?
Yes
Test Plan
We did not change the previous code. The tutorial has been tested prior to upload.
Related PRs
No documentation needs to be added.