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botorch-rust (Python package botorch_rs)

A fast Rust implementation of BoTorch with essentially the same Python API. Typical BoTorch code runs after changing the imports:

import torch
from botorch_rs.models import SingleTaskGP
from botorch_rs.models.transforms import Normalize, Standardize
from botorch_rs.mlls import ExactMarginalLogLikelihood
from botorch_rs.fit import fit_gpytorch_mll
from botorch_rs.acquisition import LogExpectedImprovement
from botorch_rs.optim import optimize_acqf

train_X = torch.rand(20, 6, dtype=torch.float64)
train_Y = (train_X - 0.5).pow(2).sum(-1, keepdim=True).neg()
model = SingleTaskGP(train_X, train_Y, input_transform=Normalize(d=6),
                     outcome_transform=Standardize(m=1))
fit_gpytorch_mll(ExactMarginalLogLikelihood(model.likelihood, model))
acqf = LogExpectedImprovement(model, best_f=train_Y.max())
bounds = torch.stack([torch.zeros(6), torch.ones(6)]).double()
candidate, value = optimize_acqf(acqf, bounds=bounds, q=1, num_restarts=10, raw_samples=512)

The package has no runtime dependencies: arrays may be torch tensors, NumPy arrays or nested lists of floats, and results have the type of the corresponding input. See the repository's README for the supported API, accuracy, benchmarks and known differences from BoTorch.

Installation

From a source checkout (requires a Rust toolchain):

$ pip install ./botorch_pyo3

License

MIT License

Metadata

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botorch_rust-0.1.0-cp311-abi3-win_amd64.whl CPython 3.11 abi3 Windows x86-64 Details
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botorch_rust-0.1.0-cp311-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.11 abi3 Linux glibc 2.17+ ARM64 Details
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botorch_rust-0.1.0-cp311-abi3-macosx_10_12_x86_64.whl CPython 3.11 abi3 macOS 10.12+ x86-64 Details

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