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diff --git a/python-botorch.spec b/python-botorch.spec new file mode 100644 index 0000000..3473750 --- /dev/null +++ b/python-botorch.spec @@ -0,0 +1,718 @@ +%global _empty_manifest_terminate_build 0 +Name: python-botorch +Version: 0.8.3 +Release: 1 +Summary: Bayesian Optimization in PyTorch +License: MIT +URL: https://botorch.org +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/db/ad/d93b5d9766b5a35bfa908e13af74b90801e820ff1eaf731e73378c27a2a3/botorch-0.8.3.tar.gz +BuildArch: noarch + +Requires: python3-multipledispatch +Requires: python3-scipy +Requires: python3-torch +Requires: python3-pyro-ppl +Requires: python3-gpytorch +Requires: python3-linear-operator +Requires: python3-pytest +Requires: python3-pytest-cov +Requires: python3-flake8 +Requires: python3-ufmt +Requires: python3-flake8-docstrings +Requires: python3-black +Requires: python3-usort +Requires: python3-sphinx +Requires: python3-pytest +Requires: python3-pytest-cov +Requires: python3-ax-platform +Requires: python3-cma +Requires: python3-jupyter +Requires: python3-kaleido +Requires: python3-matplotlib +Requires: python3-memory-profiler +Requires: python3-papermill +Requires: python3-pykeops +Requires: python3-torchvision + +%description +<a href="https://botorch.org"> + <img width="350" src="https://botorch.org/img/botorch_logo_lockup.png" alt="BoTorch Logo" /> +</a> + +<hr/> + +[](https://opensource.fb.com/support-ukraine) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ALint) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ATest) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ADocs) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ATutorials) +[](https://codecov.io/github/pytorch/botorch) + +[](https://anaconda.org/pytorch/botorch) +[](https://pypi.org/project/botorch) +[](LICENSE) + + +BoTorch is a library for Bayesian Optimization built on PyTorch. + +*BoTorch is currently in beta and under active development!* + + +#### Why BoTorch ? +BoTorch +* Provides a modular and easily extensible interface for composing Bayesian + optimization primitives, including probabilistic models, acquisition functions, + and optimizers. +* Harnesses the power of PyTorch, including auto-differentiation, native support + for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code, + and a dynamic computation graph. +* Supports Monte Carlo-based acquisition functions via the + [reparameterization trick](https://arxiv.org/abs/1312.6114), which makes it + straightforward to implement new ideas without having to impose restrictive + assumptions about the underlying model. +* Enables seamless integration with deep and/or convolutional architectures in PyTorch. +* Has first-class support for state-of-the art probabilistic models in + [GPyTorch](http://www.gpytorch.ai/), including support for multi-task Gaussian + Processes (GPs) deep kernel learning, deep GPs, and approximate inference. + + +#### Target Audience + +The primary audience for hands-on use of BoTorch are researchers and +sophisticated practitioners in Bayesian Optimization and AI. +We recommend using BoTorch as a low-level API for implementing new algorithms +for [Ax](https://ax.dev). Ax has been designed to be an easy-to-use platform +for end-users, which at the same time is flexible enough for Bayesian +Optimization researchers to plug into for handling of feature transformations, +(meta-)data management, storage, etc. +We recommend that end-users who are not actively doing research on Bayesian +Optimization simply use Ax. + + +## Installation + +**Installation Requirements** +- Python >= 3.8 +- PyTorch >= 1.12 +- gpytorch == 1.9.1 +- linear_operator == 0.3.0 +- pyro-ppl >= 1.8.4 +- scipy +- multiple-dispatch + +### Prerequisite only for MacOS users with Intel processors: +Before installing BoTorch, we recommend first manually installing PyTorch, a required dependency of +BoTorch. Installing it according to the [PyTorch installation instructions](https://pytorch.org/get-started/locally/) +ensures that it is properly linked against MKL, a library that optimizes mathematical computation for Intel processors. +This will result in up to an order-of-magnitude speed-up for Bayesian optimization, as at the moment, +installing PyTorch from pip does not link against MKL. + +The PyTorch installation instructions currently recommend: +1. Install [Anaconda](https://www.anaconda.com/distribution/#download-section). Note that there are different installers for Intel and M1 Macs. +2. Install PyTorch following the [PyTorch installation instructions](https://pytorch.org/get-started/locally/). +Currently, this suggests running `conda install pytorch torchvision -c pytorch`. + +If you want to customize your installation, please follow the [PyTorch installation instructions](https://pytorch.org/get-started/locally/) to build from source. + +### Option 1: Installing the latest release + +The latest release of BoTorch is easily installed either via +[Anaconda](https://www.anaconda.com/distribution/#download-section) (recommended) or pip. + +**To install BoTorch from Anaconda**, run +```bash +conda install botorch -c pytorch -c gpytorch -c conda-forge +``` +The above command installs BoTorch and any needed dependencies. ` -c pytorch -c gpytorch -c conda-forge` means that the most preferred source to install from is the PyTorch channel, the next most preferred is the GPyTorch channel, +and the least preferred is conda-forge. + +**Alternatively, to install with `pip`**, do +```bash +pip install botorch +``` + +_Note_: Make sure the `pip` being used is actually the one from the newly created Conda environment. If you're using a Unix-based OS, you can use `which pip` to check. + +### Option 2: Installing from latest main branch + +If you would like to try our bleeding edge features (and don't mind potentially +running into the occasional bug here or there), you can install the latest +development version directly from GitHub. If you want to also install the +current `gpytorch` and `linear_operator` development versions, you will need +to ensure that the `ALLOW_LATEST_GPYTORCH_LINOP` environment variable is set: +```bash +pip install --upgrade git+https://github.com/cornellius-gp/linear_operator.git +pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git +export ALLOW_LATEST_GPYTORCH_LINOP=true +pip install --upgrade git+https://github.com/pytorch/botorch.git +``` + +### Option 3: Editable/dev install + +If you want to [contribute](CONTRIBUTING.md) to BoTorch, you will want to install editably so that you can change files and have the +changes reflected in your local install. + +If you want to install the current `gpytorch` and `linear_operator` development versions, as in Option 2, do that +before proceeding. + +#### Option 3a: Bare-bones editable install + +```bash +git clone https://github.com/pytorch/botorch.git +cd botorch +pip install -e . +``` + +#### Option 3b: Editable install with development and tutorials dependencies + +```bash +git clone https://github.com/pytorch/botorch.git +cd botorch +export ALLOW_BOTORCH_LATEST=true +pip install -e ".[dev, tutorials]" +``` + +* `dev`: Specifies tools necessary for development + (testing, linting, docs building; see [Contributing](#contributing) below). +* `tutorials`: Also installs all packages necessary for running the tutorial notebooks. +* You can also install either the dev or tutorials dependencies without installing both, e.g. by changing the last command to `pip install -e ".[dev]"`. + +## Getting Started + +Here's a quick run down of the main components of a Bayesian optimization loop. +For more details see our [Documentation](https://botorch.org/docs/introduction) and the +[Tutorials](https://botorch.org/tutorials). + +1. Fit a Gaussian Process model to data + ```python + import torch + from botorch.models import SingleTaskGP + from botorch.fit import fit_gpytorch_mll + from gpytorch.mlls import ExactMarginalLogLikelihood + + train_X = torch.rand(10, 2) + Y = 1 - (train_X - 0.5).norm(dim=-1, keepdim=True) # explicit output dimension + Y += 0.1 * torch.rand_like(Y) + train_Y = (Y - Y.mean()) / Y.std() + + gp = SingleTaskGP(train_X, train_Y) + mll = ExactMarginalLogLikelihood(gp.likelihood, gp) + fit_gpytorch_mll(mll) + ``` + +2. Construct an acquisition function + ```python + from botorch.acquisition import UpperConfidenceBound + + UCB = UpperConfidenceBound(gp, beta=0.1) + ``` + +3. Optimize the acquisition function + ```python + from botorch.optim import optimize_acqf + + bounds = torch.stack([torch.zeros(2), torch.ones(2)]) + candidate, acq_value = optimize_acqf( + UCB, bounds=bounds, q=1, num_restarts=5, raw_samples=20, + ) + ``` + + +## Citing BoTorch + +If you use BoTorch, please cite the following paper: +> [M. Balandat, B. Karrer, D. R. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. Advances in Neural Information Processing Systems 33, 2020.](https://arxiv.org/abs/1910.06403) + +``` +@inproceedings{balandat2020botorch, + title={{BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization}}, + author={Balandat, Maximilian and Karrer, Brian and Jiang, Daniel R. and Daulton, Samuel and Letham, Benjamin and Wilson, Andrew Gordon and Bakshy, Eytan}, + booktitle = {Advances in Neural Information Processing Systems 33}, + year={2020}, + url = {http://arxiv.org/abs/1910.06403} +} +``` + +See [here](https://botorch.org/docs/papers) for an incomplete selection of peer-reviewed papers that build off of BoTorch. + + +## Contributing +See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out. + + +## License +BoTorch is MIT licensed, as found in the [LICENSE](LICENSE) file. + + +%package -n python3-botorch +Summary: Bayesian Optimization in PyTorch +Provides: python-botorch +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-botorch +<a href="https://botorch.org"> + <img width="350" src="https://botorch.org/img/botorch_logo_lockup.png" alt="BoTorch Logo" /> +</a> + +<hr/> + +[](https://opensource.fb.com/support-ukraine) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ALint) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ATest) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ADocs) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ATutorials) +[](https://codecov.io/github/pytorch/botorch) + +[](https://anaconda.org/pytorch/botorch) +[](https://pypi.org/project/botorch) +[](LICENSE) + + +BoTorch is a library for Bayesian Optimization built on PyTorch. + +*BoTorch is currently in beta and under active development!* + + +#### Why BoTorch ? +BoTorch +* Provides a modular and easily extensible interface for composing Bayesian + optimization primitives, including probabilistic models, acquisition functions, + and optimizers. +* Harnesses the power of PyTorch, including auto-differentiation, native support + for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code, + and a dynamic computation graph. +* Supports Monte Carlo-based acquisition functions via the + [reparameterization trick](https://arxiv.org/abs/1312.6114), which makes it + straightforward to implement new ideas without having to impose restrictive + assumptions about the underlying model. +* Enables seamless integration with deep and/or convolutional architectures in PyTorch. +* Has first-class support for state-of-the art probabilistic models in + [GPyTorch](http://www.gpytorch.ai/), including support for multi-task Gaussian + Processes (GPs) deep kernel learning, deep GPs, and approximate inference. + + +#### Target Audience + +The primary audience for hands-on use of BoTorch are researchers and +sophisticated practitioners in Bayesian Optimization and AI. +We recommend using BoTorch as a low-level API for implementing new algorithms +for [Ax](https://ax.dev). Ax has been designed to be an easy-to-use platform +for end-users, which at the same time is flexible enough for Bayesian +Optimization researchers to plug into for handling of feature transformations, +(meta-)data management, storage, etc. +We recommend that end-users who are not actively doing research on Bayesian +Optimization simply use Ax. + + +## Installation + +**Installation Requirements** +- Python >= 3.8 +- PyTorch >= 1.12 +- gpytorch == 1.9.1 +- linear_operator == 0.3.0 +- pyro-ppl >= 1.8.4 +- scipy +- multiple-dispatch + +### Prerequisite only for MacOS users with Intel processors: +Before installing BoTorch, we recommend first manually installing PyTorch, a required dependency of +BoTorch. Installing it according to the [PyTorch installation instructions](https://pytorch.org/get-started/locally/) +ensures that it is properly linked against MKL, a library that optimizes mathematical computation for Intel processors. +This will result in up to an order-of-magnitude speed-up for Bayesian optimization, as at the moment, +installing PyTorch from pip does not link against MKL. + +The PyTorch installation instructions currently recommend: +1. Install [Anaconda](https://www.anaconda.com/distribution/#download-section). Note that there are different installers for Intel and M1 Macs. +2. Install PyTorch following the [PyTorch installation instructions](https://pytorch.org/get-started/locally/). +Currently, this suggests running `conda install pytorch torchvision -c pytorch`. + +If you want to customize your installation, please follow the [PyTorch installation instructions](https://pytorch.org/get-started/locally/) to build from source. + +### Option 1: Installing the latest release + +The latest release of BoTorch is easily installed either via +[Anaconda](https://www.anaconda.com/distribution/#download-section) (recommended) or pip. + +**To install BoTorch from Anaconda**, run +```bash +conda install botorch -c pytorch -c gpytorch -c conda-forge +``` +The above command installs BoTorch and any needed dependencies. ` -c pytorch -c gpytorch -c conda-forge` means that the most preferred source to install from is the PyTorch channel, the next most preferred is the GPyTorch channel, +and the least preferred is conda-forge. + +**Alternatively, to install with `pip`**, do +```bash +pip install botorch +``` + +_Note_: Make sure the `pip` being used is actually the one from the newly created Conda environment. If you're using a Unix-based OS, you can use `which pip` to check. + +### Option 2: Installing from latest main branch + +If you would like to try our bleeding edge features (and don't mind potentially +running into the occasional bug here or there), you can install the latest +development version directly from GitHub. If you want to also install the +current `gpytorch` and `linear_operator` development versions, you will need +to ensure that the `ALLOW_LATEST_GPYTORCH_LINOP` environment variable is set: +```bash +pip install --upgrade git+https://github.com/cornellius-gp/linear_operator.git +pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git +export ALLOW_LATEST_GPYTORCH_LINOP=true +pip install --upgrade git+https://github.com/pytorch/botorch.git +``` + +### Option 3: Editable/dev install + +If you want to [contribute](CONTRIBUTING.md) to BoTorch, you will want to install editably so that you can change files and have the +changes reflected in your local install. + +If you want to install the current `gpytorch` and `linear_operator` development versions, as in Option 2, do that +before proceeding. + +#### Option 3a: Bare-bones editable install + +```bash +git clone https://github.com/pytorch/botorch.git +cd botorch +pip install -e . +``` + +#### Option 3b: Editable install with development and tutorials dependencies + +```bash +git clone https://github.com/pytorch/botorch.git +cd botorch +export ALLOW_BOTORCH_LATEST=true +pip install -e ".[dev, tutorials]" +``` + +* `dev`: Specifies tools necessary for development + (testing, linting, docs building; see [Contributing](#contributing) below). +* `tutorials`: Also installs all packages necessary for running the tutorial notebooks. +* You can also install either the dev or tutorials dependencies without installing both, e.g. by changing the last command to `pip install -e ".[dev]"`. + +## Getting Started + +Here's a quick run down of the main components of a Bayesian optimization loop. +For more details see our [Documentation](https://botorch.org/docs/introduction) and the +[Tutorials](https://botorch.org/tutorials). + +1. Fit a Gaussian Process model to data + ```python + import torch + from botorch.models import SingleTaskGP + from botorch.fit import fit_gpytorch_mll + from gpytorch.mlls import ExactMarginalLogLikelihood + + train_X = torch.rand(10, 2) + Y = 1 - (train_X - 0.5).norm(dim=-1, keepdim=True) # explicit output dimension + Y += 0.1 * torch.rand_like(Y) + train_Y = (Y - Y.mean()) / Y.std() + + gp = SingleTaskGP(train_X, train_Y) + mll = ExactMarginalLogLikelihood(gp.likelihood, gp) + fit_gpytorch_mll(mll) + ``` + +2. Construct an acquisition function + ```python + from botorch.acquisition import UpperConfidenceBound + + UCB = UpperConfidenceBound(gp, beta=0.1) + ``` + +3. Optimize the acquisition function + ```python + from botorch.optim import optimize_acqf + + bounds = torch.stack([torch.zeros(2), torch.ones(2)]) + candidate, acq_value = optimize_acqf( + UCB, bounds=bounds, q=1, num_restarts=5, raw_samples=20, + ) + ``` + + +## Citing BoTorch + +If you use BoTorch, please cite the following paper: +> [M. Balandat, B. Karrer, D. R. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. Advances in Neural Information Processing Systems 33, 2020.](https://arxiv.org/abs/1910.06403) + +``` +@inproceedings{balandat2020botorch, + title={{BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization}}, + author={Balandat, Maximilian and Karrer, Brian and Jiang, Daniel R. and Daulton, Samuel and Letham, Benjamin and Wilson, Andrew Gordon and Bakshy, Eytan}, + booktitle = {Advances in Neural Information Processing Systems 33}, + year={2020}, + url = {http://arxiv.org/abs/1910.06403} +} +``` + +See [here](https://botorch.org/docs/papers) for an incomplete selection of peer-reviewed papers that build off of BoTorch. + + +## Contributing +See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out. + + +## License +BoTorch is MIT licensed, as found in the [LICENSE](LICENSE) file. + + +%package help +Summary: Development documents and examples for botorch +Provides: python3-botorch-doc +%description help +<a href="https://botorch.org"> + <img width="350" src="https://botorch.org/img/botorch_logo_lockup.png" alt="BoTorch Logo" /> +</a> + +<hr/> + +[](https://opensource.fb.com/support-ukraine) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ALint) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ATest) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ADocs) +[](https://github.com/pytorch/botorch/actions?query=workflow%3ATutorials) +[](https://codecov.io/github/pytorch/botorch) + +[](https://anaconda.org/pytorch/botorch) +[](https://pypi.org/project/botorch) +[](LICENSE) + + +BoTorch is a library for Bayesian Optimization built on PyTorch. + +*BoTorch is currently in beta and under active development!* + + +#### Why BoTorch ? +BoTorch +* Provides a modular and easily extensible interface for composing Bayesian + optimization primitives, including probabilistic models, acquisition functions, + and optimizers. +* Harnesses the power of PyTorch, including auto-differentiation, native support + for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code, + and a dynamic computation graph. +* Supports Monte Carlo-based acquisition functions via the + [reparameterization trick](https://arxiv.org/abs/1312.6114), which makes it + straightforward to implement new ideas without having to impose restrictive + assumptions about the underlying model. +* Enables seamless integration with deep and/or convolutional architectures in PyTorch. +* Has first-class support for state-of-the art probabilistic models in + [GPyTorch](http://www.gpytorch.ai/), including support for multi-task Gaussian + Processes (GPs) deep kernel learning, deep GPs, and approximate inference. + + +#### Target Audience + +The primary audience for hands-on use of BoTorch are researchers and +sophisticated practitioners in Bayesian Optimization and AI. +We recommend using BoTorch as a low-level API for implementing new algorithms +for [Ax](https://ax.dev). Ax has been designed to be an easy-to-use platform +for end-users, which at the same time is flexible enough for Bayesian +Optimization researchers to plug into for handling of feature transformations, +(meta-)data management, storage, etc. +We recommend that end-users who are not actively doing research on Bayesian +Optimization simply use Ax. + + +## Installation + +**Installation Requirements** +- Python >= 3.8 +- PyTorch >= 1.12 +- gpytorch == 1.9.1 +- linear_operator == 0.3.0 +- pyro-ppl >= 1.8.4 +- scipy +- multiple-dispatch + +### Prerequisite only for MacOS users with Intel processors: +Before installing BoTorch, we recommend first manually installing PyTorch, a required dependency of +BoTorch. Installing it according to the [PyTorch installation instructions](https://pytorch.org/get-started/locally/) +ensures that it is properly linked against MKL, a library that optimizes mathematical computation for Intel processors. +This will result in up to an order-of-magnitude speed-up for Bayesian optimization, as at the moment, +installing PyTorch from pip does not link against MKL. + +The PyTorch installation instructions currently recommend: +1. Install [Anaconda](https://www.anaconda.com/distribution/#download-section). Note that there are different installers for Intel and M1 Macs. +2. Install PyTorch following the [PyTorch installation instructions](https://pytorch.org/get-started/locally/). +Currently, this suggests running `conda install pytorch torchvision -c pytorch`. + +If you want to customize your installation, please follow the [PyTorch installation instructions](https://pytorch.org/get-started/locally/) to build from source. + +### Option 1: Installing the latest release + +The latest release of BoTorch is easily installed either via +[Anaconda](https://www.anaconda.com/distribution/#download-section) (recommended) or pip. + +**To install BoTorch from Anaconda**, run +```bash +conda install botorch -c pytorch -c gpytorch -c conda-forge +``` +The above command installs BoTorch and any needed dependencies. ` -c pytorch -c gpytorch -c conda-forge` means that the most preferred source to install from is the PyTorch channel, the next most preferred is the GPyTorch channel, +and the least preferred is conda-forge. + +**Alternatively, to install with `pip`**, do +```bash +pip install botorch +``` + +_Note_: Make sure the `pip` being used is actually the one from the newly created Conda environment. If you're using a Unix-based OS, you can use `which pip` to check. + +### Option 2: Installing from latest main branch + +If you would like to try our bleeding edge features (and don't mind potentially +running into the occasional bug here or there), you can install the latest +development version directly from GitHub. If you want to also install the +current `gpytorch` and `linear_operator` development versions, you will need +to ensure that the `ALLOW_LATEST_GPYTORCH_LINOP` environment variable is set: +```bash +pip install --upgrade git+https://github.com/cornellius-gp/linear_operator.git +pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git +export ALLOW_LATEST_GPYTORCH_LINOP=true +pip install --upgrade git+https://github.com/pytorch/botorch.git +``` + +### Option 3: Editable/dev install + +If you want to [contribute](CONTRIBUTING.md) to BoTorch, you will want to install editably so that you can change files and have the +changes reflected in your local install. + +If you want to install the current `gpytorch` and `linear_operator` development versions, as in Option 2, do that +before proceeding. + +#### Option 3a: Bare-bones editable install + +```bash +git clone https://github.com/pytorch/botorch.git +cd botorch +pip install -e . +``` + +#### Option 3b: Editable install with development and tutorials dependencies + +```bash +git clone https://github.com/pytorch/botorch.git +cd botorch +export ALLOW_BOTORCH_LATEST=true +pip install -e ".[dev, tutorials]" +``` + +* `dev`: Specifies tools necessary for development + (testing, linting, docs building; see [Contributing](#contributing) below). +* `tutorials`: Also installs all packages necessary for running the tutorial notebooks. +* You can also install either the dev or tutorials dependencies without installing both, e.g. by changing the last command to `pip install -e ".[dev]"`. + +## Getting Started + +Here's a quick run down of the main components of a Bayesian optimization loop. +For more details see our [Documentation](https://botorch.org/docs/introduction) and the +[Tutorials](https://botorch.org/tutorials). + +1. Fit a Gaussian Process model to data + ```python + import torch + from botorch.models import SingleTaskGP + from botorch.fit import fit_gpytorch_mll + from gpytorch.mlls import ExactMarginalLogLikelihood + + train_X = torch.rand(10, 2) + Y = 1 - (train_X - 0.5).norm(dim=-1, keepdim=True) # explicit output dimension + Y += 0.1 * torch.rand_like(Y) + train_Y = (Y - Y.mean()) / Y.std() + + gp = SingleTaskGP(train_X, train_Y) + mll = ExactMarginalLogLikelihood(gp.likelihood, gp) + fit_gpytorch_mll(mll) + ``` + +2. Construct an acquisition function + ```python + from botorch.acquisition import UpperConfidenceBound + + UCB = UpperConfidenceBound(gp, beta=0.1) + ``` + +3. Optimize the acquisition function + ```python + from botorch.optim import optimize_acqf + + bounds = torch.stack([torch.zeros(2), torch.ones(2)]) + candidate, acq_value = optimize_acqf( + UCB, bounds=bounds, q=1, num_restarts=5, raw_samples=20, + ) + ``` + + +## Citing BoTorch + +If you use BoTorch, please cite the following paper: +> [M. Balandat, B. Karrer, D. R. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. Advances in Neural Information Processing Systems 33, 2020.](https://arxiv.org/abs/1910.06403) + +``` +@inproceedings{balandat2020botorch, + title={{BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization}}, + author={Balandat, Maximilian and Karrer, Brian and Jiang, Daniel R. and Daulton, Samuel and Letham, Benjamin and Wilson, Andrew Gordon and Bakshy, Eytan}, + booktitle = {Advances in Neural Information Processing Systems 33}, + year={2020}, + url = {http://arxiv.org/abs/1910.06403} +} +``` + +See [here](https://botorch.org/docs/papers) for an incomplete selection of peer-reviewed papers that build off of BoTorch. + + +## Contributing +See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out. + + +## License +BoTorch is MIT licensed, as found in the [LICENSE](LICENSE) file. + + +%prep +%autosetup -n botorch-0.8.3 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-botorch -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.3-1 +- Package Spec generated |