https://github.com/emdgroup/baybe
Bayesian Optimization and Design of Experiments
https://github.com/emdgroup/baybe
active-learning bayesian-optimization design-of-experiments experimental-design machine-learning optimization
Last synced: about 1 month ago
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Bayesian Optimization and Design of Experiments
- Host: GitHub
- URL: https://github.com/emdgroup/baybe
- Owner: emdgroup
- License: apache-2.0
- Created: 2023-11-27T17:02:40.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2026-01-21T14:35:04.000Z (about 1 month ago)
- Last Synced: 2026-01-21T22:57:12.038Z (about 1 month ago)
- Topics: active-learning, bayesian-optimization, design-of-experiments, experimental-design, machine-learning, optimization
- Language: Python
- Homepage: https://emdgroup.github.io/baybe/
- Size: 107 MB
- Stars: 429
- Watchers: 7
- Forks: 63
- Open Issues: 52
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- awesome-self-driving-labs - Bayesian Back End (BayBE) - source toolbox by [Merck KGaA](https://www.merckgroup.com/) for Bayesian optimization, featuring custom encodings, chemical knowledge integration, hybrid spaces, transfer learning, simulation tools, and robust, serializable code for real-world experimental campaigns. (Software / Optimization)
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# BayBE — A Bayesian Back End for Design of Experiments
The **Bay**esian **B**ack **E**nd (**BayBE**) is a general-purpose toolbox for Bayesian Design
of Experiments, focusing on additions that enable real-world experimental campaigns.
## 🔋 Batteries Included
Besides its core functionality to perform a typical recommend-measure loop, BayBE
offers a range of ✨**built‑in features**✨ crucial for real-world use cases.
The following provides a non-comprehensive overview:
- 🛠️ Custom parameter encodings: Improve your campaign with domain knowledge
- 🧪 Built-in chemical encodings: Improve your campaign with chemical knowledge
- 🎯 Numerical and binary targets with min, max and match objectives
- ⚖️ Multi-target support via Pareto optimization and desirability scalarization
- 🔍 Insights: Easily analyze feature importance and model behavior
- 🎭 Hybrid (mixed continuous and discrete) spaces
- 🚀 Transfer learning: Mix data from multiple campaigns and accelerate optimization
- 🎰 Bandit models: Efficiently find the best among many options in noisy environments (e.g. A/B Testing)
- 🔢 Cardinality constraints: Control the number of active factors in your design
- 🌎 Distributed workflows: Run campaigns asynchronously with pending experiments and partial measurements
- 🎓 Active learning: Perform smart data acquisition campaigns
- ⚙️ Custom surrogate models: Enhance your predictions through mechanistic understanding
- 📈 Comprehensive backtest, simulation and imputation utilities: Benchmark and find your best settings
- 📝 Fully typed and hypothesis-tested: Robust code base
- 🔄 All objects are fully (de-)serializable: Useful for storing results in databases or use in wrappers like APIs
## ⚡ Quick Start
Let us consider a simple experiment where we control three parameters and want to
maximize a single target called `Yield`.
First, install BayBE into your Python environment:
```bash
pip install baybe
```
For more information on this step, see our
[detailed installation instructions](#installation).
### Defining the Optimization Objective
In BayBE's language, the `Yield` can be represented as a `NumericalTarget`,
which we wrap into a `SingleTargetObjective`:
```python
from baybe.targets import NumericalTarget
from baybe.objectives import SingleTargetObjective
target = NumericalTarget(name="Yield")
objective = SingleTargetObjective(target=target)
```
In cases where we are confronted with multiple (potentially conflicting) targets,
the `ParetoObjective` or `DesirabilityObjective` can be used instead.
These allow to define additional settings, such as how the targets should be balanced.
For more details, see the
[objectives section](https://emdgroup.github.io/baybe/stable/userguide/objectives.html)
of the user guide.
### Defining the Search Space
Next, we inform BayBE about the available "control knobs", that is, the underlying
system parameters we can tune to optimize our targets. This also involves specifying
their values/ranges and other parameter-specific details.
For our example, we assume that we can control three parameters – `Granularity`,
`Pressure[bar]`, and `Solvent` – as follows:
```python
from baybe.parameters import (
CategoricalParameter,
NumericalDiscreteParameter,
SubstanceParameter,
)
parameters = [
CategoricalParameter(
name="Granularity",
values=["coarse", "medium", "fine"],
encoding="OHE", # one-hot encoding of categories
),
NumericalDiscreteParameter(
name="Pressure[bar]",
values=[1, 5, 10],
tolerance=0.2, # allows experimental inaccuracies up to 0.2 when reading values
),
SubstanceParameter(
name="Solvent",
data={
"Solvent A": "COC",
"Solvent B": "CCC", # label-SMILES pairs
"Solvent C": "O",
"Solvent D": "CS(=O)C",
},
encoding="MORDRED", # chemical encoding via scikit-fingerprints
),
]
```
For more parameter types and their details, see the
[parameters section](https://emdgroup.github.io/baybe/stable/userguide/parameters.html)
of the user guide.
Additionally, we can define a set of constraints to further specify allowed ranges and
relationships between our parameters. Details can be found in the
[constraints section](https://emdgroup.github.io/baybe/stable/userguide/constraints.html) of the user guide.
In this example, we assume no further constraints.
With the parameter definitions at hand, we can now create our
`SearchSpace` based on the Cartesian product of all possible parameter values:
```python
from baybe.searchspace import SearchSpace
searchspace = SearchSpace.from_product(parameters)
```
See the [search spaces section](https://emdgroup.github.io/baybe/stable/userguide/searchspace.html)
of our user guide for more information on the structure of search spaces
and alternative ways of construction.
### Optional: Defining the Optimization Strategy
As an optional step, we can specify details on how the optimization should be
conducted. If omitted, BayBE will choose a default setting.
For our example, we combine two recommenders via a so-called meta recommender named
`TwoPhaseMetaRecommender`:
1. In cases where no measurements have been made prior to the interaction with BayBE,
a selection via `initial_recommender` is used.
2. As soon as the first measurements are available, we switch to `recommender`.
For more details on the different recommenders, their underlying algorithmic
details, and their configuration settings, see the
[recommenders section](https://emdgroup.github.io/baybe/stable/userguide/recommenders.html)
of the user guide.
```python
from baybe.recommenders import (
BotorchRecommender,
FPSRecommender,
TwoPhaseMetaRecommender,
)
recommender = TwoPhaseMetaRecommender(
initial_recommender=FPSRecommender(), # farthest point sampling
recommender=BotorchRecommender(), # Bayesian model-based optimization
)
```
### The Optimization Loop
We can now construct a campaign object that brings all pieces of the puzzle together:
```python
from baybe import Campaign
campaign = Campaign(searchspace, objective, recommender)
```
With this object at hand, we can start our experimentation cycle.
In particular:
* We can ask BayBE to `recommend` new experiments.
* We can `add_measurements` for certain experimental settings to the campaign's
database.
Note that these two steps can be performed in any order.
In particular, available measurements can be submitted at any time and also several
times before querying the next recommendations.
```python
df = campaign.recommend(batch_size=3)
print(df)
```
```none
Granularity Pressure[bar] Solvent
15 medium 1.0 Solvent D
10 coarse 10.0 Solvent C
29 fine 5.0 Solvent B
```
Note that the specific recommendations will depend on both the data
already fed to the campaign and the random number generator seed that is used.
After having conducted the corresponding experiments, we can add our measured
targets to the table and feed it back to the campaign:
```python
df["Yield"] = [79.8, 54.1, 59.4]
campaign.add_measurements(df)
```
With the newly arrived data, BayBE can produce a refined design for the next iteration.
This loop would typically continue until a desired target value has been achieved in
the experiment.
### Advanced Example: Chemical Substances
BayBE has several modules to go beyond traditional approaches. One such example is the
use of custom encodings for categorical parameters. Chemical encodings for substances
are a special built-in case of this that comes with BayBE.
In the following picture you can see
the outcome for treating the solvent, base and ligand in a direct arylation reaction
optimization (from [Shields, B.J. et al.](https://doi.org/10.1038/s41586-021-03213-y)) with
chemical encodings compared to one-hot and a random baseline:
(installation)=
## 💻 Installation
### From Package Index
The easiest way to install BayBE is via PyPI:
```bash
pip install baybe
```
A certain released version of the package can be installed by specifying the
corresponding version tag in the form `baybe==x.y.z`.
### From GitHub
If you need finer control and would like to install a specific commit that has not been
released under a certain version tag, you can do so by installing BayBE directly from
GitHub via git and specifying the corresponding
[git ref](https://pip.pypa.io/en/stable/topics/vcs-support/#git).
For instance, to install the latest commit of the main branch, run:
```bash
pip install git+https://github.com/emdgroup/baybe.git@main
```
### From Local Clone
Alternatively, you can install the package from your own local copy.
First, clone the repository, navigate to the repository root folder, check out the
desired commit, and run:
```bash
pip install .
```
A developer would typically also install the package in editable mode ('-e'),
which ensures that changes to the code do not require a reinstallation.
```bash
pip install -e .
```
If you need to add additional dependencies, make sure to use the correct syntax
including `''`:
```bash
pip install -e '.[dev]'
```
### Optional Dependencies
There are several dependency groups that can be selected during pip installation, like
```bash
pip install 'baybe[test,lint]' # will install baybe with additional dependency groups `test` and `lint`
```
To get the most out of `baybe`, we recommend to install at least
```bash
pip install 'baybe[chem,simulation]'
```
The available groups are:
- `extras`: Installs all dependencies required for optional features.
- `benchmarking`: Required for running the benchmarking module.
- `chem`: Cheminformatics utilities (e.g. for the `SubstanceParameter`).
- `docs`: Required for creating the documentation.
- `examples`: Required for running the examples/streamlit.
- `lint`: Required for linting and formatting.
- `mypy`: Required for static type checking.
- `onnx`: Required for using custom surrogate models in [ONNX format](https://onnx.ai).
- `polars`: Required for optimized search space construction via [Polars](https://docs.pola.rs/).
- `insights`: Required for built-in model and campaign analysis (e.g. using [SHAP](https://shap.readthedocs.io/)).
- `simulation`: Enabling the [simulation](https://emdgroup.github.io/baybe/stable/_autosummary/baybe.simulation.html) module.
- `test`: Required for running the tests.
- `dev`: All of the above plus dev tools. For code contributors.
## 📡 Telemetry
Telemetry was fully and permanently removed in version 0.14.0.
## 📖 Citation
If you find BayBE useful, please consider citing [our paper](https://doi.org/10.1039/D5DD00050E):
```bibtex
@article{baybe_2025,
author = "Fitzner, Martin and {\v S}o{\v s}i{\'c}, Adrian and
Hopp, Alexander V. and M{\"u}ller, Marcel and Rihana, Rim and
Hrovatin, Karin and Liebig, Fabian and Winkel, Mathias and
Halter, Wolfgang and Brandenburg, Jan Gerit",
title = "{BayBE}: a {B}ayesian {B}ack {E}nd for experimental planning
in the low-to-no-data regime",
journal = "Digital Discovery",
year = "2025",
volume = "4",
issue = "8",
pages = "1991-2000",
publisher = "RSC",
doi = "10.1039/D5DD00050E",
url = "http://dx.doi.org/10.1039/D5DD00050E",
}
```
## 🛠️ Known Issues
A list of known issues can be found [here](https://emdgroup.github.io/baybe/stable/known_issues.html).
## 👨🏻🔧 Maintainers
- Martin Fitzner (Merck KGaA, Darmstadt, Germany), [Contact](mailto:martin.fitzner@merckgroup.com), [Github](https://github.com/Scienfitz)
- Adrian Šošić (Merck Life Science KGaA, Darmstadt, Germany), [Contact](mailto:adrian.sosic@merckgroup.com), [Github](https://github.com/AdrianSosic)
- Alexander Hopp (Merck KGaA, Darmstadt, Germany) [Contact](mailto:alexander.hopp@merckgroup.com), [Github](https://github.com/AVHopp)
## 🙏 Contributors
Thanks to our contributors!
[](https://github.com/emdgroup/baybe/graphs/contributors)
## 📄 License
Copyright 2022-2025 Merck KGaA, Darmstadt, Germany
and/or its affiliates. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.