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BayBE — A Bayesian Back End for Design of Experiments ¶

The Bayesian Back End (BayBE) provides a general-purpose toolbox for Bayesian Design of Experiments, focusing on additions that enable real-world experimental campaigns.

Besides functionality to perform a typical recommend-measure loop, BayBE’s highlights are:

Custom parameter encodings: Improve your campaign with domain knowledge
Built-in chemical encodings: Improve your campaign with chemical knowledge
Single and multiple targets with min, max and match objectives
Custom surrogate models: For specialized problems or active learning
Hybrid (mixed continuous and discrete) spaces
Transfer learning: Mix data from multiple campaigns and accelerate optimization
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:

pip install baybe 

For more information on this step, see our detailed installation instructions.

Defining the Optimization Objective¶

In BayBE’s language, the Yield can be represented as a NumericalTarget, which we pass into an Objective.

from baybe.targets import NumericalTarget
from baybe.objective import Objective

target = NumericalTarget(
    name="Yield",
    mode="MAX",
)
objective = Objective(mode="SINGLE", targets=[target])

In cases where we need to consider multiple (potentially competing) targets, the role of the Objective is to define additional settings, e.g. how these targets should be balanced. In SINGLE mode, however, there are no additional settings. For more details, see the objective section 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:

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 mordred package
    ),
]

For more parameter types and their details, see the parameters section 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 of the user guide. In this example, we assume no further constraints.

With the parameter and constraint definitions at hand, we can now create our SearchSpace based on the Cartesian product of all possible parameter values:

from baybe.searchspace import SearchSpace

searchspace = SearchSpace.from_product(parameters)

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:

In cases where no measurements have been made prior to the interaction with BayBE, a selection via initial_recommender is used.
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 of the user guide.

from baybe.recommenders import (
    SequentialGreedyRecommender,
    FPSRecommender,
    TwoPhaseMetaRecommender,
)

recommender = TwoPhaseMetaRecommender(
    initial_recommender=FPSRecommender(),  # farthest point sampling
    recommender=SequentialGreedyRecommender(),  # Bayesian model-based optimization
)

The Optimization Loop¶

We can now construct a campaign object that brings all pieces of the puzzle together:

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.

df = campaign.recommend(batch_size=3)
print(df)

   Granularity  Pressure[bar]    Solvent
    medium            1.0  Solvent D
    coarse           10.0  Solvent C
      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:

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.) with chemical encodings compared to one-hot and a random baseline: Substance Encoding Example

Installation¶

From Package Index¶

The easiest way to install BayBE is via PyPI:

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.

For instance, to install the latest commit of the main branch, run:

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:

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.

pip install -e .

If you need to add additional dependencies, make sure to use the correct syntax including '':

pip install -e '.[dev]'

Optional Dependencies¶

There are several dependency groups that can be selected during pip installation, like

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

pip install 'baybe[chem,simulation]'

The available groups are:

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.
simulation: Enabling the simulation module.
test: Required for running the tests.
dev: All of the above plus tox and pip-audit. For code contributors.

Authors¶

Martin Fitzner (Merck KGaA, Darmstadt, Germany), Contact, Github
Adrian Šošić (Merck Life Science KGaA, Darmstadt, Germany), Contact, Github
Alexander Hopp (Merck KGaA, Darmstadt, Germany) Contact, Github
Alex Lee (EMD Electronics, Tempe, Arizona, USA) Contact, Github

Known Issues¶

A list of know issues can be found here.

License¶

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.

baybe

BayBE — A Bayesian Back End for Design of Experiments.

Indices and Tables¶

Module Index

BayBE — A Bayesian Back End for Design of Experiments¶