Installation Guide
Installation from Python Package Index (PyPI)
BoFire can be installed to your Python environment by using pip. It can be done by executing
pip install bofire
Tip
The command from above will install a minimal BoFire version, consisting only of the data models. To install BoFire's including its core optimization features, execute:
pip install 'bofire[optimization]'
Additional optional dependencies
In BoFire, there are several optional dependencies that can be selected during installation via pip, like
pip install 'bofire[optimization, cheminfo] # will install bofire with additional dependencies `optimization` and `cheminfo`
To get the most our of BoFire, it is recommended to install at least
pip install 'bofire[optimization]'
The available dependencies are:
optimization: Core Bayesian optimization features.cheminfo: Cheminformatics utilities.entmoot: Entmoot functionality.xgb: XGboost surrogates.tests: Required for running the test suite.docs: Required for building the documentation.tutorials: Required for running the tutorials.all: Install all possible options (except DoE)
Warning
BoFire has the functionalities for creating D, E, A, G, K and I-optimal experimental designs via the DoEStrategy. This feature depends on cyipopt which is a python interface to ipopt. Unfortunately, it is not possible to install cyipopt including ipopt via pip. A solution is to install cyipopt and its dependencies via conda:
conda install -c conda-forge cyipopt
We are working on a solution that makes BoFire's model based DoE functionalities also accessible to users which do not have cyipopt available.
Development Installation
If you want to contribute to BoFire, it is recommended to install the repository in editable mode (-e).
After cloning the repository via
git clone https://github.com/experimental-design/bofire.git
cd bofire), you can proceed with
pip install -e ".[optimization, tests]" # include optional dependencies as you wish