OPTIMEO is a package doubled by a web application that helps you optimize your experimental process by generating a Design of Experiment, generating new experiments using Bayesian Optimization (BO), and analyzing the results of your experiments using Machine Learning models. The OPTIMEO package is aimed at helping scientists of any field to reach the optimum parameters of their process using the minimum amount of resources and effort. Therefore, it is based on BO for its data efficiency: when each experiment might take one or more day to run and characterize, it is much preferable to use BO to determine which parameters to use to minimize the number of experiments to run.

This package was developed within the frame of an academic research project, MOFSONG, funded by the French National Research Agency (N° ANR-24-CE08-7639). See the related paper reference in How to cite.

The package documentation is available here. It is generated automatically by the GitHub Actions workflow on pushes to main and published to GitHub Pages.

Release notes are available in CHANGELOG.md.

Installing the package and its dependencies should take up about 1.3 GB on your hard disk, the main "heavy" dependencies being botorch, scikit_learn, plotly, scipy, pandas and streamlit.

uv venv .venv --python 3.10
source .venv/bin/activate # Linux / macOS
uv pip install optimeo

This installs OPTIMEO from PyPI and registers the optimeo command in your environment.

python -m venv .venv
source .venv/bin/activate # Linux / macOS
python -m pip install --upgrade pip
python -m pip install optimeo

To install the latest code directly from the repository, use:

python -m venv .venv
source .venv/bin/activate # Linux / macOS
python -m pip install "git+https://github.com/colinbousige/OPTIMEO.git"

You can upgrade or uninstall when using pip:

python -m pip install --upgrade optimeo
pip uninstall optimeo

After installation, run:

optimeo

If the command is not found, activate the environment first:

source .venv/bin/activate # Linux / macOS
optimeo

If you encounter Streamlit watcher errors related to torch.classes (for example Examining the path of torch.classes raised), OPTIMEO now starts Streamlit with file watching disabled by default. To override this behavior manually, run:

optimeo --server.fileWatcherType=auto

You can also use the hosted app directly: https://optimeo.streamlit.app/. Local execution is recommended for larger datasets.

You can use the app on Streamlit.io or run it locally (see Installation). Local execution is recommended if you process many rows or use heavier BO/modeling tasks.

Choose the page you want to use in the sidebar, and follow the instructions. Hover the mouse on the question marks to get more information about the parameters.

1. Design of Experiment:
Generate a Design of Experiment (DoE) for the optimization of your process. Depending on the number of factors and levels, you can choose between different types of DoE, such as Sobol sequence, Full Factorial, Fractional Factorial, or Definitive Screening Design.

2. New experiments using Bayesian Optimization:
From a previous set of experiments and their results, generate a new set of experiments to optimize your process. You can define up to 10 outcomes. Any subset of outcomes can be marked as optimization objectives (maximize/minimize), and the others can be used as constraints.

The BO page also provides model interpretation plots, including Ax Sensitivity Analysis.

3. Data analysis and modeling:
Analyze the results of your experiments and model the response of your process.

Create the .venv environment, activate it, install the project, then launch the app:

git clone https://github.com/colinbousige/OPTIMEO.git
cd OPTIMEO
python -m venv .venv
source .venv/bin/activate # Linux / macOS
python -m pip install --upgrade pip
python -m pip install -e .
optimeo

If you prefer uv:

git clone https://github.com/colinbousige/OPTIMEO.git
cd OPTIMEO
uv venv .venv --python 3.10
source .venv/bin/activate # Linux / macOS
uv sync
optimeo

You might want to use the app as a Python package in order to integrate it in your own code, or to automate some tasks. For example:

• you are maybe using a robotic platform to run your experiments and characterize your results, and you want to use Bayesian Optimization to suggest new experiments to run automatically
• you are running a simulation and you want to optimize its parameters using Design of Experiment and Bayesian Optimization.

You can also use the app as a Python package (see Installation). You can import the different modules of the app and use them in your own code. Here is an example of how to use the app as a package:

A more detailed example is given in the notebook.

from optimeo.doe import * 
parameters = [
    {'name': 'Temperature', 'type': 'integer', 'values': [20, 40]},
    {'name': 'Pressure', 'type': 'float', 'values': [1, 2, 3]},
    {'name': 'Catalyst', 'type': 'categorical', 'values': ['A', 'B', 'C']}
]
doe = DesignOfExperiments(
    type='Sobol sequence',
    parameters=parameters,
    Nexp=8
)
doe

A more detailed example is given in the notebook.

from optimeo.bo import * 

features, outcomes = read_experimental_data('experimental_data.csv', out_pos=[-1])
bo = BOExperiment(
    features=features, 
    outcomes=outcomes,
    N = 2, # number of new points to generate
    maximize=True, # we want to maximize the response
    fixed_features=None, 
    feature_constraints=None, 
    optim = 'bo'
)
bo.suggest_next_trials()

A more detailed example is given in the notebook.

from optimeo.analysis import * 

data = pd.read_csv('dataML.csv')
factors = data.columns[:-1].tolist()
response = data.columns[-1]
analysis = DataAnalysis(data, factors, response)
analysis.model_type = "ElasticNetCV"
MLmodel = analysis.compute_ML_model()
figs = analysis.plot_ML_model()
for fig in figs:
    fig.show()

This app was made by Colin Bousige. Contact me for support or to signal a bug, or leave a message on the GitHub page of the app.

This work has been published in the article "OPTIMEO: Bayesian Optimization Web App for Process Tuning, Modeling, and Orchestration", C. Bousige, J. Open Source Softw. 10, 115 (2025), 8510. Please cite this paper if you publish using this code:

@article{bousige_optimeo_2025,
  title = {{{OPTIMEO}}: {{Bayesian Optimization Web App}} for {{Process Tuning}}, {{Modeling}}, and {{Orchestration}}},
  author = {Bousige, Colin},
  year = 2025,
  journal = {J. Open Source Softw.},
  volume = {10},
  number = {115},
  pages = {8510},
  doi = {10.21105/joss.08510}
}

This work was supported by the French National Research Agency (N° ANR-24-CE08-7639).
Also, this work was made possible thanks to the following open-source projects:

• ax
• BoTorch
• scikit-learn
• pyDOE3
• dexpy
• doepy
• definitive-screening-design

This project is licensed under the MIT License - see the LICENSE file for details