chembayes 0.1.3

Experimental design and Bayesian optimization with Gaussian Processes and QMC sampling

ChemBayes

ChemBayes is a Python library designed to streamline experimental design (DoE) and Bayesian optimization for scientific and chemical research. It leverages Gaussian Processes to model complex response surfaces and efficiently identify optimal conditions.

🚀 Key Features

• Intelligent Sampling (QMC): Generate initial experimental designs using Halton sequences (Quasi-Monte Carlo) for optimal space-filling coverage.
• Automatic Preprocessing: Integrated handling of numeric variables (scaling) and categorical variables (one-hot encoding).
• Hyperparameter Tuning: Automated Gaussian Process parameter adjustment (Matern + WhiteKernel) using Leave-One-Out (LOO) cross-validation and NLPD loss.
• Bayesian Optimization: Global maximum search using the Expected Improvement (EI) acquisition function.
• Visual Diagnostics: Automated generation of feature importance, partial dependence plots (1D and 2D), and model validation charts.

📦 Installation

You can install the latest stable version directly from PyPI:

pip install chembayes

Alternatively, for local development, you can install from the project root:

pip install .

Or for development mode:

pip install -e .

🛠️ Quick Start

1. Generating an Experimental Design (Sampling)

Define your search space with numeric and categorical parameters:

import chembayes as cb

parameters = {
    'temperature': {'type': 'float', 'l_bound': 20.0, 'u_bound': 100.0},
    'time': {'type': 'int', 'l_bound': 5, 'u_bound': 60},
    'catalyst': {'type': 'categoric', 'categories': ['Pd', 'Ni', 'Cu']}
}

# Generate 20 optimal experimental points
df_experiments = cb.qmc_sampling(parameters, n_points=20)

# Visualize the distribution
cb.plot_qmc_sampling(df_experiments)

2. Bayesian Optimization

Once you have experimental data, find the optimal conditions:

# Define input features and the target column
inputs = ['temperature', 'time', 'catalyst']
output = 'yield'

# Run the complete optimization pipeline
results = cb.optimize_experiment(
    df=df_data, 
    inputs=inputs, 
    output=output,
    n_tuning_trials=50,
    n_opt_trials=100
)

# Access the best found parameters
print(f"Best configuration: {results['best_params']}")

📂 Project Structure

• src/chembayes/sampler.py: Tools for QMC sample generation.
• src/chembayes/optimizer.py: Bayesian optimization engine and Gaussian Processes.
• src/chembayes/__init__.py: Public API exposure.
• pyproject.toml: Modern package configuration and dependencies.

✉️ Contact & Contribution

Author: Jesus Alberto Martin del Campo
Email: [e-mail]
GitHub: jesusalmartin/chembayes