Gumbi simplifies the steps needed to build a Gaussian Process model from tabular data. It takes care of shaping, transforming, and standardizing data as necessary while applying best practices and sensible defaults to the construction of the GP model itself. Taking inspiration from popular packages such as Bambi and Seaborn, Gumbi's aim is to allow quick iteration on both model structure and prediction visualization. Gumbi is primarily designed with the experimental scientist in mind, and enabling easy implementation of Bayesian Optimization into laboratory workflows. Gumbi is primarily built on top of Pymc, with a Botorch backend provided for acceleration and Bayesian Optimization.

Read in some data and store it as a Gumbi DataSet:

import gumbi as gmb
import seaborn as sns
cars = sns.load_dataset('mpg').dropna()
ds = gmb.DataSet(cars, outputs=['mpg', 'acceleration'], log_vars=['mpg', 'acceleration', 'weight', 'horsepower', 'displacement'])

Create a Gumbi GP object and fit a model that predicts mpg from horsepower:

gp = gmb.GP(ds)
gp.fit(outputs=['mpg'], continuous_dims=['horsepower']);

Make predictions and plot!

X = gp.prepare_grid()
y = gp.predict_grid()
gmb.ParrayPlotter(X, y).plot()

More complex GPs are also possible, such as correlated multi-input and multi-output systems. See the docs for more examples.

pip install gumbi

pip install git+git://github.com/JohnGoertz/Gumbi.git@develop

• Clone the repo and navigate to the new directory
  • git clone https://gitlab.com/JohnGoertz/gumbi gumbi
  • cd gumbi
• Create a new conda environment using mamba
  • conda install mamba
  • mamba install -f dev_environment.yaml
• Install gumbi via pip in editable/development mode
  • From within the gumbi repo
  • pip install --editable ./
• To update the gumbi module
  • From within the gumbi repo
  • git pull