otkerneldesign 0.1.4

Design of experiments based on kernel methods

otkerneldesign

This Python module generates designs of experiments based on kernel methods such as Kernel Herding and Support Points with the classes:

• KernelHerding
• KernelHerdingTensorized
• GreedySupportPoints

Additionally, optimal weights for quadrature and validation designs are provided by the classes:

• BayesianQuadratureWeighting
• TestSetWeighting

Installation

~$ pip install otkerneldesign

Documentation & references

• Package documentation
• Usage in Machine Learning model validation
• Support points
• Kernel herding

Example

>>> import openturns as ot
>>> import otkerneldesign as otkd
>>> # Distribution definition
>>> distribution = ot.ComposedDistribution([ot.Normal(0.5, 0.1)] * 2)
>>> dimension = distribution.getDimension()
>>> # Kernel definition
>>> ker_list = [ot.MaternModel([0.1], [1.0], 2.5)] * dimension
>>> kernel = ot.ProductCovarianceModel(ker_list)
>>> # Kernel herding design
>>> kh = otkd.KernelHerding(kernel=kernel, distribution=distribution)
>>> kh_design, kh_indices = kh.select_design(size=20)

Authors

• Elias Fekhari
• Joseph Muré