Hypervolume Kriging Model Construction

pyKriging provides n-dimensional Kriging. Plotting facilities are included for 2D and 3D models (with MayaVI installed).

Automated Model Infill

Once an initial model is trained, pyKriging can recommend where your next sample should be placed in order to improve the model. pyKriging supports infill selection based a search for the maximum Mean Squared Error (MSE) or based on Expected Improvement. Mean squared error seeks to improve the overall accuracy of the model. Expected Improvement explores the likely minimums of the model, which is useful in many optimization activities. Please see the examples directory in the Github repository of examples on using the two types of model infill.

MSE Error Infill

Example infill based on MSE search

Expected Improvement Infill Example

Example infill based on an Expected Improvement search

Training History

The Snapshot feature allows for the monitoring of the Kriging training process. This allows a user to plot hyperparameter convergence, prediction accuracy (if an analytical function is provided) and prediction convergence. Please see the Github examples folder for more information on using the training history function.

Planned Improvements
  • Co-Kriging
  • Speed Improvement
  • Multi-processor support for training
  • Optimization tools for model exploitation