Kernel Summary
The kernel_summary module parses GPy kernel trees into human-readable text.
It translates raw hyperparameter values (lengthscales, variances, noise levels)
into plain-language interpretations and displays composite kernels as an ASCII
hierarchy. Interpretation behaviour is controlled by InterpretationConfig,
which lets you adjust the numeric thresholds to match your data’s scale.
When to use: after training, to understand what structure the kernel has learned before deploying predictions or debugging unexpected model behaviour.
import gpclarity
summary = gpclarity.summarize_kernel(model)
print(summary["interpretation"]) # plain-language description
for comp in summary["components"]:
print(comp["name"], comp["lengthscale_interpretation"])
gpclarity.format_kernel_tree(model) # ASCII hierarchy to stdout