Regression, Training, and Mapping APIs
The ciderpress.models package constructs Gaussian-process density
functionals and maps them to compact inference evaluators. The training
objects operate on integrated system and reaction observations.
ciderpress.models.train provides MOLGP and MOLGP2 containers.
The original MOLGP representation maps to
MappedXC; the newer MOLGP2
representation maps to MappedXC2. The
primary difference is that MOLGP2 and MappedXC2 use libxc as a backend
for baseline functionals, making it easier to construct full-XC functionals.
Covariance kernels build on
scikit-learn primitives. DFT kernels combine those covariance functions with
feature transforms, energy-density baselines, and sparse control points.
Mapping plans under ciderpress.models.kernel_plans select an inference
evaluator for a trained kernel. A validated mapped model preserves
predictions, feature derivatives, settings, functional composition, and
correction metadata. See
Model Objects and Mapping and Training and Mapping Workflow before using
the individual APIs below.