Scikit-learn models

GPSat model based on the scikit-learn Gaussian process regression module. This is a fairly lightweight model intended to be used for simpler problems. No GPU capability.

class GPSat.models.sklearn_models.sklearnGPRModel(data=None, coords_col=None, obs_col=None, coords=None, obs=None, coords_scale=None, obs_scale=None, obs_mean=None, verbose=True, *, kernel='Matern', kernel_kwargs=None, mean_value=None, kernel_variance=1.0, likelihood_variance=None, param_bounds=None, **kwargs)

Bases: BaseGPRModel

get_kernel_variance()

get_lengthscales()

get_likelihood_variance()

get_objective_function_value()

get the marginal log likelihood

optimise_parameters(opt=None, **kwargs)

Method to fit data on model by optimising (hyper/variational)-parameters. Any inheriting class should override this method.

property param_names: list

Property method that returns the names of parameters in a list. Any inheriting class should override this method.

Each parameter name should have a get_* and set_* method. e.g. if param_names = ['A', 'B'] then methods get_A, set_A, get_B, set_B should be defined.

Additionally, one can specify a set_*_constraints method that imposes constraints on the parameters during training, if applicable.

predict(coords, full_cov=False, apply_scale=True)

method to generate prediction at given coords

set_kernel_variance(kernel_variance)

set_kernel_variance_constraints(low, high, move_within_tol=True, tol=1e-08, scale=False)

set_lengthscales(lengthscales)

set_lengthscales_constraints(low, high, move_within_tol=True, tol=1e-08, scale=False)

set_likelihood_variance(likelihood_variance)