objective¶

Objective Handling

MuyGPyS includes predefined objective functions and convenience functions for indicating them to optimization.

MuyGPyS.optimize.objective.make_loo_crossval_fn(opt_method, loss_method, loss_fn, kernel_fn, mean_fn, var_fn, sigma_sq_fn, pairwise_dists, crosswise_dists, batch_nn_targets, batch_targets)[source]¶

Prepare a leave-one-out cross validation function as a function purely of the hyperparameters to be optimized.

This function is designed for use with MuyGPyS.optimize.chassis.optimize_from_tensors(), and the format depends on the opt_method argument.

Parameters

opt_method (str) – The name of the optimization method to be utilized.
loss_method (str) – Indicates the loss function to be used.
kernel_fn (Callable) – A function that realizes kernel tensors given a list of the free parameters.
mean_fn (Callable) – A function that realizes MuyGPs posterior mean prediction given an epsilon value. The given value is unused if epsilon is fixed.
var_fn (Callable) – A function that realizes MuyGPs posterior variance prediction given an epsilon value. The given value is unused if epsilon is fixed.
sigma_sq_fn (Callable) – A function that realizes sigma_sq optimization given an epsilon value. The given value is unused if epsilon is fixed.
pairwise_dists (ndarray) – Distance tensor of floats of shape (batch_count, nn_count, nn_count) whose second two dimensions give the pairwise distances between the nearest neighbors of each batch element.
crosswise_dists (ndarray) – Distance matrix of floats of shape (batch_count, nn_count) whose rows give the distances between each batch element and its nearest neighbors.
batch_nn_targets (ndarray) – Tensor of floats of shape (batch_count, nn_count, response_count) containing the expected response for each nearest neighbor of each batch element.
batch_targets (ndarray) – Matrix of floats of shape (batch_count, response_count) whose rows give the expected response for each batch element.

Return type

Callable

Returns

A Callable objective_fn, whose format depends on opt_method.

MuyGPyS.optimize.objective.make_obj_fn(obj_method, opt_method, loss_method, *args)[source]¶

Prepare an objective function as a function purely of the hyperparameters to be optimized.