class sparse_ho.criterion.LogisticMulticlass(idx_train, idx_val, algo, idx_test=None)

Multiclass logistic loss.

idx_train: ndarray

indices of the training set

idx_val: ndarray

indices of the validation set

algo: instance of ``sparse_ho.base.AlgoModel``

A model that follows the sparse_ho API.

idx_test: ndarray

indices of the test set

dict_models: dict

dict with the models corresponding to each class.

__init__(idx_train, idx_val, algo, idx_test=None)

Initialize self. See help(type(self)) for accurate signature.


__init__(idx_train, idx_val, algo[, idx_test])

Initialize self.

get_val(model, X, y, log_alpha, …[, tol])

Get value of criterion.

get_val_grad(model, X, y, log_alpha, …[, tol])

Get value and gradient of criterion.

grad_total_loss(all_betas, all_jacs, X, Y)

Compute the gradient of the multiclass logistic loss.

proj_hyperparam(model, X, y, log_alpha)

Project hyperparameter on admissible range of values