ultraopt.optimizer.bo package

Submodules

ultraopt.optimizer.bo.config_evaluator module

class ultraopt.optimizer.bo.config_evaluator.ConfigEvaluator(budget2epm, budget, acq_func='EI', acq_func_params=<frozendict {}>, random_state=0)

基类：object

calc_weight(X_test, y_test)

update_weight(budget2weight)

class ultraopt.optimizer.bo.config_evaluator.EI(xi=0.01)

基类：object

class ultraopt.optimizer.bo.config_evaluator.LogEI(xi=0.01)

基类：object

ultraopt.optimizer.bo.etpe_opt module

class ultraopt.optimizer.bo.etpe_opt.ETPEOptimizer(top_n_percent=15, min_points_in_kde=2, bw_method='scott', cv_times=100, kde_sample_weight_scaler=None, gamma1=0.96, gamma2=3, max_bw_factor=4, min_bw_factor=1, max_try=3, min_points_in_model=20, min_n_candidates=8, n_candidates=None, n_candidates_factor=4, sort_by_EI=True, embedding_encoder='default')

基类：ultraopt.optimizer.base_opt.BaseOptimizer

get_available_max_budget()

get_available_min_budget()

property has_embedding_encoder

initialize(config_space, budgets=1, random_state=42, initial_points=None, budget2obvs=None)

tpe_sampling(epm, budget)

ultraopt.optimizer.bo.sampling_sort_opt module

class ultraopt.optimizer.bo.sampling_sort_opt.SamplingSortOptimizer(epm=None, config_transformer=None, use_local_search=False, loss_transformer='log_scaled', min_points_in_model=15, n_samples=5000, acq_func='LogEI', xi=0)

基类：ultraopt.optimizer.base_opt.BaseOptimizer

evaluate(configs: List[ConfigSpace.configuration_space.Configuration], budget, y_opt=None, return_loss_config_pairs=False, return_loss=False, return_loss_config=False)

get_available_max_budget()

get_local_search_initial_points(budget, num_points, additional_start_points)

get_y_opt(budget)

initialize(config_space, budgets=1, random_state=42, initial_points=None, budget2obvs=None)

local_search(start_points: List[ConfigSpace.configuration_space.Configuration], budget) → Tuple[numpy.ndarray, List[ConfigSpace.configuration_space.Configuration]]

transform(configs: List[ConfigSpace.configuration_space.Configuration])

ultraopt.optimizer.bo.tree_based_opt module

class ultraopt.optimizer.bo.tree_based_opt.ForestOptimizer(forest_type='ET', n_estimators=10, max_depth=None, max_features='auto', use_local_search=False, loss_transformer='log_scaled', min_points_in_model=20, n_samples=5000, acq_func='EI', xi=0, min_samples_leaf=1, min_weight_fraction_leaf=0, min_samples_split=2, max_leaf_nodes=None, min_impurity_decrease=0, bootstrap=True, oob_score=False, n_jobs=1, min_variance=0)

基类：ultraopt.optimizer.bo.sampling_sort_opt.SamplingSortOptimizer

initialize(config_space, budgets=1, random_state=42, initial_points=None, budget2obvs=None)

class ultraopt.optimizer.bo.tree_based_opt.GBRTOptimizer(n_jobs=1, use_local_search=False, loss_transformer='log_scaled', min_points_in_model=20, n_samples=5000, acq_func='EI', xi=0)

基类：ultraopt.optimizer.bo.sampling_sort_opt.SamplingSortOptimizer

initialize(config_space, budgets=1, random_state=42, initial_points=None, budget2obvs=None)

Module contents