@@ -973,7 +973,11 @@ <h1>Source code for botorch.optim.optimize</h1><div class="highlight"><pre>
973973 < span class ="n "> nonlinear_inequality_constraints</ span > < span class ="p "> :</ span > < span class ="nb "> list</ span > < span class ="p "> [</ span > < span class ="nb "> tuple</ span > < span class ="p "> [</ span > < span class ="n "> Callable</ span > < span class ="p "> ,</ span > < span class ="nb "> bool</ span > < span class ="p "> ]]</ span > < span class ="o "> |</ span > < span class ="kc "> None</ span > < span class ="o "> =</ span > < span class ="kc "> None</ span > < span class ="p "> ,</ span >
974974 < span class ="n "> post_processing_func</ span > < span class ="p "> :</ span > < span class ="n "> Callable</ span > < span class ="p "> [[</ span > < span class ="n "> Tensor</ span > < span class ="p "> ],</ span > < span class ="n "> Tensor</ span > < span class ="p "> ]</ span > < span class ="o "> |</ span > < span class ="kc "> None</ span > < span class ="o "> =</ span > < span class ="kc "> None</ span > < span class ="p "> ,</ span >
975975 < span class ="n "> batch_initial_conditions</ span > < span class ="p "> :</ span > < span class ="n "> Tensor</ span > < span class ="o "> |</ span > < span class ="kc "> None</ span > < span class ="o "> =</ span > < span class ="kc "> None</ span > < span class ="p "> ,</ span >
976+ < span class ="n "> return_best_only</ span > < span class ="p "> :</ span > < span class ="nb "> bool</ span > < span class ="o "> =</ span > < span class ="kc "> True</ span > < span class ="p "> ,</ span >
977+ < span class ="n "> gen_candidates</ span > < span class ="p "> :</ span > < span class ="n "> TGenCandidates</ span > < span class ="o "> |</ span > < span class ="kc "> None</ span > < span class ="o "> =</ span > < span class ="kc "> None</ span > < span class ="p "> ,</ span >
976978 < span class ="n "> ic_generator</ span > < span class ="p "> :</ span > < span class ="n "> TGenInitialConditions</ span > < span class ="o "> |</ span > < span class ="kc "> None</ span > < span class ="o "> =</ span > < span class ="kc "> None</ span > < span class ="p "> ,</ span >
979+ < span class ="n "> timeout_sec</ span > < span class ="p "> :</ span > < span class ="nb "> float</ span > < span class ="o "> |</ span > < span class ="kc "> None</ span > < span class ="o "> =</ span > < span class ="kc "> None</ span > < span class ="p "> ,</ span >
980+ < span class ="n "> retry_on_optimization_warning</ span > < span class ="p "> :</ span > < span class ="nb "> bool</ span > < span class ="o "> =</ span > < span class ="kc "> True</ span > < span class ="p "> ,</ span >
977981 < span class ="n "> ic_gen_kwargs</ span > < span class ="p "> :</ span > < span class ="nb "> dict</ span > < span class ="o "> |</ span > < span class ="kc "> None</ span > < span class ="o "> =</ span > < span class ="kc "> None</ span > < span class ="p "> ,</ span >
978982< span class ="p "> )</ span > < span class ="o "> -></ span > < span class ="nb "> tuple</ span > < span class ="p "> [</ span > < span class ="n "> Tensor</ span > < span class ="p "> ,</ span > < span class ="n "> Tensor</ span > < span class ="p "> ]:</ span >
979983< span class ="w "> </ span > < span class ="sa "> r</ span > < span class ="sd "> """Optimize over a list of fixed_features and returns the best solution.</ span >
@@ -1022,20 +1026,38 @@ <h1>Source code for botorch.optim.optimize</h1><div class="highlight"><pre>
10221026< span class ="sd "> transformations).</ span >
10231027< span class ="sd "> batch_initial_conditions: A tensor to specify the initial conditions. Set</ span >
10241028< span class ="sd "> this if you do not want to use default initialization strategy.</ span >
1029+ < span class ="sd "> return_best_only: If False, outputs the solutions corresponding to all</ span >
1030+ < span class ="sd "> random restart initializations of the optimization. Setting this keyword</ span >
1031+ < span class ="sd "> to False is only allowed for `q=1`. Defaults to True.</ span >
1032+ < span class ="sd "> gen_candidates: A callable for generating candidates (and their associated</ span >
1033+ < span class ="sd "> acquisition values) given a tensor of initial conditions and an</ span >
1034+ < span class ="sd "> acquisition function. Other common inputs include lower and upper bounds</ span >
1035+ < span class ="sd "> and a dictionary of options, but refer to the documentation of specific</ span >
1036+ < span class ="sd "> generation functions (e.g gen_candidates_scipy and gen_candidates_torch)</ span >
1037+ < span class ="sd "> for method-specific inputs. Default: `gen_candidates_scipy`</ span >
10251038< span class ="sd "> ic_generator: Function for generating initial conditions. Not needed when</ span >
10261039< span class ="sd "> `batch_initial_conditions` are provided. Defaults to</ span >
10271040< span class ="sd "> `gen_one_shot_kg_initial_conditions` for `qKnowledgeGradient` acquisition</ span >
10281041< span class ="sd "> functions and `gen_batch_initial_conditions` otherwise. Must be specified</ span >
10291042< span class ="sd "> for nonlinear inequality constraints.</ span >
1043+ < span class ="sd "> timeout_sec: Max amount of time optimization can run for.</ span >
1044+ < span class ="sd "> retry_on_optimization_warning: Whether to retry candidate generation with a new</ span >
1045+ < span class ="sd "> set of initial conditions when it fails with an `OptimizationWarning`.</ span >
10301046< span class ="sd "> ic_gen_kwargs: Additional keyword arguments passed to function specified by</ span >
10311047< span class ="sd "> `ic_generator`</ span >
10321048
10331049< span class ="sd "> Returns:</ span >
10341050< span class ="sd "> A two-element tuple containing</ span >
10351051
1036- < span class ="sd "> - a `q x d`-dim tensor of generated candidates.</ span >
1037- < span class ="sd "> - an associated acquisition value.</ span >
1052+ < span class ="sd "> - A tensor of generated candidates. The shape is</ span >
1053+ < span class ="sd "> -- `q x d` if `return_best_only` is True (default)</ span >
1054+ < span class ="sd "> -- `num_restarts x q x d` if `return_best_only` is False</ span >
1055+ < span class ="sd "> - a tensor of associated acquisition values of dim `num_restarts`</ span >
1056+ < span class ="sd "> if `return_best_only=False` else a scalar acquisition value.</ span >
10381057< span class ="sd "> """</ span >
1058+ < span class ="k "> if</ span > < span class ="ow "> not</ span > < span class ="n "> return_best_only</ span > < span class ="ow "> and</ span > < span class ="n "> q</ span > < span class ="o "> ></ span > < span class ="mi "> 1</ span > < span class ="p "> :</ span >
1059+ < span class ="k "> raise</ span > < span class ="ne "> NotImplementedError</ span > < span class ="p "> (</ span > < span class ="s2 "> "`return_best_only=False` is only supported for q=1."</ span > < span class ="p "> )</ span >
1060+
10391061 < span class ="k "> if</ span > < span class ="ow "> not</ span > < span class ="n "> fixed_features_list</ span > < span class ="p "> :</ span >
10401062 < span class ="k "> raise</ span > < span class ="ne "> ValueError</ span > < span class ="p "> (</ span > < span class ="s2 "> "fixed_features_list must be non-empty."</ span > < span class ="p "> )</ span >
10411063
@@ -1050,11 +1072,12 @@ <h1>Source code for botorch.optim.optimize</h1><div class="highlight"><pre>
10501072 < span class ="n "> ic_gen_kwargs</ span > < span class ="o "> =</ span > < span class ="n "> ic_gen_kwargs</ span > < span class ="ow "> or</ span > < span class ="p "> {}</ span >
10511073
10521074 < span class ="k "> if</ span > < span class ="n "> q</ span > < span class ="o "> ==</ span > < span class ="mi "> 1</ span > < span class ="p "> :</ span >
1075+ < span class ="n "> timeout_sec</ span > < span class ="o "> =</ span > < span class ="n "> timeout_sec</ span > < span class ="o "> /</ span > < span class ="nb "> len</ span > < span class ="p "> (</ span > < span class ="n "> fixed_features_list</ span > < span class ="p "> )</ span > < span class ="k "> if</ span > < span class ="n "> timeout_sec</ span > < span class ="k "> else</ span > < span class ="kc "> None</ span >
10531076 < span class ="n "> ff_candidate_list</ span > < span class ="p "> ,</ span > < span class ="n "> ff_acq_value_list</ span > < span class ="o "> =</ span > < span class ="p "> [],</ span > < span class ="p "> []</ span >
10541077 < span class ="n "> num_candidate_generation_failures</ span > < span class ="o "> =</ span > < span class ="mi "> 0</ span >
10551078 < span class ="k "> for</ span > < span class ="n "> fixed_features</ span > < span class ="ow "> in</ span > < span class ="n "> fixed_features_list</ span > < span class ="p "> :</ span >
10561079 < span class ="k "> try</ span > < span class ="p "> :</ span >
1057- < span class ="n "> candidate </ span > < span class ="p "> ,</ span > < span class ="n "> acq_value </ span > < span class ="o "> =</ span > < span class ="n "> optimize_acqf</ span > < span class ="p "> (</ span >
1080+ < span class ="n "> candidates </ span > < span class ="p "> ,</ span > < span class ="n "> acq_values </ span > < span class ="o "> =</ span > < span class ="n "> optimize_acqf</ span > < span class ="p "> (</ span >
10581081 < span class ="n "> acq_function</ span > < span class ="o "> =</ span > < span class ="n "> acq_function</ span > < span class ="p "> ,</ span >
10591082 < span class ="n "> bounds</ span > < span class ="o "> =</ span > < span class ="n "> bounds</ span > < span class ="p "> ,</ span >
10601083 < span class ="n "> q</ span > < span class ="o "> =</ span > < span class ="n "> q</ span > < span class ="p "> ,</ span >
@@ -1068,15 +1091,19 @@ <h1>Source code for botorch.optim.optimize</h1><div class="highlight"><pre>
10681091 < span class ="n "> post_processing_func</ span > < span class ="o "> =</ span > < span class ="n "> post_processing_func</ span > < span class ="p "> ,</ span >
10691092 < span class ="n "> batch_initial_conditions</ span > < span class ="o "> =</ span > < span class ="n "> batch_initial_conditions</ span > < span class ="p "> ,</ span >
10701093 < span class ="n "> ic_generator</ span > < span class ="o "> =</ span > < span class ="n "> ic_generator</ span > < span class ="p "> ,</ span >
1071- < span class ="n "> return_best_only</ span > < span class ="o "> =</ span > < span class ="kc "> True</ span > < span class ="p "> ,</ span >
1094+ < span class ="n "> return_best_only</ span > < span class ="o "> =</ span > < span class ="kc "> False</ span > < span class ="p "> ,</ span > < span class ="c1 "> # here we always return all candidates</ span >
1095+ < span class ="c1 "> # and filter later</ span >
1096+ < span class ="n "> gen_candidates</ span > < span class ="o "> =</ span > < span class ="n "> gen_candidates</ span > < span class ="p "> ,</ span >
1097+ < span class ="n "> timeout_sec</ span > < span class ="o "> =</ span > < span class ="n "> timeout_sec</ span > < span class ="p "> ,</ span >
1098+ < span class ="n "> retry_on_optimization_warning</ span > < span class ="o "> =</ span > < span class ="n "> retry_on_optimization_warning</ span > < span class ="p "> ,</ span >
10721099 < span class ="o "> **</ span > < span class ="n "> ic_gen_kwargs</ span > < span class ="p "> ,</ span >
10731100 < span class ="p "> )</ span >
10741101 < span class ="k "> except</ span > < span class ="n "> CandidateGenerationError</ span > < span class ="p "> :</ span >
10751102 < span class ="c1 "> # if candidate generation fails, we skip this candidate</ span >
10761103 < span class ="n "> num_candidate_generation_failures</ span > < span class ="o "> +=</ span > < span class ="mi "> 1</ span >
10771104 < span class ="k "> continue</ span >
1078- < span class ="n "> ff_candidate_list</ span > < span class ="o "> .</ span > < span class ="n "> append</ span > < span class ="p "> (</ span > < span class ="n "> candidate </ span > < span class ="p "> )</ span >
1079- < span class ="n "> ff_acq_value_list</ span > < span class ="o "> .</ span > < span class ="n "> append</ span > < span class ="p "> (</ span > < span class ="n "> acq_value </ span > < span class ="p "> )</ span >
1105+ < span class ="n "> ff_candidate_list</ span > < span class ="o "> .</ span > < span class ="n "> append</ span > < span class ="p "> (</ span > < span class ="n "> candidates </ span > < span class ="p "> )</ span >
1106+ < span class ="n "> ff_acq_value_list</ span > < span class ="o "> .</ span > < span class ="n "> append</ span > < span class ="p "> (</ span > < span class ="n "> acq_values </ span > < span class ="p "> )</ span >
10801107
10811108 < span class ="k "> if</ span > < span class ="nb "> len</ span > < span class ="p "> (</ span > < span class ="n "> ff_candidate_list</ span > < span class ="p "> )</ span > < span class ="o "> ==</ span > < span class ="mi "> 0</ span > < span class ="p "> :</ span >
10821109 < span class ="k "> raise</ span > < span class ="n "> CandidateGenerationError</ span > < span class ="p "> (</ span >
@@ -1091,16 +1118,25 @@ <h1>Source code for botorch.optim.optimize</h1><div class="highlight"><pre>
10911118 < span class ="n "> OptimizationWarning</ span > < span class ="p "> ,</ span >
10921119 < span class ="n "> stacklevel</ span > < span class ="o "> =</ span > < span class ="mi "> 3</ span > < span class ="p "> ,</ span >
10931120 < span class ="p "> )</ span >
1121+
10941122 < span class ="n "> ff_acq_values</ span > < span class ="o "> =</ span > < span class ="n "> torch</ span > < span class ="o "> .</ span > < span class ="n "> stack</ span > < span class ="p "> (</ span > < span class ="n "> ff_acq_value_list</ span > < span class ="p "> )</ span >
1095- < span class ="n "> best</ span > < span class ="o "> =</ span > < span class ="n "> torch</ span > < span class ="o "> .</ span > < span class ="n "> argmax</ span > < span class ="p "> (</ span > < span class ="n "> ff_acq_values</ span > < span class ="p "> )</ span >
1096- < span class ="k "> return</ span > < span class ="n "> ff_candidate_list</ span > < span class ="p "> [</ span > < span class ="n "> best</ span > < span class ="p "> ],</ span > < span class ="n "> ff_acq_values</ span > < span class ="p "> [</ span > < span class ="n "> best</ span > < span class ="p "> ]</ span >
1123+ < span class ="n "> max_res</ span > < span class ="o "> =</ span > < span class ="n "> torch</ span > < span class ="o "> .</ span > < span class ="n "> max</ span > < span class ="p "> (</ span > < span class ="n "> ff_acq_values</ span > < span class ="p "> ,</ span > < span class ="n "> dim</ span > < span class ="o "> =-</ span > < span class ="mi "> 1</ span > < span class ="p "> )</ span >
1124+ < span class ="n "> best_batch_idx</ span > < span class ="o "> =</ span > < span class ="n "> torch</ span > < span class ="o "> .</ span > < span class ="n "> argmax</ span > < span class ="p "> (</ span > < span class ="n "> max_res</ span > < span class ="o "> .</ span > < span class ="n "> values</ span > < span class ="p "> )</ span >
1125+ < span class ="n "> best_batch_candidates</ span > < span class ="o "> =</ span > < span class ="n "> ff_candidate_list</ span > < span class ="p "> [</ span > < span class ="n "> best_batch_idx</ span > < span class ="p "> ]</ span >
1126+ < span class ="n "> best_acq_values</ span > < span class ="o "> =</ span > < span class ="n "> ff_acq_value_list</ span > < span class ="p "> [</ span > < span class ="n "> best_batch_idx</ span > < span class ="p "> ]</ span >
1127+ < span class ="k "> if</ span > < span class ="ow "> not</ span > < span class ="n "> return_best_only</ span > < span class ="p "> :</ span >
1128+ < span class ="k "> return</ span > < span class ="n "> best_batch_candidates</ span > < span class ="p "> ,</ span > < span class ="n "> best_acq_values</ span >
1129+
1130+ < span class ="n "> best_idx</ span > < span class ="o "> =</ span > < span class ="n "> max_res</ span > < span class ="o "> .</ span > < span class ="n "> indices</ span > < span class ="p "> [</ span > < span class ="n "> best_batch_idx</ span > < span class ="p "> ]</ span >
1131+ < span class ="k "> return</ span > < span class ="n "> best_batch_candidates</ span > < span class ="p "> [</ span > < span class ="n "> best_idx</ span > < span class ="p "> ],</ span > < span class ="n "> best_acq_values</ span > < span class ="p "> [</ span > < span class ="n "> best_idx</ span > < span class ="p "> ]</ span >
10971132
10981133 < span class ="c1 "> # For batch optimization with q > 1 we do not want to enumerate all n_combos^n</ span >
10991134 < span class ="c1 "> # possible combinations of discrete choices. Instead, we use sequential greedy</ span >
11001135 < span class ="c1 "> # optimization.</ span >
11011136 < span class ="n "> base_X_pending</ span > < span class ="o "> =</ span > < span class ="n "> acq_function</ span > < span class ="o "> .</ span > < span class ="n "> X_pending</ span >
11021137 < span class ="n "> candidates</ span > < span class ="o "> =</ span > < span class ="n "> torch</ span > < span class ="o "> .</ span > < span class ="n "> tensor</ span > < span class ="p "> ([],</ span > < span class ="n "> device</ span > < span class ="o "> =</ span > < span class ="n "> bounds</ span > < span class ="o "> .</ span > < span class ="n "> device</ span > < span class ="p "> ,</ span > < span class ="n "> dtype</ span > < span class ="o "> =</ span > < span class ="n "> bounds</ span > < span class ="o "> .</ span > < span class ="n "> dtype</ span > < span class ="p "> )</ span >
11031138
1139+ < span class ="n "> timeout_sec</ span > < span class ="o "> =</ span > < span class ="n "> timeout_sec</ span > < span class ="o "> /</ span > < span class ="n "> q</ span > < span class ="k "> if</ span > < span class ="n "> timeout_sec</ span > < span class ="k "> else</ span > < span class ="kc "> None</ span >
11041140 < span class ="k "> for</ span > < span class ="n "> _</ span > < span class ="ow "> in</ span > < span class ="nb "> range</ span > < span class ="p "> (</ span > < span class ="n "> q</ span > < span class ="p "> ):</ span >
11051141 < span class ="n "> candidate</ span > < span class ="p "> ,</ span > < span class ="n "> acq_value</ span > < span class ="o "> =</ span > < span class ="n "> optimize_acqf_mixed</ span > < span class ="p "> (</ span >
11061142 < span class ="n "> acq_function</ span > < span class ="o "> =</ span > < span class ="n "> acq_function</ span > < span class ="p "> ,</ span >
@@ -1115,8 +1151,12 @@ <h1>Source code for botorch.optim.optimize</h1><div class="highlight"><pre>
11151151 < span class ="n "> nonlinear_inequality_constraints</ span > < span class ="o "> =</ span > < span class ="n "> nonlinear_inequality_constraints</ span > < span class ="p "> ,</ span >
11161152 < span class ="n "> post_processing_func</ span > < span class ="o "> =</ span > < span class ="n "> post_processing_func</ span > < span class ="p "> ,</ span >
11171153 < span class ="n "> batch_initial_conditions</ span > < span class ="o "> =</ span > < span class ="n "> batch_initial_conditions</ span > < span class ="p "> ,</ span >
1154+ < span class ="n "> gen_candidates</ span > < span class ="o "> =</ span > < span class ="n "> gen_candidates</ span > < span class ="p "> ,</ span >
11181155 < span class ="n "> ic_generator</ span > < span class ="o "> =</ span > < span class ="n "> ic_generator</ span > < span class ="p "> ,</ span >
11191156 < span class ="n "> ic_gen_kwargs</ span > < span class ="o "> =</ span > < span class ="n "> ic_gen_kwargs</ span > < span class ="p "> ,</ span >
1157+ < span class ="n "> timeout_sec</ span > < span class ="o "> =</ span > < span class ="n "> timeout_sec</ span > < span class ="p "> ,</ span >
1158+ < span class ="n "> retry_on_optimization_warning</ span > < span class ="o "> =</ span > < span class ="n "> retry_on_optimization_warning</ span > < span class ="p "> ,</ span >
1159+ < span class ="n "> return_best_only</ span > < span class ="o "> =</ span > < span class ="kc "> True</ span > < span class ="p "> ,</ span >
11201160 < span class ="p "> )</ span >
11211161 < span class ="n "> candidates</ span > < span class ="o "> =</ span > < span class ="n "> torch</ span > < span class ="o "> .</ span > < span class ="n "> cat</ span > < span class ="p "> ([</ span > < span class ="n "> candidates</ span > < span class ="p "> ,</ span > < span class ="n "> candidate</ span > < span class ="p "> ],</ span > < span class ="n "> dim</ span > < span class ="o "> =-</ span > < span class ="mi "> 2</ span > < span class ="p "> )</ span >
11221162 < span class ="n "> acq_function</ span > < span class ="o "> .</ span > < span class ="n "> set_X_pending</ span > < span class ="p "> (</ span >
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