physbo.search.range_multi package

Module contents

class physbo.search.range_multi.History(num_objectives, dim)

Bases: object

export_pareto_front()

export_valid()

Export the valid (successfully evaluated) observations.

Returns:

• X (numpy.ndarray) – Inputs of the valid observations (N_valid x dim).

• fx (numpy.ndarray) – Objective values of the valid observations (N_valid x num_objectives).

load(filename)

save(filename)

show_search_results_mo(N, disp_pareto_set=False)

property time_get_action

property time_run_simulator

property time_total

property time_update_predictor

property valid_mask

Mask of valid observations (True) vs failed ones (False).

An observation is failed when any of its objective values is not finite (NaN or +-Inf). Failed observations are kept in the history but excluded from the training data and the Pareto front.

Return type:

numpy.ndarray of bool, shape (total_num_search,)

write(t, action_X, time_total=None, time_update_predictor=None, time_get_action=None, time_run_simulator=None)

Overwrite fx and action_X by t and action_X.

Parameters:

• t (numpy.ndarray) – N dimensional array. The negative energy of each search candidate (value of the objective function to be optimized).

• action_X (numpy.ndarray) – N x d dimensional array. The input of each search candidate.

• time_total (numpy.ndarray) – N dimenstional array. The total elapsed time in each step. If None (default), filled by 0.0.

• time_update_predictor (numpy.ndarray) – N dimenstional array. The elapsed time for updating predictor (e.g., learning hyperparemters) in each step. If None (default), filled by 0.0.

• time_get_action (numpy.ndarray) – N dimenstional array. The elapsed time for getting next action in each step. If None (default), filled by 0.0.

• time_run_simulator (numpy.ndarray) – N dimenstional array. The elapsed time for running the simulator in each step. If None (default), filled by 0.0.

class physbo.search.range_multi.Policy(num_objectives, *, min_X=None, max_X=None, constraint=None, comm=None, config=None, initial_data=None, rng=None)

Bases: Policy

Multi objective Bayesian optimization with continuous search space

Parameters:

• num_objectives (int) – The number of objective functions.

• min_X (numpy.ndarray) – The minimum value of each dimension of the search space.

• max_X (numpy.ndarray) – The maximum value of each dimension of the search space.

• constraint (callable, optional) – Inequality constraints on the search space, as in physbo.search.range.Policy: a function from an array of shape (N, d) to a boolean array of N elements (True for feasible points). Only feasible points are proposed. Not saved by save(); pickled by save_checkpoint(), so it must be picklable.

• comm (MPI.Comm, optional) – MPI Communicator

• config (SetConfig object (physbo.misc.SetConfig))

• initial_data (tuple[np.ndarray, np.ndarray]) – The initial training datasets. The first element is the array of inputs and the second is the array of values of the objective functions, of shape (N, num_objectives). These points are not checked against the constraint.

• rng (None, "legacy", int, or numpy.random.Generator, optional) – Random number generator specification (default: “legacy”). See physbo.search.discrete.Policy for details.

bayes_search(training_list=None, max_num_probes=None, num_search_each_probe=1, predictor_list=None, is_disp=True, disp_pareto_set=False, simulator=None, score='HVPI', interval=0, num_rand_basis=0, optimizer=None, unify_method=None, ard=False)

Performing Bayesian optimization by using multi objective function

Parameters:

• training_list (list of physbo.Variable, optional) – The training datasets.

• max_num_probes (int, optional) – The maximum number of searching process by Bayesian optimization.

• num_search_each_probe (int, optional) – The number of searching by Bayesian optimization at each process.

• predictor_list (list of predictor objects, optional) – The predictor objects.

• is_disp (bool, optional) – If true, process messages are outputted.

• disp_pareto_set (bool, optional) – If true, Pareto set is displayed.

• simulator (callable, optional) – The simulator function.

• score (str or callable) – EHVI, HVPI, TS, or a callable called positionally as score(predictor_list, training, test, pareto) returning a numpy.ndarray of shape (test.X.shape[0],), with larger values preferred. pareto is self.history.pareto and must be treated as read-only; marginal scores do not update it with virtual data. Empty test data skips the callable. Under MPI, the callable evaluates local points and must not perform collective communication.

• interval (int, optional) – The interval number of learning the hyper parameter. If you set the negative value to interval, the hyper parameter learning is not performed. If you set zero to interval, the hyper parameter learning is performed only at the first step.

• num_rand_basis (int, optional) – The number of basis function. If you choose 0, ordinary Gaussian process run.

• optimizer (Optimizer object, optional) – Optimizer object for optimizing the acquisition function. If None, the default optimizer is used.

• unify_method (callable, optional) – This is for compatibility with the unified-optimization Policies. This is not used.

• ard (bool, optional) – If True, use Automatic Relevance Determination (ARD) for the Gaussian kernel. Default is False.

Returns:

history

Return type:

history object (physbo.search.range_multi.results.history)

get_kernel_length_scale()

Return the Gaussian kernel length scale(s) for each objective.

Returns:

List of length num_objectives; each element is the length scale(s) for that objective’s GP, or None if not available.

Return type:

list of numpy.ndarray or None

get_permutation_importance(n_perm: int, split_features_parallel=False)

Calculate permutation importance of models

Parameters:

• n_perm (int) – The number of permutations

• split_features_parallel (bool) – If true, split features in parallel.

Returns:

• importance_mean (numpy.ndarray) – importance_mean (num_parameters, num_objectives)

• importance_std (numpy.ndarray) – importance_std (num_parameters, num_objectives)

get_post_fcov(xs, diag=True)

Calculate covariance of predictors (post distribution)

Parameters:

• xs (physbo.Variable or np.ndarray) – input parameters to calculate covariance shape is (num_points, num_parameters)

• diag (bool) – If true, only variances (diagonal elements) are returned.

Returns:

fcov – Covariance matrix of the post distribution. Returned shape is (num_points, num_objectives) if diag=true, (num_points, num_points, num_objectives) if diag=false.

Return type:

numpy.ndarray

get_post_fmean(xs)

Calculate mean value of predictors (post distribution)

Parameters:

• xs (physbo.Variable or np.ndarray) – input parameters to calculate covariance shape is (num_points, num_parameters)

• diag (bool) – If true, only variances (diagonal elements) are returned.

Returns:

fcov – Covariance matrix of the post distribution. Returned shape is (num_points, num_objectives).

Return type:

numpy.ndarray

get_score(mode, *, xs=None, predictor_list=None, training_list=None, pareto=None, parallel=True, alpha=1)

Calculate multi-objective scores (acquisition function).

Parameters:

• mode (str or callable) – EHVI, HVPI, TS, or a callable called positionally as mode(predictor_list, training, test, pareto) returning a numpy.ndarray of shape (test.X.shape[0],), with larger values preferred. Empty test data skips the callable but still participates in MPI gathering. The callable must not perform collective communication. alpha and self.TS_candidate_num apply only to string modes.

• xs (physbo.Variable or numpy.ndarray, optional) – Input parameters to calculate scores.

• predictor_list (list of predictor objects, optional) – Predictors for each objective. Defaults to self.predictor_list.

• training_list (physbo.Variable, optional) – Training dataset containing all objectives. Defaults to self.training.

• pareto (Pareto, optional) – Passed unchanged when provided; otherwise self.history.pareto is used. Treat it as read-only. Marginal scores use the observed history’s Pareto front without adding virtual observations.

• parallel (bool) – Calculate scores in parallel by MPI (default: True).

• alpha (float) – Posterior sampling scale for string “TS” (default: 1). Ignored for callable modes.

Returns:

f – Acquisition scores of shape (num_points,).

Return type:

numpy.ndarray

Raises:

RuntimeError – If xs is not given.

Notes

When parallel is True, all ranks must receive the same input. To supply separate local inputs, set parallel=False and gather results yourself.

load(file_history, file_training_list=None, file_predictor_list=None)

Load the history, the training data and the predictors from files.

The constraint is not restored by load(); the policy keeps the one given to its constructor.

load_predictor_list(file_name)

load_training_list(file_name)

random_search(max_num_probes, num_search_each_probe=1, simulator=None, is_disp=True, disp_pareto_set=False)

Performing random search.

Parameters:

• max_num_probes (int) – Maximum number of random search process.

• num_search_each_probe (int) – Number of search at each random search process.

• simulator (callable) – Callable (function or object with __call__) from action to t Here, action is an integer which represents the index of the candidate.

• is_disp (bool) – If true, process messages are outputted.

Returns:

history

Return type:

history object (physbo.search.discrete.results.history)

save(file_history, file_training_list=None, file_predictor_list=None)

Save the history, the training data and the predictors into files.

The constraint is not saved; give it to the constructor of the policy that loads the files.

save_predictor_list(file_name)

save_training_list(file_name)

write(X, t, time_total=None, time_update_predictor=None, time_get_action=None, time_run_simulator=None)

Writing history (update history, not output to a file).

Parameters:

• X (numpy.ndarray) – N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of each search candidate.

• t (numpy.ndarray) – N dimensional array (1D) or N x 1 dimensional array (2D). The negative energy of each search candidate (value of the objective function to be optimized). Will be normalized to (N, 1) shape internally. All the values must be finite. Failed evaluations are not supported in continuous search spaces.

• time_total (numpy.ndarray) – N dimenstional array. The total elapsed time in each step. If None (default), filled by 0.0.

• time_update_predictor (numpy.ndarray) – N dimenstional array. The elapsed time for updating predictor (e.g., learning hyperparemters) in each step. If None (default), filled by 0.0.

• time_get_action (numpy.ndarray) – N dimenstional array. The elapsed time for getting next action in each step. If None (default), filled by 0.0.

• time_run_simulator (numpy.ndarray) – N dimenstional array. The elapsed time for running the simulator in each step. If None (default), filled by 0.0.

Raises:

ValueError – If t contains a non-finite value (NaN or +-Inf). Nothing is written to the history in this case.