physbo.search.range package

Module contents

class physbo.search.range.History(dim: int)

Bases: object

export_all_sequence_best_fx()

Export all fx and actions at each sequence. (The total number of data is total_num_research.)

Returns:

• best_fx (numpy.ndarray)

• best_actions (numpy.ndarray)

export_sequence_best_fx()

Export best fx and X at each sequence (each call of write function).

Returns:

• best_fx (numpy.ndarray (num_runs)) – The best fx at each sequence.

• best_X (numpy.ndarray (num_runs, dim)) – The best X at each sequence.

load(filename)

Load the information of the history.

Parameters:

filename (str) – The name of the file which stores the information of the history

save(filename)

Save the information of the history.

Parameters:

filename (str) – The name of the file which stores the information of the history

show_search_results(N)

property time_get_action

property time_run_simulator

property time_total

property time_update_predictor

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.Policy(*, min_X=None, max_X=None, config=None, initial_data=None, comm=None)

Bases: object

Single objective Bayesian optimization with continuous search space

Parameters:

• 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.

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

• initial_data (tuple[np.ndarray, np.ndarray]) – The initial training datasets. The first elements is the array of inputs and the second is the array of values of objective functions

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

bayes_search(training=None, max_num_probes=None, num_search_each_probe=1, predictor=None, is_disp=True, simulator=None, score='TS', interval=0, num_rand_basis=0, optimizer=None)

Performing Bayesian optimization.

Parameters:

• training (physbo.Variable) – Training dataset.

• max_num_probes (int) – Maximum number of searching process by Bayesian optimization.

• num_search_each_probe (int) – Number of searching by Bayesian optimization at each process.

• predictor (predictor object) – Base class is defined in physbo.predictor. If None, blm_predictor is defined.

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

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

• score (str) – The type of aquision funciton. TS (Thompson Sampling), EI (Expected Improvement) and PI (Probability of Improvement) are available.

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

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

Returns:

history

Return type:

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

export_history()

Returning the information of the history.

export_predictor()

Returning the predictor dataset

export_training()

Returning the training dataset

get_post_fcov(xs, diag=True)

Calculate covariance of predictor (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) if diag=true, (num_points, num_points) if diag=false.

Return type:

numpy.ndarray

get_post_fmean(xs)

Calculate mean value of predictor (post distribution)

Parameters:

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

Returns:

fmean – Mean value of the post distribution. Returned shape is (num_points).

Return type:

numpy.ndarray

get_score(mode, *, xs=None, predictor=None, training=None, parallel=True, alpha=1)

Calcualte score (acquisition function)

Parameters:

• mode (str) – The type of aquisition funciton. TS, EI and PI are available. These functions are defined in score.py.

• xs (physbo.Variable or np.ndarray) – input parameters to calculate score

• predictor (predictor object) – predictor used to calculate score. If not given, self.predictor will be used.

• training (physbo.Variable) – Training dataset. If not given, self.training will be used.

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

• alpha (float) – Tuning parameter which is used if mode = TS. In TS, multi variation is tuned as np.random.multivariate_normal(mean, cov*alpha**2, size).

Returns:

f – Score defined in each mode.

Return type:

float or list of float

Raises:

RuntimeError – If both actions and xs are given

Notes

When neither actions nor xs are given, scores for actions not yet searched will be calculated.

When parallel is True, it is assumed that the function receives the same input (actions or xs) for all the ranks. If you want to split the input array itself, set parallel be False and merge results by yourself.

load(file_history, file_training=None, file_predictor=None)

Loading files about history, training and predictor.

Parameters:

• file_history (str) – The name of the file that stores the information of the history.

• file_training (str) – The name of the file that stores the training dataset.

• file_predictor (str) – The name of the file that stores the predictor dataset.

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

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=None, file_predictor=None)

Saving history, training and predictor into the corresponding files.

Parameters:

• file_history (str) – The name of the file that stores the information of the history.

• file_training (str) – The name of the file that stores the training dataset.

• file_predictor (str) – The name of the file that stores the predictor dataset.

set_seed(seed)

Setting a seed parameter for np.random.

Parameters:

• seed (int) – seed number

• -------

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. The negative energy of each search candidate (value of the objective function to be optimized).

• 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.