RandomSamplingGenerator

class optimas.generators.RandomSamplingGenerator(varying_parameters, objectives, distribution='uniform', seed=None, analyzed_parameters=None)

Sample an n-dimensional space with random distributions.

This generator uses a random distribution to generate a sample of configurations where to evaluate the given objectives.

Parameters:

varying_parameterslist of VaryingParameter

List of input parameters to vary.

objectiveslist of Objective

List of optimization objectives.

distribution{‘uniform’, ‘normal’}, optional

The random distribution to use. The 'uniform' option draws samples from a uniform distribution within the lower \(l_b\) and upper \(u_b\) bounds of each parameter. The 'normal' option draws samples from a normal distribution that, for each parameter, is centered at \(c = (l_b + u_b)/2\) with standard deviation \(\sigma = u_b - c\). By default, 'uniform'.

seedint, optional

Seed to initialize the random generator.

analyzed_parameterslist of Parameter, optional

List of parameters to analyze at each trial, but which are not optimization objectives. By default None.

Methods

ask_trials(n_trials)	Ask the generator to suggest the next n_trials to evaluate.
attach_trials(trial_data[, ...])	Manually add a list of trials to the generator.
get_gen_specs(sim_workers, run_params, max_evals)	Get the libEnsemble gen_specs.
get_libe_specs()	Get the libEnsemble libe_specs.
get_trial(trial_index)	Get trial by index.
ignore_trials(trials)	Ignore trials as determined by the generator.
incorporate_history(history)	Incorporate past history into the generator.
ingest(results)	Send the results of evaluations to the generator.
mark_trial_as_failed(trial_index)	Mark an already evaluated trial as failed.
save_model_to_file()	Save model to file.
suggest(num_points)	Request the next set of points to evaluate.
tell_trials(trials[, allow_saving_model])	Give trials back to generator once they have been evaluated.
update_parameter(parameter)	Update a varying parameter of the generator.

Attributes

analyzed_parameters	Get the list of analyzed parameters.
constraints	Get the list of constraints.
dedicated_resources	Get whether the generator has dedicated resources allocated.
gpu_id	Get the ID of the GPU used by the generator.
n_completed_trials	Get the number of successfully evaluated trials.
n_evaluated_trials	Get the number of evaluated trials.
n_failed_trials	Get the number of unsuccessfully evaluated trials.
n_given_trials	Get the number of trials given for evaluation.
n_queued_trials	Get the number of trials queued for evaluation.
objectives	Get the list of objectives.
use_cuda	Get whether the generator can use CUDA.
varying_parameters	Get the list of varying parameters.