LineSamplingGenerator#

class optimas.generators.LineSamplingGenerator(vocs, n_steps)#

Sample an n-dimensional space one parameter at a time.

This generator samples the given objectives along n dimensions, where n is the number of varying_parameters, by varying only one parameter at a time. Along each direction \(i\) (i.e., along each varying parameter), the space is divided in \(n_\mathrm{steps,i}\) evenly spaced steps, resulting in a total number of evaluations \(\sum_i n_\mathrm{steps,i}\).

Since only one parameter is varied at a time, a default value that will be used when a parameter is not being varied needs to be provided for all varying parameters.

Parameters:

vocsVOCS: VOCS object specifying variables, objectives, constraints, and observables.
n_stepsndarray or list of int: A 1D array or list with the number of steps along each direction.

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.
`finalize`()	Perform any work required to close down 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.
`returns_id`	Indicates whether this generator returns IDs with the suggested points.
`use_cuda`	Get whether the generator can use CUDA.
`varying_parameters`	Get the list of varying parameters.
`vocs`	Get the VOCS object.