AxClientGenerator#

class optimas.generators.AxClientGenerator(ax_client, abandon_failed_trials=True, gpu_id=0, dedicated_resources=False, save_model=True, model_save_period=5, model_history_dir='model_history')#

Bayesian optimization generator with a user-defined AxClient.

This generator allows the user to provide a custom AxClient, allowing for maximum control of the optimization.

For this generator there is no need to provide the vocs. The generator builds a VOCS (variables, objectives, constraints, and observables) directly from the AxClient.

Parameters:

ax_clientAxClient: The Ax client from which the trials will be generated.
abandon_failed_trialsbool, optional: Whether failed trials should be abandoned (i.e., not suggested again). By default, True.
gpu_idint, optional: The ID of the GPU in which to run the generator. By default, 0. This parameter will only have an effect if any GenerationStep in the AxClient uses a GPU.
dedicated_resourcesbool, optional: Whether to allocated dedicated resources (e.g., the GPU) for the generator. These resources will not be available to the simulation workers. By default, False. This parameter will only have an effect if any GenerationStep in the AxClient uses a GPU.
save_modelbool, optional: Whether to save the optimization model (in this case, the Ax client) to disk. By default True.
model_save_periodint, optional: Periodicity, in number of evaluated Trials, with which to save the model to disk. By default, 5.
model_history_dirstr, optional: Name of the directory in which the model will be saved. By default, 'model_history'.

Notes

Outcome constraints are passed into VOCS as constraints and are correctly used by the AxClient. The optimas log/display does not yet show constraints separately; constraint metrics may appear as extra columns.

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.
`fix_value`(var_name, value)	Fix a parameter to a specific value.
`free_value`(var_name)	Free a previously fixed parameter.
`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.
`update_range`(var_name, lower_bound, upper_bound)	Update the range of a parameter.

Attributes

`analyzed_parameters`	Get the list of analyzed parameters.
`ax_client`	Get the underlying AxClient.
`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.
`model`	Get access to the underlying model using an AxModelManager.
`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.