QctrlController

class qctrlmloop.QctrlController(interface, qctrl, optimizer, test_point_count=None, learner=None, training_run_count=0, interleaved_run_count=0, **kwargs)

Controller for optimizations that uses Boulder Opal automated closed-loop optimizers.

You can use this controller to integrate an experiment managed by M-LOOP to a Boulder Opal automated closed-loop optimizer. Notice that you need to set up an M-LOOP Interface to your experiment, initiate a Boulder Opal session, and define your choice of Boulder Opal automated closed-loop optimizer prior to using this class.

Parameters:

interface (mloop.interfaces.Interface) – The M-LOOP interface from where you obtain the cost value of the test points.
qctrl (qctrl.Qctrl) – The object representing the Boulder Opal session. You must initiate it before calling the controller.
optimizer (qctrl.types.closed_loop_optimization_step.Optimizer) – The Boulder Opal automated closed-loop optimizer that you want to use. It must be a valid optimizer object (containing either an initializer or a state from a previous optimization run), as described in the Boulder Opal reference documentation.
test_point_count (int, optional) – The requested number of test points that the Boulder Opal automated closed-loop optimizer generates at each step. If chosen, it must be greater than zero. This is a hint only. The Boulder Opal automated closed-loop optimizer might choose differently.
learner (mloop.learners.Learner, optional) – The M-LOOP Learner that this controller uses to obtain extra test points, before there are enough results to run a step of the Boulder Opal automated closed-loop optimizer. Defaults to None, in which case this controller uses a RandomLearner.
training_run_count (int, optional) – The minimum number of training points that the controller obtains before calling the Boulder Opal automated closed-loop optimizer. Defaults to 0, in which case the controller uses the minimum number of points that the Boulder Opal automated closed-loop optimizer requires.
interleaved_run_count (int, optional) – The minimum number of test points that the controller obtains from the learner between two calls of the Boulder Opal automated closed-loop optimizer, in addition to the points that Boulder Opal requested. Defaults to 0.
kwargs (dict) – All the extra arguments that the Controller class from M-LOOP accepts.

Methods

`check_end_conditions`	Check whether any of the end contions have been met.
`optimize`	Optimize the experiment.
`print_results`	Print results from optimization run to the logs
`save_archive`	Save the archive associated with the controller class.