8.2.2. The Bayesian Extension and utilities

Summary of the main classes for the Bayesian Optimization extension

BayesianOptimization(dockarea, dashboard)	PyMoDAQ extension of the DashBoard to perform the optimization of a target signal taken form the detectors as a function of one or more parameters controlled by the actuators.
optimizers_base.models.OptimizerModelGeneric(...)
optimizers_base.models.OptimizerModelDefault(...)

8.2.2.1. The Extension module

class pymodaq.extensions.BayesianOptimization(dockarea, dashboard)

PyMoDAQ extension of the DashBoard to perform the optimization of a target signal taken form the detectors as a function of one or more parameters controlled by the actuators.

Methods

ini_custom_attributes()	Here you can reimplement specific attributes
thread_status(status)
update_after_actuators_changed(actuators)	Actions to do after the actuators have been updated
update_prediction_function()	Get the selected prediction function options and pass them to the Runner
value_changed(param)	to be subclassed for actions to perform when one of the param's value in self.settings is changed

config_saver
runner
set_algorithm
validate_config

ini_custom_attributes()

Here you can reimplement specific attributes

update_after_actuators_changed(actuators)

Actions to do after the actuators have been updated

update_prediction_function()

Get the selected prediction function options and pass them to the Runner

Should be reimplemented in real Optimizer implementation

value_changed(param)

to be subclassed for actions to perform when one of the param’s value in self.settings is changed

For instance:

if param.name() == 'do_something':
    if param.value():
        print('Do something')
        self.settings.child('main_settings', 'something_done').setValue(False)

Parameters:

param (Parameter) – the parameter whose value just changed

8.2.2.2. The Base Models

The models are shared by all the optimizer extensions (see pymodaq.extensions.optimizers_base).

class pymodaq.extensions.optimizers_base.models.OptimizerModelGeneric(optimization_controller)

Attributes:

optimization_algorithm

Methods

convert_input(measurements)	Convert the measurements in the units to be fed to the Optimisation Controller
convert_output(outputs[, best_individual])	Convert the output of the Optimisation Controller in units to be fed into the actuators :param outputs: output value from the controller from which the model extract a value of the same units as the actuators :type outputs: dict[str, Union[float, ndarray]] :param best_individual: the coordinates of the best individual so far :type best_individual: Optional[dict[str, float]]
has_fitness_observable()	Should return True if the model defined a 0D data to be used as fitness value
ini_model()	To be subclassed
runner_initialized()	To be subclassed
update_plots()	Called when updating the live plots
update_settings(param)	Get a parameter instance whose value has been modified by a user on the UI To be overwritten in child class

check_modules
ini_model_base
update_detector_names

convert_input(measurements)

Convert the measurements in the units to be fed to the Optimisation Controller

Parameters:

measurements (DataToExport) – data object exported from the detectors from which the model extract a float value (fitness) to be fed to the algorithm

Return type:

float

convert_output(outputs, best_individual=None)

Convert the output of the Optimisation Controller in units to be fed into the actuators :param outputs: output value from the controller from which the model extract a value of the same units as the actuators :type outputs: dict[str, Union[float, ndarray]] :param best_individual: the coordinates of the best individual so far :type best_individual: Optional[dict[str, float]]

Returns:

DataToActuatorOpti – attribute, either ‘rel’ for relative or ‘abs’ for absolute.

Return type:

DataToActuators

abstractmethod has_fitness_observable()

Should return True if the model defined a 0D data to be used as fitness value

Return type:

bool

ini_model()

To be subclassed

Initialize whatever is needed by your custom model

runner_initialized()

To be subclassed

Initialize whatever is needed by your custom model after the optimization runner is initialized

update_plots()

Called when updating the live plots

update_settings(param)

Get a parameter instance whose value has been modified by a user on the UI To be overwritten in child class

class pymodaq.extensions.optimizers_base.models.OptimizerModelDefault(optimization_controller)

Methods

convert_input(measurements)	Convert the measurements in the units to be fed to the Optimisation Controller
convert_output(outputs[, best_individual])	Convert the output of the Optimisation Controller in units to be fed into the actuators :param outputs: output value from the controller from which the model extract a value of the same units as the actuators :type outputs: dict[str, Union[float, ndarray]] :param best_individual: the coordinates of the best individual so far :type best_individual: Optional[dict[str, float]]
has_fitness_observable()	Should return True if the model defined a 0D data to be used as fitness value
ini_model()
update_settings(param)

optimize_from

convert_input(measurements)

Convert the measurements in the units to be fed to the Optimisation Controller

Parameters:

measurements (DataToExport) – data object exported from the detectors from which the model extract a float value (fitness) to be fed to the algorithm

Return type:

float

convert_output(outputs, best_individual=None)

Convert the output of the Optimisation Controller in units to be fed into the actuators :param outputs: output value from the controller from which the model extract a value of the same units as the actuators :type outputs: dict[str, Union[float, ndarray]] :param best_individual: the coordinates of the best individual so far :type best_individual: Optional[dict[str, float]]

Returns:

DataToActuatorOpti – attribute, either ‘rel’ for relative or ‘abs’ for absolute.

Return type:

DataToActuators

has_fitness_observable()

Should return True if the model defined a 0D data to be used as fitness value

Return type:

bool