ctlearn_optimizer.optimizer

Module Contents

ctlearn_optimizer.optimizer.authkey = b'1234'

class ctlearn_optimizer.optimizer.Optimizer(opt_config)

Basic class for an optimizer.

Currently, only tree parzen estimators, random search, gaussian processes and genetic algorithm based optimization using Ray Tune is supported.

set_basic_config(self)

Set basic config and fixed hyperparameters in CTLearn config file.

create_space_hyperparams(self)

Create space of hyperparameters following required syntax.

Currently, only tree parzen estimators and random search spaces based on hyperopt, gaussian processes space based on skopt and genetic algorithm space based on ray.tune.automl are supported.

Returns

space of hyperparameters following the syntax required by the optimization algorithm.

Raises

NotImplementedError – if self.optimization_type is other than tree_parzen_estimators, random_search, gaussian_processes or genetic_algorithm.

create_optimization_algorithm(self, hyperparameter_space)

Create optimization algorithm for Ray Tune.

Currently, only tree parzen estimators, random search, gaussian processes and genetic algorithm based optimization using Ray Tune is supported.

Parameters

space (dict, list or ray.tune.automl.search_space.SearchSpace) – space of hyperparameters following the syntax required by the optimization algorithm.

Returns

Optimization algorithm for Ray Tune.

Raises

NotImplementedError – if self.optimization_type is other than tree_parzen_estimators, random_search, gaussian_processes or genetic_algorithm.

get_ctlearn_metric_to_optimize(self, hyperparams)

Evaluate a CTLearn model and return metric to optimize.

Parameters

hyperparams (dict) – set of hyperparameters to evaluate provided by the optimizer.

Returns

float – metric to optimize.

optimize(self, objective_function)

Start the optimization of objective_function using Ray Tune.

Currently, only tree parzen estimators, random search, gaussian processes and genetic algorithm based optimization using Ray Tune is supported.

Parameters

objective_function – function to optimize following the syntax:

def(hyperparams, reporter):
    ...
    # compute loss to optimize
    ...
    reporter(loss=loss)

Returns

ExperimentAnalysis – object used for analyzing results from Tune run().

optimize_ctlearn_model(self)

Start the optimization of a CTLearn model using Ray Tune.

Currently, only tree parzen estimators, random search, gaussian processes and genetic algorithm based optimization using Ray Tune is supported.

Returns

ExperimentAnalysis – object used for analyzing results from Tune run().

ctlearn_optimizer.optimizer.PARSER