def test_get_best_pred(lower_is_better, expected_best): results = pandas.DataFrame({ 'x': numpy.linspace(0, 1, 10), 'Objective': numpy.linspace(0, 1, 10), 'Status': ['COMPLETED'] * 10 }) params = [sherpa.Continuous('x', [0, 1])] algorithm = GPyOpt(num_initial_data_points=2) algorithm.get_suggestion(results=results, parameters=params, lower_is_better=lower_is_better) best_params = algorithm.get_best_pred(results=results, parameters=params, lower_is_better=lower_is_better) assert best_params['x'] == expected_best
def test_types_are_correct(parameters, results): gpyopt = GPyOpt(max_concurrent=1) suggestion = gpyopt.get_suggestion(parameters, results, True) assert isinstance(suggestion['dropout'], float) assert isinstance(suggestion['lr'], float) assert isinstance(suggestion['num_hidden'], int) assert isinstance(suggestion['activation'], str)
def test_overall(): gpyopt = GPyOpt(max_concurrent=1) parameters, results, lower_is_better = sherpa.algorithms.get_sample_results_and_params( ) for i in range(51): suggestion = gpyopt.get_suggestion( parameters, results.loc[results['Trial-ID'] < i, :], lower_is_better) print(suggestion)