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)
