def test_variational_study_optimize_and_extend_and_summary():
numpy.random.seed(63351)
study = VariationalStudy(
'study',
test_ansatz,
test_objective,
black_box_type=variational_black_box.XMON_SIMULATE_STATEFUL,
target=-10.5)
assert len(study.trial_results) == 0
assert study.target == -10.5
# Optimization run 1
result = study.optimize(OptimizationParams(test_algorithm), 'run1')
assert len(study.trial_results) == 1
assert isinstance(result, OptimizationTrialResult)
assert result.repetitions == 1
# Extend optimization run 1
study.extend_result('run1', repetitions=2)
assert study.trial_results['run1'].repetitions == 3
# Optimization run 2
study.optimize(OptimizationParams(test_algorithm),
repetitions=2,
use_multiprocessing=True)
result = study.trial_results[0]
assert len(study.trial_results) == 2
assert isinstance(result, OptimizationTrialResult)
assert result.repetitions == 2
# Optimization run 3
study.optimize(OptimizationParams(test_algorithm,
initial_guess=numpy.array([4.5, 8.8]),
initial_guess_array=numpy.array(
[[7.2, 6.3], [3.6, 9.8]]),
cost_of_evaluate=1.0),
reevaluate_final_params=True,
save_x_vals=True)
result = study.trial_results[1]
assert len(study.trial_results) == 3
assert isinstance(result, OptimizationTrialResult)
assert result.repetitions == 1
assert all(result.data_frame['optimal_parameters'].apply(
lambda x: XmonSimulateVariationalBlackBox(test_ansatz, test_objective).
evaluate(x)) == result.data_frame['optimal_value'])
assert isinstance(result.results[0].cost_spent, float)
# Try extending non-existent run
with pytest.raises(KeyError):
study.extend_result('run100')
# Check that getting a summary works
assert str(study).startswith('This study contains')
def test_variational_black_box_evaluate_with_cost():
black_box = XmonSimulateVariationalBlackBox(test_ansatz, test_objective)
numpy.testing.assert_allclose(
black_box.evaluate_with_cost(test_ansatz.default_initial_params(),
2.0), 0.0)
black_box_noisy = XmonSimulateVariationalBlackBox(test_ansatz,
test_objective_noisy)
numpy.random.seed(33534)
noisy_val = black_box_noisy.evaluate_with_cost(numpy.array([0.5, 0.0]),
10.0)
assert -0.8 < noisy_val < 1.2
def test_variational_black_box_evaluate():
black_box = XmonSimulateVariationalBlackBox(test_ansatz, test_objective)
numpy.testing.assert_allclose(
black_box.evaluate(test_ansatz.default_initial_params()), 0.0)
numpy.testing.assert_allclose(black_box.evaluate(numpy.array([0.0, 0.5])),
1.0)
def test_variational_black_box_noise_bounds():
black_box = XmonSimulateVariationalBlackBox(test_ansatz, test_objective)
assert black_box.noise_bounds(100) == (-numpy.inf, numpy.inf)
def test_variational_black_box_bounds():
black_box = XmonSimulateVariationalBlackBox(test_ansatz, test_objective)
assert black_box.bounds == test_study.ansatz.param_bounds()
def test_variational_black_box_dimension():
black_box = XmonSimulateVariationalBlackBox(test_ansatz, test_objective)
assert black_box.dimension == 2