def test_optimization_algorithm_optimize():
black_box = ExampleBlackBox()
algorithm = ExampleAlgorithm()
result = algorithm.optimize(black_box)
assert isinstance(result.optimal_value, float)
assert isinstance(result.optimal_parameters, numpy.ndarray)
def test_optimize_trial_result_data_methods():
result1 = OptimizationResult(optimal_value=5.7,
optimal_parameters=numpy.array([1.3, 8.7]),
num_evaluations=59,
cost_spent=3.1,
seed=60,
status=54,
message='ZibVTBNe8',
time=0.1)
result2 = OptimizationResult(optimal_value=4.7,
optimal_parameters=numpy.array([1.7, 2.1]),
num_evaluations=57,
cost_spent=9.3,
seed=51,
status=32,
message='cicCZ8iCg0D',
time=0.2)
trial = OptimizationTrialResult([result1, result2],
params=OptimizationParams(
ExampleAlgorithm()))
assert trial.repetitions == 2
assert trial.optimal_value == 4.7
numpy.testing.assert_allclose(trial.optimal_parameters,
numpy.array([1.7, 2.1]))
assert trial.num_evaluations_quantile() == 58
numpy.testing.assert_allclose(trial.optimal_value_quantile(), 5.2)
numpy.testing.assert_allclose(trial.cost_spent_quantile(), 6.2)
numpy.testing.assert_allclose(trial.time_spent_quantile(), 0.15)
def test_optimize_trial_result_init():
result1 = OptimizationResult(optimal_value=5.7,
optimal_parameters=numpy.array([1.3, 8.7]),
num_evaluations=59,
cost_spent=3.1,
seed=60,
status=54,
message='ZibVTBNe8')
result2 = OptimizationResult(optimal_value=4.7,
optimal_parameters=numpy.array([1.7, 2.1]),
num_evaluations=57,
cost_spent=9.3,
seed=51,
status=32,
message='cicCZ8iCg0D')
trial = OptimizationTrialResult([result1, result2],
params=OptimizationParams(
ExampleAlgorithm()))
assert all(trial.data_frame['optimal_value'] == [5.7, 4.7])
numpy.testing.assert_allclose(trial.data_frame['optimal_parameters'][0],
numpy.array([1.3, 8.7]))
numpy.testing.assert_allclose(trial.data_frame['optimal_parameters'][1],
numpy.array([1.7, 2.1]))
assert all(trial.data_frame['num_evaluations'] == [59, 57])
assert all(trial.data_frame['cost_spent'] == [3.1, 9.3])
assert all(trial.data_frame['seed'] == [60, 51])
assert all(trial.data_frame['status'] == [54, 32])
assert all(trial.data_frame['message'] == ['ZibVTBNe8', 'cicCZ8iCg0D'])
def test_optimization_trial_result_extend():
result1 = OptimizationResult(optimal_value=4.7,
optimal_parameters=numpy.array([2.3, 2.7]),
num_evaluations=39,
cost_spent=3.9,
seed=63,
status=44,
message='di382j2f')
result2 = OptimizationResult(optimal_value=3.7,
optimal_parameters=numpy.array([1.2, 3.1]),
num_evaluations=47,
cost_spent=9.9,
seed=21,
status=22,
message='i328d8ie3')
trial = OptimizationTrialResult([result1],
params=OptimizationParams(
ExampleAlgorithm()))
assert len(trial.results) == 1
assert trial.repetitions == 1
trial.extend([result2])
assert len(trial.results) == 2
assert trial.repetitions == 2
ExampleStudy,
ExampleStudyNoisy)
test_ansatz = ExampleAnsatz()
a, b = test_ansatz.qubits
preparation_circuit = cirq.Circuit.from_ops(cirq.X(a))
test_study = ExampleStudy('test_study',
test_ansatz,
preparation_circuit=preparation_circuit)
test_study_noisy = ExampleStudyNoisy('test_study_noisy',
test_ansatz,
preparation_circuit=preparation_circuit)
test_algorithm = ExampleAlgorithm()
def test_variational_study_circuit():
assert (test_study.circuit.to_text_diagram().strip() == """
0: ───X───X^theta0───@───X^theta0───M('all')───
│ │
1: ───────X^theta1───@───X^theta1───M──────────
""".strip())
def test_variational_study_num_params():
assert test_study.num_params == 2
def test_variational_study_ansatz_properties():
def test_optimization_algorithm_options():
algorithm = ExampleAlgorithm()
assert algorithm.options == {}
def test_optimization_algorithm_name():
algorithm = ExampleAlgorithm()
assert algorithm.name == 'ExampleAlgorithm'