def test_qsvm_binary(self, use_circuits):
""" QSVM Binary test """
ref_kernel_training = np.array([[1., 0.85366667, 0.12341667, 0.36408333],
[0.85366667, 1., 0.11141667, 0.45491667],
[0.12341667, 0.11141667, 1., 0.667],
[0.36408333, 0.45491667, 0.667, 1.]])
ref_kernel_testing = np.array([[0.14316667, 0.18208333, 0.4785, 0.14441667],
[0.33608333, 0.3765, 0.02316667, 0.15858333]])
# ref_alpha = np.array([0.36064489, 1.49204209, 0.0264953, 1.82619169])
ref_alpha = np.array([0.34903335, 1.48325498, 0.03074852, 1.80153981])
# ref_bias = np.array([-0.03380763])
ref_bias = np.array([-0.03059226])
ref_support_vectors = np.array([[2.95309709, 2.51327412], [3.14159265, 4.08407045],
[4.08407045, 2.26194671], [4.46106157, 2.38761042]])
backend = BasicAer.get_backend('qasm_simulator')
num_qubits = 2
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2,
entangler_map=[[0, 1]])
if use_circuits:
x = ParameterVector('x', num_qubits)
feature_map = feature_map.construct_circuit(x)
feature_map.ordered_parameters = list(x)
svm = QSVM(feature_map, self.training_data, self.testing_data, None)
quantum_instance = QuantumInstance(backend,
shots=self.shots,
seed_simulator=self.random_seed,
seed_transpiler=self.random_seed)
try:
result = svm.run(quantum_instance)
np.testing.assert_array_almost_equal(
result['kernel_matrix_training'], ref_kernel_training, decimal=1)
np.testing.assert_array_almost_equal(
result['kernel_matrix_testing'], ref_kernel_testing, decimal=1)
self.assertEqual(len(result['svm']['support_vectors']), 4)
np.testing.assert_array_almost_equal(
result['svm']['support_vectors'], ref_support_vectors, decimal=4)
np.testing.assert_array_almost_equal(result['svm']['alphas'], ref_alpha, decimal=8)
np.testing.assert_array_almost_equal(result['svm']['bias'], ref_bias, decimal=8)
self.assertEqual(result['testing_accuracy'], 0.5)
except NameError as ex:
self.skipTest(str(ex))
def test_qsvm_multiclass_error_correcting_code(self, use_circuits):
""" QSVM Multiclass error Correcting Code test """
training_input = {'A': np.asarray([[0.6560706, 0.17605998], [0.25776033, 0.47628296],
[0.8690704, 0.70847635]]),
'B': np.asarray([[0.38857596, -0.33775802], [0.49946978, -0.48727951],
[0.49156185, -0.3660534]]),
'C': np.asarray([[-0.68088231, 0.46824423], [-0.56167659, 0.65270294],
[-0.82139073, 0.29941512]])}
test_input = {'A': np.asarray([[0.57483139, 0.47120732], [0.48372348, 0.25438544],
[0.48142649, 0.15931707]]),
'B': np.asarray([[-0.06048935, -0.48345293], [-0.01065613, -0.33910828],
[0.06183066, -0.53376975]]),
'C': np.asarray([[-0.74561108, 0.27047295], [-0.69942965, 0.11885162],
[-0.66489165, 0.1181712]])}
total_array = np.concatenate((test_input['A'], test_input['B'], test_input['C']))
aqua_globals.random_seed = self.random_seed
feature_map = SecondOrderExpansion(feature_dimension=get_feature_dimension(training_input),
depth=2,
entangler_map=[[0, 1]])
if use_circuits:
x = ParameterVector('x', feature_map.feature_dimension)
feature_map = feature_map.construct_circuit(x)
feature_map.ordered_parameters = list(x)
try:
svm = QSVM(feature_map, training_input, test_input, total_array,
multiclass_extension=ErrorCorrectingCode(code_size=5))
quantum_instance = QuantumInstance(BasicAer.get_backend('qasm_simulator'),
shots=self.shots,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = svm.run(quantum_instance)
self.assertAlmostEqual(result['testing_accuracy'], 0.444444444, places=4)
self.assertEqual(result['predicted_classes'], ['A', 'A', 'C', 'A',
'A', 'A', 'A', 'C', 'C'])
except NameError as ex:
self.skipTest(str(ex))
def test_qsvm_setup_data(self, use_circuits):
""" QSVM Setup Data test """
ref_kernel_testing = np. array([[0.1443953, 0.18170069, 0.47479649, 0.14691763],
[0.33041779, 0.37663733, 0.02115561, 0.16106199]])
ref_support_vectors = np.array([[2.95309709, 2.51327412], [3.14159265, 4.08407045],
[4.08407045, 2.26194671], [4.46106157, 2.38761042]])
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = 2
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2,
entangler_map=[[0, 1]])
if use_circuits:
x = ParameterVector('x', num_qubits)
feature_map = feature_map.construct_circuit(x)
feature_map.ordered_parameters = list(x)
try:
svm = QSVM(feature_map)
svm.setup_training_data(self.training_data)
svm.setup_test_data(self.testing_data)
quantum_instance = QuantumInstance(backend, seed_transpiler=self.random_seed,
seed_simulator=self.random_seed)
result = svm.run(quantum_instance)
np.testing.assert_array_almost_equal(
result['kernel_matrix_testing'], ref_kernel_testing, decimal=4)
self.assertEqual(len(result['svm']['support_vectors']), 4)
np.testing.assert_array_almost_equal(
result['svm']['support_vectors'], ref_support_vectors, decimal=4)
self.assertEqual(result['testing_accuracy'], 0.5)
except NameError as ex:
self.skipTest(str(ex))
def test_qsvm_binary_directly_statevector(self, use_circuits):
""" QSVM Binary Directly Statevector test """
ref_kernel_testing = np. array([[0.1443953, 0.18170069, 0.47479649, 0.14691763],
[0.33041779, 0.37663733, 0.02115561, 0.16106199]])
ref_support_vectors = np.array([[2.95309709, 2.51327412], [3.14159265, 4.08407045],
[4.08407045, 2.26194671], [4.46106157, 2.38761042]])
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = 2
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2,
entangler_map=[[0, 1]])
if use_circuits:
x = ParameterVector('x', num_qubits)
feature_map = feature_map.construct_circuit(x)
feature_map.ordered_parameters = list(x)
svm = QSVM(feature_map, self.training_data, self.testing_data, None)
quantum_instance = QuantumInstance(backend, seed_transpiler=self.random_seed,
seed_simulator=self.random_seed)
file_path = self.get_resource_path('qsvm_test.npz')
try:
result = svm.run(quantum_instance)
ori_alphas = result['svm']['alphas']
np.testing.assert_array_almost_equal(
result['kernel_matrix_testing'], ref_kernel_testing, decimal=4)
self.assertEqual(len(result['svm']['support_vectors']), 4)
np.testing.assert_array_almost_equal(
result['svm']['support_vectors'], ref_support_vectors, decimal=4)
self.assertEqual(result['testing_accuracy'], 0.5)
svm.save_model(file_path)
self.assertTrue(os.path.exists(file_path))
loaded_svm = QSVM(feature_map)
loaded_svm.load_model(file_path)
np.testing.assert_array_almost_equal(
loaded_svm.ret['svm']['support_vectors'], ref_support_vectors, decimal=4)
np.testing.assert_array_almost_equal(
loaded_svm.ret['svm']['alphas'], ori_alphas, decimal=4)
loaded_test_acc = loaded_svm.test(svm.test_dataset[0],
svm.test_dataset[1],
quantum_instance)
self.assertEqual(result['testing_accuracy'], loaded_test_acc)
np.testing.assert_array_almost_equal(
loaded_svm.ret['kernel_matrix_testing'], ref_kernel_testing, decimal=4)
except NameError as ex:
self.skipTest(str(ex))
finally:
if os.path.exists(file_path):
try:
os.remove(file_path)
except Exception: # pylint: disable=broad-except
pass
