def test_qsvm_binary_directly_statevector(self):
""" 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]])
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
def test_binary_directly_statevector(self):
"""Test QSVM on binary classification on BasicAer's statevector simulator.
Also tests saving and loading models."""
data_preparation = self.data_preparation
svm = QSVM(data_preparation, self.training_data, self.testing_data,
None)
file_path = self.get_resource_path('qsvm_test.npz')
try:
result = svm.run(self.statevector_simulator)
ori_alphas = result['svm']['alphas']
np.testing.assert_array_almost_equal(
result['kernel_matrix_testing'],
self.ref_kernel_testing['statevector'],
decimal=4)
self.assertEqual(len(result['svm']['support_vectors']), 4)
np.testing.assert_array_almost_equal(
result['svm']['support_vectors'],
self.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(data_preparation)
loaded_svm.load_model(file_path)
np.testing.assert_array_almost_equal(
loaded_svm.ret['svm']['support_vectors'],
self.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],
self.statevector_simulator)
self.assertEqual(result['testing_accuracy'], loaded_test_acc)
np.testing.assert_array_almost_equal(
loaded_svm.ret['kernel_matrix_testing'],
self.ref_kernel_testing['statevector'],
decimal=4)
except MissingOptionalLibraryError 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