def test_qsvm_kernel_binary_directly(self):
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_bias = np.array([-0.03380763])
ref_support_vectors = np.array([[2.95309709, 2.51327412],
[3.14159265, 4.08407045],
[4.08407045, 2.26194671],
[4.46106157, 2.38761042]])
backend = get_aer_backend('qasm_simulator')
num_qubits = 2
feature_map = SecondOrderExpansion(num_qubits=num_qubits,
depth=2,
entangler_map={0: [1]})
svm = QSVMKernel(feature_map, self.training_data, self.testing_data,
None)
svm.random_seed = self.random_seed
run_config = RunConfig(shots=self.shots,
max_credits=10,
memory=False,
seed=self.random_seed)
quantum_instance = QuantumInstance(backend,
run_config,
seed_mapper=self.random_seed)
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=4)
np.testing.assert_array_almost_equal(result['svm']['bias'],
ref_bias,
decimal=4)
self.assertEqual(result['testing_accuracy'], 0.5)
def test_qsvm_kernel_binary_directly_statevector(self):
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 = get_aer_backend('statevector_simulator')
num_qubits = 2
feature_map = SecondOrderExpansion(num_qubits=num_qubits,
depth=2,
entangler_map={0: [1]})
svm = QSVMKernel(feature_map, self.training_data, self.testing_data,
None)
svm.random_seed = self.random_seed
quantum_instance = QuantumInstance(backend,
seed_mapper=self.random_seed)
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)
file_path = self._get_resource_path('qsvm_kernel_test.npz')
svm.save_model(file_path)
self.assertTrue(os.path.exists(file_path))
loaded_svm = QSVMKernel(feature_map, self.training_data, None, None)
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)
if os.path.exists(file_path):
try:
os.remove(file_path)
except:
pass