def test_precomputed(self):
""" Test precomputed kernel in sklearn """
kernel = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.statevector_simulator)
kernel_train = kernel.evaluate(x_vec=self.sample_train)
kernel_test = kernel.evaluate(x_vec=self.sample_test,
y_vec=self.sample_train)
svc = SVC(kernel='precomputed')
svc.fit(kernel_train, self.label_train)
score = svc.score(kernel_test, self.label_test)
self.assertEqual(score, 0.5)
def test_x_more_dim(self):
""" Test incorrect x_vec dimension """
qkclass = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.qasm_simulator)
with self.assertRaises(ValueError):
_ = qkclass.evaluate(x_vec=self.sample_more_dim)
def test_x_one_dim(self):
""" Test one x_vec dimension """
qkclass = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.statevector_simulator)
kernel = qkclass.evaluate(x_vec=self.sample_train[0])
np.testing.assert_allclose(kernel,
self.ref_kernel_train['one_dim'],
rtol=1e-4)
def test_qasm_psd(self):
""" Test symmetric matrix positive semi-definite enforcement qasm sample """
qkclass = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.qasm_sample)
kernel = qkclass.evaluate(x_vec=self.sample_train)
np.testing.assert_allclose(kernel,
self.ref_kernel_train['qasm_sample_psd'],
rtol=1e-4)
def test_sv_symmetric(self):
""" Test symmetric matrix evaluation using state vector simulator """
qkclass = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.statevector_simulator)
kernel = qkclass.evaluate(x_vec=self.sample_train)
np.testing.assert_allclose(kernel,
self.ref_kernel_train['statevector'],
rtol=1e-4)
def test_qasm_unsymmetric(self):
""" Test unsymmetric matrix evaluation using qasm simulator """
qkclass = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.qasm_simulator)
kernel = qkclass.evaluate(x_vec=self.sample_train,
y_vec=self.sample_test)
np.testing.assert_allclose(kernel,
self.ref_kernel_test['qasm'],
rtol=1e-4)
def test_precomputed_kernel(self):
"""Test PegasosQSVC with a precomputed kernel matrix"""
qkernel = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.statevector_simulator)
pegasos_qsvc = PegasosQSVC(C=1000,
num_steps=self.tau,
precomputed=True)
# training
kernel_matrix_train = qkernel.evaluate(self.sample_train,
self.sample_train)
pegasos_qsvc.fit(kernel_matrix_train, self.label_train)
# testing
kernel_matrix_test = qkernel.evaluate(self.sample_test,
self.sample_train)
score = pegasos_qsvc.score(kernel_matrix_test, self.label_test)
self.assertEqual(score, 1.0)
def test_no_backend(self):
""" Test no backend provided """
qkclass = QuantumKernel(feature_map=self.feature_map)
with self.assertRaises(QiskitMachineLearningError):
_ = qkclass.evaluate(x_vec=self.sample_train)