def test_constructor(self):
"""Tests properties of PegasosQSVC"""
with self.subTest("Default parameters"):
pegasos_qsvc = PegasosQSVC()
self.assertIsInstance(pegasos_qsvc.quantum_kernel, QuantumKernel)
self.assertFalse(pegasos_qsvc.precomputed)
self.assertEqual(pegasos_qsvc.num_steps, 1000)
with self.subTest("PegasosQSVC with QuantumKernel"):
qkernel = QuantumKernel(
feature_map=self.feature_map,
quantum_instance=self.statevector_simulator)
pegasos_qsvc = PegasosQSVC(quantum_kernel=qkernel)
self.assertIsInstance(pegasos_qsvc.quantum_kernel, QuantumKernel)
self.assertFalse(pegasos_qsvc.precomputed)
with self.subTest("PegasosQSVC with precomputed kernel"):
pegasos_qsvc = PegasosQSVC(precomputed=True)
self.assertIsNone(pegasos_qsvc.quantum_kernel)
self.assertTrue(pegasos_qsvc.precomputed)
with self.subTest("PegasosQSVC with wrong parameters"):
qkernel = QuantumKernel(
feature_map=self.feature_map,
quantum_instance=self.statevector_simulator)
with self.assertRaises(ValueError):
_ = PegasosQSVC(quantum_kernel=qkernel, precomputed=True)
with self.subTest("PegasosQSVC with wrong type of kernel"):
with self.assertRaises(TypeError):
_ = PegasosQSVC(quantum_kernel=object())
def test_empty_kernel(self):
"""Test PegasosQSVC with empty QuantumKernel"""
qkernel = QuantumKernel()
pegasos_qsvc = PegasosQSVC(quantum_kernel=qkernel)
with self.assertRaises(QiskitMachineLearningError):
pegasos_qsvc.fit(self.sample_train, self.label_train)
def test_qsvc_4d(self):
"""Test PegasosQSVC with 4-dimensional input data"""
qkernel = QuantumKernel(feature_map=self.feature_map_4d,
quantum_instance=self.statevector_simulator)
pegasos_qsvc = PegasosQSVC(quantum_kernel=qkernel,
C=1000,
num_steps=self.tau)
pegasos_qsvc.fit(self.sample_train_4d, self.label_train_4d)
score = pegasos_qsvc.score(self.sample_test_4d, self.label_test_4d)
self.assertEqual(score, 1.0)
def test_qsvc(self):
"""Test PegasosQSVC"""
qkernel = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.statevector_simulator)
pegasos_qsvc = PegasosQSVC(quantum_kernel=qkernel,
C=1000,
num_steps=self.tau)
pegasos_qsvc.fit(self.sample_train, self.label_train)
score = pegasos_qsvc.score(self.sample_test, self.label_test)
self.assertEqual(score, 1.0)
def test_decision_function(self):
"""Test PegasosQSVC."""
qkernel = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.statevector_simulator)
pegasos_qsvc = PegasosQSVC(quantum_kernel=qkernel,
C=1000,
num_steps=self.tau)
pegasos_qsvc.fit(self.sample_train, self.label_train)
decision_function = pegasos_qsvc.decision_function(self.sample_test)
self.assertTrue(
np.all((decision_function > 0) == (self.label_test == 0)))
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_labels(self):
"""Test PegasosQSVC with different integer labels than {0, 1}"""
qkernel = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.statevector_simulator)
pegasos_qsvc = PegasosQSVC(quantum_kernel=qkernel,
C=1000,
num_steps=self.tau)
label_train_temp = self.label_train.copy()
label_train_temp[self.label_train == 0] = 2
label_train_temp[self.label_train == 1] = 3
label_test_temp = self.label_test.copy()
label_test_temp[self.label_test == 0] = 2
label_test_temp[self.label_test == 1] = 3
pegasos_qsvc.fit(self.sample_train, label_train_temp)
score = pegasos_qsvc.score(self.sample_test, label_test_temp)
self.assertEqual(score, 1.0)
def test_save_load(self):
"""Tests save and load models."""
features = np.array([[0, 0], [0.1, 0.2], [1, 1], [0.9, 0.8]])
labels = np.array([0, 0, 1, 1])
qkernel = QuantumKernel(feature_map=self.feature_map,
quantum_instance=self.statevector_simulator)
regressor = PegasosQSVC(quantum_kernel=qkernel,
C=1000,
num_steps=self.tau)
regressor.fit(features, labels)
# predicted labels from the newly trained model
test_features = np.array([[0.5, 0.5]])
original_predicts = regressor.predict(test_features)
# save/load, change the quantum instance and check if predicted values are the same
file_name = os.path.join(tempfile.gettempdir(), "pegasos.model")
regressor.save(file_name)
try:
regressor_load = PegasosQSVC.load(file_name)
loaded_model_predicts = regressor_load.predict(test_features)
np.testing.assert_array_almost_equal(original_predicts,
loaded_model_predicts)
# test loading warning
class FakeModel(SerializableModelMixin):
"""Fake model class for test purposes."""
pass
with self.assertRaises(TypeError):
FakeModel.load(file_name)
finally:
os.remove(file_name)