def test_minibatching_gradient_based(self):
"""Test the minibatching option with a gradient-based optimizer."""
n_dim = 2 # dimension of each data point
_, training_input, test_input, _ = ad_hoc_data(training_size=4,
test_size=2,
n=n_dim,
gap=0.3,
plot_data=False)
optimizer = L_BFGS_B(maxfun=30)
data_preparation = self.data_preparation
wavefunction = TwoLocal(2, ['ry', 'rz'],
'cz',
reps=1,
insert_barriers=True)
vqc = VQC(optimizer,
data_preparation,
wavefunction,
training_input,
test_input,
minibatch_size=2)
result = vqc.run(self.statevector_simulator)
self.log.debug(result['testing_accuracy'])
self.assertAlmostEqual(result['testing_accuracy'], 0.75, places=3)
def test_callback(self):
"""Test the callback function of the VQC."""
history = {
'eval_count': [],
'parameters': [],
'cost': [],
'batch_index': []
}
def store_intermediate_result(eval_count, parameters, cost,
batch_index):
history['eval_count'].append(eval_count)
history['parameters'].append(parameters)
history['cost'].append(cost)
history['batch_index'].append(batch_index)
optimizer = COBYLA(maxiter=3)
data_preparation = self.data_preparation
wavefunction = self.ryrz_wavefunction
# set up algorithm
vqc = VQC(optimizer,
data_preparation,
wavefunction,
self.training_data,
self.testing_data,
callback=store_intermediate_result)
vqc.run(self.qasm_simulator)
with self.subTest('eval count'):
self.assertTrue(
all(isinstance(count, int) for count in history['eval_count']))
with self.subTest('cost'):
self.assertTrue(
all(isinstance(cost, float) for cost in history['cost']))
with self.subTest('batch index'):
self.assertTrue(
all(
isinstance(index, int)
for index in history['batch_index']))
for params in history['parameters']:
with self.subTest('params'):
self.assertTrue(
all(isinstance(param, float) for param in params))
def test_save_and_load_model(self):
"""Test saving and loading a model with the VQC."""
data_preparation = self.data_preparation
wavefunction = self.ryrz_wavefunction
vqc = VQC(self.spsa, data_preparation, wavefunction,
self.training_data, self.testing_data)
result = vqc.run(self.qasm_simulator)
with self.subTest(
msg='check optimal params, training loss and testing accuracy'
):
np.testing.assert_array_almost_equal(result['opt_params'],
self.ref_opt_params,
decimal=4)
np.testing.assert_array_almost_equal(result['training_loss'],
self.ref_train_loss,
decimal=8)
self.assertEqual(0.5, result['testing_accuracy'])
file_path = self.get_resource_path('vqc_test.npz')
vqc.save_model(file_path)
with self.subTest(msg='assert saved file exists'):
self.assertTrue(os.path.exists(file_path))
loaded_vqc = VQC(self.spsa, data_preparation, wavefunction,
self.training_data, None)
loaded_vqc.load_model(file_path)
loaded_test_acc = loaded_vqc.test(vqc.test_dataset[0],
vqc.test_dataset[1],
self.qasm_simulator)
with self.subTest(
msg=
'check optimal parameters and testing accuracy of loaded model'
):
np.testing.assert_array_almost_equal(loaded_vqc.ret['opt_params'],
self.ref_opt_params,
decimal=4)
self.assertEqual(result['testing_accuracy'], loaded_test_acc)
predicted_probs, predicted_labels = loaded_vqc.predict(
self.testing_data['A'], self.qasm_simulator)
with self.subTest(msg='check probs and labels of predicted labels'):
np.testing.assert_array_almost_equal(predicted_probs,
self.ref_prediction_a_probs,
decimal=8)
np.testing.assert_array_equal(predicted_labels,
self.ref_prediction_a_label)
if os.path.exists(file_path):
try:
os.remove(file_path)
except Exception: # pylint: disable=broad-except
pass
def test_same_parameter_names_raises(self):
"""Test that the varform and feature map can have parameters with the same name."""
algorithm_globals.random_seed = self.seed
var_form = QuantumCircuit(1)
var_form.ry(Parameter('a'), 0)
feature_map = QuantumCircuit(1)
feature_map.rz(Parameter('a'), 0)
optimizer = SPSA()
vqc = VQC(optimizer, feature_map, var_form, self.training_data,
self.testing_data)
with self.assertRaises(QiskitMachineLearningError):
_ = vqc.run(BasicAer.get_backend('statevector_simulator'))
def test_usage_in_vqc(self):
"""Test using the circuit the a single VQC iteration works."""
feature_dim = 4
_, training_input, test_input, _ = wine(training_size=1,
test_size=1,
n=feature_dim,
plot_data=False)
feature_map = RawFeatureVector(feature_dimension=feature_dim)
vqc = VQC(COBYLA(maxiter=1), feature_map,
EfficientSU2(feature_map.num_qubits, reps=1), training_input,
test_input)
backend = Aer.get_backend('qasm_simulator')
result = vqc.run(backend)
self.assertTrue(result['eval_count'] > 0)
def test_statevector(self):
"""Test running the VQC on BasicAer's Statevector simulator."""
optimizer = L_BFGS_B(maxfun=200)
data_preparation = self.data_preparation
wavefunction = self.ryrz_wavefunction
vqc = VQC(optimizer, data_preparation, wavefunction,
self.training_data, self.testing_data)
result = vqc.run(self.statevector_simulator)
with self.subTest(msg='check training loss'):
self.assertLess(result['training_loss'], 0.12)
with self.subTest(msg='check testing accuracy'):
self.assertEqual(result['testing_accuracy'], 0.5)
def test_readme_sample(self):
""" readme sample test """
# pylint: disable=import-outside-toplevel,redefined-builtin
def print(*args):
""" overloads print to log values """
if args:
self.log.debug(args[0], *args[1:])
# --- Exact copy of sample code ----------------------------------------
from qiskit import BasicAer
from qiskit.utils import QuantumInstance, algorithm_globals
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import TwoLocal
from qiskit_machine_learning.algorithms import VQC
from qiskit_machine_learning.datasets import wine
from qiskit_machine_learning.circuit.library import RawFeatureVector
seed = 1376
algorithm_globals.random_seed = seed
# Use Wine data set for training and test data
feature_dim = 4 # dimension of each data point
_, training_input, test_input, _ = wine(training_size=12,
test_size=4,
n=feature_dim)
feature_map = RawFeatureVector(feature_dimension=feature_dim)
vqc = VQC(COBYLA(maxiter=100), feature_map,
TwoLocal(feature_map.num_qubits, ['ry', 'rz'], 'cz', reps=3),
training_input, test_input)
result = vqc.run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
shots=1024,
seed_simulator=seed,
seed_transpiler=seed))
print('Testing accuracy: {:0.2f}'.format(result['testing_accuracy']))
# ----------------------------------------------------------------------
self.assertGreater(result['testing_accuracy'], 0.8)
def test_raw_feature_vector_on_wine(self, ):
"""Test VQC on the wine dataset using the ``RawFeatureVector`` as data preparation."""
feature_dim = 4 # dimension of each data point
training_dataset_size = 8
testing_dataset_size = 4
_, training_input, test_input, _ = wine(
training_size=training_dataset_size,
test_size=testing_dataset_size,
n=feature_dim,
plot_data=False)
feature_map = RawFeatureVector(feature_dimension=feature_dim)
vqc = VQC(COBYLA(maxiter=100), feature_map,
TwoLocal(feature_map.num_qubits, ['ry', 'rz'], 'cz', reps=3),
training_input, test_input)
result = vqc.run(self.statevector_simulator)
self.log.debug(result['testing_accuracy'])
self.assertGreater(result['testing_accuracy'], 0.7)
def test_wine(self):
"""Test VQC on the wine dataset."""
feature_dim = 4 # dimension of each data point
training_dataset_size = 6
testing_dataset_size = 3
_, training_input, test_input, _ = wine(
training_size=training_dataset_size,
test_size=testing_dataset_size,
n=feature_dim,
plot_data=False)
algorithm_globals.random_seed = self.seed
data_preparation = ZZFeatureMap(feature_dim)
wavefunction = TwoLocal(feature_dim, ['ry', 'rz'], 'cz', reps=2)
vqc = VQC(COBYLA(maxiter=100), data_preparation, wavefunction,
training_input, test_input)
result = vqc.run(self.statevector_simulator)
self.log.debug(result['testing_accuracy'])
self.assertGreater(result['testing_accuracy'], 0.3)
def test_minibatching_gradient_free(self):
"""Test the minibatching option with a gradient-free optimizer."""
n_dim = 2 # dimension of each data point
_, training_input, test_input, _ = ad_hoc_data(training_size=6,
test_size=3,
n=n_dim,
gap=0.3,
plot_data=False)
optimizer = COBYLA(maxiter=40)
data_preparation = self.data_preparation
wavefunction = self.ryrz_wavefunction
vqc = VQC(optimizer,
data_preparation,
wavefunction,
training_input,
test_input,
minibatch_size=2)
result = vqc.run(self.qasm_simulator)
self.log.debug(result['testing_accuracy'])
self.assertAlmostEqual(result['testing_accuracy'], 0.3333333333333333)