def test_qsvm_variational_callback(self):
tmp_filename = 'qsvm_callback_test.csv'
is_file_exist = os.path.exists(self._get_resource_path(tmp_filename))
if is_file_exist:
os.remove(self._get_resource_path(tmp_filename))
def store_intermediate_result(eval_count, parameters, cost, batch_index):
with open(self._get_resource_path(tmp_filename), 'a') as f:
content = "{},{},{:.5f},{}".format(eval_count, parameters, cost, batch_index)
print(content, file=f, flush=True)
np.random.seed(self.random_seed)
backend = BasicAer.get_backend('qasm_simulator')
num_qubits = 2
optimizer = COBYLA(maxiter=3)
feature_map = SecondOrderExpansion(num_qubits=num_qubits, depth=2)
var_form = RY(num_qubits=num_qubits, depth=1)
svm = QSVMVariational(optimizer, feature_map, var_form, self.training_data,
self.testing_data, callback=store_intermediate_result)
svm.random_seed = self.random_seed
quantum_instance = QuantumInstance(backend, shots=1024, seed=self.random_seed, seed_mapper=self.random_seed)
svm.run(quantum_instance)
is_file_exist = os.path.exists(self._get_resource_path(tmp_filename))
self.assertTrue(is_file_exist, "Does not store content successfully.")
# check the content
# ref_content = [
# ["0", "[ 0.18863864 -1.08197582 1.74432295 1.29765602]", "0.53367", "0"],
# ["1", "[ 1.18863864 -1.08197582 1.74432295 1.29765602]", "0.57261", "1"],
# ["2", "[ 0.18863864 -0.08197582 1.74432295 1.29765602]", "0.47137", "2"]
# ]
ref_content = [
['0', '[ 0.18863864 -1.08197582 1.74432295 1.29765602]', '0.54802', '0'],
['1', '[ 1.18863864 -1.08197582 1.74432295 1.29765602]', '0.53862', '1'],
['2', '[ 1.18863864 -0.08197582 1.74432295 1.29765602]', '0.47278', '2'],
]
try:
with open(self._get_resource_path(tmp_filename)) as f:
idx = 0
for record in f.readlines():
eval_count, parameters, cost, batch_index = record.split(",")
self.assertEqual(eval_count.strip(), ref_content[idx][0])
self.assertEqual(parameters, ref_content[idx][1])
self.assertEqual(cost.strip(), ref_content[idx][2])
self.assertEqual(batch_index.strip(), ref_content[idx][3])
idx += 1
finally:
if is_file_exist:
os.remove(self._get_resource_path(tmp_filename))
def test_qsvm_variational_minibatching_with_gradient_support(self):
n_dim = 2 # dimension of each data point
seed = 1024
np.random.seed(seed)
sample_Total, training_input, test_input, class_labels = ad_hoc_data(
training_size=20, test_size=10, n=n_dim, gap=0.3)
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = n_dim
optimizer = L_BFGS_B(maxfun=1000)
feature_map = SecondOrderExpansion(num_qubits=num_qubits, depth=2)
var_form = RYRZ(num_qubits=num_qubits, depth=3)
svm = QSVMVariational(optimizer,
feature_map,
var_form,
training_input,
test_input,
minibatch_size=2)
aqua_globals.random_seed = seed
quantum_instance = QuantumInstance(backend, seed=seed)
result = svm.run(quantum_instance)
svm_accuracy_threshold = 0.85
print(result['testing_accuracy'])
self.assertGreater(result['testing_accuracy'], svm_accuracy_threshold)
def test_qsvm_variational_directly(self):
np.random.seed(self.random_seed)
backend = BasicAer.get_backend('qasm_simulator')
num_qubits = 2
optimizer = SPSA(max_trials=10,
save_steps=1,
c0=4.0,
skip_calibration=True)
feature_map = SecondOrderExpansion(num_qubits=num_qubits, depth=2)
var_form = RYRZ(num_qubits=num_qubits, depth=3)
svm = QSVMVariational(optimizer, feature_map, var_form,
self.training_data, self.testing_data)
aqua_globals.random_seed = self.random_seed
quantum_instance = QuantumInstance(backend,
shots=1024,
seed=self.random_seed,
seed_mapper=self.random_seed)
result = svm.run(quantum_instance)
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(result['testing_accuracy'], 0.5)
file_path = self._get_resource_path('qsvm_variational_test.npz')
svm.save_model(file_path)
self.assertTrue(os.path.exists(file_path))
loaded_svm = QSVMVariational(optimizer, feature_map, var_form,
self.training_data, None)
loaded_svm.load_model(file_path)
np.testing.assert_array_almost_equal(loaded_svm.ret['opt_params'],
self.ref_opt_params,
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)
predicted_probs, predicted_labels = loaded_svm.predict(
self.testing_data['A'], quantum_instance)
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 quantum_instance.has_circuit_caching:
self.assertLess(quantum_instance._circuit_cache.misses, 3)
if os.path.exists(file_path):
try:
os.remove(file_path)
except:
pass