def test_vqc_with_raw_feature_vector_on_wine(self):
""" vqc with raw features vector on wine test """
feature_dim = 4 # dimension of each data point
training_dataset_size = 8
testing_dataset_size = 4
_, training_input, test_input, _ = _wine_data(
training_size=training_dataset_size,
test_size=testing_dataset_size,
n=feature_dim
)
aqua_globals.random_seed = self.seed
feature_map = RawFeatureVector(feature_dimension=feature_dim)
vqc = VQC(COBYLA(maxiter=100),
feature_map,
RYRZ(feature_map.num_qubits, depth=3),
training_input,
test_input)
result = vqc.run(QuantumInstance(BasicAer.get_backend('statevector_simulator'),
shots=1024,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
self.log.debug(result['testing_accuracy'])
self.assertGreater(result['testing_accuracy'], 0.8)
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.aqua import QuantumInstance, aqua_globals
from qiskit.aqua.algorithms import VQC
from qiskit.aqua.components.optimizers import COBYLA
from qiskit.aqua.components.feature_maps import RawFeatureVector
from qiskit.ml.datasets import wine
from qiskit.circuit.library import TwoLocal
seed = 1376
aqua_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 VQE 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'], 0.8)
result = svm.run(quantum_instance)
for k, v in result.items():
print(f'{k} : {v}')
except Exception as e:
print('QSVM 2 error', e)
time0 = time.time() - start_time
print("\nQSVM finished at: {0} seconds".format(str(round(time0, 5))))
training_input, test_input, total_array = traintest(df_enc, tonum=False)
try:
seed = 1376
aqua_globals.random_seed = seed
feature_map = RawFeatureVector(feature_dimension=n)
vqc = VQC(COBYLA(maxiter=10), 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('VQC:')
print('Testing accuracy: {:0.2f}'.format(result['testing_accuracy']))
except Exception as e:
print('VQC error', e)
time0 = time.time() - start_time
print("\nVQC finished at: {0} seconds".format(str(round(time0, 5))))
from qiskit.aqua.components.feature_maps import RawFeatureVector
from qiskit.aqua.utils import get_feature_dimension
feature_dim = 4 # dimension of each data point
training_dataset_size = 20
testing_dataset_size = 10
random_seed = 10598
aqua_globals.random_seed = random_seed
sample_Total, training_input, test_input, class_labels = wine(
training_size=training_dataset_size,
test_size=testing_dataset_size,
n=feature_dim,
plot_data=False)
feature_map = RawFeatureVector(
feature_dimension=get_feature_dimension(training_input))
vqc = VQC(COBYLA(maxiter=200), 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'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
print("VQC accuracy with RawFeatureVector: ", result['testing_accuracy'])
feature_map = ZZFeatureMap(get_feature_dimension(training_input))
vqc = VQC(COBYLA(maxiter=200), 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'),
