def setUp(self):
self.random_seed = 10598
self.shots = 8192
np.random.seed(self.random_seed)
self.training_data = {
'A': np.asarray([[2.95309709, 2.51327412],
[3.14159265, 4.08407045]]),
'B': np.asarray([[4.08407045, 2.26194671],
[4.46106157, 2.38761042]])
}
self.testing_data = {
'A': np.asarray([[3.83274304, 2.45044227]]),
'B': np.asarray([[3.89557489, 0.31415927]])
}
self.ref_kernel_matrix_training = np.asarray(
[[1., 0.85632324, 0.1184082, 0.36523438],
[0.85632324, 1., 0.11352539, 0.45068359],
[0.1184082, 0.11352539, 1., 0.6730957],
[0.36523438, 0.45068359, 0.6730957, 1.]])
self.ref_kernel_matrix_testing = np.asarray(
[[0.14892578, 0.18115234, 0.47631836, 0.14709473],
[0.33239746, 0.3782959, 0.02270508, 0.16418457]])
self.ref_support_vectors = np.asarray([[2.95309709, 2.51327412],
[3.14159265, 4.08407045],
[4.08407045, 2.26194671],
[4.46106157, 2.38761042]])
self.ref_alpha = np.asarray(
[0.38038017, 1.46000306, 0.02371895, 1.81666428])
self.ref_bias = np.asarray([-0.03570662])
self.svm_input = SVMInput(self.training_data, self.testing_data)
def setUp(self):
self.random_seed = 10598
self.training_data = {'A': np.asarray([[2.95309709, 2.51327412], [3.14159265, 4.08407045]]),
'B': np.asarray([[4.08407045, 2.26194671], [4.46106157, 2.38761042]])}
self.testing_data = {'A': np.asarray([[3.83274304, 2.45044227]]),
'B': np.asarray([[3.89557489, 0.31415927]])}
self.ref_opt_params = np.asarray([2.6985, 1.5935, 2.2456, -6.255, -4.3215, -5.41,
-6.9215, 0.2656, 1.5701, -4.677, 2.6987, -11.7649,
-2.3141, -2.7084, 0.0622, -0.1577])
self.ref_train_loss = 0.6294606017231916
self.svm_input = SVMInput(self.training_data, self.testing_data)
def setUp(self):
super().setUp()
self.random_seed = 10598
self.shots = 12000
np.random.seed(self.random_seed)
self.training_data = {
'A': np.asarray([[2.95309709, 2.51327412],
[3.14159265, 4.08407045]]),
'B': np.asarray([[4.08407045, 2.26194671],
[4.46106157, 2.38761042]])
}
self.testing_data = {
'A': np.asarray([[3.83274304, 2.45044227]]),
'B': np.asarray([[3.89557489, 0.31415927]])
}
self.svm_input = SVMInput(self.training_data, self.testing_data)
def test_qsvm_kernel_binary_via_run_algorithm(self):
training_input = {
'A':
np.asarray([[0.6560706, 0.17605998], [0.14154948, 0.06201424],
[0.80202323, 0.40582692], [0.46779595, 0.39946754],
[0.57660199, 0.21821317]]),
'B':
np.asarray([[0.38857596, -0.33775802], [0.49946978, -0.48727951],
[-0.30119743, -0.11221681], [-0.16479252, -0.08640519],
[0.49156185, -0.3660534]])
}
test_input = {
'A':
np.asarray([[0.57483139, 0.47120732], [0.48372348, 0.25438544],
[0.08791134, 0.11515506], [0.45988094, 0.32854319],
[0.53015085, 0.41539212]]),
'B':
np.asarray([[-0.06048935, -0.48345293], [-0.01065613, -0.33910828],
[-0.17323832, -0.49535592], [0.14043268, -0.87869109],
[-0.15046837, -0.47340207]])
}
temp = [test_input[k] for k in test_input]
total_array = np.concatenate(temp)
params = {
'problem': {
'name': 'svm_classification',
'random_seed': self.random_seed
},
'backend': {
'shots': self.shots
},
'algorithm': {
'name': 'QSVM.Kernel'
}
}
backend = Aer.get_backend('qasm_simulator')
algo_input = SVMInput(training_input, test_input, total_array)
result = run_algorithm(params, algo_input, backend=backend)
self.assertEqual(result['testing_accuracy'], 0.6)
self.assertEqual(result['predicted_classes'],
['A', 'A', 'A', 'A', 'A', 'A', 'B', 'A', 'A', 'A'])
def setUp(self):
super().setUp()
self.random_seed = 10598
self.training_data = {
'A': np.asarray([[2.95309709, 2.51327412],
[3.14159265, 4.08407045]]),
'B': np.asarray([[4.08407045, 2.26194671],
[4.46106157, 2.38761042]])
}
self.testing_data = {
'A': np.asarray([[3.83274304, 2.45044227]]),
'B': np.asarray([[3.89557489, 0.31415927]])
}
self.ref_opt_params = np.array([
-0.09936191, -1.26202073, 1.30316646, 3.24053034, -0.50731743,
-0.6853292, 2.57404557, 1.74873317, 1.62238446, -1.83326183,
4.48499251, 0.21433137, -1.76288916, -0.15767913, 1.86321388,
0.27216782
])
self.ref_train_loss = 1.4088445273265953
self.svm_input = SVMInput(self.training_data, self.testing_data)
def test_classical_multiclass_one_against_all(self):
training_input = {
'A':
np.asarray([[0.6560706, 0.17605998], [0.25776033, 0.47628296],
[0.79687342, 0.26933706], [0.39016555, -0.08469916],
[0.3994399, 0.13601573], [0.26752049, -0.03978988],
[0.24026485, 0.01953518], [0.49490503, 0.17239737],
[0.70171827, 0.5323737], [0.43221576, 0.42357294],
[0.62864856, 0.45504447], [0.6259567, 0.30917324],
[0.58272403, 0.20760754], [0.3938784, 0.17184466],
[0.14154948, 0.06201424], [0.80202323, 0.40582692],
[0.46779595, 0.39946754], [0.57660199, 0.21821317],
[0.51044761, 0.03699459], [0.8690704, 0.70847635]]),
'B':
np.asarray([[0.38857596, -0.33775802], [0.49946978, -0.48727951],
[-0.30119743, -0.11221681], [-0.16479252, -0.08640519],
[-0.21808884, -0.56508327], [-0.14683258, -0.46528508],
[-0.05888195, -0.51474852], [0.20517435, -0.66839091],
[0.25475584, -0.21239966], [0.55194854, 0.02789679],
[-0.11542951, -0.54157026], [0.44625538, -0.49485869],
[-0.14609118, -0.60719757], [0.18121305, -0.1922198],
[0.19283785, -0.31798925], [0.29626405, -0.54563098],
[-0.39044304, -0.36527253], [-0.29432215, -0.43924164],
[-0.40294517, -0.31381308], [0.49156185, -0.3660534]]),
'C':
np.asarray([[-0.68088231, 0.46824423], [-0.56167659, 0.65270294],
[-0.54323753, 0.67630888], [-0.57685569, -0.08515631],
[-0.67765364, 0.19654347], [-0.62129115, 0.22223066],
[-0.78040851, 0.65247848], [-0.50730279, 0.59898039],
[-0.64275805, 0.63381998], [-0.72854201, 0.14151325],
[-0.57004437, 0.12344874], [-0.55215973, 0.74331215],
[-0.60916047, 0.52006917], [-0.23093745, 1.],
[-0.84025337, 0.5564536], [-0.66952391, 0.57918859],
[-0.67725082, 0.60439934], [-1., 0.23715261],
[-0.62933025, 0.19055405], [-0.82139073, 0.29941512]])
}
test_input = {
'A':
np.asarray([[0.57483139, 0.47120732], [0.48372348, 0.25438544],
[0.08791134, 0.11515506], [0.45988094, 0.32854319],
[0.53015085, 0.41539212], [0.5073321, 0.47346751],
[0.71081819, 0.19202569], [1., 0.51698289],
[0.630973, 0.19898666], [0.48142649, 0.15931707]]),
'B':
np.asarray([[-0.06048935, -0.48345293], [-0.01065613, -0.33910828],
[-0.17323832, -0.49535592], [0.14043268, -0.87869109],
[-0.15046837, -0.47340207], [-0.39600934, -0.21647957],
[-0.394202, -0.44705385], [0.15243621, -0.36695163],
[0.06195634, -0.23262325], [0.06183066, -0.53376975]]),
'C':
np.asarray([[-0.74561108, 0.27047295], [-0.69942965, 0.11885162],
[-0.52649891, 0.35265538], [-0.54345106, 0.13113995],
[-0.57181448, 0.13594725], [-0.33713329, 0.05095243],
[-0.65741384, 0.477976], [-0.79986067, 0.41733195],
[-0.73856328, 0.80699537], [-0.66489165, 0.1181712]])
}
temp = [test_input[k] for k in test_input]
total_array = np.concatenate(temp)
params = {
'problem': {
'name': 'svm_classification'
},
'algorithm': {
'name': 'SVM'
},
'multiclass_extension': {
'name': 'OneAgainstRest'
}
}
algo_input = SVMInput(training_input, test_input, total_array)
result = run_algorithm(params, algo_input)
self.assertEqual(result['testing_accuracy'], 1.0)
self.assertEqual(result['predicted_classes'], [
'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B',
'B', 'B', 'B', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'C',
'C', 'C', 'C', 'C'
])
def test_classical_binary(self):
training_input = {
'A':
np.asarray([[0.6560706, 0.17605998], [0.25776033, 0.47628296],
[0.79687342, 0.26933706], [0.39016555, -0.08469916],
[0.3994399, 0.13601573], [0.26752049, -0.03978988],
[0.24026485, 0.01953518], [0.49490503, 0.17239737],
[0.70171827, 0.5323737], [0.43221576, 0.42357294],
[0.62864856, 0.45504447], [0.6259567, 0.30917324],
[0.58272403, 0.20760754], [0.3938784, 0.17184466],
[0.14154948, 0.06201424], [0.80202323, 0.40582692],
[0.46779595, 0.39946754], [0.57660199, 0.21821317],
[0.51044761, 0.03699459], [0.8690704, 0.70847635]]),
'B':
np.asarray([[0.38857596, -0.33775802], [0.49946978, -0.48727951],
[-0.30119743, -0.11221681], [-0.16479252, -0.08640519],
[-0.21808884, -0.56508327], [-0.14683258, -0.46528508],
[-0.05888195, -0.51474852], [0.20517435, -0.66839091],
[0.25475584, -0.21239966], [0.55194854, 0.02789679],
[-0.11542951, -0.54157026], [0.44625538, -0.49485869],
[-0.14609118, -0.60719757], [0.18121305, -0.1922198],
[0.19283785, -0.31798925], [0.29626405, -0.54563098],
[-0.39044304, -0.36527253], [-0.29432215, -0.43924164],
[-0.40294517, -0.31381308], [0.49156185, -0.3660534]])
}
test_input = {
'A':
np.asarray([[0.57483139, 0.47120732], [0.48372348, 0.25438544],
[0.08791134, 0.11515506], [0.45988094, 0.32854319],
[0.53015085, 0.41539212], [0.5073321, 0.47346751],
[0.71081819, 0.19202569], [1., 0.51698289],
[0.630973, 0.19898666], [0.48142649, 0.15931707]]),
'B':
np.asarray([[-0.06048935, -0.48345293], [-0.01065613, -0.33910828],
[-0.17323832, -0.49535592], [0.14043268, -0.87869109],
[-0.15046837, -0.47340207], [-0.39600934, -0.21647957],
[-0.394202, -0.44705385], [0.15243621, -0.36695163],
[0.06195634, -0.23262325], [0.06183066, -0.53376975]])
}
temp = [test_input[k] for k in test_input]
total_array = np.concatenate(temp)
params = {
'problem': {
'name': 'svm_classification'
},
'algorithm': {
'name': 'SVM',
}
}
algo_input = SVMInput(training_input, test_input, total_array)
result = run_algorithm(params, algo_input)
self.assertEqual(result['testing_accuracy'], 1.0)
self.assertEqual(result['predicted_classes'], [
'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B',
'B', 'B', 'B', 'B', 'B', 'B', 'B'
])
from qiskit_aqua.utils import split_dataset_to_data_and_labels
from qiskit_aqua.input import SVMInput
from qiskit_qcgpu_provider import QCGPUProvider
from qiskit_aqua import run_algorithm
n = 2 # How many features to use (dimensionality)
training_dataset_size = 20
testing_dataset_size = 10
sample_Total, training_input, test_input, class_labels = breast_cancer(training_dataset_size, testing_dataset_size, n)
datapoints, class_to_label = split_dataset_to_data_and_labels(test_input)
print(class_to_label)
params = {
'problem': {'name': 'svm_classification', 'random_seed': 10598},
'algorithm': { 'name': 'QSVM.Kernel' },
'backend': {'name': 'qasm_simulator', 'shots': 1024},
'feature_map': {'name': 'SecondOrderExpansion', 'depth': 2, 'entanglement': 'linear'}
}
backend = QCGPUProvider().get_backend('qasm_simulator')
algo_input = SVMInput(training_input, test_input, datapoints[0])
%time result = run_algorithm(params, algo_input)
%time result = run_algorithm(params, algo_input, backend=backend)
print("ground truth: {}".format(datapoints[1]))
print("prediction: {}".format(result['predicted_labels']))
temp = [test_input[k] for k in test_input]
total_array = np.concatenate(temp)
aqua_dict = {
'problem': {
'name': 'svm_classification'
},
'algorithm': {
'name': 'SVM'
},
'multiclass_extension': {
'name': 'AllPairs'
}
}
algo_input = SVMInput(training_input, test_input, total_array)
from qiskit.aqua import QiskitAqua
aqua_obj = QiskitAqua(aqua_dict, algo_input)
algo_obj = aqua_obj.quantum_algorithm
result = aqua_obj.run() #run_algorithm(aqua_dict, algo_input)
for k, v in result.items():
print("'{}' : {}".format(k, v))
# 6
to_predict = singleDataItem('', 'data.csv', [1, 2, 0, 0, 0, 0, 0, 1, 0], n=3)
print(algo_obj.predict(to_predict))
# 2
def test_qsvm_kernel_multiclass_error_correcting_code(self):
backend = get_aer_backend('qasm_simulator')
training_input = {
'A':
np.asarray([[0.6560706, 0.17605998], [0.25776033, 0.47628296],
[0.8690704, 0.70847635]]),
'B':
np.asarray([[0.38857596, -0.33775802], [0.49946978, -0.48727951],
[0.49156185, -0.3660534]]),
'C':
np.asarray([[-0.68088231, 0.46824423], [-0.56167659, 0.65270294],
[-0.82139073, 0.29941512]])
}
test_input = {
'A':
np.asarray([[0.57483139, 0.47120732], [0.48372348, 0.25438544],
[0.48142649, 0.15931707]]),
'B':
np.asarray([[-0.06048935, -0.48345293], [-0.01065613, -0.33910828],
[0.06183066, -0.53376975]]),
'C':
np.asarray([[-0.74561108, 0.27047295], [-0.69942965, 0.11885162],
[-0.66489165, 0.1181712]])
}
total_array = np.concatenate(
(test_input['A'], test_input['B'], test_input['C']))
params = {
'problem': {
'name': 'svm_classification',
'random_seed': self.random_seed
},
'algorithm': {
'name': 'QSVM.Kernel',
},
'backend': {
'shots': self.shots
},
'multiclass_extension': {
'name': 'ErrorCorrectingCode',
'code_size': 5
},
'feature_map': {
'name': 'SecondOrderExpansion',
'depth': 2,
'entangler_map': {
0: [1]
}
}
}
algo_input = SVMInput(training_input, test_input, total_array)
result = run_algorithm(params, algo_input, backend=backend)
self.assertAlmostEqual(result['testing_accuracy'],
0.55555555,
places=4,
msg='Please ensure you are using C++ simulator')
self.assertEqual(result['predicted_classes'],
['A', 'A', 'C', 'A', 'A', 'A', 'C', 'C', 'C'])