def test_qsvm_multiclass_all_pairs(self): """ QSVM Multiclass All Pairs test """ 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'])) aqua_globals.random_seed = self.random_seed feature_map = SecondOrderExpansion(feature_dimension=get_feature_dimension(training_input), depth=2, entangler_map=[[0, 1]]) svm = QSVM(feature_map, training_input, test_input, total_array, multiclass_extension=AllPairs(_QSVM_Estimator, [feature_map])) quantum_instance = QuantumInstance(BasicAer.get_backend('qasm_simulator'), shots=self.shots, seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed) result = svm.run(quantum_instance) self.assertAlmostEqual(result['testing_accuracy'], 0.444444444, places=4) self.assertEqual(result['predicted_classes'], ['A', 'A', 'C', 'A', 'A', 'A', 'A', 'C', 'C'])
def test_qsvm_multiclass_all_pairs(self, data_preparation_type): """ QSVM Multiclass All Pairs test """ 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'])) aqua_globals.random_seed = self.random_seed data_preparation = self.data_preparation[data_preparation_type] try: if data_preparation_type == 'wrapped': warnings.filterwarnings('ignore', category=DeprecationWarning) svm = QSVM(data_preparation, training_input, test_input, total_array, multiclass_extension=AllPairs()) if data_preparation_type == 'wrapped': warnings.filterwarnings('always', category=DeprecationWarning) quantum_instance = QuantumInstance( BasicAer.get_backend('qasm_simulator'), shots=self.shots, seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed) result = svm.run(quantum_instance) self.assertAlmostEqual(result['testing_accuracy'], 0.444444444, places=4) self.assertEqual(result['predicted_classes'], ['A', 'A', 'C', 'A', 'A', 'A', 'A', 'C', 'C']) except NameError as ex: self.skipTest(str(ex))
def test_multiclass(self, multiclass_extension): """ QSVM Multiclass One Against All test """ train_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]])} method = {'one_vs_all': OneAgainstRest(), 'all_vs_all': AllPairs(), 'error_correcting': ErrorCorrectingCode(code_size=5)} accuracy = {'one_vs_all': 0.444444444, 'all_vs_all': 0.444444444, 'error_correcting': 0.555555555} predicted_classes = { 'one_vs_all': ['A', 'A', 'C', 'A', 'A', 'A', 'A', 'C', 'C'], 'all_vs_all': ['A', 'A', 'C', 'A', 'A', 'A', 'A', 'C', 'C'], 'error_correcting': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'C', 'C'] } total_array = np.concatenate((test_input['A'], test_input['B'], test_input['C'])) data_preparation = self.data_preparation try: svm = QSVM(data_preparation, train_input, test_input, total_array, multiclass_extension=method[multiclass_extension], lambda2=0) result = svm.run(self.qasm_simulator) self.assertAlmostEqual(result['testing_accuracy'], accuracy[multiclass_extension], places=4) self.assertEqual(result['predicted_classes'], predicted_classes[multiclass_extension]) except MissingOptionalLibraryError as ex: self.skipTest(str(ex))
temp = [test_input[k] for k in test_input] total_array = np.concatenate(temp) try: aqua_globals.random_seed = 10598 backend = BasicAer.get_backend('qasm_simulator') feature_map = ZZFeatureMap( feature_dimension=get_feature_dimension(training_input), reps=2, entanglement='linear') svm = QSVM(feature_map, training_input, test_input, total_array, multiclass_extension=AllPairs()) quantum_instance = QuantumInstance( backend, shots=1024, seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed) result = svm.run(quantum_instance) for k, v in result.items(): print(f'{k} : {v}') except Exception as e: print('QSVM error', e) # feature_dim = np.shape(X)[1] # # # Train test split
def test_multiclass_all_pairs(self): """ multiclass all pairs test """ 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 sorted(test_input)] total_array = np.concatenate(temp) result = SklearnSVM(training_input, test_input, total_array, multiclass_extension=AllPairs()).run() 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' ])
from qiskit.aqua.utils import split_dataset_to_data_and_labels from qiskit.aqua.algorithms import SklearnSVM from qiskit.aqua.components.multiclass_extensions import (OneAgainstRest, AllPairs, ErrorCorrectingCode) feature_dim = 2 # dimension of each data point sample_Total, training_input, test_input, class_labels = wine(training_size=20, test_size=10, n=feature_dim, plot_data=True) temp = [test_input[k] for k in test_input] total_array = np.concatenate(temp) extensions = [ OneAgainstRest(), AllPairs(), ErrorCorrectingCode(code_size=5) ] for extension in extensions: result = SklearnSVM(training_input, test_input, total_array, multiclass_extension=extension).run() print("\n----- Using multiclass extension: '{}' -----\n".format(extension.__class__.__name__)) for k,v in result.items(): print("'{}' : {}".format(k, v)) # 'testing_accuracy' : 1.0 # sumber = https://github.com/qiskit-community/qiskit-community-tutorials/blob/master/machine_learning/svm_classical_multiclass.ipynb
def compareMethods(class1, class2, class3 = None, backend=BasicAer.get_backend('qasm_simulator'), name = "", include_unscaled=False, include_QSVM = True, include_VQC = True, feature_dimension = 2, gamma = 'auto', C = 1.0): #Define header and chart data data = [] header = ["Algorithm", "Backend", "Time", "Accuracy", "Only one Class Predicted?"] data.append(header) #Split data into train and test class1_train, class1_test = train_test_split(class1, test_size=0.33, random_state=42) class2_train, class2_test = train_test_split(class2, test_size=0.33, random_state=42) feature_dim = feature_dimension if class3 is not None: class3_train, class3_test = train_test_split(class3, test_size=0.33, random_state=42) #Get input data for quantum training_data = {'A': np.asarray(class1_train), 'B': np.asarray(class2_train)} test_data = {'A': np.asarray(class1_test), 'B': np.asarray(class2_test)} total_array = np.concatenate((test_data['A'], test_data['B'])) if class3 is not None: training_data["C"] = class3_train test_data["C"] = class3_test total_array = np.concatenate((total_array, test_data['C'])) #Get input data for classical X_train, x_test, Y_train, y_test = convertFromQS(training_data, test_data) #Classical SVM, linear kernel (scaled and unscaled) if include_unscaled: start = time.time() clf = svm.SVC(kernel='linear') # Linear Kernel model = clf.fit(X_train, Y_train) y_pred = clf.predict(x_test) end = time.time() data.append(["SVM, Linear Kernel", "Local Processor", round(end-start), str(round(100*metrics.accuracy_score(y_test, y_pred), 2)),checkAllSame(y_pred)]) start = time.time() scaler = StandardScaler() X_train_std = scaler.fit_transform(X_train) x_test_std = scaler.fit_transform(x_test) clf = svm.SVC(kernel='linear') # Linear Kernel model = clf.fit(X_train_std, Y_train) y_pred = clf.predict(x_test_std) end = time.time() data.append(["SVM, Linear Kernel, scaled", "Local Processor", round(end-start), str(round(100*metrics.accuracy_score(y_test, y_pred), 2)),checkAllSame(y_pred)]) #Classical SVM, rbf kernel (scaled and unscaled) if include_unscaled: start = time.time() clf = svm.SVC(C=C, kernel='rbf', gamma = gamma) # rbf Kernel model = clf.fit(X_train, Y_train) y_pred = clf.predict(x_test) end = time.time() data.append(["SVM, RBF Kernel", "Local Processor", round(end-start), str(round(100*metrics.accuracy_score(y_test, y_pred), 2)),checkAllSame(y_pred)]) start = time.time() scaler = StandardScaler() X_train_std = scaler.fit_transform(X_train) x_test_std = scaler.fit_transform(x_test) clf = svm.SVC(C=C, kernel='rbf', gamma = gamma) # rbf Kernel model = clf.fit(X_train_std, Y_train) y_pred = clf.predict(x_test_std) end = time.time() data.append(["SVM, RBF Kernel, scaled", "Local Processor", round(end-start), str(round(100*metrics.accuracy_score(y_test, y_pred), 2)),checkAllSame(y_pred)]) #QSVM run if include_QSVM: start = time.time() feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement='linear') if class3 is None: qsvm = QSVM(feature_map, training_data, test_data, total_array) else: qsvm = QSVM(feature_map, training_data, test_data, total_array, multiclass_extension=AllPairs()) quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=10598, seed_transpiler=10598) resultSVM = qsvm.run(quantum_instance) end = time.time() QSVM_Summary = ["QSVM", backend.name(), round(end-start), str(round(100*resultSVM['testing_accuracy'], 2)), checkAllSame(resultSVM['predicted_classes'])] data.append(QSVM_Summary) path = 'C:\\Users\\admin\\Desktop\\QQML\\Code\\Saved_SVMs\\' + name + "_" + backend.name() + "_QSVM" if class3 is None: #Bug in package prevents saving Multiclass svms. Will find workaround or submit bug report if time. qsvm.save_model(path) #VQC run if include_VQC: start = time.time() optimizer = SPSA(max_trials=100, c0=4.0, skip_calibration=True) optimizer.set_options(save_steps=1) feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2) var_form = TwoLocal(feature_dim, ['ry', 'rz'], 'cz', reps=3) vqc = VQC(optimizer, feature_map, var_form, training_data, test_data, total_array) quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=10589, seed_transpiler=10598) resultVQC = vqc.run(quantum_instance) end = time.time() VQC_Summary = ["VQC", backend.name(), round(end-start), str(round(100*resultVQC['testing_accuracy'], 2)), checkAllSame(resultVQC['predicted_classes'])] data.append(VQC_Summary) path = 'C:\\Users\\admin\\Desktop\\QQML\\Code\\Saved_SVMs\\' + name + "_" + backend.name() + "_VQC" vqc.save_model(path) display(HTML(tabulate.tabulate(data, tablefmt='html'))) return data