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_save_and_load_model(self): """ save and load model test """ np.random.seed(self.random_seed) aqua_globals.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(feature_dimension=num_qubits, depth=2) var_form = RYRZ(num_qubits=num_qubits, depth=3) vqc = VQC(optimizer, feature_map, var_form, self.training_data, self.testing_data) quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=self.random_seed, seed_transpiler=self.random_seed) result = vqc.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(1.0, result['testing_accuracy']) file_path = self._get_resource_path('vqc_test.npz') vqc.save_model(file_path) self.assertTrue(os.path.exists(file_path)) loaded_vqc = VQC(optimizer, feature_map, var_form, self.training_data, None) loaded_vqc.load_model(file_path) np.testing.assert_array_almost_equal( loaded_vqc.ret['opt_params'], self.ref_opt_params, decimal=4) loaded_test_acc = loaded_vqc.test(vqc.test_dataset[0], vqc.test_dataset[1], quantum_instance) self.assertEqual(result['testing_accuracy'], loaded_test_acc) predicted_probs, predicted_labels = loaded_vqc.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 Exception: # pylint: disable=broad-except pass
def test_save_and_load_model(self, mode): """ save and load model test """ aqua_globals.random_seed = self.seed backend = BasicAer.get_backend('qasm_simulator') optimizer = SPSA(max_trials=10, save_steps=1, c0=4.0, skip_calibration=True) data_preparation = self.data_preparation[mode] wavefunction = self.ryrz_wavefunction[mode] if mode == 'wrapped': warnings.filterwarnings('ignore', category=DeprecationWarning) # set up algorithm vqc = VQC(optimizer, data_preparation, wavefunction, self.training_data, self.testing_data) quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=self.seed, seed_transpiler=self.seed) result = vqc.run(quantum_instance) np.testing.assert_array_almost_equal(result['opt_params'], self.ref_opt_params[mode], decimal=4) np.testing.assert_array_almost_equal(result['training_loss'], self.ref_train_loss[mode], decimal=8) self.assertEqual(1.0 if mode == 'wrapped' else 0.5, result['testing_accuracy']) file_path = self.get_resource_path('vqc_test.npz') vqc.save_model(file_path) self.assertTrue(os.path.exists(file_path)) loaded_vqc = VQC(optimizer, data_preparation, wavefunction, self.training_data, None) if mode == 'wrapped': warnings.filterwarnings('always', category=DeprecationWarning) loaded_vqc.load_model(file_path) np.testing.assert_array_almost_equal(loaded_vqc.ret['opt_params'], self.ref_opt_params[mode], decimal=4) loaded_test_acc = loaded_vqc.test(vqc.test_dataset[0], vqc.test_dataset[1], quantum_instance) self.assertEqual(result['testing_accuracy'], loaded_test_acc) predicted_probs, predicted_labels = loaded_vqc.predict( self.testing_data['A'], quantum_instance) np.testing.assert_array_almost_equal(predicted_probs, self.ref_prediction_a_probs[mode], decimal=8) np.testing.assert_array_equal(predicted_labels, self.ref_prediction_a_label[mode]) if os.path.exists(file_path): try: os.remove(file_path) except Exception: # pylint: disable=broad-except pass
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
def test_save_and_load_model(self, use_circuits): """ save and load model test """ aqua_globals.random_seed = self.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(feature_dimension=num_qubits, depth=2) var_form = RYRZ(num_qubits=num_qubits, depth=3) # convert to circuit if circuits should be used if use_circuits: x = ParameterVector('x', feature_map.feature_dimension) feature_map = feature_map.construct_circuit(x) theta = ParameterVector('theta', var_form.num_parameters) var_form = var_form.construct_circuit(theta) # set up algorithm vqc = VQC(optimizer, feature_map, var_form, self.training_data, self.testing_data) # sort parameters for reproducibility if use_circuits: vqc._feature_map_params = list(x) vqc._var_form_params = list(theta) quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=self.seed, seed_transpiler=self.seed) result = vqc.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(1.0, result['testing_accuracy']) file_path = self.get_resource_path('vqc_test.npz') vqc.save_model(file_path) self.assertTrue(os.path.exists(file_path)) loaded_vqc = VQC(optimizer, feature_map, var_form, self.training_data, None) # sort parameters for reproducibility if use_circuits: loaded_vqc._feature_map_params = list(x) loaded_vqc._var_form_params = list(theta) loaded_vqc.load_model(file_path) np.testing.assert_array_almost_equal(loaded_vqc.ret['opt_params'], self.ref_opt_params, decimal=4) loaded_test_acc = loaded_vqc.test(vqc.test_dataset[0], vqc.test_dataset[1], quantum_instance) self.assertEqual(result['testing_accuracy'], loaded_test_acc) predicted_probs, predicted_labels = loaded_vqc.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 os.path.exists(file_path): try: os.remove(file_path) except Exception: # pylint: disable=broad-except pass