dc045bebd58dbf547728ff2f34e1d9a0fcb916f9,test/aqua/test_qsvm.py,TestQSVM,test_qsvm_multiclass_one_against_all,#TestQSVM#,208

Before Change

    def test_qsvm_multiclass_one_against_all(self):
         QSVM Multiclass One Against All test 
        backend = BasicAer.get_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": "classification", "random_seed": self.random_seed},
            "algorithm": {
                "name": "QSVM",
            },
            "backend": {"shots": self.shots},
            "multiclass_extension": {"name": "OneAgainstRest"},
            "feature_map": {"name": "SecondOrderExpansion", "depth": 2, "entangler_map": [[0, 1]]}
        }

        algo_input = ClassificationInput(training_input, test_input, total_array)

        result = run_algorithm(params, algo_input, backend=backend)

        expected_accuracy = 0.444444444
        expected_classes = ["A", "A", "C", "A", "A", "A", "A", "C", "C"]
        self.assertAlmostEqual(result["testing_accuracy"], expected_accuracy, places=4)

After Change

    def test_qsvm_multiclass_one_against_all(self):
         QSVM Multiclass One Against All 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=OneAgainstRest(_QSVM_Estimator, [feature_map]))
        quantum_instance = QuantumInstance(BasicAer.get_backend("qasm_simulator"), shots=self.shots)
        result = svm.run(quantum_instance)
        expected_accuracy = 0.444444444
        expected_classes = ["A", "A", "C", "A", "A", "A", "A", "C", "C"]
        self.assertAlmostEqual(result["testing_accuracy"], expected_accuracy, places=4)
        self.assertEqual(result["predicted_classes"], expected_classes)

Instances