def METHOD_NAME(self):
    """Integration test for simple pipeline without Switch."""
    # PHOTONAI implementation
    self.pipe.add(PipelineElement('StandardScaler'))
    self.pipe += PipelineElement('PCA', hyperparameters={'n_components': IntegerRange(5, 30)})
    self.pipe += PipelineElement('SVC', hyperparameters={'kernel': Categorical(["rbf", 'poly']),
                                                         'C': FloatRange(0.001, 2, range_type='logspace')},
                                 gamma='auto')
    self.X, self.y = self.simple_classification()
    self.pipe.fit(self.X, self.y)
    # direct AUTO ML implementation
    # Build Configuration Space which defines all parameters and their ranges
    cs = ng.p.Instrumentation(
        PCA__n_components=ng.p.Scalar(lower=5, upper=30).set_integer_casting(),
        SVC__kernel=ng.p.Choice(["rbf", 'poly']),
        SVC__C=ng.p.Log(lower=0.001, upper=2)
    )
    cs.random_state.seed(42)
    # Optimize, using a Nevergrad directly
    optimizer = ng.optimizers.NGO(parametrization=cs, budget=20)
    optimizer.minimize(self.objective_function_simple)
    runhistory_photon = [x.metrics_test[0].value for x in self.pipe.results.outer_folds[0].tested_config_list]
    runhistory_original = [1-x.mean for x in list(optimizer.optim.archive.bytesdict.values())]
    self.assertListEqual(runhistory_photon, runhistory_original)