def METHOD_NAME(self, **cfg):
    cfg = {k: cfg[k] for k in cfg if cfg[k]}
    values = []
    train_indices = list(self.pipe.cross_validation.outer_folds.values())[0].train_indices
    self._validation_X, self._validation_y, _ = PhotonDataHelper.split_data(self.X, self.y,
                                                                            kwargs=None, indices=train_indices)
    for inner_fold in list(list(self.pipe.cross_validation.inner_folds.values())[0].values()):
        sc = PipelineElement("StandardScaler", {})
        pca = PipelineElement("PCA", {}, random_state=42)
        svc = PipelineElement("SVC", {}, random_state=42, gamma='auto')
        my_pipe = PhotonPipeline([('StandardScaler', sc), ('PCA', pca), ('SVC', svc)])
        my_pipe.set_params(**cfg)
        my_pipe.fit(self._validation_X[inner_fold.train_indices, :],
                    self._validation_y[inner_fold.train_indices])
        values.append(accuracy_score(self._validation_y[inner_fold.test_indices],
                                     my_pipe.predict(self._validation_X[inner_fold.test_indices, :])
                                     )
                      )
    return 1-np.mean(values)