'Score each of the estimators on the tested dimensions.
Parameters
X : array, shape (n_samples, nd_features, n_slices)
The input samples. For each data slice, the corresponding estimator
scores the prediction, e.g.:
``[estimators[ii].score(X[..., ii], y) for ii in range(n_slices)]``.
The feature dimension can be multidimensional e.g.
``X.shape = (n_samples, n_features_1, n_features_2, n_estimators)``.
y : array, shape (n_samples,) | (n_samples, n_targets)
The target values.
Returns
score : array, shape (n_samples, n_estimators, n_slices)
Score for each estimator / data slice couple.'

def score(self, X, y):

    from sklearn.metrics.scorer import check_scoring
    self._check_Xy(X)
    (parallel, p_func, n_jobs) = parallel_func(_gl_score, self.n_jobs)
    n_jobs = min(n_jobs, X.shape[(-1)])
    X_splits = np.array_split(X, n_jobs, axis=(-1))
    scoring = check_scoring(self.base_estimator, self.scoring)
    y = _fix_auc(scoring, y)
    score = parallel((p_func(self.estimators_, scoring, x, y) for x in X_splits))
    score = np.concatenate(score, axis=1)
    return score
