EigenProClassifier: check_classifier_multioutput common test fails

[rth]

EigenProClassifier fails in of the common tests from check_estimator with scikit-learn master,

____________________ test_all_estimators[EigenProClassifier()-check_classifier_multioutput] ____________________

estimator = EigenProClassifier(batch_size='auto', coef0=1, degree=3, gamma=0.02,
                   kernel='rbf', kernel_params=None, n_components=1000,
                   n_epoch=2, random_state=None, subsample_size='auto')
check = functools.partial(<function check_classifier_multioutput at 0x7f5070a540e0>, 'EigenProClassifier')

    @estimator_checks.parametrize_with_checks(ALL_ESTIMATORS)
    def test_all_estimators(estimator, check):
>       return check(estimator)

sklearn_extra/tests/test_common.py:19:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../scikit-learn/sklearn/utils/testing.py:326: in wrapper
    return fn(*args, **kwargs)
../scikit-learn/sklearn/utils/estimator_checks.py:1536: in check_classifier_multioutput
    estimator.fit(X, y)
sklearn_extra/kernel_methods/_eigenpro.py:641: in fit
    ensure_min_samples=3,
../scikit-learn/sklearn/utils/validation.py:743: in check_X_y
    y = column_or_1d(y, warn=True)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

y = array([[0, 1, 0],
       [1, 1, 1],
       [1, 1, 1],
       [1, 0, 1],
       [1, 1, 1],
       [0, 1, 1],
       [1,...[0, 1, 1],
       [1, 1, 1],
       [1, 1, 0],
       [0, 0, 0],
       [0, 1, 1],
       [0, 0, 1],
       [1, 1, 1]])
warn = True

    def column_or_1d(y, warn=False):
        """ Ravel column or 1d numpy array, else raises an error

        Parameters
        ----------
        y : array-like

        warn : boolean, default False
           To control display of warnings.

        Returns
        -------
        y : array

        """
        y = np.asarray(y)
        shape = np.shape(y)
        if len(shape) == 1:
            return np.ravel(y)
        if len(shape) == 2 and shape[1] == 1:
            if warn:
                warnings.warn("A column-vector y was passed when a 1d array was"
                              " expected. Please change the shape of y to "
                              "(n_samples, ), for example using ravel().",
                              DataConversionWarning, stacklevel=2)
            return np.ravel(y)

>       raise ValueError("bad input shape {0}".format(shape))
E       ValueError: bad input shape (42, 3)

../scikit-learn/sklearn/utils/validation.py:780: ValueError