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# n_estimators = 5
# X_dense = iris.data
# y = iris.target
#
# for sparse_format in [csc_matrix, csr_matrix, lil_matrix, coo_matrix,
# dok_matrix]:
# for views_ind in (iris.views_ind, np.array([[0, 2], [1, 3]])):
# X_sparse = sparse_format(X_dense)
#
# clf_sparse = MuCumboClassifier(
# base_estimator=CustomSVC(),
# random_state=seed,
# n_estimators=n_estimators)
# clf_sparse.fit(X_sparse, y, views_ind)
#
# clf_dense = MuCumboClassifier(
# base_estimator=CustomSVC(),
# random_state=seed,
# n_estimators=n_estimators)
# clf_dense.fit(X_dense, y, views_ind)
#
# assert_array_equal(clf_sparse.decision_function(X_sparse),
# clf_dense.decision_function(X_dense))
#
# assert_array_equal(clf_sparse.predict(X_sparse),
# clf_dense.predict(X_dense))
#
# assert_equal(clf_sparse.score(X_sparse, y),
# clf_dense.score(X_dense, y))
#
# for res_sparse, res_dense in \
# zip(clf_sparse.staged_decision_function(X_sparse),
# clf_dense.staged_decision_function(X_dense)):
# assert_array_equal(res_sparse, res_dense)
#
# for res_sparse, res_dense in \
# zip(clf_sparse.staged_predict(X_sparse),
# clf_dense.staged_predict(X_dense)):
# assert_array_equal(res_sparse, res_dense)
#
# for res_sparse, res_dense in \
# zip(clf_sparse.staged_score(X_sparse, y),
# clf_dense.staged_score(X_dense, y)):
# assert_equal(res_sparse, res_dense)
#
# # Check that sparsity of data is maintained during training
# types = [clf.data_type_ for clf in clf_sparse.estimators_]
# if sparse_format == csc_matrix:
# assert_true(all([type_ == csc_matrix for type_ in types]))
# else:
# assert_true(all([type_ == csr_matrix for type_ in types]))
#
# def test():
# cumbo = MuCumboClassifier()
if __name__ == '__main__':
unittest.main()
# suite = unittest.TestLoader().loadTestsFromTestCase
# (TestMuCumboClassifier().test_class_variation())
# unittest.TextTestRunner(verbosity=2).run(suite)