def METHOD_NAME(self):
    if self.test_data is None:
        return
    spike_times = self.test_data['spike_times']
    spike_clusters = self.test_data['spike_clusters']
    event_times = self.test_data['event_times']
    event_groups = self.test_data['event_groups']
    clf = MultinomialNB()
    cv = KFold(n_splits=2)
    times = np.column_stack(((event_times - 0.5), (event_times + 0.5)))
    counts, cluster_ids = get_spike_counts_in_bins(spike_times, spike_clusters, times)
    counts = counts.T
    accuracy, pred, prob, acc_training = classify(counts, event_groups, clf,
                                                  cross_validation=cv, return_training=True)
    self.assertTrue(accuracy == 0.2222222222222222)
    self.assertTrue(acc_training == 0.9444444444444444)
    self.assertEqual(pred.shape, event_groups.shape)
    self.assertEqual(prob.shape, event_groups.shape)