def test_drop_obs_column():
    adata = ad.AnnData(np.array(X_list), obs=obs_dict, dtype="int32")

    subset = adata[:2]
    assert subset.is_view
    # returns a copy of obs
    assert subset.obs.drop(columns=["oanno1"]).columns.tolist() == ["oanno2", "oanno3"]
    assert subset.is_view
    # would modify obs, so it should actualize subset and not modify adata
    subset.obs.drop(columns=["oanno1"], inplace=True)
    assert not subset.is_view
    assert subset.obs.columns.tolist() == ["oanno2", "oanno3"]

    assert adata.obs.columns.tolist() == ["oanno1", "oanno2", "oanno3"]
def balanced_accuracy_scoring(clf, X, y):
    """Scoring function that computes the balanced accuracy to be used
    internally in the cross-validation procedure.
    """
    y_pred = clf.predict(X)
    conf_mat = confusion_matrix(y, y_pred)
    bal_acc = 0.
    for i in range(len(conf_mat)):
        bal_acc += (float(conf_mat[i, i])) / np.sum(conf_mat[i])

    bal_acc /= len(conf_mat)
    return bal_acc