def test_views():
    X = np.array(X_list)
    adata = ad.AnnData(X, obs=obs_dict, var=var_dict, uns=uns_dict, dtype="int32")

    assert adata[:, 0].is_view
    assert adata[:, 0].X.tolist() == np.reshape([1, 4, 7], (3, 1)).tolist()

    adata[:2, 0].X = [0, 0]

    assert adata[:, 0].X.tolist() == np.reshape([0, 0, 7], (3, 1)).tolist()

    adata_subset = adata[:2, [0, 1]]

    assert adata_subset.is_view
    # now transition to actual object
    adata_subset.obs["foo"] = range(2)
    assert not adata_subset.is_view

    assert adata_subset.obs["foo"].tolist() == list(range(2))
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