def test_set_subset_varm(adata, subset_func):
    init_hash = joblib.hash(adata)
    orig_varm_val = adata.varm["o"].copy()

    while True:
        subset_idx = slice_subset(adata.var_names)
        if (adata[:, subset_idx]).shape[1] > 2:
            break
    subset = adata[:, subset_idx]

    internal_idx = _normalize_index(
        subset_func(np.arange(subset.varm["o"].shape[0])), subset.var_names
    )

    assert subset.is_view
    subset.varm["o"][internal_idx] = 1
    assert not subset.is_view
    assert np.all(adata.varm["o"] == orig_varm_val)

    assert init_hash == joblib.hash(adata)
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