def test_set_subset_obsm(adata, subset_func):
    init_hash = joblib.hash(adata)
    orig_obsm_val = adata.obsm["o"].copy()

    while True:
        subset_idx = slice_subset(adata.obs_names)
        if len(adata[subset_idx, :]) > 2:
            break
    subset = adata[subset_idx, :]

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

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

    assert init_hash == joblib.hash(adata)
def compute_svm_score_nestedCV(K, y, n_folds,
                               scoring=balanced_accuracy_scoring,
                               random_state=None,
                               param_grid=[{'C': np.logspace(-5, 5, 25)}]):
    """Compute cross-validated score of SVM using precomputed kernel.
    """
    cv = StratifiedKFold(y, n_folds=n_folds, shuffle=True,
                         random_state=random_state)
    scores = np.zeros(n_folds)
    for i, (train, test) in enumerate(cv):
        cvclf = SVC(kernel='precomputed')
        y_train = y[train]
        cvcv = StratifiedKFold(y_train, n_folds=n_folds,
                               shuffle=True,
                               random_state=random_state)
        clf = GridSearchCV(cvclf, param_grid=param_grid, scoring=scoring,
                           cv=cvcv, n_jobs=1)
        clf.fit(K[train, :][:, train], y_train)
        # print clf.best_params_
        scores[i] = clf.score(K[test, :][:, train], y[test])

    return scores.mean()