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 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()