def setup_keyring(keyring_name):
    '''Setup the keyring'''
    keyring_path = os.path.join("test", "outputdata", keyring_name)
    # Delete the entire keyring
    shutil.rmtree(keyring_path, ignore_errors=True)
    os.makedirs(keyring_path)
    gpg = GPG(gnupghome=keyring_path, gpgbinary="gpg")
    for key_name in ["key1_private", "key1_public"]:
        with open(os.path.join("test", "inputdata", key_name + ".txt"), "r") as keyfile:
            key_str = "".join(keyfile.readlines())
        import_result = gpg.import_keys(key_str)
        print("Import result:", type(import_result))
        print(import_result.__dict__)
        if import_result.count == 1 and len(set(import_result.fingerprints)) == 1:
            print("Got one import result")
    return gpg
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()