def load(self, filename, create_if_missing=True):
        filename = Path(filename)
        if not filename.exists() and create_if_missing:
            self.load_pip_requirements()
            with filename.open("w") as f:
                f.write(yaml.dump(self.serialized, default_flow_style=False,
                                  encoding=None))
            self.filename = filename
            return self.save(filename)
        with filename.open() as f:
            for group, requirements in yaml.load(f.read()).items():
                for requirement in requirements:
                    self[group].add(Requirement.coerce(requirement))
        self.filename = filename
def compute_svm_subjects(K, y, n_folds=5):
    """
    """
    cv = KFold(len(K)/2, n_folds)
    scores = np.zeros(n_folds)
    for i, (train, test) in enumerate(cv):
        train_ids = np.concatenate((train, len(K)/2+train))
        test_ids = np.concatenate((test, len(K)/2+test))
        clf = SVC(kernel='precomputed')
        clf.fit(K[train_ids, :][:, train_ids], y[train_ids])
        scores[i] = clf.score(K[test_ids, :][:, train_ids], y[test_ids])

    return scores.mean()