def load_pip_requirements(self, files_map=None, freeze=True):
        if files_map is None:
            files_map = self.default_pip_files
        for group, requirements_txt in files_map.items():
            path = Path(requirements_txt)
            if not path.exists() and group.lower() == "all" and freeze:
                cmd = envoy.run("pip freeze")
                self[group].loads(cmd.std_out)
            elif path.exists():
                self[group].load(path)
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()