df_collect_models.py

def _collect_models(self, runners, N):
    r_intercepts, r_coefs, r_weights = [], [], []
    self.models = []
    for runner in random.sample(runners, k=self.sample_size):
        r_model = runner.optimise(self.intercept_, self.coef_, self.runner_hyperparams)
        self.models.append(r_model)
        r_intercepts.append(r_model.intercept_)
        r_coefs.append(r_model.coef_)
        r_weights.append(runner.dataset_size()/N if self.combine == 'weighted' else 1/self.sample_size)
    return r_intercepts, r_coefs, r_weights