exp_linear.py 文件源码

def linear_cv_score(dataset, alpha, l1_ratio, constraints):

    fn = cache_fname("linear_cv_score", (dataset, alpha, l1_ratio,
                                         constraints))
    if os.path.exists(fn):
        logging.info("Loading {}".format(fn))
        with open(fn, "rb") as f:
            return dill.load(f)

    load, ids = get_dataset_loader(dataset, split="train")
    n_folds = 5 if dataset == 'ukp' else 3

    scores = []
    for k, (tr, val) in enumerate(KFold(n_folds).split(ids)):
        Y_marg, bl = saga_decision_function(dataset, k, alpha, alpha, l1_ratio)

        val_docs = list(load(ids[val]))
        Y_true = [doc.label for doc in val_docs]
        Y_pred = bl.fast_decode(Y_marg, val_docs, constraints)

        scores.append(bl._score(Y_true, Y_pred))

    with open(fn, "wb") as f:
        logging.info("Saving {}".format(fn))
        dill.dump(scores, f)
    return scores