exp_linear.py 文件源码

def saga_decision_function(dataset, k, link_alpha, prop_alpha, l1_ratio):

    fn = cache_fname("linear_val_df", (dataset, k, link_alpha, prop_alpha,
                                       l1_ratio))

    if os.path.exists(fn):
        logging.info("Loading {}".format(fn))
        with open(fn, "rb") as f:
            return dill.load(f)

    ds = 'erule' if dataset == 'cdcp' else 'ukp-essays'  # sorry
    path = os.path.join("data", "process", ds, "folds", "{}", "{}")

    # sorry again: get val docs
    n_folds = 5 if dataset == 'ukp' else 3
    load, ids = get_dataset_loader(dataset, "train")
    for k_, (_, val) in enumerate(KFold(n_folds).split(ids)):
        if k_ == k:
            break
    val_docs = list(load(ids[val]))

    X_tr_link, y_tr_link = load_csr(path.format(k, 'train.npz'),
                                    return_y=True)
    X_te_link, y_te_link = load_csr(path.format(k, 'val.npz'),
                                    return_y=True)

    X_tr_prop, y_tr_prop = load_csr(path.format(k, 'prop-train.npz'),
                                    return_y=True)
    X_te_prop, y_te_prop = load_csr(path.format(k, 'prop-val.npz'),
                                    return_y=True)

    baseline = BaselineStruct(link_alpha, prop_alpha, l1_ratio)
    baseline.fit(X_tr_link, y_tr_link, X_tr_prop, y_tr_prop)

    Y_marg = baseline.decision_function(X_te_link, X_te_prop, val_docs)

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

    return Y_marg, baseline