classifier.py 文件源码

def predict_kfold(cls, X, y, n_folds=10, seed=0, textModel_params={},
                      kfolds=None, pool=None, use_tqdm=True):
        try:
            from tqdm import tqdm
        except ImportError:
            def tqdm(x, **kwargs):
                return x

        le = preprocessing.LabelEncoder().fit(y)
        y = np.array(le.transform(y))
        hy = np.zeros(len(y), dtype=np.int)
        if kfolds is None:
            kfolds = StratifiedKFold(n_splits=n_folds, shuffle=True,
                                     random_state=seed).split(X, y)
        args = [(X, y, tr, ts, textModel_params) for tr, ts in kfolds]
        if pool is not None:
            if use_tqdm:
                res = [x for x in tqdm(pool.imap_unordered(cls.train_predict_pool, args),
                                       desc='Params', total=len(args))]
            else:
                res = [x for x in pool.imap_unordered(cls.train_predict_pool, args)]
        else:
            if use_tqdm:
                args = tqdm(args)
            res = [cls.train_predict_pool(x) for x in args]
        for ts, _hy in res:
            hy[ts] = _hy
        return le.inverse_transform(hy)