Make a n-fold list of Booster from random indices.

def _make_n_folds(full_data, folds, nfold, params, seed, fpreproc=None, stratified=True,
                  shuffle=True, eval_train_metric=False):
    """Make a n-fold list of Booster from random indices."""
    full_data = full_data.construct()
    num_data = full_data.num_data()
    if folds is not None:
        if not hasattr(folds, '__iter__') and not hasattr(folds, 'split'):
            raise AttributeError("folds should be a generator or iterator of (train_idx, test_idx) tuples "
                                 "or scikit-learn splitter object with split method")
        if hasattr(folds, 'split'):
            group_info = full_data.get_group()
            if group_info is not None:
                group_info = group_info.astype(int)
                flatted_group = np.repeat(range_(len(group_info)), repeats=group_info)
            else:
                flatted_group = np.zeros(num_data, dtype=int)
            folds = folds.split(X=np.zeros(num_data), y=full_data.get_label(), groups=flatted_group)
    else:
        if 'objective' in params and params['objective'] == 'lambdarank':
            if not SKLEARN_INSTALLED:
                raise LightGBMError('Scikit-learn is required for lambdarank cv.')
            # lambdarank task, split according to groups
            group_info = full_data.get_group().astype(int)
            flatted_group = np.repeat(range_(len(group_info)), repeats=group_info)
            group_kfold = _LGBMGroupKFold(n_splits=nfold)
            folds = group_kfold.split(X=np.zeros(num_data), groups=flatted_group)
        elif stratified:
            if not SKLEARN_INSTALLED:
                raise LightGBMError('Scikit-learn is required for stratified cv.')
            skf = _LGBMStratifiedKFold(n_splits=nfold, shuffle=shuffle, random_state=seed)
            folds = skf.split(X=np.zeros(num_data), y=full_data.get_label())
        else:
            if shuffle:
                randidx = np.random.RandomState(seed).permutation(num_data)
            else:
                randidx = np.arange(num_data)
            kstep = int(num_data / nfold)
            test_id = [randidx[i: i + kstep] for i in range_(0, num_data, kstep)]
            train_id = [np.concatenate([test_id[i] for i in range_(nfold) if k != i]) for k in range_(nfold)]
            folds = zip_(train_id, test_id)

    ret = _CVBooster()
    for train_idx, test_idx in folds:
        train_set = full_data.subset(train_idx)
        valid_set = full_data.subset(test_idx)
        # run preprocessing on the data set if needed
        if fpreproc is not None:
            train_set, valid_set, tparam = fpreproc(train_set, valid_set, params.copy())
        else:
            tparam = params
        cvbooster = Booster(tparam, train_set)
        if eval_train_metric:
            cvbooster.add_valid(train_set, 'train')
        cvbooster.add_valid(valid_set, 'valid')
        ret.append(cvbooster)
    return ret