def cv(params, train_set, num_boost_round=100,
       folds=None, nfold=5, stratified=True, shuffle=True,
       metrics=None, fobj=None, feval=None, init_model=None,
       feature_name='auto', categorical_feature='auto',
       early_stopping_rounds=None, fpreproc=None,
       verbose_eval=None, show_stdv=True, seed=0,
       callbacks=None, eval_train_metric=False):
    """Perform the cross-validation with given paramaters.

    Parameters
    ----------
    params : dict
        Parameters for Booster.
    train_set : Dataset
        Data to be trained on.
    num_boost_round : int, optional (default=100)
        Number of boosting iterations.
    folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None)
        If generator or iterator, it should yield the train and test indices for each fold.
        If object, it should be one of the scikit-learn splitter classes
        (https://scikit-learn.org/stable/modules/classes.html#splitter-classes)
        and have ``split`` method.
        This argument has highest priority over other data split arguments.
    nfold : int, optional (default=5)
        Number of folds in CV.
    stratified : bool, optional (default=True)
        Whether to perform stratified sampling.
    shuffle : bool, optional (default=True)
        Whether to shuffle before splitting data.
    metrics : string, list of strings or None, optional (default=None)
        Evaluation metrics to be monitored while CV.
        If not None, the metric in ``params`` will be overridden.
    fobj : callable or None, optional (default=None)
        Custom objective function.
    feval : callable or None, optional (default=None)
        Customized evaluation function.
        Should accept two parameters: preds, train_data,
        and return (eval_name, eval_result, is_higher_better) or list of such tuples.
        For multi-class task, the preds is group by class_id first, then group by row_id.
        If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
        To ignore the default metric corresponding to the used objective,
        set ``metrics`` to the string ``"None"``.
    init_model : string, Booster or None, optional (default=None)
        Filename of LightGBM model or Booster instance used for continue training.
    feature_name : list of strings or 'auto', optional (default="auto")
        Feature names.
        If 'auto' and data is pandas DataFrame, data columns names are used.
    categorical_feature : list of strings or int, or 'auto', optional (default="auto")
        Categorical features.
        If list of int, interpreted as indices.
        If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
        If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
        All values in categorical features should be less than int32 max value (2147483647).
        Large values could be memory consuming. Consider using consecutive integers starting from zero.
        All negative values in categorical features will be treated as missing values.
    early_stopping_rounds : int or None, optional (default=None)
        Activates early stopping.
        CV score needs to improve at least every ``early_stopping_rounds`` round(s)
        to continue.
        Requires at least one metric. If there's more than one, will check all of them.
        To check only the first metric you can pass in ``callbacks``
        ``early_stopping`` callback with ``first_metric_only=True``.
        Last entry in evaluation history is the one from the best iteration.
    fpreproc : callable or None, optional (default=None)
        Preprocessing function that takes (dtrain, dtest, params)
        and returns transformed versions of those.
    verbose_eval : bool, int, or None, optional (default=None)
        Whether to display the progress.
        If None, progress will be displayed when np.ndarray is returned.
        If True, progress will be displayed at every boosting stage.
        If int, progress will be displayed at every given ``verbose_eval`` boosting stage.
    show_stdv : bool, optional (default=True)
        Whether to display the standard deviation in progress.
        Results are not affected by this parameter, and always contain std.
    seed : int, optional (default=0)
        Seed used to generate the folds (passed to numpy.random.seed).
    callbacks : list of callables or None, optional (default=None)
        List of callback functions that are applied at each iteration.
        See Callbacks in Python API for more information.
    eval_train_metric : bool, optional (default=False)
        Whether to display the train metric in progress.
        The score of the metric is calculated again after each training step, so there is some impact on performance.

    Returns
    -------
    eval_hist : dict
        Evaluation history.
        The dictionary has the following format:
        {'metric1-mean': [values], 'metric1-stdv': [values],
        'metric2-mean': [values], 'metric2-stdv': [values],
        ...}.
    """
    if not isinstance(train_set, Dataset):
        raise TypeError("Traninig only accepts Dataset object")

    params = copy.deepcopy(params)
    if fobj is not None:
        params['objective'] = 'none'
    for alias in ["num_iterations", "num_iteration", "n_iter", "num_tree", "num_trees",
                  "num_round", "num_rounds", "num_boost_round", "n_estimators"]:
        if alias in params:
            warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
            num_boost_round = params.pop(alias)
            break
    for alias in ["early_stopping_round", "early_stopping_rounds", "early_stopping"]:
        if alias in params:
            warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
            early_stopping_rounds = params.pop(alias)
            break

    if num_boost_round <= 0:
        raise ValueError("num_boost_round should be greater than zero.")
    if isinstance(init_model, string_type):
        predictor = _InnerPredictor(model_file=init_model, pred_parameter=params)
    elif isinstance(init_model, Booster):
        predictor = init_model._to_predictor(dict(init_model.params, **params))
    else:
        predictor = None
    train_set._update_params(params) \
             ._set_predictor(predictor) \
             .set_feature_name(feature_name) \
             .set_categorical_feature(categorical_feature)

    if metrics is not None:
        params['metric'] = metrics

    results = collections.defaultdict(list)
    cvfolds = _make_n_folds(train_set, folds=folds, nfold=nfold,
                            params=params, seed=seed, fpreproc=fpreproc,
                            stratified=stratified, shuffle=shuffle,
                            eval_train_metric=eval_train_metric)

    # setup callbacks
    if callbacks is None:
        callbacks = set()
    else:
        for i, cb in enumerate(callbacks):
            cb.__dict__.setdefault('order', i - len(callbacks))
        callbacks = set(callbacks)
    if early_stopping_rounds is not None:
        callbacks.add(callback.early_stopping(early_stopping_rounds, verbose=False))
    if verbose_eval is True:
        callbacks.add(callback.print_evaluation(show_stdv=show_stdv))
    elif isinstance(verbose_eval, integer_types):
        callbacks.add(callback.print_evaluation(verbose_eval, show_stdv=show_stdv))

    callbacks_before_iter = {cb for cb in callbacks if getattr(cb, 'before_iteration', False)}
    callbacks_after_iter = callbacks - callbacks_before_iter
    callbacks_before_iter = sorted(callbacks_before_iter, key=attrgetter('order'))
    callbacks_after_iter = sorted(callbacks_after_iter, key=attrgetter('order'))

    for i in range_(num_boost_round):
        for cb in callbacks_before_iter:
            cb(callback.CallbackEnv(model=cvfolds,
                                    params=params,
                                    iteration=i,
                                    begin_iteration=0,
                                    end_iteration=num_boost_round,
                                    evaluation_result_list=None))
        cvfolds.update(fobj=fobj)
        res = _agg_cv_result(cvfolds.eval_valid(feval), eval_train_metric)
        for _, key, mean, _, std in res:
            results[key + '-mean'].append(mean)
            results[key + '-stdv'].append(std)
        try:
            for cb in callbacks_after_iter:
                cb(callback.CallbackEnv(model=cvfolds,
                                        params=params,
                                        iteration=i,
                                        begin_iteration=0,
                                        end_iteration=num_boost_round,
                                        evaluation_result_list=res))
        except callback.EarlyStopException as earlyStopException:
            cvfolds.best_iteration = earlyStopException.best_iteration + 1
            for k in results:
                results[k] = results[k][:cvfolds.best_iteration]
            break
    return dict(results)
Perform the cross-validation with given paramaters.

    Parameters
    ----------
    params : dict
        Parameters for Booster.
    train_set : Dataset
        Data to be trained on.
    num_boost_round : int, optional (default=100)
        Number of boosting iterations.
    folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None)
        If generator or iterator, it should yield the train and test indices for each fold.
        If object, it should be one of the scikit-learn splitter classes
        (https://scikit-learn.org/stable/modules/classes.html#splitter-classes)
        and have ``split`` method.
        This argument has highest priority over other data split arguments.
    nfold : int, optional (default=5)
        Number of folds in CV.
    stratified : bool, optional (default=True)
        Whether to perform stratified sampling.
    shuffle : bool, optional (default=True)
        Whether to shuffle before splitting data.
    metrics : string, list of strings or None, optional (default=None)
        Evaluation metrics to be monitored while CV.
        If not None, the metric in ``params`` will be overridden.
    fobj : callable or None, optional (default=None)
        Custom objective function.
    feval : callable or None, optional (default=None)
        Customized evaluation function.
        Should accept two parameters: preds, train_data,
        and return (eval_name, eval_result, is_higher_better) or list of such tuples.
        For multi-class task, the preds is group by class_id first, then group by row_id.
        If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
        To ignore the default metric corresponding to the used objective,
        set ``metrics`` to the string ``"None"``.
    init_model : string, Booster or None, optional (default=None)
        Filename of LightGBM model or Booster instance used for continue training.
    feature_name : list of strings or 'auto', optional (default="auto")
        Feature names.
        If 'auto' and data is pandas DataFrame, data columns names are used.
    categorical_feature : list of strings or int, or 'auto', optional (default="auto")
        Categorical features.
        If list of int, interpreted as indices.
        If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
        If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
        All values in categorical features should be less than int32 max value (2147483647).
        Large values could be memory consuming. Consider using consecutive integers starting from zero.
        All negative values in categorical features will be treated as missing values.
    early_stopping_rounds : int or None, optional (default=None)
        Activates early stopping.
        CV score needs to improve at least every ``early_stopping_rounds`` round(s)
        to continue.
        Requires at least one metric. If there's more than one, will check all of them.
        To check only the first metric you can pass in ``callbacks``
        ``early_stopping`` callback with ``first_metric_only=True``.
        Last entry in evaluation history is the one from the best iteration.
    fpreproc : callable or None, optional (default=None)
        Preprocessing function that takes (dtrain, dtest, params)
        and returns transformed versions of those.
    verbose_eval : bool, int, or None, optional (default=None)
        Whether to display the progress.
        If None, progress will be displayed when np.ndarray is returned.
        If True, progress will be displayed at every boosting stage.
        If int, progress will be displayed at every given ``verbose_eval`` boosting stage.
    show_stdv : bool, optional (default=True)
        Whether to display the standard deviation in progress.
        Results are not affected by this parameter, and always contain std.
    seed : int, optional (default=0)
        Seed used to generate the folds (passed to numpy.random.seed).
    callbacks : list of callables or None, optional (default=None)
        List of callback functions that are applied at each iteration.
        See Callbacks in Python API for more information.
    eval_train_metric : bool, optional (default=False)
        Whether to display the train metric in progress.
        The score of the metric is calculated again after each training step, so there is some impact on performance.

    Returns
    -------
    eval_hist : dict
        Evaluation history.
        The dictionary has the following format:
        {'metric1-mean': [values], 'metric1-stdv': [values],
        'metric2-mean': [values], 'metric2-stdv': [values],
        ...}.