Function cv

python-package/lightgbmmt/engine.py:372–581

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 o

(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)

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)
        Customized objective function.
        Should accept two parameters: preds, train_data,
        and return (grad, hess).

            preds : list or numpy 1-D array
                The predicted values.
            train_data : Dataset
                The training dataset.
            grad : list or numpy 1-D array
                The value of the first order derivative (gradient) for each sample point.
            hess : list or numpy 1-D array
                The value of the second order derivative (Hessian) for each sample point.

        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 score[j * num_data + i]
        and you should group grad and hess in this way as well.

    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.

            preds : list or numpy 1-D array
                The predicted values.
            train_data : Dataset