Function train

python-package/lightgbmmt/engine.py:18–273

Perform the training with given parameters. Parameters ---------- params : dict Parameters for training. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. valid_sets : list of Datas

(params, train_set, num_boost_round=100,
          valid_sets=None, valid_names=None,
          fobj=None, feval=None, init_model=None,
          feature_name='auto', categorical_feature='auto',
          early_stopping_rounds=None, evals_result=None,
          verbose_eval=True, learning_rates=None,
          keep_training_booster=False, callbacks=None)

def train(params, train_set, num_boost_round=100,
          valid_sets=None, valid_names=None,
          fobj=None, feval=None, init_model=None,
          feature_name='auto', categorical_feature='auto',
          early_stopping_rounds=None, evals_result=None,
          verbose_eval=True, learning_rates=None,
          keep_training_booster=False, callbacks=None):
    """Perform the training with given parameters.

    Parameters
    ----------
    params : dict
        Parameters for training.
    train_set : Dataset
        Data to be trained on.
    num_boost_round : int, optional (default=100)
        Number of boosting iterations.
    valid_sets : list of Datasets or None, optional (default=None)
        List of data to be evaluated on during training.
    valid_names : list of strings or None, optional (default=None)
        Names of ``valid_sets``.
    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
                The training dataset.
            eval_name : string
                The name of evaluation function (without whitespaces).
            eval_result : float
                The eval result.
            is_higher_better : bool
                Is eval result higher better, e.g. AUC is ``is_higher_better``.

        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,