lightgbm.cv

lightgbm.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)[source]

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
    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, 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. The output cannot be monotonically constrained with respect to a categorical feature.
  • 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, set the first_metric_only parameter to True in params. 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 – Evaluation history. The dictionary has the following format: {‘metric1-mean’: [values], ‘metric1-stdv’: [values], ‘metric2-mean’: [values], ‘metric2-stdv’: [values], …}.

Return type:

dict