lightgbm.cv

lightgbm.cv(params, train_set, num_boost_round=100, folds=None, nfold=5, stratified=True, shuffle=True, metrics=None, feval=None, init_model=None, feature_name='auto', categorical_feature='auto', fpreproc=None, seed=0, callbacks=None, eval_train_metric=False, return_cvbooster=False)[source]

Perform the cross-validation with given parameters.

Parameters:

  • params (dict) – Parameters for training. Values passed through params take precedence over those supplied via arguments.

  • 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 (str, list of str, or None, optional (default=None)) – Evaluation metrics to be monitored while CV. If not None, the metric in params will be overridden.

  • feval (callable, list of callable, or None, optional (default=None)) –

    Customized evaluation function. Each evaluation function should accept two parameters: preds, eval_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples.

    predsnumpy 1-D array or numpy 2-D array (for multi-class task)

    The predicted values. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. If custom objective function is used, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case.

    eval_dataDataset

    A Dataset to evaluate.

    eval_namestr

    The name of evaluation function (without whitespace).

    eval_resultfloat

    The eval result.

    is_higher_betterbool

    Is eval result higher better, e.g. AUC is is_higher_better.

    To ignore the default metric corresponding to the used objective, set metrics to the string "None".

  • init_model (str, pathlib.Path, Booster or None, optional (default=None)) – Filename of LightGBM model or Booster instance used for continue training.

  • feature_name (list of str, or 'auto', optional (default="auto")) – Feature names. If ‘auto’ and data is pandas DataFrame, data columns names are used.

  • categorical_feature (list of str or int, or 'auto', optional (default="auto")) – Categorical features. If list of int, interpreted as indices. If list of str, 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 will be cast to int32 and thus 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. Floating point numbers in categorical features will be rounded towards 0.

  • fpreproc (callable or None, optional (default=None)) – Preprocessing function that takes (dtrain, dtest, params) and returns transformed versions of those.

  • seed (int, optional (default=0)) – Seed used to generate the folds (passed to numpy.random.seed).

  • callbacks (list of callable, 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.

  • return_cvbooster (bool, optional (default=False)) – Whether to return Booster models trained on each fold through CVBooster.

Note

A custom objective function can be provided for the objective parameter. It should accept two parameters: preds, train_data and return (grad, hess).

predsnumpy 1-D array or numpy 2-D array (for multi-class task)

The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task.

train_dataDataset

The training dataset.

gradnumpy 1-D array or numpy 2-D array (for multi-class task)

The value of the first order derivative (gradient) of the loss with respect to the elements of preds for each sample point.

hessnumpy 1-D array or numpy 2-D array (for multi-class task)

The value of the second order derivative (Hessian) of the loss with respect to the elements of preds for each sample point.

For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format.

Returns:

eval_results – History of evaluation results of each metric. The dictionary has the following format: {‘valid metric1-mean’: [values], ‘valid metric1-stdv’: [values], ‘valid metric2-mean’: [values], ‘valid metric2-stdv’: [values], …}. If return_cvbooster=True, also returns trained boosters wrapped in a CVBooster object via cvbooster key. If eval_train_metric=True, also returns the train metric history. In this case, the dictionary has the following format: {‘train metric1-mean’: [values], ‘valid metric1-mean’: [values], ‘train metric2-mean’: [values], ‘valid metric2-mean’: [values], …}.

Return type:

dict