lightgbm.train

lightgbm.train(params, train_set, num_boost_round=100, valid_sets=None, valid_names=None, feval=None, init_model=None, feature_name='auto', categorical_feature='auto', keep_training_booster=False, callbacks=None)[source]

Perform the training 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.

  • valid_sets (list of Dataset, or None, optional (default=None)) – List of data to be evaluated on during training.

  • valid_names (list of str, or None, optional (default=None)) – Names of valid_sets.

  • 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 whitespaces).

    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 the metric parameter to the string "None" in params.

  • 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.

  • keep_training_booster (bool, optional (default=False)) – Whether the returned Booster will be used to keep training. If False, the returned value will be converted into _InnerPredictor before returning. This means you won’t be able to use eval, eval_train or eval_valid methods of the returned Booster. When your model is very large and cause the memory error, you can try to set this param to True to avoid the model conversion performed during the internal call of model_to_string. You can still use _InnerPredictor as init_model for future continue training.

  • 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.

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

booster – The trained Booster model.

Return type

Booster