LIGHTGBM(External algorithm library)¶
LightGBM is a gradient boosting framework that uses tree based learning algorithms.
Running with RecBole¶
convert_token_to_onehot (bool): If True, the token type features will be converted into onehot form. Defaults to
token_num_threhold (int): The threshold of doing onehot conversion.
lgb_silent (bool, optional): Whether print messages during construction.
lgb_model (file name of stored lgb model or 'Booster' instance):Lgb model to be loaded before training.
lgb_params (dict): Booster params.
lgb_learning_rates (int): List of learning rates for each boosting round or a customized function that calculates learning_rate in terms of current number of round.
lgb_num_boost_round (int): Number of boosting iterations.
lgb_early_stopping_rounds (int): Activates early stopping.
lgb_verbose_eval (bool or int): If verbose_eval is True then the evaluation metric on the validation set is printed at each boosting stage. If verbose_eval is an integer then the evaluation metric on the validation set is printed at every given verbose_eval boosting stage.
Please refer to [LightGBM Python package](https://lightgbm.readthedocs.io/en/latest/Python-API.html) for more details.
A Running Example:
Write the following code to a python file, such as run.py
from recbole.quick_start import run_recbole run_recbole(model='lightgbm', dataset='ml-100k')
If you want to change parameters, dataset or evaluation settings, take a look at