LIGHTGBM(External algorithm library)

Introduction

[LightGBM]

LightGBM is a gradient boosting framework that uses tree based learning algorithms.

Running with RecBole

Model Hyper-Parameters:

• convert_token_to_onehot (bool) : If True, the token type features will be converted into onehot form. Defaults to False.

• 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')

And then:

python run.py

If you want to change parameters, dataset or evaluation settings, take a look at

• Config Introduction

• Data Module Introduction

• Training & Evaluation Introduction

• Usage