recbole.quick_start

recbole.quick_start.quick_start.load_data_and_model(model_file)

Load filtered dataset, split dataloaders and saved model.

Parameters

model_file (str) – The path of saved model file.

Returns

• config (Config): An instance object of Config, which record parameter information in model_file.

• model (AbstractRecommender): The model load from model_file.

• dataset (Dataset): The filtered dataset.

• train_data (AbstractDataLoader): The dataloader for training.

• valid_data (AbstractDataLoader): The dataloader for validation.

• test_data (AbstractDataLoader): The dataloader for testing.

Return type

tuple

recbole.quick_start.quick_start.objective_function(config_dict=None, config_file_list=None, saved=True)

The default objective_function used in HyperTuning

Parameters

• config_dict (dict, optional) – Parameters dictionary used to modify experiment parameters. Defaults to None.

• config_file_list (list, optional) – Config files used to modify experiment parameters. Defaults to None.

• saved (bool, optional) – Whether to save the model. Defaults to True.

recbole.quick_start.quick_start.run_recbole(model=None, dataset=None, config_file_list=None, config_dict=None, saved=True)

A fast running api, which includes the complete process of training and testing a model on a specified dataset

Parameters

• model (str, optional) – Model name. Defaults to None.

• dataset (str, optional) – Dataset name. Defaults to None.

• config_file_list (list, optional) – Config files used to modify experiment parameters. Defaults to None.

• config_dict (dict, optional) – Parameters dictionary used to modify experiment parameters. Defaults to None.

• saved (bool, optional) – Whether to save the model. Defaults to True.