recbole.data.utils¶
-
class
recbole.data.utils.DLFriendlyAPI[source]¶ Bases:
objectA Decorator class, which helps copying
Datasetmethods toDataLoader.These methods are called DataLoader Friendly APIs.
E.g. if
train_datais an object ofDataLoader, andnum()is a method ofDataset, Cause it has been decorated,num()can be called directly bytrain_data.See the example of
set()for details.-
dataloader_apis¶ Register table that saves all the method names of DataLoader Friendly APIs.
- Type
set
-
-
recbole.data.utils.create_dataset(config)[source]¶ Create dataset according to
config['model']andconfig['MODEL_TYPE'].
-
recbole.data.utils.data_preparation(config, dataset, save=False)[source]¶ Split the dataset by
config['eval_setting']and calldataloader_construct()to create corresponding dataloader.- Parameters
- Returns
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.data.utils.get_data_loader(name, config, neg_sample_args)[source]¶ Return a dataloader class according to
configandeval_setting.- Parameters
name (str) – The stage of dataloader. It can only take two values: ‘train’ or ‘evaluation’.
config (Config) – An instance object of Config, used to record parameter information.
neg_sample_args (dict) – Settings of negative sampling.
- Returns
The dataloader class that meets the requirements in
configandeval_setting.- Return type
type