recbole.data.dataloader.abstract_dataloader

class recbole.data.dataloader.abstract_dataloader.AbstractDataLoader(config, dataset, sampler, shuffle=False)[source]

Bases: torch.utils.data.dataloader.DataLoader

AbstractDataLoader is an abstract object which would return a batch of data which is loaded by Interaction when it is iterated. And it is also the ancestor of all other dataloader.

Parameters

  • config (Config) – The config of dataloader.

  • dataset (Dataset) – The dataset of dataloader.

  • sampler (Sampler) – The sampler of dataloader.

  • shuffle (bool, optional) – Whether the dataloader will be shuffle after a round. Defaults to False.

_dataset

The dataset of this dataloader.

Type

Dataset

shuffle

If True, dataloader will shuffle before every epoch.

Type

bool

pr

Pointer of dataloader.

Type

int

step

The increment of pr for each batch.

Type

int

_batch_size

The max interaction number for all batch.

Type

int

batch_size: Optional[int]
collate_fn()[source]

Collect the sampled index, and apply neg_sampling or other methods to get the final data.

dataset: torch.utils.data.dataset.Dataset[torch.utils.data.dataloader.T_co]
drop_last: bool
num_workers: int
pin_memory: bool
pin_memory_device: str
prefetch_factor: int
sampler: Union[torch.utils.data.sampler.Sampler, Iterable]
set_batch_size(batch_size)[source]

Reset the batch_size of the dataloader, but it can’t be called when dataloader is being iterated.

Parameters

batch_size (int) – the new batch_size of dataloader.

timeout: float
update_config(config)[source]

Update configure of dataloader, such as batch_size, step etc.

Parameters

config (Config) – The new config of dataloader.

class recbole.data.dataloader.abstract_dataloader.NegSampleDataLoader(config, dataset, sampler, shuffle=True)[source]

Bases: recbole.data.dataloader.abstract_dataloader.AbstractDataLoader

NegSampleDataLoader is an abstract class which can sample negative examples by ratio. It has two neg-sampling method, the one is 1-by-1 neg-sampling (pair wise), and the other is 1-by-multi neg-sampling (point wise).

Parameters

  • config (Config) – The config of dataloader.

  • dataset (Dataset) – The dataset of dataloader.

  • sampler (Sampler) – The sampler of dataloader.

  • shuffle (bool, optional) – Whether the dataloader will be shuffle after a round. Defaults to False.

batch_size: Optional[int]
dataset: torch.utils.data.dataset.Dataset[torch.utils.data.dataloader.T_co]
drop_last: bool
get_model(model)[source]
num_workers: int
pin_memory: bool
pin_memory_device: str
prefetch_factor: int
sampler: Union[torch.utils.data.sampler.Sampler, Iterable]
timeout: float