class UserDataLoader(AbstractDataLoader):
""":class:`UserDataLoader` will return a batch of data which only contains user-id when it is iterated.
Args:
config (Config): The config of dataloader.
dataset (Dataset): The dataset of dataloader.
batch_size (int, optional): The batch_size of dataloader. Defaults to ``1``.
dl_format (InputType, optional): The input type of dataloader. Defaults to
:obj:`~recbole.utils.enum_type.InputType.POINTWISE`.
shuffle (bool, optional): Whether the dataloader will be shuffle after a round. Defaults to ``False``.
Attributes:
shuffle (bool): Whether the dataloader will be shuffle after a round.
However, in :class:`UserDataLoader`, it's guaranteed to be ``True``.
"""
dl_type = DataLoaderType.ORIGIN
def __init__(self,
config,
dataset,
batch_size=1,
dl_format=InputType.POINTWISE,
shuffle=False):
self.uid_field = dataset.uid_field
self.user_list = Interaction(
{self.uid_field: torch.arange(dataset.user_num)})
super().__init__(config=config,
dataset=dataset,
batch_size=batch_size,
dl_format=dl_format,
shuffle=shuffle)
def setup(self):
"""Make sure that the :attr:`shuffle` is True. If :attr:`shuffle` is False, it will be changed to True
and give a warning to user.
"""
if self.shuffle is False:
self.shuffle = True
self.logger.warning('UserDataLoader must shuffle the data')
@property
def pr_end(self):
return len(self.user_list)
def _shuffle(self):
self.user_list.shuffle()
def _next_batch_data(self):
cur_data = self.user_list[self.pr:self.pr + self.step]
self.pr += self.step
return cur_data
class UserDataLoader(AbstractDataLoader):
""":class:`UserDataLoader` will return a batch of data which only contains user-id when it is iterated.
Args:
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``.
Attributes:
shuffle (bool): Whether the dataloader will be shuffle after a round.
However, in :class:`UserDataLoader`, it's guaranteed to be ``True``.
"""
def __init__(self, config, dataset, sampler, shuffle=False):
if shuffle is False:
shuffle = True
self.logger.warning('UserDataLoader must shuffle the data.')
self.uid_field = dataset.uid_field
self.user_list = Interaction(
{self.uid_field: torch.arange(dataset.user_num)})
super().__init__(config, dataset, sampler, shuffle=shuffle)
def _init_batch_size_and_step(self):
batch_size = self.config['train_batch_size']
self.step = batch_size
self.set_batch_size(batch_size)
@property
def pr_end(self):
return len(self.user_list)
def _shuffle(self):
self.user_list.shuffle()
def _next_batch_data(self):
cur_data = self.user_list[self.pr:self.pr + self.step]
self.pr += self.step
return cur_data