def create_samplers(config, dataset, built_datasets):
"""Create sampler for training, validation and testing.
Args:
config (Config): An instance object of Config, used to record parameter information.
dataset (Dataset): An instance object of Dataset, which contains all interaction records.
built_datasets (list of Dataset): A list of split Dataset, which contains dataset for
training, validation and testing.
Returns:
tuple:
- train_sampler (AbstractSampler): The sampler for training.
- valid_sampler (AbstractSampler): The sampler for validation.
- test_sampler (AbstractSampler): The sampler for testing.
"""
phases = ['train', 'valid', 'test']
train_neg_sample_args = config['train_neg_sample_args']
eval_neg_sample_args = config['eval_neg_sample_args']
sampler = None
train_sampler, valid_sampler, test_sampler = None, None, None
if train_neg_sample_args['strategy'] != 'none':
if not config['repeatable']:
sampler = Sampler(phases, built_datasets,
train_neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(phases, dataset,
train_neg_sample_args['distribution'])
train_sampler = sampler.set_phase('train')
if eval_neg_sample_args['strategy'] != 'none':
if sampler is None:
if not config['repeatable']:
sampler = Sampler(phases, built_datasets,
eval_neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(
phases, dataset, eval_neg_sample_args['distribution'])
else:
sampler.set_distribution(eval_neg_sample_args['distribution'])
valid_sampler = sampler.set_phase('valid')
test_sampler = sampler.set_phase('test')
return train_sampler, valid_sampler, test_sampler
def data_preparation(config, dataset, save=False):
"""Split the dataset by :attr:`config['eval_setting']` and call :func:`dataloader_construct` to create
corresponding dataloader.
Args:
config (Config): An instance object of Config, used to record parameter information.
dataset (Dataset): An instance object of Dataset, which contains all interaction records.
save (bool, optional): If ``True``, it will call :func:`save_datasets` to save split dataset.
Defaults to ``False``.
Returns:
tuple:
- train_data (AbstractDataLoader): The dataloader for training.
- valid_data (AbstractDataLoader): The dataloader for validation.
- test_data (AbstractDataLoader): The dataloader for testing.
"""
model_type = config['MODEL_TYPE']
es_str = [_.strip() for _ in config['eval_setting'].split(',')]
es = EvalSetting(config)
kwargs = {}
if 'RS' in es_str[0]:
kwargs['ratios'] = config['split_ratio']
if kwargs['ratios'] is None:
raise ValueError('`ratios` should be set if `RS` is set')
if 'LS' in es_str[0]:
kwargs['leave_one_num'] = config['leave_one_num']
if kwargs['leave_one_num'] is None:
raise ValueError('`leave_one_num` should be set if `LS` is set')
kwargs['group_by_user'] = config['group_by_user']
getattr(es, es_str[0])(**kwargs)
if es.split_args['strategy'] != 'loo' and model_type == ModelType.SEQUENTIAL:
raise ValueError('Sequential models require "loo" split strategy.')
builded_datasets = dataset.build(es)
train_dataset, valid_dataset, test_dataset = builded_datasets
phases = ['train', 'valid', 'test']
if save:
save_datasets(config['checkpoint_dir'], name=phases, dataset=builded_datasets)
kwargs = {}
if config['training_neg_sample_num']:
es.neg_sample_by(config['training_neg_sample_num'])
if model_type != ModelType.SEQUENTIAL:
sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(phases, dataset, es.neg_sample_args['distribution'])
kwargs['sampler'] = sampler.set_phase('train')
kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args)
if model_type == ModelType.KNOWLEDGE:
kg_sampler = KGSampler(dataset, es.neg_sample_args['distribution'])
kwargs['kg_sampler'] = kg_sampler
train_data = dataloader_construct(
name='train',
config=config,
eval_setting=es,
dataset=train_dataset,
dl_format=config['MODEL_INPUT_TYPE'],
batch_size=config['train_batch_size'],
shuffle=True,
**kwargs
)
kwargs = {}
if len(es_str) > 1 and getattr(es, es_str[1], None):
getattr(es, es_str[1])()
if 'sampler' not in locals():
sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution'])
kwargs['sampler'] = [sampler.set_phase('valid'), sampler.set_phase('test')]
kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args)
valid_data, test_data = dataloader_construct(
name='evaluation',
config=config,
eval_setting=es,
dataset=[valid_dataset, test_dataset],
batch_size=config['eval_batch_size'],
**kwargs
)
return train_data, valid_data, test_data
def data_preparation(config, dataset, save=False):
"""Split the dataset by :attr:`config['eval_setting']` and call :func:`dataloader_construct` to create
corresponding dataloader.
Args:
config (Config): An instance object of Config, used to record parameter information.
dataset (Dataset): An instance object of Dataset, which contains all interaction records.
save (bool, optional): If ``True``, it will call :func:`save_datasets` to save split dataset.
Defaults to ``False``.
Returns:
tuple:
- train_data (AbstractDataLoader): The dataloader for training.
- valid_data (AbstractDataLoader): The dataloader for validation.
- test_data (AbstractDataLoader): The dataloader for testing.
"""
model_type = config['MODEL_TYPE']
es_str = [_.strip() for _ in config['eval_setting'].split(',')]
es = EvalSetting(config)
es.set_ordering_and_splitting(es_str[0])
built_datasets = dataset.build(es)
train_dataset, valid_dataset, test_dataset = built_datasets
phases = ['train', 'valid', 'test']
sampler = None
if save:
save_datasets(config['checkpoint_dir'],
name=phases,
dataset=built_datasets)
kwargs = {}
if config['training_neg_sample_num']:
if dataset.label_field in dataset.inter_feat:
raise ValueError(
f'`training_neg_sample_num` should be 0 '
f'if inter_feat have label_field [{dataset.label_field}].')
train_distribution = config[
'training_neg_sample_distribution'] or 'uniform'
es.neg_sample_by(by=config['training_neg_sample_num'],
distribution=train_distribution)
if model_type != ModelType.SEQUENTIAL:
sampler = Sampler(phases, built_datasets,
es.neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(phases, dataset,
es.neg_sample_args['distribution'])
kwargs['sampler'] = sampler.set_phase('train')
kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args)
if model_type == ModelType.KNOWLEDGE:
kg_sampler = KGSampler(dataset, es.neg_sample_args['distribution'])
kwargs['kg_sampler'] = kg_sampler
train_data = dataloader_construct(name='train',
config=config,
eval_setting=es,
dataset=train_dataset,
dl_format=config['MODEL_INPUT_TYPE'],
batch_size=config['train_batch_size'],
shuffle=True,
**kwargs)
kwargs = {}
if len(es_str) > 1 and getattr(es, es_str[1], None):
if dataset.label_field in dataset.inter_feat:
raise ValueError(
f'It can not validate with `{es_str[1]}` '
f'when inter_feat have label_field [{dataset.label_field}].')
getattr(es, es_str[1])()
if sampler is None:
if model_type != ModelType.SEQUENTIAL:
sampler = Sampler(phases, built_datasets,
es.neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(phases, dataset,
es.neg_sample_args['distribution'])
sampler.set_distribution(es.neg_sample_args['distribution'])
kwargs['sampler'] = [
sampler.set_phase('valid'),
sampler.set_phase('test')
]
kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args)
valid_data, test_data = dataloader_construct(
name='evaluation',
config=config,
eval_setting=es,
dataset=[valid_dataset, test_dataset],
batch_size=config['eval_batch_size'],
**kwargs)
return train_data, valid_data, test_data
def data_preparation(config, dataset, save=False):
"""Split the dataset by :attr:`config['eval_setting']` and call :func:`dataloader_construct` to create
corresponding dataloader.
Args:
config (Config): An instance object of Config, used to record parameter information.
dataset (Dataset): An instance object of Dataset, which contains all interaction records.
save (bool, optional): If ``True``, it will call :func:`save_datasets` to save split dataset.
Defaults to ``False``.
Returns:
tuple:
- train_data (AbstractDataLoader): The dataloader for training.
- valid_data (AbstractDataLoader): The dataloader for validation.
- test_data (AbstractDataLoader): The dataloader for testing.
"""
model_type = config['MODEL_TYPE']
es = EvalSetting(config)
built_datasets = dataset.build(es)
train_dataset, valid_dataset, test_dataset = built_datasets
phases = ['train', 'valid', 'test']
sampler = None
logger = getLogger()
train_neg_sample_args = config['train_neg_sample_args']
eval_neg_sample_args = es.neg_sample_args
# Training
train_kwargs = {
'config': config,
'dataset': train_dataset,
'batch_size': config['train_batch_size'],
'dl_format': config['MODEL_INPUT_TYPE'],
'shuffle': True,
}
if train_neg_sample_args['strategy'] != 'none':
if dataset.label_field in dataset.inter_feat:
raise ValueError(
f'`training_neg_sample_num` should be 0 '
f'if inter_feat have label_field [{dataset.label_field}].')
if model_type != ModelType.SEQUENTIAL:
sampler = Sampler(phases, built_datasets,
train_neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(phases, dataset,
train_neg_sample_args['distribution'])
train_kwargs['sampler'] = sampler.set_phase('train')
train_kwargs['neg_sample_args'] = train_neg_sample_args
if model_type == ModelType.KNOWLEDGE:
kg_sampler = KGSampler(dataset,
train_neg_sample_args['distribution'])
train_kwargs['kg_sampler'] = kg_sampler
dataloader = get_data_loader('train', config, train_neg_sample_args)
logger.info(
set_color('Build', 'pink') +
set_color(f' [{dataloader.__name__}]', 'yellow') + ' for ' +
set_color('[train]', 'yellow') + ' with format ' +
set_color(f'[{train_kwargs["dl_format"]}]', 'yellow'))
if train_neg_sample_args['strategy'] != 'none':
logger.info(
set_color('[train]', 'pink') +
set_color(' Negative Sampling', 'blue') +
f': {train_neg_sample_args}')
else:
logger.info(
set_color('[train]', 'pink') +
set_color(' No Negative Sampling', 'yellow'))
logger.info(
set_color('[train]', 'pink') + set_color(' batch_size', 'cyan') +
' = ' + set_color(f'[{train_kwargs["batch_size"]}]', 'yellow') + ', ' +
set_color('shuffle', 'cyan') + ' = ' +
set_color(f'[{train_kwargs["shuffle"]}]\n', 'yellow'))
train_data = dataloader(**train_kwargs)
# Evaluation
eval_kwargs = {
'config': config,
'batch_size': config['eval_batch_size'],
'dl_format': InputType.POINTWISE,
'shuffle': False,
}
valid_kwargs = {'dataset': valid_dataset}
test_kwargs = {'dataset': test_dataset}
if eval_neg_sample_args['strategy'] != 'none':
if dataset.label_field in dataset.inter_feat:
raise ValueError(
f'It can not validate with `{es.es_str[1]}` '
f'when inter_feat have label_field [{dataset.label_field}].')
if sampler is None:
if model_type != ModelType.SEQUENTIAL:
sampler = Sampler(phases, built_datasets,
eval_neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(
phases, dataset, eval_neg_sample_args['distribution'])
else:
sampler.set_distribution(eval_neg_sample_args['distribution'])
eval_kwargs['neg_sample_args'] = eval_neg_sample_args
valid_kwargs['sampler'] = sampler.set_phase('valid')
test_kwargs['sampler'] = sampler.set_phase('test')
valid_kwargs.update(eval_kwargs)
test_kwargs.update(eval_kwargs)
dataloader = get_data_loader('evaluation', config, eval_neg_sample_args)
logger.info(
set_color('Build', 'pink') +
set_color(f' [{dataloader.__name__}]', 'yellow') + ' for ' +
set_color('[evaluation]', 'yellow') + ' with format ' +
set_color(f'[{eval_kwargs["dl_format"]}]', 'yellow'))
logger.info(es)
logger.info(
set_color('[evaluation]', 'pink') + set_color(' batch_size', 'cyan') +
' = ' + set_color(f'[{eval_kwargs["batch_size"]}]', 'yellow') + ', ' +
set_color('shuffle', 'cyan') + ' = ' +
set_color(f'[{eval_kwargs["shuffle"]}]\n', 'yellow'))
valid_data = dataloader(**valid_kwargs)
test_data = dataloader(**test_kwargs)
if save:
save_split_dataloaders(config,
dataloaders=(train_data, valid_data, test_data))
return train_data, valid_data, test_data