def process_input(self, input_text, stage):
token_ids, entity_ids, movie_ids, word_ids = self.convert_to_id(
input_text, stage)
self.update_context(stage, token_ids, entity_ids, movie_ids, word_ids)
data = {
'role': 'Seeker',
'context_tokens': self.context[stage]['context_tokens'],
'context_entities': self.context[stage]['context_entities'],
'context_words': self.context[stage]['context_words'],
'context_items': self.context[stage]['context_items'],
'user_profile': self.context[stage]['user_profile'],
'interaction_history': self.context[stage]['interaction_history']
}
dataloader = get_dataloader(self.opt, data, self.vocab[stage])
if stage == 'rec':
data = dataloader.rec_interact(data)
elif stage == 'conv':
data = dataloader.conv_interact(data)
data = [
ele.to(self.device) if isinstance(ele, torch.Tensor) else ele
for ele in data
]
return data
def run_crslab(config_file, save_data=False, restore_data=False, save_system=False, restore_system=False,
interact=False, debug=False):
"""A fast running api, which includes the complete process of training and testing models on specified datasets.
Args:
config_file (str): path to the config file, which should be in ``yaml`` format.
You can use default config provided in the `Github repo`_, or write it by yourself.
save_data (bool): whether to save data. Defaults to False.
restore_data (bool): whether to restore data. Defaults to False.
save_system (bool): whether to save system. Defaults to False.
restore_system (bool): whether to restore system. Defaults to False.
interact (bool): whether to interact with the system. Defaults to False.
debug (bool): whether to debug the system. Defaults to False.
.. _Github repo:
https://github.com/RUCAIBox/CRSLab
"""
opt = Config(config_file, debug)
# dataset & dataloader
if isinstance(opt['tokenize'], str):
CRS_dataset = get_dataset(opt, opt['tokenize'], restore_data, save_data)
side_data = CRS_dataset.side_data
vocab = CRS_dataset.vocab
train_dataloader = get_dataloader(opt, CRS_dataset.train_data, vocab)
valid_dataloader = get_dataloader(opt, CRS_dataset.valid_data, vocab)
test_dataloader = get_dataloader(opt, CRS_dataset.test_data, vocab)
else:
tokenized_dataset = {}
train_dataloader = {}
valid_dataloader = {}
test_dataloader = {}
vocab = {}
side_data = {}
for task, tokenize in opt['tokenize'].items():
if tokenize in tokenized_dataset:
dataset = tokenized_dataset[tokenize]
else:
dataset = get_dataset(opt, tokenize, restore_data, save_data)
tokenized_dataset[tokenize] = dataset
train_data = dataset.train_data
valid_data = dataset.valid_data
test_data = dataset.test_data
side_data[task] = dataset.side_data
vocab[task] = dataset.vocab
train_dataloader[task] = get_dataloader(opt, train_data, vocab[task])
valid_dataloader[task] = get_dataloader(opt, valid_data, vocab[task])
test_dataloader[task] = get_dataloader(opt, test_data, vocab[task])
# system
CRS = get_system(opt, train_dataloader, valid_dataloader, test_dataloader, vocab, side_data, restore_system,
interact, debug)
if interact:
CRS.interact()
else:
CRS.fit()
if save_system:
CRS.save_model()
def run_crslab(config_file,
save_data=False,
restore_data=False,
save_system=False,
restore_system=False,
interact=False,
debug=False):
opt = Config(config_file, debug)
# dataset & dataloader
if isinstance(opt['tokenize'], str):
CRS_dataset = get_dataset(opt, opt['tokenize'], restore_data,
save_data)
side_data = CRS_dataset.side_data
vocab = CRS_dataset.vocab
train_dataloader = get_dataloader(opt, CRS_dataset.train_data, vocab)
valid_dataloader = get_dataloader(opt, CRS_dataset.valid_data, vocab)
test_dataloader = get_dataloader(opt, CRS_dataset.test_data, vocab)
else:
tokenized_dataset = {}
train_dataloader = {}
valid_dataloader = {}
test_dataloader = {}
vocab = {}
side_data = {}
for task, tokenize in opt['tokenize'].items():
if tokenize in tokenized_dataset:
dataset = tokenized_dataset[tokenize]
else:
dataset = get_dataset(opt, tokenize, restore_data, save_data)
tokenized_dataset[tokenize] = dataset
train_data = dataset.train_data
valid_data = dataset.valid_data
test_data = dataset.test_data
side_data[task] = dataset.side_data
vocab[task] = dataset.vocab
train_dataloader[task] = get_dataloader(opt, train_data,
vocab[task])
valid_dataloader[task] = get_dataloader(opt, valid_data,
vocab[task])
test_dataloader[task] = get_dataloader(opt, test_data, vocab[task])
# system
CRS = get_system(opt, train_dataloader, valid_dataloader, test_dataloader,
vocab, side_data, restore_system, interact, debug)
if interact:
CRS.interact()
else:
CRS.fit()
if save_system:
CRS.save_model()