def test_flatten_and_classify(self):
word_lists = ControllableTaskTeacher.build_wordlists(
ParlaiParser().parse_args([]))
utterances = [
"hello there",
"hi there dad, what's up",
"not much, do you know where your sister is?",
"I have not seen her, I thought she was with grandpa",
"well, if you see her, let me know",
"will do!",
"ok, have a good day",
"bye bye! tell mom I say hello",
]
tokens = ['f0m1', 'f1m1', 'f0m0', 'f1m0']
episode = [
Message({
'text': utterances[i],
'labels': [utterances[i + 1]],
'episode_done': False,
}) for i in range(0,
len(utterances) - 1, 2)
]
episode[-1].force_set('episode_done', True)
new_episode = flatten_and_classify(episode, -1, word_lists)
assert len(new_episode) == 4
assert all(ex['text'].endswith(tok) for ex, tok in zip(
new_episode, tokens)), f"new episode: {new_episode}"
def _setup_data(self, opt: Opt) -> List[List[Message]]:
"""
Flatten and classify the normal task data.
Save/load where applicable.
:param opt:
options dict.
"""
# create save directory, if it does not already exist
self.original_task_name = ':'.join(opt['task'].split(':')[2:])
self.save_dir = self._get_save_path(
opt['datapath'], str(datetime.datetime.today())
)
os.makedirs(self.save_dir, exist_ok=True)
fname = f"{opt['datatype'].split(':')[0]}.json"
self.save_path = os.path.join(self.save_dir, fname)
data = self.load_data(opt, fname)
if data is not None:
# successfully load data
return data
# build the original teacher
original_task_module = get_original_task_module(opt)
teacher_opt = deepcopy(opt)
teacher_opt['task'] = self.original_task_name
teacher = original_task_module(teacher_opt)
total_exs = teacher.num_examples()
if self.opt['max_examples'] > 0:
total_exs = min(self.opt['max_examples'], total_exs)
progress_bar = tqdm(
total=total_exs, unit='ex', unit_scale=True, desc='Building flattened data'
)
all_episodes = []
num_exs = 0
while num_exs < total_exs:
current_episode = []
episode_done = False
while not episode_done:
action = Message(teacher.act())
current_episode.append(action)
episode_done = action.get('episode_done', False)
num_exs += 1
# flatten the episode into 1-example episodes with context
flattened_ep = flatten_and_classify(
current_episode,
opt['flatten_max_context_length'],
include_labels=opt['flatten_include_labels'],
delimiter=opt['flatten_delimiter'],
word_lists=self.word_lists,
)
all_episodes += flattened_ep
progress_bar.update(len(flattened_ep))
# save data for future use
self.save_data(all_episodes)
return all_episodes