def test_load_multi_file_training_data(domain: Domain):
featurizer = MaxHistoryTrackerFeaturizer(SingleStateFeaturizer(), max_history=2)
trackers = training.load_data(
"data/test_yaml_stories/stories.yml", domain, augmentation_factor=0
)
trackers = sorted(trackers, key=lambda t: t.sender_id)
(tr_as_sts, tr_as_acts) = featurizer.training_states_and_labels(trackers, domain)
hashed = []
for sts, acts in zip(tr_as_sts, tr_as_acts):
hashed.append(json.dumps(sts + acts, sort_keys=True))
hashed = sorted(hashed, reverse=True)
data, label_ids, _ = featurizer.featurize_trackers(
trackers, domain, precomputations=None
)
featurizer_mul = MaxHistoryTrackerFeaturizer(SingleStateFeaturizer(), max_history=2)
trackers_mul = training.load_data(
"data/test_multifile_yaml_stories", domain, augmentation_factor=0
)
trackers_mul = sorted(trackers_mul, key=lambda t: t.sender_id)
(tr_as_sts_mul, tr_as_acts_mul) = featurizer.training_states_and_labels(
trackers_mul, domain
)
hashed_mul = []
for sts_mul, acts_mul in zip(tr_as_sts_mul, tr_as_acts_mul):
hashed_mul.append(json.dumps(sts_mul + acts_mul, sort_keys=True))
hashed_mul = sorted(hashed_mul, reverse=True)
data_mul, label_ids_mul, _ = featurizer_mul.featurize_trackers(
trackers_mul, domain, precomputations=None
)
assert hashed == hashed_mul
# we check for intents, action names and entities -- the features which
# are included in the story files
data = _surface_attributes(data)
data_mul = _surface_attributes(data_mul)
for attribute in [INTENT, ACTION_NAME, ENTITIES]:
if attribute not in data or attribute not in data_mul:
continue
assert len(data.get(attribute)) == len(data_mul.get(attribute))
for idx_tracker in range(len(data.get(attribute))):
for idx_dialogue in range(len(data.get(attribute)[idx_tracker])):
f1 = data.get(attribute)[idx_tracker][idx_dialogue]
f2 = data_mul.get(attribute)[idx_tracker][idx_dialogue]
if f1 is None or f2 is None:
assert f1 == f2
continue
for idx_turn in range(len(f1)):
f1 = data.get(attribute)[idx_tracker][idx_dialogue][idx_turn]
f2 = data_mul.get(attribute)[idx_tracker][idx_dialogue][idx_turn]
assert np.all((f1 == f2).data)
assert np.all(label_ids == label_ids_mul)
def test_featurize_trackers_with_max_history_tracker_featurizer(
moodbot_domain: Domain):
state_featurizer = SingleStateFeaturizer()
tracker_featurizer = MaxHistoryTrackerFeaturizer(state_featurizer)
tracker = tracker_from_dialogue_file("data/test_dialogues/moodbot.json",
moodbot_domain)
state_features, labels, entity_tags = tracker_featurizer.featurize_trackers(
[tracker], moodbot_domain, RegexInterpreter())
assert state_features is not None
assert len(state_features) == 7
assert labels is not None
assert len(labels) == 7
# moodbot doesn't contain e2e entities
assert not any([any(turn_tags) for turn_tags in entity_tags])
def test_generate_training_data_with_cycles(domain: Domain):
featurizer = MaxHistoryTrackerFeaturizer(SingleStateFeaturizer(), max_history=4)
training_trackers = training.load_data(
"data/test_yaml_stories/stories_with_cycle.yml", domain, augmentation_factor=0,
)
_, label_ids, _ = featurizer.featurize_trackers(
training_trackers, domain, precomputations=None
)
# how many there are depends on the graph which is not created in a
# deterministic way but should always be 3 or 4
assert len(training_trackers) == 3 or len(training_trackers) == 4
# if we have 4 trackers, there is going to be one example more for label 10
num_tens = len(training_trackers) - 1
# if new default actions are added the keys of the actions will be changed
all_label_ids = [id for ids in label_ids for id in ids]
assert Counter(all_label_ids) == {0: 6, 15: 3, 14: num_tens, 1: 2, 16: 1}
async def test_generate_training_data_with_cycles(stories_file: Text,
default_domain: Domain):
featurizer = MaxHistoryTrackerFeaturizer(SingleStateFeaturizer(),
max_history=4)
training_trackers = await training.load_data(stories_file,
default_domain,
augmentation_factor=0)
training_data, label_ids = featurizer.featurize_trackers(
training_trackers, default_domain, interpreter=RegexInterpreter())
# how many there are depends on the graph which is not created in a
# deterministic way but should always be 3 or 4
assert len(training_trackers) == 3 or len(training_trackers) == 4
# if we have 4 trackers, there is going to be one example more for label 10
num_tens = len(training_trackers) - 1
# if new default actions are added the keys of the actions will be changed
all_label_ids = [id for ids in label_ids for id in ids]
assert Counter(all_label_ids) == {0: 6, 12: num_tens, 14: 1, 1: 2, 13: 3}
async def test_load_multi_file_training_data(
stories_resources: List, default_domain: Domain
):
# the stories file in `data/test_multifile_stories` is the same as in
# `data/test_stories/stories.md`, but split across multiple files
featurizer = MaxHistoryTrackerFeaturizer(SingleStateFeaturizer(), max_history=2)
trackers = await training.load_data(
stories_resources[0], default_domain, augmentation_factor=0
)
(tr_as_sts, tr_as_acts) = featurizer.training_states_and_actions(
trackers, default_domain
)
hashed = []
for sts, acts in zip(tr_as_sts, tr_as_acts):
hashed.append(json.dumps(sts + acts, sort_keys=True))
hashed = sorted(hashed, reverse=True)
data, label_ids = featurizer.featurize_trackers(
trackers, default_domain, interpreter=RegexInterpreter()
)
featurizer_mul = MaxHistoryTrackerFeaturizer(SingleStateFeaturizer(), max_history=2)
trackers_mul = await training.load_data(
stories_resources[1], default_domain, augmentation_factor=0
)
(tr_as_sts_mul, tr_as_acts_mul) = featurizer.training_states_and_actions(
trackers_mul, default_domain
)
hashed_mul = []
for sts_mul, acts_mul in zip(tr_as_sts_mul, tr_as_acts_mul):
hashed_mul.append(json.dumps(sts_mul + acts_mul, sort_keys=True))
hashed_mul = sorted(hashed_mul, reverse=True)
data_mul, label_ids_mul = featurizer_mul.featurize_trackers(
trackers_mul, default_domain, interpreter=RegexInterpreter()
)
assert hashed == hashed_mul
# we check for intents, action names and entities -- the features which
# are included in the story files
data = surface_attributes(data)
data_mul = surface_attributes(data_mul)
for attribute in [INTENT, ACTION_NAME, ENTITIES]:
if attribute not in data or attribute not in data_mul:
continue
assert len(data.get(attribute)) == len(data_mul.get(attribute))
for idx_tracker in range(len(data.get(attribute))):
for idx_dialogue in range(len(data.get(attribute)[idx_tracker])):
f1 = data.get(attribute)[idx_tracker][idx_dialogue]
f2 = data_mul.get(attribute)[idx_tracker][idx_dialogue]
if f1 is None or f2 is None:
assert f1 == f2
continue
for idx_turn in range(len(f1)):
f1 = data.get(attribute)[idx_tracker][idx_dialogue][idx_turn]
f2 = data_mul.get(attribute)[idx_tracker][idx_dialogue][idx_turn]
assert np.all((f1 == f2).data)
assert np.all(label_ids == label_ids_mul)
