async def test_gen_batch(self, trained_policy: TEDPolicy,
default_domain: Domain, stories_path: Path):
training_trackers = await tests.core.test_policies.train_trackers(
default_domain, stories_path, augmentation_factor=0)
interpreter = RegexInterpreter()
training_data, label_ids, entity_tags = trained_policy._featurize_for_training(
training_trackers, default_domain, interpreter)
label_data, all_labels = trained_policy._create_label_data(
default_domain, interpreter)
model_data = trained_policy._create_model_data(training_data,
label_ids, entity_tags,
all_labels)
batch_size = 2
data_generator = RasaBatchDataGenerator(model_data,
batch_size=batch_size,
shuffle=False,
batch_strategy="sequence")
iterator = iter(data_generator)
# model data keys were sorted, so the order is alphabetical
(
(
batch_action_name_mask,
batch_action_name_sentence_indices,
batch_action_name_sentence_data,
batch_action_name_sentence_shape,
batch_dialogue_length,
batch_entities_mask,
batch_entities_sentence_indices,
batch_entities_sentence_data,
batch_entities_sentence_shape,
batch_intent_mask,
batch_intent_sentence_indices,
batch_intent_sentence_data,
batch_intent_sentence_shape,
batch_label_ids,
batch_slots_mask,
batch_slots_sentence_indices,
batch_slots_sentence_data,
batch_slots_sentence_shape,
),
_,
) = next(iterator)
assert (batch_label_ids.shape[0] == batch_size
and batch_dialogue_length.shape[0] == batch_size)
# batch and dialogue dimensions are NOT combined for masks
assert (batch_slots_mask.shape[0] == batch_size
and batch_intent_mask.shape[0] == batch_size
and batch_entities_mask.shape[0] == batch_size
and batch_action_name_mask.shape[0] == batch_size)
# some features might be "fake" so there sequence is `0`
seq_len = max([
batch_intent_sentence_shape[1],
batch_action_name_sentence_shape[1],
batch_entities_sentence_shape[1],
batch_slots_sentence_shape[1],
])
assert (batch_intent_sentence_shape[1] == seq_len
or batch_intent_sentence_shape[1] == 0)
assert (batch_action_name_sentence_shape[1] == seq_len
or batch_action_name_sentence_shape[1] == 0)
assert (batch_entities_sentence_shape[1] == seq_len
or batch_entities_sentence_shape[1] == 0)
assert (batch_slots_sentence_shape[1] == seq_len
or batch_slots_sentence_shape[1] == 0)
data_generator = RasaBatchDataGenerator(model_data,
batch_size=batch_size,
shuffle=True,
batch_strategy="balanced")
iterator = iter(data_generator)
(
(
batch_action_name_mask,
batch_action_name_sentence_indices,
batch_action_name_sentence_data,
batch_action_name_sentence_shape,
batch_dialogue_length,
batch_entities_mask,
batch_entities_sentence_indices,
batch_entities_sentence_data,
batch_entities_sentence_shape,
batch_intent_mask,
batch_intent_sentence_indices,
batch_intent_sentence_data,
batch_intent_sentence_shape,
batch_label_ids,
batch_slots_mask,
batch_slots_sentence_indices,
batch_slots_sentence_data,
batch_slots_sentence_shape,
),
_,
) = next(iterator)
assert (batch_label_ids.shape[0] == batch_size
and batch_dialogue_length.shape[0] == batch_size)
# some features might be "fake" so there sequence is `0`
seq_len = max([
batch_intent_sentence_shape[1],
batch_action_name_sentence_shape[1],
batch_entities_sentence_shape[1],
batch_slots_sentence_shape[1],
])
assert (batch_intent_sentence_shape[1] == seq_len
or batch_intent_sentence_shape[1] == 0)
assert (batch_action_name_sentence_shape[1] == seq_len
or batch_action_name_sentence_shape[1] == 0)
assert (batch_entities_sentence_shape[1] == seq_len
or batch_entities_sentence_shape[1] == 0)
assert (batch_slots_sentence_shape[1] == seq_len
or batch_slots_sentence_shape[1] == 0)
async def test_gen_batch(self, trained_policy: TEDPolicy, default_domain: Domain):
training_trackers = await train_trackers(default_domain, augmentation_factor=0)
interpreter = RegexInterpreter()
training_data, label_ids = trained_policy.featurize_for_training(
training_trackers, default_domain, interpreter
)
label_data, all_labels = trained_policy._create_label_data(
default_domain, interpreter
)
model_data = trained_policy._create_model_data(
training_data, label_ids, all_labels
)
batch_size = 2
(
batch_label_ids,
batch_entities_mask,
batch_entities_sentence_1,
batch_entities_sentence_2,
batch_entities_sentence_3,
batch_intent_mask,
batch_intent_sentence_1,
batch_intent_sentence_2,
batch_intent_sentence_3,
batch_slots_mask,
batch_slots_sentence_1,
batch_slots_sentence_2,
batch_slots_sentence_3,
batch_action_name_mask,
batch_action_name_sentence_1,
batch_action_name_sentence_2,
batch_action_name_sentence_3,
batch_dialogue_length,
) = next(model_data._gen_batch(batch_size=batch_size))
assert (
batch_intent_mask.shape[0] == batch_size
and batch_action_name_mask.shape[0] == batch_size
and batch_entities_mask.shape[0] == batch_size
and batch_slots_mask.shape[0] == batch_size
)
assert (
batch_intent_sentence_3[1]
== batch_action_name_sentence_3[1]
== batch_entities_sentence_3[1]
== batch_slots_sentence_3[1]
)
(
batch_label_ids,
batch_entities_mask,
batch_entities_sentence_1,
batch_entities_sentence_2,
batch_entities_sentence_3,
batch_intent_mask,
batch_intent_sentence_1,
batch_intent_sentence_2,
batch_intent_sentence_3,
batch_slots_mask,
batch_slots_sentence_1,
batch_slots_sentence_2,
batch_slots_sentence_3,
batch_action_name_mask,
batch_action_name_sentence_1,
batch_action_name_sentence_2,
batch_action_name_sentence_3,
batch_dialogue_length,
) = next(
model_data._gen_batch(
batch_size=batch_size, batch_strategy="balanced", shuffle=True
)
)
assert (
batch_intent_mask.shape[0] == batch_size
and batch_action_name_mask.shape[0] == batch_size
and batch_entities_mask.shape[0] == batch_size
and batch_slots_mask.shape[0] == batch_size
)
assert (
batch_intent_sentence_3[1]
== batch_action_name_sentence_3[1]
== batch_entities_sentence_3[1]
== batch_slots_sentence_3[1]
)
