def test_pursuit_preposition_under_learner(language_mode, learner):
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
language_generator = phase1_language_generator(language_mode)
under_train_curriculum = phase1_instances(
"Preposition Under Unit Train",
situations=sampled(
_under_template(ball,
table,
immutableset(),
is_training=True,
is_distal=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=10,
block_multiple_of_the_same_type=True,
),
language_generator=language_generator,
)
under_test_curriculum = phase1_instances(
"Preposition Under Unit Test",
situations=sampled(
_under_template(ball,
table,
immutableset(),
is_training=False,
is_distal=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=1,
block_multiple_of_the_same_type=True,
),
language_generator=language_generator,
)
processing_learner = learner(language_mode)
for (
_,
linguistic_description,
perceptual_representation,
) in under_train_curriculum.instances():
processing_learner.observe(
LearningExample(perceptual_representation, linguistic_description))
for (
_,
test_linguistic_description,
test_perceptual_representation,
) in under_test_curriculum.instances():
descriptions_from_learner = processing_learner.describe(
test_perceptual_representation)
gold = test_linguistic_description.as_token_sequence()
assert descriptions_from_learner
assert gold in [
desc.as_token_sequence() for desc in descriptions_from_learner
]
def _make_m6_under_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
return phase1_instances(
"Preposition under",
situations=chain(*[
sampled(
_under_template(
object_1,
object_2,
make_noise_objects(noise_objects),
is_training=True,
is_distal=True,
),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=num_samples if num_samples else 1,
block_multiple_of_the_same_type=True,
) for object_1 in r.sample(SMALL_OBJECT_VARS, 3)
for object_2 in r.sample(LARGE_OBJECT_VARS, 3)
]),
perception_generator=GAILA_M6_PERCEPTION_GENERATOR,
language_generator=language_generator,
)
def test_subset_preposition_under(language_mode, learner):
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
run_preposition_test(
learner(language_mode),
_under_template(ball,
table,
immutableset(),
is_training=True,
is_distal=True),
language_generator=phase1_language_generator(language_mode),
)
def test_pursuit_preposition_under_learner(language_mode):
rng = random.Random()
rng.seed(0)
learner = PrepositionPursuitLearner(
learning_factor=0.5,
graph_match_confirmation_threshold=0.7,
lexicon_entry_threshold=0.7,
rng=rng,
smoothing_parameter=0.001,
ontology=GAILA_PHASE_1_ONTOLOGY,
object_recognizer=LANGUAGE_MODE_TO_OBJECT_RECOGNIZER[language_mode],
language_mode=language_mode,
) # type: ignore
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
language_generator = phase1_language_generator(language_mode)
under_train_curriculum = phase1_instances(
"Preposition Under Unit Train",
situations=sampled(
_under_template(ball,
table,
immutableset(),
is_training=True,
is_distal=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=10,
),
language_generator=language_generator,
)
under_test_curriculum = phase1_instances(
"Preposition Under Unit Test",
situations=sampled(
_under_template(ball,
table,
immutableset(),
is_training=False,
is_distal=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=1,
),
language_generator=language_generator,
)
for (
_,
linguistic_description,
perceptual_representation,
) in under_train_curriculum.instances():
learner.observe(
LearningExample(perceptual_representation, linguistic_description))
for (
_,
test_linguistic_description,
test_perceptual_representation,
) in under_test_curriculum.instances():
descriptions_from_learner = learner.describe(
test_perceptual_representation)
gold = test_linguistic_description.as_token_sequence()
assert descriptions_from_learner
assert [
desc.as_token_sequence() for desc in descriptions_from_learner
][0] == gold