def test_pursuit_preposition_beside_learner(language_mode, learner):
language_generator = phase1_language_generator(language_mode)
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
beside_train_curriculum = phase1_instances(
"Preposition Beside Unit Train",
situations=sampled(
_beside_template(ball,
table,
immutableset(),
is_training=True,
is_right=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,
)
beside_test_curriculum = phase1_instances(
"Preposition Beside Unit Test",
situations=sampled(
_beside_template(ball,
table,
immutableset(),
is_training=False,
is_right=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 beside_train_curriculum.instances():
processing_learner.observe(
LearningExample(perceptual_representation, linguistic_description))
for (
_,
test_linguistic_description,
test_perceptual_representation,
) in beside_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_beside_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
return phase1_instances(
"Preposition on",
situations=chain(*[
sampled(
_beside_template(
object_1,
object_2,
make_noise_objects(noise_objects),
is_training=True,
is_right=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_beside(language_mode, learner):
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
run_preposition_test(
learner(language_mode),
_beside_template(ball,
table,
immutableset(),
is_training=True,
is_right=True),
language_generator=phase1_language_generator(language_mode),
)
def _prepositional_relation_described_curriculum(
max_to_sample: int,
noise_objects_sets: Iterable[Iterable[TemplateObjectVariable]],
*,
min_noise_relations: int = 0,
max_noise_relations: int = 0,
add_noise: bool,
chooser: RandomChooser,
samples_to_template_den: int = 1,
block_multiple_of_same_type: bool,
language_generator: LanguageGenerator[
HighLevelSemanticsSituation, LinearizedDependencyTree
],
include_targets_in_noise: bool = False,
min_samples: int = 6,
) -> Phase1InstanceGroup:
target_1 = standard_object(
"target_1", THING, required_properties=[INTEGRATED_EXPERIMENT_PROP]
)
target_2 = standard_object(
"target_2", THING, required_properties=[INTEGRATED_EXPERIMENT_PROP]
)
target_with_object_on = standard_object(
"target with object on",
INANIMATE_OBJECT,
required_properties=[INTEGRATED_EXPERIMENT_PROP, CAN_HAVE_THINGS_RESTING_ON_THEM],
)
templates = (
[
_on_template(
target_1,
target_with_object_on,
background_objects,
is_training=True,
background_relations=background_relations_builder(
background_objects,
num_relations,
target=target_1,
target_2=target_with_object_on,
include_targets_in_noise=include_targets_in_noise,
),
)
for background_objects in noise_objects_sets
for num_relations in range(min_noise_relations, max_noise_relations)
]
if add_noise
else [
_on_template(
target_1, target_with_object_on, immutableset(), is_training=True
)
]
)
templates.extend(
[
_beside_template(
target_1,
target_2,
background_objects,
is_right=is_right,
is_training=True,
background_relations=background_relations_builder(
background_objects,
num_relations,
target=target_1,
target_2=target_2,
include_targets_in_noise=include_targets_in_noise,
),
)
for is_right in BOOL_SET
for background_objects in noise_objects_sets
for num_relations in range(min_noise_relations, max_noise_relations)
]
if add_noise
else [
_beside_template(
target_1, target_2, immutableset(), is_right=is_right, is_training=True
)
for is_right in BOOL_SET
]
)
templates.extend(
[
_behind_template(
target_1,
target_2,
background_objects,
is_near=is_near,
is_training=True,
background_relations=background_relations_builder(
background_objects,
num_relations,
target=target_1,
target_2=target_2,
include_targets_in_noise=include_targets_in_noise,
),
)
for is_near in BOOL_SET
for background_objects in noise_objects_sets
for num_relations in range(min_noise_relations, max_noise_relations)
]
if add_noise
else [
_behind_template(
target_1, target_2, immutableset(), is_near=is_near, is_training=True
)
for is_near in BOOL_SET
]
)
templates.extend(
[
_in_front_template(
target_1,
target_2,
background_objects,
is_near=is_near,
is_training=True,
background_relations=background_relations_builder(
background_objects,
num_relations,
target=target_1,
target_2=target_2,
include_targets_in_noise=include_targets_in_noise,
),
)
for is_near in BOOL_SET
for background_objects in noise_objects_sets
for num_relations in range(min_noise_relations, max_noise_relations)
]
if add_noise
else [
_in_front_template(
target_1, target_2, immutableset(), is_near=is_near, is_training=True
)
for is_near in BOOL_SET
]
)
return phase2_instances(
"Prepositional Relation",
flatten(
[
sampled(
template,
ontology=INTEGRATED_EXPERIMENT_ONTOLOGY,
chooser=chooser,
max_to_sample=max(
math.ceil(max_to_sample / samples_to_template_den), min_samples
),
block_multiple_of_the_same_type=block_multiple_of_same_type,
)
for template in templates
]
),
language_generator=language_generator,
perception_generator=INTEGRATED_EXPERIMENT_PERCEPTION_GENERATOR,
)
def test_pursuit_preposition_beside_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
language_generator = phase1_language_generator(language_mode)
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
beside_train_curriculum = phase1_instances(
"Preposition Beside Unit Train",
situations=sampled(
_beside_template(ball,
table,
immutableset(),
is_training=True,
is_right=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=10,
),
language_generator=language_generator,
)
beside_test_curriculum = phase1_instances(
"Preposition Beside Unit Test",
situations=sampled(
_beside_template(ball,
table,
immutableset(),
is_training=False,
is_right=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=1,
),
language_generator=language_generator,
)
for (
_,
linguistic_description,
perceptual_representation,
) in beside_train_curriculum.instances():
learner.observe(
LearningExample(perceptual_representation, linguistic_description))
for (
_,
test_linguistic_description,
test_perceptual_representation,
) in beside_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