def _make_on_training(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object("ball", BALL)
figure_1 = standard_object("book", BOOK)
figure_2 = standard_object("mom", MOM)
ground_0 = standard_object("chair", CHAIR)
ground_1 = standard_object("table", TABLE)
figures = immutableset([figure_0, figure_1, figure_2])
grounds = immutableset([ground_0, ground_1])
return phase1_instances(
"Preposition Training On",
chain(*[
flatten([
sampled(
_on_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=True,
),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=num_samples if num_samples else 5,
) for figure in figures for ground in grounds
])
]),
language_generator=language_generator,
)
def _in_front_template(
figure: TemplateObjectVariable,
ground: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
*,
is_training: bool,
is_near: bool,
speaker_root_node: OntologyNode = PERSON,
) -> Phase1SituationTemplate:
handle = "training" if is_training else "testing"
direction = Direction(positive=True,
relative_to_axis=FacingAddresseeAxis(ground))
speaker = standard_object("speaker",
speaker_root_node,
added_properties=[IS_SPEAKER])
addressee = standard_object("addressee",
LEARNER,
added_properties=[IS_ADDRESSEE])
computed_background = [speaker, addressee]
computed_background.extend(background)
return Phase1SituationTemplate(
f"preposition-{handle}-{figure.handle}-behind-{ground.handle}",
salient_object_variables=[figure, ground],
background_object_variables=computed_background,
asserted_always_relations=[
near(figure, ground, direction=direction)
if is_near else far(figure, ground, direction=direction)
],
gazed_objects=[figure],
)
def _make_under_training(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object("ball", BALL)
figure_1 = standard_object("book", BOOK)
figure_2 = standard_object("mom", MOM)
ground_0 = standard_object("table", TABLE)
figures = immutableset([figure_0, figure_1, figure_2])
grounds = immutableset([ground_0])
return phase1_instances(
"Preposition Training Under",
chain(*[
sampled(
_under_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=True,
is_distal=use_above_below,
syntax_hints=[USE_ABOVE_BELOW] if use_above_below else [],
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for figure in figures for ground in grounds
# for distance in BOOL_SET
for use_above_below in BOOL_SET
]),
language_generator=language_generator,
)
def _make_put_in_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[
HighLevelSemanticsSituation, LinearizedDependencyTree
],
) -> Phase1InstanceGroup:
agent = standard_object(
"agent",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
theme = standard_object("theme", INANIMATE_OBJECT)
goal_in = standard_object("goal_in", INANIMATE_OBJECT, required_properties=[HOLLOW])
return phase1_instances(
"Capabilities - Put in",
sampled(
_put_in_template(agent, theme, goal_in, make_noise_objects(noise_objects)),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 20,
block_multiple_of_the_same_type=True,
),
language_generator=language_generator,
)
def _make_in_training(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = object_variable("water", WATER)
figure_1 = object_variable("juice", JUICE)
ground_0 = standard_object("box", BOX)
ground_1 = standard_object("cup", CUP)
figures = immutableset([figure_0, figure_1])
grounds = immutableset([ground_0, ground_1])
return phase1_instances(
"Preposition Training In",
chain(*[
sampled(
_in_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=True,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for figure in figures for ground in grounds
]),
language_generator=language_generator,
)
def make_take_grab_subtle_verb_distinction(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
taker = standard_object("tosser_passer_0",
THING,
required_properties=[ANIMATE])
takee = standard_object("tossee_passee_0",
THING,
required_properties=[INANIMATE])
background = make_noise_objects(noise_objects)
return phase1_instances(
"taking-grabbing",
chain(
flatten([
sampled(
make_take_template(
taker,
takee,
use_adverbial_path_modifier=use_adverbial_path_modifier,
operator=operator,
spatial_properties=[HARD_FORCE]
if hard_force else [SOFT_FORCE],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for use_adverbial_path_modifier in BOOL_SET
for hard_force in BOOL_SET for operator in [TOWARD, AWAY_FROM]
])),
language_generator=language_generator,
)
def make_multiple_object_situation(
num_samples: Optional[int],
num_noise_objects: Optional[int],
language_generator: LanguageGenerator[
HighLevelSemanticsSituation, LinearizedDependencyTree
],
) -> Phase1InstanceGroup:
target_object = standard_object("target_object")
noise_object_variables = [
standard_object("obj-" + str(idx), banned_properties=[IS_SPEAKER, IS_ADDRESSEE])
for idx in range(num_noise_objects if num_noise_objects else 0)
]
return phase1_instances(
"Multiple Objects",
sampled(
_make_multiple_object_template(target_object, noise_object_variables),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 20,
block_multiple_of_the_same_type=True,
),
language_generator=language_generator,
)
def make_imprecise_size_descriptions(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
theme_0 = standard_object("theme",
banned_properties=[IS_SPEAKER, IS_ADDRESSEE])
theme_1 = standard_object("theme-thing",
THING,
banned_properties=[IS_SPEAKER, IS_ADDRESSEE])
return phase1_instances(
"Imprecise Size",
chain(
flatten([
sampled(
template(theme, noise_objects),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for template in [
_big_x_template,
_little_x_template,
_tall_x_template,
_short_x_template,
] for theme in [theme_0, theme_1]
])),
language_generator=language_generator,
)
def make_german_eat_test_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
object_to_eat = standard_object("object_0", required_properties=[EDIBLE])
eater = standard_object(
"eater_0",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
background = make_noise_objects(noise_objects)
return phase1_instances(
"german-eating",
chain(*[
sampled(
make_eat_template(eater, object_to_eat, background),
max_to_sample=num_samples if num_samples else 5,
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
)
]),
language_generator=language_generator,
)
def _make_m6_in_front_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
learner_object = standard_object("learner",
LEARNER,
added_properties=[IS_ADDRESSEE])
mom = standard_object("mom", MOM, added_properties=[IS_SPEAKER])
background = [learner_object, mom]
background.extend(make_noise_objects(noise_objects))
return phase1_instances(
"Preposition behind",
situations=chain(*[
sampled(
_behind_template(object_1,
object_2,
background,
is_training=True,
is_near=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_jump(language_mode, learner):
jumper = standard_object(
"jumper_0",
THING,
required_properties=[CAN_JUMP],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
jumped_over = standard_object("jumped_over",
banned_properties=[IS_SPEAKER, IS_ADDRESSEE])
for situation_template in make_jump_templates(None):
run_verb_test(
learner(language_mode),
situation_template,
language_generator=phase1_language_generator(language_mode),
)
for situation_template in [
_jump_over_template(jumper, jumped_over, immutableset())
]:
run_verb_test(
learner(language_mode),
situation_template,
language_generator=phase1_language_generator(language_mode),
)
def test_subset_preposition_on(language_mode, learner):
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
run_preposition_test(
learner(language_mode),
_on_template(ball, table, immutableset(), is_training=True),
language_generator=phase1_language_generator(language_mode),
)
def make_move_imprecise_temporal_descriptions(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
self_mover_0 = standard_object(
"self-mover_0",
THING,
required_properties=[SELF_MOVING],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
other_mover_0 = standard_object("mover_0",
THING,
required_properties=[ANIMATE])
movee_0 = standard_object("movee_0",
THING,
required_properties=[INANIMATE])
move_goal_reference = standard_object("move-goal-reference",
THING,
required_properties=[INANIMATE])
background = make_noise_objects(noise_objects)
return phase1_instances(
"move-with-temporal-descriptions",
chain(
# bare move (e.g. "a box moves") is about half of uses in child speed
flatten(
sampled(
bare_move_template(
self_mover_0,
move_goal_reference,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
# Transitive Move
flatten(
sampled(
transitive_move_template(
other_mover_0,
movee_0,
move_goal_reference,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
),
language_generator=language_generator,
)
def test_pursuit_preposition_on_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)
on_train_curriculum = phase1_instances(
"Preposition Unit Train",
situations=sampled(
_on_template(ball, table, immutableset(), is_training=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=10,
),
language_generator=language_generator,
)
on_test_curriculum = phase1_instances(
"Preposition Unit Test",
situations=sampled(
_on_template(ball, table, immutableset(), is_training=False),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=1,
),
language_generator=language_generator,
)
for (
_,
linguistic_description,
perceptual_representation,
) in on_train_curriculum.instances():
# Get the object matches first - preposition learner can't learn without already recognized objects
learner.observe(
LearningExample(perceptual_representation, linguistic_description))
for (
_,
test_lingustics_description,
test_perceptual_representation,
) in on_test_curriculum.instances():
descriptions_from_learner = learner.describe(
test_perceptual_representation)
gold = test_lingustics_description.as_token_sequence()
assert descriptions_from_learner
assert [
desc.as_token_sequence() for desc in descriptions_from_learner
][0] == gold
def test_pursuit_preposition_has_learner(language_mode, learner):
person = standard_object("person",
PERSON,
banned_properties=[IS_SPEAKER, IS_ADDRESSEE])
inanimate_object = standard_object("inanimate-object",
INANIMATE_OBJECT,
required_properties=[PERSON_CAN_HAVE])
ball = standard_object("ball", BALL)
language_generator = phase1_language_generator(language_mode)
has_train_curriculum = phase1_instances(
"Has Unit Train",
situations=sampled(
_x_has_y_template(person, inanimate_object),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=2,
block_multiple_of_the_same_type=True,
),
language_generator=language_generator,
)
has_test_curriculum = phase1_instances(
"Has Unit Test",
situations=sampled(
_x_has_y_template(person, ball),
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 has_train_curriculum.instances():
processing_learner.observe(
LearningExample(perceptual_representation, linguistic_description))
for (
_,
test_lingustics_description,
test_perceptual_representation,
) in has_test_curriculum.instances():
descriptions_from_learner = processing_learner.describe(
test_perceptual_representation)
gold = test_lingustics_description.as_token_sequence()
assert descriptions_from_learner
assert gold in [
desc.as_token_sequence() for desc in descriptions_from_learner
]
def test_pursuit_preposition_over_learner(language_mode, learner):
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
language_generator = phase1_language_generator(language_mode)
over_train_curriculum = phase1_instances(
"Preposition Over Unit Train",
situations=sampled(
_over_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,
)
over_test_curriculum = phase1_instances(
"Preposition Over Unit Test",
situations=sampled(
_over_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 over_train_curriculum.instances():
processing_learner.observe(
LearningExample(perceptual_representation, linguistic_description))
for (
_,
test_linguistic_description,
test_perceptual_representation,
) in over_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 test_come(language_mode, learner):
movee = standard_object(
"movee",
required_properties=[SELF_MOVING],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
learner_obj = standard_object("leaner_0", LEARNER)
speaker = standard_object(
"speaker",
PERSON,
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
added_properties=[IS_SPEAKER],
)
object_ = standard_object("object_0",
THING,
banned_properties=[IS_SPEAKER, IS_ADDRESSEE])
ground = standard_object("ground", root_node=GROUND)
come_to_speaker = Phase1SituationTemplate(
"come-to-speaker",
salient_object_variables=[movee, speaker],
actions=[
Action(COME,
argument_roles_to_fillers=[(AGENT, movee), (GOAL, speaker)])
],
)
come_to_learner = Phase1SituationTemplate(
"come-to-leaner",
salient_object_variables=[movee],
actions=[
Action(COME,
argument_roles_to_fillers=[(AGENT, movee),
(GOAL, learner_obj)])
],
)
come_to_object = Phase1SituationTemplate(
"come-to-object",
salient_object_variables=[movee, object_],
actions=[
Action(COME,
argument_roles_to_fillers=[(AGENT, movee), (GOAL, object_)])
],
)
for situation_template in [
_make_come_down_template(movee, object_, speaker, ground,
immutableset()),
come_to_speaker,
come_to_learner,
come_to_object,
]:
run_verb_test(
learner(language_mode),
situation_template,
language_generator=phase1_language_generator(language_mode),
)
def _make_drink_cups_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[
HighLevelSemanticsSituation, LinearizedDependencyTree
],
) -> Phase1InstanceGroup:
templates = []
for cup in [CUP, CUP_2, CUP_3, CUP_4]:
cup_obj = standard_object("cup", cup)
liquid_0 = object_variable("liquid_0", required_properties=[LIQUID])
person_0 = standard_object(
"person_0", PERSON, banned_properties=[IS_SPEAKER, IS_ADDRESSEE]
)
templates.append(
Phase1SituationTemplate(
"drink-cup",
salient_object_variables=[liquid_0, person_0, cup_obj],
background_object_variables=make_noise_objects(noise_objects),
actions=[
Action(
DRINK,
argument_roles_to_fillers=[(AGENT, person_0), (THEME, liquid_0)],
auxiliary_variable_bindings=[(DRINK_CONTAINER_AUX, cup_obj)],
)
],
asserted_always_relations=[inside(liquid_0, cup_obj)],
)
)
return phase2_instances(
"drink - cup",
chain(
*[
sampled(
cup_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_2_ONTOLOGY,
max_to_sample=num_samples,
block_multiple_of_the_same_type=True,
)
if num_samples
else all_possible(
cup_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_2_ONTOLOGY,
)
for cup_template in templates
]
),
perception_generator=GAILA_PHASE_2_PERCEPTION_GENERATOR,
language_generator=language_generator,
)
def drink_test_template():
object_0 = standard_object(
"object_0",
required_properties=[HOLLOW, PERSON_CAN_HAVE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
liquid_0 = object_variable("liquid_0", required_properties=[LIQUID])
person_0 = standard_object("person_0",
PERSON,
banned_properties=[IS_SPEAKER, IS_ADDRESSEE])
return make_drink_template(person_0, liquid_0, object_0, None)
def test_subset_preposition_in_front(language_mode, learner):
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
speaker = standard_object("speaker", MOM, added_properties=[IS_SPEAKER])
run_preposition_test(
learner(language_mode),
_in_front_template(ball,
table, [speaker],
is_training=True,
is_near=True),
language_generator=phase1_language_generator(language_mode),
)
def test_eat_simple(language_mode, learner):
object_to_eat = standard_object("object_0", required_properties=[EDIBLE])
eater = standard_object(
"eater_0",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
run_verb_test(
learner(language_mode),
make_eat_template(eater, object_to_eat),
language_generator=phase1_language_generator(language_mode),
)
def _make_sit_on_chair_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[
HighLevelSemanticsSituation, LinearizedDependencyTree
],
) -> Phase1InstanceGroup:
templates = []
for chair_type in [CHAIR, CHAIR_2, CHAIR_3, CHAIR_4, CHAIR_5]:
sitter = standard_object(
"sitter_0",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
seat = standard_object("chair", chair_type)
templates.append(
make_sit_transitive(
sitter, seat, noise_objects, surface=False, syntax_hints=False
)
)
templates.append(
make_sit_template_intransitive(
sitter, seat, noise_objects, surface=False, syntax_hints=False
)
)
return phase2_instances(
"sit on chair",
chain(
*[
sampled(
template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_2_ONTOLOGY,
max_to_sample=num_samples,
block_multiple_of_the_same_type=True,
)
if num_samples
else all_possible(
template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_2_ONTOLOGY,
)
for template in templates
]
),
perception_generator=GAILA_PHASE_2_PERCEPTION_GENERATOR,
language_generator=language_generator,
)
def make_push_shove_subtle_verb_distinctions(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
pusher = standard_object(
"pusher_0",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
pushee = standard_object("pushee_0",
THING,
required_properties=[INANIMATE])
push_surface = standard_object("push_surface_0",
THING,
required_properties=[INANIMATE])
push_goal = standard_object("push_goal_0",
THING,
required_properties=[INANIMATE])
background = make_noise_objects(noise_objects)
# get all possible templates
templates = flatten([
make_push_templates(
pusher,
pushee,
push_surface,
push_goal,
use_adverbial_path_modifier=use_adverbial_path_modifier,
operator=operator,
spatial_properties=[HARD_FORCE] if hard_force else [SOFT_FORCE],
background=background,
) for hard_force in BOOL_SET
for use_adverbial_path_modifier in BOOL_SET
for operator in [TOWARD, AWAY_FROM]
])
return phase1_instances(
"pushing-shoving",
chain(
flatten([
sampled(
template,
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for template in templates
])),
language_generator=language_generator,
)
def _make_on_tests(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object(
"figure_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figure_1 = standard_object(
"figure_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_0 = standard_object(
"ground_0",
THING,
required_properties=[CAN_HAVE_THINGS_RESTING_ON_THEM],
banned_properties=[HOLLOW],
)
ground_1 = standard_object(
"ground_1",
THING,
required_properties=[CAN_HAVE_THINGS_RESTING_ON_THEM],
banned_properties=[HOLLOW],
)
figures = immutableset([figure_0, figure_1])
grounds = immutableset([ground_0, ground_1])
return phase1_instances(
"Preposition Testing On",
chain(*[
flatten([
sampled(
_on_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=False,
),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=num_samples if num_samples else 5,
block_multiple_of_the_same_type=True,
) for figure in figures for ground in grounds
])
]),
language_generator=language_generator,
)
def _make_over_tests(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object(
"figure_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figure_1 = standard_object(
"figure_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_0 = standard_object(
"ground_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_1 = standard_object(
"ground_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figures = immutableset([figure_0, figure_1])
grounds = immutableset([ground_0, ground_1])
return phase1_instances(
"Preposition Testing Over",
chain(*[
sampled(
_over_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=False,
is_distal=use_above_below,
syntax_hints=[USE_ABOVE_BELOW] if use_above_below else [],
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
block_multiple_of_the_same_type=True,
) for figure in figures for ground in grounds
# for distance in BOOL_SET
for use_above_below in BOOL_SET
]),
language_generator=language_generator,
)
def test_take(language_mode, learner):
run_verb_test(
learner(language_mode),
make_take_template(
agent=standard_object(
"taker_0",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
),
theme=standard_object("object_taken_0",
required_properties=[INANIMATE]),
use_adverbial_path_modifier=False,
),
language_generator=phase1_language_generator(language_mode),
)
def make_fly_imprecise_temporal_descriptions(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
bird = standard_object("bird_0", BIRD)
syntax_hints_options = ([], [USE_ADVERBIAL_PATH_MODIFIER]) # type: ignore
background = make_noise_objects(noise_objects)
return phase1_instances(
"fly-imprecise-temporal-descripttions",
chain(
# Bare Fly
flatten(
sampled(
bare_fly(
bird,
up=is_up,
syntax_hints=syntax_hints,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_up in BOOL_SET
for syntax_hints in syntax_hints_options
for is_fast in BOOL_SET)),
language_generator=language_generator,
)
def test_subset_preposition_behind(language_mode, learner):
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
run_preposition_test(
learner(language_mode),
_behind_template(
ball,
table,
immutableset(),
is_training=True,
is_near=True,
speaker_root_node=MOM,
),
language_generator=phase1_language_generator(language_mode),
)
def _make_in_front_tests(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object(
"figure_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figure_1 = standard_object(
"figure_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_0 = standard_object(
"ground_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_1 = standard_object(
"ground_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figures = immutableset([figure_0, figure_1])
grounds = immutableset([ground_0, ground_1])
return phase1_instances(
"Preposition Testing In Front",
chain(*[
flatten([
sampled(
_in_front_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=False,
is_near=close,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for figure in figures for ground in grounds
for close in BOOL_SET
])
]),
language_generator=language_generator,
)
def test_subset_preposition_in(language_mode, learner):
water = object_variable("water", WATER)
cup = standard_object("cup", CUP)
run_preposition_test(
learner(language_mode),
_in_template(water, cup, immutableset(), is_training=True),
language_generator=phase1_language_generator(language_mode),
)
