def test_composite_selections():
with open('training/' + os.listdir('training/')[205]) as f:
raw_cases = json.load(f)
cases = tuplefy_task(raw_cases)
color_0 = Property(lambda x: frozenset({0}),
np.log(2),
name=f'color {0}',
output_types=frozenset({'color'}),
entity_finder=base_entity_finder)
color_5 = Property(lambda x: frozenset({5}),
np.log(10) - 1,
name=f'color {5}',
output_types=frozenset({'color'}),
entity_finder=base_entity_finder)
take_color = Property(lambda x: x.entity.colors(),
name='the colors',
output_types=frozenset({'color'}),
entity_finder=base_entity_finder,
nll=1)
select_not_0 = Selector.make_property_selector(take_color, color_0, False)
select_not_5 = Selector.make_property_selector(take_color, color_5, False)
select_not_0_nor_5 = select_not_0.intersect(select_not_5)
entity_finder = base_entity_finder.compose(select_not_0_nor_5, True)
select_5 = Selector.make_property_selector(take_color, color_5)
center_y = Property(lambda x: x.entity.center(axis=0),
nll=np.log(2),
name='the center y coordinate',
output_types=frozenset({'y_coordinate'}),
entity_finder=base_entity_finder,
requires_entity=True)
center_x = Property(lambda x: x.entity.center(axis=1),
nll=np.log(2),
name='the center x coordinate',
output_types=frozenset({'x_coordinate'}),
entity_finder=base_entity_finder,
requires_entity=True)
center_5y = center_y.add_selector(select_5)
length_5y = Property.create_distance_property(center_5y, center_y)
center_5x = center_x.add_selector(select_5)
length_5x = Property.create_distance_property(center_5x, center_x)
vect_prop = Property.xy_length_to_vector(length_5y, length_5x)
move_to_5 = Transformer(
lambda entities, grid, copy=True: move(
entities, vector_property=vect_prop, copy=copy, extend_grid=False),
nll=vect_prop.nll + np.log(2),
name=f"{'copy' if True else 'move'} them by ({vect_prop})")
my_predictor = Predictor(entity_finder, move_to_5)
for case in cases['train']:
assert my_predictor.predict(case['input']) == case['output']
def test_case_29():
with open('training/' + os.listdir('training/')[29]) as f:
raw_task = json.load(f)
base_entity_finder = EntityFinder(
lambda grid: find_components(grid, directions=ALL_DIRECTIONS))
trivial_selector = Selector(lambda entity, grid: True, name='')
task = tuplefy_task(raw_task)
inp = task['train'][0]['input']
out = task['train'][0]['output']
# print(task['train'][0]['input'])
take_color = Property(lambda x: x.entity.colors(),
name='the colors',
output_types=frozenset({'color'}),
entity_finder=base_entity_finder,
nll=1,
requires_entity=True)
# color_2 = Property(lambda x, i=2: frozenset({2}), np.log(10) - 2, name=f'color {2}',
# output_types=frozenset({'color'}))
color_1 = Property(lambda x, i=2: frozenset({1}),
np.log(10) - 1,
name=f'color {1}',
output_types=frozenset({'color'}),
entity_finder=base_entity_finder)
color_0 = Property(lambda x, i=2: frozenset({0}),
np.log(10) - 1,
name=f'color {0}',
output_types=frozenset({'color'}),
entity_finder=base_entity_finder)
select_1 = Selector.make_property_selector(take_color, color_1)
property_0 = Property(lambda x, i=0: i,
nll=1,
name=f'{0}',
output_types=frozenset({
'x_coordinate', 'y_coordinate', 'x_length',
'y_length', 'quantity'
}),
entity_finder=base_entity_finder)
select_not_0 = Selector.make_property_selector(take_color,
color_0,
the_same=False)
smallest_y = Property(lambda x: x.entity.max_coord(axis=0),
1 + np.log(4),
name='the largest y coordinate',
output_types=frozenset({'y_coordinate'}),
entity_finder=base_entity_finder,
requires_entity=True)
min_y_of_blue = smallest_y.add_selector(select_1)
distance_to_min_y_of_blue = Property.create_distance_property(
min_y_of_blue, smallest_y)
vector_to_min_y_of_blue = Property.xy_length_to_vector(
distance_to_min_y_of_blue, property_0)
move_transform = Transformer(
lambda entities, grid, vector_prop=vector_to_min_y_of_blue: move(
entities, vector_property=vector_prop),
nll=vector_to_min_y_of_blue.nll + np.log(2),
name=f'move them by ({vector_to_min_y_of_blue})')
my_predictor = Predictor(base_entity_finder.compose(trivial_selector),
move_transform) # .compose(select_not_0)
# display_case(my_predictor.predict(inp))
# display_case(out)
assert my_predictor.predict(inp) == out
test_input = task['test'][0]['input']
test_output = task['test'][0]['output']
test_entities = base_entity_finder(test_input)
assert len(test_entities) == 4
selected_finder = base_entity_finder.compose(select_not_0)
# selected_finder(test_input)
assert len(selected_finder(test_input)) == 3
assert my_predictor.predict(test_input) == test_output