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Check what pairs of puzzles have the same size explanation. #31

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Check what pairs of puzzles have the same size explanation. #31

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2 changes: 1 addition & 1 deletion predict_sizes.json
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Original file line number Diff line number Diff line change
@@ -1 +1 @@
{"training_data":97.2,"evaluation_data":97.0}
{"training_data":51.3,"evaluation_data":51.7}
2 changes: 1 addition & 1 deletion src/logger.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
logging.getLogger("pulp").setLevel(logging.ERROR) # Suppress the verbose output from the pulp solver

logging.basicConfig(
level=logging.INFO, # change to logging.DEBUG for more verbose output
level=logging.ERROR, # change to logging.DEBUG for more verbose output
format="%(message)s",
)
logger = logging.getLogger(__name__)
213 changes: 110 additions & 103 deletions src/predict_size.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@ class Config:
task_name: str | None = None
# task_name = "746b3537.json"
find_xform_color = True
display_not_found = True
display_not_found = False


def output_size_is_input_size(grids: ExampleObjects, grid: Object, task_name: str):
Expand Down Expand Up @@ -204,9 +204,9 @@ class XformEntry(TypedDict):


xforms: List[XformEntry] = [
{"function": output_size_is_input_size, "difficulty": 1}, # Level 1: Very Simple
# {"function": output_size_is_input_size, "difficulty": 1}, # Level 1: Very Simple
# Level 2: Simple with External Dependency
{"function": output_size_is_constant, "difficulty": 2},
# {"function": output_size_is_constant, "difficulty": 2},
{
"function": output_size_is_size_of_object_inside_largest_frame,
"difficulty": 4,
Expand Down Expand Up @@ -262,7 +262,7 @@ def find_xform(
logger.debug(f"Checking xform {func.__name__} {task_type}")
if check_xform_on_examples(func, examples, task_name, task_type):
correct_xform = xform
logger.info(
logger.debug(
f"Xform {correct_xform['function'].__name__} is correct for all examples in {task_type}"
)
test_examples = task.test
Expand Down Expand Up @@ -421,7 +421,7 @@ def get_matched_objects(examples: Examples[Object, Object]) -> Optional[List[Obj
for example in examples:
input = example[0]
output = example[1]
logger.info(f" {task_type} {input.size} -> {output.size}")
logger.debug(f" {task_type} {input.size} -> {output.size}")

input_objects = candidate_objects_for_matching(input, output)
matched_object_index = find_matching_input_object(input_objects, output)
Expand Down Expand Up @@ -480,118 +480,125 @@ def predict_size_using_regularized_regression(
def process_tasks(tasks: Tasks, set: str):
num_correct = 0
num_incorrect = 0
for task_name, task in tasks.items():
if Config.task_name and task_name != Config.task_name:
continue
logger.info(f"\n***Task: {task_name} {set}***")

examples: Examples[Object, Object] = task.train
tests: Examples[Object, Object] = task.test
task_type = "train"
if True:
current_difficulty = 0

if Config.find_xform:
correct_xform = find_xform(examples, task, task_name, task_type)
if correct_xform:
logger.info(
f"Xform {correct_xform['function'].__name__} is correct for all examples in {task_type} and test"
)
num_correct += 1
continue

current_difficulty += num_difficulties_xform

if Config.find_matched_objects:
# Check if the input objects can be matched to the output objects
logger.debug(f"Checking common features for {task_name} {set}")
matched_objects = find_matched_objects(examples, task_type)
if matched_objects:
# If the input objects can be matched to the output objects, try to detect common features
# to determine the correct object to pick
for task_name1, task1 in tasks.items():
for task_name2, task2 in tasks.items():
if task_name1 == task_name2:
continue
task_name = f"{task_name1}{task_name2}"
logger.debug(f"\n***Task: {task_name} {set}***")

examples_1: Examples[Object, Object] = task1.train
examples_2: Examples[Object, Object] = task2.train
tests_1: Examples[Object, Object] = task1.test
tests_2: Examples[Object, Object] = task2.test
examples = examples_1 + examples_2
tests = tests_1 + tests_2
task = Task(examples, tests)
task_type = "train"
if True:
current_difficulty = 0

if Config.find_xform:
correct_xform = find_xform(examples, task, task_name, task_type)
if correct_xform:
logger.error(
f"{task_name}:{task_type} {correct_xform['function'].__name__} is correct"
)
num_correct += 1
continue

current_difficulty += num_difficulties_xform

if Config.find_matched_objects:
# Check if the input objects can be matched to the output objects
logger.debug(f"Checking common features for {task_name} {set}")
matched_objects = find_matched_objects(examples, task_type)
if matched_objects:
# If the input objects can be matched to the output objects, try to detect common features
# to determine the correct object to pick
logger.debug(
f"XXX Matched {len(matched_objects)}/{len(examples)} {task_name} {set}"
)
common_decision_rule, features_used = detect_common_features(
matched_objects, current_difficulty
)
if common_decision_rule:
logger.info(
f"Common decision rule ({features_used}): {common_decision_rule}"
)
num_correct += 1
continue
else:
logger.warning(
f"Could not find common decision rule for {task_name} {set}"
)
current_difficulty += num_difficulties_matching

def try_regularized_regression(exs: Examples[Object, Object]):
logger.debug(
f"XXX Matched {len(matched_objects)}/{len(examples)} {task_name} {set}"
f"Trying to determine dimensions via LP for {task_name} {set}"
)
common_decision_rule, features_used = detect_common_features(
matched_objects, current_difficulty
(
predicted_height,
predicted_width,
) = predict_size_using_regularized_regression(
exs, relative_difficulty=Config.difficulty - current_difficulty
)
if common_decision_rule:
if predicted_height and predicted_width:
logger.info(
f"Common decision rule ({features_used}): {common_decision_rule}"
)
num_correct += 1
continue
else:
logger.warning(
f"Could not find common decision rule for {task_name} {set}"
f"Predictions via LP: out.height=={pretty_print_numeric_features(predicted_height)}, out.width=={pretty_print_numeric_features(predicted_width)}"
)
current_difficulty += num_difficulties_matching
return predicted_height, predicted_width

def try_regularized_regression(exs: Examples[Object, Object]):
logger.debug(
f"Trying to determine dimensions via LP for {task_name} {set}"
)
(
predicted_height,
predicted_width,
) = predict_size_using_regularized_regression(
exs, relative_difficulty=Config.difficulty - current_difficulty
difficulty_after_regularized_regression = (
current_difficulty + numeric_features.num_difficulties + 1
)
if predicted_height and predicted_width:
logger.info(
f"Predictions via LP: out.height=={pretty_print_numeric_features(predicted_height)}, out.width=={pretty_print_numeric_features(predicted_width)}"
)
return predicted_height, predicted_width

difficulty_after_regularized_regression = (
current_difficulty + numeric_features.num_difficulties + 1
)
if (
Config.predict_size_using_regularized_regression
and Config.difficulty >= current_difficulty
):
predicted_height, predicted_width = try_regularized_regression(examples)
if predicted_height and predicted_width:
num_correct += 1
continue
if (
Config.try_remove_main_color
and Config.difficulty >= difficulty_after_regularized_regression
Config.predict_size_using_regularized_regression
and Config.difficulty >= current_difficulty
):
# try to remove main color and try again
examples2: Examples[Object, Object] = []
for example in examples:
input_obj = example[0]
# change the main color to black
new_input_data = input_obj.change_color(
input_obj.main_color(), BLACK
).copy()
examples2.append((new_input_data, example[1]))
predicted_height, predicted_width = try_regularized_regression(
examples2
)
predicted_height, predicted_width = try_regularized_regression(examples)
if predicted_height and predicted_width:
num_correct += 1
continue
current_difficulty += num_difficulties_regularized_regression

if Config.display_not_found:
obj = examples[0][0]
colored_objects = obj.detect_colored_objects(
background_color=obj.main_color(allow_black=True)
)
display_multiple(examples + tests, title=f"examples {task_name} {set}")
if colored_objects and False:
display_multiple(
colored_objects,
title=f"colored objects",
if (
Config.try_remove_main_color
and Config.difficulty >= difficulty_after_regularized_regression
):
# try to remove main color and try again
examples2: Examples[Object, Object] = []
for example in examples:
input_obj = example[0]
# change the main color to black
new_input_data = input_obj.change_color(
input_obj.main_color(), BLACK
).copy()
examples2.append((new_input_data, example[1]))
predicted_height, predicted_width = try_regularized_regression(
examples2
)
if predicted_height and predicted_width:
num_correct += 1
continue
current_difficulty += num_difficulties_regularized_regression

if Config.display_not_found:
obj = examples[0][0]
colored_objects = obj.detect_colored_objects(
background_color=obj.main_color(allow_black=True)
)
display_multiple(examples + tests, title=f"examples {task_name} {set}")
if colored_objects and False:
display_multiple(
colored_objects,
title=f"colored objects",
)

# If no valid dimensions could be determined, give up
logger.warning(
f"Could not find correct transformation or determine dimensions via Linear Programming for {task_name} {set} examples"
)
num_incorrect += 1
# If no valid dimensions could be determined, give up
logger.warning(
f"Could not find correct transformation or determine dimensions via Linear Programming for {task_name} {set} examples"
)
num_incorrect += 1

return num_correct, num_incorrect

Expand Down