File size: 5,169 Bytes
ea8c728 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | import importlib.util
import os
import json
import numpy as np
from evaluator import pass_at_2_accuracy_multi_test, extract_failure_artifacts
TASK_FILE = os.getenv("ARC_TASK_FILE", "training")
TASK_NUM = os.getenv("TASK_NUM", 0)
OUTS_DIR = os.getenv("OUTS_DIR", "")
def load_program_module():
"""Dynamically load the best_program.py module from the specified directory."""
spec = importlib.util.spec_from_file_location("program_module", os.path.join(OUTS_DIR, "best/best_program.py"))
program_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(program_module)
return program_module
def evaluate():
"""Evaluate the program module located in the specified directory."""
program_module = load_program_module()
if not hasattr(program_module, 'transform_grid_attempt_1') or not hasattr(program_module, 'transform_grid_attempt_2'):
print(f"Stage 1 validation failed: Program must define 'transform_grid_attempt_1' and 'transform_grid_attempt_2' functions.")
error_artifacts = {
"error_type": "MissingFunction",
"error_message": "Stage 1: Program is missing required 'transform_grid_attempt_1' and 'transform_grid_attempt_2' functions.",
"suggestion": "Make sure your program includes a functions named 'transform_grid_attempt_1' and 'transform_grid_attempt_2' that take as an argument a 2D numpy array and return a 2D numpy array."
}
return dict(
metrics={
"runs_successfully": 0.0,
"combined_score": 0.0,
"error": "Missing transform_grid_attempt_1 and transform_grid_attempt_2 functions"
},
artifacts=error_artifacts
)
# Load ARC tasks
challenge_path = f"/workspaces/ARC-Evolve/data/arc-prize-2025/arc-agi_{TASK_FILE}_challenges.json"
solution_path = f"/workspaces/ARC-Evolve/data/arc-prize-2025/arc-agi_{TASK_FILE}_solutions.json"
with open(challenge_path, 'r') as f:
tasks = json.load(f)
with open(solution_path, 'r') as f:
solutions = json.load(f)
task_id = list(tasks.keys())[int(TASK_NUM)]
solution = solutions[task_id]
task = tasks[task_id]
test_inputs = [np.array(inp["input"]) for inp in task['test']]
test_gts = [np.array(gt) for gt in solution]
test_attempts = []
for inp in test_inputs:
attempt_1 = program_module.transform_grid_attempt_1(inp)
if not isinstance(attempt_1, np.ndarray):
print(f"transform_grid_attempt_1 did not return a numpy array")
error_artifacts = {
"error_type": "InvalidReturnType",
"error_message": "Stage 1: transform_grid_attempt_1 did not return a numpy array.",
"suggestion": "Make sure your transform_grid_attempt_1 function returns a 2D numpy array."
}
return dict(
metrics={
"runs_successfully": 0.0,
"combined_score": 0.0,
"error": "transform_grid_attempt_1 did not return a numpy array"
},
artifacts=error_artifacts
)
attempt_2 = program_module.transform_grid_attempt_2(inp)
if not isinstance(attempt_2, np.ndarray):
print(f"transform_grid_attempt_2 did not return a numpy array")
error_artifacts = {
"error_type": "InvalidReturnType",
"error_message": "Stage 1: transform_grid_attempt_2 did not return a numpy array.",
"suggestion": "Make sure your transform_grid_attempt_2 function returns a 2D numpy array."
}
return dict(
metrics={
"runs_successfully": 0.0,
"combined_score": 0.0,
"error": "transform_grid_attempt_2 did not return a numpy array"
},
artifacts=error_artifacts
)
test_attempts.append([attempt_1, attempt_2])
pass_at_2_test, test_diagnostics_list = pass_at_2_accuracy_multi_test(test_attempts, test_gts)
metrics = {
"runs_successfully": 1.0,
"combined_score": sum(pass_at_2_test) / len(pass_at_2_test),
}
error_artifacts = {}
for i, (test_pass, test_diagnostics) in enumerate(zip(pass_at_2_test, test_diagnostics_list)):
example_name = f"test_example_{i}"
metrics[f"{example_name}_pass_at_2"] = test_pass
for attempt in test_diagnostics:
metrics[f"{example_name}_attempt_{attempt}"] = test_diagnostics[attempt]["perfect_match"]
if test_pass == 0:
error_artifacts = extract_failure_artifacts(test_diagnostics)
return dict(
metrics=metrics,
artifacts=error_artifacts
)
if __name__ == "__main__":
evaluation_result = evaluate()
with open(os.path.join(OUTS_DIR, "best", "post_evolution_evaluation_result.json"), 'w') as f:
json.dump(evaluation_result, f, indent=4) |