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"""
Tests for cascade evaluation validation functionality in openevolve.evaluator
"""
import unittest
import tempfile
import os
from unittest.mock import patch, MagicMock
from openevolve.config import Config
from openevolve.evaluator import Evaluator
from openevolve.evaluation_result import EvaluationResult
class TestCascadeValidation(unittest.IsolatedAsyncioTestCase):
"""Tests for cascade evaluation configuration validation"""
def setUp(self):
"""Set up test evaluator with cascade validation"""
self.config = Config()
# Create temporary evaluator files for testing
self.temp_dir = tempfile.mkdtemp()
def tearDown(self):
"""Clean up temporary files"""
# Clean up temp files more safely
import shutil
shutil.rmtree(self.temp_dir, ignore_errors=True)
def _create_evaluator_file(self, filename: str, content: str) -> str:
"""Helper to create temporary evaluator file"""
file_path = os.path.join(self.temp_dir, filename)
with open(file_path, "w") as f:
f.write(content)
return file_path
def test_cascade_validation_with_valid_evaluator(self):
"""Test cascade validation with evaluator that has cascade functions"""
# Create evaluator with cascade functions
evaluator_content = """
def evaluate_stage1(program_path):
return {"stage1_score": 0.5}
def evaluate_stage2(program_path):
return {"stage2_score": 0.7}
def evaluate_stage3(program_path):
return {"stage3_score": 0.9}
def evaluate(program_path):
return {"final_score": 1.0}
"""
evaluator_path = self._create_evaluator_file("valid_cascade.py", evaluator_content)
# Configure for cascade evaluation
self.config.evaluator.cascade_evaluation = True
self.config.evaluator.evaluation_file = evaluator_path
# Should not raise warnings for valid cascade evaluator
with patch("openevolve.evaluator.logger") as mock_logger:
evaluator = Evaluator(self.config.evaluator, evaluator_path)
# Should not have called warning
mock_logger.warning.assert_not_called()
def test_cascade_validation_warning_for_missing_functions(self):
"""Test cascade validation warns when cascade functions are missing"""
# Create evaluator without cascade functions
evaluator_content = """
def evaluate(program_path):
return {"score": 0.5}
"""
evaluator_path = self._create_evaluator_file("no_cascade.py", evaluator_content)
# Configure for cascade evaluation
self.config.evaluator.cascade_evaluation = True
self.config.evaluator.evaluation_file = evaluator_path
# Should warn about missing cascade functions
with patch("openevolve.evaluator.logger") as mock_logger:
evaluator = Evaluator(self.config.evaluator, evaluator_path)
# Should have warned about missing stage functions
mock_logger.warning.assert_called()
warning_call = mock_logger.warning.call_args[0][0]
self.assertIn("cascade_evaluation: true", warning_call)
self.assertIn("evaluate_stage1", warning_call)
def test_cascade_validation_partial_functions(self):
"""Test cascade validation with only some cascade functions"""
# Create evaluator with only stage1
evaluator_content = """
def evaluate_stage1(program_path):
return {"stage1_score": 0.5}
def evaluate(program_path):
return {"score": 0.5}
"""
evaluator_path = self._create_evaluator_file("partial_cascade.py", evaluator_content)
# Configure for cascade evaluation
self.config.evaluator.cascade_evaluation = True
self.config.evaluator.evaluation_file = evaluator_path
# Should warn about missing additional stages
with patch("openevolve.evaluator.logger") as mock_logger:
evaluator = Evaluator(self.config.evaluator, evaluator_path)
# Should warn about missing stage2/stage3
mock_logger.warning.assert_called_once()
warning_call = mock_logger.warning.call_args[0][0]
self.assertIn(
"defines 'evaluate_stage1' but no additional cascade stages", warning_call
)
def test_no_cascade_validation_when_disabled(self):
"""Test no validation when cascade evaluation is disabled"""
# Create evaluator without cascade functions
evaluator_content = """
def evaluate(program_path):
return {"score": 0.5}
"""
evaluator_path = self._create_evaluator_file("no_cascade.py", evaluator_content)
# Configure WITHOUT cascade evaluation
self.config.evaluator.cascade_evaluation = False
self.config.evaluator.evaluation_file = evaluator_path
# Should not perform validation or warn
with patch("openevolve.evaluator.logger") as mock_logger:
evaluator = Evaluator(self.config.evaluator, evaluator_path)
# Should not warn when cascade evaluation is disabled
mock_logger.warning.assert_not_called()
async def test_direct_evaluate_supports_evaluation_result(self):
"""Test that _direct_evaluate supports EvaluationResult returns"""
# Create evaluator that returns EvaluationResult
evaluator_content = """
from openevolve.evaluation_result import EvaluationResult
def evaluate(program_path):
return EvaluationResult(
metrics={"score": 0.8, "accuracy": 0.9},
artifacts={"debug_info": "test data"}
)
"""
evaluator_path = self._create_evaluator_file("result_evaluator.py", evaluator_content)
self.config.evaluator.cascade_evaluation = False
self.config.evaluator.evaluation_file = evaluator_path
self.config.evaluator.timeout = 10
evaluator = Evaluator(self.config.evaluator, evaluator_path)
# Create a dummy program file
program_path = self._create_evaluator_file("test_program.py", "def test(): pass")
# Mock the evaluation function
def mock_evaluate(path):
return EvaluationResult(
metrics={"score": 0.8, "accuracy": 0.9}, artifacts={"debug_info": "test data"}
)
evaluator.evaluate_function = mock_evaluate
# Should handle EvaluationResult without issues
result = await evaluator._direct_evaluate(program_path)
# Should return the EvaluationResult as-is
self.assertIsInstance(result, EvaluationResult)
self.assertEqual(result.metrics["score"], 0.8)
self.assertEqual(result.artifacts["debug_info"], "test data")
async def test_direct_evaluate_supports_dict_result(self):
"""Test that _direct_evaluate still supports dict returns"""
# Create evaluator that returns dict
evaluator_content = """
def evaluate(program_path):
return {"score": 0.7, "performance": 0.85}
"""
evaluator_path = self._create_evaluator_file("dict_evaluator.py", evaluator_content)
self.config.evaluator.cascade_evaluation = False
self.config.evaluator.evaluation_file = evaluator_path
self.config.evaluator.timeout = 10
evaluator = Evaluator(self.config.evaluator, evaluator_path)
# Create a dummy program file
program_path = self._create_evaluator_file("test_program.py", "def test(): pass")
# Mock the evaluation function directly
def mock_evaluate(path):
return {"score": 0.7, "performance": 0.85}
evaluator.evaluate_function = mock_evaluate
# Should handle dict result without issues
result = await evaluator._direct_evaluate(program_path)
# Should return the dict as-is
self.assertIsInstance(result, dict)
self.assertEqual(result["score"], 0.7)
self.assertEqual(result["performance"], 0.85)
def test_cascade_validation_with_class_based_evaluator(self):
"""Test cascade validation with class-based evaluator"""
# Create class-based evaluator with all stages
evaluator_content = """
class Evaluator:
def evaluate_stage1(self, program_path):
return {"stage1_score": 0.5}
def evaluate_stage2(self, program_path):
return {"stage2_score": 0.7}
def evaluate(self, program_path):
return {"score": 0.5}
# Module-level functions (what validation looks for)
def evaluate_stage1(program_path):
evaluator = Evaluator()
return evaluator.evaluate_stage1(program_path)
def evaluate_stage2(program_path):
evaluator = Evaluator()
return evaluator.evaluate_stage2(program_path)
def evaluate(program_path):
evaluator = Evaluator()
return evaluator.evaluate(program_path)
"""
evaluator_path = self._create_evaluator_file("class_cascade.py", evaluator_content)
# Configure for cascade evaluation
self.config.evaluator.cascade_evaluation = True
self.config.evaluator.evaluation_file = evaluator_path
# Should not warn since module-level functions exist
with patch("openevolve.evaluator.logger") as mock_logger:
evaluator = Evaluator(self.config.evaluator, evaluator_path)
mock_logger.warning.assert_not_called()
def test_cascade_validation_with_syntax_error(self):
"""Test cascade validation handles syntax errors gracefully"""
# Create evaluator with syntax error
evaluator_content = """
def evaluate_stage1(program_path) # Missing colon
return {"stage1_score": 0.5}
"""
evaluator_path = self._create_evaluator_file("syntax_error.py", evaluator_content)
# Configure for cascade evaluation
self.config.evaluator.cascade_evaluation = True
self.config.evaluator.evaluation_file = evaluator_path
# Should raise an error due to syntax error
with self.assertRaises(Exception): # Could be SyntaxError or other import error
evaluator = Evaluator(self.config.evaluator, evaluator_path)
def test_cascade_validation_nonexistent_file(self):
"""Test cascade validation with nonexistent evaluator file"""
# Configure with nonexistent file
nonexistent_path = "/nonexistent/path.py"
self.config.evaluator.cascade_evaluation = True
self.config.evaluator.evaluation_file = nonexistent_path
# Should raise ValueError for missing file
with self.assertRaises(ValueError) as context:
evaluator = Evaluator(self.config.evaluator, nonexistent_path)
self.assertIn("not found", str(context.exception))
def test_process_evaluation_result_with_artifacts(self):
"""Test that _process_evaluation_result handles artifacts correctly"""
evaluator_content = """
def evaluate(program_path):
return {"score": 0.5}
"""
evaluator_path = self._create_evaluator_file("dummy.py", evaluator_content)
self.config.evaluator.cascade_evaluation = False # Disable cascade to avoid warnings
self.config.evaluator.evaluation_file = evaluator_path
evaluator = Evaluator(self.config.evaluator, evaluator_path)
# Test with EvaluationResult containing artifacts
eval_result = EvaluationResult(
metrics={"score": 0.9}, artifacts={"log": "test log", "data": [1, 2, 3]}
)
result = evaluator._process_evaluation_result(eval_result)
self.assertEqual(result.metrics, {"score": 0.9})
self.assertEqual(result.artifacts, {"log": "test log", "data": [1, 2, 3]})
def test_process_evaluation_result_with_dict(self):
"""Test that _process_evaluation_result handles dict results correctly"""
evaluator_content = """
def evaluate(program_path):
return {"score": 0.5}
"""
evaluator_path = self._create_evaluator_file("dummy.py", evaluator_content)
self.config.evaluator.cascade_evaluation = False # Disable cascade to avoid warnings
self.config.evaluator.evaluation_file = evaluator_path
evaluator = Evaluator(self.config.evaluator, evaluator_path)
# Test with dict result
dict_result = {"score": 0.7, "accuracy": 0.8}
result = evaluator._process_evaluation_result(dict_result)
self.assertEqual(result.metrics, {"score": 0.7, "accuracy": 0.8})
self.assertEqual(result.artifacts, {})
if __name__ == "__main__":
unittest.main()
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