Fix cli.py overriding the logging level to INFO

openevolve/cli.py

@@ -42,7 +42,7 @@ def parse_args() -> argparse.Namespace:
         "-l",
         help="Logging level",
         choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
-        default="INFO",
+        default=None,
     )
 
     parser.add_argument(

openevolve/database.py

@@ -194,12 +194,12 @@ def add(
 
             if feature_key not in self.feature_map:
                 # New cell occupation
-                logging.info("New MAP-Elites cell occupied: %s", coords_dict)
+                logger.info("New MAP-Elites cell occupied: %s", coords_dict)
                 # Check coverage milestone
                 total_possible_cells = self.feature_bins ** len(self.config.feature_dimensions)
                 coverage = (len(self.feature_map) + 1) / total_possible_cells
                 if coverage in [0.1, 0.25, 0.5, 0.75, 0.9]:
-                    logging.info("MAP-Elites coverage reached %.1f%% (%d/%d cells)", 
+                    logger.info("MAP-Elites coverage reached %.1f%% (%d/%d cells)", 
                                coverage * 100, len(self.feature_map) + 1, total_possible_cells)
             else:
                 # Cell replacement - existing program being replaced
@@ -208,7 +208,7 @@ def add(
                     existing_program = self.programs[existing_program_id]
                     new_fitness = safe_numeric_average(program.metrics)
                     existing_fitness = safe_numeric_average(existing_program.metrics)
-                    logging.info("MAP-Elites cell improved: %s (fitness: %.3f -> %.3f)", 
+                    logger.info("MAP-Elites cell improved: %s (fitness: %.3f -> %.3f)", 
                                coords_dict, existing_fitness, new_fitness)
 
             self.feature_map[feature_key] = program.id
@@ -355,6 +355,10 @@ def get_top_programs(self, n: int = 10, metric: Optional[str] = None, island_idx
         Returns:
             List of top programs
         """
+        # Validate island_idx parameter
+        if island_idx is not None and (island_idx < 0 or island_idx >= len(self.islands)):
+            raise IndexError(f"Island index {island_idx} is out of range (0-{len(self.islands)-1})")
+
         if not self.programs:
             return []
 
@@ -667,7 +671,7 @@ def _calculate_feature_coords(self, program: Program) -> List[int]:
                 # Default to middle bin if feature not found
                 coords.append(self.feature_bins // 2)
         # Only log coordinates at debug level for troubleshooting
-        logging.debug(
+        logger.debug(
             "MAP-Elites coords: %s",
             str({self.config.feature_dimensions[i]: coords[i] for i in range(len(coords))}),
         )

scripts/visualizer.py

@@ -7,7 +7,7 @@
 from flask import Flask, render_template, render_template_string, jsonify
 
 
-logger = logging.getLogger("openevolve.visualizer")
+logger = logging.getLogger(__name__)
 app = Flask(__name__, template_folder="templates")
 
 
@@ -164,7 +164,7 @@ def run_static_export(args):
         shutil.rmtree(static_dst)
     shutil.copytree(static_src, static_dst)
 
-    logging.info(
+    logger.info(
         f"Static export written to {output_dir}/\nNote: This will only work correctly with a web server, not by opening the HTML file directly in a browser. Try $ python3 -m http.server --directory {output_dir} 8080"
     )
 

tests/test_cascade_validation.py

@@ -8,10 +8,10 @@
 from unittest.mock import patch, MagicMock
 from openevolve.config import Config
 from openevolve.evaluator import Evaluator
-from openevolve.database import EvaluationResult
+from openevolve.evaluation_result import EvaluationResult
 
 
-class TestCascadeValidation(unittest.TestCase):
+class TestCascadeValidation(unittest.IsolatedAsyncioTestCase):
     """Tests for cascade evaluation configuration validation"""
 
     def setUp(self):
@@ -23,10 +23,9 @@ def setUp(self):
 
     def tearDown(self):
         """Clean up temporary files"""
-        # Clean up temp files
-        for file in os.listdir(self.temp_dir):
-            os.remove(os.path.join(self.temp_dir, file))
-        os.rmdir(self.temp_dir)
+        # 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"""
@@ -59,7 +58,7 @@ def evaluate(program_path):
 
         # Should not raise warnings for valid cascade evaluator
         with patch('openevolve.evaluator.logger') as mock_logger:
-            evaluator = Evaluator(self.config.evaluator, None)
+            evaluator = Evaluator(self.config.evaluator, evaluator_path)
 
             # Should not have called warning
             mock_logger.warning.assert_not_called()
@@ -79,7 +78,7 @@ def evaluate(program_path):
 
         # Should warn about missing cascade functions
         with patch('openevolve.evaluator.logger') as mock_logger:
-            evaluator = Evaluator(self.config.evaluator, None)
+            evaluator = Evaluator(self.config.evaluator, evaluator_path)
 
             # Should have warned about missing stage functions
             mock_logger.warning.assert_called()
@@ -103,12 +102,14 @@ def evaluate(program_path):
         self.config.evaluator.cascade_evaluation = True
         self.config.evaluator.evaluation_file = evaluator_path
 
-        # Should not warn since stage1 exists (minimum requirement)
+        # Should warn about missing additional stages
         with patch('openevolve.evaluator.logger') as mock_logger:
-            evaluator = Evaluator(self.config.evaluator, None)
+            evaluator = Evaluator(self.config.evaluator, evaluator_path)
 
-            # Should not warn since stage1 exists
-            mock_logger.warning.assert_not_called()
+            # 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"""
@@ -125,16 +126,16 @@ def evaluate(program_path):
 
         # Should not perform validation or warn
         with patch('openevolve.evaluator.logger') as mock_logger:
-            evaluator = Evaluator(self.config.evaluator, None)
+            evaluator = Evaluator(self.config.evaluator, evaluator_path)
 
             # Should not warn when cascade evaluation is disabled
             mock_logger.warning.assert_not_called()
 
-    def test_direct_evaluate_supports_evaluation_result(self):
+    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.database import EvaluationResult
+from openevolve.evaluation_result import EvaluationResult
 
 def evaluate(program_path):
     return EvaluationResult(
@@ -148,27 +149,29 @@ def evaluate(program_path):
         self.config.evaluator.evaluation_file = evaluator_path
         self.config.evaluator.timeout = 10
 
-        evaluator = Evaluator(self.config.evaluator, None)
+        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 process
-        with patch('openevolve.evaluator.run_external_evaluator') as mock_run:
-            mock_run.return_value = EvaluationResult(
+        # Mock the evaluation function
+        def mock_evaluate(path):
+            return EvaluationResult(
                 metrics={"score": 0.8, "accuracy": 0.9},
                 artifacts={"debug_info": "test data"}
             )
-
-            # Should handle EvaluationResult without issues
-            result = 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")
+
+        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")
 
-    def test_direct_evaluate_supports_dict_result(self):
+    async def test_direct_evaluate_supports_dict_result(self):
         """Test that _direct_evaluate still supports dict returns"""
         # Create evaluator that returns dict
         evaluator_content = '''
@@ -181,31 +184,36 @@ def evaluate(program_path):
         self.config.evaluator.evaluation_file = evaluator_path
         self.config.evaluator.timeout = 10
 
-        evaluator = Evaluator(self.config.evaluator, None)
+        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 process
-        with patch('openevolve.evaluator.run_external_evaluator') as mock_run:
-            mock_run.return_value = {"score": 0.7, "performance": 0.85}
-
-            # Should handle dict result without issues
-            result = 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)
+        # 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
+        # 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}
 
@@ -214,6 +222,10 @@ 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)
@@ -226,7 +238,7 @@ def evaluate(program_path):
 
         # Should not warn since module-level functions exist
         with patch('openevolve.evaluator.logger') as mock_logger:
-            evaluator = Evaluator(self.config.evaluator, None)
+            evaluator = Evaluator(self.config.evaluator, evaluator_path)
 
             mock_logger.warning.assert_not_called()
 
@@ -243,58 +255,65 @@ def evaluate_stage1(program_path)  # Missing colon
         self.config.evaluator.cascade_evaluation = True
         self.config.evaluator.evaluation_file = evaluator_path
 
-        # Should handle syntax error and still warn about cascade
-        with patch('openevolve.evaluator.logger') as mock_logger:
-            evaluator = Evaluator(self.config.evaluator, None)
-
-            # Should have warned about missing functions (due to import failure)
-            mock_logger.warning.assert_called()
+        # 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.py"
+        self.config.evaluator.evaluation_file = nonexistent_path
 
-        # Should handle missing file gracefully
-        with patch('openevolve.evaluator.logger') as mock_logger:
-            evaluator = Evaluator(self.config.evaluator, None)
-
-            # Should have warned about missing functions (due to import failure)
-            mock_logger.warning.assert_called()
+        # 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_path = self._create_evaluator_file("dummy.py", "def evaluate(p): pass")
+        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, None)
+        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]}
         )
 
-        metrics, artifacts = evaluator._process_evaluation_result(eval_result)
+        result = evaluator._process_evaluation_result(eval_result)
 
-        self.assertEqual(metrics, {"score": 0.9})
-        self.assertEqual(artifacts, {"log": "test log", "data": [1, 2, 3]})
+        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_path = self._create_evaluator_file("dummy.py", "def evaluate(p): pass")
+        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, None)
+        evaluator = Evaluator(self.config.evaluator, evaluator_path)
 
         # Test with dict result
         dict_result = {"score": 0.7, "accuracy": 0.8}
 
-        metrics, artifacts = evaluator._process_evaluation_result(dict_result)
+        result = evaluator._process_evaluation_result(dict_result)
 
-        self.assertEqual(metrics, {"score": 0.7, "accuracy": 0.8})
-        self.assertEqual(artifacts, {})
+        self.assertEqual(result.metrics, {"score": 0.7, "accuracy": 0.8})
+        self.assertEqual(result.artifacts, {})
 
 
 if __name__ == "__main__":