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Unit tests for feature_stats persistence in ProgramDatabase checkpoints
"""
import json
import os
import tempfile
import shutil
import unittest
from unittest.mock import patch
from openevolve.database import ProgramDatabase, Program
from openevolve.config import DatabaseConfig
class TestFeatureStatsPersistence(unittest.TestCase):
"""Test feature_stats are correctly saved and loaded in checkpoints"""
def setUp(self):
"""Set up test environment"""
self.test_dir = tempfile.mkdtemp()
self.config = DatabaseConfig(
db_path=self.test_dir,
feature_dimensions=["score", "custom_metric1", "custom_metric2"],
feature_bins=10,
)
def tearDown(self):
"""Clean up test environment"""
shutil.rmtree(self.test_dir)
def test_feature_stats_saved_and_loaded(self):
"""Test that feature_stats are correctly saved and loaded from checkpoints"""
# Create database and add programs to build feature_stats
db1 = ProgramDatabase(self.config)
programs = []
for i in range(5):
program = Program(
id=f"test_prog_{i}",
code=f"# Test program {i}",
metrics={
"combined_score": 0.1 + i * 0.2,
"custom_metric1": 10 + i * 20,
"custom_metric2": 100 + i * 50,
},
)
programs.append(program)
db1.add(program)
# Verify feature_stats were built
self.assertIn("score", db1.feature_stats)
self.assertIn("custom_metric1", db1.feature_stats)
self.assertIn("custom_metric2", db1.feature_stats)
# Store original feature_stats for comparison
original_stats = {
dim: {"min": stats["min"], "max": stats["max"], "values": stats["values"].copy()}
for dim, stats in db1.feature_stats.items()
}
# Save checkpoint
db1.save(self.test_dir, iteration=42)
# Load into new database
db2 = ProgramDatabase(self.config)
db2.load(self.test_dir)
# Verify feature_stats were loaded correctly
self.assertEqual(len(db2.feature_stats), len(original_stats))
for dim, original in original_stats.items():
self.assertIn(dim, db2.feature_stats)
loaded = db2.feature_stats[dim]
self.assertAlmostEqual(loaded["min"], original["min"], places=5)
self.assertAlmostEqual(loaded["max"], original["max"], places=5)
self.assertEqual(loaded["values"], original["values"])
def test_empty_feature_stats_handling(self):
"""Test handling of empty feature_stats"""
db1 = ProgramDatabase(self.config)
# Save without any programs (empty feature_stats)
db1.save(self.test_dir, iteration=1)
# Load and verify
db2 = ProgramDatabase(self.config)
db2.load(self.test_dir)
self.assertEqual(db2.feature_stats, {})
def test_backward_compatibility_missing_feature_stats(self):
"""Test loading checkpoints that don't have feature_stats (backward compatibility)"""
# Create a checkpoint manually without feature_stats
os.makedirs(self.test_dir, exist_ok=True)
# Create metadata without feature_stats (simulating old checkpoint)
metadata = {
"island_feature_maps": [{}], # Updated to new format
"islands": [[]],
"archive": [],
"best_program_id": None,
"island_best_programs": [None],
"last_iteration": 10,
"current_island": 0,
"island_generations": [0],
"last_migration_generation": 0,
# Note: no "feature_stats" key
}
with open(os.path.join(self.test_dir, "metadata.json"), "w") as f:
json.dump(metadata, f)
# Load should work without errors
db = ProgramDatabase(self.config)
db.load(self.test_dir)
# feature_stats should be empty but not None
self.assertEqual(db.feature_stats, {})
def test_feature_stats_serialization_edge_cases(self):
"""Test feature_stats serialization handles edge cases correctly"""
db = ProgramDatabase(self.config)
# Test with various edge cases
db.feature_stats = {
"normal_case": {"min": 1.0, "max": 10.0, "values": [1.0, 5.0, 10.0]},
"single_value": {"min": 5.0, "max": 5.0, "values": [5.0]},
"large_values_list": {
"min": 0.0,
"max": 200.0,
"values": list(range(200)), # Should be truncated to 100
},
"empty_values": {"min": 0.0, "max": 1.0, "values": []},
}
# Test serialization
serialized = db._serialize_feature_stats()
# Check that large values list was truncated
self.assertLessEqual(len(serialized["large_values_list"]["values"]), 100)
# Test deserialization
deserialized = db._deserialize_feature_stats(serialized)
# Verify structure is maintained
self.assertIn("normal_case", deserialized)
self.assertIn("single_value", deserialized)
self.assertIn("large_values_list", deserialized)
self.assertIn("empty_values", deserialized)
# Verify types are correct
for dim, stats in deserialized.items():
self.assertIsInstance(stats["min"], float)
self.assertIsInstance(stats["max"], float)
self.assertIsInstance(stats["values"], list)
def test_feature_stats_preservation_during_load(self):
"""Test that feature_stats ranges are preserved when loading from checkpoint"""
# Create database with programs
db1 = ProgramDatabase(self.config)
test_programs = []
for i in range(3):
program = Program(
id=f"stats_test_{i}",
code=f"# Stats test {i}",
metrics={
"combined_score": 0.2 + i * 0.3,
"custom_metric1": 20 + i * 30,
"custom_metric2": 200 + i * 100,
},
)
test_programs.append(program)
db1.add(program)
# Record original feature ranges
original_ranges = {}
for dim, stats in db1.feature_stats.items():
original_ranges[dim] = {"min": stats["min"], "max": stats["max"]}
# Save checkpoint
db1.save(self.test_dir, iteration=50)
# Load into new database
db2 = ProgramDatabase(self.config)
db2.load(self.test_dir)
# Verify feature ranges are preserved
for dim, original_range in original_ranges.items():
self.assertIn(dim, db2.feature_stats)
loaded_stats = db2.feature_stats[dim]
self.assertAlmostEqual(
loaded_stats["min"],
original_range["min"],
places=5,
msg=f"Min value changed for {dim}: {original_range['min']} -> {loaded_stats['min']}",
)
self.assertAlmostEqual(
loaded_stats["max"],
original_range["max"],
places=5,
msg=f"Max value changed for {dim}: {original_range['max']} -> {loaded_stats['max']}",
)
# Test that adding a new program within existing ranges doesn't break anything
new_program = Program(
id="range_test",
code="# Program to test range stability",
metrics={
"combined_score": 0.35, # Within existing range
"custom_metric1": 35, # Within existing range
"custom_metric2": 250, # Within existing range
},
)
# Adding this program should not cause issues
db2.add(new_program)
new_coords = db2._calculate_feature_coords(new_program)
# Should get valid coordinates
self.assertEqual(len(new_coords), len(self.config.feature_dimensions))
for coord in new_coords:
self.assertIsInstance(coord, int)
self.assertGreaterEqual(coord, 0)
def test_feature_stats_with_numpy_types(self):
"""Test that numpy types are correctly handled in serialization"""
import numpy as np
db = ProgramDatabase(self.config)
# Simulate feature_stats with numpy types
db.feature_stats = {
"numpy_test": {
"min": np.float64(1.5),
"max": np.float64(9.5),
"values": [np.float64(x) for x in [1.5, 5.0, 9.5]],
}
}
# Test serialization doesn't fail
serialized = db._serialize_feature_stats()
# Verify numpy types were converted to Python types
self.assertIsInstance(serialized["numpy_test"]["min"], float)
self.assertIsInstance(serialized["numpy_test"]["max"], float)
# Test deserialization
deserialized = db._deserialize_feature_stats(serialized)
self.assertIsInstance(deserialized["numpy_test"]["min"], float)
self.assertIsInstance(deserialized["numpy_test"]["max"], float)
def test_malformed_feature_stats_handling(self):
"""Test handling of malformed feature_stats during deserialization"""
db = ProgramDatabase(self.config)
# Test with malformed data
malformed_data = {
"valid_entry": {"min": 1.0, "max": 10.0, "values": [1.0, 5.0, 10.0]},
"invalid_entry": "this is not a dict",
"missing_keys": {
"min": 1.0
# missing "max" and "values"
},
}
with patch("openevolve.database.logger") as mock_logger:
deserialized = db._deserialize_feature_stats(malformed_data)
# Should have valid entry and skip invalid ones
self.assertIn("valid_entry", deserialized)
self.assertNotIn("invalid_entry", deserialized)
self.assertIn("missing_keys", deserialized) # Should be created with defaults
# Should have logged warning for invalid entry
mock_logger.warning.assert_called()
if __name__ == "__main__":
unittest.main()
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