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Tests for MAP-Elites feature enhancements in openevolve.database
"""
import unittest
from unittest.mock import MagicMock, patch
from openevolve.config import Config
from openevolve.database import Program, ProgramDatabase
class TestMapElitesFeatures(unittest.TestCase):
"""Tests for MAP-Elites feature enhancements"""
def setUp(self):
"""Set up test database with enhanced features"""
config = Config()
config.database.in_memory = True
config.database.feature_dimensions = ["complexity", "diversity"]
config.database.feature_bins = 10
self.db = ProgramDatabase(config.database)
def test_diversity_caching(self):
"""Test diversity score caching functionality"""
# Add some reference programs
for i in range(5):
program = Program(
id=f"ref_{i}",
code=f"def func_{i}():\n return {i} * 2",
language="python",
metrics={"score": 0.5 + i * 0.1},
)
self.db.add(program)
# Create a test program
test_program = Program(
id="test", code="def test():\n return 42", language="python", metrics={"score": 0.7}
)
# First call should compute diversity
diversity1 = self.db._get_cached_diversity(test_program)
# Second call should use cache
diversity2 = self.db._get_cached_diversity(test_program)
# Should be the same value
self.assertEqual(diversity1, diversity2)
# Check cache was populated
code_hash = hash(test_program.code)
self.assertIn(code_hash, self.db.diversity_cache)
self.assertEqual(self.db.diversity_cache[code_hash]["value"], diversity1)
def test_diversity_reference_set_update(self):
"""Test diversity reference set updates"""
# Initially empty
self.assertEqual(len(self.db.diversity_reference_set), 0)
# Add programs
for i in range(30):
program = Program(
id=f"prog_{i}",
code=f"def func_{i}():\n " + "x = 1\n" * i, # Varying complexity
language="python",
metrics={"score": 0.5},
)
self.db.add(program)
# Update reference set
self.db._update_diversity_reference_set()
# Should have up to diversity_reference_size programs
expected_size = min(30, self.db.diversity_reference_size)
self.assertEqual(len(self.db.diversity_reference_set), expected_size)
# Reference set should contain diverse programs (no duplicates)
self.assertEqual(len(set(self.db.diversity_reference_set)), expected_size)
def test_feature_scaling_minmax(self):
"""Test min-max feature scaling"""
# Add programs with different complexities
complexities = [100, 300, 500, 700, 900]
for i, complexity in enumerate(complexities):
program = Program(
id=f"scale_{i}", code="x" * complexity, language="python", metrics={"score": 0.5}
)
self.db.add(program)
# Test scaling
# Min value (100) should scale to 0
scaled_min = self.db._scale_feature_value("complexity", 100.0)
self.assertAlmostEqual(scaled_min, 0.0, places=5)
# Max value (900) should scale to 1
scaled_max = self.db._scale_feature_value("complexity", 900.0)
self.assertAlmostEqual(scaled_max, 1.0, places=5)
# Middle value (500) should scale to 0.5
scaled_mid = self.db._scale_feature_value("complexity", 500.0)
self.assertAlmostEqual(scaled_mid, 0.5, places=5)
def test_feature_stats_update(self):
"""Test feature statistics are updated correctly"""
# Initially no stats
self.assertEqual(len(self.db.feature_stats), 0)
# Add programs and check stats are updated
values = [10.0, 20.0, 30.0, 40.0, 50.0]
for val in values:
self.db._update_feature_stats("test_feature", val)
# Check stats
stats = self.db.feature_stats["test_feature"]
self.assertEqual(stats["min"], 10.0)
self.assertEqual(stats["max"], 50.0)
self.assertEqual(len(stats["values"]), 5)
self.assertEqual(stats["values"], values)
def test_per_dimension_bins(self):
"""Test per-dimension bin configuration"""
# Create database with per-dimension bins
config = Config()
config.database.in_memory = True
config.database.feature_dimensions = ["complexity", "diversity", "score"]
config.database.feature_bins = {"complexity": 20, "diversity": 10, "score": 5}
db = ProgramDatabase(config.database)
# Check per-dimension bins were set correctly
self.assertEqual(db.feature_bins_per_dim["complexity"], 20)
self.assertEqual(db.feature_bins_per_dim["diversity"], 10)
self.assertEqual(db.feature_bins_per_dim["score"], 5)
# Add a program and check binning
program = Program(
id="test_bins",
code="def test():\n return 42",
language="python",
metrics={"score": 0.8},
)
db.add(program)
coords = db._calculate_feature_coords(program)
# Each coordinate should be within its dimension's range
self.assertLess(coords[0], 20) # complexity
self.assertLess(coords[1], 10) # diversity
self.assertLess(coords[2], 5) # score
def test_default_feature_dimensions(self):
"""Test default feature dimensions are complexity and diversity"""
config = Config()
# Don't set feature_dimensions, use defaults
self.assertEqual(config.database.feature_dimensions, ["complexity", "diversity"])
def test_diversity_cache_lru_eviction(self):
"""Test LRU eviction in diversity cache"""
# Set small cache size
self.db.diversity_cache_size = 3
# Add reference programs
for i in range(3):
program = Program(
id=f"ref_{i}",
code=f"def func_{i}(): pass",
language="python",
metrics={"score": 0.5},
)
self.db.add(program)
# Fill cache
programs = []
for i in range(5):
program = Program(
id=f"cache_test_{i}",
code=f"def test_{i}(): return {i}",
language="python",
metrics={"score": 0.5},
)
programs.append(program)
self.db._get_cached_diversity(program)
# Cache should only have last 3 entries
self.assertLessEqual(len(self.db.diversity_cache), 3)
# First 2 programs should be evicted
self.assertNotIn(hash(programs[0].code), self.db.diversity_cache)
self.assertNotIn(hash(programs[1].code), self.db.diversity_cache)
# Last 3 should be in cache
self.assertIn(hash(programs[2].code), self.db.diversity_cache)
self.assertIn(hash(programs[3].code), self.db.diversity_cache)
self.assertIn(hash(programs[4].code), self.db.diversity_cache)
def test_feature_scaling_with_identical_values(self):
"""Test feature scaling when all values are identical"""
# Add programs with same complexity
for i in range(3):
program = Program(
id=f"same_{i}",
code="x" * 100, # Same length
language="python",
metrics={"score": 0.5},
)
self.db.add(program)
# Scaling should return 0.5 for all values when min==max
scaled = self.db._scale_feature_value("complexity", 100.0)
self.assertEqual(scaled, 0.5)
def test_feature_coordinates_with_new_defaults(self):
"""Test feature coordinate calculation with new default dimensions"""
# Create fresh database with default config
config = Config()
config.database.in_memory = True
db = ProgramDatabase(config.database)
# Default dimensions should be complexity and diversity
self.assertEqual(db.config.feature_dimensions, ["complexity", "diversity"])
# Add some programs
for i in range(5):
program = Program(
id=f"default_test_{i}",
code=f"def func_{i}():\n " + "pass\n" * i,
language="python",
metrics={"score": 0.5 + i * 0.1},
)
db.add(program)
# Test program
test_program = Program(
id="test_defaults",
code="def test():\n return 42",
language="python",
metrics={"score": 0.7},
)
coords = db._calculate_feature_coords(test_program)
# Should have 2 coordinates (complexity, diversity)
self.assertEqual(len(coords), 2)
# Both should be valid bin indices
for coord in coords:
self.assertGreaterEqual(coord, 0)
self.assertLess(coord, db.feature_bins)
def test_missing_feature_dimension_error(self):
"""Test that missing feature dimensions raise appropriate errors"""
config = Config()
config.database.in_memory = True
config.database.feature_dimensions = ["complexity", "nonexistent_metric"]
db = ProgramDatabase(config.database)
# Add a program without the required metric
program = Program(
id="test_error",
code="def test(): pass",
language="python",
metrics={"score": 0.5}, # Missing 'nonexistent_metric'
)
# Should raise ValueError when calculating feature coordinates
with self.assertRaises(ValueError) as context:
db.add(program)
# Check error message
self.assertIn("nonexistent_metric", str(context.exception))
self.assertIn("not found in program metrics", str(context.exception))
self.assertIn("score", str(context.exception)) # Should show available metrics
def test_custom_features_override_builtin(self):
"""Test that custom complexity and diversity from evaluator override built-in calculations"""
# Create database with complexity and diversity as feature dimensions
config = Config()
config.database.in_memory = True
config.database.feature_dimensions = ["complexity", "diversity"]
config.database.feature_bins = 10
db = ProgramDatabase(config.database)
# Add a program with custom complexity and diversity metrics from evaluator
# The evaluator is providing its own definition of complexity and diversity
program = Program(
id="custom_override",
code="x" * 1000, # 1000 chars - built-in would use this
language="python",
metrics={
"complexity": 42.5, # Custom complexity from evaluator (NOT code length)
"diversity": 99.9, # Custom diversity from evaluator (NOT code structure)
"score": 0.8,
},
)
# Add program to trigger feature coordinate calculation
db.add(program)
# Manually calculate what bins the custom values should map to
# The custom values should be used, not the built-in calculations
# For complexity: custom value is 42.5
db._update_feature_stats("complexity", 42.5)
custom_complexity_scaled = db._scale_feature_value("complexity", 42.5)
expected_complexity_bin = int(custom_complexity_scaled * 10)
expected_complexity_bin = max(0, min(9, expected_complexity_bin))
# For diversity: custom value is 99.9
db._update_feature_stats("diversity", 99.9)
custom_diversity_scaled = db._scale_feature_value("diversity", 99.9)
expected_diversity_bin = int(custom_diversity_scaled * 10)
expected_diversity_bin = max(0, min(9, expected_diversity_bin))
# Get actual coordinates
coords = db._calculate_feature_coords(program)
# Verify custom metrics were used
# If built-in was used for complexity, it would use len(code) = 1000
# If built-in was used for diversity, it would calculate code structure diversity
# With custom metrics, we should see the bins for 42.5 and 99.9
self.assertEqual(coords[0], expected_complexity_bin,
"Custom complexity metric should override built-in code length")
self.assertEqual(coords[1], expected_diversity_bin,
"Custom diversity metric should override built-in code diversity")
# Additional verification: test with multiple programs to ensure consistency
program2 = Program(
id="custom_override_2",
code="y" * 500, # Different code length
language="python",
metrics={
"complexity": 10.0, # Much lower than code length
"diversity": 5.0, # Custom diversity
"score": 0.6,
},
)
db.add(program2)
coords2 = db._calculate_feature_coords(program2)
# Calculate expected bins for second program
db._update_feature_stats("complexity", 10.0)
custom_complexity_scaled_2 = db._scale_feature_value("complexity", 10.0)
expected_complexity_bin_2 = int(custom_complexity_scaled_2 * 10)
expected_complexity_bin_2 = max(0, min(9, expected_complexity_bin_2))
db._update_feature_stats("diversity", 5.0)
custom_diversity_scaled_2 = db._scale_feature_value("diversity", 5.0)
expected_diversity_bin_2 = int(custom_diversity_scaled_2 * 10)
expected_diversity_bin_2 = max(0, min(9, expected_diversity_bin_2))
self.assertEqual(coords2[0], expected_complexity_bin_2)
self.assertEqual(coords2[1], expected_diversity_bin_2)
if __name__ == "__main__":
unittest.main()
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