TNFR False-Positive Test Suite - Validation Framework
Lightweight validation of false-positive test methodology and criteria without requiring full TNFR system integration.
"""
TNFR False-Positive Test Suite - Validation Framework
Lightweight validation of false-positive test methodology and criteria
without requiring full TNFR system integration.
"""
import sys
import unittest
from pathlib import Path
# Simulated verification criteria for testing (from actual implementation)
SIMULATED_TNFR_VERIFICATION_CRITERIA = {
"min_partition_flags": 4, # require more structural conditions per partition
"dnfr_gain_min": 0.15, # minimum relative ΔNFR attenuation
"coherence_min": 0.72, # coherence similarity lower bound
"coherence_max": 1.38, # coherence similarity upper bound
"phi_delta_max": 0.35, # structural potential deviation threshold
"gradient_delta_max": 0.40, # relative phase gradient deviation limit
"curvature_delta_max": 0.45, # relative phase curvature deviation limit
"periodicity_confidence_min": 0.55, # minimum structural periodicity confidence
"required_partition_ratio": 0.5, # fraction of partition endorsements needed
"min_endorsements": 1, # absolute minimum endorsements
"min_stabilized_fraction": 0.30, # fraction of partitions with stabilized flag
"min_coverage_fraction": 0.15, # coverage across modulus (nodes_accumulated/modulus)
}
class FalsePositiveTestCase:
"""Individual test case for false-positive verification (simulation)."""
def __init__(
self, n: int, non_factor: int, reason: str, expected_periodic: bool = False
):
self.n = n
self.non_factor = non_factor
self.reason = reason
self.expected_periodic = expected_periodic
# Validate that this is indeed a non-factor
if n % non_factor == 0:
raise ValueError(f"{non_factor} is actually a factor of {n}")
def __repr__(self):
return f"FalsePositiveTestCase(n={self.n}, non_factor={self.non_factor}, reason='{self.reason}')"
class FalsePositiveMethodologyTest(unittest.TestCase):
"""Test the false-positive testing methodology and criteria validation."""
def test_false_positive_test_case_creation(self):
"""Test that false-positive test cases are created correctly."""
# Valid non-factor test case
test_case = FalsePositiveTestCase(35, 4, "close to factor 5")
self.assertEqual(test_case.n, 35)
self.assertEqual(test_case.non_factor, 4)
self.assertEqual(test_case.reason, "close to factor 5")
# Should reject actual factors
with self.assertRaises(ValueError):
FalsePositiveTestCase(35, 5, "this is actually a factor") # 5 divides 35
with self.assertRaises(ValueError):
FalsePositiveTestCase(35, 7, "this is actually a factor") # 7 divides 35
print("✓ False-positive test case creation validated")
def test_false_positive_test_case_generation_logic(self):
"""Test the logic for generating comprehensive test cases."""
# Test divisor-of-factor logic
n = 77 # 77 = 7 × 11
factors = [7, 11]
divisor_non_factors = []
for factor in factors:
for d in range(2, factor):
if factor % d == 0 and n % d != 0:
divisor_non_factors.append(d)
# 7 has no divisors other than 1,7 so no cases
# 11 has no divisors other than 1,11 so no cases
# This is expected for prime factors
# Test with composite factor
n = 35 * 6 # 210 = 2 × 3 × 5 × 7
composite_factor = 6 # 6 = 2 × 3
# If 6 were a factor of 210, we'd test its divisors 2,3
# But since 210 = 2×3×5×7, both 2 and 3 are actually factors
# So this demonstrates the importance of careful test case selection
print("✓ False-positive generation logic validated")
def test_close_to_factor_generation(self):
"""Test generation of close-to-factor non-factors."""
n = 143 # 143 = 11 × 13
factors = [11, 13]
close_non_factors = []
for factor in factors:
for delta in [-2, -1, 1, 2]:
candidate = factor + delta
if candidate > 1 and n % candidate != 0:
close_non_factors.append((candidate, f"{factor}{delta:+d}"))
# For 11: 9, 10, 12, 13 -> 13 is actually a factor, so 9,10,12
# For 13: 11, 12, 14, 15 -> 11 is actually a factor, so 12,14,15
expected_non_factors = [9, 10, 12, 12, 14, 15] # 12 appears twice
actual_non_factors = [nf[0] for nf in close_non_factors]
for nf in actual_non_factors:
self.assertNotEqual(n % nf, 0, f"{nf} should not be a factor of {n}")
print(f"✓ Close-to-factor generation: {len(close_non_factors)} cases")
def test_harmonic_pattern_generation(self):
"""Test generation of harmonic multiple/submultiple test cases."""
n = 105 # 105 = 3 × 5 × 7
factors = [3, 5, 7, 15, 21, 35] # all factors
harmonic_non_factors = []
for factor in factors:
# Test multiples
for mult in [2, 3]:
harmonic = factor * mult
if harmonic < n and n % harmonic != 0:
harmonic_non_factors.append(harmonic)
# Test submultiples
for div in [2, 3]:
if factor % div == 0:
submultiple = factor // div
if submultiple > 1 and n % submultiple != 0:
harmonic_non_factors.append(submultiple)
# Validate all are indeed non-factors
for hf in harmonic_non_factors:
self.assertNotEqual(n % hf, 0, f"{hf} should not be a factor of {n}")
print(f"✓ Harmonic pattern generation: {len(harmonic_non_factors)} candidates")
def test_verification_criteria_robustness(self):
"""Test verification criteria for false-positive resistance."""
criteria = SIMULATED_TNFR_VERIFICATION_CRITERIA
# Test minimum partition flags (multiple conditions prevent single spurious matches)
self.assertGreaterEqual(
criteria["min_partition_flags"],
3,
"Should require multiple partition conditions",
)
# Test ΔNFR gain requirement (prevents weak correlations)
self.assertGreaterEqual(
criteria["dnfr_gain_min"],
0.1,
"Should require significant structural improvement",
)
# Test periodicity confidence (prevents accidental patterns)
self.assertGreaterEqual(
criteria["periodicity_confidence_min"],
0.5,
"Should require high confidence in periodicity",
)
# Test partition ratio (prevents isolated spurious partitions)
self.assertGreaterEqual(
criteria["required_partition_ratio"],
0.3,
"Should require substantial partition endorsement",
)
print("✓ Verification criteria robustness validated")
def test_criteria_balance_assessment(self):
"""Test that verification criteria achieve appropriate balance."""
criteria = SIMULATED_TNFR_VERIFICATION_CRITERIA
# Calculate overall strictness
strictness_components = [
criteria["min_partition_flags"] / 6.0, # Normalized
criteria["dnfr_gain_min"] / 0.5,
criteria["periodicity_confidence_min"],
criteria["required_partition_ratio"],
]
avg_strictness = sum(strictness_components) / len(strictness_components)
# Should be moderately strict (not too lenient, not impossible)
self.assertGreaterEqual(avg_strictness, 0.4, "Should be reasonably strict")
self.assertLessEqual(avg_strictness, 0.8, "Should not be impossibly strict")
print(f"✓ Criteria balance: {avg_strictness:.3f} strictness (appropriate)")
def test_edge_case_boundary_conditions(self):
"""Test boundary conditions in test case generation."""
# Test edge cases that might cause issues
# Small numbers
try:
test_case = FalsePositiveTestCase(
6, 4, "small number test"
) # 6=2×3, 4 not a factor
self.assertEqual(test_case.n, 6)
except ValueError:
self.fail("Should handle small number test cases")
# Number equal to candidate (should fail)
with self.assertRaises(ValueError):
FalsePositiveTestCase(7, 7, "self-factor") # 7 divides 7
# Prime number with non-factor
test_case = FalsePositiveTestCase(
17, 3, "prime with non-factor"
) # 17 prime, 3 not factor
self.assertEqual(test_case.n, 17)
print("✓ Edge case boundary conditions validated")
def run_methodology_tests():
"""Run false-positive testing methodology validation."""
print("TNFR FALSE-POSITIVE METHODOLOGY VALIDATION")
print("=" * 48)
# Create and run test suite
suite = unittest.TestLoader().loadTestsFromTestCase(FalsePositiveMethodologyTest)
runner = unittest.TextTestRunner(verbosity=2)
result = runner.run(suite)
if result.wasSuccessful():
print("\n✓ All methodology validation tests passed")
print("False-positive testing framework is sound")
return True
else:
print(f"\n✗ {len(result.failures)} failures, {len(result.errors)} errors")
return False
if __name__ == "__main__":
success = run_methodology_tests()
sys.exit(0 if success else 1)