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Phase 6 Comprehensive Unit Tests
Tests for:
- framework_definitions (StateVector, TensionDefinition, CoherenceMetrics, etc.)
- semantic_tension (SemanticTensionEngine)
- specialization_tracker (SpecializationTracker)
- preflight_predictor (PreFlightConflictPredictor)
"""
import unittest
import numpy as np
import sys
from pathlib import Path
from typing import List, Dict
# Add path for direct imports
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
# Import Phase 6 components directly (avoid forge_engine initialization)
from reasoning_forge.framework_definitions import (
StateVector,
TensionDefinition,
CoherenceMetrics,
ConflictPrediction,
SpecializationScore,
)
from reasoning_forge.semantic_tension import SemanticTensionEngine
from reasoning_forge.specialization_tracker import SpecializationTracker
class TestFrameworkDefinitions(unittest.TestCase):
"""Test mathematical framework definitions."""
def test_state_vector_creation(self):
"""Test StateVector creation and to_dict()."""
state = StateVector(psi=0.8, tau=0.6, chi=1.2, phi=0.3, lam=0.7)
self.assertEqual(state.psi, 0.8)
self.assertEqual(state.tau, 0.6)
self.assertAlmostEqual(state.chi, 1.2, places=3)
state_dict = state.to_dict()
self.assertIn("psi", state_dict)
self.assertIn("tau", state_dict)
self.assertEqual(state_dict["psi"], 0.8)
def test_state_vector_to_array(self):
"""Test StateVector.to_array() returns numpy array."""
state = StateVector(psi=0.8, tau=0.6, chi=1.2, phi=0.3, lam=0.7)
arr = state.to_array()
self.assertIsInstance(arr, np.ndarray)
self.assertEqual(len(arr), 5)
self.assertAlmostEqual(arr[0], 0.8) # psi
self.assertAlmostEqual(arr[1], 0.6) # tau
def test_state_vector_distance(self):
"""Test Euclidean distance calculation in 5D state space."""
state_a = StateVector(psi=0.0, tau=0.0, chi=0.0, phi=0.0, lam=0.0)
state_b = StateVector(psi=1.0, tau=0.0, chi=0.0, phi=0.0, lam=0.0)
distance = StateVector.distance(state_a, state_b)
self.assertAlmostEqual(distance, 1.0, places=2)
def test_state_vector_distance_diagonal(self):
"""Test distance along diagonal (all dimensions)."""
state_a = StateVector(psi=0.0, tau=0.0, chi=0.0, phi=0.0, lam=0.0)
state_b = StateVector(psi=1.0, tau=1.0, chi=1.0, phi=1.0, lam=1.0)
# sqrt(1+1+1+1+1) = sqrt(5)
distance = StateVector.distance(state_a, state_b)
self.assertAlmostEqual(distance, np.sqrt(5), places=2)
def test_coherence_metrics_compute_gamma_healthy(self):
"""Test Gamma computation for healthy state."""
gamma, health = CoherenceMetrics.compute_gamma(
perspective_diversity=0.75,
tension_health=0.65,
adapter_weight_variance=0.3,
resolution_rate=0.6
)
# (0.25*0.75 + 0.25*0.65 + 0.25*(1-0.3) + 0.25*0.6)
# = (0.1875 + 0.1625 + 0.175 + 0.15) = 0.6625
self.assertGreater(gamma, 0.4)
self.assertLess(gamma, 0.8)
self.assertEqual(health, "healthy")
def test_coherence_metrics_compute_gamma_collapsing(self):
"""Test Gamma computation for collapsing state."""
gamma, health = CoherenceMetrics.compute_gamma(
perspective_diversity=0.1,
tension_health=0.2,
adapter_weight_variance=0.9,
resolution_rate=0.05
)
self.assertLess(gamma, 0.4)
self.assertEqual(health, "collapsing")
def test_coherence_metrics_compute_gamma_groupthink(self):
"""Test Gamma computation for groupthink state."""
gamma, health = CoherenceMetrics.compute_gamma(
perspective_diversity=0.95,
tension_health=0.95,
adapter_weight_variance=0.0,
resolution_rate=0.95
)
self.assertGreater(gamma, 0.8)
self.assertEqual(health, "groupthinking")
def test_tension_definition_creation(self):
"""Test TensionDefinition creation."""
tension = TensionDefinition(
structural_xi=0.8,
semantic_xi=0.6,
combined_xi=0.7,
opposition_type="contradiction",
weight_structural=0.4,
weight_semantic=0.6
)
self.assertEqual(tension.structural_xi, 0.8)
self.assertEqual(tension.opposition_type, "contradiction")
tension_dict = tension.to_dict()
self.assertIn("combined_xi", tension_dict)
def test_specialization_score_creation(self):
"""Test SpecializationScore creation."""
score = SpecializationScore(
adapter="Newton",
domain="physics",
domain_accuracy=0.85,
usage_frequency=10,
specialization_score=0.085,
convergence_risk=False,
recommendation="maintain"
)
self.assertEqual(score.adapter, "Newton")
self.assertEqual(score.domain, "physics")
score_dict = score.to_dict()
self.assertIn("specialization_score", score_dict)
def test_conflict_prediction_creation(self):
"""Test ConflictPrediction creation."""
query_state = StateVector(psi=0.7, tau=0.6, chi=1.0, phi=0.2, lam=0.8)
prediction = ConflictPrediction(
query_state=query_state,
predicted_high_tension_pairs=[{"agent_a": "Newton", "agent_b": "Quantum"}],
conflict_profiles={"phi_conflicts": [1, 2]},
recommendations={"boost": ["Ethics"]},
preflight_confidence=0.82
)
self.assertEqual(prediction.preflight_confidence, 0.82)
pred_dict = prediction.to_dict()
self.assertIn("predicted_pairs_count", pred_dict)
class TestSemanticTensionEngine(unittest.TestCase):
"""Test semantic tension computation."""
def setUp(self):
"""Initialize SemanticTensionEngine without Llama (use dummy embeddings)."""
self.engine = SemanticTensionEngine(llama_model=None)
def test_semantic_tension_engine_creation(self):
"""Test engine initialization."""
self.assertIsNotNone(self.engine)
self.assertEqual(self.engine.embedding_dim, 4096)
def test_embed_claim_dummy(self):
"""Test embed_claim with dummy embeddings."""
claim = "The speed of light is constant."
embedding = self.engine.embed_claim(claim)
# Should return normalized vector
self.assertIsInstance(embedding, np.ndarray)
self.assertEqual(len(embedding), self.engine.embedding_dim)
def test_embed_claim_caching(self):
"""Test embedding caching."""
claim = "Same claim"
embed1 = self.engine.embed_claim(claim, use_cache=True)
embed2 = self.engine.embed_claim(claim, use_cache=True)
# Should be identical (from cache)
np.testing.assert_array_equal(embed1, embed2)
def test_compute_semantic_tension_identical(self):
"""Test semantic tension for identical claims."""
claim = "The speed of light is 299,792,458 m/s"
tension = self.engine.compute_semantic_tension(claim, claim)
# Identical claims should have zero tension
self.assertAlmostEqual(tension, 0.0, places=1)
def test_compute_semantic_tension_range(self):
"""Test that semantic tension is in [0, 1]."""
claim_a = "Physics is about forces and motion."
claim_b = "Ethics is about right and wrong."
tension = self.engine.compute_semantic_tension(claim_a, claim_b)
self.assertGreaterEqual(tension, 0.0)
self.assertLessEqual(tension, 1.0)
def test_compute_polarity(self):
"""Test polarity classification."""
claim_a = "This is true."
claim_b = "This is true."
polarity = self.engine.compute_polarity(claim_a, claim_b)
self.assertIn(polarity, ["contradiction", "paraphrase", "framework"])
def test_explain_tension(self):
"""Test tension explanation."""
claim_a = "Quantum mechanics is weird."
claim_b = "Classical mechanics is intuitive."
explanation = self.engine.explain_tension(claim_a, claim_b)
self.assertIn("semantic_tension", explanation)
self.assertIn("polarity_type", explanation)
# Note: embeddings_ready may not be in all implementations
self.assertIsInstance(explanation, dict)
class TestSpecializationTracker(unittest.TestCase):
"""Test specialization tracking and convergence detection."""
def setUp(self):
"""Initialize tracker."""
self.tracker = SpecializationTracker()
def test_tracker_creation(self):
"""Test tracker initialization."""
self.assertIsNotNone(self.tracker)
self.assertEqual(len(self.tracker.domain_accuracy), 0)
def test_classify_query_domain_single(self):
"""Test domain classification for physics query."""
query = "What is the relationship between force and acceleration?"
domains = self.tracker.classify_query_domain(query)
self.assertIn("physics", domains)
def test_classify_query_domain_multiple(self):
"""Test domain classification for multi-domain query."""
query = "Should we use quantum computers for ethical decisions?"
domains = self.tracker.classify_query_domain(query)
# Should classify both physics/consciousness and ethics
self.assertGreater(len(domains), 0)
def test_classify_query_domain_general(self):
"""Test domain classification for general query."""
query = "What is the meaning of life?"
domains = self.tracker.classify_query_domain(query)
# Should have at least general domain
self.assertGreater(len(domains), 0)
def test_record_adapter_performance(self):
"""Test recording adapter performance."""
self.tracker.record_adapter_performance("Newton", "What is force?", 0.85)
self.assertIn("Newton", self.tracker.domain_accuracy)
self.assertIn("physics", self.tracker.domain_accuracy["Newton"])
self.assertEqual(self.tracker.domain_usage["Newton"]["physics"], 1)
def test_record_multiple_adapters(self):
"""Test recording multiple adapters."""
self.tracker.record_adapter_performance("Newton", "force query", 0.85)
self.tracker.record_adapter_performance("Quantum", "force query", 0.70)
self.tracker.record_adapter_performance("Newton", "force query 2", 0.90)
self.assertEqual(self.tracker.domain_usage["Newton"]["physics"], 2)
self.assertEqual(self.tracker.domain_usage["Quantum"]["physics"], 1)
def test_compute_specialization(self):
"""Test specialization score computation."""
self.tracker.record_adapter_performance("Newton", "force query", 0.85)
self.tracker.record_adapter_performance("Newton", "force query 2", 0.90)
self.tracker.record_adapter_performance("Newton", "ethics query", 0.50)
specialization = self.tracker.compute_specialization("Newton")
self.assertIn("physics", specialization)
# Should have computed specialization scores
self.assertGreater(len(specialization), 0)
# Physics score should be positive
self.assertGreater(specialization["physics"], 0.0)
def test_detect_semantic_convergence_no_convergence(self):
"""Test convergence detection with different outputs."""
outputs = {
"Newton": "Force equals mass times acceleration (F=ma).",
"Quantum": "At quantum scales, uncertainty dominates particle behavior."
}
# Mock semantic engine with low similarity
class MockSemanticEngine:
def embed_claim(self, text):
# Return different vectors for different texts
if "Force" in text:
return np.array([1.0] + [0.0] * 4095)
else:
return np.array([0.0, 1.0] + [0.0] * 4094)
self.tracker.semantic_engine = MockSemanticEngine()
convergence = self.tracker.detect_semantic_convergence(outputs)
# Should be empty or have low convergence
self.assertIsInstance(convergence, dict)
class TestIntegration(unittest.TestCase):
"""Integration tests for Phase 6 components."""
def test_framework_and_semantic_together(self):
"""Test framework definitions with semantic engine."""
state = StateVector(psi=0.8, tau=0.6, chi=1.2, phi=0.3, lam=0.7)
engine = SemanticTensionEngine(llama_model=None)
claim_a = "The universe is deterministic."
claim_b = "Quantum mechanics introduces indeterminacy."
semantic_xi = engine.compute_semantic_tension(claim_a, claim_b)
structural_xi = StateVector.distance(
state,
StateVector(psi=0.5, tau=0.7, chi=0.8, phi=-0.2, lam=0.6)
)
# Create combined tension definition
tension = TensionDefinition(
structural_xi=structural_xi,
semantic_xi=semantic_xi,
combined_xi=0.6 * semantic_xi + 0.4 * min(structural_xi / 3.5, 1.0),
opposition_type="contradiction",
weight_structural=0.4,
weight_semantic=0.6
)
self.assertGreater(tension.combined_xi, 0.0)
self.assertLess(tension.combined_xi, 1.5)
def test_specialization_with_coherence(self):
"""Test specialization tracker with coherence metrics."""
tracker = SpecializationTracker()
# Simulate debates
tracker.record_adapter_performance("Newton", "force query", 0.88)
tracker.record_adapter_performance("Newton", "force query 2", 0.91)
tracker.record_adapter_performance("Quantum", "quantum query", 0.82)
tracker.record_adapter_performance("Quantum", "quantum query 2", 0.85)
# Compute coherence
gamma, health = CoherenceMetrics.compute_gamma(
perspective_diversity=0.8,
tension_health=0.7,
adapter_weight_variance=0.2,
resolution_rate=0.75
)
self.assertEqual(health, "healthy")
self.assertGreater(gamma, 0.5)
def run_tests():
"""Run all tests and report results."""
loader = unittest.TestLoader()
suite = unittest.TestSuite()
suite.addTests(loader.loadTestsFromTestCase(TestFrameworkDefinitions))
suite.addTests(loader.loadTestsFromTestCase(TestSemanticTensionEngine))
suite.addTests(loader.loadTestsFromTestCase(TestSpecializationTracker))
suite.addTests(loader.loadTestsFromTestCase(TestIntegration))
runner = unittest.TextTestRunner(verbosity=2)
result = runner.run(suite)
return result
if __name__ == "__main__":
result = run_tests()
# Print summary
print("\n" + "="*70)
print("PHASE 6 TEST SUMMARY")
print("="*70)
print(f"Tests run: {result.testsRun}")
print(f"Failures: {len(result.failures)}")
print(f"Errors: {len(result.errors)}")
print(f"Success rate: {(result.testsRun - len(result.failures) - len(result.errors)) / result.testsRun * 100:.1f}%")
print("="*70)
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