#!/usr/bin/env python3
"""
Test Suite for OpenEnded Philosophy Framework
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
### Testing Methodology
This test suite verifies core framework functionality through:
- **Unit Testing**: Individual component verification
- **Integration Testing**: Inter-component interaction validation
- **Philosophical Consistency**: Epistemic commitment verification
"""
import pytest
import asyncio
from datetime import datetime
import numpy as np
from openended_philosophy.core import (
EmergentCoherenceNode,
DynamicPluralismFramework,
LanguageGameProcessor,
CoherenceLandscape,
FallibilisticInference,
SemanticPattern
)
from openended_philosophy.utils import (
calculate_epistemic_uncertainty,
semantic_similarity,
coherence_metrics
)
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
class TestEmergentCoherenceNode:
"""Test suite for EmergentCoherenceNode functionality."""
def test_node_creation(self):
"""Test basic node instantiation."""
node = EmergentCoherenceNode(
initial_pattern={"concept": "identity", "domain": "metaphysics"},
confidence=0.7,
context_sensitivity=0.8
)
assert node.pattern.content["concept"] == "identity"
assert node.pattern.confidence == 0.7
assert node.pattern.context_sensitivity == 0.8
assert len(node.revision_history) == 0
def test_contextualization(self):
"""Test meaning contextualization within language games."""
node = EmergentCoherenceNode(
initial_pattern={"concept": "causation", "features": ["necessity", "regularity"]},
confidence=0.8
)
# Create test language game
game = LanguageGameProcessor(
"scientific",
{"empirical_verification": True, "mathematical_formalism": True}
)
# Contextualize
result = node.contextualize_meaning(game)
assert "provisional_meaning" in result
assert "confidence" in result
assert result["confidence"] <= node.pattern.confidence
assert result["revision_openness"] == 1.0 - result["confidence"]
def test_adaptive_feedback(self):
"""Test pattern adaptation through feedback."""
node = EmergentCoherenceNode(
initial_pattern={"strength": 0.5, "relevance": 0.7}
)
initial_confidence = node.pattern.confidence
# Apply feedback
feedback = {
"strength": 0.8,
"relevance": 0.9,
"quality": 0.85
}
node.adapt_to_feedback(feedback, learning_rate=0.1)
# Verify adaptation
assert node.pattern.content["strength"] > 0.5
assert node.pattern.content["relevance"] > 0.7
assert node.pattern.confidence > initial_confidence
assert node.pattern.revision_count == 1
assert len(node.revision_history) == 1
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
class TestDynamicPluralismFramework:
"""Test suite for DynamicPluralismFramework."""
def test_perspective_integration(self):
"""Test integration of interpretive schemas."""
framework = DynamicPluralismFramework(openness_coefficient=0.9)
# Integrate perspectives
schema1 = {
'id': 'empiricist',
'concepts': ['observation', 'verification', 'data']
}
schema2 = {
'id': 'rationalist',
'concepts': ['reason', 'deduction', 'axioms']
}
id1 = framework.integrate_perspective(schema1)
id2 = framework.integrate_perspective(schema2)
assert len(framework.interpretive_schemas) == 2
assert framework.interpretive_schemas[id1]['weight'] <= 0.5
assert framework.interpretive_schemas[id2]['weight'] <= 0.5
def test_schema_dialogue(self):
"""Test dialogue between interpretive schemas."""
framework = DynamicPluralismFramework()
# Add schemas
s1_id = framework.integrate_perspective({
'id': 'schema1',
'concepts': ['A', 'B', 'C']
})
s2_id = framework.integrate_perspective({
'id': 'schema2',
'concepts': ['B', 'C', 'D']
})
# Enable dialogue
topic = {'content': 'test_topic', 'domain': 'philosophy'}
result = framework.dialogue_between_schemas(s1_id, s2_id, topic)
assert 'agreements' in result
assert 'tensions' in result
assert 'emergent_insights' in result
assert result['interaction_quality'] >= 0.0
def test_diversity_maintenance(self):
"""Test that framework maintains interpretive diversity."""
framework = DynamicPluralismFramework(openness_coefficient=0.8)
# Add dominant perspective
dominant_schema = {'id': 'dominant', 'concepts': ['X', 'Y', 'Z']}
framework.integrate_perspective(dominant_schema, weight=0.9)
# Verify diversity maintenance
max_weight = max(s['weight'] for s in framework.interpretive_schemas.values())
assert max_weight <= (1.0 - framework.openness)
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
class TestFallibilisticInference:
"""Test suite for FallibilisticInference engine."""
@pytest.mark.asyncio
async def test_insight_generation(self):
"""Test fallibilistic insight generation."""
engine = FallibilisticInference()
# Create test evidence
evidence_patterns = [
{'content': 'Pattern A', 'concepts': ['X', 'Y'], 'confidence': 0.8},
{'content': 'Pattern B', 'concepts': ['Y', 'Z'], 'confidence': 0.7},
{'content': 'Pattern C', 'concepts': ['X', 'Z'], 'confidence': 0.9}
]
# Generate insights
insights = await engine.derive_insights(
evidence_patterns,
confidence_threshold=0.6
)
assert len(insights) > 0
for insight in insights:
assert insight.confidence >= 0.6
assert len(insight.revision_triggers) > 0
assert len(insight.identified_limitations) > 0
assert 'temporal' in insight.expiration_conditions
def test_uncertainty_propagation(self):
"""Test uncertainty calculation and propagation."""
engine = FallibilisticInference()
# Test synthesis with varying evidence
synthesis = {
'content': 'Test synthesis',
'pattern_count': 3,
'coherence_score': 0.8
}
evidence = [{'timestamp': datetime.now()} for _ in range(3)]
confidence = engine._calculate_inference_confidence(synthesis, evidence)
assert 0.0 <= confidence <= 1.0
assert confidence < synthesis['coherence_score'] # Uncertainty reduces confidence
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
class TestCoherenceLandscape:
"""Test suite for CoherenceLandscape dynamics."""
@pytest.mark.asyncio
async def test_domain_mapping(self):
"""Test coherence landscape mapping."""
landscape = CoherenceLandscape(dimensionality='variable')
# Map domain
state = await landscape.map_domain(
domain="ethics",
depth=2,
allow_revision=True
)
assert isinstance(state.global_coherence, float)
assert 0.0 <= state.global_coherence <= 1.0
assert len(state.coherence_regions) > 0
assert state.calculate_emergence_potential() >= 0.0
def test_landscape_evolution(self):
"""Test landscape evolution with new experiences."""
landscape = CoherenceLandscape()
# Initialize test region
region = landscape.CoherenceRegion(
id="test_region",
central_concepts=["concept1", "concept2"],
stability_score=0.8,
connection_count=3
)
landscape.coherence_regions["test_region"] = region
# Apply perturbation
new_experience = {
'impact': 0.5,
'concepts': ["concept1", "concept3"],
'type': 'discovery'
}
initial_stability = region.stability_score
landscape.evolve_landscape(new_experience)
# Verify evolution
assert region.stability_score < initial_stability
assert len(landscape.phase_transitions) >= 0
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
class TestUtilityFunctions:
"""Test suite for utility functions."""
def test_epistemic_uncertainty_calculation(self):
"""Test epistemic uncertainty quantification."""
# Low evidence, low coherence = high uncertainty
u1 = calculate_epistemic_uncertainty(
evidence_count=1,
coherence_score=0.3,
temporal_factor=1.0,
domain_complexity=0.8
)
# High evidence, high coherence = low uncertainty
u2 = calculate_epistemic_uncertainty(
evidence_count=10,
coherence_score=0.9,
temporal_factor=1.0,
domain_complexity=0.2
)
assert u1 > u2
assert 0.0 <= u1 <= 1.0
assert 0.0 <= u2 <= 1.0
def test_semantic_similarity_methods(self):
"""Test different semantic similarity calculations."""
concept1 = {
'features': ['A', 'B', 'C'],
'contexts': ['X', 'Y'],
'uses': ['U1', 'U2']
}
concept2 = {
'features': ['B', 'C', 'D'],
'contexts': ['Y', 'Z'],
'uses': ['U2', 'U3']
}
# Test Jaccard similarity
jaccard_sim = semantic_similarity(concept1, concept2, method="jaccard")
assert 0.0 <= jaccard_sim <= 1.0
# Test cosine similarity
cosine_sim = semantic_similarity(concept1, concept2, method="cosine")
assert 0.0 <= cosine_sim <= 1.0
# Test Wittgensteinian family resemblance
family_sim = semantic_similarity(concept1, concept2, method="wittgenstein")
assert 0.0 <= family_sim <= 1.0
def test_coherence_metrics(self):
"""Test coherence metric calculations."""
propositions = [
{'id': 0, 'content': 'P1'},
{'id': 1, 'content': 'P2'},
{'id': 2, 'content': 'P3'}
]
relations = [
(0, 1, 'supports'),
(1, 2, 'explains'),
(0, 2, 'contradicts')
]
metrics = coherence_metrics(propositions, relations)
assert 'constraint_satisfaction' in metrics
assert 'explanatory_breadth' in metrics
assert 'analogical_fit' in metrics
assert 'overall_coherence' in metrics
assert all(0.0 <= v <= 1.0 for v in metrics.values())
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
def test_semantic_pattern_stability():
"""Test SemanticPattern stability calculation."""
pattern = SemanticPattern(
pattern_id="test_pattern",
content={"test": "data"},
confidence=0.8,
revision_count=2
)
stability = pattern.calculate_stability()
assert 0.0 <= stability <= 1.0
assert stability < pattern.confidence # Revisions reduce stability
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
if __name__ == "__main__":
"""Execute test suite with verbose output."""
pytest.main([__file__, "-v", "--tb=short"])
