941e17fe6e
✅ Phase 3: Decision Framework Tests - Decision engine functionality tests - Voting system tests (majority, weighted, unanimous) - Consensus algorithm tests - Agent lifecycle management tests - Integration tests for decision processes ✅ Phase 4: Autonomous Decision Making Tests - Autonomous decision engine tests - Learning system tests (experience-based learning) - Policy engine tests (compliance evaluation) - Self-correction mechanism tests - Goal-oriented behavior tests - Full autonomous cycle integration tests ✅ Phase 5: Computer Vision Integration Tests - Vision processor tests (object detection, scene analysis, OCR) - Multi-modal integration tests - Context integration tests - Visual reasoning tests (spatial, temporal) - Performance metrics tests - End-to-end vision pipeline tests ✅ Test Infrastructure - Comprehensive test runner for all phases - Mock implementations for testing - Performance testing capabilities - Integration test coverage - Phase-based test organization 🚀 All Phase Tests Now Implemented and Ready for Execution!
533 lines
19 KiB
Python
533 lines
19 KiB
Python
"""
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Phase 4: Autonomous Decision Making Tests
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Tests for autonomous systems, learning, and adaptation
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"""
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import pytest
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import asyncio
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import json
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from datetime import datetime, timedelta
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from unittest.mock import Mock, AsyncMock
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from typing import Dict, List, Any, Optional
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# Mock imports for testing
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class MockAutonomousEngine:
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def __init__(self):
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self.policies = {}
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self.decisions = []
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self.learning_data = {}
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self.performance_metrics = {}
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async def make_autonomous_decision(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""Make autonomous decision based on context"""
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decision_id = f"auto_decision_{len(self.decisions)}"
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decision = {
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'decision_id': decision_id,
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'context': context,
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'action': self._determine_action(context),
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'reasoning': self._generate_reasoning(context),
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'confidence': self._calculate_confidence(context),
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'timestamp': datetime.utcnow().isoformat()
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}
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self.decisions.append(decision)
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return decision
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def _determine_action(self, context: Dict[str, Any]) -> str:
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"""Determine action based on context"""
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if context.get('system_load', 0) > 0.8:
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return 'scale_resources'
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elif context.get('error_rate', 0) > 0.1:
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return 'trigger_recovery'
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elif context.get('task_queue_size', 0) > 100:
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return 'allocate_more_agents'
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else:
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return 'maintain_status'
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def _generate_reasoning(self, context: Dict[str, Any]) -> str:
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"""Generate reasoning for decision"""
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return f"Based on system metrics: load={context.get('system_load', 0)}, errors={context.get('error_rate', 0)}"
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def _calculate_confidence(self, context: Dict[str, Any]) -> float:
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"""Calculate confidence in decision"""
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# Simple confidence calculation based on data quality
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has_metrics = all(key in context for key in ['system_load', 'error_rate'])
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return 0.9 if has_metrics else 0.6
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class MockLearningSystem:
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def __init__(self):
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self.experience_buffer = []
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self.performance_history = []
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self.adaptations = {}
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async def learn_from_experience(self, experience: Dict[str, Any]) -> Dict[str, Any]:
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"""Learn from experience"""
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experience_id = f"exp_{len(self.experience_buffer)}"
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learning_data = {
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'experience_id': experience_id,
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'experience': experience,
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'lessons_learned': self._extract_lessons(experience),
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'performance_impact': self._calculate_impact(experience),
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'timestamp': datetime.utcnow().isoformat()
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}
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self.experience_buffer.append(learning_data)
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return learning_data
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def _extract_lessons(self, experience: Dict[str, Any]) -> List[str]:
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"""Extract lessons from experience"""
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lessons = []
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if experience.get('success', False):
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lessons.append("Action was successful")
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if experience.get('performance_gain', 0) > 0:
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lessons.append("Performance improved")
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return lessons
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def _calculate_impact(self, experience: Dict[str, Any]) -> float:
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"""Calculate performance impact"""
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return experience.get('performance_gain', 0.0)
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async def adapt_behavior(self, adaptation_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Adapt behavior based on learning"""
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adaptation_id = f"adapt_{len(self.adaptations)}"
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adaptation = {
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'adaptation_id': adaptation_id,
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'type': adaptation_data.get('type', 'parameter_adjustment'),
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'changes': adaptation_data.get('changes', {}),
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'expected_improvement': adaptation_data.get('expected_improvement', 0.1),
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'timestamp': datetime.utcnow().isoformat()
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}
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self.adaptations[adaptation_id] = adaptation
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return adaptation
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class MockPolicyEngine:
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def __init__(self):
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self.policies = {
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'resource_management': {
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'max_cpu_usage': 0.8,
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'max_memory_usage': 0.85,
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'auto_scale_threshold': 0.7
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},
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'error_handling': {
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'max_error_rate': 0.05,
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'retry_attempts': 3,
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'recovery_timeout': 300
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},
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'task_management': {
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'max_queue_size': 1000,
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'task_timeout': 600,
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'priority_weights': {'high': 1.0, 'normal': 0.5, 'low': 0.2}
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}
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}
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async def evaluate_policy_compliance(self, decision: Dict[str, Any]) -> Dict[str, Any]:
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"""Evaluate if decision complies with policies"""
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compliance_score = self._calculate_compliance(decision)
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violations = self._find_violations(decision)
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return {
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'decision_id': decision.get('decision_id'),
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'compliance_score': compliance_score,
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'violations': violations,
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'approved': compliance_score >= 0.8 and len(violations) == 0,
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'timestamp': datetime.utcnow().isoformat()
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}
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def _calculate_compliance(self, decision: Dict[str, Any]) -> float:
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"""Calculate policy compliance score"""
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# Simplified compliance calculation
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base_score = 1.0
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if decision.get('action') == 'scale_resources':
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# Check resource management policy
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base_score -= 0.1 # Small penalty for resource scaling
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return max(0.0, base_score)
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def _find_violations(self, decision: Dict[str, Any]) -> List[str]:
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"""Find policy violations"""
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violations = []
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context = decision.get('context', {})
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# Check resource limits
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if context.get('system_load', 0) > self.policies['resource_management']['max_cpu_usage']:
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violations.append("CPU usage exceeds policy limit")
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return violations
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class TestAutonomousEngine:
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"""Test autonomous decision making engine"""
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def setup_method(self):
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self.autonomous_engine = MockAutonomousEngine()
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@pytest.mark.asyncio
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async def test_autonomous_decision_making(self):
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"""Test basic autonomous decision making"""
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context = {
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'system_load': 0.9,
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'error_rate': 0.02,
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'task_queue_size': 50,
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'active_agents': 5
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}
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decision = await self.autonomous_engine.make_autonomous_decision(context)
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assert decision['action'] == 'scale_resources'
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assert decision['confidence'] > 0.5
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assert 'reasoning' in decision
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assert 'timestamp' in decision
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@pytest.mark.asyncio
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async def test_decision_with_high_error_rate(self):
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"""Test decision making with high error rate"""
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context = {
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'system_load': 0.4,
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'error_rate': 0.15,
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'task_queue_size': 30,
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'active_agents': 3
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}
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decision = await self.autonomous_engine.make_autonomous_decision(context)
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assert decision['action'] == 'trigger_recovery'
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assert 'error_rate' in decision['reasoning']
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@pytest.mark.asyncio
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async def test_decision_with_task_queue_pressure(self):
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"""Test decision making with task queue pressure"""
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context = {
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'system_load': 0.6,
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'error_rate': 0.03,
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'task_queue_size': 150,
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'active_agents': 4
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}
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decision = await self.autonomous_engine.make_autonomous_decision(context)
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assert decision['action'] == 'allocate_more_agents'
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@pytest.mark.asyncio
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async def test_decision_with_normal_conditions(self):
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"""Test decision making with normal conditions"""
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context = {
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'system_load': 0.5,
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'error_rate': 0.02,
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'task_queue_size': 25,
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'active_agents': 4
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}
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decision = await self.autonomous_engine.make_autonomous_decision(context)
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assert decision['action'] == 'maintain_status'
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assert decision['confidence'] > 0.8
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class TestLearningSystem:
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"""Test learning and adaptation system"""
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def setup_method(self):
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self.learning_system = MockLearningSystem()
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@pytest.mark.asyncio
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async def test_learning_from_successful_experience(self):
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"""Test learning from successful experience"""
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experience = {
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'action': 'scale_resources',
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'success': True,
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'performance_gain': 0.15,
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'context': {'system_load': 0.9}
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}
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learning_result = await self.learning_system.learn_from_experience(experience)
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assert learning_result['experience_id'].startswith('exp_')
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assert 'lessons_learned' in learning_result
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assert learning_result['performance_impact'] == 0.15
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assert 'Action was successful' in learning_result['lessons_learned']
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@pytest.mark.asyncio
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async def test_learning_from_failure(self):
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"""Test learning from failed experience"""
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experience = {
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'action': 'scale_resources',
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'success': False,
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'performance_gain': -0.05,
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'context': {'system_load': 0.9}
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}
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learning_result = await self.learning_system.learn_from_experience(experience)
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assert learning_result['experience_id'].startswith('exp_')
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assert learning_result['performance_impact'] == -0.05
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@pytest.mark.asyncio
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async def test_behavior_adaptation(self):
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"""Test behavior adaptation based on learning"""
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adaptation_data = {
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'type': 'threshold_adjustment',
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'changes': {'scale_threshold': 0.75, 'error_threshold': 0.08},
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'expected_improvement': 0.1
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}
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adaptation = await self.learning_system.adapt_behavior(adaptation_data)
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assert adaptation['type'] == 'threshold_adjustment'
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assert adaptation['expected_improvement'] == 0.1
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assert 'scale_threshold' in adaptation['changes']
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@pytest.mark.asyncio
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async def test_experience_accumulation(self):
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"""Test accumulation of experiences over time"""
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experiences = [
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{'action': 'scale_resources', 'success': True, 'performance_gain': 0.1},
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{'action': 'allocate_agents', 'success': True, 'performance_gain': 0.05},
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{'action': 'trigger_recovery', 'success': False, 'performance_gain': -0.02}
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]
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for exp in experiences:
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await self.learning_system.learn_from_experience(exp)
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assert len(self.learning_system.experience_buffer) == 3
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assert all(exp['experience_id'].startswith('exp_') for exp in self.learning_system.experience_buffer)
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class TestPolicyEngine:
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"""Test policy engine for autonomous decisions"""
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def setup_method(self):
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self.policy_engine = MockPolicyEngine()
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@pytest.mark.asyncio
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async def test_policy_compliance_evaluation(self):
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"""Test policy compliance evaluation"""
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decision = {
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'decision_id': 'test_decision_001',
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'action': 'scale_resources',
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'context': {
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'system_load': 0.7,
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'error_rate': 0.03,
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'task_queue_size': 50
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}
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}
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compliance = await self.policy_engine.evaluate_policy_compliance(decision)
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assert compliance['decision_id'] == 'test_decision_001'
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assert 'compliance_score' in compliance
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assert 'violations' in compliance
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assert 'approved' in compliance
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assert 'timestamp' in compliance
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@pytest.mark.asyncio
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async def test_policy_violation_detection(self):
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"""Test detection of policy violations"""
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decision = {
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'decision_id': 'test_decision_002',
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'action': 'scale_resources',
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'context': {
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'system_load': 0.9, # Exceeds policy limit
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'error_rate': 0.03,
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'task_queue_size': 50
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}
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}
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compliance = await self.policy_engine.evaluate_policy_compliance(decision)
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assert len(compliance['violations']) > 0
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assert any('CPU usage' in violation for violation in compliance['violations'])
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@pytest.mark.asyncio
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async def test_policy_approval(self):
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"""Test policy approval for compliant decisions"""
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decision = {
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'decision_id': 'test_decision_003',
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'action': 'maintain_status',
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'context': {
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'system_load': 0.5,
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'error_rate': 0.02,
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'task_queue_size': 25
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}
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}
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compliance = await self.policy_engine.evaluate_policy_compliance(decision)
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assert compliance['approved'] is True
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assert compliance['compliance_score'] >= 0.8
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class TestSelfCorrectionMechanism:
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"""Test self-correction mechanisms"""
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def setup_method(self):
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self.autonomous_engine = MockAutonomousEngine()
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self.learning_system = MockLearningSystem()
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self.policy_engine = MockPolicyEngine()
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@pytest.mark.asyncio
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async def test_automatic_error_correction(self):
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"""Test automatic error correction"""
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# Simulate error condition
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context = {
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'system_load': 0.9,
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'error_rate': 0.12, # High error rate
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'task_queue_size': 50
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}
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# Make initial decision
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decision = await self.autonomous_engine.make_autonomous_decision(context)
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# Simulate error in execution
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error_experience = {
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'action': decision['action'],
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'success': False,
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'error_type': 'resource_exhaustion',
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'performance_gain': -0.1
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}
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# Learn from error
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learning_result = await self.learning_system.learn_from_experience(error_experience)
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# Adapt behavior
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adaptation_data = {
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'type': 'resource_threshold_adjustment',
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'changes': {'scale_threshold': 0.8},
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'expected_improvement': 0.15
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}
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adaptation = await self.learning_system.adapt_behavior(adaptation_data)
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# Verify self-correction
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assert decision['action'] == 'trigger_recovery'
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assert learning_result['experience_id'].startswith('exp_')
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assert adaptation['type'] == 'resource_threshold_adjustment'
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@pytest.mark.asyncio
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async def test_performance_optimization(self):
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"""Test performance optimization through learning"""
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# Initial performance
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initial_context = {
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'system_load': 0.7,
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'error_rate': 0.05,
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'task_queue_size': 80
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}
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decision = await self.autonomous_engine.make_autonomous_decision(initial_context)
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# Simulate successful execution with performance gain
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success_experience = {
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'action': decision['action'],
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'success': True,
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'performance_gain': 0.2
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}
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learning_result = await self.learning_system.learn_from_experience(success_experience)
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# Adapt to optimize further
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adaptation_data = {
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'type': 'performance_optimization',
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'changes': {'aggressive_scaling': True},
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'expected_improvement': 0.1
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}
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adaptation = await self.learning_system.adapt_behavior(adaptation_data)
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# Verify optimization
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assert learning_result['performance_impact'] == 0.2
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assert adaptation['type'] == 'performance_optimization'
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@pytest.mark.asyncio
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async def test_goal_oriented_behavior(self):
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"""Test goal-oriented autonomous behavior"""
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# Define goals
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goals = {
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'primary_goal': 'maintain_system_stability',
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'secondary_goals': ['optimize_performance', 'minimize_errors'],
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'constraints': ['resource_limits', 'policy_compliance']
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}
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# Simulate goal-oriented decision making
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context = {
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'system_load': 0.6,
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'error_rate': 0.04,
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'task_queue_size': 60,
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'goals': goals
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}
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decision = await self.autonomous_engine.make_autonomous_decision(context)
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# Evaluate against goals
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compliance = await self.policy_engine.evaluate_policy_compliance(decision)
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# Verify goal alignment
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assert decision['action'] in ['maintain_status', 'allocate_more_agents']
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assert compliance['approved'] is True # Should be policy compliant
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# Integration tests
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class TestAutonomousIntegration:
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"""Integration tests for autonomous systems"""
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@pytest.mark.asyncio
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async def test_full_autonomous_cycle(self):
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"""Test complete autonomous decision cycle"""
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autonomous_engine = MockAutonomousEngine()
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learning_system = MockLearningSystem()
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policy_engine = MockPolicyEngine()
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# Step 1: Make autonomous decision
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context = {
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'system_load': 0.85,
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'error_rate': 0.08,
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'task_queue_size': 120
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}
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decision = await autonomous_engine.make_autonomous_decision(context)
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# Step 2: Evaluate policy compliance
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compliance = await policy_engine.evaluate_policy_compliance(decision)
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# Step 3: Execute and learn from result
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execution_result = {
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'action': decision['action'],
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'success': compliance['approved'],
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'performance_gain': 0.1 if compliance['approved'] else -0.05
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}
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learning_result = await learning_system.learn_from_experience(execution_result)
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# Step 4: Adapt if needed
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if not compliance['approved']:
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adaptation = await learning_system.adapt_behavior({
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'type': 'policy_compliance',
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'changes': {'more_conservative_thresholds': True}
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})
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# Verify complete cycle
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assert decision['decision_id'].startswith('auto_decision_')
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assert 'compliance_score' in compliance
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assert learning_result['experience_id'].startswith('exp_')
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@pytest.mark.asyncio
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async def test_multi_goal_optimization(self):
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"""Test optimization across multiple goals"""
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goals = {
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'stability': {'weight': 0.4, 'target': 0.95},
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'performance': {'weight': 0.3, 'target': 0.8},
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'efficiency': {'weight': 0.3, 'target': 0.75}
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}
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contexts = [
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{'system_load': 0.7, 'error_rate': 0.05, 'goals': goals},
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{'system_load': 0.8, 'error_rate': 0.06, 'goals': goals},
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|
{'system_load': 0.6, 'error_rate': 0.04, 'goals': goals}
|
|
]
|
|
|
|
autonomous_engine = MockAutonomousEngine()
|
|
decisions = []
|
|
|
|
for context in contexts:
|
|
decision = await autonomous_engine.make_autonomous_decision(context)
|
|
decisions.append(decision)
|
|
|
|
# Verify multi-goal consideration
|
|
assert len(decisions) == 3
|
|
for decision in decisions:
|
|
assert 'action' in decision
|
|
assert 'confidence' in decision
|
|
|
|
if __name__ == '__main__':
|
|
pytest.main([__file__])
|