feat: implement Phase 3-5 test suites for agent systems
✅ 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!
This commit is contained in:
aitbc
358
tests/phase3/test_decision_framework.py
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358
tests/phase3/test_decision_framework.py
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"""
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Phase 3: Decision Framework Tests
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Tests for distributed decision making, voting systems, and consensus algorithms
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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
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# Mock imports for testing
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class MockDecisionEngine:
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def __init__(self):
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self.decisions = {}
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self.votes = {}
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async def make_decision(self, decision_data: Dict[str, Any]) -> Dict[str, Any]:
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decision_id = decision_data.get('decision_id', 'test_decision')
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self.decisions[decision_id] = decision_data
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return {
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'decision_id': decision_id,
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'status': 'completed',
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'result': decision_data.get('proposal', 'approved'),
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'timestamp': datetime.utcnow().isoformat()
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}
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async def submit_vote(self, vote_data: Dict[str, Any]) -> Dict[str, Any]:
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vote_id = vote_data.get('vote_id', 'test_vote')
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self.votes[vote_id] = vote_data
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return {
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'vote_id': vote_id,
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'status': 'recorded',
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'timestamp': datetime.utcnow().isoformat()
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}
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class MockConsensusAlgorithm:
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def __init__(self):
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self.consensus_results = {}
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async def achieve_consensus(self, participants: List[str], proposal: Dict[str, Any]) -> Dict[str, Any]:
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consensus_id = f"consensus_{len(self.consensus_results)}"
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self.consensus_results[consensus_id] = {
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'participants': participants,
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'proposal': proposal,
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'result': 'consensus_reached'
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}
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return {
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'consensus_id': consensus_id,
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'status': 'consensus_reached',
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'agreement': True,
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'timestamp': datetime.utcnow().isoformat()
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}
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class TestDecisionEngine:
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"""Test the decision engine functionality"""
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def setup_method(self):
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self.decision_engine = MockDecisionEngine()
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@pytest.mark.asyncio
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async def test_make_decision(self):
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"""Test basic decision making"""
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decision_data = {
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'decision_id': 'test_decision_001',
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'proposal': 'test_proposal',
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'priority': 'high'
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}
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result = await self.decision_engine.make_decision(decision_data)
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assert result['decision_id'] == 'test_decision_001'
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assert result['status'] == 'completed'
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assert result['result'] == 'test_proposal'
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assert 'timestamp' in result
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@pytest.mark.asyncio
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async def test_submit_vote(self):
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"""Test vote submission"""
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vote_data = {
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'vote_id': 'test_vote_001',
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'voter_id': 'agent_001',
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'vote': 'approve',
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'decision_id': 'test_decision_001'
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}
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result = await self.decision_engine.submit_vote(vote_data)
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assert result['vote_id'] == 'test_vote_001'
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assert result['status'] == 'recorded'
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assert 'timestamp' in result
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@pytest.mark.asyncio
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async def test_decision_with_complex_data(self):
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"""Test decision making with complex data"""
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decision_data = {
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'decision_id': 'complex_decision_001',
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'proposal': {
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'action': 'resource_allocation',
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'resources': ['cpu', 'memory', 'storage'],
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'amounts': {'cpu': 50, 'memory': 2048, 'storage': 100}
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},
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'participants': ['agent_001', 'agent_002', 'agent_003'],
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'deadline': (datetime.utcnow() + timedelta(hours=1)).isoformat()
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}
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result = await self.decision_engine.make_decision(decision_data)
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assert result['decision_id'] == 'complex_decision_001'
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assert result['status'] == 'completed'
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assert 'timestamp' in result
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class TestConsensusAlgorithm:
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"""Test consensus algorithm functionality"""
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def setup_method(self):
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self.consensus = MockConsensusAlgorithm()
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@pytest.mark.asyncio
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async def test_achieve_consensus(self):
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"""Test basic consensus achievement"""
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participants = ['agent_001', 'agent_002', 'agent_003']
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proposal = {
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'action': 'system_update',
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'version': '1.0.0',
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'description': 'Update system to new version'
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}
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result = await self.consensus.achieve_consensus(participants, proposal)
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assert result['status'] == 'consensus_reached'
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assert result['agreement'] is True
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assert 'consensus_id' in result
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assert 'timestamp' in result
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@pytest.mark.asyncio
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async def test_consensus_with_single_agent(self):
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"""Test consensus with single participant"""
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participants = ['agent_001']
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proposal = {'action': 'test_action'}
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result = await self.consensus.achieve_consensus(participants, proposal)
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assert result['status'] == 'consensus_reached'
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assert result['agreement'] is True
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@pytest.mark.asyncio
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async def test_consensus_with_complex_proposal(self):
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"""Test consensus with complex proposal"""
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participants = ['agent_001', 'agent_002', 'agent_003', 'agent_004']
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proposal = {
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'action': 'policy_change',
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'policy': {
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'name': 'resource_allocation_policy',
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'rules': [
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{'rule': 'priority_based', 'weight': 0.6},
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{'rule': 'fair_share', 'weight': 0.4}
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],
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'effective_date': datetime.utcnow().isoformat()
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}
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}
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result = await self.consensus.achieve_consensus(participants, proposal)
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assert result['status'] == 'consensus_reached'
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assert result['agreement'] is True
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assert 'consensus_id' in result
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class TestVotingSystem:
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"""Test voting system functionality"""
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def setup_method(self):
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self.decision_engine = MockDecisionEngine()
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self.votes = {}
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@pytest.mark.asyncio
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async def test_majority_voting(self):
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"""Test majority voting mechanism"""
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votes = [
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{'voter_id': 'agent_001', 'vote': 'approve'},
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{'voter_id': 'agent_002', 'vote': 'approve'},
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{'voter_id': 'agent_003', 'vote': 'reject'}
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]
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# Simulate majority voting
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approve_votes = sum(1 for v in votes if v['vote'] == 'approve')
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total_votes = len(votes)
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majority_threshold = total_votes // 2 + 1
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result = {
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'decision': 'approve' if approve_votes >= majority_threshold else 'reject',
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'vote_count': {'approve': approve_votes, 'reject': total_votes - approve_votes},
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'threshold': majority_threshold
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}
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assert result['decision'] == 'approve'
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assert result['vote_count']['approve'] == 2
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assert result['vote_count']['reject'] == 1
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assert result['threshold'] == 2
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@pytest.mark.asyncio
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async def test_weighted_voting(self):
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"""Test weighted voting mechanism"""
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votes = [
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{'voter_id': 'agent_001', 'vote': 'approve', 'weight': 3},
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{'voter_id': 'agent_002', 'vote': 'reject', 'weight': 1},
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{'voter_id': 'agent_003', 'vote': 'approve', 'weight': 2}
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]
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# Calculate weighted votes
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approve_weight = sum(v['weight'] for v in votes if v['vote'] == 'approve')
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reject_weight = sum(v['weight'] for v in votes if v['vote'] == 'reject')
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total_weight = approve_weight + reject_weight
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result = {
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'decision': 'approve' if approve_weight > reject_weight else 'reject',
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'weighted_count': {'approve': approve_weight, 'reject': reject_weight},
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'total_weight': total_weight
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}
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assert result['decision'] == 'approve'
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assert result['weighted_count']['approve'] == 5
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assert result['weighted_count']['reject'] == 1
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assert result['total_weight'] == 6
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@pytest.mark.asyncio
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async def test_unanimous_voting(self):
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"""Test unanimous voting mechanism"""
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votes = [
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{'voter_id': 'agent_001', 'vote': 'approve'},
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{'voter_id': 'agent_002', 'vote': 'approve'},
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{'voter_id': 'agent_003', 'vote': 'approve'}
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]
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# Check for unanimity
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all_approve = all(v['vote'] == 'approve' for v in votes)
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result = {
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'decision': 'approve' if all_approve else 'reject',
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'unanimous': all_approve,
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'vote_count': len(votes)
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}
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assert result['decision'] == 'approve'
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assert result['unanimous'] is True
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assert result['vote_count'] == 3
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class TestAgentLifecycleManagement:
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"""Test agent lifecycle management"""
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def setup_method(self):
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self.agents = {}
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self.agent_states = {}
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@pytest.mark.asyncio
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async def test_agent_registration(self):
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"""Test agent registration in decision system"""
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agent_data = {
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'agent_id': 'agent_001',
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'capabilities': ['decision_making', 'voting'],
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'status': 'active',
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'join_time': datetime.utcnow().isoformat()
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}
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self.agents[agent_data['agent_id']] = agent_data
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assert agent_data['agent_id'] in self.agents
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assert self.agents[agent_data['agent_id']]['status'] == 'active'
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assert 'decision_making' in self.agents[agent_data['agent_id']]['capabilities']
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@pytest.mark.asyncio
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async def test_agent_status_update(self):
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"""Test agent status updates"""
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agent_id = 'agent_002'
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self.agents[agent_id] = {
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'agent_id': agent_id,
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'status': 'active',
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'last_update': datetime.utcnow().isoformat()
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}
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# Update agent status
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self.agents[agent_id]['status'] = 'busy'
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self.agents[agent_id]['last_update'] = datetime.utcnow().isoformat()
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assert self.agents[agent_id]['status'] == 'busy'
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assert 'last_update' in self.agents[agent_id]
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@pytest.mark.asyncio
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async def test_agent_removal(self):
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"""Test agent removal from decision system"""
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agent_id = 'agent_003'
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self.agents[agent_id] = {
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'agent_id': agent_id,
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'status': 'active'
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}
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# Remove agent
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del self.agents[agent_id]
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assert agent_id not in self.agents
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# Integration tests
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class TestDecisionIntegration:
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"""Integration tests for decision framework"""
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@pytest.mark.asyncio
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async def test_end_to_end_decision_process(self):
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"""Test complete decision making process"""
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decision_engine = MockDecisionEngine()
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consensus = MockConsensusAlgorithm()
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# Step 1: Create decision proposal
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decision_data = {
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'decision_id': 'integration_test_001',
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'proposal': 'test_proposal',
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'participants': ['agent_001', 'agent_002']
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}
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# Step 2: Make decision
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decision_result = await decision_engine.make_decision(decision_data)
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# Step 3: Achieve consensus
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consensus_result = await consensus.achieve_consensus(
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decision_data['participants'],
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{'action': decision_data['proposal']}
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)
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# Verify results
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assert decision_result['status'] == 'completed'
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assert consensus_result['status'] == 'consensus_reached'
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assert decision_result['decision_id'] == 'integration_test_001'
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@pytest.mark.asyncio
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async def test_multi_agent_coordination(self):
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"""Test coordination between multiple agents"""
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agents = ['agent_001', 'agent_002', 'agent_003']
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decision_engine = MockDecisionEngine()
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# Simulate coordinated decision making
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decisions = []
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for i, agent in enumerate(agents):
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decision_data = {
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'decision_id': f'coord_test_{i}',
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'agent_id': agent,
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'proposal': f'proposal_{i}',
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'coordinated_with': [a for a in agents if a != agent]
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}
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result = await decision_engine.make_decision(decision_data)
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decisions.append(result)
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# Verify all decisions were made
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assert len(decisions) == len(agents)
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for decision in decisions:
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assert decision['status'] == 'completed'
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if __name__ == '__main__':
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pytest.main([__file__])
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532
tests/phase4/test_autonomous_decision_making.py
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532
tests/phase4/test_autonomous_decision_making.py
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@@ -0,0 +1,532 @@
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"""
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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,
|
||||
'max_memory_usage': 0.85,
|
||||
'auto_scale_threshold': 0.7
|
||||
},
|
||||
'error_handling': {
|
||||
'max_error_rate': 0.05,
|
||||
'retry_attempts': 3,
|
||||
'recovery_timeout': 300
|
||||
},
|
||||
'task_management': {
|
||||
'max_queue_size': 1000,
|
||||
'task_timeout': 600,
|
||||
'priority_weights': {'high': 1.0, 'normal': 0.5, 'low': 0.2}
|
||||
}
|
||||
}
|
||||
|
||||
async def evaluate_policy_compliance(self, decision: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Evaluate if decision complies with policies"""
|
||||
compliance_score = self._calculate_compliance(decision)
|
||||
violations = self._find_violations(decision)
|
||||
|
||||
return {
|
||||
'decision_id': decision.get('decision_id'),
|
||||
'compliance_score': compliance_score,
|
||||
'violations': violations,
|
||||
'approved': compliance_score >= 0.8 and len(violations) == 0,
|
||||
'timestamp': datetime.utcnow().isoformat()
|
||||
}
|
||||
|
||||
def _calculate_compliance(self, decision: Dict[str, Any]) -> float:
|
||||
"""Calculate policy compliance score"""
|
||||
# Simplified compliance calculation
|
||||
base_score = 1.0
|
||||
if decision.get('action') == 'scale_resources':
|
||||
# Check resource management policy
|
||||
base_score -= 0.1 # Small penalty for resource scaling
|
||||
return max(0.0, base_score)
|
||||
|
||||
def _find_violations(self, decision: Dict[str, Any]) -> List[str]:
|
||||
"""Find policy violations"""
|
||||
violations = []
|
||||
context = decision.get('context', {})
|
||||
|
||||
# Check resource limits
|
||||
if context.get('system_load', 0) > self.policies['resource_management']['max_cpu_usage']:
|
||||
violations.append("CPU usage exceeds policy limit")
|
||||
|
||||
return violations
|
||||
|
||||
class TestAutonomousEngine:
|
||||
"""Test autonomous decision making engine"""
|
||||
|
||||
def setup_method(self):
|
||||
self.autonomous_engine = MockAutonomousEngine()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_autonomous_decision_making(self):
|
||||
"""Test basic autonomous decision making"""
|
||||
context = {
|
||||
'system_load': 0.9,
|
||||
'error_rate': 0.02,
|
||||
'task_queue_size': 50,
|
||||
'active_agents': 5
|
||||
}
|
||||
|
||||
decision = await self.autonomous_engine.make_autonomous_decision(context)
|
||||
|
||||
assert decision['action'] == 'scale_resources'
|
||||
assert decision['confidence'] > 0.5
|
||||
assert 'reasoning' in decision
|
||||
assert 'timestamp' in decision
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_decision_with_high_error_rate(self):
|
||||
"""Test decision making with high error rate"""
|
||||
context = {
|
||||
'system_load': 0.4,
|
||||
'error_rate': 0.15,
|
||||
'task_queue_size': 30,
|
||||
'active_agents': 3
|
||||
}
|
||||
|
||||
decision = await self.autonomous_engine.make_autonomous_decision(context)
|
||||
|
||||
assert decision['action'] == 'trigger_recovery'
|
||||
assert 'error_rate' in decision['reasoning']
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_decision_with_task_queue_pressure(self):
|
||||
"""Test decision making with task queue pressure"""
|
||||
context = {
|
||||
'system_load': 0.6,
|
||||
'error_rate': 0.03,
|
||||
'task_queue_size': 150,
|
||||
'active_agents': 4
|
||||
}
|
||||
|
||||
decision = await self.autonomous_engine.make_autonomous_decision(context)
|
||||
|
||||
assert decision['action'] == 'allocate_more_agents'
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_decision_with_normal_conditions(self):
|
||||
"""Test decision making with normal conditions"""
|
||||
context = {
|
||||
'system_load': 0.5,
|
||||
'error_rate': 0.02,
|
||||
'task_queue_size': 25,
|
||||
'active_agents': 4
|
||||
}
|
||||
|
||||
decision = await self.autonomous_engine.make_autonomous_decision(context)
|
||||
|
||||
assert decision['action'] == 'maintain_status'
|
||||
assert decision['confidence'] > 0.8
|
||||
|
||||
class TestLearningSystem:
|
||||
"""Test learning and adaptation system"""
|
||||
|
||||
def setup_method(self):
|
||||
self.learning_system = MockLearningSystem()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_learning_from_successful_experience(self):
|
||||
"""Test learning from successful experience"""
|
||||
experience = {
|
||||
'action': 'scale_resources',
|
||||
'success': True,
|
||||
'performance_gain': 0.15,
|
||||
'context': {'system_load': 0.9}
|
||||
}
|
||||
|
||||
learning_result = await self.learning_system.learn_from_experience(experience)
|
||||
|
||||
assert learning_result['experience_id'].startswith('exp_')
|
||||
assert 'lessons_learned' in learning_result
|
||||
assert learning_result['performance_impact'] == 0.15
|
||||
assert 'Action was successful' in learning_result['lessons_learned']
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_learning_from_failure(self):
|
||||
"""Test learning from failed experience"""
|
||||
experience = {
|
||||
'action': 'scale_resources',
|
||||
'success': False,
|
||||
'performance_gain': -0.05,
|
||||
'context': {'system_load': 0.9}
|
||||
}
|
||||
|
||||
learning_result = await self.learning_system.learn_from_experience(experience)
|
||||
|
||||
assert learning_result['experience_id'].startswith('exp_')
|
||||
assert learning_result['performance_impact'] == -0.05
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_behavior_adaptation(self):
|
||||
"""Test behavior adaptation based on learning"""
|
||||
adaptation_data = {
|
||||
'type': 'threshold_adjustment',
|
||||
'changes': {'scale_threshold': 0.75, 'error_threshold': 0.08},
|
||||
'expected_improvement': 0.1
|
||||
}
|
||||
|
||||
adaptation = await self.learning_system.adapt_behavior(adaptation_data)
|
||||
|
||||
assert adaptation['type'] == 'threshold_adjustment'
|
||||
assert adaptation['expected_improvement'] == 0.1
|
||||
assert 'scale_threshold' in adaptation['changes']
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_experience_accumulation(self):
|
||||
"""Test accumulation of experiences over time"""
|
||||
experiences = [
|
||||
{'action': 'scale_resources', 'success': True, 'performance_gain': 0.1},
|
||||
{'action': 'allocate_agents', 'success': True, 'performance_gain': 0.05},
|
||||
{'action': 'trigger_recovery', 'success': False, 'performance_gain': -0.02}
|
||||
]
|
||||
|
||||
for exp in experiences:
|
||||
await self.learning_system.learn_from_experience(exp)
|
||||
|
||||
assert len(self.learning_system.experience_buffer) == 3
|
||||
assert all(exp['experience_id'].startswith('exp_') for exp in self.learning_system.experience_buffer)
|
||||
|
||||
class TestPolicyEngine:
|
||||
"""Test policy engine for autonomous decisions"""
|
||||
|
||||
def setup_method(self):
|
||||
self.policy_engine = MockPolicyEngine()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_policy_compliance_evaluation(self):
|
||||
"""Test policy compliance evaluation"""
|
||||
decision = {
|
||||
'decision_id': 'test_decision_001',
|
||||
'action': 'scale_resources',
|
||||
'context': {
|
||||
'system_load': 0.7,
|
||||
'error_rate': 0.03,
|
||||
'task_queue_size': 50
|
||||
}
|
||||
}
|
||||
|
||||
compliance = await self.policy_engine.evaluate_policy_compliance(decision)
|
||||
|
||||
assert compliance['decision_id'] == 'test_decision_001'
|
||||
assert 'compliance_score' in compliance
|
||||
assert 'violations' in compliance
|
||||
assert 'approved' in compliance
|
||||
assert 'timestamp' in compliance
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_policy_violation_detection(self):
|
||||
"""Test detection of policy violations"""
|
||||
decision = {
|
||||
'decision_id': 'test_decision_002',
|
||||
'action': 'scale_resources',
|
||||
'context': {
|
||||
'system_load': 0.9, # Exceeds policy limit
|
||||
'error_rate': 0.03,
|
||||
'task_queue_size': 50
|
||||
}
|
||||
}
|
||||
|
||||
compliance = await self.policy_engine.evaluate_policy_compliance(decision)
|
||||
|
||||
assert len(compliance['violations']) > 0
|
||||
assert any('CPU usage' in violation for violation in compliance['violations'])
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_policy_approval(self):
|
||||
"""Test policy approval for compliant decisions"""
|
||||
decision = {
|
||||
'decision_id': 'test_decision_003',
|
||||
'action': 'maintain_status',
|
||||
'context': {
|
||||
'system_load': 0.5,
|
||||
'error_rate': 0.02,
|
||||
'task_queue_size': 25
|
||||
}
|
||||
}
|
||||
|
||||
compliance = await self.policy_engine.evaluate_policy_compliance(decision)
|
||||
|
||||
assert compliance['approved'] is True
|
||||
assert compliance['compliance_score'] >= 0.8
|
||||
|
||||
class TestSelfCorrectionMechanism:
|
||||
"""Test self-correction mechanisms"""
|
||||
|
||||
def setup_method(self):
|
||||
self.autonomous_engine = MockAutonomousEngine()
|
||||
self.learning_system = MockLearningSystem()
|
||||
self.policy_engine = MockPolicyEngine()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_automatic_error_correction(self):
|
||||
"""Test automatic error correction"""
|
||||
# Simulate error condition
|
||||
context = {
|
||||
'system_load': 0.9,
|
||||
'error_rate': 0.12, # High error rate
|
||||
'task_queue_size': 50
|
||||
}
|
||||
|
||||
# Make initial decision
|
||||
decision = await self.autonomous_engine.make_autonomous_decision(context)
|
||||
|
||||
# Simulate error in execution
|
||||
error_experience = {
|
||||
'action': decision['action'],
|
||||
'success': False,
|
||||
'error_type': 'resource_exhaustion',
|
||||
'performance_gain': -0.1
|
||||
}
|
||||
|
||||
# Learn from error
|
||||
learning_result = await self.learning_system.learn_from_experience(error_experience)
|
||||
|
||||
# Adapt behavior
|
||||
adaptation_data = {
|
||||
'type': 'resource_threshold_adjustment',
|
||||
'changes': {'scale_threshold': 0.8},
|
||||
'expected_improvement': 0.15
|
||||
}
|
||||
|
||||
adaptation = await self.learning_system.adapt_behavior(adaptation_data)
|
||||
|
||||
# Verify self-correction
|
||||
assert decision['action'] == 'trigger_recovery'
|
||||
assert learning_result['experience_id'].startswith('exp_')
|
||||
assert adaptation['type'] == 'resource_threshold_adjustment'
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_performance_optimization(self):
|
||||
"""Test performance optimization through learning"""
|
||||
# Initial performance
|
||||
initial_context = {
|
||||
'system_load': 0.7,
|
||||
'error_rate': 0.05,
|
||||
'task_queue_size': 80
|
||||
}
|
||||
|
||||
decision = await self.autonomous_engine.make_autonomous_decision(initial_context)
|
||||
|
||||
# Simulate successful execution with performance gain
|
||||
success_experience = {
|
||||
'action': decision['action'],
|
||||
'success': True,
|
||||
'performance_gain': 0.2
|
||||
}
|
||||
|
||||
learning_result = await self.learning_system.learn_from_experience(success_experience)
|
||||
|
||||
# Adapt to optimize further
|
||||
adaptation_data = {
|
||||
'type': 'performance_optimization',
|
||||
'changes': {'aggressive_scaling': True},
|
||||
'expected_improvement': 0.1
|
||||
}
|
||||
|
||||
adaptation = await self.learning_system.adapt_behavior(adaptation_data)
|
||||
|
||||
# Verify optimization
|
||||
assert learning_result['performance_impact'] == 0.2
|
||||
assert adaptation['type'] == 'performance_optimization'
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_goal_oriented_behavior(self):
|
||||
"""Test goal-oriented autonomous behavior"""
|
||||
# Define goals
|
||||
goals = {
|
||||
'primary_goal': 'maintain_system_stability',
|
||||
'secondary_goals': ['optimize_performance', 'minimize_errors'],
|
||||
'constraints': ['resource_limits', 'policy_compliance']
|
||||
}
|
||||
|
||||
# Simulate goal-oriented decision making
|
||||
context = {
|
||||
'system_load': 0.6,
|
||||
'error_rate': 0.04,
|
||||
'task_queue_size': 60,
|
||||
'goals': goals
|
||||
}
|
||||
|
||||
decision = await self.autonomous_engine.make_autonomous_decision(context)
|
||||
|
||||
# Evaluate against goals
|
||||
compliance = await self.policy_engine.evaluate_policy_compliance(decision)
|
||||
|
||||
# Verify goal alignment
|
||||
assert decision['action'] in ['maintain_status', 'allocate_more_agents']
|
||||
assert compliance['approved'] is True # Should be policy compliant
|
||||
|
||||
# Integration tests
|
||||
class TestAutonomousIntegration:
|
||||
"""Integration tests for autonomous systems"""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_full_autonomous_cycle(self):
|
||||
"""Test complete autonomous decision cycle"""
|
||||
autonomous_engine = MockAutonomousEngine()
|
||||
learning_system = MockLearningSystem()
|
||||
policy_engine = MockPolicyEngine()
|
||||
|
||||
# Step 1: Make autonomous decision
|
||||
context = {
|
||||
'system_load': 0.85,
|
||||
'error_rate': 0.08,
|
||||
'task_queue_size': 120
|
||||
}
|
||||
|
||||
decision = await autonomous_engine.make_autonomous_decision(context)
|
||||
|
||||
# Step 2: Evaluate policy compliance
|
||||
compliance = await policy_engine.evaluate_policy_compliance(decision)
|
||||
|
||||
# Step 3: Execute and learn from result
|
||||
execution_result = {
|
||||
'action': decision['action'],
|
||||
'success': compliance['approved'],
|
||||
'performance_gain': 0.1 if compliance['approved'] else -0.05
|
||||
}
|
||||
|
||||
learning_result = await learning_system.learn_from_experience(execution_result)
|
||||
|
||||
# Step 4: Adapt if needed
|
||||
if not compliance['approved']:
|
||||
adaptation = await learning_system.adapt_behavior({
|
||||
'type': 'policy_compliance',
|
||||
'changes': {'more_conservative_thresholds': True}
|
||||
})
|
||||
|
||||
# Verify complete cycle
|
||||
assert decision['decision_id'].startswith('auto_decision_')
|
||||
assert 'compliance_score' in compliance
|
||||
assert learning_result['experience_id'].startswith('exp_')
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_multi_goal_optimization(self):
|
||||
"""Test optimization across multiple goals"""
|
||||
goals = {
|
||||
'stability': {'weight': 0.4, 'target': 0.95},
|
||||
'performance': {'weight': 0.3, 'target': 0.8},
|
||||
'efficiency': {'weight': 0.3, 'target': 0.75}
|
||||
}
|
||||
|
||||
contexts = [
|
||||
{'system_load': 0.7, 'error_rate': 0.05, 'goals': goals},
|
||||
{'system_load': 0.8, 'error_rate': 0.06, 'goals': goals},
|
||||
{'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__])
|
||||
641
tests/phase5/test_vision_integration.py
Normal file
641
tests/phase5/test_vision_integration.py
Normal file
@@ -0,0 +1,641 @@
|
||||
"""
|
||||
Phase 5: Computer Vision Integration Tests
|
||||
Tests for visual intelligence, image processing, and multi-modal integration
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import asyncio
|
||||
import json
|
||||
import base64
|
||||
from datetime import datetime, timedelta
|
||||
from unittest.mock import Mock, AsyncMock
|
||||
from typing import Dict, List, Any, Optional, Tuple
|
||||
|
||||
# Mock imports for testing
|
||||
class MockVisionProcessor:
|
||||
def __init__(self):
|
||||
self.processed_images = {}
|
||||
self.detection_results = {}
|
||||
self.analysis_results = {}
|
||||
|
||||
async def process_image(self, image_data: bytes, processing_type: str = 'general') -> Dict[str, Any]:
|
||||
"""Process image data"""
|
||||
image_id = f"img_{len(self.processed_images)}"
|
||||
result = {
|
||||
'image_id': image_id,
|
||||
'processing_type': processing_type,
|
||||
'size': len(image_data),
|
||||
'format': 'processed',
|
||||
'timestamp': datetime.utcnow().isoformat(),
|
||||
'analysis': await self._analyze_image(image_data, processing_type)
|
||||
}
|
||||
self.processed_images[image_id] = result
|
||||
return result
|
||||
|
||||
async def _analyze_image(self, image_data: bytes, processing_type: str) -> Dict[str, Any]:
|
||||
"""Analyze image based on processing type"""
|
||||
if processing_type == 'object_detection':
|
||||
return await self._detect_objects(image_data)
|
||||
elif processing_type == 'scene_analysis':
|
||||
return await self._analyze_scene(image_data)
|
||||
elif processing_type == 'text_extraction':
|
||||
return await self._extract_text(image_data)
|
||||
else:
|
||||
return await self._general_analysis(image_data)
|
||||
|
||||
async def _detect_objects(self, image_data: bytes) -> Dict[str, Any]:
|
||||
"""Detect objects in image"""
|
||||
# Mock object detection
|
||||
objects = [
|
||||
{'class': 'person', 'confidence': 0.92, 'bbox': [100, 150, 200, 300]},
|
||||
{'class': 'car', 'confidence': 0.87, 'bbox': [300, 200, 500, 350]},
|
||||
{'class': 'building', 'confidence': 0.95, 'bbox': [0, 0, 600, 400]}
|
||||
]
|
||||
|
||||
self.detection_results[f"detection_{len(self.detection_results)}"] = objects
|
||||
|
||||
return {
|
||||
'objects_detected': len(objects),
|
||||
'objects': objects,
|
||||
'detection_confidence': sum(obj['confidence'] for obj in objects) / len(objects)
|
||||
}
|
||||
|
||||
async def _analyze_scene(self, image_data: bytes) -> Dict[str, Any]:
|
||||
"""Analyze scene context"""
|
||||
# Mock scene analysis
|
||||
scene_info = {
|
||||
'scene_type': 'urban_street',
|
||||
'confidence': 0.88,
|
||||
'elements': ['vehicles', 'pedestrians', 'buildings'],
|
||||
'weather': 'clear',
|
||||
'time_of_day': 'daytime',
|
||||
'complexity': 'medium'
|
||||
}
|
||||
|
||||
return scene_info
|
||||
|
||||
async def _extract_text(self, image_data: bytes) -> Dict[str, Any]:
|
||||
"""Extract text from image"""
|
||||
# Mock OCR
|
||||
text_data = {
|
||||
'text_found': True,
|
||||
'extracted_text': ['STOP', 'MAIN ST', 'NO PARKING'],
|
||||
'confidence': 0.91,
|
||||
'language': 'en',
|
||||
'text_regions': [
|
||||
{'text': 'STOP', 'bbox': [50, 100, 150, 150]},
|
||||
{'text': 'MAIN ST', 'bbox': [200, 100, 350, 150]}
|
||||
]
|
||||
}
|
||||
|
||||
return text_data
|
||||
|
||||
async def _general_analysis(self, image_data: bytes) -> Dict[str, Any]:
|
||||
"""General image analysis"""
|
||||
return {
|
||||
'brightness': 0.7,
|
||||
'contrast': 0.8,
|
||||
'sharpness': 0.75,
|
||||
'color_distribution': {'red': 0.3, 'green': 0.4, 'blue': 0.3},
|
||||
'dominant_colors': ['blue', 'green', 'white'],
|
||||
'image_quality': 'good'
|
||||
}
|
||||
|
||||
class MockMultiModalAgent:
|
||||
def __init__(self):
|
||||
self.vision_processor = MockVisionProcessor()
|
||||
self.integrated_results = {}
|
||||
|
||||
async def process_multi_modal(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Process multi-modal inputs"""
|
||||
result_id = f"multi_{len(self.integrated_results)}"
|
||||
|
||||
# Process different modalities
|
||||
results = {}
|
||||
|
||||
if 'image' in inputs:
|
||||
results['vision'] = await self.vision_processor.process_image(
|
||||
inputs['image'],
|
||||
inputs.get('vision_processing_type', 'general')
|
||||
)
|
||||
|
||||
if 'text' in inputs:
|
||||
results['text'] = await self._process_text(inputs['text'])
|
||||
|
||||
if 'sensor_data' in inputs:
|
||||
results['sensor'] = await self._process_sensor_data(inputs['sensor_data'])
|
||||
|
||||
# Integrate results
|
||||
integrated_result = {
|
||||
'result_id': result_id,
|
||||
'modalities_processed': list(results.keys()),
|
||||
'integration': await self._integrate_modalities(results),
|
||||
'timestamp': datetime.utcnow().isoformat()
|
||||
}
|
||||
|
||||
self.integrated_results[result_id] = integrated_result
|
||||
return integrated_result
|
||||
|
||||
async def _process_text(self, text: str) -> Dict[str, Any]:
|
||||
"""Process text input"""
|
||||
return {
|
||||
'text_length': len(text),
|
||||
'language': 'en',
|
||||
'sentiment': 'neutral',
|
||||
'entities': [],
|
||||
'keywords': text.split()[:5]
|
||||
}
|
||||
|
||||
async def _process_sensor_data(self, sensor_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Process sensor data"""
|
||||
return {
|
||||
'sensor_type': sensor_data.get('type', 'unknown'),
|
||||
'readings': sensor_data.get('readings', {}),
|
||||
'timestamp': sensor_data.get('timestamp', datetime.utcnow().isoformat()),
|
||||
'quality': 'good'
|
||||
}
|
||||
|
||||
async def _integrate_modalities(self, results: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Integrate results from different modalities"""
|
||||
integration = {
|
||||
'confidence': 0.85,
|
||||
'completeness': len(results) / 3.0, # Assuming 3 modalities max
|
||||
'cross_modal_insights': []
|
||||
}
|
||||
|
||||
# Add cross-modal insights
|
||||
if 'vision' in results and 'text' in results:
|
||||
if 'objects' in results['vision'].get('analysis', {}):
|
||||
integration['cross_modal_insights'].append(
|
||||
f"Visual context: {len(results['vision']['analysis']['objects'])} objects detected"
|
||||
)
|
||||
|
||||
return integration
|
||||
|
||||
class MockContextIntegration:
|
||||
def __init__(self):
|
||||
self.context_history = []
|
||||
self.context_models = {}
|
||||
|
||||
async def integrate_context(self, vision_result: Dict[str, Any], context_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Integrate vision results with context"""
|
||||
context_id = f"ctx_{len(self.context_history)}"
|
||||
|
||||
integration = {
|
||||
'context_id': context_id,
|
||||
'vision_result': vision_result,
|
||||
'context_data': context_data,
|
||||
'enhanced_understanding': await self._enhance_understanding(vision_result, context_data),
|
||||
'timestamp': datetime.utcnow().isoformat()
|
||||
}
|
||||
|
||||
self.context_history.append(integration)
|
||||
return integration
|
||||
|
||||
async def _enhance_understanding(self, vision_result: Dict[str, Any], context_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Enhance understanding with context"""
|
||||
enhanced = {
|
||||
'scene_understanding': vision_result.get('analysis', {}),
|
||||
'contextual_insights': [],
|
||||
'confidence_boost': 0.0
|
||||
}
|
||||
|
||||
# Add contextual insights
|
||||
if context_data.get('location') == 'intersection':
|
||||
enhanced['contextual_insights'].append("Traffic monitoring context")
|
||||
enhanced['confidence_boost'] += 0.1
|
||||
|
||||
if context_data.get('time_of_day') == 'night':
|
||||
enhanced['contextual_insights'].append("Low light conditions detected")
|
||||
enhanced['confidence_boost'] -= 0.05
|
||||
|
||||
return enhanced
|
||||
|
||||
class TestVisionProcessor:
|
||||
"""Test vision processing functionality"""
|
||||
|
||||
def setup_method(self):
|
||||
self.vision_processor = MockVisionProcessor()
|
||||
self.sample_image = b'sample_image_data_for_testing'
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_image_processing(self):
|
||||
"""Test basic image processing"""
|
||||
result = await self.vision_processor.process_image(self.sample_image)
|
||||
|
||||
assert result['image_id'].startswith('img_')
|
||||
assert result['size'] == len(self.sample_image)
|
||||
assert result['format'] == 'processed'
|
||||
assert 'analysis' in result
|
||||
assert 'timestamp' in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_object_detection(self):
|
||||
"""Test object detection functionality"""
|
||||
result = await self.vision_processor.process_image(self.sample_image, 'object_detection')
|
||||
|
||||
assert 'analysis' in result
|
||||
assert 'objects_detected' in result['analysis']
|
||||
assert 'objects' in result['analysis']
|
||||
assert len(result['analysis']['objects']) > 0
|
||||
|
||||
# Check object structure
|
||||
for obj in result['analysis']['objects']:
|
||||
assert 'class' in obj
|
||||
assert 'confidence' in obj
|
||||
assert 'bbox' in obj
|
||||
assert 0 <= obj['confidence'] <= 1
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_scene_analysis(self):
|
||||
"""Test scene analysis functionality"""
|
||||
result = await self.vision_processor.process_image(self.sample_image, 'scene_analysis')
|
||||
|
||||
assert 'analysis' in result
|
||||
assert 'scene_type' in result['analysis']
|
||||
assert 'confidence' in result['analysis']
|
||||
assert 'elements' in result['analysis']
|
||||
|
||||
assert result['analysis']['scene_type'] == 'urban_street'
|
||||
assert 0 <= result['analysis']['confidence'] <= 1
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_text_extraction(self):
|
||||
"""Test text extraction (OCR) functionality"""
|
||||
result = await self.vision_processor.process_image(self.sample_image, 'text_extraction')
|
||||
|
||||
assert 'analysis' in result
|
||||
assert 'text_found' in result['analysis']
|
||||
assert 'extracted_text' in result['analysis']
|
||||
|
||||
if result['analysis']['text_found']:
|
||||
assert len(result['analysis']['extracted_text']) > 0
|
||||
assert 'confidence' in result['analysis']
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_general_analysis(self):
|
||||
"""Test general image analysis"""
|
||||
result = await self.vision_processor.process_image(self.sample_image, 'general')
|
||||
|
||||
assert 'analysis' in result
|
||||
assert 'brightness' in result['analysis']
|
||||
assert 'contrast' in result['analysis']
|
||||
assert 'sharpness' in result['analysis']
|
||||
assert 'color_distribution' in result['analysis']
|
||||
|
||||
# Check value ranges
|
||||
assert 0 <= result['analysis']['brightness'] <= 1
|
||||
assert 0 <= result['analysis']['contrast'] <= 1
|
||||
assert 0 <= result['analysis']['sharpness'] <= 1
|
||||
|
||||
class TestMultiModalIntegration:
|
||||
"""Test multi-modal integration"""
|
||||
|
||||
def setup_method(self):
|
||||
self.multi_modal_agent = MockMultiModalAgent()
|
||||
self.sample_image = b'sample_image_data'
|
||||
self.sample_text = "This is a sample text for testing"
|
||||
self.sample_sensor_data = {
|
||||
'type': 'temperature',
|
||||
'readings': {'value': 25.5, 'unit': 'celsius'},
|
||||
'timestamp': datetime.utcnow().isoformat()
|
||||
}
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_vision_only_processing(self):
|
||||
"""Test processing with only vision input"""
|
||||
inputs = {'image': self.sample_image}
|
||||
|
||||
result = await self.multi_modal_agent.process_multi_modal(inputs)
|
||||
|
||||
assert result['result_id'].startswith('multi_')
|
||||
assert 'vision' in result['modalities_processed']
|
||||
assert 'integration' in result
|
||||
assert 'confidence' in result['integration']
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_text_only_processing(self):
|
||||
"""Test processing with only text input"""
|
||||
inputs = {'text': self.sample_text}
|
||||
|
||||
result = await self.multi_modal_agent.process_multi_modal(inputs)
|
||||
|
||||
assert result['result_id'].startswith('multi_')
|
||||
assert 'text' in result['modalities_processed']
|
||||
assert 'integration' in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_sensor_only_processing(self):
|
||||
"""Test processing with only sensor input"""
|
||||
inputs = {'sensor_data': self.sample_sensor_data}
|
||||
|
||||
result = await self.multi_modal_agent.process_multi_modal(inputs)
|
||||
|
||||
assert result['result_id'].startswith('multi_')
|
||||
assert 'sensor' in result['modalities_processed']
|
||||
assert 'integration' in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_full_multi_modal_processing(self):
|
||||
"""Test processing with all modalities"""
|
||||
inputs = {
|
||||
'image': self.sample_image,
|
||||
'text': self.sample_text,
|
||||
'sensor_data': self.sample_sensor_data
|
||||
}
|
||||
|
||||
result = await self.multi_modal_agent.process_multi_modal(inputs)
|
||||
|
||||
assert result['result_id'].startswith('multi_')
|
||||
assert len(result['modalities_processed']) == 3
|
||||
assert 'vision' in result['modalities_processed']
|
||||
assert 'text' in result['modalities_processed']
|
||||
assert 'sensor' in result['modalities_processed']
|
||||
assert 'integration' in result
|
||||
assert 'cross_modal_insights' in result['integration']
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_cross_modal_insights(self):
|
||||
"""Test cross-modal insight generation"""
|
||||
inputs = {
|
||||
'image': self.sample_image,
|
||||
'text': self.sample_text,
|
||||
'vision_processing_type': 'object_detection'
|
||||
}
|
||||
|
||||
result = await self.multi_modal_agent.process_multi_modal(inputs)
|
||||
|
||||
assert 'cross_modal_insights' in result['integration']
|
||||
assert len(result['integration']['cross_modal_insights']) > 0
|
||||
|
||||
class TestContextIntegration:
|
||||
"""Test context integration with vision"""
|
||||
|
||||
def setup_method(self):
|
||||
self.context_integration = MockContextIntegration()
|
||||
self.vision_processor = MockVisionProcessor()
|
||||
self.sample_image = b'sample_image_data'
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_basic_context_integration(self):
|
||||
"""Test basic context integration"""
|
||||
vision_result = await self.vision_processor.process_image(self.sample_image)
|
||||
context_data = {
|
||||
'location': 'intersection',
|
||||
'time_of_day': 'daytime',
|
||||
'weather': 'clear'
|
||||
}
|
||||
|
||||
result = await self.context_integration.integrate_context(vision_result, context_data)
|
||||
|
||||
assert result['context_id'].startswith('ctx_')
|
||||
assert 'vision_result' in result
|
||||
assert 'context_data' in result
|
||||
assert 'enhanced_understanding' in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_location_context(self):
|
||||
"""Test location-based context integration"""
|
||||
vision_result = await self.vision_processor.process_image(self.sample_image, 'object_detection')
|
||||
context_data = {
|
||||
'location': 'intersection',
|
||||
'traffic_flow': 'moderate'
|
||||
}
|
||||
|
||||
result = await self.context_integration.integrate_context(vision_result, context_data)
|
||||
|
||||
assert 'enhanced_understanding' in result
|
||||
assert 'contextual_insights' in result['enhanced_understanding']
|
||||
assert any('traffic' in insight for insight in result['enhanced_understanding']['contextual_insights'])
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_time_context(self):
|
||||
"""Test time-based context integration"""
|
||||
vision_result = await self.vision_processor.process_image(self.sample_image)
|
||||
context_data = {
|
||||
'time_of_day': 'night',
|
||||
'lighting_conditions': 'low'
|
||||
}
|
||||
|
||||
result = await self.context_integration.integrate_context(vision_result, context_data)
|
||||
|
||||
assert 'enhanced_understanding' in result
|
||||
assert 'confidence_boost' in result['enhanced_understanding']
|
||||
assert result['enhanced_understanding']['confidence_boost'] < 0 # Night time penalty
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_context_history_tracking(self):
|
||||
"""Test context history tracking"""
|
||||
for i in range(3):
|
||||
vision_result = await self.vision_processor.process_image(self.sample_image)
|
||||
context_data = {
|
||||
'location': f'location_{i}',
|
||||
'timestamp': datetime.utcnow().isoformat()
|
||||
}
|
||||
await self.context_integration.integrate_context(vision_result, context_data)
|
||||
|
||||
assert len(self.context_integration.context_history) == 3
|
||||
for context in self.context_integration.context_history:
|
||||
assert context['context_id'].startswith('ctx_')
|
||||
|
||||
class TestVisualReasoning:
|
||||
"""Test visual reasoning capabilities"""
|
||||
|
||||
def setup_method(self):
|
||||
self.vision_processor = MockVisionProcessor()
|
||||
self.multi_modal_agent = MockMultiModalAgent()
|
||||
self.sample_image = b'sample_image_data'
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_visual_scene_understanding(self):
|
||||
"""Test visual scene understanding"""
|
||||
result = await self.vision_processor.process_image(self.sample_image, 'scene_analysis')
|
||||
|
||||
assert 'analysis' in result
|
||||
assert 'scene_type' in result['analysis']
|
||||
assert 'elements' in result['analysis']
|
||||
assert 'complexity' in result['analysis']
|
||||
|
||||
# Verify scene understanding
|
||||
scene = result['analysis']
|
||||
assert len(scene['elements']) > 0
|
||||
assert scene['complexity'] in ['low', 'medium', 'high']
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_object_relationships(self):
|
||||
"""Test understanding object relationships"""
|
||||
result = await self.vision_processor.process_image(self.sample_image, 'object_detection')
|
||||
|
||||
assert 'analysis' in result
|
||||
assert 'objects' in result['analysis']
|
||||
|
||||
objects = result['analysis']['objects']
|
||||
if len(objects) > 1:
|
||||
# Mock relationship analysis
|
||||
relationships = []
|
||||
for i, obj1 in enumerate(objects):
|
||||
for obj2 in objects[i+1:]:
|
||||
if obj1['class'] == 'person' and obj2['class'] == 'car':
|
||||
relationships.append('person_near_car')
|
||||
|
||||
assert len(relationships) >= 0
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_spatial_reasoning(self):
|
||||
"""Test spatial reasoning"""
|
||||
result = await self.vision_processor.process_image(self.sample_image, 'object_detection')
|
||||
|
||||
assert 'analysis' in result
|
||||
assert 'objects' in result['analysis']
|
||||
|
||||
objects = result['analysis']['objects']
|
||||
for obj in objects:
|
||||
assert 'bbox' in obj
|
||||
assert len(obj['bbox']) == 4 # [x1, y1, x2, y2]
|
||||
|
||||
# Verify bbox coordinates
|
||||
x1, y1, x2, y2 = obj['bbox']
|
||||
assert x2 > x1
|
||||
assert y2 > y1
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_temporal_reasoning(self):
|
||||
"""Test temporal reasoning (changes over time)"""
|
||||
# Simulate processing multiple images over time
|
||||
results = []
|
||||
for i in range(3):
|
||||
result = await self.vision_processor.process_image(self.sample_image)
|
||||
results.append(result)
|
||||
await asyncio.sleep(0.01) # Small delay
|
||||
|
||||
# Analyze temporal changes
|
||||
if len(results) > 1:
|
||||
# Mock temporal analysis
|
||||
changes = []
|
||||
for i in range(1, len(results)):
|
||||
if results[i]['analysis'] != results[i-1]['analysis']:
|
||||
changes.append(f"Change detected at step {i}")
|
||||
|
||||
# Should have some analysis of changes
|
||||
assert len(results) == 3
|
||||
|
||||
class TestPerformanceMetrics:
|
||||
"""Test performance metrics for vision processing"""
|
||||
|
||||
def setup_method(self):
|
||||
self.vision_processor = MockVisionProcessor()
|
||||
self.sample_image = b'sample_image_data'
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_processing_speed(self):
|
||||
"""Test image processing speed"""
|
||||
start_time = datetime.utcnow()
|
||||
|
||||
result = await self.vision_processor.process_image(self.sample_image)
|
||||
|
||||
end_time = datetime.utcnow()
|
||||
processing_time = (end_time - start_time).total_seconds()
|
||||
|
||||
assert processing_time < 2.0 # Should process within 2 seconds
|
||||
assert result['image_id'].startswith('img_')
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_batch_processing(self):
|
||||
"""Test batch image processing"""
|
||||
images = [self.sample_image] * 5
|
||||
|
||||
start_time = datetime.utcnow()
|
||||
results = []
|
||||
for image in images:
|
||||
result = await self.vision_processor.process_image(image)
|
||||
results.append(result)
|
||||
end_time = datetime.utcnow()
|
||||
|
||||
total_time = (end_time - start_time).total_seconds()
|
||||
avg_time = total_time / len(images)
|
||||
|
||||
assert len(results) == 5
|
||||
assert avg_time < 1.0 # Average should be under 1 second per image
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_memory_usage(self):
|
||||
"""Test memory usage during processing"""
|
||||
import psutil
|
||||
import os
|
||||
|
||||
process = psutil.Process(os.getpid())
|
||||
memory_before = process.memory_info().rss
|
||||
|
||||
# Process multiple images
|
||||
for i in range(10):
|
||||
await self.vision_processor.process_image(self.sample_image)
|
||||
|
||||
memory_after = process.memory_info().rss
|
||||
memory_increase = memory_after - memory_before
|
||||
|
||||
# Memory increase should be reasonable (less than 100MB)
|
||||
assert memory_increase < 100 * 1024 * 1024 # 100MB in bytes
|
||||
|
||||
# Integration tests
|
||||
class TestVisionIntegration:
|
||||
"""Integration tests for vision system"""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_end_to_end_vision_pipeline(self):
|
||||
"""Test complete vision processing pipeline"""
|
||||
vision_processor = MockVisionProcessor()
|
||||
multi_modal_agent = MockMultiModalAgent()
|
||||
context_integration = MockContextIntegration()
|
||||
|
||||
# Step 1: Process image with object detection
|
||||
image_result = await vision_processor.process_image(b'test_image', 'object_detection')
|
||||
|
||||
# Step 2: Integrate with context
|
||||
context_data = {
|
||||
'location': 'urban_intersection',
|
||||
'time': 'daytime',
|
||||
'purpose': 'traffic_monitoring'
|
||||
}
|
||||
|
||||
context_result = await context_integration.integrate_context(image_result, context_data)
|
||||
|
||||
# Step 3: Multi-modal processing
|
||||
multi_modal_inputs = {
|
||||
'image': b'test_image',
|
||||
'text': 'Traffic monitoring report',
|
||||
'sensor_data': {'type': 'camera', 'status': 'active'}
|
||||
}
|
||||
|
||||
multi_modal_result = await multi_modal_agent.process_multi_modal(multi_modal_inputs)
|
||||
|
||||
# Verify pipeline
|
||||
assert image_result['image_id'].startswith('img_')
|
||||
assert context_result['context_id'].startswith('ctx_')
|
||||
assert multi_modal_result['result_id'].startswith('multi_')
|
||||
assert 'objects' in image_result['analysis']
|
||||
assert 'enhanced_understanding' in context_result
|
||||
assert len(multi_modal_result['modalities_processed']) == 3
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_real_time_vision_processing(self):
|
||||
"""Test real-time vision processing capabilities"""
|
||||
vision_processor = MockVisionProcessor()
|
||||
|
||||
# Simulate real-time processing
|
||||
processing_times = []
|
||||
for i in range(10):
|
||||
start_time = datetime.utcnow()
|
||||
await vision_processor.process_image(f'frame_{i}'.encode())
|
||||
end_time = datetime.utcnow()
|
||||
processing_times.append((end_time - start_time).total_seconds())
|
||||
|
||||
avg_time = sum(processing_times) / len(processing_times)
|
||||
max_time = max(processing_times)
|
||||
|
||||
# Real-time constraints
|
||||
assert avg_time < 0.5 # Average under 500ms
|
||||
assert max_time < 1.0 # Max under 1 second
|
||||
assert len(processing_times) == 10
|
||||
|
||||
if __name__ == '__main__':
|
||||
pytest.main([__file__])
|
||||
100
tests/run_all_phase_tests.py
Normal file
100
tests/run_all_phase_tests.py
Normal file
@@ -0,0 +1,100 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Run all phase tests for agent systems implementation
|
||||
"""
|
||||
|
||||
import subprocess
|
||||
import sys
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
def run_phase_tests():
|
||||
"""Run tests for all phases"""
|
||||
base_dir = Path(__file__).parent
|
||||
phases = ['phase1', 'phase2', 'phase3', 'phase4', 'phase5']
|
||||
|
||||
results = {}
|
||||
|
||||
for phase in phases:
|
||||
phase_dir = base_dir / phase
|
||||
print(f"\n{'='*60}")
|
||||
print(f"Running {phase.upper()} Tests")
|
||||
print(f"{'='*60}")
|
||||
|
||||
if not phase_dir.exists():
|
||||
print(f"❌ {phase} directory not found")
|
||||
results[phase] = {'status': 'skipped', 'reason': 'directory_not_found'}
|
||||
continue
|
||||
|
||||
# Find test files
|
||||
test_files = list(phase_dir.glob('test_*.py'))
|
||||
|
||||
if not test_files:
|
||||
print(f"❌ No test files found in {phase}")
|
||||
results[phase] = {'status': 'skipped', 'reason': 'no_test_files'}
|
||||
continue
|
||||
|
||||
# Run tests for this phase
|
||||
phase_results = {}
|
||||
for test_file in test_files:
|
||||
print(f"\n🔹 Running {test_file.name}")
|
||||
try:
|
||||
result = subprocess.run([
|
||||
sys.executable, '-m', 'pytest',
|
||||
str(test_file),
|
||||
'-v',
|
||||
'--tb=short'
|
||||
], capture_output=True, text=True, cwd=base_dir)
|
||||
|
||||
phase_results[test_file.name] = {
|
||||
'returncode': result.returncode,
|
||||
'stdout': result.stdout,
|
||||
'stderr': result.stderr
|
||||
}
|
||||
|
||||
if result.returncode == 0:
|
||||
print(f"✅ {test_file.name} - PASSED")
|
||||
else:
|
||||
print(f"❌ {test_file.name} - FAILED")
|
||||
print(f"Error: {result.stderr}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error running {test_file.name}: {e}")
|
||||
phase_results[test_file.name] = {
|
||||
'returncode': -1,
|
||||
'stdout': '',
|
||||
'stderr': str(e)
|
||||
}
|
||||
|
||||
results[phase] = {
|
||||
'status': 'completed',
|
||||
'tests': phase_results,
|
||||
'total_tests': len(test_files)
|
||||
}
|
||||
|
||||
# Print summary
|
||||
print(f"\n{'='*60}")
|
||||
print("PHASE TEST SUMMARY")
|
||||
print(f"{'='*60}")
|
||||
|
||||
total_phases = len(phases)
|
||||
completed_phases = sum(1 for phase in results.values() if phase['status'] == 'completed')
|
||||
skipped_phases = sum(1 for phase in results.values() if phase['status'] == 'skipped')
|
||||
|
||||
print(f"Total Phases: {total_phases}")
|
||||
print(f"Completed: {completed_phases}")
|
||||
print(f"Skipped: {skipped_phases}")
|
||||
|
||||
for phase, result in results.items():
|
||||
print(f"\n{phase.upper()}:")
|
||||
if result['status'] == 'completed':
|
||||
passed = sum(1 for test in result['tests'].values() if test['returncode'] == 0)
|
||||
failed = sum(1 for test in result['tests'].values() if test['returncode'] != 0)
|
||||
print(f" Tests: {result['total_tests']} (✅ {passed}, ❌ {failed})")
|
||||
else:
|
||||
print(f" Status: {result['status']} ({result.get('reason', 'unknown')})")
|
||||
|
||||
return results
|
||||
|
||||
if __name__ == '__main__':
|
||||
run_phase_tests()
|
||||
Reference in New Issue
Block a user