v1.0 - Version stable: multi-PC, détection UI-DETR-1, 3 modes exécution
- Frontend v4 accessible sur réseau local (192.168.1.40) - Ports ouverts: 3002 (frontend), 5001 (backend), 5004 (dashboard) - Ollama GPU fonctionnel - Self-healing interactif - Dashboard confiance Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Dom
175
examples/test_phase7_learning.py
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175
examples/test_phase7_learning.py
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#!/usr/bin/env python3
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"""Test Phase 7 - Learning System"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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import logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def test_imports():
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logger.info("\n=== Testing Imports ===")
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try:
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from core.learning.learning_manager import LearningManager, WorkflowStats
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logger.info("✓ LearningManager imported")
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from core.learning.feedback_processor import FeedbackProcessor, FeedbackType
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logger.info("✓ FeedbackProcessor imported")
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from core.models.workflow_graph import LearningState
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logger.info("✓ LearningState imported")
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return True
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except Exception as e:
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logger.error(f"✗ Import failed: {e}")
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return False
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def test_learning_manager():
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logger.info("\n=== Testing LearningManager ===")
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try:
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from core.learning.learning_manager import LearningManager
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from core.models.workflow_graph import Workflow, LearningState
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manager = LearningManager()
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logger.info("✓ LearningManager created")
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# Create test workflow
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workflow = Workflow(
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id="test_workflow_1",
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name="Test Workflow",
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learning_state=LearningState.OBSERVATION
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)
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manager.register_workflow(workflow)
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logger.info(f"✓ Workflow registered: {workflow.id}")
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# Test state transitions
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logger.info("\n Testing state transitions:")
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# OBSERVATION → COACHING (need 5 observations with confidence > 0.90)
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for i in range(5):
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manager.record_observation(workflow.id)
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manager.workflows[workflow.id].confidence_scores.append(0.92)
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state = manager.get_workflow_state(workflow.id)
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logger.info(f" After 5 observations: {state.value}")
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# COACHING → AUTO_CANDIDATE (need 10 executions with success > 0.90)
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for i in range(10):
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manager.record_execution(workflow.id, success=True, confidence=0.93)
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state = manager.get_workflow_state(workflow.id)
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logger.info(f" After 10 successful executions: {state.value}")
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# AUTO_CANDIDATE → AUTO_CONFIRMED (need 20 executions with success > 0.95)
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for i in range(10):
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manager.record_execution(workflow.id, success=True, confidence=0.96)
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state = manager.get_workflow_state(workflow.id)
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logger.info(f" After 20 total executions: {state.value}")
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stats = manager.get_workflow_stats(workflow.id)
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logger.info(f" Final stats: success_rate={stats.success_rate:.2f}, avg_confidence={stats.avg_confidence:.2f}")
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return True
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except Exception as e:
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logger.error(f"✗ LearningManager test failed: {e}", exc_info=True)
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return False
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def test_feedback_processor():
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logger.info("\n=== Testing FeedbackProcessor ===")
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try:
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from core.learning.feedback_processor import FeedbackProcessor, FeedbackType
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processor = FeedbackProcessor()
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logger.info("✓ FeedbackProcessor created")
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# Process different types of feedback
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result = processor.process_feedback(
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workflow_id="test_workflow_1",
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execution_id="exec_1",
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feedback_type=FeedbackType.CORRECT,
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confidence=0.95
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)
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logger.info(f"✓ CORRECT feedback processed: {len(result['suggestions'])} suggestions")
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result = processor.process_feedback(
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workflow_id="test_workflow_1",
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execution_id="exec_2",
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feedback_type=FeedbackType.INCORRECT,
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confidence=0.75,
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comment="Wrong button clicked"
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)
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logger.info(f"✓ INCORRECT feedback processed: {len(result['suggestions'])} suggestions")
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# Get stats
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stats = processor.get_feedback_stats("test_workflow_1")
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logger.info(f"✓ Feedback stats: {stats['total']} total, accuracy={stats['accuracy']:.2f}")
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return True
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except Exception as e:
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logger.error(f"✗ FeedbackProcessor test failed: {e}", exc_info=True)
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return False
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def test_rollback():
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logger.info("\n=== Testing Rollback Mechanism ===")
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try:
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from core.learning.learning_manager import LearningManager
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from core.models.workflow_graph import Workflow, LearningState
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manager = LearningManager()
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# Create workflow in AUTO_CONFIRMED state
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workflow = Workflow(
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id="test_workflow_rollback",
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name="Rollback Test",
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learning_state=LearningState.AUTO_CONFIRMED
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)
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manager.register_workflow(workflow)
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manager.workflows[workflow.id].learning_state = LearningState.AUTO_CONFIRMED
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manager.workflows[workflow.id].execution_count = 25
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logger.info(f" Initial state: {manager.get_workflow_state(workflow.id).value}")
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# Simulate confidence drop
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for i in range(10):
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manager.record_execution(workflow.id, success=False, confidence=0.70)
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state = manager.get_workflow_state(workflow.id)
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logger.info(f" After confidence drop: {state.value}")
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if state == LearningState.COACHING:
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logger.info("✓ Rollback triggered successfully")
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return True
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else:
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logger.warning("✗ Rollback not triggered")
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return False
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except Exception as e:
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logger.error(f"✗ Rollback test failed: {e}", exc_info=True)
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return False
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def main():
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logger.info("=" * 60)
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logger.info("Phase 7 - Learning System Tests")
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logger.info("=" * 60)
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tests = [
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test_imports,
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test_learning_manager,
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test_feedback_processor,
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test_rollback
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]
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results = []
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for test in tests:
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try:
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result = test()
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results.append(result)
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except Exception as e:
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logger.error(f"Test {test.__name__} crashed: {e}", exc_info=True)
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results.append(False)
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passed = sum(results)
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logger.info(f"\n{'='*60}\nResults: {passed}/{len(results)} tests passed\n{'='*60}")
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return 0 if passed == len(results) else 1
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if __name__ == '__main__':
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sys.exit(main())
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