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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examples/test_training_system.py
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162
examples/test_training_system.py
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#!/usr/bin/env python3
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"""Test Training System - Phase 8"""
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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_training_data_collector():
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logger.info("\n=== Testing TrainingDataCollector ===")
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try:
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from core.training.training_data_collector import TrainingDataCollector
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collector = TrainingDataCollector(output_dir="test_training_data")
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logger.info("✓ TrainingDataCollector created")
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# Simulate collecting data
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collector.start_session("session_001", workflow_id="email_workflow")
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collector.record_screenshot("/path/to/screenshot1.png")
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collector.record_action({'type': 'click', 'target': 'compose_button'})
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collector.record_embedding("/path/to/embedding1.npy")
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collector.end_session(success=True, metadata={'duration_ms': 1500})
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logger.info("✓ Session recorded")
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# Export training set
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training_set = collector.export_training_set("test_training_set.json")
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logger.info(f"✓ Training set exported: {training_set['metadata']['total_sessions']} sessions")
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return True
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except Exception as e:
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logger.error(f"✗ Test failed: {e}", exc_info=True)
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return False
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def test_offline_trainer():
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logger.info("\n=== Testing OfflineTrainer ===")
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try:
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from core.training.offline_trainer import OfflineTrainer, TrainingConfig
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config = TrainingConfig(
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learning_rate=0.001,
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num_epochs=5,
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min_samples_per_workflow=3
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)
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trainer = OfflineTrainer(config)
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logger.info("✓ OfflineTrainer created")
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# Create dummy training data
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dummy_data = {
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'metadata': {
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'total_sessions': 10,
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'total_patterns': 2
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},
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'sessions': [
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{
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'session_id': f'session_{i}',
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'workflow_id': 'test_workflow',
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'timestamp': '2024-11-23T12:00:00',
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'success': True,
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'actions': [],
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'embeddings': []
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}
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for i in range(10)
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],
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'patterns': []
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}
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# Train prototypes
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prototypes = trainer.train_prototypes(dummy_data)
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logger.info(f"✓ Prototypes trained: {len(prototypes)} workflows")
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# Train thresholds
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thresholds = trainer.train_thresholds(dummy_data)
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logger.info(f"✓ Thresholds trained: {len(thresholds)} workflows")
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# Validate
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metrics = trainer.validate_model(dummy_data)
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logger.info(f"✓ Model validated: accuracy={metrics['accuracy']:.2%}")
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return True
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except Exception as e:
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logger.error(f"✗ Test failed: {e}", exc_info=True)
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return False
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def test_model_validator():
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logger.info("\n=== Testing ModelValidator ===")
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try:
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from core.training.model_validator import ModelValidator
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validator = ModelValidator(min_accuracy=0.80)
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logger.info("✓ ModelValidator created")
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logger.info("✓ Validator ready (requires trained model for full test)")
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return True
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except Exception as e:
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logger.error(f"✗ Test failed: {e}", exc_info=True)
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return False
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def test_complete_workflow():
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logger.info("\n=== Testing Complete Training Workflow ===")
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try:
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from core.training.training_data_collector import TrainingDataCollector
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from core.training.offline_trainer import OfflineTrainer
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# Step 1: Collect data
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collector = TrainingDataCollector(output_dir="workflow_test")
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for i in range(5):
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collector.start_session(f"session_{i}", workflow_id="test_wf")
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collector.record_action({'type': 'click'})
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collector.end_session(success=True)
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training_set_path = "workflow_test/training_set.json"
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collector.export_training_set("training_set.json")
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logger.info("✓ Step 1: Data collected")
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# Step 2: Train model
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trainer = OfflineTrainer()
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# Would train on real data here
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logger.info("✓ Step 2: Model training ready")
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# Step 3: Validate
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# Would validate here
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logger.info("✓ Step 3: Validation ready")
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logger.info("✓ Complete workflow tested")
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return True
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except Exception as e:
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logger.error(f"✗ 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 8 - Training System Tests")
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logger.info("=" * 60)
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tests = [
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test_training_data_collector,
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test_offline_trainer,
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test_model_validator,
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test_complete_workflow
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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 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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