207 lines
7.3 KiB
Python
207 lines
7.3 KiB
Python
#!/usr/bin/env python3
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"""
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Test simple du gestionnaire d'apprentissage sans pytest
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"""
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import sys
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import tempfile
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import shutil
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from pathlib import Path
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from datetime import datetime
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import numpy as np
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# Ajouter le répertoire au path
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sys.path.insert(0, str(Path(__file__).parent))
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from geniusia2.core.learning_manager import LearningManager
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from geniusia2.core.models import Action
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from geniusia2.core.embeddings_manager import EmbeddingsManager
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from geniusia2.core.logger import Logger
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def test_learning_manager():
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"""Test basique du gestionnaire d'apprentissage."""
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print("=" * 60)
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print("Test du Gestionnaire d'Apprentissage")
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print("=" * 60)
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# Créer des répertoires temporaires
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temp_dir = tempfile.mkdtemp()
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profiles_dir = Path(temp_dir) / "profiles"
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logs_dir = Path(temp_dir) / "logs"
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index_dir = Path(temp_dir) / "index"
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keys_dir = Path(temp_dir) / "keys"
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profiles_dir.mkdir(parents=True, exist_ok=True)
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logs_dir.mkdir(parents=True, exist_ok=True)
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index_dir.mkdir(parents=True, exist_ok=True)
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keys_dir.mkdir(parents=True, exist_ok=True)
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try:
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# Créer les composants
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print("\n1. Initialisation des composants...")
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embeddings_manager = EmbeddingsManager(
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model_name="ViT-B-32",
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index_path=str(index_dir),
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device="cpu"
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)
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print(" ✓ EmbeddingsManager créé")
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logger = Logger(
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log_dir=str(logs_dir),
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key_path=str(keys_dir)
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)
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print(" ✓ Logger créé")
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config = {
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"thresholds": {
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"autopilot_observations": 20,
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"autopilot_concordance": 0.95,
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"confidence_min": 0.90,
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"rollback_confidence": 0.85
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}
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}
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learning_manager = LearningManager(
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embeddings_manager=embeddings_manager,
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logger=logger,
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config=config,
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profiles_path=str(profiles_dir)
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)
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print(" ✓ LearningManager créé")
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print(f" Mode initial: {learning_manager.mode}")
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# Test 2: Observer une action
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print("\n2. Test observation d'action...")
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action = Action(
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action_type="click",
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target_element="valider_button",
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bbox=(100, 100, 50, 30),
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confidence=0.9,
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embedding=np.random.rand(512).astype(np.float32),
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timestamp=datetime.now(),
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window_title="Test Window"
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)
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learning_manager.observe(action)
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print(f" ✓ Action observée")
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print(f" Nombre de tâches: {len(learning_manager.tasks)}")
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task_id = list(learning_manager.tasks.keys())[0]
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task = learning_manager.tasks[task_id]
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print(f" Task ID: {task_id}")
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print(f" Observations: {task.observation_count}")
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print(f" Mode: {task.mode}")
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# Test 3: Transition Shadow → Assist
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print("\n3. Test transition Shadow → Assist...")
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for i in range(4):
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learning_manager.observe(action)
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print(f" ✓ 5 observations effectuées")
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print(f" Mode actuel: {task.mode}")
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assert task.mode == "assist", "Devrait être en mode Assist"
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print(" ✓ Transition réussie vers mode Assist")
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# Test 4: Calcul de confiance
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print("\n4. Test calcul de confiance...")
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confidence = learning_manager.calculate_confidence(
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vision_conf=0.9,
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llm_score=0.8,
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task_id=task_id
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)
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print(f" ✓ Confiance calculée: {confidence:.3f}")
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expected = 0.6 * 0.9 + 0.3 * 0.8 + 0.1 * 0.0
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print(f" Attendu: {expected:.3f}")
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assert abs(confidence - expected) < 0.01, "Calcul de confiance incorrect"
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print(" ✓ Formule correcte (0.6×vision + 0.3×llm + 0.1×historique)")
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# Test 5: Confirmation d'action
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print("\n5. Test confirmation d'action...")
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learning_manager.confirm_action({
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"type": "accept",
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"task_id": task_id
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})
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print(f" ✓ Action acceptée")
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print(f" Taux de concordance: {task.concordance_rate:.2%}")
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assert task.concordance_rate == 1.0, "Concordance devrait être 100%"
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# Test 6: Évaluation de tâche
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print("\n6. Test évaluation de tâche...")
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metrics = learning_manager.evaluate_task(task_id)
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print(f" ✓ Métriques obtenues:")
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print(f" - Observations: {metrics['observation_count']}")
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print(f" - Concordance: {metrics['concordance_rate']:.2%}")
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print(f" - Corrections: {metrics['correction_count']}")
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print(f" - Mode: {metrics['mode']}")
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# Test 7: Statistiques globales
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print("\n7. Test statistiques globales...")
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stats = learning_manager.get_task_stats()
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print(f" ✓ Statistiques:")
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print(f" - Total tâches: {stats['total_tasks']}")
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print(f" - Shadow: {stats['shadow_tasks']}")
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print(f" - Assist: {stats['assist_tasks']}")
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print(f" - Auto: {stats['auto_tasks']}")
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# Test 8: Vérification critères Autopilot
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print("\n8. Test critères Autopilot...")
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# Observer 15 fois de plus (total 20)
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for i in range(15):
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learning_manager.observe(action)
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learning_manager.confirm_action({
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"type": "accept",
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"task_id": task_id
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})
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print(f" ✓ 20 observations avec 100% concordance")
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should_auto = learning_manager.should_transition_to_auto(task_id)
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print(f" Éligible pour Autopilot: {should_auto}")
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if should_auto:
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print(" ✓ Critères Autopilot remplis (≥20 obs, ≥95% concordance)")
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# Test 9: Sauvegarde et chargement
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print("\n9. Test sauvegarde et chargement...")
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learning_manager._save_profile(task_id)
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print(f" ✓ Profil sauvegardé")
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# Créer un nouveau gestionnaire pour charger
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learning_manager2 = LearningManager(
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embeddings_manager=embeddings_manager,
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logger=logger,
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config=config,
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profiles_path=str(profiles_dir)
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)
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print(f" ✓ Nouveau gestionnaire créé")
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print(f" Tâches chargées: {len(learning_manager2.tasks)}")
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if task_id in learning_manager2.tasks:
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loaded_task = learning_manager2.tasks[task_id]
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print(f" ✓ Tâche rechargée:")
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print(f" - Observations: {loaded_task.observation_count}")
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print(f" - Mode: {loaded_task.mode}")
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print(f" - Concordance: {loaded_task.concordance_rate:.2%}")
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print("\n" + "=" * 60)
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print("✓ TOUS LES TESTS RÉUSSIS!")
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print("=" * 60)
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return True
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except Exception as e:
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print(f"\n✗ ERREUR: {e}")
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import traceback
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traceback.print_exc()
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return False
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finally:
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# Nettoyer
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shutil.rmtree(temp_dir)
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print("\n✓ Fichiers temporaires nettoyés")
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if __name__ == "__main__":
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success = test_learning_manager()
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sys.exit(0 if success else 1)
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