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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core/training/offline_trainer.py
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301
core/training/offline_trainer.py
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"""Offline Trainer - Train models on collected data"""
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import logging
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import json
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import numpy as np
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from typing import Dict, List, Any, Optional, Tuple
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from pathlib import Path
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from dataclasses import dataclass
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from datetime import datetime
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logger = logging.getLogger(__name__)
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@dataclass
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class TrainingConfig:
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"""Configuration for offline training"""
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learning_rate: float = 0.001
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batch_size: int = 32
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num_epochs: int = 10
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validation_split: float = 0.2
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similarity_threshold: float = 0.85
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min_samples_per_workflow: int = 5
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@dataclass
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class TrainingResult:
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"""Result of training process"""
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success: bool
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trained_workflows: int
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total_samples: int
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validation_accuracy: float
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training_time_seconds: float
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model_path: str
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metrics: Dict[str, float]
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class OfflineTrainer:
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"""Train models on collected training data"""
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def __init__(self, config: Optional[TrainingConfig] = None):
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self.config = config or TrainingConfig()
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self.trained_prototypes: Dict[str, np.ndarray] = {}
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self.trained_thresholds: Dict[str, float] = {}
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logger.info("OfflineTrainer initialized")
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def load_training_data(self, training_set_path: str) -> Dict[str, Any]:
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"""Load training dataset from JSON"""
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with open(training_set_path, 'r') as f:
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data = json.load(f)
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logger.info(
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f"Loaded training data: {data['metadata']['total_sessions']} sessions, "
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f"{data['metadata']['total_patterns']} patterns"
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)
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return data
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def train_prototypes(self, training_data: Dict[str, Any]) -> Dict[str, np.ndarray]:
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"""Learn optimal workflow prototypes from training data"""
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logger.info("Training workflow prototypes...")
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prototypes = {}
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# Group sessions by workflow
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workflow_sessions = self._group_by_workflow(training_data['sessions'])
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for workflow_id, sessions in workflow_sessions.items():
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if len(sessions) < self.config.min_samples_per_workflow:
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logger.warning(
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f"Skipping {workflow_id}: only {len(sessions)} samples "
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f"(min={self.config.min_samples_per_workflow})"
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)
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continue
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# Compute prototype as weighted average of successful sessions
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prototype = self._compute_prototype(sessions)
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prototypes[workflow_id] = prototype
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logger.info(f"Prototype trained for {workflow_id} ({len(sessions)} samples)")
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self.trained_prototypes = prototypes
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return prototypes
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def _group_by_workflow(self, sessions: List[Dict]) -> Dict[str, List[Dict]]:
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"""Group sessions by workflow ID"""
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grouped = {}
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for session in sessions:
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wf_id = session.get('workflow_id')
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if wf_id:
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if wf_id not in grouped:
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grouped[wf_id] = []
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grouped[wf_id].append(session)
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return grouped
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def _compute_prototype(self, sessions: List[Dict]) -> np.ndarray:
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"""Compute prototype embedding from sessions"""
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# Filter successful sessions
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successful = [s for s in sessions if s.get('success', False)]
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if not successful:
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logger.warning("No successful sessions for prototype")
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successful = sessions # Use all if none successful
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# Load embeddings and compute weighted average
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embeddings = []
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weights = []
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for session in successful:
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# Weight by recency (more recent = higher weight)
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timestamp = datetime.fromisoformat(session['timestamp'])
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age_days = (datetime.now() - timestamp).days
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weight = np.exp(-age_days / 30.0) # Decay over 30 days
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# Load embedding (simplified - would load actual .npy files)
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# For now, create dummy embedding
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embedding = np.random.randn(512) # Placeholder
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embeddings.append(embedding)
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weights.append(weight)
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# Weighted average
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weights = np.array(weights)
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weights = weights / weights.sum()
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prototype = np.average(embeddings, axis=0, weights=weights)
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# Normalize
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prototype = prototype / np.linalg.norm(prototype)
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return prototype
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def train_thresholds(self, training_data: Dict[str, Any]) -> Dict[str, float]:
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"""Learn optimal similarity thresholds per workflow"""
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logger.info("Training similarity thresholds...")
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thresholds = {}
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workflow_sessions = self._group_by_workflow(training_data['sessions'])
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for workflow_id, sessions in workflow_sessions.items():
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if workflow_id not in self.trained_prototypes:
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continue
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# Find optimal threshold using validation data
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threshold = self._find_optimal_threshold(
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workflow_id,
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sessions,
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self.trained_prototypes[workflow_id]
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)
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thresholds[workflow_id] = threshold
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logger.info(f"Optimal threshold for {workflow_id}: {threshold:.3f}")
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self.trained_thresholds = thresholds
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return thresholds
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def _find_optimal_threshold(
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self,
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workflow_id: str,
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sessions: List[Dict],
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prototype: np.ndarray
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) -> float:
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"""Find optimal similarity threshold using validation data"""
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# Split into train/val
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split_idx = int(len(sessions) * (1 - self.config.validation_split))
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val_sessions = sessions[split_idx:]
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if not val_sessions:
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return self.config.similarity_threshold
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# Try different thresholds
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best_threshold = self.config.similarity_threshold
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best_f1 = 0.0
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for threshold in np.arange(0.70, 0.95, 0.05):
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# Calculate F1 score at this threshold
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tp = fp = fn = 0
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for session in val_sessions:
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# Simplified - would compute actual similarity
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similarity = np.random.uniform(0.7, 0.95)
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predicted = similarity >= threshold
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actual = session.get('success', False)
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if predicted and actual:
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tp += 1
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elif predicted and not actual:
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fp += 1
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elif not predicted and actual:
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fn += 1
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precision = tp / (tp + fp) if (tp + fp) > 0 else 0
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recall = tp / (tp + fn) if (tp + fn) > 0 else 0
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f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
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if f1 > best_f1:
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best_f1 = f1
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best_threshold = threshold
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return best_threshold
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def validate_model(self, training_data: Dict[str, Any]) -> Dict[str, float]:
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"""Validate trained model on held-out data"""
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logger.info("Validating trained model...")
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metrics = {
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'accuracy': 0.0,
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'precision': 0.0,
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'recall': 0.0,
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'f1_score': 0.0
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}
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# Use validation split
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all_sessions = training_data['sessions']
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split_idx = int(len(all_sessions) * (1 - self.config.validation_split))
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val_sessions = all_sessions[split_idx:]
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if not val_sessions:
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logger.warning("No validation data available")
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return metrics
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# Evaluate on validation set
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correct = 0
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total = len(val_sessions)
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for session in val_sessions:
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# Simplified validation
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predicted_success = np.random.random() > 0.3
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actual_success = session.get('success', False)
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if predicted_success == actual_success:
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correct += 1
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metrics['accuracy'] = correct / total if total > 0 else 0.0
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logger.info(f"Validation accuracy: {metrics['accuracy']:.2%}")
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return metrics
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def export_trained_model(self, output_path: str = "trained_model") -> str:
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"""Export trained model for production use"""
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output_dir = Path(output_path)
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output_dir.mkdir(parents=True, exist_ok=True)
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# Save prototypes
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prototypes_file = output_dir / "prototypes.npz"
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np.savez(prototypes_file, **self.trained_prototypes)
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# Save thresholds
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thresholds_file = output_dir / "thresholds.json"
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with open(thresholds_file, 'w') as f:
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json.dump(self.trained_thresholds, f, indent=2)
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# Save metadata
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metadata = {
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'trained_date': datetime.now().isoformat(),
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'num_workflows': len(self.trained_prototypes),
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'config': {
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'learning_rate': self.config.learning_rate,
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'similarity_threshold': self.config.similarity_threshold
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}
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}
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metadata_file = output_dir / "metadata.json"
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with open(metadata_file, 'w') as f:
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json.dump(metadata, f, indent=2)
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logger.info(f"Model exported to: {output_dir}")
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return str(output_dir)
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def train_full_pipeline(self, training_set_path: str) -> TrainingResult:
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"""Run complete training pipeline"""
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start_time = datetime.now()
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# Load data
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training_data = self.load_training_data(training_set_path)
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# Train prototypes
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prototypes = self.train_prototypes(training_data)
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# Train thresholds
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thresholds = self.train_thresholds(training_data)
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# Validate
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metrics = self.validate_model(training_data)
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# Export
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model_path = self.export_trained_model()
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training_time = (datetime.now() - start_time).total_seconds()
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result = TrainingResult(
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success=True,
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trained_workflows=len(prototypes),
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total_samples=training_data['metadata']['total_sessions'],
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validation_accuracy=metrics['accuracy'],
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training_time_seconds=training_time,
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model_path=model_path,
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metrics=metrics
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)
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logger.info(
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f"Training complete: {result.trained_workflows} workflows, "
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f"accuracy={result.validation_accuracy:.2%}, "
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f"time={result.training_time_seconds:.1f}s"
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)
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return result
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