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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36
core/training/__init__.py
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36
core/training/__init__.py
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"""
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Module d'entraînement et validation de qualité pour RPA Vision V3
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"""
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from .quality_validator import (
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TrainingQualityValidator,
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QualityReport,
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ClusterMetrics,
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ValidationResult,
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QualityValidatorConfig
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)
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from .session_analyzer import (
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SessionAnalyzer,
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SessionQualityReport,
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FrameQuality,
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TimingAnalysis,
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DuplicateAnalysis,
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SessionAnalyzerConfig
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)
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__all__ = [
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# Quality Validator
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'TrainingQualityValidator',
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'QualityReport',
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'ClusterMetrics',
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'ValidationResult',
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'QualityValidatorConfig',
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# Session Analyzer
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'SessionAnalyzer',
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'SessionQualityReport',
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'FrameQuality',
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'TimingAnalysis',
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'DuplicateAnalysis',
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'SessionAnalyzerConfig'
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]
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228
core/training/model_validator.py
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228
core/training/model_validator.py
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"""Model Validator - Validate trained models before production deployment"""
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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, Tuple
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from pathlib import Path
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from dataclasses import dataclass, asdict
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logger = logging.getLogger(__name__)
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@dataclass
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class ValidationReport:
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"""Validation report for trained model"""
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model_path: str
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validation_date: str
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overall_accuracy: float
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precision: float
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recall: float
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f1_score: float
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per_workflow_metrics: Dict[str, Dict[str, float]]
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recommendation: str # "DEPLOY", "RETRAIN", "COLLECT_MORE_DATA"
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issues: List[str]
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def to_dict(self) -> Dict:
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return asdict(self)
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class ModelValidator:
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"""Validate trained models before production deployment"""
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def __init__(self, min_accuracy: float = 0.85, min_samples: int = 20):
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self.min_accuracy = min_accuracy
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self.min_samples = min_samples
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logger.info(f"ModelValidator initialized (min_accuracy={min_accuracy})")
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def validate_model(
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self,
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model_path: str,
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test_data_path: str
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) -> ValidationReport:
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"""Validate trained model on test data"""
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logger.info(f"Validating model: {model_path}")
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# Load model
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model = self._load_model(model_path)
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# Load test data
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test_data = self._load_test_data(test_data_path)
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# Run validation
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metrics = self._compute_metrics(model, test_data)
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# Check for issues
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issues = self._check_issues(metrics, test_data)
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# Make recommendation
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recommendation = self._make_recommendation(metrics, issues)
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report = ValidationReport(
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model_path=model_path,
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validation_date=str(np.datetime64('now')),
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overall_accuracy=metrics['overall']['accuracy'],
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precision=metrics['overall']['precision'],
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recall=metrics['overall']['recall'],
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f1_score=metrics['overall']['f1_score'],
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per_workflow_metrics=metrics['per_workflow'],
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recommendation=recommendation,
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issues=issues
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)
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logger.info(
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f"Validation complete: accuracy={report.overall_accuracy:.2%}, "
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f"recommendation={report.recommendation}"
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)
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return report
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def _load_model(self, model_path: str) -> Dict:
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"""Load trained model"""
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model_dir = Path(model_path)
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# Load prototypes
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prototypes_file = model_dir / "prototypes.npz"
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prototypes = dict(np.load(prototypes_file))
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# Load thresholds
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thresholds_file = model_dir / "thresholds.json"
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with open(thresholds_file, 'r') as f:
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thresholds = json.load(f)
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return {
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'prototypes': prototypes,
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'thresholds': thresholds
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}
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def _load_test_data(self, test_data_path: str) -> Dict:
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"""Load test dataset"""
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with open(test_data_path, 'r') as f:
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return json.load(f)
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def _compute_metrics(self, model: Dict, test_data: Dict) -> Dict:
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"""Compute validation metrics"""
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overall_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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per_workflow_metrics = {}
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# Simplified metrics computation
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test_sessions = test_data.get('sessions', [])
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if test_sessions:
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# Overall accuracy (simplified)
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correct = sum(1 for s in test_sessions if s.get('success', False))
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overall_metrics['accuracy'] = correct / len(test_sessions)
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overall_metrics['precision'] = overall_metrics['accuracy']
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overall_metrics['recall'] = overall_metrics['accuracy']
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overall_metrics['f1_score'] = overall_metrics['accuracy']
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# Per-workflow metrics
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workflow_groups = {}
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for session in test_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 workflow_groups:
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workflow_groups[wf_id] = []
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workflow_groups[wf_id].append(session)
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for wf_id, sessions in workflow_groups.items():
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correct = sum(1 for s in sessions if s.get('success', False))
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accuracy = correct / len(sessions) if sessions else 0.0
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per_workflow_metrics[wf_id] = {
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'accuracy': accuracy,
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'samples': len(sessions),
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'success_count': correct
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}
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return {
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'overall': overall_metrics,
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'per_workflow': per_workflow_metrics
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}
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def _check_issues(self, metrics: Dict, test_data: Dict) -> List[str]:
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"""Check for potential issues"""
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issues = []
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# Check overall accuracy
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if metrics['overall']['accuracy'] < self.min_accuracy:
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issues.append(
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f"Overall accuracy ({metrics['overall']['accuracy']:.2%}) "
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f"below minimum ({self.min_accuracy:.2%})"
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)
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# Check per-workflow performance
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for wf_id, wf_metrics in metrics['per_workflow'].items():
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if wf_metrics['samples'] < self.min_samples:
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issues.append(
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f"Workflow {wf_id}: insufficient test samples "
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f"({wf_metrics['samples']} < {self.min_samples})"
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)
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if wf_metrics['accuracy'] < 0.70:
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issues.append(
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f"Workflow {wf_id}: low accuracy ({wf_metrics['accuracy']:.2%})"
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)
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return issues
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def _make_recommendation(self, metrics: Dict, issues: List[str]) -> str:
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"""Make deployment recommendation"""
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accuracy = metrics['overall']['accuracy']
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if accuracy >= 0.90 and not issues:
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return "DEPLOY"
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elif accuracy >= self.min_accuracy and len(issues) <= 2:
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return "DEPLOY_WITH_MONITORING"
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elif accuracy >= 0.70:
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return "RETRAIN"
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else:
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return "COLLECT_MORE_DATA"
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def compare_with_baseline(
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self,
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new_model_path: str,
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baseline_model_path: str,
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test_data_path: str
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) -> Dict[str, Any]:
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"""Compare new model with baseline"""
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logger.info("Comparing models...")
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# Validate both models
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new_report = self.validate_model(new_model_path, test_data_path)
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baseline_report = self.validate_model(baseline_model_path, test_data_path)
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# Calculate improvements
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accuracy_delta = new_report.overall_accuracy - baseline_report.overall_accuracy
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f1_delta = new_report.f1_score - baseline_report.f1_score
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comparison = {
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'new_model': new_report.to_dict(),
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'baseline_model': baseline_report.to_dict(),
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'improvements': {
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'accuracy_delta': accuracy_delta,
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'f1_delta': f1_delta,
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'is_better': accuracy_delta > 0
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},
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'recommendation': (
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"DEPLOY_NEW_MODEL" if accuracy_delta > 0.05
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else "KEEP_BASELINE" if accuracy_delta < -0.02
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else "EQUIVALENT"
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)
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}
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logger.info(
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f"Comparison complete: accuracy_delta={accuracy_delta:+.2%}, "
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f"recommendation={comparison['recommendation']}"
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)
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return comparison
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def save_report(self, report: ValidationReport, output_path: str) -> None:
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"""Save validation report to file"""
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with open(output_path, 'w') as f:
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json.dump(report.to_dict(), f, indent=2)
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logger.info(f"Validation report saved: {output_path}")
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301
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%}")
|
||||
return metrics
|
||||
|
||||
def export_trained_model(self, output_path: str = "trained_model") -> str:
|
||||
"""Export trained model for production use"""
|
||||
output_dir = Path(output_path)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save prototypes
|
||||
prototypes_file = output_dir / "prototypes.npz"
|
||||
np.savez(prototypes_file, **self.trained_prototypes)
|
||||
|
||||
# Save thresholds
|
||||
thresholds_file = output_dir / "thresholds.json"
|
||||
with open(thresholds_file, 'w') as f:
|
||||
json.dump(self.trained_thresholds, f, indent=2)
|
||||
|
||||
# Save metadata
|
||||
metadata = {
|
||||
'trained_date': datetime.now().isoformat(),
|
||||
'num_workflows': len(self.trained_prototypes),
|
||||
'config': {
|
||||
'learning_rate': self.config.learning_rate,
|
||||
'similarity_threshold': self.config.similarity_threshold
|
||||
}
|
||||
}
|
||||
|
||||
metadata_file = output_dir / "metadata.json"
|
||||
with open(metadata_file, 'w') as f:
|
||||
json.dump(metadata, f, indent=2)
|
||||
|
||||
logger.info(f"Model exported to: {output_dir}")
|
||||
return str(output_dir)
|
||||
|
||||
def train_full_pipeline(self, training_set_path: str) -> TrainingResult:
|
||||
"""Run complete training pipeline"""
|
||||
start_time = datetime.now()
|
||||
|
||||
# Load data
|
||||
training_data = self.load_training_data(training_set_path)
|
||||
|
||||
# Train prototypes
|
||||
prototypes = self.train_prototypes(training_data)
|
||||
|
||||
# Train thresholds
|
||||
thresholds = self.train_thresholds(training_data)
|
||||
|
||||
# Validate
|
||||
metrics = self.validate_model(training_data)
|
||||
|
||||
# Export
|
||||
model_path = self.export_trained_model()
|
||||
|
||||
training_time = (datetime.now() - start_time).total_seconds()
|
||||
|
||||
result = TrainingResult(
|
||||
success=True,
|
||||
trained_workflows=len(prototypes),
|
||||
total_samples=training_data['metadata']['total_sessions'],
|
||||
validation_accuracy=metrics['accuracy'],
|
||||
training_time_seconds=training_time,
|
||||
model_path=model_path,
|
||||
metrics=metrics
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Training complete: {result.trained_workflows} workflows, "
|
||||
f"accuracy={result.validation_accuracy:.2%}, "
|
||||
f"time={result.training_time_seconds:.1f}s"
|
||||
)
|
||||
|
||||
return result
|
||||
642
core/training/quality_validator.py
Normal file
642
core/training/quality_validator.py
Normal file
@@ -0,0 +1,642 @@
|
||||
"""
|
||||
TrainingQualityValidator - Validation de la qualité des workflows entraînés
|
||||
|
||||
Ce module évalue la qualité des workflows générés à partir des sessions d'entraînement
|
||||
en calculant des métriques de clustering, détectant les outliers, et effectuant
|
||||
une validation croisée.
|
||||
|
||||
Métriques calculées:
|
||||
- Score de silhouette (cohésion et séparation des clusters)
|
||||
- Détection d'outliers par méthode IQR
|
||||
- Validation croisée avec holdout 20%
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import List, Dict, Optional, Tuple, Any
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
import numpy as np
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
try:
|
||||
from sklearn.metrics import silhouette_score, silhouette_samples
|
||||
from sklearn.cluster import DBSCAN
|
||||
SKLEARN_AVAILABLE = True
|
||||
except ImportError:
|
||||
SKLEARN_AVAILABLE = False
|
||||
logger.warning("sklearn non installé. Installer avec: pip install scikit-learn")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Dataclasses
|
||||
# =============================================================================
|
||||
|
||||
@dataclass
|
||||
class ClusterMetrics:
|
||||
"""Métriques de qualité d'un cluster"""
|
||||
cluster_id: str
|
||||
silhouette_score: float # Score de silhouette (-1 à 1)
|
||||
cohesion: float # Cohésion intra-cluster (distance moyenne au centroïde)
|
||||
separation: float # Séparation inter-cluster (distance au cluster le plus proche)
|
||||
sample_count: int # Nombre d'échantillons dans le cluster
|
||||
is_sufficient: bool # True si sample_count >= 3
|
||||
outlier_count: int = 0 # Nombre d'outliers détectés
|
||||
|
||||
@property
|
||||
def quality_score(self) -> float:
|
||||
"""Score de qualité global du cluster (0-1)"""
|
||||
if not self.is_sufficient:
|
||||
return 0.0
|
||||
# Normaliser silhouette de [-1,1] vers [0,1]
|
||||
normalized_silhouette = (self.silhouette_score + 1) / 2
|
||||
# Pénaliser si trop d'outliers
|
||||
outlier_penalty = max(0, 1 - (self.outlier_count / max(self.sample_count, 1)))
|
||||
return normalized_silhouette * outlier_penalty
|
||||
|
||||
|
||||
@dataclass
|
||||
class ValidationResult:
|
||||
"""Résultat de la validation croisée"""
|
||||
accuracy: float # Précision de matching (0-1)
|
||||
total_samples: int # Nombre total d'échantillons testés
|
||||
correct_matches: int # Nombre de matchs corrects
|
||||
incorrect_matches: int # Nombre de matchs incorrects
|
||||
no_match_count: int # Nombre de non-matchs
|
||||
confusion_matrix: Dict[str, Dict[str, int]] = field(default_factory=dict)
|
||||
|
||||
@property
|
||||
def is_valid(self) -> bool:
|
||||
"""True si la précision est suffisante (>= 80%)"""
|
||||
return self.accuracy >= 0.80
|
||||
|
||||
|
||||
@dataclass
|
||||
class QualityReport:
|
||||
"""Rapport complet de qualité d'un workflow"""
|
||||
workflow_id: str
|
||||
overall_score: float # Score global (0-1)
|
||||
cluster_metrics: Dict[str, ClusterMetrics] = field(default_factory=dict)
|
||||
outlier_indices: List[int] = field(default_factory=list)
|
||||
validation_result: Optional[ValidationResult] = None
|
||||
recommendations: List[str] = field(default_factory=list)
|
||||
is_production_ready: bool = False
|
||||
created_at: datetime = field(default_factory=datetime.now)
|
||||
|
||||
# Seuils configurables
|
||||
min_quality_score: float = 0.7
|
||||
min_observations_per_node: int = 3
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Sérialiser en dictionnaire"""
|
||||
return {
|
||||
"workflow_id": self.workflow_id,
|
||||
"overall_score": self.overall_score,
|
||||
"is_production_ready": self.is_production_ready,
|
||||
"cluster_count": len(self.cluster_metrics),
|
||||
"outlier_count": len(self.outlier_indices),
|
||||
"validation_accuracy": self.validation_result.accuracy if self.validation_result else None,
|
||||
"recommendations": self.recommendations,
|
||||
"created_at": self.created_at.isoformat()
|
||||
}
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Configuration
|
||||
# =============================================================================
|
||||
|
||||
@dataclass
|
||||
class QualityValidatorConfig:
|
||||
"""Configuration du validateur de qualité"""
|
||||
# Seuils de qualité
|
||||
min_silhouette_score: float = 0.5 # Score silhouette minimum acceptable
|
||||
min_cluster_quality: float = 0.7 # Qualité minimum par cluster
|
||||
min_observations_per_node: int = 3 # Observations minimum par node
|
||||
|
||||
# Détection d'outliers (IQR)
|
||||
iqr_multiplier: float = 1.5 # Multiplicateur IQR pour outliers
|
||||
max_outlier_ratio: float = 0.3 # Ratio max d'outliers accepté
|
||||
|
||||
# Validation croisée
|
||||
holdout_ratio: float = 0.2 # Ratio de données pour validation
|
||||
min_validation_accuracy: float = 0.8 # Précision minimum requise
|
||||
|
||||
# Matching
|
||||
similarity_threshold: float = 0.85 # Seuil de similarité pour matching
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Validateur Principal
|
||||
# =============================================================================
|
||||
|
||||
class TrainingQualityValidator:
|
||||
"""
|
||||
Validateur de qualité des workflows entraînés.
|
||||
|
||||
Évalue la qualité des workflows en calculant:
|
||||
- Métriques de clustering (silhouette, cohésion, séparation)
|
||||
- Détection d'outliers par méthode IQR
|
||||
- Validation croisée avec holdout
|
||||
|
||||
Example:
|
||||
>>> validator = TrainingQualityValidator()
|
||||
>>> report = validator.validate_workflow(workflow, observations)
|
||||
>>> if report.is_production_ready:
|
||||
... print("Workflow prêt pour production!")
|
||||
"""
|
||||
|
||||
def __init__(self, config: Optional[QualityValidatorConfig] = None):
|
||||
"""
|
||||
Initialiser le validateur.
|
||||
|
||||
Args:
|
||||
config: Configuration du validateur (utilise défaut si None)
|
||||
"""
|
||||
self.config = config or QualityValidatorConfig()
|
||||
logger.info(f"TrainingQualityValidator initialisé (min_quality={self.config.min_cluster_quality})")
|
||||
|
||||
def validate_workflow(
|
||||
self,
|
||||
workflow: Any,
|
||||
observations: List[Any],
|
||||
embeddings: Optional[np.ndarray] = None,
|
||||
labels: Optional[np.ndarray] = None
|
||||
) -> QualityReport:
|
||||
"""
|
||||
Valider la qualité d'un workflow complet.
|
||||
|
||||
Args:
|
||||
workflow: Workflow à valider
|
||||
observations: Liste des ScreenStates utilisés pour l'entraînement
|
||||
embeddings: Matrice d'embeddings (calculée si None)
|
||||
labels: Labels de cluster pour chaque embedding
|
||||
|
||||
Returns:
|
||||
QualityReport avec métriques et recommandations
|
||||
"""
|
||||
workflow_id = getattr(workflow, 'workflow_id', 'unknown')
|
||||
logger.info(f"Validation du workflow {workflow_id} avec {len(observations)} observations")
|
||||
|
||||
recommendations = []
|
||||
cluster_metrics = {}
|
||||
outlier_indices = []
|
||||
|
||||
# Extraire embeddings si non fournis
|
||||
if embeddings is None:
|
||||
embeddings, labels = self._extract_embeddings(workflow, observations)
|
||||
|
||||
if embeddings is None or len(embeddings) == 0:
|
||||
return QualityReport(
|
||||
workflow_id=workflow_id,
|
||||
overall_score=0.0,
|
||||
recommendations=["Aucun embedding disponible pour validation"],
|
||||
is_production_ready=False
|
||||
)
|
||||
|
||||
# 1. Calculer métriques de clustering
|
||||
if labels is not None and len(np.unique(labels)) > 1:
|
||||
cluster_metrics = self.compute_cluster_metrics(embeddings, labels)
|
||||
else:
|
||||
recommendations.append("Pas assez de clusters pour calculer les métriques")
|
||||
|
||||
# 2. Détecter les outliers
|
||||
outlier_indices = self.detect_outliers(embeddings, labels)
|
||||
if len(outlier_indices) > len(embeddings) * self.config.max_outlier_ratio:
|
||||
recommendations.append(
|
||||
f"Trop d'outliers détectés ({len(outlier_indices)}/{len(embeddings)}). "
|
||||
"Considérer un ré-entraînement avec plus de données."
|
||||
)
|
||||
|
||||
# 3. Vérifier observations par node
|
||||
nodes = getattr(workflow, 'nodes', [])
|
||||
for node in nodes:
|
||||
obs_count = getattr(node, 'observation_count', 0)
|
||||
if obs_count < self.config.min_observations_per_node:
|
||||
recommendations.append(
|
||||
f"Node '{getattr(node, 'node_id', 'unknown')}' a seulement {obs_count} observations "
|
||||
f"(minimum: {self.config.min_observations_per_node})"
|
||||
)
|
||||
|
||||
# 4. Validation croisée
|
||||
validation_result = None
|
||||
if len(observations) >= 5: # Minimum pour validation croisée
|
||||
validation_result = self.cross_validate(
|
||||
workflow, observations, embeddings, labels
|
||||
)
|
||||
if validation_result and not validation_result.is_valid:
|
||||
recommendations.append(
|
||||
f"Précision de validation croisée insuffisante: {validation_result.accuracy:.2%} "
|
||||
f"(minimum: {self.config.min_validation_accuracy:.0%})"
|
||||
)
|
||||
|
||||
# 5. Calculer score global
|
||||
overall_score = self._compute_overall_score(
|
||||
cluster_metrics, outlier_indices, validation_result, len(embeddings)
|
||||
)
|
||||
|
||||
# 6. Déterminer si prêt pour production
|
||||
is_production_ready = (
|
||||
overall_score >= self.config.min_cluster_quality and
|
||||
len(outlier_indices) <= len(embeddings) * self.config.max_outlier_ratio and
|
||||
(validation_result is None or validation_result.is_valid) and
|
||||
all(
|
||||
getattr(node, 'observation_count', 0) >= self.config.min_observations_per_node
|
||||
for node in nodes
|
||||
)
|
||||
)
|
||||
|
||||
if is_production_ready:
|
||||
recommendations.append("✓ Workflow prêt pour production")
|
||||
|
||||
report = QualityReport(
|
||||
workflow_id=workflow_id,
|
||||
overall_score=overall_score,
|
||||
cluster_metrics=cluster_metrics,
|
||||
outlier_indices=outlier_indices,
|
||||
validation_result=validation_result,
|
||||
recommendations=recommendations,
|
||||
is_production_ready=is_production_ready
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Validation terminée: score={overall_score:.3f}, "
|
||||
f"production_ready={is_production_ready}"
|
||||
)
|
||||
|
||||
return report
|
||||
|
||||
def compute_cluster_metrics(
|
||||
self,
|
||||
embeddings: np.ndarray,
|
||||
labels: np.ndarray
|
||||
) -> Dict[str, ClusterMetrics]:
|
||||
"""
|
||||
Calculer les métriques de qualité pour chaque cluster.
|
||||
|
||||
Args:
|
||||
embeddings: Matrice d'embeddings (n_samples, n_features)
|
||||
labels: Labels de cluster pour chaque embedding
|
||||
|
||||
Returns:
|
||||
Dict {cluster_id: ClusterMetrics}
|
||||
"""
|
||||
if not SKLEARN_AVAILABLE:
|
||||
logger.warning("sklearn non disponible, métriques limitées")
|
||||
return {}
|
||||
|
||||
metrics = {}
|
||||
unique_labels = np.unique(labels)
|
||||
|
||||
# Filtrer les labels de bruit (-1 pour DBSCAN)
|
||||
valid_labels = unique_labels[unique_labels >= 0]
|
||||
|
||||
if len(valid_labels) < 2:
|
||||
logger.warning("Pas assez de clusters pour calculer silhouette")
|
||||
return {}
|
||||
|
||||
# Score de silhouette global
|
||||
try:
|
||||
global_silhouette = silhouette_score(embeddings, labels)
|
||||
sample_silhouettes = silhouette_samples(embeddings, labels)
|
||||
except Exception as e:
|
||||
logger.warning(f"Erreur calcul silhouette: {e}")
|
||||
global_silhouette = 0.0
|
||||
sample_silhouettes = np.zeros(len(embeddings))
|
||||
|
||||
# Calculer métriques par cluster
|
||||
for label in valid_labels:
|
||||
cluster_mask = labels == label
|
||||
cluster_embeddings = embeddings[cluster_mask]
|
||||
cluster_silhouettes = sample_silhouettes[cluster_mask]
|
||||
|
||||
# Centroïde du cluster
|
||||
centroid = np.mean(cluster_embeddings, axis=0)
|
||||
|
||||
# Cohésion: distance moyenne au centroïde
|
||||
distances_to_centroid = np.linalg.norm(
|
||||
cluster_embeddings - centroid, axis=1
|
||||
)
|
||||
cohesion = np.mean(distances_to_centroid)
|
||||
|
||||
# Séparation: distance au centroïde du cluster le plus proche
|
||||
separation = float('inf')
|
||||
for other_label in valid_labels:
|
||||
if other_label != label:
|
||||
other_mask = labels == other_label
|
||||
other_centroid = np.mean(embeddings[other_mask], axis=0)
|
||||
dist = np.linalg.norm(centroid - other_centroid)
|
||||
separation = min(separation, dist)
|
||||
|
||||
if separation == float('inf'):
|
||||
separation = 0.0
|
||||
|
||||
# Détecter outliers dans ce cluster
|
||||
outliers = self._detect_cluster_outliers(cluster_embeddings, centroid)
|
||||
|
||||
cluster_id = f"cluster_{label}"
|
||||
metrics[cluster_id] = ClusterMetrics(
|
||||
cluster_id=cluster_id,
|
||||
silhouette_score=float(np.mean(cluster_silhouettes)),
|
||||
cohesion=float(cohesion),
|
||||
separation=float(separation),
|
||||
sample_count=int(np.sum(cluster_mask)),
|
||||
is_sufficient=int(np.sum(cluster_mask)) >= self.config.min_observations_per_node,
|
||||
outlier_count=len(outliers)
|
||||
)
|
||||
|
||||
logger.debug(f"Métriques calculées pour {len(metrics)} clusters")
|
||||
return metrics
|
||||
|
||||
def detect_outliers(
|
||||
self,
|
||||
embeddings: np.ndarray,
|
||||
labels: Optional[np.ndarray] = None
|
||||
) -> List[int]:
|
||||
"""
|
||||
Détecter les outliers par méthode IQR.
|
||||
|
||||
Args:
|
||||
embeddings: Matrice d'embeddings
|
||||
labels: Labels de cluster (optionnel)
|
||||
|
||||
Returns:
|
||||
Liste des indices des outliers
|
||||
"""
|
||||
outlier_indices = []
|
||||
|
||||
if labels is None:
|
||||
# Sans labels, calculer distance au centroïde global
|
||||
centroid = np.mean(embeddings, axis=0)
|
||||
distances = np.linalg.norm(embeddings - centroid, axis=1)
|
||||
outlier_indices = self._iqr_outliers(distances)
|
||||
else:
|
||||
# Avec labels, détecter outliers par cluster
|
||||
unique_labels = np.unique(labels)
|
||||
for label in unique_labels:
|
||||
if label < 0: # Ignorer bruit DBSCAN
|
||||
continue
|
||||
|
||||
cluster_mask = labels == label
|
||||
cluster_indices = np.where(cluster_mask)[0]
|
||||
cluster_embeddings = embeddings[cluster_mask]
|
||||
|
||||
if len(cluster_embeddings) < 3:
|
||||
continue
|
||||
|
||||
centroid = np.mean(cluster_embeddings, axis=0)
|
||||
local_outliers = self._detect_cluster_outliers(
|
||||
cluster_embeddings, centroid
|
||||
)
|
||||
|
||||
# Convertir indices locaux en indices globaux
|
||||
for local_idx in local_outliers:
|
||||
outlier_indices.append(int(cluster_indices[local_idx]))
|
||||
|
||||
logger.debug(f"Détecté {len(outlier_indices)} outliers sur {len(embeddings)} embeddings")
|
||||
return outlier_indices
|
||||
|
||||
def _detect_cluster_outliers(
|
||||
self,
|
||||
cluster_embeddings: np.ndarray,
|
||||
centroid: np.ndarray
|
||||
) -> List[int]:
|
||||
"""Détecter outliers dans un cluster spécifique."""
|
||||
distances = np.linalg.norm(cluster_embeddings - centroid, axis=1)
|
||||
return self._iqr_outliers(distances)
|
||||
|
||||
def _iqr_outliers(self, values: np.ndarray) -> List[int]:
|
||||
"""
|
||||
Détecter outliers par méthode IQR.
|
||||
|
||||
Un point est outlier si: value > Q3 + 1.5*IQR
|
||||
"""
|
||||
q1 = np.percentile(values, 25)
|
||||
q3 = np.percentile(values, 75)
|
||||
iqr = q3 - q1
|
||||
|
||||
threshold = q3 + self.config.iqr_multiplier * iqr
|
||||
outlier_mask = values > threshold
|
||||
|
||||
return list(np.where(outlier_mask)[0])
|
||||
|
||||
def cross_validate(
|
||||
self,
|
||||
workflow: Any,
|
||||
observations: List[Any],
|
||||
embeddings: Optional[np.ndarray] = None,
|
||||
labels: Optional[np.ndarray] = None,
|
||||
holdout_ratio: Optional[float] = None
|
||||
) -> ValidationResult:
|
||||
"""
|
||||
Effectuer une validation croisée.
|
||||
|
||||
Args:
|
||||
workflow: Workflow à valider
|
||||
observations: Liste des observations
|
||||
embeddings: Matrice d'embeddings
|
||||
labels: Labels de cluster
|
||||
holdout_ratio: Ratio de holdout (défaut: config)
|
||||
|
||||
Returns:
|
||||
ValidationResult avec précision et détails
|
||||
"""
|
||||
holdout_ratio = holdout_ratio or self.config.holdout_ratio
|
||||
|
||||
if embeddings is None or labels is None:
|
||||
embeddings, labels = self._extract_embeddings(workflow, observations)
|
||||
|
||||
if embeddings is None or len(embeddings) < 5:
|
||||
return ValidationResult(
|
||||
accuracy=0.0,
|
||||
total_samples=0,
|
||||
correct_matches=0,
|
||||
incorrect_matches=0,
|
||||
no_match_count=0
|
||||
)
|
||||
|
||||
n_samples = len(embeddings)
|
||||
n_holdout = max(1, int(n_samples * holdout_ratio))
|
||||
|
||||
# Sélection aléatoire des indices de holdout
|
||||
np.random.seed(42) # Reproductibilité
|
||||
holdout_indices = np.random.choice(n_samples, n_holdout, replace=False)
|
||||
train_indices = np.array([i for i in range(n_samples) if i not in holdout_indices])
|
||||
|
||||
# Calculer prototypes par cluster depuis données d'entraînement
|
||||
train_embeddings = embeddings[train_indices]
|
||||
train_labels = labels[train_indices]
|
||||
|
||||
prototypes = {}
|
||||
for label in np.unique(train_labels):
|
||||
if label < 0:
|
||||
continue
|
||||
mask = train_labels == label
|
||||
prototypes[label] = np.mean(train_embeddings[mask], axis=0)
|
||||
|
||||
# Tester sur holdout
|
||||
correct = 0
|
||||
incorrect = 0
|
||||
no_match = 0
|
||||
confusion = {}
|
||||
|
||||
for idx in holdout_indices:
|
||||
query = embeddings[idx]
|
||||
true_label = labels[idx]
|
||||
|
||||
if true_label < 0:
|
||||
continue
|
||||
|
||||
# Trouver le prototype le plus proche
|
||||
best_label = None
|
||||
best_similarity = -1
|
||||
|
||||
for label, prototype in prototypes.items():
|
||||
similarity = self._cosine_similarity(query, prototype)
|
||||
if similarity > best_similarity:
|
||||
best_similarity = similarity
|
||||
best_label = label
|
||||
|
||||
# Évaluer le match
|
||||
if best_similarity < self.config.similarity_threshold:
|
||||
no_match += 1
|
||||
elif best_label == true_label:
|
||||
correct += 1
|
||||
else:
|
||||
incorrect += 1
|
||||
# Enregistrer dans matrice de confusion
|
||||
true_key = f"cluster_{true_label}"
|
||||
pred_key = f"cluster_{best_label}"
|
||||
if true_key not in confusion:
|
||||
confusion[true_key] = {}
|
||||
confusion[true_key][pred_key] = confusion[true_key].get(pred_key, 0) + 1
|
||||
|
||||
total = correct + incorrect + no_match
|
||||
accuracy = correct / total if total > 0 else 0.0
|
||||
|
||||
result = ValidationResult(
|
||||
accuracy=accuracy,
|
||||
total_samples=total,
|
||||
correct_matches=correct,
|
||||
incorrect_matches=incorrect,
|
||||
no_match_count=no_match,
|
||||
confusion_matrix=confusion
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Validation croisée: {accuracy:.2%} précision "
|
||||
f"({correct}/{total} corrects)"
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
def _extract_embeddings(
|
||||
self,
|
||||
workflow: Any,
|
||||
observations: List[Any]
|
||||
) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
|
||||
"""Extraire embeddings et labels depuis workflow et observations."""
|
||||
# Cette méthode sera connectée au StateEmbeddingBuilder existant
|
||||
try:
|
||||
from core.embedding.state_embedding_builder import StateEmbeddingBuilder
|
||||
|
||||
builder = StateEmbeddingBuilder()
|
||||
embeddings = []
|
||||
labels = []
|
||||
|
||||
nodes = getattr(workflow, 'nodes', [])
|
||||
node_id_to_label = {
|
||||
node.node_id: idx for idx, node in enumerate(nodes)
|
||||
}
|
||||
|
||||
for obs in observations:
|
||||
try:
|
||||
state_emb = builder.build(obs, compute_embeddings=True)
|
||||
vector = state_emb.get_vector()
|
||||
embeddings.append(vector)
|
||||
|
||||
# Trouver le label du node correspondant
|
||||
node_id = getattr(obs, 'matched_node_id', None)
|
||||
if node_id and node_id in node_id_to_label:
|
||||
labels.append(node_id_to_label[node_id])
|
||||
else:
|
||||
labels.append(-1) # Non assigné
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Erreur extraction embedding: {e}")
|
||||
|
||||
if embeddings:
|
||||
return np.array(embeddings), np.array(labels)
|
||||
|
||||
except ImportError:
|
||||
logger.warning("StateEmbeddingBuilder non disponible")
|
||||
|
||||
return None, None
|
||||
|
||||
def _cosine_similarity(self, a: np.ndarray, b: np.ndarray) -> float:
|
||||
"""Calculer similarité cosinus entre deux vecteurs."""
|
||||
norm_a = np.linalg.norm(a)
|
||||
norm_b = np.linalg.norm(b)
|
||||
if norm_a == 0 or norm_b == 0:
|
||||
return 0.0
|
||||
return float(np.dot(a, b) / (norm_a * norm_b))
|
||||
|
||||
def _compute_overall_score(
|
||||
self,
|
||||
cluster_metrics: Dict[str, ClusterMetrics],
|
||||
outlier_indices: List[int],
|
||||
validation_result: Optional[ValidationResult],
|
||||
total_samples: int
|
||||
) -> float:
|
||||
"""Calculer le score de qualité global."""
|
||||
scores = []
|
||||
|
||||
# Score moyen des clusters
|
||||
if cluster_metrics:
|
||||
cluster_scores = [m.quality_score for m in cluster_metrics.values()]
|
||||
scores.append(np.mean(cluster_scores))
|
||||
|
||||
# Pénalité outliers
|
||||
if total_samples > 0:
|
||||
outlier_ratio = len(outlier_indices) / total_samples
|
||||
outlier_score = max(0, 1 - outlier_ratio / self.config.max_outlier_ratio)
|
||||
scores.append(outlier_score)
|
||||
|
||||
# Score de validation
|
||||
if validation_result:
|
||||
scores.append(validation_result.accuracy)
|
||||
|
||||
return float(np.mean(scores)) if scores else 0.0
|
||||
|
||||
def get_config(self) -> QualityValidatorConfig:
|
||||
"""Récupérer la configuration actuelle."""
|
||||
return self.config
|
||||
|
||||
def set_config(self, config: QualityValidatorConfig) -> None:
|
||||
"""Mettre à jour la configuration."""
|
||||
self.config = config
|
||||
logger.info("Configuration du validateur mise à jour")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Fonctions utilitaires
|
||||
# =============================================================================
|
||||
|
||||
def create_validator(
|
||||
min_quality: float = 0.7,
|
||||
min_observations: int = 3
|
||||
) -> TrainingQualityValidator:
|
||||
"""
|
||||
Créer un validateur avec configuration personnalisée.
|
||||
|
||||
Args:
|
||||
min_quality: Score de qualité minimum
|
||||
min_observations: Observations minimum par node
|
||||
|
||||
Returns:
|
||||
TrainingQualityValidator configuré
|
||||
"""
|
||||
config = QualityValidatorConfig(
|
||||
min_cluster_quality=min_quality,
|
||||
min_observations_per_node=min_observations
|
||||
)
|
||||
return TrainingQualityValidator(config)
|
||||
546
core/training/session_analyzer.py
Normal file
546
core/training/session_analyzer.py
Normal file
@@ -0,0 +1,546 @@
|
||||
"""
|
||||
SessionAnalyzer - Analyse de la qualité des sessions d'entraînement
|
||||
|
||||
Ce module analyse:
|
||||
- Qualité des screenshots (contraste, flou, artefacts)
|
||||
- Cohérence du timing des actions
|
||||
- Détection de doublons
|
||||
- Génération de recommandations
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import List, Dict, Optional, Any, Tuple
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Dataclasses
|
||||
# =============================================================================
|
||||
|
||||
@dataclass
|
||||
class FrameQuality:
|
||||
"""Qualité d'une frame individuelle"""
|
||||
frame_index: int
|
||||
contrast_score: float # Score de contraste (0-1)
|
||||
sharpness_score: float # Score de netteté (0-1)
|
||||
artifact_score: float # Score d'artefacts (0=bon, 1=mauvais)
|
||||
overall_score: float # Score global (0-1)
|
||||
issues: List[str] = field(default_factory=list)
|
||||
|
||||
@property
|
||||
def is_acceptable(self) -> bool:
|
||||
return self.overall_score >= 0.6
|
||||
|
||||
|
||||
@dataclass
|
||||
class TimingAnalysis:
|
||||
"""Analyse du timing des actions"""
|
||||
mean_interval: float # Intervalle moyen entre actions (ms)
|
||||
std_interval: float # Écart-type des intervalles
|
||||
outlier_indices: List[int] # Indices des transitions problématiques
|
||||
is_consistent: bool # True si timing cohérent
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"mean_interval": self.mean_interval,
|
||||
"std_interval": self.std_interval,
|
||||
"outlier_count": len(self.outlier_indices),
|
||||
"is_consistent": self.is_consistent
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class DuplicateAnalysis:
|
||||
"""Analyse des doublons"""
|
||||
duplicate_pairs: List[Tuple[int, int]] # Paires de frames similaires
|
||||
duplicate_ratio: float # Ratio de doublons
|
||||
suggested_removal: List[int] # Indices suggérés pour suppression
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"duplicate_count": len(self.duplicate_pairs),
|
||||
"duplicate_ratio": self.duplicate_ratio,
|
||||
"suggested_removal_count": len(self.suggested_removal)
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class SessionQualityReport:
|
||||
"""Rapport complet de qualité d'une session"""
|
||||
session_id: str
|
||||
overall_score: float # Score global (0-1)
|
||||
frame_qualities: List[FrameQuality] # Qualité par frame
|
||||
timing_analysis: TimingAnalysis # Analyse du timing
|
||||
duplicate_analysis: DuplicateAnalysis # Analyse des doublons
|
||||
recommendations: List[str] # Recommandations
|
||||
is_acceptable: bool # Session acceptable
|
||||
created_at: datetime = field(default_factory=datetime.now)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"session_id": self.session_id,
|
||||
"overall_score": self.overall_score,
|
||||
"frame_count": len(self.frame_qualities),
|
||||
"acceptable_frames": sum(1 for f in self.frame_qualities if f.is_acceptable),
|
||||
"timing": self.timing_analysis.to_dict(),
|
||||
"duplicates": self.duplicate_analysis.to_dict(),
|
||||
"recommendations": self.recommendations,
|
||||
"is_acceptable": self.is_acceptable,
|
||||
"created_at": self.created_at.isoformat()
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class SessionAnalyzerConfig:
|
||||
"""Configuration de l'analyseur de session"""
|
||||
# Seuils de qualité d'image
|
||||
min_contrast: float = 0.3
|
||||
min_sharpness: float = 0.4
|
||||
max_artifact_ratio: float = 0.1
|
||||
|
||||
# Seuils de timing
|
||||
timing_outlier_factor: float = 2.0 # Facteur d'écart-type pour outliers
|
||||
min_action_interval_ms: float = 100
|
||||
max_action_interval_ms: float = 10000
|
||||
|
||||
# Seuils de doublons
|
||||
duplicate_similarity_threshold: float = 0.95
|
||||
max_duplicate_ratio: float = 0.3
|
||||
|
||||
# Seuils globaux
|
||||
min_acceptable_frames_ratio: float = 0.8
|
||||
min_overall_score: float = 0.6
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Analyseur de Session
|
||||
# =============================================================================
|
||||
|
||||
class SessionAnalyzer:
|
||||
"""
|
||||
Analyseur de qualité des sessions d'entraînement.
|
||||
|
||||
Évalue:
|
||||
- Qualité des screenshots (contraste, netteté, artefacts)
|
||||
- Cohérence du timing des actions
|
||||
- Présence de doublons
|
||||
- Génère des recommandations d'amélioration
|
||||
|
||||
Example:
|
||||
>>> analyzer = SessionAnalyzer()
|
||||
>>> report = analyzer.analyze_session(session)
|
||||
>>> if not report.is_acceptable:
|
||||
... print(report.recommendations)
|
||||
"""
|
||||
|
||||
def __init__(self, config: Optional[SessionAnalyzerConfig] = None):
|
||||
"""
|
||||
Initialiser l'analyseur.
|
||||
|
||||
Args:
|
||||
config: Configuration (utilise défaut si None)
|
||||
"""
|
||||
self.config = config or SessionAnalyzerConfig()
|
||||
logger.info("SessionAnalyzer initialisé")
|
||||
|
||||
def analyze_session(
|
||||
self,
|
||||
session: Any,
|
||||
screenshots: Optional[List[np.ndarray]] = None
|
||||
) -> SessionQualityReport:
|
||||
"""
|
||||
Analyser la qualité d'une session complète.
|
||||
|
||||
Args:
|
||||
session: RawSession à analyser
|
||||
screenshots: Images des screenshots (chargées si None)
|
||||
|
||||
Returns:
|
||||
SessionQualityReport avec métriques et recommandations
|
||||
"""
|
||||
session_id = getattr(session, 'session_id', 'unknown')
|
||||
logger.info(f"Analyse de la session {session_id}")
|
||||
|
||||
recommendations = []
|
||||
|
||||
# Charger screenshots si nécessaire
|
||||
if screenshots is None:
|
||||
screenshots = self._load_screenshots(session)
|
||||
|
||||
# 1. Analyser qualité des frames
|
||||
frame_qualities = self._analyze_frame_qualities(screenshots)
|
||||
|
||||
# Vérifier frames problématiques
|
||||
problematic_frames = [f for f in frame_qualities if not f.is_acceptable]
|
||||
if problematic_frames:
|
||||
recommendations.append(
|
||||
f"{len(problematic_frames)} frames ont une qualité insuffisante. "
|
||||
f"Considérer un ré-enregistrement avec meilleur éclairage/résolution."
|
||||
)
|
||||
|
||||
# 2. Analyser timing
|
||||
timing_analysis = self._analyze_timing(session)
|
||||
|
||||
if not timing_analysis.is_consistent:
|
||||
recommendations.append(
|
||||
f"Timing incohérent détecté ({len(timing_analysis.outlier_indices)} transitions problématiques). "
|
||||
f"Vérifier les pauses anormalement longues ou actions trop rapides."
|
||||
)
|
||||
|
||||
# 3. Analyser doublons
|
||||
duplicate_analysis = self._analyze_duplicates(screenshots)
|
||||
|
||||
if duplicate_analysis.duplicate_ratio > self.config.max_duplicate_ratio:
|
||||
recommendations.append(
|
||||
f"Trop de screenshots similaires ({duplicate_analysis.duplicate_ratio:.1%}). "
|
||||
f"Réduire la fréquence de capture ou optimiser le workflow."
|
||||
)
|
||||
|
||||
# 4. Calculer score global
|
||||
overall_score = self._compute_overall_score(
|
||||
frame_qualities, timing_analysis, duplicate_analysis
|
||||
)
|
||||
|
||||
# 5. Déterminer si acceptable
|
||||
acceptable_frames_ratio = sum(1 for f in frame_qualities if f.is_acceptable) / max(len(frame_qualities), 1)
|
||||
is_acceptable = (
|
||||
overall_score >= self.config.min_overall_score and
|
||||
acceptable_frames_ratio >= self.config.min_acceptable_frames_ratio and
|
||||
timing_analysis.is_consistent
|
||||
)
|
||||
|
||||
if is_acceptable:
|
||||
recommendations.append("✓ Session de qualité acceptable pour l'entraînement")
|
||||
else:
|
||||
recommendations.append("⚠ Session nécessite des améliorations avant utilisation")
|
||||
|
||||
report = SessionQualityReport(
|
||||
session_id=session_id,
|
||||
overall_score=overall_score,
|
||||
frame_qualities=frame_qualities,
|
||||
timing_analysis=timing_analysis,
|
||||
duplicate_analysis=duplicate_analysis,
|
||||
recommendations=recommendations,
|
||||
is_acceptable=is_acceptable
|
||||
)
|
||||
|
||||
logger.info(f"Analyse terminée: score={overall_score:.3f}, acceptable={is_acceptable}")
|
||||
return report
|
||||
|
||||
def _analyze_frame_qualities(
|
||||
self,
|
||||
screenshots: List[np.ndarray]
|
||||
) -> List[FrameQuality]:
|
||||
"""Analyser la qualité de chaque frame."""
|
||||
qualities = []
|
||||
|
||||
for i, img in enumerate(screenshots):
|
||||
if img is None:
|
||||
qualities.append(FrameQuality(
|
||||
frame_index=i,
|
||||
contrast_score=0.0,
|
||||
sharpness_score=0.0,
|
||||
artifact_score=1.0,
|
||||
overall_score=0.0,
|
||||
issues=["Image non chargée"]
|
||||
))
|
||||
continue
|
||||
|
||||
issues = []
|
||||
|
||||
# Calculer contraste
|
||||
contrast = self._compute_contrast(img)
|
||||
if contrast < self.config.min_contrast:
|
||||
issues.append(f"Faible contraste ({contrast:.2f})")
|
||||
|
||||
# Calculer netteté
|
||||
sharpness = self._compute_sharpness(img)
|
||||
if sharpness < self.config.min_sharpness:
|
||||
issues.append(f"Image floue ({sharpness:.2f})")
|
||||
|
||||
# Détecter artefacts
|
||||
artifact_score = self._detect_artifacts(img)
|
||||
if artifact_score > self.config.max_artifact_ratio:
|
||||
issues.append(f"Artefacts détectés ({artifact_score:.2f})")
|
||||
|
||||
# Score global
|
||||
overall = (contrast + sharpness + (1 - artifact_score)) / 3
|
||||
|
||||
qualities.append(FrameQuality(
|
||||
frame_index=i,
|
||||
contrast_score=contrast,
|
||||
sharpness_score=sharpness,
|
||||
artifact_score=artifact_score,
|
||||
overall_score=overall,
|
||||
issues=issues
|
||||
))
|
||||
|
||||
return qualities
|
||||
|
||||
def _compute_contrast(self, img: np.ndarray) -> float:
|
||||
"""Calculer le score de contraste d'une image."""
|
||||
if img is None or img.size == 0:
|
||||
return 0.0
|
||||
|
||||
# Convertir en niveaux de gris si nécessaire
|
||||
if len(img.shape) == 3:
|
||||
gray = np.mean(img, axis=2)
|
||||
else:
|
||||
gray = img
|
||||
|
||||
# Calculer écart-type normalisé
|
||||
std = np.std(gray)
|
||||
max_std = 127.5 # Max théorique pour image 8-bit
|
||||
|
||||
return min(1.0, std / max_std)
|
||||
|
||||
def _compute_sharpness(self, img: np.ndarray) -> float:
|
||||
"""Calculer le score de netteté d'une image."""
|
||||
if img is None or img.size == 0:
|
||||
return 0.0
|
||||
|
||||
# Convertir en niveaux de gris
|
||||
if len(img.shape) == 3:
|
||||
gray = np.mean(img, axis=2)
|
||||
else:
|
||||
gray = img
|
||||
|
||||
# Calculer Laplacien (mesure de netteté)
|
||||
# Approximation simple sans OpenCV
|
||||
laplacian = np.zeros_like(gray)
|
||||
laplacian[1:-1, 1:-1] = (
|
||||
gray[:-2, 1:-1] + gray[2:, 1:-1] +
|
||||
gray[1:-1, :-2] + gray[1:-1, 2:] -
|
||||
4 * gray[1:-1, 1:-1]
|
||||
)
|
||||
|
||||
variance = np.var(laplacian)
|
||||
|
||||
# Normaliser (valeur empirique)
|
||||
return min(1.0, variance / 500)
|
||||
|
||||
def _detect_artifacts(self, img: np.ndarray) -> float:
|
||||
"""Détecter les artefacts dans une image."""
|
||||
if img is None or img.size == 0:
|
||||
return 1.0
|
||||
|
||||
# Détecter zones uniformes anormales (compression artifacts)
|
||||
if len(img.shape) == 3:
|
||||
gray = np.mean(img, axis=2)
|
||||
else:
|
||||
gray = img
|
||||
|
||||
# Calculer gradient local
|
||||
grad_x = np.abs(np.diff(gray, axis=1))
|
||||
grad_y = np.abs(np.diff(gray, axis=0))
|
||||
|
||||
# Zones avec gradient très faible = potentiels artefacts
|
||||
low_grad_x = np.sum(grad_x < 1) / grad_x.size
|
||||
low_grad_y = np.sum(grad_y < 1) / grad_y.size
|
||||
|
||||
artifact_ratio = (low_grad_x + low_grad_y) / 2
|
||||
|
||||
# Normaliser (une certaine quantité de zones uniformes est normale)
|
||||
return max(0, artifact_ratio - 0.5) * 2
|
||||
|
||||
def _analyze_timing(self, session: Any) -> TimingAnalysis:
|
||||
"""Analyser le timing des actions."""
|
||||
events = getattr(session, 'events', [])
|
||||
|
||||
if len(events) < 2:
|
||||
return TimingAnalysis(
|
||||
mean_interval=0,
|
||||
std_interval=0,
|
||||
outlier_indices=[],
|
||||
is_consistent=True
|
||||
)
|
||||
|
||||
# Calculer intervalles entre événements
|
||||
intervals = []
|
||||
for i in range(1, len(events)):
|
||||
t1 = getattr(events[i-1], 't', 0)
|
||||
t2 = getattr(events[i], 't', 0)
|
||||
interval = (t2 - t1) * 1000 # Convertir en ms
|
||||
intervals.append(interval)
|
||||
|
||||
if not intervals:
|
||||
return TimingAnalysis(
|
||||
mean_interval=0,
|
||||
std_interval=0,
|
||||
outlier_indices=[],
|
||||
is_consistent=True
|
||||
)
|
||||
|
||||
intervals = np.array(intervals)
|
||||
mean_interval = np.mean(intervals)
|
||||
std_interval = np.std(intervals)
|
||||
|
||||
# Détecter outliers (> 2x écart-type)
|
||||
outlier_indices = []
|
||||
threshold = self.config.timing_outlier_factor * std_interval
|
||||
|
||||
for i, interval in enumerate(intervals):
|
||||
if abs(interval - mean_interval) > threshold:
|
||||
outlier_indices.append(i)
|
||||
elif interval < self.config.min_action_interval_ms:
|
||||
outlier_indices.append(i)
|
||||
elif interval > self.config.max_action_interval_ms:
|
||||
outlier_indices.append(i)
|
||||
|
||||
# Cohérent si moins de 10% d'outliers
|
||||
is_consistent = len(outlier_indices) / len(intervals) < 0.1
|
||||
|
||||
return TimingAnalysis(
|
||||
mean_interval=float(mean_interval),
|
||||
std_interval=float(std_interval),
|
||||
outlier_indices=outlier_indices,
|
||||
is_consistent=is_consistent
|
||||
)
|
||||
|
||||
def _analyze_duplicates(
|
||||
self,
|
||||
screenshots: List[np.ndarray]
|
||||
) -> DuplicateAnalysis:
|
||||
"""Analyser les doublons dans les screenshots."""
|
||||
if len(screenshots) < 2:
|
||||
return DuplicateAnalysis(
|
||||
duplicate_pairs=[],
|
||||
duplicate_ratio=0.0,
|
||||
suggested_removal=[]
|
||||
)
|
||||
|
||||
duplicate_pairs = []
|
||||
|
||||
# Comparer chaque paire de screenshots consécutifs
|
||||
for i in range(len(screenshots) - 1):
|
||||
if screenshots[i] is None or screenshots[i+1] is None:
|
||||
continue
|
||||
|
||||
similarity = self._compute_image_similarity(
|
||||
screenshots[i], screenshots[i+1]
|
||||
)
|
||||
|
||||
if similarity >= self.config.duplicate_similarity_threshold:
|
||||
duplicate_pairs.append((i, i+1))
|
||||
|
||||
# Calculer ratio
|
||||
duplicate_ratio = len(duplicate_pairs) / max(len(screenshots) - 1, 1)
|
||||
|
||||
# Suggérer suppressions (garder première de chaque groupe)
|
||||
suggested_removal = []
|
||||
for pair in duplicate_pairs:
|
||||
if pair[1] not in suggested_removal:
|
||||
suggested_removal.append(pair[1])
|
||||
|
||||
return DuplicateAnalysis(
|
||||
duplicate_pairs=duplicate_pairs,
|
||||
duplicate_ratio=duplicate_ratio,
|
||||
suggested_removal=suggested_removal
|
||||
)
|
||||
|
||||
def _compute_image_similarity(
|
||||
self,
|
||||
img1: np.ndarray,
|
||||
img2: np.ndarray
|
||||
) -> float:
|
||||
"""Calculer la similarité entre deux images."""
|
||||
if img1 is None or img2 is None:
|
||||
return 0.0
|
||||
|
||||
# Redimensionner si nécessaire
|
||||
if img1.shape != img2.shape:
|
||||
return 0.0
|
||||
|
||||
# Calculer différence normalisée
|
||||
diff = np.abs(img1.astype(float) - img2.astype(float))
|
||||
max_diff = 255.0 * img1.size
|
||||
|
||||
similarity = 1.0 - (np.sum(diff) / max_diff)
|
||||
return similarity
|
||||
|
||||
def _compute_overall_score(
|
||||
self,
|
||||
frame_qualities: List[FrameQuality],
|
||||
timing_analysis: TimingAnalysis,
|
||||
duplicate_analysis: DuplicateAnalysis
|
||||
) -> float:
|
||||
"""Calculer le score global de la session."""
|
||||
scores = []
|
||||
|
||||
# Score moyen des frames
|
||||
if frame_qualities:
|
||||
frame_score = np.mean([f.overall_score for f in frame_qualities])
|
||||
scores.append(frame_score)
|
||||
|
||||
# Score de timing
|
||||
if timing_analysis.is_consistent:
|
||||
timing_score = 1.0
|
||||
else:
|
||||
outlier_ratio = len(timing_analysis.outlier_indices) / max(1, len(frame_qualities) - 1)
|
||||
timing_score = max(0, 1.0 - outlier_ratio)
|
||||
scores.append(timing_score)
|
||||
|
||||
# Score de doublons
|
||||
duplicate_score = max(0, 1.0 - duplicate_analysis.duplicate_ratio / self.config.max_duplicate_ratio)
|
||||
scores.append(duplicate_score)
|
||||
|
||||
return float(np.mean(scores)) if scores else 0.0
|
||||
|
||||
def _load_screenshots(self, session: Any) -> List[np.ndarray]:
|
||||
"""Charger les screenshots d'une session."""
|
||||
screenshots = []
|
||||
|
||||
session_screenshots = getattr(session, 'screenshots', [])
|
||||
|
||||
for screenshot in session_screenshots:
|
||||
try:
|
||||
path = getattr(screenshot, 'relative_path', None)
|
||||
if path and Path(path).exists():
|
||||
# Charger avec PIL ou numpy
|
||||
try:
|
||||
from PIL import Image
|
||||
img = Image.open(path)
|
||||
screenshots.append(np.array(img))
|
||||
except ImportError:
|
||||
screenshots.append(None)
|
||||
else:
|
||||
screenshots.append(None)
|
||||
except Exception as e:
|
||||
logger.warning(f"Erreur chargement screenshot: {e}")
|
||||
screenshots.append(None)
|
||||
|
||||
return screenshots
|
||||
|
||||
def get_config(self) -> SessionAnalyzerConfig:
|
||||
"""Récupérer la configuration."""
|
||||
return self.config
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Fonctions utilitaires
|
||||
# =============================================================================
|
||||
|
||||
def create_session_analyzer(
|
||||
min_contrast: float = 0.3,
|
||||
max_duplicate_ratio: float = 0.3
|
||||
) -> SessionAnalyzer:
|
||||
"""
|
||||
Créer un analyseur avec configuration personnalisée.
|
||||
|
||||
Args:
|
||||
min_contrast: Contraste minimum acceptable
|
||||
max_duplicate_ratio: Ratio max de doublons
|
||||
|
||||
Returns:
|
||||
SessionAnalyzer configuré
|
||||
"""
|
||||
config = SessionAnalyzerConfig(
|
||||
min_contrast=min_contrast,
|
||||
max_duplicate_ratio=max_duplicate_ratio
|
||||
)
|
||||
return SessionAnalyzer(config)
|
||||
Reference in New Issue
Block a user