anotate ui phase 1

TESTCASES/core/training_simulator.py

@@ -0,0 +1,166 @@
+"""
+Training Simulator - Handles model loading, training, and inference simulation
+
+Integrates with the main system's orchestrator and models for training and testing.
+"""
+
+import logging
+import uuid
+import time
+from typing import Dict, List, Optional, Any
+from dataclasses import dataclass, asdict
+from datetime import datetime
+from pathlib import Path
+import json
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class TrainingResults:
+    """Results from training session"""
+    training_id: str
+    model_name: str
+    test_cases_used: int
+    epochs_completed: int
+    final_loss: float
+    training_duration_seconds: float
+    checkpoint_path: str
+    metrics: Dict[str, float]
+    status: str = "completed"
+
+
+@dataclass
+class InferenceResults:
+    """Results from inference simulation"""
+    annotation_id: str
+    model_name: str
+    predictions: List[Dict]
+    accuracy: float
+    precision: float
+    recall: float
+    f1_score: float
+    confusion_matrix: Dict
+    prediction_timeline: List[Dict]
+
+
+class TrainingSimulator:
+    """Simulates training and inference on annotated data"""
+    
+    def __init__(self, orchestrator=None):
+        """Initialize training simulator"""
+        self.orchestrator = orchestrator
+        self.model_cache = {}
+        self.training_sessions = {}
+        
+        # Storage for training results
+        self.results_dir = Path("TESTCASES/data/training_results")
+        self.results_dir.mkdir(parents=True, exist_ok=True)
+        
+        logger.info("TrainingSimulator initialized")
+
+    def load_model(self, model_name: str):
+        """Load model from orchestrator"""
+        if model_name in self.model_cache:
+            return self.model_cache[model_name]
+        
+        if not self.orchestrator:
+            logger.error("Orchestrator not available")
+            return None
+        
+        # Get model from orchestrator
+        # This will be implemented when we integrate with actual models
+        logger.info(f"Loading model: {model_name}")
+        return None
+    
+    def start_training(self, model_name: str, test_cases: List[Dict]) -> str:
+        """Start training session with test cases"""
+        training_id = str(uuid.uuid4())
+        
+        # Create training session
+        self.training_sessions[training_id] = {
+            'status': 'running',
+            'model_name': model_name,
+            'test_cases_count': len(test_cases),
+            'current_epoch': 0,
+            'total_epochs': 50,
+            'current_loss': 0.0,
+            'start_time': time.time()
+        }
+        
+        logger.info(f"Started training session: {training_id}")
+        
+        # TODO: Implement actual training in background thread
+        # For now, simulate training completion
+        self._simulate_training(training_id)
+        
+        return training_id
+    
+    def _simulate_training(self, training_id: str):
+        """Simulate training progress (placeholder)"""
+        import threading
+        
+        def train():
+            session = self.training_sessions[training_id]
+            total_epochs = session['total_epochs']
+            
+            for epoch in range(total_epochs):
+                time.sleep(0.1)  # Simulate training time
+                session['current_epoch'] = epoch + 1
+                session['current_loss'] = 1.0 / (epoch + 1)  # Decreasing loss
+            
+            # Mark as completed
+            session['status'] = 'completed'
+            session['final_loss'] = session['current_loss']
+            session['duration_seconds'] = time.time() - session['start_time']
+            session['accuracy'] = 0.85
+            
+            logger.info(f"Training completed: {training_id}")
+        
+        thread = threading.Thread(target=train, daemon=True)
+        thread.start()
+    
+    def get_training_progress(self, training_id: str) -> Dict:
+        """Get training progress"""
+        if training_id not in self.training_sessions:
+            return {
+                'status': 'not_found',
+                'error': 'Training session not found'
+            }
+        
+        return self.training_sessions[training_id]
+    
+    def simulate_inference(self, annotation_id: str, model_name: str) -> InferenceResults:
+        """Simulate inference on annotated period"""
+        # Placeholder implementation
+        logger.info(f"Simulating inference for annotation: {annotation_id}")
+        
+        # Generate dummy predictions
+        predictions = []
+        for i in range(10):
+            predictions.append({
+                'timestamp': datetime.now().isoformat(),
+                'predicted_action': 'BUY' if i % 2 == 0 else 'SELL',
+                'confidence': 0.7 + (i * 0.02),
+                'actual_action': 'BUY' if i % 2 == 0 else 'SELL',
+                'correct': True
+            })
+        
+        results = InferenceResults(
+            annotation_id=annotation_id,
+            model_name=model_name,
+            predictions=predictions,
+            accuracy=0.85,
+            precision=0.82,
+            recall=0.88,
+            f1_score=0.85,
+            confusion_matrix={
+                'tp_buy': 4,
+                'fn_buy': 1,
+                'fp_sell': 1,
+                'tn_sell': 4
+            },
+            prediction_timeline=predictions
+        )
+        
+        return results