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