| """ |
| Manually train the ensemble model |
| Run this to test model training or manually trigger retraining |
| """ |
| import sys |
| from pathlib import Path |
|
|
| |
| parent_dir = str(Path(__file__).parent.parent) |
| if parent_dir not in sys.path: |
| sys.path.insert(0, parent_dir) |
|
|
| from SelfTrainService.trainer import ModelTrainer |
| from SelfTrainService.data_store import ScheduleDataStore |
| from DataService.metro_data_generator import MetroDataGenerator |
| from DataService.schedule_optimizer import MetroScheduleOptimizer |
| import json |
|
|
|
|
| def generate_sample_data(num_schedules: int = 150): |
| """Generate sample schedule data for training""" |
| print(f"Generating {num_schedules} sample schedules...") |
| from datetime import datetime |
| |
| data_store = ScheduleDataStore() |
| |
| for i in range(num_schedules): |
| if (i + 1) % 10 == 0: |
| print(f" Generated {i + 1}/{num_schedules}") |
| |
| |
| num_trains = 25 + (i % 15) |
| generator = MetroDataGenerator(num_trains=num_trains) |
| route = generator.generate_route() |
| train_health = generator.generate_train_health_statuses() |
| |
| optimizer = MetroScheduleOptimizer( |
| date=datetime.now().strftime("%Y-%m-%d"), |
| num_trains=num_trains, |
| route=route, |
| train_health=train_health |
| ) |
| schedule = optimizer.optimize_schedule() |
| |
| |
| data_store.save_schedule(schedule.model_dump()) |
| |
| print(f"✓ Generated {num_schedules} schedules") |
|
|
|
|
| def main(): |
| """Train the ensemble model""" |
| print("=" * 60) |
| print("Multi-Model Ensemble Training") |
| print("=" * 60) |
| |
| |
| data_store = ScheduleDataStore() |
| count = data_store.count_schedules() |
| |
| print(f"\nCurrent data: {count} schedules") |
| |
| if count < 100: |
| print(f"Need at least 100 schedules for training") |
| generate_sample_data(150) |
| |
| |
| print("\nInitializing model trainer...") |
| trainer = ModelTrainer() |
| |
| |
| print("\nTraining ensemble models...") |
| print("Models: gradient_boosting, random_forest, xgboost, lightgbm, catboost") |
| print() |
| |
| result = trainer.train(force=True) |
| |
| if result["success"]: |
| print("\n" + "=" * 60) |
| print("Training Complete!") |
| print("=" * 60) |
| print(f"\nModels trained: {', '.join(result['models_trained'])}") |
| print(f"Best model: {result['best_model']}") |
| print(f"Samples used: {result['samples_used']}") |
| print(f"\nEnsemble Weights:") |
| for model, weight in result['ensemble_weights'].items(): |
| print(f" {model}: {weight:.4f}") |
| |
| print(f"\nModel Performance:") |
| for model, metrics in result['metrics'].items(): |
| print(f"\n{model}:") |
| print(f" Test R²: {metrics['test_r2']:.4f}") |
| print(f" Test RMSE: {metrics['test_rmse']:.4f}") |
| |
| |
| summary_path = Path("models/training_summary.json") |
| summary_path.parent.mkdir(parents=True, exist_ok=True) |
| with open(summary_path, 'w') as f: |
| json.dump(result, f, indent=2, default=str) |
| |
| print(f"\n✓ Training summary saved to {summary_path}") |
| else: |
| print(f"\n✗ Training failed: {result.get('reason', result.get('error'))}") |
| |
| |
| print("\n" + "=" * 60) |
| print("Current Model Info") |
| print("=" * 60) |
| info = trainer.get_model_info() |
| print(json.dumps(info, indent=2, default=str)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
