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"""
Manually train the ensemble model
Run this to test model training or manually trigger retraining
"""
import sys
from pathlib import Path
# Add parent directory to 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}")
# Generate schedule with varying parameters
num_trains = 25 + (i % 15) # 25-40 trains
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()
# Save 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)
# Check if we have enough data
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)
# Initialize trainer
print("\nInitializing model trainer...")
trainer = ModelTrainer()
# Train models
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}")
# Save summary
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'))}")
# Show model info
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()
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