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Refactored base.py - now uses modular structure for better maintainability.
This module has been restructured into separate files:
- models.py: Data classes and configurations
- evaluator.py: Constraint checking and objective evaluation
- genetic_algorithm.py: Genetic Algorithm implementation
- advanced_optimizers.py: CMA-ES, PSO, and Simulated Annealing
- scheduler.py: Main interface and comparison tools
For backward compatibility, the main functions are still available here.
"""
import json
from typing import Dict
# Import from new modular structure
from .scheduler import optimize_trainset_schedule, compare_optimization_methods
from .models import OptimizationResult, OptimizationConfig
# For backward compatibility, expose the main function with original signature
def optimize_trainset_schedule_main(data: Dict, method: str = 'ga') -> OptimizationResult:
"""Multi-objective optimizer for trainset scheduling using genetic algorithm.
This is a backward compatibility wrapper around the new modular system.
Args:
data: Metro synthetic data dictionary
method: Optimization method ('ga', 'cmaes', 'pso', 'sa', etc.)
Returns:
OptimizationResult with selected trainsets and performance metrics
"""
# Use the new modular system
config = OptimizationConfig()
return optimize_trainset_schedule(data, method, config)
# Usage example
if __name__ == "__main__":
# Load synthetic data
try:
with open('metro_synthetic_data.json', 'r') as f:
data = json.load(f)
except FileNotFoundError:
print("Please generate synthetic data first")
exit(1)
# Run optimization with Genetic Algorithm (backward compatibility)
result_ga = optimize_trainset_schedule_main(data, method='ga')
print(f"\nOptimization completed!")
print(f"Service trainsets: {len(result_ga.selected_trainsets)}")
print(f"Standby trainsets: {len(result_ga.standby_trainsets)}")
print(f"Maintenance trainsets: {len(result_ga.maintenance_trainsets)}")
print(f"Final fitness score: {result_ga.fitness_score:.2f}")
# You can also use the new modular interface
print(f"\nFor more advanced features, use:")
print(f"from greedyOptim import optimize_trainset_schedule, compare_optimization_methods")
print(f"from greedyOptim import optimize_with_hybrid_methods")
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