research_table_generator.py

import json
import pandas as pd
from pathlib import Path

def load_results(file_path):
    """Load results from JSON file if it exists."""
    if Path(file_path).exists():
        with open(file_path, 'r') as f:
            return json.load(f)
    return None

def format_violation_types(results):
    """Format primary violation types in research table format."""
    hard = results["results"]["hard_constraints"]
    total = hard["total_violations"]
    lecturer = hard["lecturer_conflicts"]
    capacity = hard["room_capacity_violations"]
    groups = hard["student_group_conflicts"]
    return f"{total} ({lecturer}), ({capacity}), ({groups})"

def generate_research_table():
    """Generate research comparison table."""
    
    # Load ACO results
    aco_results = load_results("aco_results.json")
    
    # Create comparison table data
    table_data = []
    
    if aco_results:
        aco_row = {
            "Algorithm": "ACO",
            "Hard Constraint Violations": aco_results["results"]["hard_constraints"]["total_violations"],
            "Primary Violation Types": format_violation_types(aco_results),
            "Soft Constraint Score": f"{aco_results['results']['soft_constraints']['overall_score']:.3f}",
            "Convergence Speed (iterations)": aco_results["results"]["convergence_speed"],
            "Parameters": f"α={aco_results['parameters']['alpha']}, β={aco_results['parameters']['beta']}, ρ={aco_results['parameters']['evaporation_rate']}"
        }
        table_data.append(aco_row)
    
    # Placeholder for other algorithms (you would load these from your GA and RL results)
    # Example template rows based on your research format:
    placeholder_algorithms = [
        {
            "Algorithm": "NSGA-II", 
            "Hard Constraint Violations": "TBD",
            "Primary Violation Types": "TBD",
            "Soft Constraint Score": "TBD", 
            "Convergence Speed (iterations)": "TBD",
            "Parameters": "TBD"
        },
        {
            "Algorithm": "MOEA/D",
            "Hard Constraint Violations": "TBD", 
            "Primary Violation Types": "TBD",
            "Soft Constraint Score": "TBD",
            "Convergence Speed (iterations)": "TBD", 
            "Parameters": "TBD"
        },
        {
            "Algorithm": "SPEA2",
            "Hard Constraint Violations": "TBD",
            "Primary Violation Types": "TBD", 
            "Soft Constraint Score": "TBD",
            "Convergence Speed (iterations)": "TBD",
            "Parameters": "TBD"
        },
        {
            "Algorithm": "Q-Learning",
            "Hard Constraint Violations": "TBD",
            "Primary Violation Types": "TBD",
            "Soft Constraint Score": "TBD", 
            "Convergence Speed (iterations)": "TBD",
            "Parameters": "TBD"
        },
        {
            "Algorithm": "DQN", 
            "Hard Constraint Violations": "TBD",
            "Primary Violation Types": "TBD",
            "Soft Constraint Score": "TBD",
            "Convergence Speed (iterations)": "TBD",
            "Parameters": "TBD"
        },
        {
            "Algorithm": "SARSA",
            "Hard Constraint Violations": "TBD", 
            "Primary Violation Types": "TBD",
            "Soft Constraint Score": "TBD",
            "Convergence Speed (iterations)": "TBD",
            "Parameters": "TBD"
        },
        {
            "Algorithm": "BCO",
            "Hard Constraint Violations": "TBD",
            "Primary Violation Types": "TBD", 
            "Soft Constraint Score": "TBD",
            "Convergence Speed (iterations)": "TBD",
            "Parameters": "TBD"
        }
    ]
    
    # Add placeholder rows
    table_data.extend(placeholder_algorithms)
    
    # Create DataFrame
    df = pd.DataFrame(table_data)
    
    # Display table
    print("="*120)
    print("RESEARCH COMPARISON TABLE - TIMETABLE SCHEDULING ALGORITHMS")
    print("="*120)
    print(df.to_string(index=False))
    print("="*120)
    
    # Save to CSV for further editing
    df.to_csv("research_comparison_table.csv", index=False)
    print("Table saved to research_comparison_table.csv")
    
    # Detailed ACO results breakdown
    if aco_results:
        print("\nDETAILED ACO RESULTS BREAKDOWN:")
        print("-" * 50)
        hard = aco_results["results"]["hard_constraints"]
        soft = aco_results["results"]["soft_constraints"]
        
        print(f"Hard Constraints:")
        print(f"  • Total Violations: {hard['total_violations']}")
        print(f"  • Vacant Rooms: {hard['vacant_rooms']}")
        print(f"  • Lecturer Conflicts: {hard['lecturer_conflicts']}")
        print(f"  • Room Capacity Violations: {hard['room_capacity_violations']}")
        print(f"  • Student Group Conflicts: {hard['student_group_conflicts']}")
        print(f"  • Unassigned Activities: {hard['unassigned_activities']}")
        
        print(f"\nSoft Constraints (Individual Components):")
        components = soft["components"]
        print(f"  • Student Fatigue: {components['student_fatigue']:.3f}")
        print(f"  • Student Idle Time: {components['student_idle']:.3f}")
        print(f"  • Student Lecture Spread: {components['student_spread']:.3f}")
        print(f"  • Lecturer Fatigue: {components['lecturer_fatigue']:.3f}")
        print(f"  • Lecturer Idle Time: {components['lecturer_idle']:.3f}")
        print(f"  • Lecturer Lecture Spread: {components['lecturer_spread']:.3f}")
        print(f"  • Lecturer Workload Balance: {components['lecturer_balance']:.3f}")
        print(f"  • Overall Score: {soft['overall_score']:.3f}")
        
        print(f"\nAlgorithm Performance:")
        print(f"  • Convergence Speed: {aco_results['results']['convergence_speed']} iterations")
        print(f"  • Final Fitness: {aco_results['results']['final_fitness']:.3f}")
        
        params = aco_results['parameters']
        print(f"\nACO Parameters:")
        print(f"  • Number of Ants: {params['n_ants']}")
        print(f"  • Iterations: {params['n_iterations']}")
        print(f"  • Alpha (pheromone): {params['alpha']}")
        print(f"  • Beta (heuristic): {params['beta']}")
        print(f"  • Evaporation Rate: {params['evaporation_rate']}")
        print(f"  • Q0 (exploitation): {params['q0']}")

if __name__ == "__main__":
    generate_research_table()