MH-P2/src/main.py

from preprocessing import parse_file
from genetic_algorithm import genetic_algorithm
from memetic_algorithm import memetic_algorithm
from sys import argv
from time import time
from itertools import combinations
from argparse import ArgumentParser


def execute_algorithm(args, n, m, data):
    if args.algorithm == "genetic":
        return genetic_algorithm(
            n, m, data, select_mode=args.selection, crossover_mode=args.crossover
        )
    else:
        return memetic_algorithm(n, m, data, hybridation=args.hybridation)


def get_row_distance(source, destination, data):
    row = data.query(
        """(source == @source and destination == @destination) or \
        (source == @destination and destination == @source)"""
    )
    return row["distance"].values[0]


def get_fitness(solutions, data):
    counter = 0
    comb = combinations(solutions.index, r=2)
    for index in list(comb):
        elements = solutions.loc[index, :]
        counter += get_row_distance(
            source=elements["point"].head(n=1).values[0],
            destination=elements["point"].tail(n=1).values[0],
            data=data,
        )
    return counter


def show_results(solutions, fitness, time_delta):
    duplicates = solutions.duplicated().any()
    print(solutions)
    print(f"Total distance: {fitness}")
    if not duplicates:
        print("No duplicates found")
    print(f"Execution time: {time_delta}")


def usage(argv):
    print(f"Usage: python {argv[0]} <file> <algorithm choice> <")
    print("algorithm choices:")
    print("genetic: genetic algorithm")
    print("memetic: memetic algorithm")
    exit(1)


def parse_arguments():
    parser = ArgumentParser()
    parser.add_argument("file", help="dataset of choice")
    subparsers = parser.add_subparsers(dest="algorithm")
    parser_genetic = subparsers.add_parser("genetic")
    parser_memetic = subparsers.add_parser("memetic")
    parser_genetic.add_argument("crossover", choices=["uniform", "position"])
    parser_genetic.add_argument("selection", choices=["generational", "stationary"])
    parser_memetic.add_argument("hybridation", choices=["all", "random", "best"])
    return parser.parse_args()


def main():
    args = parse_arguments()
    n, m, data = parse_file(args.file)
    start_time = time()
    solutions = execute_algorithm(args, n, m, data)
    end_time = time()
    fitness = get_fitness(solutions, data)
    show_results(solutions, fitness, time_delta=end_time - start_time)


if __name__ == "__main__":
    main()