MH-P2/src/genetic_algorithm.py

from numpy import sum, append, intersect1d
from numpy.random import randint, choice, shuffle
from pandas import DataFrame
from math import ceil
from functools import partial
from multiprocessing import Pool
from copy import deepcopy

from preprocessing import parse_file


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 compute_distance(element, individual, data):
    accumulator = 0
    distinct_elements = individual.query(f"point != {element}")
    for _, item in distinct_elements.iterrows():
        accumulator += get_row_distance(
            source=element, destination=item.point, data=data
        )
    return accumulator


def generate_individual(n, m, data):
    individual = DataFrame(columns=["point", "distance", "fitness"])
    individual["point"] = choice(n, size=m, replace=False)
    individual["distance"] = individual["point"].apply(
        func=compute_distance, individual=individual, data=data
    )
    return individual


def evaluate_individual(individual, data):
    fitness = []
    genotype = individual.point.values
    distances = data.query(f"source in @genotype and destination in @genotype")
    for item in genotype[:-1]:
        element_df = distances.query(f"source == {item} or destination == {item}")
        max_distance = element_df["distance"].astype(float).max()
        fitness = append(arr=fitness, values=max_distance)
        distances = distances.query(f"source != {item} and destination != {item}")
    individual["fitness"] = sum(fitness)
    return individual


def select_distinct_genes(matching_genes, parents, m):
    first_parent = parents[0].query("point not in @matching_genes")
    second_parent = parents[1].query("point not in @matching_genes")
    cutoff = randint(m - len(matching_genes))
    first_parent_genes = first_parent.point.values[cutoff:]
    second_parent_genes = second_parent.point.values[:cutoff]
    return first_parent_genes, second_parent_genes


def select_shuffled_genes(matching_genes, parents):
    first_parent = parents[0].query("point not in @matching_genes")
    second_parent = parents[1].query("point not in @matching_genes")
    first_genes = first_parent.point.values
    second_genes = second_parent.point.values
    shuffle(first_genes)
    shuffle(second_genes)
    return first_genes, second_genes


def select_random_parent(parents):
    random_index = randint(len(parents))
    random_parent = parents[random_index]
    if random_parent.point.empty:
        opposite_index = 1 - random_index
        random_parent = parents[opposite_index]
    return random_parent


def get_best_point(parents, offspring):
    while True:
        random_parent = deepcopy(select_random_parent(parents))
        best_index = random_parent["distance"].idxmax()
        best_point = random_parent["point"].iloc[best_index]
        random_parent.drop(index=best_index, inplace=True)
        if best_point not in offspring.point.values:
            return best_point


def repair_offspring(offspring, parents, m):
    while len(offspring) != m:
        if len(offspring) > m:
            best_index = offspring["distance"].idxmax()
            offspring.drop(index=best_index, inplace=True)
        elif len(offspring) < m:
            best_point = get_best_point(parents, offspring)
            offspring = offspring.append(
                {"point": best_point, "distance": 0, "fitness": 0}, ignore_index=True
            )
    return offspring


def get_matching_genes(parents):
    first_parent = parents[0].point.values
    second_parent = parents[1].point.values
    return intersect1d(first_parent, second_parent)


def populate_offspring(values):
    offspring = DataFrame(columns=["point", "distance", "fitness"])
    for element in values:
        aux = DataFrame(columns=["point", "distance", "fitness"])
        aux["point"] = element
        offspring = offspring.append(aux)
    offspring["distance"] = 0
    offspring["fitness"] = 0
    return offspring


def uniform_crossover(parents, m):
    matching_genes = get_matching_genes(parents)
    first_genes, second_genes = select_distinct_genes(matching_genes, parents, m)
    offspring = populate_offspring(values=[matching_genes, first_genes, second_genes])
    viable_offspring = repair_offspring(offspring, parents, m)
    return viable_offspring


def position_crossover(parents):
    matching_genes = get_matching_genes(parents)
    first_genes, second_genes = select_shuffled_genes(matching_genes, parents)
    first_offspring = populate_offspring(values=[matching_genes, first_genes])
    second_offspring = populate_offspring(values=[matching_genes, second_genes])
    return first_offspring, second_offspring


def crossover(mode, parents, m):
    split_parents = list(zip(*[iter(parents)] * 2))
    offspring = []
    if mode == "uniform":
        for element in split_parents:
            offspring.append(uniform_crossover(element, m))
            offspring.append(uniform_crossover(element, m))
    else:
        for element in split_parents:
            first_offspring, second_offspring = position_crossover(element)
            offspring.append(first_offspring)
            offspring.append(second_offspring)
    return offspring


def element_in_dataframe(individual, element):
    duplicates = individual.query(f"point == {element}")
    return not duplicates.empty


def select_new_gene(individual, n):
    while True:
        new_gene = randint(n)
        if not element_in_dataframe(individual=individual, element=new_gene):
            return new_gene


def mutate(offspring, data, probability=0.001):
    expected_mutations = len(offspring) * n * probability
    individuals = []
    genes = []
    for _ in range(ceil(expected_mutations)):
        individuals.append(randint(len(offspring)))
        current_individual = individuals[-1]
        genes.append(offspring[current_individual].sample().index)
    for ind, gen in zip(individuals, genes):
        individual = offspring[ind]
        individual["point"].iloc[gen] = select_new_gene(individual, n)
        individual["distance"].iloc[gen] = compute_distance(
            element=individual["point"].iloc[gen].values[0],
            individual=individual,
            data=data,
        )
    return offspring


def get_individual_index(element, population):
    for index in range(len(population)):
        if population[index].fitness.values[0] == element.fitness.values[0]:
            return index


def tournament_selection(population):
    individuals = [population[randint(len(population))] for _ in range(2)]
    best_element = max(individuals, key=lambda x: x.fitness.values[0])
    population_index = get_individual_index(best_element, population)
    return best_element, population_index


def check_element_population(element, population):
    for item in population:
        if all(element.point.values) == all(item.point.values):
            return True
    return False


def generational_replacement(prev_population, current_population):
    new_population = current_population
    best_previous_individual = max(prev_population, key=lambda x: x.fitness.values[0])
    if check_element_population(best_previous_individual, new_population):
        worst_element = min(new_population, key=lambda x: x.fitness.values[0])
        worst_index = get_individual_index(worst_element, new_population)
        new_population[worst_index] = best_previous_individual
    return new_population


def get_best_elements(population):
    select_population = deepcopy(population)
    first_element = max(select_population, key=lambda x: x.fitness.values[0])
    first_index = get_individual_index(first_element, select_population)
    select_population.pop(first_index)
    second_element = max(select_population, key=lambda x: x.fitness.values[0])
    second_index = get_individual_index(second_element, select_population)
    return first_index, second_index


def get_worst_elements(population):
    select_population = deepcopy(population)
    first_element = min(select_population, key=lambda x: x.fitness.values[0])
    first_index = get_individual_index(first_element, select_population)
    select_population.pop(first_index)
    second_element = min(select_population, key=lambda x: x.fitness.values[0])
    second_index = get_individual_index(second_element, select_population)
    return first_index, second_index


def stationary_replacement(prev_population, current_population):
    new_population = prev_population
    first_worst, second_worst = get_worst_elements(prev_population)
    first_best, second_best = get_best_elements(current_population)
    worst_indexes = [first_worst, second_worst]
    best_indexes = [first_best, second_best]
    for worst, best in zip(worst_indexes, best_indexes):
        if (
            current_population[best].fitness.values[0]
            > prev_population[worst].fitness.values[0]
        ):
            new_population[worst] = current_population[best]
    return new_population


def replace_population(prev_population, current_population, mode):
    if mode == "generational":
        return generational_replacement(prev_population, current_population)
    return stationary_replacement(prev_population, current_population)


def evaluate_population(population, data, cores=4):
    fitness_func = partial(evaluate_individual, data=data)
    with Pool(cores) as pool:
        evaluated_population = pool.map(fitness_func, population)
    return evaluated_population


def select_parents(population, n, mode):
    select_population = deepcopy(population)
    parents = []
    if mode == "generational":
        for _ in range(n):
            element, index = tournament_selection(population=select_population)
            parents.append(element)
            select_population.pop(index)
    else:
        for _ in range(2):
            element, index = tournament_selection(population=select_population)
            parents.append(element)
            select_population.pop(index)
    return parents


def genetic_algorithm(n, m, data, select_mode, crossover_mode, max_iterations=100000):
    population = [generate_individual(n, m, data) for _ in range(n)]
    population = evaluate_population(population, data)
    for _ in range(max_iterations):
        parents = select_parents(population, n, select_mode)
        offspring = crossover(crossover_mode, parents, m)
        offspring = mutate(offspring, data)
        population = replace_population(population, offspring, select_mode)
        population = evaluate_population(population, data)
    best_index, _ = get_best_elements(population)
    return population[best_index]


n, m, data = parse_file("data/GKD-c_11_n500_m50.txt")
genetic_algorithm(
    n=10,
    m=4,
    data=data,
    select_mode="generational",
    crossover_mode="uniform",
    max_iterations=10,
)