MH-P2/src/genetic_algorithm.py

from numpy import sum, append, arange, delete, where
from numpy.random import randint, choice, shuffle, random
from pandas import DataFrame


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, solution, data):
    accumulator = 0
    distinct_elements = solution.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_first_solution(n, m, data):
    solution = DataFrame(columns=["point", "distance"])
    solution["point"] = choice(n, size=m, replace=False)
    solution["distance"] = solution["point"].apply(
        func=compute_distance, solution=solution, data=data
    )
    return solution


def evaluate_element(element, data):
    fitness = []
    genotype = element.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}")
    return sum(fitness)


def select_distinct_genes(matching_genes, parents, m):
    cutoff = randint(m)
    distinct_indexes = delete(arange(m), matching_genes)
    first_parent_genes = parents[0].point.iloc[distinct_indexes[cutoff:]]
    second_parent_genes = parents[1].point.iloc[distinct_indexes[:cutoff]]
    return first_parent_genes, second_parent_genes


def select_random_genes(matching_genes, parents, m):
    random_parent = parents[randint(len(parents))]
    distinct_indexes = delete(arange(m), matching_genes)
    genes = random_parent.point.iloc[distinct_indexes].values
    shuffle(genes)
    return genes


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


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


def populate_offspring(values):
    offspring = DataFrame(columns=["point", "distance"])
    for element in values:
        aux = DataFrame(columns=["point", "distance"])
        aux["point"] = element
        offspring = offspring.append(aux)
    offspring["distance"] = 0
    offspring = offspring[1:]
    return offspring


def uniform_crossover(parents, m):
    matching_indexes = get_matching_genes(parents)
    matching_genes = parents[0].point.iloc[matching_indexes]
    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, m):
    matching_genes = get_matching_genes(parents)
    shuffled_genes = select_random_genes(matching_genes, parents, m)
    offspring = populate_offspring(values=[matching_genes, shuffled_genes])
    return offspring


def crossover(mode, parents, m):
    if mode == "uniform":
        return uniform_crossover(parents, m)
    return position_crossover(parents, m)


def mutate(solution, n, probability=0.001):
    if random() > probability:
        return solution
    row = solution.sample()
    random_element = randint(n)
    while element_in_dataframe(solution=solution, element=random_element):
        random_element = randint(n)
    solution["point"].iloc[row.index] = random_element
    solution["distance"].loc[row.index] = 0
    return solution


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


def replace_worst_element(previous, n, data):
    solution = previous.copy()
    worst_index = solution["distance"].astype(float).idxmin()
    random_element = randint(n)
    while element_in_dataframe(solution=solution, element=random_element):
        random_element = randint(n)
    solution["point"].loc[worst_index] = random_element
    solution["distance"].loc[worst_index] = compute_distance(
        element=solution["point"].loc[worst_index], solution=solution, data=data
    )
    return solution


def get_random_solution(previous, n, data):
    solution = replace_worst_element(previous, n, data)
    while solution["distance"].sum() <= previous["distance"].sum():
        solution = replace_worst_element(previous=solution, n=n, data=data)
    return solution


def explore_neighbourhood(element, n, data, max_iterations=100000):
    neighbourhood = []
    neighbourhood.append(element)
    for _ in range(max_iterations):
        previous_solution = neighbourhood[-1]
        neighbour = get_random_solution(previous=previous_solution, n=n, data=data)
        neighbourhood.append(neighbour)
    return neighbour


def genetic_algorithm(n, m, data):
    first_solution = generate_first_solution(n, m, data)
    best_solution = explore_neighbourhood(
        element=first_solution, n=n, data=data, max_iterations=100
    )
    return best_solution