MH-P2/src/local_search.py

from numpy.random import choice, seed, randint
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 get_first_random_solution(n, m, data):
    solution = DataFrame(columns=["point", "distance"])
    seed(42)
    solution["point"] = choice(n, size=m, replace=False)
    solution["distance"] = solution["point"].apply(
        func=compute_distance, solution=solution, data=data
    )
    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 local_search(first_solution, n, data):
    best_solution = explore_neighbourhood(
        element=first_solution, n=n, data=data, max_iterations=5
    )
    return best_solution