Move each algorithm into a diffent module
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55
src/greedy.py
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55
src/greedy.py
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from pandas import DataFrame, Series
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def get_first_solution(n, data):
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distance_sum = DataFrame(columns=["point", "distance"])
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for element in range(n):
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element_df = data.query(f"source == {element} or destination == {element}")
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distance = element_df["distance"].sum()
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distance_sum = distance_sum.append(
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{"point": element, "distance": distance}, ignore_index=True
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)
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furthest_index = distance_sum["distance"].astype(float).idxmax()
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furthest_row = distance_sum.iloc[furthest_index]
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furthest_row["distance"] = 0
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return furthest_row
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def get_different_element(original, row):
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if row.source == original:
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return row.destination
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return row.source
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def get_closest_element(element, data):
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element_df = data.query(f"source == {element} or destination == {element}")
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closest_index = element_df["distance"].astype(float).idxmin()
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closest_row = data.loc[closest_index]
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closest_point = get_different_element(original=element, row=closest_row)
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return Series(data={"point": closest_point, "distance": closest_row["distance"]})
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def explore_solutions(solutions, data):
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closest_elements = solutions["point"].apply(func=get_closest_element, data=data)
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furthest_index = closest_elements["distance"].astype(float).idxmax()
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return closest_elements.iloc[furthest_index]
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def remove_duplicates(current, previous, data):
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duplicate_free_df = data.query(
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f"(source != {current} or destination not in @previous) and (source not in @previous or destination != {current})"
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)
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return duplicate_free_df
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def greedy_algorithm(n, m, data):
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solutions = DataFrame(columns=["point", "distance"])
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first_solution = get_first_solution(n, data)
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solutions = solutions.append(first_solution, ignore_index=True)
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for _ in range(m):
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element = explore_solutions(solutions, data)
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solutions = solutions.append(element)
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data = remove_duplicates(
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current=element["point"], previous=solutions["point"], data=data
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)
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return solutions
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src/local_search.py
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42
src/local_search.py
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from numpy.random import choice, randint, seed
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def get_first_random_solution(m, data):
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seed(42)
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random_indexes = choice(len(data.index), size=m)
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return data.iloc[random_indexes]
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def replace_worst_element(previous, data):
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solution = previous.copy()
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worst_index = previous["distance"].astype(float).idxmin()
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random_candidate = data.loc[randint(low=0, high=len(data.index))]
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solution.loc[worst_index] = random_candidate
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return solution
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def get_random_solution(previous, data):
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solution = replace_worst_element(previous, data)
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while solution["distance"].sum() <= previous["distance"].sum():
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if solution.equals(previous):
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break
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solution = replace_worst_element(previous=solution, data=data)
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return solution
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def explore_neighbourhood(element, data, max_iterations=100000):
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neighbourhood = []
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neighbourhood.append(element)
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for _ in range(max_iterations):
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previous_solution = neighbourhood[-1]
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neighbour = get_random_solution(previous=previous_solution, data=data)
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if neighbour.equals(previous_solution):
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break
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neighbourhood.append(neighbour)
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return neighbour
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def local_search(m, data):
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first_solution = get_first_random_solution(m=m, data=data)
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best_solution = explore_neighbourhood(element=first_solution, data=data)
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return best_solution
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47
src/main.py
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47
src/main.py
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from preprocessing import parse_file
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from greedy import greedy_algorithm
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from local_search import local_search
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from sys import argv
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from time import time
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def execute_algorithm(choice, n, m, data):
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if choice == "greedy":
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return greedy_algorithm(n, m, data)
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elif choice == "local":
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return local_search(m, data)
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else:
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print("The valid algorithm choices are 'greedy' and 'local'")
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exit(1)
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def show_results(solutions, time_delta):
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distance_sum = solutions["distance"].sum()
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duplicates = solutions.duplicated().any()
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print(solutions)
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print("Total distance: " + str(distance_sum))
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if not duplicates:
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print("No duplicates found")
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print("Execution time: " + str(time_delta))
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def usage(argv):
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print(f"Usage: python {argv[0]} <file> <algorithm choice>")
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print("algorithm choices:")
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print("greedy: greedy algorithm")
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print("local: local search algorithm")
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exit(1)
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def main():
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if len(argv) != 3:
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usage(argv)
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n, m, data = parse_file(argv[1])
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start_time = time()
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solutions = execute_algorithm(choice=argv[2], n=n, m=m, data=data)
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end_time = time()
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show_results(solutions, time_delta=end_time - start_time)
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if __name__ == "__main__":
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main()
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from preprocessing import parse_file
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from numpy.random import choice, randint, seed
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from pandas import DataFrame, Series
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from sys import argv
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from time import time
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def get_first_solution(n, data):
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distance_sum = DataFrame(columns=["point", "distance"])
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for element in range(n):
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element_df = data.query(f"source == {element} or destination == {element}")
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distance = element_df["distance"].sum()
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distance_sum = distance_sum.append(
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{"point": element, "distance": distance}, ignore_index=True
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)
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furthest_index = distance_sum["distance"].astype(float).idxmax()
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furthest_row = distance_sum.iloc[furthest_index]
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furthest_row["distance"] = 0
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return furthest_row
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def get_different_element(original, row):
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if row.source == original:
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return row.destination
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return row.source
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def get_closest_element(element, data):
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element_df = data.query(f"source == {element} or destination == {element}")
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closest_index = element_df["distance"].astype(float).idxmin()
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closest_row = data.loc[closest_index]
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closest_point = get_different_element(original=element, row=closest_row)
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return Series(data={"point": closest_point, "distance": closest_row["distance"]})
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def explore_solutions(solutions, data):
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closest_elements = solutions["point"].apply(func=get_closest_element, data=data)
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furthest_index = closest_elements["distance"].astype(float).idxmax()
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return closest_elements.iloc[furthest_index]
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def remove_duplicates(current, previous, data):
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duplicate_free_df = data.query(
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f"(source != {current} or destination not in @previous) and (source not in @previous or destination != {current})"
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)
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return duplicate_free_df
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def greedy_algorithm(n, m, data):
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solutions = DataFrame(columns=["point", "distance"])
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first_solution = get_first_solution(n, data)
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solutions = solutions.append(first_solution, ignore_index=True)
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for _ in range(m):
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element = explore_solutions(solutions, data)
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solutions = solutions.append(element)
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data = remove_duplicates(
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current=element["point"], previous=solutions["point"], data=data
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)
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return solutions
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def get_first_random_solution(m, data):
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seed(42)
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random_indexes = choice(len(data.index), size=m)
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return data.iloc[random_indexes]
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def replace_worst_element(previous, data):
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solution = previous.copy()
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worst_index = previous["distance"].astype(float).idxmin()
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random_candidate = data.loc[randint(low=0, high=len(data.index))]
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solution.loc[worst_index] = random_candidate
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return solution
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def get_random_solution(previous, data):
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solution = replace_worst_element(previous, data)
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while solution["distance"].sum() <= previous["distance"].sum():
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if solution.equals(previous):
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break
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solution = replace_worst_element(previous=solution, data=data)
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return solution
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def explore_neighbourhood(element, data, max_iterations=100000):
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neighbourhood = []
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neighbourhood.append(element)
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for _ in range(max_iterations):
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previous_solution = neighbourhood[-1]
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neighbour = get_random_solution(previous=previous_solution, data=data)
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if neighbour.equals(previous_solution):
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break
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neighbourhood.append(neighbour)
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return neighbour
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def local_search(m, data):
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first_solution = get_first_random_solution(m=m, data=data)
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best_solution = explore_neighbourhood(element=first_solution, data=data)
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return best_solution
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def execute_algorithm(choice, n, m, data):
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if choice == "greedy":
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return greedy_algorithm(n, m, data)
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elif choice == "local":
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return local_search(m, data)
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else:
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print("The valid algorithm choices are 'greedy' and 'local'")
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exit(1)
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def show_results(solutions, time_delta):
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distance_sum = solutions["distance"].sum()
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duplicates = solutions.duplicated().any()
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print(solutions)
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print("Total distance: " + str(distance_sum))
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if not duplicates:
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print("No duplicates found")
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print("Execution time: " + str(time_delta))
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def usage(argv):
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print(f"Usage: python {argv[0]} <file> <algorithm choice>")
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print("algorithm choices:")
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print("greedy: greedy algorithm")
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print("local: local search algorithm")
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exit(1)
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def main():
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if len(argv) != 3:
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usage(argv)
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n, m, data = parse_file(argv[1])
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start_time = time()
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solutions = execute_algorithm(choice=argv[2], n=n, m=m, data=data)
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end_time = time()
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show_results(solutions, time_delta=end_time - start_time)
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if __name__ == "__main__":
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main()
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