AICup/src/TSP_solver.py

from time import time as t
import numpy as np
import matplotlib.pyplot as plt

from src.two_opt import loop2opt
from src.two_dot_five_opt import loop2dot5opt
from src.simulated_annealing import sa
from src.constructive_algorithms import random_method, nearest_neighbor, best_nearest_neighbor, multi_fragment_mf
from src.ant_colony import ant_colony_opt

# available_solvers = {"random": random_method,
#                      "nearest_neighbors": nearest_neighbor,
#                      "best_nn": best_nearest_neighbor,
#                      "multi_fragment": multi_fragment_mf
#                      }
available_improvers = {"2-opt": loop2opt,
                       "2.5-opt": loop2dot5opt,
                       "simulated_annealing": sa}

# available_solvers = {}
# for i in range(1, 10):
#     for j in range(1, 10):
#         available_solvers["aco_" + str(i) + "_" + str(j)] = ant_colony_opt(i/10, j)

available_solvers = {"aco": ant_colony_opt}

class TSPSolver:
    def __init__(self, algorithm_name, problem_instance, passed_avail_solvers=None, passed_avail_improvers=None):
        # assert algorithm_name in available_solvers, f"the {algorithm_name} initializer is not available currently."
        if passed_avail_improvers is None:
            passed_avail_improvers = available_improvers
        if passed_avail_solvers is None:
            passed_avail_solvers = available_solvers
        self.available_improvers = passed_avail_improvers
        self.available_solvers = passed_avail_solvers
        self.duration = np.inf
        self.found_length = np.inf
        self.algorithm_name = algorithm_name
        self.algorithms = [algorithm_name]
        self.name_method = "initialized with " + algorithm_name
        self.solved = False
        self.problem_instance = problem_instance
        self.solution = None

    def bind(self, local_or_meta):
        assert local_or_meta in self.available_improvers, f"the {local_or_meta} method is not available currently."
        self.algorithms.append(local_or_meta)
        self.name_method += ", improved with " + local_or_meta

    def pop(self):
        self.algorithms.pop()
        self.name_method = self.name_method[::-1][self.name_method[::-1].find("improved"[::-1]) + len("improved") + 2:][
                           ::-1]

    def compute_solution(self, verbose=True, return_value=True):
        self.solved = False
        if verbose:
            print(f"###  solving with {self.algorithms} ####")
        start_time = t()
        self.solution = self.available_solvers[self.algorithms[0]](self.problem_instance)
        if not self.check_if_solution_is_valid():
            print(f"Error the solution of {self.algorithm_name} for problem {self.problem_instance.name} is not valid")
            if return_value:
                return False
        for i in range(1, len(self.algorithms)):
            improver = self.algorithms[i]
            self.solution = self.available_improvers[improver](self.solution, self.problem_instance)
            if not self.check_if_solution_is_valid():
                print(
                    f"Error the solution of {self.algorithm_name} with {improver} for problem {self.problem_instance.name} is not valid")
                if return_value:
                    return False

        end_time = t()
        self.duration = np.around(end_time - start_time, 3)
        self.solved = True
        self.evaluate_solution()
        self._gap()
        if return_value:
            return self.solution

    def plot_solution(self):
        assert self.solved, "You can't plot the solution, you need to compute it first!"
        plt.figure(figsize=(8, 8))
        self._gap()
        plt.title(f"{self.problem_instance.name} solved with {self.name_method} solver, gap {self.gap}")
        ordered_points = self.problem_instance.points[self.solution]
        plt.plot(ordered_points[:, 1], ordered_points[:, 2], 'b-')
        plt.show()

    def check_if_solution_is_valid(self):
        rights_values = np.sum(
            [self.check_validation(i, self.solution[:-1]) for i in np.arange(self.problem_instance.nPoints)])
        # rights_values = np.sum(
        #     [1 if np.sum(self.solution[:-1] == i) == 1 else 0 for i in np.arange(self.problem_instance.nPoints)])
        return rights_values == self.problem_instance.nPoints

    def check_validation(self, node, solution):
        if np.sum(solution == node) == 1:
            return 1
        else:
            return 0

    def evaluate_solution(self, return_value=False):
        total_length = 0
        starting_node = self.solution[0]
        from_node = starting_node
        for node in self.solution[1:]:
            total_length += self.problem_instance.dist_matrix[from_node, node]
            from_node = node

        self.found_length = total_length
        if return_value:
            return total_length

    def pass_and_check_if_solution_is_valid(self, solution):
        rights_values = np.sum(
            [self.check_validation(i, solution[:-1]) for i in np.arange(self.problem_instance.nPoints)])
        # rights_values = np.sum(
        #     [1 if np.sum(solution[:-1] == i) == 1 else 0 for i in np.arange(self.problem_instance.nPoints)])
        return rights_values == self.problem_instance.nPoints

    def _gap(self):
        self.evaluate_solution(return_value=False)
        self.gap = np.round(
            ((self.found_length - self.problem_instance.best_sol) / self.problem_instance.best_sol) * 100, 2)