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test.py
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test.py
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# -*- coding: utf-8 -*-
import unittest
from collections import defaultdict
from evaluator import Evaluator as E
from instance import Instance
from heuristic import Heuristic
from solution import Solution
from local_search import LocalSearch
from simulated_annealing import SimulatedAnnealing
from tabu_search import TabuSearch
import similarity
class Test_heuristic(unittest.TestCase):
def setUp(self):
self.flow = [
[0, 1, 2],
[4, 0, 5],
[7, 2, 0]
]
self.distance = [
[0, 5, 2],
[1, 0, 1],
[6, 2, 0]
]
self.i = Instance(None, self.distance, self.flow)
self.h = Heuristic()
def test_sorted_list_flow(self):
expected = [(2, 0, 7), (1, 2, 5), (1, 0, 4),
(0, 2, 2), (2, 1, 2), (0, 1, 1)]
actual = self.h.sorted_list(self.i.flow, True)
self.assertEqual(actual, expected)
def test_sorted_list_distance(self):
expected = [(1, 0, 1), (1, 2, 1), (0, 2, 2),
(2, 1, 2), (0, 1, 5), (2, 0, 6)]
actual = self.h.sorted_list(self.i.distance, False)
self.assertEqual(actual, expected)
def test_unique_rows_columns_flow(self):
expected = [2, 0, 1]
actual = self.h.unique_rows_columns(
self.h.sorted_list(self.i.flow, True))
self.h.append_unpaired(actual, len(self.i.flow))
self.assertEqual(actual, expected)
def test_unique_rows_columns_distance(self):
expected = [1, 0, 2]
actual = self.h.unique_rows_columns(
self.h.sorted_list(self.i.distance, False))
self.h.append_unpaired(actual, len(self.i.distance))
self.assertEqual(actual, expected)
def test_heuristic_solution(self):
expected = Solution((0, 2, 1))
actual = self.h.solve(self.i)
self.assertTupleEqual(actual.sequence, expected.sequence)
class TestLocalSearch(unittest.TestCase):
def setUp(self):
self.flow = [
[0, 1, 2],
[4, 0, 5],
[7, 2, 0]]
self.distance = [
[0, 5, 2],
[1, 0, 1],
[6, 2, 0]]
self.i = Instance(None, self.distance, self.flow)
self.ls_steepest = LocalSearch()
self.ls_greedy = LocalSearch(greedy=True)
self.startpoint = Solution((0, 1, 2))
def test_solve_steepest_with_startpoint(self):
expected = (2, 1, 0)
actual = self.ls_steepest.solve(self.i, self.startpoint).sequence
self.assertEqual(expected, actual)
def test_solve_greedy_with_startpoint(self):
expected = (2, 1, 0)
actual = self.ls_greedy.solve(self.i, self.startpoint).sequence
self.assertEqual(expected, actual)
class TestSimulatedAnnealing(unittest.TestCase):
def setUp(self):
self.flow = [
[0, 1, 2],
[4, 0, 5],
[7, 2, 0]]
self.distance = [
[0, 5, 2],
[1, 0, 1],
[6, 2, 0]]
self.i = Instance(None, self.distance, self.flow)
self.sa = SimulatedAnnealing()
self.startpoint = Solution((0, 1, 2))
def test_solve_with_startpoint(self):
expected = (2, 1, 0)
actual = self.sa.solve(self.i, self.startpoint).sequence
self.assertEqual(expected, actual)
def test_guess_temperature(self):
expected = 255.0
actual = self.sa._guess_temp(None, prob=0.98, df=1000)
self.assertAlmostEqual(expected, actual, -1)
def test_guess_temperature_for_given_instance(self):
expected = 3.5
actual = self.sa._guess_temp(self.i, prob=0.95)
self.assertAlmostEqual(expected, actual, 0)
class TestTabuSearch(unittest.TestCase):
def setUp(self):
self.flow = [
[0, 1, 2],
[4, 0, 5],
[7, 2, 0]]
self.distance = [
[0, 5, 2],
[1, 0, 1],
[6, 2, 0]]
self.i = Instance(None, self.distance, self.flow)
self.e = E(self.i)
self.ts = TabuSearch()
self.startpoint = Solution((0, 1, 2))
def test_filter_moves(self):
moves = [(1, 2),
(1, 3),
(2, 3)]
tabu = defaultdict(int, {(1, 2): 1, (2, 3): 1})
expected = [(1, 3)]
actual = list(self.ts._get_filtered_moves(moves, tabu=tabu))
self.assertEqual(expected, actual)
def test_filter_moves_with_aspiration(self):
self.i = Instance(None, self.distance, self.flow)
self.e = E(self.i)
self.startpoint = Solution((2, 1, 0))
moves = [(0, 1),
(0, 2),
(1, 2)]
tabu = defaultdict(int, {(0, 1): 1, (1, 2): 2, (0, 2): 3})
expected = [(0, 1),
(0, 2),
(1, 2)]
actual = list(self.ts._get_filtered_moves(moves, e=self.e,
current=(self.startpoint, self.e.evaluate(self.startpoint)),
tabu=tabu))
self.assertEqual(expected, actual)
def test_decrease_tabu_penalty(self):
tabu = defaultdict(int, {(1, 2): 2, (2, 3): 1})
expected = [(1, 2)]
self.ts._decrease_tabu_penalty(tabu)
actual = tabu.keys()
self.assertEqual(expected, actual)
def test_select_best_move(self):
current = self.startpoint
moves = [(0, 1),
(0, 2),
(1, 2)]
expected = (0, 2)
actual = self.ts._select_best_move(current, moves, self.e)
self.assertEqual(expected, actual)
def test_select_best_moves(self):
current = self.startpoint
moves = [(0, 1),
(0, 2),
(1, 2)]
expected = [(0, 2),
(1, 2)]
actual = self.ts._select_best_moves(current, moves, self.e, 2)
self.assertEqual(expected, actual)
def test_with_startpoint(self):
expected = (2, 1, 0)
actual = self.ts.solve(self.i, self.startpoint).sequence
self.assertEqual(expected, actual)
class SimilarityTest(unittest.TestCase):
def setUp(self):
self.s1 = Solution((0, 1, 2))
self.s2 = Solution((0, 2, 1))
self.s3 = Solution((0, 1, 2))
self.flow = [
[0, 1, 2],
[4, 0, 5],
[7, 2, 0]]
self.distance = [
[0, 5, 5],
[4, 2, 2],
[4, 2, 2]]
self.i = Instance(None, self.distance, self.flow)
self.flow2 = [
[0, 1, 2],
[4, 0, 5],
[7, 2, 0]]
self.distance2 = [
[0, 5, 2],
[1, 0, 1],
[6, 2, 0]]
self.i2 = Instance(None, self.distance2, self.flow2)
def test_binary_similarity(self):
expected = 0.3333333333333333
actual = similarity.binary_solution_similarity(self.s1, self.s2)
self.assertAlmostEqual(actual, expected)
def test_partial_solution_similarity_identical1(self):
expected = 1.0
actual = similarity.binary_solution_similarity(self.s1, self.s3)
self.assertEqual(actual, expected)
def test_ratio_similarity(self):
expected = (1.0 + 2.0 / 3.0 + 0.5) / 3.0
actual = similarity.ratio_similarity([3, 4, 2], [3, 6, 1])
self.assertEqual(actual, expected)
def test_partial_solution_similarity_identical2(self):
expected = 1.0
actual = similarity.partial_solution_similarity(self.s1,
self.s2, self.i)
self.assertEqual(actual, expected)
def test_partial_solution_similarity_identical3(self):
expected = 1.0
actual = similarity.partial_solution_similarity(self.s1,
self.s3, self.i2)
self.assertEqual(actual, expected)
def test_partial_solution_similarity(self):
expected = (1.0 + 1.0 / 18.0 + 4.0 / 30.0) / 3.0
actual = similarity.partial_solution_similarity(self.s1,
self.s2, self.i2)
self.assertAlmostEqual(actual, expected)
class TestSolution(unittest.TestCase):
def setUp(self):
self.s = Solution((0, 1, 2))
def test_move_generation(self):
expected = [(0, 1), (0, 2), (1, 2)]
actual = list(self.s.moves())
self.assertEqual(actual, expected)
def test_make_move1(self):
expected = (1, 0, 2)
actual = self.s.make_move((0, 1)).sequence
self.assertEqual(actual, expected)
def test_make_move2(self):
expected = (2, 1, 0)
actual = self.s.make_move((0, 2)).sequence
self.assertEqual(actual, expected)
def test_make_move3(self):
expected = (0, 2, 1)
actual = self.s.make_move((1, 2)).sequence
self.assertEqual(actual, expected)
def test_neighbours_generation(self):
expected = [(1, 0, 2), (2, 1, 0), (0, 2, 1)]
actual = map(lambda s: s.sequence, list(self.s.neighbours()))
self.assertEqual(actual, expected)
if __name__ == '__main__':
unittest.main()