def test_solution_string(self):
sol1 = SolutionTuple(SOLUTIONS[0][0], SOLUTIONS[0][1])
sol2 = SolutionTuple(SOLUTIONS[1][0], SOLUTIONS[1][1])
sol3 = SolutionTuple(SOLUTIONS[2][0], SOLUTIONS[2][1])
self.assertEqual(sol1.__str__(), "[1, 2, 3] - 0.1 sense: max")
self.assertEqual(sol2.__str__(), "[1, 2, 3, 4] - 0.1 sense: max")
self.assertEqual(sol3.__str__(), "[2, 3, 4] - 0.45 sense: max")
def test_callable_pool(self):
pool = BestSolutionArchiver()
size = 3
args = {}
args.setdefault('max_archive_size', size)
population = [SolutionTuple(SOLUTIONS[0][0], SOLUTIONS[0][1]),
SolutionTuple(SOLUTIONS[1][0], SOLUTIONS[1][1]),
SolutionTuple(SOLUTIONS[2][0], SOLUTIONS[2][1]),
SolutionTuple(SOLUTIONS[3][0], SOLUTIONS[3][1]),
SolutionTuple(SOLUTIONS[4][0], SOLUTIONS[4][1]),
SolutionTuple(SOLUTIONS[5][0], SOLUTIONS[5][1]),
SolutionTuple(SOLUTIONS[6][0], SOLUTIONS[6][1])]
archive = pool(None, population, [], args)
self.assertEqual(pool.length(), size)
for sol in pool:
self.assertTrue(sol in archive)
def test_result(self):
result = KnockoutOptimizationResult(
model=self.model,
heuristic_method=None,
simulation_method=fba,
solutions=self.solutions,
objective_function=None,
ko_type="reaction",
decoder=self.decoder,
product="EX_ac_LPAREN_e_RPAREN_",
biomass="Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2",
seed=SEED,
reference=None)
self.assertEqual(result.ko_type, "reaction")
individuals = []
for index, row in result.solutions.iterrows():
individual = SolutionTuple(set(self.representation.index(r) for r in row["Knockouts"]), row["Fitness"])
self.assertNotIn(individual, individuals, msg="%s is repeated on result")
individuals.append(individual)
self.assertIn(individual, self.solutions.archive)
self.assertEqual(len(row["Knockouts"]), row["Size"])
self.assertEqual(self.solutions.archive.count(individual), 1, msg="%s is unique in archive" % individual)
def test_solution_comparison_maximization(self):
sol1 = SolutionTuple(SOLUTIONS[0][0], SOLUTIONS[0][1])
sol2 = SolutionTuple(SOLUTIONS[1][0], SOLUTIONS[1][1])
sol3 = SolutionTuple(SOLUTIONS[2][0], SOLUTIONS[2][1])
# test ordering
self.assertEqual(sol1.__cmp__(sol2), -1)
self.assertEqual(sol1.__cmp__(sol1), 0)
self.assertEqual(sol1.__cmp__(sol3), 1)
self.assertTrue(sol1 < sol2)
self.assertTrue(sol1 == sol1)
self.assertTrue(sol1 > sol3)
# test gt and lt
self.assertTrue(sol1.__lt__(sol2))
self.assertTrue(sol1.__gt__(sol3))
self.assertFalse(sol1.__lt__(sol1))
self.assertFalse(sol1.__gt__(sol1))
self.assertFalse(sol2.__lt__(sol1))
self.assertFalse(sol3.__gt__(sol1))
# testing issubset
self.assertTrue(sol1.issubset(sol2), msg="Solution 1 is subset of Solution 2")
self.assertFalse(sol2.issubset(sol1), msg="Solution 2 is not subset of Solution 1")
self.assertTrue(sol3.issubset(sol2), msg="Solution 3 is subset of Solution 2")
self.assertFalse(sol2.issubset(sol3), msg="Solution 2 is not subset of Solution 3")
self.assertFalse(sol1.issubset(sol3), msg="Solution 1 is subset of Solution 3")
self.assertFalse(sol2.issubset(sol3), msg="Solution 3 is not subset of Solution 1")
# test difference
l = len(sol2.symmetric_difference(sol1))
self.assertEqual(l, 1, msg="Difference between Solution 2 and 1 is (%s)" % sol2.symmetric_difference(sol1))
l = len(sol3.symmetric_difference(sol2))
self.assertEqual(l, 1, msg="Difference between Solution 3 and 1 is (%s)" % sol3.symmetric_difference(sol2))
l = len(sol3.symmetric_difference(sol1))
self.assertEqual(l, 2, msg="Difference between Solution 1 and 3 is (%s)" % sol3.symmetric_difference(sol1))
self.assertTrue(sol1.improves(sol2), msg="Solution 1 is better than Solution 2")
self.assertTrue(sol3.improves(sol2), msg="Solution 3 is better than Solution 2")
self.assertFalse(sol3.improves(sol1), msg="Solution 3 does not improve Solution 1")
self.assertFalse(sol2.improves(sol1), msg="Solution 2 does not improve Solution 1")
self.assertFalse(sol2.improves(sol3), msg="Solution 2 does not improve Solution 3")