def test_calc_node_depth(self):
node.set_id(self.n1, 0)
node.set_id(self.n2, 1)
node.set_children(self.n1, [self.n2, self.n3])
node.set_children(self.n2, [self.n4, self.n5, self.n6])
self.assertEqual(node.calc_node_depth(self.n1), 2)
def one_point(solution, func_bank):
"""
Core function of one_point mutation.
:param solution: solution object. solution which is applied mutation.
:param func_bank: function bank object. function bank which is defined in problem.py.
:return: solution object.
"""
point = random.choice(node.get_all_node(solution.root))
n_children = len(point.children)
function_list = func_bank.get_function_list(n_children)
if function_list is None:
raise ValueError(
"function bank must have {}'s function list, but it has no list.".
format(n_children))
if len(function_list) == 1:
return solution
candidate_id = random.sample(function_list, 2)
if point.func_id != candidate_id[0]:
node.set_id(point, candidate_id[0])
else:
node.set_id(point, candidate_id[1])
return solution
def test_get_all_node(self):
node.set_id(self.n1, 0)
node.set_id(self.n2, 1)
node.set_children(self.n1, [self.n2, self.n3])
node.set_children(self.n2, [self.n4, self.n5, self.n6])
all_nodes = [self.n1, self.n2, self.n4, self.n5, self.n6, self.n3]
self.assertEqual(node.get_all_node(self.n1), all_nodes)
def test_get_nonterminal_nodes(self):
node.set_id(self.n1, 0)
node.set_id(self.n2, 1)
node.set_children(self.n1, [self.n2, self.n3])
node.set_children(self.n2, [self.n4, self.n5])
node.set_children(self.n3, [self.n6])
nonterminal_nodes = node.get_all_nonterminal_nodes(self.n1)
expected_nonterminal_nodes = [self.n1, self.n2, self.n3]
for n in nonterminal_nodes:
self.assertTrue(n in expected_nonterminal_nodes)
def test_get_parent_node(self):
node.set_id(self.n1, 0)
node.set_id(self.n2, 1)
node.set_children(self.n1, [self.n2, self.n3])
node.set_children(self.n2, [self.n4, self.n5, self.n6])
self.assertEqual(node.get_parent_node(self.n1, self.n4), (0, self.n2))
msg = 'There is no parent of root.'
with self.assertRaises(ValueError, msg=msg):
node.get_parent_node(self.n1, self.n1)
msg = 'Invalid arguments: cannot find parent.'
with self.assertRaises(ValueError, msg=msg):
node.get_parent_node(node.Node(), node.Node())
def test__copy_nodes_along_graph(self):
node.set_id(self.n1, 0)
node.set_id(self.n2, 1)
node.set_children(self.n1, [self.n2, self.n3])
node.set_children(self.n2, [self.n4, self.n5])
node.set_children(self.n3, [self.n6])
graph = [(0, self.n1), (1, self.n2)]
pos, parent, root = node.copy_nodes_along_graph(graph)
self.assertEqual(pos, 1)
self.assertTrue(node.node_equal(parent, self.n2))
self.assertTrue(node.node_equal(root, self.n1, as_tree=True))
self.assertFalse(parent is self.n2)
self.assertFalse(root is self.n1)
def new_node(parent, depth):
current_node = node.Node()
if self.t_prob > random.random() or depth == self.max_depth:
func_id = random.choice(self.terminal_list)
else:
current_node.children = []
func_id = random.choice(self.nonterminal_list)
n_child = node.get_n_children(
func_id, self.func_bank.get_function_list())
for _ in range(n_child):
new_node(current_node, depth + 1)
node.set_id(current_node, func_id)
if parent is not None:
parent.children.append(current_node)
else:
return current_node
def test_solution_equal(self):
na1 = node.Node(0)
na2 = node.Node(1)
na3 = node.Node(0)
na4 = node.Node(1)
na5 = node.Node(0)
na6 = node.Node(1)
node.set_id(na1, 0)
node.set_id(na2, 1)
node.set_children(na1, [na2, na3])
node.set_children(na2, [na4, na5, na6])
sa = solution.Solution(na1)
self.assertTrue(solution.solution_equal(self.s1, sa, True))
self.assertFalse(solution.solution_equal(self.s1, sa, False))
na4 = node.Node(0)
node.set_children(na2, [na4, na5, na6])
self.assertFalse(solution.solution_equal(self.s1, sa, True))
def test_destructive_replace_node(self):
na1 = node.Node(1)
na2 = node.Node(0)
node.set_id(na1, 0)
node.set_id(na2, 1)
node.set_children(na1, [na2])
original_s1 = copy.deepcopy(self.s1)
new_s1 = solution.replace_node(self.s1, self.n2, na1, destructive=True)
expected_nodes = [node.Node(0), node.Node(0), node.Node(1), node.Node(0)]
node.set_children(expected_nodes[0], [expected_nodes[1], expected_nodes[3]])
node.set_children(expected_nodes[1], [expected_nodes[2]])
self.assertTrue(node.node_equal(new_s1.root, expected_nodes[0], as_tree=True))
self.assertEqual(new_s1.n_nodes, len(expected_nodes))
self.assertEqual(new_s1.depth, 2)
# Check whether the original solution is NOT protected.
self.assertFalse(solution.solution_equal(original_s1, new_s1))
self.assertTrue(self.s1 is new_s1)
def test_stop_improvement_with_shuffle(self):
root = self.s1.root
c1 = root.children[0]
c2 = root.children[1]
is_shuffle = True
flg2 = False
flg4 = False
for i in range(100):
improved = localsearch.stop_improvement(self.s1,
root,
self.problem,
is_shuffle=is_shuffle)
if i == 0:
self.assertEqual(improved, True)
if root.func_id == 2:
flg2 = True
node.set_id(root, self.root_id)
solution.set_previous_fitness(self.s1, self.root_id)
elif root.func_id == 4:
flg4 = True
node.set_id(root, self.root_id)
solution.set_previous_fitness(self.s1, self.root_id)
if flg2 and flg4:
break
self.assertEqual(flg2 and flg4, True)
improved = localsearch.stop_improvement(self.s1,
c1,
self.problem,
is_shuffle=is_shuffle)
self.assertEqual(improved, False)
improved = localsearch.stop_improvement(self.s1,
c2,
self.problem,
is_shuffle=is_shuffle)
self.assertEqual(improved, False)
def improve(target_solution, target_node, candidate_id, problem):
"""
Core function for local search.
Replace the old function with a new function and then revert it if fitness is not improved.
:param target_solution: solution object. target solution of local search.
:param target_node: node object. target node of the target solution.
:param candidate_id: int. ID of candidate function for local search.
:param problem: problem object. problem for calculation of fitness.
:return: bool. if improvement is success, return True.
"""
pre_id = target_node.func_id
node.set_id(target_node, candidate_id)
if target_solution.previous_fitness is None: # if the solution does not have previous fitness, calculate it.
pre_fit = problem.fitness(target_solution)
else:
pre_fit = target_solution.previous_fitness
new_fit = problem.fitness(target_solution) # check the fitness.
if pre_fit >= new_fit: # if it is not success, revert the function.
node.set_id(target_node, pre_id)
solution.set_previous_fitness(target_solution, pre_fit)
return False
else:
return True
def bihc(target_solution, node_list, fs_core):
"""
Best improvement hill climber (BIHC) function.
:param node_list: list of node object. candidate node list.
:param fs_core: function. search function for a target node.
:return: solution object.
"""
pre_node = None
while node_list:
ori_fit = target_solution.previous_fitness
best_fit = target_solution.previous_fitness
best_node = None
best_id = None
for target_node in node_list: # Try to find the best-improving target node and the function id.
ori_id = target_node.func_id
fs_core(target_solution, target_node)
if best_fit < target_solution.previous_fitness:
best_node = target_node
best_id = target_node.func_id
# the target solution is reverted to the original for the next iteration.
node.set_id(target_node, ori_id)
solution.set_previous_fitness(target_solution, ori_fit)
# If there is no improvement, end this local search.
if best_node is None:
break
# Otherwise, adopt the improvement to the solution.
node.set_id(best_node, best_id)
solution.set_previous_fitness(target_solution, best_fit)
if pre_node is not None:
node_list.append(pre_node)
node_list.remove(best_node) # remove the replaced node from candidate node list
pre_node = best_node
return target_solution
def make_node(func_id):
new_node = node.Node()
node.set_id(new_node, func_id)
return new_node
def test_set_id(self):
func_id = 0
node.set_id(self.n1, func_id)
self.assertEqual(self.n1.func_id, func_id)
def test_node_array_equal(self):
self.assertTrue(
node.node_array_equal([self.n1, self.n2], [self.n3, self.n4]))
node.set_id(self.n4, 0)
self.assertFalse(
node.node_array_equal([self.n1, self.n2], [self.n3, self.n4]))
def test_node_equal(self):
node.set_id(self.n1, 0)
node.set_id(self.n2, 0)
node.set_id(self.n3, 0)
node.set_id(self.n4, 0)
node.set_id(self.n5, 0)
node.set_id(self.n6, 0)
node.set_children(self.n1, [self.n2, self.n3])
node.set_children(self.n4, [self.n5, self.n6])
self.assertTrue(node.node_equal(self.n1, self.n4))
self.assertTrue(node.node_equal(self.n1, self.n4, as_tree=True))
node.set_id(self.n2, 1)
self.assertFalse(node.node_equal(self.n1, self.n3))
self.assertFalse(node.node_equal(self.n2, self.n3))
self.assertFalse(node.node_equal(self.n1, self.n4, as_tree=True))