class TreeMutation(object):
def __init__(self, config, **kwargs):
self.config = config
self.recorder = kwargs.get("recorder", None)
self.generator = TreeGenerator(self.config)
# mutation stats
self.method = None
self.index = None
self.mutation_probability = None
self.random_probability = None
self.mutated = False
self.before_mutation = None
self.after_mutation = None
def generate_new_node(self, details):
if details is None:
return None
elif details["type"] == NodeType.FUNCTION:
return Node(
NodeType.FUNCTION,
name=details["name"],
arity=details["arity"],
branches=[]
)
elif details["type"] == NodeType.CLASS_FUNCTION:
return Node(
NodeType.CLASS_FUNCTION,
name=details["name"],
arity=details["arity"],
branches=[]
)
elif details["type"] == NodeType.INPUT:
return Node(
NodeType.INPUT,
name=details["name"]
)
elif details["type"] == NodeType.CONSTANT:
return Node(
NodeType.CONSTANT,
name=details.get("name", None),
value=details["value"]
)
elif details["type"] == NodeType.RANDOM_CONSTANT:
resolved_details = self.generator.resolve_random_constant(details)
return Node(
NodeType.CONSTANT,
name=resolved_details.get("name", None),
value=resolved_details["value"]
)
def mutate_new_node_details(self, old_node):
# determine what kind of old_node it is
node_pool = []
if old_node.is_function() or old_node.is_class_function():
tmp = list(self.config["function_nodes"])
tmp = [n for n in tmp if n["arity"] == old_node.arity]
node_pool.extend(tmp)
elif old_node.is_terminal():
node_pool.extend(self.config["terminal_nodes"])
# check the node and return
retry = 0
retry_limit = 100
while True:
if retry == retry_limit:
return None
else:
retry += 1
n_details = sample(node_pool, 1)[0]
if n_details["type"] == NodeType.RANDOM_CONSTANT:
n_details = self.generator.resolve_random_constant(n_details)
elif n_details["type"] == NodeType.CLASS_FUNCTION:
n_details = self.generator.resolve_class_function(n_details)
new_node = self.generate_new_node(n_details)
if old_node.equals(new_node) is False:
return n_details
def point_mutation(self, tree, mutation_index=None):
# mutate node
self.index = randint(0, len(tree.program) - 1)
node = tree.program[self.index]
new_node = self.mutate_new_node_details(node)
if new_node is None:
return
elif node.is_function():
node.name = new_node["name"]
elif node.is_terminal():
node.node_type = new_node.get("type")
node.name = new_node.get("name", None)
node.value = new_node.get("value", None)
tree.update()
self.mutated = True
def hoist_mutation(self, tree, mutation_index=None):
# new indivdiaul generated from subtree
node = None
if mutation_index is None:
self.index = randint(0, len(tree.program) - 2)
node = tree.program[self.index]
else:
self.index = mutation_index
node = tree.program[mutation_index]
tree.root = node
tree.update()
self.mutated = True
def subtree_mutation(self, tree, mutation_index=None):
# subtree exchanged against external random subtree
node = None
if mutation_index is None:
self.index = randint(0, len(tree.program) - 1)
node = tree.program[self.index]
else:
self.index = mutation_index
node = tree.program[mutation_index]
self.generator.max_depth = randint(1, 3)
sub_tree = self.generator.generate_tree()
if node is not tree.root:
tree.replace_node(node, sub_tree.root)
else:
tree.root = sub_tree.root
tree.update()
self.mutated = True
def shrink_mutation(self, tree, mutation_index=None):
# replace subtree with terminal
if len(tree.func_nodes):
node = None
if mutation_index is None:
self.index = randint(0, len(tree.func_nodes) - 1)
node = tree.func_nodes[self.index]
while node is tree.root:
self.index = randint(0, len(tree.func_nodes) - 1)
node = tree.func_nodes[self.index]
else:
self.index = mutation_index
node = tree.program[mutation_index]
candidate_nodes = tree.term_nodes
candidate_nodes.extend(tree.input_nodes)
new_node_detail = sample(candidate_nodes, 1)[0]
node_details = self.mutate_new_node_details(new_node_detail)
new_node = self.generate_new_node(node_details)
if new_node:
tree.replace_node(node, new_node)
tree.update()
self.mutated = True
def expansion_mutation(self, tree, mutation_index=None):
# terminal exchanged against external random subtree
node = None
if mutation_index is None:
prob = random()
if tree.size == 1:
return
elif prob > 0.5 and len(tree.term_nodes) > 0:
self.index = randint(0, len(tree.term_nodes) - 1)
node = tree.term_nodes[self.index]
elif prob < 0.5 and len(tree.input_nodes) > 0:
self.index = randint(0, len(tree.input_nodes) - 1)
node = tree.input_nodes[self.index]
elif len(tree.term_nodes) > 0:
self.index = randint(0, len(tree.term_nodes) - 1)
node = tree.term_nodes[self.index]
elif len(tree.input_nodes) > 0:
self.index = randint(0, len(tree.input_nodes) - 1)
node = tree.input_nodes[self.index]
else:
self.index = mutation_index
node = tree.program[mutation_index]
sub_tree = self.generator.generate_tree()
tree.replace_node(node, sub_tree.root)
tree.update()
self.mutated = True
def mutate(self, tree):
mutation_methods = {
"POINT_MUTATION": self.point_mutation,
"HOIST_MUTATION": self.hoist_mutation,
"SUBTREE_MUTATION": self.subtree_mutation,
"SHRINK_MUTATION": self.shrink_mutation,
"EXPAND_MUTATION": self.expansion_mutation
}
self.method = sample(self.config["mutation"]["methods"], 1)[0]
self.index = None
self.mutation_probability = self.config["mutation"]["probability"]
self.random_probability = random()
self.mutated = False
self.before_mutation = None
self.after_mutation = None
# record before mutation
self.before_mutation = tree.to_dict()["program"]
# mutate
if self.mutation_probability >= self.random_probability:
mutation_func = mutation_methods[self.method]
mutation_func(tree)
# record after mutation
self.after_mutation = tree.to_dict()["program"]
# record
if self.recorder is not None:
self.recorder.record(RecordType.MUTATION, self)
def to_dict(self):
self_dict = {
"method": self.method,
"mutation_index": self.index,
"mutation_probability": self.mutation_probability,
"random_probability": self.random_probability,
"mutated": self.mutated,
"before_mutation": self.before_mutation,
"after_mutation": self.after_mutation
}
return self_dict
class TreeMutation(object):
def __init__(self, config, **kwargs):
self.config = config
self.recorder = kwargs.get("recorder", None)
self.generator = TreeGenerator(self.config)
# mutation stats
self.method = None
self.index = None
self.mutation_probability = None
self.random_probability = None
self.mutated = False
self.before_mutation = None
self.after_mutation = None
def generate_new_node(self, details):
if details is None:
return None
elif details["type"] == NodeType.FUNCTION:
return Node(NodeType.FUNCTION,
name=details["name"],
arity=details["arity"],
branches=[])
elif details["type"] == NodeType.CLASS_FUNCTION:
return Node(NodeType.CLASS_FUNCTION,
name=details["name"],
arity=details["arity"],
branches=[])
elif details["type"] == NodeType.INPUT:
return Node(NodeType.INPUT, name=details["name"])
elif details["type"] == NodeType.CONSTANT:
return Node(NodeType.CONSTANT,
name=details.get("name", None),
value=details["value"])
elif details["type"] == NodeType.RANDOM_CONSTANT:
resolved_details = self.generator.resolve_random_constant(details)
return Node(NodeType.CONSTANT,
name=resolved_details.get("name", None),
value=resolved_details["value"])
def mutate_new_node_details(self, old_node):
# determine what kind of old_node it is
node_pool = []
if old_node.is_function() or old_node.is_class_function():
tmp = list(self.config["function_nodes"])
tmp = [n for n in tmp if n["arity"] == old_node.arity]
node_pool.extend(tmp)
elif old_node.is_terminal():
node_pool.extend(self.config["terminal_nodes"])
# check the node and return
retry = 0
retry_limit = 100
while True:
if retry == retry_limit:
return None
else:
retry += 1
n_details = sample(node_pool, 1)[0]
if n_details["type"] == NodeType.RANDOM_CONSTANT:
n_details = self.generator.resolve_random_constant(n_details)
elif n_details["type"] == NodeType.CLASS_FUNCTION:
n_details = self.generator.resolve_class_function(n_details)
new_node = self.generate_new_node(n_details)
if old_node.equals(new_node) is False:
return n_details
def point_mutation(self, tree, mutation_index=None):
# mutate node
self.index = randint(0, len(tree.program) - 1)
node = tree.program[self.index]
new_node = self.mutate_new_node_details(node)
if new_node is None:
return
elif node.is_function():
node.name = new_node["name"]
elif node.is_terminal():
node.node_type = new_node.get("type")
node.name = new_node.get("name", None)
node.value = new_node.get("value", None)
tree.update()
self.mutated = True
def hoist_mutation(self, tree, mutation_index=None):
# new indivdiaul generated from subtree
node = None
if mutation_index is None:
self.index = randint(0, len(tree.program) - 2)
node = tree.program[self.index]
else:
self.index = mutation_index
node = tree.program[mutation_index]
tree.root = node
tree.update()
self.mutated = True
def subtree_mutation(self, tree, mutation_index=None):
# subtree exchanged against external random subtree
node = None
if mutation_index is None:
self.index = randint(0, len(tree.program) - 1)
node = tree.program[self.index]
else:
self.index = mutation_index
node = tree.program[mutation_index]
self.generator.max_depth = randint(1, 3)
sub_tree = self.generator.generate_tree()
if node is not tree.root:
tree.replace_node(node, sub_tree.root)
else:
tree.root = sub_tree.root
tree.update()
self.mutated = True
def shrink_mutation(self, tree, mutation_index=None):
# replace subtree with terminal
if len(tree.func_nodes):
node = None
if mutation_index is None:
self.index = randint(0, len(tree.func_nodes) - 1)
node = tree.func_nodes[self.index]
while node is tree.root:
self.index = randint(0, len(tree.func_nodes) - 1)
node = tree.func_nodes[self.index]
else:
self.index = mutation_index
node = tree.program[mutation_index]
candidate_nodes = tree.term_nodes
candidate_nodes.extend(tree.input_nodes)
new_node_detail = sample(candidate_nodes, 1)[0]
node_details = self.mutate_new_node_details(new_node_detail)
new_node = self.generate_new_node(node_details)
if new_node:
tree.replace_node(node, new_node)
tree.update()
self.mutated = True
def expansion_mutation(self, tree, mutation_index=None):
# terminal exchanged against external random subtree
node = None
if mutation_index is None:
prob = random()
if tree.size == 1:
return
elif prob > 0.5 and len(tree.term_nodes) > 0:
self.index = randint(0, len(tree.term_nodes) - 1)
node = tree.term_nodes[self.index]
elif prob < 0.5 and len(tree.input_nodes) > 0:
self.index = randint(0, len(tree.input_nodes) - 1)
node = tree.input_nodes[self.index]
elif len(tree.term_nodes) > 0:
self.index = randint(0, len(tree.term_nodes) - 1)
node = tree.term_nodes[self.index]
elif len(tree.input_nodes) > 0:
self.index = randint(0, len(tree.input_nodes) - 1)
node = tree.input_nodes[self.index]
else:
self.index = mutation_index
node = tree.program[mutation_index]
sub_tree = self.generator.generate_tree()
tree.replace_node(node, sub_tree.root)
tree.update()
self.mutated = True
def mutate(self, tree):
mutation_methods = {
"POINT_MUTATION": self.point_mutation,
"HOIST_MUTATION": self.hoist_mutation,
"SUBTREE_MUTATION": self.subtree_mutation,
"SHRINK_MUTATION": self.shrink_mutation,
"EXPAND_MUTATION": self.expansion_mutation
}
self.method = sample(self.config["mutation"]["methods"], 1)[0]
self.index = None
self.mutation_probability = self.config["mutation"]["probability"]
self.random_probability = random()
self.mutated = False
self.before_mutation = None
self.after_mutation = None
# record before mutation
self.before_mutation = tree.to_dict()["program"]
# mutate
if self.mutation_probability >= self.random_probability:
mutation_func = mutation_methods[self.method]
mutation_func(tree)
# record after mutation
self.after_mutation = tree.to_dict()["program"]
# record
if self.recorder is not None:
self.recorder.record(RecordType.MUTATION, self)
def to_dict(self):
self_dict = {
"method": self.method,
"mutation_index": self.index,
"mutation_probability": self.mutation_probability,
"random_probability": self.random_probability,
"mutated": self.mutated,
"before_mutation": self.before_mutation,
"after_mutation": self.after_mutation
}
return self_dict
class ClassifierEvaluationTests(unittest.TestCase):
def setUp(self):
self.config = {
"max_population": 5,
"tree_generation": {
"tree_type": "CLASSIFICATION_TREE",
"method": "FULL_METHOD",
"initial_max_depth": 2
},
"evaluator": {
"use_cache": True
},
"function_nodes": [
{
"type": "CLASS_FUNCTION",
"name": "GREATER_THAN",
"arity": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
},
{
"type": "CLASS_FUNCTION",
"name": "LESS_THAN",
"arity": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
},
{
"type": "CLASS_FUNCTION",
"name": "EQUALS",
"arity": 2,
"decimal_precision": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
}
],
"terminal_nodes": [
{
"type": "RANDOM_CONSTANT",
"name": "species",
"range": [
1.0,
2.0,
3.0
]
},
],
"input_variables": [
{"name": "sepal_length"},
{"name": "sepal_width"},
{"name": "petal_length"},
{"name": "petal_width"}
],
"class_attributes": [
"sepal_length",
"sepal_width",
"petal_length",
"petal_width"
],
"data_file": "tests/data/iris.dat",
"response_variables": [{"name": "species"}]
}
config.load_data(self.config)
self.functions = GPFunctionRegistry("CLASSIFICATION")
self.generator = TreeGenerator(self.config)
self.population = self.generator.init()
def test_evaluate_tree(self):
for i in range(100):
tree = self.generator.generate_tree()
score, output = evaluate_tree(tree, self.functions, self.config)
# self.assertTrue(score < 1.0)
self.assertEquals(len(output), self.config["data"]["rows"])
def test_evaluate(self):
results = []
evaluate(self.population.individuals, self.functions, self.config, results)
self.assertTrue(len(results), len(self.population.individuals))
class ClassifierEvaluationTests(unittest.TestCase):
def setUp(self):
self.config = {
"max_population":
5,
"tree_generation": {
"tree_type": "CLASSIFICATION_TREE",
"method": "FULL_METHOD",
"initial_max_depth": 2
},
"evaluator": {
"use_cache": True
},
"function_nodes": [{
"type": "CLASS_FUNCTION",
"name": "GREATER_THAN",
"arity": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
}, {
"type": "CLASS_FUNCTION",
"name": "LESS_THAN",
"arity": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
}, {
"type": "CLASS_FUNCTION",
"name": "EQUALS",
"arity": 2,
"decimal_precision": 2,
"data_range": {
"lower_bound": 0.0,
"upper_bound": 10.0,
"decimal_places": 0,
}
}],
"terminal_nodes": [
{
"type": "RANDOM_CONSTANT",
"name": "species",
"range": [1.0, 2.0, 3.0]
},
],
"input_variables": [{
"name": "sepal_length"
}, {
"name": "sepal_width"
}, {
"name": "petal_length"
}, {
"name": "petal_width"
}],
"class_attributes":
["sepal_length", "sepal_width", "petal_length", "petal_width"],
"data_file":
"tests/data/iris.dat",
"response_variables": [{
"name": "species"
}]
}
config.load_data(self.config)
self.functions = GPFunctionRegistry("CLASSIFICATION")
self.generator = TreeGenerator(self.config)
self.population = self.generator.init()
def test_evaluate_tree(self):
for i in range(100):
tree = self.generator.generate_tree()
score, output = evaluate_tree(tree, self.functions, self.config)
# self.assertTrue(score < 1.0)
self.assertEquals(len(output), self.config["data"]["rows"])
def test_evaluate(self):
results = []
evaluate(self.population.individuals, self.functions, self.config,
results)
self.assertTrue(len(results), len(self.population.individuals))
