def onepoint_crossover(chain_first: Any, chain_second: Any,
requirements) -> Any:
max_depth = requirements.max_depth
pairs_of_nodes = equivalent_subtree(chain_first, chain_second)
if pairs_of_nodes:
node_from_chain_first, node_from_chain_second = choice(pairs_of_nodes)
summary_depth = node_depth(chain_first.root_node) - node_depth(
node_from_chain_first) + node_depth(node_from_chain_second)
if summary_depth <= max_depth and summary_depth != 0:
chain_first.replace_node_with_parents(node_from_chain_first,
node_from_chain_second)
return chain_first
def get_mutation_prob(mut_id, root_node):
default_mutation_prob = 0.7
if mut_id == MutationPowerEnum.weak.value:
mutation_strength = 0.2
return mutation_strength / (node_depth(root_node) + 1)
elif mut_id == MutationPowerEnum.mean.value:
mutation_strength = 1.0
return mutation_strength / (node_depth(root_node) + 1)
elif mut_id == MutationPowerEnum.strong.value:
mutation_strength = 5.0
return mutation_strength / (node_depth(root_node) + 1)
else:
return default_mutation_prob
def growth_mutation(chain: Any,
parameters: MutationParams,
local_growth=True) -> Any:
random_layer_in_chain = randint(0, chain.depth - 1)
node_from_chain = choice(
nodes_from_height(chain.root_node, random_layer_in_chain))
if local_growth:
is_primary_node_selected = (not node_from_chain.nodes_from) or (
node_from_chain.nodes_from and node_from_chain != chain.root_node
and randint(0, 1))
else:
is_primary_node_selected = randint(0, 1) and not node_height(chain, node_from_chain) \
< parameters.requirements.max_depth
if is_primary_node_selected:
new_subtree = parameters.primary_node_func(
model_type=choice(parameters.requirements.primary))
else:
if local_growth:
max_depth = node_depth(node_from_chain)
else:
max_depth = parameters.requirements.max_depth - node_height(
chain, node_from_chain)
new_subtree = random_ml_chain(parameters.chain_class,
parameters.secondary_node_func,
parameters.primary_node_func,
parameters.requirements,
max_depth=max_depth).root_node
chain.replace_node_with_parents(node_from_chain, new_subtree)
return chain
def subtree_crossover(chain_first: Any, chain_second: Any,
requirements) -> Any:
max_depth = requirements.max_depth
random_layer_in_chain_first = randint(0, chain_first.depth - 1)
random_layer_in_chain_second = randint(0, chain_second.depth - 1)
if chain_first.depth - 1 != 0 and chain_second.depth - 1 != 0:
if random_layer_in_chain_first == 0 and random_layer_in_chain_second == 0:
if randint(0, 1):
random_layer_in_chain_first = randint(1, chain_first.depth - 1)
else:
random_layer_in_chain_second = randint(1,
chain_second.depth - 1)
node_from_chain_first = choice(
nodes_from_height(chain_first.root_node, random_layer_in_chain_first))
node_from_chain_second = choice(
nodes_from_height(chain_second.root_node,
random_layer_in_chain_second))
summary_depth = random_layer_in_chain_first + node_depth(
node_from_chain_second)
if summary_depth <= max_depth and summary_depth != 0:
chain_first.replace_node_with_parents(node_from_chain_first,
node_from_chain_second)
return chain_first
def one_point_crossover(chain_first: Any, chain_second: Any,
max_depth: int) -> Any:
"""Finds common structural parts between two trees, and after that randomly
chooses the location of nodes, subtrees of which will be swapped"""
pairs_of_nodes = equivalent_subtree(chain_first, chain_second)
if pairs_of_nodes:
node_from_chain_first, node_from_chain_second = choice(pairs_of_nodes)
layer_in_chain_first = node_depth(
chain_first.root_node) - node_depth(node_from_chain_first)
layer_in_chain_second = node_depth(
chain_second.root_node) - node_depth(node_from_chain_second)
replace_subtrees(chain_first, chain_second, node_from_chain_first,
node_from_chain_second, layer_in_chain_first,
layer_in_chain_second, max_depth)
return chain_first, chain_second
def nn_growth_mutation(chain: Any,
parameters: MutationParams,
local_growth=True) -> Any:
ComposerVisualiser.visualise(chain)
primary_nodes = parameters.requirements.primary
secondary_nodes = parameters.requirements.secondary
random_layer_in_chain = randint(0, node_depth(chain.root_node))
print(random_layer_in_chain)
print(node_depth(chain.root_node) + 1)
node_from_chain = choice(
nodes_from_height(chain.root_node, random_layer_in_chain))
if local_growth:
is_primary_node_selected = (not node_from_chain.nodes_from) or (
node_from_chain.nodes_from and node_from_chain != chain.root_node
and randint(0, 1))
else:
is_primary_node_selected = randint(0, 1) and not node_height(chain, node_from_chain) \
< parameters.requirements.max_depth
if is_primary_node_selected:
new_node_type = choice(primary_nodes)
new_layer_params = get_random_layer_params(new_node_type,
parameters.requirements)
new_subtree = parameters.primary_node_func(
layer_params=new_layer_params)
chain.replace_node_with_parents(node_from_chain, new_subtree)
else:
if local_growth:
max_depth = node_depth(node_from_chain)
else:
max_depth = parameters.requirements.max_depth - random_layer_in_chain
new_node_type = choice(secondary_nodes)
new_layer_params = get_random_layer_params(new_node_type,
parameters.requirements)
new_subtree = parameters.secondary_node_func(
layer_params=new_layer_params)
offspring_size = randint(parameters.requirements.min_arity,
parameters.requirements.max_arity)
for _ in range(offspring_size):
random_nn_branch(
secondary_node_func=parameters.secondary_node_func,
primary_node_func=parameters.primary_node_func,
requirements=parameters.requirements,
max_depth=max_depth,
start_height=(node_height(chain, node_from_chain)),
node_parent=new_subtree)
chain.replace_node_with_parents(node_from_chain, new_subtree)
def growth_mutation(chain: Any, chain_class, secondary_node_func: Callable,
primary_node_func: Callable, requirements) -> Any:
chain_copy = deepcopy(chain)
random_layer_in_chain = randint(0, chain_copy.depth - 1)
node_from_chain = choice(
nodes_from_height(chain_copy, random_layer_in_chain))
is_primary_node_selected = (not node_from_chain.nodes_from) or (
node_from_chain.nodes_from and node_from_chain != chain_copy.root_node
and randint(0, 1))
if is_primary_node_selected:
new_subtree = primary_node_func(
model_type=choice(requirements.primary))
else:
max_depth = node_depth(node_from_chain)
new_subtree = random_chain(chain_class,
secondary_node_func,
primary_node_func,
requirements,
max_depth=max_depth).root_node
chain_copy.replace_node_with_parents(node_from_chain, new_subtree)
return chain_copy