def visualise_crossover(genomes: List[Genome], count=1, parent_mutation_count = 1):
"""
pairs genomes, plots parents along with their children
"""
if len(genomes) < 2:
raise Exception("need at least 2 genomes to show cross over, num found: " + str(len(genomes)))
for i in range(count):
parent1 = copy.deepcopy(random.choice(genomes))
parent2 = None
found_unique_parent = False
while not found_unique_parent:
parent2 = copy.deepcopy(random.choice(genomes))
found_unique_parent = parent1 != parent2
child = cross.over(parent1, parent2)
if not child.validate():
print("invalid child")
continue
parent1_graph = get_graph_of(parent1, node_names="parent1", sub_graph=True, label= "parent 1")
parent2_graph = get_graph_of(parent2, node_names="parent2", sub_graph= True, label= "parent 2")
full_graph = get_graph_of(child, node_names="full", node_colour= "yellow")
full_graph.subgraph(parent1_graph)
full_graph.subgraph(parent2_graph)
full_graph.view(directory=runs_manager.get_graphs_folder_path())
def visualise_traversal_dict(traversal_dict: Dict[int, List[int]]):
g = Digraph(name="traversal_dict")
for from_id in traversal_dict.keys():
g.node(name=str(from_id))
for to_id in traversal_dict[from_id]:
g.node(name=str(to_id))
g.edge(str(from_id), str(to_id))
g.render(directory=runs_manager.get_graphs_folder_path(config.run_name),
view=config.view_graph_plots,
format="png")
def _log_imgs(generation: Generation) -> Dict[str, wandb.Image]:
imgs = {}
for root, _, files in os.walk(get_graphs_folder_path(config.run_name)):
for file in files:
if not file.endswith('.png'):
continue
for match in re.compile(r"_g[0-9]+_").finditer(str(file)):
if match[0][2:-1] == str(generation.generation_number - 1):
name = 'best_pheno' if 'phenotype' in file else 'best_geno'
imgs[name] = wandb.Image(
Image.open(os.path.join(root, file)), file)
return imgs
def visualise(pheno: neural_network,
prefix="",
suffix="",
node_colour: Any = "white"):
name = prefix + "blueprint_i" + str(
pheno.blueprint.id) + "_phenotype" + suffix
# print("saving:", name, "to",RunsManager.get_graphs_folder_path(config.run_name))
graph = graphviz.Digraph(name=name, comment='phenotype')
q: List[BaseLayer] = [pheno.model]
_node_colour = node_colour if isinstance(node_colour,
str) else node_colour(pheno.model)
graph.node(pheno.model.name,
pheno.model.get_layer_info(),
fillcolor=_node_colour,
style="filled")
visited = set()
while q:
parent_layer = q.pop()
if parent_layer.name not in visited:
visited.add(parent_layer.name)
child_layer: BaseLayer
for child_layer in parent_layer.child_layers:
_node_colour = node_colour if isinstance(
node_colour, str) else node_colour(child_layer)
description = child_layer.get_layer_info()
graph.node(child_layer.name,
child_layer.name + '\n' + description,
fillcolor=_node_colour,
style="filled")
graph.edge(parent_layer.name, child_layer.name)
q.append(child_layer)
try:
graph.render(directory=runs_manager.get_graphs_folder_path(
config.run_name),
view=config.view_graph_plots,
format="png")
except Exception as e:
print(e)
def visualise_mutation(genomes: List[Genome],
mutator: Union[module_genome_mutator, blueprint_genome_mutator],
mutation_record: mutation_record, count=1, num_mutations=1):
"""
samples genomes and plots them before and after mutation
"""
for i in range(count):
genome: Genome = random.choice(genomes)
genome = copy.deepcopy(genome)
before_graph = get_graph_of(genome, node_names="before")
mutant_genome = mutator.mutate(genome, mutation_record)
for i in range(num_mutations - 1):
# print("mutated genome")
mutant_genome = mutator.mutate(mutant_genome, mutation_record)
# print("mutated genome")
both_graph = get_graph_of(mutant_genome, node_names="after", append_graph=before_graph, node_colour="yellow")
both_graph.view(directory=runs_manager.get_graphs_folder_path())
def visualize(self):
get_graph_of(self).render(
directory=runs_manager.get_graphs_folder_path(config.run_name),
view=config.view_graph_plots)
def visualise_blueprint_genome(genome: BlueprintGenome,
sample_map: Dict[int, int] = None,
parse_number=-1,
prefix=""):
blueprint_graph = get_graph_of(
genome,
node_names="blueprint",
sample_map=sample_map,
node_colour=get_node_colour,
graph_title_prefix=prefix + "blueprint_",
graph_title_suffix=("_p" + str(parse_number) +
"_" if parse_number >= 0 else ""))
module_ids = set()
exception = None
plotted_species = set()
for bp_node_id in genome.get_fully_connected_node_ids():
bp_node = genome.nodes[bp_node_id]
if not isinstance(bp_node, BlueprintNode):
continue
species = bp_node.species_id
if species in plotted_species:
continue
plotted_species.add(species)
if bp_node.linked_module_id != -1:
module_id = bp_node.linked_module_id
elif species in sample_map.keys():
module_id = sample_map[species]
else:
exception = Exception(
"bp node is unmapped by both link and sample map: " +
repr(bp_node) + " scp_id: " + repr(species),
" sample map: " + repr(sample_map))
sub_graph_label = "Species: " + str(species) + "\nModule: " + str(
module_id)
node_names = "module_" + str(species) + "_" + str(module_id)
module = Singleton.instance.module_population[module_id]
module_graph = get_graph_of(module,
node_names=node_names,
sub_graph=True,
label=sub_graph_label,
node_colour="cyan")
blueprint_graph.subgraph(module_graph)
if config.evolve_da:
da: DAGenome = genome.get_da()
da_graph = get_graph_of(da,
sub_graph=True,
node_names="da_nodes",
label="Augmentation Scheme " + repr(da.id),
node_colour="pink")
blueprint_graph.subgraph(da_graph)
try:
blueprint_graph.render(directory=runs_manager.get_graphs_folder_path(
config.run_name),
view=config.view_graph_plots,
format="png")
except Exception as e:
print(e)
if exception is not None:
raise exception
