def elite_architecture(pop: Population,
show: bool = False,
del_cache: bool = True):
"""
Visualize each architectural change in the population's elites.
:param pop: Population object
:param show: Show the result
:param del_cache: Remove intermediate architecture-results when images are created
"""
# Initialize the architecture-list with the first genome
distance = GenomeDistanceCache(config=pop.config.genome)
new_architectures = [(0, pop.best_genome_hist[0][1])
] # Only interested in genome itself
for gen in range(1, pop.generation):
gen_genome = pop.best_genome_hist[gen][1]
if distance(
gen_genome, new_architectures[-1]
[1]) > 2: # Take only the more significant changes into account
new_architectures.append((gen, gen_genome))
new_architectures.append(
(pop.generation - 1, pop.best_genome_hist[pop.generation - 1][1]))
# Create the architectures of the unique genomes
for _, g in new_architectures:
pop.visualize_genome(
debug=False, # Keep the networks simple
genome=g,
show=False,
)
# Combine in one figure
hor = min(len(new_architectures), 5)
vert = max((len(new_architectures) - 1) // 5 + 1, 1)
plt.figure(figsize=(5 * hor, 5 * vert))
plt.tight_layout()
f_images = get_subfolder(
f"population{'_backup' if pop.use_backup else ''}/storage/{pop.folder_name}/{pop}/",
"images")
f_architectures = get_subfolder(f_images, "architectures")
for i, (gen, g) in enumerate(new_architectures):
plt.subplot(vert, hor, i + 1)
img = mpimg.imread(f'{f_architectures}genome_{g.key}.png')
plt.imshow(img)
plt.title(f'Generation {gen}')
plt.axis('off')
# Save the result
f_elites = get_subfolder(f_images, "elites")
plt.savefig(f'{f_elites}/architecture_timeline.png', bbox_inches='tight')
if show:
plt.show()
plt.close()
if del_cache:
for (_, g) in new_architectures:
path = f'{f_architectures}genome_{g.key}.png'
if os.path.exists(path): os.remove(path)
def visualize_genome(
population: Population,
genome: Genome,
debug: bool = True,
show: bool = False,
):
"""Visualize the requested genome."""
print("\n===> VISUALIZING GENOME <===\n")
print(f"Genome {genome.key} with size: {genome.size()}")
population.visualize_genome(
debug=debug,
genome=genome,
show=show,
)
def specie_representatives(pop: Population,
show: bool = True,
del_cache: bool = True):
"""Show for each of the current species their representative's architecture."""
species = pop.species.species
elite_id = dict()
for sid, s in sorted(species.items()):
elite_id[sid] = s.representative.key
pop.visualize_genome(
debug=False, # Keep the networks simple
genome=s.representative,
show=False,
)
hor = min(len(elite_id), 5)
vert = max(len(elite_id) // 5 + 1, 1)
plt.figure(figsize=(5 * hor, 5 * vert))
plt.tight_layout()
path = get_subfolder(
f"population{'_backup' if pop.use_backup else ''}/storage/{pop.folder_name}/{pop}/",
"images")
path_architectures = get_subfolder(path, "architectures")
for i, (sid, eid) in enumerate(elite_id.items()):
plt.subplot(vert, hor, i + 1)
img = mpimg.imread(f'{path_architectures}genome_{eid}.png')
plt.imshow(img)
plt.title(f'Specie {sid}')
plt.axis('off')
# Save the result
path_species = get_subfolder(path, 'species')
plt.savefig(f'{path_species}representatives_gen{pop.generation}.png',
bbox_inches='tight')
if show:
plt.show()
plt.close()
if del_cache:
for eid in elite_id.values():
path = f'{path_architectures}genome_{eid}.png'
if os.path.exists(path): os.remove(path)
