def run():
t0 = time.time()
# Get the path to the config file, which is assumed to live in
# the same directory as this script.
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'xor2_config')
# Use a pool of four workers to evaluate fitness in parallel.
pe = parallel.ParallelEvaluator(fitness, 3)
pop = population.Population(config_path)
pop.run(pe.evaluate, 400)
print("total evolution time {0:.3f} sec".format((time.time() - t0)))
print("time per generation {0:.3f} sec".format(
((time.time() - t0) / pop.generation)))
print('Number of evaluations: {0:d}'.format(pop.total_evaluations))
# Verify network output against training data.
print('\nBest network output:')
winner = pop.statistics.best_genome()
net = nn.create_feed_forward_phenotype(winner)
for i, inputs in enumerate(xor_inputs):
output = net.serial_activate(inputs) # serial activation
print("{0:1.5f} \t {1:1.5f}".format(xor_outputs[i], output[0]))
# Visualize the winner network and plot statistics.
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
visualize.draw_net(winner, view=True)
def run():
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, 'nn_config'))
pe = parallel.ParallelEvaluator(eval_fitness)
pop.run(pe.evaluate, 1000)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Display the most fit genome.
print('\nBest genome:')
winner = pop.statistics.best_genome()
print(winner)
# Verify network output against a few randomly-generated sequences.
winner_net = nn.create_recurrent_phenotype(winner)
for n in range(4):
print('\nRun {0} output:'.format(n))
seq = [random.choice((0, 1)) for _ in range(N)]
winner_net.reset()
for s in seq:
winner_net.activate([s, 0])
for s in seq:
output = winner_net.activate([0, 1])
print("expected {0:1.5f} got {1:1.5f}".format(s, output[0]))
# Visualize the winner network and plot/log statistics.
visualize.draw_net(winner, view=True, filename="nn_winner.gv")
visualize.draw_net(winner, view=True, filename="nn_winner-enabled.gv", show_disabled=False)
visualize.draw_net(winner, view=True, filename="nn_winner-enabled-pruned.gv", show_disabled=False, prune_unused=True)
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
def test_minimal():
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'test_configuration')
pop = Population(config_path)
pe = parallel.ParallelEvaluator(eval_fitness, 4)
pop.run(pe.evaluate, 400)
def train_model(features, num_generations):
timestamp = time.strftime("%Y%m%d-%H%M%S")
print("########################## Time Stamp ==== " + timestamp)
t0 = time.time()
print("## Train a NEAT model")
timestr = time.strftime("%Y%m%d-%H%M%S")
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'bnp_config')
# Use a pool of four workers to evaluate fitness in parallel.
pe = parallel.ParallelEvaluator(fitness, 3, progress_bar=True, verbose=1)
pop = population.Population(config_path)
pop.run(pe.evaluate, num_generations)
print("total evolution time {0:.3f} sec".format((time.time() - t0)))
print("time per generation {0:.3f} sec".format(
((time.time() - t0) / pop.generation)))
print('Number of evaluations: {0:d}'.format(pop.total_evaluations))
# Verify network output against training data.
print("## Test against verification data.")
winner = pop.statistics.best_genome()
net = nn.create_feed_forward_phenotype(winner)
p_train = net.array_activate(X_train[features].values)
p_valid = net.array_activate(X_valid[features].values)
score_train = sklearn.metrics.log_loss(y_train, p_train[:, 0])
score_valid = sklearn.metrics.log_loss(y_valid, p_valid[:, 0])
print("Score based on training data set = ", score_train)
print("Score based on validating data set = ", score_valid)
# Visualize the winner network and plot statistics.
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
visualize.draw_net(winner, view=True)
print("## Predicting test data")
preds = net.array_activate(test[features].values)
test[test_col_name] = preds
test[[id_col_name,
test_col_name]].to_csv("../predictions/pred_" + timestr + ".csv",
index=False)
fitness += 1.0
fitnesses.append(fitness)
# The genome's fitness is its worst performance across all runs.
return min(fitnesses)
# Load the config file, which is assumed to live in
# the same directory as this script.
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'ctrnn_config'))
config.node_gene_type = ctrnn.CTNodeGene
pop = population.Population(config)
pe = parallel.ParallelEvaluator(evaluate_genome)
pop.run(pe.evaluate, 2000)
# Save the winner.
print('Number of evaluations: {0:d}'.format(pop.total_evaluations))
winner = pop.statistics.best_genome()
with open('ctrnn_winner_genome', 'wb') as f:
pickle.dump(winner, f)
print(winner)
# Plot the evolution of the best/average fitness.
visualize.plot_stats(pop.statistics, ylog=True, filename="ctrnn_fitness.svg")
# Visualizes speciation
visualize.plot_species(pop.statistics, filename="ctrnn_speciation.svg")
# Visualize the best network.