def run():
# load settings file
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'dpole_config'))
# change the number of inputs accordingly to the type
# of experiment: markov (6) or non-markov (3)
# you can also set the configs in dpole_config as long
# as you have two config files for each type of experiment
config.input_nodes = 3
pop = population.Population(config)
pop.epoch(evaluate_population, 200, report=1, save_best=0)
winner = pop.most_fit_genomes[-1]
print('Number of evaluations: {0:d}'.format(winner.ID))
print('Winner fitness: {0:f}'.format(winner.fitness))
# save the winner
with open('winner_chromosome', 'w') as f:
cPickle.dump(winner, f)
# Plots the evolution of the best/average fitness
visualize.plot_stats(pop, ylog=True)
# Visualizes speciation
visualize.plot_species(pop)
# visualize the best topology
visualize.draw_net(winner, view=True)
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():
# load settings file
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'dpole_config'))
# change the number of inputs accordingly to the type
# of experiment: markov (6) or non-markov (3)
# you can also set the configs in dpole_config as long
# as you have two config files for each type of experiment
config.input_nodes = 3
pop = population.Population(config)
pop.epoch(evaluate_population, 200, report=1, save_best=0)
winner = pop.most_fit_genomes[-1]
print('Number of evaluations: {0:d}'.format(winner.ID))
print('Winner fitness: {0:f}'.format(winner.fitness))
# save the winner
with open('winner_chromosome', 'w') as f:
cPickle.dump(winner, f)
# Plots the evolution of the best/average fitness
visualize.plot_stats(pop, ylog=True)
# Visualizes speciation
visualize.plot_species(pop)
# visualize the best topology
visualize.draw_net(winner, view=True)
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)
outputs = net.array_activate(xor_inputs)
print("Expected XOR output : ", xor_outputs)
print("Generated output : ", outputs)
# 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 run():
# 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, 'xor2_config'))
# For this network, we use two output neurons and use the difference between
# the "time to first spike" to determine the network response. There are
# probably a great many different choices one could make for an output encoding,
# and this choice may not be the best for tackling a real problem.
config.output_nodes = 2
pop = population.Population(config)
pop.run(eval_fitness, 200)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Visualize the winner network and plot statistics.
winner = pop.statistics.best_genome()
node_names = {0: 'A', 1: 'B', 2: 'Out1', 3: 'Out2'}
visualize.draw_net(winner, view=True, node_names=node_names)
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
# Verify network output against training data.
print('\nBest network output:')
net = iznn.create_phenotype(winner, *iz_params)
sum_square_error, simulated = simulate(winner)
# Create a plot of the traces out to the max time for each set of inputs.
plt.figure(figsize=(12, 12))
for r, (inputData, outputData, t0, t1, v0, v1, neuron_data) in enumerate(simulated):
response = compute_output(t0, t1)
print("{0!r} expected {1:.3f} got {2:.3f}".format(inputData, outputData, response))
axes = plt.subplot(4, 1, r + 1)
plt.title("Traces for XOR input {{{0:.1f}, {1:.1f}}}".format(*inputData), fontsize=12)
for i, s in neuron_data.items():
if i in net.outputs:
t, v = zip(*s)
plt.plot(t, v, "-", label="neuron {0:d}".format(i))
# Circle the first peak of each output.
circle0 = patches.Ellipse((t0, v0), 1.0, 10.0, color='r', fill=False)
circle1 = patches.Ellipse((t1, v1), 1.0, 10.0, color='r', fill=False)
axes.add_artist(circle0)
axes.add_artist(circle1)
plt.ylabel("Potential (mv)", fontsize=10)
plt.ylim(-100, 50)
plt.tick_params(labelsize=8)
plt.grid()
plt.xlabel("Time (in ms)", fontsize=10)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.savefig("traces.png", dpi=90)
plt.show()
def test_run():
xor_inputs = [[0, 0], [0, 1], [1, 0], [1, 1]]
xor_outputs = [0, 1, 1, 0]
def eval_fitness(genomes):
for g in genomes:
net = nn.create_feed_forward_phenotype(g)
error = 0.0
for inputs, expected in zip(xor_inputs, xor_outputs):
# Serial activation propagates the inputs through the entire network.
output = net.serial_activate(inputs)
error += (output[0] - expected)**2
# When the output matches expected for all inputs, fitness will reach
# its maximum value of 1.0.
g.fitness = 1 - error
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
pop = population.Population(config)
pop.run(eval_fitness, 10)
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
winner = pop.statistics.best_genome()
# Validate winner.
for g in pop.statistics.most_fit_genomes:
assert winner.fitness >= g.fitness
visualize.draw_net(winner, view=False, filename="xor2-all.gv")
visualize.draw_net(winner,
view=False,
filename="xor2-enabled.gv",
show_disabled=False)
visualize.draw_net(winner,
view=False,
filename="xor2-enabled-pruned.gv",
show_disabled=False,
prune_unused=True)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
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)
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)
def test_run():
xor_inputs = [[0, 0], [0, 1], [1, 0], [1, 1]]
xor_outputs = [0, 1, 1, 0]
def eval_fitness(genomes):
for g in genomes:
net = nn.create_feed_forward_phenotype(g)
error = 0.0
for inputs, expected in zip(xor_inputs, xor_outputs):
# Serial activation propagates the inputs through the entire network.
output = net.serial_activate(inputs)
error += (output[0] - expected) ** 2
# When the output matches expected for all inputs, fitness will reach
# its maximum value of 1.0.
g.fitness = 1 - error
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
pop = population.Population(config)
pop.run(eval_fitness, 10)
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
winner = pop.statistics.best_genome()
# Validate winner.
for g in pop.statistics.most_fit_genomes:
assert winner.fitness >= g.fitness
visualize.draw_net(winner, view=False, filename="xor2-all.gv")
visualize.draw_net(winner, view=False, filename="xor2-enabled.gv", show_disabled=False)
visualize.draw_net(winner, view=False, filename="xor2-enabled-pruned.gv", show_disabled=False, prune_unused=True)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Display the most fit genome.
winner = pop.statistics.best_genome()
print('\nBest genome:\n{!s}'.format(winner))
# Verify network output against training data.
print('\nOutput:')
winner_net = nn.create_feed_forward_phenotype(winner)
for inputs, expected in zip(xor_inputs, xor_outputs):
output = winner_net.serial_activate(inputs)
print("expected {0:1.5f} got {1:1.5f}".format(expected, output[0]))
# Visualize the winner network and plot/log statistics.
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
visualize.draw_net(winner, view=True, filename="xor2-all.gv")
visualize.draw_net(winner,
view=True,
filename="xor2-enabled.gv",
show_disabled=False)
visualize.draw_net(winner,
view=True,
filename="xor2-enabled-pruned.gv",
show_disabled=False,
prune_unused=True)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
def run(output_dir, neat_config=None, generations=20, port=3001, frequency=None, unstuck=False, evaluation=None, checkpoint=None, configuration=None, timelimit=None):
if output_dir is None:
print('Error! No output dir has been set')
return
if neat_config is None:
neat_config = os.path.join(output_dir, 'nn_config')
if evaluation is None:
fitness_function = get_fitness_function(os.path.join(output_dir, 'fitness.py'))
else:
fitness_function = get_fitness_function(evaluation)
results_path, models_path, debug_path, checkpoints_path, EVAL_FUNCTION = simulation.initialize_experiments(output_dir, configuration=configuration, unstuck=unstuck, port=port)
best_model_file = os.path.join(output_dir, 'best.pickle')
if frequency is None:
frequency = generations
pop = population.Population(neat_config)
if checkpoint is not None:
print('Loading from ', checkpoint)
pop.load_checkpoint(checkpoint)
for g in range(1, generations+1):
pop.run(lambda individuals: eval_fitness(individuals,
fitness_function=fitness_function,
evaluate_function=lambda g : EVAL_FUNCTION(g, pop.generation > 13),
cleaner=lambda: simulation.clean_temp_files(results_path, models_path),
timelimit=timelimit
),
1)
if g % frequency == 0:
print('Saving best net in {}'.format(best_model_file))
best_genome = get_best_genome(pop)
pickle.dump(nn.create_recurrent_phenotype(best_genome), open(best_model_file, "wb"))
new_checkpoint = os.path.join(checkpoints_path, 'neat_gen_{}.checkpoint'.format(pop.generation))
print('Storing to ', new_checkpoint)
pop.save_checkpoint(new_checkpoint)
print('Plotting statistics')
visualize.plot_stats(pop.statistics, filename=os.path.join(output_dir, 'avg_fitness.svg'))
visualize.plot_species(pop.statistics, filename=os.path.join(output_dir, 'speciation.svg'))
print('Save network view')
visualize.draw_net(best_genome, view=False,
filename=os.path.join(output_dir, "nn_winner-enabled-pruned.gv"),
show_disabled=False, prune_unused=True)
visualize.draw_net(best_genome, view=False, filename=os.path.join(output_dir, "nn_winner.gv"))
visualize.draw_net(best_genome, view=False, filename=os.path.join(output_dir, "nn_winner-enabled.gv"),
show_disabled=False)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
print('Saving best net in {}'.format(best_model_file))
pickle.dump(nn.create_recurrent_phenotype(get_best_genome(pop)), open(best_model_file, "wb"))
# Display the most fit genome.
#print('\nBest genome:')
winner = pop.statistics.best_genome()
#print(winner)
# Visualize the winner network and plot/log statistics.
visualize.draw_net(winner, view=True, filename=os.path.join(output_dir, "nn_winner.gv"))
visualize.draw_net(winner, view=True, filename=os.path.join(output_dir, "nn_winner-enabled.gv"), show_disabled=False)
visualize.draw_net(winner, view=True, filename=os.path.join(output_dir, "nn_winner-enabled-pruned.gv"), show_disabled=False, prune_unused=True)
visualize.plot_stats(pop.statistics, filename=os.path.join(output_dir, 'avg_fitness.svg'))
visualize.plot_species(pop.statistics, filename=os.path.join(output_dir, 'speciation.svg'))
statistics.save_stats(pop.statistics, filename=os.path.join(output_dir, 'fitness_history.csv'))
statistics.save_species_count(pop.statistics, filename=os.path.join(output_dir, 'speciation.csv'))
statistics.save_species_fitness(pop.statistics, filename=os.path.join(output_dir, 'species_fitness.csv'))
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.
visualize.draw_net(winner, view=True, filename="ctrnn_winner.gv")
# its maximum value of 1.0.
g.fitness = 1 - sum_square_error
pop = population.Population('xor2_config')
pop.run(eval_fitness, 300)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Display the most fit genome.
winner = pop.statistics.best_genome()
print('\nBest genome:\n{!s}'.format(winner))
# Verify network output against training data.
print('\nOutput:')
winner_net = nn.create_feed_forward_phenotype(winner)
for inputs, expected in zip(xor_inputs, xor_outputs):
output = winner_net.serial_activate(inputs)
print("expected {0:1.5f} got {1:1.5f}".format(expected, output[0]))
# Visualize the winner network and plot/log statistics.
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
visualize.draw_net(winner, view=True, filename="xor2-all.gv")
visualize.draw_net(winner, view=True, filename="xor2-enabled.gv", show_disabled=False)
visualize.draw_net(winner, view=True, filename="xor2-enabled-pruned.gv", show_disabled=False, prune_unused=True)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
def run():
# 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, 'xor2_config'))
# For this network, we use two output neurons and use the difference between
# the "time to first spike" to determine the network response. There are
# probably a great many different choices one could make for an output encoding,
# and this choice may not be the best for tackling a real problem.
config.output_nodes = 2
pop = population.Population(config)
pop.run(eval_fitness, 200)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Visualize the winner network and plot statistics.
winner = pop.statistics.best_genome()
node_names = {0: 'A', 1: 'B', 2: 'Out1', 3: 'Out2'}
visualize.draw_net(winner, view=True, node_names=node_names)
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
# Verify network output against training data.
print('\nBest network output:')
net = iznn.create_phenotype(winner, *iz_params)
sum_square_error, simulated = simulate(winner)
# Create a plot of the traces out to the max time for each set of inputs.
plt.figure(figsize=(12, 12))
for r, (inputData, outputData, t0, t1, v0, v1,
neuron_data) in enumerate(simulated):
response = compute_output(t0, t1)
print("{0!r} expected {1:.3f} got {2:.3f}".format(
inputData, outputData, response))
axes = plt.subplot(4, 1, r + 1)
plt.title(
"Traces for XOR input {{{0:.1f}, {1:.1f}}}".format(*inputData),
fontsize=12)
for i, s in neuron_data.items():
if i in net.outputs:
t, v = zip(*s)
plt.plot(t, v, "-", label="neuron {0:d}".format(i))
# Circle the first peak of each output.
circle0 = patches.Ellipse((t0, v0), 1.0, 10.0, color='r', fill=False)
circle1 = patches.Ellipse((t1, v1), 1.0, 10.0, color='r', fill=False)
axes.add_artist(circle0)
axes.add_artist(circle1)
plt.ylabel("Potential (mv)", fontsize=10)
plt.ylim(-100, 50)
plt.tick_params(labelsize=8)
plt.grid()
plt.xlabel("Time (in ms)", fontsize=10)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.savefig("traces.png", dpi=90)
plt.show()