def mins(config):
"""Optimize a design problem score using the algorithm MINS
otherwise known as Model Inversion Networks
Args:
config: dict
a dictionary of hyper parameters such as the learning rate
"""
# create the training task and logger
logger = Logger(config['logging_dir'])
task = StaticGraphTask(config['task'], **config['task_kwargs'])
if config['normalize_ys']:
task.map_normalize_y()
if config['normalize_xs']:
task.map_normalize_x()
x = task.x
y = task.y
def map_to_probs(x, *rest):
x = task.to_logits(x)
x = tf.pad(x, [[0, 0]] * (len(x.shape) - 1) + [[1, 0]])
return (tf.math.softmax(x / 1e-5), *rest)
input_shape = x.shape[1:]
if task.is_discrete:
input_shape = list(x.shape[1:]) + [task.num_classes]
base_temp = config.get('base_temp', None)
if config['offline']:
# make several keras neural networks with two hidden layers
forward_models = [ForwardModel(
input_shape,
hidden_size=config['hidden_size'],
num_layers=config['num_layers'],
initial_max_std=config['initial_max_std'],
initial_min_std=config['initial_min_std'])
for _ in range(config['bootstraps'])]
# create a trainer for a forward model with a conservative objective
oracle = Ensemble(forward_models,
forward_model_optim=tf.keras.optimizers.Adam,
forward_model_lr=config['oracle_lr'],
is_discrete=task.is_discrete,
noise_std=config.get('noise_std', 0.0),
keep=config.get('keep', 1.0),
temp=config.get('temp', 0.001))
# build a bootstrapped data set
train_data, val_data = build_pipeline(
x=x, y=y, bootstraps=config['bootstraps'],
batch_size=config['oracle_batch_size'],
val_size=config['val_size'], buffer=1)
train_data = train_data.map(
map_to_probs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
val_data = val_data.map(
map_to_probs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
# train the model for an additional number of epochs
oracle.launch(train_data,
val_data,
logger,
config['oracle_epochs'])
# create replay buffers for both GANS
explore_pool = ReplayBuffer(config['pool_size'], input_shape)
exploit_pool = ReplayBuffer(config['pool_size'], input_shape)
if task.is_discrete:
# build a Gumbel-Softmax GAN to sample discrete outputs
explore_gen = DiscreteGenerator(
input_shape, config['latent_size'],
hidden=config['hidden_size'])
exploit_gen = DiscreteGenerator(
input_shape, config['latent_size'],
hidden=config['hidden_size'])
else:
# build an LS-GAN to sample continuous outputs
explore_gen = ContinuousGenerator(
input_shape, config['latent_size'],
hidden=config['hidden_size'])
exploit_gen = ContinuousGenerator(
input_shape, config['latent_size'],
hidden=config['hidden_size'])
# build the neural network GAN components
explore_discriminator = Discriminator(
input_shape,
hidden=config['hidden_size'],
method=config['method'])
explore_gan = WeightedGAN(
explore_gen, explore_discriminator, explore_pool,
critic_frequency=config['critic_frequency'],
flip_frac=config['flip_frac'],
pool_frac=config['pool_frac'],
pool_save=config['pool_save'],
fake_pair_frac=config['fake_pair_frac'],
penalty_weight=config['penalty_weight'],
generator_lr=config['generator_lr'],
generator_beta_1=config['generator_beta_1'],
generator_beta_2=config['generator_beta_2'],
discriminator_lr=config['discriminator_lr'],
discriminator_beta_1=config['discriminator_beta_1'],
discriminator_beta_2=config['discriminator_beta_2'],
is_discrete=task.is_discrete,
noise_std=config.get('noise_std', 0.0),
keep=config.get('keep', 1.0),
start_temp=config.get('start_temp', 5.0),
final_temp=config.get('final_temp', 1.0))
# build the neural network GAN components
exploit_discriminator = Discriminator(
input_shape,
hidden=config['hidden_size'],
method=config['method'])
exploit_gan = WeightedGAN(
exploit_gen, exploit_discriminator, exploit_pool,
critic_frequency=config['critic_frequency'],
flip_frac=config['flip_frac'],
pool_frac=config['pool_frac'],
pool_save=config['pool_save'],
fake_pair_frac=config['fake_pair_frac'],
penalty_weight=config['penalty_weight'],
generator_lr=config['generator_lr'],
generator_beta_1=config['generator_beta_1'],
generator_beta_2=config['generator_beta_2'],
discriminator_lr=config['discriminator_lr'],
discriminator_beta_1=config['discriminator_beta_1'],
discriminator_beta_2=config['discriminator_beta_2'],
is_discrete=task.is_discrete,
noise_std=config.get('noise_std', 0.0),
keep=config.get('keep', 1.0),
start_temp=config.get('start_temp', 5.0),
final_temp=config.get('final_temp', 1.0))
# build a weighted data set using newly collected samples
train_data, val_data = build_pipeline(
x=x, y=y, w=get_weights(y, base_temp=base_temp),
batch_size=config['gan_batch_size'],
val_size=config['val_size'], buffer=1)
train_data = train_data.map(
map_to_probs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
val_data = val_data.map(
map_to_probs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
# train the gan for several epochs
explore_gan.launch(
train_data, val_data, logger, config['initial_epochs'],
header="exploration/")
# sample designs from the GAN and evaluate them
condition_ys = tf.tile(tf.reduce_max(
y, keepdims=True), [config['solver_samples'], 1])
# record score percentiles
logger.record("exploration/condition_ys",
task.denormalize_y(condition_ys)
if task.is_normalized_y else condition_ys,
0,
percentile=True)
# train the gan for several epochs
exploit_gan.launch(
train_data, val_data, logger, config['initial_epochs'],
header="exploitation/")
# record score percentiles
logger.record("exploitation/condition_ys",
task.denormalize_y(condition_ys)
if task.is_normalized_y else condition_ys,
0,
percentile=True)
# prevent the temperature from being annealed further
if task.is_discrete:
explore_gan.start_temp = explore_gan.final_temp
exploit_gan.start_temp = exploit_gan.final_temp
# train the gan using an importance sampled data set
for iteration in range(config['iterations']):
# generate synthetic x paired with high performing scores
tilde_x, tilde_y = get_synthetic_data(
x, y,
exploration_samples=config['exploration_samples'],
exploration_rate=config['exploration_rate'],
base_temp=base_temp)
# build a weighted data set using newly collected samples
train_data, val_data = build_pipeline(
x=tilde_x.numpy(), y=tilde_y.numpy(),
w=get_weights(tilde_y.numpy(), base_temp=base_temp),
batch_size=config['gan_batch_size'],
val_size=config['val_size'], buffer=1)
train_data = train_data.map(
map_to_probs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
val_data = val_data.map(
map_to_probs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
# train the gan for several epochs
explore_gan.launch(
train_data, val_data, logger, config['epochs_per_iteration'],
start_epoch=config['epochs_per_iteration'] * iteration +
config['initial_epochs'],
header="exploration/")
# sample designs from the GAN and evaluate them
condition_ys = tf.tile(tf.reduce_max(
tilde_y, keepdims=True), [config['thompson_samples'], 1])
# generate samples for exploration
solver_xs = explore_gen.sample(condition_ys, temp=0.001)
if task.is_discrete:
solver_xs = tf.argmax(
solver_xs, axis=-1, output_type=tf.int32)
actual_ys = oracle.get_distribution(solver_xs).mean() \
if config['offline'] else task.predict(solver_xs)
# record score percentiles
logger.record("exploration/condition_ys",
task.denormalize_y(condition_ys)
if task.is_normalized_y else condition_ys,
0,
percentile=True)
logger.record("exploration/actual_ys",
task.denormalize_y(actual_ys)
if task.is_normalized_y else actual_ys,
0,
percentile=True)
# concatenate newly paired samples with the existing data set
x = tf.concat([x, solver_xs], 0)
y = tf.concat([y, actual_ys], 0)
# build a weighted data set using newly collected samples
train_data, val_data = build_pipeline(
x=x.numpy(), y=y.numpy(),
w=get_weights(y.numpy(), base_temp=base_temp),
batch_size=config['gan_batch_size'],
val_size=config['val_size'], buffer=1)
train_data = train_data.map(
map_to_probs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
val_data = val_data.map(
map_to_probs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
# train the gan for several epochs
exploit_gan.launch(
train_data, val_data, logger, config['epochs_per_iteration'],
start_epoch=config['epochs_per_iteration'] * iteration +
config['initial_epochs'],
header="exploitation/")
# sample designs from the GAN and evaluate them
condition_ys = tf.tile(tf.reduce_max(
y, keepdims=True), [config['solver_samples'], 1])
# record score percentiles
logger.record("exploitation/condition_ys",
task.denormalize_y(condition_ys)
if task.is_normalized_y else condition_ys,
0,
percentile=True)
# generate samples for exploration
solver_xs = exploit_gen.sample(condition_ys, temp=0.001)
solution = tf.argmax(solver_xs, axis=-1, output_type=tf.int32) \
if task.is_discrete else solver_xs
# save the current solution to the disk
np.save(os.path.join(config["logging_dir"],
f"solution.npy"), solution.numpy())
# evaluate the found solution and record a video
score = task.predict(solution)
if task.is_normalized_y:
score = task.denormalize_y(score)
logger.record("score", score, config['iterations'], percentile=True)
def bo_qei(config):
"""Optimizes over designs x in an offline optimization problem
using the CMA Evolution Strategy
Args:
config: dict
a dictionary of hyper parameters such as the learning rate
"""
# create the training task and logger
logger = Logger(config['logging_dir'])
task = StaticGraphTask(config['task'], **config['task_kwargs'])
if config['normalize_ys']:
task.map_normalize_y()
if task.is_discrete and not config["use_vae"]:
task.map_to_logits()
if config['normalize_xs']:
task.map_normalize_x()
x = task.x
y = task.y
if task.is_discrete and config["use_vae"]:
vae_model = SequentialVAE(task,
hidden_size=config['vae_hidden_size'],
latent_size=config['vae_latent_size'],
activation=config['vae_activation'],
kernel_size=config['vae_kernel_size'],
num_blocks=config['vae_num_blocks'])
vae_trainer = VAETrainer(vae_model,
vae_optim=tf.keras.optimizers.Adam,
vae_lr=config['vae_lr'],
beta=config['vae_beta'])
# create the training task and logger
train_data, val_data = build_pipeline(
x=x,
y=y,
batch_size=config['vae_batch_size'],
val_size=config['val_size'])
# estimate the number of training steps per epoch
vae_trainer.launch(train_data, val_data, logger, config['vae_epochs'])
# map the x values to latent space
x = vae_model.encoder_cnn.predict(x)[0]
mean = np.mean(x, axis=0, keepdims=True)
standard_dev = np.std(x - mean, axis=0, keepdims=True)
x = (x - mean) / standard_dev
input_shape = x.shape[1:]
input_size = np.prod(input_shape)
# create the training task and logger
train_data, val_data = build_pipeline(
x=x,
y=y,
bootstraps=config['bootstraps'],
batch_size=config['ensemble_batch_size'],
val_size=config['val_size'])
# make several keras neural networks with two hidden layers
forward_models = [
ForwardModel(input_shape,
hidden_size=config['hidden_size'],
num_layers=config['num_layers'],
initial_max_std=config['initial_max_std'],
initial_min_std=config['initial_min_std'])
for b in range(config['bootstraps'])
]
# create a trainer for a forward model with a conservative objective
ensemble = Ensemble(forward_models,
forward_model_optim=tf.keras.optimizers.Adam,
forward_model_lr=config['ensemble_lr'])
# train the model for an additional number of epochs
ensemble.launch(train_data, val_data, logger, config['ensemble_epochs'])
# select the top 1 initial designs from the dataset
indices = tf.math.top_k(y[:, 0], k=config['bo_gp_samples'])[1]
initial_x = tf.gather(x, indices, axis=0)
initial_y = tf.gather(y, indices, axis=0)
from botorch.models import FixedNoiseGP, ModelListGP
from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
from botorch.acquisition.objective import GenericMCObjective
from botorch.optim import optimize_acqf
from botorch import fit_gpytorch_model
from botorch.acquisition.monte_carlo import qExpectedImprovement
from botorch.sampling.samplers import SobolQMCNormalSampler
from botorch.exceptions import BadInitialCandidatesWarning
import torch
import time
import warnings
warnings.filterwarnings('ignore', category=BadInitialCandidatesWarning)
warnings.filterwarnings('ignore', category=RuntimeWarning)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.float32
def objective(input_x):
original_x = input_x
# convert the tensor into numpy before using a TF model
if torch.cuda.is_available():
input_x = input_x.detach().cpu().numpy()
else:
input_x = input_x.detach().numpy()
batch_shape = input_x.shape[:-1]
# pass the input into a TF model
input_x = tf.reshape(input_x, [-1, *input_shape])
# optimize teh ground truth or the learned model
if config["optimize_ground_truth"]:
if task.is_discrete and config["use_vae"]:
input_x = tf.argmax(
vae_model.decoder_cnn.predict(input_x * standard_dev +
mean),
axis=2,
output_type=tf.int32)
value = task.predict(input_x)
else:
value = ensemble.get_distribution(input_x).mean()
ys = value.numpy()
ys.reshape(list(batch_shape) + [1])
# convert the scores back to pytorch tensors
return torch.tensor(ys).type_as(original_x).to(device, dtype=dtype)
NOISE_SE = config['bo_noise_se']
train_yvar = torch.tensor(NOISE_SE**2, device=device, dtype=dtype)
def initialize_model(train_x, train_obj, state_dict=None):
# define models for objective
model_obj = FixedNoiseGP(train_x, train_obj,
train_yvar.expand_as(train_obj)).to(train_x)
# combine into a multi-output GP model
model = ModelListGP(model_obj)
mll = SumMarginalLogLikelihood(model.likelihood, model)
# load state dict if it is passed
if state_dict is not None:
model.load_state_dict(state_dict)
return mll, model
def obj_callable(Z):
return Z[..., 0]
# define a feasibility-weighted objective for optimization
obj = GenericMCObjective(obj_callable)
BATCH_SIZE = config['bo_batch_size']
bounds = torch.tensor([
np.min(x, axis=0).reshape([input_size]).tolist(),
np.max(x, axis=0).reshape([input_size]).tolist()
],
device=device,
dtype=dtype)
def optimize_acqf_and_get_observation(acq_func):
"""Optimizes the acquisition function, and returns
a new candidate and a noisy observation."""
# optimize
try:
candidates, _ = optimize_acqf(
acq_function=acq_func,
bounds=bounds,
q=BATCH_SIZE,
num_restarts=config['bo_num_restarts'],
raw_samples=config[
'bo_raw_samples'], # used for intialization heuristic
options={
"batch_limit": config['bo_batch_limit'],
"maxiter": config['bo_maxiter']
})
except RuntimeError:
return
# observe new values
new_x = candidates.detach()
exact_obj = objective(candidates)
new_obj = exact_obj + NOISE_SE * torch.randn_like(exact_obj)
return new_x, new_obj
N_BATCH = config['bo_iterations']
MC_SAMPLES = config['bo_mc_samples']
best_observed_ei = []
# call helper functions to generate initial training data and initialize model
train_x_ei = initial_x.numpy().reshape([initial_x.shape[0], input_size])
train_x_ei = torch.tensor(train_x_ei).to(device, dtype=dtype)
train_obj_ei = initial_y.numpy().reshape([initial_y.shape[0], 1])
train_obj_ei = torch.tensor(train_obj_ei).to(device, dtype=dtype)
best_observed_value_ei = train_obj_ei.max().item()
mll_ei, model_ei = initialize_model(train_x_ei, train_obj_ei)
best_observed_ei.append(best_observed_value_ei)
# run N_BATCH rounds of BayesOpt after the initial random batch
for iteration in range(1, N_BATCH + 1):
t0 = time.time()
# fit the models
fit_gpytorch_model(mll_ei)
# define the qEI acquisition module using a QMC sampler
qmc_sampler = SobolQMCNormalSampler(num_samples=MC_SAMPLES)
# for best_f, we use the best observed noisy values as an approximation
qEI = qExpectedImprovement(model=model_ei,
best_f=train_obj_ei.max(),
sampler=qmc_sampler,
objective=obj)
# optimize and get new observation
result = optimize_acqf_and_get_observation(qEI)
if result is None:
print("RuntimeError was encountered, most likely a "
"'symeig_cpu: the algorithm failed to converge'")
break
new_x_ei, new_obj_ei = result
# update training points
train_x_ei = torch.cat([train_x_ei, new_x_ei])
train_obj_ei = torch.cat([train_obj_ei, new_obj_ei])
# update progress
best_value_ei = obj(train_x_ei).max().item()
best_observed_ei.append(best_value_ei)
# reinitialize the models so they are ready for fitting on next iteration
# use the current state dict to speed up fitting
mll_ei, model_ei = initialize_model(train_x_ei, train_obj_ei,
model_ei.state_dict())
t1 = time.time()
print(
f"Batch {iteration:>2}: best_value = "
f"({best_value_ei:>4.2f}), "
f"time = {t1 - t0:>4.2f}.",
end="")
if torch.cuda.is_available():
x_sol = train_x_ei.detach().cpu().numpy()
y_sol = train_obj_ei.detach().cpu().numpy()
else:
x_sol = train_x_ei.detach().numpy()
y_sol = train_obj_ei.detach().numpy()
# select the top 1 initial designs from the dataset
indices = tf.math.top_k(y_sol[:, 0], k=config['solver_samples'])[1]
solution = tf.gather(x_sol, indices, axis=0)
solution = tf.reshape(solution, [-1, *input_shape])
if task.is_discrete and config["use_vae"]:
solution = solution * standard_dev + mean
logits = vae_model.decoder_cnn.predict(solution)
solution = tf.argmax(logits, axis=2, output_type=tf.int32)
# save the current solution to the disk
np.save(os.path.join(config["logging_dir"], f"solution.npy"),
solution.numpy())
# evaluate the found solution and record a video
score = task.predict(solution)
if task.is_normalized_y:
score = task.denormalize_y(score)
logger.record("score", score, N_BATCH, percentile=True)
def reinforce(config):
"""Optimizes over designs x in an offline optimization problem
using the REINFORCE policy gradient method
Args:
config: dict
a dictionary of hyper parameters such as the learning rate
"""
logger = Logger(config['logging_dir'])
task = StaticGraphTask(config['task'], **config['task_kwargs'])
if task.is_discrete:
task.map_to_integers()
if config['normalize_ys']:
task.map_normalize_y()
if config['normalize_xs']:
task.map_normalize_x()
x = task.x
y = task.y
# create the training task and logger
train_data, val_data = build_pipeline(
x=x, y=y, bootstraps=config['bootstraps'],
batch_size=config['ensemble_batch_size'],
val_size=config['val_size'])
# make several keras neural networks with two hidden layers
forward_models = [ForwardModel(
task,
embedding_size=config['embedding_size'],
hidden_size=config['hidden_size'],
num_layers=config['num_layers'],
initial_max_std=config['initial_max_std'],
initial_min_std=config['initial_min_std'])
for b in range(config['bootstraps'])]
# create a trainer for a forward model with a conservative objective
ensemble = Ensemble(
forward_models,
forward_model_optim=tf.keras.optimizers.Adam,
forward_model_lr=config['ensemble_lr'])
# train the model for an additional number of epochs
ensemble.launch(train_data,
val_data,
logger,
config['ensemble_epochs'])
rl_opt = tf.keras.optimizers.Adam(
learning_rate=config['reinforce_lr'])
# select the top 1 initial designs from the dataset
indices = tf.math.top_k(y[:, 0], k=config['solver_samples'])[1]
initial_x = tf.gather(x, indices, axis=0)
if task.is_discrete:
logits = tf.pad(task.to_logits(initial_x), [[0, 0], [0, 0], [1, 0]])
probs = tf.math.softmax(logits / 1e-5)
logits = tf.math.log(tf.reduce_mean(probs, axis=0))
sampler = DiscreteMarginal(logits)
else:
mean = tf.reduce_mean(initial_x, axis=0)
logstd = tf.math.log(tf.ones_like(mean) * config['exploration_std'])
sampler = ContinuousMarginal(mean, logstd)
for iteration in range(config['iterations']):
with tf.GradientTape() as tape:
td = sampler.get_distribution()
tx = td.sample(sample_shape=config['reinforce_batch_size'])
if config['optimize_ground_truth']:
ty = task.predict(tx)
else: # use the surrogate model for optimization
ty = ensemble.get_distribution(tx).mean()
mean_y = tf.reduce_mean(ty)
standard_dev_y = tf.math.reduce_std(ty - mean_y)
log_probs = td.log_prob(tf.stop_gradient(tx))
loss = tf.reduce_mean(-log_probs[:, tf.newaxis] *
tf.stop_gradient(
(ty - mean_y) / standard_dev_y))
print(f"[Iteration {iteration}] "
f"Average Prediction = {tf.reduce_mean(ty)}")
logger.record("reinforce/prediction",
ty, iteration, percentile=True)
logger.record("reinforce/loss",
loss, iteration, percentile=True)
grads = tape.gradient(
loss, sampler.trainable_variables)
rl_opt.apply_gradients(zip(
grads, sampler.trainable_variables))
td = sampler.get_distribution()
solution = td.sample(sample_shape=config['solver_samples'])
# save the current solution to the disk
np.save(os.path.join(config["logging_dir"],
f"solution.npy"), solution.numpy())
# evaluate the found solution and record a video
score = task.predict(solution)
if config['normalize_ys']:
score = task.denormalize_y(score)
logger.record(
"score", score, config['iterations'], percentile=True)
def cma_es(config):
"""Optimizes over designs x in an offline optimization problem
using the CMA Evolution Strategy
Args:
config: dict
a dictionary of hyper parameters such as the learning rate
"""
# create the training task and logger
logger = Logger(config['logging_dir'])
task = StaticGraphTask(config['task'], **config['task_kwargs'])
if config['normalize_ys']:
task.map_normalize_y()
if task.is_discrete and not config["use_vae"]:
task.map_to_logits()
if config['normalize_xs']:
task.map_normalize_x()
x = task.x
y = task.y
if task.is_discrete and config["use_vae"]:
vae_model = SequentialVAE(task,
hidden_size=config['vae_hidden_size'],
latent_size=config['vae_latent_size'],
activation=config['vae_activation'],
kernel_size=config['vae_kernel_size'],
num_blocks=config['vae_num_blocks'])
vae_trainer = VAETrainer(vae_model,
vae_optim=tf.keras.optimizers.Adam,
vae_lr=config['vae_lr'],
beta=config['vae_beta'])
# create the training task and logger
train_data, val_data = build_pipeline(
x=x,
y=y,
batch_size=config['vae_batch_size'],
val_size=config['val_size'])
# estimate the number of training steps per epoch
vae_trainer.launch(train_data, val_data, logger, config['vae_epochs'])
# map the x values to latent space
x = vae_model.encoder_cnn.predict(x)[0]
mean = np.mean(x, axis=0, keepdims=True)
standard_dev = np.std(x - mean, axis=0, keepdims=True)
x = (x - mean) / standard_dev
input_shape = x.shape[1:]
input_size = np.prod(input_shape)
# make several keras neural networks with two hidden layers
forward_models = [
ForwardModel(input_shape,
hidden_size=config['hidden_size'],
num_layers=config['num_layers'],
initial_max_std=config['initial_max_std'],
initial_min_std=config['initial_min_std'])
for b in range(config['bootstraps'])
]
# create a trainer for a forward model with a conservative objective
ensemble = Ensemble(forward_models,
forward_model_optim=tf.keras.optimizers.Adam,
forward_model_lr=config['ensemble_lr'])
# create the training task and logger
train_data, val_data = build_pipeline(
x=x,
y=y,
bootstraps=config['bootstraps'],
batch_size=config['ensemble_batch_size'],
val_size=config['val_size'])
# train the model for an additional number of epochs
ensemble.launch(train_data, val_data, logger, config['ensemble_epochs'])
# select the top 1 initial designs from the dataset
indices = tf.math.top_k(y[:, 0], k=config['solver_samples'])[1]
initial_x = tf.gather(x, indices, axis=0)
x = initial_x
# create a fitness function for optimizing the expected task score
def fitness(input_x):
input_x = tf.reshape(input_x, input_shape)[tf.newaxis]
if config["optimize_ground_truth"]:
if task.is_discrete and config["use_vae"]:
input_x = tf.argmax(
vae_model.decoder_cnn.predict(input_x * standard_dev +
mean),
axis=2,
output_type=tf.int32)
value = task.predict(input_x)
else:
value = ensemble.get_distribution(input_x).mean()
return (-value[0].numpy()).tolist()[0]
import cma
result = []
for i in range(config['solver_samples']):
xi = x[i].numpy().flatten().tolist()
es = cma.CMAEvolutionStrategy(xi, config['cma_sigma'])
step = 0
while not es.stop() and step < config['cma_max_iterations']:
solutions = es.ask()
es.tell(solutions, [fitness(x) for x in solutions])
step += 1
result.append(tf.reshape(es.result.xbest, input_shape))
print(f"CMA: {i + 1} / {config['solver_samples']}")
# convert the solution found by CMA-ES to a tensor
x = tf.stack(result, axis=0)
solution = x
if task.is_discrete and config["use_vae"]:
solution = solution * standard_dev + mean
logits = vae_model.decoder_cnn.predict(solution)
solution = tf.argmax(logits, axis=2, output_type=tf.int32)
# save the current solution to the disk
np.save(os.path.join(config["logging_dir"], f"solution.npy"),
solution.numpy())
# evaluate the found solution
score = task.predict(solution)
if task.is_normalized_y:
score = task.denormalize_y(score)
logger.record("score", score, 0, percentile=True)
def gradient_ascent(config):
"""Train a Score Function to solve a Model-Based Optimization
using gradient ascent on the input design
Args:
config: dict
a dictionary of hyper parameters such as the learning rate
"""
# create the training task and logger
logger = Logger(config['logging_dir'])
task = StaticGraphTask(config['task'], **config['task_kwargs'])
if config['normalize_ys']:
task.map_normalize_y()
if task.is_discrete and not config["use_vae"]:
task.map_to_logits()
if config['normalize_xs']:
task.map_normalize_x()
x = task.x
y = task.y
if task.is_discrete and config["use_vae"]:
vae_model = SequentialVAE(task,
hidden_size=config['vae_hidden_size'],
latent_size=config['vae_latent_size'],
activation=config['vae_activation'],
kernel_size=config['vae_kernel_size'],
num_blocks=config['vae_num_blocks'])
vae_trainer = VAETrainer(vae_model,
vae_optim=tf.keras.optimizers.Adam,
vae_lr=config['vae_lr'],
beta=config['vae_beta'])
# create the training task and logger
train_data, val_data = build_pipeline(
x=x,
y=y,
batch_size=config['vae_batch_size'],
val_size=config['val_size'])
# estimate the number of training steps per epoch
vae_trainer.launch(train_data, val_data, logger, config['vae_epochs'])
# map the x values to latent space
x = vae_model.encoder_cnn.predict(x)[0]
mean = np.mean(x, axis=0, keepdims=True)
standard_dev = np.std(x - mean, axis=0, keepdims=True)
x = (x - mean) / standard_dev
input_shape = x.shape[1:]
input_size = np.prod(input_shape)
# make several keras neural networks with different architectures
forward_models = [
ForwardModel(input_shape,
activations=activations,
hidden_size=config['hidden_size'],
initial_max_std=config['initial_max_std'],
initial_min_std=config['initial_min_std'])
for activations in config['activations']
]
# scale the learning rate based on the number of channels in x
config['solver_lr'] *= np.sqrt(np.prod(x.shape[1:]))
trs = []
for i, fm in enumerate(forward_models):
# create a bootstrapped data set
train_data, validate_data = build_pipeline(
x=x,
y=y,
batch_size=config['batch_size'],
val_size=config['val_size'],
bootstraps=1)
# create a trainer for a forward model with a conservative objective
trainer = MaximumLikelihood(
fm,
forward_model_optim=tf.keras.optimizers.Adam,
forward_model_lr=config['forward_model_lr'],
noise_std=config.get('model_noise_std', 0.0))
# train the model for an additional number of epochs
trs.append(trainer)
trainer.launch(train_data,
validate_data,
logger,
config['epochs'],
header=f'oracle_{i}/')
# select the top k initial designs from the dataset
mean_x = tf.reduce_mean(x, axis=0, keepdims=True)
indices = tf.math.top_k(y[:, 0], k=config['solver_samples'])[1]
initial_x = tf.gather(x, indices, axis=0)
x = initial_x
# evaluate the starting point
solution = x
if task.is_normalized_y:
preds = [
task.denormalize_y(fm.get_distribution(solution).mean())
for fm in forward_models
]
else:
preds = [fm.get_distribution(solution).mean() for fm in forward_models]
# record the prediction and score to the logger
logger.record("distance/travelled", tf.linalg.norm(solution - initial_x),
0)
logger.record("distance/from_mean", tf.linalg.norm(solution - mean_x), 0)
for n, prediction_i in enumerate(preds):
logger.record(f"oracle_{n}/prediction", prediction_i, 0)
if n > 0:
logger.record(f"rank_corr/0_to_{n}",
spearman(preds[0][:, 0], prediction_i[:, 0]), 0)
# perform gradient ascent on the score through the forward model
for i in range(1, config['solver_steps'] + 1):
# back propagate through the forward model
with tf.GradientTape() as tape:
tape.watch(x)
predictions = []
for fm in forward_models:
solution = x
predictions.append(fm.get_distribution(solution).mean())
if config['aggregation_method'] == 'mean':
score = tf.reduce_min(predictions, axis=0)
if config['aggregation_method'] == 'min':
score = tf.reduce_min(predictions, axis=0)
if config['aggregation_method'] == 'random':
score = predictions[np.random.randint(len(predictions))]
grads = tape.gradient(score, x)
# use the conservative optimizer to update the solution
x = x + config['solver_lr'] * grads
solution = x
# evaluate the design using the oracle and the forward model
if task.is_normalized_y:
preds = [
task.denormalize_y(fm.get_distribution(solution).mean())
for fm in forward_models
]
else:
preds = [
fm.get_distribution(solution).mean() for fm in forward_models
]
# record the prediction and score to the logger
logger.record("distance/travelled",
tf.linalg.norm(solution - initial_x), i)
logger.record("distance/from_mean", tf.linalg.norm(solution - mean_x),
i)
for n, prediction_i in enumerate(preds):
logger.record(f"oracle_{n}/prediction", prediction_i, i)
logger.record(
f"oracle_{n}/grad_norm",
tf.linalg.norm(tf.reshape(grads[n], [-1, input_size]),
axis=-1), i)
if n > 0:
logger.record(f"rank_corr/0_to_{n}",
spearman(preds[0][:, 0], prediction_i[:, 0]), i)
logger.record(
f"grad_corr/0_to_{n}",
tfp.stats.correlation(grads[0],
grads[n],
sample_axis=0,
event_axis=None), i)
if task.is_discrete and config["use_vae"]:
solution = solution * standard_dev + mean
logits = vae_model.decoder_cnn.predict(solution)
solution = tf.argmax(logits, axis=2, output_type=tf.int32)
# save the current solution to the disk
np.save(os.path.join(config["logging_dir"], f"solution.npy"),
solution.numpy())
# evaluate the found solution and record a video
score = task.predict(solution)
if task.is_normalized_y:
score = task.denormalize_y(score)
logger.record("score", score, config['solver_steps'], percentile=True)
def autofocused_cbas(config):
"""Optimize a design problem score using the algorithm CBAS
otherwise known as Conditioning by Adaptive Sampling
Args:
config: dict
a dictionary of hyper parameters such as the learning rate
"""
logger = Logger(config['logging_dir'])
task = StaticGraphTask(config['task'], **config['task_kwargs'])
if task.is_discrete:
task.map_to_integers()
if config['normalize_ys']:
task.map_normalize_y()
if config['normalize_xs']:
task.map_normalize_x()
x = task.x
y = task.y
# create the training task and logger
train_data, val_data = build_pipeline(
x=x, y=y, w=np.ones_like(y),
val_size=config['val_size'],
batch_size=config['ensemble_batch_size'],
bootstraps=config['bootstraps'])
# make several keras neural networks with two hidden layers
forward_models = [ForwardModel(
task,
embedding_size=config['embedding_size'],
hidden_size=config['hidden_size'],
num_layers=config['num_layers'],
initial_max_std=config['initial_max_std'],
initial_min_std=config['initial_min_std'])
for b in range(config['bootstraps'])]
# create a trainer for a forward model with a conservative objective
ensemble = Ensemble(
forward_models,
forward_model_optim=tf.keras.optimizers.Adam,
forward_model_lr=config['ensemble_lr'])
# train the model for an additional number of epochs
ensemble.launch(train_data,
val_data,
logger,
config['ensemble_epochs'])
# determine which arcitecture for the decoder to use
decoder = DiscreteDecoder \
if task.is_discrete else ContinuousDecoder
# build the encoder and decoder distribution and the p model
p_encoder = Encoder(task, config['latent_size'],
embedding_size=config['embedding_size'],
hidden_size=config['hidden_size'],
num_layers=config['num_layers'],
initial_max_std=config['initial_max_std'],
initial_min_std=config['initial_min_std'])
p_decoder = decoder(task, config['latent_size'],
hidden_size=config['hidden_size'],
num_layers=config['num_layers'],
initial_max_std=config['initial_max_std'],
initial_min_std=config['initial_min_std'])
p_vae = WeightedVAE(p_encoder, p_decoder,
vae_optim=tf.keras.optimizers.Adam,
vae_lr=config['vae_lr'],
vae_beta=config['vae_beta'])
# build a weighted data set
train_data, val_data = build_pipeline(
x=x, y=y, w=np.ones_like(task.y),
batch_size=config['vae_batch_size'],
val_size=config['val_size'])
# train the initial vae fit to the original data distribution
p_vae.launch(train_data,
val_data,
logger,
config['offline_epochs'])
# build the encoder and decoder distribution and the p model
q_encoder = Encoder(task, config['latent_size'],
embedding_size=config['embedding_size'],
hidden_size=config['hidden_size'],
num_layers=config['num_layers'],
initial_max_std=config['initial_max_std'],
initial_min_std=config['initial_min_std'])
q_decoder = decoder(task, config['latent_size'],
hidden_size=config['hidden_size'],
num_layers=config['num_layers'],
initial_max_std=config['initial_max_std'],
initial_min_std=config['initial_min_std'])
q_vae = WeightedVAE(q_encoder, q_decoder,
vae_optim=tf.keras.optimizers.Adam,
vae_lr=config['vae_lr'],
vae_beta=config['vae_beta'])
# create the cbas importance weight generator
cbas = CBAS(ensemble,
p_vae,
q_vae,
latent_size=config['latent_size'])
# train and validate the q_vae using online samples
q_encoder.set_weights(p_encoder.get_weights())
q_decoder.set_weights(p_decoder.get_weights())
for i in range(config['iterations']):
# generate an importance weighted dataset
x_t, y_t, w = cbas.generate_data(
config['online_batches'],
config['vae_batch_size'],
config['percentile'])
# build a weighted data set
train_data, val_data = build_pipeline(
x=x_t.numpy(),
y=y_t.numpy(),
w=w.numpy(),
batch_size=config['vae_batch_size'],
val_size=config['val_size'])
# train a vae fit using weighted maximum likelihood
start_epoch = config['online_epochs'] * i + \
config['offline_epochs']
q_vae.launch(train_data,
val_data,
logger,
config['online_epochs'],
start_epoch=start_epoch)
# autofocus the forward model using importance weights
v = cbas.autofocus_weights(
x, batch_size=config['ensemble_batch_size'])
train_data, val_data = build_pipeline(
x=x, y=y, w=v.numpy(),
bootstraps=config['bootstraps'],
batch_size=config['ensemble_batch_size'],
val_size=config['val_size'])
# train a vae fit using weighted maximum likelihood
start_epoch = config['autofocus_epochs'] * i + \
config['ensemble_epochs']
ensemble.launch(train_data,
val_data,
logger,
config['autofocus_epochs'],
start_epoch=start_epoch)
# sample designs from the prior
z = tf.random.normal([config['solver_samples'], config['latent_size']])
q_dx = q_decoder.get_distribution(z, training=False)
x_t = q_dx.sample()
np.save(os.path.join(config["logging_dir"],
f"solution.npy"), x_t.numpy())
score = task.predict(x_t)
if task.is_normalized_y:
score = task.denormalize_y(score)
logger.record("score",
score,
config['iterations'],
percentile=True)