x = rng.rand(20)
y = f(x)
grid = np.linspace(0, 1, 100)
fvals = f(grid)
plt.plot(grid, fvals, "k--")
plt.plot(x, y, "ro")
plt.grid()
plt.xlim(0, 1)
plt.show()
# -- Train Model ---
model = DNGO(do_mcmc=False)
model.train(x[:, None], y, do_optimize=True)
# -- Predict with Model ---
m, v = model.predict(grid[:, None])
plt.plot(x, y, "ro")
plt.grid()
plt.plot(grid, fvals, "k--")
plt.plot(grid, m, "blue")
plt.fill_between(grid,
m + np.sqrt(v),
m - np.sqrt(v),
color="orange",
alpha=0.8)
plt.fill_between(grid,
m + 2 * np.sqrt(v),
class DNGOTuner(Tuner):
def __init__(self, optimize_mode='maximize', sample_size=1000, trials_per_update=20, num_epochs_per_training=500):
self.searchspace_json = None
self.random_state = None
self.model = DNGO(do_mcmc=False, num_epochs=num_epochs_per_training)
self._model_initialized = False
self.sample_size = sample_size
self.trials_per_update = trials_per_update
self.optimize_mode = optimize_mode
self.x = []
self.y = []
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
self.x.append(parameters)
self.y.append(self._get_default_value(value))
if len(self.y) % self.trials_per_update == 0:
self._update_model()
def generate_parameters(self, parameter_id, **kwargs):
if not self._model_initialized:
return _random_config(self.searchspace_json, self.random_state)
else:
# random samples and pick best with model
candidate_x = [_random_config(self.searchspace_json, self.random_state) for _ in range(self.sample_size)]
x_test = np.array([np.array(list(xi.values())) for xi in candidate_x])
m, v = self.model.predict(x_test)
mean = torch.Tensor(m)
sigma = torch.Tensor(v)
u = (mean - torch.Tensor([0.95]).expand_as(mean)) / sigma
normal = Normal(torch.zeros_like(u), torch.ones_like(u))
ucdf = normal.cdf(u)
updf = torch.exp(normal.log_prob(u))
ei = sigma * (updf + u * ucdf)
if self.optimize_mode == 'maximize':
ind = torch.argmax(ei)
else:
ind = torch.argmin(ei)
new_x = candidate_x[ind]
return new_x
def update_search_space(self, search_space):
self.searchspace_json = search_space
self.random_state = np.random.RandomState()
def import_data(self, data):
for d in data:
self.x.append(d['parameter'])
self.y.append(self._get_default_value(d['value']))
self._update_model()
def _update_model(self):
_logger.info('Updating model on %d samples', len(self.x))
x_arr = []
for x in self.x:
x_arr.append([x[k] for k in sorted(x.keys())])
self.model.train(np.array(x_arr), np.array(self.y), do_optimize=True)
self._model_initialized = True
def _get_default_value(self, value):
if isinstance(value, dict) and 'default' in value:
return value['default']
elif isinstance(value, float):
return value
else:
raise ValueError(f'Unsupported value: {value}')
def dngo_search(search_space,
num_init=10,
k=10,
loss='val_loss',
total_queries=150,
encoding_type='path',
cutoff=40,
acq_opt_type='mutation',
explore_type='ucb',
deterministic=True,
verbose=True):
import torch
from pybnn import DNGO
from pybnn.util.normalization import zero_mean_unit_var_normalization, zero_mean_unit_var_denormalization
from acquisition_functions import acq_fn
def fn(arch):
return search_space.query_arch(arch, deterministic=deterministic)[loss]
# set up initial data
data = search_space.generate_random_dataset(num=num_init,
encoding_type=encoding_type,
cutoff=cutoff,
deterministic_loss=deterministic)
query = num_init + k
while query <= total_queries:
# set up data
x = np.array([d['encoding'] for d in data])
y = np.array([d[loss] for d in data])
# get a set of candidate architectures
candidates = search_space.get_candidates(data,
acq_opt_type=acq_opt_type,
encoding_type=encoding_type,
cutoff=cutoff,
deterministic_loss=deterministic)
xcandidates = np.array([d['encoding'] for d in candidates])
# train the model
model = DNGO(do_mcmc=False)
model.train(x, y, do_optimize=True)
predictions = model.predict(xcandidates)
candidate_indices = acq_fn(np.array(predictions), explore_type)
# add the k arches with the minimum acquisition function values
for i in candidate_indices[:k]:
arch_dict = search_space.query_arch(candidates[i]['spec'],
encoding_type=encoding_type,
cutoff=cutoff,
deterministic=deterministic)
data.append(arch_dict)
if verbose:
top_5_loss = sorted([(d[loss], d['epochs']) for d in data], key=lambda d: d[0])[:min(5, len(data))]
print('dngo, query {}, top 5 val losses (val, test, epoch): {}'.format(query, top_5_loss))
recent_10_loss = [(d[loss], d['epochs']) for d in data[-10:]]
print('dngo, query {}, most recent 10 (val, test, epoch): {}'.format(query, recent_10_loss))
query += k
return data
def pybnn_search(search_space,
model_type,
num_init=20,
k=DEFAULT_K,
loss=DEFAULT_LOSS,
total_queries=DEFAULT_TOTAL_QUERIES,
predictor_encoding='adj',
cutoff=0,
acq_opt_type='mutation',
explore_type='ucb',
deterministic=True,
verbose=True):
import torch
from pybnn import DNGO
from pybnn.bohamiann import Bohamiann
from pybnn.util.normalization import zero_mean_unit_var_normalization, zero_mean_unit_var_denormalization
def fn(arch):
return search_space.query_arch(arch, deterministic=deterministic)[loss]
# set up initial data
data = search_space.generate_random_dataset(
num=num_init,
predictor_encoding=predictor_encoding,
cutoff=cutoff,
deterministic_loss=deterministic)
query = num_init + k
while query <= total_queries:
# set up data
x = np.array([d['encoding'] for d in data])
y = np.array([d[loss] for d in data])
scaled_y = np.array([elt / 30 for elt in y])
# get a set of candidate architectures
candidates = search_space.get_candidates(
data,
acq_opt_type=acq_opt_type,
predictor_encoding=predictor_encoding,
cutoff=cutoff,
deterministic_loss=deterministic)
xcandidates = np.array([d['encoding'] for d in candidates])
# train the model
if model_type == 'dngo':
model = DNGO(do_mcmc=False)
model.train(x, y, do_optimize=True)
elif model_type == 'bohamiann':
model = Bohamiann()
model.train(x,
scaled_y,
num_steps=10000,
num_burn_in_steps=1000,
keep_every=50,
lr=1e-2)
predictions, var = model.predict(xcandidates)
predictions = np.array([pred * 30 for pred in predictions])
stds = np.sqrt(np.array([v * 30 for v in var]))
candidate_indices = acq_fn(np.array(predictions),
explore_type,
stds=stds)
model = None
gc.collect()
# add the k arches with the minimum acquisition function values
for i in candidate_indices[:k]:
arch_dict = search_space.query_arch(
candidates[i]['spec'],
epochs=0,
predictor_encoding=predictor_encoding,
cutoff=cutoff,
deterministic=deterministic)
data.append(arch_dict)
if verbose:
top_5_loss = sorted([d[loss] for d in data])[:min(5, len(data))]
print('dngo, query {}, top 5 val losses: {}'.format(
query, top_5_loss))
query += k
return data
func_name = 'camelback-2d'
f, bounds, _, true_fmin = get_function(func_name)
d = bounds.shape[0]
n_init = 40
var_noise = 1.0e-10
np.random.seed(3)
x = np.random.uniform(bounds[:, 0], bounds[:, 1], (n_init, d))
y = f(x)
x1, x2 = np.mgrid[-1:1:50j, -1:1:50j]
grid = np.vstack((x1.flatten(), x2.flatten())).T
fvals = f(grid)
# -- Train Model ---
model = DNGO(do_mcmc=False)
model.train(x, y.flatten(), do_optimize=True)
# -- Predict with Model ---
m, v = model.predict(grid)
figure, axes = plt.subplots(2, 1, figsize=(6, 18))
sub1 = axes[0].contourf(x1, x2, fvals.reshape(50, 50))
axes[0].plot(x[:, 0], x[:, 1], 'rx')
axes[0].set_title('objective func ')
sub2 = axes[1].contourf(x1, x2, m.reshape(50, 50))
axes[1].plot(x[:, 0], x[:, 1], 'rx')
gp_title = f'prediction by DNGO'
axes[1].set_title(gp_title)
plt.show()
class DNGOTuner(Tuner):
def __init__(self,
optimize_mode='maximize',
sample_size=1000,
trials_per_update=20,
num_epochs_per_training=500):
self.searchspace_json = None
self.random_state = None
self.model = DNGO(do_mcmc=False, num_epochs=num_epochs_per_training)
self._model_initialized = False
self.sample_size = sample_size
self.trials_per_update = trials_per_update
self.optimize_mode = optimize_mode
self.x = []
self.y = []
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
self.x.append(parameters)
self.y.append(self._get_default_value(value))
if len(self.y) % self.trials_per_update == 0:
self._update_model()
def generate_parameters(self, parameter_id, **kwargs):
if not self._model_initialized:
return _random_config(self.searchspace_json, self.random_state)
else:
# random samples and pick best with model
candidate_x = [
_random_config(self.searchspace_json, self.random_state)
for _ in range(self.sample_size)
]
# The model has NaN issue when all the candidates are same
# Also we can save the predict time when this happens
if all(x == candidate_x[0] for x in candidate_x):
return candidate_x[0]
x_test = np.array(
[np.array(list(xi.values())) for xi in candidate_x])
m, v = self.model.predict(x_test)
# The model has NaN issue when all the candidates are very close
if np.isnan(m).any() or np.isnan(v).any():
return candidate_x[0]
mean = torch.Tensor(m)
sigma = torch.Tensor(v)
u = (mean - torch.Tensor([0.95]).expand_as(mean)) / sigma
normal = Normal(torch.zeros_like(u), torch.ones_like(u))
ucdf = normal.cdf(u)
updf = torch.exp(normal.log_prob(u))
ei = sigma * (updf + u * ucdf)
if self.optimize_mode == 'maximize':
ind = torch.argmax(ei)
else:
ind = torch.argmin(ei)
new_x = candidate_x[ind]
return new_x
def update_search_space(self, search_space):
validate_search_space(search_space, [
'choice', 'randint', 'uniform', 'quniform', 'loguniform',
'qloguniform'
])
self.searchspace_json = search_space
self.random_state = np.random.RandomState()
def import_data(self, data):
for d in data:
self.x.append(d['parameter'])
self.y.append(self._get_default_value(d['value']))
self._update_model()
def _update_model(self):
_logger.info('Updating model on %d samples', len(self.x))
x_arr = []
for x in self.x:
x_arr.append([x[k] for k in sorted(x.keys())])
try:
self.model.train(np.array(x_arr),
np.array(self.y),
do_optimize=True)
except np.linalg.LinAlgError as e:
warnings.warn(
f'numpy linalg error encountered in DNGO model training: {e}')
self._model_initialized = True
def _get_default_value(self, value):
if isinstance(value, dict) and 'default' in value:
return value['default']
elif isinstance(value, float):
return value
else:
raise ValueError(f'Unsupported value: {value}')