def test_normalize_unnormalize(self):
for dtype in (torch.float, torch.double):
X = torch.tensor([0.0, 0.25, 0.5], device=self.device, dtype=dtype).view(
-1, 1
)
expected_X_normalized = torch.tensor(
[0.0, 0.5, 1.0], device=self.device, dtype=dtype
).view(-1, 1)
bounds = torch.tensor([0.0, 0.5], device=self.device, dtype=dtype).view(
-1, 1
)
X_normalized = normalize(X, bounds=bounds)
self.assertTrue(torch.equal(expected_X_normalized, X_normalized))
self.assertTrue(torch.equal(X, unnormalize(X_normalized, bounds=bounds)))
X2 = torch.tensor(
[[0.25, 0.125, 0.0], [0.25, 0.0, 0.5]], device=self.device, dtype=dtype
).transpose(1, 0)
expected_X2_normalized = torch.tensor(
[[1.0, 0.5, 0.0], [0.5, 0.0, 1.0]], device=self.device, dtype=dtype
).transpose(1, 0)
bounds2 = torch.tensor(
[[0.0, 0.0], [0.25, 0.5]], device=self.device, dtype=dtype
)
X2_normalized = normalize(X2, bounds=bounds2)
self.assertTrue(torch.equal(X2_normalized, expected_X2_normalized))
self.assertTrue(torch.equal(X2, unnormalize(X2_normalized, bounds=bounds2)))
def optimize_qehvi_and_get_observation(model, train_obj, sampler):
"""Optimizes the qEHVI acquisition function, and returns a new candidate and observation."""
# partition non-dominated space into disjoint rectangles
partitioning = NondominatedPartitioning(ref_point=problem.ref_point,
Y=train_obj)
acq_func = qExpectedHypervolumeImprovement(
model=model,
ref_point=problem.ref_point.tolist(), # use known reference point
partitioning=partitioning,
sampler=sampler,
)
# optimize
candidates, _ = optimize_acqf(
acq_function=acq_func,
bounds=standard_bounds,
q=BATCH_SIZE,
num_restarts=NUM_RESTARTS,
raw_samples=RAW_SAMPLES, # used for intialization heuristic
options={
"batch_limit": 5,
"maxiter": 200,
"nonnegative": True
},
sequential=True,
)
# observe new values
new_x = unnormalize(candidates.detach(), bounds=problem.bounds)
new_obj = problem(new_x)
return new_x, new_obj
def _torch_optimize_qehvi_and_get_observation(self):
torch_anti_ideal_point = torch.tensor(
self._transformed_anti_ideal_point, dtype=torch.double)
qehvi_partitioning = NondominatedPartitioning(
ref_point=torch_anti_ideal_point,
Y=torch.stack(self._torch_model.train_targets, dim=1))
qehvi_sampler = SobolQMCNormalSampler(num_samples=MC_SAMPLES)
self._acquisition = qExpectedHypervolumeImprovement(
model=self._torch_model,
ref_point=self._transformed_anti_ideal_point,
partitioning=qehvi_partitioning,
sampler=qehvi_sampler)
# these options all come from the tutorial
# and likely need a serious review
candidates, _ = optimize_acqf(
acq_function=self._acquisition,
bounds=self._botorch_domain,
q=BATCH_SIZE,
num_restarts=NUM_RESTARTS,
raw_samples=RAW_SAMPLES, # used for intialization heuristic
options={
"batch_limit": 5,
"maxiter": 200,
"nonnegative": True
},
sequential=True,
)
# is unnormalize necessary here?
# we are providing the same bounds here and in optimizer
new_x = unnormalize(candidates.detach(), bounds=self._botorch_domain)
transformed_eps, transformed_err = self._optimization_handler(new_x)
return new_x, transformed_eps, transformed_err
def get_bayes_pop(self, n_suggestions):
"""
Parameters
----------
n_suggestions: Number of new suggestions/trial solutions to generate using BO
Returns
The new set of trial solutions obtained by optimizing the acquisition function
-------
"""
try:
candidates, _ = optimize_acqf(
acq_function=self.acquisition,
bounds=self.min_max_bounds,
q=n_suggestions,
num_restarts=10,
raw_samples=512, # used for initialization heuristic
sequential=True)
bayes_pop = unnormalize(candidates, self.torch_bounds).numpy()
except Exception as e:
print('Error in get_bayes_pop(): {}'.format(e))
population = self.search_space.unwarp(
bayes_pop) # Translate the solution back to the original space
return population
def optimize_qparego_and_get_observation(model, train_obj, sampler):
"""Samples a set of random weights for each candidate in the batch, performs sequential greedy optimization
of the qParEGO acquisition function, and returns a new candidate and observation."""
acq_func_list = []
for _ in range(BATCH_SIZE):
weights = sample_simplex(problem.num_objectives, **tkwargs).squeeze()
objective = GenericMCObjective(
get_chebyshev_scalarization(weights=weights, Y=train_obj))
acq_func = qExpectedImprovement( # pyre-ignore: [28]
model=model,
objective=objective,
best_f=objective(train_obj).max(),
sampler=sampler,
)
acq_func_list.append(acq_func)
# optimize
candidates, _ = optimize_acqf_list(
acq_function_list=acq_func_list,
bounds=standard_bounds,
num_restarts=NUM_RESTARTS,
raw_samples=RAW_SAMPLES, # used for intialization heuristic
options={
"batch_limit": 5,
"maxiter": 200
},
)
# observe new values
new_x = unnormalize(candidates.detach(), bounds=problem.bounds)
new_obj = problem(new_x)
return new_x, new_obj
def get_calibrated_params(*,
country,
area,
multi_beta_calibration,
maxiters=None):
"""
Returns calibrated parameters for a `country` and an `area`
"""
if maxiters:
param_dict = get_calibrated_params_limited_iters(
country,
area,
multi_beta_calibration=multi_beta_calibration,
maxiters=maxiters,
)
return param_dict
state = load_state(calibration_states[country][area])
theta = state['train_theta']
best_observed_idx = state['best_observed_idx']
norm_params = theta[best_observed_idx]
param_bounds = (calibration_model_param_bounds_multi
if multi_beta_calibration else
calibration_model_param_bounds_single)
sim_bounds = pdict_to_parr(pdict=param_bounds,
multi_beta_calibration=multi_beta_calibration).T
params = transforms.unnormalize(norm_params, sim_bounds)
param_dict = parr_to_pdict(parr=params,
multi_beta_calibration=multi_beta_calibration)
return param_dict
def test_evaluate_slack_true(self):
for dtype in (torch.float, torch.double):
for f in self.functions:
f.to(device=self.device, dtype=dtype)
X = unnormalize(torch.rand(1, f.dim), bounds=f.bounds)
slack = f.evaluate_slack_true(X)
self.assertEqual(slack.shape, torch.Size([1, f.num_constraints]))
def objective_function(x: torch.Tensor):
# Caution: unnormalize and maximize
x = unnormalize(x, bounds=problem_bounds)
x = x.cpu().numpy().astype(np.float64) # caution
res = problem.evaluate(x)
res['objs'] = [-y for y in res['objs']]
return res # Caution: negative values imply feasibility in botorch
def get_calibrated_params_from_path(path,
estimate_mobility_reduction=False,
multi_beta_calibration=False):
"""
Returns calibrated parameters for a `country` and an `area`
"""
state = load_state(path)
theta = state['train_theta']
best_observed_idx = state['best_observed_idx']
norm_params = theta[best_observed_idx]
param_bounds = (calibration_model_param_bounds_multi
if multi_beta_calibration else
calibration_model_param_bounds_single)
if estimate_mobility_reduction:
param_bounds['p_stay_home'] = [0.0, 1.0]
sim_bounds = pdict_to_parr(
pdict=param_bounds,
multi_beta_calibration=multi_beta_calibration,
estimate_mobility_reduction=estimate_mobility_reduction).T
params = transforms.unnormalize(norm_params, sim_bounds)
param_dict = parr_to_pdict(
parr=params,
multi_beta_calibration=False,
estimate_mobility_reduction=estimate_mobility_reduction)
return param_dict
def objective_function(x: torch.Tensor):
# Caution: unnormalize and maximize
x = unnormalize(x, bounds=problem_bounds)
x = x.cpu().numpy().astype(np.float64) # caution
res = problem.evaluate(x)
objs = [-y for y in res['objs']]
return objs
def eval_objective(x, dim=10):
"""This is a helper function we use to unnormalize and evalaute a point"""
fun = Ackley(dim=dim, negate=True).to(dtype=dtype, device=device)
fun.bounds[0, :].fill_(low)
fun.bounds[1, :].fill_(up)
dim = fun.dim
lb, ub = fun.bounds
return fun(unnormalize(x, fun.bounds))
def test_normalize_unnormalize(self, cuda=False):
tkwargs = {"device": torch.device("cuda" if cuda else "cpu")}
for dtype in (torch.float, torch.double):
tkwargs["dtype"] = dtype
X = torch.tensor([0.0, 0.25, 0.5], **tkwargs).view(-1, 1)
expected_X_normalized = torch.tensor([0.0, 0.5, 1.0], **tkwargs).view(-1, 1)
bounds = torch.tensor([0.0, 0.5], **tkwargs).view(-1, 1)
X_normalized = normalize(X, bounds=bounds)
self.assertTrue(torch.equal(expected_X_normalized, X_normalized))
self.assertTrue(torch.equal(X, unnormalize(X_normalized, bounds=bounds)))
X2 = torch.tensor(
[[0.25, 0.125, 0.0], [0.25, 0.0, 0.5]], **tkwargs
).transpose(1, 0)
expected_X2_normalized = torch.tensor(
[[1.0, 0.5, 0.0], [0.5, 0.0, 1.0]], **tkwargs
).transpose(1, 0)
bounds2 = torch.tensor([[0.0, 0.0], [0.25, 0.5]], **tkwargs)
X2_normalized = normalize(X2, bounds=bounds2)
self.assertTrue(torch.equal(X2_normalized, expected_X2_normalized))
self.assertTrue(torch.equal(X2, unnormalize(X2_normalized, bounds=bounds2)))
def f_2(self, X: Tensor) -> Tensor:
X = torch.cat(
[X, torch.zeros_like(X)],
dim=-1,
)
# Cut out the first part of the function.
X = X * 0.95 + 0.03
X = unnormalize(X, self.levy.bounds.to(X))
Y = self.levy(X).unsqueeze(-1)
Y -= X[..., :1].pow(2) * 0.75
return Y
def test_normalize_unnormalize(self, cuda=False):
tkwargs = {"device": torch.device("cuda" if cuda else "cpu")}
for dtype in (torch.float, torch.double):
tkwargs["dtype"] = dtype
X = torch.tensor([0.0, 0.25, 0.5], **tkwargs).view(-1, 1)
expected_X_normalized = torch.tensor([0.0, 0.5, 1.0],
**tkwargs).view(-1, 1)
bounds = torch.tensor([0.0, 0.5], **tkwargs).view(-1, 1)
X_normalized = normalize(X, bounds=bounds)
self.assertTrue(torch.equal(expected_X_normalized, X_normalized))
self.assertTrue(
torch.equal(X, unnormalize(X_normalized, bounds=bounds)))
X2 = torch.tensor([[0.25, 0.125, 0.0], [0.25, 0.0, 0.5]],
**tkwargs).transpose(1, 0)
expected_X2_normalized = torch.tensor(
[[1.0, 0.5, 0.0], [0.5, 0.0, 1.0]], **tkwargs).transpose(1, 0)
bounds2 = torch.tensor([[0.0, 0.0], [0.25, 0.5]], **tkwargs)
X2_normalized = normalize(X2, bounds=bounds2)
self.assertTrue(torch.equal(X2_normalized, expected_X2_normalized))
self.assertTrue(
torch.equal(X2, unnormalize(X2_normalized, bounds=bounds2)))
def get_calibrated_params_limited_iters(country, area, multi_beta_calibration,
maxiters):
"""
Returns calibrated parameters using only the first `maxiters` iterations of BO.
"""
state = load_state(calibration_states[country][area])
train_G = state['train_G']
train_G = train_G[:min(maxiters, len(train_G))]
train_theta = state['train_theta']
mob_settings = calibration_mob_paths[country][area][0]
with open(mob_settings, 'rb') as fp:
mob_kwargs = pickle.load(fp)
mob = MobilitySimulator(**mob_kwargs)
data_start_date = calibration_start_dates[country][area]
data_end_date = calibration_lockdown_dates[country]['end']
unscaled_area_cases = collect_data_from_df(
country=country,
area=area,
datatype='new',
start_date_string=data_start_date,
end_date_string=data_end_date)
assert (len(unscaled_area_cases.shape) == 2)
# Scale down cases based on number of people in town and region
sim_cases = downsample_cases(unscaled_area_cases, mob_kwargs)
n_days, n_age = sim_cases.shape
G_obs = torch.tensor(sim_cases).reshape(1, n_days * n_age)
G_obs_aggregate = torch.tensor(sim_cases).sum(dim=-1)
def objective(G):
return -(G - G_obs_aggregate).pow(2).sum(dim=-1) / n_days
train_G_objectives = objective(train_G)
best_observed_idx = train_G_objectives.argmax()
best_observed_obj = train_G_objectives[best_observed_idx].item()
param_bounds = (calibration_model_param_bounds_multi
if multi_beta_calibration else
calibration_model_param_bounds_single)
sim_bounds = pdict_to_parr(pdict=param_bounds,
multi_beta_calibration=multi_beta_calibration).T
normalized_calibrated_params = train_theta[best_observed_idx]
calibrated_params = transforms.unnormalize(normalized_calibrated_params,
sim_bounds)
calibrated_params = parr_to_pdict(
parr=calibrated_params, multi_beta_calibration=multi_beta_calibration)
return calibrated_params
def sample_truncated_normal_perturbations(
X: Tensor,
n_discrete_points: int,
sigma: float,
bounds: Tensor,
qmc: bool = True,
) -> Tensor:
r"""Sample points around `X`.
Sample perturbed points around `X` such that the added perturbations
are sampled from N(0, sigma^2 I) and truncated to be within [0,1]^d.
Args:
X: A `n x d`-dim tensor starting points.
n_discrete_points: The number of points to sample.
sigma: The standard deviation of the additive gaussian noise for
perturbing the points.
bounds: A `2 x d`-dim tensor containing the bounds.
qmc: A boolean indicating whether to use qmc.
Returns:
A `n_discrete_points x d`-dim tensor containing the sampled points.
"""
X = normalize(X, bounds=bounds)
d = X.shape[1]
# sample points from N(X_center, sigma^2 I), truncated to be within
# [0, 1]^d.
if X.shape[0] > 1:
rand_indices = torch.randint(X.shape[0], (n_discrete_points, ),
device=X.device)
X = X[rand_indices]
if qmc:
std_bounds = torch.zeros(2, d, dtype=X.dtype, device=X.device)
std_bounds[1] = 1
u = draw_sobol_samples(bounds=std_bounds, n=n_discrete_points,
q=1).squeeze(1)
else:
u = torch.rand((n_discrete_points, d), dtype=X.dtype, device=X.device)
# compute bounds to sample from
a = -X
b = 1 - X
# compute z-score of bounds
alpha = a / sigma
beta = b / sigma
normal = Normal(0, 1)
cdf_alpha = normal.cdf(alpha)
# use inverse transform
perturbation = normal.icdf(cdf_alpha + u *
(normal.cdf(beta) - cdf_alpha)) * sigma
# add perturbation and clip points that are still outside
perturbed_X = (X + perturbation).clamp(0.0, 1.0)
return unnormalize(perturbed_X, bounds=bounds)
def optimize_acqf_and_get_observation(acq_func):
"""Optimizes the acquisition function,
and returns a new candidate and a noisy observation"""
candidates, _ = optimize_acqf(
acq_function=acq_func,
bounds=torch.stack([
torch.zeros(d, dtype=dt),
torch.ones(d, dtype=dt),
]),
q=1,
num_restarts=10,
raw_samples=200,
)
x = unnormalize(candidates.detach(), bounds=bounds)
print('Hyper-parameter: ' + str(x))
obj = self.train(x.view(-1)).unsqueeze(-1)
print(print('Error: ' + str(obj)))
return x, obj
def qparego_candidates_func(
train_x: "torch.Tensor",
train_obj: "torch.Tensor",
train_con: Optional["torch.Tensor"],
bounds: "torch.Tensor",
) -> "torch.Tensor":
"""Quasi MC-based extended ParEGO (qParEGO) for constrained multi-objective optimization.
The default value of ``candidates_func`` in :class:`~optuna.integration.BoTorchSampler`
with multi-objective optimization when the number of objectives is larger than three.
.. seealso::
:func:`~optuna.integration.botorch.qei_candidates_func` for argument and return value
descriptions.
"""
n_objectives = train_obj.size(-1)
weights = sample_simplex(n_objectives).squeeze()
scalarization = get_chebyshev_scalarization(weights=weights, Y=train_obj)
if train_con is not None:
train_y = torch.cat([train_obj, train_con], dim=-1)
constraints = []
n_constraints = train_con.size(1)
for i in range(n_constraints):
constraints.append(lambda Z, i=i: Z[..., -n_constraints + i])
objective = ConstrainedMCObjective(
objective=lambda Z: scalarization(Z[..., :n_objectives]),
constraints=constraints,
)
else:
train_y = train_obj
objective = GenericMCObjective(scalarization)
train_x = normalize(train_x, bounds=bounds)
model = SingleTaskGP(train_x,
train_y,
outcome_transform=Standardize(m=train_y.size(-1)))
mll = ExactMarginalLogLikelihood(model.likelihood, model)
fit_gpytorch_model(mll)
acqf = qExpectedImprovement(
model=model,
best_f=objective(train_y).max(),
sampler=SobolQMCNormalSampler(num_samples=256),
objective=objective,
)
standard_bounds = torch.zeros_like(bounds)
standard_bounds[1] = 1
candidates, _ = optimize_acqf(
acq_function=acqf,
bounds=standard_bounds,
q=1,
num_restarts=20,
raw_samples=1024,
options={
"batch_limit": 5,
"maxiter": 200
},
sequential=True,
)
candidates = unnormalize(candidates.detach(), bounds=bounds)
return candidates
def qehvi_candidates_func(
train_x: "torch.Tensor",
train_obj: "torch.Tensor",
train_con: Optional["torch.Tensor"],
bounds: "torch.Tensor",
) -> "torch.Tensor":
"""Quasi MC-based batch Expected Hypervolume Improvement (qEHVI).
The default value of ``candidates_func`` in :class:`~optuna.integration.BoTorchSampler`
with multi-objective optimization when the number of objectives is three or less.
.. seealso::
:func:`~optuna.integration.botorch.qei_candidates_func` for argument and return value
descriptions.
"""
n_objectives = train_obj.size(-1)
if train_con is not None:
train_y = torch.cat([train_obj, train_con], dim=-1)
is_feas = (train_con <= 0).all(dim=-1)
train_obj_feas = train_obj[is_feas]
constraints = []
n_constraints = train_con.size(1)
for i in range(n_constraints):
constraints.append(lambda Z, i=i: Z[..., -n_constraints + i])
additional_qehvi_kwargs = {
"objective":
IdentityMCMultiOutputObjective(outcomes=list(range(n_objectives))),
"constraints":
constraints,
}
else:
train_y = train_obj
train_obj_feas = train_obj
additional_qehvi_kwargs = {}
train_x = normalize(train_x, bounds=bounds)
model = SingleTaskGP(train_x,
train_y,
outcome_transform=Standardize(m=train_y.shape[-1]))
mll = ExactMarginalLogLikelihood(model.likelihood, model)
fit_gpytorch_model(mll)
# Approximate box decomposition similar to Ax when the number of objectives is large.
# https://github.com/facebook/Ax/blob/master/ax/models/torch/botorch_moo_defaults
if n_objectives > 2:
alpha = 10**(-8 + n_objectives)
else:
alpha = 0.0
partitioning = NondominatedPartitioning(num_outcomes=n_objectives,
Y=train_obj_feas,
alpha=alpha)
ref_point = train_obj.min(dim=0).values - 1e-8
ref_point_list = ref_point.tolist()
acqf = qExpectedHypervolumeImprovement(
model=model,
ref_point=ref_point_list,
partitioning=partitioning,
sampler=SobolQMCNormalSampler(num_samples=256),
**additional_qehvi_kwargs,
)
standard_bounds = torch.zeros_like(bounds)
standard_bounds[1] = 1
candidates, _ = optimize_acqf(
acq_function=acqf,
bounds=standard_bounds,
q=1,
num_restarts=20,
raw_samples=1024,
options={
"batch_limit": 5,
"maxiter": 200,
"nonnegative": True
},
sequential=True,
)
candidates = unnormalize(candidates.detach(), bounds=bounds)
return candidates
def generate_initial_observations(n, logger):
"""
Takes an integer `n` and generates `n` initial observations
from the black box function using Sobol random parameter settings
in the unit cube. Returns parameter setting and black box function outputs
"""
if n <= 0:
raise ValueError(
'qKnowledgeGradient and GP needs at least one observation to be defined properly.')
# sobol sequence
# new_thetas: [n, n_params]
new_thetas = torch.tensor(
sobol_seq.i4_sobol_generate(n_params, n), dtype=torch.float)
# simulator observations
# new_G, new_G_sem: [n, n_days * n_age] (flattened outputs)
new_G = torch.zeros((n, n_days * n_age), dtype=torch.float)
new_G_sem = torch.zeros((n, n_days * n_age), dtype=torch.float)
for i in range(n):
t0 = time.time()
# get mean and standard error of mean (sem) of every simulation output
G, G_sem = composite_simulation(new_thetas[i, :])
new_G[i, :] = G
new_G_sem[i, :] = G_sem
# log
G_objectives = objective(new_G[:i+1])
best_idx = G_objectives.argmax()
best = G_objectives[best_idx].item()
current = objective(G).item()
case_diff = (
G.reshape(n_days, n_age)[-1].sum()
- G_obs.reshape(n_days, n_age)[-1].sum())
t1 = time.time()
logger.log(
i=i - n,
time=t1 - t0,
best=best,
objective=current,
case_diff=case_diff,
theta=transforms.unnormalize(new_thetas[i, :].detach().squeeze(), sim_bounds)
)
# save state
state = {
'train_theta': new_thetas[:i+1],
'train_G': new_G[:i+1],
'train_G_sem': new_G_sem[:i+1],
'best_observed_obj': best,
'best_observed_idx': best_idx,
}
save_state(state, logger.filename + '_init')
# compute best objective from simulations
f = objective(new_G)
best_f_idx = f.argmax()
best_f = f[best_f_idx].item()
return new_thetas, new_G, new_G_sem, best_f, best_f_idx
def composite_simulation(norm_params):
"""
Takes a set of normalized (unit cube) BO parameters
and returns simulator output means and standard errors based on multiple
random restarts. This corresponds to the black-box function.
"""
# un-normalize normalized params to obtain simulation parameters
params = transforms.unnormalize(norm_params, sim_bounds)
# finalize settings based which parameters are calibrated
kwargs = copy.deepcopy(launch_kwargs)
if args.measures_optimized:
'''
Measures are calibrated
'''
measure_params = parr_to_pdict(params, measures_optimized=args.measures_optimized)
# social distancing measures: calibration is only done for `SocialDistancingForAllMeasure` for now
measure_list_ = [
SocialDistancingForPositiveMeasure(
t_window=Interval(0.0, max_time), p_stay_home=1.0),
SocialDistancingForPositiveMeasureHousehold(
t_window=Interval(0.0, max_time), p_isolate=1.0),
SocialDistancingForAllMeasure(
t_window=Interval(TO_HOURS * days_until_lockdown_start,
TO_HOURS * days_until_lockdown_end),
p_stay_home=measure_params['p_stay_home']),
]
# close sites if specified
if args.measures_close:
beta_multipliers = {'education': 1.0, 'social': 1.0,
'bus_stop': 1.0, 'office': 1.0, 'supermarket': 1.0}
for category in args.measures_close:
if category in beta_multipliers.keys():
beta_multipliers[category] = 0.0
else:
raise ValueError(f'Site type `{category}` passed in `--measures_close` is invalid.\n'
f'Available are {str(list(beta_multipliers.keys()))}')
measure_list_.append(BetaMultiplierMeasureByType(
t_window=Interval(TO_HOURS * days_until_lockdown_start,
TO_HOURS * days_until_lockdown_end),
beta_multiplier=beta_multipliers
))
kwargs['measure_list'] = MeasureList(measure_list_)
# get optimized model paramters for this country and area
calibrated_model_params = settings_optimized_town_params[args.country][args.area]
if calibrated_model_params is None:
raise ValueError(f'Cannot optimize measures for {args.country}-{args.area} because model parameters '
'have not been fitted yet. Set values in `calibration_settings.py`')
kwargs['params'] = calibrated_model_params
else:
'''
Model parameters calibrated
'''
kwargs['measure_list'] = MeasureList([
SocialDistancingForPositiveMeasure(
t_window=Interval(0.0, max_time), p_stay_home=1.0),
SocialDistancingForPositiveMeasureHousehold(
t_window=Interval(0.0, max_time), p_isolate=1.0),
])
kwargs['params'] = parr_to_pdict(params, measures_optimized=args.measures_optimized)
# run simulation in parallel,
summary = launch_parallel_simulations(**kwargs)
# (random_repeats, n_people)
posi_started = torch.tensor(summary.state_started_at['posi'])
posi_started -= test_lag_days * TO_HOURS # account for test lag
# (random_repeats, n_days)
age_groups = torch.tensor(summary.people_age)
posi_cumulative = convert_timings_to_cumulative_daily(
timings=posi_started, age_groups=age_groups, time_horizon=n_days * TO_HOURS)
if posi_cumulative.shape[0] <= 1:
raise ValueError('Must run at least 2 random restarts per setting to get estimate of noise in observation.')
# compute mean and standard error of means
G = torch.mean(posi_cumulative, dim=0)
G_sem = torch.std(posi_cumulative, dim=0) / math.sqrt(posi_cumulative.shape[0])
# make sure noise is not zero for non-degerateness
G_sem = torch.max(G_sem, MIN_NOISE)
# flatten
G = G.reshape(1, n_days * n_age)
G_sem = G_sem.reshape(1, n_days * n_age)
return G, G_sem
def unnormalize_theta(theta):
'''
Computes unnormalized parameters
'''
return transforms.unnormalize(theta, sim_bounds)
def run_RGPE(test_task: int, objective, bounds, base_model_list):
input_dim = bounds.shape[1]
best_rgpe_all = []
best_argmax_rgpe_all = []
# Average over multiple trials
for trial in range(N_TRIALS):
print(f"Trial {trial + 1} of {N_TRIALS}")
best_BMs = []
best_rgpe = []
# Initial random observations
raw_x = draw_sobol_samples(bounds=bounds,
n=RANDOM_INITIALIZATION_SIZE,
q=1,
seed=trial).squeeze(1)
train_x = normalize(raw_x, bounds=bounds)
train_y_noiseless = objective(raw_x, shift=test_task)
train_y = train_y_noiseless + noise_std * torch.randn_like(
train_y_noiseless)
train_yvar = torch.full_like(train_y, noise_std**2)
# keep track of the best observed point at each iteration
best_value = train_y.max().item()
best_rgpe.append(best_value)
# Run N_BATCH rounds of BayesOpt after the initial random batch
for iteration in range(N_BATCH):
target_model = get_fitted_model(train_x, train_y, train_yvar)
model_list = base_model_list + [target_model]
rank_weights = compute_rank_weights(
train_x,
train_y,
base_model_list,
target_model,
NUM_POSTERIOR_SAMPLES,
)
# create model and acquisition function
rgpe_model = RGPE(model_list, rank_weights)
sampler_qnei = SobolQMCNormalSampler(num_samples=MC_SAMPLES)
qNEI = qNoisyExpectedImprovement(
model=rgpe_model,
X_baseline=train_x,
sampler=sampler_qnei,
)
# optimize
candidate, _ = optimize_acqf(
acq_function=qNEI,
bounds=bounds,
q=Q_BATCH_SIZE,
num_restarts=N_RESTARTS,
raw_samples=N_RESTART_CANDIDATES,
)
# fetch the new values
new_x = candidate.detach()
new_y_noiseless = objective(unnormalize(new_x, bounds=bounds),
shift=test_task)
new_y = new_y_noiseless + noise_std * torch.randn_like(
new_y_noiseless)
new_yvar = torch.full_like(new_y, noise_std**2)
# update training points
train_x = torch.cat((train_x, new_x))
train_y = torch.cat((train_y, new_y))
train_yvar = torch.cat((train_yvar, new_yvar))
# get the new best observed value
best_value = train_y.max().item()
best_idx = torch.argmax(train_y).item()
best_candidate = train_x[best_idx].view(1, -1)
_, best_BM = objective(unnormalize(best_candidate, bounds=bounds),
shift=test_task,
include_BMs=True)
best_rgpe.append(best_value)
best_BMs.append(best_BM)
best_rgpe_all.append(best_rgpe)
best_argmax_rgpe_all.append(best_BMs)
BM_winner_idx = np.argmax(np.array(best_rgpe_all)[:, -1], axis=0)
BM_winner = np.reshape(np.array(best_argmax_rgpe_all[BM_winner_idx][-1]),
(2, input_dim))
return BM_winner
def mixin_tree(T: Tensor, bounds: Tensor, alpha: float) -> Tensor:
return (1 - alpha) * T + alpha * unnormalize(torch.rand_like(T),
bounds)
def mixin_layer(X: Tensor, bounds: Tensor, eta: float) -> Tensor:
perturbations = unnormalize(B.sample(X.shape).squeeze(-1), bounds)
return (1 - eta) * X + eta * perturbations
def evaluate_slack_true(self, X: Tensor) -> Tensor:
X_tf = unnormalize(X, self.con_bounds)
return 50 - (X_tf[..., 0:1] - 2.5).pow(2) - (X_tf[..., 1:2] -
7.5).pow(2)
def generate_initial_observations(n, logger, loaded_init_theta=None, loaded_init_G=None, loaded_init_G_sem=None):
"""
Takes an integer `n` and generates `n` initial observations
from the black box function using Sobol random parameter settings
in the unit cube. Returns parameter setting and black box function outputs.
If `loaded_init_theta/G/G_sem` are specified, initialization is loaded (possibly partially, in which
case the initialization using the Sobol random sequence is continued where left off).
"""
if n <= 0:
raise ValueError(
'qKnowledgeGradient and GP needs at least one observation to be defined properly.')
# sobol sequence proposal points
# new_thetas: [n, n_params]
new_thetas = torch.tensor(
sobol_seq.i4_sobol_generate(n_params, n), dtype=torch.float)
# check whether initial observations are loaded
loaded = (loaded_init_theta is not None
and loaded_init_G is not None
and loaded_init_G_sem is not None)
if loaded:
n_loaded = loaded_init_theta.shape[0] # loaded no. of observations total
n_loaded_init = min(n_loaded, n) # loaded no. of quasi-random initialization observations
n_init = max(n_loaded, n) # final no. of observations returned, at least quasi-random initializations
# check whether loaded proposal points are same as without loading observations
try:
assert(np.allclose(loaded_init_theta[:n_loaded_init], new_thetas[:n_loaded_init]))
except AssertionError:
print(
'\n\n\n===> Warning: parameters of loaded inital observations '
'do not coincide with initialization that would have been done. '
'Double check simulation, ninit, and parameter bounds, which could change '
'the initial random Sobol sequence. \nThe loaded parameter settings are used. \n\n\n'
)
if n_init > n:
new_thetas = loaded_init_theta # size of tensor increased to `n_init`, as more than Sobol init points loaded
else:
n_loaded = 0 # loaded no. of observations total
n_loaded_init = 0 # loaded no. of quasi-random initialization observations
n_init = n # final no. of observations returned, at least quasi-random initializations
# instantiate simulator observation tensors
if per_age_group_objective:
# new_G, new_G_sem: [n_init, n_days * n_age] (flattened outputs)
new_G = torch.zeros((n_init, n_days * n_age), dtype=torch.float)
new_G_sem = torch.zeros((n_init, n_days * n_age), dtype=torch.float)
else:
# new_G, new_G_sem: [n_init, n_days]
new_G = torch.zeros((n_init, n_days), dtype=torch.float)
new_G_sem = torch.zeros((n_init, n_days), dtype=torch.float)
# generate `n` initial evaluations at quasi random settings; if applicable, skip and load expensive evaluation result
for i in range(n_init):
# if loaded, use initial observation for this parameter settings
if loaded and i <= n_loaded - 1:
new_thetas[i] = loaded_init_theta[i]
G, G_sem = loaded_init_G[i], loaded_init_G_sem[i]
walltime = 0.0
# if not loaded, evaluate as usual
else:
t0 = time.time()
G, G_sem = composite_simulation(new_thetas[i])
walltime = time.time() - t0
new_G[i] = G
new_G_sem[i] = G_sem
# log
G_objectives = objective(new_G[:i+1])
best_idx = G_objectives.argmax()
best = G_objectives[best_idx].item()
current = objective(G).item()
if per_age_group_objective:
case_diff = G.reshape(n_days, n_age)[-1].sum() - G_obs_aggregate[-1]
else:
case_diff = G[-1] - G_obs_aggregate[-1]
logger.log(
i=i - n,
time=walltime,
best=best,
objective=current,
case_diff=case_diff,
theta=transforms.unnormalize(new_thetas[i, :].detach().squeeze(), sim_bounds)
)
# save state
state = {
'train_theta': new_thetas[:i+1],
'train_G': new_G[:i+1],
'train_G_sem': new_G_sem[:i+1],
'best_observed_obj': best,
'best_observed_idx': best_idx,
}
save_state(state, logger.filename)
# compute best objective from simulations
f = objective(new_G)
best_f_idx = f.argmax()
best_f = f[best_f_idx].item()
return new_thetas, new_G, new_G_sem, best_f, best_f_idx
def qei_candidates_func(
train_x: "torch.Tensor",
train_obj: "torch.Tensor",
train_con: Optional["torch.Tensor"],
bounds: "torch.Tensor",
) -> "torch.Tensor":
"""Quasi MC-based batch Expected Improvement (qEI).
The default value of ``candidates_func`` in :class:`~optuna.integration.BoTorchSampler`
with single-objective optimization.
Args:
train_x:
Previous parameter configurations. A ``torch.Tensor`` of shape
``(n_trials, n_params)``. ``n_trials`` is the number of already observed trials
and ``n_params`` is the number of parameters. ``n_params`` may be larger than the
actual number of parameters if categorical parameters are included in the search
space, since these parameters are one-hot encoded.
Values are not normalized.
train_obj:
Previously observed objectives. A ``torch.Tensor`` of shape
``(n_trials, n_objectives)``. ``n_trials`` is identical to that of ``train_x``.
``n_objectives`` is the number of objectives. Observations are not normalized.
train_con:
Objective constraints. A ``torch.Tensor`` of shape ``(n_trials, n_constraints)``.
``n_trials`` is identical to that of ``train_x``. ``n_constraints`` is the number of
constraints. A constraint is violated if strictly larger than 0. If no constraints are
involved in the optimization, this argument will be :obj:`None`.
bounds:
Search space bounds. A ``torch.Tensor`` of shape ``(n_params, 2)``. ``n_params`` is
identical to that of ``train_x``. The first and the second column correspond to the
lower and upper bounds for each parameter respectively.
Returns:
Next set of candidates. Usually the return value of BoTorch's ``optimize_acqf``.
"""
if train_obj.size(-1) != 1:
raise ValueError("Objective may only contain single values with qEI.")
if train_con is not None:
train_y = torch.cat([train_obj, train_con], dim=-1)
is_feas = (train_con <= 0).all(dim=-1)
train_obj_feas = train_obj[is_feas]
if train_obj_feas.numel() == 0:
# TODO(hvy): Do not use 0 as the best observation.
_logger.warning(
"No objective values are feasible. Using 0 as the best objective in qEI."
)
best_f = torch.zeros(())
else:
best_f = train_obj_feas.max()
constraints = []
n_constraints = train_con.size(1)
for i in range(n_constraints):
constraints.append(lambda Z, i=i: Z[..., -n_constraints + i])
objective = ConstrainedMCObjective(
objective=lambda Z: Z[..., 0],
constraints=constraints,
)
else:
train_y = train_obj
best_f = train_obj.max()
objective = None # Using the default identity objective.
train_x = normalize(train_x, bounds=bounds)
model = SingleTaskGP(train_x,
train_y,
outcome_transform=Standardize(m=train_y.size(-1)))
mll = ExactMarginalLogLikelihood(model.likelihood, model)
fit_gpytorch_model(mll)
acqf = qExpectedImprovement(
model=model,
best_f=best_f,
sampler=SobolQMCNormalSampler(num_samples=256),
objective=objective,
)
standard_bounds = torch.zeros_like(bounds)
standard_bounds[1] = 1
candidates, _ = optimize_acqf(
acq_function=acqf,
bounds=standard_bounds,
q=1,
num_restarts=10,
raw_samples=512,
options={
"batch_limit": 5,
"maxiter": 200
},
sequential=True,
)
candidates = unnormalize(candidates.detach(), bounds=bounds)
return candidates
def evaluate(mth, run_i, seed):
print(mth, run_i, seed, '===== start =====', flush=True)
def objective_function(x: torch.Tensor):
# Caution: unnormalize and maximize
x = unnormalize(x, bounds=problem_bounds)
x = x.cpu().numpy().astype(np.float64) # caution
res = problem.evaluate(x)
res['objs'] = [-y for y in res['objs']]
return res # Caution: negative values imply feasibility in botorch
hv_diffs = []
time_list = []
global_start_time = time.time()
# random seed
np.random.seed(seed)
torch.manual_seed(seed)
# call helper functions to generate initial training data and initialize model
train_x, train_obj, train_con = generate_initial_data(
initial_runs, objective_function, time_list, global_start_time)
# fix bug: find feasible
real_initial_runs = initial_runs
while real_initial_runs < max_runs:
# compute feasible observations
is_feas = (train_con <= 0).all(dim=-1)
# compute points that are better than the known reference point
better_than_ref = (train_obj > problem.ref_point).all(dim=-1)
if (is_feas & better_than_ref).any():
break
train_x, train_obj, train_con = expand_initial_data(
train_x, train_obj, train_con, objective_function, time_list,
global_start_time)
real_initial_runs += 1
print('=== Expand initial data to find feasible. Iter =',
real_initial_runs)
mll, model = initialize_model(train_x, train_obj, train_con)
# for plot
X_init = train_x.cpu().numpy().astype(np.float64)
Y_init = -1 * train_obj.cpu().numpy().astype(np.float64)
# calculate hypervolume of init data
for i in range(real_initial_runs):
train_obj_i = train_obj[:i + 1]
train_con_i = train_con[:i + 1]
# compute pareto front
is_feas_i = (train_con_i <= 0).all(dim=-1)
feas_train_obj_i = train_obj_i[is_feas_i]
pareto_mask = is_non_dominated(feas_train_obj_i)
pareto_y = feas_train_obj_i[pareto_mask]
# compute hypervolume
volume = hv.compute(pareto_y)
hv_diff = problem.max_hv - volume
hv_diffs.append(hv_diff)
# run (max_runs - real_initial_runs) rounds of BayesOpt after the initial random batch
for iteration in range(real_initial_runs + 1, max_runs + 1):
t0 = time.time()
try:
# fit the models
fit_gpytorch_model(mll)
# define the qEHVI acquisition modules using a QMC sampler
sampler = SobolQMCNormalSampler(num_samples=MC_SAMPLES)
# compute feasible observations
is_feas = (train_con <= 0).all(dim=-1)
# compute points that are better than the known reference point
better_than_ref = (train_obj > problem.ref_point).all(dim=-1)
# partition non-dominated space into disjoint rectangles
partitioning = NondominatedPartitioning(
num_outcomes=problem.num_objs,
# use observations that are better than the specified reference point and feasible
Y=train_obj[better_than_ref & is_feas],
)
qEHVI = qExpectedHypervolumeImprovement(
model=model,
ref_point=problem.ref_point.tolist(
), # use known reference point
partitioning=partitioning,
sampler=sampler,
# define an objective that specifies which outcomes are the objectives
objective=IdentityMCMultiOutputObjective(
outcomes=list(range(problem.num_objs))),
# specify that the constraint is on the last outcome
constraints=constraint_callable_list(
problem.num_constraints, num_objs=problem.num_objs),
)
# optimize and get new observation
new_x, new_obj, new_con = optimize_acqf_and_get_observation(
qEHVI, objective_function, time_list, global_start_time)
except Exception as e: # handle numeric problem
step = 2
print(
'===== Exception in optimization loop, restart with 1/%d of training data: %s'
% (step, str(e)))
if refit == 1:
mll, model = initialize_model(train_x[::step],
train_obj[::step],
train_con[::step])
else:
mll, model = initialize_model(
train_x[::step],
train_obj[::step],
train_con[::step],
model.state_dict(),
)
# fit the models
fit_gpytorch_model(mll)
# define the qEHVI acquisition modules using a QMC sampler
sampler = SobolQMCNormalSampler(num_samples=MC_SAMPLES)
# compute feasible observations
is_feas = (train_con[::step] <= 0).all(dim=-1)
# compute points that are better than the known reference point
better_than_ref = (train_obj[::step] > problem.ref_point).all(
dim=-1)
# partition non-dominated space into disjoint rectangles
partitioning = NondominatedPartitioning(
num_outcomes=problem.num_objs,
# use observations that are better than the specified reference point and feasible
Y=train_obj[::step][better_than_ref & is_feas],
)
qEHVI = qExpectedHypervolumeImprovement(
model=model,
ref_point=problem.ref_point.tolist(
), # use known reference point
partitioning=partitioning,
sampler=sampler,
# define an objective that specifies which outcomes are the objectives
objective=IdentityMCMultiOutputObjective(
outcomes=list(range(problem.num_objs))),
# specify that the constraint is on the last outcome
constraints=constraint_callable_list(
problem.num_constraints, num_objs=problem.num_objs),
)
# optimize and get new observation
new_x, new_obj, new_con = optimize_acqf_and_get_observation(
qEHVI, objective_function, time_list, global_start_time)
assert len(time_list) == iteration
# update training points
train_x = torch.cat([train_x, new_x])
train_obj = torch.cat([train_obj, new_obj])
train_con = torch.cat([train_con, new_con])
# update progress
# compute pareto front
is_feas = (train_con <= 0).all(dim=-1)
feas_train_obj = train_obj[is_feas]
pareto_mask = is_non_dominated(feas_train_obj)
pareto_y = feas_train_obj[pareto_mask]
# compute hypervolume
volume = hv.compute(pareto_y)
hv_diff = problem.max_hv - volume
hv_diffs.append(hv_diff)
# reinitialize the models so they are ready for fitting on next iteration
# use the current state dict to speed up fitting
# Note: they find improved performance from not warm starting the model hyperparameters
# using the hyperparameters from the previous iteration
if refit == 1:
mll, model = initialize_model(train_x, train_obj, train_con)
else:
mll, model = initialize_model(
train_x,
train_obj,
train_con,
model.state_dict(),
)
t1 = time.time()
print(
"Iter %d: x=%s, perf=%s, con=%s, hv_diff=%f, time=%.2f, global_time=%.2f"
% (iteration, unnormalize(new_x, bounds=problem_bounds), -new_obj,
new_con, hv_diff, t1 - t0, time_list[-1]),
flush=True)
# compute pareto front
is_feas = (train_con <= 0).all(dim=-1)
feas_train_obj = train_obj[is_feas]
pareto_mask = is_non_dominated(feas_train_obj)
pareto_y = feas_train_obj[pareto_mask]
pf = -1 * pareto_y.cpu().numpy().astype(np.float64)
# Save result
X = unnormalize(train_x, bounds=problem_bounds).cpu().numpy().astype(
np.float64) # caution
train_obj[~is_feas] = -INFEASIBLE_OBJ_VALUE # set infeasible
Y = -1 * train_obj.cpu().numpy().astype(np.float64)
# plot for debugging
if plot_mode == 1:
plot_pf(problem, problem_str, mth, pf, Y_init)
return hv_diffs, pf, X, Y, time_list
# ========================================================================
with torch.no_grad():
pred_r_train_mean = gp_recon_model.posterior(x_train).mean
pred_r_train_std = gp_recon_model.posterior(x_train).variance.sqrt()
pred_r_test_mean = gp_recon_model.posterior(x_val).mean
pred_r_test_std = gp_recon_model.posterior(x_val).variance.sqrt()
pred_r_train_mean = pred_r_train_mean.clamp_min(
1e-4).log() if transfo == "exp" else pred_r_train_mean
pred_r_train_std = pred_r_train_std.clamp_min(
1e-4).log() if transfo == "exp" else pred_r_train_std
pred_r_test_mean = pred_r_test_mean.clamp_min(
1e-4).log() if transfo == "exp" else pred_r_test_mean
pred_r_test_std = pred_r_test_std.clamp_min(1e-4).log() if transfo == "exp" else pred_r_test_std
pred_r_train_mean = unnormalize(pred_r_train_mean,
rbounds) if transfo == "normalize" else pred_r_train_mean
pred_r_train_std = unnormalize(pred_r_train_std,
rbounds) if transfo == "normalize" else pred_r_train_std
pred_r_test_mean = unnormalize(pred_r_test_mean,
rbounds) if transfo == "normalize" else pred_r_test_mean
pred_r_test_std = unnormalize(pred_r_test_std,
rbounds) if transfo == "normalize" else pred_r_test_std
gp_recon_model_fit_train = (pred_r_train_mean - r_train).pow(2).div(len(r_train))
gp_recon_model_fit_test = (pred_r_test_mean - r_val).pow(2).div(len(r_train))
torch.save(gp_recon_model_fit_train, os.path.join(save_folder, "gp_recon_model_fit_train.pt"))
torch.save(gp_recon_model_fit_test, os.path.join(save_folder, "gp_recon_model_fit_test.pt"))
print(f'\tMSE on r train set : {gp_recon_model_fit_train.sum().item():.3f}')
print(f'\tMSE on r validation set: {gp_recon_model_fit_test.sum().item():.3f}')
df_ = pd.DataFrame(
def evaluate(mth, run_i, seed):
print(mth, run_i, seed, '===== start =====', flush=True)
def objective_function(x: torch.Tensor):
# Caution: unnormalize and maximize
x = unnormalize(x, bounds=problem_bounds)
x = x.cpu().numpy().astype(np.float64) # caution
res = problem.evaluate(x)
objs = [-y for y in res['objs']]
return objs
hv_diffs = []
time_list = []
global_start_time = time.time()
# random seed
np.random.seed(seed)
torch.manual_seed(seed)
# call helper functions to generate initial training data and initialize model
train_x, train_obj = generate_initial_data(initial_runs,
objective_function, time_list,
global_start_time)
mll, model = initialize_model(train_x, train_obj)
# for plot
X_init = train_x.cpu().numpy().astype(np.float64)
Y_init = -1 * train_obj.cpu().numpy().astype(np.float64)
# calculate hypervolume of init data
for i in range(initial_runs):
train_obj_i = train_obj[:i + 1]
# compute pareto front
pareto_mask = is_non_dominated(train_obj_i)
pareto_y = train_obj_i[pareto_mask]
# compute hypervolume
volume = hv.compute(pareto_y)
hv_diff = problem.max_hv - volume
hv_diffs.append(hv_diff)
# run (max_runs - initial_runs) rounds of BayesOpt after the initial random batch
for iteration in range(initial_runs + 1, max_runs + 1):
t0 = time.time()
try:
# fit the models
fit_gpytorch_model(mll)
# define the qEHVI acquisition modules using a QMC sampler
sampler = SobolQMCNormalSampler(num_samples=MC_SAMPLES)
# partition non-dominated space into disjoint rectangles
partitioning = NondominatedPartitioning(
num_outcomes=problem.num_objs, Y=train_obj)
qEHVI = qExpectedHypervolumeImprovement(
model=model,
ref_point=problem.ref_point.tolist(
), # use known reference point
partitioning=partitioning,
sampler=sampler,
)
# optimize and get new observation
new_x, new_obj = optimize_acqf_and_get_observation(
qEHVI, objective_function, time_list, global_start_time)
except Exception as e:
step = 2
print(
'===== Exception in optimization loop, restart with 1/%d of training data: %s'
% (step, str(e)))
if refit == 1:
mll, model = initialize_model(train_x[::step],
train_obj[::step])
else:
mll, model = initialize_model(
train_x[::step],
train_obj[::step],
model.state_dict(),
)
# fit the models
fit_gpytorch_model(mll)
# define the qEHVI acquisition modules using a QMC sampler
sampler = SobolQMCNormalSampler(num_samples=MC_SAMPLES)
# partition non-dominated space into disjoint rectangles
partitioning = NondominatedPartitioning(
num_outcomes=problem.num_objs, Y=train_obj[::step])
qEHVI = qExpectedHypervolumeImprovement(
model=model,
ref_point=problem.ref_point.tolist(
), # use known reference point
partitioning=partitioning,
sampler=sampler,
)
# optimize and get new observation
new_x, new_obj = optimize_acqf_and_get_observation(
qEHVI, objective_function, time_list, global_start_time)
assert len(time_list) == iteration
# update training points
train_x = torch.cat([train_x, new_x])
train_obj = torch.cat([train_obj, new_obj])
# update progress
# compute pareto front
pareto_mask = is_non_dominated(train_obj)
pareto_y = train_obj[pareto_mask]
# compute hypervolume
volume = hv.compute(pareto_y)
hv_diff = problem.max_hv - volume
hv_diffs.append(hv_diff)
# reinitialize the models so they are ready for fitting on next iteration
# use the current state dict to speed up fitting
# Note: they find improved performance from not warm starting the model hyperparameters
# using the hyperparameters from the previous iteration
if refit == 1:
mll, model = initialize_model(train_x, train_obj)
else:
mll, model = initialize_model(
train_x,
train_obj,
model.state_dict(),
)
t1 = time.time()
print(
"Iter %d: x=%s, perf=%s, hv_diff=%f, time=%.2f, global_time=%.2f" %
(iteration, unnormalize(new_x, bounds=problem_bounds), -new_obj,
hv_diff, t1 - t0, time_list[-1]),
flush=True)
# Save result
X = unnormalize(train_x, bounds=problem_bounds).cpu().numpy().astype(
np.float64) # caution
Y = -1 * train_obj.cpu().numpy().astype(np.float64)
# compute pareto front
pareto_mask = is_non_dominated(train_obj)
pareto_y = train_obj[pareto_mask]
pf = -1 * pareto_y.cpu().numpy().astype(np.float64)
# plot for debugging
if plot_mode == 1:
plot_pf(problem, problem_str, mth, pf, Y_init)
return hv_diffs, pf, X, Y, time_list
