Remove maximize from info-theoretic acquisition functions (pytorch#…

…2590)

Summary:
Removes `maximize` from some info-theoretic acquisition functions (those that use `get_optimal_samples`). 



Differential Revision: D64698976

botorch/acquisition/input_constructors.py

@@ -80,6 +80,7 @@
     LearnedObjective,
     MCAcquisitionObjective,
     PosteriorTransform,
+    ScalarizedPosteriorTransform,
 )
 from botorch.acquisition.preference import (
     AnalyticExpectedUtilityOfBestOption,
@@ -1801,6 +1802,7 @@ def construct_inputs_qJES(
     bounds: list[tuple[float, float]],
     num_optima: int = 64,
     condition_noiseless: bool = True,
+    posterior_transform: ScalarizedPosteriorTransform | None = None,
     X_pending: Tensor | None = None,
     estimation_type: str = "LB",
     num_samples: int = 64,
@@ -1810,13 +1812,16 @@ def construct_inputs_qJES(
         model=model,
         bounds=torch.as_tensor(bounds, dtype=dtype).T,
         num_optima=num_optima,
+        posterior_transform=posterior_transform,
+        return_transformed=True,
     )
 
     inputs = {
         "model": model,
         "optimal_inputs": optimal_inputs,
         "optimal_outputs": optimal_outputs,
         "condition_noiseless": condition_noiseless,
+        "posterior_transform": posterior_transform,
         "X_pending": X_pending,
         "estimation_type": estimation_type,
         "num_samples": num_samples,

botorch/acquisition/joint_entropy_search.py

@@ -74,7 +74,6 @@ def __init__(
         posterior_transform: PosteriorTransform | None = None,
         X_pending: Tensor | None = None,
         estimation_type: str = "LB",
-        maximize: bool = True,
         num_samples: int = 64,
     ) -> None:
         r"""Joint entropy search acquisition function.
@@ -91,11 +90,11 @@ def __init__(
                 [Tu2022joint]_. These are sampled identically, so this only controls
                 the fashion in which the GP is reshaped as a result of conditioning
                 on the optimum.
+            posterior_transform: PosteriorTransform to negate or scalarize the output.
             estimation_type: estimation_type: A string to determine which entropy
                 estimate is computed: Lower bound" ("LB") or "Monte Carlo" ("MC").
                 Lower Bound is recommended due to the relatively high variance
                 of the MC estimator.
-            maximize: If true, we consider a maximization problem.
             X_pending: A `m x d`-dim Tensor of `m` design points that have been
                 submitted for function evaluation, but have not yet been evaluated.
             num_samples: The number of Monte Carlo samples used for the Monte Carlo
@@ -112,16 +111,13 @@ def __init__(
         # and three-dimensional otherwise.
         self.optimal_inputs = optimal_inputs.unsqueeze(-2)
         self.optimal_outputs = optimal_outputs.unsqueeze(-2)
+        self.optimal_output_values = (
+            posterior_transform.evaluate(self.optimal_outputs).unsqueeze(-1)
+            if posterior_transform
+            else self.optimal_outputs
+        )
         self.posterior_transform = posterior_transform
-        self.maximize = maximize
-
-        # The optima (can be maxima, can be minima) come in as the largest
-        # values if we optimize, or the smallest (likely substantially negative)
-        # if we minimize. Inside the acquisition function, however, we always
-        # want to consider MAX-values. As such, we need to flip them if
-        # we want to minimize.
-        if not self.maximize:
-            optimal_outputs = -optimal_outputs
+
         self.num_samples = optimal_inputs.shape[0]
         self.condition_noiseless = condition_noiseless
         self.initial_model = model
@@ -203,7 +199,9 @@ def _compute_lower_bound_information_gain(
             A `batch_shape`-dim Tensor of acquisition values at the given design
             points `X`.
         """
-        initial_posterior = self.initial_model.posterior(X, observation_noise=True)
+        initial_posterior = self.initial_model.posterior(
+            X, observation_noise=True, posterior_transform=self.posterior_transform
+        )
         # need to check if there is a two-dimensional batch shape -
         # the sampled optima appear in the dimension right after
         batch_shape = X.shape[:-2]
@@ -221,15 +219,17 @@ def _compute_lower_bound_information_gain(
 
         # Compute the mixture mean and variance
         posterior_m = self.conditional_model.posterior(
-            X.unsqueeze(MCMC_DIM), observation_noise=True
+            X.unsqueeze(MCMC_DIM),
+            observation_noise=True,
+            posterior_transform=self.posterior_transform,
         )
         noiseless_var = self.conditional_model.posterior(
-            X.unsqueeze(MCMC_DIM), observation_noise=False
+            X.unsqueeze(MCMC_DIM),
+            observation_noise=False,
+            posterior_transform=self.posterior_transform,
         ).variance
 
         mean_m = posterior_m.mean
-        if not self.maximize:
-            mean_m = -mean_m
         variance_m = posterior_m.variance
 
         check_no_nans(variance_m)
@@ -240,7 +240,7 @@ def _compute_lower_bound_information_gain(
             torch.zeros(1, device=X.device, dtype=X.dtype),
             torch.ones(1, device=X.device, dtype=X.dtype),
         )
-        normalized_mvs = (self.optimal_outputs - mean_m) / stdv
+        normalized_mvs = (self.optimal_output_values - mean_m) / stdv
         cdf_mvs = normal.cdf(normalized_mvs).clamp_min(CLAMP_LB)
         pdf_mvs = torch.exp(normal.log_prob(normalized_mvs))
 
@@ -294,7 +294,9 @@ def _compute_monte_carlo_information_gain(
             A `batch_shape`-dim Tensor of acquisition values at the given design
             points `X`.
         """
-        initial_posterior = self.initial_model.posterior(X, observation_noise=True)
+        initial_posterior = self.initial_model.posterior(
+            X, observation_noise=True, posterior_transform=self.posterior_transform
+        )
 
         batch_shape = X.shape[:-2]
         sample_dim = len(batch_shape)
@@ -311,15 +313,17 @@ def _compute_monte_carlo_information_gain(
 
         # Compute the mixture mean and variance
         posterior_m = self.conditional_model.posterior(
-            X.unsqueeze(MCMC_DIM), observation_noise=True
+            X.unsqueeze(MCMC_DIM),
+            observation_noise=True,
+            posterior_transform=self.posterior_transform,
         )
         noiseless_var = self.conditional_model.posterior(
-            X.unsqueeze(MCMC_DIM), observation_noise=False
+            X.unsqueeze(MCMC_DIM),
+            observation_noise=False,
+            posterior_transform=self.posterior_transform,
         ).variance
 
         mean_m = posterior_m.mean
-        if not self.maximize:
-            mean_m = -mean_m
         variance_m = posterior_m.variance.clamp_min(CLAMP_LB)
         conditional_samples, conditional_logprobs = self._compute_monte_carlo_variables(
             posterior_m

botorch_community/acquisition/input_constructors.py

@@ -17,6 +17,7 @@
 
 import torch
 from botorch.acquisition.input_constructors import acqf_input_constructor
+from botorch.acquisition.objective import ScalarizedPosteriorTransform
 from botorch.acquisition.utils import get_optimal_samples
 from botorch.models.model import Model
 from botorch_community.acquisition.bayesian_active_learning import (
@@ -62,7 +63,7 @@ def construct_inputs_SCoreBO(
     model: Model,
     bounds: List[Tuple[float, float]],
     num_optima: int = 8,
-    maximize: bool = True,
+    posterior_transform: Optional[ScalarizedPosteriorTransform] = None,
     distance_metric: str = "hellinger",
     X_pending: Optional[Tensor] = None,
 ):
@@ -72,14 +73,15 @@ def construct_inputs_SCoreBO(
         model=model,
         bounds=torch.as_tensor(bounds, dtype=dtype).T,
         num_optima=num_optima,
+        posterior_transform=posterior_transform,
+        return_transformed=True,
     )
-
     inputs = {
         "model": model,
         "optimal_inputs": optimal_inputs,
         "optimal_outputs": optimal_outputs,
         "distance_metric": distance_metric,
-        "maximize": maximize,
+        "posterior_transform": posterior_transform,
         "X_pending": X_pending,
     }
     return inputs

botorch_community/acquisition/scorebo.py

@@ -29,6 +29,7 @@
 from botorch.acquisition.bayesian_active_learning import (
     FullyBayesianAcquisitionFunction,
 )
+from botorch.acquisition.objective import ScalarizedPosteriorTransform
 from botorch.models.fully_bayesian import MCMC_DIM, SaasFullyBayesianSingleTaskGP
 from botorch.models.gp_regression import MIN_INFERRED_NOISE_LEVEL
 from botorch.models.utils import fantasize as fantasize_flag
@@ -50,7 +51,7 @@ def __init__(
         optimal_inputs: Optional[Tensor] = None,
         X_pending: Optional[Tensor] = None,
         distance_metric: Optional[str] = "hellinger",
-        maximize: bool = True,
+        posterior_transform: Optional[ScalarizedPosteriorTransform] = None,
     ) -> None:
         r"""Self-correcting Bayesian optimization [hvarfner2023scorebo]_ acquisition
         function. SCoreBO seeks to find accurate hyperparameters during the course
@@ -71,14 +72,15 @@ def __init__(
         super().__init__(model=model)
         # To enable fully bayesian GP conditioning, we need to unsqueeze
         # to get num_optima x num_gps unique GPs
-        self.maximize = maximize
-        if not self.maximize:
-            optimal_outputs = -optimal_outputs
-
-        # inputs come as num_optima_per_model x num_models x d
-        # but we want it four-dimensional to condition one per model.
-
+        self.optimal_inputs = optimal_inputs.unsqueeze(-2)
         self.optimal_outputs = optimal_outputs.unsqueeze(-2)
+        self.optimal_output_values = (
+            posterior_transform.evaluate(self.optimal_outputs).unsqueeze(-1)
+            if posterior_transform
+            else self.optimal_outputs
+        )
+        self.posterior_transform = posterior_transform
+
         # JES-like version of SCoreBO if optimal inputs are provided
         if optimal_inputs is not None:
             with warnings.catch_warnings():
@@ -122,13 +124,19 @@ def forward(self, X: Tensor) -> Tensor:
         # since we have two MC dims (over models and optima), we need to
         # unsqueeze a second dim to accomodate the posterior pass
         prev_posterior = self.model.posterior(
-            X.unsqueeze(MCMC_DIM), observation_noise=True
+            X.unsqueeze(MCMC_DIM),
+            observation_noise=True,
+            posterior_transform=self.posterior_transform,
         )
         noiseless_posterior = self.conditional_model.posterior(
-            X.unsqueeze(MCMC_DIM), observation_noise=False
+            X.unsqueeze(MCMC_DIM),
+            observation_noise=False,
+            posterior_transform=self.posterior_transform,
         )
         posterior = self.conditional_model.posterior(
-            X.unsqueeze(MCMC_DIM), observation_noise=True
+            X.unsqueeze(MCMC_DIM),
+            observation_noise=True,
+            posterior_transform=self.posterior_transform,
         )
 
         marg_mean = prev_posterior.mean.mean(dim=MCMC_DIM, keepdim=True)
@@ -139,7 +147,9 @@ def forward(self, X: Tensor) -> Tensor:
         # the mixture variance is squeezed, need it unsqueezed
         marg_covar = prev_posterior.mixture_covariance_matrix.unsqueeze(MCMC_DIM)
         noiseless_var = noiseless_posterior.variance
-        normalized_mvs = (self.optimal_outputs - cond_means) / noiseless_var.sqrt()
+        normalized_mvs = (
+            self.optimal_output_values - cond_means
+        ) / noiseless_var.sqrt()
         cdf_mvs = self.normal.cdf(normalized_mvs).clamp_min(CLAMP_LB)
         pdf_mvs = torch.exp(self.normal.log_prob(normalized_mvs))
         mean_truncated = cond_means - noiseless_var.sqrt() * pdf_mvs / cdf_mvs

test/acquisition/test_input_constructors.py

@@ -1620,6 +1620,9 @@ def test_construct_inputs_jes(self) -> None:
             training_data=self.blockX_blockY,
             bounds=self.bounds,
             num_optima=17,
+            posterior_transform=ScalarizedPosteriorTransform(
+                torch.rand(1, dtype=self.blockX_blockY[0].Y.dtype)
+            ),
         )
 
         self.assertEqual(self.blockX_blockY[0].X.dtype, kwargs["optimal_inputs"].dtype)

test/acquisition/test_joint_entropy_search.py

@@ -8,14 +8,15 @@
 
 import torch
 from botorch.acquisition.joint_entropy_search import qJointEntropySearch
+from botorch.acquisition.objective import ScalarizedPosteriorTransform
 from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
 from botorch.sampling.normal import SobolQMCNormalSampler
 from botorch.utils.test_helpers import get_model
 from botorch.utils.testing import BotorchTestCase
 
 
 class TestQJointEntropySearch(BotorchTestCase):
-    def test_joint_entropy_search(self):
+    def test_singleobj_joint_entropy_search(self):
         torch.manual_seed(1)
         tkwargs = {"device": self.device}
         estimation_types = ("LB", "MC")
@@ -26,15 +27,13 @@ def test_joint_entropy_search(self):
             estimation_type,
             use_model_list,
             standardize_model,
-            maximize,
             condition_noiseless,
         ) in product(
             (torch.float, torch.double),
             estimation_types,
             (False, True),
             (False, True),
             (False, True),
-            (False, True),
         ):
             tkwargs["dtype"] = dtype
             input_dim = 2
@@ -61,7 +60,6 @@ def test_joint_entropy_search(self):
                     num_samples=64,
                     X_pending=X_pending,
                     condition_noiseless=condition_noiseless,
-                    maximize=maximize,
                 )
                 self.assertIsInstance(acq.sampler, SobolQMCNormalSampler)
 
@@ -77,6 +75,18 @@ def test_joint_entropy_search(self):
                     # assess shape
                     self.assertTrue(acq_X.shape == test_Xs[j].shape[:-2])
 
+        acq = qJointEntropySearch(
+            model=model,
+            optimal_inputs=optimal_inputs,
+            optimal_outputs=optimal_outputs,
+            posterior_transform=ScalarizedPosteriorTransform(
+                weights=-torch.ones(1, **tkwargs)
+            ),
+        )
+        self.assertTrue(torch.all(acq.optimal_output_values == -acq.optimal_outputs))
+        acq_X = acq(test_Xs[j])
+        self.assertTrue(acq_X.shape == test_Xs[j].shape[:-2])
+
         with self.assertRaises(ValueError):
             acq = qJointEntropySearch(
                 model=model,
@@ -86,7 +96,6 @@ def test_joint_entropy_search(self):
                 num_samples=64,
                 X_pending=X_pending,
                 condition_noiseless=condition_noiseless,
-                maximize=maximize,
             )
             acq_X = acq(test_Xs[j])
 

test_community/acquisition/test_input_constructors.py

@@ -86,10 +86,8 @@ def test_construct_inputs_scorebo(self) -> None:
             training_data=self.blockX_blockY,
             bounds=self.bounds,
             num_optima=num_optima,
-            maximize=False,
             distance_metric="kl_divergence",
         )
-        self.assertFalse(kwargs["maximize"])
         self.assertEqual(self.blockX_blockY[0].X.dtype, kwargs["optimal_inputs"].dtype)
         self.assertEqual(len(kwargs["optimal_inputs"]), num_optima)
         self.assertEqual(len(kwargs["optimal_outputs"]), num_optima)