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Remove UnstandardizeMCMultiOutputObjective #2362

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2 changes: 0 additions & 2 deletions botorch/acquisition/multi_objective/__init__.py
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Expand Up @@ -24,7 +24,6 @@
from botorch.acquisition.multi_objective.objective import (
IdentityMCMultiOutputObjective,
MCMultiOutputObjective,
UnstandardizeMCMultiOutputObjective,
WeightedMCMultiOutputObjective,
)
from botorch.acquisition.multi_objective.utils import (
Expand All @@ -47,6 +46,5 @@
"MCMultiOutputObjective",
"MultiObjectiveAnalyticAcquisitionFunction",
"MultiObjectiveMCAcquisitionFunction",
"UnstandardizeMCMultiOutputObjective",
"WeightedMCMultiOutputObjective",
]
46 changes: 0 additions & 46 deletions botorch/acquisition/multi_objective/objective.py
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Expand Up @@ -207,49 +207,3 @@ def apply_feasibility_weights(

def forward(self, samples: Tensor, X: Optional[Tensor] = None) -> Tensor:
return self.objective(self.apply_feasibility_weights(samples), X=X)


class UnstandardizeMCMultiOutputObjective(IdentityMCMultiOutputObjective):
r"""Objective that unstandardizes the samples.

TODO: remove this when MultiTask models support outcome transforms.

Example:
>>> unstd_objective = UnstandardizeMCMultiOutputObjective(Y_mean, Y_std)
>>> samples = sampler(posterior)
>>> objective = unstd_objective(samples)
"""

def __init__(
self, Y_mean: Tensor, Y_std: Tensor, outcomes: Optional[List[int]] = None
) -> None:
r"""Initialize objective.

Args:
Y_mean: `m`-dim tensor of outcome means.
Y_std: `m`-dim tensor of outcome standard deviations.
outcomes: A list of `m' <= m` indices that specifies which of the `m` model
outputs should be considered as the outcomes for MOO. If omitted, use
all model outcomes. Typically used for constrained optimization.
"""
if Y_mean.ndim > 1 or Y_std.ndim > 1:
raise BotorchTensorDimensionError(
"Y_mean and Y_std must both be 1-dimensional, but got "
f"{Y_mean.ndim} and {Y_std.ndim}"
)
elif outcomes is not None and len(outcomes) > Y_mean.shape[-1]:
raise BotorchTensorDimensionError(
f"Cannot specify more ({len(outcomes)}) outcomes than are present in "
f"the normalization inputs ({Y_mean.shape[-1]})."
)
super().__init__(outcomes=outcomes, num_outcomes=Y_mean.shape[-1])
if outcomes is not None:
Y_mean = Y_mean.index_select(-1, self.outcomes.to(Y_mean.device))
Y_std = Y_std.index_select(-1, self.outcomes.to(Y_mean.device))

self.register_buffer("Y_mean", Y_mean)
self.register_buffer("Y_std", Y_std)

def forward(self, samples: Tensor, X: Optional[Tensor] = None) -> Tensor:
samples = super().forward(samples=samples)
return samples * self.Y_std + self.Y_mean
42 changes: 1 addition & 41 deletions botorch/acquisition/objective.py
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Expand Up @@ -13,10 +13,9 @@
from typing import Callable, List, Optional, TYPE_CHECKING, Union

import torch
from botorch.exceptions.errors import BotorchTensorDimensionError, UnsupportedError
from botorch.exceptions.errors import UnsupportedError
from botorch.exceptions.warnings import InputDataWarning
from botorch.models.model import Model
from botorch.models.transforms.outcome import Standardize
from botorch.posteriors.gpytorch import GPyTorchPosterior, scalarize_posterior
from botorch.sampling import IIDNormalSampler
from botorch.utils import apply_constraints
Expand Down Expand Up @@ -234,45 +233,6 @@ def forward(self, posterior: GPyTorchPosterior) -> GPyTorchPosterior:
return GPyTorchPosterior(distribution=new_mvn)


class UnstandardizePosteriorTransform(PosteriorTransform):
r"""Posterior transform that unstandardizes the posterior.

TODO: remove this when MultiTask models support outcome transforms.

Example:
>>> unstd_transform = UnstandardizePosteriorTransform(Y_mean, Y_std)
>>> unstd_posterior = unstd_transform(posterior)
"""

def __init__(self, Y_mean: Tensor, Y_std: Tensor) -> None:
r"""Initialize objective.

Args:
Y_mean: `m`-dim tensor of outcome means
Y_std: `m`-dim tensor of outcome standard deviations

"""
if Y_mean.ndim > 1 or Y_std.ndim > 1:
raise BotorchTensorDimensionError(
"Y_mean and Y_std must both be 1-dimensional, but got "
f"{Y_mean.ndim} and {Y_std.ndim}"
)
super().__init__()
self.outcome_transform = Standardize(m=Y_mean.shape[0]).to(Y_mean)
Y_std_unsqueezed = Y_std.unsqueeze(0)
self.outcome_transform.means = Y_mean.unsqueeze(0)
self.outcome_transform.stdvs = Y_std_unsqueezed
self.outcome_transform._stdvs_sq = Y_std_unsqueezed.pow(2)
self.outcome_transform._is_trained = torch.tensor(True)
self.outcome_transform.eval()

def evaluate(self, Y: Tensor) -> Tensor:
return self.outcome_transform.untransform(Y)[0]

def forward(self, posterior: GPyTorchPosterior) -> Tensor:
return self.outcome_transform.untransform_posterior(posterior)


class MCAcquisitionObjective(Module, ABC):
r"""Abstract base class for MC-based objectives.

Expand Down
82 changes: 5 additions & 77 deletions test/acquisition/multi_objective/test_objective.py
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Expand Up @@ -14,15 +14,10 @@
FeasibilityWeightedMCMultiOutputObjective,
IdentityMCMultiOutputObjective,
MCMultiOutputObjective,
UnstandardizeMCMultiOutputObjective,
WeightedMCMultiOutputObjective,
)
from botorch.acquisition.objective import (
IdentityMCObjective,
UnstandardizePosteriorTransform,
)
from botorch.acquisition.objective import IdentityMCObjective
from botorch.exceptions.errors import BotorchError, BotorchTensorDimensionError
from botorch.models.transforms.outcome import Standardize
from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior


Expand All @@ -37,14 +32,17 @@ def test_identity_mc_multi_output_objective(self):
objective = IdentityMCMultiOutputObjective()
with self.assertRaises(BotorchTensorDimensionError):
IdentityMCMultiOutputObjective(outcomes=[0])
# test negative outcome without specifying num_outcomes
# Test negative outcome without specifying num_outcomes.
with self.assertRaises(BotorchError):
IdentityMCMultiOutputObjective(outcomes=[0, -1])
for batch_shape, m, dtype in itertools.product(
([], [3]), (2, 3), (torch.float, torch.double)
):
samples = torch.rand(*batch_shape, 2, m, device=self.device, dtype=dtype)
self.assertTrue(torch.equal(objective(samples), samples))
# Test negative outcome with num_outcomes.
objective = IdentityMCMultiOutputObjective(outcomes=[0, -1], num_outcomes=3)
self.assertEqual(objective.outcomes.tolist(), [0, 2])


class TestWeightedMCMultiOutputObjective(BotorchTestCase):
Expand Down Expand Up @@ -138,73 +136,3 @@ def test_feasibility_weighted_mc_multi_output_objective(self):
X_baseline=X,
constraint_idcs=[1, -1],
)


class TestUnstandardizeMultiOutputObjective(BotorchTestCase):
def test_unstandardize_mo_objective(self):
Y_mean = torch.ones(2)
Y_std = torch.ones(2)
with self.assertRaises(BotorchTensorDimensionError):
UnstandardizeMCMultiOutputObjective(
Y_mean=Y_mean, Y_std=Y_std, outcomes=[0, 1, 2]
)
for objective_class in (
UnstandardizeMCMultiOutputObjective,
UnstandardizePosteriorTransform,
):
with self.assertRaises(BotorchTensorDimensionError):
objective_class(Y_mean=Y_mean.unsqueeze(0), Y_std=Y_std)
with self.assertRaises(BotorchTensorDimensionError):
objective_class(Y_mean=Y_mean, Y_std=Y_std.unsqueeze(0))
objective = objective_class(Y_mean=Y_mean, Y_std=Y_std)
for batch_shape, m, outcomes, dtype in itertools.product(
([], [3]), (2, 3), (None, [-2, -1]), (torch.float, torch.double)
):
Y_mean = torch.rand(m, dtype=dtype, device=self.device)
Y_std = torch.rand(m, dtype=dtype, device=self.device).clamp_min(1e-3)
kwargs = {}
if objective_class == UnstandardizeMCMultiOutputObjective:
kwargs["outcomes"] = outcomes
objective = objective_class(Y_mean=Y_mean, Y_std=Y_std, **kwargs)
if objective_class == UnstandardizePosteriorTransform:
objective = objective_class(Y_mean=Y_mean, Y_std=Y_std)
if outcomes is None:
# passing outcomes is not currently supported
mean = torch.rand(2, m, dtype=dtype, device=self.device)
variance = variance = torch.rand(
2, m, dtype=dtype, device=self.device
)
mock_posterior = MockPosterior(mean=mean, variance=variance)
tf_posterior = objective(mock_posterior)
tf = Standardize(m=m)
tf.means = Y_mean
tf.stdvs = Y_std
tf._stdvs_sq = Y_std.pow(2)
tf._is_trained = torch.tensor(True)
tf.eval()
expected_posterior = tf.untransform_posterior(mock_posterior)
self.assertTrue(
torch.equal(tf_posterior.mean, expected_posterior.mean)
)
self.assertTrue(
torch.equal(
tf_posterior.variance, expected_posterior.variance
)
)
# testing evaluate specifically
if objective_class == UnstandardizePosteriorTransform:
Y = torch.randn_like(Y_mean) + Y_mean
val = objective.evaluate(Y)
val_expected = Y_mean + Y * Y_std
self.assertTrue(torch.allclose(val, val_expected))
else:

samples = torch.rand(
*batch_shape, 2, m, dtype=dtype, device=self.device
)
obj_expected = samples * Y_std.to(dtype=dtype) + Y_mean.to(
dtype=dtype
)
if outcomes is not None:
obj_expected = obj_expected[..., outcomes]
self.assertTrue(torch.equal(objective(samples), obj_expected))
16 changes: 4 additions & 12 deletions test/acquisition/multi_objective/test_utils.py
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Expand Up @@ -10,8 +10,8 @@

import torch
from botorch.acquisition.multi_objective.objective import (
IdentityMCMultiOutputObjective,
MCMultiOutputObjective,
UnstandardizeMCMultiOutputObjective,
)
from botorch.acquisition.multi_objective.utils import (
compute_sample_box_decomposition,
Expand Down Expand Up @@ -92,16 +92,8 @@ def test_prune_inferior_points_multi_objective(self):
X_pruned = prune_inferior_points_multi_objective(
model=mm, X=X, ref_point=ref_point
)
self.assertTrue(torch.equal(X_pruned, X[[-1]]))
# test unstd objective
unstd_obj = UnstandardizeMCMultiOutputObjective(
Y_mean=samples.mean(dim=0), Y_std=samples.std(dim=0), outcomes=[0, 1]
)
X_pruned = prune_inferior_points_multi_objective(
model=mm, X=X, ref_point=ref_point, objective=unstd_obj
)
self.assertTrue(torch.equal(X_pruned, X[[-1]]))
# test constraints
objective = IdentityMCMultiOutputObjective(outcomes=[0, 1])
samples_constrained = torch.tensor(
[[1.0, 2.0, -1.0], [2.0, 1.0, -1.0], [3.0, 4.0, 1.0]], **tkwargs
)
Expand All @@ -110,7 +102,7 @@ def test_prune_inferior_points_multi_objective(self):
model=mm_constrained,
X=X,
ref_point=ref_point,
objective=unstd_obj,
objective=objective,
constraints=[lambda Y: Y[..., -1]],
)
self.assertTrue(torch.equal(X_pruned, X[:2]))
Expand Down Expand Up @@ -161,7 +153,7 @@ def test_prune_inferior_points_multi_objective(self):
model=mm,
X=X,
ref_point=ref_point,
objective=unstd_obj,
objective=objective,
constraints=[lambda Y: Y[..., -1] - 3.0],
marginalize_dim=-3,
)
Expand Down