Remove transform=None from GPyTorch constraints

Differential Revision: D62993873
pytorch · Sep 18, 2024 · 9e27d0c · 9e27d0c
1 parent 8536468
commit 9e27d0c
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Showing 6 changed files with 4 additions and 12 deletions.
diff --git a/botorch/models/contextual_multioutput.py b/botorch/models/contextual_multioutput.py
@@ -141,9 +141,7 @@ def __init__(
         )
         self.task_covar_module_base = RBFKernel(
             ard_num_dims=n_embs,
-            lengthscale_constraint=Interval(
-                0.0, 2.0, transform=None, initial_value=1.0
-            ),
+            lengthscale_constraint=Interval(0.0, 2.0, initial_value=1.0),
         )
         self.to(train_X)
 

diff --git a/botorch/models/gp_regression.py b/botorch/models/gp_regression.py
@@ -306,9 +306,7 @@ def __init__(
         noise_likelihood = GaussianLikelihood(
             noise_prior=SmoothedBoxPrior(-3, 5, 0.5, transform=torch.log),
             batch_shape=self._aug_batch_shape,
-            noise_constraint=GreaterThan(
-                MIN_INFERRED_NOISE_LEVEL, transform=None, initial_value=1.0
-            ),
+            noise_constraint=GreaterThan(MIN_INFERRED_NOISE_LEVEL, initial_value=1.0),
         )
         # Likelihood will always get evaluated with transformed X, so we need to
         # transform the training data before constructing the noise model.

diff --git a/botorch/models/multitask.py b/botorch/models/multitask.py
@@ -483,7 +483,6 @@ def __init__(
                 noise_prior=noise_prior,
                 noise_constraint=GreaterThan(
                     MIN_INFERRED_NOISE_LEVEL,
-                    transform=None,
                     initial_value=noise_prior.mode,
                 ),
                 rank=kwargs.get("likelihood_rank", 0),

diff --git a/botorch/models/pairwise_gp.py b/botorch/models/pairwise_gp.py
@@ -272,7 +272,7 @@ def __init__(
                     ard_num_dims=self.dim,
                     lengthscale_prior=ls_prior,
                     lengthscale_constraint=GreaterThan(
-                        lower_bound=1e-4, transform=None, initial_value=ls_prior_mode
+                        lower_bound=1e-4, initial_value=ls_prior_mode
                     ),
                     dtype=torch.float64,
                 ),

diff --git a/botorch/models/transforms/input.py b/botorch/models/transforms/input.py
@@ -1016,7 +1016,6 @@ def __init__(
             p_name = f"concentration{i}"
             constraint = GreaterThan(
                 self._min_concentration_level,
-                transform=None,
                 # set the initial value to be the identity transformation
                 initial_value=1.0,
             )

diff --git a/botorch/models/utils/gpytorch_modules.py b/botorch/models/utils/gpytorch_modules.py
@@ -65,7 +65,6 @@ def get_gaussian_likelihood_with_gamma_prior(
         batch_shape=batch_shape,
         noise_constraint=GreaterThan(
             MIN_INFERRED_NOISE_LEVEL,
-            transform=None,
             initial_value=noise_prior_mode,
         ),
     )
@@ -91,7 +90,6 @@ def get_gaussian_likelihood_with_lognormal_prior(
         batch_shape=batch_shape,
         noise_constraint=GreaterThan(
             MIN_INFERRED_NOISE_LEVEL,
-            transform=None,
             initial_value=noise_prior.mode,
         ),
     )
@@ -125,7 +123,7 @@ def get_covar_module_with_dim_scaled_prior(
         batch_shape=batch_shape,
         lengthscale_prior=lengthscale_prior,
         lengthscale_constraint=GreaterThan(
-            2.5e-2, transform=None, initial_value=lengthscale_prior.mode
+            2.5e-2, initial_value=lengthscale_prior.mode
         ),
         # pyre-ignore[6] GPyTorch type is unnecessarily restrictive.
         active_dims=active_dims,