Merge: Improve GP Fit (#472)

- scipy fit is used for all cases now
- instead, the MLL type is switched based on whether TL is active or not

Author: Scienfitz

CHANGELOG.md

@@ -4,6 +4,10 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.12.2] - 2025-01-31
+### Changed
+- More robust settings for the GP fitting
+
 ## [0.12.1] - 2025-01-29
 ### Changed
 - Default of `allow_recommending_already_recommended` is changed back to `False`

baybe/surrogates/gaussian_process/core.py

@@ -201,15 +201,19 @@ def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
             covar_module=covar_module,
             likelihood=likelihood,
         )
-        mll = gpytorch.ExactMarginalLogLikelihood(self._model.likelihood, self._model)
 
-        # TODO: This is a simple temporary workaround to avoid model overfitting
-        #   via early stopping in the presence of task parameters, which currently
-        #   have no prior configured.
+        # TODO: This is still a temporary workaround to avoid overfitting seen in
+        #  low-dimensional TL cases. More robust settings are being researched.
         if context.n_task_dimensions > 0:
-            botorch.optim.fit.fit_gpytorch_mll_torch(mll, step_limit=200)
+            mll = gpytorch.mlls.LeaveOneOutPseudoLikelihood(
+                self._model.likelihood, self._model
+            )
         else:
-            botorch.fit.fit_gpytorch_mll(mll)
+            mll = gpytorch.ExactMarginalLogLikelihood(
+                self._model.likelihood, self._model
+            )
+
+        botorch.fit.fit_gpytorch_mll(mll)
 
     @override
     def __str__(self) -> str:

baybe/surrogates/naive.py

@@ -37,7 +37,7 @@ def _estimate_moments(
         import torch
 
         # TODO: use target value bounds for covariance scaling when explicitly provided
-        mean = self._model * torch.ones([len(candidates_comp_scaled)])
+        mean = self._model * torch.ones([len(candidates_comp_scaled)])  # type: ignore[operator]
         var = torch.ones(len(candidates_comp_scaled))
         return mean, var