Version 0.1.4 (#282)

Summary:
In addition to the version bump, excludes a test that requires gpytorch master from this release.
Pull Request resolved: #282

Reviewed By: eytan

Differential Revision: D17702906

Pulled By: Balandat

fbshipit-source-id: c47d24fd1232da5b5cd3516745ede745f38e2f1d

Author: Balandat

.conda/meta.yaml

@@ -32,6 +32,7 @@ test:
     - botorch.utils
     - botorch.fit
     - botorch.gen
+    - botorch.settings
 
 about:
   home: https://botorch.org

CHANGELOG.md

@@ -2,6 +2,48 @@
 
 The release log for BoTorch.
 
+## [0.1.4] - Oct 1, 2019
+
+Knowledge Gradient acquisition function (one-shot), various maintenance
+
+#### Breaking Changes
+* Require explicit output dimensions in BoTorch models (#238)
+* Make `joint_optimize` / `sequential_optimize` return acquisition function
+  values (#149) [note deprecation notice below]
+* `standardize` now works on the second to last dimension (#263)
+* Refactor synthetic test functions (#273)
+
+#### New Features
+* Add `qKnowledgeGradient` acquisition function (#272, #276)
+* Add input scaling check to standard models (#267)
+* Add `cyclic_optimize`, convergence criterion class (#269)
+* Add `settings.debug` context manager (#242)
+
+#### Deprecations
+* Consolidate `sequential_optimize` and `joint_optimize` into `optimize_acqf`
+  (#150)
+
+#### Bug fixes
+* Properly pass noise levels to GPs using a `FixedNoiseGaussianLikelihood` (#241)
+  [requires gpytorch > 0.3.5]
+* Fix q-batch dimension issue in `ConstrainedExpectedImprovement`
+  (6c067185f56d3a244c4093393b8a97388fb1c0b3)
+* Fix parameter constraint issues on GPU (#260)
+
+#### Minor changes
+* Add decorator for concatenating pending points (#240)
+* Draw independent sample from prior for each hyperparameter (#244)
+* Allow `dim > 1111` for `gen_batch_initial_conditions` (#249)
+* Allow `optimize_acqf` to use `q>1` for `AnalyticAcquisitionFunction` (#257)
+* Allow excluding parameters in fit functions (#259)
+* Track the final iteration objective value in `fit_gpytorch_scipy` (#258)
+* Error out on unexpected dims in parameter constraint generation (#270)
+* Compute acquisition values in gen_ functions w/o grad (#274)
+
+#### Tests
+* Introduce BotorchTestCase to simplify test code (#243)
+* Refactor tests to have monolithic cuda tests (#261)
+
 
 ## [0.1.3] - Aug 9, 2019
 

botorch/__init__.py

@@ -17,7 +17,7 @@
 from .utils import manual_seed
 
 
-__version__ = "0.1.3"
+__version__ = "0.1.4"
 
 
 __all__ = [

test/models/test_model_list_gp_regression.py

@@ -155,12 +155,13 @@ def test_ModelListGP_fixed_noise(self):
             self.assertIsInstance(posterior, GPyTorchPosterior)
             self.assertIsInstance(posterior.mvn, MultitaskMultivariateNormal)
 
-            # test output_indices
-            posterior = model.posterior(
-                test_x, output_indices=[0], observation_noise=True
-            )
-            self.assertIsInstance(posterior, GPyTorchPosterior)
-            self.assertIsInstance(posterior.mvn, MultivariateNormal)
+            # TODO: Add test back in once gpytorch > 0.3.5 is released
+            # # test output_indices
+            # posterior = model.posterior(
+            #     test_x, output_indices=[0], observation_noise=True
+            # )
+            # self.assertIsInstance(posterior, GPyTorchPosterior)
+            # self.assertIsInstance(posterior.mvn, MultivariateNormal)
 
             # test condition_on_observations
             f_x = torch.rand(2, 1, **tkwargs)