Version bump to 0.1.1, update gpytorch requirement (#190)

Summary:
- bump version to 0.1.1
- update changelog
- bump gpytorch minimum requirement to 0.3.3
Pull Request resolved: #190

Reviewed By: sdaulton

Differential Revision: D16022740

Pulled By: Balandat

fbshipit-source-id: 4433955d31543722fd568549d89c7ee2ba8b1c3b

Author: Balandat

.conda/meta.yaml

@@ -16,7 +16,7 @@ requirements:
     - python>=3.6
   run:
     - pytorch>=1.1
-    - gpytorch>=0.3.2
+    - gpytorch>=0.3.3
     - scipy
 
 test:

CHANGELOG.md

@@ -2,6 +2,64 @@
 
 The release log for BoTorch.
 
+
+## [0.1.1] - June 27, 2019
+
+API updates, more robust model fitting
+
+#### Breaking changes
+* rename `botorch.qmc` to `botorch.sampling`, move MC samplers from
+  `acquisition.sampler` to `botorch.sampling.samplers` (#172)
+
+#### New Features
+* Add `condition_on_observations` and `fantasize` to the Model level API (#173)
+* Support pending observations generically for all `MCAcqusitionFunctions` (#176)
+* Add fidelity kernel for training iterations/training data points (#178)
+* Support for optimization constraints across `q`-batches (to support things like
+  sample budget constraints) (2a95a6c3f80e751d5cf8bc7240ca9f5b1529ec5b)
+* Add ModelList <-> Batched Model converter (#187)
+* New test functions
+    * basic: `neg_ackley`, `cosine8`, `neg_levy`, `neg_rosenbrock`, `neg_shekel`
+      (e26dc7576c7bf5fa2ba4cb8fbcf45849b95d324b)
+    * for multi-fidelity BO: `neg_aug_branin`, `neg_aug_hartmann6`,
+      `neg_aug_rosenbrock` (ec4aca744f65ca19847dc368f9fee4cc297533da)
+
+#### Improved functionality:
+* More robust model fitting
+    * Catch gpytorch numerical issues and return `NaN` to the optimizer (#184)
+    * Restart optimization upon failure by sampling hyperparameters from their prior (#188)
+    * Sequentially fit batched and `ModelListGP` models by default (#189)
+    * Change minimum inferred noise level (e2c64fef1e76d526a33951c5eb75ac38d5581257)
+* Introduce optional batch limit in `joint_optimize` to increases scalability of
+  parallel optimization (baab5786e8eaec02d37a511df04442471c632f8a)
+* Change constructor of `ModelListGP` to comply with GPyTorch’s `IndependentModelList`
+  constructor (a6cf739e769c75319a67c7525a023ece8806b15d)
+* Use `torch.random` to set default seed for samplers (rather than `random`) to
+  making sampling reproducible when setting `torch.manual_seed`
+  (ae507ad97255d35f02c878f50ba68a2e27017815)
+
+####  Performance Improvements
+* Use `einsum` in `LinearMCObjective` (22ca29535717cda0fcf7493a43bdf3dda324c22d)
+* Change default Sobol sample size for `MCAquisitionFunctions` to be base-2 for
+  better MC integration performance (5d8e81866a23d6bfe4158f8c9b30ea14dd82e032)
+* Add ability to fit models in `SumMarginalLogLikelihood` sequentially (and make
+  that the default setting) (#183)
+* Do not construct the full covariance matrix when computing posterior of
+  single-output BatchedMultiOutputGPyTorchModel (#185)
+
+#### Bug fixes
+* Properly handle observation_noise kwarg for BatchedMultiOutputGPyTorchModels (#182)
+* Fix a issue where `f_best` was always max for NoisyExpectedImprovement
+  (de8544a75b58873c449b41840a335f6732754c77)
+* Fix bug and numerical issues in `initialize_q_batch`
+  (844dcd1dc8f418ae42639e211c6bb8e31a75d8bf)
+* Fix numerical issues with `inv_transform` for qMC sampling (#162)
+
+#### Other
+* Bump GPyTorch minimum requirement to 0.3.3
+
+
+
 ## [0.1.0] - April 30, 2019
 
 First public beta release.

README.md

@@ -51,7 +51,7 @@ Optimization simply use Ax.
 **Installation Requirements**
 - Python >= 3.6
 - PyTorch >= 1.1
-- gpytorch >= 0.3.2
+- gpytorch >= 0.3.3
 - scipy
 
 

botorch/__init__.py

@@ -9,7 +9,7 @@
 from .utils import manual_seed
 
 
-__version__ = "0.1.0"
+__version__ = "0.1.1"
 
 
 __all__ = [

docs/getting_started.md

@@ -15,7 +15,7 @@ Before jumping the gun, we recommend you start with the high-level
 
 - Python >= 3.6
 - PyTorch >= 1.1
-- gpytorch >= 0.3.2
+- gpytorch >= 0.3.3
 - scipy
 
 BoTorch is easily installed via

environment.yml

@@ -4,5 +4,5 @@ channels:
   - gpytorch
 dependencies:
   - pytorch>=1.1
-  - gpytorch>=0.3.2
+  - gpytorch>=0.3.3
   - scipy

setup.py

@@ -64,7 +64,7 @@
     long_description=long_description,
     long_description_content_type="text/markdown",
     python_requires=">=3.6",
-    install_requires=["torch>=1.1", "gpytorch>=0.3.2", "scipy"],
+    install_requires=["torch>=1.1", "gpytorch>=0.3.3", "scipy"],
     packages=find_packages(),
     extras_require={
         "dev": DEV_REQUIRES,