trieste - Release 4.5.1

Fixes Fix one hot encoder to use input dtype and not space dtype when encoding (not normally an issue, but less fragile) (#913)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v4.5.0...v4.5.1

- Python
Published by uri-granta 9 months ago

trieste - Release 4.5.0

Improvements GPflow model wrappers can be initialised with a pre-generated posterior cache (#911) FrozenOptimizer can be used when required for models that are frozen and no longer support optimization (#911)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v4.4.0...v4.5.0

- Python
Published by uri-granta 10 months ago

trieste - Release v4.4.0

Improvements/fixes

TF datasets are now passed to DeepEnsemble.model.fit. This change enables support for training over a specified number of steps (rather than a set number of epochs) for small datasets, which may require data repetition to achieve the desired step count. https://github.com/secondmind-labs/trieste/pull/907

An optional predictfn parameter has been added to IndependentReparametrizationSampler.init_. This parameter allows generating samples where the mean and variance come from sources other than model.predict. This feature is particularly useful for drawing samples from models that separate epistemic and aleatoric uncertainty, providing greater flexibility in controlling the sources of uncertainty in the generated samples. https://github.com/secondmind-labs/trieste/pull/903

- Python
Published by ChrisMorter 12 months ago

trieste - Release v4.3.0

Improvements/fixes implement predicty and predictnoise for Deep Ensemble models, and update predict to more appropriately return the epistemic uncertainty #894 fix jitter bug in Deep Ensemble trajectory sampler #900

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v4.2.4...v4.3.0

- Python
Published by uri-granta about 1 year ago

trieste - Release v4.2.4

Fixes Remove default independent reparametrization sampler jitter (but ensure positive variance) (#888) Test against TF 2.14 (#895)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v4.2.3...v4.2.4

- Python
Published by uri-granta about 1 year ago

trieste - Release 4.2.3

Fixes Fix DeepEnsemble model serialization for some versions of tensorflow (#892)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v4.2.2...v4.2.3

- Python
Published by uri-granta about 1 year ago

trieste - Release 4.2.2

Fixes

• Don't use the same seed when sampling from the subspaces of a product space [#885]
• Fix failing ray tutorial [#856]

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v4.2.1...v4.2.2

- Python
Published by uri-granta over 1 year ago

trieste - Release 4.2.1

Fixes Make one hot encoder compilable (#880) Improve dataset_len error message (#879)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v4.2.0...v4.2.1

- Python
Published by uri-granta over 1 year ago

trieste - Release 4.2.0

Improvements/fixes Avoid duplicating initial points when using vectorization (#875)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v4.1.0...v4.2.0

- Python
Published by uri-granta over 1 year ago

trieste - Release 4.1.0

Improvements/fixes Query point encoders for Deep GP models (#873)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v4.0.1...v4.1.0

- Python
Published by uri-granta over 1 year ago

trieste - Release 4.0.1

Improvements/fixes Don't create additional feature when one-hot-encoding two-value categories (#869) Support and test against Tensorflow 2.16 (#858)

Note that (like tensorflow-probability and GPflow) Trieste uses Keras 2. Since TF 2.16 defaults to using Keras 3, tf.keras (and Keras optimizers such as Adam) must now be imported from the tf_keras package instead. Alternatively, you can import tf_keras from the gpflow.keras module, which will automatically select the right source depending on which version of TF is installed.

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v4.0.0...v4.0.1

- Python
Published by uri-granta over 1 year ago

trieste - v4.0.0

Breaking changes This release includes a minor breaking change:

As part of #864, a number of built in model classes such as GaussianProcessRegression and DeepEnsembleModel have been updated to support optional query point encoders. This involved moving the implementation of public methods such as predict to new internal methods called predict_encoded etc that work on the encoded query points. Any user-defined class that overrode the public methods should therefore switch to overriding the *_encoded internal methods instead.

New features Query point encoders for models (#864)

Improvements/fixes Categorical trust regions (#865)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v3.4.0...v4.0.0

- Python
Published by uri-granta over 1 year ago

trieste - v3.4.0

New features Preliminary support for categorical search spaces (#863)

Improvements/fixes Support Datasets with statically shaped query points and dynamically shaped observations (#866)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v3.3.4...v3.4.0

- Python
Published by uri-granta over 1 year ago

trieste - Release 3.3.4

Improvements/fixes Fix assert in filter_datasets to not insist subspace tags is a tuple (#853) Allow FixedLocalAcquisitionRule with 0 local datasets (#859)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v3.3.3...v3.3.4

- Python
Published by uri-granta over 1 year ago

trieste - Release 3.3.3

Improvements/fixes Fix uniqueness detection bug in dataset_len method (#853)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v3.3.2...v3.3.3

- Python
Published by khurram-ghani over 1 year ago

trieste - Release 3.3.2

Improvements/fixes Support tf.Variables in dataset_len method (#851)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v3.3.1...v3.3.2

- Python
Published by uri-granta over 1 year ago

trieste - Release 3.3.1

Improvements/fixes Support reinitialising AskTellOptimizer with extended datasets (#849)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v3.3.0...v3.3.1

- Python
Published by uri-granta almost 2 years ago

trieste - Release 3.3.0

Improvements/fixes Updated BatchTrustRegionRule to correctly use state, allowing it to be saved and reloaded (#841) Use correct dtype for empty Box samples (#847) Fix TREGO initialisation bug introduced in 3.1.0 (#842) Make trust region initialisation more robust (#843) Add from_state helper method to AskTellOptimizer (#841)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v3.2.0...v3.3.0

- Python
Published by uri-granta almost 2 years ago

trieste - Release 3.2.0

New features AskTellOptimizer that doesn't track datasets: instead you use tell to tell it that the datasets have been updated elsewhere (passing in the complete updated datasets, not just the new points) (#834)

Improvements/fixes Refactor SingleObjectiveTrustRegion* classes to make them more generic and reduce code duplication (#835) Fix UpdatableTrustRegion error encountered when running with extended ProbablisticModel protocol hierarchy (#834, see issue #836)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v3.1.0...v3.2.0

- Python
Published by uri-granta almost 2 years ago

trieste - Release 3.1.0

New features Trust regions with mixed search spaces (#821) Updatable discrete trust regions (#825)

Improvements/fixes Improved support for single precision pipelines (#824, #826, #829) Support for trust-region subregion reinitialisation in product spaces (#827) Zeta parameter to control trust region initial size (#832)

Build changes Automate pypi uploads (#823) Pin dill to <0.3.6 to fix tensorflow kernel serialisation issues (#822)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v3.0.0...v3.1.0

- Python
Published by uri-granta almost 2 years ago

trieste - Release 3.0.0

Breaking changes This release includes a minor breaking change:

The Record, FrozenRecord and OptimizationResult classes are now also generic in the model type. Any type-annotated code using these may need to be updated to include the model type annotations as appropriate. Runtime behaviour is unaffected.

New features Customisable initial point selection for optimization (#808) Customisable handling of model updates in AskTellOptimizer (including an AskTellOptimizerNoTraining class for use with non-trainable models) (#815) DeepEnsemble support for pass-through keras compile arguments (#816)

Improvements/fixes Support for tensorflow 2.15 (#819) Filter out local datasets when calling base rule (#805) Speed up get_unique_points_mask (used by BatchTrustRegionBox) (#813)

Documentation README and CONTRIBUTING updates (#804, #802, #810) Mixed search space notebook (#818)

New Contributors: @nfergu (#816)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v2.0.0...v3.0.0

- Python
Published by uri-granta about 2 years ago

trieste - Release 2.0.0

Breaking changes This release includes a few minor breaking changes:

1. ProbabilisticModel is now a pure interface (#797)
2. ProbabilisticModel.log is now an abstract method and needs to be explicitly specified in any concrete model class implementation (though it can be empty)

3. ProbabilisticModel.get_module_with_variables is now a utility function in trieste.models.utils

4. TREGO (TrustRegion) and TURBO were reimplemented using the new batch-trust-region classes (#778, #791)

5. a TURBO rule must now be initialised as BatchTrustRegionBox(TURBOBox(search_space)) instead of TURBO(search_space)

6. a TREGO rule must now be initialised as BatchTrustRegionBox(TREGOBox(search_space)) instead of TrustRegion()

7. the internal State structures exposed by these rules are now also different: BatchTrustRegion.State instead of Turbo.State or TrustRegion.State, with additional values such as eps accessible via the subspaces in rule._subspaces instead.

New features Multi trust region acquisition rules (#773, #777, #778, #783) Local models and datasets (#788, #791) Expose model optimization result in optimize method and get_last_optimization_result function (#774, #797)

Improvements/fixes Stop trajectory sampling ignoring active-dims in the kernel (#790) Stop randomize_hyperparameters generating repeating values (#785) Handle unconstrained priors in randomize_hyperparameters (#796) Support optimization of multiple points in batch spaces (#787) Allow Boxes with zero width (#780) Deepcopy search spaces (#776) Use int64 when calculating data size in split_acquisition_function (#795) Start using check_shape for shape checking (#770) Cleanup tutorials (#769, #771)

Build changes Parallelise integration test run (#775) Integration test fragility (#786, #798)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v1.2.0...v2.0.0

- Python
Published by uri-granta about 2 years ago

trieste - Release 1.2.0

New features SeparateIndependent kernel support (#711, #719, #736, #745, #747, #753) TuRBO acquisition rule (#739, #762) Saving models as tf.saved_model (#750)

Improvements Fix deepcopy of posterior cache and VGP parameters (#741, #752) Fix learning rate reset error with gpflux and keras models (#740) Support Tensorflow 2.11 and 2.12 (#746) Make IndependentReparametrizationSampler support XLA (#718, #748) Handle broadcasting in DeepEnsemble models (#727) Support calling qmcnormalsamples with tensors (#723) Make default tensorboard metric names unique (#726) Make Reducer AFs generic in model type and add Map reducer (#759) Add continue_optimization method to BayesianOptimizer (#755) Additional test problems (#744)

Build changes Add default PR template (#756) Support installation from pypi on MacOS (#730) Get notebooks to build with Python 3.10 (#728, #732, #734, #738, #742) Parallelise test runs (#743, #749) Reduce notebook file sizes (#721)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v1.1.2...v1.2.0

- Python
Published by uri-granta over 2 years ago

trieste - Release 1.1.2

Bug fixes Support disabling skip in QMC sampling (#715)

Documentation REMBO notebook (#710)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v1.1.1...v1.1.2

- Python
Published by uri-granta almost 3 years ago

trieste - Release 1.1.1

Bug fixes Fix Batch QMC sampling (#713)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v1.1.0...v1.1.1

- Python
Published by uri-granta almost 3 years ago

trieste - Release 1.1.0

New features MUMBO: MUlti-task Max-value Bayesian Optimization (#699) Support QMC sampling in reparameterization samplers (#708)

Improvements Fix SVGP update equations (#709) Support tf compilation in MultifidelityAutoregressive and DeepEnsemble (#691, #698)

Build changes New README.md (#690, #705, #706) Track code coverage (#702) Unpin mypy and black versions (#701) Test against Python 3.10 (#688, #696) Work around docs issue with setuptools+gym (#695)

New contributors @eltociear made their first contribution in #700

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v1.0.0...v1.1.0

- Python
Published by uri-granta almost 3 years ago

trieste - Release 1.0.0

Note: this release marks the first 1.x release but is compatible with 0.13.3. Future releases will (try to) conform to semantic versioning. Since trieste is a research-led toolbox, this may result in reasonably frequent major version increments.

New features NARGP multifidelity model (#665) BO-specific inducing point allocators (#683)

Improvements Support for explicit constraints in ExpectedImprovement (#664) Support broadcasting in search space contains method (#677) Improved logging for GPflow models (#680) Faster sampler for deep ensembles (#682)

Build changes .gitignore additions (#679) Upgrade to GPflow 2.7.0 (#684) Workaround for slowtest OOM crash (#685)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.13.3...v1.0.0

- Python
Published by uri-granta about 3 years ago

trieste - Release 0.13.3

This release reintroduces ProbabilityOfImprovement (#638) and Pareto diverse sample method (#643), which were temporarily removed in 0.13.2.

Note that the minimum supported TensorFlow version has been raised to 2.5.

New features Batch Expected Improvement (#641, #653) Portfolio method for Batch BO (#651, #659, #663) Multifidelity modeling (#621, #654) Explicit constraints (#656, #660)

Improvements Allow scipy optimizer to be changed (#655) Allow arbitrary dataset/model tags (#668) — note that this may break type checking for existing code Slight improvements to synthetic objective functgions (#671)

Build changes Support latest gpflow and gpflux (#649) Support more recent tox versions (#669)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.13.1...v0.13.3

- Python
Published by uri-granta about 3 years ago

trieste - Release 0.13.2

This release fixes the 0.13.1 release by temporarily removing the two new features (#638 and #643) as they were preventing trieste from being used with the latest release of GPflow. A future release will reintroduce them.

The fix to handle constant priors in randomize_hyperparameters (#646) remains in the release.

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.13.0...v0.13.2

- Python
Published by uri-granta over 3 years ago

trieste - Release 0.13.1

New features ProbabilityOfImprovement acquisition function (#638) Pareto diverse sample method (#643)

Improvements Handle constant priors in randomize_hyperparameters (#646)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.13.0...v0.13.1

- Python
Published by uri-granta over 3 years ago

trieste - Release 0.13.0

New features Trajectory sampler support for multiple outputs (#582) Acquisition Functions are now pickleable (#607)

Improvements TensorBoard logging improvements (#593, #600, #605, #612, #620, #630) Scalable Pareto front calculation (#633) Models with keras callbacks now pickleable (#604, #615) Keras backend fix for Deep GPs (#591) Fix issue with compiled tf.debugging.Asserts (#622, #623) Fix trajectory samplers for deep ensemble (#611, #625) Allow zero kernel samples in GPR hyperparameter init (#635)

Deprecated features Remove model config support (#626)

Build changes Code overview tutorial (#592) Expose trieste version (#613, #618) Cleaner API for single and multi objective test functions (#628) AskTell model setter (#595)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.12.0...v0.13.0

- Python
Published by uri-granta over 3 years ago

trieste - Release 0.12.0

New features Early stop callback (#579) Experimental plotting utilities package (#584)

Improvements Scipy BFGS optimizer tensorboard logging (#577) Support empty tagged product spaces (#578)

Build changes Versioned documentation (#581, #585, #586, #590) Detect unreachable code (#576) Fix docstring parameter typos (#587)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.11.3...v0.12.0

- Python
Published by uri-granta over 3 years ago

trieste - Release 0.11.3

This point release fixes support for GPflux 0.2.7, which was broken when adding support for GPflux 0.3.0 in the last release.

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.11.2...v0.11.3

- Python
Published by uri-granta over 3 years ago

trieste - Release 0.11.2

This point release fixes another bug affecting the copying and saving of deep ensemble models, and also adds support for saving deep GP models.

New features Copying and saving deep GP models (#569)

Fixes Fix copying deep ensemble model with no optimizer callbacks (#569)

Build changes Test against gpflux 0.3.0 and tensorflow 2.8.0 (#571)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.11.1...v0.11.2

- Python
Published by uri-granta over 3 years ago

trieste - Release 0.11.1

This point release fixes a bug affecting the copying and saving of deep ensemble models.

Fixes Fix copying of deep ensemble models (#567)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.11.0...v0.11.1

- Python
Published by uri-granta over 3 years ago

trieste - Release 0.11.0

New features SparseGaussianProcessRegression model wrapper (#531) MonteCarloExpectedImprovement acquisition function (#393, #554) DecoupledTrajectorySampler (#504, #556, #559) DeepGaussianProcessDecoupledTrajectorySampler (#393, #549) Fixed reference points for multi-objective optimization (#385) Inducing point selectors (#511, #564) HIPPO: HIghly Parallelizable Pareto Optimization (#519) Saving optimization history to disk (#535) Copying and saving deep ensemble models (#540) MakePositive acquisition function transformation (#516) trygetoptimal_point result helper method (#517)

Improvements TensorBoard monitoring overhaul (#514, #515, #536, #544, #551) Use gpflow posterior objects to speed up acquisition (#523, #532) DeepEnsembleTrajectorySampler improvements (#541) Avoid recompiling training loss closures (#553) Default to compiled model optimizer for gpflow (#528) Multiplication of mixed search spaces (#518) Seed argument in search space sample method (#561) Support ask-tell with uncopyable models (#545) Make gpflux builders consistent with gpflow builders (#478) Type checking improvements (#506, #507, #513, #521)

Build changes Update to gpflow 2.5.2 (#522, #550, #555) Name integration tests (#508) Make fake8 ignore build directory (#557) Thompson Sampling notebook (#527) Documentation fixes (#502, #525, #542)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.10.0...v0.11.0

- Python
Published by uri-granta over 3 years ago

trieste - Release 0.10.0

New functionality BALD active learning acquisition function (#417) Continuous Thompson Sampling acquisition functions (#475, #480, #486, #500) Random Sampling acquisition function (#493) Support for Keras models and trajectory samplers (#459, #467, #468) Utilities for quickly constructing GPFlow models (#465, #483)

Improvements Support for SVGP and VGP models with GIBBON (#491) Support for covariancebetweenpoints with multi-output GPR/SVGP/VGP models (#492) Support splitting up acquisition function calls to reduce memory usage (#497) Improve tensorboard logging to handle gpflux models, ask-tell optimization and wallclock timings (#469, #470, #488) Improve static type checking for rules and samplers that depend on specific types of models (#463, #466, #474, #479, #482, #499, #501)

Build Changes OpenAI Gym Lunar Lander tutorial (#456) Support and test with both TF 2.4 and TF 2.5 (#484, #490) Simplify optimizer code (#496)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.9.1...v0.10.0

- Python
Published by uri-granta about 4 years ago

trieste - Release 0.9.1

This point release temporarily reverts the GPFlux RFF fix (#420) so as to maintain support for Tensorflow 2.4. It also adds the following functionality.

New functionality Support for vectorized acquisition functions (#458)

Improvements Fix TF compilation issue for VGP models (#418)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.9.0...v0.9.1

- Python
Published by uri-granta about 4 years ago

trieste - Release 0.9.0

New functionality t-IMSE acquisition functions (#426, #429) Kriging Believer acquisition functions (#426, #428, #451) Initial support for Keras (#451, #452)

Improvements Refactor model-sampler interactions (#398) Parallel acquisition function optimizers (#438) Fix GPflux RandomFourierFeatures import (#420) Use default optimizers with configs (#434) Make AcquisitionFunctionBuilder generic on ProbabilisticModel (#433)

Build Changes Notebook formatting (#432) Fix test random number seeding (#450) Active learning integration tests (#441)

Breaking Changes ModelStack renamed to TrainableModelStack LocalPenalizationAcquisitionFunction renamed to LocalPenalization trieste.acquisition.function.localpenalisation renamed to greedybatch

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.8.0...v0.9.0

- Python
Published by uri-granta about 4 years ago

trieste - Release 0.8.0

New functionality

Support for deep Gaussian processes with GPflux (#357, #364, #377) Support for asynchronous Bayesian Optimization (#366, #374, #380, #381, #384, #386) Active learning: predictive variance (#294) and expected feasibility (#421) acquisition functions Tagged product search spaces (#367, #387, #403, #422) Tensorboard monitoring support (#370, #407) Trid (#378) and simpe quadratic (#404) objective functions

Improvements

Make datasets an optional keyword argument for rule acquisition and acquisition function preparation (#383) Split up function.py (interfaces must now be imported from trieste.acquisition or trieste.acquisition.interfaces) (#408) Improve config handing (support dictionary configs again; replace create_optimizer by ModelRegistry; add tutorial) (#389) Allow empty observation for non-dominated space partitions (#356) Allow specification of scipy optimizer kwargs for optimizing acqusition functions (#410) Refactor model optimizers (TFOptimizer renamed to BatchOptimizer) (#372, #405)

Build changes

Speed up CI tests (#377, #390, #391, #395, #399, #404, #409) Improved documentation (#382, #400 and various above)

Full Changelog: https://github.com/secondmind-labs/trieste/compare/v0.7.0...v0.8.0

- Python
Published by uri-granta over 4 years ago

trieste - Release 0.7.0

New functionality

Ask Tell API (#346) GPFlux interface (but no models yet) (#355) Michalewicz function (#350)

Improvements

Support in-place updates to acquisition functions to avoid having to retrace every acquisition loop. Update existing acquisition function builders to use this. (#271, #327, #340, #349, #352) Fix SVGP interface to be consistent with other GPflow interfaces (#320) Refactor Pareto code. Note that hypervolume acquisition function builders are now passed partition bounds. (#328) Simplify trust region handling (#306)

Build changes

Split model interfaces into directories (#272) Rename trieste.type module to trieste.types (#323) Remove homespun deepcopy functionality (#339) Improve type checking (#307, #331, #333) Use extend-exclude for flake8 and black (#348) Reduce RAM usage in integration tests (#330)

- Python
Published by uri-granta over 4 years ago

trieste - Release 0.6.1

This point release updates trieste.space.Box to support empty boxes. It adds no new features.

- Python
Published by uri-granta over 4 years ago

trieste - Release 0.6.0

New functionality

New acquisition functions: - AugmentedExpectedImprovement (#265) - GIBBON (#275) - ExpectedConstrainedHypervolumeImprovement (#285) - BatchMonteCarloExpectedHypervolumeImprovement (#257)

New samplers: - RandomFourierFeatureThompsonSampler (#266) - approximate (feature-based) Thompson sampling (#274)

Improvements

Better model fitting: - GPR kernel initialization (#277) - BayesianOptimizer initial model fit (#283) - Support model-specific optimization parameters (#287) - Including kernel prior term in the likelihood when choosing kernel params (#290, #291) - Sample from constrained kernel parameters before model fitting (#297, #303, #305)

Better acquisition optimization: - Better error handling in continuous acquisition optimizer (#289, #313) - Better continuous optimizers with L-BFGS-B support (#276) and recovery restarts (#313)

Experimental design support for continuous search spaces through Sobol/Halton (#259)

ExpectedConstrainedImprovement efficiency improvement (#284) Better handling of tf.function (#299, #309) Objective functions moved to a separate package, added search space variables (#302) Better numerical stability in GIBBON/MES (#310)

Build changes

More notebook documentation (#280, #288, #310) Improved instructions for contributions and discussions (#301)

- Python
Published by uri-granta over 4 years ago

trieste - Release 0.5.1

This point release updates the GPflow dependency to version 2.2. It adds no new features.

- Python
Published by uri-granta over 4 years ago

trieste - Release 0.5.0

New functionality

add support for multi-objective optimization with the expected hypervolume improvement acquisition function (#177) (#194) (#202) (#207) (#217) (#225) (#243) add support for batch optimization via local penalization (#230) (#251) allow custom acquisition function optimizers (#186) add various toy objective functions: Gramacy & Lee (#168), Goldstein-Price (#169), VLMOP2, DTLZ (#190), Hartmann (#204), Rosenbrock, Ackley (#241), Shekel (#250)

Improvements

simplify single model/dataset use case (#252) expose predict_y from GPFlow models (#254) support arbitrary tensor-likes as inputs, not just lists (#234) improve and track unit test code coverage (#222) (#236)

Build changes

simplify docs build and add it to build checks (#231) (#240) add taskipy support for running tests (#219) (#244)

- Python
Published by uri-granta almost 5 years ago

trieste - Release 0.4.0

New functionality

add Monte-Carlo-based sampler for joint distributions, using reparametrization trick (#93) add Monte-Carlo-based batch Expected Improvement acquisition function (#133) add tutorials for batch-sequential acquisition functions (#149) (#151) add predict_joint method to root model interface ProbabilisticModel for predicting the mean and variance of joint distributions (#93) support lists as lower and upper bound arguments to Box (#112) add py.typed so that trieste type hints can be used by client code (#140) add efficient astuple conversion method on Dataset (#106) add support for optimizing all GPflow model wrappers with either tf.optimizers.Optimizers (with or without mini-batching) or gpflow.optimizers.Scipy (#47)

Improvements

significant refactor of BayesianOptimizer return type, to reduce the chance of working with the result of incomplete BO runs (#17) merge equivalent tensor type aliases (those in type module) (#76) deepcopying is optimized on types typically copied while tracking state in BayesianOptimizer (#104) fix type inconsistency in VariationalGaussianProcess's constructor (#116)

Build changes

various improvements to documentation site, including "how-to" section in tutorials (#63) and formatting for bibtex references (#110) add flake8 code linter (#109) and isort import organiser (#107) to build checks add missing build dependencies to pyproject.toml (#141)

- Python
Published by joelberkeley about 5 years ago

trieste - Release 0.3.1

Improvements

add missing imports to acquisition function functionality (#100)

- Python
Published by joelberkeley-secondmind about 5 years ago

trieste - Release 0.3.0

New functionality

add Monte-Carlo samplers (and corresponding acquisition function builders) for the reparametrization trick with (non-batch) acquisition functions (#94) (#95) add an acquisition rule for batches of points (#69) add expected constrained improvement acquisition function (#9) add a static probabilistic model interface (#23) add method for Cartesian product of search spaces (#68)

Improvements

rename datasets module to data (#10) add unit tests (#62) (#60) (#59) (#58) (#44) (#31) improve VGP update efficiency and stability (#46) make BayesianOptimizer attributes observer and search_space private (#15) update Slack invitation link (#71) fix VGP model in notebook "EGO with a failure region" (#45)

Build changes

remove master branch (#22) introduce black formatter (#20) include doctests in CI run (#19) update CI to use new pip dependency resolver (#78)

- Python
Published by joelberkeley-secondmind about 5 years ago

trieste - Release 0.2.0

First PyPI release

Changelog

• Minor documentation changes only

- Python
Published by joelberkeley-secondmind over 5 years ago

trieste - Release 0.1.0

NOTE: There is no PyPI release for this version

- Python
Published by joelberkeley-secondmind over 5 years ago