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Release 0.4.0
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@joelberkeley joelberkeley released this 18 Feb 18:21
· 491 commits to develop since this release
v0.4.0
18ac9b4
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Learn about vigilant mode.

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)

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