ParMOO - v0.4.1

Release 0.4.1

:Date: Aug 30, 2024

Hotfix to packaging in setup.py:

• Added the embedders module, which was accidentally omitted in v0.4.0
• Updated the release process to ensure this doesn't get missed again

Scientific Software - Peer-reviewed - Python
Published by thchang almost 2 years ago

ParMOO - v0.4.0

Release 0.4.0

:Date: Aug 27, 2024

Major interface-breaking refactor of core MOOP builder and libraries plus minor bug-fixes.

Major changes:

• Under the hood, ParMOO now uses jax[cpu] for all gradient evaluations, constraint evaluations, and function evaluations (but not simulation evaluations) on the critical path; this means that objective and constraint functions can be just-in-time (jit) compiled, which can give massive performance improvements. However, not all Python features are supported by jax.jit(), so users must be careful to write their objective and constraint functions mindfully
• In order for jax to be effective, we have updated the interfaces to avoid using optional arguments -- this affects the objective and constraint function interfaces, and a separate gradient function must be provided by the user (as opposed to an optional der argument, as previously used)
• In order to make it easier to support mixed variable types in jax and for future maintainability, all design variables have been replaced by a library of design variable Embedder classes (corresponding ABCs also added to the structs.py)
• In order to make ParMOO more maintainable and for jax to work smoothly, we have dropped support for unnamed variables -- Closes #31
• All SurrogateFunction and AcquisitionFunction libraries have been updated to be more jax-friendly
• The SurrogateOptimizer class has been refactored to include a callback to observe simulation evaluation results. It has also been given almost full control over when model improvement steps are called in order to make implementing many DFO methods easier
• The pattern search family of optimizers has been greatly improved
• PyDOE has been dropped since most relevant DOEs now appear in the newly added scipy.stats.qmc module
• Switching to PyDOE required us to change how the numpy random seed is set. A random seed object is now passed as a hyperparameter to ParMOO and propagated to all libraries, which is the recommended way
• Updated docs to reflect above changes

Style changes:

• Overhauled some of the unit tests
• Style fixes throughout
• Renamed several methods and classes to have a consistent naming convention

Interface breaking:

• The Embedder class is now used to define custom design variables (see Major changes)
• jax is now used to evaluate gradients in the SurrogateOptimizer class (see Major changes) -- this alone should not break the interface most use-cases, but may lead to decreased performance if not careful (see notes on achieving good performance in jax in the docs). Additionally, jax defaults to single precision so double precision must be set manually using a jax.config command (see docs)
• SurrogateFunction, SurrogateOptimizer, and AcquisitionFunction interfaces have changed (only affects users using custom methods)
• The random seed must now be set using a numpy random seed object (see Major changes and examples in the docs)
• We no longer support unnamed variables (see Major changes)
• Gradient functions are now provided via an additional key in the dictionary, and cannot be set using an optional argument (see Major changes and examples in the updated docs)
• Many library functions, methods, and classes have been renamed for consistency. In some cases, the old names remain as aliases for backward compatibility. See Style changes

New features:

• ParMOO now supports jax for autograd (see Major changes above)
• SurrogateOptimizer is now notified of the results of each simulation evaluation. (This allows checks for sufficient improvements)
• Numerous new AcquisitionFunction types added
• Added an option to create a private workdir for each libEnsemble thread -- Closes #82

Minor changes:

• Updates to support numpy 2.0
• Added a code coverage badge and updated the release process to reflect the extra steps needed to make this work -- Closes #21 and #93

Requirements:

• Added jax[cpu] to list of requirements
• Removed pyDOE from list of requirements, in favor of scipy.stats (added in scipy 1.10.0)
• Released the lock on libensemble version (from Release 0.3.1)
• Updated all version requirements to be new enough to support numpy 2.0

Scientific Software - Peer-reviewed - Python
Published by thchang almost 2 years ago

ParMOO - v0.3.1

Release 0.3.1

:Date: Sep 25, 2023

Bug-fixes and minor restructuring for future releases.

Fixed several serious bugs/limitations:

• Introduced in v0.3.0: when generating batches, a bug was introduced into the lines of code that filter out duplicate candidates, resulting in significantly decreased performance but no errors being raised
• Allow for ParMOO to still generate target points for the AcqusitionFunction, even when there are no feasible points in the database
• Increase the number of characters allowed in a name when working with libEnsemble from 10 to 40 characters
• Broke the MOOP.iterate() method apart into 2 functions (iterate and filterBatch), which makes the code more maintainable and allows for future improvements to the libE_MOOP parallelism
• Updated deprecated keys in .readthedocs.yml config file

Scientific Software - Peer-reviewed - Python
Published by thchang over 2 years ago

ParMOO - v0.3.0

Release 0.3.0

:Date: Jul 6, 2023

Significant structural changes for long-term support of future solvers, bug-fixes, and significant improvements to documentation.

Major Changes:

• surrogates.GaussRBF and surrogates.LocalGaussRBF now calculate model-form uncertainties
• structural changes to MOOP class to support propagation of uncertainty information
• added EI_RandomConstraint acquisition, which can be used to implement Bayesian optimization -- note that for large budgets, this is not currently recommended due to computational expense of numerical integration
• updated LocalGPS to use trust regions, when provided, and perform multiple restarts
• SurrogateOptimizer class now has access to more information about the objective, including raw simulation outputs, in order to support more diverse structure-exploiting solvers
• Added additional stopping criteria to both MOOP.solve() and libE_MOOP.solve() -- all stopping criteria are now optional (although at least one must be specified) but they are ordered such that calling MOOP.solve(k), where k is a positional input, will pass to the iter_max criteria and produce the same behavior as before -- closes #18

API Changes:

• In most cases, none. However, it is possible that if users were previously passing arguments to the MOOP.solve() method explicitly, then the name of the first positional argument has changed: budget -> max_iters
• For users implementing their own searches, surrogates, optimizers, or acquisitions, several classes in the structs module have been updated to support the present restructuring of the MOOP class

Docs:

• Updated Quickstart guide and README to demonstrate recommended inputs and settings for ParMOO -- this includes no more lambda functions, which closes #50
• Added a FAQ page with additional usage details and responses to frequent questions -- the answers in which close #61
• Added a new tutorial on how to perform high-dimensional multiobjective optimization on a limited budget with ParMOO
• Changed examples and documentation to use and discuss pandas dataframes, which generally produce more legible outputs
• Updated libE_MOOP example to demonstrate how to retrieve data in a way that is threadsafe for both Python MP and MPI usage

Requirements:

• We now require scipy v1.10 or newer, due to usage of qmc integration tools
• At the time of this release, libEnsemble is using a deprecated version of Pydantic -- for this release only we have fixed the requirement on libEnsemble to v0.9.2, but we will relax this requirement in the future once they have patched the issue

Bug-fixes:

• Fixed an issue where in rare cases, problems with too many categorical variables could produce unexpected batch sizes
• Errors in definition of test problems: DTLZ5, 6, and 7 (new implementations have been confirmed against pymoo)
• Fixed an issue which occasionally caused the libE_MOOP class to error out during post-run cleanup when used with MPI
• Patched an issue with format="pandas" option for MOOP.getSimulationData() class and added a similar option to all libE_MOOP "getter" functions

Minor changes:

• Fixed typos in docs/doc-strings
• Updated styles to comply with new flake8 recommendations
• New unit tests added
• Added warnings when ParMOO is run with bad budget settings

Scientific Software - Peer-reviewed - Python
Published by thchang almost 3 years ago

ParMOO - v0.2.2

Release 0.2.2

:Date: Apr 25, 2023

Hot-fix for a minor issue in the plotting library without workaround.

• Resolves #58

Scientific Software - Peer-reviewed - Python
Published by thchang about 3 years ago

ParMOO - v0.2.1

Release 0.2.1

:Date: Apr 10, 2023

Minor performance improvements, maintenance, and restructuring of test cases.

• Both Gaussian RBF surrogates in parmoo/surrogates/gaussian_proc.py now use the current mean of the response values as the prior instead of the zero function. This greatly improves convergence rates in practice, especially for our structure-exploiting methods.
• Using an old version of plotly/dash for now because of a dash issue described in plotly/dash#2460
• Added additional tests to check gradient calculations of GaussRBF surrogates.
• Added whitespace to pass new flake8 standards.
• Added year to JOSS publication in several places
• Added "et al." to our docs configuration file after author names, to credit additional contributors in our documentation.

Scientific Software - Peer-reviewed - Python
Published by thchang about 3 years ago

ParMOO - v0.2.0

Release 0.2.0

:Date: Feb 2, 2023

Official release corresponding to accepted JOSS article.

• Added support for a wider variety of design variables (including integer types), as well as support for "custom" design variables that use user-provided custom embedders/extractors Documentation on design variables has been expanded accordingly. Although design variables are still specified through dicts not classes, this addresses and therefore closes the primary issue raised in parmoo/parmoo#28
• Updated extras/libe.py corresponding to interface changes made in libEnsemble Release 0.8.0. This also addresses the issues on MacOS, referenced in parmoo/parmoo#34
• Added a post-run visualization library and corresponding documentation, closing issue parmoo/parmoo#27
• Allow solvers to start from an initial point that is infeasible, so that problems with relaxable constraints and a very small feasible set can still be solved
• Various style changes and additional usage environments requested by JOSS reviewers openjournals/joss-reviews#4468 including parmoo/parmoo#32
• Added support for multistarting optimization solvers when solving surrogate problems. This is particularly important for the global GaussRBF surrogate
• Fixed an issue in how model improvement points are calculated, as implemented in the surrogate.improve method for each GaussRBF variation in surrogates/gaussian_proc.py, which was created when adding support for custom design variables
• The default design tolerance for continuous variables now depends upon the value of ub - lb

Note:

• Dropped support for Python 3.6, due to changes to GitHub Actions documented on actions/setup-python#544

Known issues:

• The visualization library uses advanced plotly/dash features, which may not support the chrome browser, as described in parmoo/parmoo#37

Scientific Software - Peer-reviewed - Python
Published by thchang over 3 years ago

ParMOO - v0.1.0

:Date: May 10, 2022

Initial release.

Known issues and desired features will be raised on GitHub post-release.

Known issues:

• update unit tests to use sim/obj/const libraries
• restructure test suite, unit tests are currently not usable as additional documentation
• solve() method(s) should support additional stopping criteria
• allow for maximizing objectives and constraint lower bounds without "hacky" solution (negating values)
• missing functions from DTLZ libraries
• README.md needs a code coverage badge

Desired features:

• update, test, and merge-in MDML interface
• allow user to choose whether or not to use named variables via useNames method, or similar
• add a funcx simulation interface, using libEnsemble release 0.9
• add predicter interface and standalone module
• a GUI interface for creating MOOPs
• static visualization tools for plotting results (from MOOP.getPF() method)
• a visualization dashboard for viewing progress interactively
• design variable types should be a class, with embed/extract methods that can be called by MOOP.__embed__() and MOOP.__extract__()

Scientific Software - Peer-reviewed - Python
Published by thchang about 4 years ago