2.2.0

What's Changed

• Update NestedLHS with design_space by @Paul-Saves in #473. It completes the work done in 2.1 on mixed-integer multi-fidelity surrogates: MFK and MFKPLS.
• Implement categorical decreed variables by @Paul-Saves in #474. This allows to define value constraints that explicitly forbid two variables from having some values simultaneously. These constraints are taken into account during the mixed-integer kriging-based surrogates training. This feature is built on top of ConfigSpace which has to be installed.

Full Changelog: v2.1.0...v2.2.0

2.1.0

What's Changed

• Add Sparse GP by @hvalayer, @relf in #463
• Add MFK and MFKPLS compatibility with mixed variables by @RemyCharayron in #467
• Update pyDOE2 dependency to pyDOE3 by @relf in #471
• Fix for expired deprecation in numpy 1.25 by @relf in #469
• Bump actions/checkout from 3 to 4 by @dependabot in #462
• Bump pypa/cibuildwheel from 2.15.0 to 2.16.2 by @dependabot in #468

New Contributors

• @hvalayer made his first contribution in #463
• @RemyCharayron made his first contribution in #467

Full Changelog: v2.0.1...v2.1.0

2.0.1

• Update deprecated setup.cfg fields (@jbussemaker #453)
• Fix PLS with noisy categorical variables (@Paul-Saves #454)
• Fix mixed variables hierachical sampling (@Paul-Saves #456)
• Add required readthedocs configuration file (@relf #458)
• Automate wheels distribution on Pypi (@relf #460)

2.0.0

Major release of the Surrogate Modeling Toolbox with handling of hierarchical and mixed variables for kriging-based surrogates.

See SMT 2.0 article (preprint).

Special thanks to @Paul-Saves for his essential contributions and @jbussemaker for his work on the API and many thanks to all contributors.

• No API breaking change since 2.0b3
• Add decoding values method for design vectors (@jbussemaker #435 )
• Now numba is opt-in: user has to set USE_NUMBA_JIT=1 once numba is installed (@Paul-Saves #443 )
• Speed up of EGO algorithm (@Paul-Saves #445 )
• Update tutorial notebooks (@NatOnera #436, #448 )

2.0b3

• Rework of the categorical and hierarchical variables API for kriging-based surrogates (@jbussemaker #428 ) :

  • Implementation of a new design space definition API in smt.utils.design_space
  • XSpecs and XType have been completely replaced by DesignSpace
  • Add numba speedup for kriging calculations (optional)
  • Documentation update

• Fixes related to categorical variables handling : (@Paul-Saves #431 )

  • Fix: kriging-based surrogates categorical_kernel option is now explicitly continuous relaxation
  • Fix: mixed-integer EGO implementation now works in folded space

• Code format with black is enforced in CI (#432 )

2.0b2

• Hierarchical variables for kriging-based surrogate models:

  • Add mixint cantilever beam and hierarchical neural network problems (@Paul-Saves #416)
  • Add architectural kernel (MixHrcKernelType.ARC_KERNEL) (@Paul-Saves #417)
  • Update documentation (@Paul-Saves #421)

• Add variable-powered exponential kernel for kriging-based surrogates (@yqliaohk #411)

• Multi-Fidelity Kriging: Reset eval_noise option to original value after reinterpolation to allow subsequent noise evaluation (@robertwenink #419)

• Update notebooks (@NatOnera #426)

• CI maintainance (@EwoutH #423, #422)

2.0b1

Breaking changes

• Kriging-based surrogates mixed integer existing support (continuous relaxation, gower distance) is reworked (@Paul-Saves #379)
• Change predict_variance_derivatives(x) for a single x to predict_variance_derivatives(x, kx) (@Paul-Saves and Ines Cardoso #390)
• Drop support for scikit-learn < 1.0.2 (related to PLS used in KPLS surrogates)
• Drop support for Python 3.7

Added:

• Kriging-based surrogates support for mixed integer variables (@Paul-Saves #379)
• Kriging-based surrogates support for hierarchical variables (@Paul-Saves #406, #400)
• Conditioned Gaussian Process sampling (@AlexThv #385): see tutorial
• Output derivatives for all correlation kernels, as it was only available for Gaussian kernel before (@Paul-Saves #389)
• Derivatives value and variance computation for all correlation kernels (@Paul-Saves #389)
• KPLS surrogates (@Paul-Saves #379):
  • automatic PLS components number determination when setting eval_n_comp option
  • PLS dimension reduction is available for categorical variables using cat_kernel_comps option
• Normalization for QP surrogate model (@Paul-Saves #396)
• Documentation and notebooks updates (@NatOnera #393, #407)

Fixed:

• Normalization for kriging based models using linear trend (@Paul-Saves #389)
• Compatibility with numpy 1.24 (@Paul-Saves #392)
• Bounds normalization when using Gower distance in kriging-based surrogate models (@Paul-Saves #394)
• EGO algorithm when discrete variables are used (@Paul-Saves #394)
• LHS to avoid generating the same doe when random state is set (#397)

1.3.0

• Breaking Changes: MGP is now compliant with the SurrogateModel API
  • mgp.predict_values() method outputs is now a 2d array (fix #375)
  • mgp.predict_variances() method now takes only one arg and returns only MGP variances (old version call predict_variances(x, both=False))
  • mgp.predict_variances_no_uq() , specific to MGP, computes variances without hyperparameters uncertainty (second value returned by the old version call predict_variances(x, both=True))
• Cleanup install_requires: remove packaging, move numpydoc and matplotlib to requirements.txt (#370)
• Documentation updates:
  • Add new example: Learning airfoil parameters using GENN surrogate model (#374 thanks @raul-rufato)
  • Update MixedInteger Tutorial: add an example of mixed integer surrogate model usage for an hybrid composites problem (#357 thanks @raul-rufato)
  • Minor fixes in notebooks (#377 thanks @NatOnera)
• Fix warnings in optimized ESE LHS (#350)
• Fix wing weight problem formula (#381)
• Use warnings.warn instead of print in Kriging-based surrogates (#367 thanks @zhoutianxun)

1.2.0

• Add EGO optimization with GEKPLS model (#340, #346, thanks @Laurentww)
• Breaking change: Remove scikit-learn < 0.22 support for KPLS surrogates family
• Remove Python 3.6 from CI tests as it has reached its end-of-life date (#342).
• Fix MOE when test data are specified (#347)
• Fix MFK to make it work even with one fidelity (#339, #341)
• Fix Kriging based surrogates to allow constant function modeling (#338)
• Fix KPLS automatic determination of components number and update notebook (#335)

1.1.0

• Mixed integer surrogate enhancements (thanks @Paul-Saves)
  • Add number of components estimation in KPLS surrogate models (#325)
  • Add ordered variables management in mixed integer surrogates (#326, #327). Deprecation warning: INT type is deprecated and superseded by ORD type.
  • Update version for the GOWER distance model. (#330)
  • Implement generalization of the homoscedastic hypersphere kernel from Pelamatti et al. (#330)
  • Refactor MixedInteger (#328, #330)
• Add propagate_uncertainty option in MFK method (#320 thanks @anfelopera) :
  • when True the variances of lower fidelity levels are taken into account.
• Add LHS expansion method (#303, #323 thanks @rconde1997)
• MOE: Fix computation of errors when choosing expert surrogates (#334)
• Breaking Changes:
  • In EGO SMT, UCB criteria mistakenly named regarding the litterature is renamed LCB! (#321)
  • In MixedInteger surrogate: use_gower_distance=True option replaced by categorical_kernel=GOWER
• Documentation:
  • Add collab links in Tutorial README (#322)
  • Add notebook about MFK with noise handling (#320)
  • Fix typos (#320, #321)