Smt

What's Changed
* Add [Sparse GP](https://smt.readthedocs.io/en/latest/_src_docs/surrogate_models/sgp.html) by hvalayer, relf in https://github.com/SMTorg/smt/pull/463
* Add MFK and MFKPLS compatibility with mixed variables by RemyCharayron in https://github.com/SMTorg/smt/pull/467
* Update pyDOE2 dependency to pyDOE3 by relf in https://github.com/SMTorg/smt/pull/471
* Fix for expired deprecation in numpy 1.25 by relf in https://github.com/SMTorg/smt/pull/469
* Bump actions/checkout from 3 to 4 by dependabot in https://github.com/SMTorg/smt/pull/462
* Bump pypa/cibuildwheel from 2.15.0 to 2.16.2 by dependabot in https://github.com/SMTorg/smt/pull/468

New Contributors
* hvalayer made his first contribution in https://github.com/SMTorg/smt/pull/463
* RemyCharayron made his first contribution in https://github.com/SMTorg/smt/pull/467

**Full Changelog**: https://github.com/SMTorg/smt/compare/v2.0.1...v2.1.0

* 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)

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

See [SMT 2.0 article](https://www.researchgate.net/publication/370982021_SMT_20_A_Surrogate_Modeling_Toolbox_with_a_focus_on_Hierarchical_and_Mixed_Variables_Gaussian_Processes) (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](https://github.com/SMTorg/smt/blob/f4a7bdddfcce53773abe83103acac4989d1f9298/tutorial/README.md) (NatOnera #436, 448 )

* 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](https://smt.readthedocs.io/en/latest/_src_docs/applications/Mixed_Hier_usage.html)

* 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 )

* 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)

**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](https://github.com/SMTorg/smt/blob/master/tutorial/SMT_GP_Sampling.ipynb)
* 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)