Smt

* 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)

* **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](https://smt.readthedocs.io/en/latest/_src_docs/examples/airfoil_parameters/learning_airfoil_parameters.html) using GENN surrogate model (#374 thanks raul-rufato)
* Update [MixedInteger Tutorial](https://github.com/SMTorg/smt/blob/72432cf639c32986d30d1251e945f867984882ea/tutorial/SMT_MixedInteger_application.ipynb): 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)

* 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](https://endoflife.date/python) (#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)

* 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](https://github.com/SMTorg/smt/blob/master/tutorial/README.md) (#322)
- Add notebook about MFK with noise handling (320)
- Fix typos (320, 321)