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)