blackboxopt.gp module

Gaussian process module.

class blackboxopt.gp.GaussianProcess(kernel=None, *, scaler=None, maxiterLBFGS: int = 15000, **kwargs) → None

Bases: GaussianProcessRegressor

Gaussian Process model.

This model uses default attributes and parameters from GaussianProcessRegressor with the following exceptions:

  • kernel: Default is sklearn.gaussian_process.kernels.RBF().

  • optimizer: Default is _optimizer().

  • normalize_y: Default is True.

  • n_restarts_optimizer: Default is 10.

Check other attributes and parameters for GaussianProcessRegressor at https://scikit-learn.org/dev/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html.

Parameters:
maxiterLBFGS

Maximum number of iterations for the L-BFGS-B optimizer. Used in the training of the gaussian process.

scaler

Scaler used to preprocess input data.

__call__(x: ndarray) → tuple[ndarray, ndarray]

Evaluates the model at one or multiple points.

Parameters:

x (ndarray) – m-by-d matrix with m point coordinates in a d-dimensional space.

Return type:

tuple[ndarray, ndarray]

Returns:

  • Mean value predicted by the GP model on each of the input points.

  • Std value predicted by the GP model on each of the input points.

check_initial_design(sample: ndarray) → bool

Check if the sample is able to generate a valid surrogate.

Parameters:

sample (ndarray) – m-by-d matrix with m training points in a d-dimensional space.

Return type:

bool

eval_kernel(x, y=None)

Evaluate the kernel function at a pair (x,y).

The structure of the kernel is the same as the one passed as parameter but with optimized hyperparameters.

Parameters:

  • x – First entry in the tuple (x,y).

  • y – Second entry in the tuple (x,y). If None, use x. (default: None)

get_iindex() → tuple[int, ...]

Return iindex, the sequence of integer variable indexes.

Return type:

tuple[int, ...]

min_design_space_size(dim: int) → int

Return the minimum design space size for a given space dimension.

Return type:

int

ntrain() → int

Get the number of sampled points.

Return type:

int

update(Xnew, ynew) → None

Updates the model with new pairs of data (x,y).

When the default optimizer method, _optimizer(), is used as optimizer, this routine reports different warnings compared to sklearn.gaussian_process.GaussianProcessRegressor.fit(). The latter reports any convergence failure in L-BFGS-B. This implementation reports the last convergence failure in the multiple L-BFGS-B runs only if there all the runs end up failing. The number of optimization runs is n_restarts_optimizer + 1.

Parameters:

  • Xnew – m-by-d matrix with m point coordinates in a d-dimensional space.

  • ynew – Function values on the sampled points.

Return type:

None

xtrain() → ndarray

Get the training data points.

Return type:

ndarray

Returns:

m-by-d matrix with m training points in a d-dimensional space.

ytrain() → ndarray

Get f(x) for the sampled points.

Return type:

ndarray