GaussianProcess

abstract type GaussianProcessesPackage

Type to dispatch which GP package to use:

• GPJL for GaussianProcesses.jl, [julia - gradient-free only]
• SKLJL for the ScikitLearn GaussianProcessRegressor, [python - gradient-free]
• AGPJL for AbstractGPs.jl, [julia - ForwardDiff compatible]

abstract type PredictionType

Predict type for GPJL in GaussianProcesses.jl:

• YType
• FType latent function.

struct GaussianProcess{GPPackage, FT, VV<:(AbstractVector)} <: CalibrateEmulateSample.Emulators.MachineLearningTool

Structure holding training input and the fitted Gaussian process regression models.

Fields

• models::Vector{Union{Nothing, PyCall.PyObject, AbstractGPs.PosteriorGP, GaussianProcesses.GPE}}: The Gaussian Process (GP) Regression model(s) that are fitted to the given input-data pairs.

• kernel::Union{Nothing, var"#s27", var"#s26", var"#s25"} where {var"#s27"<:GaussianProcesses.Kernel, var"#s26"<:PyCall.PyObject, var"#s25"<:KernelFunctions.Kernel}: Kernel object.

• noise_learn::Bool: Learn the noise with the White Noise kernel explicitly?

• alg_reg_noise::Any: Additional observational or regularization noise in used in GP algorithms

• prediction_type::CalibrateEmulateSample.Emulators.PredictionType: Prediction type (y to predict the data, f to predict the latent function).

• regularization::AbstractVector: Regularization vector for each output dimension (based on algregnoise

GaussianProcess(
    package::AbstractFloat;
    kernel,
    noise_learn,
    alg_reg_noise,
    prediction_type
)

• package - GaussianProcessPackage object.
• kernel - GaussianProcesses kernel object. Default is a Squared Exponential kernel.
• noise_learn - Boolean to additionally learn white noise in decorrelated space. Default is true.
• alg_reg_noise - Float to fix the (small) regularization parameter of algorithms when noise_learn = true
• prediction_type - PredictionType object. Default predicts data, not latent function (FType()).

build_models!(
    gp::CalibrateEmulateSample.Emulators.GaussianProcess{CalibrateEmulateSample.Emulators.GPJL},
    input_output_pairs::EnsembleKalmanProcesses.DataContainers.PairedDataContainer{FT<:AbstractFloat},
    input_structure_mats,
    output_structure_mats;
    kwargs...
) -> Any

Method to build Gaussian process models based on the package.

optimize_hyperparameters!(
    gp::CalibrateEmulateSample.Emulators.GaussianProcess{CalibrateEmulateSample.Emulators.GPJL},
    args...;
    kwargs...
)

Optimize Gaussian process hyperparameters using in-build package method.

Warning: if one uses GPJL() and wishes to modify positional arguments. The first positional argument must be the Optim method (default LBGFS()).

predict(
    gp::CalibrateEmulateSample.Emulators.GaussianProcess{CalibrateEmulateSample.Emulators.GPJL},
    new_inputs::AbstractArray{FT<:AbstractFloat, 2}
) -> Tuple{Any, Any}

Predict means and covariances in decorrelated output space using Gaussian process models.