Package 'GPM'

Title: Gaussian Process Modeling of Multi-Response and Possibly Noisy Datasets
Description: Provides a general and efficient tool for fitting a response surface to a dataset via Gaussian processes. The dataset can have multiple responses and be noisy (with stationary variance). The fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.
Authors: Ramin Bostanabad, Tucker Kearney, Siyo Tao, Daniel Apley, and Wei Chen (IDEAL)
Maintainer: Ramin Bostanabad <[email protected]>
License: GPL-2
Version: 3.0.1
Built: 2024-12-05 06:46:13 UTC
Source: CRAN

Help Index

An auxiliary function used in calculating the negative log-likelehood and its gradient

Description

Calculates some auxiliary paramters to obtain the negative log-likelehood and its gradient.

Usage

Auxil(Omega, X, Y, CorrType, MinEig, Fn, n, dy)

Arguments

Omega

The vector storing all the hyperparameters of the correlation function. The length of Omega depends on the CorrType. See reference 1.
X

Matrix containing the training (aka design or input) data points. The rows and columns of X denote individual observation settings and input dimension, respectively.
Y

Matrix containing the output (aka response) data points. The rows and columns of Y denote individual observation responses and output dimension, respectively.
CorrType

The correlation function of the GP model. Choices include 'G' (default), 'PE', 'LBG', and 'LB'. See Fit and the references.
MinEig

The smallest eigen value that the correlation matrix is allowed to have, which in return determines the appraopriate nugget that should be added to the correlation matrix.
Fn

A matrix of 1's with nrow(X) rows and 1 column. See reference 1.
n

Number of observations, nrow(X).
dy

Number of responses, ncol(Y).

Details

Since Auxil is shared between NLogL and NLogL_G during optimization, ideally it should be run only once (e.g., via memoisation). Such an implementation is left for future editions.

Value

ALL A list containing the following components (based on CorrType, some other parameters are also stored in ALL):

  • R The correlation matrix whose smallest eigen value is >= MinEig.

  • L Cholesky decomposition of R.

  • Raw_MinEig The smallest eigen value of R before adding Nug_opt.

  • Nug_opt The added nugger to R.

  • B

References

  1. Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. Int J Numer Meth Eng, 114, 501-516.

  2. Plumlee, M. & Apley, D. W. (2017) Lifted Brownian kriging models. Technometrics, 59, 165-177.

See Also

Fit to see how a GP model can be fitted to a training dataset.
Predict to use the fitted GP model for prediction.
Draw to plot the response via the fitted model.

Examples

# see the examples in the fitting function.

Two Functions for Constructing the Correlation Matrix in GPM Package

Description

The CorrMat_Sym() function builds the auto-correlation matrix corresponding to dataset X while the CorrMat_Vec() function builds the correlation matrix between datasets X1 and X2.

Usage

CorrMat_Sym(X, CorrType, Omega)
CorrMat_Vec(X1, X2, CorrType, Omega)

Arguments

X, X1, X2

Matrices containing the numeric data points. The rows and columns of both X1 and X2 denote individual observation settings and dimension, respectively.
CorrType

The correlation function of the GP model. Choices include 'G' (default), 'PE', 'LBG', and 'LB'. See the references for the details.
Omega

The vector storing all the scale (aka roughness) parameters of the correlation function. The length of Omega depends on the CorrType. See reference 1.

Value

R The Correlation matrix with size nrow(X1)-by-nrow(X2). See here.

Note

This function is NOT exported once the GPM package is loaded.

References

  1. Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.

  2. Plumlee, M. & Apley, D. W. (2017) Lifted Brownian kriging models. Technometrics, 59, 165-177.

See Also

Fit to see how a GP model can be fitted to a training dataset.
Predict to use the fitted GP model for prediction.
Draw to plot the response via the fitted model.

Examples

# see the examples in \code{\link[GPM]{Fit}}

The Plotting Function of GPM Package

Description

Plots the predicted response along with the assocaited uncertainty via the GP model fitted by Fit. Accepts multi-input and multi-output models. See Arguments for more details on the options.

Usage

Draw(Model, Plot_wrt, LB = NULL, UB = NULL, Values = NULL,
  Response_ID = NULL, res = 15, X1Label = NULL, X2Label = NULL,
  YLabel = NULL, Title = NULL, PI95 = NULL)

Arguments

Model

The GP model fitted by Fit.
Plot_wrt

A binary vector of length p where p is the dimension of the inputs in Model. A maximum (minimum) of 2 (1) elements can be 1. The elemenets set to 1, would correspond to the plotting axes.
LB, UB

Vectors of length sum(Plot_wrt) indicating the lower and upper bounds used for plotting. The first (second) element corresponds to the first (second) non-zero element of Plot_wrt.
Values

A vector of length p-sum(Plot_wrt). The values are assigned to the variables NOT used in plotting and correspond to the zeros in Plot_wrt.
Response_ID

A positive integer indicating the response that should be plotted if Model is multi-response.
res

A positive integer indicating the number of points used in plotting. Higher values will result in smoother plots.
X1Label

A string for the label of axis 1.
X2Label

A string for the label of axis 2, if plotting a surface.
YLabel

A string for the label of the response axis.
Title

A string for the title of the plot.
PI95

Flag (a scalar) indicating whether the 95% prediction interval should be plotted. Set it to a non-zero value to turn the flag "on".

References

  1. Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. Int J Numer Meth Eng, 114, 501-516.

  2. Plumlee, M. & Apley, D. W. (2017) Lifted Brownian kriging models. Technometrics, 59, 165-177.

See Also

Fit to see how a GP model can be fitted to a training dataset.
Predict to use the fitted GP model for prediction.

Examples

# See the examples in the fitting function.

The Fitting Function of GPM Package

Description

Fits a Gaussian process (GP) to a set of simulation data as described in reference 1. Both the inputs and outputs can be multi-dimensional. The outputs can be noisy in which case it is assumed that the noise is stationary (i.e., its variance is not a function of x).

Usage

Fit(X, Y, CorrType = 'G', Eps = 10^(seq(-1, -12)), AnaGr = NULL, Nopt = 5,TraceIt = 0,
MaxIter = 100, Seed = 1, LowerBound = NULL, UpperBound = NULL, 
StopFlag = 1, Progress = 0, DoParallel = 0, Ncores = NULL)

Arguments

X

Matrix containing the training (aka design or input) data points. The rows and columns of X denote individual observation settings and input dimension, respectively.
Y

Matrix containing the output (aka response) data points. The rows and columns of Y denote individual observation responses and output dimension, respectively.
CorrType

The type of the correlation function of the GP model. Choices include 'G' (default), 'PE', 'LBG', and 'LB'. See the references for the details. For smooth (or analytic) functions, choose either 'G' or 'LBG'. Fitting is faster if 'G' is chosen.
Eps

A vector containing the smallest eigen value(s) that the correlation matrix is allowed to have. The elements of Eps must be in [0, 1] and sorted in a descending order.
AnaGr

Flag indicating whether the gradient of the log-likelihood should be taken analytically (!= 0) or numerically (== 0). For now, only available when CorrType == 'G' or CorrType == 'PE'. If AnaGr != 0, the fitted model will generally be more accurate.
Nopt

The number of times the log-likelihood function is optimized when Eps[1] is used to constraint the smallest eigen value that the correlation matrix is allowed to have. Higher Nopt will increase fitting time as well as the chances of finding the global optimum. If nrow(X) is large (i.e., large training datasets), Nopt can be small.Analyzing the optimization results for Eps[1] and when Progress != 0 will determine if Nopt has been large enough.
TraceIt

Non-negative integer. If positive, tracing information on the progress of the optimization is printed. There are six levels of tracing (see optim) and higher values will produce more tracing information.
MaxIter

Maximum number of iterations allowed for each optimization (see optim).
Seed

An integer for the random number generator. Use this to make the results reproducible.
LowerBound, UpperBound

To estimate the scale (aka roughness) parameters of the correlation function, the feasible range should be defined. LowerBound and UpperBound are vectors determining, resepectively, the lower and upper bounds. Their length depends on the parametric form of the correlation function (see reference 1 for the details).
StopFlag

Flag indicating whether the optimization must be stopped if the negative log-likelihood increases with decreasing Eps[i].
Progress

Flag indicating if the fitting process should be summarized. Set it to !=0 to turn it on.
DoParallel

If != 0, optimizations will be done in parallel.
Ncores

Number of cores to use if DoParallel != 0. The default is the maximum number of physical cores.

Value

Model A list containing the following components:

  • CovFunc A list containing the type and estimated parameters of the correlation function.

  • Data A list storing the original (but scaled) data.

  • Details A list of some parameters (used in prediction) as well as some values reporting the total run-time (cost) and the added nugget (Nug_opt) for satisfying the constraint on the smallest eigen value of the correlation matrix.

  • OptimHist The optimization history.

  • Setting The default/provided settings for running the code.

References

  1. Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. Int J Numer Meth Eng, 114, 501-516.

  2. Plumlee, M. & Apley, D. W. (2017) Lifted Brownian kriging models. Technometrics, 59, 165-177.

See Also

optim for the details on L-BFGS-B algorithm used in optimization.
Predict to use the fitted GP model for prediction.
Draw to plot the response via the fitted model.

Examples

# 1D example: Fit a model (with default settings) and evaluate the performance
# by computing the root mean squared error (RMSE) in prediction.
library(lhs)
X <- 5*maximinLHS(15, 1)
Y <- 2*sin(2*X) + log(X+1)
M <- Fit(X, Y)
XF <- matrix(seq(0, 5, length.out = 100), 100, 1)
YF <- Predict(XF, M)
RMSE <- sqrt(mean((YF$YF - (2*sin(2*XF) + log(XF+1)))^2))

## Not run: 
# 1D example: Fit a model, evaluate the performance, and plot the response
# along with 95% prediction interval
X <- 10*maximinLHS(10, 1) - 5
Y <- X*cos(X)
M <- Fit(X, Y)
XF <- matrix(seq(-5, 5, length.out = 500), 500, 1)
YF <- Predict(XF, M)
RMSE <- sqrt(mean((YF$YF - (XF*cos(XF)))^2))
Draw(M, 1, res = 20)

# 2D example: Fit a model, evaluate the performance, and plot the response
# surface along with 95% prediction interval
X <- 2*maximinLHS(10, 2) - 1
Y <- X[, 1]^2 + X[, 2]^2
M <- Fit(X, Y, CorrType = "PE")
XF <- 2*maximinLHS(100, 2) - 1
YF <- Predict(XF, M)
RMSE <- sqrt(mean((YF$YF - (XF[, 1]^2 + XF[, 2]^2))^2))
library(lattice)
Draw(M, c(1, 1), res = 15, PI95=1)

# 2D example: Plot the previous model wrt X1 in the [-2, 2]
# interval with X2=1
Draw(M, c(1, 0), LB = -2, UB = 2, res = 15, PI95=1)

# 3D example: Compare the performance of Gaussian ("G") and lifted Browninan
# with Gamma=1 ("LBG")
X <- 2*maximinLHS(50, 3) - 1
Y <- cos(X[, 1]^2) + 2*sin(X[, 2]^2) + X[, 3]^2
M_G <- Fit(X, Y)
M_LBG <- Fit(X, Y, CorrType = "LBG")
XF <- 2*maximinLHS(500, 3) - 1
YF_G <- Predict(XF, M_G)
YF_LBG <- Predict(XF, M_LBG)
RMSE_G <- sqrt(mean((YF_G$YF - (cos(XF[, 1]^2) + 2*sin(XF[, 2]^2) + XF[, 3]^2))^2))
RMSE_LBG <- sqrt(mean((YF_LBG$YF - (cos(XF[, 1]^2) + 2*sin(XF[, 2]^2) + XF[, 3]^2))^2))

# 3D example: Draw the response in 2D using the M_G model when X3=0
Draw(M_G, c(1, 1, 0), PI95 = 0, Values = 0, X1Label = 'Input 1', X2Label = 'Input 2')

# 3D example: 2D response
X <- 2*maximinLHS(50, 3) - 1
Y <- cbind(cos(X[, 1]^2) + 2*sin(X[, 2]^2) + X[, 3]^2, rowSums(X))
M <- Fit(X, Y)
Draw(M, c(0, 1, 1), Response_ID = 2, Values = 0.5)

# 2D example with noise
X <- 2*maximinLHS(100, 2) - 1
Y <- X[, 1]^2 + X[, 2]^2 + matrix(rnorm(nrow(X), 0, .5), nrow(X), 1)
M <- Fit(X, Y)
# Estimating the noise variance (should be close to 0.5^2)
M$Details$Nug_opt*M$CovFunc$Parameters$Sigma2*M$Data$Yrange^2

## End(Not run)

A Set of Functions for Doing Some Calculations on Matrices in GPM Package

Description

These functions perform some matrix algebra to calculate the log-likelihood function.

Usage

Eigen(A)
CppSolve(A, B)
LowerChol(A)

Arguments

A

Numeric, symmetric, and positive definite matrix.
B

Numeric matrix or vector.

Value

Eigen(A)) returns the smallest eigen value of A. CppSolve(A, B) solves for X in AX=B. LowerChol(A) return the lower triangular Cholesky decomposition of A.

Note

These functions are NOT exported once the GPM package is loaded.

See Also

Fit to see how a GP model can be fitted to a training dataset.
Predict to use the fitted GP model for prediction.
Draw to plot the response via the fitted model.

Examples

# see the examples in \code{\link[GPM]{Fit}}

The Function for calculating the Negative Log-Likelehood in GPM Package

Description

Calculates the negative log-likelihood (excluding all the constant terms) as described in reference 1.

Usage

NLogL(Omega, X, Y, CorrType, MinEig, Fn, n, dy)

Arguments

Omega

The vector storing all the hyperparameters of the correlation function. The length of Omega depends on the CorrType. See reference 1.
X

Matrix containing the training (aka design or input) data points. The rows and columns of X denote individual observation settings and input dimension, respectively.
Y

Matrix containing the output (aka response) data points. The rows and columns of Y denote individual observation responses and output dimension, respectively.
CorrType

The correlation function of the GP model. Choices include 'G' (default), 'PE', 'LBG', and 'LB'. See Fit and the references.
MinEig

The smallest eigen value that the correlation matrix is allowed to have, which in return determines the appraopriate nugget that should be added to the correlation matrix.
Fn

A matrix of 1's with nrow(X) rows and 1 column. See reference 1.
n

Number of observations, nrow(X).
dy

Number of responses, ncol(Y).

Details

Fit calls this function with scaled X and Y. That is, when the user fits a GP model by calling Fit(X, Y), X and Y are mapped to the [0, 1] region and then passed to this function.

Value

nlogl The negative log-likelihood (excluding all the constant terms). See the references.

References

  1. Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. Int J Numer Meth Eng, 114, 501-516.

  2. Plumlee, M. & Apley, D. W. (2017) Lifted Brownian kriging models. Technometrics, 59, 165-177.

See Also

Fit to see how a GP model can be fitted to a training dataset.
Predict to use the fitted GP model for prediction.
Draw to plot the response via the fitted model.

Examples

# see the examples in the fitting function.

The Function for calculating the gradient of Negative Log-Likelehood in GPM Package

Description

Calculates the gradient of negative log-likelihood wrt Omega.

Usage

NLogL_G(Omega, X, Y, CorrType, MinEig, Fn, n, dy)

Arguments

Omega

The vector storing all the hyperparameters of the correlation function. The length of Omega depends on the CorrType. See reference 1.
X

Matrix containing the training (aka design or input) data points. The rows and columns of X denote individual observation settings and input dimension, respectively.
Y

Matrix containing the output (aka response) data points. The rows and columns of Y denote individual observation responses and output dimension, respectively.
CorrType

The correlation function of the GP model. Choices include 'G' (default), 'PE', 'LBG', and 'LB'. See Fit and the references.
MinEig

The smallest eigen value that the correlation matrix is allowed to have, which in return determines the appraopriate nugget that should be added to the correlation matrix.
Fn

A matrix of 1's with nrow(X) rows and 1 column. See reference 1.
n

Number of observations, nrow(X).
dy

Number of responses, ncol(Y).

Details

This function is used in Fit if AnaGr != 0.

Value

NLogL_G The gradient of negative log-likelihood wrt Omega. See the references.

References

  1. Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. Int J Numer Meth Eng, 114, 501-516.

  2. Plumlee, M. & Apley, D. W. (2017) Lifted Brownian kriging models. Technometrics, 59, 165-177.

See Also

Fit to see how a GP model can be fitted to a training dataset.
Predict to use the fitted GP model for prediction.
Draw to plot the response via the fitted model.

Examples

# see the examples in the fitting function.

The Prediction Function of GPM Package

Description

Predicts the reponse(s), associated prediction uncertainties, and gradient(s) of the GP model fitted by Fit.

Usage

Predict(XF, Model, MSE_on = 0, YgF_on = 0, grad_dim = rep(1, ncol(XF)))

Arguments

XF

Matrix containing the locations (settings) where the predictions are desired. The rows and columns of XF denote individual observation settings and input dimension, respectively.
Model

The GP model fitted by Fit.
MSE_on

Flag (a scalar) indicating whether the uncertainty (i.e., mean squared error MSE) associated with prediction of the response(s) should be calculated. Set to a non-zero value to calculate MSE.
YgF_on

Flag (a scalar) indicating whether the gradient(s) of the response(s) are desired. Set to a non-zero value to calculate the gradient(s). See note below.
grad_dim

A binary vector of length ncol(XF). The gradient of the response(s) will be calculated along the dimensions where the corresponding element of grad_dim is 1. grad_dim is ignored if YgF_on == 0.

Value

Output A list containing the following components:

  • YF A matrix with n rows (the number of prediction points) and dy columns (the number of responses).

  • MSE A matrix with n rows and dy columns where each element represents the prediction uncertainty (i.e., the expected value of the squared difference between the prediction and the true response) associated with the corresponding element in YF.

  • YgF An array of size n by sum{grad_dim} by dx.

Note

  1. The gradient(s) can be calculated if CorrType='G' or CorrType='LBG'. If CorrType='PE' or CorrType='LB', the gradient(s) can only be calculated if Power = 2 and Gamma = 1, respectively.

  2. For efficiency, make sure the inputs are vecotrized and then passed to Predict. Avoid passing inputs individually in a for loop.

References

  1. Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. Int J Numer Meth Eng, 114, 501-516.

  2. Plumlee, M. & Apley, D. W. (2017) Lifted Brownian kriging models. Technometrics, 59, 165-177.

See Also

Fit to see how a GP model can be fitted to a training dataset.
Draw to plot the response via the fitted model.

Examples

# See the examples in the fitting function.