gpcm: Fit and/or create a Gaussian Process Classification (GPC)...

In GPCsign: Gaussian Process Classification as Described in Bachoc et al. (2020)

Fit and/or create a Gaussian Process Classification (GPC) model

Description

gpcm is used to fit GPC models. When parameters are known, the function creates a model using given parameters. Otherwise, they are estimated by Maximum Likelihood. In both cases, the result is a gpcm object.

Usage

gpcm(f, Xf, covtype = "matern5_2", noise.var = 1e-6,
     coef.cov = NULL, coef.m = NULL, multistart = 1,
     seed = NULL, lower = NULL, upper = NULL, nsimu = 100,
     normalize = TRUE, X.mean = NULL, X.std = NULL, MeanTransform = NULL)

Arguments

f	a vector containing the binary observations (+/-1) corresponding to the class labels.
Xf	a matrix representing the design of experiments.
covtype	a character string specifying the covariance structure for the latent GP. Default is matern5_2.
noise.var	variance value standing for the homogeneous nugget effect. Default is 1e-6.
coef.cov	an optional vector containing the values for covariance parameters for the latent GP. (See below).
coef.m	an optional scalar corresponding to the mean value of the latent GP. If both coef.cov and coef.m are provided, no covariance parameter estimation is performed. If at least one of them is missing, both are estimated.
multistart	an optional integer indicating the number of initial points from which running the BFGS for covariance parameter optimization. The multiple optimizations will be performed in parallel provided that a parallel backend is registered (see package future)
seed	to fix the seed, default is NULL.
lower	(see below).
upper	lower, upper: bounds for the covariance parameters (scalars or vectors), if NULL they are set to 0.2 and 3, respectively.
nsimu	the number of samples of the latent process at observation points Xf to generate. Must be a non-null integer.
normalize	a logical parameter indicating whether to normalize the input matrix Xf. If TRUE, the matrix will be normalized using X.mean and X.std values if given; otherwise, the mean and standard deviation are computed and used for normalization.
X.mean	(see below).
X.std	optional vectors containing mean and standard deviation values for each column of the input matrix. If they are not provided, they are computed from the input matrix Xf.
MeanTransform	optional character string specifying a transform of the latent process mean coef.m. If positive (resp. negative), coef.m is constrained to be positive (resp. negative) by an exponential transform.

Details

The generation of the matrix of samples of the latent process Z_obs is done using Gibbs sampling. See rtmvnorm function in tmvtnorm package.

Value

An object of class gpcm. See gpcm-class.

Author(s)

Morgane MENZ, Céline HELBERT, Victor PICHENY, François BACHOC. Contributors: Naoual SERRAJI.

References

Bachoc, F., Helbert, C. & Picheny, V. Gaussian process optimization with failures: classification and convergence proof. J Glob Optim 78, 483–506 (2020). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10898-020-00920-0")}.

Kotecha, J. H., Djuric, P. M. (1999). Gibbs Sampling Approach For Generation of Truncated Multivariate Gaussian Random Variables. IEEE Computer Society, 1757–1760.

Wilhelm, S. tmvtnorm: Truncated Multivariate Normal and Student t Distribution. R package version 1.6. https://CRAN.R-project.org/package=tmvtnorm.

Roustant, O., Ginsbourger, D. & Deville, Y. Contributors: Chevalier, C. , Richet, Y. DiceKriging: Kriging Methods for Computer Experiments. R package version 1.6.0. https://CRAN.R-project.org/package=DiceKriging.

Byrd, R. H., Lu, P., Nocedal, J. and Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16, 1190–1208. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1137/0916069")}.

Examples

# ----------------------------------
# A 1D example - sinusoidal function
# ----------------------------------
sinusoidal_function <- function(x) {
  sin(4 * pi * x)
}

# Design of Experiments Xf and the corresponding signs f
Xf <- as.matrix(c(0.07, 0.19, 0.42, 0.56, 0.81, 0.90))
f <- rep(1, length(Xf))
f[(sinusoidal_function(Xf) < 0)] <- -1

# GPC model
GPCmodel <- gpcm(f, Xf, multistart = 3)

# Graphics of predictions
x <- as.matrix(seq(0, 1, length.out = 101))
result <- predict(object = GPCmodel, newdata = x)
probabilities <- result$prob
index <- match(Xf, x)
plot(x, probabilities, pch = "-")
points(Xf[f == 1], probabilities[index[f == 1]], pch = 20, col = "blue")
points(Xf[f == -1], probabilities[index[f == -1]], pch = 20, col = "red")
abline(h = 0.5, lty = 2)
legend("topright",title = "DoE Xf",title.cex = 0.7, legend = c("+", "-"), 
     col = c("blue", "red"), pch = 20)


# ----------------------------------
# A 2D example - Branin-Hoo function
# ----------------------------------

# 30-points DoE, and the corresponding response
d <- 2
nb_PX <- 30
require(DiceDesign)
X <- lhsDesign(nb_PX, d, seed = 123)$design
Xopt <- maximinSA_LHS(X, T0 = 10, c = 0.99, it = 10000)
x <- Xopt$design
require(DiceKriging)
fx <- apply(x, 1, branin)
f <- ifelse(fx < 14, -1, 1)
Xf <- as.matrix(x)

# Fit and create a GPC model without parallelisation
t0 <- proc.time()
GPCmodel <- gpcm(f, Xf, multistart = 3, seed = 123)
t1 = proc.time() - t0
cat(" time elapsed : ",t1[3])
print(GPCmodel)

# Graphics - Predict probabilities
ngrid <- 50
x.grid <- seq(0, 1., length.out = ngrid)
grid <- as.matrix(expand.grid(x.grid, x.grid))
probabilities <- predict(GPCmodel, newdata = grid, light.return = TRUE)
filled.contour(x.grid, x.grid, matrix(probabilities, ngrid, ngrid),
               color.palette = function(n) hcl.colors(n, "RdYlBu", rev = FALSE),
               main = "probabilities map",
               plot.axes = {
                 axis(1)
                 axis(2)
                 points(Xf[f == 1, 1], Xf[f == 1, 2], col = "blue", pch = 21, bg = "blue")
                 points(Xf[f == -1, 1], Xf[f == -1, 2], col = "red", pch = 21, bg = "red")
               }
)

# Fit and create a GPC model with parallelisation
## Use multisession futures
require(future)
plan(multisession)
t0 = proc.time()
GPCmodel2 <-  gpcm(f,Xf, multistart = 3, seed = 123 )
t1 = proc.time() - t0
cat(" time elapsed : ",t1[3])
print(GPCmodel2)
## Explicitly close multisession workers by switching plan
plan(sequential)

GPCsign documentation built on April 4, 2025, 1:55 a.m.