laGP: Localized Approximate GP Prediction At a Single Input...

In laGP: Local Approximate Gaussian Process Regression

Localized Approximate GP Prediction At a Single Input Location

Description

Build a sub-design of X of size end, and infer parameters, for approximate Gaussian process prediction at reference location(s) Xref. Return the moments of those predictive equations, and indices into the local design

Usage

laGP(Xref, start, end, X, Z, d = NULL, g = 1/10000,
     method = c("alc", "alcopt", "alcray", "mspe", "nn", "fish"), Xi.ret = TRUE,
     close = min((1000+end)*if(method[1] %in% c("alcray", "alcopt")) 10 else 1, nrow(X)), 
     alc.gpu = FALSE, numstart = if(method[1] == "alcray") ncol(X) else 1, 
     rect = NULL, lite = TRUE, verb = 0)
laGP.R(Xref, start, end, X, Z, d = NULL, g = 1/10000,
     method = c("alc", "alcopt", "alcray", "mspe", "nn", "fish"), 
     Xi.ret = TRUE, pall = FALSE, 
     close = min((1000+end)*if(method[1] %in% c("alcray", "alcopt")) 10 else 1, nrow(X)),
     parallel = c("none", "omp", "gpu"), 
     numstart = if(method[1] == "alcray") ncol(X) else 1, 
     rect = NULL, lite = TRUE, verb = 0)
laGPsep(Xref, start, end, X, Z, d = NULL, g = 1/10000,
     method = c("alc", "alcopt", "alcray", "nn"), Xi.ret = TRUE, 
     close = min((1000+end)*if(method[1] %in% c("alcray", "alcopt")) 10 else 1, nrow(X)), 
     alc.gpu = FALSE, numstart = if(method[1] == "alcray") ncol(X) else 1, 
     rect = NULL, lite = TRUE, verb=0)
laGPsep.R(Xref, start, end, X, Z, d = NULL, g = 1/10000,
     method = c("alc", "alcopt", "alcray", "nn"), 
     Xi.ret = TRUE, pall = FALSE, 
     close = min((1000+end)*if(method[1] %in% c("alcray", "alcopt")) 10 else 1, nrow(X)),
     parallel = c("none", "omp", "gpu"), 
     numstart = if(method[1] == "alcray") ncol(X) else 1, 
     rect = NULL, lite = TRUE, verb = 0)

Arguments

Xref	a vector of length ncol(X) containing a single reference location; or a matrix with ncol(Xref) = ncol(X) containing multiple reference locations (unless method = "alcray") for simultaneous sub-design and prediction
start	the number of Nearest Neighbor (NN) locations for initialization; must specify start >= 6
end	the total size of the local designs; must have start < end
X	a matrix or data.frame containing the full (large) design matrix of input locations
Z	a vector of responses/dependent values with length(Z) = nrow(X)
d	a prior or initial setting for the lengthscale parameter for a Gaussian correlation function; a (default) NULL value causes a sensible regularization (prior) and initial setting to be generated via darg; a scalar specifies an initial value, causing darg to only generate the prior; otherwise, a list or partial list matching the output of darg can be used to specify a custom prior. In the case of a partial list, the only the missing entries will be generated. Note that a default/generated list specifies MLE/MAP inference for this parameter. With laGPsep, the starting values can be an ncol(X)-by-nrow(XX) matrix or ncol(X) vector
g	a prior or initial setting for the nugget parameter; a NULL value causes a sensible regularization (prior) and initial setting to be generated via garg; a scalar (default g = 1/10000) specifies an initial value, causing garg to only generate the prior; otherwise, a list or partial list matching the output of garg can be used to specify a custom prior. In the case of a partial list, only the missing entries will be generated. Note that a default/generated list specifies no inference for this parameter; i.e., it is fixed at its starting or default value, which may be appropriate for emulating deterministic computer code output. In such situations a value much smaller than the default may work even better (i.e., yield better out-of-sample predictive performance). The default was chosen conservatively
method	Specifies the method by which end-start candidates from X are chosen in order to predict at Xref. In brief, ALC ("alc", default) minimizes predictive variance; ALCRAY ("alcray")) executes a thrifty ALC-based search focused on rays emanating from the reference location [must have nrow(Xref) = 1]; ALCOPT ("alcopt") optimizes a continuous ALC analog via derivatives to and snaps the solution back to the candidate grid; MSPE ("mspe") augments ALC with extra derivative information to minimize mean-squared prediction error (requires extra computation); NN ("nn") uses nearest neighbor; and EFI ("fish") uses the expected Fisher information - essentially 1/G from Gramacy & Apley (2015) - which is global heuristic, i.e., not localized to Xref.
Xi.ret	A scalar logical indicating whether or not a vector of indices into X, specifying the chosen sub-design, should be returned on output
pall	a scalar logical (for laGP.R only) offering the ability to obtain predictions after every update (for progress indication and debugging), rather than after just the last update
close	a non-negative integer end < close <= nrow(X) specifying the number of NNs (to Xref) in X to consider when searching for elements of the sub-design; close = 0 specifies all. For method="alcray" and method="alcopt", this argument specifies the scope used to snap solutions obtained via analog continuous searches back to elements of X, otherwise there are no restrictions on those searches. Since these approximate searches are cheaper, they can afford a larger “snapping scope” hence the larger default
alc.gpu	a scalar logical indicating if a GPU should be used to parallelize the evaluation of the ALC criteria (method = "alc"). Requires the package be compiled with CUDA flags; see README/INSTALL in the package source for more details; currently only available for nrow(Xref) == 1 via laGP, not laGPsep or the .R variants, and only supports off-loading ALC calculation to the GPU
parallel	a switch indicating if any parallel calculation of the criteria is desired. Currently parallelization at this level is only provided for option method = "alc"). For parallel = "omp", the package must be compiled with OMP flags; for parallel = "gpu", the package must be compiled with CUDA flags see README/INSTALL in the package source for more details; currently only available via laGP.R
numstart	a scalar integer indicating the number of rays for each greedy search when method="alcray" or the number of restarts when method="alcopt". More rays or restarts leads to a more thorough, but more computational intensive search. This argument is not involved in other methods
rect	an optional 2-by-ncol(X) matrix describing a bounding rectangle for X that is used by the "alcray" method. If not specified, the rectangle is calculated from range applied to the columns of X
lite	Similar to the predGP option of the same name, this argument specifies whether (TRUE, the default) or not (FALSE) to return a full covariance structure is returned, as opposed the diagonal only. A full covariance structure requires more computation and more storage. This option is only relevant when nrow(Xref) > 1
verb	a non-negative integer specifying the verbosity level; verb = 0 is quiet, and larger values cause more progress information to be printed to the screen

Details

A sub-design of X of size end is built-up according to the criteria prescribed by the method and then used to predict at Xref. The first start locations are NNs in order to initialize the first GP, via newGP or newGPsep, and thereby initialize the search. Each subsequent addition is found via the chosen criterion (method), and the GP fit is updated via updateGP or updateGPsep

The runtime is cubic in end, although the multiplicative “constant” out front depends on the method chosen, the size of the design X, and close. The "alcray" method has a smaller constant since it does not search over all candidates exhaustively.

After building the sub-design, local MLE/MAP lengthscale (and/or nugget) parameters are estimated, depending on the d and g arguments supplied. This is facilitated by calls to mleGP or jmleGP.

Finally predGP is called on the resulting local GP model, and the parameters of the resulting Student-t distribution(s) are returned. Unless Xi.ret = FALSE, the indices of the local design are also returned.

laGP.R and laGPsep.R are a prototype R-only version for debugging and transparency purposes. They are slower than laGP and laGPsep, which are primarily in C, and may not provide identical output in all cases due to differing library implementations used as subroutines; see note below for an example. laGP.R and other .R functions in the package may be useful for developing new programs that involve similar subroutines. The current version of laGP.R allows OpenMP and/or GPU parallelization of the criteria (method) if the package is compiled with the appropriate flags. See README/INSTALL in the package source for more information. For algorithmic details, see Gramacy, Niemi, & Weiss (2014)

Value

The output is a list with the following components.

mean	a vector of predictive means of length nrow(Xref)
s2	a vector of Student-t scale parameters of length nrow(Xref)
df	a Student-t degrees of freedom scalar (applies to all Xref)
llik	a scalar indicating the maximized log likelihood or log posterior probability of the data/parameter(s) under the chosen sub-design; provided up to an additive constant
time	a scalar giving the passage of wall-clock time elapsed for (substantive parts of) the calculation
method	a copy of the method argument
d	a full-list version of the d argument, possibly completed by darg
g	a full-list version of the g argument, possibly completed by garg
mle	if d$mle and/or g$mle are TRUE, then mle is a data.frame containing the values found for these parameters, and the number of required iterations
Xi	when Xi.ret = TRUE, this field contains a vector of indices of length end into X indicating the sub-design chosen
close	a copy of the input argument

Note

laGPsep provides the same functionality as laGP but deploys a separable covariance function. However criteria (methods) EFI and MSPE are not supported. This is considered “beta” functionality at this time.

Note that using method="NN" gives the same result as specifying start=end, however at some extra computational expense.

Handling multiple reference locations (nrow(Xref) > 1) is “beta” functionality. In this case the initial start locations are chosen by applying NN to the average distances to all Xref locations. Using method="alcopt" causes exhaustive search to be approximated by a continuous analog via closed form derivatives. See alcoptGP for more details. Although the approximation provided has a spirit similar to method="alcray", in that both methods are intended to offer a thrifty alternative, method="alcray" is not applicable when nrow(Xref) > 1.

Differences between the C qsort function and R's order function may cause chosen designs returned from laGP and laGP.R (code and laGPsep and laGPsep.R) to differ when multiple X values are equidistant to Xref

Author(s)

Robert B. Gramacy [e-mail] and Furong Sun [e-mail]

References

Gramacy, R. B. (2020) Surrogates: Gaussian Process Modeling, Design and Optimization for the Applied Sciences. Boca Raton, Florida: Chapman Hall/CRC. (See Chapter 9.) https://bobby.gramacy.com/surrogates/

F. Sun, R.B. Gramacy, B. Haaland, E. Lawrence, and A. Walker (2019). Emulating satellite drag from large simulation experiments, SIAM/ASA Journal on Uncertainty Quantification, 7(2), pp. 720-759; preprint on arXiv:1712.00182; https://arxiv.org/abs/1712.00182

R.B. Gramacy (2016). laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R., Journal of Statistical Software, 72(1), 1-46; \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v072.i01")} or see vignette("laGP")

R.B. Gramacy and B. Haaland (2016). Speeding up neighborhood search in local Gaussian process prediction. Technometrics, 58(3), pp. 294-303; preprint on arXiv:1409.0074 https://arxiv.org/abs/1409.0074

R.B. Gramacy and D.W. Apley (2015). Local Gaussian process approximation for large computer experiments. Journal of Computational and Graphical Statistics, 24(2), pp. 561-678; preprint on arXiv:1303.0383; https://arxiv.org/abs/1303.0383

R.B. Gramacy, J. Niemi, R.M. Weiss (2014). Massively parallel approximate Gaussian process regression. SIAM/ASA Journal on Uncertainty Quantification, 2(1), pp. 568-584; preprint on arXiv:1310.5182; https://arxiv.org/abs/1310.5182

See Also

vignette("laGP"), aGP, newGP, updateGP, predGP, mleGP, jmleGP, alcGP, mspeGP, alcrayGP, randLine ## path-based local prediction via laGP

Examples

## examining a particular laGP call from the larger analysis provided
## in the aGP documentation

## A simple 2-d test function used in Gramacy & Apley (2014);
## thanks to Lee, Gramacy, Taddy, and others who have used it before
f2d <- function(x, y=NULL)
  {
    if(is.null(y)) {
      if(!is.matrix(x) && !is.data.frame(x)) x <- matrix(x, ncol=2)
      y <- x[,2]; x <- x[,1]
    }
    g <- function(z)
      return(exp(-(z-1)^2) + exp(-0.8*(z+1)^2) - 0.05*sin(8*(z+0.1)))
    z <- -g(x)*g(y)
  }

## build up a design with N=~40K locations
x <- seq(-2, 2, by=0.02)
X <- as.matrix(expand.grid(x, x))
Z <- f2d(X)

## optional first pass of nearest neighbor
Xref <- matrix(c(-1.725, 1.725), nrow=TRUE)
out <- laGP(Xref, 6, 50, X, Z, method="nn")

## second pass via ALC, ALCOPT, MSPE, and ALC-ray respectively,
## conditioned on MLE d-values found above
out2 <- laGP(Xref, 6, 50, X, Z, d=out$mle$d)
# out2.alcopt <- laGP(Xref, 6, 50, X, Z, d=out2$mle$d, method="alcopt")
out2.mspe <- laGP(Xref, 6, 50, X, Z, d=out2$mle$d, method="mspe")
out2.alcray <- laGP(Xref, 6, 50, X, Z, d=out2$mle$d, method="alcray")

## look at the different designs
plot(rbind(X[out2$Xi,], X[out2.mspe$Xi,]), type="n",
     xlab="x1", ylab="x2", main="comparing local designs")
points(Xref[1], Xref[2], col=2, cex=0.5)
text(X[out2$Xi,], labels=1:50, cex=0.6)
# text(X[out2.alcopt$Xi,], labels=1:50, cex=0.6, col="forestgreen")
text(X[out2.mspe$Xi,], labels=1:50, cex=0.6, col="blue")
text(X[out2.alcray$Xi,], labels=1:50, cex=0.6, col="red")
legend("right", c("ALC", "ALCOPT", "MSPE", "ALCRAY"),
       text.col=c("black", "forestgreen", "blue", "red"), bty="n")

## compare computational time
c(nn=out$time, alc=out2$time, # alcopt=out2.alcopt$time, 
  mspe=out2.mspe$time, alcray=out2.alcray$time)

## Not run: 
  ## Joint path sampling: a comparison between ALC-ex, ALC-opt and NN

  ## defining a predictive path
  wx <- seq(-0.85, 0.45, length=100)
  W <- cbind(wx-0.75, wx^3+0.51)

  ## three comparators from Sun, et al. (2017)
  ## larger-than-default "close" argument to capture locations nearby path
  p.alc <- laGPsep(W, 6, 100, X, Z, close=10000, lite=FALSE)
  p.alcopt <- laGPsep(W, 6, 100, X, Z, method="alcopt", lite=FALSE)
  ## note that close=10*(1000+end) would be the default for method = "alcopt"
  p.nn <- laGPsep(W, 6, 100, X, Z, method="nn", close=10000, lite=FALSE)

  ## time comparison
  c(alc=p.alc$time, alcopt=p.alcopt$time, nn=p.nn$time)

  ## visualization
  plot(W, type="l", xlab="x1", ylab="x2", xlim=c(-2.25,0), ylim=c(-0.75,1.25), lwd=2)
  points(X[p.alc$Xi,], col=2, cex=0.6)
  lines(W[,1]+0.25, W[,2]-0.25, lwd=2)
  points(X[p.nn$Xi,1]+0.25, X[p.nn$Xi,2]-0.25, pch=22, col=3, cex=0.6)
  lines(W[,1]-0.25, W[,2]+0.25, lwd=2) 
  points(X[p.alcopt$Xi,1]-0.25, X[p.alcopt$Xi,2]+0.25, pch=23, col=4, cex=0.6)
  legend("bottomright", c("ALC-opt", "ALC-ex", "NN"), pch=c(22, 21, 23), col=c(4,2,3))

## End(Not run)

laGP documentation built on March 31, 2023, 9:46 p.m.