auc.dca: AUC optimization with DCA

In aucm: AUC Maximization

Description

optimizes SAUC using a smoothed DCA algorithm.

Usage

dcsauc (formula, data, ...)
srauc (formula, data, ...)
auc.dca (formula, data, 
    type="srauc",
    kernel="linear", para=NULL,  
    lambda=.1, zeta=.1, b=10, s=1, epsilon=1e-3, 
    method="tron", decomposition=TRUE,
    dca.control =  list(maxit=1e3, abstol=1e-5, coef.init=NULL, lincomb.init=NULL),
    tron.control = list(q=50, maxfev=1e3, gtol=1e-2, frtol=1e-12, K.thresh=1, verbose=0),
    return.K=FALSE, verbose = FALSE
)

Arguments

formula	formula, e.g. y~x1+x2
data	a data frame
type	string. Either srauc or dcsauc
kernel	See getK for more details
para	See getK for more details
lambda	scale parameter of the penalty function, defaults to 1
zeta	parameter (->0+) in writing sigmoid function as differene of two convex functions.
b	'decay rate' parameter in sigmoid function 1/(exp(bx))
s	the parameter in rauc
epsilon	the parameter in the approximation of a hinge function
method	the optimizer to use, "tron", or an optim method
decomposition	Boolean. If TRUE, decomposition strategy is used if tron is the method
dca.control	list of control parameters for the DCA algorithm
tron.control	list of control parameters to 'tron' optimizer
return.K	logical, whether to return the Kernel matrix
verbose	logical, whether to print info as alg. progresses
...	parameters passed to auc.dca

Details

dcsauc and srauc pass directly to auc.dca with the name-sake type.

Examples

#
#
#dat = sim.dat.1(n=100,seed=1)
#dat.test = sim.dat.1(n=1e3,seed=1000)
#
#t.1 = system.time({
#    fit.1=sauc.dca(y~x1+x2, dat, zeta=.1)
#})
#
#t.2 = system.time({
#    fit.2=sauc.dca(y~x1+x2, dat, zeta=1)
#})
#
## compare time
#rbind(t.1, t.2)[,3]
#
## compare performance
#RUnit::checkEqualsNumeric(
#    c(fit.1$train.auc, fit.2$train.auc)
#, c(0.7291917, 0.7282913), tolerance=1e-6)
#

aucm documentation built on Jan. 11, 2020, 9:43 a.m.