[英]Calling Rcpp functions from optim
I'm trying to obtain MAP estimates for what basically amounts to a logistic regression model. 我正在尝试获取基本上等于逻辑回归模型的MAP估计值。 I'm using the optim function which takes a log-posterior density along with its analytic gradient as arguments.
我使用的是optim函数,该函数采用对数后验密度及其解析梯度作为参数。 I have both R versions and Rcpp versions of the density and gradient functions.
我有密度和梯度函数的R版本和Rcpp版本。 I can successfully estimate the MAP with the R functions, but optim is entering asymtopia and failing to convergence to the optimum with the Rcpp functions.
我可以成功地估计与所述R的功能和MAP,但的Optim正在进入asymtopia和未能收敛到与RCPP功能最佳。
I've verified that the R version of the density function and the Rcpp version of the density function return the same value: 我已验证密度函数的R版本和密度函数的Rcpp版本返回相同的值:
ll_cpp = cpp_posterior_density(THETAi = as.vector(THETA0_LF[i,]),
Yi = as.vector(Y[i,]),
MUi =as.vector(MU_LF[i,]),
invS = invS ,TAU = TAU ,LAMBDA = LAMBDA, J = J ,K = K)
ll_R = lf_posterior_density(THETAi = as.vector(THETA0_LF[i,]),
Yi = as.vector(Y[i,]),
MUi =as.vector(MU_LF[i,]),
invS = invS ,TAU = TAU ,LAMBDA = LAMBDA, J = J ,K = K)
print(paste0(c("R: log posterior: ", ll_R)))
print(paste0(c("cpp: log posterior: ", ll_cpp)))
results in 结果是
"R: log posterior: " "15.8951804436067"
"cpp: log posterior: " "15.8951804436067"
I've also verified that the gradients are equal across the two versions. 我还验证了两个版本之间的梯度相等。
d_cpp = grad(cpp_posterior_density, x = as.vector(THETA0_LF[i,]),
Yi = as.vector(Y[i,]),
MUi =as.vector(MU_LF[i,]),
invS = invS ,TAU = TAU ,LAMBDA = LAMBDA, J = J ,K = K)
d_R = grad(lf_posterior_density, x = as.vector(THETA0_LF[i,]),
Yi = as.vector(Y[i,]),
MUi =as.vector(MU_LF[i,]),
invS = invS ,TAU = TAU ,LAMBDA = LAMBDA, J = J ,K = K)
print(paste0(c("R: gradient of log posterior: ", paste(d_R, collapse = ", "))))
print(paste0(c("cpp: gradient of log posterior: ", paste(d_cpp, collapse = ", "))))
results in 结果是
[1] "R: gradient of log posterior: "
[2] "6.49720418347811, 4.67847452089852, 5.93682469664212, 1.47670777676947"
[1] "cpp: gradient of log posterior: "
"6.49720418347811, 4.67847452089852, 5.93682469664212, 1.47670777659075"
However, when I call optim with the Rcpp functions, I fail to get convergence : 但是,当我使用Rcpp函数调用optim时,无法收敛:
#Using Rcpp
out_LF = optim(par = as.vector(THETA0_LF[i,]),
fn = cpp_posterior_density,
gr = cpp_grad_posterior_density,
Yi = as.vector(Y[i,]),
MUi = as.vector(MU_LF[i,]),
invS =invS,
TAU = TAU,
LAMBDA = LAMBDA,
J = J,
K = K,
method = "BFGS",
hessian = TRUE,
control = list(trace = 6)) #does not converge
results in 结果是
initial value 15.895180
final value -4748.586405
The final value must be strictly greater than zero indicating nonconvergence. 最终值必须严格大于零,表示不收敛。 However, with the R functions, I do get convergence:
但是,使用R函数,我确实可以收敛:
#With R functions for density and gradient
out_LF2 = optim(par = as.vector(THETA0_LF[i,]),
fn = lf_posterior_density,
gr = lf_grad_posterior_density,
Yi = as.vector(Y[i,]),
MUi = as.vector(MU_LF[i,]),
invS =invS,
TAU = TAU,
LAMBDA = LAMBDA,
J = J,
K = K,
method = "BFGS",
hessian = TRUE,
control = list(trace = 6)) #converged
results in 结果是
initial value 15.895180
final value 11.980282
Any clue what is going on? 有什么线索吗?
For reproducibility here is a link to a Dropbox folder with the required data (eg, THETA0_LF, Y, MU_LF, etc.) and objective functions and gradients (both the R version and Rcpp version). 为了重现性,这里是指向Dropbox文件夹的链接,其中包含所需的数据(例如THETA0_LF,Y,MU_LF等)以及目标函数和渐变(R版本和Rcpp版本)。 Also included is an R file that reproduces the output above (see "debug-rcpp-for-credi.R").
还包括一个R文件,该文件复制了上面的输出(请参阅“ debug-rcpp-for-credi.R”)。
Below is the Rcpp version of the objective function 以下是目标函数的Rcpp版本
#include <RcppArmadillo.h>
using namespace Rcpp;
// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
double cpp_posterior_density(const arma::vec& THETAi, const arma::vec& Yi, const arma::vec& MUi, const arma::mat& invS, const arma::vec& TAU, const arma::mat& LAMBDA, const int J, const int K) {
int j;
double lodd_j;
double b;
// PYi
arma::vec LT = LAMBDA*THETAi;
arma::vec PYi(J);
for (j = 0; j < J; j++){
lodd_j = LT(j) - TAU(j);
if(lodd_j<0){
b = 0;
} else {
b = lodd_j;
}
PYi(j) = exp(lodd_j-b)/(exp(-b) + exp(lodd_j-b));
}
double ll = 0.0;
for (j = 0; j < J; j++){
if (Yi(j)==1L){
ll += log(PYi(j));
}
if (Yi(j)==0L){
ll += log(1.0-PYi(j));
}
}
//Prior distriubtion
arma::vec dMUi = THETAi-MUi;
double twoprior = as_scalar(dMUi.t()*invS*dMUi);
// Return result
double dpost = -1.0*ll - 0.5*twoprior;
return dpost;
}
And below is the R version of the objective function: 下面是目标函数的R版本:
lf_posterior_density<-function(THETAi, Yi, MUi, invS, TAU, LAMBDA,J,K, weight = NULL){
if (is.null(weight)){weight = rep(1,J)}
# Defined variables
# PYi - J (vector)
# ll - (scalar)
# dMUi - K (vector)
# prior - (scalar)
# Computations
PYi = as.vector(1/(1 + exp(TAU - LAMBDA%*%THETAi))) # J (vector)
# likelihood component
ll = as.numeric(0) #(scalar)
for (j in 1:J){
if (Yi[j] == 1L){ll = ll + weight[j]*log(PYi[j])}
if (Yi[j] == 0L){ll = ll + weight[j]*log(1.0-PYi[j])}
}
# prior distribution component
dMUi = (THETAi - MUi) # K (vector)
prior = as.numeric(-0.5*(dMUi%*%invS%*%dMUi)) #(scalar)
# Return
return(-ll - prior)
}
There is a difference in your objective functions: 您的目标函数有所不同:
ll_cpp = cpp_posterior_density(THETAi = 2*as.vector(THETA0_LF[i,]),
Yi = as.vector(Y[i,]),
MUi =as.vector(MU_LF[i,]),
invS = invS ,TAU = TAU ,LAMBDA = LAMBDA, J = J ,K = K)
ll_R = lf_posterior_density(THETAi = 2*as.vector(THETA0_LF[i,]),
Yi = as.vector(Y[i,]),
MUi =as.vector(MU_LF[i,]),
invS = invS ,TAU = TAU ,LAMBDA = LAMBDA, J = J ,K = K)
print(paste0(c("R: log posterior: ", ll_R)))
#> [1] "R: log posterior: " "22.495400131601"
print(paste0(c("cpp: log posterior: ", ll_cpp)))
#> [1] "cpp: log posterior: " "16.7463952181814"
I have not debugged your source code to find the error. 我尚未调试您的源代码以查找错误。
In such cases it is useful to add REPORT = 1
to the control
list. 在这种情况下,将
REPORT = 1
添加到control
列表中很有用。 For R this gives: 对于R,它给出:
initial value 45.707620
iter 2 value 28.881100
iter 3 value 22.426070
iter 4 value 20.145499
iter 5 value 19.922129
iter 6 value 19.805083
iter 7 value 19.684769
iter 8 value 19.684366
iter 9 value 19.684345
iter 10 value 19.684343
iter 10 value 19.684343
final value 19.684343
converged
For Rcpp: 对于Rcpp:
initial value 45.707620
iter 2 value 23.059207
iter 3 value -33.279972
iter 4 value -77.878965
iter 4 value -77.878965
iter 5 value -93.872445
iter 5 value -93.872445
iter 6 value -2830.594586
iter 6 value -2830.594586
iter 6 value -2830.594586
final value -2830.594586
converged
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