obtim_lbfgs function in Rcppnumerical and RcppEigen with Rcpparmadillo - optimization

Question

[EDIT]
I am looking for the way to use the optim_lbfgs function in Rcppnumerical and RcppEigen with Rcpparmadillo. I follow the way in Rcpp Integration for Numerical Computing Libraries, but it was not working with the error, cannot declare variable 'obj' to be of abstract type 'scoreftn_mns'. But now, I fixed some codes to make it work. I define the beta as Eigen::VectorXd beta(p); as in the Rcppnumerical and convert it to arma::vec in the template(?).
Here's my code that I am trying to do.
// [[Rcpp::depends(RcppEigen)]]
// [[Rcpp::depends(RcppNumerical)]]
// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadillo.h>
#include <RcppNumerical.h>
using namespace Numer;
using namespace arma;
using namespace Rcpp;
// Eigen::Ref<Eigen::VectorXd>
// Eigen::Ref<const Eigen::VectorXd>
class socreftn_mns: public MFuncGrad
{
private:
const arma::vec TIME;
const arma::vec DELTA;
const arma::mat COVARI;
const arma::vec TARGETVEC;
public:
socreftn_mns(const arma::vec Time, const arma::vec Delta, const arma::mat Covari,
const arma::vec targetvec) : TIME(Time), DELTA(Delta), COVARI(Covari), TARGETVEC(targetvec) {}
double f_grad(Constvec& beta, Refvec grad){
arma::vec b_s = arma::vec(beta.data(),beta.size());
int n = COVARI.n_rows;
int p = COVARI.n_cols;
arma::vec zero_vec_p = zeros(p);
arma::mat zero_mat_np = zeros(n,p);
arma::vec tempvec_p(p);
arma::mat tempmat_np(n,p);
arma::vec resid = log(TIME) + COVARI*b_s;
arma::uvec index_resid = sort_index(resid);
TIME(index_resid);
DELTA(index_resid);
COVARI.rows(index_resid);
resid(index_resid);
tempmat_np = zero_mat_np; arma::vec U_inf = zero_vec_p;
for(int it=0; it<n; it++){
tempmat_np = COVARI.row(it) - COVARI.each_row();
U_inf += sum(tempmat_np.each_col()%conv_to<vec>::from((resid>=resid(it))),0).t()*DELTA(it);
}
U_inf = U_inf/n - TARGETVEC;
double objvalue = conv_to<double>::from(sum(pow(U_inf,2)));
double h = 1e-4;
for(int itt=0; itt<p; itt++){
tempvec_p = b_s;
tempvec_p(itt) = tempvec_p(itt) + h;
arma::vec resid_g = log(TIME) + COVARI*tempvec_p;
arma::uvec index_resid_g = sort_index(resid_g);
TIME(index_resid_g);
DELTA(index_resid_g);
COVARI.rows(index_resid_g);
resid(index_resid_g);
tempmat_np = zero_mat_np; arma::vec score_grad = zero_vec_p;
for(int it=0; it<n; it++){
tempmat_np = COVARI.row(it) - COVARI.each_row();
score_grad += sum(tempmat_np.each_col()%conv_to<vec>::from((resid_g>=resid_g(it))),0).t()*DELTA(it);
}
score_grad = score_grad/n - TARGETVEC;
double score_objvalue = conv_to<double>::from(sum(pow(score_grad,2)));
grad(itt) = (score_objvalue-objvalue)/h;
}
// beta = Eigen::Ref(b_s);
return objvalue;
}
};
// [[Rcpp::export]]
Rcpp::NumericVector aftsrr_bfgs(arma::vec Time, arma::vec Delta, arma::mat Covari, arma::vec targetvec){
const arma::vec TIME = Time;
const arma::vec DELTA = Delta;
const arma::mat COVARI = Covari;
const arma::vec TARGETVEC = targetvec;
int p = COVARI.n_cols;
// Score Function
socreftn_mns obj(TIME, DELTA, COVARI, TARGETVEC);
// Initial Guess
Eigen::VectorXd beta(p);
beta.setOnes();
double fopt;
int status = optim_lbfgs(obj, beta, fopt);
if(status < 0)
Rcpp::stop("fail to converge");
return Rcpp::wrap(beta);
}
And, this is R code that is working.
library(Rcpp)
library(RcppArmadillo)
library(RcppEigen)
library(RcppNumerical)
library(survival)
library(aftgee)
sourceCpp("C:/Users/mattw/Documents/paper_wj/exercise/aftsrr_wj_cpp/aftsrr_wj/optim_bfgs.cpp")
U_beta_r_non = function(beta,Time,Delta,Covari) {
n=length(Time)
p=ncol(Covari)
e_i_beta = as.vector(log(Time) + Covari %*% beta)
order_resid = order(e_i_beta)
Time = Time[order_resid]
Covari = matrix(Covari[order_resid,],nrow = n)
Delta = Delta[order_resid]
e_i_beta = e_i_beta[order_resid]
U_beta = list(NA)
for (i in 1:n) {
U_beta[[i]] = colSums(Delta[i]*t(Covari[i,]-t(Covari))*(e_i_beta>=e_i_beta[i]))
}
U_beta = Reduce('+',U_beta)/n
U_beta = sum(U_beta^2)
# grad = c(); h = 1e-4;
# for (it in 1:p) {
# beta_g = beta;
# beta_g[it] = beta[it] + h
#
# e_i_beta = as.vector(log(Time) + Covari %*% beta_g)
#
# order_resid = order(e_i_beta)
#
# Time = Time[order_resid]
# Covari = matrix(Covari[order_resid,],nrow = n)
# Delta = Delta[order_resid]
# e_i_beta = e_i_beta[order_resid]
#
# grad_beta = list(NA);
# for (i in 1:n) {
# grad_beta[[i]] = colSums(Delta[i]*t(Covari[i,]-t(Covari))*(e_i_beta>=e_i_beta[i]))
# }
# grad_beta = Reduce('+',grad_beta)/n
# grad_beta = sum(grad_beta^2)
#
# grad[it] = (grad_beta-U_beta)/h
# }
# return(U_beta)
return(sum(U_beta^2))
# return(grad)
}
#------------------------DATA GENERATION----------------------
set.seed(1)
n=300
beta_0=1
gamma_0=0.5
Z1=matrix(rnorm(n,3,1),nrow=n)
Z2=matrix(rexp(n,5),nrow=n)
T_aft=as.vector(exp(-beta_0*Z1-gamma_0*Z2+rnorm(n,5,1)))
C_aft=as.vector(exp(-beta_0*Z1-gamma_0*Z2+rnorm(n,6,1)))
X_aft=C_aft*(T_aft>C_aft)+T_aft*(T_aft<=C_aft)
D_aft=0*(T_aft>C_aft)+1*(T_aft<=C_aft)
table(D_aft)
beta_aftsrr=-aftsrr(Surv(X_aft,D_aft)~Z1+Z2)$beta;beta_aftsrr
init_beta = rep(0,2)
cpp_bfgs_esti = aftsrr_bfgs(init_beta,X_aft,D_aft,cbind(Z1,Z2),rep(0,2));cpp_bfgs_esti
optim_lbfgsb = optim(init_beta,function(x){U_beta_r_non(x,X_aft,D_aft,cbind(Z1,Z2))},method = "L-BFGS-B")$par;optim_lbfgsb
Now, it is working, however, it seems that it just gives the init_beta without any calculation in the R example. As some people say that it is not possible to use the above armadillo codes, I am studying the Eigen library. However, I am not familiar with the computing it is hard to convert it. So, hopefully, there's a way to use it with some modification. Thanks!
Any comments will be helpful.

Answer 1

The rest of the error message that I am getting when compiling your code is of interest:
optim_num.cpp: In function ‘Rcpp::NumericVector aftsrr_bfgs(arma::vec, arma::vec, arma::mat, arma::vec)’:
optim_num.cpp:86:16: error: cannot declare variable ‘obj’ to be of abstract type ‘socreftn_mns’
socreftn_mns obj(TIME, DELTA, COVARI, TARGETVEC);
^~~
optim_num.cpp:11:7: note: because the following virtual functions are pure within ‘socreftn_mns’:
class socreftn_mns: public MFuncGrad
^~~~~~~~~~~~
In file included from /usr/local/lib/R/site-library/RcppNumerical/include/integration/wrapper.h:13:0,
from /usr/local/lib/R/site-library/RcppNumerical/include/RcppNumerical.h:16,
from optim_num.cpp:6:
/usr/local/lib/R/site-library/RcppNumerical/include/integration/../Func.h:52:20: note: virtual double Numer::MFuncGrad::f_grad(Numer::Constvec&, Numer::Refvec)
virtual double f_grad(Constvec& x, Refvec grad) = 0;
^~~~~~
In essence that means that your class socreftn_mns is derived from the abstract class MFuncGrad. This class is abstract since it does not contain a definition for the method f_grad(Constvec& x, Refvec grad). You try to define this by defining the method f_grad(arma::vec& b_s, arma::vec grad), but due to the different function signature, the virtual function is not overloaded. Hence your class is also abstract.
If you use the same signature, things should work out. The required types are defined in terms of Eigen objects:
// Reference to a vector
typedef Eigen::Ref<Eigen::VectorXd> Refvec;
typedef const Eigen::Ref<const Eigen::VectorXd> Constvec;
So you will have to convert back and forth between Aramdillo and Eigen constructs.