TEEM Rcpp: Accuracy & Speed Comparison

source("/Users/nicholeyang/Desktop/ed/code/EM.R")
load("/Users/nicholeyang/Desktop/ed/data/zstrong.RData")
library(mvtnorm)

Result comparison (A simulate data example)

# function for simulate data
sim.data <- function(n, w, U) {
   # w: the true weight
   # mu: a k*m matrix 
   # dimension of an obs. 
   m = nrow(U[[1]])
   # k: number of classes
   k = length(w)

   # generate true class variable  
   Z = sample(1:k, n, prob = w, replace = T)  

   # store simulated data
   X = matrix(NA, ncol = m, nrow = n)

   for (i in 1:n){
     # true class for obs. i 
     j = Z[i]
     X[i, ] = rmvnorm(1,mean = rep(0,m), sigma = U[[j]])
   }
   res = list(X = X, Z = Z)
   return(res)
}
# simulate data 
set.seed(215)
n <- 1e4
w <- c(0.6,0.3,0.1)
I <- diag(c(1,1,1))
U <- list(U1 = matrix(1,3,3)+I,
          U2 = crossprod(rbind(c(1,1,0),
                               c(0,1,1)))+I,
          U3 = rbind(2*diag(3))+I)
dt <- sim.data(n, w, U)
X  <- dt$X
# initialization
w.init <- c(0.3, 0.4, 0.3)
U.init <-
  list(U1 = matrix(c(1, 0.1, 0.3, 0.1, 1, 0, 0.3, 0 ,1), ncol = 3, nrow =3), 
      U2 = matrix(c(1, 0.1, 0.9, 0.1, 1, 0, 0.9, 0 ,1), ncol = 3, nrow =3),
      U3 = diag(3))



# R version
res.R <- EM.fit(X, w.init, U.init, maxiter = 5000, tol = 1e-7, verbose = FALSE)
## Rcpp version
res.cpp = mashr:::fit_teem_rcpp(X,w.init,simplify2array(U.init), 5000, 1e-7, FALSE)
# R version result
res.R$w
res.R$U
tail(res.R$progress$obj)
length(res.R$progress$obj)

# Rcpp result
res.cpp$w
res.cpp$U
tail(res.cpp$objective)
length(res.cpp$objective)

Speed comparison (A real data example)

# The previous Gtex 5 tissue data 
set.seed(215)
test_index = sample(1:nrow(zstrong), 3000, replace = FALSE)
test = zstrong[test_index, ]
train = zstrong[-test_index,]
dim(test)
dim(train)

# Choose components = 5 to fit; Use random initialization
s = 5
w.true = rep(1/s, s)
indx = sample(1:s, nrow(train), replace = TRUE, prob = w.true)
U.init = c()
w.init = rep(NA, s)

# Calculate the empirical covariance matrix. The initialization for U = empirical Sigma + I
for (i in 1:s){
  w.init[i] = sum(indx ==i)/nrow(train)
  dt = train[indx ==i, ]
  Sigma = matrix(t(dt) %*% dt/nrow(dt), nrow = 5, ncol = 5)
  U.init[[i]] = Sigma + diag(5)
}
# ED in mashr
train.mash = mashr:::mash_set_data(train, Shat = 1)
U.pca = mashr:::cov_pca(train.mash, 4)

start= proc.time()
res.mashr = mashr:::cov_ed(train.mash, U.pca)
end = proc.time()
runtime.mashr = end - start


# newEM R version
start= proc.time()
res.R = EM.fit(train, w.init, U.init, maxiter = 5000, tol = 1e-7, verbose = FALSE)
end = proc.time()
runtime.R = end - start

# newEM CPP 
start= proc.time()
res.cpp = mashr:::fit_teem_rcpp(train , w.init,simplify2array(U.init), 5000, 1e-7, FALSE)
end = proc.time()
runtime.cpp = end - start
runtime.mashr
runtime.R
runtime.cpp


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mashr documentation built on May 24, 2021, 1:06 a.m.