+++ /dev/null
-optimBeta <- function(N, n, K, p, beta, b, link, ncores)
-{
- library(morpheus)
- res <- multiRun(
- list(n=n,p=p,beta=beta,b=b,optargs=list(K=K,link=link)),
- list(
- # morpheus
- function(fargs) {
- library(morpheus)
- K <- fargs$optargs$K
- M <- computeMoments(fargs$X, fargs$Y)
- fargs$optargs$M <- M
- mu <- computeMu(fargs$X, fargs$Y, fargs$optargs)
- op <- optimParams(K,link,fargs$optargs)
- x_init <- c( rep(1/K,K-1), as.double(mu), rep(0,K) )
- do.call(rbind, op$run(x_init))
- },
- # flexmix
- function(fargs) {
- library(flexmix)
- source("../patch_Bettina/FLXMRglm.R")
- K <- fargs$optargs$K
- dat <- as.data.frame( cbind(fargs$Y,fargs$X) )
- fm <- flexmix( cbind(V1, 1-V1) ~ .-V1, data=dat, k=K,
- model = FLXMRglm(family = binomial(link = link)) )
- p <- mean(fm@posterior[["scaled"]][,1])
- out <- refit(fm)
- beta_b <- sapply( seq_len(K), function(i) {
- as.double( out@components[[1]][[i]][,1] )
- } )
- rbind(p, beta_b[2:nrow(beta_b),], beta_b[1,])
- } ),
- prepareArgs = function(fargs, index) {
- library(morpheus)
- io = generateSampleIO(fargs$n, fargs$p, fargs$beta, fargs$b, fargs$optargs$link)
- fargs$X = io$X
- fargs$Y = io$Y
- fargs$optargs$K = ncol(fargs$beta)
- fargs$optargs$M = computeMoments(io$X,io$Y)
- fargs
- }, N=N, ncores=ncores, verbose=TRUE)
- p <- c(p, 1-sum(p))
- for (i in 1:2)
- res[[i]] <- alignMatrices(res[[i]], ref=rbind(p,beta,b), ls_mode="exact")
- res
-}
-
-#model = binomial; default values:
-link = "logit"
-N <- 10
-d <- 2
-n <- 1e4
-ncores <- 1
-
-cmd_args <- commandArgs()
-for (arg in cmd_args)
-{
- if (substr(arg,1,1)!='-') {
- spl <- strsplit(arg,'=')[[1]]
- if (spl[1] == "nc") {
- ncores <- as.integer(spl[2])
- } else if (spl[1] == "N") {
- N <- as.integer(spl[2])
- } else if (spl[1] == "n") {
- n <- as.integer(spl[2])
- } else if (spl[1] == "d") {
- d <- as.integer(spl[2])
- } else if (spl[1] == "link") {
- link <- spl[2]
- }
- }
-}
-
-if (d == 2) {
- K <- 2
- p <- .5
- b <- c(-.2, .5)
- beta <- matrix( c(1,-2, 3,1), ncol=K )
-} else if (d == 5) {
- K <- 2
- p <- .5
- b <- c(-.2, .5)
- beta <- matrix( c(1,2,-1,0,3, 2,-3,0,1,0), ncol=K )
-} else if (d == 10) {
- K <- 3
- p <- c(.3, .3)
- b <- c(-.2, 0, .5)
- beta <- matrix( c(1,2,-1,0,3,4,-1,-3,0,2, 2,-3,0,1,0,-1,-4,3,2,0, -1,1,3,-1,0,0,2,0,1,-2), ncol=K )
-} else if (d == 20) {
- K <- 3
- p <- c(.3, .3)
- b <- c(-.2, 0, .5)
- beta <- matrix( c(1,2,-1,0,3,4,-1,-3,0,2,2,-3,0,1,0,-1,-4,3,2,0, -1,1,3,-1,0,0,2,0,1,-2,1,2,-1,0,3,4,-1,-3,0,2, 2,-3,0,1,0,-1,-4,3,2,0,1,1,2,2,-2,-2,3,1,0,0), ncol=K )
-}
-
-mr <- optimBeta(N, n, K, p, beta, b, link, ncores)
-mr_params <- list("N"=N, "n"=n, "K"=K, "d"=d, "link"=link,
- "p"=c(p,1-sum(p)), "beta"=beta, "b"=b)
-
-save("mr", "mr_params", file=paste("multirun_",d,"_",link,".RData",sep=""))
+++ /dev/null
-library(morpheus)
-
-#model = binomial
-K <- 2
-p <- .5
-b <- c(-.2, .5)
-# Default values:
-link = "logit"
-N <- 100
-d <- 2
-n <- 1e4
-ncores <- 1
-nstart <- 3 #nstart-1 random starting points for each MC run
-
-cmd_args <- commandArgs()
-for (arg in cmd_args)
-{
- if (substr(arg,1,1)!='-')
- {
- spl <- strsplit(arg,'=')[[1]]
- if (spl[1] == "nc") {
- ncores <- as.integer(spl[2])
- } else if (spl[1] == "N") {
- N <- as.integer(spl[2])
- } else if (spl[1] == "n") {
- n <- as.integer(spl[2])
- } else if (spl[1] == "d") {
- d <- as.integer(spl[2])
- } else if (spl[1] == "link") {
- link <- spl[2]
- } else if (spl[1] == "nstart") {
- nstart <- spl[2]
- }
- }
-}
-betas <- list(
- matrix( c(1,-2, 3,1), ncol=K ), #d=2
- matrix( c(1,2,-1,0,3, 2,-3,0,1,0), ncol=K ), #d=5
- matrix( c(1,2,-1,0,3,4,-1,-3,0,2, 2,-3,0,1,0,-1,-4,3,2,0), ncol=K ) ) #d=10
-beta <- betas[[ ifelse( d==2, 1, ifelse(d==5,2,3) ) ]]
-
-ms <- multiRun(
- list(n=n,p=p,beta=beta,b=b,optargs=list(K=K,link=link,nstart=nstart)), list(
- function(fargs) {
- # 1 start
- library(morpheus)
- K <- fargs$optargs$K
- op <- optimParams(K, fargs$optargs$link, fargs$optargs)
- x_init <- c(rep(1/K,K-1), as.double(fargs$mu), rep(0,K))
- do.call(rbind,op$run(x_init))
- },
- function(fargs) {
- # B starts
- library(morpheus)
- K <- fargs$optargs$K
- op <- optimParams(K, fargs$optargs$link, fargs$optargs)
- best_val <- Inf
- best_par <- list()
- for (i in 1:fargs$optargs$nstart)
- {
- x_init <- c(rep(1/K,K-1), as.double(i*fargs$mu), rep(0,K))
- par <- op$run(x_init)
- val <- op$f( op$linArgs(par) )
- if (val < best_val)
- {
- best_par <- par
- best_val <- val
- }
- }
- do.call(rbind,best_par)
- }),
- prepareArgs = function(fargs) {
- library(morpheus)
- io = generateSampleIO(fargs$n, fargs$p, fargs$beta, fargs$b, fargs$optargs$link)
- fargs$optargs$M <- computeMoments(io$X, io$Y)
- mu <- computeMu(io$X, io$Y, fargs$optargs)
- fargs$mu <- mu
- }, N=N, ncores=ncores, verbose=TRUE)
-for (i in 1:2)
- ms[[i]] <- alignMatrices(ms[[i]], ref=rbind(p,beta,b), ls_mode="exact")
-
-ms_params <- list("N"=N, "nc"=ncores, "n"=n, "K"=K, "link"=link,
- "p"=p, "beta"=beta, "b"=b, "nstart"=nstart)
-
-save(ms, ms_params, file="multistart.RData")
+++ /dev/null
-#!/bin/bash
-
-#PBS -l nodes=1:ppn=15,mem=8gb,pmem=512mb
-#PBS -j oe
-
-#PBS -o .output
-rm -f .output
-
-WORKDIR=/workdir2/auder/morpheus/reports
-cd $WORKDIR
-
-module load R
-
-# arg --vanilla maybe possible on cluster
-for d in 10 20; do
- for link in "logit" "probit"; do
- R --slave --args N=1000 n=1e5 nc=15 d=$d link=$link <accuracy.R >out$d$link 2>&1
- done
-done
+++ /dev/null
-#!/bin/bash
-
-#PBS -l nodes=1:ppn=16,mem=8gb,pmem=512mb
-#PBS -j oe
-
-#PBS -o .output
-rm -f .output
-
-WORKDIR=/workdir2/auder/morpheus/reports
-cd $WORKDIR
-
-module load R
-
-# arg --vanilla maybe possible on cluster
-R --slave --args N=1000 nc=16 link=logit <timings.R >out 2>&1
+++ /dev/null
-# flexmix optimization to get beta
-fmOptim <- function(X, Y, K, link)
-{
- dat <- as.data.frame( cbind(Y,X) )
- fm <- flexmix( cbind(Y, 1-Y) ~ .-Y, data=dat, k=K,
- model = FLXMRglm(family = binomial(link = link)) )
- p <- mean(fm@posterior[["scaled"]][,1])
- out <- refit(fm)
- beta_b <- sapply( seq_len(K), function(i) as.double( out@components[[1]][[i]][,1] ) )
- list("p"=p, "beta"=beta_b[2:nrow(beta_b),], "b"=beta_b[1,])
- NULL
-}
-
-# Our package optimization for beta (using mu as a starting point)
-ourOptim <- function(X, Y, K, link)
-{
- M <- computeMoments(X, Y)
- mu <- computeMu(X, Y, list(K=K,M=M))
- x_init = c(1/2, as.double(mu), c(0,0))
- optimParams(K, link, list(M=M))$run(x_init)
- NULL
-}
-
-# Get timings for both methods with the same beta matrix
-getTimings <- function(link)
-{
- timings <- list('fm'=matrix(0,nrow=10,ncol=7),'our'=matrix(0,nrow=10,ncol=7))
- K <- 2
- for (d in c(2,5,10))
- {
- beta <- matrix(runif(d*K,min=-5,max=5),ncol=K)
- for (logn in 4:6)
- {
- n <- 10^logn
- io <- generateSampleIO(n, rep(1/K,K-1), beta, runif(K), link)
- timings[['fm']][d,logn] <- system.time(fmOptim(io$X,io$Y,K,link))[3]
- timings[['our']][d,logn] <- system.time(ourOptim(io$X,io$Y,K,link))[3]
- }
- }
- timings
-}
-
-#model = binomial
-link <- "logit"
-ncores <- 1
-N <- 100
-
-cmd_args <- commandArgs()
-for (arg in cmd_args)
-{
- if (substr(arg,1,1)!='-')
- {
- spl <- strsplit(arg,'=')[[1]]
- if (spl[1] == "link") {
- link <- spl[2]
- } else if (spl[1] == "nc") {
- ncores <- as.integer(spl[2])
- } else if (spl[1] == "N") {
- N <- as.integer(spl[2])
- }
- }
-}
-
-library(morpheus)
-library(flexmix)
-source("../patch_Bettina/FLXMRglm.R")
-
-tm <-
- if (ncores == 1) {
- lapply(1:N, function(i) {
- print(paste("Run",i))
- getTimings(link)
- })
- } else {
- library(parallel)
- mclapply(1:N, function(i) {
- print(paste("Run",i))
- getTimings(link)
- },
- mc.preschedule=FALSE, mc.cores=ncores)
- }
-tm_params <- list("link"=link, "N"=N, "nc"=ncores)
-
-save("tm", "tm_params", file="timings.RData")
projects / morpheus.git / commitdiff