####### Undocumented "tpx" utility functions #########
## ** Only referenced from topics.R
## check counts (can be an object from tm, slam, or a simple co-occurance matrix)
CheckCounts <- function(fcounts){
if(class(fcounts)[1] == "TermDocumentMatrix"){ fcounts <- t(fcounts) }
if(is.null(dimnames(fcounts)[[1]])){ dimnames(fcounts)[[1]] <- paste("doc",1:nrow(fcounts)) }
if(is.null(dimnames(fcounts)[[2]])){ dimnames(fcounts)[[2]] <- paste("wrd",1:ncol(fcounts)) }
empty <- row_sums(fcounts) == 0
if(sum(empty) != 0){
fcounts <- fcounts[!empty,]
cat(paste("Removed", sum(empty), "blank documents.\n")) }
return(as.simple_triplet_matrix(fcounts))
}
## theta initialization
## ** main workhorse function. Only Called by the above wrappers.
## topic estimation for a given number of topics (taken as ncol(theta))
ord.tpxfit <- function(fcounts, X, param_set, del_beta, a_mu, b_mu, ztree_options, tol, verb,
admix, grp, tmax, wtol, qn, acc, adapt.method)
{
## inputs and dimensions
if(!inherits(X,"simple_triplet_matrix")){ stop("X needs to be a simple_triplet_matrix") }
mu_tree_set <- mu_tree_build_set(param_set);
K <- length(param_set);
levels <- length(mu_tree_set[[1]]);
theta <- do.call(cbind, lapply(1:K, function(l) (mu_tree_set[[l]][[levels]]/(mu_tree_set[[l]][[1]]))));
n <- nrow(X)
p <- ncol(X)
m <- row_sums(X)
## recycle these in tpcweights to save time
xvo <- X$v[order(X$i)]
wrd <- X$j[order(X$i)]-1
doc <- c(0,cumsum(as.double(table(factor(X$i, levels=c(1:nrow(X)))))))
## Initialize
omega <- ord.tpxweights(n=n, p=p, xvo=xvo, wrd=wrd, doc=doc,
start=ord.tpxOmegaStart(X,theta), theta=theta)
# omega <- matrix(1/K, ncol=K, nrow=n)
## tracking
iter <- 0
dif <- tol+1+qn
update <- TRUE
if(verb){
cat("log posterior increase: " )
digits <- max(1, -floor(log(tol, base=10))) }
Y <- NULL # only used for qn > 0
Q0 <- col_sums(X)/sum(X)
L <- ord.tpxlpost(fcounts, omega_iter = omega, theta_iter = theta,
del_beta, a_mu, b_mu, ztree_options=1,
adapt.method=adapt.method);
# if(is.infinite(L)){ L <- sum( (log(Q0)*col_sums(X))[Q0>0] ) }
## Iterate towards MAP
#tmax <- 5000
while( update && iter < tmax ){
## sequential quadratic programming for conditional Y solution
move <- ord.tpxEM(X=X, m=m, theta=theta, omega=omega, method_admix = 1)
if(admix && wtol > 0){ Wfit <- ord.tpxweights(n=nrow(X), p=ncol(X), xvo=xvo, wrd=wrd, doc=doc,
start=move$omega, theta=move$theta, verb=0, nef=TRUE, wtol=wtol, tmax=20) }
if(!admix | wtol <=0){ Wfit <- move$omega }
if(!acc){
# z_tree <- z_tree_construct(fcounts, omega_iter = move$omega, theta_iter = t(move$theta), ztree_options = 1);
# param_set_fit <- param_extract_ztree(z_tree, del_beta, a_mu, b_mu);
L_new <- ord.tpxlpost(fcounts, move$omega, move$theta,
del_beta, a_mu, b_mu, ztree_options=1,
adapt.method=adapt.method)
QNup <- list("move"=move, "L"=L_new, "Y"=NULL)
Y <- QNup$Y
}
## joint parameter EM update
## move <- tpxEM(X=X, m=m, theta=theta, omega=Wfit, alpha=alpha, admix=admix, grp=grp)
if(acc){
## quasinewton acceleration
QNup <- ord.tpxQN(move=move, fcounts=fcounts, Y=Y, del_beta=del_beta, a_mu=a_mu, b_mu=b_mu,
ztree_options=ztree_options, adapt.method = adapt.method,
verb=verb, admix=admix, grp=grp, doqn=qn-dif)
move <- QNup$move
Y <- QNup$Y
}
if(adapt.method=="beta"){
## Construct the MRA of z-values given the current iterates of omega /theta
z_tree <- z_tree_construct(fcounts, omega_iter = move$omega,
theta_iter = t(move$theta),
ztree_options = 1);
## Extract the beta and mu_0 parameters from the MRA tree
param_set_fit <- param_extract_ztree(z_tree, del_beta, a_mu, b_mu);
## Build a MRA of mu-tree sets (set of clusters)
mu_tree_set_fit <- mu_tree_build_set(param_set_fit);
## Extract the theta updates from the MRA tree
levels <- length(mu_tree_set_fit[[1]]);
theta_fit <- do.call(cbind, lapply(1:K,
function(l) mu_tree_set_fit[[l]][[levels]]/mu_tree_set_fit[[l]][[1]]));
move <- list(theta=move$theta, omega=move$omega);
}
if(adapt.method=="smash"){
row_total <- rowSums(fcounts);
z_leaf_est <- round(sweep(move$theta, MARGIN=2, colSums(sweep(move$omega, MARGIN = 1,
row_total, "*")), "*"));
# plot(z_leaf_est[,1])
# plot(z_leaf_est[,2])
z_leaf_smoothed <- do.call(cbind, lapply(1:dim(z_leaf_est)[2], function(k)
{
if(sum(z_leaf_est[,k]) > 0){
out <- suppressMessages(smashr::smash.poiss(z_leaf_est[,k], ashparam = list(control=list(maxiter=500))))
return(out)
}else{
return(z_leaf_est[,k])
}
}))
# plot(z_leaf_smoothed[,1])
# plot(z_leaf_smoothed[,2])
theta_smoothed <- ordtpx::ord.normalizetpx(z_leaf_smoothed, byrow=FALSE)
move <- list(theta=theta_smoothed, omega=move$omega)
QNup$L <- ord.tpxlpost(fcounts, move$omega, move$theta,
del_beta, a_mu, b_mu, ztree_options=1,
adapt.method=adapt.method)
}
if(adapt.method=="bash"){
row_total <- rowSums(fcounts);
z_leaf_est <- round(sweep(move$theta, MARGIN=2, colSums(sweep(move$omega, MARGIN = 1, row_total, "*")), "*"));
z_leaf_smoothed <- do.call(cbind, lapply(1:dim(z_leaf_est)[2], function(k)
{
if(sum(z_leaf_est[,k])>0){
out <- suppressMessages(binshrink(z_leaf_est[,k])$est)
return(out)
}else{
return(z_leaf_est[,k])
}
}))
theta_smoothed <- ordtpx::ord.normalizetpx(z_leaf_smoothed, byrow=FALSE)
move <- list(theta=theta_smoothed, omega=omega)
QNup$L <- ord.tpxlpost(fcounts, move$omega, move$theta,
del_beta, a_mu, b_mu, ztree_options=1,
adapt.method=adapt.method)
}
# plot(move$theta[,1], type="l")
# plot(move$theta[,2], type="l")
# barplot(t(move$omega),
# col = 2:(K+1),
# axisnames = F, space = 0, border = NA,
# main=paste("No. of clusters=", K),
# las=1, ylim=c(0,1), cex.axis=1.5, cex.main=1.4)
# if(adapt.method!="bash"){
if(QNup$L < L){ # happens on bad Wfit, so fully reverse
if(verb > 10){ cat("_reversing a step_") }
# cat("We enter Qnup$L < L")
# cat("\n")
##move <- tpxEM(X=X, m=m, theta=theta, omega=omega, alpha=alpha, admix=admix, grp=grp)
if(adapt.method=="beta"){
move <- ord.tpxEM(X=X, m=m, theta=theta, omega=omega)
z_tree <- z_tree_construct(fcounts, omega_iter = move$omega, theta_iter = t(move$theta), ztree_options = 1);
param_set_fit <- param_extract_ztree(z_tree, del_beta, a_mu, b_mu);
mu_tree_set_fit <- mu_tree_build_set(param_set_fit);
levels <- length(mu_tree_set_fit[[1]]);
theta_fit <- do.call(cbind, lapply(1:K, function(l) mu_tree_set_fit[[l]][[levels]]/mu_tree_set_fit[[l]][[1]]));
move <- list(theta=theta_fit, omega=omega);
QNup$L <- ord.tpxlpost(fcounts, move$omega, move$theta,
del_beta, a_mu, b_mu, ztree_options=1,
adapt.method=adapt.method)
}
if(adapt.method=="smash"){
move <- ord.tpxEM(X=X, m=m, theta=theta, omega=omega)
z_leaf_est <- round(sweep(move$theta, MARGIN=2, colSums(sweep(move$omega, MARGIN = 1, row_total, "*")), "*"));
z_leaf_smoothed <- do.call(cbind, lapply(1:dim(z_leaf_est)[2], function(k)
{
if(sum(z_leaf_est[,k])>0){
out <- suppressMessages(smashr::smash.poiss(z_leaf_est[,k]))
return(out)
}else{
return(z_leaf_est[,k])
}
}))
theta_smoothed <- ordtpx::ord.normalizetpx(z_leaf_smoothed, byrow=FALSE)
move <- list(theta=theta_smoothed, omega=omega)
QNup$L <- ord.tpxlpost(fcounts, move$omega, move$theta,
del_beta, a_mu, b_mu, ztree_options=1,
adapt.method=adapt.method)
}
if(adapt.method=="bash"){
move <- ord.tpxEM(X=X, m=m, theta=theta, omega=omega)
z_leaf_est <- round(sweep(move$theta, MARGIN=2, colSums(sweep(move$omega, MARGIN = 1, row_total, "*")), "*"));
z_leaf_smoothed <- do.call(cbind, lapply(1:dim(z_leaf_est)[2], function(k)
{
if(sum(z_leaf_est[,k])>0){
out <- suppressMessages(binshrink(z_leaf_est[,k])$est)
return(out)
}else{
return(z_leaf_est[,k])
}
}))
theta_smoothed <- ordtpx::ord.normalizetpx(z_leaf_smoothed, byrow=FALSE)
move <- list(theta=theta_smoothed, omega=omega)
QNup$L <- ord.tpxlpost(fcounts, move$omega, move$theta,
del_beta, a_mu, b_mu, ztree_options=1,
adapt.method=adapt.method)
}
}
#}
## calculate dif
dif <- (QNup$L-L)
L <- QNup$L
## check convergence
if(abs(dif) < tol){
if(sum(abs(theta-move$theta)) < tol){ update = FALSE } }
## print
if(verb>0 && iter>0){
cat( paste( round(abs(dif),digits), #" (", sum(abs(theta-move$theta)),")",
", ", sep="") ) }
## heartbeat for long jobs
if(((iter+1)%%1000)==0){
cat(sprintf("p %d iter %d diff %g\n",
nrow(theta), iter+1,round(dif))) }
## iterate
iter <- iter+1
theta <- move$theta;
omega <- move$omega
}
## final log posterior
L <- ord.tpxlpost(fcounts, omega, theta, del_beta, a_mu, b_mu,
ztree_options=1, adapt.method=adapt.method);
## summary print
if(verb>0){
cat("done.")
if(verb>1) { cat(paste(" (L = ", round(L,digits), ")", sep="")) }
cat("\n")
}
out <- list(theta=theta, omega=omega, K=K, L=L, iter=iter)
invisible(out) }
## ** called from topics.R (predict) and tpx.R
## Conditional solution for topic weights given theta
ord.tpxweights <- function(n, p, xvo, wrd, doc, start, theta, verb=FALSE, nef=TRUE, wtol=10^{-5}, tmax=1000)
{
theta[theta==1] <- 1 - 1e-14;
theta[theta==0] <- 1e-14;
theta <- ord.normalizetpx(theta, byrow = FALSE)
K <- ncol(theta)
start[start == 0] <- 0.1/K
start <- start/rowSums(start)
omega <- .C("Romega",
n = as.integer(n),
p = as.integer(p),
K = as.integer(K),
doc = as.integer(doc),
wrd = as.integer(wrd),
X = as.double(xvo),
theta = as.double(theta),
W = as.double(t(start)),
nef = as.integer(nef),
tol = as.double(wtol),
tmax = as.integer(tmax),
verb = as.integer(verb),
PACKAGE="ordtpx")
return(t(matrix(omega$W, nrow=ncol(theta), ncol=n))) }
## ** Called only in tpx.R
ord.tpxEM <- function(X, m, theta, omega, method_admix=1){
n <- nrow(X)
p <- ncol(X)
K <- ncol(theta)
if(method_admix==1){
lambda <- omega%*%t(theta);
counts2 <- as.matrix(X);
temp <- counts2/lambda;
t_matrix <- (t(temp) %*% omega)*theta;
w_matrix <- (temp %*% theta)*omega;
theta <- normalizetpx(t_matrix, byrow=FALSE)
omega <- normalizetpx(w_matrix+(1/(n*K)), byrow=TRUE)
full_indices <- which(omega==1, arr.ind=T)
full_indices_rows <- unique(full_indices[,1]);
omega[full_indices_rows,] <- omega[full_indices_rows,] + (1/(n*K));
omega <- normalizetpx(omega, byrow=TRUE)
}
if(method_admix==2){ Xhat <- (X$v/tpxQ(theta=theta, omega=omega, doc=X$i, wrd=X$j))*(omega[X$i,]*theta[X$j,])
Zhat <- .C("Rzhat", n=as.integer(n), p=as.integer(p), K=as.integer(K), N=as.integer(nrow(Xhat)),
Xhat=as.double(Xhat), doc=as.integer(X$i-1), wrd=as.integer(X$j-1),
zj = as.double(rep(0,K*p)), zi = as.double(rep(0,K*n)), PACKAGE="maptpx")
theta <- normalizetpx(matrix(Zhat$zj, ncol=K), byrow=FALSE)
omega <- normalizetpx(matrix(Zhat$zi+1/K, ncol=K)) }
return(list(theta=theta, omega=omega))
}
## Quasi Newton update for q>0
ord.tpxQN <- function(move, fcounts, Y, del_beta, a_mu, b_mu,
ztree_options, adapt.method,
verb, admix, grp, doqn)
{
## always check likelihood
K <- ncol(move$theta);
L <- ord.tpxlpost(fcounts, move$omega, move$theta, del_beta,
a_mu, b_mu, ztree_options, adapt.method=adapt.method)
if(doqn < 0){ return(list(move=move, L=L, Y=Y)) }
temp_omega <- move$omega;
temp_theta <- move$theta;
temp_omega[temp_omega >= 1 - 1e-14]=1 - 1e-14
temp_omega[temp_omega <= 1e-14]=1e-14
temp_theta[temp_theta >= 1 - 1e-14]=1 - 1e-14
temp_theta[temp_theta <= 1e-14]=1e-14
temp_omega <- ord.normalizetpx(temp_omega, byrow=TRUE)
temp_theta <- ord.normalizetpx(temp_theta, byrow=FALSE)
## update Y accounting
Y <- cbind(Y, tpxToNEF(theta=temp_theta, omega=temp_omega))
if(ncol(Y) < 3){ return(list(Y=Y, move=move, L=L)) }
if(ncol(Y) > 3){ warning("mis-specification in quasi-newton update; please report this bug.") }
## Check quasinewton secant conditions and solve F(x) - x = 0.
U <- as.matrix(Y[,2]-Y[,1])
V <- as.matrix(Y[,3]-Y[,2])
sUU <- sum(U^2)
sVU <- sum(V*U)
Ynew <- Y[,3] + V*(sVU/(sUU-sVU))
qnup <- tpxFromNEF(Ynew, n=nrow(move$omega),
p=nrow(move$theta), K=ncol(move$theta))
## check for a likelihood improvement
Lqnup <- try(ord.tpxlpost(fcounts, qnup$omega, qnup$theta,
del_beta, a_mu, b_mu, ztree_options,
adapt.method=adapt.method), silent=TRUE)
if(inherits(Lqnup, "try-error")){
if(verb>10){ cat("(QN: try error) ") }
return(list(Y=Y[,-1], move=move, L=L)) }
if(verb>10){ cat(paste("(QN diff ", round(Lqnup-L,3), ")\n", sep="")) }
if(Lqnup < L){
return(list(Y=Y[,-1], move=move, L=L)) }
else{
L <- Lqnup
Y <- cbind(Y[,2],Ynew)
return( list(Y=Y, move=qnup, L=L) )
}
}
ord.tpxOmegaStart <- function(X, theta)
{
if(!inherits(X,"simple_triplet_matrix")){ stop("X needs to be a simple_triplet_matrix.") }
omega <- try(tcrossprod_simple_triplet_matrix(X, solve(t(theta)%*%theta)%*%t(theta)), silent=TRUE )
if(inherits(omega,"try-error")){ return( matrix( 1/ncol(theta), nrow=nrow(X), ncol=ncol(theta) ) ) }
omega[omega <= 0] <- .5
return( ord.normalizetpx(omega, byrow=TRUE) )
}
## fast computation of sparse P(X) for X>0
ord.tpxQ <- function(theta, omega, doc, wrd){
theta[theta==1] <- 1 - 1e-14;
theta[theta==0] <- 1e-14;
theta <- ord.normalizetpx(theta, byrow = FALSE)
omega[omega==1] <- 1 - 1e-14;
omega[omega==0] <- 1e-14;
theta <- ord.normalizetpx(theta, byrow = TRUE)
if(length(wrd)!=length(doc)){stop("index mis-match in ord.tpxQ") }
if(ncol(omega)!=ncol(theta)){stop("theta/omega mis-match in ord.tpxQ") }
out <- .C("RcalcQ",
n = as.integer(nrow(omega)),
p = as.integer(nrow(theta)),
K = as.integer(ncol(theta)),
doc = as.integer(doc-1),
wrd = as.integer(wrd-1),
N = as.integer(length(wrd)),
omega = as.double(omega),
theta = as.double(theta),
q = double(length(wrd)),
PACKAGE="ordtpx" )
return( out$q ) }
## model and component likelihoods for mixture model
ord.tpxMixQ <- function(X, omega, theta, grp=NULL, qhat=FALSE){
if(is.null(grp)){ grp <- rep(1, nrow(X)) }
K <- ncol(omega)
n <- nrow(X)
mixhat <- .C("RmixQ",
n = as.integer(nrow(X)),
p = as.integer(ncol(X)),
K = as.integer(K),
N = as.integer(length(X$v)),
B = as.integer(nrow(omega)),
cnt = as.double(X$v),
doc = as.integer(X$i-1),
wrd = as.integer(X$j-1),
grp = as.integer(as.numeric(grp)-1),
omega = as.double(omega),
theta = as.double(theta),
Q = double(K*n),
PACKAGE="ordtpx")
## model and component likelihoods
lQ <- matrix(mixhat$Q, ncol=K)
lqlhd <- log(row_sums(exp(lQ)))
lqlhd[is.infinite(lqlhd)] <- -600 # remove infs
if(qhat){
qhat <- exp(lQ-lqlhd)
## deal with numerical overload
infq <- row_sums(qhat) < .999
if(sum(infq)>0){
qhat[infq,] <- 0
qhat[n*(apply(matrix(lQ[infq,],ncol=K),1,which.max)-1) + (1:n)[infq]] <- 1 }
}
return(list(lQ=lQ, lqlhd=lqlhd, qhat=qhat)) }
## functions to move theta/omega to and from NEF.
tpxToNEF <- function(theta, omega){
n <- nrow(omega)
p <- nrow(theta)
K <- ncol(omega)
return(.C("RtoNEF",
n=as.integer(n), p=as.integer(p), K=as.integer(K),
Y=double((p-1)*K + n*(K-1)),
theta=as.double(theta), tomega=as.double(t(omega)),
PACKAGE="ordtpx")$Y)
}
## 'From' NEF representation back to probabilities
tpxFromNEF <- function(Y, n, p, K){
bck <- .C("RfromNEF",
n=as.integer(n), p=as.integer(p), K=as.integer(K),
Y=as.double(Y), theta=double(K*p), tomega=double(K*n),
PACKAGE="maptpx")
return(list(omega=t( matrix(bck$tomega, nrow=K) ), theta=matrix(bck$theta, ncol=K)))
}
ord.tpxinit <- function(fcounts, X, K1, alpha, verb, param_set, del_beta, a_mu, b_mu,
ztree_options, tol, admix, grp, tmax, wtol, qn, acc){
## initheta can be matrix, or c(nK, tmax, tol, verb)
ini_mu_tree_set <- mu_tree_build_set(param_set);
levels <- length(ini_mu_tree_set[[1]]);
initheta <- do.call(cbind, lapply(1:K1, function(l) ini_mu_tree_set[[l]][[levels]]/ini_mu_tree_set[[l]][[1]]));
if(is.matrix(initheta)){
if(ncol(initheta)!=K1){ stop("mis-match between initheta and K.") }
if(prod(initheta>0) != 1){ stop("use probs > 0 for initheta.") }
return(ord.normalizetpx(initheta, byrow=FALSE)) }
if(is.matrix(alpha)){
if(nrow(alpha)!=ncol(X) || ncol(alpha)!=K1){ stop("bad matrix alpha dimensions; check your K") }
return(ord.normalizetpx(alpha, byrow=FALSE)) }
if(is.null(initheta)){ ilength <- K1-1 }
else{ ilength <- initheta[1] }
if(ilength < 1){ ilength <- 1 }
## set number of initial steps
if(length(initheta)>1){ tmax <- initheta[2] }else{ tmax <- 3 }
## set the tolerance
if(length(initheta)>2){ tol <- initheta[3] }else{ tol <- 0.5 }
## print option
if(length(initheta)>3){ verb <- initheta[4] }else{ verb <- 0 }
if(verb){ cat("Building initial topics")
if(verb > 1){ cat(" for K = ") }
else{ cat("... ") } }
## Solve for map omega in NEF space
fit <- ord.tpxfit(fcounts=fcounts, X=X, param_set=param_set, del_beta=del_beta, a_mu=a_mu, b_mu=b_mu,
ztree_options=ztree_options, tol=tol, verb=verb, admix=TRUE, grp=NULL, tmax=tmax, wtol=-1,
qn=-1, acc = acc);
initheta <- fit$theta;
return(initheta)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.