### Topographical Topic models
## We assume all the cells are equally likely to be the mean centers for the topogaphical maps
mu_sim_prior <- function(topo.config, K)
{
## topo.config is a V \times D matrix, with V cells and for each cell, specifying D dimensions (latitude, longitude, elevation etc)
return(topo.config[sample(1:dim(topo.config)[1], K, replace=FALSE),])
}
lambda_sim_prior <- function(scale=0.5,K)
{
## scale represents the parameter for the Inverse-gamma distribution from which we simulate
return(1/(rgamma(K,scale,scale)))
}
## simulating the spatial proportional intensities
f_sim_prior <- function(topo.config, K, scale=0.5)
{
mu_sim <- mu_sim_prior(topo.config,K);
lambda_sim <- lambda_sim_prior(scale,K);
initopics_theta <- do.call(cbind,lapply(1:K, function(k)
{
loglik_temp <- sapply(1:dim(topo.config)[1],
function(s) dmvnorm(as.numeric(topo.config[s,]),
as.numeric(mu_sim[k,]), diag(lambda_sim[k], dim(topo.config)[2]),
log=TRUE));
weights <- exp(loglik_temp - max(loglik_temp));
prop_weights <- weights/ sum(weights);
return(prop_weights)
}))
return(initopics_theta)
}
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))
}
topo.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( normalize(omega, byrow=TRUE) )
}
topo.tpxlpost <- function(counts, omega, theta, f)
{
prob <- omega%*%t(theta);
K <- ncol(theta);
omega[omega<=0] <- 1e-20;
theta[theta<=0] <- 1e-20;
prob[prob <=0] <- 1e-20;
L <- sum(log(omega))/K + sum(f*log(theta))+sum(counts*log(prob));
return(L)
}
z_construct <- function(counts, omega_iter, theta_iter, z_options=c(1,2))
{
if(z_options==2){
row_total <- rowSums(counts);
z_est <- round(sweep(theta_iter, MARGIN=1, colSums(sweep(omega_iter, MARGIN = 1, row_total, "*")), "*"));
}
if(z_options==1){
z_est <- (t(omega_iter) %*% (counts/(omega_iter %*% theta_iter)))*theta_iter;
}
return(z_est)
}
## ** called from topics.R (predict) and tpx.R
## Conditional solution for topic weights given theta
topo.tpxweights <- function(n, p, xvo, wrd, doc, start, theta, verb=FALSE, nef=TRUE, wtol=10^{-5}, tmax=1000)
{
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="maptpx")
return(t(matrix(omega$W, nrow=ncol(theta), ncol=n))) }
topo.tpxfit <- function(counts, X, theta, f, z_options, tol, verb,
admix, grp, tmax, wtol, qn)
{
## inputs and dimensions
if(!inherits(X,"simple_triplet_matrix")){ stop("X needs to be a simple_triplet_matrix") }
K <- ncol(theta)
n <- nrow(X)
p <- ncol(X)
m <- row_sums(X)
## recycle these in tpcweights to save time
xvo <- X$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 <- topo.tpxOmegaStart(X,theta);
## tracking
iter <- 0
dif <- tol+1+qn
update <- TRUE
if(verb>0){
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 <- topo.tpxlpost(counts, omega=omega, theta=theta, f=f)
# if(is.infinite(L)){ L <- sum( (log(Q0)*col_sums(X))[Q0>0] ) }
## Iterate towards MAP
while( update && iter < tmax ){
## sequential quadratic programming for conditional Y solution
if(admix && wtol > 0){ Wfit <- topo.tpxweights(n=nrow(X), p=ncol(X), xvo=xvo, wrd=wrd, doc=doc,
start=omega, theta=theta, verb=0, nef=TRUE, wtol=wtol, tmax=50) }
else{ Wfit <- omega }
z_construct <- z_construct(counts, omega_iter = Wfit, theta_iter = t(theta), z_options = 1);
theta_fit <- t(normalize(z_construct + t(f)));
move <- list(theta=theta_fit, omega=Wfit);
QNup <- topo.tpxQN(move=move, counts=counts, Y=Y, X=X, f=f, verb=verb, admix=admix, grp=grp, doqn=qn-dif)
move <- QNup$move
Y <- QNup$Y
if(QNup$L < L){ # happens on bad Wfit, so fully reverse
if(verb > 10){ cat("_reversing a step_") }
z_construct <- z_construct(counts, omega_iter = omega, theta_iter = t(theta), z_options = 1);
theta_fit <- t(normalize(z_construct + t(f)));
move <- list(theta=theta_fit, omega=Wfit);
QNup$L <- topo.tpxlpost(counts, move$omega, move$theta, f);
}
## calculate dif
dif <- (QNup$L-L)
L <- QNup$L
## check convergence
if(abs(dif) < tol){
if(sum(abs(theta-move$theta)) < tol){ update = FALSE } }
## print
if(verb>0 && (iter-1)%%ceiling(10/verb)==0 && iter>0){
cat( paste( round(dif,digits), #" (", sum(abs(theta-move$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))) }
iter <- iter+1
theta <- move$theta;
omega <- move$omega;
mu_fit <- do.call(rbind,lapply(1:K, function(k) colSums(sweep(topo.config, MARGIN=1, theta[,k], "*"))));
lambda_fit <- do.call(rbind, lapply(1:K, function(k) colSums(sweep((topo.config-sweep(cbind(rep(1,dim(topo.config)[1]),rep(1,dim(topo.config)[1])),MARGIN = 2, (mu_fit[k,]), "*"))^2, MARGIN = 1, theta[,k],"*"))))
f <- do.call(cbind,lapply(1:K, function(k)
{
loglik_temp <- sapply(1:dim(topo.config)[1],
function(s) dmvnorm(as.numeric(topo.config[s,]),
as.numeric(mu_fit[k,]), diag(lambda_fit[k], dim(topo.config)[2]),
log=TRUE));
weights <- exp(loglik_temp - max(loglik_temp));
prop_weights <- weights/ sum(weights);
return(prop_weights)
}))
}
L <- topo.tpxlpost(counts, omega=omega, theta=theta, f=f)
## 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, f=f, L=L, mu=mu_fit, lambda=lambda_fit, iter=iter)
invisible(out) }
topo.tpxQN <- function(move, counts, Y, X, f, verb, admix, grp, doqn)
{
## always check likelihood
L <- topo.tpxlpost(counts, omega=move$omega, theta=move$theta, f=f);
if(doqn < 0){ return(list(move=move, L=L, Y=Y)) }
## update Y accounting
Y <- cbind(Y, topo.tpxToNEF(theta=move$theta, omega=move$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 <- topo.tpxFromNEF(Ynew, n=nrow(move$omega),
p=nrow(move$theta), K=ncol(move$theta))
## check for a likelihood improvement
Lqnup <- try(topo.tpxlpost(counts, omega=qnup$omega, theta=qnup$theta, f=f), silent=TRUE)
if(inherits(Lqnup, "try-error")){
if(verb>10){ cat("(QN: try error) ") }
return(list(Y=Y[,-1], move=move, L=L)) }
if(verb>10){ cat(paste("(QN diff ", round(Lqnup-L,3), ")\n", sep="")) }
if(Lqnup < L){
return(list(Y=Y[,-1], move=move, L=L)) }
else{
L <- Lqnup
Y <- cbind(Y[,2],Ynew)
return( list(Y=Y, move=qnup, L=L) )
}
}
## fast computation of sparse P(X) for X>0
topo.tpxQ <- function(theta, omega, doc, wrd){
if(length(wrd)!=length(doc)){stop("index mis-match in tpxQ") }
if(ncol(omega)!=ncol(theta)){stop("theta/omega mis-match in tpxQ") }
out <- .C("RcalcQ",
n = as.integer(nrow(omega)),
p = as.integer(nrow(theta)),
K = as.integer(ncol(theta)),
doc = as.integer(doc-1),
wrd = as.integer(wrd-1),
N = as.integer(length(wrd)),
omega = as.double(omega),
theta = as.double(theta),
q = double(length(wrd)),
PACKAGE="topotpx" )
return( out$q ) }
## model and component likelihoods for mixture model
topo.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="topotpx")
## model and component likelihoods
lQ <- matrix(mixhat$Q, ncol=K)
lqlhd <- log(row_sums(exp(lQ)))
lqlhd[is.infinite(lqlhd)] <- -600 # remove infs
if(qhat){
qhat <- exp(lQ-lqlhd)
## deal with numerical overload
infq <- row_sums(qhat) < .999
if(sum(infq)>0){
qhat[infq,] <- 0
qhat[n*(apply(matrix(lQ[infq,],ncol=K),1,which.max)-1) + (1:n)[infq]] <- 1 }
}
return(list(lQ=lQ, lqlhd=lqlhd, qhat=qhat)) }
## functions to move theta/omega to and from NEF.
topo.tpxToNEF <- function(theta, omega){
n <- nrow(omega)
p <- nrow(theta)
K <- ncol(omega)
return(.C("RtoNEF",
n=as.integer(n), p=as.integer(p), K=as.integer(K),
Y=double((p-1)*K + n*(K-1)),
theta=as.double(theta), tomega=as.double(t(omega)),
PACKAGE="topotpx")$Y)
}
## 'From' NEF representation back to probabilities
topo.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="topotpx")
return(list(omega=t( matrix(bck$tomega, nrow=K) ), theta=matrix(bck$theta, ncol=K)))
}
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