inst/doc/RDFTensor-Demo.R

In RDFTensor: Different Tensor Factorization (Decomposition) Techniques for RDF Tensors (Three-Mode-Tensors)

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## ---- echo=FALSE, include=FALSE-----------------------------------------------
 library(RDFTensor)

  data('umls_tnsr')
   ntnsr=umls_tnsr#old format

## ---- echo=TRUE---------------------------------------------------------------
    tt=rescal(ntnsr$X,rnk=10,ainit='nvecs',verbose=1,lambdaA=0,epsilon=1e-4,lambdaR=0)
#tt=scRescal(ntnsr$X,rnk=10,ainit='nvecs',verbose=1,lambdaA=0,epsilon=1e-4,lambdaR=0,ncores = 2,OS_WIN = TRUE)
    A=tt$A
    R=tt$R

## ---- echo=TRUE---------------------------------------------------------------

      res=rescal_Trp_Val(R=R,A=A,ntnsr,verbose=0)
    
    plot(density(res[,'val']),main='RESCAL Factorization rank=10, density of triples of UMLS')
    print(summary(res[,'val']))

## ---- echo=TRUE---------------------------------------------------------------
    RecRes=RescalReconstructBack(R=R,A=A,otnsr=ntnsr,ncore=2,verbose=0,OS_WIN=TRUE,generateLog=TRUE)

## ---- echo=TRUE---------------------------------------------------------------
    print(sprintf('True positive rate:%.2f %%',100*sum(RecRes$TP)/length(RecRes$TP)))
    s=2#<affects> predicate 
    stats=NULL
    ijk=RecRes[[1]]$ijk
    val=RecRes[[1]]$val
    tp_flg=RecRes$TP
    
    for(thr in sort(unique(val[tp_flg&ijk[,2]==s]),decreasing=TRUE)){
        tp=sum(tp_flg[val>=thr & ijk[,2]==s])
        fp=sum(val>=thr & ijk[,2]==s)-tp
        fn=sum(ntnsr$X[[s]])-tp
        stats=rbind(stats,cbind(thr=thr,R=tp/(tp+fn),P=tp/(tp+fp),tp=tp,fn=fn,fp=fp))
    }
    HM=apply(stats,1,function(x){2/(1/x['P']+1/x['R'])})

     plot(stats[,'thr'],stats[,'R']*100,type='l',col='red',lwd=2,
    main=sprintf('Slice:%d, Predicate:<%s>, #Triples:%d, Max HM @ %.4f',s,ntnsr$P[s],sum(ntnsr$X[[s]]),
     stats[which.max(HM),'thr']), ylab="",xlab='Threshold ',cex.main=0.85,
                     xlim=c(0,max(thr,1)),ylim=c(0,100))
    abline(h = c(0,20,40,60,80,100), lty = 2, col = "grey")
    abline(v = seq(0.1,1,0.1),  lty = 2, col = "grey")
    lines(stats[,'thr'],stats[,'P']*100,col='blue',lwd=2)
    lines(stats[,'thr'],100*HM,col='green',lwd=2)
    # grid(nx=10, lty = "dotted", lwd = 1)
    legend(legend=c('Recall','Precision','Harmonic mean'),col=c('red','blue','green'),x=0.6,y=20,pch=1,cex=0.75,lwd=2)
    abline(v=stats[which.max(HM),'thr'],col='grey')