vector<pair<T_ID, uint32> > InvertedIndexMulDim::topkQuery(vector<int>& queryLow, vector<int>& queryHigh, uint32 k){
map<T_ID,uint32> counter;
scanIndex(queryLow, queryHigh, counter);
vector<pair<T_ID, uint32> > topk(k);
partial_sort_copy(counter.begin(),
counter.end(),
topk.begin(),
topk.end(),
compfunc);
cout<<"topk.size():"<<topk.size()<<endl;
return topk;
}
void hca_displaytopics(char *stem, char *resstem, int topword,
enum ScoreType scoretype, int pmicount, int fullreport) {
int w,k;
uint32_t *termindk = NULL;
uint32_t *indk = NULL;
int Nk_tot = 0;
double (*termtscore)(int) = NULL;
double (*tscore)(int) = NULL;
double sparsityword = 0;
double sparsitydoc = 0;
double underused = 0;
uint32_t *top1cnt = NULL;
FILE *fp;
float *tpmi = NULL;
char *topfile;
char *repfile;
uint32_t *psort;
FILE *rp = NULL;
float *gtvec = globalprop();
//#define XTRA // prints model topic probs after observed
#ifdef XTRA
double *gtavec = calloc(ddN.T,sizeof(gtavec[0]));
#endif
float *gpvec = calloc(ddN.W,sizeof(gpvec[0]));
float *pvec = calloc(ddN.W,sizeof(pvec[0]));
#ifdef KL
float *dfvec = calloc(ddN.W,sizeof(dfvec[0]));
#endif
double *ngalpha = NULL;
T_stats_t *termstats;
#ifdef XTRA
get_probs(gtavec);
#endif
if ( pmicount>topword )
pmicount = topword;
if ( scoretype == ST_idf ) {
tscore = idfscore;
} else if ( scoretype == ST_phirat ) {
tscore = phiratioscore;
} else if ( scoretype == ST_phi ) {
tscore = phiscore;
} else if ( scoretype == ST_count ) {
tscore = countscore;
} else if ( scoretype == ST_cost ) {
tscore = costscore;
} else if ( scoretype == ST_Q ) {
tscore = Qscore;
lowerQ = 1.0/ddN.T;
}
if ( ddS.TwT==NULL && ddP.phi==NULL && scoretype == ST_phirat )
yap_quit("Cannot use '-orat' option with this model/settings.\n");
if ( ddP.PYalpha==H_NG ) {
/*
* provide an estimate of alpha
*/
ngalpha = dvec(ddN.T);
get_probs(ngalpha);
for (k=0; k<ddN.T; k++) {
ddP.alphapr[k] = ngalpha[k];
}
}
/*
* returns null if no relevant data file
*/
termstats = tstats_init(ddS.z, ddD.NdTcum, ddN.T, ddN.DT, stem);
if ( termstats ) {
if ( scoretype == ST_idf ) {
termtscore = termidfscore;
} else
termtscore = termcountscore;
}
/*
* first collect counts of each word/term,
* and build gpvec (mean word probs)
*/
build_NwK();
if ( termstats )
build_termNwK(termstats);
{
/*
* gpvec[] is normalised NwK[]
*/
double tot = 0;
for (w=0; w<ddN.W; w++)
tot += gpvec[w] = NwK[w]+0.1;
for (w=0; w<ddN.W; w++)
gpvec[w] /= tot;
}
if ( ddS.Nwt ) {
for (k=0; k<ddN.T; k++) {
Nk_tot += ddS.NWt[k];
}
}
psort = sorttops(gtvec, ddN.T);
top1cnt = hca_top1cnt();
if ( !top1cnt )
yap_quit("Cannot allocate top1cnt in hca_displaytopics()\n");
if ( pmicount ) {
tpmi = malloc(sizeof(*tpmi)*(ddN.T+1));
if ( !tpmi )
yap_quit("Cannot allocate tpmi in hca_displaytopics()\n");
}
indk = malloc(sizeof(*indk)*ddN.W);
if ( !indk )
yap_quit("Cannot allocate indk in hca_displaytopics()\n");
if ( termstats ) {
termindk = malloc(sizeof(*indk)*termstats->K);
if ( !termindk )
yap_quit("Cannot allocate termindk in hca_displaytopics()\n");
}
data_df(stem);
#ifdef KL
for (w=0; w<ddN.W; w++)
dfvec[w] = ddD.df[w];
#endif
/*
* two passes through,
* first to build the top words and dump to file
*/
repfile = yap_makename(resstem,".topset");
topfile = yap_makename(resstem,".toplst");
fp = fopen(topfile,"w");
if ( !fp )
yap_sysquit("Cannot open file '%s' for write\n", topfile);
yap_message("\n");
for (k=0; k<ddN.T; k++) {
int cnt, termcnt = 0;
tscorek = k;
/*
* build sorted word list
*/
cnt = buildindk(k, indk);
topk(topword, cnt, indk, tscore);
if ( cnt==0 )
continue;
if ( termstats ) {
termcnt = buildtermindk(k, termindk, termstats);
topk(topword, termcnt, termindk, termtscore);
}
/*
* dump words to file
*/
fprintf(fp,"%d: ", k);
for (w=0; w<topword && w<cnt; w++) {
fprintf(fp," %d", (int)indk[w]);
}
if ( termstats ) {
for (w=0; w<topword && w<termcnt; w++) {
fprintf(fp," %d", (int)termstats->Kmin+termindk[w]);
}
}
fprintf(fp, "\n");
}
if ( ddP.PYbeta && (ddP.phi==NULL || ddP.betapr) ) {
int cnt;
/*
* dump root words
*/
tscorek = -1;
cnt = buildindk(-1, indk);
topk(topword, cnt, indk, (ddP.phi==NULL)?countscore:phiscore);
fprintf(fp,"-1:");
for (w=0; w<topword && w<cnt; w++) {
fprintf(fp," %d", (int)indk[w]);
}
fprintf(fp, "\n");
}
fclose(fp);
if ( verbose>1 ) yap_message("\n");
if ( pmicount ) {
/*
* compute PMI
*/
char *toppmifile;
char *pmifile;
double *tp;
tp = dvec(ddN.T);
pmifile=yap_makename(stem,".pmi");
toppmifile=yap_makename(resstem,".toppmi");
get_probs(tp);
report_pmi(topfile, pmifile, toppmifile, ddN.T, ddN.W, 1,
pmicount, tp, tpmi);
free(toppmifile);
free(pmifile);
free(tp);
}
/*
* now report words and diagnostics
*/
//ttop_open(topfile);
if ( fullreport ) {
rp = fopen(repfile,"w");
if ( !rp )
yap_sysquit("Cannot open file '%s' for write\n", repfile);
fprintf(rp, "#topic index rank prop word-sparse doc-sparse eff-words eff-docs docs-bound top-one "
"dist-unif dist-unigrm");
if ( PCTL_BURSTY() )
fprintf(rp, " burst-concent");
if ( ddN.tokens )
fprintf(rp, " ave-length");
fprintf(rp, " coher");
if ( pmicount )
fprintf(rp, " pmi");
fprintf(rp, "\n#word topic index rank");
if ( ddS.Nwt )
fprintf(rp, " count");
fprintf(rp, " prop cumm df coher\n");
}
for (k=0; k<ddN.T; k++) {
int cnt, termcnt = 0;
int kk = psort[k];
uint32_t **dfmtx;
if ( ddP.phi==NULL && ddS.NWt[kk]==0 )
continue;
/*
* grab word prob vec for later use
*/
if ( ddS.Nwt ) {
int w;
for (w=0; w<ddN.W; w++)
pvec[w] = wordprob(w,kk);
} else if ( ddP.phi )
fv_copy(pvec, ddP.phi[kk], ddN.W);
else if ( ddS.phi )
fv_copy(pvec, ddS.phi[kk], ddN.W);
/*
* rebuild word list
*/
tscorek = kk;
cnt = buildindk(kk, indk);
topk(topword, cnt, indk, tscore);
if ( topword<cnt )
cnt = topword;
assert(cnt>0);
if ( termstats ) {
termcnt = buildtermindk(kk, termindk, termstats);
topk(topword, termcnt, termindk, termtscore);
if ( topword<termcnt )
termcnt = topword;
}
/*
* df stats for topic returned as matrix
*/
dfmtx = hca_dfmtx(indk, cnt, kk);
if ( ddS.Nwt && (ddS.NWt[kk]*ddN.T*100<Nk_tot || ddS.NWt[kk]<5 ))
underused++;
/*
* print stats for topic
* Mallet: tokens, doc_ent, ave-word-len, coher.,
* uni-dist, corp-dist, eff-no-words
*/
yap_message("Topic %d/%d", kk, k);
{
/*
* compute diagnostics
*/
double prop = gtvec[kk];
float *dprop = docprop(kk);
double spw = 0;
double spd = ((double)nonzero_Ndt(kk))/((double)ddN.DT);
#ifdef KL
double ew = fv_kl(dfvec,pvec,ddN.W);
#else
double ew = exp(fv_entropy(pvec,ddN.W));
#endif
double ud = fv_helldistunif(pvec,ddN.W);
double pd = fv_helldist(pvec,gpvec,ddN.W);
double sl = fv_avestrlen(pvec,ddN.tokens,ddN.W);
double co = coherence(dfmtx, cnt);
double ed = dprop?exp(fv_entropy(dprop,ddN.DT)):ddN.DT;
#define MALLET_EW
#ifdef MALLET_EW
double ewp = dprop?(1.0/fv_expprob(pvec,ddN.W)):ddN.W;
#endif
double da = dprop?fv_bound(dprop,ddN.DT,1.0/sqrt((double)ddN.T)):0;
sparsitydoc += spd;
yap_message((ddN.T>200)?" p=%.3lf%%":" p=%.2lf%%",100*prop);
#ifdef XTRA
yap_message((ddN.T>200)?"/%.3lf%%":"/%.2lf%%",100*gtavec[kk]);
#endif
if ( ddS.Nwt ) {
spw = ((double)nonzero_Nwt(kk))/((double)ddN.W);
sparsityword += spw;
yap_message(" ws=%.1lf%%", 100*(1-spw));
}
yap_message(" ds=%.1lf%%", 100*(1-spd) );
#ifdef KL
yap_message(" ew=%lf", ew);
#else
yap_message(" ew=%.0lf", ew);
#endif
#ifdef MALLET_EW
yap_message(" ewp=%.1lf", ewp);
#endif
yap_message(" ed=%.1lf", ed);
yap_message(" da=%.0lf", da+0.1);
yap_message(" t1=%u", top1cnt[kk]);
yap_message(" ud=%.3lf", ud);
yap_message(" pd=%.3lf", pd);
if ( PCTL_BURSTY() )
yap_message(" bd=%.3lf", ddP.bdk[kk]);
if ( ddP.NGbeta ) {
/*
* approx. as sqrt(var(lambda_k)/lambda-normaliser
*/
double ngvar = sqrt(ddP.NGalpha[kk])
* (ngalpha[kk]/ddP.NGalpha[kk]);
yap_message(" ng=%.4lf,%.4lf",
ngalpha[kk], ngvar/ngalpha[kk]);
if ( ddS.sparse )
yap_message(",%.4f", 1-((float)ddS.sparseD[kk])/ddN.DTused);
if ( verbose>2 )
yap_message(" ngl=%.4lf,%.4lf, nga=%.4lf,%.4lf",
ddP.NGalpha[kk]/ddP.NGbeta[kk],
sqrt(ddP.NGalpha[kk]/ddP.NGbeta[kk]/ddP.NGbeta[kk]),
ddP.NGalpha[kk], ddP.NGbeta[kk]);
}
if ( ddN.tokens )
yap_message(" sl=%.2lf", sl);
yap_message(" co=%.3lf%%", co);
if ( pmicount )
yap_message(" pmi=%.3f", tpmi[kk]);
if ( fullreport ) {
fprintf(rp,"topic %d %d", kk, k);
fprintf(rp," %.6lf", prop);
if ( ddS.Nwt ) {
fprintf(rp," %.6lf", (1-spw));
} else {
fprintf(rp," 0");
}
fprintf(rp," %.6lf", (1-spd) );
#ifdef KL
yap_message(" %lf", ew);
#else
fprintf(rp," %.2lf", ew);
#endif
#ifdef MALLET_EW
fprintf(rp," %.2lf", ewp);
#endif
fprintf(rp," %.2lf", ed);
fprintf(rp," %.0lf", da+0.1);
fprintf(rp," %u", top1cnt[kk]);
fprintf(rp," %.6lf", ud);
fprintf(rp," %.6lf", pd);
if ( PCTL_BURSTY() )
fprintf(rp," %.3lf", ddP.bdk[kk]);
fprintf(rp," %.4lf", (ddN.tokens)?sl:0);
fprintf(rp," %.6lf", co);
if ( pmicount )
fprintf(rp," %.4f", tpmi[kk]);
fprintf(rp,"\n");
}
if ( dprop) free(dprop);
}
if ( verbose>1 ) {
double pcumm = 0;
/*
* print top words:
* Mallet: rank, count, prob, cumm, docs, coh
*/
yap_message("\ntopic %d/%d", kk, k);
yap_message(" words=");
for (w=0; w<cnt; w++) {
if ( w>0 ) yap_message(",");
if ( ddN.tokens )
yap_message("%s", ddN.tokens[indk[w]]);
else
yap_message("%d", indk[w]);
if ( verbose>2 ) {
if ( scoretype == ST_count )
yap_message("(%d)", (int)(tscore(indk[w])+0.2));
else
yap_message("(%6lf)", tscore(indk[w]));
}
if ( fullreport ) {
fprintf(rp, "word %d %d %d", kk, indk[w], w);
if ( ddS.Nwt )
fprintf(rp, " %d", ddS.Nwt[indk[w]][kk]);
pcumm += pvec[indk[w]];
fprintf(rp, " %.6f %.6f", pvec[indk[w]], pcumm);
fprintf(rp, " %d", dfmtx[w][w]);
fprintf(rp, " %.6f", coherence_word(dfmtx, cnt, w));
if ( ddN.tokens )
fprintf(rp, " %s", ddN.tokens[indk[w]]);
fprintf(rp, "\n");
}
}
if ( termstats ) {
yap_message(" terms=");
for (w=0; w<termcnt; w++) {
if ( w>0 ) yap_message(",");
if ( ddN.tokens )
yap_message("%s", termstats->tokens[termindk[w]]);
else
yap_message("%d", termstats->Kmin+termindk[w]);
if ( verbose>2 ) {
if ( scoretype == ST_count )
yap_message("(%d)", (int)(termtscore(termindk[w])+0.2));
else
yap_message("(%6lf)", termtscore(termindk[w]));
}
if ( fullreport ) {
fprintf(rp, "term %d %d %d", kk, termindk[w], w);
fprintf(rp, " %d", termstats->Nkt[termindk[w]][kk]);
fprintf(rp, " %s", termstats->tokens[termindk[w]]);
fprintf(rp, "\n");
}
}
}
}
yap_message("\n");
free(dfmtx[0]); free(dfmtx);
}
if ( verbose>1 && ddP.PYbeta ) {
int cnt;
double pcumm = 0;
/*
* print root words
*/
tscorek = -1;
cnt = buildindk(-1,indk);
/* this case gives bad results */
// if ( scoretype == ST_phirat ) topk(topword, cnt, indk, phiratioscore);
topk(topword, cnt, indk, (ddP.phi==NULL)?countscore:phiscore);
/*
* cannot build df mtx for root because
* it is latent w.r.t. topics
*/
yap_message("Topic root words=");
if ( fullreport ) {
int w;
if ( ddP.phi && ddP.PYbeta!=H_PDP ) {
for (w=0; w<ddN.W; w++)
pvec[w] = ddS.phi[ddN.T][w];
} else {
for (w=0; w<ddN.W; w++)
pvec[w] = betabasewordprob(w);
}
#ifdef KL
double ew = fv_kl(dfvec,pvec,ddN.W);
#else
double ew = exp(fv_entropy(pvec,ddN.W));
#endif
double ud = fv_helldistunif(pvec,ddN.W);
double pd = fv_helldist(pvec,gpvec,ddN.W);
fprintf(rp,"topic -1 -1 0 0");
fprintf(rp," %.4lf", ew);
fprintf(rp," %.6lf", ud);
fprintf(rp," %.6lf", pd);
fprintf(rp,"\n");
}
for (w=0; w<topword && w<cnt; w++) {
if ( w>0 ) yap_message(",");
if ( ddN.tokens )
yap_message("%s", ddN.tokens[indk[w]]);
else
yap_message("%d", indk[w]);
if ( verbose>2 && !ddP.phi )
yap_message("(%6lf)", countscore(indk[w]));
if ( fullreport ) {
fprintf(rp, "word %d %d %d", -1, indk[w], w);
if ( ddS.TwT )
fprintf(rp, " %d", ddS.TwT[w]);
pcumm += pvec[indk[w]];
fprintf(rp, " %.6f %.6f", pvec[indk[w]], pcumm);
fprintf(rp, " 0 0");
if ( ddN.tokens )
fprintf(rp, " %s", ddN.tokens[indk[w]]);
fprintf(rp, "\n");
}
}
yap_message("\nTopical words=");
topk(topword, cnt, indk, phiinvratioscore);
for (w=0; w<topword && w<cnt; w++) {
if ( w>0 ) yap_message(",");
if ( ddN.tokens )
yap_message("%s", ddN.tokens[indk[w]]);
else
yap_message("%d", indk[w]);
}
yap_message("\n");
}
yap_message("\n");
if ( rp )
fclose(rp);
if ( ddS.Nwt )
yap_message("Average topicXword sparsity = %.2lf%%\n",
100*(1-sparsityword/ddN.T) );
yap_message("Average docXtopic sparsity = %.2lf%%\n"
"Underused topics = %.1lf%%\n",
100*(1-sparsitydoc/ddN.T),
100.0*underused/(double)ddN.T);
if ( ddS.sparse && ddP.PYalpha==H_NG ) {
double avesp = 0;
// correct_docsp();
for (k=0; k<ddN.T; k++) {
avesp += gtvec[k];
}
// check gtvec[] sums to 1
assert(fabs(avesp-1.0)<0.00001);
avesp = 0;
for (k=0; k<ddN.T; k++) {
avesp += gtvec[k]*((float)ddS.sparseD[k])/ddN.DTused;
assert(ddS.sparseD[k]<=ddN.DTused);
}
assert(avesp<=1.0);
assert(avesp>0.0);
yap_message("IBP sparsity = %.2lf%%\n", 100*(1-avesp));
}
if ( pmicount )
yap_message("Average PMI = %.3f\n", tpmi[ddN.T]);
/*
* print
*/
if ( 1 ) {
float **cmtx = hca_topmtx();
int t1, t2;
int m1, m2;
float mval;
char *corfile = yap_makename(resstem,".topcor");
fp = fopen(corfile,"w");
if ( !fp )
yap_sysquit("Cannot open file '%s' for write\n", corfile);
/*
* print file
*/
for (t1=0; t1<ddN.T; t1++) {
for (t2=0; t2<t1; t2++)
if ( cmtx[t1][t2]>1.0e-7 )
fprintf(fp, "%d %d %0.6f\n", t1, t2, cmtx[t1][t2]);
}
fclose(fp);
free(corfile);
/*
* display maximum
*/
m1 = 1; m2 = 0;
mval = cmtx[1][0];
for (t1=0; t1<ddN.T; t1++) {
for (t2=0; t2<t1; t2++) {
if ( mval<cmtx[t1][t2] ) {
mval = cmtx[t1][t2];
m1 = t1;
m2 = t2;
}
}
}
yap_message("Maximum correlated topics (%d,%d) = %f\n", m1, m2, mval);
free(cmtx[0]); free(cmtx);
}
/*
* print burstiness report
*/
if ( PCTL_BURSTY() ) {
int tottbl = 0;
int totmlttbl = 0;
int totmlt = 0;
int i;
for (i=0; i<ddN.NT; i++) {
if ( Z_issetr(ddS.z[i]) ) {
if ( M_multi(i) )
totmlttbl++;
tottbl++;
}
if ( M_multi(i) )
totmlt++;
}
yap_message("Burst report: multis=%.2lf%%, tables=%.2lf%%, tbls-in-multis=%.2lf%%\n",
100.0*((double)ddM.dim_multiind)/ddN.N,
100.0*((double)tottbl)/ddN.NT,
100.0*((double)totmlttbl)/totmlt);
}
yap_message("\n");
free(topfile);
if ( repfile ) free(repfile);
if ( top1cnt ) free(top1cnt);
free(indk);
free(psort);
if ( ngalpha )
free(ngalpha);
if ( pmicount )
free(tpmi);
if ( NwK ) {
free(NwK);
NwK = NULL;
}
#ifdef KL
free(dfvec);
#endif
free(pvec);
free(gtvec);
free(gpvec);
tstats_free(termstats);
}
void hca_displaytopics(char *resstem, int topword, enum ScoreType scoretype) {
int w,k;
int *indk = NULL;
int Nk_tot = 0;
double (*tscore)(int) = NULL;
double sparsityword = 0;
double sparsitydoc = 0;
double underused = 0;
char *fname = yap_makename(resstem,".top");
int nophi = (ddP.phi==NULL) && (ddS.phi==NULL);
FILE *fp;
if ( scoretype == ST_idf ) {
tscore = idfscore;
} else if ( scoretype == ST_phi ) {
tscore = phiscore;
} else if ( scoretype == ST_count ) {
tscore = countscore;
} else if ( scoretype == ST_cost ) {
tscore = costscore;
} else if ( scoretype == ST_Q ) {
tscore = Qscore;
lowerQ = 1.0/ddN.T;
}
fp = fopen(fname,"w");
if ( !fp )
yap_sysquit("Cannot open file '%s' for write\n", fname);
/*
* first collect counts of each word/term
*/
if ( scoretype != ST_count && scoretype != ST_phi ) {
NwK = u32vec(ddN.W);
if ( !NwK )
yap_quit("Out of memory in hca_displaytopics()\n");
for (w=0; w<ddN.W; w++) {
NwK[w] = 0;
}
NWK = 0;
for (w=0; w<ddN.W; w++) {
for (k=0; k<ddN.T; k++) {
NwK[w] += ddS.Nwt[w][k]; // should use CCT_ReadN()
}
NWK += NwK[w];
}
}
assert(ddN.tokens);
for (k=0; k<ddN.T; k++) {
Nk_tot += ddS.NWt[k];
}
indk = malloc(sizeof(*indk)*ddN.W);
if ( !indk )
yap_quit("Cannot allocate indk\n");
for (k=0; k<ddN.T; k++) {
int cnt;
double spw;
double spd;
tscorek = k;
/*
* print top words
*/
cnt=0;
if ( ddP.phi==NULL ) {
for (w=0; w<ddN.W; w++) {
if ( ddS.Nwt[w][k]>0 ) indk[cnt++] = w;
}
} else {
float **phi;
if ( ddP.phi )
phi = ddP.phi;
else
phi = ddS.phi;
for (w=0; w<ddN.W; w++) {
if ( phi[k][w]>0.5/ddN.W ) indk[cnt++] = w;
}
}
topk(topword, cnt, indk, tscore);
spd = ((double)nonzero_Ndt(k))/((double)ddN.DT);
sparsitydoc += spd;
if ( nophi ) {
spw = ((double)nonzero_Nwt(k))/((double)ddN.W);
sparsityword += spw;
}
if ( ddS.NWt[k]*ddN.T*100<Nk_tot )
underused++;
yap_message("\nTopic %d (", k);
if ( ddP.phi==NULL )
yap_message((ddN.T>200)?"p=%.3lf%%,":"p=%.2lf%%,",
100*((double)ddS.NWt[k])/(double)Nk_tot);
if ( nophi )
yap_message("ws=%.1lf%%,", 100*(1-spw));
else
yap_message("#=%.0lf,", exp(phi_entropy(k)));
yap_message("ds=%.1lf%%", 100*(1-spd) );
fprintf(fp,"%d: ", k);
yap_message(") words =");
for (w=0; w<topword && w<cnt; w++) {
fprintf(fp," %d", (int)indk[w]);
if ( verbose>2 ) {
double score = tscore(indk[w]);
yap_message(",%s(%6lf)", ddN.tokens[indk[w]], score);
} else
yap_message(",%s", ddN.tokens[indk[w]]);
}
yap_message("\n");
fprintf(fp, "\n");
}
if ( ddP.PYbeta && nophi ) {
int cnt;
/*
* print root words
*/
tscorek = -1;
cnt=0;
for (w=0; w<ddN.W; w++) {
if ( ddS.TwT[w]>0 ) indk[cnt++] = w;
}
topk(topword, cnt, indk, tscore);
yap_message("\nTopic root words =");
fprintf(fp,"-1:");
for (w=0; w<topword && w<cnt; w++) {
fprintf(fp," %d", (int)indk[w]);
if ( verbose>2 ) {
double score = tscore(indk[w]);
yap_message(",%s(%6lf)", ddN.tokens[indk[w]], score);
} else
yap_message(",%s", ddN.tokens[indk[w]]);
}
yap_message("\n");
fprintf(fp, "\n");
}
if ( nophi )
yap_message("Average topicXword sparsity = %.2lf%%, ",
100*(1-sparsityword/ddN.T) );
yap_message("Average docXtopic sparsity = %.2lf%%, "
"underused topics = %.1lf%%\n",
100*(1-sparsitydoc/ddN.T),
100.0*underused/(double)ddN.T);
if ( ddP.bdk!=NULL) {
int tottbl = 0;
int totmlttbl = 0;
int totmlt = 0;
int i;
for (i=0; i<ddN.NT; i++) {
if ( Z_issetr(ddS.z[i]) ) {
if ( M_multi(i) )
totmlttbl++;
tottbl++;
}
if ( M_multi(i) )
totmlt++;
}
yap_message("doc PYP report: multis=%.2lf%%, tables=%.2lf%%, tbls-in-multis=%.2lf%%\n",
100.0*((double)ddM.dim_multiind)/ddN.N,
100.0*((double)tottbl)/ddN.NT,
100.0*((double)totmlttbl)/totmlt);
}
fclose(fp);
free(fname);
free(indk);
if ( scoretype != ST_count ) {
free(NwK);
NwK = NULL;
}
}
SEXP deterministic(SEXP Y, SEXP X, SEXP Rprobinit, SEXP Rmodeldim, SEXP incint, SEXP Ralpha, SEXP method, SEXP modelprior)
{
SEXP RXwork = PROTECT(duplicate(X)), RYwork = PROTECT(duplicate(Y));
int nProtected = 2;
int nModels=LENGTH(Rmodeldim);
SEXP ANS = PROTECT(allocVector(VECSXP, 12)); ++nProtected;
SEXP ANS_names = PROTECT(allocVector(STRSXP, 12)); ++nProtected;
SEXP Rprobs = PROTECT(duplicate(Rprobinit)); ++nProtected;
SEXP R2 = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP shrinkage = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP modelspace = PROTECT(allocVector(VECSXP, nModels)); ++nProtected;
SEXP modeldim = PROTECT(duplicate(Rmodeldim)); ++nProtected;
SEXP beta = PROTECT(allocVector(VECSXP, nModels)); ++nProtected;
SEXP se = PROTECT(allocVector(VECSXP, nModels)); ++nProtected;
SEXP mse = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP modelprobs = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP priorprobs = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP logmarg = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP sampleprobs = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP Rse_m, Rcoef_m, Rmodel_m;
double *Xwork, *Ywork, *coefficients,*probs,
SSY, yty, ybar, mse_m, *se_m, pigamma,
R2_m, RSquareFull, alpha, logmarg_m, shrinkage_m;
double *XtX, *XtY, *XtXwork, *XtYwork;
double one, zero;
int inc, p2;
int nobs, p, k, i, j, m, n, l, pmodel, *xdims, *model_m, *model;
char uplo[] = "U", trans[]="T";
Bit **models;
struct Var *vars; /* Info about the model variables. */
/* get dimsensions of all variables */
nobs = LENGTH(Y);
xdims = INTEGER(getAttrib(X,R_DimSymbol));
p = xdims[1];
k = LENGTH(modelprobs);
Ywork = REAL(RYwork);
Xwork = REAL(RXwork);
XtX = (double *) R_alloc(p * p, sizeof(double));
XtXwork = (double *) R_alloc(p * p, sizeof(double));
XtY = vecalloc(p);
XtYwork = vecalloc(p);
/* create X matrix */
for (j=0, l=0; j < p; j++) {
for (i = 0; i < p; i++) {
XtX[j*p + i] = 0.0;}
}
p2 = p*p;
one = 1.0; zero = 0.0; ybar = 0.0; SSY = 0.0; yty = 0.0;
inc = 1;
F77_NAME(dsyrk)(uplo, trans, &p, &nobs, &one, &Xwork[0], &nobs, &zero, &XtX[0], &p);
yty = F77_NAME(ddot)(&nobs, &Ywork[0], &inc, &Ywork[0], &inc);
for (i = 0; i< nobs; i++) {
ybar += Ywork[i];
}
ybar = ybar/ (double) nobs;
SSY = yty - (double) nobs* ybar *ybar;
F77_NAME(dgemv)(trans, &nobs, &p, &one, &Xwork[0], &nobs, &Ywork[0], &inc, &zero, &XtY[0],&inc);
alpha = REAL(Ralpha)[0];
vars = (struct Var *) R_alloc(p, sizeof(struct Var));
probs = REAL(Rprobs);
n = sortvars(vars, probs, p);
/* Make space for the models and working variables. */
models = cmatalloc(k,p);
model = (int *) R_alloc(p, sizeof(int));
k = topk(models, probs, k, vars, n, p);
/* Fit Full model */
if (nobs <= p) {RSquareFull = 1.0;}
else {
Rcoef_m = NEW_NUMERIC(p); PROTECT(Rcoef_m);
Rse_m = NEW_NUMERIC(p); PROTECT(Rse_m);
coefficients = REAL(Rcoef_m); se_m = REAL(Rse_m);
memcpy(coefficients, XtY, p*sizeof(double));
memcpy(XtXwork, XtX, p2*sizeof(double));
memcpy(XtYwork, XtY, p*sizeof(double));
mse_m = yty;
cholreg(XtYwork, XtXwork, coefficients, se_m, &mse_m, p, nobs);
/* olsreg(Ywork, Xwork, coefficients, se_m, &mse_m, &p, &nobs, pivot,qraux,work,residuals,effects,v, betaols); */
RSquareFull = 1.0 - (mse_m * (double) ( nobs - p))/SSY;
UNPROTECT(2);
}
/* now fit all top k models */
for (m=0; m < k; m++) {
pmodel = 0;
pigamma = 1.0;
for (j = 0; j < p; j++) {
model[j] = (int) models[m][j];
pmodel += (int) models[m][j];
pigamma *= (double)((int) models[m][j])*probs[j] +
(1.0 - (double)((int) models[m][j]))*(1.0 - probs[j]);
}
REAL(sampleprobs)[m] = pigamma;
INTEGER(modeldim)[m] = pmodel;
Rmodel_m = NEW_INTEGER(pmodel); PROTECT(Rmodel_m);
model_m = INTEGER(Rmodel_m);
for (j = 0, l=0; j < p; j++) {
if (models[m][j]) {
model_m[l] = j;
l +=1; }
}
INTEGER(modeldim)[m] = pmodel;
SET_ELEMENT(modelspace, m, Rmodel_m);
UNPROTECT(1);
Rcoef_m = NEW_NUMERIC(pmodel); PROTECT(Rcoef_m);
Rse_m = NEW_NUMERIC(pmodel); PROTECT(Rse_m);
coefficients = REAL(Rcoef_m);
se_m = REAL(Rse_m);
for (j=0, l=0; j < pmodel; j++) {
XtYwork[j] = XtY[model_m[j]];
for ( i = 0; i < pmodel; i++) {
XtXwork[j*pmodel + i] = XtX[model_m[j]*p + model_m[i]];
}
}
mse_m = yty;
memcpy(coefficients, XtYwork, sizeof(double)*pmodel);
cholreg(XtYwork, XtXwork, coefficients, se_m, &mse_m, pmodel, nobs);
/* olsreg(Ywork, Xwork, coefficients, se_m, &mse_m, &pmodel, &nobs, pivot,qraux,work,residuals,effects,v, betaols); */
R2_m = 1.0 - (mse_m * (double) ( nobs - pmodel))/SSY;
SET_ELEMENT(beta, m, Rcoef_m);
SET_ELEMENT(se, m, Rse_m);
REAL(R2)[m] = R2_m;
REAL(mse)[m] = mse_m;
gexpectations(p, pmodel, nobs, R2_m, alpha, INTEGER(method)[0], RSquareFull,
SSY, &logmarg_m, &shrinkage_m);
REAL(logmarg)[m] = logmarg_m;
REAL(priorprobs)[m] = compute_prior_probs( model, pmodel,p, modelprior);
REAL(shrinkage)[m] = shrinkage_m;
UNPROTECT(2);
}
compute_modelprobs(modelprobs, logmarg, priorprobs, k);
compute_margprobs_old(models, modelprobs, probs, k, p);
/* freechmat(models,k); */
SET_VECTOR_ELT(ANS, 0, Rprobs);
SET_STRING_ELT(ANS_names, 0, mkChar("probne0"));
SET_VECTOR_ELT(ANS, 1, modelspace);
SET_STRING_ELT(ANS_names, 1, mkChar("which"));
SET_VECTOR_ELT(ANS, 2, logmarg);
SET_STRING_ELT(ANS_names, 2, mkChar("logmarg"));
SET_VECTOR_ELT(ANS, 3, modelprobs);
SET_STRING_ELT(ANS_names, 3, mkChar("postprobs"));
SET_VECTOR_ELT(ANS, 4, priorprobs);
SET_STRING_ELT(ANS_names, 4, mkChar("priorprobs"));
SET_VECTOR_ELT(ANS, 5,sampleprobs);
SET_STRING_ELT(ANS_names, 5, mkChar("sampleprobs"));
SET_VECTOR_ELT(ANS, 6, mse);
SET_STRING_ELT(ANS_names, 6, mkChar("mse"));
SET_VECTOR_ELT(ANS, 7, beta);
SET_STRING_ELT(ANS_names, 7, mkChar("ols"));
SET_VECTOR_ELT(ANS, 8, se);
SET_STRING_ELT(ANS_names, 8, mkChar("ols.se"));
SET_VECTOR_ELT(ANS, 9, shrinkage);
SET_STRING_ELT(ANS_names, 9, mkChar("shrinkage"));
SET_VECTOR_ELT(ANS, 10, modeldim);
SET_STRING_ELT(ANS_names, 10, mkChar("size"));
SET_VECTOR_ELT(ANS, 11, R2);
SET_STRING_ELT(ANS_names, 11, mkChar("R2"));
setAttrib(ANS, R_NamesSymbol, ANS_names);
UNPROTECT(nProtected);
return(ANS);
}
SEXP glm_deterministic(SEXP Y, SEXP X, SEXP Roffset, SEXP Rweights,
SEXP Rprobinit, SEXP Rmodeldim, SEXP modelprior, SEXP betaprior,
SEXP family, SEXP Rcontrol, SEXP Rlaplace) {
int nProtected = 0;
int nModels=LENGTH(Rmodeldim);
glmstptr * glmfamily;
glmfamily = make_glmfamily_structure(family);
betapriorptr *betapriorfamily;
betapriorfamily = make_betaprior_structure(betaprior, family);
// Rprintf("Allocating Space for %d Models\n", nModels) ;
SEXP ANS = PROTECT(allocVector(VECSXP, 14)); ++nProtected;
SEXP ANS_names = PROTECT(allocVector(STRSXP, 14)); ++nProtected;
SEXP Rprobs = PROTECT(duplicate(Rprobinit)); ++nProtected;
SEXP R2 = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP shrinkage = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP modelspace = PROTECT(allocVector(VECSXP, nModels)); ++nProtected;
SEXP modeldim = PROTECT(duplicate(Rmodeldim)); ++nProtected;
SEXP beta = PROTECT(allocVector(VECSXP, nModels)); ++nProtected;
SEXP se = PROTECT(allocVector(VECSXP, nModels)); ++nProtected;
SEXP deviance = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP modelprobs = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP priorprobs = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP logmarg = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP sampleprobs = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP Q = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
SEXP Rintercept = PROTECT(allocVector(REALSXP, nModels)); ++nProtected;
double *probs,shrinkage_m,logmargy;
//get dimsensions of all variables
int p = INTEGER(getAttrib(X,R_DimSymbol))[1];
int k = LENGTH(modelprobs);
struct Var *vars = (struct Var *) R_alloc(p, sizeof(struct Var)); // Info about the model variables.
probs = REAL(Rprobs);
int n = sortvars(vars, probs, p);
Bit **models = cmatalloc(k,p);
int *model = (int *) R_alloc(p, sizeof(int));
k = topk(models, probs, k, vars, n, p);
/* now fit all top k models */
for (int m=0; m < k; m++) {
int pmodel = 0;
double pigamma = 1.0;
for (int j = 0; j < p; j++) {
model[j] = (int) models[m][j];
pmodel += (int) models[m][j];
pigamma *= (double)((int) models[m][j])*probs[j] +
(1.0 - (double)((int) models[m][j]))*(1.0 - probs[j]);
}
SEXP Rmodel_m = PROTECT(allocVector(INTSXP,pmodel));
GetModel_m(Rmodel_m, model, p);
//evaluate logmargy and shrinkage
SEXP glm_fit = PROTECT(glm_FitModel(X, Y, Rmodel_m, Roffset, Rweights,
glmfamily, Rcontrol, Rlaplace,
betapriorfamily));
double prior_m = compute_prior_probs(model,pmodel,p, modelprior);
logmargy = REAL(getListElement(getListElement(glm_fit, "lpy"),"lpY"))[0];
shrinkage_m = REAL(getListElement(getListElement(glm_fit, "lpy"),
"shrinkage"))[0];
SetModel2(logmargy, shrinkage_m, prior_m, sampleprobs, logmarg, shrinkage, priorprobs, m);
REAL(sampleprobs)[m] = pigamma;
SetModel1(glm_fit, Rmodel_m, beta, se, modelspace, deviance,
R2, Q, Rintercept, m);
UNPROTECT(2);
}
compute_modelprobs(modelprobs, logmarg, priorprobs, k);
compute_margprobs_old(models, modelprobs, probs, k, p);
/* freechmat(models,k); */
SET_VECTOR_ELT(ANS, 0, Rprobs);
SET_STRING_ELT(ANS_names, 0, mkChar("probne0"));
SET_VECTOR_ELT(ANS, 1, modelspace);
SET_STRING_ELT(ANS_names, 1, mkChar("which"));
SET_VECTOR_ELT(ANS, 2, logmarg);
SET_STRING_ELT(ANS_names, 2, mkChar("logmarg"));
SET_VECTOR_ELT(ANS, 3, modelprobs);
SET_STRING_ELT(ANS_names, 3, mkChar("postprobs"));
SET_VECTOR_ELT(ANS, 4, priorprobs);
SET_STRING_ELT(ANS_names, 4, mkChar("priorprobs"));
SET_VECTOR_ELT(ANS, 5,sampleprobs);
SET_STRING_ELT(ANS_names, 5, mkChar("sampleprobs"));
SET_VECTOR_ELT(ANS, 6, deviance);
SET_STRING_ELT(ANS_names, 6, mkChar("deviance"));
SET_VECTOR_ELT(ANS, 7, beta);
SET_STRING_ELT(ANS_names, 7, mkChar("mle"));
SET_VECTOR_ELT(ANS, 8, se);
SET_STRING_ELT(ANS_names, 8, mkChar("mle.se"));
SET_VECTOR_ELT(ANS, 9, shrinkage);
SET_STRING_ELT(ANS_names, 9, mkChar("shrinkage"));
SET_VECTOR_ELT(ANS, 10, modeldim);
SET_STRING_ELT(ANS_names, 10, mkChar("size"));
SET_VECTOR_ELT(ANS, 11, R2);
SET_STRING_ELT(ANS_names, 11, mkChar("R2"));
SET_VECTOR_ELT(ANS, 12, Q);
SET_STRING_ELT(ANS_names, 12, mkChar("Q"));
SET_VECTOR_ELT(ANS, 13, Rintercept);
SET_STRING_ELT(ANS_names, 13, mkChar("intercept"));
setAttrib(ANS, R_NamesSymbol, ANS_names);
UNPROTECT(nProtected);
return(ANS);
}
void hca_displaytopics(char *stem, char *resstem, int topword,
enum ScoreType scoretype, int pmicount, int fullreport) {
int w,k;
uint32_t *indk = NULL;
int Nk_tot = 0;
double (*tscore)(int) = NULL;
double sparsityword = 0;
double sparsitydoc = 0;
double underused = 0;
uint32_t *top1cnt = NULL;
FILE *fp;
float *tpmi = NULL;
char *topfile;
char *repfile;
uint32_t *psort;
FILE *rp = NULL;
float *gtvec = globalprop();
float *gpvec = calloc(ddN.W,sizeof(gpvec[0]));
float *pvec = calloc(ddN.W,sizeof(pvec[0]));
if ( pmicount>topword )
pmicount = topword;
if ( scoretype == ST_idf ) {
tscore = idfscore;
} else if ( scoretype == ST_phi ) {
tscore = phiscore;
} else if ( scoretype == ST_count ) {
tscore = countscore;
} else if ( scoretype == ST_cost ) {
tscore = costscore;
} else if ( scoretype == ST_Q ) {
tscore = Qscore;
lowerQ = 1.0/ddN.T;
}
/*
* first collect counts of each word/term,
* and build gpvec (mean word probs)
*/
build_NwK();
{
/*
* gpvec[] is normalised NwK[]
*/
double tot = 0;
for (w=0; w<ddN.W; w++)
tot += gpvec[w] = NwK[w]+0.1;
for (w=0; w<ddN.W; w++)
gpvec[w] /= tot;
}
if ( ddS.Nwt ) {
for (k=0; k<ddN.T; k++) {
Nk_tot += ddS.NWt[k];
}
}
psort = sorttops(gtvec, ddN.T);
top1cnt = hca_top1cnt();
if ( !top1cnt )
yap_quit("Cannot allocate top1cnt in hca_displaytopics()\n");
if ( pmicount ) {
tpmi = malloc(sizeof(*tpmi)*(ddN.T+1));
if ( !tpmi )
yap_quit("Cannot allocate tpmi in hca_displaytopics()\n");
}
indk = malloc(sizeof(*indk)*ddN.W);
if ( !indk )
yap_quit("Cannot allocate indk in hca_displaytopics()\n");
/*
* two passes through,
* first to build the top words and dump to file
*/
repfile = yap_makename(resstem,".topset");
topfile = yap_makename(resstem,".toplst");
fp = fopen(topfile,"w");
if ( !fp )
yap_sysquit("Cannot open file '%s' for write\n", topfile);
yap_message("\n");
for (k=0; k<ddN.T; k++) {
int cnt;
tscorek = k;
/*
* build sorted word list
*/
cnt = buildindk(k, indk);
topk(topword, cnt, indk, tscore);
if ( cnt==0 )
continue;
/*
* dump words to file
*/
fprintf(fp,"%d: ", k);
for (w=0; w<topword && w<cnt; w++) {
fprintf(fp," %d", (int)indk[w]);
}
fprintf(fp, "\n");
}
if ( ddP.PYbeta && (ddP.phi==NULL || ddP.betapr) ) {
int cnt;
/*
* dump root words
*/
tscorek = -1;
cnt = buildindk(-1, indk);
topk(topword, cnt, indk, (ddP.phi==NULL)?countscore:phiscore);
fprintf(fp,"-1:");
for (w=0; w<topword && w<cnt; w++) {
fprintf(fp," %d", (int)indk[w]);
}
fprintf(fp, "\n");
}
fclose(fp);
if ( verbose>1 ) yap_message("\n");
if ( pmicount ) {
/*
* compute PMI
*/
char *toppmifile;
char *pmifile;
double *tp;
tp = dvec(ddN.T);
pmifile=yap_makename(stem,".pmi");
toppmifile=yap_makename(resstem,".toppmi");
get_probs(tp);
report_pmi(topfile, pmifile, toppmifile, ddN.T, ddN.W, 1,
pmicount, tp, tpmi);
free(toppmifile);
free(pmifile);
free(tp);
}
/*
* now report words and diagnostics
*/
//ttop_open(topfile);
if ( fullreport ) {
rp = fopen(repfile,"w");
if ( !rp )
yap_sysquit("Cannot open file '%s' for write\n", repfile);
fprintf(rp, "#topic index rank prop word-sparse doc-sparse eff-words eff-docs docs-bound top-one "
"dist-unif dist-unigrm");
if ( PCTL_BURSTY() )
fprintf(rp, " burst-concent");
if ( ddN.tokens )
fprintf(rp, " ave-length");
fprintf(rp, " coher");
if ( pmicount )
fprintf(rp, " pmi");
fprintf(rp, "\n#word topic index rank");
if ( ddS.Nwt )
fprintf(rp, " count");
fprintf(rp, " prop cumm df coher\n");
}
for (k=0; k<ddN.T; k++) {
int cnt;
int kk = psort[k];
uint32_t **dfmtx;
if ( ddP.phi==NULL && ddS.NWt[kk]==0 )
continue;
/*
* grab word prob vec for later use
*/
if ( ddS.Nwt ) {
int w;
for (w=0; w<ddN.W; w++)
pvec[w] = wordprob(w,kk);
} else if ( ddP.phi )
fv_copy(pvec, ddP.phi[kk], ddN.W);
else if ( ddS.phi )
fv_copy(pvec, ddS.phi[kk], ddN.W);
/*
* rebuild word list
*/
tscorek = kk;
cnt = buildindk(kk, indk);
topk(topword, cnt, indk, tscore);
if ( topword<cnt )
cnt = topword;
assert(cnt>0);
/*
* df stats for topic returned as matrix
*/
dfmtx = hca_dfmtx(indk, cnt, kk);
if ( ddS.Nwt && (ddS.NWt[kk]*ddN.T*100<Nk_tot || ddS.NWt[kk]<5 ))
underused++;
/*
* print stats for topic
* Mallet: tokens, doc_ent, ave-word-len, coher.,
* uni-dist, corp-dist, eff-no-words
*/
yap_message("Topic %d/%d", kk, k);
{
/*
* compute diagnostics
*/
double prop = gtvec[kk];
float *dprop = docprop(kk);
double spw = 0;
double spd = ((double)nonzero_Ndt(kk))/((double)ddN.DT);
double ew = exp(fv_entropy(pvec,ddN.W));
double ud = fv_helldistunif(pvec,ddN.W);
double pd = fv_helldist(pvec,gpvec,ddN.W);
double sl = fv_avestrlen(pvec,ddN.tokens,ddN.W);
double co = coherence(dfmtx, cnt);
double ed = dprop?exp(fv_entropy(dprop,ddN.DT)):ddN.DT;
double da = dprop?fv_bound(dprop,ddN.DT,1.0/sqrt((double)ddN.T)):0;
sparsitydoc += spd;
yap_message((ddN.T>200)?" p=%.3lf%%":" p=%.2lf%%",100*prop);
if ( ddS.Nwt ) {
spw = ((double)nonzero_Nwt(kk))/((double)ddN.W);
sparsityword += spw;
yap_message(" ws=%.1lf%%", 100*(1-spw));
}
yap_message(" ds=%.1lf%%", 100*(1-spd) );
yap_message(" ew=%.0lf", ew);
yap_message(" ed=%.1lf", ed);
yap_message(" da=%.0lf", da+0.1);
yap_message(" t1=%u", top1cnt[kk]);
yap_message(" ud=%.3lf", ud);
yap_message(" pd=%.3lf", pd);
if ( PCTL_BURSTY() )
yap_message(" bd=%.3lf", ddP.bdk[kk]);
if ( ddN.tokens )
yap_message(" sl=%.2lf", sl);
yap_message(" co=%.3lf%%", co);
if ( pmicount )
yap_message(" pmi=%.3f", tpmi[kk]);
if ( fullreport ) {
fprintf(rp,"topic %d %d", kk, k);
fprintf(rp," %.6lf", prop);
if ( ddS.Nwt ) {
fprintf(rp," %.6lf", (1-spw));
} else {
fprintf(rp," 0");
}
fprintf(rp," %.6lf", (1-spd) );
fprintf(rp," %.2lf", ew);
fprintf(rp," %.2lf", ed);
fprintf(rp," %.0lf", da+0.1);
fprintf(rp," %u", top1cnt[kk]);
fprintf(rp," %.6lf", ud);
fprintf(rp," %.6lf", pd);
if ( PCTL_BURSTY() )
fprintf(rp," %.3lf", ddP.bdk[kk]);
fprintf(rp," %.4lf", (ddN.tokens)?sl:0);
fprintf(rp," %.6lf", co);
if ( pmicount )
fprintf(rp," %.4f", tpmi[kk]);
fprintf(rp,"\n");
}
if ( dprop) free(dprop);
}
if ( verbose>1 ) {
double pcumm = 0;
/*
* print top words:
* Mallet: rank, count, prob, cumm, docs, coh
*/
yap_message("\ntopic %d/%d", kk, k);
yap_message(" words=");
for (w=0; w<cnt; w++) {
if ( w>0 ) yap_message(",");
if ( ddN.tokens )
yap_message("%s", ddN.tokens[indk[w]]);
else
yap_message("%d", indk[w]);
if ( verbose>2 )
yap_message("(%6lf)", tscore(indk[w]));
if ( fullreport ) {
fprintf(rp, "word %d %d %d", kk, indk[w], w);
if ( ddS.Nwt )
fprintf(rp, " %d", ddS.Nwt[indk[w]][kk]);
pcumm += pvec[indk[w]];
fprintf(rp, " %.6f %.6f", pvec[indk[w]], pcumm);
fprintf(rp, " %d", dfmtx[w][w]);
fprintf(rp, " %.6f", coherence_word(dfmtx, cnt, w));
if ( ddN.tokens )
fprintf(rp, " %s", ddN.tokens[indk[w]]);
fprintf(rp, "\n");
}
}
}
yap_message("\n");
free(dfmtx[0]); free(dfmtx);
}
if ( verbose>1 && ddP.PYbeta && (ddP.phi==NULL || ddP.betapr) ) {
int cnt;
double pcumm = 0;
/*
* print root words
*/
tscorek = -1;
cnt = buildindk(-1,indk);
topk(topword, cnt, indk, (ddP.phi==NULL)?countscore:phiscore);
/*
* cannot build df mtx for root because
* it is latent w.r.t. topics
*/
yap_message("Topic root words=");
if ( fullreport ) {
int w;
for (w=0; w<ddN.W; w++)
pvec[w] = betabasewordprob(w);
double ew = exp(fv_entropy(pvec,ddN.W));
double ud = fv_helldistunif(pvec,ddN.W);
double pd = fv_helldist(pvec,gpvec,ddN.W);
fprintf(rp,"topic -1 -1 0 0");
fprintf(rp," %.4lf", ew);
fprintf(rp," %.6lf", ud);
fprintf(rp," %.6lf", pd);
fprintf(rp,"\n");
}
for (w=0; w<topword && w<cnt; w++) {
if ( w>0 ) yap_message(",");
if ( ddN.tokens )
yap_message("%s", ddN.tokens[indk[w]]);
else
yap_message("%d", indk[w]);
if ( verbose>2 )
yap_message("(%6lf)", countscore(indk[w]));
if ( fullreport ) {
fprintf(rp, "word %d %d %d", -1, indk[w], w);
if ( ddS.TwT )
fprintf(rp, " %d", ddS.TwT[w]);
pcumm += pvec[indk[w]];
fprintf(rp, " %.6f %.6f", pvec[indk[w]], pcumm);
fprintf(rp, " 0 0");
if ( ddN.tokens )
fprintf(rp, " %s", ddN.tokens[indk[w]]);
fprintf(rp, "\n");
}
}
yap_message("\n");
}
yap_message("\n");
if ( rp )
fclose(rp);
if ( ddS.Nwt )
yap_message("Average topicXword sparsity = %.2lf%%\n",
100*(1-sparsityword/ddN.T) );
yap_message("Average docXtopic sparsity = %.2lf%%\n"
"Underused topics = %.1lf%%\n",
100*(1-sparsitydoc/ddN.T),
100.0*underused/(double)ddN.T);
if ( pmicount )
yap_message("Average PMI = %.3f\n", tpmi[ddN.T]);
/*
* print
*/
if ( 1 ) {
float **cmtx = hca_topmtx();
int t1, t2;
int m1, m2;
float mval;
char *corfile = yap_makename(resstem,".topcor");
fp = fopen(corfile,"w");
if ( !fp )
yap_sysquit("Cannot open file '%s' for write\n", corfile);
/*
* print file
*/
for (t1=0; t1<ddN.T; t1++) {
for (t2=0; t2<t1; t2++)
if ( cmtx[t1][t2]>1.0e-3 )
fprintf(fp, "%d %d %0.6f\n", t1, t2, cmtx[t1][t2]);
}
fclose(fp);
free(corfile);
/*
* display maximum
*/
m1 = 1; m2 = 0;
mval = cmtx[1][0];
for (t1=0; t1<ddN.T; t1++) {
for (t2=0; t2<t1; t2++) {
if ( mval<cmtx[t1][t2] ) {
mval = cmtx[t1][t2];
m1 = t1;
m2 = t2;
}
}
}
yap_message("Maximum correlated topics (%d,%d) = %f\n", m1, m2, mval);
free(cmtx[0]); free(cmtx);
}
/*
* print burstiness report
*/
if ( PCTL_BURSTY() ) {
int tottbl = 0;
int totmlttbl = 0;
int totmlt = 0;
int i;
for (i=0; i<ddN.NT; i++) {
if ( Z_issetr(ddS.z[i]) ) {
if ( M_multi(i) )
totmlttbl++;
tottbl++;
}
if ( M_multi(i) )
totmlt++;
}
yap_message("Burst report: multis=%.2lf%%, tables=%.2lf%%, tbls-in-multis=%.2lf%%\n",
100.0*((double)ddM.dim_multiind)/ddN.N,
100.0*((double)tottbl)/ddN.NT,
100.0*((double)totmlttbl)/totmlt);
}
yap_message("\n");
free(topfile);
if ( repfile ) free(repfile);
if ( top1cnt ) free(top1cnt);
free(indk);
free(psort);
if ( pmicount )
free(tpmi);
if ( NwK ) {
free(NwK);
NwK = NULL;
}
free(pvec);
free(gtvec);
free(gpvec);
}