bool TreeProbability::splitNodeInternal(size_t nodeID, std::vector<size_t>& possible_split_varIDs) {
// Check node size, stop if maximum reached
if (sampleIDs[nodeID].size() <= min_node_size) {
addToTerminalNodes(nodeID);
return true;
}
// Check if node is pure and set split_value to estimate and stop if pure
bool pure = true;
double pure_value = 0;
for (size_t i = 0; i < sampleIDs[nodeID].size(); ++i) {
double value = data->get(sampleIDs[nodeID][i], dependent_varID);
if (i != 0 && value != pure_value) {
pure = false;
break;
}
pure_value = value;
}
if (pure) {
addToTerminalNodes(nodeID);
return true;
}
// Find best split, stop if no decrease of impurity
bool stop = findBestSplit(nodeID, possible_split_varIDs);
if (stop) {
addToTerminalNodes(nodeID);
return true;
}
return false;
}
bool TreeRegression::splitNodeInternal(size_t nodeID, std::vector<size_t>& possible_split_varIDs) {
// Check node size, stop if maximum reached
if (sampleIDs[nodeID].size() <= min_node_size) {
split_values[nodeID] = estimate(nodeID);
return true;
}
// Find best split, stop if no decrease of impurity
bool stop = findBestSplit(nodeID, possible_split_varIDs);
if (stop) {
split_values[nodeID] = estimate(nodeID);
return true;
}
return false;
}
void KdTreeNode::computeChildren()
{
int axis = depth % 3;
if (terminate()) { // the node is a leaf
return;
}
float limit;
if (! findBestSplit(axis, &limit)) return;
createChildren(axis, limit);
splitObjects(axis, &objects, &(left->objects), &(right->objects));
objects.clear(); // only leaves keep their objects
#pragma omp task
left->computeChildren();
#pragma omp task
right->computeChildren();
#pragma omp taskwait
}
void regTree(double *x, double *y, int mdim, int nsample, int *lDaughter,
int *rDaughter,
double *upper, double *avnode, SMALL_INT *nodestatus, int nrnodes,
int *treeSize, int nthsize, int mtry, int *mbest, int *cat,
double *tgini, int *varUsed) {
int i, j, k, m, ncur;
static int *jdex, *nodestart, *nodepop;
int ndstart, ndend, ndendl, nodecnt, jstat, msplit;
double d, ss, av, decsplit, ubest, sumnode;
if (in_regTree==-99){
free(nodestart);
free(jdex);
free(nodepop);
// PRINTF("giving up mem in in_regTree\n");
return;
}
if (in_regTree==0){
in_regTree=1;
nodestart = (int *) calloc(nrnodes, sizeof(int));
nodepop = (int *) calloc(nrnodes, sizeof(int));
jdex = (int *) calloc(nsample, sizeof(int));
}
/* initialize some arrays for the tree */
zeroSMALLInt(nodestatus, nrnodes);
zeroInt(nodestart, nrnodes);
zeroInt(nodepop, nrnodes);
zeroDouble(avnode, nrnodes);
for (i = 1; i <= nsample; ++i) jdex[i-1] = i;
ncur = 0;
nodestart[0] = 0;
nodepop[0] = nsample;
nodestatus[0] = NODE_TOSPLIT;
/* compute mean and sum of squares for Y */
av = 0.0;
ss = 0.0;
for (i = 0; i < nsample; ++i) {
d = y[jdex[i] - 1];
ss += i * (av - d) * (av - d) / (i + 1);
av = (i * av + d) / (i + 1);
}
avnode[0] = av;
/* start main loop */
for (k = 0; k < nrnodes - 2; ++k) {
if (k > ncur || ncur >= nrnodes - 2) break;
/* skip if the node is not to be split */
if (nodestatus[k] != NODE_TOSPLIT) continue;
/* initialize for next call to findbestsplit */
ndstart = nodestart[k];
ndend = ndstart + nodepop[k] - 1;
nodecnt = nodepop[k];
sumnode = nodecnt * avnode[k];
jstat = 0;
decsplit = 0.0;
findBestSplit(x, jdex, y, mdim, nsample, ndstart, ndend, &msplit,
&decsplit, &ubest, &ndendl, &jstat, mtry, sumnode,
nodecnt, cat);
if (jstat == 1) {
/* Node is terminal: Mark it as such and move on to the next. */
nodestatus[k] = NODE_TERMINAL;
continue;
}
/* Found the best split. */
mbest[k] = msplit;
varUsed[msplit - 1] = 1;
upper[k] = ubest;
tgini[msplit - 1] += decsplit;
nodestatus[k] = NODE_INTERIOR;
/* leftnode no.= ncur+1, rightnode no. = ncur+2. */
nodepop[ncur + 1] = ndendl - ndstart + 1;
nodepop[ncur + 2] = ndend - ndendl;
nodestart[ncur + 1] = ndstart;
nodestart[ncur + 2] = ndendl + 1;
/* compute mean and sum of squares for the left daughter node */
av = 0.0;
ss = 0.0;
for (j = ndstart; j <= ndendl; ++j) {
d = y[jdex[j]-1];
m = j - ndstart;
ss += m * (av - d) * (av - d) / (m + 1);
av = (m * av + d) / (m+1);
}
avnode[ncur+1] = av;
nodestatus[ncur+1] = NODE_TOSPLIT;
if (nodepop[ncur + 1] <= nthsize) {
nodestatus[ncur + 1] = NODE_TERMINAL;
}
/* compute mean and sum of squares for the right daughter node */
av = 0.0;
ss = 0.0;
for (j = ndendl + 1; j <= ndend; ++j) {
d = y[jdex[j]-1];
m = j - (ndendl + 1);
ss += m * (av - d) * (av - d) / (m + 1);
av = (m * av + d) / (m + 1);
}
avnode[ncur + 2] = av;
nodestatus[ncur + 2] = NODE_TOSPLIT;
if (nodepop[ncur + 2] <= nthsize) {
nodestatus[ncur + 2] = NODE_TERMINAL;
}
/* map the daughter nodes */
lDaughter[k] = ncur + 1 + 1;
rDaughter[k] = ncur + 2 + 1;
/* Augment the tree by two nodes. */
ncur += 2;
}
*treeSize = nrnodes;
for (k = nrnodes - 1; k >= 0; --k) {
if (nodestatus[k] == 0) (*treeSize)--;
if (nodestatus[k] == NODE_TOSPLIT) {
nodestatus[k] = NODE_TERMINAL;
}
}
}
void regRF(double *x, double *y, int *xdim, int *sampsize,
int *nthsize, int *nrnodes, int *nTree, int *mtry, int *imp,
int *cat, int maxcat, int *jprint, int doProx, int oobprox,
int biasCorr, double *yptr, double *errimp, double *impmat,
double *impSD, double *prox, int *treeSize, SMALL_INT *nodestatus,
int *lDaughter, int *rDaughter, double *avnode, int *mbest,
double *upper, double *mse, const int *keepf, int *replace,
int testdat, double *xts, int *nts, double *yts, int labelts,
double *yTestPred, double *proxts, double *msets, double *coef,
int *nout, int *inbag) {
/*************************************************************************
* Input:
* mdim=number of variables in data set
* nsample=number of cases
*
* nthsize=number of cases in a node below which the tree will not split,
* setting nthsize=5 generally gives good results.
*
* nTree=number of trees in run. 200-500 gives pretty good results
*
* mtry=number of variables to pick to split on at each node. mdim/3
* seems to give genrally good performance, but it can be
* altered up or down
*
* imp=1 turns on variable importance. This is computed for the
* mth variable as the percent rise in the test set mean sum-of-
* squared errors when the mth variable is randomly permuted.
*
*************************************************************************/
//PRINTF( "*jprint: %d\n", *jprint );
//mexEvalString( "pause(0.0001)" );
double errts = 0.0, averrb, meanY, meanYts, varY, varYts, r, xrand,
errb = 0.0, resid=0.0, ooberr, ooberrperm, delta, *resOOB;
double *yb, *xtmp, *xb, *ytr, *ytree = NULL, *tgini;
int k, m, mr, n, nOOB, j, jout, idx, ntest, last, ktmp, nPerm,
nsample, mdim, keepF, keepInbag;
int *oobpair, varImp, localImp, *varUsed;
int *in, *nind, *nodex, *nodexts = NULL;
//Abhi:temp variable
double tmp_d = 0;
int tmp_i;
SMALL_INT tmp_c;
//Do initialization for COKUS's Random generator
seedMT(2*rand()+1); //works well with odd number so why don't use that
nsample = xdim[0];
mdim = xdim[1];
ntest = *nts;
varImp = imp[0];
localImp = imp[1];
nPerm = imp[2]; //PRINTF("nPerm %d\n",nPerm);
keepF = keepf[0];
keepInbag = keepf[1];
if (*jprint == 0) *jprint = *nTree + 1;
yb = (double *) calloc(*sampsize, sizeof(double));
xb = (double *) calloc(mdim * *sampsize, sizeof(double));
ytr = (double *) calloc(nsample, sizeof(double));
xtmp = (double *) calloc(nsample, sizeof(double));
resOOB = (double *) calloc(nsample, sizeof(double));
in = (int *) calloc(nsample, sizeof(int));
nodex = (int *) calloc(nsample, sizeof(int));
varUsed = (int *) calloc(mdim, sizeof(int));
nind = *replace ? NULL : (int *) calloc(nsample, sizeof(int));
if (testdat) {
ytree = (double *) calloc(ntest, sizeof(double));
nodexts = (int *) calloc(ntest, sizeof(int));
}
oobpair = (doProx && oobprox) ?
(int *) calloc(nsample * nsample, sizeof(int)) : NULL;
/* If variable importance is requested, tgini points to the second
"column" of errimp, otherwise it's just the same as errimp. */
tgini = varImp ? errimp + mdim : errimp;
averrb = 0.0;
meanY = 0.0;
varY = 0.0;
zeroDouble(yptr, nsample);
zeroInt(nout, nsample);
for (n = 0; n < nsample; ++n) {
varY += n * (y[n] - meanY)*(y[n] - meanY) / (n + 1);
meanY = (n * meanY + y[n]) / (n + 1);
}
varY /= nsample;
varYts = 0.0;
meanYts = 0.0;
if (testdat) {
for (n = 0; n < ntest; ++n) {
varYts += n * (yts[n] - meanYts)*(yts[n] - meanYts) / (n + 1);
meanYts = (n * meanYts + yts[n]) / (n + 1);
}
varYts /= ntest;
}
if (doProx) {
zeroDouble(prox, nsample * nsample);
if (testdat) zeroDouble(proxts, ntest * (nsample + ntest));
}
if (varImp) {
zeroDouble(errimp, mdim * 2);
if (localImp) zeroDouble(impmat, nsample * mdim);
} else {
zeroDouble(errimp, mdim);
}
if (labelts) zeroDouble(yTestPred, ntest);
/* print header for running output */
if (*jprint <= *nTree) {
PRINTF(" | Out-of-bag ");
if (testdat) PRINTF("| Test set ");
PRINTF("|\n");
PRINTF("Tree | MSE %%Var(y) ");
if (testdat) PRINTF("| MSE %%Var(y) ");
PRINTF("|\n");
// mexEvalString( "pause(0.001)" );
}
GetRNGstate();
/*************************************
* Start the loop over trees.
*************************************/
for (j = 0; j < *nTree; ++j) {
//PRINTF("tree num %d\n",j);fflush(stdout);
//PRINTF("1. maxcat %d, jprint %d, doProx %d, oobProx %d, biasCorr %d\n", *maxcat, *jprint, doProx, oobprox, biasCorr);
idx = keepF ? j * *nrnodes : 0;
zeroInt(in, nsample);
zeroInt(varUsed, mdim);
/* Draw a random sample for growing a tree. */
// PRINTF("1.8. maxcat %d, jprint %d, doProx %d, oobProx %d, biasCorr %d testdat %d\n", maxcat, *jprint, doProx, oobprox, biasCorr,testdat);
if (*replace) { /* sampling with replacement */
for (n = 0; n < *sampsize; ++n) {
xrand = unif_rand();
k = (int)(xrand * nsample);
in[k] = 1;
yb[n] = y[k];
for(m = 0; m < mdim; ++m) {
xb[m + n * mdim] = x[m + k * mdim];
}
}
} else { /* sampling w/o replacement */
for (n = 0; n < nsample; ++n) nind[n] = n;
last = nsample - 1;
for (n = 0; n < *sampsize; ++n) {
ktmp = (int) (unif_rand() * (last+1));
k = nind[ktmp];
swapInt(nind[ktmp], nind[last]);
last--;
in[k] = 1;
yb[n] = y[k];
for(m = 0; m < mdim; ++m) {
xb[m + n * mdim] = x[m + k * mdim];
}
}
}
if (keepInbag) {
for (n = 0; n < nsample; ++n) inbag[n + j * nsample] = in[n];
}
// PRINTF("1.9. maxcat %d, jprint %d, doProx %d, oobProx %d, biasCorr %d testdat %d\n", maxcat, *jprint, doProx, oobprox, biasCorr,testdat);
/* grow the regression tree */
regTree(xb, yb, mdim, *sampsize, lDaughter + idx, rDaughter + idx,
upper + idx, avnode + idx, nodestatus + idx, *nrnodes,
treeSize + j, *nthsize, *mtry, mbest + idx, cat, tgini,
varUsed);
/* predict the OOB data with the current tree */
/* ytr is the prediction on OOB data by the current tree */
// PRINTF("2. maxcat %d, jprint %d, doProx %d, oobProx %d, biasCorr %d testdat %d\n", maxcat, *jprint, doProx, oobprox, biasCorr,testdat);
predictRegTree(x, nsample, mdim, lDaughter + idx,
rDaughter + idx, nodestatus + idx, ytr, upper + idx,
avnode + idx, mbest + idx, treeSize[j], cat, maxcat,
nodex);
/* yptr is the aggregated prediction by all trees grown so far */
errb = 0.0;
ooberr = 0.0;
jout = 0; /* jout is the number of cases that has been OOB so far */
nOOB = 0; /* nOOB is the number of OOB samples for this tree */
for (n = 0; n < nsample; ++n) {
if (in[n] == 0) {
nout[n]++;
nOOB++;
yptr[n] = ((nout[n]-1) * yptr[n] + ytr[n]) / nout[n];
resOOB[n] = ytr[n] - y[n];
ooberr += resOOB[n] * resOOB[n];
}
if (nout[n]) {
jout++;
errb += (y[n] - yptr[n]) * (y[n] - yptr[n]);
}
}
errb /= jout;
/* Do simple linear regression of y on yhat for bias correction. */
if (biasCorr) simpleLinReg(nsample, yptr, y, coef, &errb, nout);
//PRINTF("2.5.maxcat %d, jprint %d, doProx %d, oobProx %d, biasCorr %d\n", maxcat, *jprint, doProx, oobprox, biasCorr);
/* predict testset data with the current tree */
if (testdat) {
predictRegTree(xts, ntest, mdim, lDaughter + idx,
rDaughter + idx, nodestatus + idx, ytree,
upper + idx, avnode + idx,
mbest + idx, treeSize[j], cat, maxcat, nodexts);
/* ytree is the prediction for test data by the current tree */
/* yTestPred is the average prediction by all trees grown so far */
errts = 0.0;
for (n = 0; n < ntest; ++n) {
yTestPred[n] = (j * yTestPred[n] + ytree[n]) / (j + 1);
}
/* compute testset MSE */
if (labelts) {
for (n = 0; n < ntest; ++n) {
resid = biasCorr ?
yts[n] - (coef[0] + coef[1]*yTestPred[n]) :
yts[n] - yTestPred[n];
errts += resid * resid;
}
errts /= ntest;
}
}
//PRINTF("2.6.maxcat %d, jprint %d, doProx %d, oobProx %d, biasCorr %d, testdat %d\n", maxcat, *jprint, doProx, oobprox, biasCorr,testdat);
/* Print running output. */
if ((j + 1) % *jprint == 0) {
PRINTF("%4d |", j + 1);
PRINTF(" %8.4g %8.2f ", errb, 100 * errb / varY);
if(labelts == 1) PRINTF("| %8.4g %8.2f ",
errts, 100.0 * errts / varYts);
PRINTF("|\n");
fflush(stdout);
// mexEvalString("pause(.001);"); // to dump string.
}
//PRINTF("2.7.maxcat %d, jprint %d, doProx %d, oobProx %d, biasCorr %d, testdat %d\n", maxcat, *jprint, doProx, oobprox, biasCorr,testdat);
mse[j] = errb;
if (labelts) msets[j] = errts;
//PRINTF("2.701 j %d, nTree %d, errts %f errb %f \n", j, *nTree, errts,errb);
//PRINTF("2.71.maxcat %d, jprint %d, doProx %d, oobProx %d, biasCorr %d, testdat %d\n", maxcat, *jprint, doProx, oobprox, biasCorr,testdat);
/* DO PROXIMITIES */
if (doProx) {
computeProximity(prox, oobprox, nodex, in, oobpair, nsample);
/* proximity for test data */
if (testdat) {
/* In the next call, in and oobpair are not used. */
computeProximity(proxts, 0, nodexts, in, oobpair, ntest);
for (n = 0; n < ntest; ++n) {
for (k = 0; k < nsample; ++k) {
if (nodexts[n] == nodex[k]) {
proxts[n + ntest * (k+ntest)] += 1.0;
}
}
}
}
}
//PRINTF("2.8.maxcat %d, jprint %d, doProx %d, oobProx %d, biasCorr %d, testdat %d\n", maxcat, *jprint, doProx, oobprox, biasCorr,testdat);
/* Variable importance */
if (varImp) {
for (mr = 0; mr < mdim; ++mr) {
if (varUsed[mr]) { /* Go ahead if the variable is used */
/* make a copy of the m-th variable into xtmp */
for (n = 0; n < nsample; ++n)
xtmp[n] = x[mr + n * mdim];
ooberrperm = 0.0;
for (k = 0; k < nPerm; ++k) {
permuteOOB(mr, x, in, nsample, mdim);
predictRegTree(x, nsample, mdim, lDaughter + idx,
rDaughter + idx, nodestatus + idx, ytr,
upper + idx, avnode + idx, mbest + idx,
treeSize[j], cat, maxcat, nodex);
for (n = 0; n < nsample; ++n) {
if (in[n] == 0) {
r = ytr[n] - y[n];
ooberrperm += r * r;
if (localImp) {
impmat[mr + n * mdim] +=
(r*r - resOOB[n]*resOOB[n]) / nPerm;
}
}
}
}
delta = (ooberrperm / nPerm - ooberr) / nOOB;
errimp[mr] += delta;
impSD[mr] += delta * delta;
/* copy original data back */
for (n = 0; n < nsample; ++n)
x[mr + n * mdim] = xtmp[n];
}
}
}
// PRINTF("3. maxcat %d, jprint %d, doProx %d, oobProx %d, biasCorr %d testdat %d\n", maxcat, *jprint, doProx, oobprox, biasCorr,testdat);
}
PutRNGstate();
/* end of tree iterations=======================================*/
if (biasCorr) { /* bias correction for predicted values */
for (n = 0; n < nsample; ++n) {
if (nout[n]) yptr[n] = coef[0] + coef[1] * yptr[n];
}
if (testdat) {
for (n = 0; n < ntest; ++n) {
yTestPred[n] = coef[0] + coef[1] * yTestPred[n];
}
}
}
if (doProx) {
for (n = 0; n < nsample; ++n) {
for (k = n + 1; k < nsample; ++k) {
prox[nsample*k + n] /= oobprox ?
(oobpair[nsample*k + n] > 0 ? oobpair[nsample*k + n] : 1) :
*nTree;
prox[nsample * n + k] = prox[nsample * k + n];
}
prox[nsample * n + n] = 1.0;
}
if (testdat) {
for (n = 0; n < ntest; ++n)
for (k = 0; k < ntest + nsample; ++k)
proxts[ntest*k + n] /= *nTree;
}
}
if (varImp) {
for (m = 0; m < mdim; ++m) {
errimp[m] = errimp[m] / *nTree;
impSD[m] = sqrt( ((impSD[m] / *nTree) -
(errimp[m] * errimp[m])) / *nTree );
if (localImp) {
for (n = 0; n < nsample; ++n) {
impmat[m + n * mdim] /= nout[n];
}
}
}
}
for (m = 0; m < mdim; ++m) tgini[m] /= *nTree;
//addition by abhi
//in order to release the space stored by the variable in findBestSplit
// call by setting
in_findBestSplit=-99;
findBestSplit(&tmp_d, &tmp_i, &tmp_d, tmp_i, tmp_i,
tmp_i, tmp_i, &tmp_i, &tmp_d,
&tmp_d, &tmp_i, &tmp_i, tmp_i,
tmp_d, tmp_i, &tmp_i);
//do the same freeing of space by calling with -99
in_regTree=-99;
regTree(&tmp_d, &tmp_d, tmp_i, tmp_i, &tmp_i,
&tmp_i,
&tmp_d, &tmp_d, &tmp_c, tmp_i,
&tmp_i, tmp_i, tmp_i, &tmp_i, &tmp_i,
&tmp_d, &tmp_i);
free(yb);
free(xb);
free(ytr);
free(xtmp);
free(resOOB);
free(in);
free(nodex);
free(varUsed);
if (!(*replace) )
free(nind);
if (testdat) {
free(ytree);
free(nodexts);
}
if (doProx && oobprox)
free(oobpair) ;
}
