@Override
public void onCompletion(CountedCompleter caller) {
ScoreBuildHistogram sbh = (ScoreBuildHistogram) caller;
// System.out.println(sbh.profString());
final int leafk = _leafs[_k];
int tmax = _tree.len(); // Number of total splits in tree K
for (int leaf = leafk; leaf < tmax; leaf++) { // Visit all the new splits (leaves)
DTree.UndecidedNode udn = _tree.undecided(leaf);
// System.out.println((_st._nclass==1?"Regression":("Class
// "+_fr2.vecs()[_st._ncols].domain()[_k]))+",\n Undecided node:"+udn);
// Replace the Undecided with the Split decision
DTree.DecidedNode dn = _st.makeDecided(udn, sbh._hcs[leaf - leafk]);
// System.out.println(dn +
// " > Split: " + dn._split + " L/R:" + dn._split._n0+" +
// "+dn._split._n1);
if (dn._split._col == -1) udn.do_not_split();
else {
_did_split = true;
DTree.Split s = dn._split; // Accumulate squared error improvements per variable
AtomicUtils.FloatArray.add(_improvPerVar, s.col(), (float) (s.pre_split_se() - s.se()));
}
}
_leafs[_k] = tmax; // Setup leafs for next tree level
int new_leafs = _tree.len() - tmax;
_hcs[_k] = new DHistogram[new_leafs][ /*ncol*/];
for (int nl = tmax; nl < _tree.len(); nl++) _hcs[_k][nl - tmax] = _tree.undecided(nl)._hs;
if (_did_split) _tree._depth++;
}
// Find the column with the best split (lowest score). Unlike RF, GBM
// scores on all columns and selects splits on all columns.
@Override
public DTree.Split bestCol(UndecidedNode u, DHistogram[] hs) {
DTree.Split best =
new DTree.Split(-1, -1, false, Double.MAX_VALUE, Double.MAX_VALUE, 0L, 0L, 0, 0);
if (hs == null) return best;
for (int i = 0; i < hs.length; i++) {
if (hs[i] == null || hs[i].nbins() <= 1) continue;
DTree.Split s = hs[i].scoreMSE(i);
if (s == null) continue;
if (best == null || s.se() < best.se()) best = s;
if (s.se() <= 0) break; // No point in looking further!
}
return best;
}
