// --------------------------------------------------------------------------
// Build the next k-trees, which is trying to correct the residual error from
// the prior trees. From LSE2, page 387. Step 2b ii, iii.
private DTree[] buildNextKTrees(Frame fr) {
// We're going to build K (nclass) trees - each focused on correcting
// errors for a single class.
final DTree[] ktrees = new DTree[_nclass];
// Initial set of histograms. All trees; one leaf per tree (the root
// leaf); all columns
DHistogram hcs[][][] = new DHistogram[_nclass][1 /*just root leaf*/][_ncols];
for (int k = 0; k < _nclass; k++) {
// Initially setup as-if an empty-split had just happened
if (_distribution == null || _distribution[k] != 0) {
// The Boolean Optimization
// This optimization assumes the 2nd tree of a 2-class system is the
// inverse of the first. This is false for DRF (and true for GBM) -
// DRF picks a random different set of columns for the 2nd tree.
if (k == 1 && _nclass == 2) continue;
ktrees[k] = new DTree(fr._names, _ncols, (char) nbins, (char) _nclass, min_rows);
new GBMUndecidedNode(
ktrees[k],
-1,
DHistogram.initialHist(fr, _ncols, nbins, hcs[k][0], false)); // The "root" node
}
}
int[] leafs = new int[_nclass]; // Define a "working set" of leaf splits, from here to tree._len
// ----
// ESL2, page 387. Step 2b ii.
// One Big Loop till the ktrees are of proper depth.
// Adds a layer to the trees each pass.
int depth = 0;
for (; depth < max_depth; depth++) {
if (!Job.isRunning(self())) return null;
hcs = buildLayer(fr, ktrees, leafs, hcs, false, false);
// If we did not make any new splits, then the tree is split-to-death
if (hcs == null) break;
}
// Each tree bottomed-out in a DecidedNode; go 1 more level and insert
// LeafNodes to hold predictions.
for (int k = 0; k < _nclass; k++) {
DTree tree = ktrees[k];
if (tree == null) continue;
int leaf = leafs[k] = tree.len();
for (int nid = 0; nid < leaf; nid++) {
if (tree.node(nid) instanceof DecidedNode) {
DecidedNode dn = tree.decided(nid);
for (int i = 0; i < dn._nids.length; i++) {
int cnid = dn._nids[i];
if (cnid == -1
|| // Bottomed out (predictors or responses known constant)
tree.node(cnid) instanceof UndecidedNode
|| // Or chopped off for depth
(tree.node(cnid) instanceof DecidedNode
&& // Or not possible to split
((DecidedNode) tree.node(cnid))._split.col() == -1))
dn._nids[i] = new GBMLeafNode(tree, nid).nid(); // Mark a leaf here
}
// Handle the trivial non-splitting tree
if (nid == 0 && dn._split.col() == -1) new GBMLeafNode(tree, -1, 0);
}
}
} // -- k-trees are done
// ----
// ESL2, page 387. Step 2b iii. Compute the gammas, and store them back
// into the tree leaves. Includes learn_rate.
// gamma_i_k = (nclass-1)/nclass * (sum res_i / sum (|res_i|*(1-|res_i|)))
// For regression:
// gamma_i_k = sum res_i / count(res_i)
GammaPass gp = new GammaPass(ktrees, leafs).doAll(fr);
double m1class = _nclass > 1 ? (double) (_nclass - 1) / _nclass : 1.0; // K-1/K
for (int k = 0; k < _nclass; k++) {
final DTree tree = ktrees[k];
if (tree == null) continue;
for (int i = 0; i < tree._len - leafs[k]; i++) {
double g =
gp._gss[k][i] == 0 // Constant response?
? (gp._rss[k][i] == 0
? 0
: 1000) // Cap (exponential) learn, instead of dealing with Inf
: learn_rate * m1class * gp._rss[k][i] / gp._gss[k][i];
assert !Double.isNaN(g);
((LeafNode) tree.node(leafs[k] + i))._pred = g;
}
}
// ----
// ESL2, page 387. Step 2b iv. Cache the sum of all the trees, plus the
// new tree, in the 'tree' columns. Also, zap the NIDs for next pass.
// Tree <== f(Tree)
// Nids <== 0
new MRTask2() {
@Override
public void map(Chunk chks[]) {
// For all tree/klasses
for (int k = 0; k < _nclass; k++) {
final DTree tree = ktrees[k];
if (tree == null) continue;
final Chunk nids = chk_nids(chks, k);
final Chunk ct = chk_tree(chks, k);
for (int row = 0; row < nids._len; row++) {
int nid = (int) nids.at80(row);
if (nid < 0) continue;
ct.set0(row, (float) (ct.at0(row) + ((LeafNode) tree.node(nid))._pred));
nids.set0(row, 0);
}
}
}
}.doAll(fr);
// Collect leaves stats
for (int i = 0; i < ktrees.length; i++)
if (ktrees[i] != null) ktrees[i].leaves = ktrees[i].len() - leafs[i];
// DEBUG: Print the generated K trees
// printGenerateTrees(ktrees);
return ktrees;
}
// --------------------------------------------------------------------------
// Build the next random k-trees representing tid-th tree
private void buildNextKTrees(Frame fr, int mtrys, float sample_rate, Random rand, int tid) {
// We're going to build K (nclass) trees - each focused on correcting
// errors for a single class.
final DTree[] ktrees = new DTree[_nclass];
// Initial set of histograms. All trees; one leaf per tree (the root
// leaf); all columns
DHistogram hcs[][][] = new DHistogram[_nclass][1 /*just root leaf*/][_ncols];
// Adjust real bins for the top-levels
int adj_nbins = Math.max(_parms._nbins_top_level, _parms._nbins);
// Use for all k-trees the same seed. NOTE: this is only to make a fair
// view for all k-trees
final double[] _distribution = _model._output._distribution;
long rseed = rand.nextLong();
// Initially setup as-if an empty-split had just happened
for (int k = 0; k < _nclass; k++) {
if (_distribution[k] != 0) { // Ignore missing classes
// The Boolean Optimization
// This optimization assumes the 2nd tree of a 2-class system is the
// inverse of the first (and that the same columns were picked)
if (k == 1 && _nclass == 2 && _model.binomialOpt()) continue;
ktrees[k] =
new DRFTree(
fr,
_ncols,
(char) _parms._nbins,
(char) _parms._nbins_cats,
(char) _nclass,
_parms._min_rows,
mtrys,
rseed);
new DRFUndecidedNode(
ktrees[k],
-1,
DHistogram.initialHist(
fr, _ncols, adj_nbins, _parms._nbins_cats, hcs[k][0])); // The "root" node
}
}
// Sample - mark the lines by putting 'OUT_OF_BAG' into nid(<klass>) vector
Timer t_1 = new Timer();
Sample ss[] = new Sample[_nclass];
for (int k = 0; k < _nclass; k++)
if (ktrees[k] != null)
ss[k] =
new Sample((DRFTree) ktrees[k], sample_rate)
.dfork(0, new Frame(vec_nids(fr, k), vec_resp(fr)), _parms._build_tree_one_node);
for (int k = 0; k < _nclass; k++) if (ss[k] != null) ss[k].getResult();
Log.debug("Sampling took: + " + t_1);
int[] leafs =
new int[_nclass]; // Define a "working set" of leaf splits, from leafs[i] to tree._len for
// each tree i
// ----
// One Big Loop till the ktrees are of proper depth.
// Adds a layer to the trees each pass.
Timer t_2 = new Timer();
int depth = 0;
for (; depth < _parms._max_depth; depth++) {
if (!isRunning()) return;
hcs =
buildLayer(
fr,
_parms._nbins,
_parms._nbins_cats,
ktrees,
leafs,
hcs,
true,
_parms._build_tree_one_node);
// If we did not make any new splits, then the tree is split-to-death
if (hcs == null) break;
}
Log.debug("Tree build took: " + t_2);
// Each tree bottomed-out in a DecidedNode; go 1 more level and insert
// LeafNodes to hold predictions.
Timer t_3 = new Timer();
for (int k = 0; k < _nclass; k++) {
DTree tree = ktrees[k];
if (tree == null) continue;
int leaf = leafs[k] = tree.len();
for (int nid = 0; nid < leaf; nid++) {
if (tree.node(nid) instanceof DecidedNode) {
DecidedNode dn = tree.decided(nid);
if (dn._split._col == -1) { // No decision here, no row should have this NID now
if (nid == 0) { // Handle the trivial non-splitting tree
LeafNode ln = new DRFLeafNode(tree, -1, 0);
ln._pred =
(float)
(isClassifier() ? _model._output._priorClassDist[k] : _initialPrediction);
}
continue;
}
for (int i = 0; i < dn._nids.length; i++) {
int cnid = dn._nids[i];
if (cnid == -1
|| // Bottomed out (predictors or responses known constant)
tree.node(cnid) instanceof UndecidedNode
|| // Or chopped off for depth
(tree.node(cnid) instanceof DecidedNode
&& // Or not possible to split
((DecidedNode) tree.node(cnid))._split.col() == -1)) {
LeafNode ln = new DRFLeafNode(tree, nid);
ln._pred = (float) dn.pred(i); // Set prediction into the leaf
dn._nids[i] = ln.nid(); // Mark a leaf here
}
}
}
}
} // -- k-trees are done
Log.debug("Nodes propagation: " + t_3);
// ----
// Move rows into the final leaf rows
Timer t_4 = new Timer();
CollectPreds cp =
new CollectPreds(ktrees, leafs, _model.defaultThreshold())
.doAll(fr, _parms._build_tree_one_node);
if (isClassifier())
asVotes(_treeMeasuresOnOOB)
.append(cp.rightVotes, cp.allRows); // Track right votes over OOB rows for this tree
else /* regression */ asSSE(_treeMeasuresOnOOB).append(cp.sse, cp.allRows);
Log.debug("CollectPreds done: " + t_4);
// Grow the model by K-trees
_model._output.addKTrees(ktrees);
}
