@Override
public void map(Chunk[] chks) {
final Chunk y = importance ? chk_resp(chks) : null; // Response
final double[] rpred = importance ? new double[1 + _nclass] : null; // Row prediction
final double[] rowdata = importance ? new double[_ncols] : null; // Pre-allocated row data
final Chunk oobt = chk_oobt(chks); // Out-of-bag rows counter over all trees
// Iterate over all rows
for (int row = 0; row < oobt._len; row++) {
final boolean wasOOBRow = ScoreBuildHistogram.isOOBRow((int) chk_nids(chks, 0).at8(row));
// For all tree (i.e., k-classes)
for (int k = 0; k < _nclass; k++) {
final DTree tree = _trees[k];
if (tree == null) continue; // Empty class is ignored
final Chunk nids = chk_nids(chks, k); // Node-ids for this tree/class
int nid = (int) nids.at8(row); // Get Node to decide from
// Update only out-of-bag rows
// This is out-of-bag row - but we would like to track on-the-fly prediction for the row
if (wasOOBRow) {
final Chunk ct =
chk_tree(chks, k); // k-tree working column holding votes for given row
nid = ScoreBuildHistogram.oob2Nid(nid);
if (tree.node(nid) instanceof UndecidedNode) // If we bottomed out the tree
nid = tree.node(nid).pid(); // Then take parent's decision
int leafnid;
if (tree.root() instanceof LeafNode) {
leafnid = 0;
} else {
DecidedNode dn = tree.decided(nid); // Must have a decision point
if (dn._split.col() == -1) // Unable to decide?
dn = tree.decided(tree.node(nid).pid()); // Then take parent's decision
leafnid = dn.ns(chks, row); // Decide down to a leafnode
}
// Setup Tree(i) - on the fly prediction of i-tree for row-th row
// - for classification: cumulative number of votes for this row
// - for regression: cumulative sum of prediction of each tree - has to be
// normalized by number of trees
double prediction =
((LeafNode) tree.node(leafnid))
.pred(); // Prediction for this k-class and this row
if (importance)
rpred[1 + k] = (float) prediction; // for both regression and classification
ct.set(row, (float) (ct.atd(row) + prediction));
}
// reset help column for this row and this k-class
nids.set(row, 0);
} /* end of k-trees iteration */
// For this tree this row is out-of-bag - i.e., a tree voted for this row
if (wasOOBRow) oobt.set(row, oobt.atd(row) + 1); // track number of trees
if (importance) {
if (wasOOBRow && !y.isNA(row)) {
if (isClassifier()) {
int treePred = getPrediction(rpred, data_row(chks, row, rowdata), _threshold);
int actuPred = (int) y.at8(row);
if (treePred == actuPred) rightVotes++; // No miss !
} else { // regression
double treePred = rpred[1];
double actuPred = y.atd(row);
sse += (actuPred - treePred) * (actuPred - treePred);
}
allRows++;
}
}
}
}
// --------------------------------------------------------------------------
// 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] : responseMean());
}
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);
}