@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++;
}
/**
* Compute relative variable importance for GBM model.
*
* <p>See (45), (35) formulas in Friedman: Greedy Function Approximation: A Gradient boosting
* machine. Algo used here can be used for computation individual importance of features per
* output class.
*/
@Override
protected VarImp doVarImpCalc(
GBMModel model, DTree[] ktrees, int tid, Frame validationFrame, boolean scale) {
assert model.ntrees() - 1 == tid
: "varimp computation expect model with already serialized trees: tid=" + tid;
// Iterates over k-tree
for (DTree t : ktrees) { // Iterate over trees
if (t != null) {
for (int n = 0; n < t.len() - t.leaves; n++)
if (t.node(n) instanceof DecidedNode) { // it is split node
Split split = t.decided(n)._split;
_improvPerVar[split._col] += split.improvement(); // least squares improvement
}
}
}
// Compute variable importance for all trees in model
float[] varimp = new float[model.nfeatures()];
int ntreesTotal = model.ntrees() * model.nclasses();
int maxVar = 0;
for (int var = 0; var < _improvPerVar.length; var++) {
varimp[var] = _improvPerVar[var] / ntreesTotal;
if (varimp[var] > varimp[maxVar]) maxVar = var;
}
// GBM scale varimp to scale 0..100
if (scale) {
float maxVal = varimp[maxVar];
for (int var = 0; var < varimp.length; var++) varimp[var] /= maxVal;
}
return new VarImp(varimp);
}
// helper for debugging
@SuppressWarnings("unused")
protected static void printGenerateTrees(DTree[] trees) {
for (DTree dtree : trees)
if (dtree != null) {
try {
PrintWriter writer = new PrintWriter("/tmp/h2o-3.tree" + ++counter + ".txt", "UTF-8");
writer.println(dtree.root().toString2(new StringBuilder(), 0));
writer.close();
} catch (FileNotFoundException | UnsupportedEncodingException e) {
e.printStackTrace();
}
System.out.println(dtree.root().toString2(new StringBuilder(), 0));
}
}
private DirDiffElement(
DTree parent,
@Nullable DiffElement source,
@Nullable DiffElement target,
DType type,
String name) {
myParent = parent.getParent();
myNode = parent;
myType = type;
mySource = source;
mySourceLength = source == null || source.isContainer() ? -1 : source.getSize();
myTarget = target;
myTargetLength = target == null || target.isContainer() ? -1 : target.getSize();
myName = name;
if (type == DType.ERROR) {
myDefaultOperation = NONE;
} else if (isSource()) {
myDefaultOperation = COPY_TO;
} else if (isTarget()) {
myDefaultOperation = COPY_FROM;
} else if (type == DType.EQUAL) {
myDefaultOperation = EQUAL;
} else if (type == DType.CHANGED) {
assert source != null;
myDefaultOperation = MERGE;
}
}
@Override
public void map(Chunk[] chks) {
_gss = new double[_nclass][];
_rss = new double[_nclass][];
// For all tree/klasses
for (int k = 0; k < _nclass; k++) {
final DTree tree = _trees[k];
final int leaf = _leafs[k];
if (tree == null) continue; // Empty class is ignored
// A leaf-biased array of all active Tree leaves.
final double gs[] = _gss[k] = new double[tree._len - leaf];
final double rs[] = _rss[k] = new double[tree._len - leaf];
final Chunk nids = chk_nids(chks, k); // Node-ids for this tree/class
final Chunk ress = chk_work(chks, k); // Residuals for this tree/class
// If we have all constant responses, then we do not split even the
// root and the residuals should be zero.
if (tree.root() instanceof LeafNode) continue;
for (int row = 0; row < nids._len; row++) { // For all rows
int nid = (int) nids.at80(row); // Get Node to decide from
if (nid < 0) continue; // Missing response
if (tree.node(nid) instanceof UndecidedNode) // If we bottomed out the tree
nid = tree.node(nid)._pid; // Then take parent's decision
DecidedNode dn = tree.decided(nid); // Must have a decision point
if (dn._split._col == -1) // Unable to decide?
dn = tree.decided(nid = dn._pid); // Then take parent's decision
int leafnid = dn.ns(chks, row); // Decide down to a leafnode
assert leaf <= leafnid && leafnid < tree._len;
assert tree.node(leafnid) instanceof LeafNode;
// Note: I can which leaf/region I end up in, but I do not care for
// the prediction presented by the tree. For GBM, we compute the
// sum-of-residuals (and sum/abs/mult residuals) for all rows in the
// leaf, and get our prediction from that.
nids.set0(row, leafnid);
assert !ress.isNA0(row);
double res = ress.at0(row);
double ares = Math.abs(res);
gs[leafnid - leaf] += _nclass > 1 ? ares * (1 - ares) : 1;
rs[leafnid - leaf] += res;
}
}
}
// --------------------------------------------------------------------------
// 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;
}
@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,
_model._output._priorClassDist,
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] : _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);
}
