// 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));
}
}
@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;
}
}
}
@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++;
}
}
}
}
