@Override
protected float[] score0(double data[], float preds[]) {
float[] p = super.score0(data, preds);
int ntrees = ntrees();
if (p.length == 1) {
if (ntrees > 0) div(p, ntrees);
} // regression - compute avg over all trees
else { // classification
float s = sum(p);
if (s > 0) div(p, s); // unify over all classes
p[0] = ModelUtils.getPrediction(p, data);
}
return p;
}
@Override
public void map(Chunk[] chks) {
final Chunk y = importance ? chk_resp(chks) : null; // Response
final float[] rpred = importance ? new float[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++) {
boolean wasOOBRow = false;
// 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
// 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;
final Chunk nids = chk_nids(chks, k); // Node-ids for this tree/class
final Chunk ct = chk_tree(chks, k); // k-tree working column holding votes for given row
int nid = (int) nids.at80(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 (isOOBRow(nid)) { // The row should be OOB for all k-trees !!!
assert k == 0 || wasOOBRow
: "Something is wrong: k-class trees oob row computing is broken! All k-trees should agree on oob row!";
wasOOBRow = true;
nid = oob2Nid(nid);
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(tree.node(nid).pid()); // Then take parent's decision
int 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.set0(row, (float) (ct.at0(row) + prediction));
// For this tree this row is out-of-bag - i.e., a tree voted for this row
oobt.set0(
row,
_nclass > 1
? 1
: oobt.at0(row)
+ 1); // for regression track number of trees, for classification boolean
// flag is enough
}
// reset help column for this row and this k-class
nids.set0(row, 0);
} /* end of k-trees iteration */
if (importance) {
if (wasOOBRow && !y.isNA0(row)) {
if (classification) {
int treePred = ModelUtils.getPrediction(rpred, data_row(chks, row, rowdata));
int actuPred = (int) y.at80(row);
if (treePred == actuPred) rightVotes++; // No miss !
} else { // regression
float treePred = rpred[1];
float actuPred = (float) y.at0(row);
sse += (actuPred - treePred) * (actuPred - treePred);
}
allRows++;
}
}
}
}