@Override
public void map(Chunk strata, Chunk newW) {
for (int i = 0; i < strata._len; ++i) {
// Log.info("NID:" + ((int) strata.at8(i)));
if ((int) strata.at8(i) != stratumToKeep) newW.set(i, 0);
}
}
@Override
public void map(Chunk nids, Chunk ys) {
Random rand = _tree.rngForChunk(nids.cidx());
for (int row = 0; row < nids._len; row++)
if (rand.nextFloat() >= _rate || Double.isNaN(ys.atd(row))) {
nids.set(row, ScoreBuildHistogram.OUT_OF_BAG); // Flag row as being ignored by sampling
}
}
@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++;
}
}
}
}
