@Override
protected DRFModel buildModel(
DRFModel model, final Frame fr, String names[], String domains[][], final Timer t_build) {
// Append number of trees participating in on-the-fly scoring
fr.add("OUT_BAG_TREES", response.makeZero());
// The RNG used to pick split columns
Random rand = createRNG(_seed);
// Prepare working columns
new SetWrkTask().doAll(fr);
int tid;
DTree[] ktrees = null;
// Prepare tree statistics
TreeStats tstats = new TreeStats();
// Build trees until we hit the limit
for (tid = 0; tid < ntrees; tid++) { // Building tid-tree
model =
doScoring(
model, fr, ktrees, tid, tstats, tid == 0, !hasValidation(), build_tree_one_node);
// At each iteration build K trees (K = nclass = response column domain size)
// TODO: parallelize more? build more than k trees at each time, we need to care about
// temporary data
// Idea: launch more DRF at once.
Timer kb_timer = new Timer();
ktrees = buildNextKTrees(fr, _mtry, sample_rate, rand, tid);
Log.info(Sys.DRF__, (tid + 1) + ". tree was built " + kb_timer.toString());
if (!Job.isRunning(self())) break; // If canceled during building, do not bulkscore
// Check latest predictions
tstats.updateBy(ktrees);
}
model = doScoring(model, fr, ktrees, tid, tstats, true, !hasValidation(), build_tree_one_node);
// Make sure that we did not miss any votes
assert !importance
|| _treeMeasuresOnOOB.npredictors() == _treeMeasuresOnSOOB[0 /*variable*/].npredictors()
: "Missing some tree votes in variable importance voting?!";
return model;
}
// Start by splitting all the data according to some criteria (minimize
// variance at the leaves). Record on each row which split it goes to, and
// assign a split number to it (for next pass). On *this* pass, use the
// split-number to build a per-split histogram, with a per-histogram-bucket
// variance.
@Override
protected GBMModel buildModel(
GBMModel model, final Frame fr, String names[], String domains[][], Timer t_build) {
// Tag out rows missing the response column
new ExcludeNAResponse().doAll(fr);
// Build trees until we hit the limit
int tid;
DTree[] ktrees = null; // Trees
TreeStats tstats = new TreeStats(); // Tree stats
for (tid = 0; tid < ntrees; tid++) {
// During first iteration model contains 0 trees, then 0-trees, then 1-tree,...
// BUT if validation is not specified model does not participate in voting
// but on-the-fly computed data are used
model = doScoring(model, fr, ktrees, tid, tstats, false, false, false);
// ESL2, page 387
// Step 2a: Compute prediction (prob distribution) from prior tree results:
// Work <== f(Tree)
new ComputeProb().doAll(fr);
// ESL2, page 387
// Step 2b i: Compute residuals from the prediction (probability distribution)
// Work <== f(Work)
new ComputeRes().doAll(fr);
// ESL2, page 387, Step 2b ii, iii, iv
Timer kb_timer = new Timer();
ktrees = buildNextKTrees(fr);
Log.info(Sys.GBM__, (tid + 1) + ". tree was built in " + kb_timer.toString());
if (!Job.isRunning(self())) break; // If canceled during building, do not bulkscore
// Check latest predictions
tstats.updateBy(ktrees);
}
// Final scoring
model = doScoring(model, fr, ktrees, tid, tstats, true, false, false);
return model;
}
@Override
protected void buildModel() {
// Start with class distribution as null-model
// FIXME: Test/Investigate this
// if( _nclass >= 2 ) {
// for( int c=0; c<_nclass; c++ ) {
// final double init = _model._output._priorClassDist[c];
// new MRTask() {
// @Override public void map(Chunk tree) { for( int i=0; i<tree._len; i++ )
// tree.set(i, init); }
// }.doAll(vec_tree(_train,c));
// }
// }
_mtry =
(_parms._mtries == -1)
? // classification: mtry=sqrt(_ncols), regression: mtry=_ncols/3
(isClassifier() ? Math.max((int) Math.sqrt(_ncols), 1) : Math.max(_ncols / 3, 1))
: _parms._mtries;
if (!(1 <= _mtry && _mtry <= _ncols))
throw new IllegalArgumentException(
"Computed mtry should be in interval <1,#cols> but it is " + _mtry);
// Initialize TreeVotes for classification, MSE arrays for regression
initTreeMeasurements();
// Append number of trees participating in on-the-fly scoring
_train.add("OUT_BAG_TREES", _response.makeZero());
// Prepare working columns
new SetWrkTask().doAll(_train);
// If there was a check point recompute tree_<_> and oob columns based on predictions from
// previous trees
// but only if OOB validation is requested.
if (_parms._checkpoint) {
Timer t = new Timer();
// Compute oob votes for each output level
new OOBScorer(
_ncols,
_nclass,
(hasWeights() ? 1 : 0) + (hasOffset() ? 1 : 0),
_parms._sample_rate,
_model._output._treeKeys)
.doAll(_train);
Log.info("Reconstructing oob stats from checkpointed model took " + t);
}
// The RNG used to pick split columns
Random rand = createRNG(_parms._seed);
// To be deterministic get random numbers for previous trees and
// put random generator to the same state
for (int i = 0; i < _ntreesFromCheckpoint; i++) rand.nextLong();
int tid;
DTree[] ktrees = null;
// Prepare tree statistics
// Build trees until we hit the limit
for (tid = 0; tid < _parms._ntrees; tid++) { // Building tid-tree
if (tid != 0 || !_parms._checkpoint) { // do not make initial scoring if model already exist
double training_r2 = doScoringAndSaveModel(false, true, _parms._build_tree_one_node);
if (training_r2 >= _parms._r2_stopping) return; // Stop when approaching round-off error
}
// At each iteration build K trees (K = nclass = response column domain size)
// TODO: parallelize more? build more than k trees at each time, we need to care about
// temporary data
// Idea: launch more DRF at once.
Timer kb_timer = new Timer();
buildNextKTrees(_train, _mtry, _parms._sample_rate, rand, tid);
Log.info((tid + 1) + ". tree was built " + kb_timer.toString());
DRF.this.update(1);
if (!isRunning()) return; // If canceled during building, do not bulkscore
}
doScoringAndSaveModel(true, true, _parms._build_tree_one_node);
}
