Java TreeStats Example

Introduction

The java treestats example is extracted from the most popular open source projects, you can refer to the following example for usage.

Programming language: Java

Namespace/package name: hex.gbm.DTree.TreeModel

Example#1

File: GBM.javaProject: jgustave/h2o

  // 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[][],
      String[] cmDomain,
      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, cmDomain, 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, cmDomain, tstats, true, false, false);

    return model;
  }

Example#2

File: DRF.javaProject: rohit2412/h2o

  @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;
  }