hex.tree.Score Maven / Gradle / Ivy

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package hex.tree;

import hex.ModelCategory;
import hex.ModelMetrics;
import hex.ModelMetricsSupervised;
import hex.genmodel.GenModel;
import water.MRTask;
import water.fvec.Chunk;
import water.fvec.Frame;

/** Score the tree columns, and produce a confusion matrix and AUC
 */
public class Score extends MRTask {
  final SharedTree _bldr;
  final boolean _is_train;      // Scoring on pre-scored training data vs full-score data
  final boolean _oob;           // Computed on OOB
  final ModelCategory _mcat;    // Model category (Binomial, Regression, etc)
  ModelMetrics.MetricBuilder _mb;

  /** Compute ModelMetrics on the testing dataset.
   *  It expect already adapted validation dataset which is adapted to a model
   *  and contains a response which is adapted to confusion matrix domain.
   */
  public Score(SharedTree bldr, boolean is_train, boolean oob, ModelCategory mcat) { _bldr = bldr; _is_train = is_train; _oob = oob; _mcat = mcat; }
  
  @Override public void map( Chunk chks[] ) {
    Chunk ys = _bldr.chk_resp(chks);  // Response
    final int nclass = _bldr._nclass; 
    // Because of adaption - the validation training set has at least as many
    // classes as the training set (it may have more).  The Confusion Matrix
    // needs to be at least as big as the training set domain.
    String[] domain = _bldr.vresponse().domain();
    // If this is a score-on-train AND DRF, then oobColIdx makes sense,
    // otherwise this field is unused.
    final int oobColIdx = _bldr.idx_oobt();
    _mb = _bldr._model.makeMetricBuilder(domain);
    final double[] cdists = _mb._work; // Temp working array for class distributions
    // If working a validation set, need to push thru official model scoring
    // logic which requires a temp array to hold the features.
    final double[] tmp = _is_train ? null : new double[_bldr._ncols];

    // Score all Rows
    float [] val= new float[1];
    for( int row=0; row 1 ) cdists[0] = GenModel.getPrediction(cdists, tmp, _bldr._model.defaultThreshold()); // Fill in prediction
      val[0] = (float)ys.atd(row);
      _mb.perRow(cdists,val, _bldr._model);
    }
  }

  @Override public void reduce( Score t ) { _mb.reduce(t._mb); }

  // Run after the doAll scoring to convert the MetricsBuilder to a ModelMetrics
  ModelMetricsSupervised makeModelMetrics(SharedTreeModel model, Frame fr, String resp) { return (ModelMetricsSupervised)_mb.makeModelMetrics(model,fr, fr.vec(resp).sigma()); }
}