@Override
protected SB toJavaInit(SB sb, SB fileContextSB) {
sb = super.toJavaInit(sb, fileContextSB);
sb.ip("public boolean isSupervised() { return " + isSupervised() + "; }").nl();
sb.ip("public int nfeatures() { return " + _output.nfeatures() + "; }").nl();
sb.ip("public int nclasses() { return " + _parms._k + "; }").nl();
if (_output._nnums > 0) {
JCodeGen.toStaticVar(
sb,
"NORMMUL",
_output._normMul,
"Standardization/Normalization scaling factor for numerical variables.");
JCodeGen.toStaticVar(
sb,
"NORMSUB",
_output._normSub,
"Standardization/Normalization offset for numerical variables.");
}
JCodeGen.toStaticVar(sb, "CATOFFS", _output._catOffsets, "Categorical column offsets.");
JCodeGen.toStaticVar(sb, "PERMUTE", _output._permutation, "Permutation index vector.");
JCodeGen.toStaticVar(sb, "EIGVECS", _output._eigenvectors_raw, "Eigenvector matrix.");
return sb;
}
// Note: POJO scoring code doesn't support per-row offsets (the scoring API would need to be
// changed to pass in offsets)
@Override
protected void toJavaUnifyPreds(SB body, SB file) {
// Preds are filled in from the trees, but need to be adjusted according to
// the loss function.
if (_parms._distribution == Distributions.Family.bernoulli) {
body.ip("preds[2] = preds[1] + ").p(_output._init_f).p(";").nl();
body.ip("preds[2] = " + _parms._distribution.linkInvString("preds[2]") + ";").nl();
body.ip("preds[1] = 1.0-preds[2];").nl();
if (_parms._balance_classes)
body.ip(
"hex.genmodel.GenModel.correctProbabilities(preds, PRIOR_CLASS_DISTRIB, MODEL_CLASS_DISTRIB);")
.nl();
body.ip(
"preds[0] = hex.genmodel.GenModel.getPrediction(preds, data, "
+ defaultThreshold()
+ ");")
.nl();
return;
}
if (_output.nclasses() == 1) { // Regression
body.ip("preds[0] += ").p(_output._init_f).p(";").nl();
body.ip("preds[0] = " + _parms._distribution.linkInvString("preds[0]") + ";").nl();
return;
}
if (_output.nclasses() == 2) { // Kept the initial prediction for binomial
body.ip("preds[1] += ").p(_output._init_f).p(";").nl();
body.ip("preds[2] = - preds[1];").nl();
}
body.ip("hex.genmodel.GenModel.GBM_rescale(preds);").nl();
if (_parms._balance_classes)
body.ip(
"hex.genmodel.GenModel.correctProbabilities(preds, PRIOR_CLASS_DISTRIB, MODEL_CLASS_DISTRIB);")
.nl();
body.ip(
"preds[0] = hex.genmodel.GenModel.getPrediction(preds, data, "
+ defaultThreshold()
+ ");")
.nl();
}
