Example #1
0
 @Override
 public String toJavaString() {
   double[] ary = expand();
   if (ary == null || ary.length == 0) return "\"null\"";
   SB sb = new SB().p('{');
   for (int i = 0; i < ary.length - 1; ++i) sb.p(ary[i]).p(',');
   return sb.p('}').toString();
 }
Example #2
0
 @Override
 protected void toJavaUnifyPreds(SB bodySb) {
   if (isClassifier()) {
     bodySb.i().p("float sum = 0;").nl();
     bodySb.i().p("for(int i=1; i<preds.length; i++) sum += preds[i];").nl();
     bodySb.i().p("if (sum>0) for(int i=1; i<preds.length; i++) preds[i] /= sum;").nl();
   } else bodySb.i().p("preds[1] = preds[1]/NTREES;").nl();
 }
Example #3
0
  @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;
  }
Example #4
0
 @Override
 public String str() {
   SB sb = new SB().p('[');
   for (int i = 0; i < _bases.length; i++) {
     sb.p(_bases[i]);
     if (_cnts[i] != 1) {
       sb.p(':').p(_bases[i] + _cnts[i] * _strides[i]);
       if (_strides[i] != 1 || ((long) _bases[i]) != _bases[i]) sb.p(':').p(_strides[i]);
     }
     if (i < _bases.length - 1) sb.p(',');
   }
   return sb.p(']').toString();
 }
Example #5
0
 @Override
 protected void toJavaUnifyPreds(SB bodyCtxSB) {
   if (isClassifier()) {
     bodyCtxSB
         .i()
         .p(
             "// Compute Probabilities for classifier (scale via http://www.hongliangjie.com/2011/01/07/logsum/)")
         .nl();
     bodyCtxSB.i().p("float dsum = 0, maxval = Float.NEGATIVE_INFINITY;").nl();
     if (nclasses() == 2) {
       bodyCtxSB.i().p("preds[2] = -preds[1];").nl();
     }
     bodyCtxSB
         .i()
         .p("for(int i=1; i<preds.length; i++) maxval = Math.max(maxval, preds[i]);")
         .nl();
     bodyCtxSB
         .i()
         .p(
             "for(int i=1; i<preds.length; i++) dsum += (preds[i]=(float) Math.exp(preds[i] - maxval));")
         .nl();
     bodyCtxSB.i().p("for(int i=1; i<preds.length; i++) preds[i] = preds[i] / dsum;").nl();
   }
 }
Example #6
0
 @Override
 public String str() {
   SB sb = new SB().p('(');
   for (AST ast : _asts) sb.p(ast.toString()).p(' ');
   return sb.p(')').toString();
 }
Example #7
0
  @Override
  protected void toJavaPredictBody(final SB bodySb, final SB classCtxSb, final SB fileCtxSb) {
    SB model = new SB();
    bodySb.i().p("java.util.Arrays.fill(preds,0);").nl();
    final int cats = _output._ncats;
    final int nums = _output._nnums;
    bodySb.i().p("final int nstart = CATOFFS[CATOFFS.length-1];").nl();
    bodySb.i().p("for(int i = 0; i < ").p(_parms._k).p("; i++) {").nl();
    // Categorical columns
    bodySb.i(1).p("for(int j = 0; j < ").p(cats).p("; j++) {").nl();
    bodySb.i(2).p("double d = data[PERMUTE[j]];").nl();
    bodySb.i(2).p("if(Double.isNaN(d)) continue;").nl();
    bodySb.i(2).p("int last = CATOFFS[j+1]-CATOFFS[j]-1;").nl();
    bodySb.i(2).p("int c = (int)d").p(_parms._use_all_factor_levels ? ";" : "-1;").nl();
    bodySb.i(2).p("if(c < 0 || c > last) continue;").nl();
    bodySb.i(2).p("preds[i] += EIGVECS[CATOFFS[j]+c][i];").nl();
    bodySb.i(1).p("}").nl();

    // Numeric columns
    bodySb.i(1).p("for(int j = 0; j < ").p(nums).p("; j++) {").nl();
    bodySb
        .i(2)
        .p(
            "preds[i] += (data[PERMUTE[j"
                + (cats > 0 ? "+" + cats : "")
                + "]]-NORMSUB[j])*NORMMUL[j]*EIGVECS[j"
                + (cats > 0 ? "+ nstart" : "")
                + "][i];")
        .nl();
    bodySb.i(1).p("}").nl();
    bodySb.i().p("}").nl();
    fileCtxSb.p(model);
  }
Example #8
0
 // 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();
 }