// NewRescaledFastForestEvaluator returns an evalator for a tree
// that automatically corrects for various scaling factors required
// for a given evaluation
func NewRescaledFastForestEvaluator(f *pb.Forest) (Evaluator, error) {
e := &fastForestEvaluator{
trees: make([]Evaluator, 0, len(f.GetTrees())),
}
for _, t := range f.GetTrees() {
evaluator, err := newFastTreeEvaluator(t)
if err != nil {
return nil, err
}
e.trees = append(e.trees, evaluator)
}
switch f.GetRescaling() {
case pb.Rescaling_NONE:
return e, nil
case pb.Rescaling_AVERAGING:
return EvaluatorFunc(func(features []float64) float64 {
return e.Evaluate(features) / float64(len(e.trees))
}), nil
case pb.Rescaling_LOG_ODDS:
return EvaluatorFunc(func(features []float64) float64 {
return 1.0 / (1.0 + math.Exp(-2.0*e.Evaluate(features)))
}), nil
}
return nil, fmt.Errorf("unknown rescaling method: %v", f.GetRescaling)
}
// LearningCurve computes the progressive learning curve after each epoch on the
// given examples
func LearningCurve(f *pb.Forest, e Examples) *pb.TrainingResults {
tr := &pb.TrainingResults{
EpochResults: make([]*pb.EpochResult, 0, len(f.GetTrees())),
}
for i := range f.GetTrees() {
evaluator, err := NewRescaledFastForestEvaluator(&pb.Forest{
Trees: f.GetTrees()[:i],
Rescaling: f.GetRescaling().Enum(),
})
if err != nil {
glog.Fatal(err)
}
er := computeEpochResult(evaluator, e)
tr.EpochResults = append(tr.EpochResults, &er)
}
return tr
}