//
// runTPSet will run true-positive set into trained stage, to get the
// false-positive. The FP samples will be added to training set.
//
func (crf *Runtime) runTPSet(samples tabula.ClasetInterface) {
// Skip the first stage, because we just got tnset from them.
if len(crf.weights) <= 1 {
return
}
tnIds := numerus.IntCreateSeq(0, crf.tnset.Len()-1)
_, cm, _ := crf.ClassifySetByWeight(crf.tnset, tnIds)
crf.refillWithFP(samples, crf.tnset, cm)
}
//
// Performance given an actuals class label and their probabilities, compute
// the performance statistic of classifier.
//
// Algorithm,
// (1) Sort the probabilities in descending order.
// (2) Sort the actuals and predicts using sorted index from probs
// (3) Compute tpr, fpr, precision
// (4) Write performance to file.
//
func (rt *Runtime) Performance(samples tabula.ClasetInterface,
predicts []string, probs []float64,
) (
perfs Stats,
) {
// (1)
actuals := samples.GetClassAsStrings()
sortedIds := numerus.IntCreateSeq(0, len(probs)-1)
numerus.Floats64InplaceMergesort(probs, sortedIds, 0, len(probs),
false)
// (2)
tekstus.StringsSortByIndex(&actuals, sortedIds)
tekstus.StringsSortByIndex(&predicts, sortedIds)
// (3)
rt.computePerfByProbs(samples, actuals, probs)
return rt.perfs
}
