// 输出的度量名字为 "confusion:M/N" 其中M为真实标注，N为预测标注
func (e *ConfusionMatrixEvaluator) Evaluate(m supervised.Model, set data.Dataset) (result Evaluation) {
result.Metrics = make(map[string]float64)
iter := set.CreateIterator()
iter.Start()
for !iter.End() {
instance := iter.GetInstance()
out := m.Predict(instance)
name := fmt.Sprintf("confusion:%d/%d", instance.Output.Label, out.Label)
result.Metrics[name]++
iter.Next()
}
return
}
func (e *PREvaluator) Evaluate(m supervised.Model, set data.Dataset) (result Evaluation) {
tp := 0 // true-positive
tn := 0 // true-negative
fp := 0 // false-positive
fn := 0 // false-negative
iter := set.CreateIterator()
iter.Start()
for !iter.End() {
instance := iter.GetInstance()
if instance.Output.Label > 2 {
log.Fatal("调用PREvaluator但不是二分类问题")
}
out := m.Predict(instance)
if out.Label == 0 {
if instance.Output.Label == 0 {
tn++
} else {
fn++
}
} else {
if instance.Output.Label == 0 {
fp++
} else {
tp++
}
}
iter.Next()
}
result.Metrics = make(map[string]float64)
result.Metrics["precision"] = float64(tp) / float64(tp+fp)
result.Metrics["recall"] = float64(tp) / float64(tp+fn)
result.Metrics["tp"] = float64(tp)
result.Metrics["fp"] = float64(fp)
result.Metrics["tn"] = float64(tn)
result.Metrics["fn"] = float64(fn)
result.Metrics["fscore"] =
2 * result.Metrics["precision"] * result.Metrics["recall"] / (result.Metrics["precision"] + result.Metrics["recall"])
return
}
func (e *AccuracyEvaluator) Evaluate(m supervised.Model, set data.Dataset) (result Evaluation) {
correctPrediction := 0
totalPrediction := 0
iter := set.CreateIterator()
iter.Start()
for !iter.End() {
instance := iter.GetInstance()
out := m.Predict(instance)
if instance.Output.Label == out.Label {
correctPrediction++
}
totalPrediction++
iter.Next()
}
result.Metrics = make(map[string]float64)
result.Metrics["accuracy"] = float64(correctPrediction) / float64(totalPrediction)
return
}
