func EvalModel(testdataset *gbdt.DataSet, g *gbdt.GBDT, treecount int) (auc_score, logloss float32) {
var totalweight float32 = 0.0
auc := gbdt.NewAuc()
for j := 0; j < len(testdataset.Samples); j++ {
totalweight += testdataset.Samples[j].GetWeight()
score := g.Predict(testdataset.Samples[j], treecount)
p := gbdt.LogitCtr(score)
auc.Add(float64(p), float64(testdataset.Samples[j].GetWeight()), testdataset.Samples[j].GetLabel())
if testdataset.Samples[j].GetLabel() == 1 {
if score < -15 {
logloss -= score * 2 * testdataset.Samples[j].GetWeight()
} else {
logloss -= float32(math.Log(float64(p))) * testdataset.Samples[j].GetWeight()
}
} else {
if score > 15 {
logloss += score * 2 * testdataset.Samples[j].GetWeight()
} else {
logloss -= float32(math.Log(1-float64(p))) * testdataset.Samples[j].GetWeight()
}
}
}
auc_score = float32(auc.CalculateAuc())
logloss = logloss / totalweight
return
}
func main() {
//defer profile.Start(profile.CPUProfile).Stop() //monitor program performance
if gbdt.Conf == nil {
fmt.Println("nil pointer")
} else {
fmt.Println(gbdt.Conf)
}
modelname := "./gbdt.model"
start := time.Now()
trainpath := "./train.data"
//trainpath:="/opt/tmp/gbdt/data/train.data"
train_sample_number := 4584
//train_sample_number:=4186052
dataset := &gbdt.DataSet{}
dataset.LoadDataFromFile(trainpath, train_sample_number)
g := gbdt.NewGBDT()
g.Init(dataset)
g.Train(dataset)
model := g.Save()
if err := ioutil.WriteFile(modelname, []byte(model), 0666); err != nil {
fmt.Println(err)
}
latency := time.Since(start)
fmt.Println("train time:", latency)
start2 := time.Now()
testpath := "./test.data"
//testpath:="/opt/tmp/gbdt/data/smalltest.data"
//test_sample_number := 837210
test_sample_number := 4584
testdataset := &gbdt.DataSet{}
testdataset.LoadDataFromFile(testpath, test_sample_number)
/*model,err:=ioutil.ReadFile(modelname)
if err!=nil {
log.Fatal(err)
}
g:=&gbdt.GBDT{}
g.Load(string(model))*/ //load model from local file
g.Load(model)
samples := testdataset.GetSamples()
tree_count := gbdt.Conf.GetTreecount()
var click_sum, no_click_sum float64
for i := 0; i < len(samples); i++ {
if samples[i].GetLabel() == 1 {
click_sum += float64(samples[i].GetWeight())
} else {
no_click_sum += float64(samples[i].GetWeight())
}
}
latency = time.Since(start2)
fmt.Println("precision time:", latency)
auc := gbdt.NewAuc()
for j := 0; j < len(samples); j++ {
p := gbdt.LogitCtr(g.Predict(samples[j], tree_count))
auc.Add(float64(p), float64(samples[j].GetWeight()), samples[j].GetLabel())
}
fmt.Println("auc:", auc.CalculateAuc())
auc.PrintConfusionTable()
/*FeatureMapFile:="./feature.map"
feature_data,err:=ioutil.ReadFile(FeatureMapFile)
if err!=nil {
log.Fatal(err)
}
feature_map:=gbdt.LoadFeatureMap(string(feature_data))
feature_weight_list:=g.GetFeatureWeight()
for i := 0; i < len(feature_weight_list); i++ {
fid:=feature_weight_list[i].Key
fmt.Println(feature_map[fid],":",feature_weight_list[i].Value)
}*/
}