func StreamRun(model_file string, instances []string) (string, error) {
log := util.GetLogger()
if !util.FileExists(model_file) || len(instances) == 0 {
log.Error("[Predictor-StreamRun] Model file or instances error.")
return fmt.Sprintf(errorjson, "[Predictor-StreamRun] Model file or instances error."), errors.New("[Predictor-StreamRun] Model file or instances error.")
}
var rtstr string
var model solver.LRModel
model.Initialize(model_file)
for i := 0; i < len(instances); i++ {
res, _, x := util.ParseSample(instances[i])
if res != nil {
break
}
pred := model.Predict(x)
pred = math.Max(math.Min(pred, 1.-10e-15), 10e-15)
if i == len(instances)-1 {
rtstr += strconv.FormatFloat(pred, 'f', 6, 64)
} else {
rtstr += strconv.FormatFloat(pred, 'f', 6, 64) + ","
}
}
return fmt.Sprintf(streamjson, rtstr), nil
}
func Run(argc int, argv []string) (string, error) {
var job_name string
var test_file string
var model_file string
var output_file string
var threshold float64
log := util.GetLogger()
if len(argv) == 5 {
job_name = argv[0]
test_file = argv[1]
model_file = argv[2]
output_file = argv[3]
threshold, _ = strconv.ParseFloat(argv[4], 64)
} else {
print_usage(argc, argv)
log.Error("[Predictor-Run] Input parameters error.")
return fmt.Sprintf(errorjson, "[Predictor-Run] Input parameters error."), errors.New("[Predictor-Run] Input parameters error.")
}
if len(job_name) == 0 || len(test_file) == 0 || len(model_file) == 0 || len(output_file) == 0 {
print_usage(argc, argv)
log.Error("[Predictor-Run] Input parameters error.")
return fmt.Sprintf(errorjson, "[Predictor-Run] Input parameters error."), errors.New("[Predictor-Run] Input parameters error.")
}
var model solver.LRModel
model.Initialize(model_file)
var wfp *os.File
var err1 error
exist := func(filename string) bool {
var exist = true
if _, err := os.Stat(filename); os.IsNotExist(err) {
exist = false
}
return exist
}
if exist(output_file) {
wfp, err1 = os.OpenFile(output_file, os.O_SYNC, 0666)
} else {
wfp, err1 = os.Create(output_file)
}
if err1 != nil {
log.Error("[Predictor-Run] Open file error." + err1.Error())
return fmt.Sprintf(errorjson, err1.Error()), errors.New("[Predictor-Run] Open file error." + err1.Error())
}
defer wfp.Close()
cnt := 0 //样本总数
pcorrect := 0 //正样本预测正确数
pcnt := 0 //正样本总数
ncorrect := 0 //负样本预测正确数
var loss float64 = 0.
var parser trainer.FileParser
err := parser.OpenFile(test_file)
if err != nil {
log.Error("[Predictor-Run] Open file error." + err.Error())
return fmt.Sprintf(errorjson, err.Error()), errors.New("[Predictor-Run] Open file error." + err.Error())
}
var pred_scores util.Dvector
for {
res, y, x := parser.ReadSample()
if res != nil {
break
}
pred := model.Predict(x)
pred = math.Max(math.Min(pred, 1.-10e-15), 10e-15)
wfp.WriteString(fmt.Sprintf("%f\n", pred))
pred_scores = append(pred_scores, util.DPair{pred, y})
cnt++
if util.UtilFloat64Equal(y, 1.0) {
pcnt++
}
var pred_label float64 = 0
if pred > threshold {
pred_label = 1
}
if util.UtilFloat64Equal(pred_label, y) {
if util.UtilFloat64Equal(y, 1.0) {
pcorrect++
} else {
ncorrect++
}
}
pred = math.Max(math.Min(pred, 1.-10e-15), 10e-15)
if y > 0 {
loss += -math.Log(pred)
} else {
loss += -math.Log(1. - pred)
}
}
auc := calc_auc(pred_scores)
if auc < 0.5 {
auc = 0.5
}
if cnt > 0 {
log.Info(fmt.Sprintf("[%s] Log-likelihood = %f\n", job_name, float64(loss)/float64(cnt)))
log.Info(fmt.Sprintf("[%s] Precision = %.2f%% (%d/%d)\n", job_name,
float64(pcorrect*100)/float64(cnt-pcnt-ncorrect+pcorrect),
pcorrect, cnt-pcnt-ncorrect+pcorrect))
log.Info(fmt.Sprintf("[%s] Recall = %.2f%% (%d/%d)\n", job_name,
float64(pcorrect*100)/float64(pcnt), pcorrect, pcnt))
log.Info(fmt.Sprintf("[%s] Accuracy = %.2f%% (%d/%d)\n", job_name,
float64((pcorrect+ncorrect)*100)/float64(cnt), (pcorrect + ncorrect), cnt))
log.Info(fmt.Sprintf("[%s] AUC = %f\n", job_name, auc))
}
parser.CloseFile()
util.Write2File(output_file, fmt.Sprintf(" Log-likelihood = %f\n Precision = %f (%d/%d)\n Recall = %f (%d/%d)\n Accuracy = %f (%d/%d)\n AUC = %f\n",
float64(loss)/float64(cnt),
float64(pcorrect)/float64(cnt-pcnt-ncorrect+pcorrect), pcorrect, cnt-pcnt-ncorrect+pcorrect,
float64(pcorrect)/float64(pcnt), pcorrect, pcnt,
float64(pcorrect+ncorrect)/float64(cnt), pcorrect+ncorrect, cnt,
auc))
return fmt.Sprintf(returnJson,
job_name,
fmt.Sprintf("Log-likelihood = %f", float64(loss)/float64(cnt)),
fmt.Sprintf("Precision = %f (%d/%d)", float64(pcorrect)/float64(cnt-pcnt-ncorrect+pcorrect), pcorrect, cnt-pcnt-ncorrect+pcorrect),
fmt.Sprintf("Recall = %f (%d/%d)", float64(pcorrect)/float64(pcnt), pcorrect, pcnt),
fmt.Sprintf("Accuracy = %f (%d/%d)", float64((pcorrect+ncorrect))/float64(cnt), (pcorrect+ncorrect), cnt),
fmt.Sprintf("AUC = %f", auc),
output_file), nil
}