func AlgorithmRunOnDataSet(classifier algo.Classifier, train_dataset, test_dataset *core.DataSet, pred_path string, params map[string]string) (float64, []*eval.LabelPrediction) {
if train_dataset != nil {
classifier.Train(train_dataset)
}
predictions := []*eval.LabelPrediction{}
var pred_file *os.File
if pred_path != "" {
pred_file, _ = os.Create(pred_path)
}
for _, sample := range test_dataset.Samples {
prediction := classifier.Predict(sample)
if pred_file != nil {
pred_file.WriteString(strconv.FormatFloat(prediction, 'g', 5, 64) + "\n")
}
predictions = append(predictions, &(eval.LabelPrediction{Label: sample.Label, Prediction: prediction}))
}
if pred_path != "" {
defer pred_file.Close()
}
auc := eval.AUC(predictions)
return auc, predictions
}
func AlgorithmTrain(classifier algo.Classifier, train_path string, params map[string]string) error {
global, _ := strconv.ParseInt(params["global"], 10, 64)
train_dataset := core.NewDataSet()
err := train_dataset.Load(train_path, global)
if err != nil {
return err
}
classifier.Init(params)
classifier.Train(train_dataset)
model_path, _ := params["model"]
if model_path != "" {
classifier.SaveModel(model_path)
}
return nil
}