func AlgorithmRun(classifier algo.Classifier, train_path string, test_path string, pred_path string, params map[string]string) (float64, []*eval.LabelPrediction, error) {
global, _ := strconv.ParseInt(params["global"], 10, 64)
train_dataset := core.NewDataSet()
err := train_dataset.Load(train_path, global)
if err != nil {
return 0.5, nil, err
}
test_dataset := core.NewDataSet()
err = test_dataset.Load(test_path, global)
if err != nil {
return 0.5, nil, err
}
classifier.Init(params)
auc, predictions := AlgorithmRunOnDataSet(classifier, train_dataset, test_dataset, pred_path, params)
return auc, predictions, nil
}
func AlgorithmTest(classifier algo.Classifier, test_path string, pred_path string, params map[string]string) (float64, []*eval.LabelPrediction, error) {
global, _ := strconv.ParseInt(params["global"], 10, 64)
model_path, _ := params["model"]
classifier.Init(params)
if model_path != "" {
classifier.LoadModel(model_path)
} else {
return 0.0, nil, nil
}
test_dataset := core.NewDataSet()
err := test_dataset.Load(test_path, global)
if err != nil {
return 0.0, nil, err
}
auc, predictions := AlgorithmRunOnDataSet(classifier, nil, test_dataset, pred_path, params)
return auc, predictions, nil
}
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
}