public static double CalcMSE(XGBRegressor regr)
{
var target = GetKlaThickness(LoadedDatas);
var pred = regr.Predict(GetReflectivity(LoadedDatas));
return(MSE(pred, target));
}
// 나중에는 세팅 , 데이터 , 로드 여부 순으로 모델 만들기
public static XGBRegressor CreateModel(List <IpsDataSet> datas)
{
LoadedDatas = datas;
Regr = new XGBRegressor();
Regr.Fit(GetReflectivity(datas), GetKlaThickness(datas));
return(Regr);
}
[TestMethod] public void Predict()
{
var dataTrain = TestUtils.GetRegressorDataTrain();
var labelsTrain = TestUtils.GetRegressorLabelsTrain();
var dataTest = TestUtils.GetRegressorDataTest();
var xgbr = new XGBRegressor();
xgbr.Fit(dataTrain, labelsTrain);
var preds = xgbr.Predict(dataTest);
Assert.IsTrue(TestUtils.RegressorPredsCorrect(preds));
}
public void TestRegressorSaveAndLoadWithParameters()
{
var dataTrain = TestUtils.GetRegressorDataTrain();
var labelsTrain = TestUtils.GetRegressorLabelsTrain();
var dataTest = TestUtils.GetRegressorDataTest();
var xgbr = new XGBRegressor();
xgbr.Fit(dataTrain, labelsTrain);
var preds1 = xgbr.Predict(dataTest);
xgbr.SaveModelToFile(TEST_FILE);
var xgbr2 = BaseXgbModel.LoadRegressorFromFile(TEST_FILE);
var preds2 = xgbr2.Predict(dataTest);
Assert.IsTrue(TestUtils.AreEqual(preds1, preds2));
}
public static XGBRegressor LoadModel(string path) // Excuted when ScanAutorun is fired
{
Regr = LoadRegressorFromFile(path);
return(Regr);
}
public static void Reset()
{
Regr = new XGBRegressor();
LoadedDatas = new List <IpsDataSet>();
}
