void AssertModel(F64Matrix observations, double[] targets, RegressionXGBoostModel model)
{
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0795934933096642, actual, m_delta);
}
public void RegressionXGBoostModel_Save_Load()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var learner = CreateLearner();
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var modelFilePath = "model.xgb";
using (var sutPreSave = learner.Learn(observations, targets))
{
AssertModel(observations, targets, sutPreSave);
sutPreSave.Save(modelFilePath);
}
using (var sutAfterSave = RegressionXGBoostModel.Load(modelFilePath))
{
AssertModel(observations, targets, sutAfterSave);
}
}
public void RegressionXGBoostModel_Save_Load()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var learner = CreateLearner();
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var modelFilePath = "model.xgb";
using (var sutPreSave = learner.Learn(observations, targets))
{
AssertModel(observations, targets, sutPreSave);
sutPreSave.Save(modelFilePath);
}
using (var sutAfterSave = RegressionXGBoostModel.Load(modelFilePath))
{
AssertModel(observations, targets, sutAfterSave);
}
}
