public void ClassificationDecisionTreeModel_Load() { var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet(); var reader = new StringReader(ClassificationDecisionTreeModelString); var sut = ClassificationDecisionTreeModel.Load(() => reader); var predictions = sut.Predict(observations); var evaluator = new TotalErrorClassificationMetric <double>(); var error = evaluator.Error(targets, predictions); Assert.AreEqual(0.19230769230769232, error, 0.0000001); }
public void ClassificationDecisionTreeModel_Load() { var parser = new CsvParser(() => new StringReader(Resources.AptitudeData)); var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix(); var targets = parser.EnumerateRows("Pass").ToF64Vector(); var reader = new StringReader(ClassificationDecisionTreeModelString); var sut = ClassificationDecisionTreeModel.Load(() => reader); var predictions = sut.Predict(observations); var evaluator = new TotalErrorClassificationMetric <double>(); var error = evaluator.Error(targets, predictions); Assert.AreEqual(0.19230769230769232, error, 0.0000001); }
public void ClassificationModel_Save_Load() { #region learner creation // Use StreamReader(filepath) when running from filesystem var parser = new CsvParser(() => new StringReader(Resources.winequality_white)); var targetName = "quality"; // read feature matrix var observations = parser.EnumerateRows(c => c != targetName) .ToF64Matrix(); // read classification targets var targets = parser.EnumerateRows(targetName) .ToF64Vector(); // create learner var learner = new ClassificationDecisionTreeLearner(); #endregion // learns a ClassificationDecisionTreeModel var model = learner.Learn(observations, targets); var writer = new StringWriter(); model.Save(() => writer); // save to file //model.Save(() => new StreamWriter(filePath)); var text = writer.ToString(); var loadedModel = ClassificationDecisionTreeModel.Load(() => new StringReader(text)); // load from file //ClassificationDecisionTreeModel.Load(() => new StreamReader(filePath)); }