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));
}
