double ClassificationExtremelyRandomizedTreesLearner_Learn_Aptitude(int trees, double subSampleRatio = 1.0)
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var sut = new ClassificationExtremelyRandomizedTreesLearner(trees, 1, 100, 1, 0.0001, subSampleRatio, 42, false);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var error = evaluator.Error(targets, predictions);
return(error);
}
public void ClassificationExtremelyRandomizedTreesLearner_Learn_Glass_100_Trees_Parallel()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new ClassificationExtremelyRandomizedTreesLearner(100, 1, 100, 1, 0.0001, 1.0, 42, true);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0560747663551402, error, m_delta);
}
double ClassificationExtremelyRandomizedTreesLearner_Learn_Aptitude(int trees, double subSampleRatio = 1.0)
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var rows = targets.Length;
var sut = new ClassificationExtremelyRandomizedTreesLearner(trees, 1, 100, 1, 0.0001, subSampleRatio, 42, false);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var error = evaluator.Error(targets, predictions);
return(error);
}
public void ClassificationExtremelyRandomizedTreesLearner_Learn_Glass_100_Trees_Parallel()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var sut = new ClassificationExtremelyRandomizedTreesLearner(100, 1, 100, 1, 0.0001, 1.0, 42, true);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0560747663551402, error, m_delta);
}
public void ClassificationExtremelyRandomizedTreesLearner_Learn_Glass_100_Indices()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new ClassificationExtremelyRandomizedTreesLearner(100, 1, 100, 1, 0.0001, 1.0, 42, false);
var indices = Enumerable.Range(0, targets.Length).ToArray();
indices.Shuffle(new Random(42));
indices = indices.Take((int)(targets.Length * 0.7))
.ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.11214953271028, error, m_delta);
}
public void ClassificationExtremelyRandomizedTreesLearner_Learn_Glass_100_Indices()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var rows = targets.Length;
var sut = new ClassificationExtremelyRandomizedTreesLearner(100, 1, 100, 1, 0.0001, 1.0, 42, false);
var indices = Enumerable.Range(0, targets.Length).ToArray();
indices.Shuffle(new Random(42));
indices = indices.Take((int)(targets.Length * 0.7))
.ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.14485981308411214, error, 0.0000001);
}
