public void ClassificationGradientBoostLearner_LearnWithEarlyStopping_ToFewIterations()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var splitter = new StratifiedTrainingTestIndexSplitter <double>(0.6, 1234);
var split = splitter.SplitSet(observations, targets);
var sut = new ClassificationBinomialGradientBoostLearner(10, 0.01, 9, 1, 1e-6, .5, 1);
var evaluator = new TotalErrorClassificationMetric <double>();
var model = sut.LearnWithEarlyStopping(split.TrainingSet.Observations, split.TrainingSet.Targets,
split.TestSet.Observations, split.TestSet.Targets, evaluator, 10);
}
public void ClassificationGradientBoostLearner_LearnWithEarlyStopping_ToFewIterations()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(r => r != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var splitter = new StratifiedTrainingTestIndexSplitter <double>(0.6, 1234);
var split = splitter.SplitSet(observations, targets);
var sut = new ClassificationBinomialGradientBoostLearner(10, 0.01, 9, 1, 1e-6, .5, 1);
var evaluator = new TotalErrorClassificationMetric <double>();
var model = sut.LearnWithEarlyStopping(split.TrainingSet.Observations, split.TrainingSet.Targets,
split.TestSet.Observations, split.TestSet.Targets, evaluator, 10);
}
public void ClassificationGradientBoostLearner_LearnWithEarlyStopping()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var splitter = new StratifiedTrainingTestIndexSplitter <double>(0.6, 1234);
var split = splitter.SplitSet(observations, targets);
var sut = new ClassificationBinomialGradientBoostLearner(100, 0.01, 9, 1, 1e-6, .5, 0, false);
var evaluator = new TotalErrorClassificationMetric <double>();
var model = sut.LearnWithEarlyStopping(split.TrainingSet.Observations, split.TrainingSet.Targets,
split.TestSet.Observations, split.TestSet.Targets, evaluator, 10);
var predictions = model.Predict(split.TestSet.Observations);
var actual = evaluator.Error(split.TestSet.Targets, predictions);
Assert.AreEqual(0.16279069767441862, actual, 0.000001);
Assert.AreEqual(90, model.Trees.First().ToArray().Length);
}
public void ClassificationGradientBoostLearner_LearnWithEarlyStopping()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(r => r != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var splitter = new StratifiedTrainingTestIndexSplitter <double>(0.6, 1234);
var split = splitter.SplitSet(observations, targets);
var sut = new ClassificationBinomialGradientBoostLearner(100, 0.01, 9, 1, 1e-6, .5, 0, false);
var evaluator = new TotalErrorClassificationMetric <double>();
var model = sut.LearnWithEarlyStopping(split.TrainingSet.Observations, split.TrainingSet.Targets,
split.TestSet.Observations, split.TestSet.Targets, evaluator, 10);
var predictions = model.Predict(split.TestSet.Observations);
var actual = evaluator.Error(split.TestSet.Targets, predictions);
Assert.AreEqual(0.16279069767441862, actual, 0.000001);
Assert.AreEqual(90, model.Trees.First().ToArray().Length);
}
