public void RegressionGradientBoostLearner_LearnWithEarlyStopping_ToFewIterations()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var splitter = new RandomTrainingTestIndexSplitter <double>(0.6, 1234);
var split = splitter.SplitSet(observations, targets);
var sut = new RegressionSquareLossGradientBoostLearner(5, 0.1, 3, 1, 1e-6, 1.0, 0, false);
var evaluator = new MeanSquaredErrorRegressionMetric();
var model = sut.LearnWithEarlyStopping(split.TrainingSet.Observations, split.TrainingSet.Targets,
split.TestSet.Observations, split.TestSet.Targets, evaluator, 5);
}
public void RegressionGradientBoostLearner_LearnWithEarlyStopping_ToFewIterations()
{
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var splitter = new RandomTrainingTestIndexSplitter <double>(0.6, 1234);
var split = splitter.SplitSet(observations, targets);
var sut = new RegressionSquareLossGradientBoostLearner(5, 0.1, 3, 1, 1e-6, 1.0, 0, false);
var evaluator = new MeanSquaredErrorRegressionMetric();
var model = sut.LearnWithEarlyStopping(split.TrainingSet.Observations, split.TrainingSet.Targets,
split.TestSet.Observations, split.TestSet.Targets, evaluator, 5);
}
public void RegressionGradientBoostLearner_LearnWithEarlyStopping()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var splitter = new RandomTrainingTestIndexSplitter <double>(0.6, 1234);
var split = splitter.SplitSet(observations, targets);
var sut = new RegressionSquareLossGradientBoostLearner(1000, 0.1, 3, 1, 1e-6, 1.0, 0, false);
var evaluator = new MeanSquaredErrorRegressionMetric();
var model = sut.LearnWithEarlyStopping(split.TrainingSet.Observations, split.TrainingSet.Targets,
split.TestSet.Observations, split.TestSet.Targets, evaluator, 5);
var predictions = model.Predict(split.TestSet.Observations);
var actual = evaluator.Error(split.TestSet.Targets, predictions);
Assert.AreEqual(0.061035472792879512, actual, 0.000001);
Assert.AreEqual(40, model.Trees.Length);
}
public void RegressionGradientBoostLearner_LearnWithEarlyStopping()
{
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var splitter = new RandomTrainingTestIndexSplitter <double>(0.6, 1234);
var split = splitter.SplitSet(observations, targets);
var sut = new RegressionSquareLossGradientBoostLearner(1000, 0.1, 3, 1, 1e-6, 1.0, 0, false);
var evaluator = new MeanSquaredErrorRegressionMetric();
var model = sut.LearnWithEarlyStopping(split.TrainingSet.Observations, split.TrainingSet.Targets,
split.TestSet.Observations, split.TestSet.Targets, evaluator, 5);
var predictions = model.Predict(split.TestSet.Observations);
var actual = evaluator.Error(split.TestSet.Targets, predictions);
Assert.AreEqual(0.061035472792879512, actual, 0.000001);
Assert.AreEqual(40, model.Trees.Length);
}
public void RegressionGradientBoostLearner_LearnWithEarlyStopping_when_more_featuresPerSlit_than_featureCount_Throw()
{
var sut = new RegressionSquareLossGradientBoostLearner(500, 0.1, 10, 15, 0.01, 0.8,
featuresPrSplit: 4);
IRegressionMetric metric = new MeanSquaredErrorRegressionMetric();
var trainingRows = 5;
var testRows = 6;
var cols = 3;
var split = new TrainingTestSetSplit(
new F64Matrix(trainingRows, cols), new double[trainingRows],
new F64Matrix(testRows, cols), new double[testRows]);
var model = sut.LearnWithEarlyStopping(
split.TrainingSet.Observations, split.TrainingSet.Targets,
split.TestSet.Observations, split.TestSet.Targets,
metric,
earlyStoppingRounds: 20);
}