public void RegressionHuberLossGradientBoostLearner_Stochastic_Learn()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var sut = new RegressionHuberLossGradientBoostLearner(50, 0.1, 3, 1, 1e-6, .5, 0, 0.9, false);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0268131680885004, actual, 0.0001);
}
public void RegressionHuberLossGradientBoostLearner_FeaturesPrSplit_Learn()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var sut = new RegressionHuberLossGradientBoostLearner(50, 0.1, 3, 1, 1e-6, 1.0, 1, 0.9, false);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.076395719114033783, actual);
}
public void RegressionHuberLossGradientBoostLearner_FeaturesPrSplit_Learn()
{
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var sut = new RegressionHuberLossGradientBoostLearner(50, 0.1, 3, 1, 1e-6, 1.0, 1, 0.9, false);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.076395719114033783, actual);
}
public void RegressionHuberLossGradientBoostLearner_Stochastic_Learn()
{
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var sut = new RegressionHuberLossGradientBoostLearner(50, 0.1, 3, 1, 1e-6, .5, 0, 0.9, false);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0268131680885004, actual, 0.0001);
}
public void RegressionHuberLossGradientBoostLearner_Learn_Indexed()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new RegressionHuberLossGradientBoostLearner(50, 0.1, 3, 1, 1e-6, 1.0, 0, 0.9, 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 indexedPredictions = predictions.GetIndices(indices);
var indexedTargets = targets.GetIndices(indices);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(indexedTargets, indexedPredictions);
Assert.AreEqual(0.21635632980552189, actual, 0.0001);
}
public void RegressionHuberLossGradientBoostLearner_Learn_Indexed()
{
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 RegressionHuberLossGradientBoostLearner(50, 0.1, 3, 1, 1e-6, 1.0, 0, 0.9, 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 indexedPredictions = predictions.GetIndices(indices);
var indexedTargets = targets.GetIndices(indices);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(indexedTargets, indexedPredictions);
Assert.AreEqual(0.21635632980552189, actual, 0.0001);
}
