public void MeanAbsolutErrorRegressionMetric_Error_Zero_Error()
{
var targets = new double[] { 0.0, 0.0 };
var predictions = new double[] { 0.0, 0.0 };
var sut = new MeanAbsolutErrorRegressionMetric();
var actual = sut.Error(targets, predictions);
Assert.AreEqual(0.0, actual);
}
public void MeanAbsolutErrorRegressionMetric_Error()
{
var targets = new double[] { 1.0, 2.3, 3.1, 4.4, 5.8 };
var predictions = new double[] { 1.0, 2.0, 3.0, 4.0, 5.0 };
var sut = new MeanAbsolutErrorRegressionMetric();
var actual = sut.Error(targets, predictions);
Assert.AreEqual(0.32, actual, 0.00001);
}
public void RegressionAdaBoostModel_Load()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var reader = new StringReader(RegressionDecisionTreeModelString);
var sut = RegressionAdaBoostModel.Load(() => reader);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.22527472527472531, error, 0.0000001);
}
public void RegressionAdaBoostLearner_Learn_Glass()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new RegressionAdaBoostLearner(10);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.54723570404775324, actual);
}
public void RegressionAdaBoostModel_Precit_Multiple()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new RegressionAdaBoostLearner(10);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.14185814185814186, error, 0.0000001);
}
public void RegressionGradientBoostModel_Precit_Multiple()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new RegressionGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1.0, 0, new GradientBoostSquaredLoss(), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.045093177702025665, error, 0.0000001);
}
public void RegressionAdaBoostLearner_Learn_AptitudeData_LinearLoss()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var sut = new RegressionAdaBoostLearner(10);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.14185814185814186, actual);
}
public void RegressionAdaBoostLearner_Learn_AptitudeData_ExponentialLoss()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var sut = new RegressionAdaBoostLearner(10, 1, 0, AdaBoostRegressionLoss.Exponential);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.10370879120879124, actual);
}
public void RegressionGradientBoostModel_Load()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var reader = new StringReader(RegressionGradientBoostModelString);
var sut = RegressionGradientBoostModel.Load(() => reader);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.42445562130177528, error, 0.0000001);
}
public void RegressionGradientBoostModel_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(RegressionGradientBoostModelString);
var sut = RegressionGradientBoostModel.Load(() => reader);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.42445562130177528, error, 0.0000001);
}
public void RegressionAdaBoostLearner_Learn_Glass()
{
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 RegressionAdaBoostLearner(10);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.54723570404775324, actual);
}
public void RegressionAdaBoostLearner_Learn_AptitudeData_ExponentialLoss()
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var sut = new RegressionAdaBoostLearner(10, 1, 0, AdaBoostRegressionLoss.Exponential);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.10370879120879124, actual);
}
public void RegressionAdaBoostModel_Precit_Multiple()
{
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 learner = new RegressionAdaBoostLearner(10);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.14185814185814186, error, 0.0000001);
}
public void RegressionGradientBoostModel_Precit_Multiple()
{
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 learner = new RegressionGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1.0, 0, new GradientBoostSquaredLoss(), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.045093177702025665, error, 0.0000001);
}
public void RegressionGradientBoostModel_Predict_Single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new RegressionGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1.0, 0, new GradientBoostSquaredLoss(), false);
var sut = learner.Learn(observations, targets);
var rows = targets.Length;
var predictions = new double[rows];
for (int i = 0; i < rows; i++)
{
predictions[i] = sut.Predict(observations.Row(i));
}
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.045093177702025665, error, 0.0000001);
}
public void RegressionAdaBoostModel_Predict_Single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var rows = targets.Length;
var learner = new RegressionAdaBoostLearner(10);
var sut = learner.Learn(observations, targets);
var predictions = new double[rows];
for (int i = 0; i < rows; i++)
{
predictions[i] = sut.Predict(observations.Row(i));
}
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.14185814185814186, error, 0.0000001);
}
public void RegressionAbsoluteLossGradientBoostLearner_Learn_Indexed()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new RegressionAbsoluteLossGradientBoostLearner(50, 0.1, 3, 1, 1e-6, 1.0, 0, 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(0.41246374405350877, actual, 0.0001);
}
public void RegressionAdaBoostLearner_Learn_Glass_Indexed()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new RegressionAdaBoostLearner(10, 1, 5);
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(0.22181054803405248, actual);
}
public void RegressionAbsoluteLossGradientBoostLearner_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 RegressionAbsoluteLossGradientBoostLearner(50, 0.1, 3, 1, 1e-6, 1.0, 0, 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(0.41246374405350877, actual, 0.0001);
}
public void RegressionAdaBoostLearner_Learn_Glass_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 RegressionAdaBoostLearner(10, 1, 5);
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(0.22181054803405248, actual);
}
