public void TimeSeriesCrossValidation_Validate_InitialTrainingSize_Is_Larger_Than_Obsevations_Length()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var sut = new TimeSeriesCrossValidation <double>(initialTrainingSize: 300);
var timeSeriesPredictions = sut.Validate(new RegressionDecisionTreeLearner(), observations, targets);
}
public void TimeSeriesCrossValidation_Validate_Observations_And_Targets_Length_Does_Not_Match()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
targets = targets.Take(100).ToArray();
var sut = new TimeSeriesCrossValidation <double>(initialTrainingSize: 5);
var timeSeriesPredictions = sut.Validate(new RegressionDecisionTreeLearner(), observations, targets);
}
public void TimeSeriesCrossValidation_Validate_InitialTrainingSize_Is_Larger_Than_Obsevations_Length()
{
var targetName = "T";
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows(v => !v.Contains(targetName)).ToF64Matrix();
var targets = parser.EnumerateRows(targetName).ToF64Vector();
var sut = new TimeSeriesCrossValidation <double>(initialTrainingSize: 300);
var timeSeriesPredictions = sut.Validate(new RegressionDecisionTreeLearner(), observations, targets);
}
public void TimeSeriesCrossValidation_GetValidationIndices()
{
var targets = Enumerable.Range(0, 100).Select(v => (double)v).ToArray();
var initialTrainingSize = 5;
var sut = new TimeSeriesCrossValidation <double>(initialTrainingSize);
var actual = sut.GetValidationIndices(targets);
var expected = targets.Skip(initialTrainingSize).Select(v => (int)v).ToArray();
CollectionAssert.AreEqual(expected, actual);
}
public void TimeSeriesCrossValidation_Validate_MaxTrainingSetSize_And_RetrainInterval()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var sut = new TimeSeriesCrossValidation <double>(initialTrainingSize: 5, maxTrainingSetSize: 30, retrainInterval: 5);
var timeSeriesPredictions = sut.Validate(new RegressionDecisionTreeLearner(), observations, targets);
var timeSeriesTargets = sut.GetValidationTargets(targets);
var metric = new MeanSquaredErrorRegressionMetric();
var error = metric.Error(timeSeriesTargets, timeSeriesPredictions);
Assert.AreEqual(0.13010151998135897, error, 0.00001);
}
public void TimeSeriesCrossValidation_Validate_MaxTrainingSetSize()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var sut = new TimeSeriesCrossValidation <double>(initialTrainingSize: 5, maxTrainingSetSize: 10);
var timeSeriesPredictions = sut.Validate(new RegressionDecisionTreeLearner(), observations, targets);
var timeSeriesTargets = sut.GetValidationTargets(targets);
var metric = new MeanSquaredErrorRegressionMetric();
var error = metric.Error(timeSeriesTargets, timeSeriesPredictions);
Assert.AreEqual(0.27296549371962692, error, 0.00001);
}
public void TimeSeriesCrossValidation_Validate_RetrainInterval()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var sut = new TimeSeriesCrossValidation <double>(initialTrainingSize: 5, retrainInterval: 5);
var learner = new RegressionDecisionTreeLearner();
var timeSeriesPredictions = sut.Validate(learner, observations, targets);
var timeSeriesTargets = sut.GetValidationTargets(targets);
var metric = new MeanSquaredErrorRegressionMetric();
var error = metric.Error(timeSeriesTargets, timeSeriesPredictions);
Assert.AreEqual(0.096346937132994928, error, 0.00001);
}
public void TimeSeriesCrossValidation_Validate_MaxTrainingSetSize_And_RetrainInterval()
{
var targetName = "T";
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows(v => !v.Contains(targetName)).ToF64Matrix();
var targets = parser.EnumerateRows(targetName).ToF64Vector();
var sut = new TimeSeriesCrossValidation <double>(initialTrainingSize: 5, maxTrainingSetSize: 30, retrainInterval: 5);
var timeSeriesPredictions = sut.Validate(new RegressionDecisionTreeLearner(), observations, targets);
var timeSeriesTargets = sut.GetValidationTargets(targets);
var metric = new MeanSquaredErrorRegressionMetric();
var error = metric.Error(timeSeriesTargets, timeSeriesPredictions);
Assert.AreEqual(0.11347313000447182, error, 0.00001);
}
