public void RandomClassificationEnsembleSelection_Constructor_Number_Of_Availible_Models_Lower_Than_Number_Of_Models_To_Select()
{
var sut = new RandomClassificationEnsembleSelection(
new LogLossClassificationProbabilityMetric(),
new MeanProbabilityClassificationEnsembleStrategy(), 5, 1, true);
var observations = new ProbabilityPrediction[3][];
observations.Select(t => new ProbabilityPrediction[10]).ToArray();
var targets = new double[10];
sut.Select(observations, targets);
}
public void RandomClassificationEnsembleSelection_Select_No_Replacements()
{
var sut = new RandomClassificationEnsembleSelection(new LogLossClassificationProbabilityMetric(),
new MeanProbabilityClassificationEnsembleStrategy(), 3, 10, false);
var random = new Random(42);
var observations = CreateModelPredictions(random);
var targets = Enumerable.Range(0, 10).Select(v => (double)random.Next(1)).ToArray();
var actual = sut.Select(observations, targets);
var expected = new int[] { 1, 0, 2 };
CollectionAssert.AreEqual(expected, actual);
}
public void RandomClassificationEnsembleSelection_Constructor_Number_Of_Models_Too_Low()
{
var sut = new RandomClassificationEnsembleSelection(
new LogLossClassificationProbabilityMetric(),
new MeanProbabilityClassificationEnsembleStrategy(), 0, 1, true);
}
public void RandomClassificationEnsembleSelection_Constructor_EnsembleStratey_Null()
{
var sut = new RandomClassificationEnsembleSelection(
new LogLossClassificationProbabilityMetric(), null, 5, 1, true);
}
public void RandomClassificationEnsembleSelection_Constructor_Metric_Null()
{
var sut = new RandomClassificationEnsembleSelection(null,
new MeanProbabilityClassificationEnsembleStrategy(), 5, 1, true);
}
