public void ClassificationModelSelectingEnsembleLearner_Learn_Start_With_3_Models()
{
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9),
new ClassificationDecisionTreeLearner(11),
new ClassificationDecisionTreeLearner(21),
new ClassificationDecisionTreeLearner(23),
new ClassificationDecisionTreeLearner(1),
new ClassificationDecisionTreeLearner(14),
new ClassificationDecisionTreeLearner(17),
new ClassificationDecisionTreeLearner(19),
new ClassificationDecisionTreeLearner(33)
};
var metric = new LogLossClassificationProbabilityMetric();
var ensembleStrategy = new MeanProbabilityClassificationEnsembleStrategy();
var ensembleSelection = new ForwardSearchClassificationEnsembleSelection(metric, ensembleStrategy, 5, 3, true);
var sut = new ClassificationModelSelectingEnsembleLearner(learners, new RandomCrossValidation <ProbabilityPrediction>(5, 23),
ensembleStrategy, ensembleSelection);
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.PredictProbability(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.55183985816428427, actual, 0.0001);
}
public void ClassificationModelSelectingEnsembleLearner_Constructor_CrossValidation_Null()
{
var learners = new IIndexedLearner <ProbabilityPrediction> [4];
var metric = new LogLossClassificationProbabilityMetric();
var ensembleStrategy = new MeanProbabilityClassificationEnsembleStrategy();
var ensembleSelection = new ForwardSearchClassificationEnsembleSelection(metric, ensembleStrategy, 5, 1, true);
var sut = new ClassificationModelSelectingEnsembleLearner(learners, null, ensembleStrategy, ensembleSelection);
}
public void ClassificationModelSelectingEnsembleLearner_Constructor_Learners_Null()
{
var metric = new LogLossClassificationProbabilityMetric();
var ensembleStrategy = new MeanProbabilityClassificationEnsembleStrategy();
var ensembleSelection = new ForwardSearchClassificationEnsembleSelection(metric, ensembleStrategy, 5, 1, true);
var crossValidation = new RandomCrossValidation <ProbabilityPrediction>(5);
var sut = new ClassificationModelSelectingEnsembleLearner(null, crossValidation, ensembleStrategy, ensembleSelection);
}
public void ForwardSearchClassificationEnsembleSelection_Constructor_Number_Of_Availible_Models_Lower_Than_Number_Of_Models_To_Select()
{
var sut = new ForwardSearchClassificationEnsembleSelection(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 ForwardSearchClassificationEnsembleSelection_Select_Start_With_2_Models()
{
var sut = new ForwardSearchClassificationEnsembleSelection(new LogLossClassificationProbabilityMetric(),
new MeanProbabilityClassificationEnsembleStrategy(), 3, 2, true);
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[3] {
0, 1, 1
};
CollectionAssert.AreEqual(expected, actual);
}
public void ClassificationModelSelectingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9),
new ClassificationDecisionTreeLearner(11),
new ClassificationDecisionTreeLearner(21),
new ClassificationDecisionTreeLearner(23),
new ClassificationDecisionTreeLearner(1),
new ClassificationDecisionTreeLearner(14),
new ClassificationDecisionTreeLearner(17),
new ClassificationDecisionTreeLearner(19),
new ClassificationDecisionTreeLearner(33)
};
var metric = new LogLossClassificationProbabilityMetric();
var ensembleStrategy = new MeanProbabilityClassificationEnsembleStrategy();
var ensembleSelection = new ForwardSearchClassificationEnsembleSelection(metric,
ensembleStrategy, 5, 1, true);
var sut = new ClassificationModelSelectingEnsembleLearner(learners,
new RandomCrossValidation <ProbabilityPrediction>(5, 23),
ensembleStrategy, ensembleSelection);
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var indices = Enumerable.Range(0, 25).ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.PredictProbability(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(2.3682546920482164, actual, 0.0001);
}
public void ClassificationModelSelectingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9),
new ClassificationDecisionTreeLearner(11),
new ClassificationDecisionTreeLearner(21),
new ClassificationDecisionTreeLearner(23),
new ClassificationDecisionTreeLearner(1),
new ClassificationDecisionTreeLearner(14),
new ClassificationDecisionTreeLearner(17),
new ClassificationDecisionTreeLearner(19),
new ClassificationDecisionTreeLearner(33)
};
var metric = new LogLossClassificationProbabilityMetric();
var ensembleStrategy = new MeanProbabilityClassificationEnsembleStrategy();
var ensembleSelection = new ForwardSearchClassificationEnsembleSelection(metric, ensembleStrategy, 5, 1, true);
var sut = new ClassificationModelSelectingEnsembleLearner(learners, new RandomCrossValidation <ProbabilityPrediction>(5, 23),
ensembleStrategy, ensembleSelection);
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var indices = Enumerable.Range(0, 25).ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.PredictProbability(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(2.3682546920482164, actual, 0.0001);
}
public void ForwardSearchClassificationEnsembleSelection_Constructor_Number_Of_Models_From_Start_Larger_Than_Number_Of_Models_To_Select()
{
var sut = new ForwardSearchClassificationEnsembleSelection(
new LogLossClassificationProbabilityMetric(),
new MeanProbabilityClassificationEnsembleStrategy(), 3, 5, true);
}
public void ForwardSearchClassificationEnsembleSelection_Constructor_Number_Of_Models_Too_Low()
{
var sut = new ForwardSearchClassificationEnsembleSelection(
new LogLossClassificationProbabilityMetric(),
new MeanProbabilityClassificationEnsembleStrategy(), 0, 1, true);
}
public void ForwardSearchClassificationEnsembleSelection_Constructor_EnsembleStratey_Null()
{
var sut = new ForwardSearchClassificationEnsembleSelection(
new LogLossClassificationProbabilityMetric(), null, 5, 1, true);
}
public void ForwardSearchClassificationEnsembleSelection_Constructor_Metric_Null()
{
var sut = new ForwardSearchClassificationEnsembleSelection(null,
new MeanProbabilityClassificationEnsembleStrategy(), 5, 1, true);
}
