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 ClassificationBackwardEliminationModelSelectingEnsembleLearner_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 sut = new ClassificationBackwardEliminationModelSelectingEnsembleLearner(learners, 5,
new RandomCrossValidation <ProbabilityPrediction>(5, 23), ensembleStrategy, metric);
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 ClassificationForwardSearchModelSelectingEnsembleLearner_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 sut = new ClassificationForwardSearchModelSelectingEnsembleLearner(learners, 5,
new StratifiedCrossValidation <ProbabilityPrediction>(5, 23), ensembleStrategy, metric, 3, true);
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var model = sut.Learn(observations, targets);
var predictions = model.PredictProbability(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.54434276244488244, actual, 0.0001);
}
public void ClassificationRandomModelSelectingEnsembleLearner_Learn_Without_Replacement()
{
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 sut = new ClassificationRandomModelSelectingEnsembleLearner(learners, 5,
new StratifiedCrossValidation <ProbabilityPrediction>(5, 23), ensembleStrategy, metric, 1, false);
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.PredictProbability(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.5805783545646459, actual, 0.0001);
}
public void ClassificationModelSelectingEnsembleLearner_Constructor_EnsembleSelection_Null()
{
var learners = new IIndexedLearner <ProbabilityPrediction> [4];
var metric = new LogLossClassificationProbabilityMetric();
var ensembleStrategy = new MeanProbabilityClassificationEnsembleStrategy();
var crossValidation = new RandomCrossValidation <ProbabilityPrediction>(5);
var sut = new ClassificationModelSelectingEnsembleLearner(learners, crossValidation, ensembleStrategy, null);
}
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 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 MeanProbabilityClassificationEnsembleStrategy_Combine()
{
var values = new ProbabilityPrediction[]
{
new ProbabilityPrediction(1.0, new Dictionary <double, double> {
{ 0.0, 0.3 }, { 1.0, 0.88 }
}),
new ProbabilityPrediction(0.0, new Dictionary <double, double> {
{ 0.0, 0.66 }, { 1.0, 0.33 }
}),
new ProbabilityPrediction(1.0, new Dictionary <double, double> {
{ 0.0, 0.01 }, { 1.0, 0.99 }
}),
};
var sut = new MeanProbabilityClassificationEnsembleStrategy();
var actual = sut.Combine(values);
var expected = new ProbabilityPrediction(1.0, new Dictionary <double, double> {
{ 0.0, 0.323333333333333 }, { 1.0, 0.733333333333333 }
});
Assert.AreEqual(expected, actual);
}
