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 ClassificationBackwardEliminationModelSelectingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
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 sut = new ClassificationBackwardEliminationModelSelectingEnsembleLearner(learners, 5);
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.SelectModels(observations, metaObservations, targets);
var predictions = model.PredictProbability(observations);
var metric = new LogLossClassificationProbabilityMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.52351727716455632, actual, 0.0001);
}
public void ClassificationBackwardEliminationModelSelectingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
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 sut = new ClassificationBackwardEliminationModelSelectingEnsembleLearner(learners, 5);
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.SelectModels(observations, metaObservations, targets);
var predictions = model.PredictProbability(observations);
var metric = new LogLossClassificationProbabilityMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.52351727716455632, 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 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);
}
