public void ClassificationStackingEnsembleModel_Predict_Multiple()
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var rows = targets.Length;
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var learner = new ClassificationStackingEnsembleLearner(learners, new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var metric = new TotalErrorClassificationMetric <double>();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.34615384615384615, actual, 0.0000001);
}
public void ClassificationStackingEnsembleModel_PredictProbability_single()
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var rows = targets.Length;
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var learner = new ClassificationStackingEnsembleLearner(learners, new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), false);
var sut = learner.Learn(observations, targets);
var predictions = new ProbabilityPrediction[rows];
for (int i = 0; i < rows; i++)
{
predictions[i] = sut.PredictProbability(observations.Row(i));
}
var metric = new LogLossClassificationProbabilityMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.6696598716465223, actual, 0.0000001);
}
public void ClassificationStackingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var sut = new ClassificationStackingEnsembleLearner(learners, new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), false);
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.Predict(observations);
var metric = new TotalErrorClassificationMetric <double>();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.67289719626168221, actual, 0.0001);
}
public void ClassificationStackingEnsembleModel_GetRawVariableImportance()
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var learner = new ClassificationStackingEnsembleLearner(learners, new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 0.12545787545787546, 0, 0.16300453932032882, 0.0345479082321188, 0.15036245805476572, 0, 0, 0 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.000001);
}
}
public void ClassificationStackingEnsembleLearner_Learn_Include_Original_Features()
{
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var sut = new ClassificationStackingEnsembleLearner(learners, new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), 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.Predict(observations);
var metric = new TotalErrorClassificationMetric <double>();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.26168224299065418, actual, 0.0001);
}
public void ClassificationStackingEnsembleModel_PredictProbability_single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var learner = new ClassificationStackingEnsembleLearner(learners,
new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), false);
var sut = learner.Learn(observations, targets);
var rows = targets.Length;
var predictions = new ProbabilityPrediction[rows];
for (int i = 0; i < rows; i++)
{
predictions[i] = sut.PredictProbability(observations.Row(i));
}
var metric = new LogLossClassificationProbabilityMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.6696598716465223, actual, 0.0000001);
}
public void ClassificationStackingEnsembleModel_Predict_Multiple()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var learner = new ClassificationStackingEnsembleLearner(learners,
new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var metric = new TotalErrorClassificationMetric <double>();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.34615384615384615, actual, 0.0000001);
}
public void ClassificationStackingEnsembleModel_GetRawVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var learner = new ClassificationStackingEnsembleLearner(learners,
new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 0.12545787545787546, 0, 0.16300453932032882, 0.0345479082321188, 0.15036245805476572, 0, 0, 0 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.000001);
}
}
public void ClassificationStackingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var sut = new ClassificationStackingEnsembleLearner(learners, new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), false);
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var indices = Enumerable.Range(0, 25).ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var metric = new TotalErrorClassificationMetric <double>();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.67289719626168221, actual, 0.0001);
}
public void ClassificationStackingEnsembleModel_GetVariableImportance()
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var featureNameToIndex = new Dictionary <string, int> {
{ "AptitudeTestScore", 0 },
{ "PreviousExperience_month", 1 }
};
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var learner = new ClassificationStackingEnsembleLearner(learners, new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetVariableImportance(featureNameToIndex);
WriteImportances(actual);
var expected = new Dictionary <string, double> {
{ "ClassificationDecisionTreeModel_1_Class_Probability_0", 100 }, { "ClassificationDecisionTreeModel_2_Class_Probability_0", 92.2443379072288 }, { "ClassificationDecisionTreeModel_0_Class_Probability_0", 76.9658783620323 }, { "ClassificationDecisionTreeModel_1_Class_Probability_1", 21.1944454897829 }, { "ClassificationDecisionTreeModel_0_Class_Probability_1", 0 }, { "ClassificationDecisionTreeModel_2_Class_Probability_1", 0 }, { "ClassificationDecisionTreeModel_3_Class_Probability_0", 0 }, { "ClassificationDecisionTreeModel_3_Class_Probability_1", 0 }
};
Assert.AreEqual(expected.Count, actual.Count);
var zip = expected.Zip(actual, (e, a) => new { Expected = e, Actual = a });
foreach (var item in zip)
{
Assert.AreEqual(item.Expected.Key, item.Actual.Key);
Assert.AreEqual(item.Expected.Value, item.Actual.Value, 0.000001);
}
}
public void ClassificationStackingEnsembleLearner_Learn_Include_Original_Features()
{
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var sut = new ClassificationStackingEnsembleLearner(learners, new ClassificationDecisionTreeLearner(9),
new RandomCrossValidation <ProbabilityPrediction>(5, 23), true);
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var metric = new TotalErrorClassificationMetric <double>();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.26168224299065418, actual, 0.0001);
}