public void ClassificationEnsembleModel_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 ClassificationEnsembleLearner(learners, new MeanProbabilityClassificationEnsembleStrategy());
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(0.32562112824941963, actual, 0.0000001);
}
public void ClassificationEnsembleModel_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 ClassificationEnsembleLearner(learners, new MeanProbabilityClassificationEnsembleStrategy());
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 100.0, 15.6771501925546 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.000001);
}
}
public void ClassificationEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var sut = new ClassificationEnsembleLearner(learners,
new MeanProbabilityClassificationEnsembleStrategy());
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 ClassificationEnsembleModel_GetVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
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 ClassificationEnsembleLearner(learners, new MeanProbabilityClassificationEnsembleStrategy());
var sut = learner.Learn(observations, targets);
var actual = sut.GetVariableImportance(featureNameToIndex);
var expected = new Dictionary <string, double> {
{ "PreviousExperience_month", 100.0 },
{ "AptitudeTestScore", 15.6771501925546 }
};
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 ClassificationEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var sut = new ClassificationEnsembleLearner(learners, new MeanProbabilityClassificationEnsembleStrategy());
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 ClassificationEnsembleModel_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 ClassificationEnsembleLearner(learners, new MeanProbabilityClassificationEnsembleStrategy());
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(0.32562112824941963, actual, 0.0000001);
}
public void ClassificationEnsembleModel_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 ClassificationEnsembleLearner(learners, new MeanProbabilityClassificationEnsembleStrategy());
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var metric = new TotalErrorClassificationMetric <double>();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.076923076923076927, actual, 0.0000001);
}
public void ClassificationEnsembleModel_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 ClassificationEnsembleLearner(learners, new MeanProbabilityClassificationEnsembleStrategy());
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 100.0, 15.6771501925546 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.000001);
}
}
public void ClassificationEnsembleModel_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 ClassificationEnsembleLearner(learners, new MeanProbabilityClassificationEnsembleStrategy());
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var metric = new TotalErrorClassificationMetric <double>();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.076923076923076927, actual, 0.0000001);
}
public void ClassificationEnsembleLearner_Learn_Bagging()
{
var learners = new IIndexedLearner <ProbabilityPrediction>[]
{
new ClassificationDecisionTreeLearner(2),
new ClassificationDecisionTreeLearner(5),
new ClassificationDecisionTreeLearner(7),
new ClassificationDecisionTreeLearner(9)
};
var sut = new ClassificationEnsembleLearner(learners, new MeanProbabilityClassificationEnsembleStrategy(), 0.7);
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(0.16822429906542055, actual, 0.0001);
}
