public void ClassificationStackingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
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 metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.LearnStackingModel(observations, metaObservations, targets);
var predictions = model.Predict(observations);
var metric = new TotalErrorClassificationMetric <double>();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.63551401869158874, actual, 0.0001);
}
public void RegressionRandomModelSelectingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var indices = Enumerable.Range(0, 25).ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.13601421174394385, actual, 0.0001);
}
public void RegressionRandomModelSelectingEnsembleLearner_Learn_Start_With_3_Models()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var metric = new MeanSquaredErrorRegressionMetric();
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5,
new RandomCrossValidation <double>(5, 42), new MeanRegressionEnsembleStrategy(), metric, 3, false);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.0090143589987671056, actual, 0.0001);
}
public void RegressionStackingEnsembleModel_Predict_Multiple()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var learner = new RegressionStackingEnsembleLearner(learners,
new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.26175213675213671, actual, 0.0000001);
}
public void RegressionForwardSearchModelSelectingEnsembleLearner_Learn_Without_Replacement()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var metric = new MeanSquaredErrorRegressionMetric();
var sut = new RegressionForwardSearchModelSelectingEnsembleLearner(learners, 5,
new RandomCrossValidation <double>(5, 42),
new MeanRegressionEnsembleStrategy(), metric, 1, false);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.010316259438112841, 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 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 SharpLearning_Example()
{
// Load data
var(observations, targets) = DataSetUtilities.LoadWinequalityWhite();
// transform data for neural net
var transform = new MinMaxTransformer(0.0, 1.0);
transform.Transform(observations, observations);
var featureCount = observations.ColumnCount;
// define the neural net.
var net = new NeuralNet();
net.Add(new InputLayer(inputUnits: featureCount));
net.Add(new DenseLayer(32, Activation.Relu));
net.Add(new DenseLayer(32, Activation.Relu));
net.Add(new SquaredErrorRegressionLayer());
// using only 10 iteration to make the example run faster.
// using square error as error metric. This is only used for reporting progress.
var learner = new RegressionNeuralNetLearner(net, iterations: 10, loss: new SquareLoss(),
optimizerMethod: OptimizerMethod.Adam);
var cv = new RandomCrossValidation <double>(10, seed: 232);
var predictions = cv.CrossValidate(learner, observations, targets);
Trace.WriteLine(FormatErrorString(targets, predictions));
}
public void RegressionEnsembleModel_GetRawVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var learner = new RegressionEnsembleLearner(learners, new MeanRegressionEnsembleStrategy());
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 100.0, 3.46067371526717 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.000001);
}
}
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 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 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 GBMTree_AddRawFeatureImportances()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var inSample = targets.Select(t => true).ToArray();
var orderedElements = new int[observations.ColumnCount][];
var rows = observations.RowCount;
for (int i = 0; i < observations.ColumnCount; i++)
{
var feature = observations.Column(i);
var indices = Enumerable.Range(0, rows).ToArray();
feature.SortWith(indices);
orderedElements[i] = indices;
}
var sut = new GBMDecisionTreeLearner(10);
var tree = sut.Learn(observations, targets, targets, targets, orderedElements, inSample);
var actual = new double[observations.ColumnCount];
tree.AddRawVariableImportances(actual);
var expected = new double[] { 0.0, 105017.48701572006 };
Assert.AreEqual(expected.Length, actual.Length);
Assert.AreEqual(expected[0], actual[0], 0.01);
Assert.AreEqual(expected[1], actual[1], 0.01);
}
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 RegressionEnsembleModel_Predict_single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var learner = new RegressionEnsembleLearner(learners, new MeanRegressionEnsembleStrategy());
var sut = learner.Learn(observations, targets);
var rows = targets.Length;
var predictions = new double[rows];
for (int i = 0; i < rows; i++)
{
predictions[i] = sut.Predict(observations.Row(i));
}
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.033195970695970689, actual, 0.0000001);
}
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 GBMDecisionTreeLearner_Learn()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var inSample = targets.Select(t => true).ToArray();
var orderedElements = new int[observations.ColumnCount][];
var rows = observations.RowCount;
for (int i = 0; i < observations.ColumnCount; i++)
{
var feature = observations.Column(i);
var indices = Enumerable.Range(0, rows).ToArray();
feature.SortWith(indices);
orderedElements[i] = indices;
}
var sut = new GBMDecisionTreeLearner(10);
var tree = sut.Learn(observations, targets, targets, targets, orderedElements, inSample);
var predictions = tree.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0046122425037232661, actual);
}
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 ClassificationRandomModelSelectingEnsembleLearner_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 ClassificationRandomModelSelectingEnsembleLearner(learners, 5);
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.PredictProbability(observations);
var metric = new LogLossClassificationProbabilityMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.60969181130388794, actual, 0.0001);
}
public void RegressionStackingEnsembleModel_GetRawVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var learner = new RegressionStackingEnsembleLearner(learners,
new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 0.255311355311355, 0.525592463092463, 0.753846153846154, 0.0128205128205128 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.000001);
}
}
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_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 RegressionStackingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var sut = new RegressionStackingEnsembleLearner(learners, new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.LearnStackingModel(observations, metaObservations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.06951934687172627, actual, 0.0001);
}
public void ClassificationAdaBoostModel_GetVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var featureNameToIndex = new Dictionary <string, int> {
{ "AptitudeTestScore", 0 },
{ "PreviousExperience_month", 1 }
};
var learner = new ClassificationAdaBoostLearner(10, 1, 3);
var sut = learner.Learn(observations, targets);
var actual = sut.GetVariableImportance(featureNameToIndex);
var expected = new Dictionary <string, double> {
{ "PreviousExperience_month", 100.0 },
{ "AptitudeTestScore", 24.0268096428771 }
};
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 RegressionStackingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var sut = new RegressionStackingEnsembleLearner(learners, new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var indices = Enumerable.Range(0, 25).ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.133930222950635, actual, 0.0001);
}
public void RegressionRandomModelSelectingEnsembleLearner_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.017238327841614508, actual, 0.0001);
}
public void RegressionBackwardEliminationModelSelectingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionBackwardEliminationModelSelectingEnsembleLearner(learners, 5);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.SelectModels(observations, metaObservations, targets);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.010316259438112841, actual, 0.0001);
}
public void RegressionGradientBoostModel_GetVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var featureNameToIndex = new Dictionary <string, int> {
{ "AptitudeTestScore", 0 },
{ "PreviousExperience_month", 1 }
};
var learner = new RegressionGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1.0, 0, new GradientBoostSquaredLoss(), false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetVariableImportance(featureNameToIndex);
var expected = new Dictionary <string, double> {
{ "PreviousExperience_month", 100.0 },
{ "AptitudeTestScore", 72.1682473281495 }
};
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 RegressionForestModel_GetVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var featureNameToIndex = new Dictionary <string, int> {
{ "AptitudeTestScore", 0 },
{ "PreviousExperience_month", 1 }
};
var learner = new RegressionRandomForestLearner(100, 5, 100, 1, 0.0001, 1.0, 42, false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetVariableImportance(featureNameToIndex);
var expected = new Dictionary <string, double> {
{ "PreviousExperience_month", 100 },
{ "AptitudeTestScore", 42.3879919692465 }
};
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, m_delta);
}
}
