public void ClassificationGradientBoostModel_GetVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var featureNameToIndex = new Dictionary <string, int> {
{ "AptitudeTestScore", 0 },
{ "PreviousExperience_month", 1 }
};
var learner = new ClassificationGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1, 0, new GradientBoostBinomialLoss(), false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetVariableImportance(featureNameToIndex);
var expected = new Dictionary <string, double> {
{ "PreviousExperience_month", 100 },
{ "AptitudeTestScore", 56.81853305612 }
};
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, 0.01);
}
}
public void ClassificationGradientBoostModel_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 learner = new ClassificationGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1, 0, new GradientBoostBinomialLoss(), false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetVariableImportance(featureNameToIndex);
var expected = new Dictionary <string, double> {
{ "PreviousExperience_month", 100 },
{ "AptitudeTestScore", 56.81853305612 }
};
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, 0.01);
}
}
public void ClassificationGradientBoostModel_Predict_Multiple()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new ClassificationGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1, 0, new GradientBoostBinomialLoss(), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.038461538461538464, error, 0.0000001);
}
public void ClassificationGradientBoostModel_GetRawVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new ClassificationGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1, 0, new GradientBoostBinomialLoss(), false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 26.287331114005394, 46.265416757664667 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.001);
}
}
public void ClassificationGradientBoostModel_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 learner = new ClassificationGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1, 0, new GradientBoostBinomialLoss(), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.038461538461538464, error, 0.0000001);
}
public void ClassificationGradientBoostModel_GetRawVariableImportance()
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var learner = new ClassificationGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1, 0, new GradientBoostBinomialLoss(), false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 26.287331114005394, 46.265416757664667 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.001);
}
}
public void ClassificationGradientBoostModel_Predict_Single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new ClassificationGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1, 0, new GradientBoostBinomialLoss(), false);
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 evaluator = new TotalErrorClassificationMetric <double>();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.038461538461538464, error, 0.0000001);
}
public void ClassificationGradientBoostModel_Save()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new ClassificationGradientBoostLearner(5);
var sut = learner.Learn(observations, targets);
// save model.
var writer = new StringWriter();
sut.Save(() => writer);
// load model and assert prediction results.
sut = ClassificationGradientBoostModel.Load(() => new StringReader(writer.ToString()));
var predictions = sut.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.15384615384615385, actual, 0.0000001);
}
public void ClassificationGradientBoostModel_Save()
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var learner = new ClassificationGradientBoostLearner(5);
var sut = learner.Learn(observations, targets);
// save model.
var writer = new StringWriter();
sut.Save(() => writer);
// load model and assert prediction results.
sut = ClassificationGradientBoostModel.Load(() => new StringReader(writer.ToString()));
var predictions = sut.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.15384615384615385, actual, 0.0000001);
}
public void ClassificationGradientBoostModel_PredictProbability_Single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new ClassificationGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1, 0, new GradientBoostBinomialLoss(), false);
var sut = learner.Learn(observations, targets);
var rows = targets.Length;
var actual = new ProbabilityPrediction[rows];
for (int i = 0; i < rows; i++)
{
actual[i] = sut.PredictProbability(observations.Row(i));
}
var evaluator = new TotalErrorClassificationMetric <double>();
var error = evaluator.Error(targets, actual.Select(p => p.Prediction).ToArray());
Assert.AreEqual(0.038461538461538464, error, 0.0000001);
var expected = new ProbabilityPrediction[] { new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00153419685769873 }, { 0, 0.998465803142301 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.497135615200052 }, { 0, 0.502864384799948 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00674291737944022 }, { 0, 0.99325708262056 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00153419685769873 }, { 0, 0.998465803142301 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.497135615200052 }, { 0, 0.502864384799948 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00428497228545111 }, { 0, 0.995715027714549 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 1, 0.987907185249206 }, { 0, 0.0120928147507945 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 1, 0.982783250692275 }, { 0, 0.0172167493077254 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00262490179961228 }, { 0, 0.997375098200388 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 1, 0.996417847055106 }, { 0, 0.00358215294489364 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 1, 0.995341658753364 }, { 0, 0.00465834124663571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00674291737944022 }, { 0, 0.99325708262056 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.0118633115475969 }, { 0, 0.988136688452403 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00048646805791186 }, { 0, 0.999513531942088 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 1, 0.999891769651047 }, { 0, 0.000108230348952856 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00334655581934884 }, { 0, 0.996653444180651 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00428497228545111 }, { 0, 0.995715027714549 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.0118633115475969 }, { 0, 0.988136688452403 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 1, 0.996417847055106 }, { 0, 0.00358215294489362 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 1, 0.993419876193791 }, { 0, 0.00658012380620933 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00262490179961228 }, { 0, 0.997375098200388 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 1, 0.996417847055106 }, { 0, 0.00358215294489362 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 1, 0.988568859753437 }, { 0, 0.0114311402465632 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00334655581934884 }, { 0, 0.996653444180651 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00428497228545111 }, { 0, 0.995715027714549 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 1, 0.00262490179961228 }, { 0, 0.997375098200388 },
}), };
CollectionAssert.AreEqual(expected, actual);
}
