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 RegressionGradientBoostModel_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 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 RegressionGradientBoostModel_Precit_Multiple()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new RegressionGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1.0, 0, new GradientBoostSquaredLoss(), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.045093177702025665, error, 0.0000001);
}
public void RegressionGradientBoostModel_GetRawVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
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.GetRawVariableImportance();
var expected = new double[] { 31.124562320186836, 43.127779144563753 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.000001);
}
}
public void RegressionGradientBoostModel_Precit_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 RegressionGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1.0, 0, new GradientBoostSquaredLoss(), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.045093177702025665, error, 0.0000001);
}
public void RegressionGradientBoostModel_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 RegressionGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1.0, 0, new GradientBoostSquaredLoss(), false);
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 31.124562320186836, 43.127779144563753 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.000001);
}
}
public void RegressionGradientBoostModel_Predict_Single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new RegressionGradientBoostLearner(100, 0.1, 3, 1, 1e-6, 1.0, 0, new GradientBoostSquaredLoss(), 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 MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.045093177702025665, error, 0.0000001);
}
public void RegressionGradientBoostModel_Save()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new RegressionGradientBoostLearner(2, 0.1, 3, 1, 1e-6, 1.0, 0, new GradientBoostSquaredLoss(), false);
var sut = learner.Learn(observations, targets);
// save model.
var writer = new StringWriter();
sut.Save(() => writer);
// load model and assert prediction results.
sut = RegressionGradientBoostModel.Load(() => new StringReader(writer.ToString()));
var predictions = sut.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.192163332018409, actual, 0.0001);
}
public void RegressionGradientBoostModel_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 RegressionGradientBoostLearner(2, 0.1, 3, 1, 1e-6, 1.0, 0, new GradientBoostSquaredLoss(), false);
var sut = learner.Learn(observations, targets);
// save model.
var writer = new StringWriter();
sut.Save(() => writer);
// load model and assert prediction results.
sut = RegressionGradientBoostModel.Load(() => new StringReader(writer.ToString()));
var predictions = sut.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.192163332018409, actual, 0.0001);
}
