public void ClassificationAdaBoostModel_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 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 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 ClassificationAdaBoostLearner_Learn_Glass() { var(observations, targets) = DataSetUtilities.LoadGlassDataSet(); var sut = new ClassificationAdaBoostLearner(10, 1, 5); var model = sut.Learn(observations, targets); var predictions = model.Predict(observations); var evaluator = new TotalErrorClassificationMetric <double>(); var actual = evaluator.Error(targets, predictions); Assert.AreEqual(0.0, actual); }
public void ClassificationAdaBoostModel_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 ClassificationAdaBoostLearner(2); var sut = learner.Learn(observations, targets); var writer = new StringWriter(); sut.Save(() => writer); Assert.AreEqual(ClassificationAdaBoostModelString, writer.ToString()); }
public void ClassificationAdaBoostModel_GetRawVariableImportance() { var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet(); var learner = new ClassificationAdaBoostLearner(10, 1, 3); var sut = learner.Learn(observations, targets); var actual = sut.GetRawVariableImportance(); var expected = new double[] { 0.65083327864662022, 2.7087794356399844 }; Assert.AreEqual(expected.Length, actual.Length); for (int i = 0; i < expected.Length; i++) { Assert.AreEqual(expected[i], actual[i], 0.000001); } }
public void ClassificationAdaBoostModel_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 ClassificationAdaBoostLearner(10); 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 ClassificationAdaBoostLearner_Learn_AptitudeData_SequenceContainNoItemIssue_Solved() { var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet(); var indices = new int[] { 22, 6, 23, 12 }; var sut = new ClassificationAdaBoostLearner(10); var model = sut.Learn(observations, targets, indices); var predictions = model.Predict(observations); var indexedPredictions = predictions.GetIndices(indices); var indexedTargets = targets.GetIndices(indices); var evaluator = new TotalErrorClassificationMetric <double>(); var actual = evaluator.Error(indexedTargets, indexedPredictions); Assert.AreEqual(0.0, actual); }
public void ClassificationAdaBoostModel_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 ClassificationAdaBoostLearner(10, 1, 3); var sut = learner.Learn(observations, targets); var actual = sut.GetRawVariableImportance(); var expected = new double[] { 0.65083327864662022, 2.7087794356399844 }; Assert.AreEqual(expected.Length, actual.Length); for (int i = 0; i < expected.Length; i++) { Assert.AreEqual(expected[i], actual[i], 0.000001); } }
public void ClassificationAdaBoostModel_Predict_Single() { var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet(); var learner = new ClassificationAdaBoostLearner(10); 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 ClassificationAdaBoostLearner_Learn_Glass_Indexed() { var(observations, targets) = DataSetUtilities.LoadGlassDataSet(); var sut = new ClassificationAdaBoostLearner(10, 1, 5); var indices = Enumerable.Range(0, targets.Length).ToArray(); indices.Shuffle(new Random(42)); indices = indices.Take((int)(targets.Length * 0.7)) .ToArray(); var model = sut.Learn(observations, targets, indices); var predictions = model.Predict(observations); var indexedPredictions = predictions.GetIndices(indices); var indexedTargets = targets.GetIndices(indices); var evaluator = new TotalErrorClassificationMetric <double>(); var actual = evaluator.Error(indexedTargets, indexedPredictions); Assert.AreEqual(0.0, actual); }