public void ClassificationNeuralNetModel_PredictProbability_Single()
{
var numberOfObservations = 500;
var numberOfFeatures = 5;
var numberOfClasses = 5;
var random = new Random(32);
var observations = new F64Matrix(numberOfObservations, numberOfFeatures);
observations.Map(() => random.NextDouble());
var targets = Enumerable.Range(0, numberOfObservations)
.Select(i => (double)random.Next(0, numberOfClasses)).ToArray();
var sut = ClassificationNeuralNetModel.Load(() => new StringReader(m_classificationNeuralNetModelText));
var predictions = new ProbabilityPrediction[numberOfObservations];
for (int i = 0; i < numberOfObservations; i++)
{
predictions[i] = sut.PredictProbability(observations.Row(i));
}
var evaluator = new TotalErrorClassificationMetric <double>();
var actual = evaluator.Error(targets, predictions.Select(p => p.Prediction).ToArray());
Assert.AreEqual(0.762, actual);
}
public void RandomClassificationEnsembleSelection_Constructor_Number_Of_Availible_Models_Lower_Than_Number_Of_Models_To_Select()
{
var sut = new RandomClassificationEnsembleSelection(
new LogLossClassificationProbabilityMetric(),
new MeanProbabilityClassificationEnsembleStrategy(), 5, 1, true);
var observations = new ProbabilityPrediction[3][];
observations.Select(t => new ProbabilityPrediction[10]).ToArray();
var targets = new double[10];
sut.Select(observations, targets);
}
public void ClassificationAdaBoostModel_PredictProbability_Single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new ClassificationAdaBoostLearner(10, 1, 3);
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> {
{ 0, 0.553917222019051 }, { 1, 0.446082777980949 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.455270122123639 }, { 1, 0.544729877876361 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.590671208378385 }, { 1, 0.409328791621616 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.564961572849738 }, { 1, 0.435038427150263 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.455270122123639 }, { 1, 0.544729877876361 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.549970403132686 }, { 1, 0.450029596867314 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.417527839140627 }, { 1, 0.582472160859373 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.409988559960094 }, { 1, 0.590011440039906 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.630894242807786 }, { 1, 0.369105757192214 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.436954866525023 }, { 1, 0.563045133474978 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.461264944069783 }, { 1, 0.538735055930217 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.590671208378385 }, { 1, 0.409328791621616 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.549503146925505 }, { 1, 0.450496853074495 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.537653803214063 }, { 1, 0.462346196785938 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.37650723540928 }, { 1, 0.62349276459072 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.573579890413618 }, { 1, 0.426420109586382 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.549970403132686 }, { 1, 0.450029596867314 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.524371409810479 }, { 1, 0.475628590189522 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.436954866525023 }, { 1, 0.563045133474978 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.471117379964633 }, { 1, 0.528882620035367 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.630894242807786 }, { 1, 0.369105757192214 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.436954866525023 }, { 1, 0.563045133474978 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.404976804073458 }, { 1, 0.595023195926542 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.573579890413618 }, { 1, 0.426420109586382 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.549970403132686 }, { 1, 0.450029596867314 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.630894242807786 }, { 1, 0.369105757192214 },
}), };
CollectionAssert.AreEqual(expected, actual);
}
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);
}
public void ClassificationForestModel_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 learner = new ClassificationRandomForestLearner(100, 1, 100, 1, 0.0001, 1.0, 42, false);
var sut = learner.Learn(observations, targets);
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(0.076923076923076927, error, m_delta);
var expected = new ProbabilityPrediction[] { new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.650149027443145 }, { 1, 0.349850972556855 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.566943847818848 }, { 1, 0.433056152181152 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.726936489980608 }, { 1, 0.273063510019392 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.752781908451026 }, { 1, 0.247218091548974 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.566943847818848 }, { 1, 0.433056152181152 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.792506836300954 }, { 1, 0.207493163699046 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.491736055611056 }, { 1, 0.508263944388944 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.574583315377433 }, { 1, 0.425416684622567 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.838724674018791 }, { 1, 0.161275325981208 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.241480824730825 }, { 1, 0.758519175269175 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.385258186258186 }, { 1, 0.614741813741813 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.726936489980608 }, { 1, 0.273063510019392 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.706733044733045 }, { 1, 0.293266955266955 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.801266011766012 }, { 1, 0.198733988233988 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.294952297702298 }, { 1, 0.705047702297702 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.821706914001031 }, { 1, 0.178293085998968 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.780062391856509 }, { 1, 0.21993760814349 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.554444388944389 }, { 1, 0.445555611055611 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.261349872349872 }, { 1, 0.738650127650127 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.419758186258186 }, { 1, 0.580241813741813 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.71382231249143 }, { 1, 0.28617768750857 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.241480824730825 }, { 1, 0.758519175269175 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.47562148962149 }, { 1, 0.52437851037851 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.821706914001031 }, { 1, 0.178293085998968 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.792506836300954 }, { 1, 0.207493163699046 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.666244987039105 }, { 1, 0.333755012960895 },
}) };
CollectionAssert.AreEqual(expected, actual);
}
public void ClassificationDecisionTreeModel_PredictProbability_Single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new ClassificationDecisionTreeLearner(100, 5, 2, 0.001, 42);
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(0.23076923076923078, error, 0.0000001);
var expected = new ProbabilityPrediction[] { new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.571428571428571 }, { 1, 0.428571428571429 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.428571428571429 }, { 1, 0.571428571428571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.857142857142857 }, { 1, 0.142857142857143 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.571428571428571 }, { 1, 0.428571428571429 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.428571428571429 }, { 1, 0.571428571428571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.285714285714286 }, { 1, 0.714285714285714 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.857142857142857 }, { 1, 0.142857142857143 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.285714285714286 }, { 1, 0.714285714285714 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.571428571428571 }, { 1, 0.428571428571429 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.857142857142857 }, { 1, 0.142857142857143 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.428571428571429 }, { 1, 0.571428571428571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.571428571428571 }, { 1, 0.428571428571429 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.428571428571429 }, { 1, 0.571428571428571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.285714285714286 }, { 1, 0.714285714285714 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.285714285714286 }, { 1, 0.714285714285714 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.571428571428571 }, { 1, 0.428571428571429 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.857142857142857 }, { 1, 0.142857142857143 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.285714285714286 }, { 1, 0.714285714285714 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.428571428571429 }, { 1, 0.571428571428571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.857142857142857 }, { 1, 0.142857142857143 },
}), };
CollectionAssert.AreEqual(expected, actual);
}
public void ClassificationDecisionTreeModel_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 learner = new ClassificationDecisionTreeLearner(100, 5, 2, 0.001, 42);
var sut = learner.Learn(observations, targets);
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(0.23076923076923078, error, 0.0000001);
var expected = new ProbabilityPrediction[] { new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.571428571428571 }, { 1, 0.428571428571429 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.428571428571429 }, { 1, 0.571428571428571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.857142857142857 }, { 1, 0.142857142857143 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.571428571428571 }, { 1, 0.428571428571429 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.428571428571429 }, { 1, 0.571428571428571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.285714285714286 }, { 1, 0.714285714285714 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.857142857142857 }, { 1, 0.142857142857143 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.285714285714286 }, { 1, 0.714285714285714 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.571428571428571 }, { 1, 0.428571428571429 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.857142857142857 }, { 1, 0.142857142857143 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.428571428571429 }, { 1, 0.571428571428571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.571428571428571 }, { 1, 0.428571428571429 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.428571428571429 }, { 1, 0.571428571428571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.285714285714286 }, { 1, 0.714285714285714 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.285714285714286 }, { 1, 0.714285714285714 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.571428571428571 }, { 1, 0.428571428571429 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.857142857142857 }, { 1, 0.142857142857143 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.285714285714286 }, { 1, 0.714285714285714 },
}), new ProbabilityPrediction(1, new Dictionary <double, double> {
{ 0, 0.428571428571429 }, { 1, 0.571428571428571 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.75 }, { 1, 0.25 },
}), new ProbabilityPrediction(0, new Dictionary <double, double> {
{ 0, 0.857142857142857 }, { 1, 0.142857142857143 },
}), };
CollectionAssert.AreEqual(expected, actual);
}
