public void RegressionAdaBoostModel_GetVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var featureNameToIndex = new Dictionary <string, int> {
{ "AptitudeTestScore", 0 },
{ "PreviousExperience_month", 1 }
};
var learner = new RegressionAdaBoostLearner(10);
var sut = learner.Learn(observations, targets);
var actual = sut.GetVariableImportance(featureNameToIndex);
var expected = new Dictionary <string, double> {
{ "PreviousExperience_month", 100.0 },
{ "AptitudeTestScore", 33.8004886838701 }
};
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 RegressionAdaBoostModel_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 RegressionAdaBoostLearner(10);
var sut = learner.Learn(observations, targets);
var actual = sut.GetVariableImportance(featureNameToIndex);
var expected = new Dictionary <string, double> {
{ "PreviousExperience_month", 100.0 },
{ "AptitudeTestScore", 33.8004886838701 }
};
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 RegressionAdaBoostLearner_Learn_AptitudeData_ExponentialLoss()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var sut = new RegressionAdaBoostLearner(10, 1, 0, AdaBoostRegressionLoss.Exponential);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.10370879120879124, actual);
}
public void RegressionAdaBoostModel_Precit_Multiple()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new RegressionAdaBoostLearner(10);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.14185814185814186, error, 0.0000001);
}
public void RegressionAdaBoostLearner_Learn_Glass()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new RegressionAdaBoostLearner(10);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.54723570404775324, actual);
}
public void RegressionAdaBoostLearner_Learn_AptitudeData_LinearLoss()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var sut = new RegressionAdaBoostLearner(10);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.14185814185814186, actual);
}
public void RegressionAdaBoostModel_Save()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new RegressionAdaBoostLearner(2);
var sut = learner.Learn(observations, targets);
var writer = new StringWriter();
sut.Save(() => writer);
var actual = writer.ToString();
Assert.AreEqual(RegressionDecisionTreeModelString, actual);
}
public void RegressionAdaBoostLearner_Learn_Glass()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var sut = new RegressionAdaBoostLearner(10);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.54723570404775324, actual);
}
public void RegressionAdaBoostLearner_Learn_AptitudeData_ExponentialLoss()
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var sut = new RegressionAdaBoostLearner(10, 1, 0, AdaBoostRegressionLoss.Exponential);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.10370879120879124, actual);
}
public void RegressionAdaBoostModel_GetRawVariableImportance()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learner = new RegressionAdaBoostLearner(10);
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 5.9121817956101106, 17.49140922459933 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.000001);
}
}
public void RegressionAdaBoostModel_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 RegressionAdaBoostLearner(10);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.14185814185814186, error, 0.0000001);
}
public void RegressionAdaBoostModel_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 RegressionAdaBoostLearner(2);
var sut = learner.Learn(observations, targets);
var writer = new StringWriter();
sut.Save(() => writer);
var actual = writer.ToString();
Assert.AreEqual(RegressionDecisionTreeModelString, actual);
}
public void RegressionAdaBoostModel_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 RegressionAdaBoostLearner(10);
var sut = learner.Learn(observations, targets);
var actual = sut.GetRawVariableImportance();
var expected = new double[] { 5.9121817956101106, 17.49140922459933 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.000001);
}
}
public void RegressionAdaBoostModel_Predict_Single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var rows = targets.Length;
var learner = new RegressionAdaBoostLearner(10);
var sut = learner.Learn(observations, targets);
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(0.14185814185814186, error, 0.0000001);
}
public void RegressionAdaBoostLearner_Learn_Glass_Indexed()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new RegressionAdaBoostLearner(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 MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(indexedTargets, indexedPredictions);
Assert.AreEqual(0.22181054803405248, actual);
}
public void RegressionAdaBoostLearner_Learn_Glass_Indexed()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var sut = new RegressionAdaBoostLearner(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 MeanAbsolutErrorRegressionMetric();
var actual = evaluator.Error(indexedTargets, indexedPredictions);
Assert.AreEqual(0.22181054803405248, actual);
}
/// <summary>
/// Predição de Floresta Aleatória e Rede Neural
/// </summary>
public void RegressionLearner_Learn_And_Predict()
{
#region Treinamento da Floresta Aleatória
var parser = new CsvParser(() => new StringReader(treinamento));
var targetName = "T";
var observations = parser.EnumerateRows(c => c != targetName)
.ToF64Matrix();
var targets = parser.EnumerateRows(targetName)
.ToF64Vector();
UltimaObservacao = new double[] { observations[observations.RowCount - 1, 0], observations[observations.RowCount - 1, 2], observations[observations.RowCount - 1, 3], observations[observations.RowCount - 1, 4], observations[observations.RowCount - 1, 5], targets[targets.Count() - 1] };
var learner = new RegressionRandomForestLearner(trees: 500);
model = learner.Learn(observations, targets);
#endregion
#region Teste da Floresta Aleatória
parser = new CsvParser(() => new StringReader(teste));
var observationsTeste = parser.EnumerateRows(c => c != targetName)
.ToF64Matrix();
var targetsTeste = parser.EnumerateRows(targetName)
.ToF64Vector();
// predict the training and test set.
var trainPredictions = model.Predict(observations);
var testPredictions = model.Predict(observationsTeste);
// create the metric
var metric = new MeanSquaredErrorRegressionMetric();
// measure the error on training and test set.
trainError = metric.Error(targets, trainPredictions);
testError = metric.Error(targetsTeste, testPredictions);
#endregion
#region Treinamento da Rede Neural
var net = new NeuralNet();
net.Add(new InputLayer(6));
net.Add(new DropoutLayer(0.2));
net.Add(new DenseLayer(800, Activation.Relu));
net.Add(new DropoutLayer(0.5));
net.Add(new DenseLayer(800, Activation.Relu));
net.Add(new DropoutLayer(0.5));
net.Add(new SquaredErrorRegressionLayer());
var learnernet = new RegressionNeuralNetLearner(net, iterations: 500, loss: new SquareLoss());
modelnet = learnernet.Learn(observations, targets);
#endregion
#region Teste da Rede Neural
trainPredictions = modelnet.Predict(observations);
testPredictions = modelnet.Predict(observationsTeste);
trainErrorNet = metric.Error(targets, trainPredictions);
testErrorNet = metric.Error(targetsTeste, testPredictions);
#endregion
#region Treinamento Ada
var learnerada = new RegressionAdaBoostLearner(maximumTreeDepth: 35, iterations: 2000, learningRate: 0.1);
modelada = learnerada.Learn(observations, targets);
#endregion
#region Teste Ada
trainPredictions = modelada.Predict(observations);
testPredictions = modelada.Predict(observationsTeste);
trainErrorAda = metric.Error(targets, trainPredictions);
testErrorAda = metric.Error(targetsTeste, testPredictions);
string stargets = "";
string strainPredictions = "";
string stargetsTeste = "";
string stestPredictions = "";
foreach (var i in targets)
{
stargets += i + ";";
}
foreach (var i in trainPredictions)
{
strainPredictions += i + ";";
}
foreach (var i in targetsTeste)
{
stargetsTeste += i + ";";
}
foreach (var i in testPredictions)
{
stestPredictions += i + ";";
}
#endregion
}