public void RegressionForwardSearchModelSelectingEnsembleLearner_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionForwardSearchModelSelectingEnsembleLearner(learners, 5);
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.016150842834795006, actual, 0.0001);
}
public void RegressionStackingEnsembleLearner_Learn_Keep_Original_Features()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var sut = new RegressionStackingEnsembleLearner(learners, new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), true);
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.066184865331534531, actual, 0.0001);
}
public void RegressionStackingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var sut = new RegressionStackingEnsembleLearner(learners, new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.LearnStackingModel(observations, metaObservations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.06951934687172627, actual, 0.0001);
}
public void RegressionStackingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var sut = new RegressionStackingEnsembleLearner(learners, new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var indices = Enumerable.Range(0, 25).ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.133930222950635, actual, 0.0001);
}
public void RegressionForwardSearchModelSelectingEnsembleLearner_Learn_Start_With_3_Models()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var metric = new MeanSquaredErrorRegressionMetric();
var sut = new RegressionForwardSearchModelSelectingEnsembleLearner(learners, 5,
new RandomCrossValidation <double>(5, 42), new MeanRegressionEnsembleStrategy(), metric, 3, false);
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.010316259438112848, actual, 0.0001);
}
public void RegressionBackwardEliminationModelSelectingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionBackwardEliminationModelSelectingEnsembleLearner(learners, 5);
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var indices = Enumerable.Range(0, 25).ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.13601421174394385, actual, 0.0001);
}
// Update is called once per frame
void Update()
{
double[] input = get_input();
double[] output = get_output();
F64Matrix input_M = new F64Matrix(input, 1, 2);
RegressionNeuralNetModel model;
if (!agent_play)
{
model = learner.Learn(input_M, output);
}
else
{
model = new RegressionNeuralNetModel(net);
}
double[] predictions = model.Predict(input_M);
if (agent_play)
{
agent_work(predictions);
}
MeanSquaredErrorRegressionMetric metric = new MeanSquaredErrorRegressionMetric();
var error = metric.Error(output, predictions);
TM.text = error.ToString("F2");
TM2.text = predictions[0].ToString("F2");
}
public void RegressionStackingEnsembleModel_Predict_Multiple()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var learner = new RegressionStackingEnsembleLearner(learners,
new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.26175213675213671, actual, 0.0000001);
}
public void RegressionForwardSearchModelSelectingEnsembleLearner_Learn_Without_Replacement()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var metric = new MeanSquaredErrorRegressionMetric();
var sut = new RegressionForwardSearchModelSelectingEnsembleLearner(learners, 5,
new RandomCrossValidation <double>(5, 42),
new MeanRegressionEnsembleStrategy(), metric, 1, false);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.010316259438112841, actual, 0.0001);
}
public void Hyper_Parameter_Tuning()
{
#region Read data
// Use StreamReader(filepath) when running from filesystem
var parser = new CsvParser(() => new StringReader(Resources.winequality_white));
var targetName = "quality";
// read feature matrix
var observations = parser.EnumerateRows(c => c != targetName)
.ToF64Matrix();
// read classification targets
var targets = parser.EnumerateRows(targetName)
.ToF64Vector();
#endregion
// metric to minimize
var metric = new MeanSquaredErrorRegressionMetric();
// Parameter ranges for the optimizer
var paramers = new ParameterBounds[]
{
new ParameterBounds(min: 1, max: 100, transform: Transform.Linear), // maximumTreeDepth
new ParameterBounds(min: 1, max: 16, transform: Transform.Linear), // minimumSplitSize
};
// create random search optimizer
var optimizer = new RandomSearchOptimizer(paramers, iterations: 30, runParallel: true);
// other availible optimizers
// GridSearchOptimizer
// GlobalizedBoundedNelderMeadOptimizer
// ParticleSwarmOptimizer
// BayesianOptimizer
// function to minimize
Func <double[], OptimizerResult> minimize = p =>
{
var cv = new RandomCrossValidation <double>(crossValidationFolds: 5, seed: 42);
var optlearner = new RegressionDecisionTreeLearner(maximumTreeDepth: (int)p[0], minimumSplitSize: (int)p[1]);
var predictions = cv.CrossValidate(optlearner, observations, targets);
var error = metric.Error(targets, predictions);
Trace.WriteLine("Error: " + error);
return(new OptimizerResult(p, error));
};
// run optimizer
var result = optimizer.OptimizeBest(minimize);
var bestParameters = result.ParameterSet;
Trace.WriteLine("Result: " + result.Error);
// create learner with found parameters
var learner = new RegressionDecisionTreeLearner(maximumTreeDepth: (int)bestParameters[0], minimumSplitSize: (int)bestParameters[1]);
// learn model with found parameters
var model = learner.Learn(observations, targets);
}
public void RegressionRandomForestLearnerTest_Learn_Glass_100_Indices()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var rows = targets.Length;
var sut = new RegressionRandomForestLearner(100, 1, 100, 1, 0.0001, 1.0, 42, false);
var indices = Enumerable.Range(0, targets.Length).ToArray();
indices.Shuffle(new Random(42));
indices = indices.Take((int)(targets.Length * 0.7))
.ToArray();
var decisionTreeModels = sut.Learn(observations, targets, indices, out var rawVariableImportance).ToArray();
var model = new RegressionForestModel(decisionTreeModels, rawVariableImportance);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.49709813080602938, error, m_delta);
}
public void RegressionRandomModelSelectingEnsembleLearner_Learn_Start_With_3_Models()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var metric = new MeanSquaredErrorRegressionMetric();
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5,
new RandomCrossValidation <double>(5, 42), new MeanRegressionEnsembleStrategy(), metric, 3, false);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.0090143589987671056, actual, 0.0001);
}
public void RegressionNeuralNetModel_Save()
{
var numberOfObservations = 500;
var numberOfFeatures = 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.NextDouble()).ToArray();
var net = new NeuralNet();
net.Add(new InputLayer(numberOfFeatures));
net.Add(new DenseLayer(10));
net.Add(new SquaredErrorRegressionLayer());
var learner = new RegressionNeuralNetLearner(net, new AccuracyLoss());
var sut = learner.Learn(observations, targets);
// save model.
var writer = new StringWriter();
sut.Save(() => writer);
// load model and assert prediction results.
sut = RegressionNeuralNetModel.Load(() => new StringReader(writer.ToString()));
var predictions = sut.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0871547675024143, actual, 0.0001);
}
public void RegressionBackwardEliminationModelSelectingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionBackwardEliminationModelSelectingEnsembleLearner(learners, 5);
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.SelectModels(observations, metaObservations, targets);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.010316259438112841, actual, 0.0001);
}
public void RegressionRandomModelSelectingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var indices = Enumerable.Range(0, 25).ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.13601421174394385, actual, 0.0001);
}
public void RegressionEnsembleModel_Predict_single()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var learner = new RegressionEnsembleLearner(learners, new MeanRegressionEnsembleStrategy());
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 metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.033195970695970689, actual, 0.0000001);
}
public void GBMDecisionTreeLearner_Learn()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var inSample = targets.Select(t => true).ToArray();
var orderedElements = new int[observations.ColumnCount][];
var rows = observations.RowCount;
for (int i = 0; i < observations.ColumnCount; i++)
{
var feature = observations.Column(i);
var indices = Enumerable.Range(0, rows).ToArray();
feature.SortWith(indices);
orderedElements[i] = indices;
}
var sut = new GBMDecisionTreeLearner(10);
var tree = sut.Learn(observations, targets, targets, targets, orderedElements, inSample);
var predictions = tree.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0046122425037232661, actual);
}
public void RegressionForestModel_PredictCertainty_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 RegressionRandomForestLearner(100, 5, 100, 1, 0.0001, 1.0, 42, false);
var sut = learner.Learn(observations, targets);
var actual = new CertaintyPrediction[rows];
for (int i = 0; i < rows; i++)
{
actual[i] = sut.PredictCertainty(observations.Row(i));
}
var evaluator = new MeanSquaredErrorRegressionMetric();
var error = evaluator.Error(targets, actual.Select(p => p.Prediction).ToArray());
Assert.AreEqual(0.15381141277554411, error, 0.0000001);
var expected = new CertaintyPrediction[] { new CertaintyPrediction(0.379151515151515, 0.0608255007215007), new CertaintyPrediction(0.411071351850763, 0.0831655436577049), new CertaintyPrediction(0.243420918950331, 0.0452827034233046), new CertaintyPrediction(0.302332251082251, 0.0699917594408057), new CertaintyPrediction(0.411071351850763, 0.0831655436577049), new CertaintyPrediction(0.175743762773174, 0.0354069437824887), new CertaintyPrediction(0.574083361083361, 0.0765858693929188), new CertaintyPrediction(0.259063776093188, 0.0491198812971218), new CertaintyPrediction(0.163878898878899, 0.0331543420321184), new CertaintyPrediction(0.671753996003996, 0.0624466591504497), new CertaintyPrediction(0.418472943722944, 0.0607014359023913), new CertaintyPrediction(0.243420918950331, 0.0452827034233046), new CertaintyPrediction(0.443779942279942, 0.0941961872991865), new CertaintyPrediction(0.156999361749362, 0.0435804333960299), new CertaintyPrediction(0.591222034501446, 0.0873624628347336), new CertaintyPrediction(0.123822406351818, 0.0283119805431255), new CertaintyPrediction(0.162873993653405, 0.0333697457759022), new CertaintyPrediction(0.596261932511932, 0.0695341060210394), new CertaintyPrediction(0.671753996003996, 0.0624466591504497), new CertaintyPrediction(0.418472943722944, 0.0607014359023913), new CertaintyPrediction(0.329000027750028, 0.0788869852405852), new CertaintyPrediction(0.671753996003996, 0.0624466591504497), new CertaintyPrediction(0.499770375049787, 0.0913884936411888), new CertaintyPrediction(0.140025508804921, 0.0309875116490099), new CertaintyPrediction(0.161207326986739, 0.0336321035325246), new CertaintyPrediction(0.389553418803419, 0.0744433596104835), };
CollectionAssert.AreEqual(expected, actual);
}
public void RegressionStackingEnsembleModel_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 learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var learner = new RegressionStackingEnsembleLearner(learners, new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var sut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.26175213675213671, actual, 0.0000001);
}
string FormatErrorString(double[] targets, double[] predictions)
{
var metric = new MeanSquaredErrorRegressionMetric();
var error = metric.Error(targets, predictions);
return($"MSE: {error}");
}
public void RegressionNeuralNetLearner_Learn()
{
var numberOfObservations = 500;
var numberOfFeatures = 5;
var random = new Random(32);
F64Matrix observations;
double[] targets;
CreateData(numberOfObservations, numberOfFeatures, random, out observations, out targets);
var net = new NeuralNet();
net.Add(new InputLayer(numberOfFeatures));
net.Add(new DenseLayer(10));
net.Add(new SquaredErrorRegressionLayer());
var sut = new RegressionNeuralNetLearner(net, new SquareLoss());
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0871547675024143, actual, 0.0001);
}
public void GBMDecisionTreeLearner_Learn()
{
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var inSample = targets.Select(t => true).ToArray();
var orderedElements = new int[observations.ColumnCount][];
var rows = observations.RowCount;
for (int i = 0; i < observations.ColumnCount; i++)
{
var feature = observations.Column(i);
var indices = Enumerable.Range(0, rows).ToArray();
feature.SortWith(indices);
orderedElements[i] = indices;
}
var sut = new GBMDecisionTreeLearner(10);
var tree = sut.Learn(observations, targets, targets, targets, orderedElements, inSample);
var predictions = tree.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0046122425037232661, actual);
}
public void RegressionNeuralNetLearner_Learn_Early_Stopping()
{
var numberOfObservations = 500;
var numberOfFeatures = 5;
var random = new Random(32);
F64Matrix observations;
double[] targets;
CreateData(numberOfObservations, numberOfFeatures, random, out observations, out targets);
F64Matrix validationObservations;
double[] validationTargets;
CreateData(numberOfObservations, numberOfFeatures, random, out validationObservations, out validationTargets);
var net = new NeuralNet();
net.Add(new InputLayer(numberOfFeatures));
net.Add(new DenseLayer(10));
net.Add(new SquaredErrorRegressionLayer());
var sut = new RegressionNeuralNetLearner(net, new SquareLoss(), 0.01, 150);
var model = sut.Learn(observations, targets,
validationObservations, validationTargets);
var validationPredictions = model.Predict(validationObservations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(validationTargets, validationPredictions);
Assert.AreEqual(0.093500629562319859, actual, 0.0001);
}
public void RegressionStackingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var sut = new RegressionStackingEnsembleLearner(learners, new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.LearnStackingModel(observations, metaObservations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.06951934687172627, actual, 0.0001);
}
public void RegressionRandomModelSelectingEnsembleLearner_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.017238327841614508, actual, 0.0001);
}
public void RegressionStackingEnsembleLearner_Learn_Indexed()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var sut = new RegressionStackingEnsembleLearner(learners, new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var indices = Enumerable.Range(0, 25).ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.133930222950635, actual, 0.0001);
}
public void RegressionNeuralNetModel_Predict_Single()
{
var numberOfObservations = 500;
var numberOfFeatures = 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.NextDouble()).ToArray();
var sut = RegressionNeuralNetModel.Load(() => new StringReader(m_regressionNeuralNetModelText));
var predictions = new double[numberOfObservations];
for (int i = 0; i < numberOfObservations; i++)
{
predictions[i] = sut.Predict(observations.Row(i));
}
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0871547675024143, actual, 0.0001);
}
public void RegressionBackwardEliminationModelSelectingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9),
new RegressionDecisionTreeLearner(11),
new RegressionDecisionTreeLearner(21),
new RegressionDecisionTreeLearner(23),
new RegressionDecisionTreeLearner(1),
new RegressionDecisionTreeLearner(14),
new RegressionDecisionTreeLearner(17),
new RegressionDecisionTreeLearner(19),
new RegressionDecisionTreeLearner(33)
};
var sut = new RegressionBackwardEliminationModelSelectingEnsembleLearner(learners, 5);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.SelectModels(observations, metaObservations, targets);
var predictions = model.Predict(observations);
var metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.010316259438112841, actual, 0.0001);
}
public void RegressionEnsembleModel_Predict_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 learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var learner = new RegressionEnsembleLearner(learners, new MeanRegressionEnsembleStrategy());
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 metric = new MeanSquaredErrorRegressionMetric();
var actual = metric.Error(targets, predictions);
Assert.AreEqual(0.033195970695970689, actual, 0.0000001);
}
void AssertModel(F64Matrix observations, double[] targets, RegressionXGBoostModel model)
{
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.0795934933096642, actual, m_delta);
}
