public void RegressionDecisionTreeModel_Load()
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var reader = new StringReader(m_regressionDecisionTreeModelString);
var sut = RegressionDecisionTreeModel.Load(() => reader);
var predictions = sut.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.032120286249559482, error, 0.0000001);
}
public void RegressionDecisionTreeModel_Load()
{
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var reader = new StringReader(RegressionDecisionTreeModelString);
var sut = RegressionDecisionTreeModel.Load(() => reader);
var predictions = sut.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.032120286249559482, error, 0.0000001);
}
RegressionDecisionTreeModel CreateTree(F64Matrix observations, double[] targets, int[] indices, Random random)
{
var learner = new DecisionTreeLearner(
new DepthFirstTreeBuilder(m_maximumTreeDepth,
m_featuresPrSplit,
m_minimumInformationGain,
m_random.Next(),
new RandomSplitSearcher(m_minimumSplitSize, m_random.Next()),
new RegressionImpurityCalculator()));
var treeIndicesLength = (int)Math.Round(m_subSampleRatio * (double)indices.Length);
var treeIndices = new int[treeIndicesLength];
for (int j = 0; j < treeIndicesLength; j++)
{
treeIndices[j] = indices[random.Next(indices.Length)];
}
var model = new RegressionDecisionTreeModel(learner.Learn(observations, targets, treeIndices));
return(model);
}
public void RegressionModel_Save_Load()
{
#region learner creation
// 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 regression targets
var targets = parser.EnumerateRows(targetName)
.ToF64Vector();
// create learner
var learner = new RegressionDecisionTreeLearner();
#endregion
// learns a ClassificationDecisionTreeModel
var model = learner.Learn(observations, targets);
var writer = new StringWriter();
model.Save(() => writer);
// save to file
//model.Save(() => new StreamWriter(filePath));
var text = writer.ToString();
var loadedModel = RegressionDecisionTreeModel.Load(() => new StringReader(text));
// load from file
//RegressionDecisionTreeModel.Load(() => new StreamReader(filePath));
}
