public void ClassificationMetric_On_Strings()
{
var targets = new string[] { "Quality1", "Quality2", "Quality2", "Quality2", "Quality3", "Quality1", "Quality1", "Quality2", "Quality3" };
var predictions = new string[] { "Quality1", "Quality2", "Quality2", "Quality2", "Quality1", "Quality2", "Quality2", "Quality1", "Quality3" };
var metric = new TotalErrorClassificationMetric <string>();
Trace.WriteLine(metric.ErrorString(targets, predictions));
}
public void ClassificationMetric_ErrorString()
{
var targets = new double[] { 1, 2, 2, 2, 3, 1, 1, 2, 3 };
var predictions = new double[] { 1, 2, 2, 2, 1, 2, 2, 1, 3 };
var metric = new TotalErrorClassificationMetric <double>();
Trace.WriteLine(metric.ErrorString(targets, predictions));
}
public void TotalErrorClassificationMetric_ErrorString()
{
var predictions = new double[] { 0, 1, 1, 2, 3, 4, 4 };
var targets = new double[] { 0, 1, 1, 2, 2, 3, 4 };
var sut = new TotalErrorClassificationMetric <double>();
var actual = sut.ErrorString(targets, predictions);
var expected = ";0;1;2;3;4;0;1;2;3;4\r\n0;1.000;0.000;0.000;0.000;0.000;100.000;0.000;0.000;0.000;0.000\r\n1;0.000;2.000;0.000;0.000;0.000;0.000;100.000;0.000;0.000;0.000\r\n2;0.000;0.000;1.000;1.000;0.000;0.000;0.000;50.000;50.000;0.000\r\n3;0.000;0.000;0.000;0.000;1.000;0.000;0.000;0.000;0.000;100.000\r\n4;0.000;0.000;0.000;0.000;1.000;0.000;0.000;0.000;0.000;100.000\r\nError: 28.571\r\n";;
Assert.AreEqual(expected, actual);
}
public void ClassificationMetric_ErrorString_Translate_Target_Values_To_Names()
{
var targets = new double[] { 1, 2, 2, 2, 3, 1, 1, 2, 3 };
var predictions = new double[] { 1, 2, 2, 2, 1, 2, 2, 1, 3 };
var translation = new Dictionary <double, string> {
{ 1.0, "Quality1" }, { 2.0, "Quality2" }, { 3.0, "Quality3" }
};
var metric = new TotalErrorClassificationMetric <double>();
Trace.WriteLine(metric.ErrorString(targets, predictions, translation));
}
public void TotalErrorClassificationMetric_ErrorString_TargetStringMapping()
{
var predictions = new double[] { 0, 1, 1, 2, 3, 4, 4 };
var targets = new double[] { 0, 1, 1, 2, 2, 3, 4 };
var sut = new TotalErrorClassificationMetric <double>();
var targetStringMapping = new Dictionary <double, string> {
{ 0, "One" }, { 1, "Two" }, { 2, "Three" }, { 3, "Four" }, { 4, "Five" }
};
var actual = sut.ErrorString(targets, predictions, targetStringMapping);
var expected = ";One;Two;Three;Four;Five;One;Two;Three;Four;Five\r\nOne;1.000;0.000;0.000;0.000;0.000;100.000;0.000;0.000;0.000;0.000\r\nTwo;0.000;2.000;0.000;0.000;0.000;0.000;100.000;0.000;0.000;0.000\r\nThree;0.000;0.000;1.000;1.000;0.000;0.000;0.000;50.000;50.000;0.000\r\nFour;0.000;0.000;0.000;0.000;1.000;0.000;0.000;0.000;0.000;100.000\r\nFive;0.000;0.000;0.000;0.000;1.000;0.000;0.000;0.000;0.000;100.000\r\nError: 28.571\r\n";;
Assert.AreEqual(expected, actual);
}
double ClassificationDecisionTreeLearner_Learn_Glass_Weighted(int treeDepth, double weight)
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var weights = targets.Select(v => Weight(v, 1, weight)).ToArray();
var sut = new ClassificationDecisionTreeLearner(treeDepth, 1, observations.ColumnCount, 0.001, 42);
var model = sut.Learn(observations, targets, weights);
var predictions = model.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
Trace.WriteLine(evaluator.ErrorString(targets, predictions));
var error = evaluator.Error(targets, predictions);
return(error);
}
double ClassificationDecisionTreeLearner_Learn_Glass_Weighted(int treeDepth, double weight)
{
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 weights = targets.Select(v => Weight(v, 1, weight)).ToArray();
var sut = new ClassificationDecisionTreeLearner(treeDepth, 1, observations.ColumnCount, 0.001, 42);
var model = sut.Learn(observations, targets, weights);
var predictions = model.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
Trace.WriteLine(evaluator.ErrorString(targets, predictions));
var error = evaluator.Error(targets, predictions);
return(error);
}