public static IEnumerable <Tuple <string, double> > CalculateImpacts(
IClassificationSolution solution,
ReplacementMethodEnum replacementMethod = ReplacementMethodEnum.Shuffle,
FactorReplacementMethodEnum factorReplacementMethod = FactorReplacementMethodEnum.Best,
DataPartitionEnum dataPartition = DataPartitionEnum.Training)
{
IEnumerable <int> rows = GetPartitionRows(dataPartition, solution.ProblemData);
IEnumerable <double> estimatedClassValues = solution.GetEstimatedClassValues(rows);
var model = (IClassificationModel)solution.Model.Clone(); //mkommend: clone of model is necessary, because the thresholds for IDiscriminantClassificationModels are updated
return(CalculateImpacts(model, solution.ProblemData, estimatedClassValues, rows, replacementMethod, factorReplacementMethod));
}
private void CheckDefaultAsserts(IClassificationSolution solution, Dictionary <string, double> expectedImpacts)
{
IClassificationProblemData problemData = solution.ProblemData;
IEnumerable <double> estimatedValues = solution.GetEstimatedClassValues(solution.ProblemData.TrainingIndices);
var solutionImpacts = ClassificationSolutionVariableImpactsCalculator.CalculateImpacts(solution);
var modelImpacts = ClassificationSolutionVariableImpactsCalculator.CalculateImpacts(solution.Model, problemData, estimatedValues, problemData.TrainingIndices);
//Both ways should return equal results
Assert.IsTrue(solutionImpacts.SequenceEqual(modelImpacts));
//Check if impacts are as expected
Assert.AreEqual(modelImpacts.Count(), expectedImpacts.Count);
Assert.IsTrue(modelImpacts.All(v => v.Item2.IsAlmost(expectedImpacts[v.Item1])));
}
