public void EvaluateClassifier(
IBaseClassifier <Classifier> classifier,
Runtime testing,
bool crossFoldsEvaluate = true,
bool runPredictions = true,
string saveModelToDiskFile = null,
string loadModelfromDiskFile = null,
int numFolds = 10)
{
Console.WriteLine("EvaluateClassifier");
var start = DateTime.Now;
if (classifier != null)
{
classifier.Build();
}
if (!String.IsNullOrWhiteSpace(loadModelfromDiskFile))
{
throw new NotSupportedException("loadModelfromDiskFile not supported " +
"as BaseClassifier.Read returns a Classifier not a IBaseClassifier as it should");
// clas = BaseClassifier.Read(loadModelfromDiskFile);
// Console.WriteLine("Loading Model From Disk Took: {0}", DateTime.Now.Subtract(start));
}
else if (!String.IsNullOrWhiteSpace(saveModelToDiskFile))
{
classifier.FlushToFile(saveModelToDiskFile);
}
if (crossFoldsEvaluate)
{
classifier.Runtime.EvaluateWithCrossValidation(classifier, numFolds);
}
if (runPredictions)
{
testing.GeneratePredictions(null, classifier);
}
Console.WriteLine("\n\nEvaluation Took: {0}", DateTime.Now.Subtract(start));
}
public ClassifierEvaluator(Runtime runtime, IBaseClassifier <Classifier> classifier)
{
this.runtime = runtime;
this.classifier = classifier;
}
public double[] GetClassifications(IBaseClassifier <Classifier> classifier)
{
return(this.Select(classifier.ClassifyInstance).ToArray());
}
public PmlEvaluation EvaluateWithCrossValidation(IBaseClassifier <Classifier> classifier, int numfolds = 10, bool quiet = false)
{
return(new ClassifierEvaluator(this, classifier).
EvaluateWithCrossValidateion(numfolds, quiet));
}
public double EvaluateModelOnce(IBaseClassifier <Classifier> classifier, PmlInstance instance)
{
return(Impl.evaluateModelOnce(classifier.Impl, instance.Impl));
}
public double EvaluateModelOnceAndRecordPrediction(IBaseClassifier <Classifier> classifier, PmlInstance instance)
{
return(Impl.evaluateModelOnceAndRecordPrediction(classifier.Impl, instance.Impl));
}
public static string EvaluateModel(IBaseClassifier <Classifier> classifier, string[] options)
{
return(Evaluation.evaluateModel(classifier.Impl, options));
}
public double[] EvaluateModel(IBaseClassifier <Classifier> classifier, Runtime data, object[] forPredictionsPrinting)
{
return(Impl.evaluateModel(classifier.Impl, data.Impl, forPredictionsPrinting));
}
