public ClassifierAccuracyAnalysis(IEventSeriesProbabalisticClassifier <Ty> classifier, string classifierName, DiscreteSeriesDatabase <Ty> labeledData, string criterionByWhichToClassify, double trainSplitFrac, int iterations, double bucketSize)
{
this.classifier = classifier;
this.classifierName = classifierName;
this.labeledData = labeledData;
this.criterionByWhichToClassify = criterionByWhichToClassify;
this.trainSplitFrac = trainSplitFrac;
this.iterations = iterations;
this.bucketSize = bucketSize;
}
public static string GetAlgorithmName <Ty>(this IEventSeriesProbabalisticClassifier <Ty> model)
{
return(GetAlgorithmName((object)model));
}
//Name, true class, predicted class, scores, winning score;
public static Tuple <string, string, string, double[], double> classificationInfo(IEventSeriesProbabalisticClassifier <Ty> classifier, string[] classifierSchema, Dictionary <string, int> trueSchemaMapping, DiscreteEventSeries <Ty> data, string nameCriterion, string criterionByWhichToClassify)
{
//scores in the synthesizer scorespace
double[] synthScores = classifier.Classify(data);
int maxIndex = synthScores.MaxIndex();
/*
* classifierSchema = classifier.GetClasses();
* if (maxIndex >= classifierSchema.Length) {
* Console.WriteLine ("Schema not long enough. synthlen, max, schema = " + synthScores.Length + ", " + maxIndex + ", " + classifierSchema.Length);
* Console.WriteLine ("Classifier Info:");
* Console.WriteLine (classifier.ToString ());
* Console.WriteLine ("Synth Features:");
* Console.WriteLine (((SeriesFeatureSynthesizerToVectorProbabalisticClassifierEventSeriesProbabalisticClassifier<string>)classifier).synthesizer.GetFeatureSchema().FoldToString ());
* Console.WriteLine ("Classifier Features:");
* Console.WriteLine (((SeriesFeatureSynthesizerToVectorProbabalisticClassifierEventSeriesProbabalisticClassifier<string>)classifier).classifier.GetClasses().FoldToString ());
* return null;
* }
*/
string predictedClass = classifierSchema[maxIndex];
double maxScore = synthScores[maxIndex];
//convert scores to the true space.
double[] trueScores = new double[trueSchemaMapping.Count];
for (int j = 0; j < classifierSchema.Length; j++)
{
trueScores[trueSchemaMapping[classifierSchema[j]]] = synthScores[j];
}
return(new Tuple <string, string, string, double[], double> (data.labels[nameCriterion], data.labels[criterionByWhichToClassify], predictedClass, trueScores, maxScore));
}