/// <summary>
/// Computes the score for the given current results.
/// </summary>
/// <param name="baselineMatrices">The baseline matrices.</param>
/// <param name="currentResults">The current results.</param>
/// <returns></returns>
public static double ComputeScore(TLSimilarityMatricesCollection baselineMatrices, TLSimilarityMatricesCollection currentResultsMatrices,
TLDatasetsList datasets, MetricComputationComponentConfig config)
{
double score = 0.0;
double sum = 0;
double counts = 0;
IMetricComputation metricComputation = GetMetricComputation(config);
foreach (TLDataset dataset in datasets)
{
TLSimilarityMatrix baseline = baselineMatrices[dataset.Name];
TLSimilarityMatrix current = currentResultsMatrices[dataset.Name];
//score is computed based on delta between two techniques from metric computation
SortedDictionary <string, double> baselineValues = metricComputation.Calculate(baseline, dataset);
SortedDictionary <string, double> currentValues = metricComputation.Calculate(current, dataset);
var deltas = ScoreComputationHelper.Delta(baselineValues, currentValues);
//now compute average of computed deltas, and that's the score
foreach (double delta in deltas.Values)
{
sum += delta;
}
counts += deltas.Count;
}
score = sum / counts;
return(score);
}
private List <double> PrepareDataForWilcox(SingleTracingResults techniqueOneResults, SingleTracingResults techniqueTwoResults, TLDataset dataset)
{
var avePrec1 = (SortedDictionary <string, double>)m_baseMetricComputation.Calculate(techniqueOneResults.ResultMatrix, dataset);
var avePrec2 = (SortedDictionary <string, double>)m_baseMetricComputation.Calculate(techniqueTwoResults.ResultMatrix, dataset);
var deltas = ScoreComputationHelper.Delta(avePrec1, avePrec2);
return(deltas.Values.ToList());
}
public BoxSummaryData Calculate(SingleTracingResults singleTechniqueResults, TLDataset dataset)
{
SortedDictionary <string, double> intermediateResults = m_metricComputation.Calculate(singleTechniqueResults.ResultMatrix, dataset);
double[] dataPoints = intermediateResults.Values.ToArray();
if (dataPoints.Length == 0 && m_logger != null)
{
m_logger.Warn("Metric computation of '" + m_metricName + "' returned zero matching results for " + dataset.Name + " for one of the techniques. It may be valid results, but it may also mean that there is mismatch of ids in the answer matrix and corresponding artifacts.");
}
var summaryData = new BoxSummaryData(m_metricName, m_metricDescription);
summaryData.AddPoint(new BoxPlotPoint(dataPoints));
return(summaryData);
}