/// <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);
}
/// <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;
}
/// <summary>
/// Gets the metric computation.
/// </summary>
/// <param name="config">The config.</param>
/// <returns></returns>
private static IMetricComputation GetMetricComputation(MetricComputationComponentConfig config)
{
IMetricComputation metricComputation;
if (config.ScoreBaseMetric == ScoreBaseMetric.PrecisionAtRecall100)
{
metricComputation = new PrecisionAtRecall100();
}
else if (config.ScoreBaseMetric == ScoreBaseMetric.Precision)
{
metricComputation = new Precision(config.Threshold);
}
else if (config.ScoreBaseMetric == ScoreBaseMetric.Recall)
{
metricComputation = new Recall(config.Threshold);
}
else
{
metricComputation = new AveragePrecision();
}
return metricComputation;
}
/// <summary>
/// Gets the metric computation.
/// </summary>
/// <param name="config">The config.</param>
/// <returns></returns>
private static IMetricComputation GetMetricComputation(MetricComputationComponentConfig config)
{
IMetricComputation metricComputation;
if (config.ScoreBaseMetric == ScoreBaseMetric.PrecisionAtRecall100)
{
metricComputation = new PrecisionAtRecall100();
}
else if (config.ScoreBaseMetric == ScoreBaseMetric.Precision)
{
metricComputation = new Precision(config.Threshold);
}
else if (config.ScoreBaseMetric == ScoreBaseMetric.Recall)
{
metricComputation = new Recall(config.Threshold);
}
else
{
metricComputation = new AveragePrecision();
}
return(metricComputation);
}
//public MetricComputationEngine(GroupOfTracingResults<SingleTracingResults> allTracingResults, TLDatasetsList datasets, ComponentLogger logger)
// : this(allTracingResults, datasets, logger, 0.01)
//{
//}
//public MetricComputationEngine(GroupOfTracingResults<SingleTracingResults> allTracingResults, TLDatasetsList datasets, ComponentLogger logger, double threshold)
// : base(allTracingResults, datasets)
//{
// m_logger = logger;
// InitMetricComputationsPerDataset(threshold);
// InitMetricComputationAcrossAllDatasets();
//}
private void InitMetricComputationsPerDataset(MetricComputationComponentConfig config)
{
if (config.AveragePrecision == true)
{
RegisterMetricComputation(new AveragePrecisionMetricComputation(m_logger));
}
if (config.Recall == true)
{
RegisterMetricComputation(new RecallMetricComputation(config.Threshold, m_logger));
}
if (config.Precision == true)
{
RegisterMetricComputation(new PrecisionMetricComputation(config.Threshold, m_logger));
}
if (config.PrecisionAtRecall100 == true)
{
RegisterMetricComputation(new PrecisionAtRecall100MetricComputation(m_logger));
}
if (config.PrecisionRecallCurve == true)
{
RegisterMetricComputation(new PrecisionRecallCurveMetricComputation(m_logger));
}
//RegisterMetricComputation(new DataStatisticsMetricSingleDatasetComputation<SingleTracingResults>());
}
public MetricComputationComponent(ComponentLogger log) : base(log)
{
m_config = new MetricComputationComponentConfig();
Configuration = m_config;
}
public MetricComputationComponentForSingleDataset(ComponentLogger log) : base(log)
{
m_config = new MetricComputationComponentConfig();
Configuration = m_config;
}
public MetricComputationEngine(TLDatasetsList datasets, ComponentLogger logger, MetricComputationComponentConfig config)
: base(datasets)
{
if (logger == null)
{
throw new ArgumentNullException("logger");
}
m_logger = logger;
InitMetricComputationsPerDataset(config);
}
