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;
}
/// <summary>
/// Generates a summarizing Metric for results analysis
/// </summary>
/// <returns>Metric object</returns>
protected override Metric GenerateSummaryImplementation()
{
BoxSummaryData data = new BoxSummaryData(Name, Description);
data.AddPoint(new BoxPlotPoint(Results.Values.ToArray()));
return data;
}
public void ExperimentResultsRawSerializationTest()
{
int n = 0;
TLExperimentResults expResultsIn = new TLExperimentResults("Technique " + n++);
for (int k = 0; k < 5; k++)
{
DatasetResults dataResults = new DatasetResults("Dataset " + n++);
for (int i = 0; i < 10; i++)
{
LineSeries line = new LineSeries("Line " + i, "Description " + n++);
for (int j = 1000 * i; j < 1000; j++)
{
line.AddPoint(new Point(j, j + 1));
}
dataResults.AddMetric(line);
BoxSummaryData box = new BoxSummaryData("Box " + i, "Description " + n++);
for (int j = 0; j < 100; j++)
{
box.AddPoint(new BoxPlotPoint(j, j + 1, j + 2, j + 3, j + 4, j + 5, j + 6, j + 7));
}
dataResults.AddMetric(box);
}
expResultsIn.AddDatasetResult(dataResults);
}
BinaryWriter binWriter = new BinaryWriter(new MemoryStream());
BinaryReader binReader = new BinaryReader(binWriter.BaseStream);
expResultsIn.WriteData(binWriter);
binReader.BaseStream.Position = 0;
TLExperimentResults expResultsOut = (TLExperimentResults)Activator.CreateInstance(typeof(TLExperimentResults), true);
expResultsOut.ReadData(binReader);
Assert.AreEqual(expResultsIn.TechniqueName, expResultsOut.TechniqueName);
Assert.AreEqual(expResultsIn.DatasetsResults.Count(), expResultsOut.DatasetsResults.Count());
foreach (DatasetResults result1 in expResultsIn.DatasetsResults)
{
bool sameDatasetResultExists = false;
DatasetResults result2 = null;
foreach (DatasetResults res in expResultsOut.DatasetsResults)
{
if (res.DatasetName == result1.DatasetName)
{
sameDatasetResultExists = true;
result2 = res;
break;
}
}
Assert.IsTrue(sameDatasetResultExists);
Assert.AreEqual(result1.DatasetName, result2.DatasetName);
Assert.AreEqual(result1.Metrics.Count(), result2.Metrics.Count());
foreach (Metric m1 in result1.Metrics)
{
bool sameMetricExists = false;
Metric m2 = null;
foreach (Metric metric in result2.Metrics)
{
if (m1.MetricName == metric.MetricName)
{
sameMetricExists = true;
m2 = metric;
break;
}
}
Assert.IsTrue(sameMetricExists);
Assert.AreEqual(m1.Description, m2.Description);
if (m1 is LineSeries)
{
Assert.IsTrue(m2 is LineSeries);
LineSeries l1 = (LineSeries)m1;
LineSeries l2 = (LineSeries)m2;
Assert.AreEqual(l1.Points.Count(), l2.Points.Count());
}
else
{
Assert.IsTrue(m2 is BoxSummaryData);
BoxSummaryData b1 = (BoxSummaryData)m1;
BoxSummaryData b2 = (BoxSummaryData)m2;
Assert.AreEqual(b1.Points.Count(), b2.Points.Count());
}
}
}
}