/// <summary>
/// Generates a TLExperimentsResultsCollection containing summary data of results.
/// This operation is thread-safe.
/// </summary>
/// <returns>Results summaries</returns>
public TLExperimentsResultsCollection GenerateSummaryResults()
{
lock (_lock)
{
TLExperimentsResultsCollection ExperimentsResultsCollection = new TLExperimentsResultsCollection();
// iterate over techniques
foreach (string technique in _techniques)
{
TLExperimentResults TechniqueResults = new TLExperimentResults(technique);
// iterate over datasets
foreach (string dataset in _datasets)
{
// get list of results for technique + dataset
List <IMetricComputation> list = null;
string key = ComputeKey(technique, dataset);
_results.TryGetValue(key, out list);
if (list != null)
{
DatasetResults data = new DatasetResults(dataset);
// add results to dataset
foreach (IMetricComputation computation in list)
{
if (!computation.HasRun)
{
computation.Compute();
}
data.AddMetric(computation.GenerateSummary());
}
// add dataset to technique
if (data.Metrics.Count() > 0)
{
TechniqueResults.AddDatasetResult(data);
}
}
}
// add technique to collection
if (TechniqueResults.DatasetsResults.Count() > 0)
{
ExperimentsResultsCollection.Add(TechniqueResults);
}
}
return(ExperimentsResultsCollection);
}
}
private TLExperimentResults ComputeMetricResultsForDataset(GroupOfTracingResults <T> tracingResults)
{
//create experiment results container for this technique
TLExperimentResults experimentResult = new TLExperimentResults(tracingResults.TechniqueName);
foreach (TLDataset dataset in m_datasets)
{
DatasetResults datasetResults = new DatasetResults(dataset.Name);
//iterate through all computation and calculate metric results for this dataset
foreach (MetricComputationForSingleDataset <T> computation in metricComputationsPerDataset)
{
T tracingResult = default(T);
if (tracingResults.Contains(dataset.Name))
{
tracingResult = tracingResults[dataset.Name];
}
var metric = computation.Compute(tracingResult, dataset);
if (metric == null)
{
throw new InvalidOperationException("The metric computation method failed to return the metric. " +
"Even if tracing results are empty computation must return metric with name and description, although it may have empty data");
}
if (computation is IStatisticallyComparableMetric <T> )
{
//collect results, so that statistical comparison can be computed afterwards, if there are two techniques
}
datasetResults.AddMetric(metric);
}
experimentResult.AddDatasetResult(datasetResults);
}
//compute also set of metrics across all datasets combined
//experimentResult.AcrossAllDatasetsResults = ComputeMetricResultsAcrossAllDatasets(tracingResults);
return(experimentResult);
}
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());
}
}
}
}
