/// <summary>
/// Evaluates the test results.
/// </summary>
/// <param name="testResults">Set of test results.</param>
/// <param name="_testName">Descriptive name to be attached at results report.</param>
/// <param name="logger">The logger - to log any problems, if occourred.</param>
/// <param name="averagingMethod">The averaging method.</param>
/// <returns></returns>
public classificationReport EvaluateTestResults(List <FeatureVectorWithLabelID> testResults, String _testName, ILogBuilder logger, classificationMetricComputation averagingMethod = classificationMetricComputation.macroAveraging)
{
//classificationReport report = new classificationReport()
classificationReport report = new classificationReport(_testName);
classificationEvalMetricSet metric = EvaluateTestResultsToMetricSet(testResults, _testName, logger);
report.GetSetMetrics(metric);
report.AddValues(metric, averagingMethod);
return(report);
}
/// <summary>
/// Generates the final reports and read me files
/// </summary>
public void CloseExperiment(ILogBuilder logger, long startOfLog)
{
if (!testSummaries.Any())
{
logger.log("No experiment procedures performes");
return;
}
DataTableTypeExtended <classificationReport> summaryTable = new DataTableTypeExtended <classificationReport>("Test results", "k-fold cross valudation results");
classificationReport sumRow = new classificationReport(runName);
sumRow.Comment = runName + ", " + description;
// classificationEvalMetricSet metric = new classificationEvalMetricSet("Total", truthTable.labels_without_unknown);
foreach (classificationReport s in testSummaries)
{
summaryTable.AddRow(s);
//metric = metric + s;
if (sumRow.Classifier.isNullOrEmpty())
{
sumRow.Classifier = s.Classifier;
}
sumRow.AddValues(s);
}
sumRow.DivideValues(testSummaries.Count);
sumRow.SetReportDataFields(crossValidation, this);
summaryTable.SetDescription(description);
summaryTable.SetAdditionalInfoEntry("RunName", runName);
summaryTable.SetAdditionalInfoEntry("Description", description);
summaryTable.SetAdditionalInfoEntry("Averaging", averagingMethod.ToString());
summaryTable.AddRow(sumRow);
summaryTable.GetReportAndSave(notes.folder, signature);
finalReport = sumRow;
//sumRow.ReportToLog(logger);
sumRow.ReportToLog(notes);
objectSerialization.saveObjectToXML(sumRow, notes.folder.pathFor("results.xml", imbSCI.Data.enums.getWritableFileMode.overwrite, "Experiment results", true));
logger.log("Experiment completed");
notes.SaveNote("note");
String logPrintout = logger.GetContent(startOfLog);
String p = notes.folder.pathFor("log.txt", imbSCI.Data.enums.getWritableFileMode.overwrite, "Log printout during experiment execution");
File.WriteAllText(p, logPrintout);
experimentRootFolder.generateReadmeFiles(signature);
}