/// <summary>
/// Creates the report from metrics given, using averaging computation specified
/// </summary>
/// <param name="_metrics">The metrics to build report entry from</param>
/// <param name="averageComputation">The average computation method to apply for F1, P and R</param>
public classificationReport(classificationEvalMetricSet _metrics, classificationMetricComputation averageComputation)
{
Name = _metrics.name;
GetSetMetrics(_metrics);
this.AddValues(_metrics, averageComputation);
method = averageComputation;
}
/// <summary>
/// Deploys the specified report options.
/// </summary>
/// <param name="_reportOptions">The report options.</param>
/// <param name="_signature">The signature.</param>
/// <param name="_averagingMethod">The averaging method.</param>
/// <param name="_rootFolder">The root folder.</param>
/// <param name="_experimentDescription">The experiment description.</param>
/// <param name="logger">The logger.</param>
public void Deploy(aceAuthorNotation _signature, classificationMetricComputation _averagingMethod, folderNode _rootFolder, String _experimentDescription, ILogBuilder logger)
{
// reportOptions = _reportOptions;
signature = _signature;
averagingMethod = _averagingMethod;
description = _experimentDescription;
experimentRootFolder = _rootFolder;
notes = new ToolkitExperimentNotes(experimentRootFolder, _experimentDescription);
notes.log("Starting [" + runName + "]");
notes.log("[" + description + "]:[" + signature + "]");
//ToolkitExperimentNotes.[" + _experimentDescription + "]:[" + signature + "]");
imbSCI.Core.screenOutputControl.logToConsoleControl.setAsOutput(notes, "Note");
}
/// <summary>
/// Gets the F1 measure, computed in respect to the <c>method</c> specified
/// </summary>
/// <param name="method">The method of F1 measure computation.</param>
/// <seealso cref="classificationEval.GetF1"/>
/// <returns></returns>
public Double GetF1(classificationMetricComputation method = classificationMetricComputation.microAveraging)
{
Double output = 0;
switch (method)
{
case classificationMetricComputation.microAveraging:
return(GetSummary().GetF1());
break;
case classificationMetricComputation.macroAveraging:
foreach (var pair in items)
{
output += this[pair.Key].GetF1();
}
return(output.GetRatio(items.Count));
break;
}
return(output);
}
/// <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>
/// Sets or Adds the values from specified <c>metrics</c> object.
/// </summary>
/// <param name="a">a.</param>
/// <param name="metrics">The metrics.</param>
/// <param name="method">The method of ratios computation (F1, Precision, Recall)</param>
public static void AddValues(this IClassificationReport a, classificationEvalMetricSet metrics, classificationMetricComputation method)
{
a.Precision += metrics.GetPrecision(method);
a.Recall += metrics.GetRecall(method);
a.F1measure += metrics.GetF1(method);
foreach (var p in metrics)
{
a.Correct += p.Value.correct;
a.Wrong += p.Value.wrong;
a.Targets += p.Value.correct + p.Value.wrong;
}
a.method = method;
}
