void IAiTest.Evaluate()
{
IDataView dataView = _context.Data.LoadFromTextFile <GitHubIssue>($"{RootFolder}/{EvaluateDataFile}", hasHeader: true);
Console.WriteLine("=============== Evaluating Model accuracy with Test data===============");
var predictions = _model.Transform(dataView);
var metrics = _context.MulticlassClassification.Evaluate(predictions, "Area", "Score");
Common.ConsoleHelper.PrintMultiClassClassificationMetrics(_trainer.ToString(), metrics);
Utility.SaveModelAsFile(_context, _model, dataView, $"{RootFolder}/{ModelFileName}");
}
/// <summary>
/// Show accuracy stats.
/// </summary>
/// <param name="trainer"></param>
/// <param name="metrics"></param>
private static void ShowAccuracyStats(EstimatorChain <KeyToValueMappingTransformer> trainer, MulticlassClassificationMetrics metrics)
{
Console.WriteLine($"************************************************************");
Console.WriteLine($"* Metrics for {trainer.ToString()} multi-class classification model ");
Console.WriteLine($"*-----------------------------------------------------------");
Console.WriteLine($" AccuracyMacro = {metrics.MacroAccuracy.ToString(CultureInfo.CurrentCulture)}, a value between 0 and 1, the closer to 1, the better");
Console.WriteLine($" AccuracyMicro = {metrics.MicroAccuracy.ToString(CultureInfo.CurrentCulture)}, a value between 0 and 1, the closer to 1, the better");
Console.WriteLine($" LogLoss = {metrics.LogLoss.ToString(CultureInfo.CurrentCulture)}, the closer to 0, the better");
Console.WriteLine($" LogLoss for class 1 = {metrics.PerClassLogLoss[0].ToString(CultureInfo.CurrentCulture)}, the closer to 0, the better");
Console.WriteLine($" LogLoss for class 2 = {metrics.PerClassLogLoss[1].ToString(CultureInfo.CurrentCulture)}, the closer to 0, the better");
Console.WriteLine($" LogLoss for class 3 = {metrics.PerClassLogLoss[2].ToString(CultureInfo.CurrentCulture)}, the closer to 0, the better");
Console.WriteLine($"************************************************************");
}
