/// <summary>
/// Calculates the total error metric on a multi label or binary classification problem
/// </summary>
/// <param name="targets"></param>
/// <param name="predictions"></param>
/// <returns></returns>
public double Error(T[] targets, T[] predictions)
{
var uniques = UniqueTargets(targets, predictions);
var confusionMatrix = ClassificationMatrix.ConfusionMatrix(uniques, targets, predictions);
var errorMatrix = ClassificationMatrix.ErrorMatrix(uniques, confusionMatrix);
return(TotalError(uniques, confusionMatrix));
}
internal static string ClassificationMatrixString <T>(T[] targets, T[] predictions, double error)
{
var uniques = UniqueTargetValues(targets, predictions);
var confusionMatrix = ClassificationMatrix.ConfusionMatrix(uniques, targets, predictions);
var errorMatrix = ClassificationMatrix.ErrorMatrix(uniques, confusionMatrix);
return(ClassificationMatrixStringConverter.Convert(uniques, confusionMatrix,
errorMatrix, error));
}
/// <summary>
/// Gets a string representation of the classification matrix with counts and percentages
/// </summary>
/// <param name="targets"></param>
/// <param name="predictions"></param>
/// <returns></returns>
public string ErrorString(T[] targets, T[] predictions)
{
var uniques = UniqueTargets(targets, predictions);
var confusionMatrix = ClassificationMatrix.ConfusionMatrix(uniques, targets, predictions);
var errorMatrix = ClassificationMatrix.ErrorMatrix(uniques, confusionMatrix);
var error = 1.0 - Recall(targets, predictions);
return(ClassificationMatrixStringConverter.Convert(uniques, confusionMatrix, errorMatrix, error));
}
/// <summary>
/// Gets a string representation of the classification matrix with counts and percentages
/// Using the target names provided in the targetStringMapping
/// </summary>
/// <param name="targets"></param>
/// <param name="predictions"></param>
/// <param name="targetStringMapping"></param>
/// <returns></returns>
public string ErrorString(T[] targets, T[] predictions, Dictionary <T, string> targetStringMapping)
{
var uniques = UniqueTargets(targets, predictions);
var confusionMatrix = ClassificationMatrix.ConfusionMatrix(uniques, targets, predictions);
var errorMatrix = ClassificationMatrix.ErrorMatrix(uniques, confusionMatrix);
var error = Error(targets, predictions);
return(ClassificationMatrixStringConverter.Convert(uniques, targetStringMapping, confusionMatrix, errorMatrix, error));
}
/// <summary>
/// Gets a string representation of the classification matrix with counts and percentages
/// Using the target names provided in the targetStringMapping
/// </summary>
/// <param name="targets"></param>
/// <param name="predictions"></param>
/// <param name="targetStringMapping"></param>
/// <returns></returns>
public string ErrorString(double[] targets, ProbabilityPrediction[] predictions, Dictionary <double, string> targetStringMapping)
{
var classPredictions = predictions.Select(p => p.Prediction).ToArray();
var uniques = UniqueTargets(targets, classPredictions);
var confusionMatrix = ClassificationMatrix.ConfusionMatrix(uniques, targets, classPredictions);
var errorMatrix = ClassificationMatrix.ErrorMatrix(uniques, confusionMatrix);
var error = Error(targets, predictions);
return(ClassificationMatrixStringConverter.Convert(uniques, targetStringMapping, confusionMatrix, errorMatrix, error));
}
