internal BinaryClassificationMetrics(IHost host, DataViewRow overallResult, IDataView confusionMatrix)
{
double Fetch(string name) => Fetch <double>(host, overallResult, name);
AreaUnderRocCurve = Fetch(BinaryClassifierEvaluator.Auc);
Accuracy = Fetch(BinaryClassifierEvaluator.Accuracy);
PositivePrecision = Fetch(BinaryClassifierEvaluator.PosPrecName);
PositiveRecall = Fetch(BinaryClassifierEvaluator.PosRecallName);
NegativePrecision = Fetch(BinaryClassifierEvaluator.NegPrecName);
NegativeRecall = Fetch(BinaryClassifierEvaluator.NegRecallName);
F1Score = Fetch(BinaryClassifierEvaluator.F1);
AreaUnderPrecisionRecallCurve = Fetch(BinaryClassifierEvaluator.AuPrc);
ConfusionMatrix = MetricWriter.GetConfusionMatrix(host, confusionMatrix);
}
internal MulticlassClassificationMetrics(IHost host, DataViewRow overallResult, int topKPredictionCount, IDataView confusionMatrix)
{
double FetchDouble(string name) => RowCursorUtils.Fetch<double>(host, overallResult, name);
MicroAccuracy = FetchDouble(MulticlassClassificationEvaluator.AccuracyMicro);
MacroAccuracy = FetchDouble(MulticlassClassificationEvaluator.AccuracyMacro);
LogLoss = FetchDouble(MulticlassClassificationEvaluator.LogLoss);
LogLossReduction = FetchDouble(MulticlassClassificationEvaluator.LogLossReduction);
TopKPredictionCount = topKPredictionCount;
if (topKPredictionCount > 0)
TopKAccuracy = FetchDouble(MulticlassClassificationEvaluator.TopKAccuracy);
var perClassLogLoss = RowCursorUtils.Fetch<VBuffer<double>>(host, overallResult, MulticlassClassificationEvaluator.PerClassLogLoss);
PerClassLogLoss = perClassLogLoss.DenseValues().ToImmutableArray();
ConfusionMatrix = MetricWriter.GetConfusionMatrix(host, confusionMatrix, binary: false, perClassLogLoss.Length);
}
