internal AnomalyDetectionMetrics(IExceptionContext ectx, DataViewRow overallResult)
{
double FetchDouble(string name) => RowCursorUtils.Fetch <double>(ectx, overallResult, name);
Auc = FetchDouble(BinaryClassifierEvaluator.Auc);
DrAtK = FetchDouble(AnomalyDetectionEvaluator.OverallMetrics.DrAtK);
}
internal Result(IExceptionContext ectx, IRow overallResult)
{
double Fetch(string name) => RowCursorUtils.Fetch <double>(ectx, overallResult, name);
L1 = Fetch(RegressionEvaluator.L1);
L2 = Fetch(RegressionEvaluator.L2);
Rms = Fetch(RegressionEvaluator.Rms);
LossFn = Fetch(RegressionEvaluator.Loss);
RSquared = Fetch(RegressionEvaluator.RSquared);
}
internal Result(IExceptionContext ectx, IRow overallResult, bool calculateDbi)
{
double Fetch(string name) => RowCursorUtils.Fetch <double>(ectx, overallResult, name);
Nmi = Fetch(ClusteringEvaluator.Nmi);
AvgMinScore = Fetch(ClusteringEvaluator.AvgMinScore);
if (calculateDbi)
{
Dbi = Fetch(ClusteringEvaluator.Dbi);
}
}
internal MultiClassClassifierMetrics(IExceptionContext ectx, Row overallResult, int topK)
{
double FetchDouble(string name) => RowCursorUtils.Fetch <double>(ectx, overallResult, name);
AccuracyMicro = FetchDouble(MultiClassClassifierEvaluator.AccuracyMicro);
AccuracyMacro = FetchDouble(MultiClassClassifierEvaluator.AccuracyMacro);
LogLoss = FetchDouble(MultiClassClassifierEvaluator.LogLoss);
LogLossReduction = FetchDouble(MultiClassClassifierEvaluator.LogLossReduction);
TopK = topK;
if (topK > 0)
{
TopKAccuracy = FetchDouble(MultiClassClassifierEvaluator.TopKAccuracy);
}
var perClassLogLoss = RowCursorUtils.Fetch <VBuffer <double> >(ectx, overallResult, MultiClassClassifierEvaluator.PerClassLogLoss);
PerClassLogLoss = new double[perClassLogLoss.Length];
perClassLogLoss.CopyTo(PerClassLogLoss);
}
