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);
}
internal BinaryClassificationMetrics(double auc, double accuracy, double positivePrecision, double positiveRecall,
double negativePrecision, double negativeRecall, double f1Score, double auprc, ConfusionMatrix confusionMatrix)
: this(auc, accuracy, positivePrecision, positiveRecall, negativePrecision, negativeRecall, f1Score, auprc)
{
ConfusionMatrix = confusionMatrix;
}
internal MulticlassClassificationMetrics(double accuracyMicro, double accuracyMacro, double logLoss, double logLossReduction,
int topKPredictionCount, double topKAccuracy, double[] perClassLogLoss, ConfusionMatrix confusionMatrix)
: this(accuracyMicro, accuracyMacro, logLoss, logLossReduction, topKPredictionCount, topKAccuracy, perClassLogLoss)
{
ConfusionMatrix = confusionMatrix;
}
