scripts/builtin/f1Score.dml

#-------------------------------------------------------------
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# Computes the F1 score as the harmonic mean of precision and recall.
# F1 = 2TP / (2TP + FP + FN)
#
# INPUT:
# ------------------------------------------------------------------------------
# P              vector of predictions (1-based, recoded)
# Y              vector of actual labels (1-based, recoded)
# ------------------------------------------------------------------------------
#
# OUTPUT:
# ------------------------------------------------------------------------------
# score          the F1 score
# ------------------------------------------------------------------------------

m_f1Score = function(Matrix[Double] P, Matrix[Double] Y)
  return(Double score)
{
  [cS, cA] = confusionMatrix(P, Y);
  if(nrow(cS)>2 | ncol(cS)>2)
    stop("f1Score: currently only supported for binary class labels.");
  score = as.scalar(2*cS[1,1] / (2*cS[1,1] + cS[2,1] + cS[1,2]));
}