scripts/builtin/confusionMatrix.dml

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# Accepts a vector for prediction and a one-hot-encoded matrix
# Then it computes the max value of each vector and compare them
# After which, it calculates and returns the sum of classifications
# and the average of each true class.
#
# .. code-block::
#
#                   True Labels
#                     1    2
#                 1   TP | FP
#   Predictions      ----+----
#                 2   FN | TN
#
# INPUT:
# --------------------------------------------------------------------------------
# P     vector of Predictions
# Y     vector of Golden standard One Hot Encoded; the one hot
#       encoded vector of actual labels
# --------------------------------------------------------------------------------
#
# OUTPUT:
# ------------------------------------------------------------------------------------------------
# confusionSum   The Confusion Matrix Sums of classifications
# confusionAvg   The Confusion Matrix averages of each true class
# ------------------------------------------------------------------------------------------------

m_confusionMatrix = function(Matrix[Double] P, Matrix[Double] Y)
  return(Matrix[Double] confusionSum, Matrix[Double] confusionAvg)
{
  if(ncol(P) > 1  | ncol(Y) > 1)
    stop("CONFUSION MATRIX: Invalid input number of cols should be 1 in both P ["+ncol(P)+"] and Y ["+ncol(Y)+"]")
  if(nrow(P) != nrow(Y))
    stop("CONFUSION MATRIX: The number of rows have to be equal in both P ["+nrow(P)+"] and Y ["+nrow(Y)+"]")
  if(min(P) < 1 | min(Y) < 1)
    stop("CONFUSION MATRIX: All Values in P and Y should be abore or equal to 1, min(P):" + min(P) + " min(Y):" + min(Y) )

  dim = max(max(Y),max(P))
  confusionSum = table(P, Y,  dim, dim)
  # max to avoid devision by 0, in case a colum contain no entries.
  confusionAvg = confusionSum / max(1,colSums(confusionSum))
}