classification_report.R

##' calculate summary statistics from a confusion matrix.
##'
##' @param cm a confusion matrix, where rows = predicted, cols = actual.
##' @param dp round decimal place (default 2).
##' @return a classification report, similar to sklean.
##' @examples
##' \dontrun{
##' # 3 classes.
##' cm <- matrix(c(4, 6, 3, 1, 2, 0, 1, 2, 6), ncol=3, byrow=T)
##' cr(cm)
##'
##' $summary
##'   tp tn fp fn precision recall f1_score support
##' 1  4 10  9  2      0.31   0.67     0.42       6
##' 2  2 14  1  8      0.67   0.20     0.31      10
##' 3  6 13  3  3      0.67   0.67     0.67       9
##'
##' $accuracy
##' [1] 0.48
##'
##' $support
##' [1] 25
##'
##' }
cr <- function(cm, dp=2) {
  ct <- sum(cm)
  cs <- colSums(cm)
  rs <- rowSums(cm)
  tp <- diag(cm)
  tn <- ct - (rs + cs - tp)
  fp <- rs - tp
  fn <- cs - tp
  pr <- tp / (tp + fp)
  re <- tp / (tp + fn)
  f1 <- 2 * pr * re / (pr + re)
  ac <- sum(tp) / ct
  list(summary=round(data.frame(tp, tn, fp, fn, precision=pr, recall=re, f1_score=f1, support=cs), dp),
       accuracy=round(ac, dp),
       support=ct)
}