R/tool_perfmeas.R

In evalclass/prcbench: Testing Workbench for Precision-Recall Curves

#
# PerfMeas
#
.pm_wrapper <- function(testset, calc_auc = FALSE, store_res = TRUE) {
  # Prepare data
  scores <- testset$get_scores()
  labels <- testset$get_labels()

  # Calculate Precision-Recall curve
  prc <- .perfmeas.precision.at.all.recall.levels(scores, labels)

  # Get AUC
  if (calc_auc) {
    aucscore <- .perfmeas.AUPRC(list(prc), comp.precision = TRUE)
    names(aucscore) <- NULL
  } else {
    aucscore <- NA
  }

  # Return x and y values if requested
  if (store_res) {
    x <- prc[["recall"]]
    y <- prc[["precision"]]

    list(x = x, y = y, auc = aucscore)
  } else {
    NULL
  }
}

# Function to compute the precision at all recall levels  for a single class
# Input:
#   scores : vector of the predicted scores in [0,1]
#   labels : 0/1 vector of the true labels
# Output:
#   a list with 3 elements:
#   precision : precision at different thresholds
#   recall : recall at different thresholds
#   f.score : f.score at different thresholds
.perfmeas.precision.at.all.recall.levels <- function(scores, labels) {
  n <- length(scores)
  if (n != length(labels)) {
    stop(paste0(
      "precision.at.recall.level: ",
      "length of labels and scores does not match"
    ))
  }

  if (length(which(labels > 0)) == 0) {
    return(list(res = 0, precision = rep(0, n), recall = rep(0, n)))
  }

  scores_ordered <- order(scores, decreasing = TRUE)
  precision <- recall <- rep(0, n)
  res <- perfmeas_prec_recall(
    as.integer(scores_ordered), as.integer(labels),
    as.integer(n)
  )

  precision <- res[["precision"]]
  recall <- res[["recall"]]

  f_score <- (2 * precision * recall) / (precision + recall)
  f_score[is.nan(f_score)] <- 0

  list(precision = precision, recall = recall, f.score = f_score)
}

# Function to compute multiple AUPRC (Area Under Precision and Recall Curves)
# Input:
#   z : a list of lists. Each component list is a list returned
#       from precision.at.all.recall.levels  that reports precision,
#       recall and f-score results at different levels for different
#       methods or tasks
#   comp.precision: boolean. It TRUE (default) the AUPRC is computed
#     otherwise the area under the F-score curve is computed
# Output:
#   a named vector with the AUPRC (or the AUFRC) for different methods or tasks
.perfmeas.AUPRC <- function(z, comp.precision = TRUE) {
  n <- length(z)
  curve.names <- names(z)
  if (is.null(names(curve.names))) {
    curve.names <- as.character(1:n)
  }
  integral <- numeric(n)
  names(integral) <- curve.names
  for (i in 1:n) {
    if (comp.precision) {
      integral[i] <- .perfmeas.trap.rule.integral(z[[i]][[2]], z[[i]][[1]])
    } else {
      integral[i] <- .perfmeas.trap.rule.integral(z[[i]][[2]], z[[i]][[3]])
    }
  }

  integral
}

# Function that implements the trapezoidal rule for integration
# Input:
#   x : abscissa values in increasing order
#   y : ordinate values
# Output:
#   value of the integral
.perfmeas.trap.rule.integral <- function(x, y) {
  if (length(x) != length(y)) {
    stop("trap.rule.integral: length of x and y vectors must match")
  }
  perfmeas_trap_rule(as.double(x), as.double(y), as.integer(length(x)))
}