add phragmen's rule in efs

R/EnsembleFSResult.R

@@ -143,6 +143,9 @@ EnsembleFSResult = R6Class("EnsembleFSResult",
  #' Voters who approve more candidates contribute a lesser score to the individual approved candidates.
  #' - `"seq_pav"|"seq_pav_weighted"` (sequential proportional approval voting) sequentially builds a committee by iteratively selecting the candidate that maximizes the PAV score when added, ensuring proportional representation.
  #' The **PAV score** (Proportional Approval Voting score) is a metric that calculates the weighted sum of harmonic numbers corresponding to the number of elected candidates supported by each voter, reflecting the overall satisfaction of voters in a committee selection process.
+ #' - `"seq_phragmen"|"seq_phragmen_weighted"` (sequential Phragmen's rule) distributes "loads" equally among voters for each candidate added to the committee.
+ #' The rule iteratively selects the candidate that results in the smallest increase in voter load.
+ #' This approach is suitable for scenarios where a balanced representation is desired, as it seeks to evenly distribute the "burden" of representation among all voters.
  #'
  #' @param method (`character(1)`)\cr
  #' The method to calculate the feature ranking.
@@ -156,7 +159,8 @@ EnsembleFSResult = R6Class("EnsembleFSResult",
  #'
  feature_ranking = function(method = "av", committee_size = NULL) {
  assert_choice(method, choices = c("av", "av_weighted", "sav", "sav_weighted",
- "seq_pav", "seq_pav_weighted"))
+ "seq_pav", "seq_pav_weighted", "seq_phragmen",
+ "seq_phragmen_weighted"))
  assert_int(committee_size, lower = 1, null.ok = TRUE)
 
  # cached results
@@ -189,6 +193,8 @@ EnsembleFSResult = R6Class("EnsembleFSResult",
  res = satisfaction_approval_voting(voters, candidates, weights)
  } else if (startsWith(method, "seq_pav")) {
  res = seq_proportional_approval_voting(voters, candidates, weights, committee_size)
+ } else if (startsWith(method, "seq_phragmen")) {
+ res = seq_phragmen_rule(voters, candidates, weights, committee_size)
  }
 
  private$.feature_ranking[[method]] = res

R/voting_methods.R

@@ -57,6 +57,17 @@ seq_proportional_approval_voting = function(voters, candidates, weights, committ
  add_borda_score(res)
 }
 
+seq_phragmen_rule = function(voters, candidates, weights, committee_size = NULL) {
+ if (is.null(committee_size)) {
+ committee_size = length(candidates)
+ }
+
+ # returns ranked features from best to worst (up to committee_size)
+ res = as.data.table(seq_PAV_rcpp(voters, candidates, weights, committee_size))
+
+ add_borda_score(res)
+}
+
 # add normalized borda scores
 add_borda_score = function(dt) {
  borda_score = NULL # silence data.table note: "no visible global binding"