modify documentation

src/kemeny_young.py

@@ -1,3 +1,7 @@
+"""
+This Module defines functions to compute the kendall tau distance between two
+rankings, and the kemeny-young rank aggregation method.
+"""
 import numpy as np
 from numba import jit, njit
 from itertools import permutations
@@ -18,7 +22,6 @@ Number = int|float
 
 
 
-
 def kendall_tau_dist(ranking_a: list[int], ranking_b: list[int]) -> Number:
     """The kendall τ distance between two rankings / permutations.
     It is the number of inversions that don't have the same sign within all pairs of an inversion of ranking_a and an inversion of ranking_b.
@@ -42,9 +45,9 @@ def __tau(A: list[int], B: list[int]) -> int:
 
 
 def rank_aggregation(rankings: list[list[int]]) -> tuple[int, tuple[int, ...]]:
-    """Brute-force kemeny-young rank aggregation.
+    """Return the order elected by the kemeny-young method.
     Args:
-        ranks: A list of the ranks (2D numpy array).
+        ranks: A list of the ranks (2D numpy array) to elect from.
     Returns:
         int, list: The minimal sum of distances to ranks, the rank of minimal distance.
     """
@@ -67,6 +70,9 @@ def rank_aggregation(rankings: list[list[int]]) -> tuple[int, tuple[int, ...]]:
     return min_dist, best_ranking
 
 
+
+#################################### TESTS #####################################
+
 if __name__ == '__main__':
     ranks = np.array([[0, 1, 2, 3, 4],
                       [0, 1, 3, 2, 4],