Fix bugs in borda count and STV.

Added new general votemethod unit test.

setup.py

@@ -21,7 +21,8 @@
 #
 setup(
       name='votesim',
-      version='1.0.9',   # Mar 3, 2021 update, Seq Monroe Update
+      version='1.0.10',   # Mar 4, 2021 update
+      # version='1.0.9',   # Mar 3, 2021 update, Seq Monroe Update
       # version='1.0.8',   # Mar 3, 2021 update
       # version='1.0.7',   # Mar 2, 2021 update
       # version='1.0.6',   # Mar 1, 2021 update

votesim/votemethods/condcalcs.py

@@ -170,7 +170,7 @@ def smith_set(ranks=None, vm=None, wl=None):
     return smith_set1
 
 
-def condorcet_check_one(ranks=None, scores=None):
+def condorcet_check_one(ranks=None, scores=None, matrix=None):
     """Calculate condorcet winner from ranked data if winner exists.
     
     Partial election method; function does not handle outcomes when
@@ -201,6 +201,8 @@ def condorcet_check_one(ranks=None, scores=None):
         m = pairwise_rank_matrix(ranks)
     elif scores is not None:
         m = pairwise_scored_matrix(scores)
+    elif matrix is not None:
+        m = np.asarray(matrix)
     else:
         raise ValueError('You must set either argument ranks or scores.'
                          'Both are currently not set as None.')
@@ -217,6 +219,31 @@ def condorcet_check_one(ranks=None, scores=None):
         return i[0]
     else:
         raise RuntimeError('Something went wrong that should not have happened')
+
+
+def check_win_loss_matrix(wl: np.ndarray):
+    """Given win-loss matrix, find condorcet winner.
+    
+    Parameters
+    ----------
+    wl : array shape (a, a)
+        Win loss matrix
+    
+    Returns
+    -------
+    out : int
+        Winner index location, -1 if no winner found.
+    """
+    beats = wl > 0
+    cnum = len(wl) - 1
+    beatnum = np.sum(beats, axis=1)
+    ii = np.where(beatnum == cnum)[0]
+    if len(ii) == 0:
+        return -1
+    elif len(ii) == 1:
+        return ii[0]
+    else:
+        raise RuntimeError('Something went wrong that should not have happened')
 
 
 def condorcet_winners_check(ranks=None, matrix=None, pairs=None, numwin=1,

votesim/votemethods/condorcet.py

@@ -15,6 +15,7 @@
                                            has_cycle,
                                            pairwise_rank_matrix,
                                            condorcet_winners_check,
+                                           check_win_loss_matrix,
                                            VoteMatrix
                                            )
 
@@ -341,11 +342,18 @@ def black(data, numwin=1):
     """Condorcet-black."""
     m = pairwise_rank_matrix(data)
     win_losses = m - m.T
-    winners, ties, scores = condorcet_winners_check(matrix=win_losses)
+
+    winner = check_win_loss_matrix(win_losses)
+
+    # winners, ties, scores = condorcet_winners_check(matrix=win_losses)
     output = {}
     output['margin_matrix'] = win_losses    
     output['tally'] = None
-    if len(winners) > 0:
+
+
+    if winner >= 0:
+        winners = np.array([winner])
+        ties = np.array([], dtype=int)
         return winners, ties, output    
     else:
         winners, ties, b_output = borda(data, numwin=1)
@@ -360,7 +368,7 @@ def copeland(data, numwin=1):
     win_losses = m - m.T
 
     # Create copeland results matrix
-    r_matrix = np.zeros(m.size)
+    r_matrix = np.zeros(m.shape)
 
     # Give score 1 if more voters prefer candidate to other. 
     r_matrix[win_losses > 0] = 1

votesim/votemethods/irv.py

@@ -260,6 +260,7 @@ def irv_eliminate(data):
     round_history = []
 
     logger.debug('irv elimination start.')
+    logger.debug('start losers = %s', start_losers)
     logger.debug('# losers = %s', losernum)
 
     active_bool = np.ones(candidate_num, dtype=bool)    
@@ -369,6 +370,7 @@ def irv_stv(data, numwin=1, reallocation='hare',
     num_ranked, num_candidates = data.shape
 
     quota = droop_quota(num_ranked, numwin)
+    logger.info('\n\n------ STARTING STV ----------------------')
     logger.info('stv droop quota = %s', quota)
 
     if reallocation=='hare':
@@ -403,7 +405,7 @@ def irv_stv(data, numwin=1, reallocation='hare',
 
 
         logger.info('\n\nstv round #%d', ii)
-        logger.info('stv votes = %s', tally)
+        logger.info('stv tally = %s', tally)
         logger.info('stv winners = %s', winners)
 
         # Break if we've gotten all winners        
@@ -435,7 +437,8 @@ def irv_stv(data, numwin=1, reallocation='hare',
 
                 # Check if there are too many ties to complete STV
                 if winner_count + tienum > numwin + survivor_count - tienum:
-                    logger.warning('Ties %s too close to winner. Outputting ties', ties)
+                    logger.warning(
+                        'Ties %s too close to winner. Outputting ties', ties)
                     break
                 else: 
                     jj = rstate.randint(0, tienum)
@@ -528,12 +531,12 @@ def hare_reallocation(data, tally, winners, quota, weights, rstate=None):
         win_voter_locs = data[:, winner] == 1
         win_voter_index = np.flatnonzero(win_voter_locs)
 
-        vote_num = tally[ii]
+        # vote_num = int(tally[ii])
 
         # Remove ballots the size of the quota
         remove_index = rstate.choice(
             win_voter_index,
-            size = min(vote_num, quota),
+            size = quota,
             replace = False,
             )
 

votesim/votemethods/tests/test_condorcet_minimax.py

@@ -96,6 +96,9 @@ def tests_minimax_condorcet(self):
 
 
 
+
+
+
 #    def test_wiki(self):
 #        """
 #        Test example from wikipedia, retrieved Dec 19, 2019.

votesim/votemethods/tests/test_general.py

@@ -0,0 +1,53 @@
+# -*- coding: utf-8 -*-
+"""
+Test every voting method.
+"""
+import pdb 
+import logging
+
+import votesim
+from votesim import votemethods
+from votesim.votemethods import scored_methods, ranked_methods, eRunner
+from votesim.models import spatial
+
+
+def test_all():
+    numvoters = 50    
+    num_candidates = 6
+    numwinners_list = [1, 3, 5]
+
+    for ii in range(50):
+        v = spatial.Voters(seed=ii,)
+        v.add_random(numvoters=numvoters, ndim=2, )
+        c = spatial.Candidates(voters=v, seed=0)
+        c.add_random(cnum=num_candidates, sdev=1.0)
+        e = spatial.Election(voters=v, candidates=c)
+
+        scores = e.ballotgen.get_honest_ballots(
+            etype=votesim.votemethods.SCORE
+        )
+        ranks = e.ballotgen.get_honest_ballots(
+            etype=votesim.votemethods.IRV
+        )       
+
+        for etype in ranked_methods:
+            for numwinners in numwinners_list:
+                runner = eRunner(etype=etype,
+                                 ballots=ranks, 
+                                 numwinners=numwinners)
+                assert len(runner.winners) == numwinners, etype
+
+        for etype in scored_methods:
+            for numwinners in numwinners_list:
+                runner = eRunner(etype=etype,
+                                 ballots=scores,
+                                 numwinners=numwinners)    
+                assert len(runner.winners) == numwinners, etype
+
+
+if __name__ == '__main__':
+    # logging.basicConfig()
+    # logger = logging.getLogger('votesim.votemethods.irv')
+    # logger.setLevel(logging.DEBUG)
+
+    test_all()

votesim/votemethods/tools.py

@@ -236,7 +236,7 @@ def multi_win_eliminate(func, data, numwin=1, **kwargs):
     num_left = numwin
     data = data.copy()
     outputs = []
-    ties = None
+    ties = []
     while num_left > 0:
 
         winners_ii, ties_ii, output_ii = run_with_eliminated(
@@ -269,8 +269,8 @@ def multi_win_eliminate(func, data, numwin=1, **kwargs):
         num_left = numwin - len(winners)
 
     winners = np.array(winners, dtype=int)
-    if ties:
-        ties = ties.asarray(dtype=int)
+    if len(ties) > 0:
+        ties = np.asarray(ties, dtype=int)
     else:        
         ties = np.array([], dtype=int)
     return winners, ties, outputs