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Completed sequential monroe update.
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@johnh865
johnh865 committed Mar 4, 2021
1 parent b940a68 commit 3c24297
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Showing 2 changed files with 66 additions and 20 deletions.
60 changes: 43 additions & 17 deletions votesim/votemethods/score.py
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Original file line number Diff line number Diff line change
Expand Up @@ -444,6 +444,11 @@ def sequential_monroe(data, numwin=1, maxscore=None ):
4. Elect the candidate with the highest hare quota score and exhaust
the votes that contribute to that candidate's hare quota score.
(JCH - for our implementation, because of discretized scores,
there may be voter scores that exceed the hare quota. IE,
quota is 20 votes, but we have 30 votes of top rating 5.0/5.0.
Here there are 10 surplus votes to deal with.
We will use fractional exhaustion to take care of this.)
5. Repeat this process until all the seats are filled.
Expand Down Expand Up @@ -479,6 +484,7 @@ def sequential_monroe(data, numwin=1, maxscore=None ):
num_candidates = data.shape[1]
num_voters = data.shape[0]
quota = tools.hare_quota(num_voters, numwin)
logger.debug('quota=%s', quota)
if maxscore is None:
maxscore = data.max()

Expand All @@ -490,19 +496,19 @@ def sequential_monroe(data, numwin=1, maxscore=None ):
weights = np.ones(num_voters)

# Get sort array for candidate's scores from highest to lowest
candidate_sort_indices = []
for ic in range(num_candidates):
ic_votes = data[:, ic]
ic_sort = np.argsort(ic_votes)
candidate_sort_indices.append(ic_sort)
# candidate_sort_indices = []
# for ic in range(num_candidates):
# ic_votes = data[:, ic]
# ic_sort = np.argsort(ic_votes)
# candidate_sort_indices.append(ic_sort)


# Find for each required number of winners
for ii in range(numwin):
tally = []
top_voter_record = []
# unique_scores_record = []
logger.debug('\n\nIteration #%s', ii)
logger.debug('\n\n---------- Iteration #%s --------------', ii)
for ic in range(num_candidates):

ic_votes = data[:, ic]
Expand All @@ -514,8 +520,13 @@ def sequential_monroe(data, numwin=1, maxscore=None ):
for score_floor in unique_scores:
top_index = ic_votes >= score_floor
top_weights = weights[top_index]

if np.sum(top_weights) >= quota:
top_weight_sum = np.sum(top_weights)
if top_weight_sum >= quota:
logger.debug(
'top_weight_sum=%.0f @ score_floor=%s',
top_weight_sum,
score_floor,
)
break

top_voter_record.append(top_index)
Expand All @@ -524,13 +535,14 @@ def sequential_monroe(data, numwin=1, maxscore=None ):

# Score values of candidate's top voters
top_scores = ic_votes[top_index]
top_voter_num = len(top_scores)

# Get unique score values of top voters, sort highest to lowest
# unique_scores = np.unique(ic_top_scores)[::-1]
# unique_scores_record.append(unique_scores)

# Average scores of each candidate within top voter quota
mean_score = np.sum(top_scores * top_weights) / quota
mean_score = np.sum(top_scores * top_weights) / top_voter_num
tally.append(mean_score)

# Top voter index locations for all candidates
Expand All @@ -544,13 +556,19 @@ def sequential_monroe(data, numwin=1, maxscore=None ):
winners_ii, ties_ii = tools.winner_check(tally, 1)

remaining_slots = numwin - len(winners)
if len(ties_ii) <= remaining_slots:
if len(winners_ii) > 0:
winners.extend(winners_ii)

elif len(ties_ii) <= remaining_slots:
winners_ii = ties_ii
winners.extend(ties_ii)
logger.debug('Ties found to fill out rest of winner')
logger.debug('Tie winners = %s', ties_ii)
else:
ties = ties_ii
break

winners.extend(winners_ii)
logger.debug('Winners: %s', winners)
if len(winners) >= numwin:
break

Expand All @@ -571,31 +589,39 @@ def sequential_monroe(data, numwin=1, maxscore=None ):

# Get net weight of winning voters
k_weight = np.sum(weights[voter_locs])

votes_exhausted += k_weight
logger.debug('Adding votes for score %s', score_k)
logger.debug('votes_exhausted=%.3f', votes_exhausted)

# Now we need to calculate the surplus vote per voter.
# It's possible that some voters don't have enough
# weight to contribute 100%,
# so we have to take that from other voters.

if votes_exhausted > quota:
surplus_weight = k_weight - quota
surplus_weight = votes_exhausted - quota
factor = surplus_weight / k_weight
weights[voter_locs] = weights[voter_locs] * factor

logger.debug('votes_exhausted=%s', votes_exhausted)
logger.debug('surplus_weight=%s', surplus_weight)
logger.debug('factor=%s', factor)

logger.debug('surplus_weight=%.3f', surplus_weight)
logger.debug('factor=%.3f', factor)
if logger.isEnabledFor(logging.DEBUG):
new_weight = np.sum(weights[voter_locs])
logger.debug('new_weight=%s', new_weight)
logger.debug('new_weight=%.3f', new_weight)
logger.debug('residual=%.3f', new_weight - surplus_weight)
break

elif votes_exhausted <= quota:
factor = 0.0
weights[voter_locs] = 0.0
logger.debug('new_weight=0')



## Set winner data to zero
data[:, jj] = 0

winners = np.array(winners, dtype=int)


Expand Down
26 changes: 23 additions & 3 deletions votesim/votemethods/tests/test_score.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -199,7 +199,7 @@ def test_result(self):
def test_spatial(self):

numvoters = 99
num_candidates = 5
num_candidates = 20

v = spatial.Voters(seed=400,)
v.add_random(numvoters=numvoters, ndim=2, )
Expand All @@ -210,9 +210,29 @@ def test_spatial(self):
etype=votesim.votemethods.SCORE
)

winners, ties, output = score.sequential_monroe(scores, numwin=3)
winners, ties, output = score.sequential_monroe(scores, numwin=6)
return


def test_score_compare(self):
"""Sequential monroe ought to devolve to score at numwinners=1"""
numvoters = 50
num_candidates = 6
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
)

winners1, ties1, output = score.sequential_monroe(scores, numwin=1)
winners2, ties2, output = score.score(scores, numwin=1)
assert np.all(winners1 == winners2)
assert np.all(ties1 == ties2)




Expand All @@ -228,7 +248,7 @@ def test_spatial(self):


t= TestSequentialMonroe()
t.test_spatial()
t.test_score_compare()

# t = TestRRW()
# t.test_result()
Expand Down

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