def learn_voting_rule(self, voting_rule, one_vs_one=None):
headers = []
sample_set = []
labels = []
for i in range(len(self.preference_profiles)):
preference_profile = self.preference_profiles[i]
positional_score_matrix = data_generator.positional_score_matrix(
self.candidates, preference_profile)
weighted_majority_graph = data_generator.weighted_majority_graph(
self.candidates, preference_profile)
histogram = data_generator.histogram(self.candidates,
preference_profile)
x = data_generator.get_feature_vector(positional_score_matrix,
weighted_majority_graph,
histogram,
preference_profile,
self.candidates)
y = voting_rule(self.candidates, preference_profile)
feature_names = data_generator.get_feature_names(
positional_score_matrix, weighted_majority_graph)
if i == 0:
headers = feature_names
sample_set.append(x)
labels.append(y)
print(len(labels))
x_data = learn.load_x(headers, sample_set)
y_data = learn.load_y(self.candidates, labels)
#print(x_data)
#print(y_data)
if (one_vs_one): learn.one_vs_one(x_data, y_data)
else: learn.train(x_data, y_data)
def voting_rule7(candidates, preference_profile):
weighted_majority_graph = data_generator.weighted_majority_graph(
candidates, preference_profile)
winners = voting_rules.Borda(candidates, preference_profile)
#return random.choice(winners)
return max(
voting_rules.minimax_tiebreaking(candidates, winners,
preference_profile))
def learned_rule(candidates, preference_profile, learned_model):
positional_score_matrix = data_generator.positional_score_matrix(
candidates, preference_profile)
weighted_majority_graph = data_generator.weighted_majority_graph(
candidates, preference_profile)
features = data_generator.get_feature_vector(positional_score_matrix,
weighted_majority_graph)
x = learn.load_x(None, [features])
return candidates[learned_model.predict(x)[0]]
def Condorcet(candidates, preference_profile):
weighted_majority_graph = data_generator.weighted_majority_graph(
candidates, preference_profile)
for row in weighted_majority_graph:
not_win = 0
for column in row:
if column[1] == 0:
not_win += 1
if not_win == 1:
return column[0][0]
return None
def voting_rule1(candidates, preference_profile):
weighted_majority_graph = data_generator.weighted_majority_graph(
candidates, preference_profile)
winners = voting_rules.Borda(candidates, preference_profile)
#return random.choice(winners)
#return max(voting_rules.minimax_tiebreaking(candidates,winners,preference_profile))
"""
mpsr = voting_rules.MPSR_tiebreaking(candidates,preference_profile,winners)
if mpsr !=None:
return mpsr
"""
return max(winners)
def get_Xs(candidates, preference_profiles):
Xs = []
for i in range(len(preference_profiles)):
preference_profile = preference_profiles[i]
positional_score_matrix = data_generator.positional_score_matrix(
candidates, preference_profile)
weighted_majority_graph = data_generator.weighted_majority_graph(
candidates, preference_profile)
histogram = data_generator.histogram(candidates,
preference_profile)
X = data_generator.get_feature_vector(positional_score_matrix,
weighted_majority_graph,
histogram,
preference_profile,
candidates)
Xs.append(X)
return Xs
def Minimax(candidates, preference_profile):
weighted_majority_graph = data_generator.weighted_majority_graph(
candidates, preference_profile)
winner = []
minimax_loss = 1000
for i in range(len(weighted_majority_graph)):
max_loss = 0
for j in range(len(weighted_majority_graph[0])):
loss = -weighted_majority_graph[i][j][
1] + weighted_majority_graph[j][i][1]
if loss > max_loss:
max_loss = loss
if max_loss < minimax_loss:
minimax_loss = max_loss
winner = []
winner.append(weighted_majority_graph[i][j][0][0])
elif max_loss == minimax_loss:
winner.append(weighted_majority_graph[i][j][0][0])
return winner
def Copland(candidates, preference_profile):
weighted_majority_graph = data_generator.weighted_majority_graph(
candidates, preference_profile)
winner = []
max_wins = 0
for i in range(len(weighted_majority_graph)):
wins = 0
for j in range(len(weighted_majority_graph[0])):
if weighted_majority_graph[i][j][1] > weighted_majority_graph[
j][i][1]:
wins += 1
elif weighted_majority_graph[i][j][
1] < weighted_majority_graph[j][i][1]:
wins -= 1
if wins > max_wins:
max_wins = wins
winner = []
winner.append(weighted_majority_graph[i][j][0][0])
elif wins == max_wins:
winner.append(weighted_majority_graph[i][j][0][0])
return winner