def draw_histogram(out_dir, multigoal_rule, k, percentages, distribution, reps, n, m, method, threshold=None):
distribution_name = get_distribution_name(distribution)
perc = '_'.join([str(p) for p in percentages])
winner_filename = '{}_{}_{}_k{}_n{}_m{}_{}'.format(multigoal_rule.__name__, distribution_name, perc, k, n, m, method)
file_path = os.path.join(out_dir, winner_filename)
mw2d_generate_histogram(file_path, reps)
histogram_filename = file_path + '.hist'
mw2d_draw_histogram(histogram_filename, threshold)
os.remove(histogram_filename)
def draw_pareto_chart_from_winner_files(current_dir, m, n, k, multigoal_rule, distribution, distribution_params=None):
print("{}, k={}".format(multigoal_rule.__name__, k))
# We assume that there are "repetitions" files generated for each threshold.
rule_name = multigoal_rule.__name__
distribution_name = get_distribution_name(distribution)
rules = get_multigoal_rules(multigoal_rule)
if not rules:
return
xy = {}
for dir_name in os.listdir(current_dir):
dir_pattern = '{}_{}_(\d+)_\d+_k{}_n{}_m{}'.format(rule_name, distribution_name, k, n, m)
r1_match = re.match(dir_pattern, dir_name)
if r1_match is None:
continue
r1 = r1_match.group(1)
for filename in os.listdir(os.path.join(current_dir, dir_name)):
rep = get_repetition_from_filename(dir_name, filename)
if not rep:
continue
win_filename = os.path.join(current_dir, dir_name, filename)
best_filename = '{}_{}.best'.format(dir_name, rep)
best_filename = os.path.join(current_dir, dir_name, best_filename)
scores = read_scores(win_filename)
best = read_scores(best_filename)
approx = scores[1] / best[1]
if r1 in xy:
xy[r1].append(approx)
else:
xy[r1] = [approx]
xy_list = list(xy.items())
xy_list = sorted(xy_list, key=lambda e: int(e[0]))
x = [int(x) for x, _ in xy_list]
y_min = [np.min(ys) for _, ys in xy_list]
if distribution_params is None:
filename = '{}_{}_k{}_n{}_m{}'.format(rule_name, distribution_name, k, n, m)
else:
distribution_params_string = '_'.join([dpk + str(dpv) for dpk, dpv in distribution_params.items()])
distribution_params_string = distribution_params_string.replace('.', '')
filename = '{}_{}_{}_k{}_n{}_m{}'.format(rule_name, distribution_name, distribution_params_string, k, n, m)
title = "voters: {}, candidates: {}, committee size: {}".format(n, m, k)
plot(filename, x, y_min, rules, title=title)
def draw_approximation_charts(experiment, ms, k_percs, distribution, approximations):
out_dir = experiment.get_generated_dir_path()
distribution_name = get_distribution_name(distribution)
for im, m in enumerate(ms):
filename = os.path.join(out_dir, 'approx_{}_m{}'.format(distribution_name, m))
ks = k_percs * m
axes = plt.gca()
axes.set_xlim([0, m])
axes.set_ylim([0, 1.1])
plt.xlabel('k')
plt.ylabel('approximation')
plt.plot(ks, approximations['Approx_Greedy'][im, :])
plt.plot(ks, approximations['Approx_P'][im, :])
plt.legend(['Greedy', 'P'])
plt.title('Approximation in CC+kB')
plt.savefig(filename)
plt.clf()
def draw_transition_from_winner_files(current_dir, m, n, k, multigoal_rule, distribution, repetitions):
print("{}, k={}".format(multigoal_rule.__name__, k))
# We assume that there are "repetitions" files generated for each threshold.
rule_name = multigoal_rule.__name__
distribution_name = get_distribution_name(distribution)
rules = get_multigoal_rules(multigoal_rule)
if not rules:
return
for dir_name in os.listdir(current_dir):
dir_path = os.path.join(current_dir, dir_name)
if not os.path.isdir(dir_path):
continue
dir_pattern = '{}_{}_(\d+)_(\d+)_k{}_n{}_m{}'.format(rule_name, distribution_name, k, n, m)
r1_r2_match = re.match(dir_pattern, dir_name)
if r1_r2_match is None:
continue
percentages = r1_r2_match.group(1), r1_r2_match.group(2)
draw_histogram(dir_path, multigoal_rule, k, percentages, distribution, repetitions, n, m, 'ILP')
m = 100 # candidates number
n = 100 # voters number
repetitions = 60
k_ccs = range(0, k + 1)
methods = ['Approx_P', 'Approx_Greedy']
multigoal_rule = MultigoalCCBorda
distribution = generate_uniform
experiments = []
for _ in range(repetitions):
config = MultigoalExperimentConfig()
config.add_candidates(distribution(-3, -3, 3, 3, m, 'None'))
config.add_voters(distribution(-3, -3, 3, 3, n, 'None'))
config.set_distribution_name(get_distribution_name(distribution))
experiment = MultigoalExperiment(config)
experiment.set_multigoal_election(MultigoalCCBorda,
k,
percent_thresholds=[0, 0])
experiments.append(experiment)
for repetition, experiment in enumerate(experiments):
for i, k_cc in enumerate(k_ccs):
file_prefix = 'kcc' + str(k_cc)
experiment.set_filename(file_prefix)
experiment.run(n=1,
n_start=repetition + 1,
methods=methods,
k_cc=k_cc,
draw_pareto_chart_from_winner_files
from pmp.multigoal.helpers import get_distribution_name
from pmp.rules import MultigoalBlocBorda
current_file = os.path.abspath(__file__)
current_dir = os.path.dirname(current_file)
repetitions = 4
k = 10
n = 100
m = 100
phi = 0.1
multigoal_rule = MultigoalBlocBorda
distribution = mallows
distribution_name = get_distribution_name(distribution)
start = 90
step = 2
dir_name = 'results_{}_{}_{}_{}_phi{}_k{}_n{}_m{}'.format(
start, step, multigoal_rule.__name__, distribution_name, phi, k, n, m)
configs = []
for _ in range(repetitions):
config = MultigoalExperimentConfig()
config.mallows(phi, m, n)
config.set_distribution_name(distribution_name)
configs.append(config)
generate_winner_files_for_pareto(dir_name,