def test_simple_minerva():
simple_minerva = Minerva(.1, .1, default_contest)
assert simple_minerva.alpha == .1
assert simple_minerva.beta == 0.0
assert simple_minerva.max_fraction_to_draw == .1
assert len(simple_minerva.rounds) == 0
assert len(simple_minerva.sub_audits) == 1
assert simple_minerva.get_risk_level() is None
simple_minerva.rounds.append(10)
simple_minerva.stopped = True
assert simple_minerva.next_sample_size() == 10
assert simple_minerva.next_sample_size(verbose=True) == (10, 0, 1)
def test_minerva_georgia_senate_2020():
ga_senate_race = Contest(2453876 + 2358432, {
'A': 2453876,
'B': 2358432
}, 1, ['A'], ContestType.PLURALITY)
ga_senate_audit = Minerva(.1, 1.0, ga_senate_race)
irrelevant_scale_up = 1.0238785631
estimates = []
for sprob in [.7, .8, .9]:
estimates.append(
math.ceil(irrelevant_scale_up *
ga_senate_audit.next_sample_size(sprob=sprob)))
assert estimates == [10486, 13205, 18005]
ga_senate_audit.execute_round(9903, {'A': 4950, 'B': 9903 - 4950})
assert abs(ga_senate_audit.pvalue_schedule[-1] -
0.527638189598802) < .000001
ga_senate_audit.execute_round(24000, {'A': 11900, 'B': 24000 - 11900})
assert abs(ga_senate_audit.pvalue_schedule[-1] -
2.663358309286826) < .000001
ga_senate_audit.execute_round(45600, {'A': 24000, 'B': 45600 - 24000})
assert abs(ga_senate_audit.pvalue_schedule[-1]) < 0.000001
ga_senate_audit = Minerva(.1, 1.0, ga_senate_race)
ga_senate_audit.execute_round(17605, {'A': 8900, 'B': 17605 - 8900})
assert abs(ga_senate_audit.get_risk_level() - 0.081750333563781) < .000001
ga_senate_audit = Minerva(.1, 1.0, ga_senate_race)
ga_senate_audit.execute_round(17605, {'A': 17605, 'B': 0})
assert ga_senate_audit.get_risk_level() == 0
ga_senate_audit = Minerva(.1, 1.0, ga_senate_race)
ga_senate_audit.execute_round(17605, {'A': 0, 'B': 17605})
assert abs(ga_senate_audit.get_risk_level() - 1) < 0.000001
def test_minerva_second_round_estimate():
contest1 = Contest(100000, {
'A': 60000,
'B': 40000
}, 1, ['A'], ContestType.MAJORITY)
minerva1 = Minerva(.1, .1, contest1)
minerva1.compute_min_winner_ballots(minerva1.sub_audits['A-B'], [100])
minerva1.sample_ballots['A'].append(54)
minerva1.sample_ballots['B'].append(100 - 54)
contest2 = Contest(4504975 + 4617886, {
'Trump': 4617886,
'Clinton': 4504975
}, 1, ['Trump'], ContestType.PLURALITY)
minerva2 = Minerva(.1, 1.0, contest2)
minerva2.compute_min_winner_ballots(minerva2.sub_audits['Trump-Clinton'],
[45081])
minerva2.sample_ballots['Trump'].append(22634)
minerva2.sample_ballots['Clinton'].append(45081 - 22634)
assert minerva1.next_sample_size() == 305
assert minerva2.next_sample_size() == 111257
def test_minerva_first_round_estimate():
contest1 = Contest(100000, {
'A': 60000,
'B': 40000
}, 1, ['A'], ContestType.MAJORITY)
minerva1 = Minerva(.1, .1, contest1)
contest2 = Contest(100000, {
'A': 51000,
'B': 49000
}, 1, ['A'], ContestType.MAJORITY)
minerva2 = Minerva(.1, .1, contest2)
contest3 = Contest(10000000, {
'A': 5040799,
'B': 10000000 - 5040799
}, 1, ['A'], ContestType.MAJORITY)
minerva3 = Minerva(.1, 1.0, contest3)
assert minerva1.next_sample_size() == 179
assert minerva2.next_sample_size() == 17272
assert minerva3.next_sample_size() == 103483
def test_minerva_second_round_estimate_2016():
with open('tests/data/2016_pres_trials.json', 'r') as json_file:
data = json.load(json_file)
out = {}
out['data_check'] = {}
for state in data:
out['data_check'][state] = {}
clinton = data[state]['tally']['Clinton']
trump = data[state]['tally']['Trump']
print(state)
tally = {"Clinton": clinton, "Trump": trump}
margin = abs((clinton - trump) / (clinton + trump))
if margin < .10:
continue
contest = Contest(clinton + trump, tally, 1,
[max(tally, key=tally.get)], ContestType.PLURALITY)
if tally['Clinton'] > tally['Trump']:
rep_winner = 'Clinton'
rep_loser = 'Trump'
else:
rep_winner = 'Trump'
rep_loser = 'Clinton'
for sim_type in [
'underlying_reported_first_5',
'underlying_reported_not_stop_5', 'underlying_tied_first_5'
]:
out['data_check'][state][sim_type] = []
for trial in data[state][sim_type]:
n = trial['relevant_sample_size']
k = trial['winner_ballots']
minerva = Minerva(.1, 1.0, contest)
minerva.execute_round(n, {rep_winner: k, rep_loser: n - k})
p_value = minerva.get_risk_level()
stop = minerva.stopped
if stop:
minerva.next_min_winner_ballots()
minerva.truncate_dist_null()
minerva.truncate_dist_reported()
next_round_data = minerva.next_sample_size(verbose=True)
out['data_check'][state][sim_type].append({
"n":
n,
"k":
k,
"p_value":
p_value,
"stop":
bool(stop),
"kmin":
minerva.sub_audits[rep_winner + '-' +
rep_loser].min_winner_ballots[-1],
"next_round_size":
next_round_data[0],
"next_round_kmin":
next_round_data[1],
"next_round_sprob":
next_round_data[2]
})
with open(
'tests/data/test_minerva_second_round_estimate_2016.json',
'w') as output:
json.dump(out, output, sort_keys=True, indent=4)
# Now that the file has been generated, compare to PV version.
with open('tests/data/gm_test_minerva_second_round_estimate_2016.json',
'r') as json_file:
data_canonical = json.load(json_file)
with open('tests/data/test_minerva_second_round_estimate_2016.json',
'r') as json_file:
data_test = json.load(json_file)
for state in data_canonical['data_check']:
if data_canonical['data_check'][state] == {}:
continue
for sim_type in [
'underlying_reported_first_5',
'underlying_reported_not_stop_5', 'underlying_tied_first_5'
]:
for i in range(5):
assert data_canonical['data_check'][state][sim_type][i][
'n'] == data_test['data_check'][state][sim_type][i]['n']
assert data_canonical['data_check'][state][sim_type][i][
'k'] == data_test['data_check'][state][sim_type][i]['k']
assert data_canonical['data_check'][state][sim_type][i][
'kmin'] == data_test['data_check'][state][sim_type][i][
'kmin']
assert data_canonical['data_check'][state][sim_type][i][
'stop'] == data_test['data_check'][state][sim_type][i][
'stop']
assert data_canonical['data_check'][state][sim_type][i]['next_round_size'] == \
data_test['data_check'][state][sim_type][i]['next_round_size']
assert abs(data_canonical['data_check'][state][sim_type][i]
['p_value'] - data_test['data_check'][state]
[sim_type][i]['p_value']) < .000001
assert abs(data_canonical['data_check'][state][sim_type][i]
['next_round_sprob'] - data_test['data_check']
[state][sim_type][i]['next_round_sprob']) < .000001
