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stratified_round_size_script.py
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stratified_round_size_script.py
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"""
This script generates computes and stores in a json file the polling stratum
round sizes that achieve a desired probability of stopping under the alternative
hypothesis that the election is truly as announced. It produces both
Minerva and R2 Bravo round sizes for various polling stratum sizes (as a
percentage of relevant ballots).
Oliver Broadrick 2020
"""
from round_sizes import find_sample_size_for_stopping_prob_efficiently, find_sample_size_for_stopping_prob_efficiently_r2bravo
import math
import matplotlib.pyplot as plt
import json
# loop through the various margins for which I would like results (run overnight)
for fractional_margin in [.01, .02, .03, .04, .05, .06, .07, .08]:
# track all data in a struct for each margin, output to a json file
data = {}
data['audits'] = []
# file for data named based on margin
data_file_name = 'data/data_stratified_'+str(round(fractional_margin * 100))+'_percent_margin.txt'
# loop through the various percent sizes of the polling stratum
for percent_polling in [.05,.1,.15,.2,.25,.3,.35,.4,.45,.5,.55,.6,.65,.7,.75,.8,.85,.9,.95]:
minerva_right = None
r2bravo_right = None
"""
# define a right bounds for the round size searchs based on the previous size found
if (percent_polling == .05):
minerva_right = None
r2bravo_right = None
elif (percent_polling < .25):
# times 4 is sufficient here (I tested it)
minerva_prev = data['audits'][len(data['audits']) - 1]['minerva_round_size']
r2bravo_prev = data['audits'][len(data['audits']) - 1]['r2bravo_round_size']
minerva_right = 4 * minerva_prev
print(minerva_right)
r2bravo_right = 4 * r2bravo_prev
print(r2bravo_right)
else:
# times 2 is sufficient here (I tested it)
minerva_prev = data['audits'][len(data['audits']) - 1]['minerva_round_size']
r2bravo_prev = data['audits'][len(data['audits']) - 1]['r2bravo_round_size']
minerva_right = 2 * minerva_prev
print(minerva_right)
r2bravo_right = 2 * r2bravo_prev
print(r2bravo_right)
"""
# risk limit (same as suite example 1)
alpha = 0.1
# overall relevant ballot tallies (same as suite example 1)
N_relevant = 104000
N_w = round(N_relevant * (1 + fractional_margin) / 2)
N_l = 104000 - N_w
N_w_fraction = N_w / N_relevant
# divide the ballots into strata
# all strata will have the same margin as the overall contest
N_2 = round(percent_polling * N_relevant)
N_1 = N_relevant - N_2
N_w1 = round(N_w_fraction * N_1)
N_l1 = N_1 - N_w1
N_w2 = N_w - N_w1
N_l2 = N_2 - N_w2
margin = N_w1 + N_w2 - N_l1 - N_l2
# sanity check
assert (N_l2 + N_l1 == N_l)
assert (N_w2 + N_w1 == N_w)
assert (N_1 + N_2 == N_relevant)