def test_mtable_generation(k, p, alpha):
f = fair.Fair(k, p, alpha)
# create an adjusted mtable with alpha unadjusted
mtable = f.create_adjusted_mtable()
# get alpha adjusted
alpha_adjusted = f.adjust_alpha()
# create a new unadjusted mtable with the new alpha
f_adjusted = fair.Fair(k, p, alpha_adjusted)
mtable_adjusted = f_adjusted.create_unadjusted_mtable()
assert mtable == mtable_adjusted # they should be same to the 5th decimal
def test_adjust_alpha(k, p, alpha, result):
f = fair.Fair(k, p, alpha)
adjusted_alpha = f.adjust_alpha()
assert abs(adjusted_alpha -
result) < 0.0001 # they should be same to the 5th decimal
def test_compute_fail_probability(k, p, alpha, result):
f = fair.Fair(k, p, alpha)
adjusted_mtable = f.create_adjusted_mtable()
prob = f.compute_fail_probability(adjusted_mtable)
assert abs(prob -
result) < 0.0001 # they should be same to the 5th decimal
def test_re_rank(k, p, alpha, ranking):
f = fair.Fair(k, p, alpha)
re_ranked = f.re_rank(ranking)
# input should not be fair
assert not f.is_fair(ranking)
# check length
assert len(ranking) == len(re_ranked)
# check content
assert set([r.id for r in ranking]) == set([r.id for r in re_ranked])
# output should be fair
assert f.is_fair(re_ranked)
def test_fail_probability_calcualtors():
Ms = [1000, 10000]
ks = [10, 20, 50, 100, 200]
ps = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
alphas = [0.01, 0.05, 0.1, 0.15]
allowed_offset = 0.02 # we tolerate an absolute difference in probability of 0.02
for M in Ms:
for k in ks:
for p in ps:
rankings = simulator.generate_rankings(M, k, p)
for alpha in alphas:
f = fair.Fair(k, p, alpha)
mtable = f.create_adjusted_mtable()
experimental = simulator.compute_fail_probability(
rankings, mtable)
analytical = f.compute_fail_probability(mtable)
# Not pretty, but adding all the parameters in the assert, so we know what combination fails
assert M > 0 and k > 0 and p > 0 and alpha > 0 \
and abs(experimental - analytical) < (allowed_offset + alpha * 0.01 / allowed_offset)
def test_create_adjusted_mtable(k, p, alpha, result):
f = fair.Fair(k, p, alpha)
mtable = f.create_adjusted_mtable()
assert mtable == result
def test_is_fair(k, p, alpha, ranking):
f = fair.Fair(k, p, alpha)
assert len(ranking) == k
assert f.is_fair(ranking)