def test_lladser_ci():
"""lladser_ci estimate using defaults contains p with 95% prob"""
reps = 100
sum = 0
for i in range(reps):
fake_obs, exp_p = create_fake_observation()
(low, high) = lladser_ci(fake_obs, r=10)
if (low <= exp_p <= high):
sum += 1
assert_true(sum / reps >= 0.95)
def test_lladser_ci():
"""lladser_ci estimate using defaults contains p with 95% prob"""
reps = 100
sum = 0
for i in range(reps):
fake_obs, exp_p = create_fake_observation()
(low, high) = lladser_ci(fake_obs, r=10)
if (low <= exp_p <= high):
sum += 1
assert_true(sum/reps >= 0.95)
def test_lladser_ci_f3():
"""lladser_ci estimate using f=3 contains p with 95% prob"""
# Test different values of f=3 and r=14, which lie exactly on the
# 95% interval line. For 100 reps using simple cumulative binomial
# probs we expect to have more than 5 misses of the interval in 38%
# of all test runs. To make this test pass reliable we thus have to
# set a defined seed
np.random.seed(12345678)
reps = 100
sum = 0
for i in range(reps):
# re-create the obs for every estimate, such that they are truly
# independent events
fake_obs, exp_p = create_fake_observation()
(low, high) = lladser_ci(fake_obs, r=14, f=3)
if (low <= exp_p <= high):
sum += 1
assert_true(sum / reps >= 0.95)
def test_lladser_ci_f3():
"""lladser_ci estimate using f=3 contains p with 95% prob"""
# Test different values of f=3 and r=14, which lie exactly on the
# 95% interval line. For 100 reps using simple cumulative binomial
# probs we expect to have more than 5 misses of the interval in 38%
# of all test runs. To make this test pass reliable we thus have to
# set a defined seed
np.random.seed(12345678)
reps = 100
sum = 0
for i in range(reps):
# re-create the obs for every estimate, such that they are truly
# independent events
fake_obs, exp_p = create_fake_observation()
(low, high) = lladser_ci(fake_obs, r=14, f=3)
if (low <= exp_p <= high):
sum += 1
assert_true(sum/reps >= 0.95)
def test_lladser_ci_f3():
"""lladser_ci estimate using f=3 contains p with 95% prob"""
# Test different values of f=3 and r=14, which lie exactly on th
# 95% interval line. For 100 reps using simple cumulative binomial
# probs we expect to have more than 5 misses of the interval in 38%
# of all test runs. To make this test pass reliable we thus have to
# set the threshold much lower and do more reps. For 0.93 and 1000 reps,
# the binomial distribution tells us that the test will pass more
# often than 99%.
reps = 1000
sum = 0
for i in range(reps):
# re-create the obs for every estimate, such that they are truly
# independent events
fake_obs, exp_p = create_fake_observation()
(low, high) = lladser_ci(fake_obs, r=14, f=3)
if (low <= exp_p <= high):
sum += 1
assert_true(sum / reps >= 0.93)
def test_lladser_ci_f3():
"""lladser_ci estimate using f=3 contains p with 95% prob"""
# Test different values of f=3 and r=14, which lie exactly on th
# 95% interval line. For 100 reps using simple cumulative binomial
# probs we expect to have more than 5 misses of the interval in 38%
# of all test runs. To make this test pass reliable we thus have to
# set the threshold much lower and do more reps. For 0.93 and 1000 reps,
# the binomial distribution tells us that the test will pass more
# often than 99%.
reps = 1000
sum = 0
for i in range(reps):
# re-create the obs for every estimate, such that they are truly
# independent events
fake_obs, exp_p = create_fake_observation()
(low, high) = lladser_ci(fake_obs, r=14, f=3)
if (low <= exp_p <= high):
sum += 1
assert_true(sum/reps >= 0.93)
def test_lladser_ci_nan():
"""lladser_ci returns nan if sample is too short to make an estimate"""
obs = lladser_ci([3], r=4)
assert_true(len(obs) == 2 and np.isnan(obs[0]) and np.isnan(obs[1]))
def test_lladser_ci_nan():
"""lladser_ci returns nan if sample is too short to make an estimate"""
obs = lladser_ci([3], r=4)
assert_true(len(obs) == 2 and np.isnan(obs[0]) and np.isnan(obs[1]))
