def test_bca_confidence_interval_bounded_estimator(ci_method, rng):
def fn(data):
return max(np.mean(data), 0)
data = (-3, -2, -1)
ci = confidence_interval(fn,
data,
ci_method=ci_method,
size=5,
random_state=rng)
assert_allclose((0.0, 0.0), ci)
def test_bca_confidence_interval_estimator_returns_int(rng):
def fn(data):
return int(np.mean(data))
data = (1, 2, 3)
ci = confidence_interval(fn,
data,
ci_method="bca",
size=5,
random_state=rng)
assert_allclose((1.0, 2.0), ci)
def test_confidence_interval(ci_method, rng):
data = rng.normal(size=1000)
par = stats.norm.fit(data)
dist = stats.norm(par[0], par[1] /
len(data)**0.5) # accuracy of mean is sqrt(n) better
cl = 0.9
ci_ref = dist.ppf(0.05), dist.ppf(0.95)
ci = confidence_interval(np.mean,
data,
cl=cl,
size=1000,
ci_method=ci_method)
assert_almost_equal(ci_ref, ci, decimal=2)
def test_confidence_interval_deprecation(rng):
from numpy import VisibleDeprecationWarning
d = [1, 2, 3]
with pytest.warns(VisibleDeprecationWarning):
r = confidence_interval(np.mean, d, 0.6, random_state=1)
assert_equal(r, confidence_interval(np.mean, d, cl=0.6, random_state=1))
with pytest.warns(VisibleDeprecationWarning):
r = confidence_interval(np.mean, d, 0.6, "percentile", random_state=1)
assert_equal(
r,
confidence_interval(np.mean,
d,
cl=0.6,
ci_method="percentile",
random_state=1),
)
with pytest.warns(VisibleDeprecationWarning):
with pytest.raises(ValueError):
confidence_interval(np.mean, d, 0.6, "percentile", 1)
def test_confidence_interval_invalid_ci_method_raises():
msg = "method must be 'percentile' or 'bca'"
with pytest.raises(ValueError, match=msg):
confidence_interval(np.mean, (1, 2, 3), ci_method="foobar")
def test_confidence_interval_invalid_p_raises():
msg = "must be between zero and one"
with pytest.raises(ValueError, match=msg):
confidence_interval(np.mean, (1, 2, 3), cl=2)
def run_confidence_interval(n, ci_method):
x = np.arange(n)
confidence_interval(np.mean, x, ci_method=ci_method)
