def test_ppf(self, u):
dist = StandardNormal()
rng = NumericalInversePolynomial(dist, u_resolution=1e-14)
# Older versions of NumPy throw RuntimeWarnings for comparisons
# with nan.
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "invalid value encountered in greater")
sup.filter(RuntimeWarning, "invalid value encountered in "
"greater_equal")
sup.filter(RuntimeWarning, "invalid value encountered in less")
sup.filter(RuntimeWarning, "invalid value encountered in "
"less_equal")
res = rng.ppf(u)
expected = stats.norm.ppf(u)
assert_allclose(res, expected, rtol=1e-11, atol=1e-11)
assert res.shape == expected.shape
def test_bad_pdf(self, pdf, err, msg):
class dist:
pass
dist.pdf = pdf
with pytest.raises(err, match=msg):
NumericalInversePolynomial(dist, domain=[0, 5])
def test_bad_args(self):
dist = StandardNormal()
rng = NumericalInversePolynomial(dist)
msg = r"CDF is not available."
with pytest.raises(ValueError, match=msg):
rng.cdf([1, 2, 3])
msg = r"`sample_size` must be greater than or equal to 1000."
with pytest.raises(ValueError, match=msg):
rng.u_error(10)
class Distribution:
def pdf(self, x):
return np.exp(-0.5 * x * x)
dist = Distribution()
rng = NumericalInversePolynomial(dist)
msg = r"Exact CDF required but not found."
with pytest.raises(ValueError, match=msg):
rng.u_error()
def test_u_error(self):
dist = StandardNormal()
rng = NumericalInversePolynomial(dist, u_resolution=1e-10)
max_error, mae = rng.u_error()
assert max_error < 1e-10
assert mae <= max_error
rng = NumericalInversePolynomial(dist, u_resolution=1e-14)
max_error, mae = rng.u_error()
assert max_error < 1e-14
assert mae <= max_error
def test_basic_all_scipy_dists(self, distname, params):
if distname in self.very_slow_dists:
pytest.skip(f"PINV too slow for {distname}")
if distname in self.fail_dists:
pytest.skip(f"PINV fails for {distname}")
if distname in self.unbounded_pdf_fail_dists:
pytest.skip("PINV fails for unbounded PDFs.")
if distname in self.fatal_fail_dists:
pytest.xfail(f"PINV segmentation faults for {distname}")
dist = (getattr(stats, distname)
if isinstance(distname, str) else distname)
dist = dist(*params)
with suppress_warnings() as sup:
sup.filter(RuntimeWarning)
rng = NumericalInversePolynomial(dist, random_state=42)
check_cont_samples(rng, dist, [dist.mean(), dist.var()])
def test_bad_max_intervals(self, max_intervals):
msg = (r"`max_intervals` must be an integer in the range "
r"\[100, 1000000\].")
with pytest.raises(ValueError, match=msg):
NumericalInversePolynomial(StandardNormal(),
max_intervals=max_intervals)
def test_bad_u_resolution(self, u_resolution):
msg = r"`u_resolution` must be between 1e-15 and 1e-5."
with pytest.raises(ValueError, match=msg):
NumericalInversePolynomial(StandardNormal(),
u_resolution=u_resolution)
def test_bad_orders(self, order):
dist = StandardNormal()
msg = r"`order` must be an integer in the range \[3, 17\]."
with pytest.raises(ValueError, match=msg):
NumericalInversePolynomial(dist, order=order)
def test_inf_nan_domains(self, domain, err, msg):
with pytest.raises(err, match=msg):
NumericalInversePolynomial(StandardNormal(), domain=domain)
def test_basic(self, dist, mv_ex):
rng = NumericalInversePolynomial(dist, random_state=42)
check_cont_samples(rng, dist, mv_ex)