425 lines
13 KiB
Python
425 lines
13 KiB
Python
"""
|
|
Test cdflib functions versus mpmath, if available.
|
|
|
|
The following functions still need tests:
|
|
|
|
- ncfdtr
|
|
- ncfdtri
|
|
- ncfdtridfn
|
|
- ncfdtridfd
|
|
- ncfdtrinc
|
|
- nbdtrik
|
|
- nbdtrin
|
|
- nrdtrimn
|
|
- nrdtrisd
|
|
- pdtrik
|
|
- nctdtr
|
|
- nctdtrit
|
|
- nctdtridf
|
|
- nctdtrinc
|
|
|
|
"""
|
|
import itertools
|
|
|
|
import numpy as np
|
|
from numpy.testing import assert_equal, assert_allclose
|
|
import pytest
|
|
|
|
import scipy.special as sp
|
|
from scipy.special._testutils import (
|
|
MissingModule, check_version, FuncData)
|
|
from scipy.special._mptestutils import (
|
|
Arg, IntArg, get_args, mpf2float, assert_mpmath_equal)
|
|
|
|
try:
|
|
import mpmath
|
|
except ImportError:
|
|
mpmath = MissingModule('mpmath')
|
|
|
|
|
|
class ProbArg:
|
|
"""Generate a set of probabilities on [0, 1]."""
|
|
def __init__(self):
|
|
# Include the endpoints for compatibility with Arg et. al.
|
|
self.a = 0
|
|
self.b = 1
|
|
|
|
def values(self, n):
|
|
"""Return an array containing approximatively n numbers."""
|
|
m = max(1, n//3)
|
|
v1 = np.logspace(-30, np.log10(0.3), m)
|
|
v2 = np.linspace(0.3, 0.7, m + 1, endpoint=False)[1:]
|
|
v3 = 1 - np.logspace(np.log10(0.3), -15, m)
|
|
v = np.r_[v1, v2, v3]
|
|
return np.unique(v)
|
|
|
|
|
|
class EndpointFilter:
|
|
def __init__(self, a, b, rtol, atol):
|
|
self.a = a
|
|
self.b = b
|
|
self.rtol = rtol
|
|
self.atol = atol
|
|
|
|
def __call__(self, x):
|
|
mask1 = np.abs(x - self.a) < self.rtol*np.abs(self.a) + self.atol
|
|
mask2 = np.abs(x - self.b) < self.rtol*np.abs(self.b) + self.atol
|
|
return np.where(mask1 | mask2, False, True)
|
|
|
|
|
|
class _CDFData:
|
|
def __init__(self, spfunc, mpfunc, index, argspec, spfunc_first=True,
|
|
dps=20, n=5000, rtol=None, atol=None,
|
|
endpt_rtol=None, endpt_atol=None):
|
|
self.spfunc = spfunc
|
|
self.mpfunc = mpfunc
|
|
self.index = index
|
|
self.argspec = argspec
|
|
self.spfunc_first = spfunc_first
|
|
self.dps = dps
|
|
self.n = n
|
|
self.rtol = rtol
|
|
self.atol = atol
|
|
|
|
if not isinstance(argspec, list):
|
|
self.endpt_rtol = None
|
|
self.endpt_atol = None
|
|
elif endpt_rtol is not None or endpt_atol is not None:
|
|
if isinstance(endpt_rtol, list):
|
|
self.endpt_rtol = endpt_rtol
|
|
else:
|
|
self.endpt_rtol = [endpt_rtol]*len(self.argspec)
|
|
if isinstance(endpt_atol, list):
|
|
self.endpt_atol = endpt_atol
|
|
else:
|
|
self.endpt_atol = [endpt_atol]*len(self.argspec)
|
|
else:
|
|
self.endpt_rtol = None
|
|
self.endpt_atol = None
|
|
|
|
def idmap(self, *args):
|
|
if self.spfunc_first:
|
|
res = self.spfunc(*args)
|
|
if np.isnan(res):
|
|
return np.nan
|
|
args = list(args)
|
|
args[self.index] = res
|
|
with mpmath.workdps(self.dps):
|
|
res = self.mpfunc(*tuple(args))
|
|
# Imaginary parts are spurious
|
|
res = mpf2float(res.real)
|
|
else:
|
|
with mpmath.workdps(self.dps):
|
|
res = self.mpfunc(*args)
|
|
res = mpf2float(res.real)
|
|
args = list(args)
|
|
args[self.index] = res
|
|
res = self.spfunc(*tuple(args))
|
|
return res
|
|
|
|
def get_param_filter(self):
|
|
if self.endpt_rtol is None and self.endpt_atol is None:
|
|
return None
|
|
|
|
filters = []
|
|
for rtol, atol, spec in zip(self.endpt_rtol, self.endpt_atol, self.argspec):
|
|
if rtol is None and atol is None:
|
|
filters.append(None)
|
|
continue
|
|
elif rtol is None:
|
|
rtol = 0.0
|
|
elif atol is None:
|
|
atol = 0.0
|
|
|
|
filters.append(EndpointFilter(spec.a, spec.b, rtol, atol))
|
|
return filters
|
|
|
|
def check(self):
|
|
# Generate values for the arguments
|
|
args = get_args(self.argspec, self.n)
|
|
param_filter = self.get_param_filter()
|
|
param_columns = tuple(range(args.shape[1]))
|
|
result_columns = args.shape[1]
|
|
args = np.hstack((args, args[:,self.index].reshape(args.shape[0], 1)))
|
|
FuncData(self.idmap, args,
|
|
param_columns=param_columns, result_columns=result_columns,
|
|
rtol=self.rtol, atol=self.atol, vectorized=False,
|
|
param_filter=param_filter).check()
|
|
|
|
|
|
def _assert_inverts(*a, **kw):
|
|
d = _CDFData(*a, **kw)
|
|
d.check()
|
|
|
|
|
|
def _binomial_cdf(k, n, p):
|
|
k, n, p = mpmath.mpf(k), mpmath.mpf(n), mpmath.mpf(p)
|
|
if k <= 0:
|
|
return mpmath.mpf(0)
|
|
elif k >= n:
|
|
return mpmath.mpf(1)
|
|
|
|
onemp = mpmath.fsub(1, p, exact=True)
|
|
return mpmath.betainc(n - k, k + 1, x2=onemp, regularized=True)
|
|
|
|
|
|
def _f_cdf(dfn, dfd, x):
|
|
if x < 0:
|
|
return mpmath.mpf(0)
|
|
dfn, dfd, x = mpmath.mpf(dfn), mpmath.mpf(dfd), mpmath.mpf(x)
|
|
ub = dfn*x/(dfn*x + dfd)
|
|
res = mpmath.betainc(dfn/2, dfd/2, x2=ub, regularized=True)
|
|
return res
|
|
|
|
|
|
def _student_t_cdf(df, t, dps=None):
|
|
if dps is None:
|
|
dps = mpmath.mp.dps
|
|
with mpmath.workdps(dps):
|
|
df, t = mpmath.mpf(df), mpmath.mpf(t)
|
|
fac = mpmath.hyp2f1(0.5, 0.5*(df + 1), 1.5, -t**2/df)
|
|
fac *= t*mpmath.gamma(0.5*(df + 1))
|
|
fac /= mpmath.sqrt(mpmath.pi*df)*mpmath.gamma(0.5*df)
|
|
return 0.5 + fac
|
|
|
|
|
|
def _noncentral_chi_pdf(t, df, nc):
|
|
res = mpmath.besseli(df/2 - 1, mpmath.sqrt(nc*t))
|
|
res *= mpmath.exp(-(t + nc)/2)*(t/nc)**(df/4 - 1/2)/2
|
|
return res
|
|
|
|
|
|
def _noncentral_chi_cdf(x, df, nc, dps=None):
|
|
if dps is None:
|
|
dps = mpmath.mp.dps
|
|
x, df, nc = mpmath.mpf(x), mpmath.mpf(df), mpmath.mpf(nc)
|
|
with mpmath.workdps(dps):
|
|
res = mpmath.quad(lambda t: _noncentral_chi_pdf(t, df, nc), [0, x])
|
|
return res
|
|
|
|
|
|
def _tukey_lmbda_quantile(p, lmbda):
|
|
# For lmbda != 0
|
|
return (p**lmbda - (1 - p)**lmbda)/lmbda
|
|
|
|
|
|
@pytest.mark.slow
|
|
@check_version(mpmath, '0.19')
|
|
class TestCDFlib:
|
|
|
|
@pytest.mark.xfail(run=False)
|
|
def test_bdtrik(self):
|
|
_assert_inverts(
|
|
sp.bdtrik,
|
|
_binomial_cdf,
|
|
0, [ProbArg(), IntArg(1, 1000), ProbArg()],
|
|
rtol=1e-4)
|
|
|
|
def test_bdtrin(self):
|
|
_assert_inverts(
|
|
sp.bdtrin,
|
|
_binomial_cdf,
|
|
1, [IntArg(1, 1000), ProbArg(), ProbArg()],
|
|
rtol=1e-4, endpt_atol=[None, None, 1e-6])
|
|
|
|
def test_btdtria(self):
|
|
_assert_inverts(
|
|
sp.btdtria,
|
|
lambda a, b, x: mpmath.betainc(a, b, x2=x, regularized=True),
|
|
0, [ProbArg(), Arg(0, 1e2, inclusive_a=False),
|
|
Arg(0, 1, inclusive_a=False, inclusive_b=False)],
|
|
rtol=1e-6)
|
|
|
|
def test_btdtrib(self):
|
|
# Use small values of a or mpmath doesn't converge
|
|
_assert_inverts(
|
|
sp.btdtrib,
|
|
lambda a, b, x: mpmath.betainc(a, b, x2=x, regularized=True),
|
|
1, [Arg(0, 1e2, inclusive_a=False), ProbArg(),
|
|
Arg(0, 1, inclusive_a=False, inclusive_b=False)],
|
|
rtol=1e-7, endpt_atol=[None, 1e-18, 1e-15])
|
|
|
|
@pytest.mark.xfail(run=False)
|
|
def test_fdtridfd(self):
|
|
_assert_inverts(
|
|
sp.fdtridfd,
|
|
_f_cdf,
|
|
1, [IntArg(1, 100), ProbArg(), Arg(0, 100, inclusive_a=False)],
|
|
rtol=1e-7)
|
|
|
|
def test_gdtria(self):
|
|
_assert_inverts(
|
|
sp.gdtria,
|
|
lambda a, b, x: mpmath.gammainc(b, b=a*x, regularized=True),
|
|
0, [ProbArg(), Arg(0, 1e3, inclusive_a=False),
|
|
Arg(0, 1e4, inclusive_a=False)], rtol=1e-7,
|
|
endpt_atol=[None, 1e-7, 1e-10])
|
|
|
|
def test_gdtrib(self):
|
|
# Use small values of a and x or mpmath doesn't converge
|
|
_assert_inverts(
|
|
sp.gdtrib,
|
|
lambda a, b, x: mpmath.gammainc(b, b=a*x, regularized=True),
|
|
1, [Arg(0, 1e2, inclusive_a=False), ProbArg(),
|
|
Arg(0, 1e3, inclusive_a=False)], rtol=1e-5)
|
|
|
|
def test_gdtrix(self):
|
|
_assert_inverts(
|
|
sp.gdtrix,
|
|
lambda a, b, x: mpmath.gammainc(b, b=a*x, regularized=True),
|
|
2, [Arg(0, 1e3, inclusive_a=False), Arg(0, 1e3, inclusive_a=False),
|
|
ProbArg()], rtol=1e-7,
|
|
endpt_atol=[None, 1e-7, 1e-10])
|
|
|
|
def test_stdtr(self):
|
|
# Ideally the left endpoint for Arg() should be 0.
|
|
assert_mpmath_equal(
|
|
sp.stdtr,
|
|
_student_t_cdf,
|
|
[IntArg(1, 100), Arg(1e-10, np.inf)], rtol=1e-7)
|
|
|
|
@pytest.mark.xfail(run=False)
|
|
def test_stdtridf(self):
|
|
_assert_inverts(
|
|
sp.stdtridf,
|
|
_student_t_cdf,
|
|
0, [ProbArg(), Arg()], rtol=1e-7)
|
|
|
|
def test_stdtrit(self):
|
|
_assert_inverts(
|
|
sp.stdtrit,
|
|
_student_t_cdf,
|
|
1, [IntArg(1, 100), ProbArg()], rtol=1e-7,
|
|
endpt_atol=[None, 1e-10])
|
|
|
|
def test_chdtriv(self):
|
|
_assert_inverts(
|
|
sp.chdtriv,
|
|
lambda v, x: mpmath.gammainc(v/2, b=x/2, regularized=True),
|
|
0, [ProbArg(), IntArg(1, 100)], rtol=1e-4)
|
|
|
|
@pytest.mark.xfail(run=False)
|
|
def test_chndtridf(self):
|
|
# Use a larger atol since mpmath is doing numerical integration
|
|
_assert_inverts(
|
|
sp.chndtridf,
|
|
_noncentral_chi_cdf,
|
|
1, [Arg(0, 100, inclusive_a=False), ProbArg(),
|
|
Arg(0, 100, inclusive_a=False)],
|
|
n=1000, rtol=1e-4, atol=1e-15)
|
|
|
|
@pytest.mark.xfail(run=False)
|
|
def test_chndtrinc(self):
|
|
# Use a larger atol since mpmath is doing numerical integration
|
|
_assert_inverts(
|
|
sp.chndtrinc,
|
|
_noncentral_chi_cdf,
|
|
2, [Arg(0, 100, inclusive_a=False), IntArg(1, 100), ProbArg()],
|
|
n=1000, rtol=1e-4, atol=1e-15)
|
|
|
|
def test_chndtrix(self):
|
|
# Use a larger atol since mpmath is doing numerical integration
|
|
_assert_inverts(
|
|
sp.chndtrix,
|
|
_noncentral_chi_cdf,
|
|
0, [ProbArg(), IntArg(1, 100), Arg(0, 100, inclusive_a=False)],
|
|
n=1000, rtol=1e-4, atol=1e-15,
|
|
endpt_atol=[1e-6, None, None])
|
|
|
|
def test_tklmbda_zero_shape(self):
|
|
# When lmbda = 0 the CDF has a simple closed form
|
|
one = mpmath.mpf(1)
|
|
assert_mpmath_equal(
|
|
lambda x: sp.tklmbda(x, 0),
|
|
lambda x: one/(mpmath.exp(-x) + one),
|
|
[Arg()], rtol=1e-7)
|
|
|
|
def test_tklmbda_neg_shape(self):
|
|
_assert_inverts(
|
|
sp.tklmbda,
|
|
_tukey_lmbda_quantile,
|
|
0, [ProbArg(), Arg(-25, 0, inclusive_b=False)],
|
|
spfunc_first=False, rtol=1e-5,
|
|
endpt_atol=[1e-9, 1e-5])
|
|
|
|
@pytest.mark.xfail(run=False)
|
|
def test_tklmbda_pos_shape(self):
|
|
_assert_inverts(
|
|
sp.tklmbda,
|
|
_tukey_lmbda_quantile,
|
|
0, [ProbArg(), Arg(0, 100, inclusive_a=False)],
|
|
spfunc_first=False, rtol=1e-5)
|
|
|
|
|
|
def test_nonfinite():
|
|
funcs = [
|
|
("btdtria", 3),
|
|
("btdtrib", 3),
|
|
("bdtrik", 3),
|
|
("bdtrin", 3),
|
|
("chdtriv", 2),
|
|
("chndtr", 3),
|
|
("chndtrix", 3),
|
|
("chndtridf", 3),
|
|
("chndtrinc", 3),
|
|
("fdtridfd", 3),
|
|
("ncfdtr", 4),
|
|
("ncfdtri", 4),
|
|
("ncfdtridfn", 4),
|
|
("ncfdtridfd", 4),
|
|
("ncfdtrinc", 4),
|
|
("gdtrix", 3),
|
|
("gdtrib", 3),
|
|
("gdtria", 3),
|
|
("nbdtrik", 3),
|
|
("nbdtrin", 3),
|
|
("nrdtrimn", 3),
|
|
("nrdtrisd", 3),
|
|
("pdtrik", 2),
|
|
("stdtr", 2),
|
|
("stdtrit", 2),
|
|
("stdtridf", 2),
|
|
("nctdtr", 3),
|
|
("nctdtrit", 3),
|
|
("nctdtridf", 3),
|
|
("nctdtrinc", 3),
|
|
("tklmbda", 2),
|
|
]
|
|
|
|
np.random.seed(1)
|
|
|
|
for func, numargs in funcs:
|
|
func = getattr(sp, func)
|
|
|
|
args_choices = [(float(x), np.nan, np.inf, -np.inf) for x in
|
|
np.random.rand(numargs)]
|
|
|
|
for args in itertools.product(*args_choices):
|
|
res = func(*args)
|
|
|
|
if any(np.isnan(x) for x in args):
|
|
# Nan inputs should result to nan output
|
|
assert_equal(res, np.nan)
|
|
else:
|
|
# All other inputs should return something (but not
|
|
# raise exceptions or cause hangs)
|
|
pass
|
|
|
|
|
|
def test_chndtrix_gh2158():
|
|
# test that gh-2158 is resolved; previously this blew up
|
|
res = sp.chndtrix(0.999999, 2, np.arange(20.)+1e-6)
|
|
|
|
# Generated in R
|
|
# options(digits=16)
|
|
# ncp <- seq(0, 19) + 1e-6
|
|
# print(qchisq(0.999999, df = 2, ncp = ncp))
|
|
res_exp = [27.63103493142305, 35.25728589950540, 39.97396073236288,
|
|
43.88033702110538, 47.35206403482798, 50.54112500166103,
|
|
53.52720257322766, 56.35830042867810, 59.06600769498512,
|
|
61.67243118946381, 64.19376191277179, 66.64228141346548,
|
|
69.02756927200180, 71.35726934749408, 73.63759723904816,
|
|
75.87368842650227, 78.06984431185720, 80.22971052389806,
|
|
82.35640899964173, 84.45263768373256]
|
|
assert_allclose(res, res_exp)
|