352 lines
12 KiB
Python
352 lines
12 KiB
Python
import pickle
|
|
|
|
import numpy as np
|
|
import numpy.testing as npt
|
|
from numpy.testing import assert_allclose, assert_equal
|
|
from pytest import raises as assert_raises
|
|
|
|
import numpy.ma.testutils as ma_npt
|
|
|
|
from scipy._lib._util import (
|
|
getfullargspec_no_self as _getfullargspec, np_long
|
|
)
|
|
from scipy import stats
|
|
|
|
|
|
def check_named_results(res, attributes, ma=False):
|
|
for i, attr in enumerate(attributes):
|
|
if ma:
|
|
ma_npt.assert_equal(res[i], getattr(res, attr))
|
|
else:
|
|
npt.assert_equal(res[i], getattr(res, attr))
|
|
|
|
|
|
def check_normalization(distfn, args, distname):
|
|
norm_moment = distfn.moment(0, *args)
|
|
npt.assert_allclose(norm_moment, 1.0)
|
|
|
|
if distname == "rv_histogram_instance":
|
|
atol, rtol = 1e-5, 0
|
|
else:
|
|
atol, rtol = 1e-7, 1e-7
|
|
|
|
normalization_expect = distfn.expect(lambda x: 1, args=args)
|
|
npt.assert_allclose(normalization_expect, 1.0, atol=atol, rtol=rtol,
|
|
err_msg=distname, verbose=True)
|
|
|
|
_a, _b = distfn.support(*args)
|
|
normalization_cdf = distfn.cdf(_b, *args)
|
|
npt.assert_allclose(normalization_cdf, 1.0)
|
|
|
|
|
|
def check_moment(distfn, arg, m, v, msg):
|
|
m1 = distfn.moment(1, *arg)
|
|
m2 = distfn.moment(2, *arg)
|
|
if not np.isinf(m):
|
|
npt.assert_almost_equal(m1, m, decimal=10,
|
|
err_msg=msg + ' - 1st moment')
|
|
else: # or np.isnan(m1),
|
|
npt.assert_(np.isinf(m1),
|
|
msg + ' - 1st moment -infinite, m1=%s' % str(m1))
|
|
|
|
if not np.isinf(v):
|
|
npt.assert_almost_equal(m2 - m1 * m1, v, decimal=10,
|
|
err_msg=msg + ' - 2ndt moment')
|
|
else: # or np.isnan(m2),
|
|
npt.assert_(np.isinf(m2), msg + f' - 2nd moment -infinite, {m2=}')
|
|
|
|
|
|
def check_mean_expect(distfn, arg, m, msg):
|
|
if np.isfinite(m):
|
|
m1 = distfn.expect(lambda x: x, arg)
|
|
npt.assert_almost_equal(m1, m, decimal=5,
|
|
err_msg=msg + ' - 1st moment (expect)')
|
|
|
|
|
|
def check_var_expect(distfn, arg, m, v, msg):
|
|
dist_looser_tolerances = {"rv_histogram_instance" , "ksone"}
|
|
kwargs = {'rtol': 5e-6} if msg in dist_looser_tolerances else {}
|
|
if np.isfinite(v):
|
|
m2 = distfn.expect(lambda x: x*x, arg)
|
|
npt.assert_allclose(m2, v + m*m, **kwargs)
|
|
|
|
|
|
def check_skew_expect(distfn, arg, m, v, s, msg):
|
|
if np.isfinite(s):
|
|
m3e = distfn.expect(lambda x: np.power(x-m, 3), arg)
|
|
npt.assert_almost_equal(m3e, s * np.power(v, 1.5),
|
|
decimal=5, err_msg=msg + ' - skew')
|
|
else:
|
|
npt.assert_(np.isnan(s))
|
|
|
|
|
|
def check_kurt_expect(distfn, arg, m, v, k, msg):
|
|
if np.isfinite(k):
|
|
m4e = distfn.expect(lambda x: np.power(x-m, 4), arg)
|
|
npt.assert_allclose(m4e, (k + 3.) * np.power(v, 2),
|
|
atol=1e-5, rtol=1e-5,
|
|
err_msg=msg + ' - kurtosis')
|
|
elif not np.isposinf(k):
|
|
npt.assert_(np.isnan(k))
|
|
|
|
|
|
def check_munp_expect(dist, args, msg):
|
|
# If _munp is overridden, test a higher moment. (Before gh-18634, some
|
|
# distributions had issues with moments 5 and higher.)
|
|
if dist._munp.__func__ != stats.rv_continuous._munp:
|
|
res = dist.moment(5, *args) # shouldn't raise an error
|
|
ref = dist.expect(lambda x: x ** 5, args, lb=-np.inf, ub=np.inf)
|
|
if not np.isfinite(res): # could be valid; automated test can't know
|
|
return
|
|
# loose tolerance, mostly to see whether _munp returns *something*
|
|
assert_allclose(res, ref, atol=1e-10, rtol=1e-4,
|
|
err_msg=msg + ' - higher moment / _munp')
|
|
|
|
|
|
def check_entropy(distfn, arg, msg):
|
|
ent = distfn.entropy(*arg)
|
|
npt.assert_(not np.isnan(ent), msg + 'test Entropy is nan')
|
|
|
|
|
|
def check_private_entropy(distfn, args, superclass):
|
|
# compare a generic _entropy with the distribution-specific implementation
|
|
npt.assert_allclose(distfn._entropy(*args),
|
|
superclass._entropy(distfn, *args))
|
|
|
|
|
|
def check_entropy_vect_scale(distfn, arg):
|
|
# check 2-d
|
|
sc = np.asarray([[1, 2], [3, 4]])
|
|
v_ent = distfn.entropy(*arg, scale=sc)
|
|
s_ent = [distfn.entropy(*arg, scale=s) for s in sc.ravel()]
|
|
s_ent = np.asarray(s_ent).reshape(v_ent.shape)
|
|
assert_allclose(v_ent, s_ent, atol=1e-14)
|
|
|
|
# check invalid value, check cast
|
|
sc = [1, 2, -3]
|
|
v_ent = distfn.entropy(*arg, scale=sc)
|
|
s_ent = [distfn.entropy(*arg, scale=s) for s in sc]
|
|
s_ent = np.asarray(s_ent).reshape(v_ent.shape)
|
|
assert_allclose(v_ent, s_ent, atol=1e-14)
|
|
|
|
|
|
def check_edge_support(distfn, args):
|
|
# Make sure that x=self.a and self.b are handled correctly.
|
|
x = distfn.support(*args)
|
|
if isinstance(distfn, stats.rv_discrete):
|
|
x = x[0]-1, x[1]
|
|
|
|
npt.assert_equal(distfn.cdf(x, *args), [0.0, 1.0])
|
|
npt.assert_equal(distfn.sf(x, *args), [1.0, 0.0])
|
|
|
|
if distfn.name not in ('skellam', 'dlaplace'):
|
|
# with a = -inf, log(0) generates warnings
|
|
npt.assert_equal(distfn.logcdf(x, *args), [-np.inf, 0.0])
|
|
npt.assert_equal(distfn.logsf(x, *args), [0.0, -np.inf])
|
|
|
|
npt.assert_equal(distfn.ppf([0.0, 1.0], *args), x)
|
|
npt.assert_equal(distfn.isf([0.0, 1.0], *args), x[::-1])
|
|
|
|
# out-of-bounds for isf & ppf
|
|
npt.assert_(np.isnan(distfn.isf([-1, 2], *args)).all())
|
|
npt.assert_(np.isnan(distfn.ppf([-1, 2], *args)).all())
|
|
|
|
|
|
def check_named_args(distfn, x, shape_args, defaults, meths):
|
|
## Check calling w/ named arguments.
|
|
|
|
# check consistency of shapes, numargs and _parse signature
|
|
signature = _getfullargspec(distfn._parse_args)
|
|
npt.assert_(signature.varargs is None)
|
|
npt.assert_(signature.varkw is None)
|
|
npt.assert_(not signature.kwonlyargs)
|
|
npt.assert_(list(signature.defaults) == list(defaults))
|
|
|
|
shape_argnames = signature.args[:-len(defaults)] # a, b, loc=0, scale=1
|
|
if distfn.shapes:
|
|
shapes_ = distfn.shapes.replace(',', ' ').split()
|
|
else:
|
|
shapes_ = ''
|
|
npt.assert_(len(shapes_) == distfn.numargs)
|
|
npt.assert_(len(shapes_) == len(shape_argnames))
|
|
|
|
# check calling w/ named arguments
|
|
shape_args = list(shape_args)
|
|
|
|
vals = [meth(x, *shape_args) for meth in meths]
|
|
npt.assert_(np.all(np.isfinite(vals)))
|
|
|
|
names, a, k = shape_argnames[:], shape_args[:], {}
|
|
while names:
|
|
k.update({names.pop(): a.pop()})
|
|
v = [meth(x, *a, **k) for meth in meths]
|
|
npt.assert_array_equal(vals, v)
|
|
if 'n' not in k.keys():
|
|
# `n` is first parameter of moment(), so can't be used as named arg
|
|
npt.assert_equal(distfn.moment(1, *a, **k),
|
|
distfn.moment(1, *shape_args))
|
|
|
|
# unknown arguments should not go through:
|
|
k.update({'kaboom': 42})
|
|
assert_raises(TypeError, distfn.cdf, x, **k)
|
|
|
|
|
|
def check_random_state_property(distfn, args):
|
|
# check the random_state attribute of a distribution *instance*
|
|
|
|
# This test fiddles with distfn.random_state. This breaks other tests,
|
|
# hence need to save it and then restore.
|
|
rndm = distfn.random_state
|
|
|
|
# baseline: this relies on the global state
|
|
np.random.seed(1234)
|
|
distfn.random_state = None
|
|
r0 = distfn.rvs(*args, size=8)
|
|
|
|
# use an explicit instance-level random_state
|
|
distfn.random_state = 1234
|
|
r1 = distfn.rvs(*args, size=8)
|
|
npt.assert_equal(r0, r1)
|
|
|
|
distfn.random_state = np.random.RandomState(1234)
|
|
r2 = distfn.rvs(*args, size=8)
|
|
npt.assert_equal(r0, r2)
|
|
|
|
# check that np.random.Generator can be used (numpy >= 1.17)
|
|
if hasattr(np.random, 'default_rng'):
|
|
# obtain a np.random.Generator object
|
|
rng = np.random.default_rng(1234)
|
|
distfn.rvs(*args, size=1, random_state=rng)
|
|
|
|
# can override the instance-level random_state for an individual .rvs call
|
|
distfn.random_state = 2
|
|
orig_state = distfn.random_state.get_state()
|
|
|
|
r3 = distfn.rvs(*args, size=8, random_state=np.random.RandomState(1234))
|
|
npt.assert_equal(r0, r3)
|
|
|
|
# ... and that does not alter the instance-level random_state!
|
|
npt.assert_equal(distfn.random_state.get_state(), orig_state)
|
|
|
|
# finally, restore the random_state
|
|
distfn.random_state = rndm
|
|
|
|
|
|
def check_meth_dtype(distfn, arg, meths):
|
|
q0 = [0.25, 0.5, 0.75]
|
|
x0 = distfn.ppf(q0, *arg)
|
|
x_cast = [x0.astype(tp) for tp in (np_long, np.float16, np.float32,
|
|
np.float64)]
|
|
|
|
for x in x_cast:
|
|
# casting may have clipped the values, exclude those
|
|
distfn._argcheck(*arg)
|
|
x = x[(distfn.a < x) & (x < distfn.b)]
|
|
for meth in meths:
|
|
val = meth(x, *arg)
|
|
npt.assert_(val.dtype == np.float64)
|
|
|
|
|
|
def check_ppf_dtype(distfn, arg):
|
|
q0 = np.asarray([0.25, 0.5, 0.75])
|
|
q_cast = [q0.astype(tp) for tp in (np.float16, np.float32, np.float64)]
|
|
for q in q_cast:
|
|
for meth in [distfn.ppf, distfn.isf]:
|
|
val = meth(q, *arg)
|
|
npt.assert_(val.dtype == np.float64)
|
|
|
|
|
|
def check_cmplx_deriv(distfn, arg):
|
|
# Distributions allow complex arguments.
|
|
def deriv(f, x, *arg):
|
|
x = np.asarray(x)
|
|
h = 1e-10
|
|
return (f(x + h*1j, *arg)/h).imag
|
|
|
|
x0 = distfn.ppf([0.25, 0.51, 0.75], *arg)
|
|
x_cast = [x0.astype(tp) for tp in (np_long, np.float16, np.float32,
|
|
np.float64)]
|
|
|
|
for x in x_cast:
|
|
# casting may have clipped the values, exclude those
|
|
distfn._argcheck(*arg)
|
|
x = x[(distfn.a < x) & (x < distfn.b)]
|
|
|
|
pdf, cdf, sf = distfn.pdf(x, *arg), distfn.cdf(x, *arg), distfn.sf(x, *arg)
|
|
assert_allclose(deriv(distfn.cdf, x, *arg), pdf, rtol=1e-5)
|
|
assert_allclose(deriv(distfn.logcdf, x, *arg), pdf/cdf, rtol=1e-5)
|
|
|
|
assert_allclose(deriv(distfn.sf, x, *arg), -pdf, rtol=1e-5)
|
|
assert_allclose(deriv(distfn.logsf, x, *arg), -pdf/sf, rtol=1e-5)
|
|
|
|
assert_allclose(deriv(distfn.logpdf, x, *arg),
|
|
deriv(distfn.pdf, x, *arg) / distfn.pdf(x, *arg),
|
|
rtol=1e-5)
|
|
|
|
|
|
def check_pickling(distfn, args):
|
|
# check that a distribution instance pickles and unpickles
|
|
# pay special attention to the random_state property
|
|
|
|
# save the random_state (restore later)
|
|
rndm = distfn.random_state
|
|
|
|
# check unfrozen
|
|
distfn.random_state = 1234
|
|
distfn.rvs(*args, size=8)
|
|
s = pickle.dumps(distfn)
|
|
r0 = distfn.rvs(*args, size=8)
|
|
|
|
unpickled = pickle.loads(s)
|
|
r1 = unpickled.rvs(*args, size=8)
|
|
npt.assert_equal(r0, r1)
|
|
|
|
# also smoke test some methods
|
|
medians = [distfn.ppf(0.5, *args), unpickled.ppf(0.5, *args)]
|
|
npt.assert_equal(medians[0], medians[1])
|
|
npt.assert_equal(distfn.cdf(medians[0], *args),
|
|
unpickled.cdf(medians[1], *args))
|
|
|
|
# check frozen pickling/unpickling with rvs
|
|
frozen_dist = distfn(*args)
|
|
pkl = pickle.dumps(frozen_dist)
|
|
unpickled = pickle.loads(pkl)
|
|
|
|
r0 = frozen_dist.rvs(size=8)
|
|
r1 = unpickled.rvs(size=8)
|
|
npt.assert_equal(r0, r1)
|
|
|
|
# check pickling/unpickling of .fit method
|
|
if hasattr(distfn, "fit"):
|
|
fit_function = distfn.fit
|
|
pickled_fit_function = pickle.dumps(fit_function)
|
|
unpickled_fit_function = pickle.loads(pickled_fit_function)
|
|
assert fit_function.__name__ == unpickled_fit_function.__name__ == "fit"
|
|
|
|
# restore the random_state
|
|
distfn.random_state = rndm
|
|
|
|
|
|
def check_freezing(distfn, args):
|
|
# regression test for gh-11089: freezing a distribution fails
|
|
# if loc and/or scale are specified
|
|
if isinstance(distfn, stats.rv_continuous):
|
|
locscale = {'loc': 1, 'scale': 2}
|
|
else:
|
|
locscale = {'loc': 1}
|
|
|
|
rv = distfn(*args, **locscale)
|
|
assert rv.a == distfn(*args).a
|
|
assert rv.b == distfn(*args).b
|
|
|
|
|
|
def check_rvs_broadcast(distfunc, distname, allargs, shape, shape_only, otype):
|
|
np.random.seed(123)
|
|
sample = distfunc.rvs(*allargs)
|
|
assert_equal(sample.shape, shape, "%s: rvs failed to broadcast" % distname)
|
|
if not shape_only:
|
|
rvs = np.vectorize(lambda *allargs: distfunc.rvs(*allargs), otypes=otype)
|
|
np.random.seed(123)
|
|
expected = rvs(*allargs)
|
|
assert_allclose(sample, expected, rtol=1e-13)
|