BUG: special: test_kolmogorov fails on 32-bit platforms #9509
Description
Test was marked as xfail in gh-9494. It should be investigated why it fails though, looks like a real bug.
First observed on MacPython/scipy-wheels#37.
/venv/lib/python3.7/site-packages/scipy/special/tests/test_kolmogorov.py:243:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <Data for _smirnovp>
data = array([[ 3. , 0.33333333, -1.66666667],
[ 4. , 0.25 , -2.125 ],
[ 5. , ...5882353, -3.71393676],
[18. , 0.05555556, -3.750371 ],
[19. , 0.05263158, -3.78331196]])
dtype = dtype('float64')
dtypes = [<class 'int'>, <class 'float'>, <class 'float'>]
def check(self, data=None, dtype=None, dtypes=None):
"""Check the special function against the data."""
if self.knownfailure:
pytest.xfail(reason=self.knownfailure)
if data is None:
data = self.data
if dtype is None:
dtype = data.dtype
else:
data = data.astype(dtype)
rtol, atol = self.get_tolerances(dtype)
# Apply given filter functions
if self.param_filter:
param_mask = np.ones((data.shape[0],), np.bool_)
for j, filter in zip(self.param_columns, self.param_filter):
if filter:
param_mask &= list(filter(data[:,j]))
data = data[param_mask]
# Pick parameters from the correct columns
params = []
for idx, j in enumerate(self.param_columns):
if np.iscomplexobj(j):
j = int(j.imag)
params.append(data[:,j].astype(complex))
elif dtypes and idx < len(dtypes):
params.append(data[:, j].astype(dtypes[idx]))
else:
params.append(data[:,j])
# Helper for evaluating results
def eval_func_at_params(func, skip_mask=None):
if self.vectorized:
got = func(*params)
else:
got = []
for j in range(len(params[0])):
if skip_mask is not None and skip_mask[j]:
got.append(np.nan)
continue
got.append(func(*tuple([params[i][j] for i in range(len(params))])))
got = np.asarray(got)
if not isinstance(got, tuple):
got = (got,)
return got
# Evaluate function to be tested
got = eval_func_at_params(self.func)
# Grab the correct results
if self.result_columns is not None:
# Correct results passed in with the data
wanted = tuple([data[:,icol] for icol in self.result_columns])
else:
# Function producing correct results passed in
skip_mask = None
if self.nan_ok and len(got) == 1:
# Don't spend time evaluating what doesn't need to be evaluated
skip_mask = np.isnan(got[0])
wanted = eval_func_at_params(self.result_func, skip_mask=skip_mask)
# Check the validity of each output returned
assert_(len(got) == len(wanted))
for output_num, (x, y) in enumerate(zip(got, wanted)):
if np.issubdtype(x.dtype, np.complexfloating) or self.ignore_inf_sign:
pinf_x = np.isinf(x)
pinf_y = np.isinf(y)
minf_x = np.isinf(x)
minf_y = np.isinf(y)
else:
pinf_x = np.isposinf(x)
pinf_y = np.isposinf(y)
minf_x = np.isneginf(x)
minf_y = np.isneginf(y)
nan_x = np.isnan(x)
nan_y = np.isnan(y)
olderr = np.seterr(all='ignore')
try:
abs_y = np.absolute(y)
abs_y[~np.isfinite(abs_y)] = 0
diff = np.absolute(x - y)
diff[~np.isfinite(diff)] = 0
rdiff = diff / np.absolute(y)
rdiff[~np.isfinite(rdiff)] = 0
finally:
np.seterr(**olderr)
tol_mask = (diff <= atol + rtol*abs_y)
pinf_mask = (pinf_x == pinf_y)
minf_mask = (minf_x == minf_y)
nan_mask = (nan_x == nan_y)
bad_j = ~(tol_mask & pinf_mask & minf_mask & nan_mask)
point_count = bad_j.size
if self.nan_ok:
bad_j &= ~nan_x
bad_j &= ~nan_y
point_count -= (nan_x | nan_y).sum()
if not self.distinguish_nan_and_inf and not self.nan_ok:
# If nan's are okay we've already covered all these cases
inf_x = np.isinf(x)
inf_y = np.isinf(y)
both_nonfinite = (inf_x & nan_y) | (nan_x & inf_y)
bad_j &= ~both_nonfinite
point_count -= both_nonfinite.sum()
if np.any(bad_j):
# Some bad results: inform what, where, and how bad
msg = [""]
msg.append("Max |adiff|: %g" % diff.max())
msg.append("Max |rdiff|: %g" % rdiff.max())
msg.append("Bad results (%d out of %d) for the following points (in output %d):"
% (np.sum(bad_j), point_count, output_num,))
for j in np.nonzero(bad_j)[0]:
j = int(j)
fmt = lambda x: "%30s" % np.array2string(x[j], precision=18)
a = " ".join(map(fmt, params))
b = " ".join(map(fmt, got))
c = " ".join(map(fmt, wanted))
d = fmt(rdiff)
msg.append("%s => %s != %s (rdiff %s)" % (a, b, c, d))
> assert_(False, "\n".join(msg))
E AssertionError:
E Max |adiff|: 0.0121951
E Max |rdiff|: 0.00370991
E Bad results (9 out of 17) for the following points (in output 0):
E 10 0.10000000000000045 => -3.299372741951227 != -3.2871776200000262 (rdiff 0.003709906601031427)
E 11 0.09090909090909136 => -3.3821190947447826 != -3.3765011172167076 (rdiff 0.001663845896401149)
E 12 0.08333333333333377 => -3.442344918605228 != -3.452983216477585 (rdiff 0.003080900544662628)
E 13 0.07692307692307737 => -3.5286334525977225 != -3.51919949033359 (rdiff 0.002680712556945199)
E 14 0.07142857142857187 => -3.567990913442175 != -3.5770818225331054 (rdiff 0.002541431687042837)
E 15 0.06666666666666711 => -3.626015070796918 != -3.6281071210061473 (rdiff 0.000576623054241369)
E 17 0.05882352941176515 => -3.7120917372777273 != -3.713936755727941 (rdiff 0.000496782409492605)
E 18 0.055555555555556 => -3.743328749446484 != -3.7503710029676434 (rdiff 0.001877748498905016)
E 19 0.05263157894736886 => -3.776645292919359 != -3.7833119595860536 (rdiff 0.001762124492484152)
a = ' 19 0.05263157894736886'
abs_y = array([1.66666667, 2.125 , 2.456 , 2.70524691, 2.89932766,
3.05457306, 3.18150316, 3.28717762, 3.37650112, 3.45298322,
3.51919949, 3.57708182, 3.62810712, 3.6734235 , 3.71393676,
3.750371 , 3.78331196])
atol = 1.1125369292536007e-307
b = ' -3.776645292919359'
bad_j = array([False, False, False, False, False, False, False, True, True,
True, True, True, True, False, True, True, True])
c = ' -3.7833119595860536'
d = ' 0.001762124492484152'
data = array([[ 3. , 0.33333333, -1.66666667],
[ 4. , 0.25 , -2.125 ],
[ 5. , ...5882353, -3.71393676],
[18. , 0.05555556, -3.750371 ],
[19. , 0.05263158, -3.78331196]])
diff = array([3.55271368e-15, 5.32907052e-15, 6.66133815e-15, 9.76996262e-15,
1.02140518e-14, 1.24344979e-14, 1.554312...226e-03, 9.09090909e-03,
2.09205021e-03, 2.70894418e-14, 1.84501845e-03, 7.04225352e-03,
6.66666667e-03])
dtype = dtype('float64')
dtypes = [<class 'int'>, <class 'float'>, <class 'float'>]
eval_func_at_params = <function FuncData.check.<locals>.eval_func_at_params at 0xea13e7c4>
filter = None
fmt = <function FuncData.check.<locals>.<lambda> at 0xea13e6a4>
got = (array([-1.66666667, -2.125 , -2.456 , -2.70524691, -2.89932766,
-3.05457306, -3.18150316, -3.29937274,...3.44234492,
-3.52863345, -3.56799091, -3.62601507, -3.6734235 , -3.71209174,
-3.74332875, -3.77664529]),)
idx = 1
j = 16
minf_mask = array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True])
minf_x = array([False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False])
minf_y = array([False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False])
msg = ['', 'Max |adiff|: 0.0121951', 'Max |rdiff|: 0.00370991', 'Bad results (9 out of 17) for the following points (in outp...9136 => -3.3821190947447826 != -3.3765011172167076 (rdiff 0.001663845896401149)', ...]
nan_mask = array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True])
nan_x = array([False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False])
nan_y = array([False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False])
olderr = {'divide': 'warn', 'invalid': 'warn', 'over': 'warn', 'under': 'ignore'}
output_num = 0
param_mask = array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True])
params = [array([ 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]), array([0.33333333, 0.25 , 0.2 ...090909, 0.08333333,
0.07692308, 0.07142857, 0.06666667, 0.0625 , 0.05882353,
0.05555556, 0.05263158])]
pinf_mask = array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True])
pinf_x = array([False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False])
pinf_y = array([False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False])
point_count = 17
rdiff = array([2.13162821e-15, 2.50779789e-15, 2.71227123e-15, 3.61148647e-15,
3.52290359e-15, 4.07078097e-15, 4.885465...256e-03, 2.54143169e-03,
5.76623054e-04, 7.37444017e-15, 4.96782409e-04, 1.87774850e-03,
1.76212449e-03])
rtol = 1e-10
self = <Data for _smirnovp>
tol_mask = array([ True, True, True, True, True, True, True, False, False,
False, False, False, False, True, False, False, False])
wanted = (array([-1.66666667, -2.125 , -2.456 , -2.70524691, -2.89932766,
-3.05457306, -3.18150316, -3.28717762,...3.45298322,
-3.51919949, -3.57708182, -3.62810712, -3.6734235 , -3.71393676,
-3.750371 , -3.78331196]),)
x = array([-1.66666667, -2.125 , -2.456 , -2.70524691, -2.89932766,
-3.05457306, -3.18150316, -3.29937274, ... -3.44234492,
-3.52863345, -3.56799091, -3.62601507, -3.6734235 , -3.71209174,
-3.74332875, -3.77664529])
y = array([-1.66666667, -2.125 , -2.456 , -2.70524691, -2.89932766,
-3.05457306, -3.18150316, -3.28717762, ... -3.45298322,
-3.51919949, -3.57708182, -3.62810712, -3.6734235 , -3.71393676,
-3.750371 , -3.78331196])
/venv/lib/python3.7/site-packages/scipy/special/_testutils.py:307: AssertionError
``