Source: statsmodels
Version: 0.13.5+dfsg-4
Severity: serious
Tags: ftbfs
https://buildd.debian.org/status/logs.php?pkg=statsmodels&ver=0.13.5%2Bdfsg-4%2Bb1
...
=================================== FAILURES ===================================
_______________________ TestDiscretizedGamma.test_basic ________________________
self = <statsmodels.distributions.tests.test_discrete.TestDiscretizedGamma
object at 0xefb687f0>
def test_basic(self):
d_offset = self.d_offset
ddistr = self.ddistr
paramg = self.paramg
paramd = self.paramd
shapes = self.shapes
start_params = self.start_params
np.random.seed(987146)
dp = DiscretizedCount(ddistr, d_offset)
assert dp.shapes == shapes
xi = np.arange(5)
p = dp._pmf(xi, *paramd)
cdf1 = ddistr.cdf(xi, *paramg)
p1 = np.diff(cdf1)
assert_allclose(p[: len(p1)], p1, rtol=1e-13)
cdf = dp._cdf(xi, *paramd)
assert_allclose(cdf[: len(cdf1) - 1], cdf1[1:], rtol=1e-13)
# check that scipy dispatch methods work
p2 = dp.pmf(xi, *paramd)
assert_allclose(p2, p, rtol=1e-13)
cdf2 = dp.cdf(xi, *paramd)
assert_allclose(cdf2, cdf, rtol=1e-13)
sf = dp.sf(xi, *paramd)
assert_allclose(sf, 1 - cdf, rtol=1e-13)
nobs = 2000
xx = dp.rvs(*paramd, size=nobs) # , random_state=987146)
# check that we go a non-trivial rvs
assert len(xx) == nobs
assert xx.var() > 0.001
mod = DiscretizedModel(xx, distr=dp)
res = mod.fit(start_params=start_params)
p = mod.predict(res.params, which="probs")
args = self.convert_params(res.params)
p1 = -np.diff(ddistr.sf(np.arange(21), *args))
assert_allclose(p, p1, rtol=1e-13)
# using cdf limits precision to computation around 1
p1 = np.diff(ddistr.cdf(np.arange(21), *args))
assert_allclose(p, p1, rtol=1e-13, atol=1e-15)
freq = np.bincount(xx.astype(int))
# truncate at last observed
k = len(freq)
if k > 10:
# reduce low count bins for heavy tailed distributions
k = 10
freq[k - 1] += freq[k:].sum()
freq = freq[:k]
p = mod.predict(res.params, which="probs", k_max=k)
p[k - 1] += 1 - p[:k].sum()
tchi2 = stats.chisquare(freq, p[:k] * nobs)
assert tchi2.pvalue > 0.01
# estimated distribution methods rvs, ppf
# frozen distribution with estimated parameters
# Todo results method
dfr = mod.get_distr(res.params)
nobs_rvs = 500
rvs = dfr.rvs(size=nobs_rvs)
> freq = np.bincount(rvs)
../.pybuild/cpython3_3.11_statsmodels/build/statsmodels/distributions/tests/test_discrete.py:302:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
args = (array([2, 0, 3, 1, 1, 2, 2, 5, 1, 1, 1, 3, 2, 1, 2, 0, 2, 2, 4, 2, 2, 1,
3, 2, 1, 1, 1, 1, 4, 2, 4, 2, 1, 1, 2... 2, 4, 1, 2, 1, 2, 0, 1, 1, 2,
1, 2, 2, 2, 4, 0,
4, 1, 1, 2, 2, 1, 1, 3, 2, 3, 2, 1, 3, 1, 1, 2], dtype=int64),)
kwargs = {}
relevant_args = (array([2, 0, 3, 1, 1, 2, 2, 5, 1, 1, 1, 3, 2, 1, 2, 0, 2, 2,
4, 2, 2, 1,
3, 2, 1, 1, 1, 1, 4, 2, 4, 2, 1, 1, 2..., 1, 2, 1, 2, 0, 1, 1, 2, 1, 2,
2, 2, 4, 0,
4, 1, 1, 2, 2, 1, 1, 3, 2, 3, 2, 1, 3, 1, 1, 2], dtype=int64), None)
> ???
E TypeError: Cannot cast array data from dtype('int64') to dtype('int32')
according to the rule 'safe'
<__array_function__ internals>:200: TypeError
----------------------------- Captured stdout call -----------------------------
Optimization terminated successfully.
Current function value: 1.491508
Iterations: 64
Function evaluations: 122
____________________ TestDiscretizedExponential.test_basic _____________________
self =
<statsmodels.distributions.tests.test_discrete.TestDiscretizedExponential
object at 0xef920d90>
def test_basic(self):
d_offset = self.d_offset
ddistr = self.ddistr
paramg = self.paramg
paramd = self.paramd
shapes = self.shapes
start_params = self.start_params
np.random.seed(987146)
dp = DiscretizedCount(ddistr, d_offset)
assert dp.shapes == shapes
xi = np.arange(5)
p = dp._pmf(xi, *paramd)
cdf1 = ddistr.cdf(xi, *paramg)
p1 = np.diff(cdf1)
assert_allclose(p[: len(p1)], p1, rtol=1e-13)
cdf = dp._cdf(xi, *paramd)
assert_allclose(cdf[: len(cdf1) - 1], cdf1[1:], rtol=1e-13)
# check that scipy dispatch methods work
p2 = dp.pmf(xi, *paramd)
assert_allclose(p2, p, rtol=1e-13)
cdf2 = dp.cdf(xi, *paramd)
assert_allclose(cdf2, cdf, rtol=1e-13)
sf = dp.sf(xi, *paramd)
assert_allclose(sf, 1 - cdf, rtol=1e-13)
nobs = 2000
xx = dp.rvs(*paramd, size=nobs) # , random_state=987146)
# check that we go a non-trivial rvs
assert len(xx) == nobs
assert xx.var() > 0.001
mod = DiscretizedModel(xx, distr=dp)
res = mod.fit(start_params=start_params)
p = mod.predict(res.params, which="probs")
args = self.convert_params(res.params)
p1 = -np.diff(ddistr.sf(np.arange(21), *args))
assert_allclose(p, p1, rtol=1e-13)
# using cdf limits precision to computation around 1
p1 = np.diff(ddistr.cdf(np.arange(21), *args))
assert_allclose(p, p1, rtol=1e-13, atol=1e-15)
freq = np.bincount(xx.astype(int))
# truncate at last observed
k = len(freq)
if k > 10:
# reduce low count bins for heavy tailed distributions
k = 10
freq[k - 1] += freq[k:].sum()
freq = freq[:k]
p = mod.predict(res.params, which="probs", k_max=k)
p[k - 1] += 1 - p[:k].sum()
tchi2 = stats.chisquare(freq, p[:k] * nobs)
assert tchi2.pvalue > 0.01
# estimated distribution methods rvs, ppf
# frozen distribution with estimated parameters
# Todo results method
dfr = mod.get_distr(res.params)
nobs_rvs = 500
rvs = dfr.rvs(size=nobs_rvs)
> freq = np.bincount(rvs)
../.pybuild/cpython3_3.11_statsmodels/build/statsmodels/distributions/tests/test_discrete.py:302:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
args = (array([ 0, 0, 5, 2, 1, 1, 6, 1, 2, 1, 3, 3, 1, 2, 1, 9,
1,
10, 4, 1, 7, 1, 2, 13, 1, 4,... 2, 0, 10, 11, 5, 0, 7, 1,
8, 1, 6, 0, 1, 1, 3, 0, 2,
1, 4, 1, 7, 0, 7, 9], dtype=int64),)
kwargs = {}
relevant_args = (array([ 0, 0, 5, 2, 1, 1, 6, 1, 2, 1, 3, 3, 1, 2,
1, 9, 1,
10, 4, 1, 7, 1, 2, 13, 1, 4,... 0, 10, 11, 5, 0, 7, 1, 8,
1, 6, 0, 1, 1, 3, 0, 2,
1, 4, 1, 7, 0, 7, 9], dtype=int64), None)
> ???
E TypeError: Cannot cast array data from dtype('int64') to dtype('int32')
according to the rule 'safe'
<__array_function__ internals>:200: TypeError
----------------------------- Captured stdout call -----------------------------
Optimization terminated successfully.
Current function value: 2.617352
Iterations: 22
Function evaluations: 44
_______________________ TestDiscretizedLomax.test_basic ________________________
self = <statsmodels.distributions.tests.test_discrete.TestDiscretizedLomax
object at 0xef9209d0>
def test_basic(self):
d_offset = self.d_offset
ddistr = self.ddistr
paramg = self.paramg
paramd = self.paramd
shapes = self.shapes
start_params = self.start_params
np.random.seed(987146)
dp = DiscretizedCount(ddistr, d_offset)
assert dp.shapes == shapes
xi = np.arange(5)
p = dp._pmf(xi, *paramd)
cdf1 = ddistr.cdf(xi, *paramg)
p1 = np.diff(cdf1)
assert_allclose(p[: len(p1)], p1, rtol=1e-13)
cdf = dp._cdf(xi, *paramd)
assert_allclose(cdf[: len(cdf1) - 1], cdf1[1:], rtol=1e-13)
# check that scipy dispatch methods work
p2 = dp.pmf(xi, *paramd)
assert_allclose(p2, p, rtol=1e-13)
cdf2 = dp.cdf(xi, *paramd)
assert_allclose(cdf2, cdf, rtol=1e-13)
sf = dp.sf(xi, *paramd)
assert_allclose(sf, 1 - cdf, rtol=1e-13)
nobs = 2000
xx = dp.rvs(*paramd, size=nobs) # , random_state=987146)
# check that we go a non-trivial rvs
assert len(xx) == nobs
assert xx.var() > 0.001
mod = DiscretizedModel(xx, distr=dp)
res = mod.fit(start_params=start_params)
p = mod.predict(res.params, which="probs")
args = self.convert_params(res.params)
p1 = -np.diff(ddistr.sf(np.arange(21), *args))
assert_allclose(p, p1, rtol=1e-13)
# using cdf limits precision to computation around 1
p1 = np.diff(ddistr.cdf(np.arange(21), *args))
assert_allclose(p, p1, rtol=1e-13, atol=1e-15)
freq = np.bincount(xx.astype(int))
# truncate at last observed
k = len(freq)
if k > 10:
# reduce low count bins for heavy tailed distributions
k = 10
freq[k - 1] += freq[k:].sum()
freq = freq[:k]
p = mod.predict(res.params, which="probs", k_max=k)
p[k - 1] += 1 - p[:k].sum()
tchi2 = stats.chisquare(freq, p[:k] * nobs)
assert tchi2.pvalue > 0.01
# estimated distribution methods rvs, ppf
# frozen distribution with estimated parameters
# Todo results method
dfr = mod.get_distr(res.params)
nobs_rvs = 500
rvs = dfr.rvs(size=nobs_rvs)
> freq = np.bincount(rvs)
../.pybuild/cpython3_3.11_statsmodels/build/statsmodels/distributions/tests/test_discrete.py:302:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
args = (array([ 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0,
2, 0, 0, 1, 0, 0, 4, 0, 0,... 0, 0, 2, 3, 1, 0, 1, 0,
2, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 2], dtype=int64),)
kwargs = {}
relevant_args = (array([ 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 2, 0,
2, 0, 0, 1, 0, 0, 4, 0, 0,... 0, 2, 3, 1, 0, 1, 0, 2,
0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 2], dtype=int64), None)
> ???
E TypeError: Cannot cast array data from dtype('int64') to dtype('int32')
according to the rule 'safe'
<__array_function__ internals>:200: TypeError
----------------------------- Captured stdout call -----------------------------
Optimization terminated successfully.
Current function value: 1.294980
Iterations: 51
Function evaluations: 101
_______________________ TestDiscretizedBurr12.test_basic _______________________
self = <statsmodels.distributions.tests.test_discrete.TestDiscretizedBurr12
object at 0xef920990>
def test_basic(self):
d_offset = self.d_offset
ddistr = self.ddistr
paramg = self.paramg
paramd = self.paramd
shapes = self.shapes
start_params = self.start_params
np.random.seed(987146)
dp = DiscretizedCount(ddistr, d_offset)
assert dp.shapes == shapes
xi = np.arange(5)
p = dp._pmf(xi, *paramd)
cdf1 = ddistr.cdf(xi, *paramg)
p1 = np.diff(cdf1)
assert_allclose(p[: len(p1)], p1, rtol=1e-13)
cdf = dp._cdf(xi, *paramd)
assert_allclose(cdf[: len(cdf1) - 1], cdf1[1:], rtol=1e-13)
# check that scipy dispatch methods work
p2 = dp.pmf(xi, *paramd)
assert_allclose(p2, p, rtol=1e-13)
cdf2 = dp.cdf(xi, *paramd)
assert_allclose(cdf2, cdf, rtol=1e-13)
sf = dp.sf(xi, *paramd)
assert_allclose(sf, 1 - cdf, rtol=1e-13)
nobs = 2000
xx = dp.rvs(*paramd, size=nobs) # , random_state=987146)
# check that we go a non-trivial rvs
assert len(xx) == nobs
assert xx.var() > 0.001
mod = DiscretizedModel(xx, distr=dp)
res = mod.fit(start_params=start_params)
p = mod.predict(res.params, which="probs")
args = self.convert_params(res.params)
p1 = -np.diff(ddistr.sf(np.arange(21), *args))
assert_allclose(p, p1, rtol=1e-13)
# using cdf limits precision to computation around 1
p1 = np.diff(ddistr.cdf(np.arange(21), *args))
assert_allclose(p, p1, rtol=1e-13, atol=1e-15)
freq = np.bincount(xx.astype(int))
# truncate at last observed
k = len(freq)
if k > 10:
# reduce low count bins for heavy tailed distributions
k = 10
freq[k - 1] += freq[k:].sum()
freq = freq[:k]
p = mod.predict(res.params, which="probs", k_max=k)
p[k - 1] += 1 - p[:k].sum()
tchi2 = stats.chisquare(freq, p[:k] * nobs)
assert tchi2.pvalue > 0.01
# estimated distribution methods rvs, ppf
# frozen distribution with estimated parameters
# Todo results method
dfr = mod.get_distr(res.params)
nobs_rvs = 500
rvs = dfr.rvs(size=nobs_rvs)
> freq = np.bincount(rvs)
../.pybuild/cpython3_3.11_statsmodels/build/statsmodels/distributions/tests/test_discrete.py:302:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
args = (array([ 0, 0, 2, 1, 0, 1, 2, 0, 1, 0, 1, 1, 0, 1, 0, 3,
0,
4, 1, 0, 2, 0, 1, 5, 0, 1,... 1, 0, 4, 4, 2, 0, 2, 1,
3, 0, 2, 0, 0, 0, 1, 0, 1,
0, 1, 1, 2, 0, 2, 3], dtype=int64),)
kwargs = {}
relevant_args = (array([ 0, 0, 2, 1, 0, 1, 2, 0, 1, 0, 1, 1, 0, 1,
0, 3, 0,
4, 1, 0, 2, 0, 1, 5, 0, 1,... 0, 4, 4, 2, 0, 2, 1, 3,
0, 2, 0, 0, 0, 1, 0, 1,
0, 1, 1, 2, 0, 2, 3], dtype=int64), None)
> ???
E TypeError: Cannot cast array data from dtype('int64') to dtype('int32')
according to the rule 'safe'
<__array_function__ internals>:200: TypeError
...
=========================== short test summary info ============================
FAILED
../.pybuild/cpython3_3.11_statsmodels/build/statsmodels/distributions/tests/test_discrete.py::TestDiscretizedGamma::test_basic
FAILED
../.pybuild/cpython3_3.11_statsmodels/build/statsmodels/distributions/tests/test_discrete.py::TestDiscretizedExponential::test_basic
FAILED
../.pybuild/cpython3_3.11_statsmodels/build/statsmodels/distributions/tests/test_discrete.py::TestDiscretizedLomax::test_basic
FAILED
../.pybuild/cpython3_3.11_statsmodels/build/statsmodels/distributions/tests/test_discrete.py::TestDiscretizedBurr12::test_basic
= 4 failed, 16442 passed, 292 skipped, 142 xfailed, 10 xpassed, 307 warnings in
3029.45s (0:50:29) =
make[1]: *** [debian/rules:105: override_dh_auto_test] Error 1
