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projektAI/venv/Lib/site-packages/scipy/stats/tests/test_continuous_basic.py

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2021-06-06 22:13:05 +02:00
import pickle
import numpy as np
import numpy.testing as npt
import pytest
from pytest import raises as assert_raises
from scipy.integrate import IntegrationWarning
from scipy import stats
from scipy.special import betainc
from. common_tests import (check_normalization, check_moment, check_mean_expect,
check_var_expect, check_skew_expect,
check_kurt_expect, check_entropy,
check_private_entropy, check_entropy_vect_scale,
check_edge_support, check_named_args,
check_random_state_property,
check_meth_dtype, check_ppf_dtype, check_cmplx_deriv,
check_pickling, check_rvs_broadcast, check_freezing)
from scipy.stats._distr_params import distcont
"""
Test all continuous distributions.
Parameters were chosen for those distributions that pass the
Kolmogorov-Smirnov test. This provides safe parameters for each
distributions so that we can perform further testing of class methods.
These tests currently check only/mostly for serious errors and exceptions,
not for numerically exact results.
"""
# Note that you need to add new distributions you want tested
# to _distr_params
DECIMAL = 5 # specify the precision of the tests # increased from 0 to 5
# Last three of these fail all around. Need to be checked
distcont_extra = [
['betaprime', (100, 86)],
['fatiguelife', (5,)],
['invweibull', (0.58847112119264788,)],
# burr: sample mean test fails still for c<1
['burr', (0.94839838075366045, 4.3820284068855795)],
# genextreme: sample mean test, sf-logsf test fail
['genextreme', (3.3184017469423535,)],
]
distslow = ['kstwo', 'ksone', 'kappa4', 'gausshyper', 'recipinvgauss',
'genexpon', 'vonmises', 'vonmises_line', 'cosine', 'invweibull',
'powerlognorm', 'johnsonsu', 'kstwobign']
# distslow are sorted by speed (very slow to slow)
# skip check_fit_args (test is slow)
skip_fit_test = ['exponpow', 'exponweib', 'gausshyper', 'genexpon',
'halfgennorm', 'gompertz', 'johnsonsb', 'johnsonsu',
'kappa4', 'ksone', 'kstwo', 'kstwobign', 'mielke', 'ncf', 'nct',
'powerlognorm', 'powernorm', 'recipinvgauss', 'trapezoid',
'vonmises', 'vonmises_line',
'levy_stable', 'rv_histogram_instance']
# skip check_fit_args_fix (test is slow)
skip_fit_fix_test = ['burr', 'exponpow', 'exponweib',
'gausshyper', 'genexpon', 'halfgennorm',
'gompertz', 'johnsonsb', 'johnsonsu', 'kappa4',
'ksone', 'kstwo', 'kstwobign', 'levy_stable', 'mielke', 'ncf',
'ncx2', 'powerlognorm', 'powernorm', 'rdist',
'recipinvgauss', 'trapezoid', 'vonmises', 'vonmises_line']
# These distributions fail the complex derivative test below.
# Here 'fail' mean produce wrong results and/or raise exceptions, depending
# on the implementation details of corresponding special functions.
# cf https://github.com/scipy/scipy/pull/4979 for a discussion.
fails_cmplx = set(['beta', 'betaprime', 'chi', 'chi2', 'dgamma', 'dweibull',
'erlang', 'f', 'gamma', 'gausshyper', 'gengamma',
'geninvgauss', 'gennorm', 'genpareto',
'halfgennorm', 'invgamma',
'ksone', 'kstwo', 'kstwobign', 'levy_l', 'loggamma', 'logistic',
'loguniform', 'maxwell', 'nakagami',
'ncf', 'nct', 'ncx2', 'norminvgauss', 'pearson3', 'rdist',
'reciprocal', 'rice', 'skewnorm', 't', 'tukeylambda',
'vonmises', 'vonmises_line', 'rv_histogram_instance'])
_h = np.histogram([1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6,
6, 6, 6, 7, 7, 7, 8, 8, 9], bins=8)
histogram_test_instance = stats.rv_histogram(_h)
def cases_test_cont_basic():
for distname, arg in distcont[:] + [(histogram_test_instance, tuple())]:
if distname == 'levy_stable':
continue
elif distname in distslow:
yield pytest.param(distname, arg, marks=pytest.mark.slow)
else:
yield distname, arg
@pytest.mark.parametrize('distname,arg', cases_test_cont_basic())
def test_cont_basic(distname, arg):
# this test skips slow distributions
if distname == 'truncnorm':
pytest.xfail(reason=distname)
try:
distfn = getattr(stats, distname)
except TypeError:
distfn = distname
distname = 'rv_histogram_instance'
rng = np.random.RandomState(765456)
sn = 500
rvs = distfn.rvs(size=sn, *arg, random_state=rng)
sm = rvs.mean()
sv = rvs.var()
m, v = distfn.stats(*arg)
check_sample_meanvar_(distfn, arg, m, v, sm, sv, sn, distname + 'sample mean test')
check_cdf_ppf(distfn, arg, distname)
check_sf_isf(distfn, arg, distname)
check_pdf(distfn, arg, distname)
check_pdf_logpdf(distfn, arg, distname)
check_pdf_logpdf_at_endpoints(distfn, arg, distname)
check_cdf_logcdf(distfn, arg, distname)
check_sf_logsf(distfn, arg, distname)
check_ppf_broadcast(distfn, arg, distname)
alpha = 0.01
if distname == 'rv_histogram_instance':
check_distribution_rvs(distfn.cdf, arg, alpha, rvs)
elif distname != 'geninvgauss':
# skip kstest for geninvgauss since cdf is too slow; see test for
# rv generation in TestGenInvGauss in test_distributions.py
check_distribution_rvs(distname, arg, alpha, rvs)
locscale_defaults = (0, 1)
meths = [distfn.pdf, distfn.logpdf, distfn.cdf, distfn.logcdf,
distfn.logsf]
# make sure arguments are within support
spec_x = {'weibull_max': -0.5, 'levy_l': -0.5,
'pareto': 1.5, 'tukeylambda': 0.3,
'rv_histogram_instance': 5.0}
x = spec_x.get(distname, 0.5)
if distname == 'invweibull':
arg = (1,)
elif distname == 'ksone':
arg = (3,)
check_named_args(distfn, x, arg, locscale_defaults, meths)
check_random_state_property(distfn, arg)
check_pickling(distfn, arg)
check_freezing(distfn, arg)
# Entropy
if distname not in ['kstwobign', 'kstwo']:
check_entropy(distfn, arg, distname)
if distfn.numargs == 0:
check_vecentropy(distfn, arg)
if (distfn.__class__._entropy != stats.rv_continuous._entropy
and distname != 'vonmises'):
check_private_entropy(distfn, arg, stats.rv_continuous)
with npt.suppress_warnings() as sup:
sup.filter(IntegrationWarning, "The occurrence of roundoff error")
sup.filter(IntegrationWarning, "Extremely bad integrand")
sup.filter(RuntimeWarning, "invalid value")
check_entropy_vect_scale(distfn, arg)
check_retrieving_support(distfn, arg)
check_edge_support(distfn, arg)
check_meth_dtype(distfn, arg, meths)
check_ppf_dtype(distfn, arg)
if distname not in fails_cmplx:
check_cmplx_deriv(distfn, arg)
if distname != 'truncnorm':
check_ppf_private(distfn, arg, distname)
if distname not in skip_fit_test:
check_fit_args(distfn, arg, rvs[0:200])
if distname not in skip_fit_fix_test:
check_fit_args_fix(distfn, arg, rvs[0:200])
@pytest.mark.parametrize('distname,arg', cases_test_cont_basic())
def test_rvs_scalar(distname, arg):
# rvs should return a scalar when given scalar arguments (gh-12428)
try:
distfn = getattr(stats, distname)
except TypeError:
distfn = distname
distname = 'rv_histogram_instance'
assert np.isscalar(distfn.rvs(*arg))
assert np.isscalar(distfn.rvs(*arg, size=()))
assert np.isscalar(distfn.rvs(*arg, size=None))
def test_levy_stable_random_state_property():
# levy_stable only implements rvs(), so it is skipped in the
# main loop in test_cont_basic(). Here we apply just the test
# check_random_state_property to levy_stable.
check_random_state_property(stats.levy_stable, (0.5, 0.1))
def cases_test_moments():
fail_normalization = set(['vonmises'])
fail_higher = set(['vonmises', 'ncf'])
for distname, arg in distcont[:] + [(histogram_test_instance, tuple())]:
if distname == 'levy_stable':
continue
cond1 = distname not in fail_normalization
cond2 = distname not in fail_higher
yield distname, arg, cond1, cond2, False
if not cond1 or not cond2:
# Run the distributions that have issues twice, once skipping the
# not_ok parts, once with the not_ok parts but marked as knownfail
yield pytest.param(distname, arg, True, True, True,
marks=pytest.mark.xfail)
@pytest.mark.slow
@pytest.mark.parametrize('distname,arg,normalization_ok,higher_ok,is_xfailing',
cases_test_moments())
def test_moments(distname, arg, normalization_ok, higher_ok, is_xfailing):
try:
distfn = getattr(stats, distname)
except TypeError:
distfn = distname
distname = 'rv_histogram_instance'
with npt.suppress_warnings() as sup:
sup.filter(IntegrationWarning,
"The integral is probably divergent, or slowly convergent.")
if is_xfailing:
sup.filter(IntegrationWarning)
m, v, s, k = distfn.stats(*arg, moments='mvsk')
if normalization_ok:
check_normalization(distfn, arg, distname)
if higher_ok:
check_mean_expect(distfn, arg, m, distname)
check_skew_expect(distfn, arg, m, v, s, distname)
check_var_expect(distfn, arg, m, v, distname)
check_kurt_expect(distfn, arg, m, v, k, distname)
check_loc_scale(distfn, arg, m, v, distname)
check_moment(distfn, arg, m, v, distname)
@pytest.mark.parametrize('dist,shape_args', distcont)
def test_rvs_broadcast(dist, shape_args):
if dist in ['gausshyper', 'genexpon']:
pytest.skip("too slow")
# If shape_only is True, it means the _rvs method of the
# distribution uses more than one random number to generate a random
# variate. That means the result of using rvs with broadcasting or
# with a nontrivial size will not necessarily be the same as using the
# numpy.vectorize'd version of rvs(), so we can only compare the shapes
# of the results, not the values.
# Whether or not a distribution is in the following list is an
# implementation detail of the distribution, not a requirement. If
# the implementation the rvs() method of a distribution changes, this
# test might also have to be changed.
shape_only = dist in ['argus', 'betaprime', 'dgamma', 'dweibull',
'exponnorm', 'geninvgauss', 'levy_stable', 'nct',
'norminvgauss', 'rice', 'skewnorm', 'semicircular']
distfunc = getattr(stats, dist)
loc = np.zeros(2)
scale = np.ones((3, 1))
nargs = distfunc.numargs
allargs = []
bshape = [3, 2]
# Generate shape parameter arguments...
for k in range(nargs):
shp = (k + 4,) + (1,)*(k + 2)
allargs.append(shape_args[k]*np.ones(shp))
bshape.insert(0, k + 4)
allargs.extend([loc, scale])
# bshape holds the expected shape when loc, scale, and the shape
# parameters are all broadcast together.
check_rvs_broadcast(distfunc, dist, allargs, bshape, shape_only, 'd')
def test_rvs_gh2069_regression():
# Regression tests for gh-2069. In scipy 0.17 and earlier,
# these tests would fail.
#
# A typical example of the broken behavior:
# >>> norm.rvs(loc=np.zeros(5), scale=np.ones(5))
# array([-2.49613705, -2.49613705, -2.49613705, -2.49613705, -2.49613705])
rng = np.random.RandomState(123)
vals = stats.norm.rvs(loc=np.zeros(5), scale=1, random_state=rng)
d = np.diff(vals)
npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
vals = stats.norm.rvs(loc=0, scale=np.ones(5), random_state=rng)
d = np.diff(vals)
npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
vals = stats.norm.rvs(loc=np.zeros(5), scale=np.ones(5), random_state=rng)
d = np.diff(vals)
npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
vals = stats.norm.rvs(loc=np.array([[0], [0]]), scale=np.ones(5),
random_state=rng)
d = np.diff(vals.ravel())
npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
assert_raises(ValueError, stats.norm.rvs, [[0, 0], [0, 0]],
[[1, 1], [1, 1]], 1)
assert_raises(ValueError, stats.gamma.rvs, [2, 3, 4, 5], 0, 1, (2, 2))
assert_raises(ValueError, stats.gamma.rvs, [1, 1, 1, 1], [0, 0, 0, 0],
[[1], [2]], (4,))
def test_nomodify_gh9900_regression():
# Regression test for gh-9990
# Prior to gh-9990, calls to stats.truncnorm._cdf() use what ever was
# set inside the stats.truncnorm instance during stats.truncnorm.cdf().
# This could cause issues wth multi-threaded code.
# Since then, the calls to cdf() are not permitted to modify the global
# stats.truncnorm instance.
tn = stats.truncnorm
# Use the right-half truncated normal
# Check that the cdf and _cdf return the same result.
npt.assert_almost_equal(tn.cdf(1, 0, np.inf), 0.6826894921370859)
npt.assert_almost_equal(tn._cdf(1, 0, np.inf), 0.6826894921370859)
# Now use the left-half truncated normal
npt.assert_almost_equal(tn.cdf(-1, -np.inf, 0), 0.31731050786291415)
npt.assert_almost_equal(tn._cdf(-1, -np.inf, 0), 0.31731050786291415)
# Check that the right-half truncated normal _cdf hasn't changed
npt.assert_almost_equal(tn._cdf(1, 0, np.inf), 0.6826894921370859) # NOT 1.6826894921370859
npt.assert_almost_equal(tn.cdf(1, 0, np.inf), 0.6826894921370859)
# Check that the left-half truncated normal _cdf hasn't changed
npt.assert_almost_equal(tn._cdf(-1, -np.inf, 0), 0.31731050786291415) # Not -0.6826894921370859
npt.assert_almost_equal(tn.cdf(1, -np.inf, 0), 1) # Not 1.6826894921370859
npt.assert_almost_equal(tn.cdf(-1, -np.inf, 0), 0.31731050786291415) # Not -0.6826894921370859
def test_broadcast_gh9990_regression():
# Regression test for gh-9990
# The x-value 7 only lies within the support of 4 of the supplied
# distributions. Prior to 9990, one array passed to
# stats.reciprocal._cdf would have 4 elements, but an array
# previously stored by stats.reciprocal_argcheck() would have 6, leading
# to a broadcast error.
a = np.array([1, 2, 3, 4, 5, 6])
b = np.array([8, 16, 1, 32, 1, 48])
ans = [stats.reciprocal.cdf(7, _a, _b) for _a, _b in zip(a,b)]
npt.assert_array_almost_equal(stats.reciprocal.cdf(7, a, b), ans)
ans = [stats.reciprocal.cdf(1, _a, _b) for _a, _b in zip(a,b)]
npt.assert_array_almost_equal(stats.reciprocal.cdf(1, a, b), ans)
ans = [stats.reciprocal.cdf(_a, _a, _b) for _a, _b in zip(a,b)]
npt.assert_array_almost_equal(stats.reciprocal.cdf(a, a, b), ans)
ans = [stats.reciprocal.cdf(_b, _a, _b) for _a, _b in zip(a,b)]
npt.assert_array_almost_equal(stats.reciprocal.cdf(b, a, b), ans)
def test_broadcast_gh7933_regression():
# Check broadcast works
stats.truncnorm.logpdf(
np.array([3.0, 2.0, 1.0]),
a=(1.5 - np.array([6.0, 5.0, 4.0])) / 3.0,
b=np.inf,
loc=np.array([6.0, 5.0, 4.0]),
scale=3.0
)
def test_gh2002_regression():
# Add a check that broadcast works in situations where only some
# x-values are compatible with some of the shape arguments.
x = np.r_[-2:2:101j]
a = np.r_[-np.ones(50), np.ones(51)]
expected = [stats.truncnorm.pdf(_x, _a, np.inf) for _x, _a in zip(x, a)]
ans = stats.truncnorm.pdf(x, a, np.inf)
npt.assert_array_almost_equal(ans, expected)
def test_gh1320_regression():
# Check that the first example from gh-1320 now works.
c = 2.62
stats.genextreme.ppf(0.5, np.array([[c], [c + 0.5]]))
# The other examples in gh-1320 appear to have stopped working
# some time ago.
# ans = stats.genextreme.moment(2, np.array([c, c + 0.5]))
# expected = np.array([25.50105963, 115.11191437])
# stats.genextreme.moment(5, np.array([[c], [c + 0.5]]))
# stats.genextreme.moment(5, np.array([c, c + 0.5]))
def check_sample_meanvar_(distfn, arg, m, v, sm, sv, sn, msg):
# this did not work, skipped silently by nose
if np.isfinite(m):
check_sample_mean(sm, sv, sn, m)
if np.isfinite(v):
check_sample_var(sv, sn, v)
def check_sample_mean(sm, v, n, popmean):
# from stats.stats.ttest_1samp(a, popmean):
# Calculates the t-obtained for the independent samples T-test on ONE group
# of scores a, given a population mean.
#
# Returns: t-value, two-tailed prob
df = n-1
svar = ((n-1)*v) / float(df) # looks redundant
t = (sm-popmean) / np.sqrt(svar*(1.0/n))
prob = betainc(0.5*df, 0.5, df/(df + t*t))
# return t,prob
npt.assert_(prob > 0.01, 'mean fail, t,prob = %f, %f, m, sm=%f,%f' %
(t, prob, popmean, sm))
def check_sample_var(sv, n, popvar):
# two-sided chisquare test for sample variance equal to
# hypothesized variance
df = n-1
chi2 = (n - 1)*sv/popvar
pval = stats.distributions.chi2.sf(chi2, df) * 2
npt.assert_(pval > 0.01, 'var fail, t, pval = %f, %f, v, sv=%f, %f' %
(chi2, pval, popvar, sv))
def check_cdf_ppf(distfn, arg, msg):
values = [0.001, 0.5, 0.999]
npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
values, decimal=DECIMAL, err_msg=msg +
' - cdf-ppf roundtrip')
def check_sf_isf(distfn, arg, msg):
npt.assert_almost_equal(distfn.sf(distfn.isf([0.1, 0.5, 0.9], *arg), *arg),
[0.1, 0.5, 0.9], decimal=DECIMAL, err_msg=msg +
' - sf-isf roundtrip')
npt.assert_almost_equal(distfn.cdf([0.1, 0.9], *arg),
1.0 - distfn.sf([0.1, 0.9], *arg),
decimal=DECIMAL, err_msg=msg +
' - cdf-sf relationship')
def check_pdf(distfn, arg, msg):
# compares pdf at median with numerical derivative of cdf
median = distfn.ppf(0.5, *arg)
eps = 1e-6
pdfv = distfn.pdf(median, *arg)
if (pdfv < 1e-4) or (pdfv > 1e4):
# avoid checking a case where pdf is close to zero or
# huge (singularity)
median = median + 0.1
pdfv = distfn.pdf(median, *arg)
cdfdiff = (distfn.cdf(median + eps, *arg) -
distfn.cdf(median - eps, *arg))/eps/2.0
# replace with better diff and better test (more points),
# actually, this works pretty well
msg += ' - cdf-pdf relationship'
npt.assert_almost_equal(pdfv, cdfdiff, decimal=DECIMAL, err_msg=msg)
def check_pdf_logpdf(distfn, args, msg):
# compares pdf at several points with the log of the pdf
points = np.array([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
vals = distfn.ppf(points, *args)
vals = vals[np.isfinite(vals)]
pdf = distfn.pdf(vals, *args)
logpdf = distfn.logpdf(vals, *args)
pdf = pdf[(pdf != 0) & np.isfinite(pdf)]
logpdf = logpdf[np.isfinite(logpdf)]
msg += " - logpdf-log(pdf) relationship"
npt.assert_almost_equal(np.log(pdf), logpdf, decimal=7, err_msg=msg)
def check_pdf_logpdf_at_endpoints(distfn, args, msg):
# compares pdf with the log of the pdf at the (finite) end points
points = np.array([0, 1])
vals = distfn.ppf(points, *args)
vals = vals[np.isfinite(vals)]
with npt.suppress_warnings() as sup:
# Several distributions incur divide by zero or encounter invalid values when computing
# the pdf or logpdf at the endpoints.
suppress_messsages = [
"divide by zero encountered in true_divide", # multiple distributions
"divide by zero encountered in log", # multiple distributions
"divide by zero encountered in power", # gengamma
"invalid value encountered in add", # genextreme
"invalid value encountered in subtract", # gengamma
"invalid value encountered in multiply" # recipinvgauss
]
for msg in suppress_messsages:
sup.filter(category=RuntimeWarning, message=msg)
pdf = distfn.pdf(vals, *args)
logpdf = distfn.logpdf(vals, *args)
pdf = pdf[(pdf != 0) & np.isfinite(pdf)]
logpdf = logpdf[np.isfinite(logpdf)]
msg += " - logpdf-log(pdf) relationship"
npt.assert_almost_equal(np.log(pdf), logpdf, decimal=7, err_msg=msg)
def check_sf_logsf(distfn, args, msg):
# compares sf at several points with the log of the sf
points = np.array([0.0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1.0])
vals = distfn.ppf(points, *args)
vals = vals[np.isfinite(vals)]
sf = distfn.sf(vals, *args)
logsf = distfn.logsf(vals, *args)
sf = sf[sf != 0]
logsf = logsf[np.isfinite(logsf)]
msg += " - logsf-log(sf) relationship"
npt.assert_almost_equal(np.log(sf), logsf, decimal=7, err_msg=msg)
def check_cdf_logcdf(distfn, args, msg):
# compares cdf at several points with the log of the cdf
points = np.array([0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1.0])
vals = distfn.ppf(points, *args)
vals = vals[np.isfinite(vals)]
cdf = distfn.cdf(vals, *args)
logcdf = distfn.logcdf(vals, *args)
cdf = cdf[cdf != 0]
logcdf = logcdf[np.isfinite(logcdf)]
msg += " - logcdf-log(cdf) relationship"
npt.assert_almost_equal(np.log(cdf), logcdf, decimal=7, err_msg=msg)
def check_ppf_broadcast(distfn, arg, msg):
# compares ppf for multiple argsets.
num_repeats = 5
args = [] * num_repeats
if arg:
args = [np.array([_] * num_repeats) for _ in arg]
median = distfn.ppf(0.5, *arg)
medians = distfn.ppf(0.5, *args)
msg += " - ppf multiple"
npt.assert_almost_equal(medians, [median] * num_repeats, decimal=7, err_msg=msg)
def check_distribution_rvs(dist, args, alpha, rvs):
# dist is either a cdf function or name of a distribution in scipy.stats.
# args are the args for scipy.stats.dist(*args)
# alpha is a significance level, ~0.01
# rvs is array_like of random variables
# test from scipy.stats.tests
# this version reuses existing random variables
D, pval = stats.kstest(rvs, dist, args=args, N=1000)
if (pval < alpha):
# The rvs passed in failed the K-S test, which _could_ happen
# but is unlikely if alpha is small enough.
# Repeat the the test with a new sample of rvs.
# Generate 1000 rvs, perform a K-S test that the new sample of rvs
# are distributed according to the distribution.
D, pval = stats.kstest(dist, dist, args=args, N=1000)
npt.assert_(pval > alpha, "D = " + str(D) + "; pval = " + str(pval) +
"; alpha = " + str(alpha) + "\nargs = " + str(args))
def check_vecentropy(distfn, args):
npt.assert_equal(distfn.vecentropy(*args), distfn._entropy(*args))
def check_loc_scale(distfn, arg, m, v, msg):
loc, scale = 10.0, 10.0
mt, vt = distfn.stats(loc=loc, scale=scale, *arg)
npt.assert_allclose(m*scale + loc, mt)
npt.assert_allclose(v*scale*scale, vt)
def check_ppf_private(distfn, arg, msg):
# fails by design for truncnorm self.nb not defined
ppfs = distfn._ppf(np.array([0.1, 0.5, 0.9]), *arg)
npt.assert_(not np.any(np.isnan(ppfs)), msg + 'ppf private is nan')
def check_retrieving_support(distfn, args):
loc, scale = 1, 2
supp = distfn.support(*args)
supp_loc_scale = distfn.support(*args, loc=loc, scale=scale)
npt.assert_almost_equal(np.array(supp)*scale + loc,
np.array(supp_loc_scale))
def check_fit_args(distfn, arg, rvs):
with np.errstate(all='ignore'), npt.suppress_warnings() as sup:
sup.filter(category=RuntimeWarning,
message="The shape parameter of the erlang")
sup.filter(category=RuntimeWarning,
message="floating point number truncated")
vals = distfn.fit(rvs)
vals2 = distfn.fit(rvs, optimizer='powell')
# Only check the length of the return
# FIXME: should check the actual results to see if we are 'close'
# to what was created --- but what is 'close' enough
npt.assert_(len(vals) == 2+len(arg))
npt.assert_(len(vals2) == 2+len(arg))
def check_fit_args_fix(distfn, arg, rvs):
with np.errstate(all='ignore'), npt.suppress_warnings() as sup:
sup.filter(category=RuntimeWarning,
message="The shape parameter of the erlang")
vals = distfn.fit(rvs, floc=0)
vals2 = distfn.fit(rvs, fscale=1)
npt.assert_(len(vals) == 2+len(arg))
npt.assert_(vals[-2] == 0)
npt.assert_(vals2[-1] == 1)
npt.assert_(len(vals2) == 2+len(arg))
if len(arg) > 0:
vals3 = distfn.fit(rvs, f0=arg[0])
npt.assert_(len(vals3) == 2+len(arg))
npt.assert_(vals3[0] == arg[0])
if len(arg) > 1:
vals4 = distfn.fit(rvs, f1=arg[1])
npt.assert_(len(vals4) == 2+len(arg))
npt.assert_(vals4[1] == arg[1])
if len(arg) > 2:
vals5 = distfn.fit(rvs, f2=arg[2])
npt.assert_(len(vals5) == 2+len(arg))
npt.assert_(vals5[2] == arg[2])
@pytest.mark.parametrize('method', ['pdf', 'logpdf', 'cdf', 'logcdf',
'sf', 'logsf', 'ppf', 'isf'])
@pytest.mark.parametrize('distname, args', distcont)
def test_methods_with_lists(method, distname, args):
# Test that the continuous distributions can accept Python lists
# as arguments.
dist = getattr(stats, distname)
f = getattr(dist, method)
if distname == 'invweibull' and method.startswith('log'):
x = [1.5, 2]
else:
x = [0.1, 0.2]
shape2 = [[a]*2 for a in args]
loc = [0, 0.1]
scale = [1, 1.01]
result = f(x, *shape2, loc=loc, scale=scale)
npt.assert_allclose(result,
[f(*v) for v in zip(x, *shape2, loc, scale)],
rtol=1e-14, atol=5e-14)
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