274 lines
9.4 KiB
Python
274 lines
9.4 KiB
Python
|
import numpy.testing as npt
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from scipy import stats
|
||
|
|
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_edge_support,
|
||
|
|
check_named_args, check_random_state_property,
|
||
|
|
check_pickling, check_rvs_broadcast, check_freezing)
|
||
|
|
from scipy.stats._distr_params import distdiscrete
|
||
|
|
|
||
|
|
vals = ([1, 2, 3, 4], [0.1, 0.2, 0.3, 0.4])
|
||
|
|
distdiscrete += [[stats.rv_discrete(values=vals), ()]]
|
||
|
|
|
||
|
|
|
||
|
|
def cases_test_discrete_basic():
|
||
|
|
seen = set()
|
||
|
|
for distname, arg in distdiscrete:
|
||
|
|
yield distname, arg, distname not in seen
|
||
|
|
seen.add(distname)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('distname,arg,first_case', cases_test_discrete_basic())
|
||
|
|
def test_discrete_basic(distname, arg, first_case):
|
||
|
|
try:
|
||
|
|
distfn = getattr(stats, distname)
|
||
|
|
except TypeError:
|
||
|
|
distfn = distname
|
||
|
|
distname = 'sample distribution'
|
||
|
|
np.random.seed(9765456)
|
||
|
|
rvs = distfn.rvs(size=2000, *arg)
|
||
|
|
supp = np.unique(rvs)
|
||
|
|
m, v = distfn.stats(*arg)
|
||
|
|
check_cdf_ppf(distfn, arg, supp, distname + ' cdf_ppf')
|
||
|
|
|
||
|
|
check_pmf_cdf(distfn, arg, distname)
|
||
|
|
check_oth(distfn, arg, supp, distname + ' oth')
|
||
|
|
check_edge_support(distfn, arg)
|
||
|
|
|
||
|
|
alpha = 0.01
|
||
|
|
check_discrete_chisquare(distfn, arg, rvs, alpha,
|
||
|
|
distname + ' chisquare')
|
||
|
|
|
||
|
|
if first_case:
|
||
|
|
locscale_defaults = (0,)
|
||
|
|
meths = [distfn.pmf, distfn.logpmf, distfn.cdf, distfn.logcdf,
|
||
|
|
distfn.logsf]
|
||
|
|
# make sure arguments are within support
|
||
|
|
spec_k = {'randint': 11, 'hypergeom': 4, 'bernoulli': 0, }
|
||
|
|
k = spec_k.get(distname, 1)
|
||
|
|
check_named_args(distfn, k, arg, locscale_defaults, meths)
|
||
|
|
if distname != 'sample distribution':
|
||
|
|
check_scale_docstring(distfn)
|
||
|
|
check_random_state_property(distfn, arg)
|
||
|
|
check_pickling(distfn, arg)
|
||
|
|
check_freezing(distfn, arg)
|
||
|
|
|
||
|
|
# Entropy
|
||
|
|
check_entropy(distfn, arg, distname)
|
||
|
|
if distfn.__class__._entropy != stats.rv_discrete._entropy:
|
||
|
|
check_private_entropy(distfn, arg, stats.rv_discrete)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('distname,arg', distdiscrete)
|
||
|
|
def test_moments(distname, arg):
|
||
|
|
try:
|
||
|
|
distfn = getattr(stats, distname)
|
||
|
|
except TypeError:
|
||
|
|
distfn = distname
|
||
|
|
distname = 'sample distribution'
|
||
|
|
m, v, s, k = distfn.stats(*arg, moments='mvsk')
|
||
|
|
check_normalization(distfn, arg, distname)
|
||
|
|
|
||
|
|
# compare `stats` and `moment` methods
|
||
|
|
check_moment(distfn, arg, m, v, distname)
|
||
|
|
check_mean_expect(distfn, arg, m, distname)
|
||
|
|
check_var_expect(distfn, arg, m, v, distname)
|
||
|
|
check_skew_expect(distfn, arg, m, v, s, distname)
|
||
|
|
if distname not in ['zipf', 'yulesimon']:
|
||
|
|
check_kurt_expect(distfn, arg, m, v, k, distname)
|
||
|
|
|
||
|
|
# frozen distr moments
|
||
|
|
check_moment_frozen(distfn, arg, m, 1)
|
||
|
|
check_moment_frozen(distfn, arg, v+m*m, 2)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('dist,shape_args', distdiscrete)
|
||
|
|
def test_rvs_broadcast(dist, shape_args):
|
||
|
|
# 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 ['betabinom', 'skellam', 'yulesimon', 'dlaplace']
|
||
|
|
|
||
|
|
try:
|
||
|
|
distfunc = getattr(stats, dist)
|
||
|
|
except TypeError:
|
||
|
|
distfunc = dist
|
||
|
|
dist = 'rv_discrete(values=(%r, %r))' % (dist.xk, dist.pk)
|
||
|
|
loc = np.zeros(2)
|
||
|
|
nargs = distfunc.numargs
|
||
|
|
allargs = []
|
||
|
|
bshape = []
|
||
|
|
# Generate shape parameter arguments...
|
||
|
|
for k in range(nargs):
|
||
|
|
shp = (k + 3,) + (1,)*(k + 1)
|
||
|
|
param_val = shape_args[k]
|
||
|
|
allargs.append(np.full(shp, param_val))
|
||
|
|
bshape.insert(0, shp[0])
|
||
|
|
allargs.append(loc)
|
||
|
|
bshape.append(loc.size)
|
||
|
|
# 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, [np.int_])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('dist,args', distdiscrete)
|
||
|
|
def test_ppf_with_loc(dist, args):
|
||
|
|
try:
|
||
|
|
distfn = getattr(stats, dist)
|
||
|
|
except TypeError:
|
||
|
|
distfn = dist
|
||
|
|
#check with a negative, no and positive relocation.
|
||
|
|
np.random.seed(1942349)
|
||
|
|
re_locs = [np.random.randint(-10, -1), 0, np.random.randint(1, 10)]
|
||
|
|
_a, _b = distfn.support(*args)
|
||
|
|
for loc in re_locs:
|
||
|
|
npt.assert_array_equal(
|
||
|
|
[_a-1+loc, _b+loc],
|
||
|
|
[distfn.ppf(0.0, *args, loc=loc), distfn.ppf(1.0, *args, loc=loc)]
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def check_cdf_ppf(distfn, arg, supp, msg):
|
||
|
|
# cdf is a step function, and ppf(q) = min{k : cdf(k) >= q, k integer}
|
||
|
|
npt.assert_array_equal(distfn.ppf(distfn.cdf(supp, *arg), *arg),
|
||
|
|
supp, msg + '-roundtrip')
|
||
|
|
npt.assert_array_equal(distfn.ppf(distfn.cdf(supp, *arg) - 1e-8, *arg),
|
||
|
|
supp, msg + '-roundtrip')
|
||
|
|
|
||
|
|
if not hasattr(distfn, 'xk'):
|
||
|
|
_a, _b = distfn.support(*arg)
|
||
|
|
supp1 = supp[supp < _b]
|
||
|
|
npt.assert_array_equal(distfn.ppf(distfn.cdf(supp1, *arg) + 1e-8, *arg),
|
||
|
|
supp1 + distfn.inc, msg + ' ppf-cdf-next')
|
||
|
|
# -1e-8 could cause an error if pmf < 1e-8
|
||
|
|
|
||
|
|
|
||
|
|
def check_pmf_cdf(distfn, arg, distname):
|
||
|
|
if hasattr(distfn, 'xk'):
|
||
|
|
index = distfn.xk
|
||
|
|
else:
|
||
|
|
startind = int(distfn.ppf(0.01, *arg) - 1)
|
||
|
|
index = list(range(startind, startind + 10))
|
||
|
|
cdfs = distfn.cdf(index, *arg)
|
||
|
|
pmfs_cum = distfn.pmf(index, *arg).cumsum()
|
||
|
|
|
||
|
|
atol, rtol = 1e-10, 1e-10
|
||
|
|
if distname == 'skellam': # ncx2 accuracy
|
||
|
|
atol, rtol = 1e-5, 1e-5
|
||
|
|
npt.assert_allclose(cdfs - cdfs[0], pmfs_cum - pmfs_cum[0],
|
||
|
|
atol=atol, rtol=rtol)
|
||
|
|
|
||
|
|
|
||
|
|
def check_moment_frozen(distfn, arg, m, k):
|
||
|
|
npt.assert_allclose(distfn(*arg).moment(k), m,
|
||
|
|
atol=1e-10, rtol=1e-10)
|
||
|
|
|
||
|
|
|
||
|
|
def check_oth(distfn, arg, supp, msg):
|
||
|
|
# checking other methods of distfn
|
||
|
|
npt.assert_allclose(distfn.sf(supp, *arg), 1. - distfn.cdf(supp, *arg),
|
||
|
|
atol=1e-10, rtol=1e-10)
|
||
|
|
|
||
|
|
q = np.linspace(0.01, 0.99, 20)
|
||
|
|
npt.assert_allclose(distfn.isf(q, *arg), distfn.ppf(1. - q, *arg),
|
||
|
|
atol=1e-10, rtol=1e-10)
|
||
|
|
|
||
|
|
median_sf = distfn.isf(0.5, *arg)
|
||
|
|
npt.assert_(distfn.sf(median_sf - 1, *arg) > 0.5)
|
||
|
|
npt.assert_(distfn.cdf(median_sf + 1, *arg) > 0.5)
|
||
|
|
|
||
|
|
|
||
|
|
def check_discrete_chisquare(distfn, arg, rvs, alpha, msg):
|
||
|
|
"""Perform chisquare test for random sample of a discrete distribution
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
distname : string
|
||
|
|
name of distribution function
|
||
|
|
arg : sequence
|
||
|
|
parameters of distribution
|
||
|
|
alpha : float
|
||
|
|
significance level, threshold for p-value
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
result : bool
|
||
|
|
0 if test passes, 1 if test fails
|
||
|
|
|
||
|
|
"""
|
||
|
|
wsupp = 0.05
|
||
|
|
|
||
|
|
# construct intervals with minimum mass `wsupp`.
|
||
|
|
# intervals are left-half-open as in a cdf difference
|
||
|
|
_a, _b = distfn.support(*arg)
|
||
|
|
lo = int(max(_a, -1000))
|
||
|
|
high = int(min(_b, 1000)) + 1
|
||
|
|
distsupport = range(lo, high)
|
||
|
|
last = 0
|
||
|
|
distsupp = [lo]
|
||
|
|
distmass = []
|
||
|
|
for ii in distsupport:
|
||
|
|
current = distfn.cdf(ii, *arg)
|
||
|
|
if current - last >= wsupp - 1e-14:
|
||
|
|
distsupp.append(ii)
|
||
|
|
distmass.append(current - last)
|
||
|
|
last = current
|
||
|
|
if current > (1 - wsupp):
|
||
|
|
break
|
||
|
|
if distsupp[-1] < _b:
|
||
|
|
distsupp.append(_b)
|
||
|
|
distmass.append(1 - last)
|
||
|
|
distsupp = np.array(distsupp)
|
||
|
|
distmass = np.array(distmass)
|
||
|
|
|
||
|
|
# convert intervals to right-half-open as required by histogram
|
||
|
|
histsupp = distsupp + 1e-8
|
||
|
|
histsupp[0] = _a
|
||
|
|
|
||
|
|
# find sample frequencies and perform chisquare test
|
||
|
|
freq, hsupp = np.histogram(rvs, histsupp)
|
||
|
|
chis, pval = stats.chisquare(np.array(freq), len(rvs)*distmass)
|
||
|
|
|
||
|
|
npt.assert_(pval > alpha,
|
||
|
|
'chisquare - test for %s at arg = %s with pval = %s' %
|
||
|
|
(msg, str(arg), str(pval)))
|
||
|
|
|
||
|
|
|
||
|
|
def check_scale_docstring(distfn):
|
||
|
|
if distfn.__doc__ is not None:
|
||
|
|
# Docstrings can be stripped if interpreter is run with -OO
|
||
|
|
npt.assert_('scale' not in distfn.__doc__)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('method', ['pmf', 'logpmf', 'cdf', 'logcdf',
|
||
|
|
'sf', 'logsf', 'ppf', 'isf'])
|
||
|
|
@pytest.mark.parametrize('distname, args', distdiscrete)
|
||
|
|
def test_methods_with_lists(method, distname, args):
|
||
|
|
# Test that the discrete distributions can accept Python lists
|
||
|
|
# as arguments.
|
||
|
|
try:
|
||
|
|
dist = getattr(stats, distname)
|
||
|
|
except TypeError:
|
||
|
|
return
|
||
|
|
if method in ['ppf', 'isf']:
|
||
|
|
z = [0.1, 0.2]
|
||
|
|
else:
|
||
|
|
z = [0, 1]
|
||
|
|
p2 = [[p]*2 for p in args]
|
||
|
|
loc = [0, 1]
|
||
|
|
result = dist.pmf(z, *p2, loc=loc)
|
||
|
|
npt.assert_allclose(result,
|
||
|
|
[dist.pmf(*v) for v in zip(z, *p2, loc)],
|
||
|
|
rtol=1e-15, atol=1e-15)
|