from __future__ import division, print_function, absolute_import
import numpy.testing as npt
import numpy as np
from scipy._lib.six import xrange
from wafo import stats
from wafo.stats.tests.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)
from wafo.stats._distr_params import distdiscrete
knf = npt.dec.knownfailureif
vals = ([1, 2, 3, 4], [0.1, 0.2, 0.3, 0.4])
distdiscrete += [[stats.rv_discrete(values=vals), ()]]
def test_discrete_basic():
for distname, arg in distdiscrete:
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)
yield check_cdf_ppf, distfn, arg, supp, distname + ' cdf_ppf'
yield check_pmf_cdf, distfn, arg, distname
yield check_oth, distfn, arg, supp, distname + ' oth'
yield check_edge_support, distfn, arg
alpha = 0.01
yield (check_discrete_chisquare, distfn, arg, rvs, alpha,
distname + ' chisquare')
seen = set()
for distname, arg in distdiscrete:
if distname in seen:
continue
seen.add(distname)
try:
distfn = getattr(stats, distname)
except TypeError:
distfn = distname
distname = 'sample distribution'
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)
yield check_named_args, distfn, k, arg, locscale_defaults, meths
if distname != 'sample distribution':
yield check_scale_docstring, distfn
yield check_random_state_property, distfn, arg
yield check_pickling, distfn, arg
# Entropy
yield check_entropy, distfn, arg, distname
if distfn.__class__._entropy != stats.rv_discrete._entropy:
yield check_private_entropy, distfn, arg, stats.rv_discrete
def test_moments():
for distname, arg in distdiscrete:
try:
distfn = getattr(stats, distname)
except TypeError:
distfn = distname
distname = 'sample distribution'
m, v, s, k = distfn.stats(*arg, moments='mvsk')
yield check_normalization, distfn, arg, distname
# compare `stats` and `moment` methods
yield check_moment, distfn, arg, m, v, distname
yield check_mean_expect, distfn, arg, m, distname
yield check_var_expect, distfn, arg, m, v, distname
yield check_skew_expect, distfn, arg, m, v, s, distname
cond = distname in ['zipf']
msg = distname + ' fails kurtosis'
yield knf(cond, msg)(check_kurt_expect), distfn, arg, m, v, k, distname
# frozen distr moments
yield check_moment_frozen, distfn, arg, m, 1
yield check_moment_frozen, distfn, arg, v+m*m, 2
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')
supp1 = supp[supp < distfn.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):
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
lo = max(distfn.a, -1000)
distsupport = xrange(lo, min(distfn.b, 1000) + 1)
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] < distfn.b:
distsupp.append(distfn.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] = distfn.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__)
if __name__ == "__main__":
npt.run_module_suite()