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355 lines
14 KiB
Python
355 lines
14 KiB
Python
from __future__ import division, print_function, absolute_import
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import warnings
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import numpy as np
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import numpy.testing as npt
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from scipy import integrate
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from wafo import stats
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from wafo.stats.tests.common_tests import (check_normalization, check_moment,
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check_mean_expect,
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check_var_expect, check_skew_expect, check_kurt_expect,
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check_entropy, check_private_entropy, NUMPY_BELOW_1_7,
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check_edge_support, check_named_args)
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from wafo.stats._distr_params import distcont
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"""
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Test all continuous distributions.
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Parameters were chosen for those distributions that pass the
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Kolmogorov-Smirnov test. This provides safe parameters for each
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distributions so that we can perform further testing of class methods.
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These tests currently check only/mostly for serious errors and exceptions,
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not for numerically exact results.
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"""
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DECIMAL = 5 # specify the precision of the tests # increased from 0 to 5
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## Last four of these fail all around. Need to be checked
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distcont_extra = [
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['betaprime', (100, 86)],
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['fatiguelife', (5,)],
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['mielke', (4.6420495492121487, 0.59707419545516938)],
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['invweibull', (0.58847112119264788,)],
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# burr: sample mean test fails still for c<1
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['burr', (0.94839838075366045, 4.3820284068855795)],
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# genextreme: sample mean test, sf-logsf test fail
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['genextreme', (3.3184017469423535,)],
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]
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# for testing only specific functions
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# distcont = [
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## ['fatiguelife', (29,)], #correction numargs = 1
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## ['loggamma', (0.41411931826052117,)]]
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# for testing ticket:767
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# distcont = [
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## ['genextreme', (3.3184017469423535,)],
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## ['genextreme', (0.01,)],
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## ['genextreme', (0.00001,)],
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## ['genextreme', (0.0,)],
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## ['genextreme', (-0.01,)]
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## ]
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# distcont = [['gumbel_l', ()],
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## ['gumbel_r', ()],
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## ['norm', ()]
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## ]
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# distcont = [['norm', ()]]
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distmissing = ['wald', 'gausshyper', 'genexpon', 'rv_continuous',
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'loglaplace', 'rdist', 'semicircular', 'invweibull', 'ksone',
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'cosine', 'kstwobign', 'truncnorm', 'mielke', 'recipinvgauss', 'levy',
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'johnsonsu', 'levy_l', 'powernorm', 'wrapcauchy',
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'johnsonsb', 'truncexpon', 'rice', 'invgauss', 'invgamma',
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'powerlognorm']
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distmiss = [[dist,args] for dist,args in distcont if dist in distmissing]
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distslow = ['rdist', 'gausshyper', 'recipinvgauss', 'ksone', 'genexpon',
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'vonmises', 'vonmises_line', 'mielke', 'semicircular',
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'cosine', 'invweibull', 'powerlognorm', 'johnsonsu', 'kstwobign']
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# distslow are sorted by speed (very slow to slow)
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# NB: not needed anymore?
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def _silence_fp_errors(func):
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# warning: don't apply to test_ functions as is, then those will be skipped
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def wrap(*a, **kw):
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olderr = np.seterr(all='ignore')
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try:
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return func(*a, **kw)
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finally:
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np.seterr(**olderr)
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wrap.__name__ = func.__name__
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return wrap
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def test_cont_basic():
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# this test skips slow distributions
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=integrate.IntegrationWarning)
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for distname, arg in distcont[:]:
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if distname in distslow:
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continue
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if distname is 'levy_stable':
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continue
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distfn = getattr(stats, distname)
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np.random.seed(765456)
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sn = 500
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rvs = distfn.rvs(size=sn, *arg)
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sm = rvs.mean()
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sv = rvs.var()
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m, v = distfn.stats(*arg)
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yield check_sample_meanvar_, distfn, arg, m, v, sm, sv, sn, \
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distname + 'sample mean test'
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yield check_cdf_ppf, distfn, arg, distname
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yield check_sf_isf, distfn, arg, distname
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yield check_pdf, distfn, arg, distname
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yield check_pdf_logpdf, distfn, arg, distname
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yield check_cdf_logcdf, distfn, arg, distname
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yield check_sf_logsf, distfn, arg, distname
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if distname in distmissing:
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alpha = 0.01
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yield check_distribution_rvs, distname, arg, alpha, rvs
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locscale_defaults = (0, 1)
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meths = [distfn.pdf, distfn.logpdf, distfn.cdf, distfn.logcdf,
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distfn.logsf]
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# make sure arguments are within support
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spec_x = {'frechet_l': -0.5, 'weibull_max': -0.5, 'levy_l': -0.5,
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'pareto': 1.5, 'tukeylambda': 0.3}
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x = spec_x.get(distname, 0.5)
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yield check_named_args, distfn, x, arg, locscale_defaults, meths
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# Entropy
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skp = npt.dec.skipif
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yield check_entropy, distfn, arg, distname
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if distfn.numargs == 0:
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yield skp(NUMPY_BELOW_1_7)(check_vecentropy), distfn, arg
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if distfn.__class__._entropy != stats.rv_continuous._entropy:
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yield check_private_entropy, distfn, arg, stats.rv_continuous
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yield check_edge_support, distfn, arg
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knf = npt.dec.knownfailureif
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yield knf(distname == 'truncnorm')(check_ppf_private), distfn, \
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arg, distname
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@npt.dec.slow
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def test_cont_basic_slow():
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# same as above for slow distributions
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=integrate.IntegrationWarning)
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for distname, arg in distcont[:]:
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if distname not in distslow:
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continue
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if distname is 'levy_stable':
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continue
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distfn = getattr(stats, distname)
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np.random.seed(765456)
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sn = 500
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rvs = distfn.rvs(size=sn,*arg)
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sm = rvs.mean()
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sv = rvs.var()
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m, v = distfn.stats(*arg)
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yield check_sample_meanvar_, distfn, arg, m, v, sm, sv, sn, \
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distname + 'sample mean test'
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yield check_cdf_ppf, distfn, arg, distname
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yield check_sf_isf, distfn, arg, distname
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yield check_pdf, distfn, arg, distname
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yield check_pdf_logpdf, distfn, arg, distname
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yield check_cdf_logcdf, distfn, arg, distname
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yield check_sf_logsf, distfn, arg, distname
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# yield check_oth, distfn, arg # is still missing
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if distname in distmissing:
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alpha = 0.01
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yield check_distribution_rvs, distname, arg, alpha, rvs
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locscale_defaults = (0, 1)
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meths = [distfn.pdf, distfn.logpdf, distfn.cdf, distfn.logcdf,
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distfn.logsf]
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# make sure arguments are within support
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x = 0.5
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if distname == 'invweibull':
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arg = (1,)
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elif distname == 'ksone':
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arg = (3,)
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yield check_named_args, distfn, x, arg, locscale_defaults, meths
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# Entropy
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skp = npt.dec.skipif
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ks_cond = distname in ['ksone', 'kstwobign']
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yield skp(ks_cond)(check_entropy), distfn, arg, distname
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if distfn.numargs == 0:
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yield skp(NUMPY_BELOW_1_7)(check_vecentropy), distfn, arg
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if distfn.__class__._entropy != stats.rv_continuous._entropy:
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yield check_private_entropy, distfn, arg, stats.rv_continuous
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yield check_edge_support, distfn, arg
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@npt.dec.slow
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def test_moments():
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=integrate.IntegrationWarning)
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knf = npt.dec.knownfailureif
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fail_normalization = set(['vonmises', 'ksone'])
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fail_higher = set(['vonmises', 'ksone', 'ncf'])
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for distname, arg in distcont[:]:
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if distname is 'levy_stable':
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continue
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distfn = getattr(stats, distname)
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m, v, s, k = distfn.stats(*arg, moments='mvsk')
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cond1, cond2 = distname in fail_normalization, distname in fail_higher
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msg = distname + ' fails moments'
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yield knf(cond1, msg)(check_normalization), distfn, arg, distname
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yield knf(cond2, msg)(check_mean_expect), distfn, arg, m, distname
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yield knf(cond2, msg)(check_var_expect), distfn, arg, m, v, distname
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yield knf(cond2, msg)(check_skew_expect), distfn, arg, m, v, s, \
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distname
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yield knf(cond2, msg)(check_kurt_expect), distfn, arg, m, v, k, \
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distname
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yield check_loc_scale, distfn, arg, m, v, distname
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yield check_moment, distfn, arg, m, v, distname
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def check_sample_meanvar_(distfn, arg, m, v, sm, sv, sn, msg):
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# this did not work, skipped silently by nose
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if not np.isinf(m):
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check_sample_mean(sm, sv, sn, m)
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if not np.isinf(v):
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check_sample_var(sv, sn, v)
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def check_sample_mean(sm,v,n, popmean):
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# from stats.stats.ttest_1samp(a, popmean):
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# Calculates the t-obtained for the independent samples T-test on ONE group
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# of scores a, given a population mean.
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#
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# Returns: t-value, two-tailed prob
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df = n-1
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svar = ((n-1)*v) / float(df) # looks redundant
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t = (sm-popmean) / np.sqrt(svar*(1.0/n))
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prob = stats.betai(0.5*df, 0.5, df/(df+t*t))
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# return t,prob
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npt.assert_(prob > 0.01, 'mean fail, t,prob = %f, %f, m, sm=%f,%f' %
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(t, prob, popmean, sm))
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def check_sample_var(sv,n, popvar):
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# two-sided chisquare test for sample variance equal to hypothesized variance
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df = n-1
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chi2 = (n-1)*popvar/float(popvar)
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pval = stats.chisqprob(chi2,df)*2
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npt.assert_(pval > 0.01, 'var fail, t, pval = %f, %f, v, sv=%f, %f' %
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(chi2,pval,popvar,sv))
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def check_cdf_ppf(distfn,arg,msg):
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values = [0.001, 0.5, 0.999]
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npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
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values, decimal=DECIMAL, err_msg=msg +
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' - cdf-ppf roundtrip')
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def check_sf_isf(distfn,arg,msg):
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npt.assert_almost_equal(distfn.sf(distfn.isf([0.1,0.5,0.9], *arg), *arg),
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[0.1,0.5,0.9], decimal=DECIMAL, err_msg=msg +
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' - sf-isf roundtrip')
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npt.assert_almost_equal(distfn.cdf([0.1,0.9], *arg),
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1.0-distfn.sf([0.1,0.9], *arg),
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decimal=DECIMAL, err_msg=msg +
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' - cdf-sf relationship')
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def check_pdf(distfn, arg, msg):
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# compares pdf at median with numerical derivative of cdf
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median = distfn.ppf(0.5, *arg)
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eps = 1e-6
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pdfv = distfn.pdf(median, *arg)
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if (pdfv < 1e-4) or (pdfv > 1e4):
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# avoid checking a case where pdf is close to zero or huge (singularity)
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median = median + 0.1
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pdfv = distfn.pdf(median, *arg)
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cdfdiff = (distfn.cdf(median + eps, *arg) -
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distfn.cdf(median - eps, *arg))/eps/2.0
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# replace with better diff and better test (more points),
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# actually, this works pretty well
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npt.assert_almost_equal(pdfv, cdfdiff,
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decimal=DECIMAL, err_msg=msg + ' - cdf-pdf relationship')
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def check_pdf_logpdf(distfn, args, msg):
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# compares pdf at several points with the log of the pdf
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points = np.array([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
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vals = distfn.ppf(points, *args)
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pdf = distfn.pdf(vals, *args)
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logpdf = distfn.logpdf(vals, *args)
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pdf = pdf[pdf != 0]
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logpdf = logpdf[np.isfinite(logpdf)]
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npt.assert_almost_equal(np.log(pdf), logpdf, decimal=7, err_msg=msg + " - logpdf-log(pdf) relationship")
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def check_sf_logsf(distfn, args, msg):
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# compares sf at several points with the log of the sf
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points = np.array([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
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vals = distfn.ppf(points, *args)
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sf = distfn.sf(vals, *args)
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logsf = distfn.logsf(vals, *args)
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sf = sf[sf != 0]
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logsf = logsf[np.isfinite(logsf)]
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npt.assert_almost_equal(np.log(sf), logsf, decimal=7, err_msg=msg + " - logsf-log(sf) relationship")
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def check_cdf_logcdf(distfn, args, msg):
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# compares cdf at several points with the log of the cdf
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points = np.array([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
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vals = distfn.ppf(points, *args)
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cdf = distfn.cdf(vals, *args)
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logcdf = distfn.logcdf(vals, *args)
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cdf = cdf[cdf != 0]
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logcdf = logcdf[np.isfinite(logcdf)]
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npt.assert_almost_equal(np.log(cdf), logcdf, decimal=7, err_msg=msg + " - logcdf-log(cdf) relationship")
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def check_distribution_rvs(dist, args, alpha, rvs):
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# test from scipy.stats.tests
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# this version reuses existing random variables
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D,pval = stats.kstest(rvs, dist, args=args, N=1000)
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if (pval < alpha):
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D,pval = stats.kstest(dist,'',args=args, N=1000)
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npt.assert_(pval > alpha, "D = " + str(D) + "; pval = " + str(pval) +
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"; alpha = " + str(alpha) + "\nargs = " + str(args))
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def check_vecentropy(distfn, args):
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npt.assert_equal(distfn.vecentropy(*args), distfn._entropy(*args))
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@npt.dec.skipif(NUMPY_BELOW_1_7)
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def check_loc_scale(distfn, arg, m, v, msg):
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loc, scale = 10.0, 10.0
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mt, vt = distfn.stats(loc=loc, scale=scale, *arg)
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npt.assert_allclose(m*scale + loc, mt)
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npt.assert_allclose(v*scale*scale, vt)
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def check_ppf_private(distfn, arg, msg):
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#fails by design for truncnorm self.nb not defined
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ppfs = distfn._ppf(np.array([0.1, 0.5, 0.9]), *arg)
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npt.assert_(not np.any(np.isnan(ppfs)), msg + 'ppf private is nan')
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if __name__ == "__main__":
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npt.run_module_suite()
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