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657 lines
26 KiB
Python
657 lines
26 KiB
Python
import pickle
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import numpy as np
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import numpy.testing as npt
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import pytest
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from pytest import raises as assert_raises
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from scipy.integrate import IntegrationWarning
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from scipy import stats
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from scipy.special import betainc
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from. common_tests import (check_normalization, check_moment, check_mean_expect,
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check_var_expect, check_skew_expect,
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check_kurt_expect, check_entropy,
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check_private_entropy, check_entropy_vect_scale,
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check_edge_support, check_named_args,
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check_random_state_property,
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check_meth_dtype, check_ppf_dtype, check_cmplx_deriv,
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check_pickling, check_rvs_broadcast, check_freezing)
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from scipy.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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# Note that you need to add new distributions you want tested
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# to _distr_params
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DECIMAL = 5 # specify the precision of the tests # increased from 0 to 5
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# Last three 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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['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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distslow = ['kstwo', 'ksone', 'kappa4', 'gausshyper', 'recipinvgauss',
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'genexpon', 'vonmises', 'vonmises_line', 'cosine', 'invweibull',
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'powerlognorm', 'johnsonsu', 'kstwobign']
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# distslow are sorted by speed (very slow to slow)
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# skip check_fit_args (test is slow)
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skip_fit_test = ['exponpow', 'exponweib', 'gausshyper', 'genexpon',
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'halfgennorm', 'gompertz', 'johnsonsb', 'johnsonsu',
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'kappa4', 'ksone', 'kstwo', 'kstwobign', 'mielke', 'ncf', 'nct',
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'powerlognorm', 'powernorm', 'recipinvgauss', 'trapezoid',
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'vonmises', 'vonmises_line',
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'levy_stable', 'rv_histogram_instance']
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# skip check_fit_args_fix (test is slow)
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skip_fit_fix_test = ['burr', 'exponpow', 'exponweib',
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'gausshyper', 'genexpon', 'halfgennorm',
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'gompertz', 'johnsonsb', 'johnsonsu', 'kappa4',
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'ksone', 'kstwo', 'kstwobign', 'levy_stable', 'mielke', 'ncf',
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'ncx2', 'powerlognorm', 'powernorm', 'rdist',
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'recipinvgauss', 'trapezoid', 'vonmises', 'vonmises_line']
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# These distributions fail the complex derivative test below.
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# Here 'fail' mean produce wrong results and/or raise exceptions, depending
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# on the implementation details of corresponding special functions.
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# cf https://github.com/scipy/scipy/pull/4979 for a discussion.
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fails_cmplx = set(['beta', 'betaprime', 'chi', 'chi2', 'dgamma', 'dweibull',
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'erlang', 'f', 'gamma', 'gausshyper', 'gengamma',
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'geninvgauss', 'gennorm', 'genpareto',
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'halfgennorm', 'invgamma',
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'ksone', 'kstwo', 'kstwobign', 'levy_l', 'loggamma', 'logistic',
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'loguniform', 'maxwell', 'nakagami',
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'ncf', 'nct', 'ncx2', 'norminvgauss', 'pearson3', 'rdist',
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'reciprocal', 'rice', 'skewnorm', 't', 'tukeylambda',
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'vonmises', 'vonmises_line', 'rv_histogram_instance'])
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_h = np.histogram([1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6,
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6, 6, 6, 7, 7, 7, 8, 8, 9], bins=8)
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histogram_test_instance = stats.rv_histogram(_h)
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def cases_test_cont_basic():
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for distname, arg in distcont[:] + [(histogram_test_instance, tuple())]:
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if distname == 'levy_stable':
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continue
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elif distname in distslow:
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yield pytest.param(distname, arg, marks=pytest.mark.slow)
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else:
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yield distname, arg
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@pytest.mark.parametrize('distname,arg', cases_test_cont_basic())
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def test_cont_basic(distname, arg):
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# this test skips slow distributions
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if distname == 'truncnorm':
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pytest.xfail(reason=distname)
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try:
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distfn = getattr(stats, distname)
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except TypeError:
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distfn = distname
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distname = 'rv_histogram_instance'
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rng = np.random.RandomState(765456)
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sn = 500
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rvs = distfn.rvs(size=sn, *arg, random_state=rng)
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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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check_sample_meanvar_(distfn, arg, m, v, sm, sv, sn, distname + 'sample mean test')
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check_cdf_ppf(distfn, arg, distname)
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check_sf_isf(distfn, arg, distname)
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check_pdf(distfn, arg, distname)
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check_pdf_logpdf(distfn, arg, distname)
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check_pdf_logpdf_at_endpoints(distfn, arg, distname)
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check_cdf_logcdf(distfn, arg, distname)
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check_sf_logsf(distfn, arg, distname)
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check_ppf_broadcast(distfn, arg, distname)
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alpha = 0.01
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if distname == 'rv_histogram_instance':
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check_distribution_rvs(distfn.cdf, arg, alpha, rvs)
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elif distname != 'geninvgauss':
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# skip kstest for geninvgauss since cdf is too slow; see test for
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# rv generation in TestGenInvGauss in test_distributions.py
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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 = {'weibull_max': -0.5, 'levy_l': -0.5,
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'pareto': 1.5, 'tukeylambda': 0.3,
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'rv_histogram_instance': 5.0}
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x = spec_x.get(distname, 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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check_named_args(distfn, x, arg, locscale_defaults, meths)
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check_random_state_property(distfn, arg)
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check_pickling(distfn, arg)
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check_freezing(distfn, arg)
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# Entropy
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if distname not in ['kstwobign', 'kstwo']:
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check_entropy(distfn, arg, distname)
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if distfn.numargs == 0:
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check_vecentropy(distfn, arg)
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if (distfn.__class__._entropy != stats.rv_continuous._entropy
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and distname != 'vonmises'):
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check_private_entropy(distfn, arg, stats.rv_continuous)
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with npt.suppress_warnings() as sup:
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sup.filter(IntegrationWarning, "The occurrence of roundoff error")
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sup.filter(IntegrationWarning, "Extremely bad integrand")
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sup.filter(RuntimeWarning, "invalid value")
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check_entropy_vect_scale(distfn, arg)
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check_retrieving_support(distfn, arg)
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check_edge_support(distfn, arg)
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check_meth_dtype(distfn, arg, meths)
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check_ppf_dtype(distfn, arg)
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if distname not in fails_cmplx:
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check_cmplx_deriv(distfn, arg)
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if distname != 'truncnorm':
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check_ppf_private(distfn, arg, distname)
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if distname not in skip_fit_test:
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check_fit_args(distfn, arg, rvs[0:200])
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if distname not in skip_fit_fix_test:
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check_fit_args_fix(distfn, arg, rvs[0:200])
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@pytest.mark.parametrize('distname,arg', cases_test_cont_basic())
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def test_rvs_scalar(distname, arg):
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# rvs should return a scalar when given scalar arguments (gh-12428)
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try:
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distfn = getattr(stats, distname)
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except TypeError:
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distfn = distname
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distname = 'rv_histogram_instance'
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assert np.isscalar(distfn.rvs(*arg))
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assert np.isscalar(distfn.rvs(*arg, size=()))
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assert np.isscalar(distfn.rvs(*arg, size=None))
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def test_levy_stable_random_state_property():
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# levy_stable only implements rvs(), so it is skipped in the
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# main loop in test_cont_basic(). Here we apply just the test
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# check_random_state_property to levy_stable.
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check_random_state_property(stats.levy_stable, (0.5, 0.1))
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def cases_test_moments():
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fail_normalization = set(['vonmises'])
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fail_higher = set(['vonmises', 'ncf'])
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for distname, arg in distcont[:] + [(histogram_test_instance, tuple())]:
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if distname == 'levy_stable':
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continue
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cond1 = distname not in fail_normalization
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cond2 = distname not in fail_higher
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yield distname, arg, cond1, cond2, False
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if not cond1 or not cond2:
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# Run the distributions that have issues twice, once skipping the
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# not_ok parts, once with the not_ok parts but marked as knownfail
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yield pytest.param(distname, arg, True, True, True,
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marks=pytest.mark.xfail)
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@pytest.mark.slow
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@pytest.mark.parametrize('distname,arg,normalization_ok,higher_ok,is_xfailing',
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cases_test_moments())
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def test_moments(distname, arg, normalization_ok, higher_ok, is_xfailing):
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try:
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distfn = getattr(stats, distname)
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except TypeError:
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distfn = distname
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distname = 'rv_histogram_instance'
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with npt.suppress_warnings() as sup:
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sup.filter(IntegrationWarning,
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"The integral is probably divergent, or slowly convergent.")
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if is_xfailing:
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sup.filter(IntegrationWarning)
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m, v, s, k = distfn.stats(*arg, moments='mvsk')
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if normalization_ok:
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check_normalization(distfn, arg, distname)
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if higher_ok:
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check_mean_expect(distfn, arg, m, distname)
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check_skew_expect(distfn, arg, m, v, s, distname)
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check_var_expect(distfn, arg, m, v, distname)
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check_kurt_expect(distfn, arg, m, v, k, distname)
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check_loc_scale(distfn, arg, m, v, distname)
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check_moment(distfn, arg, m, v, distname)
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@pytest.mark.parametrize('dist,shape_args', distcont)
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def test_rvs_broadcast(dist, shape_args):
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if dist in ['gausshyper', 'genexpon']:
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pytest.skip("too slow")
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# If shape_only is True, it means the _rvs method of the
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# distribution uses more than one random number to generate a random
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# variate. That means the result of using rvs with broadcasting or
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# with a nontrivial size will not necessarily be the same as using the
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# numpy.vectorize'd version of rvs(), so we can only compare the shapes
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# of the results, not the values.
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# Whether or not a distribution is in the following list is an
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# implementation detail of the distribution, not a requirement. If
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# the implementation the rvs() method of a distribution changes, this
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# test might also have to be changed.
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shape_only = dist in ['argus', 'betaprime', 'dgamma', 'dweibull',
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'exponnorm', 'geninvgauss', 'levy_stable', 'nct',
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'norminvgauss', 'rice', 'skewnorm', 'semicircular']
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distfunc = getattr(stats, dist)
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loc = np.zeros(2)
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scale = np.ones((3, 1))
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nargs = distfunc.numargs
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allargs = []
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bshape = [3, 2]
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# Generate shape parameter arguments...
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for k in range(nargs):
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shp = (k + 4,) + (1,)*(k + 2)
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allargs.append(shape_args[k]*np.ones(shp))
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bshape.insert(0, k + 4)
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allargs.extend([loc, scale])
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# bshape holds the expected shape when loc, scale, and the shape
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# parameters are all broadcast together.
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check_rvs_broadcast(distfunc, dist, allargs, bshape, shape_only, 'd')
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def test_rvs_gh2069_regression():
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# Regression tests for gh-2069. In scipy 0.17 and earlier,
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# these tests would fail.
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#
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# A typical example of the broken behavior:
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# >>> norm.rvs(loc=np.zeros(5), scale=np.ones(5))
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# array([-2.49613705, -2.49613705, -2.49613705, -2.49613705, -2.49613705])
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rng = np.random.RandomState(123)
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vals = stats.norm.rvs(loc=np.zeros(5), scale=1, random_state=rng)
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d = np.diff(vals)
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npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
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vals = stats.norm.rvs(loc=0, scale=np.ones(5), random_state=rng)
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d = np.diff(vals)
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npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
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vals = stats.norm.rvs(loc=np.zeros(5), scale=np.ones(5), random_state=rng)
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d = np.diff(vals)
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npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
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vals = stats.norm.rvs(loc=np.array([[0], [0]]), scale=np.ones(5),
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random_state=rng)
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d = np.diff(vals.ravel())
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npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
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assert_raises(ValueError, stats.norm.rvs, [[0, 0], [0, 0]],
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[[1, 1], [1, 1]], 1)
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assert_raises(ValueError, stats.gamma.rvs, [2, 3, 4, 5], 0, 1, (2, 2))
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assert_raises(ValueError, stats.gamma.rvs, [1, 1, 1, 1], [0, 0, 0, 0],
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[[1], [2]], (4,))
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def test_nomodify_gh9900_regression():
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# Regression test for gh-9990
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# Prior to gh-9990, calls to stats.truncnorm._cdf() use what ever was
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# set inside the stats.truncnorm instance during stats.truncnorm.cdf().
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# This could cause issues wth multi-threaded code.
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# Since then, the calls to cdf() are not permitted to modify the global
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# stats.truncnorm instance.
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tn = stats.truncnorm
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# Use the right-half truncated normal
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# Check that the cdf and _cdf return the same result.
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npt.assert_almost_equal(tn.cdf(1, 0, np.inf), 0.6826894921370859)
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npt.assert_almost_equal(tn._cdf(1, 0, np.inf), 0.6826894921370859)
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# Now use the left-half truncated normal
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npt.assert_almost_equal(tn.cdf(-1, -np.inf, 0), 0.31731050786291415)
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npt.assert_almost_equal(tn._cdf(-1, -np.inf, 0), 0.31731050786291415)
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# Check that the right-half truncated normal _cdf hasn't changed
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npt.assert_almost_equal(tn._cdf(1, 0, np.inf), 0.6826894921370859) # NOT 1.6826894921370859
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npt.assert_almost_equal(tn.cdf(1, 0, np.inf), 0.6826894921370859)
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# Check that the left-half truncated normal _cdf hasn't changed
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npt.assert_almost_equal(tn._cdf(-1, -np.inf, 0), 0.31731050786291415) # Not -0.6826894921370859
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npt.assert_almost_equal(tn.cdf(1, -np.inf, 0), 1) # Not 1.6826894921370859
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npt.assert_almost_equal(tn.cdf(-1, -np.inf, 0), 0.31731050786291415) # Not -0.6826894921370859
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def test_broadcast_gh9990_regression():
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# Regression test for gh-9990
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# The x-value 7 only lies within the support of 4 of the supplied
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# distributions. Prior to 9990, one array passed to
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# stats.reciprocal._cdf would have 4 elements, but an array
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# previously stored by stats.reciprocal_argcheck() would have 6, leading
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# to a broadcast error.
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a = np.array([1, 2, 3, 4, 5, 6])
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b = np.array([8, 16, 1, 32, 1, 48])
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ans = [stats.reciprocal.cdf(7, _a, _b) for _a, _b in zip(a,b)]
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npt.assert_array_almost_equal(stats.reciprocal.cdf(7, a, b), ans)
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ans = [stats.reciprocal.cdf(1, _a, _b) for _a, _b in zip(a,b)]
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npt.assert_array_almost_equal(stats.reciprocal.cdf(1, a, b), ans)
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ans = [stats.reciprocal.cdf(_a, _a, _b) for _a, _b in zip(a,b)]
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npt.assert_array_almost_equal(stats.reciprocal.cdf(a, a, b), ans)
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ans = [stats.reciprocal.cdf(_b, _a, _b) for _a, _b in zip(a,b)]
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npt.assert_array_almost_equal(stats.reciprocal.cdf(b, a, b), ans)
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def test_broadcast_gh7933_regression():
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# Check broadcast works
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stats.truncnorm.logpdf(
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np.array([3.0, 2.0, 1.0]),
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a=(1.5 - np.array([6.0, 5.0, 4.0])) / 3.0,
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b=np.inf,
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loc=np.array([6.0, 5.0, 4.0]),
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scale=3.0
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)
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def test_gh2002_regression():
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# Add a check that broadcast works in situations where only some
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# x-values are compatible with some of the shape arguments.
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x = np.r_[-2:2:101j]
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a = np.r_[-np.ones(50), np.ones(51)]
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expected = [stats.truncnorm.pdf(_x, _a, np.inf) for _x, _a in zip(x, a)]
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ans = stats.truncnorm.pdf(x, a, np.inf)
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npt.assert_array_almost_equal(ans, expected)
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def test_gh1320_regression():
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# Check that the first example from gh-1320 now works.
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c = 2.62
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stats.genextreme.ppf(0.5, np.array([[c], [c + 0.5]]))
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# The other examples in gh-1320 appear to have stopped working
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# some time ago.
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# ans = stats.genextreme.moment(2, np.array([c, c + 0.5]))
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# expected = np.array([25.50105963, 115.11191437])
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# stats.genextreme.moment(5, np.array([[c], [c + 0.5]]))
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# stats.genextreme.moment(5, np.array([c, c + 0.5]))
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|
|
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)
|