Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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120 lines
3.6 KiB
120 lines
3.6 KiB
import os
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import numpy as np
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from numpy.testing import assert_allclose, suppress_warnings
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import pytest
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from scipy import stats
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from .test_continuous_basic import distcont
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# this is not a proper statistical test for convergence, but only
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# verifies that the estimate and true values don't differ by too much
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fit_sizes = [1000, 5000] # sample sizes to try
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thresh_percent = 0.25 # percent of true parameters for fail cut-off
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thresh_min = 0.75 # minimum difference estimate - true to fail test
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failing_fits = [
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'burr',
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'chi2',
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'gausshyper',
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'genexpon',
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'gengamma',
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'kappa4',
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'ksone',
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'kstwo',
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'mielke',
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'ncf',
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'ncx2',
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'pearson3',
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'powerlognorm',
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'truncexpon',
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'tukeylambda',
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'vonmises',
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'wrapcauchy',
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'levy_stable',
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'trapz'
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]
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# Don't run the fit test on these:
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skip_fit = [
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'erlang', # Subclass of gamma, generates a warning.
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]
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def cases_test_cont_fit():
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# this tests the closeness of the estimated parameters to the true
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# parameters with fit method of continuous distributions
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# Note: is slow, some distributions don't converge with sample size <= 10000
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for distname, arg in distcont:
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if distname not in skip_fit:
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yield distname, arg
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@pytest.mark.slow
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@pytest.mark.parametrize('distname,arg', cases_test_cont_fit())
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def test_cont_fit(distname, arg):
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if distname in failing_fits:
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# Skip failing fits unless overridden
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try:
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xfail = not int(os.environ['SCIPY_XFAIL'])
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except Exception:
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xfail = True
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if xfail:
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msg = "Fitting %s doesn't work reliably yet" % distname
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msg += " [Set environment variable SCIPY_XFAIL=1 to run this test nevertheless.]"
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pytest.xfail(msg)
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distfn = getattr(stats, distname)
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truearg = np.hstack([arg, [0.0, 1.0]])
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diffthreshold = np.max(np.vstack([truearg*thresh_percent,
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np.full(distfn.numargs+2, thresh_min)]),
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0)
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for fit_size in fit_sizes:
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# Note that if a fit succeeds, the other fit_sizes are skipped
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np.random.seed(1234)
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with np.errstate(all='ignore'), suppress_warnings() as sup:
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sup.filter(category=DeprecationWarning, message=".*frechet_")
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rvs = distfn.rvs(size=fit_size, *arg)
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est = distfn.fit(rvs) # start with default values
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diff = est - truearg
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# threshold for location
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diffthreshold[-2] = np.max([np.abs(rvs.mean())*thresh_percent,thresh_min])
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if np.any(np.isnan(est)):
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raise AssertionError('nan returned in fit')
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else:
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if np.all(np.abs(diff) <= diffthreshold):
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break
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else:
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txt = 'parameter: %s\n' % str(truearg)
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txt += 'estimated: %s\n' % str(est)
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txt += 'diff : %s\n' % str(diff)
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raise AssertionError('fit not very good in %s\n' % distfn.name + txt)
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def _check_loc_scale_mle_fit(name, data, desired, atol=None):
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d = getattr(stats, name)
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actual = d.fit(data)[-2:]
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assert_allclose(actual, desired, atol=atol,
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err_msg='poor mle fit of (loc, scale) in %s' % name)
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def test_non_default_loc_scale_mle_fit():
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data = np.array([1.01, 1.78, 1.78, 1.78, 1.88, 1.88, 1.88, 2.00])
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_check_loc_scale_mle_fit('uniform', data, [1.01, 0.99], 1e-3)
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_check_loc_scale_mle_fit('expon', data, [1.01, 0.73875], 1e-3)
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def test_expon_fit():
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"""gh-6167"""
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data = [0, 0, 0, 0, 2, 2, 2, 2]
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phat = stats.expon.fit(data, floc=0)
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assert_allclose(phat, [0, 1.0], atol=1e-3)
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