1039 lines
45 KiB
Python
1039 lines
45 KiB
Python
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import os
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import numpy as np
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import numpy.testing as npt
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from numpy.testing import assert_allclose, assert_equal
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import pytest
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from scipy import stats
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from scipy.optimize import differential_evolution
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from .test_continuous_basic import distcont
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from scipy.stats._distn_infrastructure import FitError
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from scipy.stats._distr_params import distdiscrete
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from scipy.stats import goodness_of_fit
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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, 10000] # 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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mle_failing_fits = [
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'gausshyper',
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'genexpon',
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'gengamma',
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'irwinhall',
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'kappa4',
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'ksone',
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'kstwo',
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'ncf',
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'ncx2',
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'truncexpon',
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'tukeylambda',
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'vonmises',
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'levy_stable',
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'trapezoid',
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'truncweibull_min',
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'studentized_range',
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]
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# these pass but are XSLOW (>1s)
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mle_Xslow_fits = ['betaprime', 'crystalball', 'exponweib', 'f', 'geninvgauss',
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'jf_skew_t', 'recipinvgauss', 'rel_breitwigner', 'vonmises_line']
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# The MLE fit method of these distributions doesn't perform well when all
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# parameters are fit, so test them with the location fixed at 0.
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mle_use_floc0 = [
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'burr',
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'chi',
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'chi2',
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'mielke',
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'pearson3',
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'genhalflogistic',
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'rdist',
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'pareto',
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'powerlaw', # distfn.nnlf(est2, rvs) > distfn.nnlf(est1, rvs) otherwise
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'powerlognorm',
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'wrapcauchy',
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'rel_breitwigner',
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]
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mm_failing_fits = ['alpha', 'betaprime', 'burr', 'burr12', 'cauchy', 'chi',
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'chi2', 'crystalball', 'dgamma', 'dweibull', 'f',
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'fatiguelife', 'fisk', 'foldcauchy', 'genextreme',
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'gengamma', 'genhyperbolic', 'gennorm', 'genpareto',
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'halfcauchy', 'invgamma', 'invweibull', 'irwinhall', 'jf_skew_t',
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'johnsonsu', 'kappa3', 'ksone', 'kstwo', 'levy', 'levy_l',
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'levy_stable', 'loglaplace', 'lomax', 'mielke', 'nakagami',
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'ncf', 'nct', 'ncx2', 'pareto', 'powerlognorm', 'powernorm',
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'rel_breitwigner', 'skewcauchy', 't', 'trapezoid', 'triang',
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'truncpareto', 'truncweibull_min', 'tukeylambda',
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'studentized_range']
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# not sure if these fail, but they caused my patience to fail
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mm_XXslow_fits = ['argus', 'exponpow', 'exponweib', 'gausshyper', 'genexpon',
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'genhalflogistic', 'halfgennorm', 'gompertz', 'johnsonsb',
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'kappa4', 'kstwobign', 'recipinvgauss',
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'truncexpon', 'vonmises', 'vonmises_line']
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# these pass but are XSLOW (>1s)
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mm_Xslow_fits = ['wrapcauchy']
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failing_fits = {"MM": mm_failing_fits + mm_XXslow_fits, "MLE": mle_failing_fits}
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xslow_fits = {"MM": mm_Xslow_fits, "MLE": mle_Xslow_fits}
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fail_interval_censored = {"truncpareto"}
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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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'genhyperbolic', 'norminvgauss', # too slow
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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
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# 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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@pytest.mark.parametrize('method', ["MLE", "MM"])
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def test_cont_fit(distname, arg, method):
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run_xfail = int(os.getenv('SCIPY_XFAIL', default=False))
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run_xslow = int(os.getenv('SCIPY_XSLOW', default=False))
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if distname in failing_fits[method] and not run_xfail:
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# The generic `fit` method can't be expected to work perfectly for all
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# distributions, data, and guesses. Some failures are expected.
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msg = "Failure expected; set environment variable SCIPY_XFAIL=1 to run."
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pytest.xfail(msg)
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if distname in xslow_fits[method] and not run_xslow:
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msg = "Very slow; set environment variable SCIPY_XSLOW=1 to run."
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pytest.skip(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'):
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rvs = distfn.rvs(size=fit_size, *arg)
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if method == 'MLE' and distfn.name in mle_use_floc0:
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kwds = {'floc': 0}
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else:
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kwds = {}
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# start with default values
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est = distfn.fit(rvs, method=method, **kwds)
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if method == 'MLE':
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# Trivial test of the use of CensoredData. The fit() method
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# will check that data contains no actual censored data, and
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# do a regular uncensored fit.
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data1 = stats.CensoredData(rvs)
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est1 = distfn.fit(data1, **kwds)
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msg = ('Different results fitting uncensored data wrapped as'
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f' CensoredData: {distfn.name}: est={est} est1={est1}')
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assert_allclose(est1, est, rtol=1e-10, err_msg=msg)
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if method == 'MLE' and distname not in fail_interval_censored:
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# Convert the first `nic` values in rvs to interval-censored
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# values. The interval is small, so est2 should be close to
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# est.
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nic = 15
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interval = np.column_stack((rvs, rvs))
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interval[:nic, 0] *= 0.99
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interval[:nic, 1] *= 1.01
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interval.sort(axis=1)
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data2 = stats.CensoredData(interval=interval)
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est2 = distfn.fit(data2, **kwds)
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msg = ('Different results fitting interval-censored'
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f' data: {distfn.name}: est={est} est2={est2}')
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assert_allclose(est2, est, rtol=0.05, err_msg=msg)
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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,
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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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def test_fit_error():
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data = np.concatenate([np.zeros(29), np.ones(21)])
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message = "Optimization converged to parameters that are..."
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with pytest.raises(FitError, match=message), \
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pytest.warns(RuntimeWarning):
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stats.beta.fit(data)
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@pytest.mark.parametrize("dist, params",
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[(stats.norm, (0.5, 2.5)), # type: ignore[attr-defined]
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(stats.binom, (10, 0.3, 2))]) # type: ignore[attr-defined]
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def test_nnlf_and_related_methods(dist, params):
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rng = np.random.default_rng(983459824)
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if hasattr(dist, 'pdf'):
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logpxf = dist.logpdf
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else:
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logpxf = dist.logpmf
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x = dist.rvs(*params, size=100, random_state=rng)
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ref = -logpxf(x, *params).sum()
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res1 = dist.nnlf(params, x)
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res2 = dist._penalized_nnlf(params, x)
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assert_allclose(res1, ref)
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assert_allclose(res2, ref)
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def cases_test_fit_mle():
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# These fail default test or hang
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skip_basic_fit = {'argus', 'irwinhall', 'foldnorm', 'truncpareto',
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'truncweibull_min', 'ksone', 'levy_stable',
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'studentized_range', 'kstwo', 'arcsine'}
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# Please keep this list in alphabetical order...
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slow_basic_fit = {'alpha', 'betaprime', 'binom', 'bradford', 'burr12',
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'chi', 'crystalball', 'dweibull', 'erlang', 'exponnorm',
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'exponpow', 'f', 'fatiguelife', 'fisk', 'foldcauchy', 'gamma',
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'genexpon', 'genextreme', 'gennorm', 'genpareto',
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'gompertz', 'halfgennorm', 'invgamma', 'invgauss', 'invweibull',
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'jf_skew_t', 'johnsonsb', 'johnsonsu', 'kappa3',
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'kstwobign', 'loglaplace', 'lognorm', 'lomax', 'mielke',
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'nakagami', 'nbinom', 'norminvgauss',
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'pareto', 'pearson3', 'powerlaw', 'powernorm',
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'randint', 'rdist', 'recipinvgauss', 'rice', 'skewnorm',
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't', 'uniform', 'weibull_max', 'weibull_min', 'wrapcauchy'}
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# Please keep this list in alphabetical order...
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xslow_basic_fit = {'beta', 'betabinom', 'betanbinom', 'burr', 'exponweib',
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'gausshyper', 'gengamma', 'genhalflogistic',
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'genhyperbolic', 'geninvgauss',
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'hypergeom', 'kappa4', 'loguniform',
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'ncf', 'nchypergeom_fisher', 'nchypergeom_wallenius',
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'nct', 'ncx2', 'nhypergeom',
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'powerlognorm', 'reciprocal', 'rel_breitwigner',
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'skellam', 'trapezoid', 'triang', 'truncnorm',
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'tukeylambda', 'vonmises', 'zipfian'}
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for dist in dict(distdiscrete + distcont):
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if dist in skip_basic_fit or not isinstance(dist, str):
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reason = "tested separately"
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yield pytest.param(dist, marks=pytest.mark.skip(reason=reason))
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elif dist in slow_basic_fit:
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reason = "too slow (>= 0.25s)"
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yield pytest.param(dist, marks=pytest.mark.slow(reason=reason))
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elif dist in xslow_basic_fit:
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reason = "too slow (>= 1.0s)"
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yield pytest.param(dist, marks=pytest.mark.xslow(reason=reason))
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else:
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yield dist
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def cases_test_fit_mse():
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# the first four are so slow that I'm not sure whether they would pass
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skip_basic_fit = {'levy_stable', 'studentized_range', 'ksone', 'skewnorm',
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'irwinhall', # hangs
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'norminvgauss', # super slow (~1 hr) but passes
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'kstwo', # very slow (~25 min) but passes
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'geninvgauss', # quite slow (~4 minutes) but passes
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'gausshyper', 'genhyperbolic', # integration warnings
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'tukeylambda', # close, but doesn't meet tolerance
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'vonmises', # can have negative CDF; doesn't play nice
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'argus'} # doesn't meet tolerance; tested separately
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# Please keep this list in alphabetical order...
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slow_basic_fit = {'alpha', 'anglit', 'arcsine', 'betabinom', 'bradford',
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'chi', 'chi2', 'crystalball', 'dweibull',
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'erlang', 'exponnorm', 'exponpow', 'exponweib',
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'fatiguelife', 'fisk', 'foldcauchy', 'foldnorm',
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'gamma', 'genexpon', 'genextreme', 'genhalflogistic',
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'genlogistic', 'genpareto', 'gompertz',
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'hypergeom', 'invweibull',
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'johnsonsu', 'kappa3', 'kstwobign',
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'laplace_asymmetric', 'loggamma', 'loglaplace',
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'lognorm', 'lomax',
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'maxwell', 'nhypergeom',
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'pareto', 'powernorm', 'randint', 'recipinvgauss',
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'semicircular',
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't', 'triang', 'truncexpon', 'truncpareto',
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'uniform',
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'wald', 'weibull_max', 'weibull_min', 'wrapcauchy'}
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# Please keep this list in alphabetical order...
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xslow_basic_fit = {'argus', 'beta', 'betaprime', 'burr', 'burr12',
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'dgamma', 'f', 'gengamma', 'gennorm',
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'halfgennorm', 'invgamma', 'invgauss', 'jf_skew_t',
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'johnsonsb', 'kappa4', 'loguniform', 'mielke',
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'nakagami', 'ncf', 'nchypergeom_fisher',
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'nchypergeom_wallenius', 'nct', 'ncx2',
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'pearson3', 'powerlaw', 'powerlognorm',
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'rdist', 'reciprocal', 'rel_breitwigner', 'rice',
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'trapezoid', 'truncnorm', 'truncweibull_min',
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'vonmises_line', 'zipfian'}
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warns_basic_fit = {'skellam'} # can remove mark after gh-14901 is resolved
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|
|
||
|
|
for dist in dict(distdiscrete + distcont):
|
||
|
|
if dist in skip_basic_fit or not isinstance(dist, str):
|
||
|
|
reason = "Fails. Oh well."
|
||
|
|
yield pytest.param(dist, marks=pytest.mark.skip(reason=reason))
|
||
|
|
elif dist in slow_basic_fit:
|
||
|
|
reason = "too slow (>= 0.25s)"
|
||
|
|
yield pytest.param(dist, marks=pytest.mark.slow(reason=reason))
|
||
|
|
elif dist in xslow_basic_fit:
|
||
|
|
reason = "too slow (>= 1.0s)"
|
||
|
|
yield pytest.param(dist, marks=pytest.mark.xslow(reason=reason))
|
||
|
|
elif dist in warns_basic_fit:
|
||
|
|
mark = pytest.mark.filterwarnings('ignore::RuntimeWarning')
|
||
|
|
yield pytest.param(dist, marks=mark)
|
||
|
|
else:
|
||
|
|
yield dist
|
||
|
|
|
||
|
|
|
||
|
|
def cases_test_fitstart():
|
||
|
|
for distname, shapes in dict(distcont).items():
|
||
|
|
if (not isinstance(distname, str) or
|
||
|
|
distname in {'studentized_range', 'recipinvgauss'}): # slow
|
||
|
|
continue
|
||
|
|
yield distname, shapes
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('distname, shapes', cases_test_fitstart())
|
||
|
|
def test_fitstart(distname, shapes):
|
||
|
|
dist = getattr(stats, distname)
|
||
|
|
rng = np.random.default_rng(216342614)
|
||
|
|
data = rng.random(10)
|
||
|
|
|
||
|
|
with np.errstate(invalid='ignore', divide='ignore'): # irrelevant to test
|
||
|
|
guess = dist._fitstart(data)
|
||
|
|
|
||
|
|
assert dist._argcheck(*guess[:-2])
|
||
|
|
|
||
|
|
|
||
|
|
def assert_nlff_less_or_close(dist, data, params1, params0, rtol=1e-7, atol=0,
|
||
|
|
nlff_name='nnlf'):
|
||
|
|
nlff = getattr(dist, nlff_name)
|
||
|
|
nlff1 = nlff(params1, data)
|
||
|
|
nlff0 = nlff(params0, data)
|
||
|
|
if not (nlff1 < nlff0):
|
||
|
|
np.testing.assert_allclose(nlff1, nlff0, rtol=rtol, atol=atol)
|
||
|
|
|
||
|
|
|
||
|
|
class TestFit:
|
||
|
|
dist = stats.binom # type: ignore[attr-defined]
|
||
|
|
seed = 654634816187
|
||
|
|
rng = np.random.default_rng(seed)
|
||
|
|
data = stats.binom.rvs(5, 0.5, size=100, random_state=rng) # type: ignore[attr-defined] # noqa: E501
|
||
|
|
shape_bounds_a = [(1, 10), (0, 1)]
|
||
|
|
shape_bounds_d = {'n': (1, 10), 'p': (0, 1)}
|
||
|
|
atol = 5e-2
|
||
|
|
rtol = 1e-2
|
||
|
|
tols = {'atol': atol, 'rtol': rtol}
|
||
|
|
|
||
|
|
def opt(self, *args, **kwds):
|
||
|
|
return differential_evolution(*args, seed=0, **kwds)
|
||
|
|
|
||
|
|
def test_dist_iv(self):
|
||
|
|
message = "`dist` must be an instance of..."
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(10, self.data, self.shape_bounds_a)
|
||
|
|
|
||
|
|
def test_data_iv(self):
|
||
|
|
message = "`data` must be exactly one-dimensional."
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, [[1, 2, 3]], self.shape_bounds_a)
|
||
|
|
|
||
|
|
message = "All elements of `data` must be finite numbers."
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, [1, 2, 3, np.nan], self.shape_bounds_a)
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, [1, 2, 3, np.inf], self.shape_bounds_a)
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, ['1', '2', '3'], self.shape_bounds_a)
|
||
|
|
|
||
|
|
def test_bounds_iv(self):
|
||
|
|
message = "Bounds provided for the following unrecognized..."
|
||
|
|
shape_bounds = {'n': (1, 10), 'p': (0, 1), '1': (0, 10)}
|
||
|
|
with pytest.warns(RuntimeWarning, match=message):
|
||
|
|
stats.fit(self.dist, self.data, shape_bounds)
|
||
|
|
|
||
|
|
message = "Each element of a `bounds` sequence must be a tuple..."
|
||
|
|
shape_bounds = [(1, 10, 3), (0, 1)]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, shape_bounds)
|
||
|
|
|
||
|
|
message = "Each element of `bounds` must be a tuple specifying..."
|
||
|
|
shape_bounds = [(1, 10, 3), (0, 1, 0.5)]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, shape_bounds)
|
||
|
|
shape_bounds = [1, 0]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, shape_bounds)
|
||
|
|
|
||
|
|
message = "A `bounds` sequence must contain at least 2 elements..."
|
||
|
|
shape_bounds = [(1, 10)]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, shape_bounds)
|
||
|
|
|
||
|
|
message = "A `bounds` sequence may not contain more than 3 elements..."
|
||
|
|
bounds = [(1, 10), (1, 10), (1, 10), (1, 10)]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, bounds)
|
||
|
|
|
||
|
|
message = "There are no values for `p` on the interval..."
|
||
|
|
shape_bounds = {'n': (1, 10), 'p': (1, 0)}
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, shape_bounds)
|
||
|
|
|
||
|
|
message = "There are no values for `n` on the interval..."
|
||
|
|
shape_bounds = [(10, 1), (0, 1)]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, shape_bounds)
|
||
|
|
|
||
|
|
message = "There are no integer values for `n` on the interval..."
|
||
|
|
shape_bounds = [(1.4, 1.6), (0, 1)]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, shape_bounds)
|
||
|
|
|
||
|
|
message = "The intersection of user-provided bounds for `n`"
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data)
|
||
|
|
shape_bounds = [(-np.inf, np.inf), (0, 1)]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, shape_bounds)
|
||
|
|
|
||
|
|
def test_guess_iv(self):
|
||
|
|
message = "Guesses provided for the following unrecognized..."
|
||
|
|
guess = {'n': 1, 'p': 0.5, '1': 255}
|
||
|
|
with pytest.warns(RuntimeWarning, match=message):
|
||
|
|
stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
|
||
|
|
|
||
|
|
message = "Each element of `guess` must be a scalar..."
|
||
|
|
guess = {'n': 1, 'p': 'hi'}
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
|
||
|
|
guess = [1, 'f']
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
|
||
|
|
guess = [[1, 2]]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
|
||
|
|
|
||
|
|
message = "A `guess` sequence must contain at least 2..."
|
||
|
|
guess = [1]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
|
||
|
|
|
||
|
|
message = "A `guess` sequence may not contain more than 3..."
|
||
|
|
guess = [1, 2, 3, 4]
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
|
||
|
|
|
||
|
|
message = "Guess for parameter `n` rounded.*|Guess for parameter `p` clipped.*"
|
||
|
|
guess = {'n': 4.5, 'p': -0.5}
|
||
|
|
with pytest.warns(RuntimeWarning, match=message):
|
||
|
|
stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
|
||
|
|
|
||
|
|
message = "Guess for parameter `loc` rounded..."
|
||
|
|
guess = [5, 0.5, 0.5]
|
||
|
|
with pytest.warns(RuntimeWarning, match=message):
|
||
|
|
stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
|
||
|
|
|
||
|
|
message = "Guess for parameter `p` clipped..."
|
||
|
|
guess = {'n': 5, 'p': -0.5}
|
||
|
|
with pytest.warns(RuntimeWarning, match=message):
|
||
|
|
stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
|
||
|
|
|
||
|
|
message = "Guess for parameter `loc` clipped..."
|
||
|
|
guess = [5, 0.5, 1]
|
||
|
|
with pytest.warns(RuntimeWarning, match=message):
|
||
|
|
stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
|
||
|
|
|
||
|
|
def basic_fit_test(self, dist_name, method):
|
||
|
|
|
||
|
|
N = 5000
|
||
|
|
dist_data = dict(distcont + distdiscrete)
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
dist = getattr(stats, dist_name)
|
||
|
|
shapes = np.array(dist_data[dist_name])
|
||
|
|
bounds = np.empty((len(shapes) + 2, 2), dtype=np.float64)
|
||
|
|
bounds[:-2, 0] = shapes/10.**np.sign(shapes)
|
||
|
|
bounds[:-2, 1] = shapes*10.**np.sign(shapes)
|
||
|
|
bounds[-2] = (0, 10)
|
||
|
|
bounds[-1] = (1e-16, 10)
|
||
|
|
loc = rng.uniform(*bounds[-2])
|
||
|
|
scale = rng.uniform(*bounds[-1])
|
||
|
|
ref = list(dist_data[dist_name]) + [loc, scale]
|
||
|
|
|
||
|
|
if getattr(dist, 'pmf', False):
|
||
|
|
ref = ref[:-1]
|
||
|
|
ref[-1] = np.floor(loc)
|
||
|
|
data = dist.rvs(*ref, size=N, random_state=rng)
|
||
|
|
bounds = bounds[:-1]
|
||
|
|
if getattr(dist, 'pdf', False):
|
||
|
|
data = dist.rvs(*ref, size=N, random_state=rng)
|
||
|
|
|
||
|
|
with npt.suppress_warnings() as sup:
|
||
|
|
sup.filter(RuntimeWarning, "overflow encountered")
|
||
|
|
res = stats.fit(dist, data, bounds, method=method,
|
||
|
|
optimizer=self.opt)
|
||
|
|
|
||
|
|
nlff_names = {'mle': 'nnlf', 'mse': '_penalized_nlpsf'}
|
||
|
|
nlff_name = nlff_names[method]
|
||
|
|
assert_nlff_less_or_close(dist, data, res.params, ref, **self.tols,
|
||
|
|
nlff_name=nlff_name)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dist_name", cases_test_fit_mle())
|
||
|
|
def test_basic_fit_mle(self, dist_name):
|
||
|
|
self.basic_fit_test(dist_name, "mle")
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dist_name", cases_test_fit_mse())
|
||
|
|
def test_basic_fit_mse(self, dist_name):
|
||
|
|
self.basic_fit_test(dist_name, "mse")
|
||
|
|
|
||
|
|
def test_arcsine(self):
|
||
|
|
# Can't guarantee that all distributions will fit all data with
|
||
|
|
# arbitrary bounds. This distribution just happens to fail above.
|
||
|
|
# Try something slightly different.
|
||
|
|
N = 1000
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
dist = stats.arcsine
|
||
|
|
shapes = (1., 2.)
|
||
|
|
data = dist.rvs(*shapes, size=N, random_state=rng)
|
||
|
|
shape_bounds = {'loc': (0.1, 10), 'scale': (0.1, 10)}
|
||
|
|
res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
|
||
|
|
assert_nlff_less_or_close(dist, data, res.params, shapes, **self.tols)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ('mle', 'mse'))
|
||
|
|
def test_argus(self, method):
|
||
|
|
# Can't guarantee that all distributions will fit all data with
|
||
|
|
# arbitrary bounds. This distribution just happens to fail above.
|
||
|
|
# Try something slightly different.
|
||
|
|
N = 1000
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
dist = stats.argus
|
||
|
|
shapes = (1., 2., 3.)
|
||
|
|
data = dist.rvs(*shapes, size=N, random_state=rng)
|
||
|
|
shape_bounds = {'chi': (0.1, 10), 'loc': (0.1, 10), 'scale': (0.1, 10)}
|
||
|
|
res = stats.fit(dist, data, shape_bounds, optimizer=self.opt, method=method)
|
||
|
|
|
||
|
|
assert_nlff_less_or_close(dist, data, res.params, shapes, **self.tols)
|
||
|
|
|
||
|
|
def test_foldnorm(self):
|
||
|
|
# Can't guarantee that all distributions will fit all data with
|
||
|
|
# arbitrary bounds. This distribution just happens to fail above.
|
||
|
|
# Try something slightly different.
|
||
|
|
N = 1000
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
dist = stats.foldnorm
|
||
|
|
shapes = (1.952125337355587, 2., 3.)
|
||
|
|
data = dist.rvs(*shapes, size=N, random_state=rng)
|
||
|
|
shape_bounds = {'c': (0.1, 10), 'loc': (0.1, 10), 'scale': (0.1, 10)}
|
||
|
|
res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
|
||
|
|
|
||
|
|
assert_nlff_less_or_close(dist, data, res.params, shapes, **self.tols)
|
||
|
|
|
||
|
|
def test_truncpareto(self):
|
||
|
|
# Can't guarantee that all distributions will fit all data with
|
||
|
|
# arbitrary bounds. This distribution just happens to fail above.
|
||
|
|
# Try something slightly different.
|
||
|
|
N = 1000
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
dist = stats.truncpareto
|
||
|
|
shapes = (1.8, 5.3, 2.3, 4.1)
|
||
|
|
data = dist.rvs(*shapes, size=N, random_state=rng)
|
||
|
|
shape_bounds = [(0.1, 10)]*4
|
||
|
|
res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
|
||
|
|
|
||
|
|
assert_nlff_less_or_close(dist, data, res.params, shapes, **self.tols)
|
||
|
|
|
||
|
|
def test_truncweibull_min(self):
|
||
|
|
# Can't guarantee that all distributions will fit all data with
|
||
|
|
# arbitrary bounds. This distribution just happens to fail above.
|
||
|
|
# Try something slightly different.
|
||
|
|
N = 1000
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
dist = stats.truncweibull_min
|
||
|
|
shapes = (2.5, 0.25, 1.75, 2., 3.)
|
||
|
|
data = dist.rvs(*shapes, size=N, random_state=rng)
|
||
|
|
shape_bounds = [(0.1, 10)]*5
|
||
|
|
res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
|
||
|
|
|
||
|
|
assert_nlff_less_or_close(dist, data, res.params, shapes, **self.tols)
|
||
|
|
|
||
|
|
def test_missing_shape_bounds(self):
|
||
|
|
# some distributions have a small domain w.r.t. a parameter, e.g.
|
||
|
|
# $p \in [0, 1]$ for binomial distribution
|
||
|
|
# User does not need to provide these because the intersection of the
|
||
|
|
# user's bounds (none) and the distribution's domain is finite
|
||
|
|
N = 1000
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
|
||
|
|
dist = stats.binom
|
||
|
|
n, p, loc = 10, 0.65, 0
|
||
|
|
data = dist.rvs(n, p, loc=loc, size=N, random_state=rng)
|
||
|
|
shape_bounds = {'n': np.array([0, 20])} # check arrays are OK, too
|
||
|
|
res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
|
||
|
|
assert_allclose(res.params, (n, p, loc), **self.tols)
|
||
|
|
|
||
|
|
dist = stats.bernoulli
|
||
|
|
p, loc = 0.314159, 0
|
||
|
|
data = dist.rvs(p, loc=loc, size=N, random_state=rng)
|
||
|
|
res = stats.fit(dist, data, optimizer=self.opt)
|
||
|
|
assert_allclose(res.params, (p, loc), **self.tols)
|
||
|
|
|
||
|
|
def test_fit_only_loc_scale(self):
|
||
|
|
# fit only loc
|
||
|
|
N = 5000
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
|
||
|
|
dist = stats.norm
|
||
|
|
loc, scale = 1.5, 1
|
||
|
|
data = dist.rvs(loc=loc, size=N, random_state=rng)
|
||
|
|
loc_bounds = (0, 5)
|
||
|
|
bounds = {'loc': loc_bounds}
|
||
|
|
res = stats.fit(dist, data, bounds, optimizer=self.opt)
|
||
|
|
assert_allclose(res.params, (loc, scale), **self.tols)
|
||
|
|
|
||
|
|
# fit only scale
|
||
|
|
loc, scale = 0, 2.5
|
||
|
|
data = dist.rvs(scale=scale, size=N, random_state=rng)
|
||
|
|
scale_bounds = (0.01, 5)
|
||
|
|
bounds = {'scale': scale_bounds}
|
||
|
|
res = stats.fit(dist, data, bounds, optimizer=self.opt)
|
||
|
|
assert_allclose(res.params, (loc, scale), **self.tols)
|
||
|
|
|
||
|
|
# fit only loc and scale
|
||
|
|
dist = stats.norm
|
||
|
|
loc, scale = 1.5, 2.5
|
||
|
|
data = dist.rvs(loc=loc, scale=scale, size=N, random_state=rng)
|
||
|
|
bounds = {'loc': loc_bounds, 'scale': scale_bounds}
|
||
|
|
res = stats.fit(dist, data, bounds, optimizer=self.opt)
|
||
|
|
assert_allclose(res.params, (loc, scale), **self.tols)
|
||
|
|
|
||
|
|
def test_everything_fixed(self):
|
||
|
|
N = 5000
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
|
||
|
|
dist = stats.norm
|
||
|
|
loc, scale = 1.5, 2.5
|
||
|
|
data = dist.rvs(loc=loc, scale=scale, size=N, random_state=rng)
|
||
|
|
|
||
|
|
# loc, scale fixed to 0, 1 by default
|
||
|
|
res = stats.fit(dist, data)
|
||
|
|
assert_allclose(res.params, (0, 1), **self.tols)
|
||
|
|
|
||
|
|
# loc, scale explicitly fixed
|
||
|
|
bounds = {'loc': (loc, loc), 'scale': (scale, scale)}
|
||
|
|
res = stats.fit(dist, data, bounds)
|
||
|
|
assert_allclose(res.params, (loc, scale), **self.tols)
|
||
|
|
|
||
|
|
# `n` gets fixed during polishing
|
||
|
|
dist = stats.binom
|
||
|
|
n, p, loc = 10, 0.65, 0
|
||
|
|
data = dist.rvs(n, p, loc=loc, size=N, random_state=rng)
|
||
|
|
shape_bounds = {'n': (0, 20), 'p': (0.65, 0.65)}
|
||
|
|
res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
|
||
|
|
assert_allclose(res.params, (n, p, loc), **self.tols)
|
||
|
|
|
||
|
|
def test_failure(self):
|
||
|
|
N = 5000
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
|
||
|
|
dist = stats.nbinom
|
||
|
|
shapes = (5, 0.5)
|
||
|
|
data = dist.rvs(*shapes, size=N, random_state=rng)
|
||
|
|
|
||
|
|
assert data.min() == 0
|
||
|
|
# With lower bounds on location at 0.5, likelihood is zero
|
||
|
|
bounds = [(0, 30), (0, 1), (0.5, 10)]
|
||
|
|
res = stats.fit(dist, data, bounds)
|
||
|
|
message = "Optimization converged to parameter values that are"
|
||
|
|
assert res.message.startswith(message)
|
||
|
|
assert res.success is False
|
||
|
|
|
||
|
|
@pytest.mark.xslow
|
||
|
|
def test_guess(self):
|
||
|
|
# Test that guess helps DE find the desired solution
|
||
|
|
N = 2000
|
||
|
|
# With some seeds, `fit` doesn't need a guess
|
||
|
|
rng = np.random.default_rng(196390444561)
|
||
|
|
dist = stats.nhypergeom
|
||
|
|
params = (20, 7, 12, 0)
|
||
|
|
bounds = [(2, 200), (0.7, 70), (1.2, 120), (0, 10)]
|
||
|
|
|
||
|
|
data = dist.rvs(*params, size=N, random_state=rng)
|
||
|
|
|
||
|
|
res = stats.fit(dist, data, bounds, optimizer=self.opt)
|
||
|
|
assert not np.allclose(res.params, params, **self.tols)
|
||
|
|
|
||
|
|
res = stats.fit(dist, data, bounds, guess=params, optimizer=self.opt)
|
||
|
|
assert_allclose(res.params, params, **self.tols)
|
||
|
|
|
||
|
|
def test_mse_accuracy_1(self):
|
||
|
|
# Test maximum spacing estimation against example from Wikipedia
|
||
|
|
# https://en.wikipedia.org/wiki/Maximum_spacing_estimation#Examples
|
||
|
|
data = [2, 4]
|
||
|
|
dist = stats.expon
|
||
|
|
bounds = {'loc': (0, 0), 'scale': (1e-8, 10)}
|
||
|
|
res_mle = stats.fit(dist, data, bounds=bounds, method='mle')
|
||
|
|
assert_allclose(res_mle.params.scale, 3, atol=1e-3)
|
||
|
|
res_mse = stats.fit(dist, data, bounds=bounds, method='mse')
|
||
|
|
assert_allclose(res_mse.params.scale, 3.915, atol=1e-3)
|
||
|
|
|
||
|
|
def test_mse_accuracy_2(self):
|
||
|
|
# Test maximum spacing estimation against example from Wikipedia
|
||
|
|
# https://en.wikipedia.org/wiki/Maximum_spacing_estimation#Examples
|
||
|
|
rng = np.random.default_rng(9843212616816518964)
|
||
|
|
|
||
|
|
dist = stats.uniform
|
||
|
|
n = 10
|
||
|
|
data = dist(3, 6).rvs(size=n, random_state=rng)
|
||
|
|
bounds = {'loc': (0, 10), 'scale': (1e-8, 10)}
|
||
|
|
res = stats.fit(dist, data, bounds=bounds, method='mse')
|
||
|
|
# (loc=3.608118420015416, scale=5.509323262055043)
|
||
|
|
|
||
|
|
x = np.sort(data)
|
||
|
|
a = (n*x[0] - x[-1])/(n - 1)
|
||
|
|
b = (n*x[-1] - x[0])/(n - 1)
|
||
|
|
ref = a, b-a # (3.6081133632151503, 5.509328130317254)
|
||
|
|
assert_allclose(res.params, ref, rtol=1e-4)
|
||
|
|
|
||
|
|
|
||
|
|
# Data from Matlab: https://www.mathworks.com/help/stats/lillietest.html
|
||
|
|
examgrades = [65, 61, 81, 88, 69, 89, 55, 84, 86, 84, 71, 81, 84, 81, 78, 67,
|
||
|
|
96, 66, 73, 75, 59, 71, 69, 63, 79, 76, 63, 85, 87, 88, 80, 71,
|
||
|
|
65, 84, 71, 75, 81, 79, 64, 65, 84, 77, 70, 75, 84, 75, 73, 92,
|
||
|
|
90, 79, 80, 71, 73, 71, 58, 79, 73, 64, 77, 82, 81, 59, 54, 82,
|
||
|
|
57, 79, 79, 73, 74, 82, 63, 64, 73, 69, 87, 68, 81, 73, 83, 73,
|
||
|
|
80, 73, 73, 71, 66, 78, 64, 74, 68, 67, 75, 75, 80, 85, 74, 76,
|
||
|
|
80, 77, 93, 70, 86, 80, 81, 83, 68, 60, 85, 64, 74, 82, 81, 77,
|
||
|
|
66, 85, 75, 81, 69, 60, 83, 72]
|
||
|
|
|
||
|
|
|
||
|
|
class TestGoodnessOfFit:
|
||
|
|
|
||
|
|
def test_gof_iv(self):
|
||
|
|
dist = stats.norm
|
||
|
|
x = [1, 2, 3]
|
||
|
|
|
||
|
|
message = r"`dist` must be a \(non-frozen\) instance of..."
|
||
|
|
with pytest.raises(TypeError, match=message):
|
||
|
|
goodness_of_fit(stats.norm(), x)
|
||
|
|
|
||
|
|
message = "`data` must be a one-dimensional array of numbers."
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
goodness_of_fit(dist, [[1, 2, 3]])
|
||
|
|
|
||
|
|
message = "`statistic` must be one of..."
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
goodness_of_fit(dist, x, statistic='mm')
|
||
|
|
|
||
|
|
message = "`n_mc_samples` must be an integer."
|
||
|
|
with pytest.raises(TypeError, match=message):
|
||
|
|
goodness_of_fit(dist, x, n_mc_samples=1000.5)
|
||
|
|
|
||
|
|
message = "'herring' cannot be used to seed a"
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
goodness_of_fit(dist, x, random_state='herring')
|
||
|
|
|
||
|
|
def test_against_ks(self):
|
||
|
|
rng = np.random.default_rng(8517426291317196949)
|
||
|
|
x = examgrades
|
||
|
|
known_params = {'loc': np.mean(x), 'scale': np.std(x, ddof=1)}
|
||
|
|
res = goodness_of_fit(stats.norm, x, known_params=known_params,
|
||
|
|
statistic='ks', random_state=rng)
|
||
|
|
ref = stats.kstest(x, stats.norm(**known_params).cdf, method='exact')
|
||
|
|
assert_allclose(res.statistic, ref.statistic) # ~0.0848
|
||
|
|
assert_allclose(res.pvalue, ref.pvalue, atol=5e-3) # ~0.335
|
||
|
|
|
||
|
|
def test_against_lilliefors(self):
|
||
|
|
rng = np.random.default_rng(2291803665717442724)
|
||
|
|
x = examgrades
|
||
|
|
res = goodness_of_fit(stats.norm, x, statistic='ks', random_state=rng)
|
||
|
|
known_params = {'loc': np.mean(x), 'scale': np.std(x, ddof=1)}
|
||
|
|
ref = stats.kstest(x, stats.norm(**known_params).cdf, method='exact')
|
||
|
|
assert_allclose(res.statistic, ref.statistic) # ~0.0848
|
||
|
|
assert_allclose(res.pvalue, 0.0348, atol=5e-3)
|
||
|
|
|
||
|
|
def test_against_cvm(self):
|
||
|
|
rng = np.random.default_rng(8674330857509546614)
|
||
|
|
x = examgrades
|
||
|
|
known_params = {'loc': np.mean(x), 'scale': np.std(x, ddof=1)}
|
||
|
|
res = goodness_of_fit(stats.norm, x, known_params=known_params,
|
||
|
|
statistic='cvm', random_state=rng)
|
||
|
|
ref = stats.cramervonmises(x, stats.norm(**known_params).cdf)
|
||
|
|
assert_allclose(res.statistic, ref.statistic) # ~0.090
|
||
|
|
assert_allclose(res.pvalue, ref.pvalue, atol=5e-3) # ~0.636
|
||
|
|
|
||
|
|
def test_against_anderson_case_0(self):
|
||
|
|
# "Case 0" is where loc and scale are known [1]
|
||
|
|
rng = np.random.default_rng(7384539336846690410)
|
||
|
|
x = np.arange(1, 101)
|
||
|
|
# loc that produced critical value of statistic found w/ root_scalar
|
||
|
|
known_params = {'loc': 45.01575354024957, 'scale': 30}
|
||
|
|
res = goodness_of_fit(stats.norm, x, known_params=known_params,
|
||
|
|
statistic='ad', random_state=rng)
|
||
|
|
assert_allclose(res.statistic, 2.492) # See [1] Table 1A 1.0
|
||
|
|
assert_allclose(res.pvalue, 0.05, atol=5e-3)
|
||
|
|
|
||
|
|
def test_against_anderson_case_1(self):
|
||
|
|
# "Case 1" is where scale is known and loc is fit [1]
|
||
|
|
rng = np.random.default_rng(5040212485680146248)
|
||
|
|
x = np.arange(1, 101)
|
||
|
|
# scale that produced critical value of statistic found w/ root_scalar
|
||
|
|
known_params = {'scale': 29.957112639101933}
|
||
|
|
res = goodness_of_fit(stats.norm, x, known_params=known_params,
|
||
|
|
statistic='ad', random_state=rng)
|
||
|
|
assert_allclose(res.statistic, 0.908) # See [1] Table 1B 1.1
|
||
|
|
assert_allclose(res.pvalue, 0.1, atol=5e-3)
|
||
|
|
|
||
|
|
def test_against_anderson_case_2(self):
|
||
|
|
# "Case 2" is where loc is known and scale is fit [1]
|
||
|
|
rng = np.random.default_rng(726693985720914083)
|
||
|
|
x = np.arange(1, 101)
|
||
|
|
# loc that produced critical value of statistic found w/ root_scalar
|
||
|
|
known_params = {'loc': 44.5680212261933}
|
||
|
|
res = goodness_of_fit(stats.norm, x, known_params=known_params,
|
||
|
|
statistic='ad', random_state=rng)
|
||
|
|
assert_allclose(res.statistic, 2.904) # See [1] Table 1B 1.2
|
||
|
|
assert_allclose(res.pvalue, 0.025, atol=5e-3)
|
||
|
|
|
||
|
|
def test_against_anderson_case_3(self):
|
||
|
|
# "Case 3" is where both loc and scale are fit [1]
|
||
|
|
rng = np.random.default_rng(6763691329830218206)
|
||
|
|
# c that produced critical value of statistic found w/ root_scalar
|
||
|
|
x = stats.skewnorm.rvs(1.4477847789132101, loc=1, scale=2, size=100,
|
||
|
|
random_state=rng)
|
||
|
|
res = goodness_of_fit(stats.norm, x, statistic='ad', random_state=rng)
|
||
|
|
assert_allclose(res.statistic, 0.559) # See [1] Table 1B 1.2
|
||
|
|
assert_allclose(res.pvalue, 0.15, atol=5e-3)
|
||
|
|
|
||
|
|
@pytest.mark.xslow
|
||
|
|
def test_against_anderson_gumbel_r(self):
|
||
|
|
rng = np.random.default_rng(7302761058217743)
|
||
|
|
# c that produced critical value of statistic found w/ root_scalar
|
||
|
|
x = stats.genextreme(0.051896837188595134, loc=0.5,
|
||
|
|
scale=1.5).rvs(size=1000, random_state=rng)
|
||
|
|
res = goodness_of_fit(stats.gumbel_r, x, statistic='ad',
|
||
|
|
random_state=rng)
|
||
|
|
ref = stats.anderson(x, dist='gumbel_r')
|
||
|
|
assert_allclose(res.statistic, ref.critical_values[0])
|
||
|
|
assert_allclose(res.pvalue, ref.significance_level[0]/100, atol=5e-3)
|
||
|
|
|
||
|
|
def test_against_filliben_norm(self):
|
||
|
|
# Test against `stats.fit` ref. [7] Section 8 "Example"
|
||
|
|
rng = np.random.default_rng(8024266430745011915)
|
||
|
|
y = [6, 1, -4, 8, -2, 5, 0]
|
||
|
|
known_params = {'loc': 0, 'scale': 1}
|
||
|
|
res = stats.goodness_of_fit(stats.norm, y, known_params=known_params,
|
||
|
|
statistic="filliben", random_state=rng)
|
||
|
|
# Slight discrepancy presumably due to roundoff in Filliben's
|
||
|
|
# calculation. Using exact order statistic medians instead of
|
||
|
|
# Filliben's approximation doesn't account for it.
|
||
|
|
assert_allclose(res.statistic, 0.98538, atol=1e-4)
|
||
|
|
assert 0.75 < res.pvalue < 0.9
|
||
|
|
|
||
|
|
# Using R's ppcc library:
|
||
|
|
# library(ppcc)
|
||
|
|
# options(digits=16)
|
||
|
|
# x < - c(6, 1, -4, 8, -2, 5, 0)
|
||
|
|
# set.seed(100)
|
||
|
|
# ppccTest(x, "qnorm", ppos="Filliben")
|
||
|
|
# Discrepancy with
|
||
|
|
assert_allclose(res.statistic, 0.98540957187084, rtol=2e-5)
|
||
|
|
assert_allclose(res.pvalue, 0.8875, rtol=2e-3)
|
||
|
|
|
||
|
|
def test_filliben_property(self):
|
||
|
|
# Filliben's statistic should be independent of data location and scale
|
||
|
|
rng = np.random.default_rng(8535677809395478813)
|
||
|
|
x = rng.normal(loc=10, scale=0.5, size=100)
|
||
|
|
res = stats.goodness_of_fit(stats.norm, x,
|
||
|
|
statistic="filliben", random_state=rng)
|
||
|
|
known_params = {'loc': 0, 'scale': 1}
|
||
|
|
ref = stats.goodness_of_fit(stats.norm, x, known_params=known_params,
|
||
|
|
statistic="filliben", random_state=rng)
|
||
|
|
assert_allclose(res.statistic, ref.statistic, rtol=1e-15)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('case', [(25, [.928, .937, .950, .958, .966]),
|
||
|
|
(50, [.959, .965, .972, .977, .981]),
|
||
|
|
(95, [.977, .979, .983, .986, .989])])
|
||
|
|
def test_against_filliben_norm_table(self, case):
|
||
|
|
# Test against `stats.fit` ref. [7] Table 1
|
||
|
|
rng = np.random.default_rng(504569995557928957)
|
||
|
|
n, ref = case
|
||
|
|
x = rng.random(n)
|
||
|
|
known_params = {'loc': 0, 'scale': 1}
|
||
|
|
res = stats.goodness_of_fit(stats.norm, x, known_params=known_params,
|
||
|
|
statistic="filliben", random_state=rng)
|
||
|
|
percentiles = np.array([0.005, 0.01, 0.025, 0.05, 0.1])
|
||
|
|
res = stats.scoreatpercentile(res.null_distribution, percentiles*100)
|
||
|
|
assert_allclose(res, ref, atol=2e-3)
|
||
|
|
|
||
|
|
@pytest.mark.xslow
|
||
|
|
@pytest.mark.parametrize('case', [(5, 0.95772790260469, 0.4755),
|
||
|
|
(6, 0.95398832257958, 0.3848),
|
||
|
|
(7, 0.9432692889277, 0.2328)])
|
||
|
|
def test_against_ppcc(self, case):
|
||
|
|
# Test against R ppcc, e.g.
|
||
|
|
# library(ppcc)
|
||
|
|
# options(digits=16)
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# x < - c(0.52325412, 1.06907699, -0.36084066, 0.15305959, 0.99093194)
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# set.seed(100)
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# ppccTest(x, "qrayleigh", ppos="Filliben")
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n, ref_statistic, ref_pvalue = case
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rng = np.random.default_rng(7777775561439803116)
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|
x = rng.normal(size=n)
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|
res = stats.goodness_of_fit(stats.rayleigh, x, statistic="filliben",
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|
random_state=rng)
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|
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assert_allclose(res.statistic, ref_statistic, rtol=1e-4)
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|
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assert_allclose(res.pvalue, ref_pvalue, atol=1.5e-2)
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||
|
|
|
||
|
|
def test_params_effects(self):
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|
# Ensure that `guessed_params`, `fit_params`, and `known_params` have
|
||
|
|
# the intended effects.
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|
|
rng = np.random.default_rng(9121950977643805391)
|
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|
|
x = stats.skewnorm.rvs(-5.044559778383153, loc=1, scale=2, size=50,
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|
|
random_state=rng)
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||
|
|
|
||
|
|
# Show that `guessed_params` don't fit to the guess,
|
||
|
|
# but `fit_params` and `known_params` respect the provided fit
|
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|
|
guessed_params = {'c': 13.4}
|
||
|
|
fit_params = {'scale': 13.73}
|
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|
|
known_params = {'loc': -13.85}
|
||
|
|
rng = np.random.default_rng(9121950977643805391)
|
||
|
|
res1 = goodness_of_fit(stats.weibull_min, x, n_mc_samples=2,
|
||
|
|
guessed_params=guessed_params,
|
||
|
|
fit_params=fit_params,
|
||
|
|
known_params=known_params, random_state=rng)
|
||
|
|
assert not np.allclose(res1.fit_result.params.c, 13.4)
|
||
|
|
assert_equal(res1.fit_result.params.scale, 13.73)
|
||
|
|
assert_equal(res1.fit_result.params.loc, -13.85)
|
||
|
|
|
||
|
|
# Show that changing the guess changes the parameter that gets fit,
|
||
|
|
# and it changes the null distribution
|
||
|
|
guessed_params = {'c': 2}
|
||
|
|
rng = np.random.default_rng(9121950977643805391)
|
||
|
|
res2 = goodness_of_fit(stats.weibull_min, x, n_mc_samples=2,
|
||
|
|
guessed_params=guessed_params,
|
||
|
|
fit_params=fit_params,
|
||
|
|
known_params=known_params, random_state=rng)
|
||
|
|
assert not np.allclose(res2.fit_result.params.c,
|
||
|
|
res1.fit_result.params.c, rtol=1e-8)
|
||
|
|
assert not np.allclose(res2.null_distribution,
|
||
|
|
res1.null_distribution, rtol=1e-8)
|
||
|
|
assert_equal(res2.fit_result.params.scale, 13.73)
|
||
|
|
assert_equal(res2.fit_result.params.loc, -13.85)
|
||
|
|
|
||
|
|
# If we set all parameters as fit_params and known_params,
|
||
|
|
# they're all fixed to those values, but the null distribution
|
||
|
|
# varies.
|
||
|
|
fit_params = {'c': 13.4, 'scale': 13.73}
|
||
|
|
rng = np.random.default_rng(9121950977643805391)
|
||
|
|
res3 = goodness_of_fit(stats.weibull_min, x, n_mc_samples=2,
|
||
|
|
guessed_params=guessed_params,
|
||
|
|
fit_params=fit_params,
|
||
|
|
known_params=known_params, random_state=rng)
|
||
|
|
assert_equal(res3.fit_result.params.c, 13.4)
|
||
|
|
assert_equal(res3.fit_result.params.scale, 13.73)
|
||
|
|
assert_equal(res3.fit_result.params.loc, -13.85)
|
||
|
|
assert not np.allclose(res3.null_distribution, res1.null_distribution)
|
||
|
|
|
||
|
|
def test_custom_statistic(self):
|
||
|
|
# Test support for custom statistic function.
|
||
|
|
|
||
|
|
# References:
|
||
|
|
# [1] Pyke, R. (1965). "Spacings". Journal of the Royal Statistical
|
||
|
|
# Society: Series B (Methodological), 27(3): 395-436.
|
||
|
|
# [2] Burrows, P. M. (1979). "Selected Percentage Points of
|
||
|
|
# Greenwood's Statistics". Journal of the Royal Statistical
|
||
|
|
# Society. Series A (General), 142(2): 256-258.
|
||
|
|
|
||
|
|
# Use the Greenwood statistic for illustration; see [1, p.402].
|
||
|
|
def greenwood(dist, data, *, axis):
|
||
|
|
x = np.sort(data, axis=axis)
|
||
|
|
y = dist.cdf(x)
|
||
|
|
d = np.diff(y, axis=axis, prepend=0, append=1)
|
||
|
|
return np.sum(d ** 2, axis=axis)
|
||
|
|
|
||
|
|
# Run the Monte Carlo test with sample size = 5 on a fully specified
|
||
|
|
# null distribution, and compare the simulated quantiles to the exact
|
||
|
|
# ones given in [2, Table 1, column (n = 5)].
|
||
|
|
rng = np.random.default_rng(9121950977643805391)
|
||
|
|
data = stats.expon.rvs(size=5, random_state=rng)
|
||
|
|
result = goodness_of_fit(stats.expon, data,
|
||
|
|
known_params={'loc': 0, 'scale': 1},
|
||
|
|
statistic=greenwood, random_state=rng)
|
||
|
|
p = [.01, .05, .1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99]
|
||
|
|
exact_quantiles = [
|
||
|
|
.183863, .199403, .210088, .226040, .239947, .253677, .268422,
|
||
|
|
.285293, .306002, .334447, .382972, .432049, .547468]
|
||
|
|
simulated_quantiles = np.quantile(result.null_distribution, p)
|
||
|
|
assert_allclose(simulated_quantiles, exact_quantiles, atol=0.005)
|
||
|
|
|
||
|
|
class TestFitResult:
|
||
|
|
def test_plot_iv(self):
|
||
|
|
rng = np.random.default_rng(1769658657308472721)
|
||
|
|
data = stats.norm.rvs(0, 1, size=100, random_state=rng)
|
||
|
|
|
||
|
|
def optimizer(*args, **kwargs):
|
||
|
|
return differential_evolution(*args, **kwargs, seed=rng)
|
||
|
|
|
||
|
|
bounds = [(0, 30), (0, 1)]
|
||
|
|
res = stats.fit(stats.norm, data, bounds, optimizer=optimizer)
|
||
|
|
try:
|
||
|
|
import matplotlib # noqa: F401
|
||
|
|
message = r"`plot_type` must be one of \{'..."
|
||
|
|
with pytest.raises(ValueError, match=message):
|
||
|
|
res.plot(plot_type='llama')
|
||
|
|
except (ModuleNotFoundError, ImportError):
|
||
|
|
# Avoid trying to call MPL with numpy 2.0-dev, because that fails
|
||
|
|
# too often due to ABI mismatches and is hard to avoid. This test
|
||
|
|
# will work fine again once MPL has done a 2.0-compatible release.
|
||
|
|
if not np.__version__.startswith('2.0.0.dev0'):
|
||
|
|
message = r"matplotlib must be installed to use method `plot`."
|
||
|
|
with pytest.raises(ModuleNotFoundError, match=message):
|
||
|
|
res.plot(plot_type='llama')
|