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