123 lines
3.7 KiB
Python
123 lines
3.7 KiB
Python
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
|
|
xfail = True
|
|
try:
|
|
xfail = not int(os.environ['SCIPY_XFAIL'])
|
|
except:
|
|
pass
|
|
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)
|
|
|