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Mini Shell
Mini Shell
import os
import numpy as np
from numpy.testing import assert_allclose
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, 10000] # 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
mle_failing_fits = [
'burr',
'chi2',
'gausshyper',
'genexpon',
'gengamma',
'kappa4',
'ksone',
'kstwo',
'mielke',
'ncf',
'ncx2',
'pearson3',
'powerlognorm',
'truncexpon',
'tukeylambda',
'vonmises',
'levy_stable',
'trapezoid',
'studentized_range'
]
mm_failing_fits = ['alpha', 'betaprime', 'burr', 'burr12', 'cauchy', 'chi',
'chi2', 'crystalball', 'dgamma', 'dweibull', 'f',
'fatiguelife', 'fisk', 'foldcauchy', 'genextreme',
'gengamma', 'genhyperbolic', 'gennorm', 'genpareto',
'halfcauchy', 'invgamma', 'invweibull', 'johnsonsu',
'kappa3', 'ksone', 'kstwo', 'levy', 'levy_l',
'levy_stable', 'loglaplace', 'lomax', 'mielke', 'nakagami',
'ncf', 'nct', 'ncx2', 'pareto', 'powerlognorm', 'powernorm',
'skewcauchy', 't',
'trapezoid', 'triang', 'tukeylambda', 'studentized_range']
# not sure if these fail, but they caused my patience to fail
mm_slow_fits = ['argus', 'exponpow', 'exponweib', 'gausshyper', 'genexpon',
'genhalflogistic', 'halfgennorm', 'gompertz', 'johnsonsb',
'kappa4', 'kstwobign', 'recipinvgauss', 'skewnorm',
'truncexpon', 'vonmises', 'vonmises_line']
failing_fits = {"MM": mm_failing_fits + mm_slow_fits, "MLE": mle_failing_fits}
# 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())
@pytest.mark.parametrize('method', ["MLE", 'MM'])
def test_cont_fit(distname, arg, method):
if distname in failing_fits[method]:
# 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.full(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'):
rvs = distfn.rvs(size=fit_size, *arg)
est = distfn.fit(rvs, method=method) # 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)
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