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from __future__ import division, print_function, absolute_import
import os
import numpy as np
from numpy.testing import dec, assert_allclose
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.
]
@dec.slow
def 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 check_cont_fit, distname,arg
def check_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.]"
dec.knownfailureif(True, msg)(lambda: None)()
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'):
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])
yield _check_loc_scale_mle_fit, 'uniform', data, [1.01, 0.99], 1e-3
yield _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)
if __name__ == "__main__":
np.testing.run_module_suite()
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