1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
|
from __future__ import division, print_function, absolute_import
import os
import numpy as np
from numpy.testing import dec
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',
'chi',
'chi2',
'gausshyper',
'genexpon',
'gengamma',
'ksone',
'mielke',
'ncf',
'ncx2',
'pearson3',
'powerlognorm',
'truncexpon',
'tukeylambda',
'vonmises',
'wrapcauchy',
'levy_stable'
]
# 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)
if __name__ == "__main__":
np.testing.run_module_suite()
|
