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test_continuous_basic.py
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test_continuous_basic.py
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import warnings
import numpy.testing as npt
import numpy as np
import nose
from scipy import stats
"""
Test all continuous distributions.
Parameters were chosen for those distributions that pass the
Kolmogorov-Smirnov test. This provides safe parameters for each
distributions so that we can perform further testing of class methods.
These tests currently check only/mostly for serious errors and exceptions,
not for numerically exact results.
TODO:
* make functioning test for skew and kurtosis
still known failures - skip for now
"""
#currently not used
DECIMAL = 5 # specify the precision of the tests # increased from 0 to 5
DECIMAL_kurt = 0
distcont = [
['alpha', (3.5704770516650459,)],
['anglit', ()],
['arcsine', ()],
['beta', (2.3098496451481823, 0.62687954300963677)],
['betaprime', (5, 6)], # avoid unbound error in entropy with (100, 86)],
['bradford', (0.29891359763170633,)],
['burr', (10.5, 4.3)], #incorrect mean and var for(0.94839838075366045, 4.3820284068855795)],
['cauchy', ()],
['chi', (78,)],
['chi2', (55,)],
['cosine', ()],
['dgamma', (1.1023326088288166,)],
['dweibull', (2.0685080649914673,)],
['erlang', (20,)], #correction numargs = 1
['expon', ()],
['exponpow', (2.697119160358469,)],
['exponweib', (2.8923945291034436, 1.9505288745913174)],
['f', (29, 18)],
['fatiguelife', (29,)], #correction numargs = 1
['fisk', (3.0857548622253179,)],
['foldcauchy', (4.7164673455831894,)],
['foldnorm', (1.9521253373555869,)],
['frechet_l', (3.6279911255583239,)],
['frechet_r', (1.8928171603534227,)],
['gamma', (1.9932305483800778,)],
['gausshyper', (13.763771604130699, 3.1189636648681431,
2.5145980350183019, 5.1811649903971615)], #veryslow
['genexpon', (9.1325976465418908, 16.231956600590632, 3.2819552690843983)],
['genextreme', (-0.1,)], # sample mean test fails for (3.3184017469423535,)],
['gengamma', (4.4162385429431925, 3.1193091679242761)],
['genhalflogistic', (0.77274727809929322,)],
['genlogistic', (0.41192440799679475,)],
['genpareto', (0.1,)], # use case with finite moments
['gilbrat', ()],
['gompertz', (0.94743713075105251,)],
['gumbel_l', ()],
['gumbel_r', ()],
['halfcauchy', ()],
['halflogistic', ()],
['halfnorm', ()],
['hypsecant', ()],
['invgamma', (2.0668996136993067,)],
['invgauss', (0.14546264555347513,)],
['invweibull', (10.58,)], # sample mean test fails at(0.58847112119264788,)]
['johnsonsb', (4.3172675099141058, 3.1837781130785063)],
['johnsonsu', (2.554395574161155, 2.2482281679651965)],
['ksone', (1000,)], #replace 22 by 100 to avoid failing range, ticket 956
['kstwobign', ()],
['laplace', ()],
['levy', ()],
['levy_l', ()],
# ['levy_stable', (0.35667405469844993,
# -0.67450531578494011)], #NotImplementedError
# rvs not tested
['loggamma', (0.41411931826052117,)],
['logistic', ()],
['loglaplace', (3.2505926592051435,)],
['lognorm', (0.95368226960575331,)],
['lomax', (1.8771398388773268,)],
['maxwell', ()],
['mielke', (10.4, 3.6)], # sample mean test fails for (4.6420495492121487, 0.59707419545516938)],
# mielke: good results if 2nd parameter >2, weird mean or var below
['nakagami', (4.9673794866666237,)],
['ncf', (27, 27, 0.41578441799226107)],
['nct', (14, 0.24045031331198066)],
['ncx2', (21, 1.0560465975116415)],
['norm', ()],
['pareto', (2.621716532144454,)],
['powerlaw', (1.6591133289905851,)],
['powerlognorm', (2.1413923530064087, 0.44639540782048337)],
['powernorm', (4.4453652254590779,)],
['rayleigh', ()],
['rdist', (0.9,)], # feels also slow
# ['rdist', (3.8266985793976525,)], #veryslow, especially rvs
#['rdist', (541.0,)], # from ticket #758 #veryslow
['recipinvgauss', (0.63004267809369119,)],
['reciprocal', (0.0062309367010521255, 1.0062309367010522)],
['rice', (0.7749725210111873,)],
['semicircular', ()],
['t', (2.7433514990818093,)],
['triang', (0.15785029824528218,)],
['truncexpon', (4.6907725456810478,)],
['truncnorm', (-1.0978730080013919, 2.7306754109031979)],
['tukeylambda', (3.1321477856738267,)],
['uniform', ()],
['vonmises', (3.9939042581071398,)],
['wald', ()],
['weibull_max', (2.8687961709100187,)],
['weibull_min', (1.7866166930421596,)],
['wrapcauchy', (0.031071279018614728,)]]
# for testing only specific functions
##distcont = [
## ['erlang', (20,)], #correction numargs = 1
## ['fatiguelife', (29,)], #correction numargs = 1
## ['loggamma', (0.41411931826052117,)]]
# for testing ticket:767
##distcont = [
## ['genextreme', (3.3184017469423535,)],
## ['genextreme', (0.01,)],
## ['genextreme', (0.00001,)],
## ['genextreme', (0.0,)],
## ['genextreme', (-0.01,)]
## ]
##distcont = [['gumbel_l', ()],
## ['gumbel_r', ()],
## ['norm', ()]
## ]
##distcont = [['norm', ()]]
distmissing = ['wald', 'gausshyper', 'genexpon', 'rv_continuous',
'loglaplace', 'rdist', 'semicircular', 'invweibull', 'ksone',
'cosine', 'kstwobign', 'truncnorm', 'mielke', 'recipinvgauss', 'levy',
'johnsonsu', 'levy_l', 'powernorm', 'wrapcauchy',
'johnsonsb', 'truncexpon', 'rice', 'invgauss', 'invgamma',
'powerlognorm']
distmiss = [[dist,args] for dist,args in distcont if dist in distmissing]
distslow = ['rdist', 'gausshyper', 'recipinvgauss', 'ksone', 'genexpon',
'vonmises', 'rice', 'mielke', 'semicircular', 'cosine', 'invweibull',
'powerlognorm', 'johnsonsu', 'kstwobign']
#distslow are sorted by speed (very slow to slow)
def _silence_fp_errors(func):
def wrap(*a, **kw):
olderr = np.seterr(all='ignore')
try:
return func(*a, **kw)
finally:
np.seterr(**olderr)
wrap.__name__ = func.__name__
return wrap
@_silence_fp_errors
def test_cont_basic():
# this test skips slow distributions
for distname, arg in distcont[:]:
if distname in distslow:
continue
distfn = getattr(stats, distname)
np.random.seed(765456)
sn = 1000
rvs = distfn.rvs(size=sn,*arg)
sm = rvs.mean()
sv = rvs.var()
skurt = stats.kurtosis(rvs)
sskew = stats.skew(rvs)
m,v = distfn.stats(*arg)
yield check_sample_meanvar_, distfn, arg, m, v, sm, sv, sn, distname + \
'sample mean test'
# the sample skew kurtosis test has known failures, not very good distance measure
#yield check_sample_skew_kurt, distfn, arg, sskew, skurt, distname
yield check_moment, distfn, arg, m, v, distname
yield check_cdf_ppf, distfn, arg, distname
yield check_sf_isf, distfn, arg, distname
yield check_pdf, distfn, arg, distname
if distname in ['wald']:
continue
yield check_pdf_logpdf, distfn, arg, distname
yield check_cdf_logcdf, distfn, arg, distname
yield check_sf_logsf, distfn, arg, distname
if distname in distmissing:
alpha = 0.01
yield check_distribution_rvs, distname, arg, alpha, rvs
@npt.dec.slow
def test_cont_basic_slow():
# same as above for slow distributions
for distname, arg in distcont[:]:
if distname not in distslow: continue
distfn = getattr(stats, distname)
np.random.seed(765456)
sn = 1000
rvs = distfn.rvs(size=sn,*arg)
sm = rvs.mean()
sv = rvs.var()
skurt = stats.kurtosis(rvs)
sskew = stats.skew(rvs)
m,v = distfn.stats(*arg)
yield check_sample_meanvar_, distfn, arg, m, v, sm, sv, sn, distname + \
'sample mean test'
# the sample skew kurtosis test has known failures, not very good distance measure
#yield check_sample_skew_kurt, distfn, arg, sskew, skurt, distname
yield check_moment, distfn, arg, m, v, distname
yield check_cdf_ppf, distfn, arg, distname
yield check_sf_isf, distfn, arg, distname
yield check_pdf, distfn, arg, distname
yield check_pdf_logpdf, distfn, arg, distname
yield check_cdf_logcdf, distfn, arg, distname
yield check_sf_logsf, distfn, arg, distname
#yield check_oth, distfn, arg # is still missing
if distname in distmissing:
alpha = 0.01
yield check_distribution_rvs, distname, arg, alpha, rvs
@_silence_fp_errors
def check_moment(distfn, arg, m, v, msg):
m1 = distfn.moment(1,*arg)
m2 = distfn.moment(2,*arg)
if not np.isinf(m):
npt.assert_almost_equal(m1, m, decimal=10, err_msg= msg + \
' - 1st moment')
else: # or np.isnan(m1),
npt.assert_(np.isinf(m1),
msg + ' - 1st moment -infinite, m1=%s' % str(m1))
#np.isnan(m1) temporary special treatment for loggamma
if not np.isinf(v):
npt.assert_almost_equal(m2-m1*m1, v, decimal=10, err_msg= msg + \
' - 2ndt moment')
else: #or np.isnan(m2),
npt.assert_(np.isinf(m2),
msg + ' - 2nd moment -infinite, m2=%s' % str(m2))
#np.isnan(m2) temporary special treatment for loggamma
@_silence_fp_errors
def check_sample_meanvar_(distfn, arg, m, v, sm, sv, sn, msg):
#this did not work, skipped silently by nose
#check_sample_meanvar, sm, m, msg + 'sample mean test'
#check_sample_meanvar, sv, v, msg + 'sample var test'
if not np.isinf(m):
check_sample_mean(sm, sv, sn, m)
if not np.isinf(v):
check_sample_var(sv, sn, v)
## check_sample_meanvar( sm, m, msg + 'sample mean test')
## check_sample_meanvar( sv, v, msg + 'sample var test')
def check_sample_mean(sm,v,n, popmean):
"""
from stats.stats.ttest_1samp(a, popmean):
Calculates the t-obtained for the independent samples T-test on ONE group
of scores a, given a population mean.
Returns: t-value, two-tailed prob
"""
## a = asarray(a)
## x = np.mean(a)
## v = np.var(a, ddof=1)
## n = len(a)
df = n-1
svar = ((n-1)*v) / float(df) #looks redundant
t = (sm-popmean)/np.sqrt(svar*(1.0/n))
prob = stats.betai(0.5*df,0.5,df/(df+t*t))
#return t,prob
npt.assert_(prob > 0.01, 'mean fail, t,prob = %f, %f, m,sm=%f,%f' % (t,prob,popmean,sm))
def check_sample_var(sv,n, popvar):
'''
two-sided chisquare test for sample variance equal to hypothesized variance
'''
df = n-1
chi2 = (n-1)*popvar/float(popvar)
pval = stats.chisqprob(chi2,df)*2
npt.assert_(pval > 0.01, 'var fail, t,pval = %f, %f, v,sv=%f,%f' % (chi2,pval,popvar,sv))
def check_sample_skew_kurt(distfn, arg, ss, sk, msg):
skew,kurt = distfn.stats(moments='sk',*arg)
## skew = distfn.stats(moment='s',*arg)[()]
## kurt = distfn.stats(moment='k',*arg)[()]
check_sample_meanvar( sk, kurt, msg + 'sample kurtosis test')
check_sample_meanvar( ss, skew, msg + 'sample skew test')
def check_sample_meanvar(sm,m,msg):
if not np.isinf(m) and not np.isnan(m):
npt.assert_almost_equal(sm, m, decimal=DECIMAL, err_msg= msg + \
' - finite moment')
## else:
## npt.assert_(abs(sm) > 10000), msg='infinite moment, sm = ' + str(sm))
@_silence_fp_errors
def check_cdf_ppf(distfn,arg,msg):
values = [0.001,0.5,0.999]
npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
values, decimal=DECIMAL, err_msg= msg + \
' - cdf-ppf roundtrip')
@_silence_fp_errors
def check_sf_isf(distfn,arg,msg):
npt.assert_almost_equal(distfn.sf(distfn.isf([0.1,0.5,0.9], *arg), *arg),
[0.1,0.5,0.9], decimal=DECIMAL, err_msg= msg + \
' - sf-isf roundtrip')
npt.assert_almost_equal(distfn.cdf([0.1,0.9], *arg),
1.0-distfn.sf([0.1,0.9], *arg),
decimal=DECIMAL, err_msg= msg + \
' - cdf-sf relationship')
@_silence_fp_errors
def check_pdf(distfn, arg, msg):
# compares pdf at median with numerical derivative of cdf
median = distfn.ppf(0.5, *arg)
eps = 1e-6
pdfv = distfn.pdf(median, *arg)
if (pdfv < 1e-4) or (pdfv > 1e4):
# avoid checking a case where pdf is close to zero or huge (singularity)
median = median + 0.1
pdfv = distfn.pdf(median, *arg)
cdfdiff = (distfn.cdf(median + eps, *arg) -
distfn.cdf(median - eps, *arg))/eps/2.0
#replace with better diff and better test (more points),
#actually, this works pretty well
npt.assert_almost_equal(pdfv, cdfdiff,
decimal=DECIMAL, err_msg= msg + ' - cdf-pdf relationship')
@_silence_fp_errors
def check_pdf_logpdf(distfn, args, msg):
# compares pdf at several points with the log of the pdf
points = np.array([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
vals = distfn.ppf(points, *args)
pdf = distfn.pdf(vals, *args)
logpdf = distfn.logpdf(vals, *args)
pdf = pdf[pdf != 0]
logpdf = logpdf[np.isfinite(logpdf)]
npt.assert_almost_equal(np.log(pdf), logpdf, decimal=7, err_msg=msg + " - logpdf-log(pdf) relationship")
@_silence_fp_errors
def check_sf_logsf(distfn, args, msg):
# compares sf at several points with the log of the sf
points = np.array([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
vals = distfn.ppf(points, *args)
sf = distfn.sf(vals, *args)
logsf = distfn.logsf(vals, *args)
sf = sf[sf != 0]
logsf = logsf[np.isfinite(logsf)]
npt.assert_almost_equal(np.log(sf), logsf, decimal=7, err_msg=msg + " - logsf-log(sf) relationship")
@_silence_fp_errors
def check_cdf_logcdf(distfn, args, msg):
# compares cdf at several points with the log of the cdf
points = np.array([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
vals = distfn.ppf(points, *args)
cdf = distfn.cdf(vals, *args)
logcdf = distfn.logcdf(vals, *args)
cdf = cdf[cdf != 0]
logcdf = logcdf[np.isfinite(logcdf)]
npt.assert_almost_equal(np.log(cdf), logcdf, decimal=7, err_msg=msg + " - logcdf-log(cdf) relationship")
@_silence_fp_errors
def check_distribution_rvs(dist, args, alpha, rvs):
#test from scipy.stats.tests
#this version reuses existing random variables
D,pval = stats.kstest(rvs, dist, args=args, N=1000)
if (pval < alpha):
D,pval = stats.kstest(dist,'',args=args, N=1000)
npt.assert_(pval > alpha, "D = " + str(D) + "; pval = " + str(pval) +
"; alpha = " + str(alpha) + "\nargs = " + str(args))
if __name__ == "__main__":
#nose.run(argv=['', __file__])
nose.runmodule(argv=[__file__,'-s'], exit=False)