def test_match_distribution_semifrozen(self):
"""Handle frozen params in match_distribution
"""
matches = match_distribution(
np.arange(10),
distributions=['uniform', ('uniform', {
'loc': 0
})],
p=-1 # so we get all matches
)
self.assertEqual(len(matches), 2) # we must get some match
self.assertTrue(abs(matches[0][-1][0]) <
4e-1) # full fit should get close to true loc
self.assertEqual(matches[1][-1][0],
0) # frozen should maintain the loc
if externals.versions['scipy'] >= '0.10':
# known to work on 0.10 and fail on 0.7.3
self.assertTrue(abs(matches[0][-1][1] - 9) <
1e-1) # full fit should get close to true scale
else:
raise SkipTest("KnownFailure to fit uniform on older scipy")
# actually it fails ATM to fit uniform with frozen loc=0
# nicely -- sets scale = 1 :-/ TODO
raise SkipTest("TODO: Known failure to fit uniform with frozen loc")
self.assertTrue(
abs(matches[1][-1][1] - 9) < 1e-1) # frozen fit of scale
def test_match_distribution(self):
"""Some really basic testing for match_distribution
"""
ds = datasets['uni2medium'] # large to get stable stats
data = ds.samples[:, ds.a.bogus_features[0]]
# choose bogus feature, which
# should have close to normal distribution
# Lets test ad-hoc rv_semifrozen
floc = rv_semifrozen(scipy.stats.norm, loc=0).fit(data)
self.assertTrue(floc[0] == 0)
fscale = rv_semifrozen(scipy.stats.norm, scale=1.0).fit(data)
self.assertTrue(fscale[1] == 1)
flocscale = rv_semifrozen(scipy.stats.norm, loc=0, scale=1.0).fit(data)
self.assertTrue(flocscale[1] == 1 and flocscale[0] == 0)
full = scipy.stats.norm.fit(data)
for res in [floc, fscale, flocscale, full]:
self.assertTrue(len(res) == 2)
data_mean = np.mean(data)
for loc in [None, data_mean]:
for test in ['p-roc', 'kstest']:
# some really basic testing
matched = match_distribution(data=data,
distributions=[
'scipy',
('norm', {
'name': 'norm_fixed',
'loc': 0.2,
'scale': 0.3
})
],
test=test,
loc=loc,
p=0.05)
# at least norm should be in there
names = [m[2] for m in matched]
if test == 'p-roc':
if cfg.getboolean('tests', 'labile', default='yes'):
# we can guarantee that only for norm_fixed
self.assertTrue('norm' in names)
self.assertTrue('norm_fixed' in names)
inorm = names.index('norm_fixed')
# and it should be at least in the first
# 30 best matching ;-)
self.assertTrue(inorm <= 30)
# Test plotting only once
if loc is None and externals.exists("pylab plottable"):
import pylab as pl
from mvpa2.clfs.stats import plot_distribution_matches
fig = pl.figure()
plot_distribution_matches(data,
matched,
legend=1,
nbest=5)
#pl.show()
pl.close(fig)
def test_match_distribution_semifrozen(self):
"""Handle frozen params in match_distribution
"""
matches = match_distribution(np.arange(10),
distributions=[
'uniform',
('uniform', {'loc': 0})
],
p= -1 # so we get all matches
)
self.assertEqual(len(matches), 2) # we must get some match
self.assertTrue(abs(matches[0][-1][0]) < 4e-1) # full fit should get close to true loc
self.assertEqual(matches[1][-1][0], 0) # frozen should maintain the loc
if externals.versions['scipy'] >= '0.10':
# known to work on 0.10 and fail on 0.7.3
self.assertTrue(abs(matches[0][-1][1] - 9) < 1e-1) # full fit should get close to true scale
else:
raise SkipTest("KnownFailure to fit uniform on older scipy")
# actually it fails ATM to fit uniform with frozen loc=0
# nicely -- sets scale = 1 :-/ TODO
raise SkipTest("TODO: Known failure to fit uniform with frozen loc")
self.assertTrue(abs(matches[1][-1][1] - 9) < 1e-1) # frozen fit of scale
def test_match_distribution(self):
"""Some really basic testing for match_distribution
"""
ds = datasets['uni2medium'] # large to get stable stats
data = ds.samples[:, ds.a.bogus_features[0]]
# choose bogus feature, which
# should have close to normal distribution
# Lets test ad-hoc rv_semifrozen
floc = rv_semifrozen(scipy.stats.norm, loc=0).fit(data)
self.assertTrue(floc[0] == 0)
fscale = rv_semifrozen(scipy.stats.norm, scale=1.0).fit(data)
self.assertTrue(fscale[1] == 1)
flocscale = rv_semifrozen(scipy.stats.norm, loc=0, scale=1.0).fit(data)
self.assertTrue(flocscale[1] == 1 and flocscale[0] == 0)
full = scipy.stats.norm.fit(data)
for res in [floc, fscale, flocscale, full]:
self.assertTrue(len(res) == 2)
data_mean = np.mean(data)
for loc in [None, data_mean]:
for test in ['p-roc', 'kstest']:
# some really basic testing
matched = match_distribution(
data=data,
distributions = ['scipy',
('norm',
{'name': 'norm_fixed',
'loc': 0.2,
'scale': 0.3})],
test=test, loc=loc, p=0.05)
# at least norm should be in there
names = [m[2] for m in matched]
if test == 'p-roc':
if cfg.getboolean('tests', 'labile', default='yes'):
# we can guarantee that only for norm_fixed
self.assertTrue('norm' in names)
self.assertTrue('norm_fixed' in names)
inorm = names.index('norm_fixed')
# and it should be at least in the first
# 30 best matching ;-)
self.assertTrue(inorm <= 30)
# Test plotting only once
if loc is None and externals.exists("pylab plottable"):
import pylab as pl
from mvpa2.clfs.stats import plot_distribution_matches
fig = pl.figure()
plot_distribution_matches(data, matched, legend=1, nbest=5)
#pl.show()
pl.close(fig)
def test_match_distribution_semifrozen(self):
"""Handle frozen params in match_distribution
"""
matches = match_distribution(np.arange(10),
distributions=[
'uniform',
('uniform', {'loc': 0})
],
p=-1 # so we get all matches
)
self.assertEqual(len(matches), 2) # we must get some match
self.assertTrue(abs(matches[0][-1][0]) < 4e-1) # full fit should get close to true loc
self.assertTrue(abs(matches[0][-1][1]-9) < 1e-1) # full fit should get close to true scale
self.assertEqual(matches[1][-1][0], 0) # frozen should maintain the loc
# actually it fails ATM to fit uniform with frozen loc=0
# nicely -- sets scale = 1 :-/ TODO
raise SkipTest("TODO: Known failure to fit uniform with frozen loc")
self.assertTrue(abs(matches[1][-1][1]-9) < 1e-1) # frozen fit of scale
