def test_gnb_sensitivities():
gnb = GNB(common_variance=True)
ds = normal_feature_dataset(perlabel=4,
nlabels=3,
nfeatures=5,
nchunks=4,
snr=10,
nonbogus_features=[0, 1, 2])
s = gnb.get_sensitivity_analyzer()(ds)
assert_in('targets', s.sa)
assert_equal(s.shape, (((len(ds.uniquetargets) *
(len(ds.uniquetargets) - 1)) / 2), ds.nfeatures))
# test zero variance case
# set variance of feature to zero
ds.samples[:, 3] = 0.3
s_zerovar = gnb.get_sensitivity_analyzer()
sens = s_zerovar(ds)
assert_true(all(sens.samples[:, 3] == 0))
# test whether tagging and untagging works
assert 'has_sensitivity' in gnb.__tags__
gnb.untrain()
assert 'has_sensitivity' not in gnb.__tags__
# test whether content of sensitivities makes rough sense
# e.g.: sensitivity of first feature should be larger than of bogus last feature
assert_true(
abs(sens.samples[i, 0]) > abs(sens.samples[i, 4])
for i in range(np.shape(sens.samples)[0]))
def test_gnb_sensitivities():
gnb = GNB(common_variance=True)
ds = normal_feature_dataset(perlabel=4,
nlabels=3,
nfeatures=5,
nchunks=4,
snr=10,
nonbogus_features=[0, 1, 2]
)
s = gnb.get_sensitivity_analyzer()(ds)
assert_in('targets', s.sa)
assert_equal(s.shape, (((len(ds.uniquetargets) * (len(ds.uniquetargets) - 1))/2), ds.nfeatures))
# test zero variance case
# set variance of feature to zero
ds.samples[:,3]=0.3
s_zerovar = gnb.get_sensitivity_analyzer()
sens = s_zerovar(ds)
assert_true(all(sens.samples[:, 3] == 0))
# test whether tagging and untagging works
assert 'has_sensitivity' in gnb.__tags__
gnb.untrain()
assert 'has_sensitivity' not in gnb.__tags__
# test whether content of sensitivities makes rough sense
# e.g.: sensitivity of first feature should be larger than of bogus last feature
assert_true(abs(sens.samples[i, 0]) > abs(sens.samples[i, 4]) for i in range(np.shape(sens.samples)[0]))
def test_gnb(self):
gnb = GNB()
gnb_nc = GNB(common_variance=False)
gnb_n = GNB(normalize=True)
gnb_n_nc = GNB(normalize=True, common_variance=False)
gnb_lin = GNB(common_variance=True)
ds = datasets['uni2medium']
# Generic silly coverage just to assure that it works in all
# possible scenarios:
bools = (True, False)
# There should be better way... heh
for cv in bools: # common_variance?
for prior in ('uniform', 'laplacian_smoothing', 'ratio'):
tp = None # predictions -- all above should
# result in the same predictions
for n in bools: # normalized?
for ls in bools: # logspace?
for es in ((), ('estimates')):
gnb_ = GNB(common_variance=cv,
prior=prior,
normalize=n,
logprob=ls,
enable_ca=es)
tm = TransferMeasure(gnb_, Splitter('train'))
predictions = tm(ds).samples[:, 0]
if tp is None:
tp = predictions
assert_array_equal(predictions, tp)
# if normalized -- check if estimates are such
if n and 'estimates' in es:
v = gnb_.ca.estimates
if ls: # in log space -- take exp ;)
v = np.exp(v)
d1 = np.sum(v, axis=1) - 1.0
self.assertTrue(np.max(np.abs(d1)) < 1e-5)
# smoke test to see whether invocation of sensitivity analyser blows
# if gnb classifier isn't linear, and to see whether it doesn't blow
# when it is linear.
if cv:
assert 'has_sensitivity' in gnb_.__tags__
gnb_.get_sensitivity_analyzer()
if not cv:
with self.assertRaises(NotImplementedError):
gnb_.get_sensitivity_analyzer()
def test_gnb(self):
gnb = GNB()
gnb_nc = GNB(common_variance=False)
gnb_n = GNB(normalize=True)
gnb_n_nc = GNB(normalize=True, common_variance=False)
gnb_lin = GNB(common_variance=True)
ds = datasets['uni2medium']
# Generic silly coverage just to assure that it works in all
# possible scenarios:
bools = (True, False)
# There should be better way... heh
for cv in bools: # common_variance?
for prior in ('uniform', 'laplacian_smoothing', 'ratio'):
tp = None # predictions -- all above should
# result in the same predictions
for n in bools: # normalized?
for ls in bools: # logspace?
for es in ((), ('estimates')):
gnb_ = GNB(common_variance=cv,
prior=prior,
normalize=n,
logprob=ls,
enable_ca=es)
tm = TransferMeasure(gnb_, Splitter('train'))
predictions = tm(ds).samples[:,0]
if tp is None:
tp = predictions
assert_array_equal(predictions, tp)
# if normalized -- check if estimates are such
if n and 'estimates' in es:
v = gnb_.ca.estimates
if ls: # in log space -- take exp ;)
v = np.exp(v)
d1 = np.sum(v, axis=1) - 1.0
self.assertTrue(np.max(np.abs(d1)) < 1e-5)
# smoke test to see whether invocation of sensitivity analyser blows
# if gnb classifier isn't linear, and to see whether it doesn't blow
# when it is linear.
if cv:
assert 'has_sensitivity' in gnb_.__tags__
gnb_.get_sensitivity_analyzer()
if not cv:
with self.assertRaises(NotImplementedError):
gnb_.get_sensitivity_analyzer()
def test_gnb_sensitivities(logprob):
gnb = GNB(common_variance=True, logprob=logprob)
ds = normal_feature_dataset(perlabel=4,
nlabels=3,
nfeatures=5,
nchunks=4,
snr=20,
nonbogus_features=[0, 1, 2])
s = gnb.get_sensitivity_analyzer()(ds)
assert_in('targets', s.sa)
assert_equal(s.shape, (((len(ds.uniquetargets) *
(len(ds.uniquetargets) - 1)) / 2), ds.nfeatures))
# test zero variance case
# set variance of feature to zero
ds.samples[:, 3] = 0.3
s_zerovar = gnb.get_sensitivity_analyzer()
sens = s_zerovar(ds)
assert_equal(sens.T.dtype, 'O') # we store pairs
assert_equal(sens.T[0], ('L0', 'L1'))
assert_true(all(sens.samples[:, 3] == 0))
gnb.untrain()
# test whether content of sensitivities makes rough sense
# First feature has information only about L0, so it would be of
# no use for L1 -vs- L2 classification, so we will go through each pair
# and make sure that signs etc all correct for each pair.
# This in principle should be a generic test for multiclass sensitivities
abssens = abs(sens.samples)
for (t1, t2), t1t2sens in zip(sens.T, sens.samples):
# go from literal L1 to 1, L0 to 0 - corresponds to feature
i1 = int(t1[1])
i2 = int(t2[1])
assert t1t2sens[i1] < 0
assert t1t2sens[i2] > 0
assert t1t2sens[i2] > t1t2sens[4]
def test_gnb_sensitivities():
gnb = GNB(common_variance=True)
ds = normal_feature_dataset(perlabel=4,
nlabels=3,
nfeatures=5,
nchunks=4,
snr=20,
nonbogus_features=[0, 1, 2]
)
s = gnb.get_sensitivity_analyzer()(ds)
assert_in('targets', s.sa)
assert_equal(s.shape, (((len(ds.uniquetargets) * (len(ds.uniquetargets) - 1))/2), ds.nfeatures))
# test zero variance case
# set variance of feature to zero
ds.samples[:, 3] = 0.3
s_zerovar = gnb.get_sensitivity_analyzer()
sens = s_zerovar(ds)
assert_equal(sens.T.dtype, 'O') # we store pairs
assert_equal(sens.T[0], ('L0', 'L1'))
assert_true(all(sens.samples[:, 3] == 0))
gnb.untrain()
# test whether content of sensitivities makes rough sense
# First feature has information only about L0, so it would be of
# no use for L1 -vs- L2 classification, so we will go through each pair
# and make sure that signs etc all correct for each pair.
# This in principle should be a generic test for multiclass sensitivities
abssens = abs(sens.samples)
for (t1, t2), t1t2sens in zip(sens.T, sens.samples):
# go from literal L1 to 1, L0 to 0 - corresponds to feature
i1 = int(t1[1])
i2 = int(t2[1])
assert t1t2sens[i1] < 0
assert t1t2sens[i2] > 0
assert t1t2sens[i2] > t1t2sens[4]