def _forward_dataset(self, ds):
# local binding
chunks_attr = self.__chunks_attr
dtype = self.__dtype
if __debug__ and chunks_attr is not None:
nsamples_per_chunk = get_nsamples_per_attr(ds, chunks_attr)
min_nsamples_per_chunk = np.min(nsamples_per_chunk.values())
if min_nsamples_per_chunk in range(3, 6):
warning(
"Z-scoring chunk-wise having a chunk with only "
"%d samples is 'discouraged'. "
"You have chunks with following number of samples: %s" % (
min_nsamples_per_chunk,
nsamples_per_chunk,
))
if min_nsamples_per_chunk <= 2:
warning(
"Z-scoring chunk-wise having a chunk with less "
"than three samples will set features in these "
"samples to either zero (with 1 sample in a chunk) "
"or -1/+1 (with 2 samples in a chunk). "
"You have chunks with following number of samples: %s" %
(nsamples_per_chunk, ))
params = self.__params_dict
if params is None:
raise RuntimeError, \
"ZScoreMapper needs to be trained before call to forward"
if self._secret_inplace_zscore:
mds = ds
else:
# shallow copy to put the new stuff in
mds = ds.copy(deep=False)
# but deepcopy the samples since _zscore would modify inplace
mds.samples = mds.samples.copy()
# cast the data to float, since in-place operations below do not upcast!
if np.issubdtype(mds.samples.dtype, np.integer):
mds.samples = mds.samples.astype(dtype)
if '__all__' in params:
# we have a global parameter set
mds.samples = self._zscore(mds.samples, *params['__all__'])
else:
# per chunk z-scoring
for c in mds.sa[chunks_attr].unique:
if not c in params:
raise RuntimeError(
"%s has no parameters for chunk '%s'. It probably "
"wasn't present in the training dataset!?" %
(self.__class__.__name__, c))
slicer = np.where(mds.sa[chunks_attr].value == c)[0]
mds.samples[slicer] = self._zscore(mds.samples[slicer],
*params[c])
return mds
def _forward_dataset(self, ds):
# local binding
chunks_attr = self.__chunks_attr
dtype = self.__dtype
if __debug__ and not chunks_attr is None:
nsamples_per_chunk = get_nsamples_per_attr(ds, chunks_attr)
min_nsamples_per_chunk = np.min(nsamples_per_chunk.values())
if min_nsamples_per_chunk in range(3, 6):
warning(
"Z-scoring chunk-wise having a chunk with only "
"%d samples is 'discouraged'. "
"You have chunks with following number of samples: %s"
% (min_nsamples_per_chunk, nsamples_per_chunk)
)
if min_nsamples_per_chunk <= 2:
warning(
"Z-scoring chunk-wise having a chunk with less "
"than three samples will set features in these "
"samples to either zero (with 1 sample in a chunk) "
"or -1/+1 (with 2 samples in a chunk). "
"You have chunks with following number of samples: %s" % (nsamples_per_chunk,)
)
params = self.__params_dict
if params is None:
raise RuntimeError, "ZScoreMapper needs to be trained before call to forward"
if self._secret_inplace_zscore:
mds = ds
else:
# shallow copy to put the new stuff in
mds = ds.copy(deep=False)
# but deepcopy the samples since _zscore would modify inplace
mds.samples = mds.samples.copy()
# cast the data to float, since in-place operations below do not upcast!
if np.issubdtype(mds.samples.dtype, np.integer):
mds.samples = mds.samples.astype(dtype)
if "__all__" in params:
# we have a global parameter set
mds.samples = self._zscore(mds.samples, *params["__all__"])
else:
# per chunk z-scoring
for c in mds.sa[chunks_attr].unique:
if not c in params:
raise RuntimeError(
"%s has no parameters for chunk '%s'. It probably "
"wasn't present in the training dataset!?" % (self.__class__.__name__, c)
)
slicer = np.where(mds.sa[chunks_attr].value == c)[0]
mds.samples[slicer] = self._zscore(mds.samples[slicer], *params[c])
return mds
def _forward_dataset(self, ds):
# local binding
chunks_attr = self.__chunks_attr
dtype = self.__dtype
if (
__debug__
and not chunks_attr is None
and np.array(get_nsamples_per_attr(ds, chunks_attr).values()).min() <= 2
):
warning(
"Z-scoring chunk-wise having a chunk with less than three "
"samples will set features in these samples to either zero "
"(with 1 sample in a chunk) "
"or -1/+1 (with 2 samples in a chunk)."
)
params = self.__params_dict
if params is None:
raise RuntimeError, "ZScoreMapper needs to be trained before call to forward"
if self._secret_inplace_zscore:
mds = ds
else:
# shallow copy to put the new stuff in
mds = ds.copy(deep=False)
# cast the data to float, since in-place operations below do not upcast!
if np.issubdtype(mds.samples.dtype, np.integer):
mds.samples = mds.samples.astype(dtype)
if "__all__" in params:
# we have a global parameter set
mds.samples = self._zscore(mds.samples, *params["__all__"])
else:
# per chunk z-scoring
for c in mds.sa[chunks_attr].unique:
if not c in params:
raise RuntimeError(
"%s has no parameters for chunk '%s'. It probably "
"wasn't present in the training dataset!?" % (self.__class__.__name__, c)
)
slicer = np.where(mds.sa[chunks_attr].value == c)[0]
mds.samples[slicer] = self._zscore(mds.samples[slicer], *params[c])
return mds
def test_cper_class(self, clf):
if not (clf.params.has_key('C')):
# skip those without C
return
ds = datasets['uni2medium'].copy()
ds__ = datasets['uni2medium'].copy()
#
# ballanced set
# Lets add a bit of noise to drive classifier nuts. same
# should be done for disballanced set
ds__.samples = ds__.samples + \
0.5 * np.random.normal(size=(ds__.samples.shape))
#
# disballanced set
# lets overpopulate label 0
times = 20
ds_ = ds[(range(ds.nsamples) + range(ds.nsamples//2) * times)]
ds_.samples = ds_.samples + \
0.5 * np.random.normal(size=(ds_.samples.shape))
spl = get_nsamples_per_attr(ds_, 'targets') #_.samplesperlabel
#print ds_.targets, ds_.chunks
cve = CrossValidation(clf, NFoldPartitioner(), enable_ca='stats')
# on balanced
e = cve(ds__)
tpr_1 = cve.ca.stats.stats["TPR"][1]
# on disbalanced
e = cve(ds_)
tpr_2 = cve.ca.stats.stats["TPR"][1]
# Set '1 C per label'
# recreate cvte since previous might have operated on copies
cve = CrossValidation(clf, NFoldPartitioner(),
enable_ca='stats')
oldC = clf.params.C
# TODO: provide clf.params.C not with a tuple but dictionary
# with C per label (now order is deduced in a cruel way)
ratio = np.sqrt(float(spl[ds_.UT[0]])/spl[ds_.UT[1]])
clf.params.C = (-1/ratio, -1*ratio)
try:
# on disbalanced but with balanced C
e_ = cve(ds_)
# reassign C
clf.params.C = oldC
except:
clf.params.C = oldC
raise
tpr_3 = cve.ca.stats.stats["TPR"][1]
# Actual tests
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(tpr_1 > 0.25,
msg="Without disballance we should have some "
"hits, but got TPR=%.3f" % tpr_1)
self.assertTrue(tpr_2 < 0.25,
msg="With disballance we should have almost no "
"hits for minor, but got TPR=%.3f" % tpr_2)
self.assertTrue(tpr_3 > 0.25,
msg="With disballanced data but ratio-based Cs "
"we should have some hits for minor, but got "
"TPR=%.3f" % tpr_3)
def test_cper_class(self, clf):
if not ('C' in clf.params):
# skip those without C
return
ds = datasets['uni2medium'].copy()
ds__ = datasets['uni2medium'].copy()
#
# ballanced set
# Lets add a bit of noise to drive classifier nuts. same
# should be done for disballanced set
ds__.samples += 0.5 * np.random.normal(size=(ds__.samples.shape))
#
# disballanced set
# lets overpopulate label 0
times = 20
ds_ = ds[(list(range(ds.nsamples)) +
list(range(ds.nsamples // 2)) * times)]
ds_.samples += 0.5 * np.random.normal(size=(ds_.samples.shape))
spl = get_nsamples_per_attr(ds_, 'targets') #_.samplesperlabel
cve = CrossValidation(clf, NFoldPartitioner(), enable_ca='stats')
# on balanced
e = cve(ds__)
tpr_1 = cve.ca.stats.stats["TPR"][1]
# we should be able to print summary for the classifier
clf_summary = clf.summary()
if externals.exists('libsvm') and isinstance(clf, libsvm.SVM):
self.assertIn(" #SVs:", clf_summary)
self.assertIn(" #bounded_SVs:", clf_summary)
self.assertIn(" used_C:", clf_summary)
# on disbalanced
e = cve(ds_)
tpr_2 = cve.ca.stats.stats["TPR"][1]
# Set '1 C per label'
# recreate cvte since previous might have operated on copies
cve = CrossValidation(clf, NFoldPartitioner(), enable_ca='stats')
oldC = clf.params.C
# TODO: provide clf.params.C not with a tuple but dictionary
# with C per label (now order is deduced in a cruel way)
ratio = np.sqrt(float(spl[ds_.UT[0]]) / spl[ds_.UT[1]])
clf.params.C = (-1 / ratio, -1 * ratio)
try:
# on disbalanced but with balanced C
e_ = cve(ds_)
# reassign C
clf.params.C = oldC
except:
clf.params.C = oldC
raise
tpr_3 = cve.ca.stats.stats["TPR"][1]
# Actual tests
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(tpr_1 > 0.25,
msg="Without disballance we should have some "
"hits, but got TPR=%.3f" % tpr_1)
self.assertTrue(tpr_2 < 0.25,
msg="With disballance we should have almost no "
"hits for minor, but got TPR=%.3f" % tpr_2)
self.assertTrue(tpr_3 > 0.25,
msg="With disballanced data but ratio-based Cs "
"we should have some hits for minor, but got "
"TPR=%.3f" % tpr_3)