def _call(self, dataset):
"""Computes featurewise f-scores using compound comparisons."""
targets_sa = dataset.sa[self._targets_attr]
orig_labels = targets_sa.value
labels = orig_labels.copy()
# Lets create a very shallow copy of a dataset with just
# samples and targets_attr
dataset_mod = Dataset(dataset.samples, sa={self._targets_attr: labels})
results = []
for ul in targets_sa.unique:
labels[orig_labels == ul] = 1
labels[orig_labels != ul] = 2
f_ds = OneWayAnova._call(self, dataset_mod)
if 'fprob' in f_ds.fa:
# rename the fprob attribute to something label specific
# to survive final aggregation stage
f_ds.fa['fprob_' + str(ul)] = f_ds.fa.fprob
del f_ds.fa['fprob']
results.append(f_ds)
results = vstack(results)
results.sa[self._targets_attr] = targets_sa.unique
return results
def _call(self, dataset):
"""Computes featurewise f-scores using compound comparisons."""
targets_sa = dataset.sa[self._targets_attr]
orig_labels = targets_sa.value
labels = orig_labels.copy()
# Lets create a very shallow copy of a dataset with just
# samples and targets_attr
dataset_mod = Dataset(dataset.samples,
sa={self._targets_attr : labels})
results = []
for ul in targets_sa.unique:
labels[orig_labels == ul] = 1
labels[orig_labels != ul] = 2
f_ds = OneWayAnova._call(self, dataset_mod)
if 'fprob' in f_ds.fa:
# rename the fprob attribute to something label specific
# to survive final aggregation stage
f_ds.fa['fprob_' + str(ul)] = f_ds.fa.fprob
del f_ds.fa['fprob']
results.append(f_ds)
results = vstack(results)
results.sa[self._targets_attr] = targets_sa.unique
return results
def test_mergeds():
data0 = Dataset.from_wizard(np.ones((5, 5)), targets=1)
data0.fa['one'] = np.ones(5)
data1 = Dataset.from_wizard(np.ones((5, 5)), targets=1, chunks=1)
data1.fa['one'] = np.zeros(5)
data2 = Dataset.from_wizard(np.ones((3, 5)), targets=2, chunks=1)
data3 = Dataset.from_wizard(np.ones((4, 5)), targets=2)
data4 = Dataset.from_wizard(np.ones((2, 5)), targets=3, chunks=2)
data4.fa['test'] = np.arange(5)
# cannot merge if there are attributes missing in one of the datasets
assert_raises(DatasetError, data1.append, data0)
merged = data1.copy()
merged.append(data2)
ok_( merged.nfeatures == 5 )
l12 = [1]*5 + [2]*3
l1 = [1]*8
ok_((merged.targets == l12).all())
ok_((merged.chunks == l1).all())
data_append = data1.copy()
data_append.append(data2)
ok_(data_append.nfeatures == 5)
ok_((data_append.targets == l12).all())
ok_((data_append.chunks == l1).all())
#
# appending
#
# we need the same samples attributes in both datasets
assert_raises(DatasetError, data2.append, data3)
#
# vstacking
#
if __debug__:
# tested only in __debug__
assert_raises(ValueError, vstack, (data0, data1, data2, data3))
datasets = (data1, data2, data4)
merged = vstack(datasets)
assert_equal(merged.shape,
(np.sum([len(ds) for ds in datasets]), data1.nfeatures))
assert_true('test' in merged.fa)
assert_array_equal(merged.sa.targets, [1]*5 + [2]*3 + [3]*2)
#
# hstacking
#
assert_raises(ValueError, hstack, datasets)
datasets = (data0, data1)
merged = hstack(datasets)
assert_equal(merged.shape,
(len(data1), np.sum([ds.nfeatures for ds in datasets])))
assert_true('chunks' in merged.sa)
assert_array_equal(merged.fa.one, [1]*5 + [0]*5)
def test_ifs(self, svm):
# measure for feature selection criterion and performance assesment
# use the SAME clf!
errorfx = mean_mismatch_error
fmeasure = CrossValidation(svm, NFoldPartitioner(), postproc=mean_sample())
pmeasure = ProxyMeasure(svm, postproc=BinaryFxNode(errorfx, 'targets'))
ifs = IFS(fmeasure,
pmeasure,
Splitter('purpose', attr_values=['train', 'test']),
fselector=\
# go for lower tail selection as data_measure will return
# errors -> low is good
FixedNElementTailSelector(1, tail='lower', mode='select'),
)
wdata = self.get_data()
wdata.sa['purpose'] = np.repeat('train', len(wdata))
tdata = self.get_data()
tdata.sa['purpose'] = np.repeat('test', len(tdata))
ds = vstack((wdata, tdata))
orig_nfeatures = ds.nfeatures
ifs.train(ds)
resds = ifs(ds)
# fail if orig datasets are changed
self.failUnless(ds.nfeatures == orig_nfeatures)
# check that the features set with the least error is selected
self.failUnless(len(ifs.ca.errors))
e = np.array(ifs.ca.errors)
self.failUnless(resds.nfeatures == e.argmin() + 1)
# repeat with dataset where selection order is known
wsignal = datasets['dumb2'].copy()
wsignal.sa['purpose'] = np.repeat('train', len(wsignal))
tsignal = datasets['dumb2'].copy()
tsignal.sa['purpose'] = np.repeat('test', len(tsignal))
signal = vstack((wsignal, tsignal))
ifs.train(signal)
resds = ifs(signal)
self.failUnless((resds.samples[:,0] == signal.samples[:,0]).all())
def _call(self, dataset):
"""Computes featurewise f-scores using compound comparisons."""
orig_labels = dataset.targets
labels = orig_labels.copy()
results = []
for ul in dataset.sa['targets'].unique:
labels[orig_labels == ul] = 1
labels[orig_labels != ul] = 2
f_ds = OneWayAnova._call(self, dataset, labels)
if 'fprob' in f_ds.fa:
# rename the fprob attribute to something label specific
# to survive final aggregation stage
f_ds.fa['fprob_' + str(ul)] = f_ds.fa.fprob
del f_ds.fa['fprob']
results.append(f_ds)
results = vstack(results)
results.sa['targets'] = dataset.sa['targets'].unique
return results
def _forward_dataset(self, ds):
if self.__chunks_attr is None:
return self._forward_dataset_helper(ds)
else:
# strip down dataset to speedup local processing
if self.__attr_strategy == 'remove':
keep_sa = []
else:
keep_sa = None
proc_ds = ds.copy(deep=False, sa=keep_sa, fa=[], a=[])
# process all chunks individually
# use a customsplitter to speed-up splitting
spl = Splitter(self.__chunks_attr)
dses = [self._forward_dataset_helper(d)
for d in spl.generate(proc_ds)]
# and merge them again
mds = vstack(dses)
# put back attributes
mds.fa.update(ds.fa)
mds.a.update(ds.a)
return mds
def _forward_dataset(self, ds):
if self.__chunks_attr is None:
return self._forward_dataset_helper(ds)
else:
# strip down dataset to speedup local processing
if self.__attr_strategy == 'remove':
keep_sa = []
else:
keep_sa = None
proc_ds = ds.copy(deep=False, sa=keep_sa, fa=[], a=[])
# process all chunks individually
# use a customsplitter to speed-up splitting
spl = CustomSplitter([((i, ), )
for i in ds.sa[self.__chunks_attr].unique],
attr=self.__chunks_attr)
dses = [self._forward_dataset_helper(d[0]) for d in spl(proc_ds)]
# and merge them again
mds = vstack(dses)
# put back attributes
mds.fa.update(ds.fa)
mds.a.update(ds.a)
return mds
def test_vstack_and_origids_issue(self):
# That is actually what swaroop hit
skip_if_no_external('shogun', ver_dep='shogun:rev', min_version=4455)
# Inspired by the problem Swaroop ran into
k = LinearSGKernel(normalizer_cls=False)
k_ = LinearSGKernel(normalizer_cls=False) # to be cached
ck = CachedKernel(k_)
clf = sgSVM(svm_impl='libsvm', kernel=k, C=-1)
clf_ = sgSVM(svm_impl='libsvm', kernel=ck, C=-1)
cvte = CrossValidatedTransferError(
TransferError(clf), NFoldSplitter())
cvte_ = CrossValidatedTransferError(
TransferError(clf_), NFoldSplitter())
ds = datasets['uni2large_test'].copy(deep=True)
ok_(~('orig_ids' in ds.sa)) # assure that there are None
ck.compute(ds) # so we initialize origids
ok_('origids' in ds.sa)
ds2 = ds.copy(deep=True)
ds2.samples = np.zeros(ds2.shape)
from mvpa.base.dataset import vstack
ds_vstacked = vstack((ds2, ds))
# should complaint now since there would not be unique
# samples' origids
if __debug__:
assert_raises(ValueError, ck.compute, ds_vstacked)
ds_vstacked.init_origids('samples') # reset origids
ck.compute(ds_vstacked)
errs = cvte(ds_vstacked)
errs_ = cvte_(ds_vstacked)
# Following test would have failed since origids
# were just ints, and then non-unique after vstack
assert_array_equal(errs.samples, errs_.samples)
def test_vstack_and_origids_issue(self):
# That is actually what swaroop hit
skip_if_no_external('shogun', ver_dep='shogun:rev', min_version=4455)
# Inspired by the problem Swaroop ran into
k = LinearSGKernel(normalizer_cls=False)
k_ = LinearSGKernel(normalizer_cls=False) # to be cached
ck = CachedKernel(k_)
clf = sgSVM(svm_impl='libsvm', kernel=k, C=-1)
clf_ = sgSVM(svm_impl='libsvm', kernel=ck, C=-1)
cvte = CrossValidatedTransferError(TransferError(clf), NFoldSplitter())
cvte_ = CrossValidatedTransferError(TransferError(clf_),
NFoldSplitter())
ds = datasets['uni2large_test'].copy(deep=True)
ok_(~('orig_ids' in ds.sa)) # assure that there are None
ck.compute(ds) # so we initialize origids
ok_('origids' in ds.sa)
ds2 = ds.copy(deep=True)
ds2.samples = np.zeros(ds2.shape)
from mvpa.base.dataset import vstack
ds_vstacked = vstack((ds2, ds))
# should complaint now since there would not be unique
# samples' origids
if __debug__:
assert_raises(ValueError, ck.compute, ds_vstacked)
ds_vstacked.init_origids('samples') # reset origids
ck.compute(ds_vstacked)
errs = cvte(ds_vstacked)
errs_ = cvte_(ds_vstacked)
# Following test would have failed since origids
# were just ints, and then non-unique after vstack
assert_array_equal(errs.samples, errs_.samples)
