def selectFeatures(self, ids, plain=False, sort=False):
"""Select features given their ids.
The methods behaves similar to Dataset.selectFeatures(), but
additionally takes care of adjusting the embedded mapper
appropriately.
:Parameters:
ids: sequence
Iterable container to select ids
plain: boolean
Flag whether to return MappedDataset (or just Dataset)
sort: boolean
Flag whether to sort Ids. Order matters and selectFeatures
assumes incremental order. If not such, in non-optimized
code selectFeatures would verify the order and sort
"""
# call base method to get selected feature subset
if plain:
sdata = Dataset(self._data, self._dsattr, check_data=False,
copy_samples=False, copy_data=False,
copy_dsattr=False)
return sdata.selectFeatures(ids=ids, sort=sort)
else:
sdata = Dataset.selectFeatures(self, ids=ids, sort=sort)
# since we have new DataSet we better have a new mapper
sdata._dsattr['mapper'] = copy.deepcopy(sdata._dsattr['mapper'])
if sort:
sdata._dsattr['mapper'].selectOut(sorted(ids))
else:
sdata._dsattr['mapper'].selectOut(ids)
return sdata
def testEvilSelects(self):
"""Test some obscure selections of samples via select() or __getitem__
"""
origdata = datasets["uni2large"].samples[:100, :10].T
data = Dataset(
samples=origdata,
# 0 1 2 3 4 5 6 7 8 9
labels=[8, 9, 4, 3, 3, 3, 3, 2, 8, 9],
chunks=[1, 2, 3, 2, 3, 1, 5, 6, 3, 6],
)
# malformed getitem
if __debug__:
# check is enforced only in __debug__
self.failUnlessRaises(ValueError, data.__getitem__, "labels", "featu")
# too many indicies
self.failUnlessRaises(ValueError, data.__getitem__, 1, 1, 1)
# various getitems which should carry the same result
for sel in [
data.select("chunks", [2, 6], labels=[3, 2], features=slice(None)),
data.select("all", "all", labels=[2, 3], chunks=[2, 6]),
data["chunks", [2, 6], "labels", [3, 2]],
data[:, :, "chunks", [2, 6], "labels", [3, 2]],
# get warnings but should work as the rest for now
data[3:8, "chunks", [2, 6, 2, 6], "labels", [3, 2]],
]:
self.failUnless(N.all(sel.origids == [3, 7]))
self.failUnless(sel.nfeatures == 100)
self.failUnless(N.all(sel.samples == origdata[[3, 7]]))
target = origdata[[3, 7]]
target = target[:, [1, 3]]
# various getitems which should carry the same result
for sel in [
data.select("all", [1, 3], "chunks", [2, 6], labels=[3, 2]),
data[:, [1, 3], "chunks", [2, 6], "labels", [3, 2]],
data[:, [1, 3], "chunks", [2, 6], "labels", [3, 2]],
# get warnings but should work as the rest for now
data[3:8, [1, 1, 3, 1], "chunks", [2, 6, 2, 6], "labels", [3, 2]],
]:
self.failUnless(N.all(sel.origids == [3, 7]))
self.failUnless(sel.nfeatures == 2)
self.failUnless(N.all(sel.samples == target))
# Check if we get empty selection if requesting impossible
self.failUnless(data.select(chunks=[23]).nsamples == 0)
# Check .where()
self.failUnless(N.all(data.where(chunks=[2, 6]) == [1, 3, 7, 9]))
self.failUnless(N.all(data.where(chunks=[2, 6], labels=[22, 3]) == [3]))
# both samples and features
idx = data.where("all", [1, 3, 10], labels=[2, 3, 4])
self.failUnless(N.all(idx[1] == [1, 3, 10]))
self.failUnless(N.all(idx[0] == range(2, 8)))
# empty query
self.failUnless(data.where() is None)
# empty result
self.failUnless(data.where(labels=[123]) == [])
def testCombinedPatternAndFeatureMasking(self):
data = Dataset(samples=N.arange(20).reshape((4, 5)), labels=1, chunks=1)
self.failUnless(data.nsamples == 4)
self.failUnless(data.nfeatures == 5)
fsel = data.selectFeatures([1, 2])
fpsel = fsel.selectSamples([0, 3])
self.failUnless(fpsel.nsamples == 2)
self.failUnless(fpsel.nfeatures == 2)
self.failUnless((fpsel.samples == [[1, 2], [16, 17]]).all())
def testFeatureMaskConversion(self):
dataset = Dataset(samples=N.arange(12).reshape((4, 3)), labels=1, chunks=1)
mask = dataset.convertFeatureIds2FeatureMask(range(dataset.nfeatures))
self.failUnless(len(mask) == dataset.nfeatures)
self.failUnless((mask == True).all())
self.failUnless((dataset.convertFeatureMask2FeatureIds(mask) == range(3)).all())
mask[1] = False
self.failUnless((dataset.convertFeatureMask2FeatureIds(mask) == [0, 2]).all())
def testApplyMapper(self):
"""Test creation of new dataset by applying a mapper"""
mapper = MaskMapper(N.array([1, 0, 1]))
dataset = Dataset(samples=N.arange(12).reshape((4, 3)), labels=1, chunks=1)
seldataset = dataset.applyMapper(featuresmapper=mapper)
self.failUnless((dataset.selectFeatures([0, 2]).samples == seldataset.samples).all())
# Lets do simple test on maskmapper reverse since it seems to
# do evil things. Those checks are done only in __debug__
if __debug__:
# should fail since in mask we have just 2 features now
self.failUnlessRaises(ValueError, mapper.reverse, [10, 20, 30])
self.failUnlessRaises(ValueError, mapper.forward, [10, 20])
def testLabelsMapping(self):
"""Test mapping of the labels from strings to numericals
"""
od = {"apple": 0, "orange": 1}
samples = [[3], [2], [3]]
labels_l = ["apple", "orange", "apple"]
# test broadcasting of the label
ds = Dataset(samples=samples, labels="orange")
self.failUnless(N.all(ds.labels == ["orange"] * 3))
# Test basic mapping of litteral labels
for ds in [
Dataset(samples=samples, labels=labels_l, labels_map=od),
# Figure out mapping
Dataset(samples=samples, labels=labels_l, labels_map=True),
]:
self.failUnless(N.all(ds.labels == [0, 1, 0]))
self.failUnless(ds.labels_map == od)
ds_ = ds[1]
self.failUnless(ds_.labels_map == od, msg="selectSamples should provide full mapping preserved")
# We should complaint about insufficient mapping
self.failUnlessRaises(ValueError, Dataset, samples=samples, labels=labels_l, labels_map={"apple": 0})
# Conformance to older behavior -- if labels are given in
# strings, no mapping occur by default
ds2 = Dataset(samples=samples, labels=labels_l)
self.failUnlessEqual(ds2.labels_map, None)
# We should label numerical labels if it was requested:
od3 = {1: 100, 2: 101, 3: 100}
ds3 = Dataset(samples=samples, labels=[1, 2, 3], labels_map=od3)
self.failUnlessEqual(ds3.labels_map, od3)
self.failUnless(N.all(ds3.labels == [100, 101, 100]))
ds3_ = ds3[1]
self.failUnlessEqual(ds3.labels_map, od3)
ds4 = Dataset(samples=samples, labels=labels_l)
# Lets check setting the labels map
ds = Dataset(samples=samples, labels=labels_l, labels_map=od)
self.failUnlessRaises(ValueError, ds.setLabelsMap, {"orange": 1, "nonorange": 3})
new_map = {"tasty": 0, "crappy": 1}
ds.labels_map = new_map.copy()
self.failUnlessEqual(ds.labels_map, new_map)
def _call(self, dataset):
"""Extract weights from GPR
"""
clf = self.clf
kernel = clf.kernel
train_fv = clf._train_fv
if isinstance(kernel, LinearKernel):
Sigma_p = 1.0
else:
Sigma_p = kernel.params.Sigma_p
weights = Ndot(Sigma_p, Ndot(train_fv.T, clf._alpha))
if self.ca.is_enabled('variances'):
# super ugly formulas that can be quite surely improved:
tmp = np.linalg.inv(clf._L)
Kyinv = Ndot(tmp.T, tmp)
# XXX in such lengthy matrix manipulations you might better off
# using np.matrix where * is a matrix product
self.ca.variances = Ndiag(
Sigma_p -
Ndot(Sigma_p,
Ndot(train_fv.T, Ndot(Kyinv, Ndot(train_fv, Sigma_p)))))
return Dataset(np.atleast_2d(weights))
def _call(self, dataset):
sensitivities = []
for ind, analyzer in enumerate(self.__analyzers):
if __debug__:
debug("SA", "Computing sensitivity for SA#%d:%s" %
(ind, analyzer))
sensitivity = analyzer(dataset)
sensitivities.append(sensitivity)
if __debug__:
debug("SA",
"Returning %d sensitivities from %s" %
(len(sensitivities), self.__class__.__name__))
sa_attr = self._sa_attr
if isinstance(sensitivities[0], AttrDataset):
smerged = None
for i, s in enumerate(sensitivities):
s.sa[sa_attr] = np.repeat(i, len(s))
if smerged is None:
smerged = s
else:
smerged.append(s)
sensitivities = smerged
else:
sensitivities = \
Dataset(sensitivities,
sa={sa_attr: np.arange(len(sensitivities))})
self.ca.sensitivities = sensitivities
return sensitivities
def _forward_dataset_helper(self, ds):
# local binding
num = self.__num
pos = None
if not self.__position_attr is None:
# we know something about sample position
pos = ds.sa[self.__position_attr].value
rsamples, pos = resample(ds.samples,
self.__num,
t=pos,
window=self.__window_args)
else:
# we know nothing about samples position
rsamples = resample(ds.samples,
self.__num,
t=None,
window=self.__window_args)
# new dataset that reuses that feature and dataset attributes of the
# source
mds = Dataset(rsamples, fa=ds.fa, a=ds.a)
# the tricky part is what to do with the samples attributes, since their
# number has changes
if self.__attr_strategy == 'remove':
# nothing to be done
pass
elif self.__attr_strategy == 'sample':
step = int(len(ds) / num)
sa = dict([(k, ds.sa[k].value[0::step][:num]) for k in ds.sa])
mds.sa.update(sa)
elif self.__attr_strategy == 'resample':
# resample the attributes themselves
sa = {}
for k in ds.sa:
v = ds.sa[k].value
if pos is None:
sa[k] = resample(v,
self.__num,
t=None,
window=self.__window_args)
else:
if k == self.__position_attr:
# position attr will be handled separately at the end
continue
sa[k] = resample(v,
self.__num,
t=pos,
window=self.__window_args)[0]
# inject them all
mds.sa.update(sa)
else:
raise ValueError("Unkown attribute handling strategy '%s'." %
self.__attr_strategy)
if not pos is None:
# we got the new sample positions and can store them
mds.sa[self.__position_attr] = pos
return mds
def test_linear_kernel(self):
"""Simplistic testing of linear kernel"""
d1 = Dataset(np.asarray([range(5)] * 10, dtype=float))
lk = npK.LinearKernel()
lk.compute(d1)
self.failUnless(lk._k.shape == (10, 10),
"Failure computing LinearKernel (Size mismatch)")
self.failUnless((lk._k == 30).all(), "Failure computing LinearKernel")
def test_resample():
time = np.linspace(0, 2 * np.pi, 100)
ds = Dataset(np.vstack((np.sin(time), np.cos(time))).T,
sa={
'time': time,
'section': np.repeat(range(10), 10)
})
assert_equal(ds.shape, (100, 2))
# downsample
num = 10
rm = FFTResampleMapper(num,
window=('gauss', 50),
position_attr='time',
attr_strategy='sample')
mds = rm(ds)
assert_equal(mds.shape, (num, ds.nfeatures))
# didn't change the orig
assert_equal(len(ds), 100)
# check position-based resampling
ds_partial = ds[0::10]
mds_partial = rm(ds_partial)
# despite different input sampling should yield the same output timepoints
assert_array_almost_equal(mds.sa.time, mds_partial.sa.time)
# exclude the first points to prevent edge effects, but the data should be
# very similar too
assert_array_almost_equal(mds.samples[2:],
mds_partial.samples[2:],
decimal=2)
# simple sample of sa's should give meaningful stuff
assert_array_equal(mds.sa.section, range(10))
# and now for a dataset with chunks
cds = vstack([ds.copy(), ds.copy()])
cds.sa['chunks'] = np.repeat([0, 1], len(ds))
rm = FFTResampleMapper(num,
attr_strategy='sample',
chunks_attr='chunks',
window=('gauss', 50))
mcds = rm(cds)
assert_equal(mcds.shape, (20, 2))
assert_array_equal(mcds.sa.section, np.tile(range(10), 2))
# each individual chunks should be identical to previous dataset
assert_array_almost_equal(mds.samples, mcds.samples[:10])
assert_array_almost_equal(mds.samples, mcds.samples[10:])
def testId(self):
"""Test Dataset.idhash() if it gets changed if any of the
labels/chunks changes
"""
dataset = Dataset(samples=N.arange(12).reshape((4, 3)), labels=1, chunks=1)
origid = dataset.idhash
dataset.labels = [3, 1, 2, 3] # change all labels
self.failUnless(origid != dataset.idhash, msg="Changing all labels should alter dataset's idhash")
origid = dataset.idhash
z = dataset.labels[1]
self.failUnlessEqual(origid, dataset.idhash, msg="Accessing shouldn't change idhash")
z = dataset.chunks
self.failUnlessEqual(origid, dataset.idhash, msg="Accessing shouldn't change idhash")
z[2] = 333
self.failUnless(origid != dataset.idhash, msg="Changing value in attribute should change idhash")
origid = dataset.idhash
dataset.samples[1, 1] = 1000
self.failUnless(origid != dataset.idhash, msg="Changing value in data should change idhash")
origid = dataset.idhash
dataset.permuteLabels(True)
self.failUnless(origid != dataset.idhash, msg="Permutation also changes idhash")
dataset.permuteLabels(False)
self.failUnless(origid == dataset.idhash, msg="idhash should be restored after " "permuteLabels(False)")
def test_cached_kernel(self):
nchunks = 5
n = 50 * nchunks
d = Dataset(np.random.randn(n, 132))
d.sa.chunks = np.random.randint(nchunks, size=n)
# We'll compare against an Rbf just because it has a parameter to change
rk = npK.RbfKernel(sigma=1.5)
# Assure two kernels are independent for this test
ck = CachedKernel(kernel=npK.RbfKernel(sigma=1.5))
ck.compute(d) # Initial cache of all data
self.failUnless(ck._recomputed,
'CachedKernel was not initially computed')
# Try some splitting
for chunk in [d[d.sa.chunks == i] for i in range(nchunks)]:
rk.compute(chunk)
ck.compute(chunk)
self.kernel_equiv(rk, ck) #, accuracy=1e-12)
self.failIf(ck._recomputed,
"CachedKernel incorrectly recomputed it's kernel")
# Test what happens when a parameter changes
ck.params.sigma = 3.5
ck.compute(d)
self.failUnless(ck._recomputed,
"CachedKernel doesn't recompute on kernel change")
rk.params.sigma = 3.5
rk.compute(d)
self.failUnless(np.all(rk._k == ck._k),
'Cached and rbf kernels disagree after kernel change')
# Now test handling new data
d2 = Dataset(np.random.randn(32, 43))
ck.compute(d2)
self.failUnless(
ck._recomputed,
"CachedKernel did not automatically recompute new data")
ck.compute(d)
self.failUnless(ck._recomputed,
"CachedKernel did not recompute old data which had\n" +\
"previously been computed, but had the cache overriden")
def testFeatureSelection(self):
"""Testing feature selection: sorted/not sorted, feature groups
"""
origdata = datasets["uni2large"].samples[:10, :20]
data = Dataset(samples=origdata, labels=2, chunks=2)
# define some feature groups
data.defineFeatureGroups(N.repeat(range(4), 5))
unmasked = data.samples.copy()
# default must be no mask
self.failUnless(data.nfeatures == 20)
features_to_select = [3, 0, 17]
features_to_select_copy = copy.deepcopy(features_to_select)
features_to_select_sorted = copy.deepcopy(features_to_select)
features_to_select_sorted.sort()
bsel = N.array([False] * 20)
bsel[features_to_select] = True
# check selection with feature list
for sel, issorted in [
(data.selectFeatures(features_to_select, sort=False), False),
(data.selectFeatures(features_to_select, sort=True), True),
(data.select(slice(None), features_to_select), True),
(data.select(slice(None), N.array(features_to_select)), True),
(data.select(slice(None), bsel), True),
]:
self.failUnless(sel.nfeatures == 3)
# check size of the masked patterns
self.failUnless(sel.samples.shape == (10, 3))
# check that the right features are selected
fts = (features_to_select, features_to_select_sorted)[int(issorted)]
self.failUnless((unmasked[:, fts] == sel.samples).all())
# check grouping information
self.failUnless((sel._dsattr["featuregroups"] == [0, 0, 3]).all())
# check side effect on features_to_select parameter:
self.failUnless(features_to_select == features_to_select_copy)
# check selection by feature group id
gsel = data.selectFeatures(groups=[2, 3])
self.failUnless(gsel.nfeatures == 10)
self.failUnless(set(gsel._dsattr["featuregroups"]) == set([2, 3]))
def testAttributes(self):
"""Test adding custom attributes to a dataset
"""
# class BlobbyDataset(Dataset):
# pass
# TODO: we can't assign attributes to those for now...
ds = Dataset(samples=range(5), labels=1, chunks=1)
self.failUnlessRaises(AttributeError, lambda x: x.blobs, ds)
"""Dataset.blobs should fail since .blobs wasn't yet registered"""
# register new attribute but it would alter only new instances
Dataset._registerAttribute("blobs", "_data", hasunique=True)
ds = Dataset(samples=range(5), labels=1, chunks=1)
self.failUnless(not ds.blobs != [0], msg="By default new attributes supposed to get 0 as the value")
try:
ds.blobs = [1, 2]
self.fail(msg="Dataset.blobs=[1,2] should fail since " "there is 5 samples")
except ValueError, e:
pass
def _call(self, dataset):
"""Computes featurewise I-RELIEF weights."""
samples = dataset.samples
NS, NF = samples.shape[:2]
if self.w_guess == None:
self.w = np.ones(NF, 'd')
# do normalization in all cases to be safe :)
self.w = self.w / (self.w**2).sum()
M, H = self.compute_M_H(dataset.targets)
while True:
self.k = self.kernel(length_scale=self.kernel_width / self.w)
d_w_k = self.k.computed(samples).as_raw_np()
# set d_w_k to zero where distance=0 (i.e. kernel ==
# 1.0), otherwise I-RELIEF could not converge.
# XXX Note that kernel==1 for distance=0 only for
# exponential kernels!! IMPROVE
d_w_k[np.abs(d_w_k - 1.0) < 1.0e-15] = 0.0
ni = np.zeros(NF, 'd')
for n in range(NS):
# d_w_k[n,n] could be omitted since == 0.0
gamma_n = 1.0 - np.nan_to_num(d_w_k[n, M[n]].sum() \
/ (d_w_k[n, :].sum()-d_w_k[n, n]))
alpha_n = np.nan_to_num(d_w_k[n, M[n]] /
(d_w_k[n, M[n]].sum()))
beta_n = np.nan_to_num(d_w_k[n, H[n]] / (d_w_k[n, H[n]].sum()))
m_n = (np.abs(samples[n, :] - samples[M[n], :]) \
* alpha_n[:, None]).sum(0)
h_n = (np.abs(samples[n, :] - samples[H[n], :]) \
* beta_n[:, None]).sum(0)
ni += gamma_n * (m_n - h_n)
ni = ni / NS
ni_plus = np.clip(ni, 0.0,
np.inf) # set all negative elements to zero
w_new = np.nan_to_num(ni_plus / (np.sqrt((ni_plus**2).sum())))
change = np.abs(w_new - self.w).sum()
if __debug__ and 'IRELIEF' in debug.active:
debug(
'IRELIEF',
"change=%.4f max=%f min=%.4f mean=%.4f std=%.4f #nan=%d" %
(change, w_new.max(), w_new.min(), w_new.mean(),
w_new.std(), np.isnan(w_new).sum()))
# update weights:
self.w = w_new
if change < self.threshold:
break
return Dataset(self.w[np.newaxis])
def test_resample():
time = np.linspace(0, 2*np.pi, 100)
ds = Dataset(np.vstack((np.sin(time), np.cos(time))).T,
sa = {'time': time,
'section': np.repeat(range(10), 10)})
assert_equal(ds.shape, (100, 2))
# downsample
num = 10
rm = FFTResampleMapper(num, window=('gauss', 50),
position_attr='time',
attr_strategy='sample')
mds = rm(ds)
assert_equal(mds.shape, (num, ds.nfeatures))
# didn't change the orig
assert_equal(len(ds), 100)
# check position-based resampling
ds_partial = ds[0::10]
mds_partial = rm(ds_partial)
# despite different input sampling should yield the same output timepoints
assert_array_almost_equal(mds.sa.time, mds_partial.sa.time)
# exclude the first points to prevent edge effects, but the data should be
# very similar too
assert_array_almost_equal(mds.samples[2:], mds_partial.samples[2:], decimal=2)
# simple sample of sa's should give meaningful stuff
assert_array_equal(mds.sa.section, range(10))
# and now for a dataset with chunks
cds = vstack([ds.copy(), ds.copy()])
cds.sa['chunks'] = np.repeat([0,1], len(ds))
rm = FFTResampleMapper(num, attr_strategy='sample', chunks_attr='chunks',
window=('gauss', 50))
mcds = rm(cds)
assert_equal(mcds.shape, (20, 2))
assert_array_equal(mcds.sa.section, np.tile(range(10),2))
# each individual chunks should be identical to previous dataset
assert_array_almost_equal(mds.samples, mcds.samples[:10])
assert_array_almost_equal(mds.samples, mcds.samples[10:])
def test_datasetmapping():
# 6 samples, 4 features
data = np.arange(24).reshape(6, 4)
ds = Dataset(data,
sa={
'timepoints': np.arange(6),
'multidim': data.copy()
},
fa={'fid': np.arange(4)})
# with overlapping and non-overlapping boxcars
startpoints = [0, 1, 4]
boxlength = 2
bm = BoxcarMapper(startpoints, boxlength, inspace='boxy')
# train is critical
bm.train(ds)
mds = bm.forward(ds)
assert_equal(len(mds), len(startpoints))
assert_equal(mds.nfeatures, boxlength)
# all samples attributes remain, but the can rotated/compressed into
# multidimensional attributes
assert_equal(sorted(mds.sa.keys()),
['boxy_onsetidx'] + sorted(ds.sa.keys()))
assert_equal(mds.sa.multidim.shape,
(len(startpoints), boxlength, ds.nfeatures))
assert_equal(mds.sa.timepoints.shape, (len(startpoints), boxlength))
assert_array_equal(mds.sa.timepoints.flatten(),
np.array([(s, s + 1) for s in startpoints]).flatten())
assert_array_equal(mds.sa.boxy_onsetidx, startpoints)
# feature attributes also get rotated and broadcasted
assert_array_equal(mds.fa.fid, [ds.fa.fid, ds.fa.fid])
# and finally there is a new one
assert_array_equal(mds.fa.boxy_offsetidx,
np.repeat(np.arange(boxlength), 4).reshape(2, -1))
# now see how it works on reverse()
rds = bm.reverse(mds)
# we got at least something of all original attributes back
assert_equal(sorted(rds.sa.keys()), sorted(ds.sa.keys()))
assert_equal(sorted(rds.fa.keys()), sorted(ds.fa.keys()))
# it is not possible to reconstruct the full samples array
# some samples even might show up multiple times (when there are overlapping
# boxcars
assert_array_equal(
rds.samples,
np.array([[0, 1, 2, 3], [4, 5, 6, 7], [4, 5, 6, 7], [8, 9, 10, 11],
[16, 17, 18, 19], [20, 21, 22, 23]]))
assert_array_equal(rds.sa.timepoints, [0, 1, 1, 2, 4, 5])
assert_array_equal(rds.sa.multidim, ds.sa.multidim[rds.sa.timepoints])
# but feature attributes should be fully recovered
assert_array_equal(rds.fa.fid, ds.fa.fid)
def testSimple(self):
data = N.arange(24).reshape(8,3)
labels = [0, 1] * 4
chunks = N.repeat(N.array((0,1)),4)
# correct results
csamples = [[3, 4, 5], [6, 7, 8], [15, 16, 17], [18, 19, 20]]
clabels = [0, 1, 0, 1]
cchunks = [0, 0, 1, 1]
ds = Dataset(samples=data, labels=labels, chunks=chunks)
# default behavior
m = SampleGroupMapper()
# error if not trained
self.failUnlessRaises(RuntimeError, m, data)
# train mapper first
m.train(ds)
self.failUnless((m.forward(ds.samples) == csamples).all())
self.failUnless((m.forward(ds.labels) == clabels).all())
self.failUnless((m.forward(ds.chunks) == cchunks).all())
# directly apply to dataset
# using untrained mapper!
mapped = ds.applyMapper(samplesmapper=SampleGroupMapper())
self.failUnless(mapped.nsamples == 4)
self.failUnless(mapped.nfeatures == 3)
self.failUnless((mapped.samples == csamples).all())
self.failUnless((mapped.labels == clabels).all())
self.failUnless((mapped.chunks == cchunks).all())
# make sure origids get regenerated
self.failUnless((mapped.origids == range(4)).all())
def test_1d_multispace_searchlight(self):
ds = Dataset([np.arange(6)])
ds.fa['coord1'] = np.repeat(np.arange(3), 2)
# add a second space to the dataset
ds.fa['coord2'] = np.tile(np.arange(2), 3)
measure = lambda x: "+".join([str(x) for x in x.samples[0]])
# simply select each feature once
res = Searchlight(measure,
IndexQueryEngine(coord1=Sphere(0),
coord2=Sphere(0)),
nproc=1)(ds)
assert_array_equal(res.samples, [['0', '1', '2', '3', '4', '5']])
res = Searchlight(measure,
IndexQueryEngine(coord1=Sphere(0),
coord2=Sphere(1)),
nproc=1)(ds)
assert_array_equal(res.samples,
[['0+1', '0+1', '2+3', '2+3', '4+5', '4+5']])
res = Searchlight(measure,
IndexQueryEngine(coord1=Sphere(1),
coord2=Sphere(0)),
nproc=1)(ds)
assert_array_equal(res.samples,
[['0+2', '1+3', '0+2+4', '1+3+5', '2+4', '3+5']])
def test_1d_multispace_searchlight(self):
ds = Dataset([np.arange(6)])
ds.fa['coord1'] = np.repeat(np.arange(3), 2)
# add a second space to the dataset
ds.fa['coord2'] = np.tile(np.arange(2), 3)
measure = lambda x: "+".join([str(x) for x in x.samples[0]])
# simply select each feature once
res = Searchlight(measure,
IndexQueryEngine(coord1=Sphere(0),
coord2=Sphere(0)),
nproc=1)(ds)
assert_array_equal(res.samples, [['0', '1', '2', '3', '4', '5']])
res = Searchlight(measure,
IndexQueryEngine(coord1=Sphere(0),
coord2=Sphere(1)),
nproc=1)(ds)
assert_array_equal(res.samples,
[['0+1', '0+1', '2+3', '2+3', '4+5', '4+5']])
res = Searchlight(measure,
IndexQueryEngine(coord1=Sphere(1),
coord2=Sphere(0)),
nproc=1)(ds)
assert_array_equal(res.samples,
[['0+2', '1+3', '0+2+4', '1+3+5', '2+4', '3+5']])
def normalFeatureDataset(perlabel=50, nlabels=2, nfeatures=4, nchunks=5,
means=None, nonbogus_features=None, snr=1.0):
"""Generate a dataset where each label is some normally
distributed beastie around specified mean (0 if None).
snr is assuming that signal has std 1.0 so we just divide noise by snr
Probably it is a generalization of pureMultivariateSignal where
means=[ [0,1], [1,0] ]
Specify either means or nonbogus_features so means get assigned
accordingly
"""
data = N.random.standard_normal((perlabel*nlabels, nfeatures))/N.sqrt(snr)
if (means is None) and (not nonbogus_features is None):
if len(nonbogus_features) > nlabels:
raise ValueError, "Can't assign simply a feature to a " + \
"class: more nonbogus_features than labels"
means = N.zeros((len(nonbogus_features), nfeatures))
# pure multivariate -- single bit per feature
for i in xrange(len(nonbogus_features)):
means[i, nonbogus_features[i]] = 1.0
if not means is None:
# add mean
data += N.repeat(N.array(means, ndmin=2), perlabel, axis=0)
# bring it 'under 1', since otherwise some classifiers have difficulties
# during optimization
data = 1.0/(N.max(N.abs(data))) * data
labels = N.concatenate([N.repeat('L%d' % i, perlabel)
for i in range(nlabels)])
chunks = N.concatenate([N.repeat(range(nchunks),
perlabel/nchunks) for i in range(nlabels)])
ds = Dataset(samples=data, labels=labels, chunks=chunks, labels_map=True)
ds.nonbogus_features = nonbogus_features
return ds
def build_streamline_things(self):
# Build a dataset having samples of different lengths. This is
# trying to mimic a possible interface for streamlines
# datasets, i.e., an iterable container of Mx3 points, where M
# depends on each single streamline.
# trying to pack it into an 'object' array to prevent conversion in the
# Dataset
self.streamline_samples = np.array([
np.random.rand(3,3),
np.random.rand(5,3),
np.random.rand(7,3)],
dtype='object')
self.dataset = Dataset(self.streamline_samples)
self.similarities = [StreamlineSimilarity(distance=corouge)]
def _call(self, dataset):
"""Computes featurewise I-RELIEF weights."""
samples = dataset.samples
NS, NF = samples.shape[:2]
if self.w_guess == None:
w = np.ones(NF, 'd')
w /= (w**2).sum() # do normalization in all cases to be safe :)
M, H = self.compute_M_H(dataset.targets)
while True:
d_w_k = self.k(pnorm_w(data1=samples, weight=w, p=1))
ni = np.zeros(NF, 'd')
for n in range(NS):
# d_w_k[n, n] could be omitted since == 0.0
gamma_n = 1.0 - np.nan_to_num(d_w_k[n, M[n]].sum() \
/ (d_w_k[n, :].sum() - d_w_k[n, n]))
alpha_n = np.nan_to_num(d_w_k[n, M[n]] /
(d_w_k[n, M[n]].sum()))
beta_n = np.nan_to_num(d_w_k[n, H[n]] / (d_w_k[n, H[n]].sum()))
m_n = (np.abs(samples[n, :] - samples[M[n], :]) \
* alpha_n[:, None]).sum(0)
h_n = (np.abs(samples[n, :] - samples[H[n], :]) \
* beta_n[:, None]).sum(0)
ni += gamma_n * (m_n - h_n)
ni = ni / NS
ni_plus = np.clip(ni, 0.0,
np.inf) # set all negative elements to zero
w_new = np.nan_to_num(ni_plus / (np.sqrt((ni_plus**2).sum())))
change = np.abs(w_new - w).sum()
if __debug__ and 'IRELIEF' in debug.active:
debug('IRELIEF',
"change=%.4f max=%f min=%.4f mean=%.4f std=%.4f #nan=%d" \
% (change, w_new.max(), w_new.min(), w_new.mean(),
w_new.std(), np.isnan(w_new).sum()))
# update weights:
w = w_new
if change < self.threshold:
break
self.w = w
return Dataset(self.w[np.newaxis])
def testLabelRandomizationAndSampling(self):
"""
"""
data = Dataset(samples=N.ones((5, 1)), labels=range(5), chunks=1)
data += Dataset(samples=N.ones((5, 1)) + 1, labels=range(5), chunks=2)
data += Dataset(samples=N.ones((5, 1)) + 2, labels=range(5), chunks=3)
data += Dataset(samples=N.ones((5, 1)) + 3, labels=range(5), chunks=4)
data += Dataset(samples=N.ones((5, 1)) + 4, labels=range(5), chunks=5)
self.failUnless(data.samplesperlabel == {0: 5, 1: 5, 2: 5, 3: 5, 4: 5})
sample = data.getRandomSamples(2)
self.failUnless(sample.samplesperlabel.values() == [2, 2, 2, 2, 2])
self.failUnless((data.uniquechunks == range(1, 6)).all())
# store the old labels
origlabels = data.labels.copy()
data.permuteLabels(True)
self.failIf((data.labels == origlabels).all())
data.permuteLabels(False)
self.failUnless((data.labels == origlabels).all())
# now try another object with the same data
data2 = Dataset(samples=data.samples, labels=data.labels, chunks=data.chunks)
# labels are the same as the originals
self.failUnless((data2.labels == origlabels).all())
# now permute in the new object
data2.permuteLabels(True)
# must not affect the old one
self.failUnless((data.labels == origlabels).all())
# but only the new one
self.failIf((data2.labels == origlabels).all())
def eep_dataset(samples, targets=None, chunks=None):
"""Create a dataset using an EEP binary file as source.
EEP files are used by *eeprobe* a software for analysing even-related
potentials (ERP), which was developed at the Max-Planck Institute for
Cognitive Neuroscience in Leipzig, Germany.
http://www.ant-neuro.com/products/eeprobe
Parameters
----------
samples : str or EEPBin instance
This is either a filename of an EEP file, or an EEPBin instance, providing
the samples data in EEP format.
targets, chunks : sequence or scalar or None
Values are pass through to `Dataset.from_wizard()`. See its documentation
for more information.
Returns
-------
Dataset
Besides is usual attributes (e.g. targets, chunks, and a mapper). The
returned dataset also includes feature attributes associating each same
with a channel (by id), and a specific timepoint -- based on information
read from the EEP data.
"""
if isinstance(samples, str):
# open the eep file
eb = EEPBin(samples)
elif isinstance(samples, EEPBin):
# nothing special
eb = samples
else:
raise ValueError("eep_dataset takes the filename of an "
"EEP file or a EEPBin object as 'samples' argument.")
# init dataset
ds = Dataset.from_channeltimeseries(eb.data,
targets=targets,
chunks=chunks,
t0=eb.t0,
dt=eb.dt,
channelids=eb.channels)
return ds
def bench_pymvpa(X, Y):
"""
bench with pymvpa (by default uses a custom swig-generated wrapper
around libsvm)
"""
from mvpa.datasets import Dataset
from mvpa.clfs import svm
gc.collect()
# start time
tstart = datetime.now()
data = Dataset(samples=X, labels=Y)
clf = svm.RbfCSVMC(C=1.)
clf.train(data)
Z = clf.predict(X)
delta = (datetime.now() - tstart)
# stop time
mvpa_results.append(delta.seconds + delta.microseconds / mu_second)
def eep_dataset(samples, targets=None, chunks=None):
"""Create a dataset using an EEP binary file as source.
EEP files are used by *eeprobe* a software for analysing even-related
potentials (ERP), which was developed at the Max-Planck Institute for
Cognitive Neuroscience in Leipzig, Germany.
http://www.ant-neuro.com/products/eeprobe
Parameters
----------
samples : str or EEPBin instance
This is either a filename of an EEP file, or an EEPBin instance, providing
the samples data in EEP format.
targets, chunks : sequence or scalar or None
Values are pass through to `Dataset.from_wizard()`. See its documentation
for more information.
Returns
-------
Dataset
Besides is usual attributes (e.g. targets, chunks, and a mapper). The
returned dataset also includes feature attributes associating each same
with a channel (by id), and a specific timepoint -- based on information
read from the EEP data.
"""
if isinstance(samples, str):
# open the eep file
eb = EEPBin(samples)
elif isinstance(samples, EEPBin):
# nothing special
eb = samples
else:
raise ValueError("eep_dataset takes the filename of an "
"EEP file or a EEPBin object as 'samples' argument.")
# init dataset
ds = Dataset.from_channeltimeseries(
eb.data, targets=targets, chunks=chunks, t0=eb.t0, dt=eb.dt,
channelids=eb.channels)
return ds
def __init__(self, samples=None, mapper=None, dsattr=None, **kwargs):
"""
If `samples` and `mapper` arguments are not `None` the mapper is
used to forward-map the samples array and the result is passed
to the `Dataset` constructor.
:Parameters:
mapper: Instance of `Mapper`
This mapper will be embedded in the dataset and is used and
updated, by all subsequent mapping or feature selection
procedures.
**kwargs:
All other arguments are simply passed to and handled by
the constructor of `Dataset`.
"""
# there are basically two mode for the constructor:
# 1. internal mode - only data and dsattr dict
# 2. user mode - samples != None # and mapper != None
# see if dsattr is none, if so, set to empty dict
if dsattr is None:
dsattr = {}
# if a mapper was passed, store it in dsattr dict that gets passed
# to base Dataset
if not mapper is None:
# TODO: check mapper for compliance with dimensionality within _data
# may be only within __debug__
dsattr['mapper'] = mapper
# if the samples are passed to the special arg, use the mapper to
# transform them.
if not samples is None:
if not dsattr.has_key('mapper') or dsattr['mapper'] is None:
raise DatasetError, \
"Constructor of MappedDataset requires a mapper " \
"if unmapped samples are provided."
Dataset.__init__(self,
samples=mapper.forward(samples),
dsattr=dsattr,
**(kwargs))
else:
Dataset._checkCopyConstructorArgs(samples=samples,
dsattr=dsattr,
**kwargs)
Dataset.__init__(self, dsattr=dsattr, **(kwargs))
def testIdsonboundaries(self):
"""Test detection of transition points
Shame on Yarik -- he didn't create unittests right away... damn me
"""
ds = Dataset(
samples=N.array(range(10), ndmin=2).T,
labels=[0, 0, 1, 1, 0, 0, 1, 1, 0, 0],
chunks=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
)
self.failUnless(
ds.idsonboundaries() == [0, 2, 4, 5, 6, 8],
"We should have got ids whenever either chunk or " "label changes",
)
self.failUnless(ds.idsonboundaries(attributes_to_track=["chunks"]) == [0, 5])
# Preceding samples
self.failUnless(ds.idsonboundaries(prior=1, post=-1, attributes_to_track=["chunks"]) == [4, 9])
self.failUnless(ds.idsonboundaries(prior=2, post=-1, attributes_to_track=["chunks"]) == [3, 4, 8, 9])
self.failUnless(
ds.idsonboundaries(prior=2, post=-1, attributes_to_track=["chunks"], revert=True) == [0, 1, 2, 5, 6, 7]
)
self.failUnless(ds.idsonboundaries(prior=1, post=1, attributes_to_track=["chunks"]) == [0, 1, 4, 5, 6, 9])
# all should be there
self.failUnless(ds.idsonboundaries(prior=2) == range(10))
def test_polydetrend():
samples_forwhole = np.array( [[1.0, 2, 3, 4, 5, 6],
[-2.0, -4, -6, -8, -10, -12]], ndmin=2 ).T
samples_forchunks = np.array( [[1.0, 2, 3, 3, 2, 1],
[-2.0, -4, -6, -6, -4, -2]], ndmin=2 ).T
chunks = [0, 0, 0, 1, 1, 1]
chunks_bad = [ 0, 0, 1, 1, 1, 0]
target_whole = np.array( [[-3.0, -2, -1, 1, 2, 3],
[-6, -4, -2, 2, 4, 6]], ndmin=2 ).T
target_chunked = np.array( [[-1.0, 0, 1, 1, 0, -1],
[2, 0, -2, -2, 0, 2]], ndmin=2 ).T
ds = Dataset(samples_forwhole)
# this one will auto-train the mapper on first use
dm = PolyDetrendMapper(polyord=1, inspace='police')
mds = dm(ds)
# features are linear trends, so detrending should remove all
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# we get the information where each sample is assumed to be in the
# space spanned by the polynomials
assert_array_equal(mds.sa.police, np.arange(len(ds)))
# hackish way to get the previous regressors into a dataset
ds.sa['opt_reg_const'] = dm._regs[:,0]
ds.sa['opt_reg_lin'] = dm._regs[:,1]
# using these precomputed regressors, we should get the same result as
# before even if we do not generate a regressor for linear
dm_optreg = PolyDetrendMapper(polyord=0,
opt_regs=['opt_reg_const', 'opt_reg_lin'])
mds_optreg = dm_optreg(ds)
assert_array_almost_equal(mds_optreg, np.zeros(mds.shape))
ds = Dataset(samples_forchunks)
# 'constant' detrending removes the mean
mds = PolyDetrendMapper(polyord=0)(ds)
assert_array_almost_equal(
mds.samples,
samples_forchunks - np.mean(samples_forchunks, axis=0))
# if there is no GLOBAL linear trend it should be identical to mean removal
# even if trying to remove linear
mds2 = PolyDetrendMapper(polyord=1)(ds)
assert_array_almost_equal(mds, mds2)
# chunk-wise detrending
ds = dataset_wizard(samples_forchunks, chunks=chunks)
dm = PolyDetrendMapper(chunks_attr='chunks', polyord=1, inspace='police')
mds = dm(ds)
# features are chunkswise linear trends, so detrending should remove all
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# we get the information where each sample is assumed to be in the
# space spanned by the polynomials, which is the identical linspace in both
# chunks
assert_array_equal(mds.sa.police, range(3) * 2)
# non-matching number of samples cannot be mapped
assert_raises(ValueError, dm, ds[:-1])
# however, if the dataset knows about the space it is possible
ds.sa['police'] = mds.sa.police
# XXX this should be
#mds2 = dm(ds[1:-1])
#assert_array_equal(mds[1:-1], mds2)
# XXX but right now is
assert_raises(NotImplementedError, dm, ds[1:-1])
# Detrend must preserve the size of dataset
assert_equal(mds.shape, ds.shape)
# small additional test for break points
# although they are no longer there
ds = dataset_wizard(np.array([[1.0, 2, 3, 1, 2, 3]], ndmin=2).T,
targets=chunks, chunks=chunks)
mds = PolyDetrendMapper(chunks_attr='chunks', polyord=1)(ds)
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# test of different polyord on each chunk
target_mixed = np.array( [[-1.0, 0, 1, 0, 0, 0],
[2.0, 0, -2, 0, 0, 0]], ndmin=2 ).T
ds = dataset_wizard(samples_forchunks.copy(), targets=chunks, chunks=chunks)
mds = PolyDetrendMapper(chunks_attr='chunks', polyord=[0,1])(ds)
assert_array_almost_equal(mds, target_mixed)
# test irregluar spacing of samples, but with corrective time info
samples_forwhole = np.array( [[1.0, 4, 6, 8, 2, 9],
[-2.0, -8, -12, -16, -4, -18]], ndmin=2 ).T
ds = Dataset(samples_forwhole, sa={'time': samples_forwhole[:,0]})
# linear detrending that makes use of temporal info from dataset
dm = PolyDetrendMapper(polyord=1, inspace='time')
mds = dm(ds)
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# and now the same stuff, but with chunking and ordered by time
samples_forchunks = np.array( [[1.0, 3, 3, 2, 2, 1],
[-2.0, -6, -6, -4, -4, -2]], ndmin=2 ).T
chunks = [0, 1, 0, 1, 0, 1]
time = [4, 4, 12, 8, 8, 12]
ds = Dataset(samples_forchunks.copy(), sa={'chunks': chunks, 'time': time})
mds = PolyDetrendMapper(chunks_attr='chunks', polyord=1, inspace='time')(ds)
# the whole thing must not affect the source data
assert_array_equal(ds, samples_forchunks)
# but if done inplace that is no longer true
poly_detrend(ds, chunks_attr='chunks', polyord=1, inspace='time')
assert_array_equal(ds, mds)
def testSampleSelection(self):
origdata = datasets["uni2large"].samples[:100, :10].T
data = Dataset(samples=origdata, labels=2, chunks=2)
self.failUnless(data.nsamples == 10)
# set single pattern to enabled
for sel in [data.selectSamples(5), data.select(5), data.select(slice(5, 6))]:
self.failUnless(sel.nsamples == 1)
self.failUnless(data.nfeatures == 100)
self.failUnless(sel.origids == [5])
# check duplicate selections
for sel in [
data.selectSamples([5, 5]),
# Following ones would fail since select removes
# repetitions (XXX)
# data.select([5,5]),
# data.select([5,5], 'all'),
# data.select([5,5], slice(None)),
]:
self.failUnless(sel.nsamples == 2)
self.failUnless((sel.samples[0] == data.samples[5]).all())
self.failUnless((sel.samples[0] == sel.samples[1]).all())
self.failUnless(len(sel.labels) == 2)
self.failUnless(len(sel.chunks) == 2)
self.failUnless((sel.origids == [5, 5]).all())
self.failUnless(sel.samples.shape == (2, 100))
# check selection by labels
for sel in [
data.selectSamples(data.idsbylabels(2)),
data.select(labels=2),
data.select("labels", 2),
data.select("labels", [2]),
data["labels", [2]],
data["labels":[2], "labels":2],
data["labels":[2]],
]:
self.failUnless(sel.nsamples == data.nsamples)
self.failUnless(N.all(sel.samples == data.samples))
# not present label
for sel in [
data.selectSamples(data.idsbylabels(3)),
data.select(labels=3),
data.select("labels", 3),
data.select("labels", [3]),
]:
self.failUnless(sel.nsamples == 0)
data = Dataset(samples=origdata, labels=[8, 9, 4, 3, 3, 3, 4, 2, 8, 9], chunks=2)
for sel in [
data.selectSamples(data.idsbylabels([2, 3])),
data.select("labels", [2, 3]),
data.select("labels", [2, 3], labels=[1, 2, 3, 4]),
data.select("labels", [2, 3], chunks=[1, 2, 3, 4]),
data["labels":[2, 3], "chunks":[1, 2, 3, 4]],
data["chunks":[1, 2, 3, 4], "labels":[2, 3]],
]:
self.failUnless(N.all(sel.origids == [3.0, 4.0, 5.0, 7.0]))
# lets cause it to compute unique labels
self.failUnless((data.uniquelabels == [2, 3, 4, 8, 9]).all())
# select some samples removing some labels completely
sel = data.selectSamples(data.idsbylabels([3, 4, 8, 9]))
self.failUnlessEqual(Set(sel.uniquelabels), Set([3, 4, 8, 9]))
self.failUnless((sel.origids == [0, 1, 2, 3, 4, 5, 6, 8, 9]).all())
datasets[basename] = dataset
# sample 3D
total = 2 * spec['perlabel']
nchunks = spec['nchunks']
data = np.random.standard_normal((total, 3, 6, 6))
labels = np.concatenate(
(np.repeat(0, spec['perlabel']), np.repeat(1, spec['perlabel'])))
data[:, 1, 0, 0] += 2 * labels # add some signal
chunks = np.asarray(range(nchunks) * (total / nchunks))
mask = np.ones((3, 6, 6), dtype='bool')
mask[0, 0, 0] = 0
mask[1, 3, 2] = 0
ds = Dataset.from_wizard(samples=data,
targets=labels,
chunks=chunks,
mask=mask,
space='myspace')
datasets['3d%s' % kind] = ds
# some additional datasets
datasets['dumb2'] = dumb_feature_binary_dataset()
datasets['dumb'] = dumb_feature_dataset()
# dataset with few invariant features
_dsinv = dumb_feature_dataset()
_dsinv.samples = np.hstack((_dsinv.samples, np.zeros(
(_dsinv.nsamples, 1)), np.ones((_dsinv.nsamples, 1))))
datasets['dumbinv'] = _dsinv
# Datasets for regressions testing
datasets['sin_modulated'] = multiple_chunks(sin_modulated, 4, 30, 1)
def test_polydetrend():
samples_forwhole = np.array(
[[1.0, 2, 3, 4, 5, 6], [-2.0, -4, -6, -8, -10, -12]], ndmin=2).T
samples_forchunks = np.array(
[[1.0, 2, 3, 3, 2, 1], [-2.0, -4, -6, -6, -4, -2]], ndmin=2).T
chunks = [0, 0, 0, 1, 1, 1]
chunks_bad = [0, 0, 1, 1, 1, 0]
target_whole = np.array([[-3.0, -2, -1, 1, 2, 3], [-6, -4, -2, 2, 4, 6]],
ndmin=2).T
target_chunked = np.array([[-1.0, 0, 1, 1, 0, -1], [2, 0, -2, -2, 0, 2]],
ndmin=2).T
ds = Dataset(samples_forwhole)
# this one will auto-train the mapper on first use
dm = PolyDetrendMapper(polyord=1, inspace='police')
mds = dm(ds)
# features are linear trends, so detrending should remove all
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# we get the information where each sample is assumed to be in the
# space spanned by the polynomials
assert_array_equal(mds.sa.police, np.arange(len(ds)))
# hackish way to get the previous regressors into a dataset
ds.sa['opt_reg_const'] = dm._regs[:, 0]
ds.sa['opt_reg_lin'] = dm._regs[:, 1]
# using these precomputed regressors, we should get the same result as
# before even if we do not generate a regressor for linear
dm_optreg = PolyDetrendMapper(polyord=0,
opt_regs=['opt_reg_const', 'opt_reg_lin'])
mds_optreg = dm_optreg(ds)
assert_array_almost_equal(mds_optreg, np.zeros(mds.shape))
ds = Dataset(samples_forchunks)
# 'constant' detrending removes the mean
mds = PolyDetrendMapper(polyord=0)(ds)
assert_array_almost_equal(
mds.samples, samples_forchunks - np.mean(samples_forchunks, axis=0))
# if there is no GLOBAL linear trend it should be identical to mean removal
# even if trying to remove linear
mds2 = PolyDetrendMapper(polyord=1)(ds)
assert_array_almost_equal(mds, mds2)
# chunk-wise detrending
ds = dataset_wizard(samples_forchunks, chunks=chunks)
dm = PolyDetrendMapper(chunks_attr='chunks', polyord=1, inspace='police')
mds = dm(ds)
# features are chunkswise linear trends, so detrending should remove all
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# we get the information where each sample is assumed to be in the
# space spanned by the polynomials, which is the identical linspace in both
# chunks
assert_array_equal(mds.sa.police, range(3) * 2)
# non-matching number of samples cannot be mapped
assert_raises(ValueError, dm, ds[:-1])
# however, if the dataset knows about the space it is possible
ds.sa['police'] = mds.sa.police
# XXX this should be
#mds2 = dm(ds[1:-1])
#assert_array_equal(mds[1:-1], mds2)
# XXX but right now is
assert_raises(NotImplementedError, dm, ds[1:-1])
# Detrend must preserve the size of dataset
assert_equal(mds.shape, ds.shape)
# small additional test for break points
# although they are no longer there
ds = dataset_wizard(np.array([[1.0, 2, 3, 1, 2, 3]], ndmin=2).T,
targets=chunks,
chunks=chunks)
mds = PolyDetrendMapper(chunks_attr='chunks', polyord=1)(ds)
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# test of different polyord on each chunk
target_mixed = np.array([[-1.0, 0, 1, 0, 0, 0], [2.0, 0, -2, 0, 0, 0]],
ndmin=2).T
ds = dataset_wizard(samples_forchunks.copy(),
targets=chunks,
chunks=chunks)
mds = PolyDetrendMapper(chunks_attr='chunks', polyord=[0, 1])(ds)
assert_array_almost_equal(mds, target_mixed)
# test irregluar spacing of samples, but with corrective time info
samples_forwhole = np.array(
[[1.0, 4, 6, 8, 2, 9], [-2.0, -8, -12, -16, -4, -18]], ndmin=2).T
ds = Dataset(samples_forwhole, sa={'time': samples_forwhole[:, 0]})
# linear detrending that makes use of temporal info from dataset
dm = PolyDetrendMapper(polyord=1, inspace='time')
mds = dm(ds)
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# and now the same stuff, but with chunking and ordered by time
samples_forchunks = np.array(
[[1.0, 3, 3, 2, 2, 1], [-2.0, -6, -6, -4, -4, -2]], ndmin=2).T
chunks = [0, 1, 0, 1, 0, 1]
time = [4, 4, 12, 8, 8, 12]
ds = Dataset(samples_forchunks.copy(), sa={'chunks': chunks, 'time': time})
mds = PolyDetrendMapper(chunks_attr='chunks', polyord=1,
inspace='time')(ds)
# the whole thing must not affect the source data
assert_array_equal(ds, samples_forchunks)
# but if done inplace that is no longer true
poly_detrend(ds, chunks_attr='chunks', polyord=1, inspace='time')
assert_array_equal(ds, mds)
def _call(self, dataset):
"""Computes featurewise I-RELIEF-2 weights. Online version."""
NS = dataset.samples.shape[0]
NF = dataset.samples.shape[1]
if self.w_guess == None:
self.w = np.ones(NF, 'd')
# do normalization in all cases to be safe :)
self.w = self.w / (self.w**2).sum()
M, H = self.compute_M_H(dataset.targets)
ni = np.zeros(NF, 'd')
pi = np.zeros(NF, 'd')
if self.permute:
# indices to go through samples in random order
random_sequence = np.random.permutation(NS)
else:
random_sequence = np.arange(NS)
change = self.threshold + 1.0
iteration = 0
counter = 0.0
while change > self.threshold and iteration < self.max_iter:
if __debug__:
debug('IRELIEF', "Iteration %d" % iteration)
for t in range(NS):
counter += 1.0
n = random_sequence[t]
self.k = self.kernel(length_scale=self.kernel_width / self.w)
d_w_k_xn_Mn = self.k.computed(
dataset.samples[None, n, :],
dataset.samples[M[n], :]).as_raw_np().squeeze()
d_w_k_xn_Mn_sum = d_w_k_xn_Mn.sum()
d_w_k_xn_x = self.k.computed(
dataset.samples[None, n, :],
dataset.samples).as_raw_np().squeeze()
gamma_n = 1.0 - d_w_k_xn_Mn_sum / d_w_k_xn_x.sum()
alpha_n = d_w_k_xn_Mn / d_w_k_xn_Mn_sum
d_w_k_xn_Hn = self.k.computed(
dataset.samples[None, n, :],
dataset.samples[H[n], :]).as_raw_np().squeeze()
beta_n = d_w_k_xn_Hn / d_w_k_xn_Hn.sum()
m_n = (np.abs(dataset.samples[n, :] - dataset.samples[M[n], :]) \
* alpha_n[:, np.newaxis]).sum(0)
h_n = (np.abs(dataset.samples[n, :] - dataset.samples[H[n], :]) \
* beta_n[:, np.newaxis]).sum(0)
pi = gamma_n * (m_n - h_n)
learning_rate = 1.0 / (counter * self.a + 1.0)
ni_new = ni + learning_rate * (pi - ni)
ni = ni_new
# set all negative elements to zero
ni_plus = np.clip(ni, 0.0, np.inf)
w_new = np.nan_to_num(ni_plus / (np.sqrt((ni_plus**2).sum())))
change = np.abs(w_new - self.w).sum()
if t % 10 == 0 and __debug__ and 'IRELIEF' in debug.active:
debug(
'IRELIEF',
"t=%d change=%.4f max=%f min=%.4f mean=%.4f std=%.4f"
" #nan=%d" %
(t, change, w_new.max(), w_new.min(), w_new.mean(),
w_new.std(), np.isnan(w_new).sum()))
self.w = w_new
if change < self.threshold and iteration > 0:
break
iteration += 1
return Dataset(self.w[np.newaxis])
datasets["%s_%s" % (basename, replication)] = dataset_
# full dataset
datasets[basename] = dataset
# sample 3D
total = 2*spec['perlabel']
nchunks = spec['nchunks']
data = np.random.standard_normal(( total, 3, 6, 6 ))
labels = np.concatenate( ( np.repeat( 0, spec['perlabel'] ),
np.repeat( 1, spec['perlabel'] ) ) )
chunks = np.asarray(range(nchunks)*(total/nchunks))
mask = np.ones((3, 6, 6), dtype='bool')
mask[0, 0, 0] = 0
mask[1, 3, 2] = 0
ds = Dataset.from_wizard(samples=data, targets=labels, chunks=chunks,
mask=mask, space='myspace')
datasets['3d%s' % kind] = ds
# some additional datasets
datasets['dumb2'] = dumb_feature_binary_dataset()
datasets['dumb'] = dumb_feature_dataset()
# dataset with few invariant features
_dsinv = dumb_feature_dataset()
_dsinv.samples = np.hstack((_dsinv.samples,
np.zeros((_dsinv.nsamples, 1)),
np.ones((_dsinv.nsamples, 1))))
datasets['dumbinv'] = _dsinv
# Datasets for regressions testing
datasets['sin_modulated'] = multiple_chunks(sin_modulated, 4, 30, 1)
def _call(self, dataset):
"""Computes featurewise I-RELIEF-2 weights. Online version."""
# local bindings
samples = dataset.samples
NS, NF = samples.shape[:2]
threshold = self.threshold
a = self.a
if self.w_guess == None:
w = np.ones(NF, 'd')
# do normalization in all cases to be safe :)
w /= (w**2).sum()
M, H = self.compute_M_H(dataset.targets)
ni = np.zeros(NF, 'd')
pi = np.zeros(NF, 'd')
if self.permute:
# indices to go through x in random order
random_sequence = np.random.permutation(NS)
else:
random_sequence = np.arange(NS)
change = threshold + 1.0
iteration = 0
counter = 0.0
while change > threshold and iteration < self.max_iter:
if __debug__:
debug('IRELIEF', "Iteration %d" % iteration)
for t in range(NS):
counter += 1.0
n = random_sequence[t]
d_xn_x = np.abs(samples[n, :] - samples)
d_w_k_xn_x = self.k((d_xn_x * w).sum(1))
d_w_k_xn_Mn = d_w_k_xn_x[M[n]]
d_w_k_xn_Mn_sum = d_w_k_xn_Mn.sum()
gamma_n = 1.0 - d_w_k_xn_Mn_sum / d_w_k_xn_x.sum()
alpha_n = d_w_k_xn_Mn / d_w_k_xn_Mn_sum
d_w_k_xn_Hn = d_w_k_xn_x[H[n]]
beta_n = d_w_k_xn_Hn / d_w_k_xn_Hn.sum()
m_n = (d_xn_x[M[n], :] * alpha_n[:, None]).sum(0)
h_n = (d_xn_x[H[n], :] * beta_n[:, None]).sum(0)
pi = gamma_n * (m_n - h_n)
learning_rate = 1.0 / (counter * a + 1.0)
ni_new = ni + learning_rate * (pi - ni)
ni = ni_new
# set all negative elements to zero
ni_plus = np.clip(ni, 0.0, np.inf)
w_new = np.nan_to_num(ni_plus / (np.sqrt((ni_plus**2).sum())))
change = np.abs(w_new - w).sum()
if t % 10 == 0 and __debug__ and 'IRELIEF' in debug.active:
debug(
'IRELIEF',
"t=%d change=%.4f max=%f min=%.4f mean=%.4f std=%.4f"
" #nan=%d" %
(t, change, w_new.max(), w_new.min(), w_new.mean(),
w_new.std(), np.isnan(w_new).sum()))
w = w_new
if change < threshold and iteration > 0:
break
iteration += 1
self.w = w
return Dataset(self.w[np.newaxis])
lbp = np.asarray(lbp) i3_histo = np.asarray(i3_histo) rgb_histo = np.asarray(rgb_histo) id_index = 15 lbp_predictdata = lbp[[id_index]] i3_histo_predictdata = lbp[[id_index]] print #print predictdata print classID[id_index] #print "len lbp:", len(lbp) #print "shape:", lbp.shape #mvpa lbp_training = Dataset(samples=lbp,labels=classID) i3_histo_training = Dataset(samples=lbp,labels=classID) clf = kNN(k=1, voting='majority') print "clf = ", clf clf.train(lbp_training) lbp_predicted_classID = clf.predict(lbp_predictdata) clf.train(i3_histo_training) i3_histo_predicted_classID = clf.predict(i3_histo_predictdata) print "lbp_predicted_classID: ", lbp_predicted_classID print "i3_histo__predicted_classID :", i3_histo_predicted_classID #if predicted_classID[0] == 1.0: print "Image is of class: GRASS" #if predicted_classID[0] == 2.0: print "Image is of class: DIRT/GRAVEL" #if predicted_classID[0] == 3.0: print "Image is of class: CEMENT/ASPHALT"
from mvpa.datasets import Dataset
#pymvpa stuff
f_handle = open("classdatafile.txt", 'r')
f_handle2 = open("classidfile.txt", 'r')
f_handle3 = open("predictdata.txt", 'r')
features = genfromtxt(f_handle, dtype=float)
classes = genfromtxt(f_handle2, dtype=int)
predictdata = genfromtxt(f_handle3, dtype=float)
predictdata = np.expand_dims(predictdata, axis=0)
print predictdata
print np.shape(features), features.ndim, features.dtype
print np.shape(classes), classes.ndim, classes.dtype
print np.shape(predictdata), predictdata.ndim, predictdata.dtype
f_handle.close()
f_handle2.close()
f_handle3.close()
training = Dataset(samples=features, labels=classes)
clf = kNN(k=2)
print "clf = ", clf
clf.train(training)
#print np.mean(clf.predict(training.samples) == training.labels)
classID = clf.predict(predictdata)
print "classID = ", classID
#print clf.trained_labels
if classID[0] == 1: print "Image is of class: GRASS"
if classID[0] == 2: print "Image is of class: DIRT/GRAVEL"
if classID[0] == 3:
print "Image is of class: CEMENT/ASPHALT"
