def _get_trained_queryengines(self, datasets, queryengine, radius, ref_ds):
"""Helper to return trained query engine(s), either list of one or one per each dataset
if queryengine is None then IndexQueryEngine based on radius is created
"""
ndatasets = len(datasets)
if queryengine:
if isinstance(queryengine, (list, tuple)):
queryengines = queryengine
if len(queryengines) != ndatasets:
raise ValueError(
"%d query engines were specified although %d datasets "
"provided" % (len(queryengines), ndatasets))
_shpaldebug("Training provided query engines")
for qe, ds in zip(queryengines, datasets):
qe.train(ds)
else:
queryengine.train(datasets[ref_ds])
queryengines = [queryengine]
else:
_shpaldebug('No custom query engines were provided. Setting up the '
'volumetric query engine on voxel_indices.')
queryengine = IndexQueryEngine(voxel_indices=Sphere(radius))
queryengine.train(datasets[ref_ds])
queryengines = [queryengine]
return queryengines
def local_random_affine_transformations(
ds, distort_seeds, distort_neighbor, space, scale_fac=100,
shift_fac=10):
"""Distort a dataset in the local neighborhood of selected features.
This function is similar to ``random_affine_transformation()``, but applies
multiple random affine transformations to a spatially constraint local
neighborhood.
Parameters
----------
ds : Dataset
The to be transformed/distorted dataset.
distort_seeds : list(int)
This a sequence of feature ids (corresponding to the input dataset) that
serve as anchor to determine the local neighborhood for a distortion. The
number of seeds also determines the number of different local distortions
that are going to be applied.
distort_neighbor : callable
And object that when called with a coordinate generates a sequence of
coordinates that comprise its neighborhood (see e.g. ``Sphere()``).
space : str
Name of the feature attribute of the input dataset that contains the
relevant feature coordinates (e.g. 'voxel_indices').
scale_fac : float
See ``random_affine_transformation()``
shift_fac : float
See ``random_affine_transformation()``
Returns
-------
Dataset
A dataset derived from the input dataset with added local distortions.
"""
# which dataset attributes to aggregate
random_stats = ['random_rotation', 'random_scale', 'random_shift']
kwa = {space: distort_neighbor}
qe = IndexQueryEngine(**kwa)
qe.train(ds)
ds_distorted = ds.copy()
for stat in random_stats:
ds_distorted.a[stat + 's'] = {}
# for each seed region
for seed in distort_seeds:
# select the neighborhood for this seed
# take data from the distorted dataset to avoid
# 'loosing' previous distortions
distort_ids = qe[seed]
ds_d = random_affine_transformation(
ds_distorted[:, distort_ids],
scale_fac=scale_fac,
shift_fac=shift_fac)
# recover the distortions stats for this seed
for stat in random_stats:
ds_distorted.a[stat + 's'].value[seed] = ds_d.a[stat].value
# put the freshly distorted data back
ds_distorted.samples[:, distort_ids] = ds_d.samples
return ds_distorted
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 sphere_searchlight(datameasure,
radius=1,
center_ids=None,
space='voxel_indices',
**kwargs):
"""Creates a `Searchlight` to run a scalar `Measure` on
all possible spheres of a certain size within a dataset.
The idea for a searchlight algorithm stems from a paper by
:ref:`Kriegeskorte et al. (2006) <KGB06>`.
Parameters
----------
datameasure : callable
Any object that takes a :class:`~mvpa2.datasets.base.Dataset`
and returns some measure when called.
radius : int
All features within this radius around the center will be part
of a sphere. Radius is in grid-indices, i.e. ``1`` corresponds
to all immediate neighbors, regardless of the physical distance.
center_ids : list of int
List of feature ids (not coordinates) the shall serve as sphere
centers. Alternatively, this can be the name of a feature attribute
of the input dataset, whose non-zero values determine the feature
ids. By default all features will be used (it is passed as ``roi_ids``
argument of Searchlight).
space : str
Name of a feature attribute of the input dataset that defines the spatial
coordinates of all features.
**kwargs
In addition this class supports all keyword arguments of its
base-class :class:`~mvpa2.measures.base.Measure`.
Notes
-----
If `Searchlight` is used as `SensitivityAnalyzer` one has to make
sure that the specified scalar `Measure` returns large
(absolute) values for high sensitivities and small (absolute) values
for low sensitivities. Especially when using error functions usually
low values imply high performance and therefore high sensitivity.
This would in turn result in sensitivity maps that have low
(absolute) values indicating high sensitivities and this conflicts
with the intended behavior of a `SensitivityAnalyzer`.
"""
# build a matching query engine from the arguments
kwa = {space: Sphere(radius)}
qe = IndexQueryEngine(**kwa)
# init the searchlight with the queryengine
return Searchlight(datameasure,
queryengine=qe,
roi_ids=center_ids,
**kwargs)
def sphere_gnbsearchlight(gnb,
generator,
radius=1,
center_ids=None,
space='voxel_indices',
*args,
**kwargs):
"""Creates a `GNBSearchlight` to assess :term:`cross-validation`
classification performance of GNB on all possible spheres of a
certain size within a dataset.
The idea of taking advantage of naiveness of GNB for the sake of
quick searchlight-ing stems from Francisco Pereira (paper under
review).
Parameters
----------
radius : float
All features within this radius around the center will be part
of a sphere.
center_ids : list of int
List of feature ids (not coordinates) the shall serve as sphere
centers. By default all features will be used (it is passed
roi_ids argument for Searchlight).
space : str
Name of a feature attribute of the input dataset that defines the spatial
coordinates of all features.
**kwargs
In addition this class supports all keyword arguments of
:class:`~mvpa2.measures.gnbsearchlight.GNBSearchlight`.
Notes
-----
If any `BaseSearchlight` is used as `SensitivityAnalyzer` one has to make
sure that the specified scalar `Measure` returns large
(absolute) values for high sensitivities and small (absolute) values
for low sensitivities. Especially when using error functions usually
low values imply high performance and therefore high sensitivity.
This would in turn result in sensitivity maps that have low
(absolute) values indicating high sensitivities and this conflicts
with the intended behavior of a `SensitivityAnalyzer`.
"""
# build a matching query engine from the arguments
kwa = {space: Sphere(radius)}
qe = IndexQueryEngine(**kwa)
# init the searchlight with the queryengine
return GNBSearchlight(gnb,
generator,
qe,
roi_ids=center_ids,
*args,
**kwargs)
def test_add_center_fa(self):
# just a smoke test pretty much
ds = datasets['3dsmall'].copy()
# check that we do not mark anything as center whenever there is none
def check_no_center(ds):
assert (not np.any(ds.fa.center))
return 1.0
# or just a single center in our case
def check_center(ds):
assert (np.sum(ds.fa.center) == 1)
return 1.0
for n, check in [(HollowSphere(1, 0), check_no_center),
(Sphere(0), check_center), (Sphere(1), check_center)]:
Searchlight(check,
IndexQueryEngine(myspace=n),
add_center_fa='center')(ds)
# and no changes to original ds data, etc
assert_array_equal(datasets['3dsmall'].fa.keys(), ds.fa.keys())
assert_array_equal(datasets['3dsmall'].samples, ds.samples)
N_BLOCKS=128
cnx_tx = 489
toutdir = os.path.join(basedir, 'transformation_matrices', 'iterative_cha_olp4cbp_mappers' +'_' + 'subs-' + str(nsubs) + '_'+ 'radius1-10_radius2-' + str(HYPERALIGNMENT_RADIUS) + '.hdf5.gz')
print(toutdir)
# load nifti as a pymvpa dataset and then use that as ref_ds in the queryengine definition
# mask with data in brainmask so only 170k (size of connectomes) voxels are included
ref_ds = fmri_dataset(os.path.join(helperfiles,'newbrainmask.nii'), mask=os.path.join(helperfiles,'newbrainmask.nii'))
print('Size of brain mask:')
print(str(len(ref_ds.fa.voxel_indices)))
# set searchlight sphere radius
sl_radius = HYPERALIGNMENT_RADIUS
#create query engine
qe = IndexQueryEngine(voxel_indices=Sphere(sl_radius))
qe.train(ref_ds)
# load all subject
nfiles = glob.glob(os.path.join(chamats, '*commonspace_subs*'))
print('Loading participant data from: ')
print(chamats)
mysubs = nfiles[0:nsubs]
# import connectomes into pymvpa dataset, zscore, then add chunks and voxel indices, append to list of datsets
dss = []
for sub in range(len(mysubs)):
ds = mv.Dataset(np.load(mysubs[sub]))
ds.fa['voxel_indices'] = range(ds.shape[1])
#ds.sa['chunks'] = np.repeat(i,cnx_tx)
mv.zscore(ds, chunks_attr=None)
def test_voxel_selection(self):
'''Compare surface and volume based searchlight'''
'''
Tests to see whether results are identical for surface-based
searchlight (just one plane; Euclidean distnace) and volume-based
searchlight.
Note that the current value is a float; if it were int, it would
specify the number of voxels in each searchlight'''
radius = 10.
'''Define input filenames'''
epi_fn = os.path.join(pymvpa_dataroot, 'bold.nii.gz')
maskfn = os.path.join(pymvpa_dataroot, 'mask.nii.gz')
'''
Use the EPI datafile to define a surface.
The surface has as many nodes as there are voxels
and is parallel to the volume 'slice'
'''
vg = volgeom.from_any(maskfn, mask_volume=True)
aff = vg.affine
nx, ny, nz = vg.shape[:3]
'''Plane goes in x and y direction, so we take these vectors
from the affine transformation matrix of the volume'''
plane = surf.generate_plane(aff[:3, 3], aff[:3, 0], aff[:3, 1], nx, ny)
'''
Simulate pial and white matter as just above and below
the central plane
'''
normal_vec = aff[:3, 2]
outer = plane + normal_vec
inner = plane + -normal_vec
'''
Combine volume and surface information
'''
vsm = volsurf.VolSurfMaximalMapping(vg, outer, inner)
'''
Run voxel selection with specified radius (in mm), using
Euclidean distance measure
'''
surf_voxsel = surf_voxel_selection.voxel_selection(vsm,
radius,
distance_metric='e')
'''Define the measure'''
# run_slow=True would give an actual cross-validation with meaningful
# accuracies. Because this is a unit-test only the number of voxels
# in each searchlight is tested.
run_slow = False
if run_slow:
meas = CrossValidation(GNB(),
OddEvenPartitioner(),
errorfx=lambda p, t: np.mean(p == t))
postproc = mean_sample
else:
meas = _Voxel_Count_Measure()
postproc = lambda x: x
'''
Surface analysis: define the query engine, cross validation,
and searchlight
'''
surf_qe = SurfaceVerticesQueryEngine(surf_voxsel)
surf_sl = Searchlight(meas, queryengine=surf_qe, postproc=postproc)
'''
new (Sep 2012): also test 'simple' queryengine wrapper function
'''
surf_qe2 = disc_surface_queryengine(radius,
maskfn,
inner,
outer,
plane,
volume_mask=True,
distance_metric='euclidean')
surf_sl2 = Searchlight(meas, queryengine=surf_qe2, postproc=postproc)
'''
Same for the volume analysis
'''
element_sizes = tuple(map(abs, (aff[0, 0], aff[1, 1], aff[2, 2])))
sph = Sphere(radius, element_sizes=element_sizes)
kwa = {'voxel_indices': sph}
vol_qe = IndexQueryEngine(**kwa)
vol_sl = Searchlight(meas, queryengine=vol_qe, postproc=postproc)
'''The following steps are similar to start_easy.py'''
attr = SampleAttributes(
os.path.join(pymvpa_dataroot, 'attributes_literal.txt'))
mask = surf_voxsel.get_mask()
dataset = fmri_dataset(samples=os.path.join(pymvpa_dataroot,
'bold.nii.gz'),
targets=attr.targets,
chunks=attr.chunks,
mask=mask)
if run_slow:
# do chunkswise linear detrending on dataset
poly_detrend(dataset, polyord=1, chunks_attr='chunks')
# zscore dataset relative to baseline ('rest') mean
zscore(dataset,
chunks_attr='chunks',
param_est=('targets', ['rest']))
# select class face and house for this demo analysis
# would work with full datasets (just a little slower)
dataset = dataset[np.array(
[l in ['face', 'house'] for l in dataset.sa.targets],
dtype='bool')]
'''Apply searchlight to datasets'''
surf_dset = surf_sl(dataset)
surf_dset2 = surf_sl2(dataset)
vol_dset = vol_sl(dataset)
surf_data = surf_dset.samples
surf_data2 = surf_dset2.samples
vol_data = vol_dset.samples
assert_array_equal(surf_data, surf_data2)
assert_array_equal(surf_data, vol_data)
def run(args):
if os.path.isfile(args.payload) and args.payload.endswith('.py'):
measure = script2obj(args.payload)
elif args.payload == 'cv':
if args.cv_learner is None or args.cv_partitioner is None:
raise ValueError(
'cross-validation payload requires --learner and --partitioner'
)
# get CV instance
measure = get_crossvalidation_instance(
args.cv_learner, args.cv_partitioner, args.cv_errorfx,
args.cv_sampling_repetitions, args.cv_learner_space,
args.cv_balance_training, args.cv_permutations,
args.cv_avg_datafold_results, args.cv_prob_tail)
else:
raise RuntimeError("this should not happen")
ds = arg2ds(args.data)
if not args.ds_preproc_fx is None:
ds = args.ds_preproc_fx(ds)
# setup neighborhood
# XXX add big switch to allow for setting up surface-based neighborhoods
from mvpa2.misc.neighborhood import IndexQueryEngine
qe = IndexQueryEngine(**dict(args.neighbors))
# determine ROIs
rids = None # all by default
aggregate_fx = args.aggregate_fx
if args.roi_attr is not None:
# first figure out which roi features should be processed
if len(args.roi_attr) == 1 and args.roi_attr[0] in ds.fa.keys():
# name of an attribute -> pull non-zeroes
rids = ds.fa[args.roi_attr[0]].value.nonzero()[0]
else:
# an expression?
from .cmd_select import _eval_attr_expr
rids = _eval_attr_expr(args.roi_attr, ds.fa).nonzero()[0]
seed_ids = None
if args.scatter_rois is not None:
# scatter_neighborhoods among available ids if was requested
from mvpa2.misc.neighborhood import scatter_neighborhoods
attr, nb = args.scatter_rois
coords = ds.fa[attr].value
if rids is not None:
# select only those which were chosen by ROI
coords = coords[rids]
_, seed_ids = scatter_neighborhoods(nb, coords)
if aggregate_fx is None:
# no custom one given -> use default "fill in" function
aggregate_fx = _fill_in_scattered_results
if args.enable_ca is None:
args.enable_ca = ['roi_feature_ids']
elif 'roi_feature_ids' not in args.enable_ca:
args.enable_ca += ['roi_feature_ids']
if seed_ids is None:
roi_ids = rids
else:
if rids is not None:
# we had to sub-select by scatterring among available rids
# so we would need to get original ids
roi_ids = rids[seed_ids]
else:
# scattering happened on entire feature-set
roi_ids = seed_ids
verbose(
3, 'Attempting %i ROI analyses' %
((roi_ids is None) and ds.nfeatures or len(roi_ids)))
from mvpa2.measures.searchlight import Searchlight
sl = Searchlight(measure,
queryengine=qe,
roi_ids=roi_ids,
nproc=args.nproc,
results_backend=args.multiproc_backend,
results_fx=aggregate_fx,
enable_ca=args.enable_ca,
disable_ca=args.disable_ca)
# XXX support me too!
# add_center_fa
# tmp_prefix
# nblocks
# null_dist
# run
res = sl(ds)
if (seed_ids is not None) and ('mapper' in res.a):
# strip the last mapper link in the chain, which would be the seed ID selection
res.a['mapper'] = res.a.mapper[:-1]
# XXX create more output
# and store
ds2hdf5(res, args.output, compression=args.hdf5_compression)
return res
