def test_scattered_neighborhoods():
radius = 1
sphere = ne.Sphere(radius)
coords = range(50)
scoords, sidx = ne.scatter_neighborhoods(sphere,
coords,
deterministic=False)
# for this specific case of 1d coordinates the coords and idx should be
# identical
assert_array_equal(scoords, sidx)
# minimal difference of successive coordinates must be larger than the
# radius of the spheres. Test only works for 1d coords and sorted return
# values
assert (np.diff(scoords).min() > radius)
# now the same for the case where a particular coordinate appears multiple
# times
coords = range(10) + range(10)
scoords, sidx = ne.scatter_neighborhoods(sphere,
coords,
deterministic=False)
sidx = sorted(sidx)
assert_array_equal(scoords, sidx[:5])
assert_array_equal(scoords, [i - 10 for i in sidx[5:]])
def test_scattered_neighborhoods():
radius = 1
sphere = ne.Sphere(radius)
coords = range(50)
scoords, sidx = ne.scatter_neighborhoods(sphere, coords,
deterministic=False)
# for this specific case of 1d coordinates the coords and idx should be
# identical
assert_array_equal(scoords, sidx)
# minimal difference of successive coordinates must be larger than the
# radius of the spheres. Test only works for 1d coords and sorted return
# values
assert(np.diff(scoords).min() > radius)
# now the same for the case where a particular coordinate appears multiple
# times
coords = range(10) + range(10)
scoords, sidx = ne.scatter_neighborhoods(sphere, coords,
deterministic=False)
sidx = sorted(sidx)
assert_array_equal(scoords, sidx[:5])
assert_array_equal(scoords, [i - 10 for i in sidx[5:]])
def test_searchlight_hyperalignment(self):
skip_if_no_external('scipy')
skip_if_no_external('h5py')
ds_orig = datasets['3dsmall'].copy()[:, :15]
ds_orig.fa['voxel_indices'] = ds_orig.fa.myspace
space = 'voxel_indices'
# total number of datasets for the analysis
nds = 5
zscore(ds_orig, chunks_attr=None)
dss = [ds_orig]
# create a few distorted datasets to match the desired number of datasets
# not sure if this truly mimics the real data, but at least we can test
# implementation
while len(dss) < nds - 1:
sd = local_random_affine_transformations(
ds_orig,
scatter_neighborhoods(
Sphere(1),
ds_orig.fa[space].value, deterministic=True)[1],
Sphere(2),
space=space,
scale_fac=1.0, shift_fac=0.0)
# sometimes above function returns dataset with nans, infs, we don't want that.
if np.sum(np.isnan(sd.samples)+np.isinf(sd.samples)) == 0 \
and np.all(sd.samples.std(0)):
dss.append(sd)
ds_orig_noisy = ds_orig.copy()
ds_orig_noisy.samples += 0.1 * np.random.random(size=ds_orig_noisy.shape)
dss.append(ds_orig_noisy)
_ = [zscore(sd, chunks_attr=None) for sd in dss[1:]]
# we should have some distortion
for ds in dss[1:]:
assert_false(np.all(ds_orig.samples == ds.samples))
# testing checks
slhyp = SearchlightHyperalignment(ref_ds=1, exclude_from_model=[1])
self.assertRaises(ValueError, slhyp, dss[:3])
slhyp = SearchlightHyperalignment(ref_ds=3)
self.assertRaises(ValueError, slhyp, dss[:3])
# store projections for each mapper separately
projs = list()
# run the algorithm with all combinations of the two major parameters
# for projection calculation.
for kwargs in [{'combine_neighbormappers': True, 'nproc': 2},
{'combine_neighbormappers': True, 'dtype': 'float64', 'compute_recon': True},
{'combine_neighbormappers': True, 'exclude_from_model': [2, 4]},
{'combine_neighbormappers': False},
{'combine_neighbormappers': False, 'mask_node_ids': np.arange(dss[0].nfeatures).tolist()},
{'combine_neighbormappers': True, 'sparse_radius': 1},
{'combine_neighbormappers': True, 'nblocks': 2}]:
slhyp = SearchlightHyperalignment(radius=2, **kwargs)
mappers = slhyp(dss)
# one mapper per input ds
assert_equal(len(mappers), nds)
projs.append(mappers)
# some checks
for midx in range(nds):
# making sure mask_node_ids options works as expected
assert_array_almost_equal(projs[3][midx].proj.todense(),
projs[4][midx].proj.todense())
# recon check
assert_array_almost_equal(projs[0][midx].proj.todense(),
projs[1][midx].recon.T.todense(), decimal=5)
assert_equal(projs[1][midx].proj.dtype, 'float64')
assert_equal(projs[0][midx].proj.dtype, 'float32')
# making sure the projections make sense
for proj in projs:
# no .max on sparse matrices on older scipy (e.g. on precise) so conver to array first
max_weight = proj[0].proj.toarray().max(0).squeeze()
diag_weight = proj[0].proj.diagonal()
# Check to make sure diagonal is the max weight, in almost all rows for reference subject
assert(np.sum(max_weight == diag_weight) / float(len(diag_weight)) > 0.90)
# and not true for other subjects
for i in range(1, nds - 1):
assert(np.sum(proj[i].proj.toarray().max(0).squeeze() == proj[i].proj.diagonal())
/ float(proj[i].proj.shape[0]) < 0.80)
# Check to make sure projection weights match across duplicate datasets
max_weight = proj[-1].proj.toarray().max(0).squeeze()
diag_weight = proj[-1].proj.diagonal()
# Check to make sure diagonal is the max weight, in almost all rows for reference subject
assert(np.sum(max_weight == diag_weight) / float(len(diag_weight)) > 0.90)
# project data
dss_hyper = [hm.forward(sd) for hm, sd in zip(projs[0], dss)]
_ = [zscore(sd, chunks_attr=None) for sd in dss_hyper]
ndcss = []
nf = ds_orig.nfeatures
for ds_hyper in dss_hyper:
ndcs = np.diag(np.corrcoef(ds_hyper.samples.T,
ds_orig.samples.T)[nf:, :nf],
k=0)
ndcss += [ndcs]
assert_true(np.median(ndcss[0]) > 0.9)
# noisy copy of original dataset should be similar to original after hyperalignment
assert_true(np.median(ndcss[-1]) > 0.9)
assert_true(np.all([np.median(ndcs) > 0.2 for ndcs in ndcss[1:-2]]))
def __call__(self, datasets):
"""Estimate mappers for each dataset using searchlight-based
hyperalignment.
Parameters
----------
datasets : list or tuple of datasets
Returns
-------
A list of trained StaticProjectionMappers of the same length as datasets
"""
# Perform some checks first before modifying internal state
params = self.params
ndatasets = len(datasets)
if len(datasets) <= 1:
raise ValueError("SearchlightHyperalignment needs > 1 dataset to "
"operate on. Got: %d" % self.ndatasets)
if params.ref_ds in params.exclude_from_model:
raise ValueError("Requested reference dataset %i is also "
"in the exclude list." % params.ref_ds)
if params.ref_ds >= ndatasets:
raise ValueError("Requested reference dataset %i is out of "
"bounds. We have only %i datasets provided" %
(params.ref_ds, self.ndatasets))
# The rest of the checks are just warnings
self.ndatasets = ndatasets
_shpaldebug("SearchlightHyperalignment %s for %i datasets" %
(self, self.ndatasets))
selected = [
_ for _ in range(ndatasets) if _ not in params.exclude_from_model
]
ref_ds_train = selected.index(params.ref_ds)
params.hyperalignment.params.ref_ds = ref_ds_train
warning('Using %dth dataset as the reference dataset (%dth after '
'excluding datasets)' % (params.ref_ds, ref_ds_train))
if len(params.exclude_from_model) > 0:
warning("These datasets will not participate in building common "
"model: %s" % params.exclude_from_model)
if __debug__:
# verify that datasets were zscored prior the alignment since it is
# assumed/required preprocessing step
for ids, ds in enumerate(datasets):
for f, fname, tval in ((np.mean, 'means', 0), (np.std, 'stds',
1)):
vals = f(ds, axis=0)
vals_comp = np.abs(vals - tval) > 1e-5
if np.any(vals_comp):
warning(
'%d %s are too different (max diff=%g) from %d in '
'dataset %d to come from a zscored dataset. '
'Please zscore datasets first for correct operation '
'(unless if was intentional)' %
(np.sum(vals_comp), fname, np.max(
np.abs(vals)), tval, ids))
# Setting up SearchlightHyperalignment
# we need to know which original features where comprising the
# individual SL ROIs
_shpaldebug('Initializing FeatureSelectionHyperalignment.')
hmeasure = FeatureSelectionHyperalignment(
ref_ds=params.ref_ds,
featsel=params.featsel,
hyperalignment=params.hyperalignment,
full_matrix=params.combine_neighbormappers,
use_same_features=params.use_same_features,
exclude_from_model=params.exclude_from_model,
dtype=params.dtype)
# Performing SL processing manually
_shpaldebug("Setting up for searchlights")
if params.nproc is None and externals.exists('pprocess'):
import pprocess
try:
params.nproc = pprocess.get_number_of_cores() or 1
except AttributeError:
warning("pprocess version %s has no API to figure out maximal "
"number of cores. Using 1" %
externals.versions['pprocess'])
params.nproc = 1
# XXX I think this class should already accept a single dataset only.
# It should have a ``space`` setting that names a sample attribute that
# can be used to identify individual/original datasets.
# Taking a single dataset as argument would be cleaner, because the
# algorithm relies on the assumption that there is a coarse feature
# alignment, i.e. the SL ROIs cover roughly the same area
queryengines = self._get_trained_queryengines(datasets,
params.queryengine,
params.radius,
params.ref_ds)
# For surface nodes to voxels queryengines, roi_seed hardly makes sense
qe = queryengines[(0 if len(queryengines) == 1 else params.ref_ds)]
if isinstance(qe, SurfaceVerticesQueryEngine):
self.force_roi_seed = False
if not self.params.combine_neighbormappers:
raise NotImplementedError(
"Mapping from voxels to surface nodes is not "
"implmented yet. Try setting combine_neighbormappers to True."
)
self.nfeatures = datasets[params.ref_ds].nfeatures
_shpaldebug("Performing Hyperalignment in searchlights")
# Setting up centers for running SL Hyperalignment
if params.sparse_radius is None:
roi_ids = self._get_verified_ids(queryengines) \
if params.mask_node_ids is None \
else params.mask_node_ids
else:
if params.queryengine is not None:
raise NotImplementedError(
"using sparse_radius whenever custom queryengine is "
"provided is not yet supported.")
_shpaldebug("Setting up sparse neighborhood")
from mvpa2.misc.neighborhood import scatter_neighborhoods
if params.mask_node_ids is None:
scoords, sidx = scatter_neighborhoods(
Sphere(params.sparse_radius),
datasets[params.ref_ds].fa.voxel_indices,
deterministic=True)
roi_ids = sidx
else:
scoords, sidx = scatter_neighborhoods(
Sphere(params.sparse_radius),
datasets[params.ref_ds].fa.voxel_indices[
params.mask_node_ids],
deterministic=True)
roi_ids = [params.mask_node_ids[sid] for sid in sidx]
# Initialize projections
_shpaldebug('Initializing projection matrices')
self.projections = [
csc_matrix((self.nfeatures, self.nfeatures), dtype=params.dtype)
for isub in range(self.ndatasets)
]
# compute
if params.nproc is not None and params.nproc > 1:
# split all target ROIs centers into `nproc` equally sized blocks
nproc_needed = min(len(roi_ids), params.nproc)
params.nblocks = nproc_needed \
if params.nblocks is None else params.nblocks
params.nblocks = min(len(roi_ids), params.nblocks)
node_blocks = np.array_split(roi_ids, params.nblocks)
# the next block sets up the infrastructure for parallel computing
# this can easily be changed into a ParallelPython loop, if we
# decide to have a PP job server in PyMVPA
import pprocess
p_results = pprocess.Map(limit=nproc_needed)
if __debug__:
debug(
'SLC', "Starting off %s child processes for nblocks=%i" %
(nproc_needed, params.nblocks))
compute = p_results.manage(pprocess.MakeParallel(self._proc_block))
seed = mvpa2.get_random_seed()
for iblock, block in enumerate(node_blocks):
# should we maybe deepcopy the measure to have a unique and
# independent one per process?
compute(block,
datasets,
copy.copy(hmeasure),
queryengines,
seed=seed,
iblock=iblock)
else:
# otherwise collect the results in an 1-item list
_shpaldebug('Using 1 process to compute mappers.')
if params.nblocks is None:
params.nblocks = 1
params.nblocks = min(len(roi_ids), params.nblocks)
node_blocks = np.array_split(roi_ids, params.nblocks)
p_results = [
self._proc_block(block, datasets, hmeasure, queryengines)
for block in node_blocks
]
results_ds = self.__handle_all_results(p_results)
# Dummy iterator for, you know, iteration
list(results_ds)
_shpaldebug(
'Wrapping projection matrices into StaticProjectionMappers')
self.projections = [
StaticProjectionMapper(proj=proj, recon=proj.T)
if params.compute_recon else StaticProjectionMapper(proj=proj)
for proj in self.projections
]
return self.projections
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 args.ds_preproc_fx is not 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
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
def __call__(self, datasets):
"""Estimate mappers for each dataset using searchlight-based
hyperalignment.
Parameters
----------
datasets : list or tuple of datasets
Returns
-------
A list of trained StaticProjectionMappers of the same length as datasets
"""
# Perform some checks first before modifying internal state
params = self.params
ndatasets = len(datasets)
if len(datasets) <= 1:
raise ValueError("SearchlightHyperalignment needs > 1 dataset to "
"operate on. Got: %d" % self.ndatasets)
if params.ref_ds in params.exclude_from_model:
raise ValueError("Requested reference dataset %i is also "
"in the exclude list." % params.ref_ds)
if params.ref_ds >= ndatasets:
raise ValueError("Requested reference dataset %i is out of "
"bounds. We have only %i datasets provided"
% (params.ref_ds, self.ndatasets))
# The rest of the checks are just warnings
self.ndatasets = ndatasets
_shpaldebug("SearchlightHyperalignment %s for %i datasets"
% (self, self.ndatasets))
if params.ref_ds != params.hyperalignment.params.ref_ds:
warning('Supplied ref_ds & hyperalignment instance ref_ds:%d differ.'
% params.hyperalignment.params.ref_ds)
warning('Using default hyperalignment instance with ref_ds: %d' % params.ref_ds)
params.hyperalignment = Hyperalignment(ref_ds=params.ref_ds)
if len(params.exclude_from_model) > 0:
warning("These datasets will not participate in building common "
"model: %s" % params.exclude_from_model)
if __debug__:
# verify that datasets were zscored prior the alignment since it is
# assumed/required preprocessing step
for ids, ds in enumerate(datasets):
for f, fname, tval in ((np.mean, 'means', 0),
(np.std, 'stds', 1)):
vals = f(ds, axis=0)
vals_comp = np.abs(vals - tval) > 1e-5
if np.any(vals_comp):
warning('%d %s are too different (max diff=%g) from %d in '
'dataset %d to come from a zscored dataset. '
'Please zscore datasets first for correct operation '
'(unless if was intentional)'
% (np.sum(vals_comp), fname,
np.max(np.abs(vals)), tval, ids))
# Setting up SearchlightHyperalignment
# we need to know which original features where comprising the
# individual SL ROIs
_shpaldebug('Initializing FeatureSelectionHyperalignment.')
hmeasure = FeatureSelectionHyperalignment(
featsel=params.featsel,
hyperalignment=params.hyperalignment,
full_matrix=params.combine_neighbormappers,
use_same_features=params.use_same_features,
exclude_from_model=params.exclude_from_model,
dtype=params.dtype)
# Performing SL processing manually
_shpaldebug("Setting up for searchlights")
if params.nproc is None and externals.exists('pprocess'):
import pprocess
try:
params.nproc = pprocess.get_number_of_cores() or 1
except AttributeError:
warning("pprocess version %s has no API to figure out maximal "
"number of cores. Using 1"
% externals.versions['pprocess'])
params.nproc = 1
# XXX I think this class should already accept a single dataset only.
# It should have a ``space`` setting that names a sample attribute that
# can be used to identify individual/original datasets.
# Taking a single dataset as argument would be cleaner, because the
# algorithm relies on the assumption that there is a coarse feature
# alignment, i.e. the SL ROIs cover roughly the same area
queryengines = self._get_trained_queryengines(
datasets, params.queryengine, params.radius, params.ref_ds)
# For surface nodes to voxels queryengines, roi_seed hardly makes sense
if isinstance(queryengines[params.ref_ds], SurfaceVerticesQueryEngine):
self.force_roi_seed = False
if not self.params.combine_neighbormappers:
raise NotImplementedError("Mapping from voxels to surface nodes is not "
"implmented yet. Try setting combine_neighbormappers to True.")
self.nfeatures = datasets[params.ref_ds].nfeatures
_shpaldebug("Performing Hyperalignment in searchlights")
# Setting up centers for running SL Hyperalignment
if params.sparse_radius is None:
roi_ids = self._get_verified_ids(queryengines) \
if params.mask_node_ids is None \
else params.mask_node_ids
else:
if params.queryengine is not None:
raise NotImplementedError(
"using sparse_radius whenever custom queryengine is "
"provided is not yet supported.")
_shpaldebug("Setting up sparse neighborhood")
from mvpa2.misc.neighborhood import scatter_neighborhoods
if params.mask_node_ids is None:
scoords, sidx = scatter_neighborhoods(
Sphere(params.sparse_radius),
datasets[params.ref_ds].fa.voxel_indices,
deterministic=True)
roi_ids = sidx
else:
scoords, sidx = scatter_neighborhoods(
Sphere(params.sparse_radius),
datasets[params.ref_ds].fa.voxel_indices[params.mask_node_ids],
deterministic=True)
roi_ids = [params.mask_node_ids[sid] for sid in sidx]
# Initialize projections
_shpaldebug('Initializing projection matrices')
self.projections = [
csc_matrix((self.nfeatures, self.nfeatures), dtype=params.dtype)
for isub in range(self.ndatasets)]
# compute
if params.nproc is not None and params.nproc > 1:
# split all target ROIs centers into `nproc` equally sized blocks
nproc_needed = min(len(roi_ids), params.nproc)
params.nblocks = nproc_needed \
if params.nblocks is None else params.nblocks
params.nblocks = min(len(roi_ids), params.nblocks)
node_blocks = np.array_split(roi_ids, params.nblocks)
# the next block sets up the infrastructure for parallel computing
# this can easily be changed into a ParallelPython loop, if we
# decide to have a PP job server in PyMVPA
import pprocess
p_results = pprocess.Map(limit=nproc_needed)
if __debug__:
debug('SLC', "Starting off %s child processes for nblocks=%i"
% (nproc_needed, params.nblocks))
compute = p_results.manage(
pprocess.MakeParallel(self._proc_block))
seed = mvpa2.get_random_seed()
for iblock, block in enumerate(node_blocks):
# should we maybe deepcopy the measure to have a unique and
# independent one per process?
compute(block, datasets, copy.copy(hmeasure), queryengines,
seed=seed, iblock=iblock)
else:
# otherwise collect the results in an 1-item list
_shpaldebug('Using 1 process to compute mappers.')
if params.nblocks is None:
params.nblocks = 1
params.nblocks = min(len(roi_ids), params.nblocks)
node_blocks = np.array_split(roi_ids, params.nblocks)
p_results = [self._proc_block(block, datasets, hmeasure, queryengines)
for block in node_blocks]
results_ds = self.__handle_all_results(p_results)
# Dummy iterator for, you know, iteration
list(results_ds)
_shpaldebug('Wrapping projection matrices into StaticProjectionMappers')
self.projections = [
StaticProjectionMapper(proj=proj, recon=proj.T) if params.compute_recon
else StaticProjectionMapper(proj=proj)
for proj in self.projections]
return self.projections
def test_searchlight_hyperalignment(self):
skip_if_no_external('scipy')
skip_if_no_external('h5py')
ds_orig = datasets['3dsmall'].copy()[:, :15]
ds_orig.fa['voxel_indices'] = ds_orig.fa.myspace
space = 'voxel_indices'
# total number of datasets for the analysis
nds = 5
zscore(ds_orig, chunks_attr=None)
dss = [ds_orig]
# create a few distorted datasets to match the desired number of datasets
# not sure if this truly mimics the real data, but at least we can test
# implementation
while len(dss) < nds - 1:
sd = local_random_affine_transformations(
ds_orig,
scatter_neighborhoods(Sphere(1),
ds_orig.fa[space].value,
deterministic=True)[1],
Sphere(2),
space=space,
scale_fac=1.0,
shift_fac=0.0)
# sometimes above function returns dataset with nans, infs, we don't want that.
if np.sum(np.isnan(sd.samples)+np.isinf(sd.samples)) == 0 \
and np.all(sd.samples.std(0)):
dss.append(sd)
ds_orig_noisy = ds_orig.copy()
ds_orig_noisy.samples += 0.1 * np.random.random(
size=ds_orig_noisy.shape)
dss.append(ds_orig_noisy)
_ = [zscore(sd, chunks_attr=None) for sd in dss[1:]]
# we should have some distortion
for ds in dss[1:]:
assert_false(np.all(ds_orig.samples == ds.samples))
# testing checks
slhyp = SearchlightHyperalignment(ref_ds=1, exclude_from_model=[1])
self.assertRaises(ValueError, slhyp, dss[:3])
slhyp = SearchlightHyperalignment(ref_ds=3)
self.assertRaises(ValueError, slhyp, dss[:3])
# explicit test of exclude_from_model
slhyp = SearchlightHyperalignment(ref_ds=2,
exclude_from_model=[1],
featsel=0.7)
projs1 = slhyp(dss)
aligned1 = [proj.forward(ds) for proj, ds in zip(projs1, dss)]
samples = dss[1].samples.copy()
dss[1].samples += 0.1 * np.random.random(size=dss[1].shape)
projs2 = slhyp(dss)
aligned2 = [proj.forward(ds) for proj, ds in zip(projs1, dss)]
for i in [0, 2, 3, 4]:
assert_array_almost_equal(projs1[i].proj.todense(),
projs2[i].proj.todense())
assert_array_almost_equal(aligned1[i].samples, aligned2[i].samples)
assert_false(
np.all(projs1[1].proj.todense() == projs1[2].proj.todense()))
assert_false(np.all(aligned1[1].samples == aligned2[1].samples))
dss[1].samples = samples
# store projections for each mapper separately
projs = list()
# run the algorithm with all combinations of the two major parameters
# for projection calculation.
for kwargs in [{
'combine_neighbormappers': True,
'nproc': 1 + int(externals.exists('pprocess'))
}, {
'combine_neighbormappers': True,
'dtype': 'float64',
'compute_recon': True
}, {
'combine_neighbormappers': True,
'exclude_from_model': [2, 4]
}, {
'combine_neighbormappers': False
}, {
'combine_neighbormappers': False,
'mask_node_ids': np.arange(dss[0].nfeatures).tolist()
}, {
'combine_neighbormappers': True,
'sparse_radius': 1
}, {
'combine_neighbormappers': True,
'nblocks': 2
}]:
slhyp = SearchlightHyperalignment(radius=2, **kwargs)
mappers = slhyp(dss)
# one mapper per input ds
assert_equal(len(mappers), nds)
projs.append(mappers)
# some checks
for midx in range(nds):
# making sure mask_node_ids options works as expected
assert_array_almost_equal(projs[3][midx].proj.todense(),
projs[4][midx].proj.todense())
# recon check
assert_array_almost_equal(projs[0][midx].proj.todense(),
projs[1][midx].recon.T.todense(),
decimal=5)
assert_equal(projs[1][midx].proj.dtype, 'float64')
assert_equal(projs[0][midx].proj.dtype, 'float32')
# making sure the projections make sense
for proj in projs:
# no .max on sparse matrices on older scipy (e.g. on precise) so conver to array first
max_weight = proj[0].proj.toarray().max(1).squeeze()
diag_weight = proj[0].proj.diagonal()
# Check to make sure diagonal is the max weight, in almost all rows for reference subject
assert (np.sum(max_weight == diag_weight) / float(len(diag_weight))
>= 0.80)
# and not true for other subjects
for i in range(1, nds - 1):
assert (np.sum(proj[i].proj.toarray().max(1).squeeze() ==
proj[i].proj.diagonal()) /
float(proj[i].proj.shape[0]) < 0.80)
# Check to make sure projection weights match across duplicate datasets
max_weight = proj[-1].proj.toarray().max(1).squeeze()
diag_weight = proj[-1].proj.diagonal()
# Check to make sure diagonal is the max weight, in almost all rows for reference subject
assert (np.sum(max_weight == diag_weight) / float(len(diag_weight))
>= 0.80)
# project data
dss_hyper = [hm.forward(sd) for hm, sd in zip(projs[0], dss)]
_ = [zscore(sd, chunks_attr=None) for sd in dss_hyper]
ndcss = []
nf = ds_orig.nfeatures
for ds_hyper in dss_hyper:
ndcs = np.diag(np.corrcoef(ds_hyper.samples.T,
ds_orig.samples.T)[nf:, :nf],
k=0)
ndcss += [ndcs]
assert_true(np.median(ndcss[0]) > 0.9)
# noisy copy of original dataset should be similar to original after hyperalignment
assert_true(np.median(ndcss[-1]) > 0.9)
assert_true(np.all([np.median(ndcs) > 0.2 for ndcs in ndcss[1:-2]]))