def run(args):
dss = hdf2ds(args.data)
verbose(3, 'Loaded %i dataset(s)' % len(dss))
ds = vstack(dss)
verbose(3, 'Concatenation yielded %i samples with %i features' % ds.shape)
# get CV instance
cv = get_crossvalidation_instance(args.learner, args.partitioner,
args.errorfx, args.sampling_repetitions,
args.learner_space,
args.balance_training, args.permutations,
args.avg_datafold_results,
args.prob_tail)
res = cv(ds)
# some meaningful output
# XXX make condition on classification analysis only?
print cv.ca.stats
print 'Results\n-------'
if args.permutations > 0:
nprob = cv.ca.null_prob.samples
if res.shape[1] == 1:
# simple result structure
if args.permutations > 0:
p = ', p-value (%s tail)' % args.prob_tail
else:
p = ''
print 'Fold, Result%s' % p
for i in xrange(len(res)):
if args.permutations > 0:
p = ', %f' % nprob[i, 0]
else:
p = ''
print '%s, %f%s' % (res.sa.cvfolds[i], res.samples[i, 0], p)
# and store
ds2hdf5(res, args.output, compression=args.hdf5_compression)
if args.permutations > 0:
if args.output.endswith('.hdf5'):
args.output = args.output[:-5]
ds2hdf5(cv.ca.null_prob,
'%s_nullprob' % args.output,
compression=args.hdf5_compression)
return res
def run(args):
dss = hdf2ds(args.data)
verbose(3, 'Loaded %i dataset(s)' % len(dss))
ds = vstack(dss)
verbose(3, 'Concatenation yielded %i samples with %i features' % ds.shape)
# get CV instance
cv = get_crossvalidation_instance(
args.learner, args.partitioner, args.errorfx, args.sampling_repetitions,
args.learner_space, args.balance_training, args.permutations,
args.avg_datafold_results, args.prob_tail)
res = cv(ds)
# some meaningful output
# XXX make condition on classification analysis only?
print cv.ca.stats
print 'Results\n-------'
if args.permutations > 0:
nprob = cv.ca.null_prob.samples
if res.shape[1] == 1:
# simple result structure
if args.permutations > 0:
p=', p-value (%s tail)' % args.prob_tail
else:
p=''
print 'Fold, Result%s' % p
for i in xrange(len(res)):
if args.permutations > 0:
p = ', %f' % nprob[i, 0]
else:
p = ''
print '%s, %f%s' % (res.sa.cvfolds[i], res.samples[i, 0], p)
# and store
ds2hdf5(res, args.output, compression=args.hdf5_compression)
if args.permutations > 0:
if args.output.endswith('.hdf5'):
args.output = args.output[:-5]
ds2hdf5(cv.ca.null_prob, '%s_nullprob' % 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 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
