def test_remove_invariant_as_a_mapper():
from mvpa2.featsel.helpers import RangeElementSelector
from mvpa2.featsel.base import StaticFeatureSelection, SensitivityBasedFeatureSelection
from mvpa2.testing.datasets import datasets
from mvpa2.datasets.miscfx import remove_invariant_features
mapper = SensitivityBasedFeatureSelection(
lambda x: np.std(x, axis=0),
RangeElementSelector(lower=0, inclusive=False),
train_analyzer=False,
auto_train=True)
ds = datasets['uni2large'].copy()
ds.a['mapper'] = StaticFeatureSelection(np.arange(ds.nfeatures))
ds.fa['index'] = np.arange(ds.nfeatures)
ds.samples[:, [1, 8]] = 10
ds_out = mapper(ds)
# Validate that we are getting the same results as remove_invariant_features
ds_rifs = remove_invariant_features(ds)
assert_array_equal(ds_out.samples, ds_rifs.samples)
assert_array_equal(ds_out.fa.index, ds_rifs.fa.index)
assert_equal(ds_out.fa.index[1], 2)
assert_equal(ds_out.fa.index[8], 10)
def test_invar_features_removal(self):
r = np.random.normal(size=(3, 1))
ds = dataset_wizard(samples=np.hstack((np.zeros((3, 2)), r)), targets=1)
self.failUnless(ds.nfeatures == 3)
dsc = remove_invariant_features(ds)
self.failUnless(dsc.nfeatures == 1)
self.failUnless((dsc.samples == r).all())
def test_invar_features_removal(self):
r = np.random.normal(size=(3,1))
ds = dataset_wizard(samples=np.hstack((np.zeros((3,2)), r)),
targets=1)
self.assertTrue(ds.nfeatures == 3)
dsc = remove_invariant_features(ds)
self.assertTrue(dsc.nfeatures == 1)
self.assertTrue((dsc.samples == r).all())
def _test_gnb_overflow_haxby(): # pragma: no cover
# example from https://github.com/PyMVPA/PyMVPA/issues/581
# a heavier version of the above test
import os
import numpy as np
from mvpa2.datasets.sources.native import load_tutorial_data
from mvpa2.clfs.gnb import GNB
from mvpa2.measures.base import CrossValidation
from mvpa2.generators.partition import HalfPartitioner
from mvpa2.mappers.zscore import zscore
from mvpa2.mappers.detrend import poly_detrend
from mvpa2.datasets.miscfx import remove_invariant_features
from mvpa2.testing.datasets import *
datapath = '/usr/share/data/pymvpa2-tutorial/'
haxby = load_tutorial_data(datapath,
roi='vt',
add_fa={
'vt_thr_glm':
os.path.join(datapath, 'haxby2001',
'sub001', 'masks', 'orig',
'vt.nii.gz')
})
# poly_detrend(haxby, polyord=1, chunks_attr='chunks')
haxby = haxby[np.array(
[
l in ['rest', 'scrambled'] # ''house', 'face']
for l in haxby.targets
],
dtype='bool')]
#zscore(haxby, chunks_attr='chunks', param_est=('targets', ['rest']),
# dtype='float32')
# haxby = haxby[haxby.sa.targets != 'rest']
haxby = remove_invariant_features(haxby)
clf = GNB(enable_ca='estimates', logprob=True, normalize=True)
#clf.train(haxby)
#clf.predict(haxby)
# estimates a bit "overfit" to judge in the train/predict on the same data
cv = CrossValidation(clf,
HalfPartitioner(attr='chunks'),
postproc=None,
enable_ca=['stats'])
cv_results = cv(haxby)
res1_est = clf.ca.estimates
print "Estimates:\n", res1_est
print "Exp(estimates):\n", np.round(np.exp(res1_est), 3)
assert np.all(np.isfinite(res1_est))
fds_z = detrended_fds
#print fds.a.mapper
#pring fds_z.a.mapper
#use a balancer to make a balanced dataset of even amounts of samples in each class
#if model == 'MVPA-01':
balancer = ChainNode([
NFoldPartitioner(),
Balancer(attr='targets', count=1, limit='partitions', apply_selection=True)
],
space='partitions')
##WHATCHA
# Removing inv features #pleases the SVM but messes up dimensions. ##triplecheck
fds = remove_invariant_features(fds_z)
if model == 'MVPA-05' or model == 'MVPA-04':
fds = (fds_z)
#cross validate using NFoldPartioner - which makes cross validation folds by chunk/run
#if model == 'MVPA-01':
cv = CrossValidation(clf, balancer, errorfx=lambda p, t: np.mean(p == t))
if model == 'MVPA-03' or model == 'MVPA-05':
cv = CrossValidation(clf,
NFoldPartitioner(),
errorfx=lambda p, t: np.mean(p == t))
#cv = CrossValidation(clf, NFoldPartitioner(1), errorfx=lambda p, t: np.mean(p == t))
#no balance!
def run(args):
if not args.chunks is None:
# apply global "chunks" setting
for cattr in ('detrend_chunks', 'zscore_chunks'):
if getattr(args, cattr) is None:
# only overwrite if individual option is not given
args.__setattr__(cattr, args.chunks)
ds = arg2ds(args.data)
if not args.poly_detrend is None:
if not args.detrend_chunks is None \
and not args.detrend_chunks in ds.sa:
raise ValueError(
"--detrend-chunks attribute '%s' not found in dataset" %
args.detrend_chunks)
from mvpa2.mappers.detrend import poly_detrend
verbose(1, "Detrend")
poly_detrend(ds,
polyord=args.poly_detrend,
chunks_attr=args.detrend_chunks,
opt_regs=args.detrend_regrs,
space=args.detrend_coords)
if args.filter_passband is not None:
from mvpa2.mappers.filters import iir_filter
from scipy.signal import butter, buttord
if args.sampling_rate is None or args.filter_stopband is None:
raise ValueError("spectral filtering requires specification of "
"--filter-stopband and --sampling-rate")
# determine filter type
nyquist = args.sampling_rate / 2.0
if len(args.filter_passband) > 1:
btype = 'bandpass'
if not len(args.filter_passband) == len(args.filter_stopband):
raise ValueError(
"passband and stopband specifications have to "
"match in size")
wp = [v / nyquist for v in args.filter_passband]
ws = [v / nyquist for v in args.filter_stopband]
elif args.filter_passband[0] < args.filter_stopband[0]:
btype = 'lowpass'
wp = args.filter_passband[0] / nyquist
ws = args.filter_stopband[0] / nyquist
elif args.filter_passband[0] > args.filter_stopband[0]:
btype = 'highpass'
wp = args.filter_passband[0] / nyquist
ws = args.filter_stopband[0] / nyquist
else:
raise ValueError("invalid specification of Butterworth filter")
# create filter
verbose(1, "Spectral filtering (%s)" % (btype, ))
try:
ord, wn = buttord(wp,
ws,
args.filter_passloss,
args.filter_stopattenuation,
analog=False)
b, a = butter(ord, wn, btype=btype)
except OverflowError:
raise ValueError(
"cannot contruct Butterworth filter for the given "
"specification")
ds = iir_filter(ds, b, a)
if args.zscore:
from mvpa2.mappers.zscore import zscore
verbose(1, "Z-score")
zscore(ds, chunks_attr=args.zscore_chunks, params=args.zscore_params)
verbose(3, "Dataset summary %s" % (ds.summary()))
# invariants?
if not args.strip_invariant_features is None:
from mvpa2.datasets.miscfx import remove_invariant_features
ds = remove_invariant_features(ds)
# and store
ds2hdf5(ds, args.output, compression=args.hdf5_compression)
return ds
def run(args):
if args.chunks is not None:
# apply global "chunks" setting
for cattr in ("detrend_chunks", "zscore_chunks"):
if getattr(args, cattr) is None:
# only overwrite if individual option is not given
args.__setattr__(cattr, args.chunks)
ds = arg2ds(args.data)
if args.poly_detrend is not None:
if args.detrend_chunks is not None and not args.detrend_chunks in ds.sa:
raise ValueError("--detrend-chunks attribute '%s' not found in dataset" % args.detrend_chunks)
from mvpa2.mappers.detrend import poly_detrend
verbose(1, "Detrend")
poly_detrend(
ds,
polyord=args.poly_detrend,
chunks_attr=args.detrend_chunks,
opt_regs=args.detrend_regrs,
space=args.detrend_coords,
)
if args.filter_passband is not None:
from mvpa2.mappers.filters import iir_filter
from scipy.signal import butter, buttord
if args.sampling_rate is None or args.filter_stopband is None:
raise ValueError("spectral filtering requires specification of " "--filter-stopband and --sampling-rate")
# determine filter type
nyquist = args.sampling_rate / 2.0
if len(args.filter_passband) > 1:
btype = "bandpass"
if not len(args.filter_passband) == len(args.filter_stopband):
raise ValueError("passband and stopband specifications have to " "match in size")
wp = [v / nyquist for v in args.filter_passband]
ws = [v / nyquist for v in args.filter_stopband]
elif args.filter_passband[0] < args.filter_stopband[0]:
btype = "lowpass"
wp = args.filter_passband[0] / nyquist
ws = args.filter_stopband[0] / nyquist
elif args.filter_passband[0] > args.filter_stopband[0]:
btype = "highpass"
wp = args.filter_passband[0] / nyquist
ws = args.filter_stopband[0] / nyquist
else:
raise ValueError("invalid specification of Butterworth filter")
# create filter
verbose(1, "Spectral filtering (%s)" % (btype,))
try:
ord, wn = buttord(wp, ws, args.filter_passloss, args.filter_stopattenuation, analog=False)
b, a = butter(ord, wn, btype=btype)
except OverflowError:
raise ValueError("cannot contruct Butterworth filter for the given " "specification")
ds = iir_filter(ds, b, a)
if args.zscore:
from mvpa2.mappers.zscore import zscore
verbose(1, "Z-score")
zscore(ds, chunks_attr=args.zscore_chunks, params=args.zscore_params)
verbose(3, "Dataset summary %s" % (ds.summary()))
# invariants?
if args.strip_invariant_features is not None:
from mvpa2.datasets.miscfx import remove_invariant_features
ds = remove_invariant_features(ds)
# and store
ds2hdf5(ds, args.output, compression=args.hdf5_compression)
return ds
