def test_mapped_classifier_sensitivity_analyzer(self, clf):
"""Test sensitivity of the mapped classifier
"""
# Assuming many defaults it is as simple as
mclf = FeatureSelectionClassifier(
clf,
SensitivityBasedFeatureSelection(
OneWayAnova(),
FractionTailSelector(0.5, mode='select', tail='upper')),
enable_ca=['training_stats'])
sana = mclf.get_sensitivity_analyzer(postproc=sumofabs_sample(),
enable_ca=["sensitivities"])
# and lets look at all sensitivities
dataset = datasets['uni2small']
# and we get sensitivity analyzer which works on splits
sens = sana(dataset)
self.assertEqual(sens.shape, (1, dataset.nfeatures))
def test_mapped_classifier_sensitivity_analyzer(self, clf):
"""Test sensitivity of the mapped classifier
"""
# Assuming many defaults it is as simple as
mclf = FeatureSelectionClassifier(
clf,
SensitivityBasedFeatureSelection(
OneWayAnova(),
FractionTailSelector(0.5, mode='select', tail='upper')),
enable_ca=['training_stats'])
sana = mclf.get_sensitivity_analyzer(postproc=sumofabs_sample(),
enable_ca=["sensitivities"])
# and lets look at all sensitivities
dataset = datasets['uni2small']
# and we get sensitivity analyzer which works on splits
sens = sana(dataset)
self.assertEqual(sens.shape, (1, dataset.nfeatures))
def test_rfe_sensmap():
# http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/2013q3/002538.html
# just a smoke test. fails with
from mvpa2.clfs.svm import LinearCSVMC
from mvpa2.clfs.meta import FeatureSelectionClassifier
from mvpa2.measures.base import CrossValidation, RepeatedMeasure
from mvpa2.generators.splitters import Splitter
from mvpa2.generators.partition import NFoldPartitioner
from mvpa2.misc.errorfx import mean_mismatch_error
from mvpa2.mappers.fx import mean_sample
from mvpa2.mappers.fx import maxofabs_sample
from mvpa2.generators.base import Repeater
from mvpa2.featsel.rfe import RFE
from mvpa2.featsel.helpers import FractionTailSelector, BestDetector
from mvpa2.featsel.helpers import NBackHistoryStopCrit
from mvpa2.datasets import vstack
from mvpa2.misc.data_generators import normal_feature_dataset
# Let's simulate the beast -- 6 categories total groupped into 3
# super-ordinate, and actually without any 'superordinate' effect
# since subordinate categories independent
fds = normal_feature_dataset(nlabels=3,
snr=1, # 100, # pure signal! ;)
perlabel=9,
nfeatures=6,
nonbogus_features=range(3),
nchunks=3)
clfsvm = LinearCSVMC()
rfesvm = RFE(clfsvm.get_sensitivity_analyzer(postproc=maxofabs_sample()),
CrossValidation(
clfsvm,
NFoldPartitioner(),
errorfx=mean_mismatch_error, postproc=mean_sample()),
Repeater(2),
fselector=FractionTailSelector(0.70, mode='select', tail='upper'),
stopping_criterion=NBackHistoryStopCrit(BestDetector(), 10),
update_sensitivity=True)
fclfsvm = FeatureSelectionClassifier(clfsvm, rfesvm)
sensanasvm = fclfsvm.get_sensitivity_analyzer(postproc=maxofabs_sample())
# manually repeating/splitting so we do both RFE sensitivity and classification
senses, errors = [], []
for i, pset in enumerate(NFoldPartitioner().generate(fds)):
# split partitioned dataset
split = [d for d in Splitter('partitions').generate(pset)]
senses.append(sensanasvm(split[0])) # and it also should train the classifier so we would ask it about error
errors.append(mean_mismatch_error(fclfsvm.predict(split[1]), split[1].targets))
senses = vstack(senses)
errors = vstack(errors)
# Let's compare against rerunning the beast simply for classification with CV
errors_cv = CrossValidation(fclfsvm, NFoldPartitioner(), errorfx=mean_mismatch_error)(fds)
# and they should match
assert_array_equal(errors, errors_cv)
# buggy!
cv_sensana_svm = RepeatedMeasure(sensanasvm, NFoldPartitioner())
senses_rm = cv_sensana_svm(fds)
#print senses.samples, senses_rm.samples
#print errors, errors_cv.samples
assert_raises(AssertionError,
assert_array_almost_equal,
senses.samples, senses_rm.samples)
raise SkipTest("Known failure for repeated measures: https://github.com/PyMVPA/PyMVPA/issues/117")
