def test_sensitivity_based_feature_selection(self, clf):
# sensitivity analyser and transfer error quantifier use the SAME clf!
sens_ana = clf.get_sensitivity_analyzer(postproc=maxofabs_sample())
# of features to remove
Nremove = 2
# because the clf is already trained when computing the sensitivity
# map, prevent retraining for transfer error calculation
# Use absolute of the svm weights as sensitivity
fe = SensitivityBasedFeatureSelection(
sens_ana,
feature_selector=FixedNElementTailSelector(2),
enable_ca=["sensitivity", "selected_ids"])
data = self.get_data()
data_nfeatures = data.nfeatures
fe.train(data)
resds = fe(data)
# fail if orig datasets are changed
self.assertTrue(data.nfeatures == data_nfeatures)
# silly check if nfeatures got a single one removed
self.assertEqual(data.nfeatures,
resds.nfeatures + Nremove,
msg="We had to remove just a single feature")
self.assertEqual(
fe.ca.sensitivity.nfeatures,
data_nfeatures,
msg="Sensitivity have to have # of features equal to original")
def test_feature_selection_pipeline(self):
sens_ana = SillySensitivityAnalyzer()
data = self.get_data()
data_nfeatures = data.nfeatures
# test silly one first ;-)
self.assertEqual(
sens_ana(data).samples[0, 0], -int(data_nfeatures / 2))
# OLD: first remove 25% == 6, and then 4, total removing 10
# NOW: test should be independent of the numerical number of features
feature_selections = [
SensitivityBasedFeatureSelection(sens_ana,
FractionTailSelector(0.25)),
SensitivityBasedFeatureSelection(sens_ana,
FixedNElementTailSelector(4))
]
# create a FeatureSelection pipeline
feat_sel_pipeline = ChainMapper(feature_selections)
feat_sel_pipeline.train(data)
resds = feat_sel_pipeline(data)
self.assertEqual(len(feat_sel_pipeline),
len(feature_selections),
msg="Test the property feature_selections")
desired_nfeatures = int(np.ceil(data_nfeatures * 0.75))
self.assertEqual([fe._oshape[0] for fe in feat_sel_pipeline],
[desired_nfeatures, desired_nfeatures - 4])
def test_sensitivity_based_feature_selection(self, clf):
# sensitivity analyser and transfer error quantifier use the SAME clf!
sens_ana = clf.get_sensitivity_analyzer(postproc=maxofabs_sample())
# of features to remove
Nremove = 2
# because the clf is already trained when computing the sensitivity
# map, prevent retraining for transfer error calculation
# Use absolute of the svm weights as sensitivity
fe = SensitivityBasedFeatureSelection(sens_ana,
feature_selector=FixedNElementTailSelector(2),
enable_ca=["sensitivity", "selected_ids"])
data = self.get_data()
data_nfeatures = data.nfeatures
fe.train(data)
resds = fe(data)
# fail if orig datasets are changed
self.assertTrue(data.nfeatures == data_nfeatures)
# silly check if nfeatures got a single one removed
self.assertEqual(data.nfeatures, resds.nfeatures+Nremove,
msg="We had to remove just a single feature")
self.assertEqual(fe.ca.sensitivity.nfeatures, data_nfeatures,
msg="Sensitivity have to have # of features equal to original")
def test_feature_selection_classifier(self):
from mvpa2.featsel.base import \
SensitivityBasedFeatureSelection
from mvpa2.featsel.helpers import \
FixedNElementTailSelector
# should give lowest weight to the feature with lowest index
sens_ana = SillySensitivityAnalyzer()
# should give lowest weight to the feature with highest index
sens_ana_rev = SillySensitivityAnalyzer(mult=-1)
# corresponding feature selections
feat_sel = SensitivityBasedFeatureSelection(
sens_ana, FixedNElementTailSelector(1, mode='discard'))
feat_sel_rev = SensitivityBasedFeatureSelection(
sens_ana_rev, FixedNElementTailSelector(1))
samples = np.array([[0, 0, -1], [1, 0, 1], [-1, -1, 1], [-1, 0, 1],
[1, -1, 1]])
testdata3 = dataset_wizard(samples=samples, targets=1)
# dummy train data so proper mapper gets created
traindata = dataset_wizard(samples=np.array([[0, 0, -1], [1, 0, 1]]),
targets=[1, 2])
# targets
res110 = [1, 1, 1, -1, -1]
res011 = [-1, 1, -1, 1, -1]
# first classifier -- 0th feature should be discarded
clf011 = FeatureSelectionClassifier(self.clf_sign,
feat_sel,
enable_ca=['feature_ids'])
self.clf_sign.ca.change_temporarily(enable_ca=['estimates'])
clf011.train(traindata)
self.assertEqual(clf011.predict(testdata3.samples), res011)
# just silly test if we get values assigned in the 'ProxyClassifier'
self.assertTrue(len(clf011.ca.estimates) == len(res110),
msg="We need to pass values into ProxyClassifier")
self.clf_sign.ca.reset_changed_temporarily()
self.assertEqual(clf011.mapper._oshape, (2, ))
"Feature selection classifier had to be trained on 2 features"
# first classifier -- last feature should be discarded
clf011 = FeatureSelectionClassifier(self.clf_sign, feat_sel_rev)
clf011.train(traindata)
self.assertEqual(clf011.predict(testdata3.samples), res110)
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_feature_selection_classifier_with_regression(self):
from mvpa2.featsel.base import \
SensitivityBasedFeatureSelection
from mvpa2.featsel.helpers import \
FixedNElementTailSelector
if sample_clf_reg is None:
# none regression was found, so nothing to test
return
# should give lowest weight to the feature with lowest index
sens_ana = SillySensitivityAnalyzer()
# corresponding feature selections
feat_sel = SensitivityBasedFeatureSelection(
sens_ana, FixedNElementTailSelector(1, mode='discard'))
# now test with regression-based classifier. The problem is
# that it is determining predictions twice from values and
# then setting the values from the results, which the second
# time is set to predictions. The final outcome is that the
# values are actually predictions...
dat = dataset_wizard(samples=np.random.randn(4, 10),
targets=[-1, -1, 1, 1])
clf_reg = FeatureSelectionClassifier(sample_clf_reg, feat_sel)
clf_reg.train(dat)
_ = clf_reg.predict(dat.samples)
self.failIf(
(np.array(clf_reg.ca.estimates) -
clf_reg.ca.predictions).sum() == 0,
msg="Values were set to the predictions in %s." % sample_clf_reg)
def test_custom_combined_selectors(self):
"""Test combination of the selectors in a single function
"""
def custom_tail_selector(seq):
seq1 = FractionTailSelector(0.01, mode='discard',
tail='upper')(seq)
seq2 = FractionTailSelector(0.05, mode='select', tail='upper')(seq)
return list(set(seq1).intersection(seq2))
seq = np.arange(100)
seq_ = custom_tail_selector(seq)
assert_array_equal(sorted(seq_), [95, 96, 97, 98])
# verify that this function could be used in place of the selector
fs = SensitivityBasedFeatureSelection(OneWayAnova(),
custom_tail_selector)
ds = datasets['3dsmall']
fs.train(ds) # XXX: why needs to be trained here explicitly?
ds_ = fs(ds)
assert_equal(ds_.nfeatures, int(ds.nfeatures * 0.04))
def test_custom_combined_selectors(self):
"""Test combination of the selectors in a single function
"""
def custom_tail_selector(seq):
seq1 = FractionTailSelector(0.01, mode='discard', tail='upper')(seq)
seq2 = FractionTailSelector(0.05, mode='select', tail='upper')(seq)
return list(set(seq1).intersection(seq2))
seq = np.arange(100)
seq_ = custom_tail_selector(seq)
assert_array_equal(sorted(seq_), [95, 96, 97, 98])
# verify that this function could be used in place of the selector
fs = SensitivityBasedFeatureSelection(
OneWayAnova(),
custom_tail_selector)
ds = datasets['3dsmall']
fs.train(ds) # XXX: why needs to be trained here explicitly?
ds_ = fs(ds)
assert_equal(ds_.nfeatures, int(ds.nfeatures * 0.04))
def test_union_feature_selection(self):
# two methods: 5% highes F-scores, non-zero SMLR weights
fss = [SensitivityBasedFeatureSelection(
OneWayAnova(),
FractionTailSelector(0.05, mode='select', tail='upper')),
SensitivityBasedFeatureSelection(
SMLRWeights(SMLR(lm=1, implementation="C"),
postproc=sumofabs_sample()),
RangeElementSelector(mode='select'))]
fs = CombinedFeatureSelection(fss, method='union')
od_union = fs(self.dataset)
self.assertTrue(fs.method == 'union')
# check output dataset
self.assertTrue(od_union.nfeatures <= self.dataset.nfeatures)
# again for intersection
fs = CombinedFeatureSelection(fss, method='intersection')
od_intersect = fs(self.dataset)
assert_true(od_intersect.nfeatures < od_union.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 setup_classifier(**kwargs):
'''
Thinked!
'''
for arg in kwargs:
if arg == 'clf_type':
clf_type = kwargs[arg]
if arg == 'fsel':
f_sel = kwargs[arg]
if arg == 'cv_type':
cv_approach = kwargs[arg]
if arg == 'cv_folds':
if np.int(kwargs[arg]) == 0:
cv_type = np.float(kwargs[arg])
else:
cv_type = np.int(kwargs[arg])
if arg == 'permutations':
permutations = np.int(kwargs[arg])
if arg == 'cv_attribute':
attribute = kwargs[arg]
cv_n = cv_type
################# Classifier #######################
if clf_type == 'SVM':
clf = LinearCSVMC(C=1, probability=1, enable_ca=['probabilities'])
elif clf_type == 'GNB':
clf = GNB()
elif clf_type == 'LDA':
clf = LDA()
elif clf_type == 'QDA':
clf = QDA()
elif clf_type == 'SMLR':
clf = SMLR()
elif clf_type == 'RbfSVM':
sk_clf = SVC(gamma=0.1, C=1)
clf = SKLLearnerAdapter(sk_clf, enable_ca=['probabilities'])
elif clf_type == 'GP':
clf = GPR()
else:
clf = LinearCSVMC(C=1, probability=1, enable_ca=['probabilities'])
############## Feature Selection #########################
if f_sel == 'True':
logger.info('Feature Selection selected.')
fsel = SensitivityBasedFeatureSelection(
OneWayAnova(),
FractionTailSelector(0.05, mode='select', tail='upper'))
fclf = FeatureSelectionClassifier(clf, fsel)
elif f_sel == 'Fixed':
logger.info('Fixed Feature Selection selected.')
fsel = SensitivityBasedFeatureSelection(
OneWayAnova(),
FixedNElementTailSelector(100, mode='select', tail='upper'))
fclf = FeatureSelectionClassifier(clf, fsel)
elif f_sel == 'PCA':
from mvpa2.mappers.skl_adaptor import SKLTransformer
from sklearn.decomposition import PCA
logger.info('Fixed Feature Selection selected.')
fsel = SKLTransformer(PCA(n_components=45))
fclf = FeatureSelectionClassifier(clf, fsel)
else:
fclf = clf
######################### Permutations #############################
if permutations != 0:
if __debug__:
debug.active += ["STATMC"]
repeater = Repeater(count=permutations)
permutator = AttributePermutator('targets',
limit={'partitions': 1},
count=1)
partitioner = NFoldPartitioner(cvtype=cv_n, attr=attribute)
null_cv = CrossValidation(clf,
ChainNode([partitioner, permutator],
space=partitioner.get_space()),
errorfx=mean_mismatch_error)
distr_est = MCNullDist(repeater,
tail='left',
measure=null_cv,
enable_ca=['dist_samples'])
#postproc = mean_sample()
else:
distr_est = None
#postproc = None
########################################################
if cv_approach == 'n_fold':
if cv_type != 0:
splitter_used = NFoldPartitioner(cvtype=cv_type, attr=attribute)
else:
splitter_used = NFoldPartitioner(cvtype=1, attr=attribute)
else:
splitter_used = HalfPartitioner(attr=attribute)
chain_splitter = ChainNode([
splitter_used,
Balancer(
attr='targets', count=1, limit='partitions', apply_selection=True)
],
space='partitions')
#############################################################
if distr_est == None:
cvte = CrossValidation(fclf,
chain_splitter,
enable_ca=['stats', 'repetition_results'])
else:
cvte = CrossValidation(fclf,
chain_splitter,
errorfx=mean_mismatch_error,
null_dist=distr_est,
enable_ca=['stats', 'repetition_results'])
logger.info('Classifier set...')
return [fclf, cvte]
tags=_lars_tags,
descr='skl.LassoLarsIC()')
regrswh += [_lasso_lars_ic]
clfswh += [
RegressionAsClassifier(_lasso_lars_ic, descr='skl.LassoLarsIC_C()')
]
# kNN
clfswh += kNN(k=5, descr="kNN(k=5)")
clfswh += kNN(k=5, voting='majority', descr="kNN(k=5, voting='majority')")
clfswh += \
FeatureSelectionClassifier(
kNN(),
SensitivityBasedFeatureSelection(
SMLRWeights(SMLR(lm=1.0, implementation="C"),
postproc=maxofabs_sample()),
RangeElementSelector(mode='select')),
descr="kNN on SMLR(lm=1) non-0")
clfswh += \
FeatureSelectionClassifier(
kNN(),
SensitivityBasedFeatureSelection(
OneWayAnova(),
FractionTailSelector(0.05, mode='select', tail='upper')),
descr="kNN on 5%(ANOVA)")
clfswh += \
FeatureSelectionClassifier(
kNN(),
SensitivityBasedFeatureSelection(
data = np.concatenate(data)
labels = np.concatenate(labels)
return data, labels.astype(np.int)
rois = ['aSTG', 'HG', 'pSTG']
for sub_id in range(1, 21):
data = []
for roi in rois:
data_path = os.path.join(data_dir, roi)
tmp_data, label = load_data(data_path, sub_id)
data.append(tmp_data)
data = np.concatenate(data, axis=1)
data = np.concatenate([data[i,:,:].T for i in range(len(data))])
ds = Dataset(data)
ds.sa['time_coords'] = np.linspace(0, len(ds)-1, len(ds))
events = [{'onset': i*5, 'duration': 5, 'targets':label[i], 'chunks':i+1} for i in range(int(len(ds)/5))]
hrf_estimates = fit_event_hrf_model(ds, events, time_attr='time_coords', condition_attr=('targets', 'chunks'),
design_kwargs=dict(drift_model='blank'), glmfit_kwargs=dict(model='ols'),
return_model=True)
fsel = SensitivityBasedFeatureSelection(OneWayAnova(), FixedNElementTailSelector(5000, mode='select', tail='upper'))
fsel.train(hrf_estimates)
ds_p = fsel(hrf_estimates)
np.save('feat_sub{:03d}'.format(sub_id), ds_p.samples)
