def test_feature_selection_classifier(self):
from mvpa.featsel.base import \
SensitivityBasedFeatureSelection
from mvpa.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.failUnlessEqual(clf011.predict(testdata3.samples), res011)
# just silly test if we get values assigned in the 'ProxyClassifier'
self.failUnless(len(clf011.ca.estimates) == len(res110),
msg="We need to pass values into ProxyClassifier")
self.clf_sign.ca.reset_changed_temporarily()
self.failUnlessEqual(len(clf011.ca.feature_ids), 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.failUnlessEqual(clf011.predict(testdata3.samples), res110)
def test_feature_selection_classifier(self):
from mvpa.featsel.base import \
SensitivityBasedFeatureSelection
from mvpa.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.failUnlessEqual(clf011.predict(testdata3.samples), res011)
# just silly test if we get values assigned in the 'ProxyClassifier'
self.failUnless(len(clf011.ca.estimates) == len(res110),
msg="We need to pass values into ProxyClassifier")
self.clf_sign.ca.reset_changed_temporarily()
self.failUnlessEqual(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.failUnlessEqual(clf011.predict(testdata3.samples), res110)
def test_feature_selection_classifier_with_regression(self):
from mvpa.featsel.base import \
SensitivityBasedFeatureSelection
from mvpa.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_feature_selection_classifier_with_regression(self):
from mvpa.featsel.base import \
SensitivityBasedFeatureSelection
from mvpa.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)
