def test_plr(self):
data = datasets['dumb2']
clf = PLR()
clf.train(data)
# prediction has to be perfect
self.failUnless((clf.predict(data.samples) == data.targets).all())
def test_plr(self):
data = datasets['dumb2']
clf = PLR()
clf.train(data)
# prediction has to be perfect
self.assertTrue((clf.predict(data.samples) == data.targets).all())
def test_plr_state(self):
data = datasets['dumb2']
clf = PLR()
clf.train(data)
clf.ca.enable('estimates')
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.failUnless((p == clf.ca.predictions).all())
self.failUnless(np.array(clf.ca.estimates).shape == np.array(p).shape)
def test_plr_state(self):
data = datasets['dumb2']
clf = PLR()
clf.train(data)
# Also get "sensitivity". Was introduced to check a bug with
# processing dataset with numeric labels
sa = clf.get_sensitivity_analyzer()
sens = sa(data)
clf.ca.enable('estimates')
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.assertTrue((p == clf.ca.predictions).all())
self.assertTrue(np.array(clf.ca.estimates).shape == np.array(p).shape)
def test_plr_state(self):
data = datasets['dumb2']
clf = PLR()
clf.train(data)
# Also get "sensitivity". Was introduced to check a bug with
# processing dataset with numeric labels
sa = clf.get_sensitivity_analyzer()
sens = sa(data)
clf.ca.enable('estimates')
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.assertTrue((p == clf.ca.predictions).all())
self.assertTrue(np.array(clf.ca.estimates).shape == np.array(p).shape)
# lets remove multiclass label from it
gprcb.__tags__.pop(gprcb.__tags__.index('multiclass'))
clfswh += gprcb
# and create a proper multiclass one
clfswh += MulticlassClassifier(RegressionAsClassifier(
GPR(kernel=GeneralizedLinearKernel())),
descr="GPRCM(kernel='linear')")
# BLR
from mvpa2.clfs.blr import BLR
clfswh += RegressionAsClassifier(BLR(descr="BLR()"), descr="BLR Classifier")
#PLR
from mvpa2.clfs.plr import PLR
clfswh += PLR(descr="PLR()")
if externals.exists('scipy'):
clfswh += PLR(reduced=0.05, descr="PLR(reduced=0.01)")
# SVM stuff
if len(clfswh['linear', 'svm']) > 0:
linearSVMC = clfswh['linear', 'svm',
cfg.get('svm', 'backend', default='libsvm').lower()][0]
# "Interesting" classifiers
clfswh += \
FeatureSelectionClassifier(
linearSVMC.clone(),
SensitivityBasedFeatureSelection(
