def test_glmnet_c_sensitivities():
data = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)
# use GLMNET on binary problem
clf = GLMNET_C()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_train=False)(None)
#failUnless(sens.shape == (data.nfeatures,))
assert_equal(sens.shape, (len(data.UT), data.nfeatures))
def test_glmnet_c_sensitivities():
data = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)
# use GLMNET on binary problem
clf = GLMNET_C()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_training=False)()
#failUnless(sens.shape == (data.nfeatures,))
assert_equal(sens.shape, (len(data.UT), data.nfeatures))
def test_glmnet_c():
# define binary prob
data = datasets['dumb2']
# use GLMNET on binary problem
clf = GLMNET_C()
clf.ca.enable('estimates')
clf.train(data)
# test predictions
pre = clf.predict(data.samples)
assert_array_equal(pre, data.targets)
def test_glmnet_c():
# define binary prob
data = datasets['dumb2']
# use GLMNET on binary problem
clf = GLMNET_C()
clf.ca.enable('estimates')
clf.train(data)
# test predictions
pre = clf.predict(data.samples)
assert_array_equal(pre, data.targets)
regrswh += lars_regr
# clfswh += MulticlassClassifier(lars,
# descr='Multiclass %s' % lars.descr)
## Still fails unittests battery although overhauled otherwise.
## # enet from R via RPy2
## if externals.exists('elasticnet'):
## from mvpa.clfs.enet import ENET
## clfswh += RegressionAsClassifier(ENET(),
## descr="RegressionAsClassifier(ENET())")
## regrswh += ENET(descr="ENET()")
# glmnet from R via RPy
if externals.exists('glmnet'):
from mvpa.clfs.glmnet import GLMNET_C, GLMNET_R
clfswh += GLMNET_C(descr="GLMNET_C()")
regrswh += GLMNET_R(descr="GLMNET_R()")
# 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")
