def test_glmnet_r_sensitivities():
data = datasets['chirp_linear']
clf = GLMNET_R()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_train=False)(None)
assert_equal(sens.shape, (1, data.nfeatures))
def test_glmnet_r_sensitivities():
data = datasets['chirp_linear']
clf = GLMNET_R()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_training=False)()
assert_equal(sens.shape, (1, data.nfeatures))
def test_glmnet_state():
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = GLMNET_R()
clf.train(data)
clf.ca.enable('predictions')
p = clf.predict(data.samples)
assert_array_equal(p, clf.ca.predictions)
def test_glmnet_state():
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = GLMNET_R()
clf.train(data)
clf.ca.enable('predictions')
p = clf.predict(data.samples)
assert_array_equal(p, clf.ca.predictions)
def test_glmnet_r():
# not the perfect dataset with which to test, but
# it will do for now.
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = GLMNET_R()
clf.train(data)
# prediction has to be almost perfect
# test with a correlation
pre = clf.predict(data.samples)
corerr = corr_error(pre, data.targets)
if cfg.getboolean('tests', 'labile', default='yes'):
assert_true(corerr < .2)
def test_glmnet_r():
# not the perfect dataset with which to test, but
# it will do for now.
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = GLMNET_R()
clf.train(data)
# prediction has to be almost perfect
# test with a correlation
pre = clf.predict(data.samples)
corerr = CorrErrorFx()(pre, data.targets)
if cfg.getboolean('tests', 'labile', default='yes'):
assert_true(corerr < .2)
# 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")
clfswh += \
