def testENETSensitivities(self):
data = normalFeatureDataset(perlabel=10, nlabels=2, nfeatures=4)
# use ENET on binary problem
clf = ENET()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.getSensitivityAnalyzer(force_training=False)()
self.failUnless(sens.shape == (data.nfeatures,))
def test_enet_sensitivities(self):
data = datasets['chirp_linear']
# use ENET on binary problem
clf = ENET()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_train=False)()
self.failUnless(sens.shape == (data.nfeatures,))
def test_enet_sensitivities(self):
data = datasets['chirp_linear']
# use ENET on binary problem
clf = ENET()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_training=False)()
self.failUnless(sens.shape == (data.nfeatures,))
def test_enet_state(self):
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = ENET()
clf.train(data)
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.failUnless((p == clf.ca.predictions).all())
def test_enet_state(self):
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = ENET()
clf.train(data)
clf.ca.enable('predictions')
p = clf.predict(data.samples)
self.failUnless((p == clf.ca.predictions).all())
def test_enet(self):
# 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 = ENET()
clf.train(data)
# prediction has to be almost perfect
# test with a correlation
pre = clf.predict(data.samples)
cor = pearsonr(pre, data.targets)
if cfg.getboolean('tests', 'labile', default='yes'):
self.failUnless(cor[0] > .8)
def test_enet(self):
# 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 = ENET()
clf.train(data)
# prediction has to be almost perfect
# test with a correlation
pre = clf.predict(data.samples)
cor = pearsonr(pre, data.targets)
if cfg.getboolean('tests', 'labile', default='yes'):
self.failUnless(cor[0] > .8)