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_train=False)(None)
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 = corr_error(pre, data.targets)
if cfg.getboolean('tests', 'labile', default='yes'):
assert_true(corerr < .2)
