Exemplo n.º 1
0
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))
Exemplo n.º 2
0
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))
Exemplo n.º 3
0
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)
Exemplo n.º 4
0
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)
Exemplo n.º 5
0
        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 mvpa2.clfs.enet import ENET
##     clfswh += RegressionAsClassifier(ENET(),
##                                      descr="RegressionAsClassifier(ENET())")
##     regrswh += ENET(descr="ENET()")

# glmnet from R via RPy
if externals.exists('glmnet'):
    from mvpa2.clfs.glmnet import GLMNET_C, GLMNET_R
    clfswh += GLMNET_C(descr="GLMNET_C()")
    regrswh += GLMNET_R(descr="GLMNET_R()")

# LDA/QDA
clfswh += LDA(descr='LDA()')
clfswh += QDA(descr='QDA()')

if externals.exists('skl'):
    _skl_version = externals.versions['skl']
    _skl_api09 = _skl_version >= '0.9'

    def _skl_import(submod, class_):
        if _skl_api09:
            submod_ = __import__('sklearn.%s' % submod, fromlist=[submod])
        else:
            submod_ = __import__('scikits.learn.%s' % submod,