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)
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")