예제 #1
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    def test_lars_sensitivities(self):
        data = datasets['chirp_linear']

        # use LARS on binary problem
        clf = LARS()
        clf.train(data)

        # now ask for the sensitivities WITHOUT having to pass the dataset
        # again
        sens = clf.get_sensitivity_analyzer(force_train=False)(None)

        self.failUnless(sens.shape == (1, data.nfeatures))
예제 #2
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    def test_lars_sensitivities(self):
        data = datasets['chirp_linear']

        # use LARS on binary problem
        clf = LARS()
        clf.train(data)

        # now ask for the sensitivities WITHOUT having to pass the dataset
        # again
        sens = clf.get_sensitivity_analyzer(force_train=False)(None)

        self.assertTrue(sens.shape == (1, data.nfeatures))
예제 #3
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    def test_lars_state(self):
        #data = datasets['dumb2']
        # for some reason the R code fails with the dumb data
        data = datasets['chirp_linear']

        clf = LARS()

        clf.train(data)

        clf.ca.enable('predictions')

        p = clf.predict(data.samples)

        self.assertTrue((p == clf.ca.predictions).all())
예제 #4
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    def test_lars_state(self):
        #data = datasets['dumb2']
        # for some reason the R code fails with the dumb data
        data = datasets['chirp_linear']


        clf = LARS()

        clf.train(data)

        clf.ca.enable('predictions')

        p = clf.predict(data.samples)

        self.failUnless((p == clf.ca.predictions).all())
예제 #5
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    def test_lars(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 = LARS()

        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.assertTrue(cor[0] > .8)
예제 #6
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    def test_lars(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 = LARS()

        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)
예제 #7
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            #sg.SVM(svm_impl=impl, kernel_type='RBF',
            #       descr='sg.RBFSVMR()/%s' % impl),
        ]

if len(clfswh['svm', 'linear']) > 0:
    # if any SVM implementation is known, import default ones
    from mvpa2.clfs.svm import *

# lars from R via RPy
if externals.exists('lars'):
    import mvpa2.clfs.lars as lars
    from mvpa2.clfs.lars import LARS
    for model in lars.known_models:
        # XXX create proper repository of classifiers!
        lars_clf = RegressionAsClassifier(
            LARS(descr="LARS(%s)" % model, model_type=model),
            descr='LARS(model_type=%r) classifier' % model)
        clfswh += lars_clf

        # is a regression, too
        lars_regr = LARS(descr="_LARS(%s)" % model, model_type=model)
        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())")