Exemple #1
0
    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)(None)

        self.assertTrue(sens.shape == (data.nfeatures, ))
Exemple #2
0
    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)(None)

        self.assertTrue(sens.shape == (data.nfeatures,))
Exemple #3
0
    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.assertTrue((p == clf.ca.predictions).all())
Exemple #4
0
    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.assertTrue((p == clf.ca.predictions).all())
Exemple #5
0
    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.assertTrue(cor[0] > .8)
Exemple #6
0
    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.assertTrue(cor[0] > .8)