Source Code for Module mvpa.tests.test_enet

 1  # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- 
 2  # vi: set ft=python sts=4 ts=4 sw=4 et: 
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 4  # 
 5  #   See COPYING file distributed along with the PyMVPA package for the 
 6  #   copyright and license terms. 
 7  # 
 8  ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## 
 9  """Unit tests for PyMVPA least angle regression (ENET) classifier""" 
10   
11  from mvpa import cfg 
12  from mvpa.clfs.enet import ENET 
13  from scipy.stats import pearsonr 
14  from tests_warehouse import * 
15  from mvpa.misc.data_generators import normalFeatureDataset 
16   
17 -class ENETTests(unittest.TestCase): 
18   
19 -    def testENET(self): 
20          # not the perfect dataset with which to test, but 
21          # it will do for now. 
22          #data = datasets['dumb2'] 
23          # for some reason the R code fails with the dumb data 
24          data = datasets['chirp_linear'] 
25   
26          clf = ENET() 
27   
28          clf.train(data) 
29   
30          # prediction has to be almost perfect 
31          # test with a correlation 
32          pre = clf.predict(data.samples) 
33          cor = pearsonr(pre, data.labels) 
34          if cfg.getboolean('tests', 'labile', default='yes'): 
35              self.failUnless(cor[0] > .8) 
36   
37 -    def testENETState(self): 
38          #data = datasets['dumb2'] 
39          # for some reason the R code fails with the dumb data 
40          data = datasets['chirp_linear'] 
41   
42          clf = ENET() 
43   
44          clf.train(data) 
45   
46          clf.states.enable('predictions') 
47   
48          p = clf.predict(data.samples) 
49   
50          self.failUnless((p == clf.predictions).all()) 
51   
52   
53 -    def testENETSensitivities(self): 
54          data = normalFeatureDataset(perlabel=10, nlabels=2, nfeatures=4) 
55   
56          # use ENET on binary problem 
57          clf = ENET() 
58          clf.train(data) 
59   
60          # now ask for the sensitivities WITHOUT having to pass the dataset 
61          # again 
62          sens = clf.getSensitivityAnalyzer(force_training=False)() 
63   
64          self.failUnless(sens.shape == (data.nfeatures,)) 
65   
66   
67 -def suite(): 
68      return unittest.makeSuite(ENETTests) 
69   
70   
71  if __name__ == '__main__': 
72      import runner 
73