Source Code for Module mvpa.clfs.sg.sens

 1  # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- 
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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  """Provide sensitivity measures for sg's SVM.""" 
10   
11  __docformat__ = 'restructuredtext' 
12   
13  import numpy as N 
14   
15  from mvpa.base import externals 
16  if externals.exists('shogun', raiseException=True): 
17      import shogun.Classifier 
18   
19  from mvpa.misc.state import StateVariable 
20  from mvpa.measures.base import Sensitivity 
21   
22  if __debug__: 
23      from mvpa.base import debug 
24   
25   
26 -class LinearSVMWeights(Sensitivity): 
27      """`Sensitivity` that reports the weights of a linear SVM trained 
28      on a given `Dataset`. 
29      """ 
30   
31      biases = StateVariable(enabled=True, 
32                             doc="Offsets of separating hyperplanes") 
33   
34 -    def __init__(self, clf, **kwargs): 
35          """Initialize the analyzer with the classifier it shall use. 
36   
37          :Parameters: 
38            clf: LinearSVM 
39              classifier to use. Only classifiers sub-classed from 
40              `LinearSVM` may be used. 
41          """ 
42          # init base classes first 
43          Sensitivity.__init__(self, clf, **kwargs) 
44   
45   
46 -    def __sg_helper(self, svm): 
47          """Helper function to compute sensitivity for a single given SVM""" 
48          self.offsets = svm.get_bias() 
49          svcoef = N.matrix(svm.get_alphas()) 
50          svnums = svm.get_support_vectors() 
51          svs = self.clf.traindataset.samples[svnums,:] 
52          res = (svcoef * svs).mean(axis=0).A1 
53          return res 
54   
55   
56 -    def _call(self, dataset): 
57          # XXX Hm... it might make sense to unify access functions 
58          # naming across our swig libsvm wrapper and sg access 
59          # functions for svm 
60          svm = self.clf.svm 
61          if isinstance(svm, shogun.Classifier.MultiClassSVM): 
62              sens = [] 
63              for i in xrange(svm.get_num_svms()): 
64                  sens.append(self.__sg_helper(svm.get_svm(i))) 
65          else: 
66              sens = self.__sg_helper(svm) 
67          return N.asarray(sens) 
68