Source Code for Module mvpa.tests.test_params

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  5  #   See COPYING file distributed along with the PyMVPA package for the 
  6  #   copyright and license terms. 
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  8  ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## 
  9  """Unit tests for PyMVPA Parameter class.""" 
 10   
 11  import unittest, copy 
 12   
 13  import numpy as N 
 14   
 15  from mvpa.datasets import Dataset 
 16  from mvpa.misc.state import ClassWithCollections, StateVariable 
 17  from mvpa.misc.param import Parameter, KernelParameter 
 18   
 19  from tests_warehouse_clfs import SameSignClassifier 
 20   
 21 -class ParametrizedClassifier(SameSignClassifier): 
 22      p1 = Parameter(1.0) 
 23      kp1 = KernelParameter(100.0) 
 24   
 25 -class ParametrizedClassifierExtended(ParametrizedClassifier): 
 26 -    def __init__(self): 
 27          ParametrizedClassifier.__init__(self) 
 28          self.kernel_params.add(KernelParameter(200.0, doc="Very useful param", name="kp2")) 
 29   
 30 -class BlankClass(ClassWithCollections): 
 31      pass 
 32   
 33 -class SimpleClass(ClassWithCollections): 
 34      C = Parameter(1.0, min=0, doc="C parameter") 
 35   
 36 -class MixedClass(ClassWithCollections): 
 37      C = Parameter(1.0, min=0, doc="C parameter") 
 38      D = Parameter(3.0, min=0, doc="D parameter") 
 39      state1 = StateVariable(doc="bogus") 
 40   
 41 -class ParamsTests(unittest.TestCase): 
 42   
 43 -    def testBlank(self): 
 44          blank  = BlankClass() 
 45   
 46          self.failUnlessRaises(AttributeError, blank.__getattribute__, 'states') 
 47          self.failUnlessRaises(IndexError, blank.__getattribute__, '') 
 48   
 49 -    def testSimple(self): 
 50          simple  = SimpleClass() 
 51   
 52          self.failUnlessEqual(len(simple.params.items), 1) 
 53          self.failUnlessRaises(AttributeError, simple.__getattribute__, 'dummy') 
 54          self.failUnlessRaises(IndexError, simple.__getattribute__, '') 
 55   
 56          self.failUnlessEqual(simple.C, 1.0) 
 57          self.failUnlessEqual(simple.params.isSet("C"), False) 
 58          self.failUnlessEqual(simple.params.isSet(), False) 
 59          self.failUnlessEqual(simple.params["C"].isDefault, True) 
 60          self.failUnlessEqual(simple.params["C"].equalDefault, True) 
 61   
 62          simple.C = 1.0 
 63          # we are not actually setting the value if == default 
 64          self.failUnlessEqual(simple.params["C"].isDefault, True) 
 65          self.failUnlessEqual(simple.params["C"].equalDefault, True) 
 66   
 67          simple.C = 10.0 
 68          self.failUnlessEqual(simple.params.isSet("C"), True) 
 69          self.failUnlessEqual(simple.params.isSet(), True) 
 70          self.failUnlessEqual(simple.params["C"].isDefault, False) 
 71          self.failUnlessEqual(simple.params["C"].equalDefault, False) 
 72   
 73          self.failUnlessEqual(simple.C, 10.0) 
 74          simple.params["C"].resetvalue() 
 75          self.failUnlessEqual(simple.params.isSet("C"), True) 
 76          # TODO: Test if we 'train' a classifier f we get isSet to false 
 77          self.failUnlessEqual(simple.C, 1.0) 
 78          self.failUnlessRaises(AttributeError, simple.params.__getattribute__, 'B') 
 79   
 80 -    def testMixed(self): 
 81          mixed  = MixedClass() 
 82   
 83          self.failUnlessEqual(len(mixed.params.items), 2) 
 84          self.failUnlessEqual(len(mixed.states.items), 1) 
 85          self.failUnlessRaises(AttributeError, mixed.__getattribute__, 'kernel_params') 
 86   
 87          self.failUnlessEqual(mixed.C, 1.0) 
 88          self.failUnlessEqual(mixed.params.isSet("C"), False) 
 89          self.failUnlessEqual(mixed.params.isSet(), False) 
 90          mixed.C = 10.0 
 91          self.failUnlessEqual(mixed.params.isSet("C"), True) 
 92          self.failUnlessEqual(mixed.params.isSet("D"), False) 
 93          self.failUnlessEqual(mixed.params.isSet(), True) 
 94          self.failUnlessEqual(mixed.D, 3.0) 
 95   
 96   
 97 -    def testClassifier(self): 
 98          clf  = ParametrizedClassifier() 
 99          self.failUnlessEqual(len(clf.params.items), 3) # + regression/retrainable 
100          self.failUnlessEqual(len(clf.kernel_params.items), 1) 
101   
102          clfe  = ParametrizedClassifierExtended() 
103          self.failUnlessEqual(len(clfe.params.items), 3) 
104          self.failUnlessEqual(len(clfe.kernel_params.items), 2) 
105          self.failUnlessEqual(len(clfe.kernel_params.listing), 2) 
106   
107          # check assignment once again 
108          self.failUnlessEqual(clfe.kp2, 200.0) 
109          clfe.kp2 = 201.0 
110          self.failUnlessEqual(clfe.kp2, 201.0) 
111          self.failUnlessEqual(clfe.kernel_params.isSet("kp2"), True) 
112          clfe.train(Dataset(samples=[[0,0]], labels=[1], chunks=[1])) 
113          self.failUnlessEqual(clfe.kernel_params.isSet("kp2"), False) 
114          self.failUnlessEqual(clfe.kernel_params.isSet(), False) 
115          self.failUnlessEqual(clfe.params.isSet(), False) 
116   
117 -def suite(): 
118      return unittest.makeSuite(ParamsTests) 
119   
120   
121  if __name__ == '__main__': 
122      import runner 
123