Source Code for Module paramz.core.gradcheckable

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30  from . import HierarchyError 
31  from .pickleable import Pickleable 
32  from .parentable import Parentable 
33   
34 -class Gradcheckable(Pickleable, Parentable): 
35      """ 
36      Adds the functionality for an object to be gradcheckable. 
37      It is just a thin wrapper of a call to the highest parent for now. 
38      TODO: Can be done better, by only changing parameters of the current parameter handle, 
39      such that object hierarchy only has to change for those. 
40      """ 
41 -    def __init__(self, *a, **kw): 
42          super(Gradcheckable, self).__init__(*a, **kw) 
43   
44 -    def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3, df_tolerance=1e-12): 
45          """ 
46          Check the gradient of this parameter with respect to the highest parent's 
47          objective function. 
48          This is a three point estimate of the gradient, wiggling at the parameters 
49          with a stepsize step. 
50          The check passes if either the ratio or the difference between numerical and 
51          analytical gradient is smaller then tolerance. 
52   
53          :param bool verbose: whether each parameter shall be checked individually. 
54          :param float step: the stepsize for the numerical three point gradient estimate. 
55          :param float tolerance: the tolerance for the gradient ratio or difference. 
56          :param float df_tolerance: the tolerance for df_tolerance 
57   
58          .. note:: 
59             The *dF_ratio* indicates the limit of accuracy of numerical gradients. 
60             If it is too small, e.g., smaller than 1e-12, the numerical gradients 
61             are usually not accurate enough for the tests (shown with blue). 
62          """ 
63          # Make sure we always call the gradcheck on the highest parent 
64          # This ensures the assumption of the highest parent to hold the fixes 
65          # In the checkgrad function we take advantage of that, so it needs 
66          # to be set in place here. 
67          if self.has_parent(): 
68              return self._highest_parent_._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance, df_tolerance=df_tolerance) 
69          return self._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance, df_tolerance=df_tolerance) 
70   
71 -    def _checkgrad(self, param, verbose=0, step=1e-6, tolerance=1e-3, df_tolerance=1e-12): 
72          """ 
73          Perform the checkgrad on the model. 
74          TODO: this can be done more efficiently, when doing it inside here 
75          """ 
76          raise HierarchyError("This parameter is not in a model with a likelihood, and, therefore, cannot be gradient checked!") 
77