Merge pull request #273 from tBuLi/fix_271

Fixed #271 by lazily creating Hessian and Jacobian models

symfit/core/fit.py

@@ -516,14 +516,14 @@ def _init_minimizer(self, minimizer, **minimizer_options):
             # that, otherwise we let the minimizer estimate it itself.
             # Hence the check of jacobian_model, as this is the
             # py function version of the analytical jacobian.
-            if hasattr(self.model, 'jacobian_model') and hasattr(self.objective, 'eval_jacobian'):
+            if hasattr(self.model, 'eval_jacobian') and hasattr(self.objective, 'eval_jacobian'):
                 minimizer_options['jacobian'] = self.objective.eval_jacobian
         if issubclass(minimizer, HessianMinimizer):
             # If an analytical version of the Hessian exists we should use
             # that, otherwise we let the minimizer estimate it itself.
             # Hence the check of hessian_model, as this is the
             # py function version of the analytical hessian.
-            if hasattr(self.model, 'hessian_model') and hasattr(self.objective, 'eval_hessian'):
+            if hasattr(self.model, 'eval_hessian') and hasattr(self.objective, 'eval_hessian'):
                 minimizer_options['hessian'] = self.objective.eval_hessian
 
         if issubclass(minimizer, ConstrainedMinimizer):

symfit/core/fit_results.py

@@ -112,7 +112,7 @@ def stdev(self, param):
         """
         try:
             return np.sqrt(self.variance(param))
-        except AttributeError:
+        except (AttributeError, TypeError):
             # This happens when variance returns None.
             return None
 

symfit/core/models.py

@@ -593,13 +593,14 @@ def numerical_components(self):
              '`numerical_components`.').format(self.__class__)
         )
 
-    def _get_params(self):
+    @property
+    def params(self):
         return self._params
 
-    def _set_params(self, value):
+    @params.setter
+    def params(self, value):
         self._params = value
         self.__signature__ = self._make_signature()
-    params = property(_get_params, _set_params)  # Properties cannot use `super`
 
     def _make_signature(self):
         # Handle args and kwargs according to the allowed names.
@@ -789,14 +790,12 @@ class GradientModel(CallableModel, BaseGradientModel):
     """
     def __init__(self, *args, **kwargs):
         super(GradientModel, self).__init__(*args, **kwargs)
-        self.jacobian_model = jacobian_from_model(self)
 
-    def _set_params(self, value):
-        super(GradientModel, self)._set_params(value)
-        if hasattr(self, 'jacobian_model'):
-            self.jacobian_model.params = value
-    # Properties cannot use `super` unless when used in this way
-    params = property(CallableModel._get_params, _set_params)
+    @cached_property
+    def jacobian_model(self):
+        jac_model = jacobian_from_model(self)
+        jac_model.params = self.params
+        return jac_model
 
     @cached_property
     def jacobian(self):
@@ -843,14 +842,12 @@ class HessianModel(GradientModel):
     """
     def __init__(self, *args, **kwargs):
         super(HessianModel, self).__init__(*args, **kwargs)
-        self.hessian_model = hessian_from_model(self)
-
-    def _set_params(self, value):
-        super(HessianModel, self)._set_params(value)
-        if hasattr(self, 'hessian_model'):
-            self.hessian_model.params = value
-    # Properties cannot use `super` unless when used in this way
-    params = property(GradientModel._get_params, _set_params)
+
+    @cached_property
+    def hessian_model(self):
+        hess_model = hessian_from_model(self)
+        hess_model.params = self.params
+        return hess_model
 
     @property
     def hessian(self):