autopep8 pymc3/variational --select=E101,E121 --in-place --recursive

Author: ferrine

pymc3/variational/approximations.py

@@ -439,7 +439,7 @@ def _check_user_params(self, **kwargs):
             raise opvi.ParametrizationError(
                 'Passed parameters do not have a needed set of keys, '
                 'they should be equal, needed {needed}, got {givens}'.format(
-                 givens=list(sorted(givens)), needed='[0, 1, ..., %d]' % len(formula.flows)))
+                    givens=list(sorted(givens)), needed='[0, 1, ..., %d]' % len(formula.flows)))
         for i in needed:
             flow = flows.flow_for_params(params[i])
             flow_keys = set(flow.__param_spec__)
@@ -448,7 +448,7 @@ def _check_user_params(self, **kwargs):
                 raise opvi.ParametrizationError(
                     'Passed parameters for flow `{i}` ({cls}) do not have a needed set of keys, '
                     'they should be equal, needed {needed}, got {givens}'.format(
-                     givens=user_keys, needed=flow_keys, i=i, cls=flow.__name__))
+                        givens=user_keys, needed=flow_keys, i=i, cls=flow.__name__))
         return True
 
     @property

pymc3/variational/callbacks.py

@@ -101,15 +101,15 @@ class Tracker(Callback):
     >>> tracker = Tracker(time=time.time)
     >>> with model:
     ...     approx = pm.fit(callbacks=[tracker])
-    
+
     Time can be accessed via :code:`tracker['time']` now
     For more complex summary one can use callable that takes
     (approx, hist, i) as arguments
     >>> with model:
     ...     my_callable = lambda ap, h, i: h[-1]
     ...     tracker = Tracker(some_stat=my_callable)
     ...     approx = pm.fit(callbacks=[tracker])
-    
+
     Multiple stats are valid too
     >>> with model:
     ...     tracker = Tracker(some_stat=my_callable, time=time.time)

pymc3/variational/inference.py

@@ -553,7 +553,7 @@ class ASVGD(ImplicitGradient):
     -   Dilin Wang, Qiang Liu (2016)
         Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
         arXiv:1611.01722
-    
+
     -   Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng (2017)
         Stein Variational Policy Gradient
         arXiv:1704.02399

pymc3/variational/opvi.py

@@ -797,7 +797,7 @@ def _check_user_params(self, **kwargs):
             raise ParametrizationError(
                 'Passed parameters do not have a needed set of keys, '
                 'they should be equal, got {givens}, needed {needed}'.format(
-                 givens=givens, needed=needed))
+                    givens=givens, needed=needed))
         self._user_params = dict()
         spec = self.get_param_spec_for(d=self.ddim, **kwargs.pop('spec_kw', {}))
         for name, param in self.user_params.items():