V4 update test framework for distributions random method 2nd attempt

pymc3/distributions/discrete.py

@@ -713,24 +713,23 @@ def NegBinom(a, m, x):
 
     @classmethod
     def dist(cls, mu=None, alpha=None, p=None, n=None, *args, **kwargs):
-        n, p = cls.get_mu_alpha(mu, alpha, p, n)
+        n, p = cls.get_n_p(mu, alpha, p, n)
         n = at.as_tensor_variable(floatX(n))
         p = at.as_tensor_variable(floatX(p))
         return super().dist([n, p], *args, **kwargs)
 
     @classmethod
-    def get_mu_alpha(cls, mu=None, alpha=None, p=None, n=None):
+    def get_n_p(cls, mu=None, alpha=None, p=None, n=None):
         if n is None:
             if alpha is not None:
-                n = at.as_tensor_variable(floatX(alpha))
+                n = alpha
             else:
                 raise ValueError("Incompatible parametrization. Must specify either alpha or n.")
         elif alpha is not None:
             raise ValueError("Incompatible parametrization. Can't specify both alpha and n.")
 
         if p is None:
             if mu is not None:
-                mu = at.as_tensor_variable(floatX(mu))
                 p = n / (mu + n)
             else:
                 raise ValueError("Incompatible parametrization. Must specify either mu or p.")

pymc3/tests/helpers.py

@@ -27,6 +27,7 @@
 
 class SeededTest:
     random_seed = 20160911
+    random_state = None
 
     @classmethod
     def setup_class(cls):
@@ -40,6 +41,11 @@ def setup_method(self):
     def teardown_method(self):
         set_at_rng(self.old_at_rng)
 
+    def get_random_state(self, reset=False):
+        if self.random_state is None or reset:
+            self.random_state = nr.RandomState(self.random_seed)
+        return self.random_state
+
 
 class LoggingHandler(BufferingHandler):
     def __init__(self, matcher):