Fix pickling support

__reduce__ methods were not added in subclasses, only in superclass (_MKLRandomState), so the proper constructors would never be called, thus pickling would produce different objects

Author: ndgrigorian

mkl_random/interfaces/_numpy_random.py

@@ -60,7 +60,7 @@ def __init__(self, seed=None):
 
     def seed(self, seed=None):
         """
-        seed(seed=Nonee)
+        seed(seed=None)
 
         Seed the generator.
 
@@ -104,6 +104,11 @@ def set_state(self, state):
         """
         return super().set_state(state=state)
 
+    # pickling support
+    def __reduce__(self):
+        global __NPRandomState_ctor
+        return (__NPRandomState_ctor, (), self.get_state())
+
     def random_sample(self, size=None):
         """
         random_sample(size=None)
@@ -223,7 +228,7 @@ def beta(self, a, b, size=None):
         For full documentation refer to `numpy.random.beta`.
 
         """
-        return super().beta(a=a, b=b, size=size,)
+        return super().beta(a=a, b=b, size=size)
 
     def exponential(self, scale=1.0, size=None):
         """
@@ -591,6 +596,19 @@ def permutation(self, x):
         return super().permutation(x=x)
 
 
+def __NPRandomState_ctor():
+    """
+    Return a RandomState instance.
+    This function exists solely to assist (un)pickling.
+    Note that the state of the RandomState returned here is irrelevant, as this function's
+    entire purpose is to return a newly allocated RandomState whose state pickle can set.
+    Consequently the RandomState returned by this function is a freshly allocated copy
+    with a seed=0.
+    See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
+    """
+    return RandomState(seed=0)
+
+
 # instantiate a default RandomState object to be used by module-level functions
 _rand = RandomState()
 # define module-level functions using methods of a default RandomState object

mkl_random/mklrand.pyx

@@ -1261,10 +1261,6 @@ cdef class _MKLRandomState:
     def __setstate__(self, state):
         self.set_state(state)
 
-    def __reduce__(self):
-        global __RandomState_ctor
-        return (__RandomState_ctor, (), self.get_state())
-
     # Basic distributions:
     def random_sample(self, size=None):
         """
@@ -5605,6 +5601,11 @@ cdef class MKLRandomState(_MKLRandomState):
 
     """
 
+    # pickling support
+    def __reduce__(self):
+        global __MKLRandomState_ctor
+        return (__MKLRandomState_ctor, (), self.get_state())
+
     def leapfrog(self, int k, int nstreams):
         """
         leapfrog(k, nstreams)
@@ -5966,6 +5967,11 @@ class RandomState(MKLRandomState):
         )
         super().__init__(seed=seed, brng=brng)
 
+    # pickling support
+    def __reduce__(self):
+        global __RandomState_ctor
+        return (__RandomState_ctor, (), self.get_state())
+
 
 def __MKLRandomState_ctor():
     """