Improve random for ObservedRV

pymc3/distributions/distribution.py

@@ -383,7 +383,17 @@ def _draw_value(param, point=None, givens=None, size=None):
         elif (hasattr(param, 'distribution') and
                 hasattr(param.distribution, 'random') and
                 param.distribution.random is not None):
-            return param.distribution.random(point=point, size=size)
+            # reset the dist shape for ObservedRV
+            if hasattr(param, 'observations'):
+                dist_tmp = param.distribution
+                try:
+                    distshape = param.observations.shape.eval()
+                except AttributeError:
+                    distshape = param.observations.shape
+                dist_tmp.shape = distshape
+                return dist_tmp.random(point=point, size=size)
+            else:
+                return param.distribution.random(point=point, size=size)
         else:
             if givens:
                 variables, values = list(zip(*givens))

pymc3/tests/test_sampling.py

@@ -353,6 +353,7 @@ def test_ignores_observed(self):
         assert (prior['mu'] < 90).all()
         assert (prior['positive_mu'] > 90).all()
         assert (prior['x_obs'] < 90).all()
+        assert prior['x_obs'].shape == (500, 200)
         npt.assert_array_almost_equal(prior['positive_mu'], np.abs(prior['mu']), decimal=4)
 
     def test_respects_shape(self):
@@ -395,9 +396,28 @@ def test_transformed(self):
 
             thetas = pm.Beta('thetas', alpha=phi*kappa, beta=(1.0-phi)*kappa, shape=n)
 
-            y = pm.Binomial('y', n=at_bats, p=thetas, shape=n, observed=hits)
+            y = pm.Binomial('y', n=at_bats, p=thetas, observed=hits)
             gen = pm.sample_prior_predictive(draws)
 
         assert gen['phi'].shape == (draws,)
         assert gen['y'].shape == (draws, n)
-        assert 'thetas_logodds__' in gen
+        assert 'thetas_logodds__' in gen
+
+    def test_shared(self):
+        n1 = 10
+        obs = shared(np.random.rand(n1) < .5)
+        draws = 50
+
+        with pm.Model() as m:
+            p = pm.Beta('p', 1., 1.)
+            y = pm.Bernoulli('y', p, observed=obs)
+            gen1 = pm.sample_prior_predictive(draws)
+
+        assert gen1['y'].shape == (draws, n1)
+
+        n2 = 20
+        obs.set_value(np.random.rand(n2) < .5)
+        with m:
+            gen2 = pm.sample_prior_predictive(draws)
+
+        assert gen2['y'].shape == (draws, n2)