Fix Dirichlet.logp (#4454)

* Fix Dirichlet.logp by checking number of categories > 1 only at event dims

* Update test_distributions.py

* Removed the shape validation check to even work for last dimensional shape as 1.

Modified the `test_dirichlet` function to check for the same.

* Added a test to check Dirichlet.logp with different batch shapes.

* Tested exact Dirichlet.logp values againt scipy implementation

Given a mention in RELEASE-NOTES.md

RELEASE-NOTES.md

@@ -13,6 +13,7 @@
 - `math.log1mexp_numpy` no longer raises RuntimeWarning when given very small inputs. These were commonly observed during NUTS sampling (see [#4428](https://github.com/pymc-devs/pymc3/pull/4428)).
 - `ScalarSharedVariable` can now be used as an input to other RVs directly (see [#4445](https://github.com/pymc-devs/pymc3/pull/4445)).
 - `pm.sample` and `pm.find_MAP` no longer change the `start` argument (see [#4458](https://github.com/pymc-devs/pymc3/pull/4458)).
+- Fixed `Dirichlet.logp` method to work with unit batch or event shapes (see [#4454](https://github.com/pymc-devs/pymc3/pull/4454)).
 
 ## PyMC3 3.11.0 (21 January 2021)
 

pymc3/distributions/multivariate.py

@@ -522,7 +522,6 @@ def logp(self, value):
             tt.sum(logpow(value, a - 1) - gammaln(a), axis=-1) + gammaln(tt.sum(a, axis=-1)),
             tt.all(value >= 0),
             tt.all(value <= 1),
-            np.logical_not(a.broadcastable),
             tt.all(a > 0),
             broadcast_conditions=False,
         )

pymc3/tests/test_distributions.py

@@ -1692,10 +1692,21 @@ def test_lkj(self, x, eta, n, lp):
         decimals = select_by_precision(float64=6, float32=4)
         assert_almost_equal(model.fastlogp(pt), lp, decimal=decimals, err_msg=str(pt))
 
-    @pytest.mark.parametrize("n", [2, 3])
+    @pytest.mark.parametrize("n", [1, 2, 3])
     def test_dirichlet(self, n):
         self.pymc3_matches_scipy(Dirichlet, Simplex(n), {"a": Vector(Rplus, n)}, dirichlet_logpdf)
 
+    @pytest.mark.parametrize("dist_shape", [1, (2, 1), (1, 2), (2, 4, 3)])
+    def test_dirichlet_with_batch_shapes(self, dist_shape):
+        a = np.ones(dist_shape)
+        with pm.Model() as model:
+            d = pm.Dirichlet("a", a=a)
+
+        pymc3_res = d.distribution.logp(d.tag.test_value).eval()
+        for idx in np.ndindex(a.shape[:-1]):
+            scipy_res = scipy.stats.dirichlet(a[idx]).logpdf(d.tag.test_value[idx])
+            assert_almost_equal(pymc3_res[idx], scipy_res)
+
     def test_dirichlet_shape(self):
         a = tt.as_tensor_variable(np.r_[1, 2])
         with pytest.warns(DeprecationWarning):