Check that concentration parameters of Dirichlet distribution are all > 0 (#3853)

* Added check that a>0 in Dirichlet

* Cast a as array for tests

* Test a>0 only when a not an RV and convert to array when list

* Added test for init of Dirichlet with negative values

* Added release note

* Resolved conflict in release notes

* Escaped parenthesis in match regexp

Author: AlexAndorra

Diff for: RELEASE-NOTES.md

@@ -20,6 +20,7 @@
 - `pm.sample` now takes 1000 draws and 1000 tuning samples by default, instead of 500 previously (see [#3855](https://github.com/pymc-devs/pymc3/pull/3855)).
 - Dropped the outdated 'nuts' initialization method for `pm.sample` (see [#3863](https://github.com/pymc-devs/pymc3/pull/3863)).
 - Moved argument division out of `NegativeBinomial` `random` method. Fixes [#3864](https://github.com/pymc-devs/pymc3/issues/3864) in the style of [#3509](https://github.com/pymc-devs/pymc3/pull/3509).
+- The Dirichlet distribution now raises a ValueError when it's initialized with <= 0 values (see [#3853](https://github.com/pymc-devs/pymc3/pull/3853)).
 
 ## PyMC3 3.8 (November 29 2019)
 

Diff for: pymc3/distributions/multivariate.py

@@ -488,6 +488,16 @@ class Dirichlet(Continuous):
 
     def __init__(self, a, transform=transforms.stick_breaking,
                  *args, **kwargs):
+
+        if not isinstance(a, pm.model.TensorVariable):
+            if not isinstance(a, list) and not isinstance(a, np.ndarray):
+                raise TypeError(
+                    'The vector of concentration parameters (a) must be a python list '
+                    'or numpy array.')
+            a = np.array(a)
+            if (a <= 0).any():
+               raise ValueError("All concentration parameters (a) must be > 0.")
+
         shape = np.atleast_1d(a.shape)[-1]
 
         kwargs.setdefault("shape", shape)

Diff for: pymc3/tests/test_distributions.py

@@ -944,17 +944,43 @@ def test_lkj(self, x, eta, n, lp):
 
     @pytest.mark.parametrize('n', [2, 3])
     def test_dirichlet(self, n):
-        self.pymc3_matches_scipy(Dirichlet, Simplex(
-            n), {'a': Vector(Rplus, n)}, dirichlet_logpdf)
+        self.pymc3_matches_scipy(
+            Dirichlet,
+            Simplex(n),
+            {'a': Vector(Rplus, n)},
+            dirichlet_logpdf
+        )
+
+    @pytest.mark.parametrize('n', [3, 4])
+    def test_dirichlet_init_fail(self, n):
+        with Model():
+            with pytest.raises(
+                    ValueError,
+                    match=r"All concentration parameters \(a\) must be > 0."
+            ):
+                _ = Dirichlet('x', a=np.zeros(n), shape=n)
+            with pytest.raises(
+                    ValueError,
+                    match=r"All concentration parameters \(a\) must be > 0."
+            ):
+                _ = Dirichlet('x', a=np.array([-1.] * n), shape=n)
 
     def test_dirichlet_2D(self):
-        self.pymc3_matches_scipy(Dirichlet, MultiSimplex(2, 2),
-                                 {'a': Vector(Vector(Rplus, 2), 2)}, dirichlet_logpdf)
+        self.pymc3_matches_scipy(
+            Dirichlet,
+            MultiSimplex(2, 2),
+            {'a': Vector(Vector(Rplus, 2), 2)},
+            dirichlet_logpdf
+        )
 
     @pytest.mark.parametrize('n', [2, 3])
     def test_multinomial(self, n):
-        self.pymc3_matches_scipy(Multinomial, Vector(Nat, n), {'p': Simplex(n), 'n': Nat},
-                                 multinomial_logpdf)
+        self.pymc3_matches_scipy(
+            Multinomial,
+            Vector(Nat, n),
+            {'p': Simplex(n), 'n': Nat},
+            multinomial_logpdf
+        )
 
     @pytest.mark.parametrize('p,n', [
         [[.25, .25, .25, .25], 1],