Reintroduce informative xfails in still faulty distributions

pymc-devs · Mar 19, 2021 · 3ca2162 · 3ca2162
1 parent 37d60ec
commit 3ca2162
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diff --git a/pymc3/tests/test_distributions.py b/pymc3/tests/test_distributions.py
@@ -1140,6 +1140,7 @@ def modified_scipy_hypergeom_logcdf(value, N, k, n):
             {"N": NatSmall, "k": NatSmall, "n": NatSmall},
         )
 
+    @pytest.mark.xfail(reason="Logp method not refactored yet")
     def test_negative_binomial(self):
         def scipy_mu_alpha_logpmf(value, mu, alpha):
             return sp.nbinom.logpmf(value, alpha, 1 - mu / (mu + alpha))
@@ -1639,6 +1640,7 @@ def test_zeroinflatedbinomial_logcdf(self):
             n_samples=10,
         )
 
+    @pytest.mark.xfail(reason="Distribution not refactored yet")
     @pytest.mark.parametrize("n", [1, 2, 3])
     def test_mvnormal(self, n):
         self.check_logp(

diff --git a/pymc3/tests/test_distributions_random.py b/pymc3/tests/test_distributions_random.py
@@ -787,6 +787,7 @@ def ref_rand(size, q, beta):
             pm.DiscreteWeibull, {"q": Unit, "beta": Rplusdunif}, ref_rand=ref_rand
         )
 
+    @pytest.mark.xfail(reason="Needs debugging")
     @pytest.mark.parametrize("s", [2, 3, 4])
     def test_categorical_random(self, s):
         def ref_rand(size, p):
@@ -801,6 +802,7 @@ def ref_rand(size, c):
 
         pymc3_random_discrete(pm.Constant, {"c": I}, ref_rand=ref_rand)
 
+    @pytest.mark.xfail(reason="Not refactored yet")
     def test_mv_normal(self):
         def ref_rand(size, mu, cov):
             return st.multivariate_normal.rvs(mean=mu, cov=cov, size=size)