consolidate regression test into existing one

+ test_issue_4186 was merged into TestStrAndLatexRepr and now all 4 formatting options are covered.

Author: michaelosthege

pymc3/tests/test_distributions.py

@@ -1776,6 +1776,12 @@ def setup_class(self):
             # Test Cholesky parameterization
             Z = MvNormal("Z", mu=np.zeros(2), chol=np.eye(2), shape=(2,))
 
+            # NegativeBinomial representations to test issue 4186
+            nb1 = pm.NegativeBinomial(
+                "nb_with_mu_alpha", mu=pm.Normal("nbmu"), alpha=pm.Gamma("nbalpha", mu=6, sigma=1)
+            )
+            nb2 = pm.NegativeBinomial("nb_with_p_n", p=pm.Uniform("nbp"), n=10)
+
             # Expected value of outcome
             mu = Deterministic("mu", floatX(alpha + tt.dot(X, b)))
 
@@ -1799,14 +1805,16 @@ def setup_class(self):
             # Likelihood (sampling distribution) of observations
             Y_obs = Normal("Y_obs", mu=mu, sigma=sigma, observed=Y)
 
-        self.distributions = [alpha, sigma, mu, b, Z, Y_obs, bound_var]
+        self.distributions = [alpha, sigma, mu, b, Z, nb1, nb2, Y_obs, bound_var]
         self.expected = {
             "latex": (
                 r"$\text{alpha} \sim \text{Normal}$",
                 r"$\text{sigma} \sim \text{HalfNormal}$",
                 r"$\text{mu} \sim \text{Deterministic}$",
                 r"$\text{beta} \sim \text{Normal}$",
                 r"$\text{Z} \sim \text{MvNormal}$",
+                r"$\text{nb_with_mu_alpha} \sim \text{NegativeBinomial}$",
+                r"$\text{nb_with_p_n} \sim \text{NegativeBinomial}$",
                 r"$\text{Y_obs} \sim \text{Normal}$",
                 r"$\text{bound_var} \sim \text{Bound}$ -- \text{Normal}$",
                 r"$\text{kron_normal} \sim \text{KroneckerNormal}$",
@@ -1818,6 +1826,8 @@ def setup_class(self):
                 r"mu ~ Deterministic",
                 r"beta ~ Normal",
                 r"Z ~ MvNormal",
+                r"nb_with_mu_alpha ~ NegativeBinomial",
+                r"nb_with_p_n ~ NegativeBinomial",
                 r"Y_obs ~ Normal",
                 r"bound_var ~ Bound-Normal",
                 r"kron_normal ~ KroneckerNormal",
@@ -1829,6 +1839,8 @@ def setup_class(self):
                 r"$\text{mu} \sim \text{Deterministic}(\text{alpha},~\text{Constant},~\text{beta})$",
                 r"$\text{beta} \sim \text{Normal}(\mathit{mu}=0.0,~\mathit{sigma}=10.0)$",
                 r"$\text{Z} \sim \text{MvNormal}(\mathit{mu}=array,~\mathit{chol_cov}=array)$",
+                r"$\text{nb_with_mu_alpha} \sim \text{NegativeBinomial}(\mathit{mu}=\text{nbmu},~\mathit{alpha}=\text{nbalpha})$",
+                r"$\text{nb_with_p_n} \sim \text{NegativeBinomial}(\mathit{p}=\text{nbp},~\mathit{n}=10)$",
                 r"$\text{Y_obs} \sim \text{Normal}(\mathit{mu}=\text{mu},~\mathit{sigma}=f(\text{sigma}))$",
                 r"$\text{bound_var} \sim \text{Bound}(\mathit{lower}=1.0,~\mathit{upper}=\text{None})$ -- \text{Normal}(\mathit{mu}=0.0,~\mathit{sigma}=10.0)$",
                 r"$\text{kron_normal} \sim \text{KroneckerNormal}(\mathit{mu}=array)$",
@@ -1840,6 +1852,8 @@ def setup_class(self):
                 r"mu ~ Deterministic(alpha, Constant, beta)",
                 r"beta ~ Normal(mu=0.0, sigma=10.0)",
                 r"Z ~ MvNormal(mu=array, chol_cov=array)",
+                r"nb_with_mu_alpha ~ NegativeBinomial(mu=nbmu, alpha=nbalpha)",
+                r"nb_with_p_n ~ NegativeBinomial(p=nbp, n=10)",
                 r"Y_obs ~ Normal(mu=mu, sigma=f(sigma))",
                 r"bound_var ~ Bound(lower=1.0, upper=None)-Normal(mu=0.0, sigma=10.0)",
                 r"kron_normal ~ KroneckerNormal(mu=array)",
@@ -1929,17 +1943,6 @@ def test_issue_3051(self, dims, dist_cls, kwargs):
         assert actual_a.shape == (X.shape[0],)
         pass
 
-    def test_issue_4186(self):
-        with pm.Model():
-            nb = pm.NegativeBinomial(
-                "nb", mu=pm.Normal("mu"), alpha=pm.Gamma("alpha", mu=6, sigma=1)
-            )
-        assert str(nb) == "nb ~ NegativeBinomial(mu=mu, alpha=alpha)"
-
-        with pm.Model():
-            nb = pm.NegativeBinomial("nb", p=pm.Uniform("p"), n=10)
-        assert str(nb) == "nb ~ NegativeBinomial(p=p, n=10)"
-
 
 def test_serialize_density_dist():
     def func(x):