Rename ZeroInflatedPoisson theta parameter to mu

For consistency with the base Poisson distribution

RELEASE-NOTES.md

@@ -74,6 +74,7 @@ All of the above apply to:
   - The function `replace_with_values` function has been added to `gp.utils`.
   - `MarginalSparse` has been renamed `MarginalApprox`.
   - Removed `MixtureSameFamily`. `Mixture` is now capable of handling batched multivariate components (see [#5438](https://github.com/pymc-devs/pymc/pull/5438)).
+  - `ZeroInflatedPoisson` `theta` parameter was renamed to `mu` (see [#5584](https://github.com/pymc-devs/pymc/pull/5584)).
 - ...
 
 ### Expected breaks

pymc/distributions/discrete.py

@@ -1414,8 +1414,8 @@ class ZeroInflatedPoisson:
 
     .. math::
 
-        f(x \mid \psi, \theta) = \left\{ \begin{array}{l}
-            (1-\psi) + \psi e^{-\theta}, \text{if } x = 0 \\
+        f(x \mid \psi, \mu) = \left\{ \begin{array}{l}
+            (1-\psi) + \psi e^{-\mu}, \text{if } x = 0 \\
             \psi \frac{e^{-\theta}\theta^x}{x!}, \text{if } x=1,2,3,\ldots
             \end{array} \right.
 
@@ -1428,42 +1428,42 @@ class ZeroInflatedPoisson:
         plt.style.use('arviz-darkgrid')
         x = np.arange(0, 22)
         psis = [0.7, 0.4]
-        thetas = [8, 4]
-        for psi, theta in zip(psis, thetas):
-            pmf = st.poisson.pmf(x, theta)
+        mus = [8, 4]
+        for psi, mu in zip(psis, mus):
+            pmf = st.poisson.pmf(x, mu)
             pmf[0] = (1 - psi) + pmf[0]
             pmf[1:] =  psi * pmf[1:]
             pmf /= pmf.sum()
-            plt.plot(x, pmf, '-o', label='$\\psi$ = {}, $\\theta$ = {}'.format(psi, theta))
+            plt.plot(x, pmf, '-o', label='$\\psi$ = {}, $\\mu$ = {}'.format(psi, mu))
         plt.xlabel('x', fontsize=12)
         plt.ylabel('f(x)', fontsize=12)
         plt.legend(loc=1)
         plt.show()
 
     ========  ==========================
     Support   :math:`x \in \mathbb{N}_0`
-    Mean      :math:`\psi\theta`
-    Variance  :math:`\theta + \frac{1-\psi}{\psi}\theta^2`
+    Mean      :math:`\psi\mu`
+    Variance  :math:`\mu + \frac{1-\psi}{\psi}\mu^2`
     ========  ==========================
 
     Parameters
     ----------
     psi: float
         Expected proportion of Poisson variates (0 < psi < 1)
-    theta: float
+    mu: float
         Expected number of occurrences during the given interval
-        (theta >= 0).
+        (mu >= 0).
     """
 
-    def __new__(cls, name, psi, theta, **kwargs):
+    def __new__(cls, name, psi, mu, **kwargs):
         return _zero_inflated_mixture(
-            name=name, nonzero_p=psi, nonzero_dist=Poisson.dist(mu=theta), **kwargs
+            name=name, nonzero_p=psi, nonzero_dist=Poisson.dist(mu=mu), **kwargs
         )
 
     @classmethod
-    def dist(cls, psi, theta, **kwargs):
+    def dist(cls, psi, mu, **kwargs):
         return _zero_inflated_mixture(
-            name=None, nonzero_p=psi, nonzero_dist=Poisson.dist(mu=theta), **kwargs
+            name=None, nonzero_p=psi, nonzero_dist=Poisson.dist(mu=mu), **kwargs
         )
 
 

pymc/tests/test_distributions.py

@@ -1747,33 +1747,33 @@ def test_constantdist(self):
         self.check_logcdf(Constant, I, {"c": I}, lambda value, c: np.log(value >= c))
 
     def test_zeroinflatedpoisson(self):
-        def logp_fn(value, psi, theta):
+        def logp_fn(value, psi, mu):
             if value == 0:
-                return np.log((1 - psi) * sp.poisson.pmf(0, theta))
+                return np.log((1 - psi) * sp.poisson.pmf(0, mu))
             else:
-                return np.log(psi * sp.poisson.pmf(value, theta))
+                return np.log(psi * sp.poisson.pmf(value, mu))
 
-        def logcdf_fn(value, psi, theta):
-            return np.log((1 - psi) + psi * sp.poisson.cdf(value, theta))
+        def logcdf_fn(value, psi, mu):
+            return np.log((1 - psi) + psi * sp.poisson.cdf(value, mu))
 
         self.check_logp(
             ZeroInflatedPoisson,
             Nat,
-            {"psi": Unit, "theta": Rplus},
+            {"psi": Unit, "mu": Rplus},
             logp_fn,
         )
 
         self.check_logcdf(
             ZeroInflatedPoisson,
             Nat,
-            {"psi": Unit, "theta": Rplus},
+            {"psi": Unit, "mu": Rplus},
             logcdf_fn,
         )
 
         self.check_selfconsistency_discrete_logcdf(
             ZeroInflatedPoisson,
             Nat,
-            {"theta": Rplus, "psi": Unit},
+            {"mu": Rplus, "psi": Unit},
         )
 
     def test_zeroinflatednegativebinomial(self):

pymc/tests/test_distributions_moments.py

@@ -624,17 +624,17 @@ def test_constant_moment(c, size, expected):
 
 
 @pytest.mark.parametrize(
-    "psi, theta, size, expected",
+    "psi, mu, size, expected",
     [
         (0.9, 3.0, None, 3),
         (0.8, 2.9, 5, np.full(5, 2)),
         (0.2, np.arange(1, 5) * 5, None, np.arange(1, 5)),
         (0.2, np.arange(1, 5) * 5, (2, 4), np.full((2, 4), np.arange(1, 5))),
     ],
 )
-def test_zero_inflated_poisson_moment(psi, theta, size, expected):
+def test_zero_inflated_poisson_moment(psi, mu, size, expected):
     with Model() as model:
-        ZeroInflatedPoisson("x", psi=psi, theta=theta, size=size)
+        ZeroInflatedPoisson("x", psi=psi, mu=mu, size=size)
     assert_moment_is_expected(model, expected)
 
 

pymc/tests/test_sampling.py

@@ -1128,11 +1128,11 @@ def test_shape_edgecase(self):
 
     def test_zeroinflatedpoisson(self):
         with pm.Model():
-            theta = pm.Beta("theta", alpha=1, beta=1)
+            mu = pm.Beta("mu", alpha=1, beta=1)
             psi = pm.HalfNormal("psi", sd=1)
-            pm.ZeroInflatedPoisson("suppliers", psi=psi, theta=theta, size=20)
+            pm.ZeroInflatedPoisson("suppliers", psi=psi, mu=mu, size=20)
             gen_data = pm.sample_prior_predictive(samples=5000)
-            assert gen_data.prior["theta"].shape == (1, 5000)
+            assert gen_data.prior["mu"].shape == (1, 5000)
             assert gen_data.prior["psi"].shape == (1, 5000)
             assert gen_data.prior["suppliers"].shape == (1, 5000, 20)