Parametrize Binomial and Categorical distributions via logit_p

RELEASE-NOTES.md

@@ -133,6 +133,7 @@ This includes API changes we did not warn about since at least `3.11.0` (2021-01
 - Adding support for blackjax's NUTS sampler `pymc.sampling_jax` (see [#5477](ihttps://github.com/pymc-devs/pymc/pull/5477))
 - `pymc.sampling_jax` samplers support `log_likelihood`, `observed_data`, and `sample_stats` in returned InferenceData object (see [#5189](https://github.com/pymc-devs/pymc/pull/5189))
 - Adding support for `pm.Deterministic` in `pymc.sampling_jax` (see [#5182](https://github.com/pymc-devs/pymc/pull/5182))
+- Added an alternative parametrization, `logit_p` to `pm.Binomial` and `pm.Categorical` distributions (see [5637](https://github.com/pymc-devs/pymc/pull/5637)).
 - ...
 
 

pymc/distributions/discrete.py

@@ -106,15 +106,25 @@ class Binomial(Discrete):
 
     Parameters
     ----------
-    n: int
+    n : int
         Number of Bernoulli trials (n >= 0).
-    p: float
+    p : float
         Probability of success in each trial (0 < p < 1).
+    logit_p : float
+        Alternative log odds for the probability of success.
     """
     rv_op = binomial
 
     @classmethod
-    def dist(cls, n, p, *args, **kwargs):
+    def dist(cls, n, p=None, logit_p=None, *args, **kwargs):
+        if p is not None and logit_p is not None:
+            raise ValueError("Incompatible parametrization. Can't specify both p and logit_p.")
+        elif p is None and logit_p is None:
+            raise ValueError("Incompatible parametrization. Must specify either p or logit_p.")
+
+        if logit_p is not None:
+            p = at.sigmoid(logit_p)
+
         n = at.as_tensor_variable(intX(n))
         p = at.as_tensor_variable(floatX(p))
         return super().dist([n, p], **kwargs)
@@ -1245,14 +1255,24 @@ class Categorical(Discrete):
 
     Parameters
     ----------
-    p: array of floats
+    p : array of floats
         p > 0 and the elements of p must sum to 1. They will be automatically
         rescaled otherwise.
+    logit_p : float
+        Alternative log odds for the probability of success.
     """
     rv_op = categorical
 
     @classmethod
-    def dist(cls, p, **kwargs):
+    def dist(cls, p=None, logit_p=None, **kwargs):
+        if p is not None and logit_p is not None:
+            raise ValueError("Incompatible parametrization. Can't specify both p and logit_p.")
+        elif p is None and logit_p is None:
+            raise ValueError("Incompatible parametrization. Must specify either p or logit_p.")
+
+        if logit_p is not None:
+            p = pm.math.softmax(logit_p, axis=-1)
+
         if isinstance(p, np.ndarray) or isinstance(p, list):
             if (np.asarray(p) < 0).any():
                 raise ValueError(f"Negative `p` parameters are not valid, got: {p}")

pymc/tests/test_distributions_random.py

@@ -37,7 +37,7 @@ def random_polyagamma(*args, **kwargs):
         raise RuntimeError("polyagamma package is not installed!")
 
 
-from scipy.special import expit
+from scipy.special import expit, softmax
 
 import pymc as pm
 
@@ -1005,6 +1005,25 @@ class TestBinomial(BaseTestDistributionRandom):
     checks_to_run = ["check_pymc_params_match_rv_op"]
 
 
+class TestLogitBinomial(BaseTestDistributionRandom):
+    pymc_dist = pm.Binomial
+    pymc_dist_params = {"n": 100, "logit_p": 0.5}
+    expected_rv_op_params = {"n": 100, "p": expit(0.5)}
+    tests_to_run = ["check_pymc_params_match_rv_op"]
+
+    @pytest.mark.parametrize(
+        "n, p, logit_p, expected",
+        [
+            (5, None, None, "Must specify either p or logit_p."),
+            (5, 0.5, 0.5, "Can't specify both p and logit_p."),
+        ],
+    )
+    def test_binomial_init_fail(self, n, p, logit_p, expected):
+        with pm.Model() as model:
+            with pytest.raises(ValueError, match=f"Incompatible parametrization. {expected}"):
+                pm.Binomial("x", n=n, p=p, logit_p=logit_p)
+
+
 class TestNegativeBinomial(BaseTestDistributionRandom):
     pymc_dist = pm.NegativeBinomial
     pymc_dist_params = {"n": 100, "p": 0.33}
@@ -1410,6 +1429,30 @@ class TestCategorical(BaseTestDistributionRandom):
     ]
 
 
+class TestLogitCategorical(BaseTestDistributionRandom):
+    pymc_dist = pm.Categorical
+    pymc_dist_params = {"logit_p": np.array([[0.28, 0.62, 0.10], [0.28, 0.62, 0.10]])}
+    expected_rv_op_params = {
+        "p": softmax(np.array([[0.28, 0.62, 0.10], [0.28, 0.62, 0.10]]), axis=-1)
+    }
+    tests_to_run = [
+        "check_pymc_params_match_rv_op",
+        "check_rv_size",
+    ]
+
+    @pytest.mark.parametrize(
+        "p, logit_p, expected",
+        [
+            (None, None, "Must specify either p or logit_p."),
+            (0.5, 0.5, "Can't specify both p and logit_p."),
+        ],
+    )
+    def test_categorical_init_fail(self, p, logit_p, expected):
+        with pm.Model() as model:
+            with pytest.raises(ValueError, match=f"Incompatible parametrization. {expected}"):
+                pm.Categorical("x", p=p, logit_p=logit_p)
+
+
 class TestGeometric(BaseTestDistributionRandom):
     pymc_dist = pm.Geometric
     pymc_dist_params = {"p": 0.9}