Added type hints to discrete distributions in pymc/distributions/discrete.pyAdd type hints to discrete distributions

pymc/distributions/discrete.py

@@ -13,6 +13,8 @@
 #   limitations under the License.
 import warnings
 
+from typing import Optional
+
 import numpy as np
 import pytensor.tensor as pt
 
@@ -118,7 +120,14 @@ class Binomial(Discrete):
     rv_op = binomial
 
     @classmethod
-    def dist(cls, n, p=None, logit_p=None, *args, **kwargs):
+     def dist(
+        cls,
+        n: DIST_PARAMETER_TYPES,
+        p: Optional[DIST_PARAMETER_TYPES] = None,
+        logit_p: Optional[DIST_PARAMETER_TYPES] = 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:
@@ -234,7 +243,14 @@ def BetaBinom(a, b, n, x):
     rv_op = betabinom
 
     @classmethod
-    def dist(cls, alpha, beta, n, *args, **kwargs):
+    def dist(
+        cls,
+        alpha: DIST_PARAMETER_TYPES,
+        beta: DIST_PARAMETER_TYPES,
+        n: DIST_PARAMETER_TYPES,
+        *args,
+        **kwargs,
+    ):
         alpha = pt.as_tensor_variable(alpha)
         beta = pt.as_tensor_variable(beta)
         n = pt.as_tensor_variable(n, dtype=int)
@@ -341,7 +357,13 @@ class Bernoulli(Discrete):
     rv_op = bernoulli
 
     @classmethod
-    def dist(cls, p=None, logit_p=None, *args, **kwargs):
+    def dist(
+        cls,
+        p: Optional[DIST_PARAMETER_TYPES] = None,
+        logit_p: Optional[DIST_PARAMETER_TYPES] = 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:
@@ -465,7 +487,8 @@ def DiscreteWeibull(q, b, x):
     rv_op = DiscreteWeibullRV.rv_op
 
     @classmethod
-    def dist(cls, q, beta, *args, **kwargs):
+    def dist(cls, q: DIST_PARAMETER_TYPES, beta: DIST_PARAMETER_TYPES, *args, **kwargs):
+
         return super().dist([q, beta], **kwargs)
 
     def support_point(rv, size, q, beta):
@@ -553,7 +576,8 @@ class Poisson(Discrete):
     rv_op = poisson
 
     @classmethod
-    def dist(cls, mu, *args, **kwargs):
+    def dist(cls, mu: DIST_PARAMETER_TYPES, *args, **kwargs):
+
         mu = pt.as_tensor_variable(mu)
         return super().dist([mu], *args, **kwargs)
 
@@ -677,7 +701,16 @@ def NegBinom(a, m, x):
     rv_op = nbinom
 
     @classmethod
-    def dist(cls, mu=None, alpha=None, p=None, n=None, *args, **kwargs):
+    def dist(
+        cls,
+        mu: Optional[DIST_PARAMETER_TYPES] = None,
+        alpha: Optional[DIST_PARAMETER_TYPES] = None,
+        p: Optional[DIST_PARAMETER_TYPES] = None,
+        n: Optional[DIST_PARAMETER_TYPES] = None,
+        *args,
+        **kwargs,
+    ):
+
         n, p = cls.get_n_p(mu=mu, alpha=alpha, p=p, n=n)
         n = pt.as_tensor_variable(n)
         p = pt.as_tensor_variable(p)
@@ -790,7 +823,8 @@ class Geometric(Discrete):
     rv_op = geometric
 
     @classmethod
-    def dist(cls, p, *args, **kwargs):
+    def dist(cls, p: DIST_PARAMETER_TYPES, *args, **kwargs):
+
         p = pt.as_tensor_variable(p)
         return super().dist([p], *args, **kwargs)
 
@@ -1027,7 +1061,8 @@ class DiscreteUniform(Discrete):
     rv_op = discrete_uniform
 
     @classmethod
-    def dist(cls, lower, upper, *args, **kwargs):
+    def dist(cls, lower: DIST_PARAMETER_TYPES, upper: DIST_PARAMETER_TYPES, *args, **kwargs):
+
         lower = pt.floor(lower)
         upper = pt.floor(upper)
         return super().dist([lower, upper], **kwargs)
@@ -1123,7 +1158,12 @@ class Categorical(Discrete):
     rv_op = categorical
 
     @classmethod
-    def dist(cls, p=None, logit_p=None, **kwargs):
+    def dist(
+        cls,
+        p: Optional[DIST_PARAMETER_TYPES] = None,
+        logit_p: Optional[DIST_PARAMETER_TYPES] = 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:

pymc/distributions/distribution.py

@@ -23,7 +23,7 @@
 from functools import singledispatch
 from typing import Any, TypeAlias
 
-import numpy as np
+import numpy as np # type: ignore
 
 from pytensor import tensor as pt
 from pytensor.compile.builders import OpFromGraph