Ignore finite upper limit in Nat domains.

Move new checks to `check_logcdf`.

pymc3/tests/test_distributions.py

@@ -575,28 +575,6 @@ def check_logcdf(
                 err_msg=str(pt),
             )
 
-    def check_selfconsistency_discrete_logcdf(
-        self, distribution, domain, paramdomains, decimal=None, n_samples=100
-    ):
-        """
-        Check that logcdf of discrete distributions matches sum of logps up to value
-        """
-        domains = paramdomains.copy()
-        domains["value"] = domain
-        if decimal is None:
-            decimal = select_by_precision(float64=6, float32=3)
-        for pt in product(domains, n_samples=n_samples):
-            params = dict(pt)
-            value = params.pop("value")
-            values = np.arange(domain.lower, value + 1)
-            dist = distribution.dist(**params)
-            assert_almost_equal(
-                dist.logcdf(value).tag.test_value,
-                logsumexp(dist.logp(values), keepdims=False).tag.test_value,
-                decimal=decimal,
-                err_msg=str(pt),
-            )
-
         # Test that values below domain evaluate to -np.inf
         if np.isfinite(domain.lower):
             below_domain = domain.lower - 1
@@ -607,7 +585,9 @@ def check_selfconsistency_discrete_logcdf(
             )
 
         # Test that values above domain evaluate to 0
-        if np.isfinite(domain.upper):
+        # Natural domains do not have inf as the upper edge, but should also be ignored
+        not_nat_domain = domain not in (NatSmall, Nat, NatBig, PosNat)
+        if not_nat_domain and np.isfinite(domain.upper):
             above_domain = domain.upper + 1
             assert_equal(
                 dist.logcdf(above_domain).tag.test_value,
@@ -619,11 +599,31 @@ def check_selfconsistency_discrete_logcdf(
         try:
             dist.logcdf(np.array([value, value])).tag.test_value
         except TypeError as err:
-            if not str(err).endswith(
-                ".logcdf expects a scalar value but received a 1-dimensional object."
-            ):
+            if not str(err).endswith(".logcdf expects a scalar value but received a 1-dimensional object."):
                 raise
 
+    def check_selfconsistency_discrete_logcdf(
+        self, distribution, domain, paramdomains, decimal=None, n_samples=100
+    ):
+        """
+        Check that logcdf of discrete distributions matches sum of logps up to value
+        """
+        domains = paramdomains.copy()
+        domains["value"] = domain
+        if decimal is None:
+            decimal = select_by_precision(float64=6, float32=3)
+        for pt in product(domains, n_samples=n_samples):
+            params = dict(pt)
+            value = params.pop("value")
+            values = np.arange(domain.lower, value + 1)
+            dist = distribution.dist(**params)
+            assert_almost_equal(
+                dist.logcdf(value).tag.test_value,
+                logsumexp(dist.logp(values), keepdims=False).tag.test_value,
+                decimal=decimal,
+                err_msg=str(pt),
+            )
+
     def check_int_to_1(self, model, value, domain, paramdomains):
         pdf = model.fastfn(exp(model.logpt))
         for pt in product(paramdomains, n_samples=10):