Improve Categorical and Multinomial checks

- Fixes bug that occurred when constructing probability parameters from lists or tuples of Aesara variables
- Improves logp checks for valid probabilty parameters

pymc/distributions/discrete.py

@@ -16,6 +16,7 @@
 import aesara.tensor as at
 import numpy as np
 
+from aesara.tensor import TensorConstant
 from aesara.tensor.random.basic import (
     RandomVariable,
     ScipyRandomVariable,
@@ -1285,17 +1286,21 @@ def dist(cls, p=None, logit_p=None, **kwargs):
         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}")
-            p_sum = np.sum([p], axis=-1)
-            if not np.all(np.isclose(p_sum, 1.0)):
+        p = at.as_tensor_variable(p)
+        if isinstance(p, TensorConstant):
+            p_ = np.asarray(p.data)
+            if np.any(p_ < 0):
+                raise ValueError(f"Negative `p` parameters are not valid, got: {p_}")
+            p_sum_ = np.sum([p_], axis=-1)
+            if not np.all(np.isclose(p_sum_, 1.0)):
                 warnings.warn(
-                    f"`p` parameters sum to {p_sum}, instead of 1.0. They will be automatically rescaled. You can rescale them directly to get rid of this warning.",
+                    f"`p` parameters sum to {p_sum_}, instead of 1.0. "
+                    "They will be automatically rescaled. "
+                    "You can rescale them directly to get rid of this warning.",
                     UserWarning,
                 )
-                p = p / at.sum(p, axis=-1, keepdims=True)
-        p = at.as_tensor_variable(floatX(p))
+                p_ = p_ / at.sum(p_, axis=-1, keepdims=True)
+                p = at.as_tensor_variable(p_)
         return super().dist([p], **kwargs)
 
     def moment(rv, size, p):
@@ -1341,7 +1346,11 @@ def logp(value, p):
         )
 
         return check_parameters(
-            res, at.all(p_ >= 0, axis=-1), at.all(p <= 1, axis=-1), msg="0 <= p <=1"
+            res,
+            p_ >= 0,
+            p_ <= 1,
+            at.isclose(at.sum(p, axis=-1), 1),
+            msg="0 <= p <=1, sum(p) = 1",
         )
 
 

pymc/distributions/multivariate.py

@@ -30,7 +30,7 @@
 from aesara.graph.op import Op
 from aesara.raise_op import Assert
 from aesara.sparse.basic import sp_sum
-from aesara.tensor import gammaln, sigmoid
+from aesara.tensor import TensorConstant, gammaln, sigmoid
 from aesara.tensor.nlinalg import det, eigh, matrix_inverse, trace
 from aesara.tensor.random.basic import dirichlet, multinomial, multivariate_normal
 from aesara.tensor.random.op import RandomVariable, default_supp_shape_from_params
@@ -543,16 +543,21 @@ class Multinomial(Discrete):
 
     @classmethod
     def dist(cls, n, p, *args, **kwargs):
-        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}")
-            p_sum = np.sum([p], axis=-1)
-            if not np.all(np.isclose(p_sum, 1.0)):
+        p = at.as_tensor_variable(p)
+        if isinstance(p, TensorConstant):
+            p_ = np.asarray(p.data)
+            if np.any(p_ < 0):
+                raise ValueError(f"Negative `p` parameters are not valid, got: {p_}")
+            p_sum_ = np.sum([p_], axis=-1)
+            if not np.all(np.isclose(p_sum_, 1.0)):
                 warnings.warn(
-                    f"`p` parameters sum up to {p_sum}, instead of 1.0. They will be automatically rescaled. You can rescale them directly to get rid of this warning.",
+                    f"`p` parameters sum to {p_sum_}, instead of 1.0. "
+                    "They will be automatically rescaled. "
+                    "You can rescale them directly to get rid of this warning.",
                     UserWarning,
                 )
-                p = p / at.sum(p, axis=-1, keepdims=True)
+                p_ = p_ / at.sum(p_, axis=-1, keepdims=True)
+                p = at.as_tensor_variable(p_)
         n = at.as_tensor_variable(n)
         p = at.as_tensor_variable(p)
         return super().dist([n, p], *args, **kwargs)
@@ -591,10 +596,11 @@ def logp(value, n, p):
         )
         return check_parameters(
             res,
+            p >= 0,
             p <= 1,
             at.isclose(at.sum(p, axis=-1), 1),
             at.ge(n, 0),
-            msg="p <= 1, sum(p) = 1, n >= 0",
+            msg="0 <= p <= 1, sum(p) = 1, n >= 0",
         )
 
 

pymc/tests/distributions/test_discrete.py

@@ -497,32 +497,39 @@ def test_categorical_bounds(self):
             # entries if there is a single or pair number of negative values
             # and the rest are zero
             np.array([-1, -1, 0, 0]),
+            at.as_tensor_variable([-1, -1, 0, 0]),
         ],
     )
     def test_categorical_negative_p(self, p):
-        with pytest.raises(ValueError, match=f"{p}"):
+        with pytest.raises(ValueError, match="Negative `p` parameters are not valid"):
             with pm.Model():
                 x = pm.Categorical("x", p=p)
 
     def test_categorical_p_not_normalized(self):
         # test UserWarning is raised for p vals that sum to more than 1
         # and normaliation is triggered
-        with pytest.warns(UserWarning, match="[5]"):
+        with pytest.warns(UserWarning, match="They will be automatically rescaled"):
             with pm.Model() as m:
                 x = pm.Categorical("x", p=[1, 1, 1, 1, 1])
         assert np.isclose(m.x.owner.inputs[3].sum().eval(), 1.0)
 
     def test_categorical_negative_p_symbolic(self):
+        value = np.array([[1, 1, 1]])
+
+        x = at.scalar("x")
+        invalid_dist = pm.Categorical.dist(p=[x, x, x])
+
         with pytest.raises(ParameterValueError):
-            value = np.array([[1, 1, 1]])
-            invalid_dist = pm.Categorical.dist(p=at.as_tensor_variable([-1, 0.5, 0.5]))
-            pm.logp(invalid_dist, value).eval()
+            pm.logp(invalid_dist, value).eval({x: -1 / 3})
 
     def test_categorical_p_not_normalized_symbolic(self):
+        value = np.array([[1, 1, 1]])
+
+        x = at.scalar("x")
+        invalid_dist = pm.Categorical.dist(p=(x, x, x))
+
         with pytest.raises(ParameterValueError):
-            value = np.array([[1, 1, 1]])
-            invalid_dist = pm.Categorical.dist(p=at.as_tensor_variable([2, 2, 2]))
-            pm.logp(invalid_dist, value).eval()
+            pm.logp(invalid_dist, value).eval({x: 0.5})
 
     @pytest.mark.parametrize("n", [2, 3, 4])
     def test_orderedlogistic(self, n):

pymc/tests/distributions/test_multivariate.py

@@ -548,14 +548,14 @@ def test_multinomial_invalid_value(self):
 
     def test_multinomial_negative_p(self):
         # test passing a list/numpy with negative p raises an immediate error
-        with pytest.raises(ValueError, match="[-1, 1, 1]"):
+        with pytest.raises(ValueError, match="Negative `p` parameters are not valid"):
             with pm.Model() as model:
                 x = pm.Multinomial("x", n=5, p=[-1, 1, 1])
 
     def test_multinomial_p_not_normalized(self):
         # test UserWarning is raised for p vals that sum to more than 1
         # and normaliation is triggered
-        with pytest.warns(UserWarning, match="[5]"):
+        with pytest.warns(UserWarning, match="They will be automatically rescaled"):
             with pm.Model() as m:
                 x = pm.Multinomial("x", n=5, p=[1, 1, 1, 1, 1])
         # test stored p-vals have been normalised
@@ -564,18 +564,23 @@ def test_multinomial_p_not_normalized(self):
     def test_multinomial_negative_p_symbolic(self):
         # Passing symbolic negative p does not raise an immediate error, but evaluating
         # logp raises a ParameterValueError
+        value = np.array([[1, 1, 1]])
+
+        x = at.scalar("x")
+        invalid_dist = pm.Multinomial.dist(n=1, p=[x, x, x])
+
         with pytest.raises(ParameterValueError):
-            value = np.array([[1, 1, 1]])
-            invalid_dist = pm.Multinomial.dist(n=1, p=at.as_tensor_variable([-1, 0.5, 0.5]))
-            pm.logp(invalid_dist, value).eval()
+            pm.logp(invalid_dist, value).eval({x: -1 / 3})
 
     def test_multinomial_p_not_normalized_symbolic(self):
         # Passing symbolic p that do not add up to on does not raise any warning, but evaluating
         # logp raises a ParameterValueError
+        value = np.array([[1, 1, 1]])
+
+        x = at.scalar("x")
+        invalid_dist = pm.Multinomial.dist(n=1, p=(x, x, x))
         with pytest.raises(ParameterValueError):
-            value = np.array([[1, 1, 1]])
-            invalid_dist = pm.Multinomial.dist(n=1, p=at.as_tensor_variable([1, 0.5, 0.5]))
-            pm.logp(invalid_dist, value).eval()
+            pm.logp(invalid_dist, value).eval({x: 0.5})
 
     @pytest.mark.parametrize("n", [(10), ([10, 11]), ([[5, 6], [10, 11]])])
     @pytest.mark.parametrize(