Fix deprecation warning: use xlogy

pytensor_distributions/beta.py

@@ -1,7 +1,6 @@
 import pytensor.tensor as pt
 from pytensor.tensor.math import betaincinv
-from pytensor.tensor.special import betaln
-from pytensor.tensor.xlogx import xlogy0
+from pytensor.tensor.special import betaln, xlogy
 
 from pytensor_distributions.helper import ppf_bounds_cont
 from pytensor_distributions.lmoments import _lmoments
@@ -120,8 +119,8 @@ def logpdf(x, alpha, beta):
     return pt.switch(
         pt.bitwise_or(pt.lt(x, 0), pt.gt(x, 1)),
         -pt.inf,
-        (xlogy0((alpha - 1), x) + xlogy0((beta - 1), 1 - x))
-        - (xlogy0((alpha + beta - 1), 1) + betaln(alpha, beta)),
+        (xlogy((alpha - 1), x) + xlogy((beta - 1), 1 - x))
+        - (xlogy((alpha + beta - 1), 1) + betaln(alpha, beta)),
     )
 
 

pytensor_distributions/betascaled.py

@@ -1,7 +1,6 @@
 import pytensor.tensor as pt
 from pytensor.tensor.math import betaincinv
-from pytensor.tensor.special import betaln
-from pytensor.tensor.xlogx import xlogy0
+from pytensor.tensor.special import betaln, xlogy
 
 from pytensor_distributions.helper import ppf_bounds_cont
 from pytensor_distributions.lmoments import _lmoments
@@ -124,8 +123,8 @@ def logpdf(x, alpha, beta, lower, upper):
     return pt.switch(
         pt.bitwise_or(pt.lt(x, lower), pt.gt(x, upper)),
         -pt.inf,
-        (xlogy0((alpha - 1), (x - lower)) + xlogy0((beta - 1), (upper - x)))
-        - (xlogy0((alpha + beta - 1), (upper - lower)) + betaln(alpha, beta)),
+        (xlogy((alpha - 1), (x - lower)) + xlogy((beta - 1), (upper - x)))
+        - (xlogy((alpha + beta - 1), (upper - lower)) + betaln(alpha, beta)),
     )
 
 

pytensor_distributions/binomial.py

@@ -1,5 +1,5 @@
 import pytensor.tensor as pt
-from pytensor.tensor.xlogx import xlogy0
+from pytensor.tensor.special import xlogy
 
 from pytensor_distributions.helper import cdf_bounds, discrete_entropy
 from pytensor_distributions.lmoments import _lmoments
@@ -87,7 +87,7 @@ def logpdf(x, n, p):
         pt.gammaln(n + 1)
         - pt.gammaln(x + 1)
         - pt.gammaln(n - x + 1)
-        + xlogy0(x, p)
+        + xlogy(x, p)
         + (n - x) * pt.log1p(-p),
     )
 

pytensor_distributions/chisquared.py

@@ -1,5 +1,5 @@
 import pytensor.tensor as pt
-from pytensor.tensor.xlogx import xlogy0
+from pytensor.tensor.special import xlogy
 
 from pytensor_distributions.helper import cdf_bounds, ppf_bounds_cont
 from pytensor_distributions.lmoments import _lmoments
@@ -87,7 +87,7 @@ def logpdf(x, nu):
     return pt.switch(
         pt.lt(x, 0),
         -pt.inf,
-        xlogy0(nu / 2 - 1, x) - x / 2 - pt.gammaln(nu / 2) - (nu * pt.log(2)) / 2,
+        xlogy(nu / 2 - 1, x) - x / 2 - pt.gammaln(nu / 2) - (nu * pt.log(2)) / 2,
     )
 
 

pytensor_distributions/kumaraswamy.py

@@ -1,6 +1,5 @@
 import pytensor.tensor as pt
-from pytensor.tensor.special import betaln
-from pytensor.tensor.xlogx import xlogy0
+from pytensor.tensor.special import betaln, xlogy
 
 from pytensor_distributions.helper import cdf_bounds, ppf_bounds_cont, sf_bounds
 
@@ -112,7 +111,7 @@ def logpdf(x, a, b):
     return pt.switch(
         pt.bitwise_or(pt.le(x, 0), pt.ge(x, 1)),
         -pt.inf,
-        pt.log(a * b) + xlogy0(a - 1, x) + xlogy0(b - 1, 1 - x**a),
+        pt.log(a * b) + xlogy(a - 1, x) + xlogy(b - 1, 1 - x**a),
     )
 
 

pytensor_distributions/negativebinomial.py

@@ -1,5 +1,5 @@
 import pytensor.tensor as pt
-from pytensor.tensor.xlogx import xlogy0
+from pytensor.tensor.special import xlogy
 
 from pytensor_distributions.helper import cdf_bounds, discrete_entropy, sf_bounds
 from pytensor_distributions.lmoments import _lmoments
@@ -88,7 +88,7 @@ def logpdf(x, n, p):
     return pt.switch(
         pt.lt(x, 0),
         -pt.inf,
-        pt.gammaln(x + n) - pt.gammaln(n) - pt.gammaln(x + 1) + xlogy0(n, p) + xlogy0(x, 1 - p),
+        pt.gammaln(x + n) - pt.gammaln(n) - pt.gammaln(x + 1) + xlogy(n, p) + xlogy(x, 1 - p),
     )
 
 

pytensor_distributions/poisson.py

@@ -1,5 +1,5 @@
 import pytensor.tensor as pt
-from pytensor.tensor.xlogx import xlogy0
+from pytensor.tensor.special import xlogy
 
 from pytensor_distributions.helper import cdf_bounds, discrete_entropy, sf_bounds
 from pytensor_distributions.lmoments import _lmoments
@@ -85,7 +85,7 @@ def rvs(mu, size=None, random_state=None):
 
 
 def logpdf(x, mu):
-    return xlogy0(x, mu) - pt.gammaln(x + 1) - mu
+    return xlogy(x, mu) - pt.gammaln(x + 1) - mu
 
 
 def logcdf(x, mu):

pytensor_distributions/weibull.py

@@ -1,6 +1,5 @@
 import pytensor.tensor as pt
-from pytensor.tensor.special import gamma
-from pytensor.tensor.xlogx import xlogy0
+from pytensor.tensor.special import gamma, xlogy
 
 from pytensor_distributions.helper import ppf_bounds_cont
 
@@ -106,7 +105,7 @@ def logpdf(x, alpha, beta):
     return pt.switch(
         pt.lt(x, 0),
         -pt.inf,
-        pt.log(alpha / beta) + xlogy0(alpha - 1, x / beta) - (x / beta) ** alpha,
+        pt.log(alpha / beta) + xlogy(alpha - 1, x / beta) - (x / beta) ** alpha,
     )