Implement `tensor.special.logit` helper (#645)

pytensor/tensor/special.py

@@ -8,7 +8,7 @@ from pytensor.graph.replace import _vectorize_node
 from pytensor.link.c.op import COp
 from pytensor.tensor.basic import as_tensor_variable
 from pytensor.tensor.elemwise import get_normalized_batch_axes
-from pytensor.tensor.math import gamma, gammaln, neg, sum
+from pytensor.tensor.math import gamma, gammaln, log, neg, sum
 
 
 class SoftmaxGrad(COp):
@@ -780,6 +780,14 @@ def factorial(n):
     return gamma(n + 1)
 
 
+def logit(x):
+    """
+    Logit function.
+
+    """
+    return log(x / (1 - x))
+
+
 def beta(a, b):
     """
     Beta function.
@@ -801,6 +809,7 @@ __all__ = [
     "log_softmax",
     "poch",
     "factorial",
+    "logit",
     "beta",
     "betaln",
 ]

tests/tensor/test_special.py

@@ -3,6 +3,7 @@ import pytest
 from scipy.special import beta as scipy_beta
 from scipy.special import factorial as scipy_factorial
 from scipy.special import log_softmax as scipy_log_softmax
+from scipy.special import logit as scipy_logit
 from scipy.special import poch as scipy_poch
 from scipy.special import softmax as scipy_softmax
 
@@ -18,6 +19,7 @@ from pytensor.tensor.special import (
     betaln,
     factorial,
     log_softmax,
+    logit,
     poch,
     softmax,
 )
@@ -206,6 +208,18 @@ def test_factorial(n):
     )
 
 
+def test_logit():
+    x = vector("x")
+    actual_fn = function([x], logit(x), allow_input_downcast=True)
+
+    x_test = np.linspace(0, 1)
+    actual = actual_fn(x_test)
+    expected = scipy_logit(x_test)
+    np.testing.assert_allclose(
+        actual, expected, rtol=1e-7 if config.floatX == "float64" else 1e-5
+    )
+
+
 def test_beta():
     _a, _b = vectors("a", "b")
     actual_fn = function([_a, _b], beta(_a, _b))