Refactor get_tau_sigma and support lists of variables

pymc/distributions/continuous.py

@@ -229,32 +229,21 @@
     -----
     If neither tau nor sigma is provided, returns (1., 1.)
     """
-    if tau is None:
-        if sigma is None:
-            sigma = 1.0
-            tau = 1.0
-        else:
-            if isinstance(sigma, Variable):
-                # Keep tau negative, if sigma was negative, so that it will fail when used
-                tau = (sigma**-2.0) * pt.sign(sigma)
-            else:
-                sigma_ = np.asarray(sigma)
-                if np.any(sigma_ <= 0):
-                    raise ValueError("sigma must be positive")
-                tau = sigma_**-2.0
-
+    if tau is not None and sigma is not None:
+        raise ValueError("Can't pass both tau and sigma")
+    if tau is None and sigma is None:
+        sigma = pt.as_tensor_variable(1.0)
+        tau = pt.as_tensor_variable(1.0)
+    elif tau is None:
+        sigma = pt.as_tensor_variable(sigma)
+        # Keep tau negative, if sigma was negative, so that it will
+        # fail when used
+        tau = (sigma**-2.0) * pt.sign(sigma)
     else:
-        if sigma is not None:
-            raise ValueError("Can't pass both tau and sigma")
-        else:
-            if isinstance(tau, Variable):
-                # Keep sigma negative, if tau was negative, so that it will fail when used
-                sigma = pt.abs(tau) ** (-0.5) * pt.sign(tau)
-            else:
-                tau_ = np.asarray(tau)
-                if np.any(tau_ <= 0):
-                    raise ValueError("tau must be positive")
-                sigma = tau_**-0.5
+        tau = pt.as_tensor_variable(tau)
+        # Keep tau negative, if sigma was negative, so that it will
+        # fail when used
+        sigma = pt.abs(tau) ** -0.5 * pt.sign(tau)
 
     return tau, sigma
 

tests/distributions/test_continuous.py

@@ -13,7 +13,6 @@
 #   limitations under the License.
 
 import functools as ft
-import warnings
 
 import numpy as np
 import numpy.testing as npt
@@ -998,26 +997,45 @@ def scipy_logcdf(value, mu, sigma, lower, upper):
         assert np.isinf(logp[2])
 
     def test_get_tau_sigma(self):
-        # Fail on warnings
-        with warnings.catch_warnings():
-            warnings.simplefilter("error")
+        sigma = np.array(2)
+        tau, _ = get_tau_sigma(sigma=sigma)
+        npt.assert_almost_equal(tau.eval(), 1.0 / sigma**2)
 
-            sigma = np.array(2)
-            npt.assert_almost_equal(get_tau_sigma(sigma=sigma), [1.0 / sigma**2, sigma])
+        tau = np.array(2)
+        _, sigma = get_tau_sigma(tau=tau)
+        npt.assert_almost_equal(sigma.eval(), tau**-0.5)
 
-            tau = np.array(2)
-            npt.assert_almost_equal(get_tau_sigma(tau=tau), [tau, tau**-0.5])
+        tau, _ = get_tau_sigma(sigma=pt.constant(-2))
+        npt.assert_almost_equal(tau.eval(), -0.25)
 
-            tau, _ = get_tau_sigma(sigma=pt.constant(-2))
-            npt.assert_almost_equal(tau.eval(), -0.25)
+        _, sigma = get_tau_sigma(tau=pt.constant(-2))
+        npt.assert_almost_equal(sigma.eval(), -1.0 / np.sqrt(2.0))
 
-            _, sigma = get_tau_sigma(tau=pt.constant(-2))
-            npt.assert_almost_equal(sigma.eval(), -np.sqrt(1 / 2))
+        sigma = [1, 2]
+        tau, _ = get_tau_sigma(sigma=sigma)
+        npt.assert_almost_equal(tau.eval(), 1.0 / np.array(sigma) ** 2)
 
-            sigma = [1, 2]
-            npt.assert_almost_equal(
-                get_tau_sigma(sigma=sigma), [1.0 / np.array(sigma) ** 2, np.array(sigma)]
-            )
+        # Test null arguments
+        tau, sigma = get_tau_sigma()
+        npt.assert_almost_equal(tau.eval(), 1.0)
+        npt.assert_almost_equal(sigma.eval(), 1.0)
+
+        # Test exception upon passing both sigma and tau
+        msg = "Can't pass both tau and sigma"
+        with pytest.raises(ValueError, match=msg):
+            _, _ = get_tau_sigma(sigma=1.0, tau=1.0)
+
+        # These are regression test for #6988: Check that get_tau_sigma works
+        # for lists of tensors
+        sigma = [pt.constant(2), pt.constant(2)]
+        expect_tau = np.array([0.25, 0.25])
+        tau, _ = get_tau_sigma(sigma=sigma)
+        npt.assert_almost_equal(tau.eval(), expect_tau)
+
+        tau = [pt.constant(2), pt.constant(2)]
+        expect_sigma = np.array([2.0, 2.0]) ** -0.5
+        _, sigma = get_tau_sigma(tau=tau)
+        npt.assert_almost_equal(sigma.eval(), expect_sigma)
 
     @pytest.mark.parametrize(
         "value,mu,sigma,nu,logp",
@@ -2042,7 +2060,7 @@ class TestStudentTLam(BaseTestDistributionRandom):
     lam, sigma = get_tau_sigma(tau=2.0)
     pymc_dist_params = {"nu": 5.0, "mu": -1.0, "lam": lam}
     expected_rv_op_params = {"nu": 5.0, "mu": -1.0, "lam": sigma}
-    reference_dist_params = {"df": 5.0, "loc": -1.0, "scale": sigma}
+    reference_dist_params = {"df": 5.0, "loc": -1.0, "scale": sigma.eval()}
     reference_dist = seeded_scipy_distribution_builder("t")
     checks_to_run = ["check_pymc_params_match_rv_op"]