Don't use check_parameters in get_tau_sigma.

The default use of `check_parameters` indicates that an expression can be replaced by -inf, if the constraints aren't met. Instead, if `can_be_replaced_by_ninf=False`, sampling would fail for negative tau/sigma.

To avoid this, the conversion now returns the right value for positive tau or sigma, but negative images if the inputs were negative. The methods that then validate the paramters (such as logp, logcdf, random), can later catch this.

pymc/distributions/continuous.py

@@ -234,24 +234,26 @@ def get_tau_sigma(tau=None, sigma=None):
             tau = 1.0
         else:
             if isinstance(sigma, Variable):
-                sigma_ = check_parameters(sigma, sigma > 0, msg="sigma > 0")
+                # Keep tau negative, if sigma was negative, so that it will fail when used
+                tau = (sigma**-2.0) * pt.sgn(sigma)
             else:
                 sigma_ = np.asarray(sigma)
                 if np.any(sigma_ <= 0):
                     raise ValueError("sigma must be positive")
-            tau = sigma_**-2.0
+                tau = sigma_**-2.0
 
     else:
         if sigma is not None:
             raise ValueError("Can't pass both tau and sigma")
         else:
             if isinstance(tau, Variable):
-                tau_ = check_parameters(tau, tau > 0, msg="tau > 0")
+                # Keep sigma negative, if tau was negative, so that it will fail when used
+                sigma = pt.abs(tau) ** (-0.5) * pt.sgn(tau)
             else:
                 tau_ = np.asarray(tau)
                 if np.any(tau_ <= 0):
                     raise ValueError("tau must be positive")
-            sigma = tau_**-0.5
+                sigma = tau_**-0.5
 
     return floatX(tau), floatX(sigma)
 

tests/distributions/test_continuous.py

@@ -13,6 +13,7 @@
 #   limitations under the License.
 
 import functools as ft
+import warnings
 
 import numpy as np
 import numpy.testing as npt
@@ -890,24 +891,26 @@ def scipy_logp(value, mu, sigma, lower, upper):
         assert np.isinf(logp[2])
 
     def test_get_tau_sigma(self):
-        sigma = np.array(2)
-        npt.assert_almost_equal(get_tau_sigma(sigma=sigma), [1.0 / sigma**2, sigma])
+        # Fail on warnings
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
 
-        tau = np.array(2)
-        npt.assert_almost_equal(get_tau_sigma(tau=tau), [tau, tau**-0.5])
+            sigma = np.array(2)
+            npt.assert_almost_equal(get_tau_sigma(sigma=sigma), [1.0 / sigma**2, sigma])
 
-        tau, _ = get_tau_sigma(sigma=pt.constant(-2))
-        with pytest.raises(ParameterValueError):
-            tau.eval()
+            tau = np.array(2)
+            npt.assert_almost_equal(get_tau_sigma(tau=tau), [tau, tau**-0.5])
 
-        _, sigma = get_tau_sigma(tau=pt.constant(-2))
-        with pytest.raises(ParameterValueError):
-            sigma.eval()
+            tau, _ = get_tau_sigma(sigma=pt.constant(-2))
+            npt.assert_almost_equal(tau.eval(), -0.25)
 
-        sigma = [1, 2]
-        npt.assert_almost_equal(
-            get_tau_sigma(sigma=sigma), [1.0 / np.array(sigma) ** 2, np.array(sigma)]
-        )
+            _, sigma = get_tau_sigma(tau=pt.constant(-2))
+            npt.assert_almost_equal(sigma.eval(), -np.sqrt(1 / 2))
+
+            sigma = [1, 2]
+            npt.assert_almost_equal(
+                get_tau_sigma(sigma=sigma), [1.0 / np.array(sigma) ** 2, np.array(sigma)]
+            )
 
     @pytest.mark.parametrize(
         "value,mu,sigma,nu,logp",