changed default values for TruncatedNormal lower and upper from None to -np.inf…

RELEASE-NOTES.md

@@ -29,6 +29,7 @@
 - Changed initialization of `Categorical.p`. `p` is now normalized to sum to `1` inside `logp` and `random`, but not during initialization. This could hide negative values supplied to `p` as mentioned in #2082.
 - `Categorical` now accepts elements of `p` equal to `0`. `logp` will return `-inf` if there are `values` that index to the zero probability categories.
 - Add `sigma`, `tau`, and `sd` to signature of `NormalMixture`.
+- Resolved issue #3248. Set default lower and upper values of -inf and inf for pm.distributions.continuous.TruncatedNormal. This avoids errors caused by their previous values of None.
 
 ### Deprecations
 

docs/release-notes/pymc3-3.0.md

@@ -0,0 +1 @@
+

pymc3/distributions/continuous.py

@@ -571,15 +571,17 @@ def __init__(self, mu=0, sigma=None, tau=None, lower=None, upper=None,
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         self.sigma = self.sd = tt.as_tensor_variable(sigma)
         self.tau = tt.as_tensor_variable(tau)
-        self.lower = tt.as_tensor_variable(floatX(lower)) if lower is not None else lower
-        self.upper = tt.as_tensor_variable(floatX(upper)) if upper is not None else upper
+        self.lower_check = tt.as_tensor_variable(floatX(lower)) if lower is not None else lower
+        self.upper_check = tt.as_tensor_variable(floatX(upper)) if upper is not None else upper
+        self.lower = tt.as_tensor_variable(floatX(lower)) if lower is not None else tt.as_tensor_variable(-np.inf)
+        self.upper = tt.as_tensor_variable(floatX(upper)) if upper is not None else tt.as_tensor_variable(np.inf)
         self.mu = tt.as_tensor_variable(floatX(mu))
 
-        if self.lower is None and self.upper is None:
+        if self.lower_check is None and self.upper_check is None:
             self._defaultval = mu
-        elif self.lower is None and self.upper is not None:
+        elif self.lower_check is None and self.upper_check is not None:
             self._defaultval = self.upper - 1.
-        elif self.lower is not None and self.upper is None:
+        elif self.lower_check is not None and self.upper_check is None:
             self._defaultval = self.lower + 1.
         else:
             self._defaultval = (self.lower + self.upper) / 2
@@ -639,19 +641,19 @@ def logp(self, value):
         logp = Normal.dist(mu=mu, sigma=sigma).logp(value) - norm
 
         bounds = [sigma > 0]
-        if self.lower is not None:
+        if self.lower_check is not None:
             bounds.append(value >= self.lower)
-        if self.upper is not None:
+        if self.upper_check is not None:
             bounds.append(value <= self.upper)
         return bound(logp, *bounds)
 
     def _normalization(self):
         mu, sigma = self.mu, self.sigma
 
-        if self.lower is None and self.upper is None:
+        if self.lower_check is None and self.upper_check is None:
             return 0.
 
-        if self.lower is not None and self.upper is not None:
+        if self.lower_check is not None and self.upper_check is not None:
             lcdf_a = normal_lcdf(mu, sigma, self.lower)
             lcdf_b = normal_lcdf(mu, sigma, self.upper)
             lsf_a = normal_lccdf(mu, sigma, self.lower)
@@ -663,7 +665,7 @@ def _normalization(self):
                 logdiffexp(lcdf_b, lcdf_a),
             )
 
-        if self.lower is not None:
+        if self.lower_check is not None:
             return normal_lccdf(mu, sigma, self.lower)
         else:
             return normal_lcdf(mu, sigma, self.upper)

pymc3/tests/test_distributions_random.py

@@ -257,7 +257,15 @@ class TestNormal(BaseTestCases.BaseTestCase):
 
 class TestTruncatedNormal(BaseTestCases.BaseTestCase):
     distribution = pm.TruncatedNormal
-    params = {'mu': 0., 'tau': 1., 'lower':-0.5, 'upper':0.5}
+    params = {'mu': 0., 'tau': 1., 'lower': -0.5, 'upper': 0.5}
+
+class TestTruncatedNormalLower(BaseTestCases.BaseTestCase):
+    distribution = pm.TruncatedNormal
+    params = {'mu': 0., 'tau': 1., 'lower': -0.5}
+
+class TestTruncatedNormalUpper(BaseTestCases.BaseTestCase):
+    distribution = pm.TruncatedNormal
+    params = {'mu': 0., 'tau': 1., 'upper': 0.5}
 
 class TestSkewNormal(BaseTestCases.BaseTestCase):
     distribution = pm.SkewNormal
@@ -461,7 +469,6 @@ def ref_rand(size, tau):
     def test_uniform(self):
         def ref_rand(size, lower, upper):
             return st.uniform.rvs(size=size, loc=lower, scale=upper - lower)
-
         pymc3_random(pm.Uniform, {'lower': -Rplus, 'upper': Rplus}, ref_rand=ref_rand)
 
     def test_normal(self):
@@ -471,8 +478,20 @@ def ref_rand(size, mu, sigma):
 
     def test_truncated_normal(self):
         def ref_rand(size, mu, sigma, lower, upper):
-            return st.truncnorm.rvs((lower-mu)/sigma, (upper-mu)/sigma, size=size, loc=mu, scale=sigma)
-        pymc3_random(pm.TruncatedNormal, {'mu': R, 'sigma': Rplusbig, 'lower':-Rplusbig, 'upper':Rplusbig},
+            return st.truncnorm.rvs((lower - mu) / sigma, (upper - mu) / sigma, size=size, loc=mu, scale=sigma)
+        pymc3_random(pm.TruncatedNormal, {'mu': R, 'sigma': Rplusbig, 'lower': -Rplusbig, 'upper': Rplusbig},
+                     ref_rand=ref_rand)
+
+    def test_truncated_normal_lower(self):
+        def ref_rand(size, mu, sigma, lower):
+            return st.truncnorm.rvs((lower - mu) / sigma, np.inf, size=size, loc=mu, scale=sigma)
+        pymc3_random(pm.TruncatedNormal, {'mu': R, 'sigma': Rplusbig, 'lower': -Rplusbig},
+                     ref_rand=ref_rand)
+
+    def test_truncated_normal_upper(self):
+        def ref_rand(size, mu, sigma, upper):
+            return st.truncnorm.rvs(-np.inf, (upper - mu) / sigma, size=size, loc=mu, scale=sigma)
+        pymc3_random(pm.TruncatedNormal, {'mu': R, 'sigma': Rplusbig, 'upper': Rplusbig},
                      ref_rand=ref_rand)
 
     def test_skew_normal(self):