Reintroduce logit_p argument in Bernoulli

Author: ricardoV94

pymc3/distributions/discrete.py

@@ -333,8 +333,15 @@ class Bernoulli(Discrete):
 
     @classmethod
     def dist(cls, p=None, logit_p=None, *args, **kwargs):
+        if p is not None and logit_p is not None:
+            raise ValueError("Incompatible parametrization. Can't specify both p and logit_p.")
+        elif p is None and logit_p is None:
+            raise ValueError("Incompatible parametrization. Must specify either p or logit_p.")
+
+        if logit_p is not None:
+            p = at.sigmoid(logit_p)
+
         p = at.as_tensor_variable(floatX(p))
-        # mode = at.cast(tround(p), "int8")
         return super().dist([p], **kwargs)
 
     def logp(value, p):
@@ -351,12 +358,9 @@ def logp(value, p):
         -------
         TensorVariable
         """
-        # if self._is_logit:
-        #     lp = at.switch(value, self._logit_p, -self._logit_p)
-        #     return -log1pexp(-lp)
-        # else:
+
         return bound(
-            at.switch(value, at.log(p), at.log(1 - p)),
+            at.switch(value, at.log(p), at.log1p(-p)),
             value >= 0,
             value <= 1,
             p >= 0,

pymc3/tests/test_distributions.py

@@ -1608,40 +1608,50 @@ def test_beta_binomial(self):
             {"alpha": Rplus, "beta": Rplus, "n": NatSmall},
         )
 
-    @pytest.mark.xfail(reason="Bernoulli logit_p not refactored yet")
-    def test_bernoulli_logit_p(self):
+    def test_bernoulli(self):
         self.check_logp(
             Bernoulli,
             Bool,
-            {"logit_p": R},
-            lambda value, logit_p: sp.bernoulli.logpmf(value, scipy.special.expit(logit_p)),
+            {"p": Unit},
+            lambda value, p: sp.bernoulli.logpmf(value, p),
         )
-        self.check_logcdf(
+        self.check_logp(
             Bernoulli,
             Bool,
             {"logit_p": R},
-            lambda value, logit_p: sp.bernoulli.logcdf(value, scipy.special.expit(logit_p)),
+            lambda value, logit_p: sp.bernoulli.logpmf(value, scipy.special.expit(logit_p)),
         )
-
-    def test_bernoulli(self):
-        self.check_logp(
+        self.check_logcdf(
             Bernoulli,
             Bool,
             {"p": Unit},
-            lambda value, p: sp.bernoulli.logpmf(value, p),
+            lambda value, p: sp.bernoulli.logcdf(value, p),
         )
         self.check_logcdf(
             Bernoulli,
             Bool,
-            {"p": Unit},
-            lambda value, p: sp.bernoulli.logcdf(value, p),
+            {"logit_p": R},
+            lambda value, logit_p: sp.bernoulli.logcdf(value, scipy.special.expit(logit_p)),
         )
         self.check_selfconsistency_discrete_logcdf(
             Bernoulli,
             Bool,
             {"p": Unit},
         )
 
+    def test_bernoulli_wrong_arguments(self):
+        m = pm.Model()
+
+        msg = "Incompatible parametrization. Can't specify both p and logit_p"
+        with m:
+            with pytest.raises(ValueError, match=msg):
+                Bernoulli("x", p=0.5, logit_p=0)
+
+        msg = "Incompatible parametrization. Must specify either p or logit_p"
+        with m:
+            with pytest.raises(ValueError, match=msg):
+                Bernoulli("x")
+
     def test_discrete_weibull(self):
         self.check_logp(
             DiscreteWeibull,

pymc3/tests/test_distributions_random.py

@@ -1025,11 +1025,10 @@ class TestBernoulli(BaseTestDistribution):
     ]
 
 
-@pytest.mark.skip("Still not implemented")
 class TestBernoulliLogitP(BaseTestDistribution):
     pymc_dist = pm.Bernoulli
     pymc_dist_params = {"logit_p": 1.0}
-    expected_rv_op_params = {"mean": 0, "sigma": 10.0}
+    expected_rv_op_params = {"p": expit(1.0)}
     tests_to_run = ["check_pymc_params_match_rv_op"]
 
 

pymc3/tests/test_examples.py

@@ -51,7 +51,6 @@ def get_city_data():
     return data.merge(unique, "inner", on="fips")
 
 
-@pytest.mark.xfail(reason="Bernoulli logitp distribution not refactored")
 class TestARM5_4(SeededTest):
     def build_model(self):
         data = pd.read_csv(