Add user-friendly logp and logcdf methods

docs/source/developer_guide_implementing_distribution.md

@@ -188,33 +188,25 @@ Some notes:
 1. As mentioned above, `v4` works in a very {functional}`Functional_Programming` way, and all the information that is needed in the `logp` and `logcdf` methods is expected to be "carried" via the `RandomVariable` inputs. You may pass numerical arguments that are not strictly needed for the `rng_fn` method but are used in the `logp` and `logcdf` methods. Just keep in mind whether this affects the correct shape inference behavior of the `RandomVariable`. If specialized non-numeric information is needed you might need to define your custom`_logp` and `_logcdf` {dispatch}`Dispatching` functions, but this should be done as a last resort.
 1. The `logcdf` method is not a requirement, but it's a nice plus!
 
-For a quick check that things are working you can try to create the new distribution in a model context:
+For a quick check that things are working you can try the following:
 
 ```python
 
 import pymc3 as pm
-from pymc3.distributions.logp import logpt
 
-with pm.Model() as model:
-   # pm.blah = pm.Uniform in this example
-   blah = pm.Blah('blah', [0, 0], [1, 2])
+# pm.blah = pm.Uniform in this example
+blah = pm.Blah.dist([0, 0], [1, 2])
 
 # Test that the returned blah_op is still working fine
 blah.eval()
 # array([0.62778803, 1.95165513])
 
-# logpt will replace the blah RandomVariable with the corresponding logp
-# expression, which takes as input the `blah_value` `TensorVariable` and
-# `blah.owner.inputs`. We pass `transformed=False` to return the original
-# `logp` expression just for simplicity of the example.
-blah_value = model.rvs_to_values[blah]
-blah_logp = logpt(blah, {blah: blah_value}, transformed=False)
-blah_logp.eval({blah_value: [1.5, 1.5]})
+# Test the logp
+pm.logp(blah, [1.5, 1.5]).eval()
 # array([       -inf, -0.69314718])
 
-# It will instead introduce the `logcdf` expression if we pass `cdf=True`
-blah_logcdf = logpt(blah, {blah: blah_value}, cdf=True)
-blah_logcdf.eval({blah_value: [1.5, 1.5]})
+# Test the logcdf
+pm.logcdf(blah, [1.5, 1.5]).eval()
 # array([ 0.        , -0.28768207])
 ```
 

pymc3/distributions/__init__.py

@@ -12,10 +12,11 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-from pymc3.distributions.logp import (  # isort:skip
+from pymc3.distributions.logprob import (  # isort:skip
     _logcdf,
     _logp,
     logcdf,
+    logp,
     logp_transform,
     logpt,
     logpt_sum,
@@ -189,6 +190,7 @@
     "BART",
     "CAR",
     "logpt",
+    "logp",
     "_logp",
     "logp_transform",
     "logcdf",

pymc3/distributions/discrete.py

@@ -42,7 +42,7 @@
     normal_lcdf,
 )
 from pymc3.distributions.distribution import Discrete
-from pymc3.distributions.logp import _logcdf, _logp
+from pymc3.distributions.logprob import _logcdf, _logp
 from pymc3.math import log1mexp, logaddexp, logsumexp, sigmoid
 
 __all__ = [

pymc3/distributions/logp.py → pymc3/distributions/logprob.py

@@ -342,9 +342,24 @@ def subtensor_logp(op, var, rvs_to_values, indexed_rv_var, *indices, **kwargs):
     return logp_var
 
 
-def logcdf(*args, **kwargs):
+def logp(var, rv_values, **kwargs):
+    """Create a log-probability graph."""
+
+    # Attach the value_var to the tag of var when it does not have one
+    if not hasattr(var.tag, "value_var"):
+        if isinstance(rv_values, Mapping):
+            value_var = rv_values[var]
+        else:
+            value_var = rv_values
+        var.tag.value_var = at.as_tensor_variable(value_var, dtype=var.dtype)
+
+    return logpt(var, rv_values, **kwargs)
+
+
+def logcdf(var, rv_values, **kwargs):
     """Create a log-CDF graph."""
-    return logpt(*args, cdf=True, **kwargs)
+
+    return logp(var, rv_values, cdf=True, **kwargs)
 
 
 @singledispatch