Add backports module and backport logp syntax (#5083)

* Add backports module and backport logp syntax

Closes #5052

Co-authored-by: Ravin Kumar <ravinsdrive@gmail.com>

Author: michaelosthege

.github/workflows/pytest.yml

@@ -13,6 +13,7 @@ jobs:
         floatx: [float32, float64]
         test-subset:
           - |
+            --ignore=pymc3/tests/test_backports.py
             --ignore=pymc3/tests/test_dist_math.py
             --ignore=pymc3/tests/test_distribution_defaults.py
             --ignore=pymc3/tests/test_distributions.py
@@ -42,6 +43,7 @@ jobs:
             pymc3/tests/test_shared.py
             pymc3/tests/test_smc.py
           - |
+            pymc3/tests/test_backports.py
             pymc3/tests/test_examples.py
             pymc3/tests/test_mixture.py
             pymc3/tests/test_ode.py

RELEASE-NOTES.md

@@ -1,5 +1,9 @@
 # Release Notes
 
+## PyMC 3.11.5 (TBD)
+### Backports
++ The `pm.logp(rv, x)` syntax is now available and recommended to make your model code `v4`-ready. Note that this backport is just an alias and much less capable than what's available with `pymc >=4` (see [#5083](https://github.com/pymc-devs/pymc/pulls/5083)).
+
 ## PyMC3 3.11.4 (20 August 2021)
 
 ### New Features

docs/source/developer_guide.rst

@@ -158,7 +158,7 @@ explicit about the conversion. For example:
     with pm.Model() as model:
         z = pm.Normal('z', mu=0., sigma=5.)             # ==> pymc3.model.FreeRV, or theano.tensor with logp
         x = pm.Normal('x', mu=z, sigma=1., observed=5.) # ==> pymc3.model.ObservedRV, also has logp properties
-    x.logp({'z': 2.5})                                  # ==> -4.0439386
+    pm.logp(x, {'z': 2.5})                              # ==> -4.0439386
     model.logp({'z': 2.5})                              # ==> -6.6973152
 
 **TFP**

pymc3/__init__.py

@@ -82,6 +82,7 @@ def _hotfix_theano_printing():
 from pymc3 import gp, ode, sampling
 from pymc3.backends import load_trace, save_trace
 from pymc3.backends.tracetab import *
+from pymc3.backports import logp
 from pymc3.blocking import *
 from pymc3.data import *
 from pymc3.distributions import *

pymc3/backports.py

@@ -0,0 +1,43 @@
+#   Copyright 2021 The PyMC Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+from typing import Union
+
+import numpy as np
+
+from theano.tensor import TensorVariable
+
+from pymc3.distributions.distribution import Distribution
+from pymc3.model import Factor
+
+
+def logp(
+    rv: Union[Factor, Distribution], value: Union[TensorVariable, np.ndarray]
+) -> Union[TensorVariable, np.ndarray]:
+    """
+    Calculate log-probability of a distribution at specified value.
+
+    This function is a limited functionality backported version of PyMC >=4.0 like capabilities.
+
+    Parameters
+    ----------
+    value : numeric
+        Value(s) for which log-probability is calculated. If the log-probabilities for multiple
+        values are desired the values must be provided in a numpy array or theano tensor
+
+    Returns
+    -------
+    logp : TensorVariable or np.ndarray
+    """
+    return rv.logp(value)

pymc3/tests/test_backports.py

@@ -0,0 +1,32 @@
+#   Copyright 2021 The PyMC Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+import numpy as np
+
+import pymc3 as pm
+
+
+class TestLogpSyntax:
+    def test_equivalence(self):
+        with pm.Model():
+            rv = pm.Normal("n")
+        input = {"n": 2}
+        np.testing.assert_array_equal(rv.logp(input), pm.logp(rv, input))
+
+    def test_equivalence_dist(self):
+        rv = pm.Normal.dist()
+        assert rv.logp(2).eval() == pm.logp(rv, 2).eval()
+        np.testing.assert_array_equal(
+            rv.logp(np.arange(3)).eval(), pm.logp(rv, np.arange(3)).eval()
+        )