Merge branch 'distsigs' of https://github.com/cluhmann/pymc into dist…

…sigs

.github/workflows/autoupdate-pre-commit-config.yml

@@ -14,7 +14,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v2
       - name: Cache multiple paths
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         with:
           path: |
             ~/.cache/pre-commit

.github/workflows/dispatched_pre-commit.yml

@@ -13,7 +13,7 @@ jobs:
           ref: ${{github.event.client_payload.pull_request.head.ref}}
           token: ${{ secrets.ACTION_TRIGGER_TOKEN }}
       - name: Cache multiple paths
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         with:
           path: |
             ~/.cache/pre-commit

.github/workflows/tests.yml

@@ -86,7 +86,7 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - name: Cache conda
-        uses: actions/cache@v1
+        uses: actions/cache@v3
         env:
           # Increase this value to reset cache if environment-test-py37.yml has not changed
           CACHE_NUMBER: 0
@@ -95,7 +95,7 @@ jobs:
           key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{
             hashFiles('conda-envs/environment-test-py37.yml') }}
       - name: Cache multiple paths
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         env:
           # Increase this value to reset cache if requirements.txt has not changed
           CACHE_NUMBER: 0
@@ -154,7 +154,7 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - name: Cache conda
-        uses: actions/cache@v1
+        uses: actions/cache@v3
         env:
           # Increase this value to reset cache if conda-envs/environment-test-py38.yml has not changed
           CACHE_NUMBER: 0
@@ -163,7 +163,7 @@ jobs:
           key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{
             hashFiles('conda-envs/windows-environment-test-py38.yml') }}
       - name: Cache multiple paths
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         env:
           # Increase this value to reset cache if requirements.txt has not changed
           CACHE_NUMBER: 0
@@ -230,7 +230,7 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - name: Cache conda
-        uses: actions/cache@v1
+        uses: actions/cache@v3
         env:
           # Increase this value to reset cache if environment-test-py39.yml has not changed
           CACHE_NUMBER: 0
@@ -239,7 +239,7 @@ jobs:
           key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{
             hashFiles('conda-envs/environment-test-py39.yml') }}
       - name: Cache multiple paths
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         env:
           # Increase this value to reset cache if requirements.txt has not changed
           CACHE_NUMBER: 0
@@ -292,7 +292,7 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - name: Cache conda
-        uses: actions/cache@v1
+        uses: actions/cache@v3
         env:
           # Increase this value to reset cache if environment-test-py39.yml has not changed
           CACHE_NUMBER: 0
@@ -301,7 +301,7 @@ jobs:
           key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{
             hashFiles('conda-envs/environment-test-py39.yml') }}
       - name: Cache multiple paths
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         env:
           # Increase this value to reset cache if requirements.txt has not changed
           CACHE_NUMBER: 0
@@ -359,7 +359,7 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - name: Cache conda
-        uses: actions/cache@v1
+        uses: actions/cache@v3
         env:
           # Increase this value to reset cache if conda-envs/environment-test-py38.yml has not changed
           CACHE_NUMBER: 0
@@ -368,7 +368,7 @@ jobs:
           key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{
             hashFiles('conda-envs/windows-environment-test-py38.yml') }}
       - name: Cache multiple paths
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         env:
           # Increase this value to reset cache if requirements.txt has not changed
           CACHE_NUMBER: 0

docs/source/contributing/developer_guide_implementing_distribution.md

@@ -4,7 +4,7 @@ This guide provides an overview on how to implement a distribution for version 4
 It is designed for developers who wish to add a new distribution to the library.
 Users will not be aware of all this complexity and should instead make use of helper methods such as `~pymc.distributions.DensityDist`.
 
-PyMC {class}`~pymc.distributions.Distribution` builds on top of Aesara's {class}`~aesara.tensor.random.op.RandomVariable`, and implements `logp`, `logcdf` and `get_moment` methods as well as other initialization and validation helpers.
+PyMC {class}`~pymc.distributions.Distribution` builds on top of Aesara's {class}`~aesara.tensor.random.op.RandomVariable`, and implements `logp`, `logcdf` and `moment` methods as well as other initialization and validation helpers.
 Most notably `shape/dims` kwargs, alternative parametrizations, and default `transforms`.
 
 Here is a summary check-list of the steps needed to implement a new distribution.
@@ -13,7 +13,7 @@ Each section will be expanded below:
 1. Creating a new `RandomVariable` `Op`
 1. Implementing the corresponding `Distribution` class
 1. Adding tests for the new `RandomVariable`
-1. Adding tests for `logp` / `logcdf` and `get_moment` methods
+1. Adding tests for `logp` / `logcdf` and `moment` methods
 1. Documenting the new `Distribution`.
 
 This guide does not attempt to explain the rationale behind the `Distributions` current implementation, and details are provided only insofar as they help to implement new "standard" distributions.
@@ -119,7 +119,7 @@ After implementing the new `RandomVariable` `Op`, it's time to make use of it in
 PyMC 4.x works in a very {term}`functional <Functional Programming>` way, and the `distribution` classes are there mostly to facilitate porting the `PyMC3` v3.x code to the new `PyMC` v4.x version, add PyMC API features and keep related methods organized together.
 In practice, they take care of:
 
-1. Linking ({term}`Dispatching`) a rv_op class with the corresponding `get_moment`, `logp` and `logcdf` methods.
+1. Linking ({term}`Dispatching`) a rv_op class with the corresponding `moment`, `logp` and `logcdf` methods.
 1. Defining a standard transformation (for continuous distributions) that converts a bounded variable domain (e.g., positive line) to an unbounded domain (i.e., the real line), which many samplers prefer.
 1. Validating the parametrization of a distribution and converting non-symbolic inputs (i.e., numeric literals or numpy arrays) to symbolic variables.
 1. Converting multiple alternative parametrizations to the standard parametrization that the `RandomVariable` is defined in terms of.
@@ -154,9 +154,9 @@ class Blah(PositiveContinuous):
         # the rv_op needs in order to be instantiated
         return super().dist([param1, param2], **kwargs)
 
-    # get_moment returns a symbolic expression for the stable moment from which to start sampling
+    # moment returns a symbolic expression for the stable moment from which to start sampling
     # the variable, given the implicit `rv`, `size` and `param1` ... `paramN`
-    def get_moment(rv, size, param1, param2):
+    def moment(rv, size, param1, param2):
         moment, _ = at.broadcast_arrays(param1, param2)
         if not rv_size_is_none(size):
             moment = at.full(size, moment)
@@ -193,30 +193,29 @@ class Blah(PositiveContinuous):
 
 Some notes:
 
-1. A distribution should at the very least inherit from {class}`~pymc.distributions.Discrete` or {class}`~pymc.distributions.Continuous`. For the latter, more specific subclasses exist: `PositiveContinuous`, `UnitContinuous`, `BoundedContinuous`, `CircularContinuous`, which specify default transformations for the variables. If you need to specify a one-time custom transform you can also override the `__new__` method, as is done for the {class}`~pymc.distributions.multivariate.Dirichlet`.
-1. If a distribution does not have a corresponding `random` implementation, a `RandomVariable` should still be created that raises a `NotImplementedError`. This is the case for the {class}`~pymc.distributions.continuous.Flat`. In this case it will be necessary to provide a standard `initval` by
-   overriding `__new__`.
+1. A distribution should at the very least inherit from {class}`~pymc.distributions.Discrete` or {class}`~pymc.distributions.Continuous`. For the latter, more specific subclasses exist: `PositiveContinuous`, `UnitContinuous`, `BoundedContinuous`, `CircularContinuous`, `SimplexContinuous`, which specify default transformations for the variables. If you need to specify a one-time custom transform you can also create a `_default_transform` dispatch function as is done for the {class}`~pymc.distributions.multivariate.LKJCholeskyCov`.
+1. If a distribution does not have a corresponding `random` implementation, a `RandomVariable` should still be created that raises a `NotImplementedError`. This is the case for the {class}`~pymc.distributions.continuous.Flat`. In this case it will be necessary to provide a `moment` method.
 1. As mentioned above, `PyMC` v4.x works in a very {term}`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` {term}`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!
-1. Currently only one moment is supported in the `get_moment` method, and probably the "higher-order" one is the most useful (that is `mean` > `median` > `mode`)... You might need to truncate the moment if you are dealing with a discrete distribution.
-1. When creating the `get_moment` method, we have to be careful with `size != None` and broadcast properly when some parameters that are not used in the moment may nevertheless inform about the shape of the distribution. E.g. `pm.Normal.dist(mu=0, sigma=np.arange(1, 6))` returns a moment of `[mu, mu, mu, mu, mu]`.
+1. Currently only one moment is supported in the `moment` method, and probably the "higher-order" one is the most useful (that is `mean` > `median` > `mode`)... You might need to truncate the moment if you are dealing with a discrete distribution.
+1. When creating the `moment` method, we have to be careful with `size != None` and broadcast properly when some parameters that are not used in the moment may nevertheless inform about the shape of the distribution. E.g. `pm.Normal.dist(mu=0, sigma=np.arange(1, 6))` returns a moment of `[mu, mu, mu, mu, mu]`.
 
 For a quick check that things are working you can try the following:
 
 ```python
 
 import pymc as pm
-from pymc.distributions.distribution import get_moment
+from pymc.distributions.distribution import moment
 
 # pm.blah = pm.Normal in this example
-blah = pm.blah.dist(mu = 0, sigma = 1)
+blah = pm.blah.dist(mu=0, sigma=1)
 
 # Test that the returned blah_op is still working fine
 blah.eval()
 # array(-1.01397228)
 
-# Test the get_moment method
-get_moment(blah).eval()
+# Test the moment method
+moment(blah).eval()
 # array(0.)
 
 # Test the logp method
@@ -367,9 +366,9 @@ def test_blah_logcdf(self):
 
 ```
 
-## 5. Adding tests for the `get_moment` method
+## 5. Adding tests for the `moment` method
 
-Tests for the `get_moment` method are contained in `pymc/tests/test_distributions_moments.py`, and make use of the function `assert_moment_is_expected`
+Tests for the `moment` method are contained in `pymc/tests/test_distributions_moments.py`, and make use of the function `assert_moment_is_expected`
 which checks if:
 1. Moments return the `expected` values
 1. Moments have the expected size and shape

pymc/distributions/bound.py

@@ -20,11 +20,12 @@
 from aesara.tensor.var import TensorVariable
 
 from pymc.aesaraf import floatX, intX
-from pymc.distributions.continuous import BoundedContinuous
+from pymc.distributions.continuous import BoundedContinuous, bounded_cont_transform
 from pymc.distributions.dist_math import check_parameters
 from pymc.distributions.distribution import Continuous, Discrete
 from pymc.distributions.logprob import logp
 from pymc.distributions.shape_utils import to_tuple
+from pymc.distributions.transforms import _default_transform
 from pymc.model import modelcontext
 from pymc.util import check_dist_not_registered
 
@@ -82,6 +83,11 @@ def logp(value, distribution, lower, upper):
         )
 
 
+@_default_transform.register(BoundRV)
+def bound_default_transform(op, rv):
+    return bounded_cont_transform(op, rv, _ContinuousBounded.bound_args_indices)
+
+
 class DiscreteBoundRV(BoundRV):
     name = "discrete_bound"
     dtype = "int64"
@@ -94,8 +100,8 @@ class _DiscreteBounded(Discrete):
     rv_op = discrete_boundrv
 
     def __new__(cls, *args, **kwargs):
-        transform = kwargs.get("transform", None)
-        if transform is not None:
+        kwargs.setdefault("transform", None)
+        if kwargs.get("transform") is not None:
             raise ValueError("Cannot transform discrete variable.")
         return super().__new__(cls, *args, **kwargs)
 

pymc/distributions/censored.py

@@ -18,7 +18,7 @@
 from aesara.tensor import TensorVariable
 from aesara.tensor.random.op import RandomVariable
 
-from pymc.distributions.distribution import SymbolicDistribution, _get_moment
+from pymc.distributions.distribution import SymbolicDistribution, _moment
 from pymc.util import check_dist_not_registered
 
 
@@ -124,8 +124,8 @@ def graph_rvs(cls, rv):
         return (rv.tag.dist,)
 
 
-@_get_moment.register(Clip)
-def get_moment_censored(op, rv, dist, lower, upper):
+@_moment.register(Clip)
+def moment_censored(op, rv, dist, lower, upper):
     moment = at.switch(
         at.eq(lower, -np.inf),
         at.switch(