Remove NoDistribution class

Author: ricardoV94

pymc/distributions/__init__.py

@@ -81,7 +81,6 @@
     DensityDist,
     Discrete,
     Distribution,
-    NoDistribution,
     SymbolicRandomVariable,
 )
 from pymc.distributions.mixture import Mixture, NormalMixture
@@ -159,7 +158,6 @@
     "SymbolicRandomVariable",
     "Continuous",
     "Discrete",
-    "NoDistribution",
     "MvNormal",
     "MatrixNormal",
     "KroneckerNormal",

pymc/distributions/distribution.py

@@ -62,7 +62,6 @@
     "Distribution",
     "Continuous",
     "Discrete",
-    "NoDistribution",
     "SymbolicRandomVariable",
 ]
 
@@ -470,13 +469,6 @@ class Continuous(Distribution):
     """Base class for continuous distributions"""
 
 
-class NoDistribution(Distribution):
-    """Base class for artifical distributions
-
-    RandomVariables that share this type are allowed in logprob graphs
-    """
-
-
 class DensityDistRV(RandomVariable):
     """
     Base class for DensityDistRV
@@ -494,7 +486,7 @@ def rng_fn(cls, rng, *args):
         return cls._random_fn(*args, rng=rng, size=size)
 
 
-class DensityDist(NoDistribution):
+class DensityDist(Distribution):
     """A distribution that can be used to wrap black-box log density functions.
 
     Creates a Distribution and registers the supplied log density function to be used

pymc/distributions/simulator.py

@@ -25,7 +25,7 @@
 from scipy.spatial import cKDTree
 
 from pymc.aesaraf import floatX
-from pymc.distributions.distribution import NoDistribution, _moment
+from pymc.distributions.distribution import Distribution, _moment
 
 __all__ = ["Simulator"]
 
@@ -63,7 +63,7 @@ def sum_stat(cls, *args, **kwargs):
         return cls._sum_stat(*args, **kwargs)
 
 
-class Simulator(NoDistribution):
+class Simulator(Distribution):
     r"""
     Simulator distribution, used for Approximate Bayesian Inference (ABC)
     with Sequential Monte Carlo (SMC) sampling via :func:`~pymc.sample_smc`.
@@ -213,11 +213,8 @@ def __new__(
         )()
 
         # The logp function is registered to the more general SimulatorRV,
-        # in order to avoid issues with multiprocessing / pickling
-
-        # rv_type = type(sim_op)
-        # NoDistribution.register(rv_type)
-        NoDistribution.register(SimulatorRV)
+        # in order to avoid issues with multiprocessing / pickling,
+        # otherwise it would be registered to `type(sim_op)`
 
         @_logprob.register(SimulatorRV)
         def logp(op, value_var_list, *dist_params, **kwargs):

pymc/gp/util.py

@@ -29,9 +29,9 @@
 from pymc.aesaraf import compile_pymc, walk_model
 
 # Avoid circular dependency when importing modelcontext
-from pymc.distributions.distribution import NoDistribution
+from pymc.distributions.distribution import Distribution
 
-assert NoDistribution  # keep both pylint and black happy
+assert Distribution  # keep both pylint and black happy
 from pymc.model import modelcontext
 
 JITTER_DEFAULT = 1e-6

pymc/tests/distributions/test_distribution.py

@@ -102,7 +102,6 @@ def test_all_distributions_have_moments():
         dist_module.Distribution,
         dist_module.Discrete,
         dist_module.Continuous,
-        dist_module.NoDistribution,
         dist_module.DensityDist,
         dist_module.simulator.Simulator,
     }