Remove PyPolyaGamma

aesara/tensor/random/basic.py

@@ -15,14 +15,6 @@ from aesara.tensor.random.var import (
 )
 
 
-try:
-    from pypolyagamma import PyPolyaGamma
-except ImportError:  # pragma: no cover
-
-    def PyPolyaGamma(*args, **kwargs):
-        raise RuntimeError("pypolygamma not installed!")
-
-
 try:
     broadcast_shapes = np.broadcast_shapes
 except AttributeError:
@@ -635,47 +627,6 @@ class CategoricalRV(RandomVariable):
 categorical = CategoricalRV()
 
 
-class PolyaGammaRV(RandomVariable):
-    """Polya-Gamma random variable.
-
-    XXX: This doesn't really use the given RNG, due to the narrowness of the
-    sampler package's implementation.
-    """
-
-    name = "polya-gamma"
-    ndim_supp = 0
-    ndims_params = [0, 0]
-    dtype = "floatX"
-    _print_name = ("PG", "\\operatorname{PG}")
-
-    @classmethod
-    def rng_fn(cls, rng, b, c, size):
-        rand_method = rng.integers if hasattr(rng, "integers") else rng.randint
-        pg = PyPolyaGamma(rand_method(2 ** 16))
-
-        if not size and b.shape == c.shape == ():
-            return pg.pgdraw(b, c)
-        else:
-            b, c = np.broadcast_arrays(b, c)
-            size = tuple(size or ())
-
-            if len(size) > 0:
-                b = np.broadcast_to(b, size)
-                c = np.broadcast_to(c, size)
-
-            smpl_val = np.empty(b.shape, dtype="double")
-
-            pg.pgdrawv(
-                np.asarray(b.flat).astype("double", copy=True),
-                np.asarray(c.flat).astype("double", copy=True),
-                np.asarray(smpl_val.flat),
-            )
-            return smpl_val
-
-
-polyagamma = PolyaGammaRV()
-
-
 class RandIntRV(RandomVariable):
     name = "randint"
     ndim_supp = 0

environment.yml

@@ -14,7 +14,6 @@ dependencies:
   - numpy
   - scipy
   - sympy
-  - pypolyagamma
   # Intel BLAS
   - mkl
   - mkl-service

requirements.txt

@@ -18,5 +18,4 @@ numba-scipy>=0.3.0
 diff-cover
 pre-commit
 isort
-pypolyagamma
 packaging

tests/tensor/random/test_basic.py

@@ -45,7 +45,6 @@ from aesara.tensor.random.basic import (
     pareto,
     permutation,
     poisson,
-    polyagamma,
     randint,
     standard_normal,
     triangular,
@@ -1157,39 +1156,6 @@ def test_categorical_basic():
         categorical.rng_fn(rng, p, size=10)
 
 
-@config.change_flags(compute_test_value="raise")
-def test_polyagamma_samples():
-
-    _ = pytest.importorskip("pypolyagamma")
-
-    # Sampled values should be scalars
-    a = np.array(1.1, dtype=config.floatX)
-    b = np.array(-10.5, dtype=config.floatX)
-    pg_rv = polyagamma(a, b)
-    assert get_test_value(pg_rv).shape == ()
-
-    pg_rv = polyagamma(a, b, size=[1])
-    assert get_test_value(pg_rv).shape == (1,)
-
-    pg_rv = polyagamma(a, b, size=[2, 3])
-    bcast_smpl = get_test_value(pg_rv)
-    assert bcast_smpl.shape == (2, 3)
-    # Make sure they're not all equal
-    assert np.all(np.abs(np.diff(bcast_smpl.flat)) > 0.0)
-
-    a = np.array([1.1, 3], dtype=config.floatX)
-    b = np.array(-10.5, dtype=config.floatX)
-    pg_rv = polyagamma(a, b)
-    bcast_smpl = get_test_value(pg_rv)
-    assert bcast_smpl.shape == (2,)
-    assert np.all(np.abs(np.diff(bcast_smpl.flat)) > 0.0)
-
-    pg_rv = polyagamma(a, b, size=(3, 2))
-    bcast_smpl = get_test_value(pg_rv)
-    assert bcast_smpl.shape == (3, 2)
-    assert np.all(np.abs(np.diff(bcast_smpl.flat)) > 0.0)
-
-
 def test_randint_samples():
 
     with pytest.raises(TypeError):