Remove tests for random variable samples shape and size

Most of the random variable logic has been moved to aesara, as well as
most of the relative tests. More details can be found on issue #4554

Author: matteo-pallini

pymc3/tests/test_distributions_random.py

@@ -245,12 +245,6 @@ class TestGaussianRandomWalk(BaseTestCases.BaseTestCase):
     default_shape = (1,)
 
 
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestNormal(BaseTestCases.BaseTestCase):
-    distribution = pm.Normal
-    params = {"mu": 0.0, "tau": 1.0}
-
-
 @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
 class TestTruncatedNormal(BaseTestCases.BaseTestCase):
     distribution = pm.TruncatedNormal
@@ -275,18 +269,6 @@ class TestSkewNormal(BaseTestCases.BaseTestCase):
     params = {"mu": 0.0, "sigma": 1.0, "alpha": 5.0}
 
 
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestHalfNormal(BaseTestCases.BaseTestCase):
-    distribution = pm.HalfNormal
-    params = {"tau": 1.0}
-
-
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestUniform(BaseTestCases.BaseTestCase):
-    distribution = pm.Uniform
-    params = {"lower": 0.0, "upper": 1.0}
-
-
 @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
 class TestTriangular(BaseTestCases.BaseTestCase):
     distribution = pm.Triangular
@@ -310,12 +292,6 @@ class TestKumaraswamy(BaseTestCases.BaseTestCase):
     params = {"a": 1.0, "b": 1.0}
 
 
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestExponential(BaseTestCases.BaseTestCase):
-    distribution = pm.Exponential
-    params = {"lam": 1.0}
-
-
 @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
 class TestLaplace(BaseTestCases.BaseTestCase):
     distribution = pm.Laplace
@@ -346,30 +322,6 @@ class TestPareto(BaseTestCases.BaseTestCase):
     params = {"alpha": 0.5, "m": 1.0}
 
 
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestCauchy(BaseTestCases.BaseTestCase):
-    distribution = pm.Cauchy
-    params = {"alpha": 1.0, "beta": 1.0}
-
-
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestHalfCauchy(BaseTestCases.BaseTestCase):
-    distribution = pm.HalfCauchy
-    params = {"beta": 1.0}
-
-
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestGamma(BaseTestCases.BaseTestCase):
-    distribution = pm.Gamma
-    params = {"alpha": 1.0, "beta": 1.0}
-
-
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestInverseGamma(BaseTestCases.BaseTestCase):
-    distribution = pm.InverseGamma
-    params = {"alpha": 0.5, "beta": 0.5}
-
-
 @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
 class TestChiSquared(BaseTestCases.BaseTestCase):
     distribution = pm.ChiSquared
@@ -412,42 +364,18 @@ class TestLogitNormal(BaseTestCases.BaseTestCase):
     params = {"mu": 0.0, "sigma": 1.0}
 
 
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestBinomial(BaseTestCases.BaseTestCase):
-    distribution = pm.Binomial
-    params = {"n": 5, "p": 0.5}
-
-
 @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
 class TestBetaBinomial(BaseTestCases.BaseTestCase):
     distribution = pm.BetaBinomial
     params = {"n": 5, "alpha": 1.0, "beta": 1.0}
 
 
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestBernoulli(BaseTestCases.BaseTestCase):
-    distribution = pm.Bernoulli
-    params = {"p": 0.5}
-
-
 @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
 class TestDiscreteWeibull(BaseTestCases.BaseTestCase):
     distribution = pm.DiscreteWeibull
     params = {"q": 0.25, "beta": 2.0}
 
 
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestPoisson(BaseTestCases.BaseTestCase):
-    distribution = pm.Poisson
-    params = {"mu": 1.0}
-
-
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestNegativeBinomial(BaseTestCases.BaseTestCase):
-    distribution = pm.NegativeBinomial
-    params = {"mu": 1.0, "alpha": 1.0}
-
-
 @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
 class TestConstant(BaseTestCases.BaseTestCase):
     distribution = pm.Constant
@@ -496,45 +424,6 @@ class TestMoyal(BaseTestCases.BaseTestCase):
     params = {"mu": 0.0, "sigma": 1.0}
 
 
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestCategorical(BaseTestCases.BaseTestCase):
-    distribution = pm.Categorical
-    params = {"p": np.ones(BaseTestCases.BaseTestCase.shape)}
-
-    def get_random_variable(
-        self, shape, with_vector_params=False, **kwargs
-    ):  # don't transform categories
-        return super().get_random_variable(shape, with_vector_params=False, **kwargs)
-
-    def test_probability_vector_shape(self):
-        """Check that if a 2d array of probabilities are passed to categorical correct shape is returned"""
-        p = np.ones((10, 5))
-        assert pm.Categorical.dist(p=p).random().shape == (10,)
-        assert pm.Categorical.dist(p=p).random(size=4).shape == (4, 10)
-        p = np.ones((3, 7, 5))
-        assert pm.Categorical.dist(p=p).random().shape == (3, 7)
-        assert pm.Categorical.dist(p=p).random(size=4).shape == (4, 3, 7)
-
-
-@pytest.mark.skip(reason="This test is covered by Aesara")
-class TestDirichlet(SeededTest):
-    @pytest.mark.parametrize(
-        "shape, size",
-        [
-            ((2), (1)),
-            ((2), (2)),
-            ((2, 2), (2, 100)),
-            ((3, 4), (3, 4)),
-            ((3, 4), (3, 4, 100)),
-            ((3, 4), (100)),
-            ((3, 4), (1)),
-        ],
-    )
-    def test_dirichlet_random_shape(self, shape, size):
-        out_shape = to_tuple(size) + to_tuple(shape)
-        assert pm.Dirichlet.dist(a=np.ones(shape)).random(size=size).shape == out_shape
-
-
 class TestCorrectParametrizationMappingPymcToScipy(SeededTest):
     @staticmethod
     def get_inputs_from_apply_node_outputs(outputs):