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Remove tests for random variable samples shape and size
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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
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@matteo-pallini
matteo-pallini committed Apr 3, 2021
1 parent 069533f commit 53dfb75
Showing 1 changed file with 0 additions and 111 deletions.
111 changes: 0 additions & 111 deletions pymc3/tests/test_distributions_random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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):
Expand Down

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