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Allow OrderedProbit distribution to take vector inputs #5418

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merged 5 commits into from
Feb 6, 2022
Merged

Allow OrderedProbit distribution to take vector inputs #5418

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cb75f13
fix and add test_vector_inputs for OrderedProbit
danhphan Jan 29, 2022
8d1d9d9
simplify test_vector_inputs
danhphan Jan 29, 2022
32f6c89
simplify test_shape_inputs for _OrderedProbit
danhphan Feb 4, 2022
466a941
add test_shape_inputs for _OrderedLogistic
danhphan Feb 4, 2022
9b311bf
add 2d cutpoints and positive sigma
danhphan Feb 5, 2022
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4 changes: 3 additions & 1 deletion pymc/distributions/discrete.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1964,7 +1964,9 @@ def dist(cls, eta, cutpoints, sigma=1, *args, **kwargs):
_log_p = at.concatenate(
[
at.shape_padright(normal_lccdf(0, sigma, probits[..., 0])),
log_diff_normal_cdf(0, sigma, probits[..., :-1], probits[..., 1:]),
log_diff_normal_cdf(
0, at.shape_padright(sigma), probits[..., :-1], probits[..., 1:]
),
at.shape_padright(normal_lcdf(0, sigma, probits[..., -1])),
],
axis=-1,
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46 changes: 46 additions & 0 deletions pymc/tests/test_distributions_random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1688,6 +1688,28 @@ class TestOrderedLogistic(BaseTestDistributionRandom):
"check_rv_size",
]

@pytest.mark.parametrize(
"eta, cutpoints, expected",
[
(0, [-2.0, 0, 2.0], (4,)),
([-1], [-2.0, 0, 2.0], (1, 4)),
([1.0, -2.0], [-1.0, 0, 1.0], (2, 4)),
(np.zeros((3, 2)), [-2.0, 0, 1.0], (3, 2, 4)),
(np.ones((5, 2)), [[-2.0, 0, 1.0], [-1.0, 0, 1.0]], (5, 2, 4)),
(np.ones((3, 5, 2)), [[-2.0, 0, 1.0], [-1.0, 0, 1.0]], (3, 5, 2, 4)),
],
)
def test_shape_inputs(self, eta, cutpoints, expected):
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"""
This test checks when providing different shapes for `eta` parameters.
"""
categorical = _OrderedLogistic.dist(
eta=eta,
cutpoints=cutpoints,
)
p = categorical.owner.inputs[3].eval()
assert p.shape == expected


class TestOrderedProbit(BaseTestDistributionRandom):
pymc_dist = _OrderedProbit
Expand All @@ -1698,6 +1720,30 @@ class TestOrderedProbit(BaseTestDistributionRandom):
"check_rv_size",
]

@pytest.mark.parametrize(
"eta, cutpoints, sigma, expected",
[
(0, [-2.0, 0, 2.0], 1.0, (4,)),
([-1], [-1.0, 0, 2.0], [2.0], (1, 4)),
([1.0, -2.0], [-1.0, 0, 1.0], 1.0, (2, 4)),
([1.0, -2.0, 3.0], [-1.0, 0, 2.0], np.ones((1, 3)), (1, 3, 4)),
(np.zeros((2, 3)), [-2.0, 0, 1.0], [1.0, 2.0, 5.0], (2, 3, 4)),
(np.ones((2, 3)), [-1.0, 0, 1.0], np.ones((2, 3)), (2, 3, 4)),
(np.zeros((5, 2)), [[-2, 0, 1], [-1, 0, 1]], np.ones((2, 5, 2)), (2, 5, 2, 4)),
],
)
def test_shape_inputs(self, eta, cutpoints, sigma, expected):
"""
This test checks when providing different shapes for `eta` and `sigma` parameters.
"""
categorical = _OrderedProbit.dist(
eta=eta,
cutpoints=cutpoints,
sigma=sigma,
)
p = categorical.owner.inputs[3].eval()
assert p.shape == expected


class TestOrderedMultinomial(BaseTestDistributionRandom):
pymc_dist = _OrderedMultinomial
Expand Down