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Make Multinomial robust against batches #4169

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merged 4 commits into from
Oct 14, 2020
Merged

Make Multinomial robust against batches #4169

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825f98e
Allow Multinomial to work with batches of n and p that have more than…
lucianopaz Oct 13, 2020
6ca644d
Fix failing tests
lucianopaz Oct 14, 2020
5f712a6
Fix the float32 errors
lucianopaz Oct 14, 2020
50b2e65
Added line to release notes
lucianopaz Oct 14, 2020
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4 changes: 0 additions & 4 deletions pymc3/distributions/multivariate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -597,14 +597,10 @@ def __init__(self, n, p, *args, **kwargs):
super().__init__(*args, **kwargs)

p = p / tt.sum(p, axis=-1, keepdims=True)
n = np.squeeze(n) # works also if n is a tensor
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pretty simple fix, just remove some code 👍


if len(self.shape) > 1:
self.n = tt.shape_padright(n)
self.p = p if p.ndim > 1 else tt.shape_padleft(p)
elif n.ndim == 1:
self.n = tt.shape_padright(n)
self.p = p if p.ndim > 1 else tt.shape_padleft(p)
else:
# n is a scalar, p is a 1d array
self.n = tt.as_tensor_variable(n)
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22 changes: 22 additions & 0 deletions pymc3/tests/test_distributions.py
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Expand Up @@ -1447,6 +1447,28 @@ def test_multinomial_vec_2d_p(self):
decimal=4,
)

def test_batch_multinomial(self):
n = 10
vals = np.zeros((4, 5, 3))
p = np.zeros_like(vals)
inds = np.random.randint(vals.shape[-1], size=vals.shape[:-1])[..., None]
np.put_along_axis(vals, inds, n, axis=-1)
np.put_along_axis(p, inds, 1, axis=-1)

dist = Multinomial.dist(n=n, p=p, shape=vals.shape)
value = tt.tensor3()
value.tag.test_value = np.zeros_like(vals)
logp = tt.exp(dist.logp(value))
f = theano.function(inputs=[value], outputs=logp)
assert_almost_equal(
f(vals),
np.ones(vals.shape[:-1] + (1,)),
decimal=select_by_precision(float64=6, float32=3),
)

sample = dist.random(size=2)
assert_allclose(sample, np.stack([vals, vals], axis=0))

def test_categorical_bounds(self):
with Model():
x = Categorical("x", p=np.array([0.2, 0.3, 0.5]))
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