def test_allow_input_downcast_int(self):
a = tensor.wvector("a") # int16
b = tensor.bvector("b") # int8
c = tensor.bscalar("c") # int8
f = pfunc([a, b, c], (a + b + c), allow_input_downcast=True)
# Value too big for a, b, or c, silently ignored
assert f([2**20], [1], 0) == 1
assert f([3], [312], 0) == 59
assert f([3], [1], 806) == 42
g = pfunc([a, b, c], (a + b + c), allow_input_downcast=False)
# All values are in range. Since they're not ndarrays (but lists
# or scalars), they will be converted, and their value checked.
assert np.all(g([3], [6], 0) == 9)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
with pytest.raises(TypeError):
g([3], np.array([6], dtype="int16"), 0)
# Value too big for b, raises TypeError
with pytest.raises(TypeError):
g([3], [312], 0)
h = pfunc([a, b, c], (a + b + c)) # Default: allow_input_downcast=None
# Everything here should behave like with False
assert np.all(h([3], [6], 0) == 9)
with pytest.raises(TypeError):
h([3], np.array([6], dtype="int16"), 0)
with pytest.raises(TypeError):
h([3], [312], 0)
def test_param_allow_downcast_int(self):
a = tensor.wvector("a") # int16
b = tensor.bvector("b") # int8
c = tensor.bscalar("c") # int8
f = pfunc(
[
In(a, allow_downcast=True),
In(b, allow_downcast=False),
In(c, allow_downcast=None),
],
(a + b + c),
)
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert np.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
with pytest.raises(TypeError):
f([3], np.array([6], dtype="int16"), 1)
# Value too big for a, silently ignored
assert np.all(f([2**20], np.ones(1, dtype="int8"), 1) == 2)
# Value too big for b, raises TypeError
with pytest.raises(TypeError):
f([3], [312], 1)
# Value too big for c, raises TypeError
with pytest.raises(TypeError):
f([3], [6], 806)
def test_replacements(binomial_model_inference):
d = aet.bscalar()
d.tag.test_value = 1
approx = binomial_model_inference.approx
p = approx.model.p
p_t = p**3
p_s = approx.sample_node(p_t)
if aesara.config.compute_test_value != "off":
assert p_s.tag.test_value.shape == p_t.tag.test_value.shape
sampled = [p_s.eval() for _ in range(100)]
assert any(map(operator.ne, sampled[1:], sampled[:-1])) # stochastic
p_d = approx.sample_node(p_t, deterministic=True)
sampled = [p_d.eval() for _ in range(100)]
assert all(map(operator.eq, sampled[1:], sampled[:-1])) # deterministic
p_r = approx.sample_node(p_t, deterministic=d)
sampled = [p_r.eval({d: 1}) for _ in range(100)]
assert all(map(operator.eq, sampled[1:], sampled[:-1])) # deterministic
sampled = [p_r.eval({d: 0}) for _ in range(100)]
assert any(map(operator.ne, sampled[1:], sampled[:-1])) # stochastic
def test_replacements(binomial_model_inference):
d = at.bscalar()
d.tag.test_value = 1
approx = binomial_model_inference.approx
p = approx.model.p
p_t = p**3
p_s = approx.sample_node(p_t)
assert not any(
isinstance(n.owner.op, aesara.tensor.random.basic.BetaRV)
for n in aesara.graph.ancestors([p_s])
if n.owner), "p should be replaced"
if aesara.config.compute_test_value != "off":
assert p_s.tag.test_value.shape == p_t.tag.test_value.shape
sampled = [p_s.eval() for _ in range(100)]
assert any(map(operator.ne, sampled[1:], sampled[:-1])) # stochastic
p_z = approx.sample_node(p_t, deterministic=False, size=10)
assert p_z.shape.eval() == (10, )
try:
p_z = approx.sample_node(p_t, deterministic=True, size=10)
assert p_z.shape.eval() == (10, )
except NotImplementedInference:
pass
try:
p_d = approx.sample_node(p_t, deterministic=True)
sampled = [p_d.eval() for _ in range(100)]
assert all(map(operator.eq, sampled[1:],
sampled[:-1])) # deterministic
except NotImplementedInference:
pass
p_r = approx.sample_node(p_t, deterministic=d)
sampled = [p_r.eval({d: 1}) for _ in range(100)]
assert all(map(operator.eq, sampled[1:], sampled[:-1])) # deterministic
sampled = [p_r.eval({d: 0}) for _ in range(100)]
assert any(map(operator.ne, sampled[1:], sampled[:-1])) # stochastic