def test_select_proportional_to_weight(self):
# Tests that multinomial_wo_replacement selects elements, on average,
# proportional to the their probabilities
th_rng = RandomStream(12345)
p = fmatrix()
n = iscalar()
m = th_rng.choice(size=n, p=p, replace=False)
f = function([p, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
rng = np.random.default_rng(12345)
pvals = rng.integers(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = np.zeros((n_elements, ), dtype=config.floatX)
for rep in range(10000):
res = f(pvals, n_selected)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol
def test_target_parameter():
srng = MRG_RandomStream()
pvals = np.array([[0.98, 0.01, 0.01], [0.01, 0.49, 0.50]])
def basic_target_parameter_test(x):
f = function([], x)
assert isinstance(f(), np.ndarray)
basic_target_parameter_test(srng.uniform((3, 2), target="cpu"))
basic_target_parameter_test(srng.normal((3, 2), target="cpu"))
basic_target_parameter_test(srng.truncated_normal((3, 2), target="cpu"))
basic_target_parameter_test(srng.binomial((3, 2), target="cpu"))
basic_target_parameter_test(
srng.multinomial(pvals=pvals.astype("float32"), target="cpu"))
basic_target_parameter_test(
srng.choice(p=pvals.astype("float32"), replace=False, target="cpu"))
basic_target_parameter_test(
srng.multinomial_wo_replacement(pvals=pvals.astype("float32"),
target="cpu"))
def test_undefined_grad():
srng = MRG_RandomStream(seed=1234)
# checking uniform distribution
low = scalar()
out = srng.uniform((), low=low)
with pytest.raises(NullTypeGradError):
grad(out, low)
high = scalar()
out = srng.uniform((), low=0, high=high)
with pytest.raises(NullTypeGradError):
grad(out, high)
out = srng.uniform((), low=low, high=high)
with pytest.raises(NullTypeGradError):
grad(out, (low, high))
# checking binomial distribution
prob = scalar()
out = srng.binomial((), p=prob)
with pytest.raises(NullTypeGradError):
grad(out, prob)
# checking multinomial distribution
prob1 = scalar()
prob2 = scalar()
p = [as_tensor_variable([prob1, 0.5, 0.25])]
out = srng.multinomial(size=None, pvals=p, n=4)[0]
with pytest.raises(NullTypeGradError):
grad(at_sum(out), prob1)
p = [as_tensor_variable([prob1, prob2])]
out = srng.multinomial(size=None, pvals=p, n=4)[0]
with pytest.raises(NullTypeGradError):
grad(at_sum(out), (prob1, prob2))
# checking choice
p = [as_tensor_variable([prob1, prob2, 0.1, 0.2])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(NullTypeGradError):
grad(out[0], (prob1, prob2))
p = [as_tensor_variable([prob1, prob2])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(NullTypeGradError):
grad(out[0], (prob1, prob2))
p = [as_tensor_variable([prob1, 0.2, 0.3])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(NullTypeGradError):
grad(out[0], prob1)
# checking normal distribution
avg = scalar()
out = srng.normal((), avg=avg)
with pytest.raises(NullTypeGradError):
grad(out, avg)
std = scalar()
out = srng.normal((), avg=0, std=std)
with pytest.raises(NullTypeGradError):
grad(out, std)
out = srng.normal((), avg=avg, std=std)
with pytest.raises(NullTypeGradError):
grad(out, (avg, std))
# checking truncated normal distribution
avg = scalar()
out = srng.truncated_normal((), avg=avg)
with pytest.raises(NullTypeGradError):
grad(out, avg)
std = scalar()
out = srng.truncated_normal((), avg=0, std=std)
with pytest.raises(NullTypeGradError):
grad(out, std)
out = srng.truncated_normal((), avg=avg, std=std)
with pytest.raises(NullTypeGradError):
grad(out, (avg, std))
