def test_log_prob(batch_shape, event_shape, dtype):
samples = []
for i in range(5):
samples.append(torch.ones(event_shape, dtype=dtype) * i)
samples = torch.stack(samples).expand(batch_shape + [5] + event_shape)
weights = torch.tensor(1.0).expand(batch_shape + [5])
empirical_dist = Empirical(samples, weights)
sample_to_score = torch.tensor(1, dtype=dtype).expand(batch_shape +
event_shape)
log_prob = empirical_dist.log_prob(sample_to_score)
assert_equal(log_prob,
(weights.new_ones(batch_shape + [1]) * 0.2).sum(-1).log())
# Value outside support returns -Inf
sample_to_score = torch.tensor(
1, dtype=dtype).expand(batch_shape + event_shape) * 6
log_prob = empirical_dist.log_prob(sample_to_score)
assert log_prob.shape == torch.Size(batch_shape)
assert torch.isinf(log_prob).all()
# Vectorized ``log_prob`` raises ValueError
with pytest.raises(ValueError):
sample_to_score = torch.ones([3] + batch_shape + event_shape,
dtype=dtype)
empirical_dist.log_prob(sample_to_score)
def test_log_prob(batch_shape, dtype):
empirical_dist = Empirical()
for i in range(5):
empirical_dist.add(torch.ones(batch_shape, dtype=dtype) * i)
sample_to_score = torch.ones(batch_shape, dtype=dtype)
log_prob = empirical_dist.log_prob(sample_to_score)
assert_equal(log_prob, torch.tensor(0.2).log())
# Value outside support returns -Inf
sample_to_score = torch.ones(batch_shape, dtype=dtype) * 6
log_prob = empirical_dist.log_prob(sample_to_score)
assert log_prob == -float("inf")
# Vectorized ``log_prob`` raises ValueError
with pytest.raises(ValueError):
sample_to_score = torch.ones((3,) + batch_shape, dtype=dtype)
empirical_dist.log_prob(sample_to_score)
def test_log_prob(batch_shape, dtype):
empirical_dist = Empirical()
for i in range(5):
empirical_dist.add(torch.ones(batch_shape, dtype=dtype) * i)
sample_to_score = torch.ones(batch_shape, dtype=dtype)
log_prob = empirical_dist.log_prob(sample_to_score)
assert_equal(log_prob, torch.tensor(0.2).log())
# Value outside support returns -Inf
sample_to_score = torch.ones(batch_shape, dtype=dtype) * 6
log_prob = empirical_dist.log_prob(sample_to_score)
assert log_prob == -float("inf")
# Vectorized ``log_prob`` raises ValueError
with pytest.raises(ValueError):
sample_to_score = torch.ones((3, ) + batch_shape, dtype=dtype)
empirical_dist.log_prob(sample_to_score)
def test_weighted_sample_coherence(event_shape, dtype):
samples = [(1.0, 0.5), (0.0, 1.5), (1.0, 0.5), (0.0, 1.5)]
empirical_dist = Empirical()
for sample, weight in samples:
empirical_dist.add(sample * torch.ones(event_shape, dtype=dtype), weight=weight)
assert_equal(empirical_dist.event_shape, event_shape)
assert_equal(empirical_dist.sample_size, 4)
sample_to_score = torch.ones(event_shape, dtype=dtype) * 1.0
assert_equal(empirical_dist.log_prob(sample_to_score), torch.tensor(0.25).log())
samples = empirical_dist.sample(sample_shape=torch.Size((1000,)))
zeros = torch.zeros(event_shape, dtype=dtype)
ones = torch.ones(event_shape, dtype=dtype)
num_zeros = samples.eq(zeros).contiguous().view(1000, -1).min(dim=-1)[0].float().sum()
num_ones = samples.eq(ones).contiguous().view(1000, -1).min(dim=-1)[0].float().sum()
assert_equal(num_zeros.item() / 1000, 0.75, prec=0.02)
assert_equal(num_ones.item() / 1000, 0.25, prec=0.02)
def test_weighted_sample_coherence(event_shape, dtype):
data = [(1.0, 0.5), (0.0, 1.5), (1.0, 0.5), (0.0, 1.5)]
samples, weights = [], []
for sample, weight in data:
samples.append(sample * torch.ones(event_shape, dtype=dtype))
weights.append(torch.tensor(weight).log())
samples, weights = torch.stack(samples), torch.stack(weights)
empirical_dist = Empirical(samples, weights)
assert_equal(empirical_dist.event_shape, torch.Size(event_shape))
assert_equal(empirical_dist.sample_size, 4)
sample_to_score = torch.ones(event_shape, dtype=dtype) * 1.0
assert_equal(empirical_dist.log_prob(sample_to_score), torch.tensor(0.25).log())
samples = empirical_dist.sample(sample_shape=torch.Size((1000,)))
zeros = torch.zeros(event_shape, dtype=dtype)
ones = torch.ones(event_shape, dtype=dtype)
num_zeros = samples.eq(zeros).contiguous().view(1000, -1).min(dim=-1)[0].float().sum()
num_ones = samples.eq(ones).contiguous().view(1000, -1).min(dim=-1)[0].float().sum()
assert_equal(num_zeros.item() / 1000, 0.75, prec=0.02)
assert_equal(num_ones.item() / 1000, 0.25, prec=0.02)