def get_reconstruction_loss(
self,
x: torch.Tensor,
y: torch.Tensor,
px_: Dict[str, torch.Tensor],
py_: Dict[str, torch.Tensor],
pro_batch_mask_minibatch: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Compute reconstruction loss
"""
# Reconstruction Loss
if self.reconstruction_loss_gene == "zinb":
reconst_loss_gene = (-ZeroInflatedNegativeBinomial(
mu=px_["rate"], theta=px_["r"],
zi_logits=px_["dropout"]).log_prob(x).sum(dim=-1))
else:
reconst_loss_gene = (-NegativeBinomial(
mu=px_["rate"], theta=px_["r"]).log_prob(x).sum(dim=-1))
reconst_loss_protein_full = -log_mixture_nb(
y, py_["rate_back"], py_["rate_fore"], py_["r"], None,
py_["mixing"])
if pro_batch_mask_minibatch is not None:
temp_pro_loss_full = torch.zeros_like(reconst_loss_protein_full)
temp_pro_loss_full.masked_scatter_(pro_batch_mask_minibatch.bool(),
reconst_loss_protein_full)
reconst_loss_protein = temp_pro_loss_full.sum(dim=-1)
else:
reconst_loss_protein = reconst_loss_protein_full.sum(dim=-1)
return reconst_loss_gene, reconst_loss_protein
def get_reconstruction_loss(
self,
x: torch.Tensor,
px_rate: torch.Tensor,
px_r: torch.Tensor,
px_dropout: torch.Tensor,
bernoulli_params: torch.Tensor,
eps_log: float = 1e-8,
**kwargs,
) -> torch.Tensor:
# LLs for NB and ZINB
ll_zinb = torch.log(1.0 - bernoulli_params +
eps_log) + ZeroInflatedNegativeBinomial(
mu=px_rate, theta=px_r,
zi_logits=px_dropout).log_prob(x)
ll_nb = torch.log(bernoulli_params + eps_log) + NegativeBinomial(
mu=px_rate, theta=px_r).log_prob(x)
# Reconstruction loss using a logsumexp-type computation
ll_max = torch.max(ll_zinb, ll_nb)
ll_tot = ll_max + torch.log(
torch.exp(ll_nb - ll_max) + torch.exp(ll_zinb - ll_max))
reconst_loss = -ll_tot.sum(dim=-1)
return reconst_loss
def get_reconstruction_loss(self, x, px_rate, px_r, px_dropout,
**kwargs) -> torch.Tensor:
# Reconstruction Loss
if self.reconstruction_loss == "zinb":
reconst_loss = (-ZeroInflatedNegativeBinomial(
mu=px_rate, theta=px_r,
zi_logits=px_dropout).log_prob(x).sum(dim=-1))
elif self.reconstruction_loss == "nb":
reconst_loss = (-NegativeBinomial(
mu=px_rate, theta=px_r).log_prob(x).sum(dim=-1))
elif self.reconstruction_loss == "poisson":
reconst_loss = -Poisson(px_rate).log_prob(x).sum(dim=-1)
return reconst_loss
def reconstruction_loss(
self,
x: torch.Tensor,
px_rate: torch.Tensor,
px_r: torch.Tensor,
px_dropout: torch.Tensor,
mode: int,
) -> torch.Tensor:
reconstruction_loss = None
if self.reconstruction_losses[mode] == "zinb":
reconstruction_loss = (-ZeroInflatedNegativeBinomial(
mu=px_rate, theta=px_r,
zi_logits=px_dropout).log_prob(x).sum(dim=-1))
elif self.reconstruction_losses[mode] == "nb":
reconstruction_loss = (-NegativeBinomial(
mu=px_rate, theta=px_r).log_prob(x).sum(dim=-1))
elif self.reconstruction_losses[mode] == "poisson":
reconstruction_loss = -Poisson(px_rate).log_prob(x).sum(dim=1)
return reconstruction_loss
def test_zinb_distribution():
theta = 100.0 + torch.rand(size=(2, ))
mu = 15.0 * torch.ones_like(theta)
pi = torch.randn_like(theta)
x = torch.randint_like(mu, high=20)
log_p_ref = log_zinb_positive(x, mu, theta, pi)
dist = ZeroInflatedNegativeBinomial(mu=mu, theta=theta, zi_logits=pi)
log_p_zinb = dist.log_prob(x)
assert (log_p_ref - log_p_zinb).abs().max().item() <= 1e-8
torch.manual_seed(0)
s1 = dist.sample((100, ))
assert s1.shape == (100, 2)
s2 = dist.sample(sample_shape=(4, 3))
assert s2.shape == (4, 3, 2)
log_p_ref = log_nb_positive(x, mu, theta)
dist = NegativeBinomial(mu=mu, theta=theta)
log_p_nb = dist.log_prob(x)
assert (log_p_ref - log_p_nb).abs().max().item() <= 1e-8
s1 = dist.sample((1000, ))
assert s1.shape == (1000, 2)
assert (s1.mean(0) - mu).abs().mean() <= 1e0
assert (s1.std(0) - (mu + mu * mu / theta)**0.5).abs().mean() <= 1e0
size = (50, 3)
theta = 100.0 + torch.rand(size=size)
mu = 15.0 * torch.ones_like(theta)
pi = torch.randn_like(theta)
x = torch.randint_like(mu, high=20)
dist1 = ZeroInflatedNegativeBinomial(mu=mu, theta=theta, zi_logits=pi)
dist2 = NegativeBinomial(mu=mu, theta=theta)
assert dist1.log_prob(x).shape == size
assert dist2.log_prob(x).shape == size
with pytest.raises(ValueError):
ZeroInflatedNegativeBinomial(mu=-mu, theta=theta, zi_logits=pi)
with pytest.warns(UserWarning):
dist1.log_prob(-x) # ensures neg values raise warning
with pytest.warns(UserWarning):
dist2.log_prob(0.5 * x) # ensures float values raise warning