def forward(self, x, local_l_mean, local_l_var, batch_index=None, y=None):
r""" Returns the reconstruction loss and the Kullback divergences
:param x: tensor of values with shape (batch_size, n_input)
:param local_l_mean: tensor of means of the prior distribution of latent variable l
with shape (batch_size, 1)
:param local_l_var: tensor of variancess of the prior distribution of latent variable l
with shape (batch_size, 1)
:param batch_index: array that indicates which batch the cells belong to with shape ``batch_size``
:param y: tensor of cell-types labels with shape (batch_size, n_labels)
:return: the reconstruction loss and the Kullback divergences
:rtype: 2-tuple of :py:class:`torch.FloatTensor`
"""
# assert self.trained_decoder, "If you train the encoder alone please use the `ratio_loss`" \
# "In `forward`, the KL terms are wrong"
px_rate, qz_m, qz_v, z, ql_m, ql_v, library = self.inference(
x, batch_index, y)
# KL Divergence
mean, scale = self.get_prior_params(device=qz_m.device)
kl_divergence_z = kl(self.z_encoder.distrib(qz_m, qz_v),
self.z_encoder.distrib(mean, scale))
if len(kl_divergence_z.size()) == 2:
kl_divergence_z = kl_divergence_z.sum(dim=1)
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean, torch.sqrt(local_l_var)),
).sum(dim=1)
kl_divergence = kl_divergence_z
reconst_loss = self.get_reconstruction_loss(x, px_rate)
return reconst_loss + kl_divergence_l, kl_divergence
def forward(self, x, local_l_mean, local_l_var, batch_index=None, y=None):
# Parameters for z latent distribution
x_ = x
if self.log_variational:
x_ = torch.log(1 + x_)
# Sampling
qz_m, qz_v, z = self.z_encoder(x_)
ql_m, ql_v, library = self.l_encoder(x_)
px_scale, px_r, px_rate, px_dropout = self.decoder(
self.dispersion, z, library, batch_index)
reconst_loss = self._reconstruction_loss(x, px_rate, px_r, px_dropout,
batch_index, y)
# KL Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)),
Normal(mean, scale)).sum(dim=1)
kl_divergence_l = kl(Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean,
torch.sqrt(local_l_var))).sum(dim=1)
kl_divergence = kl_divergence_z + kl_divergence_l
return reconst_loss, kl_divergence
def forward(self, x, local_l_mean, local_l_var, batch_index=None, y=None):
r""" Returns the reconstruction loss and the Kullback divergences
:param x: tensor of values with shape (batch_size, n_input)
:param local_l_mean: tensor of means of the prior distribution of latent variable l
with shape (batch_size, 1)
:param local_l_var: tensor of variancess of the prior distribution of latent variable l
with shape (batch_size, 1)
:param batch_index: array that indicates which batch the cells belong to with shape ``batch_size``
:param y: tensor of cell-types labels with shape (batch_size, n_labels)
:return: the reconstruction loss and the Kullback divergences
:rtype: 2-tuple of :py:class:`torch.FloatTensor`
"""
# Parameters for z latent distribution
px_scale, px_r, px_rate, px_dropout, qz_m, qz_v, z, ql_m, ql_v, library = self.inference(x, batch_index, y)
reconst_loss = self._reconstruction_loss(x, px_rate, px_r, px_dropout)
# KL Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean, scale)).sum(dim=1)
kl_divergence_l = kl(Normal(ql_m, torch.sqrt(ql_v)), Normal(local_l_mean, torch.sqrt(local_l_var))).sum(dim=1)
kl_divergence = kl_divergence_z
return reconst_loss + kl_divergence_l, kl_divergence
def forward(self, x, local_l_mean, local_l_var, batch_index=None, y=None):
# Prepare for sampling
x_ = torch.log(1 + x)
ql_m, ql_v, library = self.l_encoder(x_)
# Enumerate choices of label
ys, xs, library_s, batch_index_s = (
broadcast_labels(
y, x, library, batch_index, n_broadcast=self.n_labels
)
)
if self.log_variational:
xs_ = torch.log(1 + xs)
# Sampling
qz_m, qz_v, zs = self.z_encoder(xs_, batch_index_s, ys)
px_scale, px_r, px_rate, px_dropout = self.decoder(self.dispersion, zs, library_s, batch_index_s, ys)
reconst_loss = self._reconstruction_loss(xs, px_rate, px_r, px_dropout, batch_index_s, ys)
# KL Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean, scale)).sum(dim=1)
kl_divergence_l = kl(Normal(ql_m, torch.sqrt(ql_v)), Normal(local_l_mean, torch.sqrt(local_l_var))).sum(dim=1)
return reconst_loss, kl_divergence_z + kl_divergence_l
def forward(self, x, local_l_mean, local_l_var, batch_index=None, y=None):
is_labelled = False if y is None else True
outputs = self.inference(x, batch_index, y)
px_r = outputs["px_r"]
px_rate = outputs["px_rate"]
px_dropout = outputs["px_dropout"]
qz1_m = outputs["qz_m"]
qz1_v = outputs["qz_v"]
z1 = outputs["z"]
ql_m = outputs["ql_m"]
ql_v = outputs["ql_v"]
# Enumerate choices of label
ys, z1s = broadcast_labels(y, z1, n_broadcast=self.n_labels)
qz2_m, qz2_v, z2 = self.encoder_z2_z1(z1s, ys)
pz1_m, pz1_v = self.decoder_z1_z2(z2, ys)
reconst_loss = self.get_reconstruction_loss(x, px_rate, px_r,
px_dropout)
# KL Divergence
mean = torch.zeros_like(qz2_m)
scale = torch.ones_like(qz2_v)
kl_divergence_z2 = kl(Normal(qz2_m, torch.sqrt(qz2_v)),
Normal(mean, scale)).sum(dim=1)
loss_z1_unweight = -Normal(pz1_m,
torch.sqrt(pz1_v)).log_prob(z1s).sum(dim=-1)
loss_z1_weight = Normal(qz1_m,
torch.sqrt(qz1_v)).log_prob(z1).sum(dim=-1)
if not self.use_observed_lib_size:
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean, torch.sqrt(local_l_var)),
).sum(dim=1)
else:
kl_divergence_l = 0.0
if is_labelled:
return (
reconst_loss + loss_z1_weight + loss_z1_unweight,
kl_divergence_z2 + kl_divergence_l,
0.0,
)
probs = self.classifier(z1)
reconst_loss += loss_z1_weight + (
(loss_z1_unweight).view(self.n_labels, -1).t() * probs).sum(dim=1)
kl_divergence = (kl_divergence_z2.view(self.n_labels, -1).t() *
probs).sum(dim=1)
kl_divergence += kl(
Categorical(probs=probs),
Categorical(probs=self.y_prior.repeat(probs.size(0), 1)),
)
kl_divergence += kl_divergence_l
return reconst_loss, kl_divergence, 0.0
def forward(
self,
x: torch.Tensor,
local_l_mean: torch.Tensor,
local_l_var: torch.Tensor,
batch_index: Optional[torch.Tensor] = None,
y: Optional[torch.Tensor] = None,
mode: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Return the reconstruction loss and the Kullback divergences
:param x: tensor of values with shape ``(batch_size, n_input)``
or ``(batch_size, n_input_fish)`` depending on the mode
:param local_l_mean: tensor of means of the prior distribution of latent variable l
with shape (batch_size, 1)
:param local_l_var: tensor of variances of the prior distribution of latent variable l
with shape (batch_size, 1)
:param batch_index: array that indicates which batch the cells belong to with shape ``batch_size``
:param y: tensor of cell-types labels with shape (batch_size, n_labels)
:param mode: indicates which head/tail to use in the joint network
:return: the reconstruction loss and the Kullback divergences
"""
if mode is None:
if len(self.n_input_list) == 1:
mode = 0
else:
raise Exception("Must provide a mode")
qz_m, qz_v, z, ql_m, ql_v, library = self.encode(x, mode)
px_scale, px_r, px_rate, px_dropout = self.decode(
z, mode, library, batch_index, y
)
# mask loss to observed genes
mapping_indices = self.indices_mappings[mode]
reconstruction_loss = self.reconstruction_loss(
x,
px_rate[:, mapping_indices],
px_r[:, mapping_indices],
px_dropout[:, mapping_indices],
mode,
)
# KL Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean, scale)).sum(
dim=1
)
if self.model_library_bools[mode]:
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean, torch.sqrt(local_l_var)),
).sum(dim=1)
else:
kl_divergence_l = torch.zeros_like(kl_divergence_z)
return reconstruction_loss, kl_divergence_l + kl_divergence_z, 0.0
def forward(self,
x,
local_l_mean,
local_l_var,
batch_index=None,
y=None) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Returns the reconstruction loss and the KL divergences.
Parameters
----------
x
tensor of values with shape (batch_size, n_input)
local_l_mean
tensor of means of the prior distribution of latent variable l
with shape (batch_size, 1)
local_l_var
tensor of variancess of the prior distribution of latent variable l
with shape (batch_size, 1)
batch_index
array that indicates which batch the cells belong to with shape ``batch_size`` (Default value = None)
y
tensor of cell-types labels with shape (batch_size, n_labels) (Default value = None)
Returns
-------
type
the reconstruction loss and the Kullback divergences
"""
# Parameters for z latent distribution
outputs = self.inference(x, batch_index, y)
qz_m = outputs["qz_m"]
qz_v = outputs["qz_v"]
ql_m = outputs["ql_m"]
ql_v = outputs["ql_v"]
px_rate = outputs["px_rate"]
px_r = outputs["px_r"]
px_dropout = outputs["px_dropout"]
# KL Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)),
Normal(mean, scale)).sum(dim=1)
if not self.use_observed_lib_size:
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean, torch.sqrt(local_l_var)),
).sum(dim=1)
else:
kl_divergence_l = 0.0
kl_divergence = kl_divergence_z
reconst_loss = self.get_reconstruction_loss(x, px_rate, px_r,
px_dropout)
return reconst_loss + kl_divergence_l, kl_divergence, 0.0
def forward(self, X1, X2, local_l_mean, local_l_var, local_l_mean1,
local_l_var1):
result = self.inference(X1, X2)
disper_x = result["disper_x"]
recon_x1 = result["recon_x1"]
dropout_rate = result["dropout_rate"]
disper_x2 = result["disper_x2"]
recon_x_2 = result["recon_x_2"]
dropout_rate_2 = result["dropout_rate_2"]
if X1 is not None:
mean_l = result["mean_l"]
logvar_l = result["logvar_l"]
kl_divergence_l = kl(Normal(mean_l, logvar_l),
Normal(local_l_mean,
torch.sqrt(local_l_var))).sum(dim=1)
else:
kl_divergence_l = torch.tensor(0.0)
if X2 is not None:
if self.Type == 'ZINB':
mean_l2 = result["mean_l2"]
logvar_l2 = result["library2"]
kl_divergence_l2 = kl(
Normal(mean_l2, logvar_l2),
Normal(local_l_mean1, torch.sqrt(local_l_var1))).sum(dim=1)
else:
kl_divergence_l2 = torch.tensor(0.0)
else:
kl_divergence_l2 = torch.tensor(0.0)
mean_z = result["mean_z"]
logvar_z = result["logvar_z"]
latent_z = result["latent_z"]
if self.penality == "GMM":
gamma, mu_c, var_c, pi = self.get_gamma(
latent_z) #, self.n_centroids, c_params)
kl_divergence_z = GMM_loss(gamma, (mu_c, var_c, pi),
(mean_z, logvar_z))
else:
mean = torch.zeros_like(mean_z)
scale = torch.ones_like(logvar_z)
kl_divergence_z = kl(Normal(mean_z, logvar_z),
Normal(mean, scale)).sum(dim=1)
loss1, loss2 = get_both_recon_loss(X1, recon_x1, disper_x,
dropout_rate, X2, recon_x_2,
disper_x2, dropout_rate_2, "ZINB",
self.Type)
return loss1, loss2, kl_divergence_l, kl_divergence_l2, kl_divergence_z
def loss(
self,
tensors,
inference_outputs,
generative_outputs,
kl_weight: int = 1.0,
n_obs: int = 1.0,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
# Parameters for z latent distribution
qz_m = inference_outputs["qz_m"]
qz_v = inference_outputs["qz_v"]
px_rate = generative_outputs["px_rate"]
px_r = generative_outputs["px_r"]
px_dropout = generative_outputs["px_dropout"]
bernoulli_params = generative_outputs["bernoulli_params"]
x = tensors[_CONSTANTS.X_KEY]
batch_index = tensors[_CONSTANTS.BATCH_KEY]
# KL divergences wrt z_n,l_n
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)),
Normal(mean, scale)).sum(dim=1)
if not self.use_observed_lib_size:
ql_m = inference_outputs["ql_m"]
ql_v = inference_outputs["ql_v"]
(
local_library_log_means,
local_library_log_vars,
) = self._compute_local_library_params(batch_index)
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_library_log_means,
torch.sqrt(local_library_log_vars)),
).sum(dim=1)
else:
kl_divergence_l = 0.0
# KL divergence wrt Bernoulli parameters
kl_divergence_bernoulli = self.compute_global_kl_divergence()
# Reconstruction loss
reconst_loss = self.get_reconstruction_loss(x, px_rate, px_r,
px_dropout,
bernoulli_params)
kl_global = kl_divergence_bernoulli
kl_local_for_warmup = kl_divergence_z
kl_local_no_warmup = kl_divergence_l
weighted_kl_local = kl_weight * kl_local_for_warmup + kl_local_no_warmup
loss = n_obs * torch.mean(reconst_loss + weighted_kl_local) + kl_global
kl_local = dict(kl_divergence_l=kl_divergence_l,
kl_divergence_z=kl_divergence_z)
return LossRecorder(loss, reconst_loss, kl_local, kl_global)
def forward(
self,
x: torch.Tensor,
y: torch.Tensor,
local_l_mean_gene: torch.Tensor,
local_l_var_gene: torch.Tensor,
batch_index: Optional[torch.Tensor] = None,
label: Optional[torch.Tensor] = None,
):
r""" Returns the reconstruction loss and the Kullback divergences
:param x: tensor of values with shape (batch_size, n_input_genes)
:param y: tensor of values with shape (batch_size, n_input_proteins)
:param local_l_mean_gene: tensor of means of the prior distribution of latent variable l
with shape (batch_size, 1)
:param local_l_var_gene: tensor of variancess of the prior distribution of latent variable l
with shape (batch_size, 1)
:param batch_index: array that indicates which batch the cells belong to with shape ``batch_size``
:param label: tensor of cell-types labels with shape (batch_size, n_labels)
:return: the reconstruction loss and the Kullback divergences
:rtype: 4-tuple of :py:class:`torch.FloatTensor`
"""
# Parameters for z latent distribution
outputs = self.inference(x, y, batch_index, label)
qz_m = outputs["qz_m"]
qz_v = outputs["qz_v"]
ql_m = outputs["ql_m"]
ql_v = outputs["ql_v"]
px_ = outputs["px_"]
py_ = outputs["py_"]
reconst_loss_gene, reconst_loss_protein = self.get_reconstruction_loss(
x, y, px_, py_
)
# KL Divergence
kl_div_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(0, 1)).sum(dim=1)
kl_div_l_gene = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean_gene, torch.sqrt(local_l_var_gene)),
).sum(dim=1)
kl_div_back_pro = kl(
Normal(py_["back_alpha"], py_["back_beta"]), self.back_mean_prior
).sum(dim=-1)
return (
reconst_loss_gene,
reconst_loss_protein,
kl_div_z,
kl_div_l_gene,
kl_div_back_pro,
)
def forward(
self,
x: torch.Tensor,
local_l_mean: torch.Tensor,
local_l_var: torch.Tensor,
batch_index: Optional[torch.Tensor] = None,
y: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
r""" Returns the reconstruction loss and the Kullback divergences
:param x: tensor of values with shape (batch_size, n_input)
:param local_l_mean: tensor of means of the prior distribution of latent variable l
with shape (batch_size, 1)
:param local_l_var: tensor of variancess of the prior distribution of latent variable l
with shape (batch_size, 1)
:param batch_index: array that indicates which batch the cells belong to with shape ``batch_size``
:param y: tensor of cell-types labels with shape (batch_size, n_labels)
:return: the reconstruction loss and the Kullback divergences
:rtype: 2-tuple of :py:class:`torch.FloatTensor`
"""
# Parameters for z latent distribution
outputs = self.inference(x, batch_index, y)
qz_m = outputs["qz_m"]
qz_v = outputs["qz_v"]
ql_m = outputs["ql_m"]
ql_v = outputs["ql_v"]
px_rate = outputs["px_rate"]
px_r = outputs["px_r"]
px_dropout = outputs["px_dropout"]
bernoulli_params = outputs["bernoulli_params"]
# KL divergences wrt z_n,l_n
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean, scale)).sum(
dim=1
)
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean, torch.sqrt(local_l_var)),
).sum(dim=1)
# KL divergence wrt Bernoulli parameters
kl_divergence_bernoulli = self.compute_global_kl_divergence()
# Reconstruction loss
reconst_loss = self.get_reconstruction_loss(
x, px_rate, px_r, px_dropout, bernoulli_params
)
return reconst_loss + kl_divergence_l, kl_divergence_z, kl_divergence_bernoulli
def loss(
self,
tensors,
inference_outputs,
generative_outputs,
kl_weight: float = 1.0,
):
x = tensors[REGISTRY_KEYS.X_KEY]
batch_index = tensors[REGISTRY_KEYS.BATCH_KEY]
qz_m = inference_outputs["qz_m"]
qz_v = inference_outputs["qz_v"]
px_rate = generative_outputs["px_rate"]
px_r = generative_outputs["px_r"]
px_dropout = generative_outputs["px_dropout"]
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, qz_v.sqrt()), Normal(mean, scale)).sum(dim=1)
if not self.use_observed_lib_size:
ql_m = inference_outputs["ql_m"]
ql_v = inference_outputs["ql_v"]
(
local_library_log_means,
local_library_log_vars,
) = self._compute_local_library_params(batch_index)
kl_divergence_l = kl(
Normal(ql_m, ql_v.sqrt()),
Normal(local_library_log_means, local_library_log_vars.sqrt()),
).sum(dim=1)
else:
kl_divergence_l = 0.0
reconst_loss = self.get_reconstruction_loss(x, px_rate, px_r, px_dropout)
kl_local_for_warmup = kl_divergence_z
kl_local_no_warmup = kl_divergence_l
weighted_kl_local = kl_weight * kl_local_for_warmup + kl_local_no_warmup
loss = torch.mean(reconst_loss + weighted_kl_local)
kl_local = dict(
kl_divergence_l=kl_divergence_l, kl_divergence_z=kl_divergence_z
)
kl_global = torch.tensor(0.0)
return LossRecorder(loss, reconst_loss, kl_local, kl_global)
def loss(
self,
tensors,
inference_outputs,
generative_outputs,
kl_weight: float = 1.0,
):
kl_weight = self.kl_factor * kl_weight
x = tensors[_CONSTANTS.X_KEY]
local_l_mean = tensors[_CONSTANTS.LOCAL_L_MEAN_KEY]
local_l_var = tensors[_CONSTANTS.LOCAL_L_VAR_KEY]
qz_m = inference_outputs["qz_m"]
qz_v = inference_outputs["qz_v"]
ql_m = inference_outputs["ql_m"]
ql_v = inference_outputs["ql_v"]
px_rate = generative_outputs["px_rate"]
px_r = generative_outputs["px_r"]
px_dropout = generative_outputs["px_dropout"]
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean, scale)).sum(
dim=1
)
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean, torch.sqrt(local_l_var)),
).sum(dim=1)
reconst_loss = (
-ZeroInflatedNegativeBinomial(mu=px_rate, theta=px_r, zi_logits=px_dropout)
.log_prob(x)
.sum(dim=-1)
)
kl_local_for_warmup = kl_divergence_z
kl_local_no_warmup = kl_divergence_l
weighted_kl_local = kl_weight * kl_local_for_warmup + kl_local_no_warmup
loss = torch.mean(reconst_loss + weighted_kl_local)
kl_local = dict(
kl_divergence_l=kl_divergence_l, kl_divergence_z=kl_divergence_z
)
kl_global = 0.0
return LossRecorder(loss, reconst_loss, kl_local, kl_global)
def forward(self,
x,
local_l_mean,
local_l_var,
batch_index=None,
y=None): # same signature as loss
# Parameters for z latent distribution
x_ = x
if self.log_variational:
x_ = torch.log(1 + x_)
# Sampling
qz_m, qz_v, z = self.z_encoder(x_)
ql_m, ql_v, library = self.l_encoder(x_)
if self.dispersion == "gene-cell":
px_scale, self.px_r, px_rate, px_dropout = self.decoder(
self.dispersion, z, library, batch_index)
else: # self.dispersion == "gene", "gene-batch", "gene-label"
px_scale, px_rate, px_dropout = self.decoder(
self.dispersion, z, library, batch_index)
if self.dispersion == "gene-label":
px_r = F.linear(
one_hot(y, self.n_labels),
self.px_r) # px_r gets transposed - last dimension is nb genes
elif self.dispersion == "gene-batch":
px_r = F.linear(one_hot(batch_index, self.n_batch), self.px_r)
else:
px_r = self.px_r
# Reconstruction Loss
if self.reconstruction_loss == 'zinb':
reconst_loss = -log_zinb_positive(x, px_rate, torch.exp(px_r),
px_dropout)
elif self.reconstruction_loss == 'nb':
reconst_loss = -log_nb_positive(x, px_rate, torch.exp(px_r))
# KL Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)),
Normal(mean, scale)).sum(dim=1)
kl_divergence_l = kl(Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean,
torch.sqrt(local_l_var))).sum(dim=1)
kl_divergence = kl_divergence_z + kl_divergence_l
return reconst_loss, kl_divergence
def loss(
self,
tensors,
inference_outputs,
generative_outputs,
kl_weight: int = 1.0,
n_obs: int = 1.0,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
# Parameters for z latent distribution
qz_m = inference_outputs["qz_m"]
qz_v = inference_outputs["qz_v"]
ql_m = inference_outputs["ql_m"]
ql_v = inference_outputs["ql_v"]
px_rate = generative_outputs["px_rate"]
px_r = generative_outputs["px_r"]
px_dropout = generative_outputs["px_dropout"]
bernoulli_params = generative_outputs["bernoulli_params"]
x = tensors[_CONSTANTS.X_KEY]
local_l_mean = tensors[_CONSTANTS.LOCAL_L_MEAN_KEY]
local_l_var = tensors[_CONSTANTS.LOCAL_L_VAR_KEY]
# KL divergences wrt z_n,l_n
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)),
Normal(mean, scale)).sum(dim=1)
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean, torch.sqrt(local_l_var)),
).sum(dim=1)
# KL divergence wrt Bernoulli parameters
kl_divergence_bernoulli = self.compute_global_kl_divergence()
# Reconstruction loss
reconst_loss = self.get_reconstruction_loss(x, px_rate, px_r,
px_dropout,
bernoulli_params)
kl_global = kl_divergence_bernoulli
kl_local_for_warmup = kl_divergence_l
kl_local_no_warmup = kl_divergence_z
weighted_kl_local = kl_weight * kl_local_for_warmup + kl_local_no_warmup
loss = n_obs * torch.mean(reconst_loss + weighted_kl_local) + kl_global
kl_local = dict(kl_divergence_l=kl_divergence_l,
kl_divergence_z=kl_divergence_z)
return SCVILoss(loss, reconst_loss, kl_local, kl_global)
def forward(self, x, local_l_mean, local_l_var, batch_index=None, y=None):
is_labelled = False if y is None else True
# Prepare for sampling
x_ = torch.log(1 + x)
ql_m, ql_v, library = self.l_encoder(x_)
# Enumerate choices of label
ys, xs, library_s, batch_index_s = broadcast_labels(
y, x, library, batch_index, n_broadcast=self.n_labels
)
# Sampling
outputs = self.inference(xs, batch_index_s, ys)
px_r = outputs["px_r"]
px_rate = outputs["px_rate"]
px_dropout = outputs["px_dropout"]
qz_m = outputs["qz_m"]
qz_v = outputs["qz_v"]
reconst_loss = self.get_reconstruction_loss(xs, px_rate, px_r, px_dropout)
# KL Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean, scale)).sum(
dim=1
)
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean, torch.sqrt(local_l_var)),
).sum(dim=1)
if is_labelled:
return reconst_loss, kl_divergence_z + kl_divergence_l, 0.0
reconst_loss = reconst_loss.view(self.n_labels, -1)
probs = self.classifier(x_)
reconst_loss = (reconst_loss.t() * probs).sum(dim=1)
kl_divergence = (kl_divergence_z.view(self.n_labels, -1).t() * probs).sum(dim=1)
kl_divergence += kl(
Categorical(probs=probs),
Categorical(probs=self.y_prior.repeat(probs.size(0), 1)),
)
kl_divergence += kl_divergence_l
return reconst_loss, kl_divergence, 0.0
def forward(self, x, local_l_mean, local_l_var, batch_index=None, y=None):
is_labelled = False if y is None else True
# Prepare for sampling
x_ = torch.log(1 + x)
ql_m, ql_v, library = self.l_encoder(x_)
# Enumerate choices of label
ys, xs, library_s, batch_index_s = (broadcast_labels(
y, x, library, batch_index, n_broadcast=self.n_labels))
if self.log_variational:
xs_ = torch.log(1 + xs)
# Sampling
qz_m, qz_v, zs = self.z_encoder(xs_, ys)
px_scale, px_r, px_rate, px_dropout = self.decoder(
self.dispersion, zs, library_s, batch_index_s, ys)
reconst_loss = self._reconstruction_loss(xs, px_rate, px_r, px_dropout,
batch_index_s, ys)
# KL Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)),
Normal(mean, scale)).sum(dim=1)
kl_divergence_l = kl(Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean,
torch.sqrt(local_l_var))).sum(dim=1)
if is_labelled:
return reconst_loss, kl_divergence_z + kl_divergence_l
reconst_loss = reconst_loss.view(self.n_labels, -1)
probs = self.classifier(x_)
reconst_loss = (reconst_loss.t() * probs).sum(dim=1)
kl_divergence = (kl_divergence_z.view(self.n_labels, -1).t() *
probs).sum(dim=1)
kl_divergence += kl(
Categorical(probs=probs),
Categorical(probs=self.y_prior.repeat(probs.size(0), 1)))
kl_divergence += kl_divergence_l
return reconst_loss, kl_divergence
def loss(
self,
tensors,
inference_outputs,
generative_outputs,
kl_weight: float = 1.0,
):
x = tensors[_CONSTANTS.X_KEY]
local_l_mean = tensors[_CONSTANTS.LOCAL_L_MEAN_KEY]
local_l_var = tensors[_CONSTANTS.LOCAL_L_VAR_KEY]
qz_m = inference_outputs["qz_m"]
qz_v = inference_outputs["qz_v"]
ql_m = inference_outputs["ql_m"]
ql_v = inference_outputs["ql_v"]
px_rate = generative_outputs["px_rate"]
px_r = generative_outputs["px_r"]
px_dropout = generative_outputs["px_dropout"]
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean, scale)).sum(
dim=1
)
if not self.use_observed_lib_size:
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean, torch.sqrt(local_l_var)),
).sum(dim=1)
else:
kl_divergence_l = 0.0
reconst_loss = self.get_reconstruction_loss(x, px_rate, px_r, px_dropout)
kl_local_for_warmup = kl_divergence_l
kl_local_no_warmup = kl_divergence_z
weighted_kl_local = kl_weight * kl_local_for_warmup + kl_local_no_warmup
loss = torch.mean(reconst_loss + weighted_kl_local)
kl_local = dict(
kl_divergence_l=kl_divergence_l, kl_divergence_z=kl_divergence_z
)
kl_global = 0.0
return SCVILoss(loss, reconst_loss, kl_local, kl_global)
def forward(self,
x,
local_l_mean,
local_l_var,
batch_index=None,
y=None,
mode="scRNA",
weighting=1):
r""" Returns the reconstruction loss and the Kullback divergences
:param x: tensor of values with shape ``(batch_size, n_input)``
or ``(batch_size, n_input_fish)`` depending on the mode
:param local_l_mean: tensor of means of the prior distribution of latent variable l
with shape (batch_size, 1)
:param local_l_var: tensor of variances of the prior distribution of latent variable l
with shape (batch_size, 1)
:param batch_index: array that indicates which batch the cells belong to with shape ``batch_size``
:param y: tensor of cell-types labels with shape (batch_size, n_labels)
:param mode: string that indicates the type of data we analyse
:param weighting: used in none of these methods
:return: the reconstruction loss and the Kullback divergences
:rtype: 2-tuple of :py:class:`torch.FloatTensor`
"""
# Parameters for z latent distribution
px_scale, px_r, px_rate, px_dropout, qz_m, qz_v, z, ql_m, ql_v, library = self.inference(
x, batch_index, y, mode, weighting)
# Reconstruction Loss
reconst_loss = self.get_reconstruction_loss(x, px_rate, px_r,
px_dropout, mode,
weighting)
# KL Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)),
Normal(mean, scale)).sum(dim=1)
if self.model_library:
kl_divergence_l = kl(Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean,
torch.sqrt(local_l_var))).sum(dim=1)
kl_divergence = kl_divergence_z + kl_divergence_l
else:
kl_divergence = kl_divergence_z
return reconst_loss, kl_divergence
def loss(
self,
tensors,
inference_outputs,
generative_outputs,
kl_weight: float = 1.0,
):
x = tensors[REGISTRY_KEYS.X_KEY]
y = tensors[REGISTRY_KEYS.LABELS_KEY]
qz_m = inference_outputs["qz_m"]
qz_v = inference_outputs["qz_v"]
px_rate = generative_outputs["px_rate"]
px_r = generative_outputs["px_r"]
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)),
Normal(mean, scale)).sum(dim=1)
reconst_loss = -NegativeBinomial(px_rate,
logits=px_r).log_prob(x).sum(-1)
scaling_factor = self.ct_weight[y.long()[:, 0]]
loss = torch.mean(scaling_factor *
(reconst_loss + kl_weight * kl_divergence_z))
return LossRecorder(loss, reconst_loss, kl_divergence_z,
torch.tensor(0.0))
def loss(
self,
tensors,
inference_outputs,
generative_outputs,
):
x = tensors[_CONSTANTS.X_KEY]
qz_m = inference_outputs["qz_m"]
qz_v = inference_outputs["qz_v"]
px_logit = generative_outputs["px_logit"]
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean, scale)).sum(
dim=1
)
reconst_loss = (
-Bernoulli(logits=px_logit)
.log_prob(x)
.sum(dim=-1)
)
loss = torch.mean(reconst_loss + kl_divergence_z)
kl_local = dict(
kl_divergence_z=kl_divergence_z
)
kl_global = 0.0
return LossRecorder(loss, reconst_loss, kl_local, kl_global)
def compute_global_kl_divergence(self) -> torch.Tensor:
outputs = self.get_alphas_betas(as_numpy=False)
alpha_posterior = outputs["alpha_posterior"]
beta_posterior = outputs["beta_posterior"]
alpha_prior = outputs["alpha_prior"]
beta_prior = outputs["beta_prior"]
return kl(Beta(alpha_posterior, beta_posterior),
Beta(alpha_prior, beta_prior)).sum()
def forward(self, x, local_l_mean, local_l_var, batch_index=None, y=None):
is_labelled = False if y is None else True
x_ = torch.log(1 + x)
qz1_m, qz1_v, z1 = self.z_encoder(x_)
ql_m, ql_v, library = self.l_encoder(x_)
# Enumerate choices of label
ys, z1s = (broadcast_labels(y, z1, n_broadcast=self.n_labels))
qz2_m, qz2_v, z2 = self.encoder_z2_z1(z1s, ys)
pz1_m, pz1_v = self.decoder_z1_z2(z2, ys)
px_scale, px_r, px_rate, px_dropout = self.decoder(
self.dispersion, z1, library, batch_index)
reconst_loss = self._reconstruction_loss(x, px_rate, px_r, px_dropout,
batch_index, y)
# KL Divergence
mean = torch.zeros_like(qz2_m)
scale = torch.ones_like(qz2_v)
kl_divergence_z2 = kl(Normal(qz2_m, torch.sqrt(qz2_v)),
Normal(mean, scale)).sum(dim=1)
loss_z1_unweight = -Normal(pz1_m,
torch.sqrt(pz1_v)).log_prob(z1s).sum(dim=-1)
loss_z1_weight = Normal(qz1_m,
torch.sqrt(qz1_v)).log_prob(z1).sum(dim=-1)
kl_divergence_l = kl(Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean,
torch.sqrt(local_l_var))).sum(dim=1)
if is_labelled:
return reconst_loss + loss_z1_weight + loss_z1_unweight, kl_divergence_z2 + kl_divergence_l
probs = self.classifier(z1)
reconst_loss += (loss_z1_weight + (
(loss_z1_unweight).view(self.n_labels, -1).t() * probs).sum(dim=1))
kl_divergence = (kl_divergence_z2.view(self.n_labels, -1).t() *
probs).sum(dim=1)
kl_divergence += kl(
Categorical(probs=probs),
Categorical(probs=self.y_prior.repeat(probs.size(0), 1)))
kl_divergence += kl_divergence_l
return reconst_loss, kl_divergence
def kl(self):
w_mus = [weight_mu.view([-1]) for weight_mu in self.weight_mus]
b_mus = [bias_mu.view([-1]) for bias_mu in self.bias_mus]
mus = torch.cat(w_mus+b_mus)
w_logsigs = [weight_logsig.view([-1]) for weight_logsig in self.weight_logsigs]
b_logsigs = [bias_logsigs.view([-1]) for bias_logsigs in self.bias_logsigs]
sigs = torch.cat(w_logsigs+b_logsigs).exp()
q = Normal(mus, sigs)
N = Normal(torch.zeros(len(mus), device=mus.device), torch.ones(len(mus), device=mus.device))
return kl(q, N)
def forward(self, x, local_l_mean, local_l_var, batch_index=None, y=None):
r""" Returns the reconstruction loss and the Kullback divergences
:param x: tensor of values with shape (batch_size, n_input)
:param local_l_mean: tensor of means of the prior distribution of latent variable l
with shape (batch_size, 1)
:param local_l_var: tensor of variancess of the prior distribution of latent variable l
with shape (batch_size, 1)
:param batch_index: array that indicates which batch the cells belong to with shape ``batch_size``
:param y: tensor of cell-types labels with shape (batch_size, n_labels)
:return: the reconstruction loss and the Kullback divergences
:rtype: 2-tuple of :py:class:`torch.FloatTensor`
"""
# Parameters for z latent distribution
outputs = self.inference(x, batch_index, None)
qz_m = outputs["qz_m"]
qz_v = outputs["qz_v"]
ql_m = outputs["ql_m"]
ql_v = outputs["ql_v"]
px_rate = outputs["px_rate"]
px_r = outputs["px_r"]
px_dropout = outputs["px_dropout"]
# KL Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)),
Normal(mean, scale)).sum(dim=1)
kl_divergence_l = kl(
Normal(ql_m, torch.sqrt(ql_v)),
Normal(local_l_mean, torch.sqrt(local_l_var)),
).sum(dim=1)
kl_divergence = kl_divergence_z
reconst_loss = self.get_reconstruction_loss(x, px_rate, px_r,
px_dropout)
if self.reconstruction_loss == "mse" or self.reconstruction_loss == "nb":
kl_divergence_l = 1.0
print("reconst_loss=%f, kl_divergence=%f" %
(torch.mean(reconst_loss), torch.mean(kl_divergence)))
return reconst_loss + kl_divergence_l, kl_divergence, 0.0
def forward(self, X, local_l_mean=None, local_l_var=None):
result = self.inference(X)
latent_z_mu = result["latent_z_mu"]
latent_z_logvar = result["latent_z_logvar"]
latent_z = result["latent_z"]
latent_l_mu = result["latent_l_mu"]
latent_l_logvar = result["latent_l_logvar"]
imputation = result["imputation"]
disperation = result["disperation"]
dropoutrate = result["dropoutrate"]
# KL Divergence for library factor
if local_l_mean is not None:
kl_divergence_l = kl(Normal(latent_l_mu, latent_l_logvar),
Normal(local_l_mean,
torch.sqrt(local_l_var))).sum(dim=1)
else:
kl_divergence_l = torch.tensor(0.0)
# KL Divergence for latent code
if self.penality == "GMM":
gamma, mu_c, var_c, pi = self.get_gamma(
latent_z) #, self.n_centroids, c_params)
kl_divergence_z = GMM_loss(gamma, (mu_c, var_c, pi),
(latent_z_mu, latent_z_logvar))
else:
mean = torch.zeros_like(latent_z_mu)
scale = torch.ones_like(latent_z_logvar)
kl_divergence_z = kl(Normal(latent_z_mu, latent_z_logvar),
Normal(mean, scale)).sum(dim=1)
reconst_loss = self.get_reconstruction_loss(X, imputation, disperation,
dropoutrate)
return reconst_loss, kl_divergence_l, kl_divergence_z
def forward(self, x, local_l_mean, local_l_var, batch_index=None, y=None):
r""" Returns the reconstruction loss and the Kullback divergences
:param x: tensor of values with shape (batch_size, n_input)
:param local_l_mean: tensor of means of the prior distribution of latent variable l
with shape (batch_size, 1)
:param local_l_var: tensor of variancess of the prior distribution of latent variable l
with shape (batch_size, 1)
:param batch_index: array that indicates which batch the cells belong to with shape ``batch_size``
:param y: tensor of cell-types labels with shape (batch_size, n_labels)
:return: the reconstruction loss and the Kullback divergences
:rtype: 2-tuple of :py:class:`torch.FloatTensor`
"""
# Parameters for z latent distribution
outputs = self.inference(x, batch_index, y)
qz_m = outputs['qz_m']
qz_v = outputs['qz_v']
ql_m = outputs['ql_m']
ql_v = outputs['ql_v']
px_rate = outputs['px_rate']
px_r = outputs['px_r']
px_dropout = outputs['px_dropout']
# KL Divergence
mean, scale = self.get_prior_params(device=qz_m.device)
kl_divergence_z = kl(self.z_encoder.distrib(qz_m, qz_v),
self.z_encoder.distrib(mean, scale))
if len(kl_divergence_z.size()) == 2:
kl_divergence_z = kl_divergence_z.sum(dim=1)
kl_divergence_l = kl(Normal(ql_m, torch.sqrt(ql_v)), Normal(local_l_mean, torch.sqrt(local_l_var))).sum(dim=1)
kl_divergence = kl_divergence_z
reconst_loss = self.get_reconstruction_loss(x, px_rate, px_r, px_dropout)
return reconst_loss + kl_divergence_l, kl_divergence
def forward(self, x, y=None):
is_labelled = False if y is None else True
qz1_m, qz1_v, z1 = self.z_encoder(x)
# Enumerate choices of label
ys, z1s = (
broadcast_labels(
y, z1, n_broadcast=self.n_labels
)
)
qz2_m, qz2_v, z2 = self.encoder_z2_z1(z1s, ys)
pz1_m, pz1_v = self.decoder_z1_z2(z2, ys)
qx_m, qx_v = self.decoder(z1)
reconst_loss = self._reconstruction_loss(x, qx_m, qx_v)
# KL Divergence
mean = torch.zeros_like(qz2_m)
scale = torch.ones_like(qz2_v)
kl_divergence_z2 = kl(Normal(qz2_m, torch.sqrt(qz2_v)), Normal(mean, scale)).sum(dim=1)
loss_z1_unweight = - Normal(pz1_m, torch.sqrt(pz1_v)).log_prob(z1s).sum(dim=-1)
loss_z1_weight = Normal(qz1_m, torch.sqrt(qz1_v)).log_prob(z1).sum(dim=-1)
if is_labelled:
return reconst_loss + loss_z1_weight + loss_z1_unweight, kl_divergence_z2
probs = self.classifier(z1)
reconst_loss += (loss_z1_weight + ((loss_z1_unweight).view(self.n_labels, -1).t() * probs).sum(dim=1))
kl_divergence = (kl_divergence_z2.view(self.n_labels, -1).t() * probs).sum(dim=1)
kl_divergence += kl(Categorical(probs=probs),
Categorical(probs=self.y_prior.repeat(probs.size(0), 1)))
return reconst_loss, kl_divergence
def forward(self, x):
r""" Returns the reconstruction loss
:param x: tensor of values with shape (batch_size, n_input)
:return: the reconstruction loss and the Kullback divergences
:rtype: 2-tuple of :py:class:`torch.FloatTensor`
"""
# Parameters for z latent distribution
outputs = self.inference(x)
qz_m = outputs["qz_m"]
qz_v = outputs["qz_v"]
px_rate = outputs["px_rate"]
px_r = outputs["px_r"]
z = outputs["z"]
library = outputs["library"]
self.encoder_variance.append(
np.linalg.norm(qz_v.detach().cpu().numpy(), axis=1))
if self.use_MP:
# Message passing likelihood
self.initialize_visit()
self.initialize_messages(z, self.barcodes, self.n_latent)
self.perform_message_passing((self.tree & self.root), z.shape[1],
False)
mp_lik = self.aggregate_messages_into_leaves_likelihood(
z.shape[1], add_prior=True)
# Gaussian variational likelihood
qz = Normal(qz_m, torch.sqrt(qz_v)).log_prob(z).sum(dim=-1)
else:
mp_lik = None
# scVI Kl Divergence
mean = torch.zeros_like(qz_m)
scale = torch.ones_like(qz_v)
qz = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean,
scale)).sum(dim=1)
# Reconstruction Loss
if 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, qz, mp_lik
def loss(
self,
tensors,
inference_outputs,
generative_outputs,
):
x = tensors[REGISTRY_KEYS.X_KEY]
qz_m = inference_outputs["qz_m"]
qz_v = inference_outputs["qz_v"]
p = generative_outputs["px"]
kld = kl(
Normal(qz_m, torch.sqrt(qz_v)),
Normal(0, 1),
).sum(dim=1)
rl = self.get_reconstruction_loss(p, x)
loss = (0.5 * rl + 0.5 * (kld * self.kl_weight)).mean()
return LossRecorder(loss, rl, kld)
