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23 unused created variables #24

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Merged
edircoli merged 2 commits into from
Aug 14, 2025
Merged

23 unused created variables #24

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32be0d0
Commented unused variables
edircoli Aug 14, 2025
dcbb087
Commented unused variables
edircoli Aug 14, 2025
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20 changes: 10 additions & 10 deletions src/abaco/ABaCo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -769,7 +769,7 @@ def sample(self, sample_shape=torch.Size()):
dm_sample:
Sample(s) from the Dirichlet Multinomial distribution.
"""
shape = self._extended_shape(sample_shape)
# shape = self._extended_shape(sample_shape)
p = td.Dirichlet(self.concentration).sample(sample_shape)

batch_dims = p.shape[:-1]
Expand Down Expand Up @@ -1207,7 +1207,7 @@ def kl_div_loss(self, x):
KL-divergence loss
"""
q = self.encoder(x)
z = q.rsample()
# z = q.rsample()
kl_loss = torch.mean(
self.beta * td.kl_divergence(q, self.prior()),
dim=0,
Expand Down Expand Up @@ -1359,7 +1359,7 @@ def elbo(self, x):

def kl_div_loss(self, x):
q = self.encoder(x)
z = q.rsample()
# z = q.rsample()
kl_loss = torch.mean(
self.beta * td.kl_divergence(q, self.prior()),
dim=0,
Expand Down Expand Up @@ -2035,7 +2035,7 @@ def train_abaco(
for loader_data in data_iter:
x = loader_data[0].to(device)
y = loader_data[1].to(device).float() # Batch label
z = loader_data[2].to(device).float() # Bio type label
# z = loader_data[2].to(device).float() # Bio type label

# VAE ELBO computation with masked batch label
vae_optim_post.zero_grad()
Expand All @@ -2050,8 +2050,8 @@ def train_abaco(
p_xz = vae.decoder(torch.cat([latent_points, alpha * y], dim=1))

# Log probabilities of prior and posterior
log_q_zx = q_zx.log_prob(latent_points)
log_p_z = vae.log_prob(latent_points)
# log_q_zx = q_zx.log_prob(latent_points)
# log_p_z = vae.log_prob(latent_points)

# Compute ELBO
recon_term = p_xz.log_prob(x).mean()
Expand Down Expand Up @@ -2829,7 +2829,7 @@ def train_abaco_ensemble(
for loader_data in data_iter:
x = loader_data[0].to(device)
y = loader_data[1].to(device).float() # Batch label
z = loader_data[2].to(device).float() # Bio type label
# z = loader_data[2].to(device).float() # Bio type label

# VAE ELBO computation with masked batch label
vae_optim_post.zero_grad()
Expand All @@ -2849,8 +2849,8 @@ def train_abaco_ensemble(
p_xzs.append(p_xz)

# Log probabilities of prior and posterior
log_q_zx = q_zx.log_prob(latent_points)
log_p_z = vae.log_prob(latent_points)
# log_q_zx = q_zx.log_prob(latent_points)
# log_p_z = vae.log_prob(latent_points)

# Compute ELBO

Expand Down Expand Up @@ -4529,7 +4529,7 @@ def correct(
for loader_data in iter(self.dataloader):
x = loader_data[0].to(self.device)
ohe_batch = loader_data[1].to(self.device).float() # Batch label
ohe_bio = loader_data[2].to(self.device).float() # Bio type label
# ohe_bio = loader_data[2].to(self.device).float() # Bio type label

# Encode and decode the input data along with the one-hot encoded batch label
q_zx = self.vae.encoder(torch.cat([x, ohe_batch], dim=1)) # td.Distribution
Expand Down
2 changes: 1 addition & 1 deletion src/abaco/batch_correction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -547,7 +547,7 @@ def fit(self, df, y=None):
cc = [self._col_order.index(c) for c in self.covariate_cols]
design_idx = bc + cc
Xd = X_full[:, design_idx]
Xd_ref = X_ref[:, design_idx]
# Xd_ref = X_ref[:, design_idx]

n, p = X_full.shape
feat_idx = list(range(len(design_idx), p))
Expand Down