From 32be0d0c27da81b21bc76af96ced75903ec29122 Mon Sep 17 00:00:00 2001 From: edircoli Date: Thu, 14 Aug 2025 11:31:37 +0200 Subject: [PATCH 1/2] Commented unused variables --- src/abaco/ABaCo.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/src/abaco/ABaCo.py b/src/abaco/ABaCo.py index 675c3f5..dd93495 100644 --- a/src/abaco/ABaCo.py +++ b/src/abaco/ABaCo.py @@ -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] @@ -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, @@ -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, @@ -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() @@ -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() @@ -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() @@ -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 @@ -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 From dcbb0878711e96da3a0fc263f4261b071afee8cf Mon Sep 17 00:00:00 2001 From: edircoli Date: Thu, 14 Aug 2025 12:13:16 +0200 Subject: [PATCH 2/2] Commented unused variables --- src/abaco/batch_correction.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/abaco/batch_correction.py b/src/abaco/batch_correction.py index eca602f..8d970d2 100644 --- a/src/abaco/batch_correction.py +++ b/src/abaco/batch_correction.py @@ -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))