Developer continuous

setup.cfg

@@ -37,4 +37,6 @@ console_scripts =
 
 [flake8]
 max-line-length = 120
-aggressive = 2
+aggressive = 2
+extend-select = B950
+extend-ignore = E203,E501,E701

src/move/data/perturbations.py

@@ -41,7 +41,7 @@ def perturb_categorical_data(
     splits = np.cumsum(
         [0] + [int.__mul__(*shape) for shape in baseline_dataset.cat_shapes]
     )
-    slice_ = slice(*splits[target_idx: target_idx + 2])
+    slice_ = slice(*splits[target_idx : target_idx + 2])
 
     target_shape = baseline_dataset.cat_shapes[target_idx]
     num_features = target_shape[0]  # CHANGE
@@ -93,7 +93,7 @@ def perturb_continuous_data(
 
     target_idx = con_dataset_names.index(target_dataset_name)
     splits = np.cumsum([0] + baseline_dataset.con_shapes)
-    slice_ = slice(*splits[target_idx: target_idx + 2])
+    slice_ = slice(*splits[target_idx : target_idx + 2])
 
     num_features = baseline_dataset.con_shapes[target_idx]
 
@@ -154,7 +154,7 @@ def perturb_continuous_data_extended(
 
     target_idx = con_dataset_names.index(target_dataset_name)  # dataset index
     splits = np.cumsum([0] + baseline_dataset.con_shapes)
-    slice_ = slice(*splits[target_idx: target_idx + 2])
+    slice_ = slice(*splits[target_idx : target_idx + 2])
 
     num_features = baseline_dataset.con_shapes[target_idx]
     dataloaders = []

src/move/models/vae.py

@@ -190,7 +190,7 @@ def decompose_categorical(self, reconstruction: torch.Tensor) -> list[torch.Tens
         cat_out = []
         pos = 0
         for cat_shape in self.categorical_shapes:
-            cat_dataset = cat_tmp[:, pos: (cat_shape[0] * cat_shape[1] + pos)]
+            cat_dataset = cat_tmp[:, pos : (cat_shape[0] * cat_shape[1] + pos)]
 
             cat_out_tmp = cat_dataset.view(
                 cat_dataset.shape[0], cat_shape[0], cat_shape[1]
@@ -287,7 +287,7 @@ def calculate_cat_error(
         cat_errors = []
         pos = 0
         for cat_shape in self.categorical_shapes:
-            cat_dataset = cat_in[:, pos: (cat_shape[0] * cat_shape[1] + pos)]
+            cat_dataset = cat_in[:, pos : (cat_shape[0] * cat_shape[1] + pos)]
 
             cat_dataset = cat_dataset.view(cat_in.shape[0], cat_shape[0], cat_shape[1])
             cat_target = cat_dataset
@@ -327,8 +327,8 @@ def calculate_con_error(
         total_shape = 0
         con_errors_list: list[torch.Tensor] = []
         for s in self.continuous_shapes:
-            c_in = con_in[:, total_shape: (s + total_shape - 1)]
-            c_re = con_out[:, total_shape: (s + total_shape - 1)]
+            c_in = con_in[:, total_shape : (s + total_shape - 1)]
+            c_re = con_out[:, total_shape : (s + total_shape - 1)]
             error = loss(c_re, c_in) / batch_size
             con_errors_list.append(error)
             total_shape += s
@@ -451,7 +451,9 @@ def encoding(
             elif self.num_continuous > 0:
                 tensor = con
             else:
-                raise ValueError("Must have at least 1 categorial or 1 continuous feature")
+                raise ValueError(
+                    "Must have at least 1 categorial or 1 continuous feature"
+                )
 
             optimizer.zero_grad()
 
@@ -538,21 +540,21 @@ def get_cat_recon(
         shape_1 = 0
         for cat_shape in self.categorical_shapes:
             # Get input categorical data
-            cat_in_tmp = cat[:, pos: (cat_shape[0] * cat_shape[1] + pos)]
+            cat_in_tmp = cat[:, pos : (cat_shape[0] * cat_shape[1] + pos)]
             cat_in_tmp = cat_in_tmp.view(cat.shape[0], cat_shape[0], cat_shape[1])
 
             # Calculate target values for input
             cat_target_tmp = cat_in_tmp
             cat_target_tmp = torch.argmax(cat_target_tmp.detach(), dim=2)
             cat_target_tmp[cat_in_tmp.sum(dim=2) == 0] = -1
-            cat_target[:, shape_1: (cat_shape[0] + shape_1)] = (
+            cat_target[:, shape_1 : (cat_shape[0] + shape_1)] = (
                 cat_target_tmp  # .numpy()
             )
 
             # Get reconstructed categorical data
             cat_out_tmp = cat_out[count]
             cat_out_tmp = cat_out_tmp.transpose(1, 2)
-            cat_out_class[:, shape_1: (cat_shape[0] + shape_1)] = torch.argmax(
+            cat_out_class[:, shape_1 : (cat_shape[0] + shape_1)] = torch.argmax(
                 cat_out_tmp, dim=2
             )  # .numpy()
 
@@ -694,7 +696,9 @@ def latent(
             elif self.num_continuous > 0:
                 tensor = con
             else:
-                raise ValueError("Must have at least 1 categorial or 1 continuous feature")
+                raise ValueError(
+                    "Must have at least 1 categorial or 1 continuous feature"
+                )
 
             # Evaluate
             cat_out, con_out, mu, logvar = self(tensor)
@@ -713,14 +717,14 @@ def latent(
                 cat_out_class, cat_target = self.get_cat_recon(
                     batch, cat_total_shape, cat, cat_out
                 )
-                cat_recon[row: row + len(cat_out_class)] = torch.Tensor(cat_out_class)
-                cat_class[row: row + len(cat_target)] = torch.Tensor(cat_target)
+                cat_recon[row : row + len(cat_out_class)] = torch.Tensor(cat_out_class)
+                cat_class[row : row + len(cat_target)] = torch.Tensor(cat_target)
 
             if self.num_continuous > 0:
-                con_recon[row: row + len(con_out)] = con_out
+                con_recon[row : row + len(con_out)] = con_out
 
-            latent_var[row: row + len(logvar)] = logvar
-            latent[row: row + len(mu)] = mu
+            latent_var[row : row + len(logvar)] = logvar
+            latent[row : row + len(mu)] = mu
             row += len(mu)
 
         test_loss /= len(dataloader)

src/move/tasks/identify_associations.py

@@ -598,11 +598,11 @@ def _ks_approach(
                         edges,
                         hist_base,
                         hist_pert,
-                        f"Cumulative_perturbed_{i}_measuring_{k}_stats_{stats[j,i,k]}",
+                        f"Cumulative_perturbed_{i}_measuring_{k}_stats_{stats[j, i, k]}",
                     )
                     fig.savefig(
                         figure_path
-                        / f"Cumulative_refit_{j}_perturbed_{i}_measuring_{k}_stats_{stats[j,i,k]}.png"
+                        / f"Cumulative_refit_{j}_perturbed_{i}_measuring_{k}_stats_{stats[j, i, k]}.png"
                     )
 
                     # Feature changes:

src/move/tasks/tune_model.py

@@ -116,7 +116,7 @@ def _tune_stability(
         cosine_sim0 = None
         cosine_sim_diffs = []
         for j in range(task_config.num_refits):
-            logger.debug(f"Refit: {j+1}/{task_config.num_refits}")
+            logger.debug(f"Refit: {j + 1}/{task_config.num_refits}")
             model: VAE = hydra.utils.instantiate(
                 task_config.model,
                 continuous_shapes=train_dataset.con_shapes,