[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
     - id: check-toml
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: 'v0.5.6'
+    rev: 'v0.6.1'
     hooks:
     - id: ruff
       args: [--fix, --exit-non-zero-on-fix]

odyssey/data/processor.py

@@ -529,13 +529,17 @@ def get_pretrain_test_split(
             test_size=test_size,
         )
     else:
-        test_patients = dataset.sample(n=int(test_size * len(dataset)), random_state=seed)
+        test_patients = dataset.sample(
+            n=int(test_size * len(dataset)), random_state=seed
+        )
         test_ids = test_patients["patient_id"].tolist()
-        pretrain_ids = dataset[~dataset["patient_id"].isin(test_ids)]["patient_id"].tolist()
-
+        pretrain_ids = dataset[~dataset["patient_id"].isin(test_ids)][
+            "patient_id"
+        ].tolist()
+
     random.seed(seed)
     random.shuffle(pretrain_ids)
-    
+
     return pretrain_ids, test_ids
 
 

odyssey/models/ehr_mamba/mamba_utils.py

@@ -112,7 +112,8 @@ def forward(
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
             `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
 
-        Returns:
+        Returns
+        -------
         """
         if inputs_embeds is not None:
             sequence_outputs = self.backbone(
@@ -222,7 +223,8 @@ def forward(
         task_indices (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Task indices to specify which classification head to use for each example in the batch.
 
-        Returns:
+        Returns
+        -------
         """
         if inputs_embeds is not None:
             sequence_outputs = self.backbone(