Formatting fixes

finetune.py

@@ -19,7 +19,6 @@
 from skmultilearn.model_selection import iterative_train_test_split
 from torch.utils.data import DataLoader
 
-from odyssey.utils.utils import seed_everything
 from odyssey.data.dataset import FinetuneDataset, FinetuneMultiDataset
 from odyssey.data.tokenizer import ConceptTokenizer
 from odyssey.models.cehr_bert.model import BertFinetune, BertPretrain
@@ -29,6 +28,7 @@
     load_config,
     load_finetune_data,
 )
+from odyssey.utils.utils import seed_everything
 
 
 def main(
@@ -251,7 +251,7 @@ def main(
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    
+
     # project configuration
     parser.add_argument(
         "--model-type",

odyssey/data/dataset.py

@@ -4,7 +4,6 @@
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import pandas as pd
-
 import torch
 from torch.utils.data import Dataset
 

odyssey/evals/prediction.py

@@ -4,8 +4,8 @@
 
 import torch
 
-from odyssey.models.cehr_big_bird.model import BigBirdFinetune, BigBirdPretrain
 from odyssey.data.tokenizer import ConceptTokenizer
+from odyssey.models.cehr_big_bird.model import BigBirdFinetune, BigBirdPretrain
 
 
 def load_finetuned_model(

odyssey/models/baseline/Bi-LSTM.py

@@ -18,8 +18,8 @@
 from tqdm import tqdm
 
 from odyssey.data.dataset import FinetuneDataset
-from odyssey.models.cehr_big_bird.embeddings import Embeddings
 from odyssey.data.tokenizer import HuggingFaceConceptTokenizer
+from odyssey.models.cehr_big_bird.embeddings import Embeddings
 
 
 ROOT = "/fs01/home/afallah/odyssey/odyssey"

odyssey/models/cehr_big_bird/model.py

@@ -4,15 +4,14 @@
 
 import numpy as np
 import pytorch_lightning as pl
+import torch
 from sklearn.metrics import (
     accuracy_score,
     f1_score,
     precision_score,
     recall_score,
     roc_auc_score,
 )
-
-import torch
 from torch import nn, optim
 from torch.cuda.amp import autocast
 from torch.optim import AdamW
@@ -453,7 +452,7 @@ def on_test_epoch_end(self) -> Any:
             auc = roc_auc_score(labels, preds)
             precision = precision_score(labels, preds)
             recall = recall_score(labels, preds)
-        
+
         self.log("test_loss", loss)
         self.log("test_acc", accuracy)
         self.log("test_f1", f1)

odyssey/utils/utils.py

@@ -1,13 +1,12 @@
 """General utility functions and constants for the project."""
 
-from typing import Any
-
 import pickle
 import random
+from typing import Any
 
 import numpy as np
-import torch
 import pytorch_lightning as pl
+import torch
 
 
 def seed_everything(seed: int) -> None:

pretrain.py

@@ -13,7 +13,6 @@
 from sklearn.model_selection import train_test_split
 from torch.utils.data import DataLoader
 
-from odyssey.utils.utils import seed_everything
 from odyssey.data.dataset import PretrainDataset
 from odyssey.data.tokenizer import ConceptTokenizer
 from odyssey.models.cehr_bert.model import BertPretrain
@@ -23,6 +22,7 @@
     load_config,
     load_pretrain_data,
 )
+from odyssey.utils.utils import seed_everything
 
 
 def main(args: argparse.Namespace, model_config: Dict[str, Any]) -> None:
@@ -152,7 +152,7 @@ def main(args: argparse.Namespace, model_config: Dict[str, Any]) -> None:
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    
+
     # project configuration
     parser.add_argument(
         "--model-type",