[pre-commit.ci] auto fixes from pre-commit.com hooks

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src/create_train_test.py

@@ -2,6 +2,7 @@
 import os
 
 import polars as pl
+
 from utils.functions import load_pickle
 
 parser = argparse.ArgumentParser(description="Create train/val/test split.")

src/datasets.py

@@ -4,6 +4,7 @@
 import torch
 from sklearn.model_selection import train_test_split
 from torch.utils.data import Dataset
+
 from utils.functions import load_pickle, preview_data
 
 

src/evaluate.py

@@ -7,7 +7,6 @@
 import polars as pl
 import shap
 import toml
-from datasets import CollateTimeSeries, MIMIC4Dataset
 from fairlearn.metrics import (
     MetricFrame,
     count,
@@ -19,7 +18,6 @@
     selection_rate,
 )
 from lightning.pytorch import Trainer
-from models import MMModel
 from sklearn.metrics import (
     accuracy_score,
     average_precision_score,
@@ -28,6 +26,9 @@
 )
 from torch import concat
 from torch.utils.data import DataLoader
+
+from datasets import CollateTimeSeries, MIMIC4Dataset
+from models import MMModel
 from utils.functions import load_pickle, read_from_txt
 from utils.preprocessing import transform_race
 

src/postprocess.py

@@ -5,13 +5,14 @@
 import numpy as np
 import polars as pl
 import toml
-from datasets import MIMIC4Dataset
 from fairlearn.postprocessing import ThresholdOptimizer, plot_threshold_optimizer
 from sklearn.metrics import (
     accuracy_score,
     balanced_accuracy_score,
     confusion_matrix,
 )
+
+from datasets import MIMIC4Dataset
 from utils.functions import load_pickle, read_from_txt
 
 if __name__ == "__main__":

src/prepare_data.py

@@ -6,6 +6,7 @@
 
 import polars as pl
 from tqdm import tqdm
+
 from utils.functions import scale_numeric_features
 from utils.preprocessing import (
     add_time_elapsed_to_events,

src/train.py

@@ -2,15 +2,16 @@
 
 import lightning as L
 import toml
-from datasets import CollateFn, CollateTimeSeries, MIMIC4Dataset
 from lightning.pytorch.callbacks import (
     EarlyStopping,
     LearningRateMonitor,
     ModelCheckpoint,
 )
 from lightning.pytorch.loggers import CSVLogger, WandbLogger
-from models import MMModel
 from torch.utils.data import DataLoader
+
+from datasets import CollateFn, CollateTimeSeries, MIMIC4Dataset
+from models import MMModel
 from utils.functions import read_from_txt
 
 if __name__ == "__main__":

src/train_rf.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 import toml
-from datasets import MIMIC4Dataset
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import (
     accuracy_score,
@@ -13,6 +12,8 @@
     roc_auc_score,
 )
 from sklearn.model_selection import GridSearchCV
+
+from datasets import MIMIC4Dataset
 from utils.functions import read_from_txt
 
 if __name__ == "__main__":