Modify train_test_indexes_patient_wise according to new dataset methods

split.py

 import numpy as np
 from sklearn.model_selection import train_test_split
 
-def train_test_indexes_patient_wise(dataset, test_size=0.2, stratify=True):
-    
-    patients = dataset.get_patients()
+
+def train_test_indexes_patient_wise(dataset, test_size=0.2, stratify=True, seed=1234):
+
+    patients = dataset.patients
     unique_patients = np.unique(patients)
 
     # print(len(files), len(patients))
@@ -11,28 +12,38 @@ def train_test_indexes_patient_wise(dataset, test_size=0.2, stratify=True):
     patients_labels = []
 
     for patient in unique_patients:
-        idx = np.where(patient == np.array(patients))[0][0] # index of the first im belonging to the patient
-        label = dataset.get_labels()[idx]
+        idx = np.where(patient == np.array(patients))[0][
+            0
+        ]  # index of the first im belonging to the patient
+        label = dataset.labels[idx]
         patients_labels.append(label)
 
     # print(len(patients_labels))
-        
+
     # print(train_patients)
     if stratify:
-        train_patients, test_patients = train_test_split(unique_patients, test_size=test_size, random_state=dataset.seed, stratify=patients_labels)
+        train_patients, test_patients = train_test_split(
+            unique_patients,
+            test_size=test_size,
+            random_state=seed,
+            stratify=patients_labels,
+        )
     else:
-        train_patients, test_patients = train_test_split(unique_patients, test_size=test_size, random_state=dataset.seed) 
-    
+        train_patients, test_patients = train_test_split(
+            unique_patients, test_size=test_size, random_state=seed
+        )
+
     train_indexes = []
     test_indexes = []
-    
-    
+
     for train_patient in train_patients:
         idxs = np.where(train_patient == np.array(patients))[0].tolist()
         train_indexes.extend(idxs)
-    
+
     for test_patient in test_patients:
         idxs = np.where(test_patient == np.array(patients))[0].tolist()
         test_indexes.extend(idxs)
-    
-    return train_indexes, test_indexes
\ No newline at end of file
+
+    return train_indexes, test_indexes
+
+