Update rsna-2023-abdominal-trauma-detection.py

rsna-2023-abdominal-trauma-detection.py

@@ -2,6 +2,7 @@ import urllib
 import numpy as np
 import pandas as pd
 import datasets
+from sklearn.model_selection import train_test_split
 
 
 _CITATION = """\
@@ -173,16 +174,39 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
             or self.config.name == "segmentation"
         ):
             series_meta_df = series_meta_df.loc[series_meta_df["has_segmentation"] == 1]
-            img_files = dl_manager.download(
-                series_meta_df.apply(
+
+            train_series_meta_df, test_series_meta_df = train_test_split(
+                series_meta_df, test_size=0.1, random_state=42, shuffle=True
+            )
+
+            train_img_files = dl_manager.download(
+                train_series_meta_df.apply(
+                    lambda x: urllib.parse.urljoin(
+                        _URL,
+                        f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz",
+                    ),
+                    axis=1,
+                ).tolist()
+            )
+            test_img_files = dl_manager.download(
+                test_series_meta_df.apply(
                     lambda x: urllib.parse.urljoin(
-                        _URL, f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz"
+                        _URL,
+                        f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz",
+                    ),
+                    axis=1,
+                ).tolist()
+            )
+            train_seg_files = dl_manager.download(
+                train_series_meta_df.apply(
+                    lambda x: urllib.parse.urljoin(
+                        _URL, f"segmentations/{int(x['series_id'])}.nii.gz"
                     ),
                     axis=1,
                 ).tolist()
             )
-            seg_files = dl_manager.download(
-                series_meta_df.apply(
+            test_seg_files = dl_manager.download(
+                train_series_meta_df.apply(
                     lambda x: urllib.parse.urljoin(
                         _URL, f"segmentations/{int(x['series_id'])}.nii.gz"
                     ),
@@ -190,26 +214,52 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
                 ).tolist()
             )
         else:
-            img_files = dl_manager.download(
-                series_meta_df.apply(
+            train_series_meta_df, test_series_meta_df = train_test_split(
+                series_meta_df, test_size=0.1, random_state=42, shuffle=True
+            )
+
+            train_img_files = dl_manager.download(
+                train_series_meta_df.apply(
+                    lambda x: urllib.parse.urljoin(
+                        _URL,
+                        f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz",
+                    ),
+                    axis=1,
+                ).tolist()
+            )
+            test_img_files = dl_manager.download(
+                test_series_meta_df.apply(
                     lambda x: urllib.parse.urljoin(
-                        _URL, f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz"
+                        _URL,
+                        f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz",
                     ),
                     axis=1,
                 ).tolist()
             )
-            seg_files = None
+            train_seg_files = None
+            test_seg_files = None
 
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.ALL,
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "series_ids": train_series_meta_df["series_id"].tolist(),
+                    "dicom_tags_file": dicom_tags_file,
+                    "series_meta_file": series_meta_file,
+                    "labels_file": labels_file,
+                    "img_files": train_img_files,
+                    "seg_files": train_seg_files,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
                 gen_kwargs={
-                    "series_ids": series_meta_df["series_id"].tolist(),
+                    "series_ids": test_series_meta_df["series_id"].tolist(),
                     "dicom_tags_file": dicom_tags_file,
                     "series_meta_file": series_meta_file,
                     "labels_file": labels_file,
-                    "img_files": img_files,
-                    "seg_files": seg_files,
+                    "img_files": test_img_files,
+                    "seg_files": test_seg_files,
                 },
             ),
         ]