feat: return ICUType in the physionet_2012 dataset;

WenjieDu · Oct 9, 2023 · 60b42c9 · 60b42c9
1 parent 2dd0857
commit 60b42c9
Show file tree

Hide file tree

Showing 2 changed files with 19 additions and 2 deletions.
diff --git a/pypots/data/generating.py b/pypots/data/generating.py
@@ -350,6 +350,8 @@ def gene_physionet2012(artificially_missing_rate: float = 0.1):
     dataset = load_specific_dataset("physionet_2012")
     X = dataset["X"]
     y = dataset["y"]
+    ICUType = dataset["ICUType"]
+
     all_recordID = X["RecordID"].unique()
     train_set_ids, test_set_ids = train_test_split(all_recordID, test_size=0.2)
     train_set_ids, val_set_ids = train_test_split(train_set_ids, test_size=0.2)
@@ -385,16 +387,28 @@ def gene_physionet2012(artificially_missing_rate: float = 0.1):
     test_y = y[y.index.isin(test_set_ids)].sort_index()
     train_y, val_y, test_y = train_y.to_numpy(), val_y.to_numpy(), test_y.to_numpy()
 
+    train_ICUType = ICUType[ICUType.index.isin(train_set_ids)].sort_index()
+    val_ICUType = ICUType[ICUType.index.isin(val_set_ids)].sort_index()
+    test_ICUType = ICUType[ICUType.index.isin(test_set_ids)].sort_index()
+    train_ICUType, val_ICUType, test_ICUType = (
+        train_ICUType.to_numpy(),
+        val_ICUType.to_numpy(),
+        test_ICUType.to_numpy(),
+    )
+
     data = {
         "n_classes": 2,
         "n_steps": 48,
         "n_features": train_X.shape[-1],
         "train_X": train_X,
         "train_y": train_y.flatten(),
+        "train_ICUType": train_ICUType.flatten(),
         "val_X": val_X,
         "val_y": val_y.flatten(),
+        "val_ICUType": val_ICUType.flatten(),
         "test_X": test_X,
         "test_y": test_y.flatten(),
+        "test_ICUType": test_ICUType.flatten(),
         "scaler": scaler,
     }
 

diff --git a/pypots/data/load_preprocessing.py b/pypots/data/load_preprocessing.py
@@ -25,7 +25,8 @@ def preprocess_physionet2012(data: dict) -> dict:
             y : pandas.Series
                 The 11988 classification labels of all patients, indicating whether they were deceased.
     """
-    # remove the static features, e.g. age, gender
+    data["static_features"].remove("ICUType")  # keep ICUType for now
+    # remove the other static features, e.g. age, gender
     X = data["X"].drop(data["static_features"], axis=1)
 
     def apply_func(df_temp):  # pad and truncate to set the max length of samples as 48
@@ -41,11 +42,13 @@ def apply_func(df_temp):  # pad and truncate to set the max length of samples as
     X = X.groupby("RecordID").apply(apply_func)
     X = X.drop("RecordID", axis=1)
     X = X.reset_index()
-    X = X.drop(["level_1"], axis=1)
+    ICUType = X[["RecordID", "ICUType"]].set_index("RecordID").dropna()
+    X = X.drop(["level_1", "ICUType"], axis=1)
 
     dataset = {
         "X": X,
         "y": data["y"],
+        "ICUType": ICUType,
     }
 
     return dataset