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milk10k_effb2_dermoscopic_metadata/data.py

@@ -113,6 +113,10 @@ def synthetic_mask(df: pd.DataFrame) -> np.ndarray:
     return mask
 
 
+def source_lesion_id(value: Any) -> str:
+    return str(value).split("__sdpair_", 1)[0]
+
+
 def create_or_load_split(
     df: pd.DataFrame, manifest: Path, val_size: float, seed: int,
     synthetic_train_only: bool = False, fold_index: int = 0, k_folds: int = 1,
@@ -138,12 +142,18 @@ def create_or_load_split(
             raise ValueError(f"Split manifest has overlapping train/validation IDs: {manifest}")
         unknown = (train_ids | val_ids) - all_ids
         missing = all_ids - (train_ids | val_ids)
-        if unknown or missing:
+        allowed_missing = set()
+        if synthetic_train_only:
+            allowed_missing = {
+                lesion_id for lesion_id in missing
+                if "__sdpair_" in lesion_id and source_lesion_id(lesion_id) in val_ids
+            }
+        unexpected_missing = missing - allowed_missing
+        if unknown or unexpected_missing:
             raise ValueError(f"Split manifest does not match dataset (unknown={len(unknown)}, missing={len(missing)}).")
     else:
         synthetic = synthetic_mask(df)
         base = df.loc[~synthetic].copy() if synthetic_train_only else df.copy()
-        extra_train_ids = set(df.loc[synthetic, "lesion_id"].astype(str)) if synthetic_train_only else set()
         folds = []
         if k_folds == 1:
             train_rows, val_rows = train_test_split(base, test_size=val_size, stratify=base["label"], random_state=seed)
@@ -155,9 +165,23 @@ def create_or_load_split(
             splitter = StratifiedKFold(k_folds, shuffle=True, random_state=seed)
             pairs = [(base.iloc[tr], base.iloc[va]) for tr, va in splitter.split(base, base["label"])]
         for train_rows, val_rows in pairs:
+            train_real_ids = set(train_rows["lesion_id"].astype(str))
+            val_real_ids = set(val_rows["lesion_id"].astype(str))
+            extra_train_ids = set()
+            excluded_synthetic_ids = set()
+            if synthetic_train_only:
+                for lesion_id in df.loc[synthetic, "lesion_id"].astype(str):
+                    source_id = source_lesion_id(lesion_id)
+                    if source_id in train_real_ids:
+                        extra_train_ids.add(lesion_id)
+                    elif source_id in val_real_ids:
+                        excluded_synthetic_ids.add(lesion_id)
+                    else:
+                        raise ValueError(f"Synthetic lesion has unknown source ID: {lesion_id}")
             folds.append({
                 "train_lesion_ids": sorted(set(train_rows["lesion_id"].astype(str)) | extra_train_ids),
                 "val_lesion_ids": sorted(set(val_rows["lesion_id"].astype(str))),
+                "excluded_synthetic_lesion_ids": sorted(excluded_synthetic_ids),
             })
         train_ids = set(folds[fold_index]["train_lesion_ids"]); val_ids = set(folds[fold_index]["val_lesion_ids"])
         manifest.parent.mkdir(parents=True, exist_ok=True)
@@ -180,6 +204,14 @@ def append_augmented_rows(base_df: pd.DataFrame, train_df: pd.DataFrame, args) -
     if args.augmented_data_dir is None: return train_df
     augmented = load_dermoscopic_dataframe(args.augmented_data_dir)
     augmented = augmented[~augmented["lesion_id"].astype(str).isin(set(base_df["lesion_id"].astype(str)))].copy()
+    train_source_ids = set(train_df["lesion_id"].astype(str).map(source_lesion_id))
+    base_source_ids = set(base_df["lesion_id"].astype(str).map(source_lesion_id))
+    augmented["source_lesion_id"] = augmented["lesion_id"].astype(str).map(source_lesion_id)
+    unknown = ~augmented["source_lesion_id"].isin(base_source_ids)
+    if unknown.any():
+        examples = augmented.loc[unknown, "lesion_id"].astype(str).head(5).tolist()
+        raise ValueError(f"Augmented lesions have unknown source IDs. Examples: {examples}")
+    augmented = augmented[augmented["source_lesion_id"].isin(train_source_ids)].copy()
     if args.augmented_classes:
         allowed = {name.upper() for name in args.augmented_classes}
         unknown = allowed - {name.upper() for name in base_df["label"].unique()}