moved_repo_first_update

2026-02-24 10:39:48 +01:00
commit 9894a23f09
98 changed files with 35387 additions and 0 deletions
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+"""Filter segmentation metrics rows with near-zero Dice scores."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import pandas as pd
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Drop samples where both disc and cup Dice are below a threshold "
+            "(default 0.01) and report how many were removed."
+        )
+    )
+    parser.add_argument("input", type=Path, help="Path to metrics CSV to filter")
+    parser.add_argument(
+        "--output",
+        type=Path,
+        help="Destination CSV. Defaults to <input stem>_filtered.csv in the same directory.",
+    )
+    parser.add_argument(
+        "--threshold",
+        type=float,
+        default=0.01,
+        help="Dice cutoff; rows with both dice_disc and dice_cup below this are removed.",
+    )
+    parser.add_argument(
+        "--keep-summary",
+        action="store_true",
+        help="Always keep summary rows (sample_id == '__mean__').",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    df = pd.read_csv(args.input)
+
+    mask_low = (df["dice_disc"] < args.threshold) & (df["dice_cup"] < args.threshold)
+    if args.keep_summary and "sample_id" in df.columns:
+        mask_low &= df["sample_id"].ne("__mean__")
+
+    removed = int(mask_low.sum())
+    filtered = df.loc[~mask_low].copy()
+
+    # Recompute summary if original file contained one
+    if "sample_id" in filtered.columns:
+        summary_mask = filtered["sample_id"].eq("__mean__")
+        filtered = filtered.loc[~summary_mask].copy()
+        if not filtered.empty:
+            summary = filtered[["dice_disc", "dice_cup"]].mean()
+            summary_row = {
+                "sample_id": "__mean__",
+                "dataset": "summary",
+                "split": "summary",
+                "dice_disc": summary["dice_disc"],
+                "dice_cup": summary["dice_cup"],
+            }
+            filtered = pd.concat([filtered, pd.DataFrame([summary_row])], ignore_index=True)
+
+    remaining = len(filtered)
+
+    output_path = args.output
+    if output_path is None:
+        output_path = args.input.with_name(f"{args.input.stem}_filtered.csv")
+
+    filtered.to_csv(output_path, index=False)
+
+    print(f"Removed rows: {removed}")
+    print(f"Remaining rows: {remaining}")
+    print(f"Filtered metrics saved to: {output_path}")
+
+
+if __name__ == "__main__":
+    main()