moved_repo_first_update

classes/v2/papila_builders.py

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+from __future__ import annotations
+
+from typing import Dict, List
+
+import numpy as np
+import pandas as pd
+
+from classes.v2.data_bundle import DataBundle
+
+# ---- Pachymetry → IOP correction (per PAPILA Table 3) ----
+_PACHY_TABLE: Dict[int, int] = {
+    475: +5,
+    485: +4,
+    495: +4,
+    505: +3,
+    515: +2,
+    525: +1,
+    535: +1,
+    545: 0,
+    555: -1,
+    565: -1,
+    575: -2,
+    585: -3,
+    595: -4,
+    605: -4,
+    615: -5,
+}
+_PACHY_KEYS = np.array(sorted(_PACHY_TABLE.keys()))
+
+
+def _nearest_pachy_key(x: float) -> int:
+    idx = int(np.argmin(np.abs(_PACHY_KEYS - float(x))))
+    return int(_PACHY_KEYS[idx])
+
+
+def _pick_iop(row: pd.Series) -> float:
+    """Prefer Pneumatic, else Perkins; may return NaN."""
+    raw = row["Pneumatic"] if not pd.isna(row.get("Pneumatic", np.nan)) else row.get("Perkins", np.nan)
+    return float(raw) if not pd.isna(raw) else np.nan
+
+
+def _correct_iop(raw_iop: float, pachy: float) -> float:
+    """Return corrected IOP using nearest pachymetry bin; if pachy missing, return raw."""
+    if pd.isna(raw_iop):
+        return np.nan
+    if pd.isna(pachy):
+        return float(raw_iop)
+    key = _nearest_pachy_key(float(pachy))
+    return float(raw_iop) + float(_PACHY_TABLE[key])
+
+
+def _apply_iop_and_drop_md(df: pd.DataFrame) -> pd.DataFrame:
+    """Add IOP_raw/IOP_corr and drop VF_MD if present (in-place safe)."""
+    df["IOP_raw"] = df.apply(_pick_iop, axis=1)
+    pachy = df.get("Pachymetry", pd.Series(np.nan, index=df.index))
+    df["IOP_corr"] = [
+        _correct_iop(r, p) for r, p in zip(df["IOP_raw"].values, pachy.values)
+    ]
+    if "VF_MD" in df.columns:
+        df.drop(columns=["VF_MD"], inplace=True)
+    return df
+
+
+def _canonicalize_eye_column(df: pd.DataFrame) -> None:
+    if "eyeID" in df.columns:
+        src = "eyeID"
+    else:
+        src = None
+        for c in df.columns:
+            if "eye" in c.lower():
+                src = c
+                break
+        if src is None:
+            df["eyeID"] = "OS"
+            return
+
+    s = df[src]
+
+    def norm(v):
+        if pd.isna(v):
+            return None
+        x = str(v).strip().upper()
+        if x in {"OS", "L", "LEFT", "0"}:
+            return "OS"
+        if x in {"OD", "R", "RIGHT", "1"}:
+            return "OD"
+        try:
+            num = int(float(x))
+            return "OD" if num % 2 == 1 else "OS"
+        except Exception:
+            return None
+
+    mapped = s.map(norm)
+    uniq = {u for u in mapped.dropna().unique().tolist()}
+    if not uniq.issubset({"OS", "OD"}):
+        raise ValueError(f"eyeID must be binary; found values {sorted(uniq)}")
+    df["eyeID"] = mapped.fillna("OS")
+
+
+def build_papila_data(
+    *,
+    image_dir: str,
+    clinical_dir: str,
+    label_col: str,
+    cat_cols: List[str],
+    n_splits: int = 5,
+    random_seed: int = 42,
+) -> DataBundle:
+    """
+    Build a DataBundle for PAPILA with dataset-specific preprocessing:
+      - load OD/OS Excel sheets
+      - normalize Patient ID
+      - canonicalize eyeID
+      - compute IOP_raw / IOP_corr, drop VF_MD
+      - build feature typing & folds
+    """
+    bundle = DataBundle(
+        image_dir=image_dir,
+        clinical_dir=clinical_dir,
+        label_col=label_col,
+        patient_col="Patient ID",
+        cat_cols=cat_cols,
+        n_splits=n_splits,
+        random_seed=random_seed,
+        filename_template="RET{pid:03d}{eye}.jpg",
+    )
+
+    od = pd.read_excel(f"{clinical_dir}/patient_data_od.xlsx", header=1)
+    od["eyeID"] = "OD"
+    os = pd.read_excel(f"{clinical_dir}/patient_data_os.xlsx", header=1)
+    os["eyeID"] = "OS"
+
+    for frame in (od, os):
+        if "Patient ID" not in frame.columns and "ID" in frame.columns:
+            frame.rename(columns={"ID": "Patient ID"}, inplace=True)
+        frame["Patient ID"] = frame["Patient ID"].astype(str).str.extract(r"(\d+)")[0].astype(int)
+        _canonicalize_eye_column(frame)
+
+    bundle.add_df(od, id_column="ID")
+    bundle.add_df(os, id_column="ID")
+
+    for i in range(len(bundle.frames)):
+        bundle.frames[i] = _apply_iop_and_drop_md(bundle.frames[i])
+
+    bundle._refresh_master_df()
+    bundle._infer_or_validate_feature_types()
+    bundle._compute_numeric_stats()
+    bundle._build_cat_maps()
+    bundle._compute_feature_dim()
+    bundle._build_kfold_indices()
+
+    return bundle