moved_repo_first_update

classes/papila_builders.py

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+# papila_builders.py
+from typing import List, Dict
+import numpy as np
+import pandas as pd
+
+from classes import ClinicalData  # adjust import path if needed
+
+# ---- 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)."""
+    # IOP_raw
+    df["IOP_raw"] = df.apply(_pick_iop, axis=1)
+
+    # IOP_corr
+    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)
+    ]
+
+    # Drop VF_MD if present
+    if "VF_MD" in df.columns:
+        df.drop(columns=["VF_MD"], inplace=True)
+    return df
+
+
+def build_papila_clinical(
+    image_dir: str,
+    clinical_dir: str,
+    label_col: str,
+    cat_cols: List[str],
+    n_splits: int = 5,
+    random_seed: int = 42,
+) -> ClinicalData:
+    """
+    Build ClinicalData exactly like the user's original build_clinical:
+      - add_df(OD), set eyeID='OD'
+      - add_df(OS), set eyeID='OS'
+      - normalize 'Patient ID' on frames
+    THEN:
+      - compute IOP_raw / IOP_corr on each frame
+      - drop VF_MD
+      - refresh master df + kfold indices
+    """
+    clinical = ClinicalData(
+        image_dir=image_dir,
+        clinical_dir=clinical_dir,
+        label_col=label_col,
+        cat_cols=cat_cols,
+        n_splits=n_splits,
+        random_seed=random_seed,
+    )
+
+    # --- Load exactly like original build_clinical ---
+    clinical.add_df(pd.read_excel(f"{clinical_dir}/patient_data_od.xlsx", header=1), id_column="ID")
+    clinical.frames[0]["eyeID"] = "OD"
+
+    clinical.add_df(pd.read_excel(f"{clinical_dir}/patient_data_os.xlsx", header=1), id_column="ID")
+    clinical.frames[1]["eyeID"] = "OS"
+
+    # Normalize 'Patient ID' on the per-eye frames (string → int)
+    for frame in clinical.frames:
+        frame["Patient ID"] = frame["Patient ID"].astype(str).str.extract(r"(\d+)")[0].astype(int)
+
+    # Build initial master as in original
+    clinical._refresh_master_df()
+
+    # --- Post-processing ON THE FRAMES (so everything stays consistent) ---
+    for i in range(len(clinical.frames)):
+        clinical.frames[i] = _apply_iop_and_drop_md(clinical.frames[i])
+
+    # Refresh master again so IOP_raw/IOP_corr & MD removal propagate
+    clinical._refresh_master_df()
+    clinical._build_kfold_indices()
+
+    return clinical