Report AUCs in Delphi2M format

delphi2m_auc_report.py

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+"""Build Delphi2M-style sex-specific AUC reports.
+
+The Delphi2M evaluation code uses 0.1 years for the no-gap evaluation.  The
+published report displays that point as 0 months, while retaining the actual
+0.1-year evaluation period in this project's report output.
+"""
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Dict, Optional
+
+import numpy as np
+import pandas as pd
+
+
+DEFAULT_DELPHI2M_PERIODS_YEARS = (0.1, 1.0, 5.0, 10.0)
+
+_CHAPTER_SHORT_NAMES = {
+    "I": "I. Infectious Diseases",
+    "II": "II. Neoplasms",
+    "III": "III. Blood & Immune Disorders",
+    "IV": "IV. Metabolic Diseases",
+    "V": "V. Mental Disorders",
+    "VI": "VI. Nervous System Diseases",
+    "VII": "VII. Eye Diseases",
+    "VIII": "VIII. Ear Diseases",
+    "IX": "IX. Circulatory Diseases",
+    "X": "X. Respiratory Diseases",
+    "XI": "XI. Digestive Diseases",
+    "XII": "XII. Skin Diseases",
+    "XIII": "XIII. Musculoskeletal Diseases",
+    "XIV": "XIV. Genitourinary Diseases",
+    "XV": "XV. Pregnancy & Childbirth",
+    "XVI": "XVI. Perinatal Conditions",
+    "XVII": "XVII. Congenital Abnormalities",
+    "XVIII": "XVIII. Symptoms & Signs",
+    "XIX": "XIX. Injury & Poisoning",
+    "XX": "XX. External Causes",
+    "XXI": "XXI. Health Services",
+    "XXII": "XXII. Special Purposes",
+    "Death": "Death",
+    "Unmapped": "Unmapped",
+}
+
+
+def _is_no_gap(period_years: float) -> bool:
+    return bool(np.isclose(float(period_years), 0.1, rtol=0.0, atol=1e-8))
+
+
+def _canonical_period_years(period_years: float) -> float:
+    value = float(period_years)
+    for canonical in DEFAULT_DELPHI2M_PERIODS_YEARS:
+        if np.isclose(value, canonical, rtol=0.0, atol=1e-6):
+            return float(canonical)
+    return value
+
+
+def _gap_months(period_years: float) -> int:
+    if _is_no_gap(period_years):
+        return 0
+    return int(round(float(period_years) * 12.0))
+
+
+def _gap_label(period_years: float) -> str:
+    if _is_no_gap(period_years):
+        return "No gap"
+    value = float(period_years)
+    value_text = f"{value:g}"
+    unit = "year" if np.isclose(value, 1.0) else "years"
+    return f"{value_text} {unit}"
+
+
+def _load_chapter_by_code(
+    chapter_mapping_path: Optional[str | Path] = None,
+) -> Dict[str, str]:
+    if chapter_mapping_path is None:
+        chapter_mapping_path = Path(__file__).with_name(
+            "icd10_chapter_organ_mapping.csv"
+        )
+    path = Path(chapter_mapping_path)
+    if not path.exists():
+        return {}
+
+    mapping = pd.read_csv(
+        path,
+        usecols=["code", "icd10_chapter"],
+        dtype={"code": str, "icd10_chapter": str},
+    )
+    mapping["code"] = mapping["code"].str.strip()
+    mapping["chapter"] = (
+        mapping["icd10_chapter"]
+        .str.strip()
+        .map(_CHAPTER_SHORT_NAMES)
+        .fillna("Unmapped")
+    )
+    return dict(zip(mapping["code"], mapping["chapter"]))
+
+
+def build_delphi2m_auc_report(
+    df_unpooled: pd.DataFrame,
+    *,
+    period_col: str,
+    chapter_mapping_path: Optional[str | Path] = None,
+) -> pd.DataFrame:
+    """Aggregate age strata by sex and return a Delphi2M-style AUC report.
+
+    Required input columns are ``token``, ``label_code``, ``sex``,
+    ``auc_delong``, and the supplied ``period_col`` (``offset`` or
+    ``horizon``).  The output begins with the five columns used by Delphi2M
+    Fig. 2e and then records the actual evaluation period and ICD-10 code.
+    """
+    required = {"token", "label_code", "sex", "auc_delong", period_col}
+    missing = sorted(required - set(df_unpooled.columns))
+    if missing:
+        raise ValueError(
+            "Cannot build Delphi2M AUC report; missing columns: "
+            + ", ".join(missing)
+        )
+
+    source = df_unpooled.loc[
+        :,
+        ["token", "label_code", "sex", "auc_delong", period_col],
+    ].copy()
+    source["sex"] = source["sex"].astype(str).str.strip().str.lower()
+    source = source[source["sex"].isin(["female", "male"])]
+    source["auc_delong"] = pd.to_numeric(
+        source["auc_delong"], errors="coerce"
+    )
+    source[period_col] = pd.to_numeric(source[period_col], errors="coerce")
+    source = source.dropna(subset=[period_col, "auc_delong"])
+    source[period_col] = source[period_col].map(_canonical_period_years)
+
+    if source.empty:
+        raise ValueError("Cannot build Delphi2M AUC report from empty AUC data.")
+
+    grouped = (
+        source.groupby(
+            ["token", "label_code", period_col, "sex"],
+            dropna=False,
+            as_index=False,
+        )
+        .agg(auc=("auc_delong", "mean"))
+    )
+    report = (
+        grouped.pivot(
+            index=["token", "label_code", period_col],
+            columns="sex",
+            values="auc",
+        )
+        .reset_index()
+        .rename_axis(columns=None)
+        .rename(columns={"female": "Female", "male": "Male"})
+    )
+    for col in ["Female", "Male"]:
+        if col not in report.columns:
+            report[col] = np.nan
+
+    chapter_by_code = _load_chapter_by_code(chapter_mapping_path)
+    report["chapter"] = (
+        report["label_code"].astype(str).map(chapter_by_code).fillna("Unmapped")
+    )
+    report["Gap, months"] = report[period_col].map(_gap_months).astype("Int64")
+    report["Gap label"] = report[period_col].map(_gap_label)
+    report["icd10"] = pd.to_numeric(report["token"], errors="coerce").astype(
+        "Int64"
+    )
+
+    report = report.sort_values(
+        ["icd10", period_col], kind="stable", ignore_index=True
+    )
+    return report.loc[
+        :,
+        [
+            "Gap, months",
+            "chapter",
+            "icd10",
+            "Female",
+            "Male",
+            period_col,
+            "Gap label",
+            "label_code",
+        ],
+    ]