features.py

"""Optional composite features — extend as the group finalises predictors."""
from __future__ import annotations

import pandas as pd


def add_optional_features(df: pd.DataFrame) -> pd.DataFrame:
    """
    Placeholder for derived variables (e.g. counts, ratios).
    Returns a copy with new columns; no-op if you add nothing below.
    """
    out = df.copy()
    # Example (uncomment when variables exist and are agreed):
    # out["chronic_count"] = out[["cond1", "cond2"]].sum(axis=1)
    return out


def list_numeric_predictors(
    df: pd.DataFrame,
    id_col: str,
    exclude: frozenset[str],
) -> list[str]:
    """All numeric columns suitable for sklearn, excluding ID and outcome-related names."""
    cols = []
    for c in df.columns:
        if c == id_col or c in exclude:
            continue
        if pd.api.types.is_numeric_dtype(df[c]):
            cols.append(c)
    return sorted(cols)