| from __future__ import annotations |
|
|
| import numpy as np |
| import pandas as pd |
|
|
|
|
| def resolve_reference_rank_percentile( |
| merged_df: pd.DataFrame, |
| reference_ligand_id: str, |
| *, |
| oracle_rank_col: str = "rank_percentile", |
| run_rank_col: str = "run_rank_percentile", |
| ) -> tuple[float, str]: |
| """ |
| Resolve reference rank percentile with robust fallback. |
| |
| Returns: |
| - rank percentile (float) |
| - source ("oracle", "run_fallback", "missing") |
| """ |
| if merged_df.empty: |
| return float("nan"), "missing" |
|
|
| d = merged_df.copy() |
| if "ligand_id" not in d.columns: |
| return float("nan"), "missing" |
| d["ligand_id"] = d["ligand_id"].astype(str) |
| ref = d[d["ligand_id"] == str(reference_ligand_id)] |
| if ref.empty: |
| return float("nan"), "missing" |
|
|
| if oracle_rank_col in ref.columns: |
| oracle_val = pd.to_numeric(ref[oracle_rank_col], errors="coerce").iloc[0] |
| if np.isfinite(float(oracle_val)): |
| return float(oracle_val), "oracle" |
|
|
| if run_rank_col in d.columns: |
| run_val = pd.to_numeric(ref[run_rank_col], errors="coerce").iloc[0] |
| if np.isfinite(float(run_val)): |
| return float(run_val), "run_fallback" |
|
|
| return float("nan"), "missing" |
|
|
|
|
