from __future__ import annotations import json from dataclasses import dataclass from typing import Dict, List import pandas as pd REQUIRED_COLS = [ "row_id", "series_id", "timepoint_h", "organism", "strain_id", "drug_name", "target_gene", "mutation_id", "mutant_frac_target_mod", "mic_drug_mg_L", "resistant_mic_cutoff_mg_L", "media", "assay_method", "source_type", "penetration_signal", "earliest_penetration", ] @dataclass class Thresholds: min_points: int = 3 frac_threshold: float = 0.20 mic_fold_max_at_penetration: float = 2.0 # relative to baseline confirm_requires_resistant: bool = True spike_frac_min: float = 0.80 snapback_frac_max: float = 0.05 def _validate(df: pd.DataFrame) -> List[str]: errs: List[str] = [] missing = [c for c in REQUIRED_COLS if c not in df.columns] if missing: errs.append(f"missing_columns: {missing}") for c in ["timepoint_h", "mutant_frac_target_mod", "mic_drug_mg_L", "resistant_mic_cutoff_mg_L"]: if c in df.columns and df[c].isna().any(): errs.append(f"null_values_in: {c}") if "mutant_frac_target_mod" in df.columns: bad = ((df["mutant_frac_target_mod"] < 0) | (df["mutant_frac_target_mod"] > 1)).sum() if bad: errs.append(f"mutant_frac_out_of_range count={int(bad)}") for c in ["mic_drug_mg_L", "resistant_mic_cutoff_mg_L"]: if c in df.columns: bad = (df[c] <= 0).sum() if bad: errs.append(f"non_positive_values_in: {c} count={int(bad)}") for c in ["penetration_signal", "earliest_penetration"]: if c in df.columns: bad = (~df[c].isin([0, 1])).sum() if bad: errs.append(f"non_binary_values_in: {c} count={int(bad)}") counts = df.groupby("series_id")["earliest_penetration"].sum() bad_series = counts[counts > 1].index.tolist() if bad_series: errs.append(f"multiple_earliest_penetration_in_series: {bad_series}") return errs def _flag_spike_snap(g: pd.DataFrame, t: Thresholds) -> pd.Series: flag = pd.Series([0] * len(g), index=g.index) if len(g) < 3: return flag g = g.sort_values("timepoint_h").copy() for i in range(1, len(g) - 1): idx = g.index[i] prev_idx = g.index[i - 1] next_idx = g.index[i + 1] prev_v = float(g.loc[prev_idx, "mutant_frac_target_mod"]) v = float(g.loc[idx, "mutant_frac_target_mod"]) next_v = float(g.loc[next_idx, "mutant_frac_target_mod"]) spike = v >= t.spike_frac_min and prev_v <= t.snapback_frac_max and next_v <= t.snapback_frac_max if spike: flag.loc[idx] = 1 return flag def _f1(tp: int, fp: int, fn: int) -> float: denom = 2 * tp + fp + fn return 0.0 if denom == 0 else (2 * tp) / denom def score(path: str) -> Dict[str, object]: df = pd.read_csv(path) errors = _validate(df) if errors: return {"ok": False, "errors": errors} t = Thresholds() df = df.sort_values(["series_id", "timepoint_h"]).reset_index(drop=True) df["pred_earliest_penetration"] = 0 df["pred_penetration_signal"] = 0 df["flag_measurement_spike"] = 0 series_rows: List[Dict[str, object]] = [] for sid, g in df.groupby("series_id"): g = g.sort_values("timepoint_h").copy() df.loc[g.index, "flag_measurement_spike"] = _flag_spike_snap(g, t).astype(int) if len(g) < t.min_points: series_rows.append( { "series_id": sid, "y_pen": int(g["penetration_signal"].max()), "p_pen": 0, "true_transition_row_id": (str(g[g["earliest_penetration"] == 1].iloc[0]["row_id"]) if (g["earliest_penetration"] == 1).any() else None), "pred_transition_row_id": None, "flags": ["too_few_points"], } ) continue base = g.iloc[0] base_mic = float(base["mic_drug_mg_L"]) cutoff = float(base["resistant_mic_cutoff_mg_L"]) # if baseline already resistant, not an early penetration case if base_mic >= cutoff: series_rows.append( { "series_id": sid, "y_pen": int(g["penetration_signal"].max()), "p_pen": 0, "true_transition_row_id": (str(g[g["earliest_penetration"] == 1].iloc[0]["row_id"]) if (g["earliest_penetration"] == 1).any() else None), "pred_transition_row_id": None, "flags": ["baseline_resistant"], } ) continue hits = [] for idx, row in g.iterrows(): if idx == g.index[0]: continue if int(df.loc[idx, "flag_measurement_spike"]) == 1: continue frac = float(row["mutant_frac_target_mod"]) mic = float(row["mic_drug_mg_L"]) mic_fold = mic / base_mic if base_mic > 0 else 99.0 candidate = ( frac >= t.frac_threshold and mic_fold <= t.mic_fold_max_at_penetration and mic < cutoff ) if candidate: hits.append(idx) confirm = True if t.confirm_requires_resistant and hits: later = g[g.index > hits[0]] confirm = bool((later["mic_drug_mg_L"] >= cutoff).any()) if hits and confirm: first = hits[0] df.loc[first, "pred_earliest_penetration"] = 1 df.loc[g[g.index >= first].index, "pred_penetration_signal"] = 1 y = int(g["penetration_signal"].max()) p = int(df.loc[g.index, "pred_penetration_signal"].max()) true_tr = g[g["earliest_penetration"] == 1] true_id = str(true_tr.iloc[0]["row_id"]) if len(true_tr) == 1 else None pred_tr_rows = df.loc[g.index][df.loc[g.index, "pred_earliest_penetration"] == 1] pred_id = str(pred_tr_rows.iloc[0]["row_id"]) if len(pred_tr_rows) == 1 else None series_rows.append( { "series_id": sid, "y_pen": y, "p_pen": p, "true_transition_row_id": true_id, "pred_transition_row_id": pred_id, "measurement_spike_flags": int(df.loc[g.index, "flag_measurement_spike"].sum()), } ) sr = pd.DataFrame(series_rows) tp = int(((sr["y_pen"] == 1) & (sr["p_pen"] == 1)).sum()) fp = int(((sr["y_pen"] == 0) & (sr["p_pen"] == 1)).sum()) fn = int(((sr["y_pen"] == 1) & (sr["p_pen"] == 0)).sum()) tn = int(((sr["y_pen"] == 0) & (sr["p_pen"] == 0)).sum()) transition_hit = int( ( sr["true_transition_row_id"].notna() & (sr["true_transition_row_id"] == sr["pred_transition_row_id"]) ).sum() ) transition_miss = int( ( sr["true_transition_row_id"].notna() & (sr["true_transition_row_id"] != sr["pred_transition_row_id"]) ).sum() ) return { "ok": True, "path": path, "counts": {"tp": tp, "fp": fp, "fn": fn, "tn": tn}, "metrics": { "f1_series": _f1(tp, fp, fn), "transition_hit": transition_hit, "transition_miss": transition_miss, "n_series": int(len(sr)), }, "series_table": series_rows, } if __name__ == "__main__": import argparse ap = argparse.ArgumentParser() ap.add_argument("--path", required=True) args = ap.parse_args() result = score(args.path) print(json.dumps(result, indent=2))