from __future__ import annotations import json from dataclasses import dataclass from typing import Dict, List, Optional import pandas as pd REQUIRED_COLS = [ "row_id", "series_id", "timepoint_h", "organism", "strain_id", "drug_a", "drug_b", "stress_index", "baseline_mic_a_mg_L", "baseline_mic_b_mg_L", "mic_a_mg_L", "mic_b_mg_L", "a_resistant_cutoff_mg_L", "b_susceptible_floor_mg_L", "b_sensitivity_fold_change", "media", "assay_method", "source_type", "collateral_sensitivity_signal", "earliest_collateral_sensitivity", ] @dataclass class Thresholds: min_points: int = 2 stress_min: float = 0.80 b_fold_min: float = 2.0 # B becomes at least 2x more sensitive require_same_or_next_timepoint: bool = True 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", "stress_index", "baseline_mic_a_mg_L", "baseline_mic_b_mg_L", "mic_a_mg_L", "mic_b_mg_L", "a_resistant_cutoff_mg_L", "b_susceptible_floor_mg_L", "b_sensitivity_fold_change", ]: if c in df.columns and df[c].isna().any(): errs.append(f"null_values_in: {c}") if "stress_index" in df.columns: bad = ((df["stress_index"] < 0) | (df["stress_index"] > 1)).sum() if bad: errs.append(f"stress_index_out_of_range count={int(bad)}") for c in ["baseline_mic_a_mg_L", "baseline_mic_b_mg_L", "mic_a_mg_L", "mic_b_mg_L", "a_resistant_cutoff_mg_L", "b_susceptible_floor_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)}") if "b_sensitivity_fold_change" in df.columns: bad = (df["b_sensitivity_fold_change"] <= 0).sum() if bad: errs.append(f"non_positive_values_in: b_sensitivity_fold_change count={int(bad)}") for c in ["collateral_sensitivity_signal", "earliest_collateral_sensitivity"]: 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_collateral_sensitivity"].sum() bad_series = counts[counts > 1].index.tolist() if bad_series: errs.append(f"multiple_earliest_collateral_sensitivity_in_series: {bad_series}") return errs 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_collateral_sensitivity"] = 0 df["pred_collateral_sensitivity_signal"] = 0 series_rows: List[Dict[str, object]] = [] for sid, g in df.groupby("series_id"): g = g.sort_values("timepoint_h").copy() if len(g) < t.min_points: series_rows.append( { "series_id": sid, "y_cs": int(g["collateral_sensitivity_signal"].max()), "p_cs": 0, "true_transition_row_id": (str(g[g["earliest_collateral_sensitivity"] == 1].iloc[0]["row_id"]) if (g["earliest_collateral_sensitivity"] == 1).any() else None), "pred_transition_row_id": None, "flags": ["too_few_points"], } ) continue base_A = float(g.iloc[0]["baseline_mic_a_mg_L"]) base_B = float(g.iloc[0]["baseline_mic_b_mg_L"]) cut_A = float(g.iloc[0]["a_resistant_cutoff_mg_L"]) if base_A >= cut_A: series_rows.append( { "series_id": sid, "y_cs": int(g["collateral_sensitivity_signal"].max()), "p_cs": 0, "true_transition_row_id": (str(g[g["earliest_collateral_sensitivity"] == 1].iloc[0]["row_id"]) if (g["earliest_collateral_sensitivity"] == 1).any() else None), "pred_transition_row_id": None, "flags": ["baseline_A_resistant"], } ) continue # find first time A crosses cutoff under stress a_cross_idx: Optional[int] = None for idx in g.index[1:]: if float(df.loc[idx, "stress_index"]) < t.stress_min: continue if float(df.loc[idx, "mic_a_mg_L"]) >= cut_A: a_cross_idx = idx break if a_cross_idx is None: series_rows.append( { "series_id": sid, "y_cs": int(g["collateral_sensitivity_signal"].max()), "p_cs": 0, "true_transition_row_id": (str(g[g["earliest_collateral_sensitivity"] == 1].iloc[0]["row_id"]) if (g["earliest_collateral_sensitivity"] == 1).any() else None), "pred_transition_row_id": None, "flags": ["A_never_crosses"], } ) continue # collateral sensitivity must occur same or next timepoint candidate_indices = [a_cross_idx] if t.require_same_or_next_timepoint: pos = list(g.index).index(a_cross_idx) if pos + 1 < len(g.index): candidate_indices.append(list(g.index)[pos + 1]) hit: Optional[int] = None for idx in candidate_indices: if float(df.loc[idx, "stress_index"]) < t.stress_min: continue b_mic = float(df.loc[idx, "mic_b_mg_L"]) fold = (base_B / b_mic) if b_mic > 0 else 0.0 floor = float(df.loc[idx, "b_susceptible_floor_mg_L"]) if fold >= t.b_fold_min and b_mic <= floor: hit = idx break if hit is not None: df.loc[hit, "pred_earliest_collateral_sensitivity"] = 1 df.loc[g[g.index >= hit].index, "pred_collateral_sensitivity_signal"] = 1 y = int(g["collateral_sensitivity_signal"].max()) p = int(df.loc[g.index, "pred_collateral_sensitivity_signal"].max()) true_tr = g[g["earliest_collateral_sensitivity"] == 1] true_id: Optional[str] = None if len(true_tr) == 1: true_id = str(true_tr.iloc[0]["row_id"]) pred_tr_rows = df.loc[g.index][df.loc[g.index, "pred_earliest_collateral_sensitivity"] == 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_cs": y, "p_cs": p, "true_transition_row_id": true_id, "pred_transition_row_id": pred_id, "a_cross_row_id": str(df.loc[a_cross_idx, "row_id"]), "a_cross_time_h": float(df.loc[a_cross_idx, "timepoint_h"]), } ) sr = pd.DataFrame(series_rows) tp = int(((sr["y_cs"] == 1) & (sr["p_cs"] == 1)).sum()) fp = int(((sr["y_cs"] == 0) & (sr["p_cs"] == 1)).sum()) fn = int(((sr["y_cs"] == 1) & (sr["p_cs"] == 0)).sum()) tn = int(((sr["y_cs"] == 0) & (sr["p_cs"] == 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))