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f12d3b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | #!/usr/bin/env python3
"""
Aggregate the EGFR-VHL ablation outputs into one table.
Walks generation_logs/ablation_egfr/<tag>/ and for each, parses the summary CSV
into batch_report's compute_stats. Emits a TSV with one row per (tag, seed).
Also prints a per-cell (scorer x weight) and acceptance summary averaged
across seeds.
"""
import argparse
import csv
import importlib.util
import re
import statistics
from pathlib import Path
import pandas as pd
# Locate batch_report.py in scripts/
SCRIPT_DIR = Path(__file__).resolve().parent.parent
spec = importlib.util.spec_from_file_location("batch_report", SCRIPT_DIR / "batch_report.py")
br = importlib.util.module_from_spec(spec)
spec.loader.exec_module(br)
REPO_ROOT = SCRIPT_DIR.parent
OUT_BASE = REPO_ROOT / "generation_logs" / "ablation_egfr"
def parse_tag(tag: str) -> dict:
m = re.match(r"egfr_baseline_seed(?P<seed>\d+)$", tag)
if m:
return {"kind": "baseline", "seed": int(m["seed"])}
m = re.match(r"egfr_(?P<sc>[a-z]+)_w(?P<w>[\d.]+)_seed(?P<seed>\d+)$", tag)
if m:
return {"kind": "main", "scorer": m["sc"],
"weight": float(m["w"]), "seed": int(m["seed"])}
m = re.match(r"egfr_accept_(?P<acc>on|off)_seed(?P<seed>\d+)$", tag)
if m:
return {"kind": "accept", "accept": m["acc"], "seed": int(m["seed"])}
return {}
def stats_for_csv(csv_path: Path, protac_module: str) -> dict:
br._PROTAC_MODULE = protac_module
br._protac_analyzer_state = None
br._PROTACDB_FP_CACHE = getattr(br, "_PROTACDB_FP_CACHE", {})
df = br.run_evaluate_full_protac_file(csv_path) if csv_path.suffix == ".txt" \
else br.run_evaluate(csv_path)
return br.compute_stats(df)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--out", default=str(OUT_BASE / "ablation_results.tsv"),
help="output TSV path")
ap.add_argument("--module", default="ErlotinibVHL")
args = ap.parse_args()
rows = []
for run_dir in sorted(OUT_BASE.iterdir()):
if not run_dir.is_dir():
continue
csvs = list(run_dir.glob("defog_local_edit_*_summary.csv"))
if not csvs:
print(f"[skip] {run_dir.name}: no summary csv")
continue
# In case of multiple (resumed runs), take the newest
csv_path = sorted(csvs, key=lambda p: p.stat().st_mtime)[-1]
try:
s = stats_for_csv(csv_path, args.module)
except Exception as e:
print(f"[fail] {run_dir.name}: {e}")
continue
meta = parse_tag(run_dir.name)
rows.append({
"tag": run_dir.name,
**meta,
"n_total": s["n_total"],
"std_valid": round(s["std_valid_rate"] * s["n_total"] / 100),
"asm_valid": s["n_valid"],
"asm_pct": round(s["asm_valid_rate"], 1),
"sa_mean": round(s["sa_mean"], 2),
"qed_mean": round(s["qed_mean"], 3),
"dm_mean": round(s["degrad_mean"], 3),
"dm_std": round(s["degrad_std"], 3),
"dm_ge05": round(s["degrad_ge05"], 1),
"mem_ge08": round(s.get("train_sim_pct_ge_0_8", 0), 1),
"logp_in": round(s["logp_in_window_pct"], 1),
"mw_in": round(s["mw_in_window_pct"], 1),
"rotb_in": round(s["rot_in_window_pct"], 1),
})
print(f"[ok] {run_dir.name}: asm={s['n_valid']}/{s['n_total']}, "
f"dm={s['degrad_mean']:.3f}")
if not rows:
print("No results to aggregate.")
return
# Write per-run TSV (union of all keys across baseline / main / accept rows)
fieldnames = []
for r in rows:
for k in r:
if k not in fieldnames:
fieldnames.append(k)
with open(args.out, "w", newline="") as f:
w = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t",
extrasaction="ignore", restval="")
w.writeheader()
w.writerows(rows)
print(f"\nPer-run TSV: {args.out} ({len(rows)} rows)")
# Aggregate baseline (shared across all w=1 cells)
base_rows = [r for r in rows if r.get("kind") == "baseline"]
base_acc_off = base_rows # acceptance=off baseline reuses the baseline runs
def agg(group):
asm = [g["asm_valid"] for g in group]
dm = [g["dm_mean"] for g in group]
mem = [g["mem_ge08"] for g in group]
if not group:
return None
return {
"asm_m": statistics.mean(asm),
"asm_s": statistics.stdev(asm) if len(asm) > 1 else 0,
"dm_m": statistics.mean(dm),
"dm_s": statistics.stdev(dm) if len(dm) > 1 else 0,
"mem_m": statistics.mean(mem),
"mem_s": statistics.stdev(mem) if len(mem) > 1 else 0,
"n": len(group),
}
main_rows = [r for r in rows if r.get("kind") == "main"]
if main_rows or base_rows:
# collect into (scorer, weight) -> aggregate
by_cell = {}
for r in main_rows:
by_cell.setdefault((r["scorer"], r["weight"]), []).append(r)
scorers = sorted({r["scorer"] for r in main_rows})
weights = sorted({r["weight"] for r in main_rows} | {1.0})
print("\n## Main ablation (EGFR-VHL, 100 samples, mean ± std over 3 seeds)\n")
# Markdown table
header_w = "| scorer | " + " | ".join(f"w={w:g}" for w in weights) + " |"
sep = "|" + "|".join(["---"] * (len(weights) + 1)) + "|"
print("**AsmVal (out of 100)**\n")
print(header_w); print(sep)
base_agg = agg(base_acc_off)
for sc in scorers:
cells = []
for w in weights:
if w == 1.0:
a = base_agg
else:
a = agg(by_cell.get((sc, w), []))
cells.append(f"{a['asm_m']:.1f} ± {a['asm_s']:.1f}" if a else "—")
print(f"| {sc} | " + " | ".join(cells) + " |")
print("\n**DegradeMaster mean**\n")
print(header_w); print(sep)
for sc in scorers:
cells = []
for w in weights:
if w == 1.0:
a = base_agg
else:
a = agg(by_cell.get((sc, w), []))
cells.append(f"{a['dm_m']:.3f} ± {a['dm_s']:.3f}" if a else "—")
print(f"| {sc} | " + " | ".join(cells) + " |")
print("\n**Memorization % (> 0.80 Tanimoto)**\n")
print(header_w); print(sep)
for sc in scorers:
cells = []
for w in weights:
if w == 1.0:
a = base_agg
else:
a = agg(by_cell.get((sc, w), []))
cells.append(f"{a['mem_m']:.1f} ± {a['mem_s']:.1f}" if a else "—")
print(f"| {sc} | " + " | ".join(cells) + " |")
# Acceptance ablation
acc_on = [r for r in rows if r.get("kind") == "accept" and r["accept"] == "on"]
acc_off = base_acc_off # reuse baseline
if acc_on or acc_off:
print("\n## Acceptance ablation (EGFR-VHL, baseline weights, mean ± std over 3 seeds)\n")
print("| accept | AsmVal | DegradeMaster | Mem % |")
print("|---|---|---|---|")
for name, group in (("off", acc_off), ("on", acc_on)):
a = agg(group)
if a is None:
continue
print(f"| {name} | {a['asm_m']:.1f} ± {a['asm_s']:.1f} | "
f"{a['dm_m']:.3f} ± {a['dm_s']:.3f} | "
f"{a['mem_m']:.1f} ± {a['mem_s']:.1f} |")
if __name__ == "__main__":
main()
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