File size: 4,481 Bytes
ea8bfa1 | 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 | #!/usr/bin/env python3
"""Aggregate ablation summaries across multiple seeds."""
from __future__ import annotations
import argparse
import csv
import json
import statistics
from pathlib import Path
from typing import Dict, List
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Aggregate ablation CSV files across seeds.")
parser.add_argument("--results-pattern", required=True, help="Pattern with {seed} placeholder")
parser.add_argument("--seeds", type=int, nargs="+", default=[1, 3, 5, 7, 11])
parser.add_argument("--output-dir", required=True)
parser.add_argument(
"--variant-order",
nargs="*",
default=["Full", "w/o Verification", "w/o Feedback", "w/o Co-Attention", "Text-only", "Vision-only", "w/o Text", "w/o Image"],
)
return parser.parse_args()
def agg(values: List[float]) -> tuple[float, float]:
if not values:
return float("nan"), float("nan")
if len(values) == 1:
return values[0], 0.0
return float(statistics.mean(values)), float(statistics.stdev(values))
def main() -> None:
args = parse_args()
metric_map: Dict[str, Dict[str, List[float]]] = {}
f1_key = ""
for seed in args.seeds:
path = Path(args.results_pattern.format(seed=seed))
if not path.exists():
raise FileNotFoundError(f"Missing ablation CSV for seed {seed}: {path}")
with path.open("r", encoding="utf-8") as f:
rows = list(csv.DictReader(f))
if not rows:
continue
if not f1_key:
f1_candidates = [k for k in rows[0].keys() if k.startswith("f1_")]
if not f1_candidates:
raise RuntimeError(f"Could not find F1 column in {path}")
f1_key = f1_candidates[0]
for row in rows:
variant = row["variant"]
metric_map.setdefault(variant, {"accuracy": [], f1_key: []})
metric_map[variant]["accuracy"].append(float(row["accuracy"]))
metric_map[variant][f1_key].append(float(row[f1_key]))
ordered_variants: List[str] = []
for variant in args.variant_order:
if variant in metric_map:
ordered_variants.append(variant)
for variant in metric_map.keys():
if variant not in ordered_variants:
ordered_variants.append(variant)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
rows_out: List[Dict[str, object]] = []
for variant in ordered_variants:
acc_mean, acc_std = agg(metric_map[variant]["accuracy"])
f1_mean, f1_std = agg(metric_map[variant][f1_key])
rows_out.append(
{
"variant": variant,
"accuracy_mean": acc_mean,
"accuracy_std": acc_std,
f"{f1_key}_mean": f1_mean,
f"{f1_key}_std": f1_std,
"n_seeds": len(metric_map[variant]["accuracy"]),
"accuracy_pm": f"{acc_mean * 100.0:.2f} ± {acc_std * 100.0:.2f}",
"f1_pm": f"{f1_mean * 100.0:.2f} ± {f1_std * 100.0:.2f}",
}
)
csv_path = output_dir / "ablation_multiseed_aggregate.csv"
with csv_path.open("w", encoding="utf-8", newline="") as f:
fieldnames = [
"variant",
"accuracy_mean",
"accuracy_std",
f"{f1_key}_mean",
f"{f1_key}_std",
"n_seeds",
"accuracy_pm",
"f1_pm",
]
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
for row in rows_out:
writer.writerow(row)
md_lines = [
f"| Variant | Accuracy (%) | {f1_key} (%) | n |",
"|---|---:|---:|---:|",
]
for row in rows_out:
md_lines.append(
"| {variant} | {acc} | {f1} | {n} |".format(
variant=row["variant"],
acc=row["accuracy_pm"],
f1=row["f1_pm"],
n=int(row["n_seeds"]),
)
)
md_path = output_dir / "ablation_multiseed_aggregate.md"
md_path.write_text("\n".join(md_lines) + "\n", encoding="utf-8")
json_path = output_dir / "ablation_multiseed_aggregate.json"
json_path.write_text(json.dumps(rows_out, indent=2), encoding="utf-8")
print(f"Saved CSV: {csv_path}")
print(f"Saved MD: {md_path}")
print(f"Saved JSON:{json_path}")
if __name__ == "__main__":
main()
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